The Government of the Kingdom of Swaziland

Ministry of Health

Health Worker Staffing Norms Analysis

Optimal Allocation of Health Workers across Swaziland’s

Government and Mission Health Facilities

February 12, 2014

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Table of Contents

I. Executive Summary......................................................................................................................................... 6

II. Introduction .................................................................................................................................................... 8

Rationale for Analysis ............................................................................................................................. 8

Potential Applications of Staffing Norms Analysis.................................................................................. 8

III. Methodology ................................................................................................................................................ 10

Model Logic........................................................................................................................................... 10

Data Sources for Model ........................................................................................................................ 11

Limitations of Workload-Based Demand Models ................................................................................. 11

Interpretation of Results ...................................................................................................................... 12

IV. Results ........................................................................................................................................................... 14

Modeling Results: National Summary .................................................................................................. 14

Modeling Results: Medical Cadre ......................................................................................................... 22

Modeling Results: Nursing Cadre ......................................................................................................... 24

Modeling Results: Dental Cadre ........................................................................................................... 27

Modeling Results: Medical Imaging Cadre ........................................................................................... 30

Modeling Results: Laboratory Services Cadre ...................................................................................... 33

Modeling Results: Pharmacy Cadre ...................................................................................................... 36

Modeling Results: Adherence Support Cadre ...................................................................................... 39

Case Study: Environmental Health Cadre ............................................................................................. 45

V. Recommendations ........................................................................................................................................ 49

VI. Next Steps ..................................................................................................................................................... 55

Annex I. Technical Methods .................................................................................................................................. 56

Annex II. Considerations for Selecting a Workload-Based Demand Model .......................................................... 67

Annex III. Participants in Swaziland Staffing Norms Analysis ............................................................................... 69

Annex IV. Health Worker Types ............................................................................................................................ 72

Annex V. Service Delivery Activity Time Inputs for Modeling ............................................................................... 74

Annex VI. Facility-Level Optimal Staffing Requirements .................................................................................... 106

References .......................................................................................................................................................... 128

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List of Tables

Table 1. Workforce requirements by government- versus mission-operated facility types ................................................ 16

Table 2. All health workers by type ....................................................................................................................................... 21

Table 3. Medical staff by region ............................................................................................................................................ 23

Table 4. Medical staff by urban versus rural areas ............................................................................................................... 23

Table 5. Medical staff by facility type .................................................................................................................................. 24

Table 6. Nursing staff by region ............................................................................................................................................ 25

Table 7. Nursing staff by facility type .................................................................................................................................... 26

Table 8. Nursing cadre by staff type ..................................................................................................................................... 26

Table 9. Dental staff by region .............................................................................................................................................. 28

Table 10. Dental staff by facility type ................................................................................................................................... 28

Table 11. Dental cadre by staff type .................................................................................................................................... 29

Table 12. Medical imaging staff by region ............................................................................................................................ 31

Table 13. Medical imaging staff by facility type .................................................................................................................... 31

Table 14. Medical imaging cadre by staff type ..................................................................................................................... 32

Table 15. Laboratory services staff by region (adjusted results) .......................................................................................... 34

Table 16. Laboratory services staff by facility type (adjusted results) .................................................................................. 35

Table 17. Laboratory services cadre by staff type (adjusted results) ................................................................................... 35

Table 18. Pharmacy staff by region ....................................................................................................................................... 37

Table 19. Pharmacy staff by facility type .............................................................................................................................. 37

Table 20. Pharmacy cadre by staff type ................................................................................................................................ 38

Table 21. Adherence support staff by region ....................................................................................................................... 40

Table 22. Adherence support staff by facility type ............................................................................................................... 41

Table 23. National health workforce costing requirements (in SZL millions) ....................................................................... 44

Table 24. Responsibilities of environmental health cadre by activity type and percentage of time per week required ..... 45

Table 25. Current distribution of Environmental Health staff by region .............................................................................. 46

Table 26. Recommended population-based proportional staffing levels for environmental health cadre ......................... 47

Table 27. Required nursing and adherence support costs in different task shifting scenarios (M SZL) ............................... 54

Table 28. Timeline for staffing norms analysis (2012) .......................................................................................................... 56

Table 29. Timeline for staffing norms validation and refinement (2013) ............................................................................. 56

Table 30. Types of health facilities in Swaziland ................................................................................................................... 58

Table 31. Rationale and methodology for cadres and facilities categorized as special cases .............................................. 59

Table 32. Entitlements used to calculate health worker productivity .................................................................................. 62

Table 33. Time Estimate Inputs Validated in Field Visits ...................................................................................................... 63

Table 34. Descriptive comparison of Workload Indicator of Staffing Needs and Human Resources for Health Optimization

Models .................................................................................................................................................................................. 68

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List of Figures

Figure 1. Workload-based demand methodology used in staffing norms analysis .............................................................. 10

Figure 2. National workforce requirements by government- versus mission-operated facilities ........................................ 15

Figure 3. National health workforce requirements (summary) ............................................................................................ 16

Figure 4. Government-funded as compared to donor-funded positions ............................................................................. 17

Figure 5. All health workers by region .................................................................................................................................. 18

Figure 6. All health workers by facility type .......................................................................................................................... 19

Figure 7. All health workers by cadre ................................................................................................................................... 20

Figure 8. Medical cadre by type ............................................................................................................................................ 22

Figure 9. Nursing cadre by type ............................................................................................................................................ 25

Figure 10. Dental cadre by type ............................................................................................................................................ 27

Figure 11. Medical imagining cadre by type ......................................................................................................................... 30

Figure 12. Laboratory cadre by type (adjusted results) ........................................................................................................ 34

Figure 13. Pharmacy cadre by type ....................................................................................................................................... 36

Figure 14. Adherence support cadre by type ....................................................................................................................... 40

Figure 15. National health workforce costing requirements (in SZL millions) ...................................................................... 42

Figure 16. National health workforce costing requirements by region (in SZL millions) ...................................................... 43

Figure 17. National health workforce costing requirements by facility type (in SZL millions) ............................................. 43

Figure 18. National health workforce costing requirements by cadre (in SZL millions) ....................................................... 44

Figure 19. Conceptual approach for linking health workforce requirements and supply projections ................................. 50

Figure 20. Swaziland’s highest priority clinics for HRH investment ...................................................................................... 51

Figure 21. Effect of additional staffing on patient load in Zambian facilities ....................................................................... 51

Figure 22. Facility type breakdown of nursing gaps between optimal staffing and current staffing total .......................... 52

Figure 23. Optimal nursing and adherence support staffing in different task shifting scenarios ........................................ 53

Figure 24. Workload-based demand methodology used in staffing norms analysis ........................................... 57

Figure 25. Screenshot of data collection tool used to define health service activities and record activity times ............... 60

Figure 26. Validation of results between HRH Optimization Model and WISN Model (based on December 2012 staffing

norms results which have since been refined) ..................................................................................................................... 68

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List of Abbreviations

CMS

CHAI

EHA

EHO

EHCP

HIV/AIDS

HMIS

HRAA

HRH

HRH TWG

HRIS

HTC

MDR-TB

MOH

MOPS

m2m

PMTCT

RHM

SZL

TB

WHO

WISN

Central Medical Stores

Clinton Health Access Initiative

Environmental Health Assistant

Environmental Health Officer

Essential Health Care Package

Human immunodeficiency virus/Acquired immunodeficiency syndrome

Health Information Management System

Human Resources Alliance for Africa

Human Resources for Health

Human Resources for Health Technical Working Group

Human Resources Information Management System

HIV testing and counseling

Multidrug-resistant tuberculosis

Ministry of Health

Ministry of Public Service

mothers2mothers

Prevention of mother-to-child transmission of HIV

Rural health motivators

Swazi Emalangeni

Tuberculosis

World Health Organization

Workload Indicators of Staffing Need

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I. Executive Summary The primary purpose of the Health Workers Staffing Norms Analysis is to meet and operationalize the goal set by the Ministry of Health in the Human Resources for Health Strategic Plan “…the adequate numbers of motivated and performing health workforce with the proper skills and knowledge to tackle current and future health challenges…to realize at least 75% of the minimum staffing forms at each level of the health system” (Ministry of Health 2012). The Staffing Norms Analysis will provide the information required to create evidence-based policies and interventions that are needed to ensure an equitable distribution of Swaziland’s health workforce across the government-managed health system. In terms of methodology, the staffing norms analysis relied on a workload-based demand methodology, which is used to identify where workload pressures on staff are especially high due to the facility-level demand for health services. An Excel-based “HRH workforce optimization model” was built using data from the MOH’s Health Management Information System (HMIS) along with other key inputs to calculate the number of medical, nursing, laboratory, dental, medical imaging, pharmacy, and adherence support staff required at each of the country’s government and mission health facilities to meet actual health demands. Additionally, a case study was used to assess the demand for the environmental health cadre as this subset of the health workforce required a different analytical approach. Modeling results are summarized to provide a national overview. Then, the disaggregated findings for each of the seven cadres included in the modeling exercise are reported individually: medical, nursing, dental, medical imaging, laboratory, pharmacy, and adherence support. When reviewing the data it is vital to keep in mind that every facility is distinct and assumptions around averages, minimums and maximums in terms of meeting optimal staffing levels by facility type, region, cadre or etc. cannot be made. Instead, detailed analysis around each facility needs to be conducted in order to recommend the appropriate staffing level changes. The results of the staffing norms analysis highlights, at a national level, that there is a gap of 1,136 staff between the current government funded staff and the optimal health workforce of medical, nursing, dental, medical imaging, laboratory, pharmacy, and adherence support cadres required to meet the population’s current demand for health services (1,902 current government-funded filled positions as compared to 3,038 optimal staff required). This means that, if filled, the current establishment posts would meet only 67.6% of the current need, based on the currently established work flows and definition of responsibilities.

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In order for the country to meet optimal staffing levels, an effective and efficient mobilization of human resources for health is essential for resource-limited countries working to improve the performance of their health systems with limited dollars. The analysis recommends building on the findings and implementing the following short to medium term priorities:

1. Use staffing norms findings to determine and prioritize the short-, medium-, and long-term investments required to expand the country’s health workforce

2. Use staffing norms findings to identify immediate optimization opportunities within the existing workforce 3. Reduce staffing costs by implementing appropriate task shifting for key cadres 4. Address quality of care impact from staff absences through greater coordination and structured substitution 5. Increase community outreach in facilities to improve their quality of care and ability to meet patient demand

One concrete, immediate next step would be to institutionalize the HRH optimization analysis exercise as a tool for HRH investment decision making within the MOH. The model used for this analysis could be streamlined and developed a tool to be used by the MOH HR department to update and use as needed for a sustainable, dynamic, and evidence-driven HRH investment decision making process. Additionally, further evaluation of the recommendations for working towards optimal staffing levels presented in this report for feasibility and prioritization should be conducted. This would include prioritization, infrastructure analysis and other due diligence for potential investment in additional healthcare staff; feasibility analyses for redistribution, staff substitution, and community outreach; and standardization and roll-out for the EHCP task shifting framework. Looking forward, several components of the staffing norms would benefit from further investigation as well. Further analysis could be conducted to validate pharmacy results as the least data was available for that analysis. Additionally, it was not possible to create case studies for the operations support staff (ambulance drivers, orderlies, kitchen staff, and laundry staff) with the information available. Further interviews and data collection is needed to assess the metrics that are unique to these types of critical support staff, including but not limited to the number of ambulance transports provided by facility; total number of square footage for each facility and the time required to clean each square footage per person per day; total number of meals served per facility; and the total pounds of clothes and linens cleaned per facility. After this information is collected, benchmarks and staffing requirements for the operations support staff can be analyzed.

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II. Introduction Health workers have been widely recognized as a key ingredient for a healthy population, yet 57 countries face critical health worker shortages that have left an estimated one billion people without access to essential health services (World Health Organization 2006). Forty of these 57 crisis countries are in Africa, which has 11% of the world’s population and 24% of its disease burden, yet only 3% of the global health workforce. As countries work towards closing health staffing gaps and improving health service performance, crucial decisions will need to be made about future investments in their most important capital – health workers. Health worker salaries account for roughly 65 to 80% of a country’s recurrent health expenditures, and these costs are inherently linked to how human resources are deployed and utilized across the health system. If the majority of clients seen at rural health facilities are seeking basic health services, deploying more physicians to rural areas may be less cost-effective than considering a different staffing model that employs more nurses at those facilities. Strategic health workforce planning, therefore, is a fundamental first step towards an effective and efficient national health system.

In Swaziland, the Ministry of Health (MOH) has recognized that the persistent shortage of health workers is the country’s greatest obstacle to reducing the burden of disease in line with the targets defined in the country’s Primary Health Care strategy (Ministry of Health Swaziland 2012). Meeting these targets is critical, given that Swaziland has experienced a marked decline in a number of important health indicators, including a drop in life expectancy (falling from 59.3 years in 1990 to 48.6 years in 2011) that is attributed to the impact of the country’s HIV/AIDS epidemic and an increase in the child mortality rate (81.8 deaths of children under 5 per 1,000 live births in 1992 to 103.8 in 2011) (World Bank 2011). Yet, improvements in health outcomes will only be achieved with a sufficient supply of health workers, and Swaziland currently has just 1.87 clinicians per 1,000 population working in its public sector health system, far below the World Health Organization’s (WHO’s) benchmark of 2.28 per 1,000.

The need for a practical and stepwise approach for human resources for health (HRH) planning in Swaziland is further underlined when examining the scale of the staffing shortages within and across each cadre of health workers. Physicians, for example, play a key role in health care provision, yet, Swaziland has only 152 government-funded Medical Officers and Medical Specialists to serve the entire country. Significant investments of time and funds will be needed to scale up Swaziland’s current ratio of 0.18 physicians per 1,000 population to meet even just the WHO’s minimum threshold of 0.55 physicians per 1,000. Broadly, the need for more health workers in Swaziland is clear, but it is difficult to know where to focus resources to close staffing gaps without a closer examination.

Rationale for Analysis Evidence-based policies and interventions are needed to ensure an equitable distribution of Swaziland’s health workforce across the government-managed health system, and the following staffing norms analysis will provide the data required to chart the way forward. In Swaziland’s HRH Strategic Plan, the MOH set a goal to establish “…the adequate numbers of motivated and performing health workforce with the proper skills and knowledge to tackle current and future health challenges…to realize at least 75% of the minimum staffing norms at each level of the health system” (Ministry of Health 2012). To determine the country’s staffing norms, the MOH requested technical support from the Human Resources Alliance for Africa (HRAA) and the Clinton Health Access Initiative (CHAI), in partnership with the WHO, to conduct a staffing norms analysis by using a demand based workload methodology to calculate the optimal number, type, and distribution of health workers in Swaziland based on the existing facility-level demands for health services. As the country works to expand its HRH capacity and rolls out initiatives such as the Essential Health Care Package and the Standard Treatment Guidelines, this analysis arms the MOH with the data needed to identify opportunities to optimize the use of the existing health workforce and prioritize HRH spending for health facilities that are struggling to meet the current demands for services.

Potential Applications of Staffing Norms Analysis This analysis provides the optimal number of health workers needed to meet current demand for patient services at each of Swaziland’s government and mission health facilities. These findings can be used in the following ways:

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Identify immediate opportunities for optimization of existing resources This analysis shed light on a number of potential inefficiencies within the current health workforce system that should be targeted for further investigation. Some facilities have patient demands for health services that are outpacing their health worker supply, and a better understanding of the facility-level workloads across the country’s health system should be used to inform resource allocation decisions. The model used for this analysis is tailored to Swaziland’s human resources for health context, and it is also dynamic. It can, therefore, be used to further investigate the potential impact of changes to work processes and staffing models (i.e. task shifting and task sharing within and across cadres) on staff workloads and staffing level requirements.

Quantify need for increased budget support The current financial and systems constraints make it almost impossible to reach optimal staffing targets in the short-term, but this analysis provides the data needed to calculate the financial gap between the current staff available and the staff required to meet the existing demands for health services. This evidence can be used to advocate for increased positions within the Funded Establishment, and prioritize pre-service training investments for cadres with the largest gaps.

Build foundation for evidence-based health worker staffing strategy These findings can be used by the Swaziland MOH to identify where workload pressures on current staff are especially high due to the demand for health services and prioritize the deployment of clinicians to those health facilities most in need of additional staff. If this methodology is integrated into the current HRH planning systems, the MOH would be able to quickly identify staffing gaps and adjust the distribution and allocation of its health workers in response to the always-evolving health demands of the population.

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III. Methodology The staffing norms analysis relied on a workload-based demand methodology, which is used to identify where workload pressures on staff are especially high due to the facility-level demand for health services. An Excel-based “HRH workforce optimization model” was built using data from the MOH’s Health Management Information System (HMIS) along with other key inputs to calculate the number of medical, nursing, laboratory, dental, medical imaging, pharmacy, and adherence support staff required at each of the country’s government and mission health facilities to meet actual health demands. A case study was used to assess the demand for the environmental health cadre as this subset of the health workforce required a different analytical approach. A summary of the methodological approach used and the data sources for the staffing norms analysis are described below, and a detailed step-by-step description of the methods can be found in Annex I. A description of the data sources for the costing exercises is also provided in Annex I. Two models were used to validate the findings of this analysis: “Workload Indicator of Staffing Needs” developed by WHO and the “Human Resources for Health Optimization Model” developed by CHAI. The validation process is described in Annex II. As described Annex II, the outputs of the two models were not significantly different, and so the results presented in the following report are from the “HRH Optimization Model”.

Model Logic The HRH workforce optimization analysis was based on a rigorous analytical framework that was structured to maximize the country’s existing data sources. The model logic is outlined in Error! Reference source not found.1 below. Some adjustments were made for in-patient care and the pharmacy cadre. These adjustments are outlined in Annex I. Figure 1. Workload-based demand methodology used in staffing norms analysis

Activities and Times

Data collected from facility observations

and interviews

Services offered at hospitals, health

centers, clinics, and PHUs

Time spent on each activity by each of the priority health

worker cadres

Proportion of time different cadres

perform each activity

Incidence Data

Data collected from HMIS

Incidence should match activity areas

outlined in EHCP

Monthly incidence converted to yearly incidence for each

activity

Total Time to Meet

Demand for Services

Aggregate activities and times by HCW cadre to get total time needed to

meet demand for services

Multiply aggregated activity times by

incidence for each activity to get total time needed for all activities for each

cadre

Health Worker

Productivity

Scheduling and availability of health

worker cadres

Number of working days per year, hours

available per day, etc.

Optimal Workforce

for Each Health

Worker Cadre

Optimal workforce for each health worker cadre

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Data Sources for Model Data was synthesized from a comprehensive range of sources, and organized as follows: Model inputs

Health activities: Defined by the Essential Health Care Package (EHCP)

Activity times: Defined using time-motion observations (n = 526) and validated through interviews with clinical experts (n = 42 experts), further validated using additional time-motion observations (n = 1,258) and interviews with clinical experts and staff (n = 65 interviews)

Incidence: Used Health Management Information System (HMIS) service volume data (2012)

Productivity: Defined using staff entitlements per the Ministry of Public Service (MOPS) General Orders

Model parameters

Health facilities: Represents 88% of all Swaziland public and mission health facilities (n = 132 facilities). See Annex I for further detail on the facility types. As explained in Annex I, 18 facilities were removed from the analysis either because: a) they were missing HMIS or staffing data or b) the specialized types of services offered by their health staff did not fit into the modeling parameters, which are structured to calculate the staff required to meet the demands of an “average” patient.

Health worker cadres: Analyzes seven priority cadres (Medical, Nursing, Dental, Medical Imaging, Laboratory, Pharmacy, and Adherence Support). Data available for Environmental Health Services so this cadre was presented as a case study. An overview of the staffing model for each facility type is presented in Annex IV.

Number of staff currently employed: Used current staffing data from the Service Availability Mapping (2013) exercise to calculate the number of staff currently employed in each facility.

Number of funded positions: Used staffing data from MOPS Establishment Register (2012/2013) to define government-funded positions and data from the Donor Supported Positions in Health Sector Swaziland Report (2013) to define donor-funded positions.

Limitations of Workload-Based Demand Models There are several limitations with any workload-based demand analyses. First, the facility-level targets calculated through this model should be interpreted with some caution since demand indicators may be imprecise. HMIS data was not available for all health facilities across all of the health service indicators and some facilities may not have reported a full year of data. Averages were used to represent missing values where appropriate. Under-reporting is also a concern with HMIS data collection, in which case the true demand for human resources could be higher. Second, this analysis relied on the judgment of clinical experts and observations of patient-provider interactions at health facilities to determine the work activity measurements needed by different health workers to manage different types of patient cases. Since patient management is not an exact science, data collected from these patient-provider interactions are inherently subjective. The optimal prototypes for each individual health facility that are output by the optimization model are meant to reflect the health worker staffing requirements to meet health demands of an average patient seeking care. The model does not include extraneous variables that may impact how different populations utilize their surrounding health facilities, such as the physical distance between an individual’s home to the nearest health center, age distribution across catchment areas, medical equipment availability at the health facility, or disease incidence across different communities. To limit potential observation bias, multiple observations of the same activity were captured within and across facilities during the data collection period. A number of different experts were interviewed to further validate the time estimates that were used as inputs for the model. Further, during the observations, the research team communicated to both the clients and the providers that the observations will be aggregated across many facilities to calculate the “average clinician interaction with the average patient” and that the information collected is intended only to determine how many staff members are needed to deliver services. The data collection team was trained to communicate to providers that these observations are not intended to judge their performance or interfere with their patient care.

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Finally, since the most recently updated HMIS data was used to determine demand frequencies, the outputs of the optimization model reflect only the health worker staffing requirements to meet the current demand for health services, not future health demands. To adjust for future changes to health demands or health worker responsibilities in Swaziland, the optimization methodology should be integrated into Swaziland’s current HRH staffing strategy so that it can be updated with new HMIS and HRIS data regularly.

Interpretation of Results The following section provides guidance on how to interpret the results of the HRH workforce optimization model. First, data results were summarized into the following categories:

Optimal Required Staffing (Modeling): Represents the number of health workers needed to meet current demand for patient services at each of Swaziland’s government and mission health facilities

Current Government Funded to Establishment Post % Gap: Represents the gap or % required to meet the staffing norm levels from the current establishment posts (establishment includes the current government positions available through the MOPS Establishment Register). Mission health facilities are not recorded within the Establishment, therefore the assumption was made that the current mission health facilities positions are equivalent to the Establishment.

Current Government Funded to Optimal Required % Gap: Represents the gap or % required to meet the staffing norm level from the current filled government-funded posts.

Results of this study are shown through a collection of charts and tables and are summarized by facility type, cadre, region, health worker, and other combinations. The subsequent chart and table represent examples of the types of figures found within this study. Interpreting Charts – The below chart shows staffing levels broken out by cadre, posts and optimal staff required. The difference between current filled positions/available establishment post vs. optimal required staff represents the gap or additional staff required by region to meet staffing norms. As an example, in order to meet optimal staffing levels, the Nursing cadre requires an addition of 333 government-funded nurses and an additional 229 nursing positions added to the government establishment.

Interpreting Tables – The below table shows staffing broken out by facility type, number of facilities, posts and optimal staff required. The difference between current filled positions/available establishment post vs. optimal required staff represents the gap or additional staff required by facility type to meet staffing norms. As an example, in order to meet optimal staffing levels, regional hospitals require an addition of 58 government-funded staff members which represents a gap of 15.2%.

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When reviewing the data it is vital to keep in mind that every facility is distinct and assumptions around averages, minimums and maximums in terms of meeting optimal staffing levels by facility type, region, cadre or etc. cannot be made. The below chart provides an example of why such assumptions would be leading to misinterpretation. The chart provides a detailed overview by Public Health Unit (PHU) for the nursing cadre, including registered nurses and assistant nurses. Overall, the gap between government-funded versus optimal required staff differs facility by facility. Taking examples, Siteki PHU is overstaffed by 11 nurses (minimum gap of all 6 PHU), Mankayane PHU is understaffed by 6 nurses (maximum gap of all 6 PHU) and the average assumes 3 additional nurses are needed at each PHU to meet optimal staffing levels. The table below also demonstrates that applying an average number of nurses needed by PHU would also be misleading. The average optimal required nurses is 20 per PHU, however this ranges between 9 nurses in Mankayane and Piggs’ Peak PHUs and 33 in King Sobhuza II PHU, which again explain why using average, minimum and maximum will distort the actual needs of each facility. Instead, detailed analysis around each facility needs to be conducted in order to recommend the appropriate staffing level changes.

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IV. Results The following section is organized as follows. First, the modeling results are summarized to provide a national overview. Then, the disaggregated findings for each of the seven cadres included in the modeling exercise are reported individually: medical, nursing, dental, medical imaging, laboratory, pharmacy, and adherence support. Following the presentation of the national and cadre-specific modeling results, a high-level costing analysis to determine the funds required to close the gap between Swaziland’s current staff and the optimal required is described. Finally, an individual case study is presented for the environmental health cadre.

Modeling Results: National Summary

Key takeaways

- Swaziland currently has 1,902 government-funded medical, nursing, dental, medical imaging, laboratory,

pharmacy, and adherence support staff, which is 67.6% of the health workforce it requires as compared to 3,038

optimal staff required

- Based on the existing demands for health services, the country requires 1,136 more government-funded health

workers, compared to the current government-funded, filled positions, to reach optimal staffing, of which 749

are required in government-operated facilities compared to 387 in mission-operated facilities

- A 32.4% increase in the number of available establishment posts is needed to meet the optimal staffing

requirements (2,055 available establishment posts as compared to 3,038 optimal staff required)

- Across facility types, the most significant staffing gaps in current government-funded to optimal staffing were

found in government clinics and mission-operated sub-regional hospitals with gaps of 474 (57.5%) and 135

(53.8%) health workers respectively

- Among the regions, Manzini has the largest current government-funded to optimal staffing gap at 49.7% or 423

health workers

- The nursing cadre will require the largest absolute number of new staff to meet the optimal staffing levels (333

government-funded positions), but the laboratory and pharmacy cadres have the most significant percentage

gaps between the current government-funded filled positions and the optimal staff required (73.2% for

laboratory and 80.4% for pharmacy)

Swaziland’s health system includes both government- and mission-operated health facilities at all levels of care, such as hospitals, health centers, and clinics. Therefore, both government- and mission-operated facilities were included in this analysis so as to provide a comprehensive assessment of the staffing required across the country. However, when reviewing the findings that are presented in this report, it is important to note a key difference in how government- and mission-operated facilities define the number of funded positions at their facilities. The Government of the Kingdom of Swaziland provides a subvention to mission-operated facilities, which represents the majority of overall funds collected by these facilities. These funds are largely used to finance health worker salaries. However, the positions at mission-operated facilities are not included in MOPS Establishment Register, and since these positions are effectively funded by the government, the number of current staff at the mission facilities was set equal to the number of available funded posts (defined in the following graphs as “Available Establishment Posts”) for the purposes of this analysis. As presented in Figure 22, the current government-funded to optimal staff required gap for government-operated facilities is 33.6% as compared to 48.0% at mission-operated facilities. Government-operated facilities represent 74.2% (n = 98) of the health facilities included in this analysis, and 73.5% (n = 2,232) of the optimal staff required to meet

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existing demands for health services. The 34 mission-operated facilities included in the staffing norms analysis currently have 419 government-funded staff and 275 donor-funded staff, for a total staffing level of 694 health workers. Figure 2. National workforce requirements by government- versus mission-operated facilities

Across the facilities included in the staffing norms analysis, mission-operated clinics accounted for 24.2% (n = 32) of all facilities, and 6.1% (n = 116) of all current government-funded staff (See Table 1). Two mission-operated regional hospitals (Good Shepherd Hospital and Raleigh Fitkin Memorial Hospital) were also included in this analysis. These two mission-operated hospitals account for 15.9% of current government funded staff (n=303). When considering the staffing requirements across the government- and mission-operated facility types, the greatest current-to-optimal staffing gaps were found at mission-operated sub-regional hospitals (45.4% current-to-optimal gap), government-operated clinics (57.5% current-to-optimal gap), and mission-operated clinics (53.8% current-to-optimal gap). Government-operated clinics will require the largest absolute increase in staff to meet their optimal staffing requirements – an additional 474 health workers (825 optimal staff required compared to 351 current filled government-funded positions). Appropriately, more than half of all donor-funded health workers (61.0% or 426 currently filled positions) are currently working in government-operated clinics or mission-operated sub-regional hospitals, two facility groups with the largest gaps between current government-funded staff and optimal staffing. As a result, these facilities are still functioning relatively well compared to their gaps due to donor support.

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Table 1. Workforce requirements by government- versus mission-operated facility types

Moving forward, the findings for the government- and mission-operated facilities will be presented together. As presented in Figure 33, the country will need to add 1,136 government-funded staff and 983 establishment positions to its current health workforce within the medical, nursing, dental, medical imaging, laboratory, pharmacy, and adherence support cadres to meet the population’s current demand for health services (1,902 current government-funded filled positions as compared to 3,038 optimal staff required). This means that the country currently has 67.6% of the human resources required to accommodate the current need, based on the currently established work flows and definition of responsibilities. Figure 3. National health workforce requirements (summary)

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There are 698 positions in Swaziland’s existing health workforce that are currently funded by donors. When adding these donor-funded staff to the current government-funded workforce, the country has a total current workforce of 2,600 health workers (see Figure 4). When comparing the current workforce to the number of funded positions, the difference between the total workforce of 2,600 staff and the available Establishment posts (n = 2,055) is 545 positions. If the government were to absorb these 545 donor-funded positions into the Establishment, the country would need to create at least an additional 221 positions to meet the optimal number of staff required or 3,038 medical, nursing, dental, medical imaging, laboratory, pharmacy, and adherence support staff. This shows that absorbing donor-funded positions into the establishment could be a significant step towards reaching optimal staffing levels; however, the donor position absorption process should still incorporate an evaluation of the positions as not all donor-funded positions should be absorbed. Overall, to close the gap between the current number of available establishment posts (n = 2,055) and the optimal staff required (n = 3,038), Swaziland’s health workforce will require a 14.4% increase in the number of filled positions. Figure 4. Government-funded as compared to donor-funded positions

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When considering how the workforce is distributed across the country, it is important to note that the current staffing arrangement was likely influenced by differences between the regions in population demographics, diseases burdens, and facility types. As seen in Figure 55, the regions are currently facing current government staffing to optimal staffing gaps ranging from 26.4% to 49.7%. Based on the demand for patient services in the facilities as compared to the current staffing levels, Manzini currently has the largest current government-funded to optimal required gap (49.7%) and Hhohho has the smallest (26.4%). Additionally, Manzini has a disproportionally high portion of the country’s donor-funded staff at 45.3% (n = 316). Figure 5. All health workers by region

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As the country works towards a comprehensive implementation of the EHCP and further decentralization of services, it is important to recognize the existing significant staffing gaps found at the country’s primary health care level. As presented in Figure 66, government clinics (Type A and B) have a gap of 57.5% between current government staff and optimal staff required to meet the existing demand for patient services and mission sub-regional hospitals have a gap of 45.4%. Among the tertiary facilities, government hospitals currently have a gap of only 3.3% to reach the optimal staffing levels required to meet their existing demand for patient services. The government health centers, however, have a gap of 44.2% in the health workforce required. Further details on the facility-level staffing requirements are provided in Annex VI. Figure 6. All health workers by facility type

Figure 77 provides a national comparison of the staffing requirements broken out across the seven cadres included in the model: medical, nursing, dental, medical imaging, laboratory, pharmacy, and adherence support. Across all the cadres, the pharmacy and laboratory cadres have the largest gaps between the current government-funded filled positions and the optimal staff required: 80.4% for pharmacy and 73.2% for laboratory. In the laboratory cadre, much of the country’s laboratory demands are currently being supported by donor-funded staff. 66.5% (109 out of 164) of the country’s working laboratory staff are currently donor-funded. Finally, while the demands for additional staff in the pharmacy and laboratory cadres are significant, the nursing cadre requires the largest absolute number of new staff to meet the optimal staff requirements; an additional 229 establishment posts for nurses are required to meet the optimal staffing levels. Nurses comprise 61.0% (n = 1,851) of the overall staff requirements.

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Figure 7. All health workers by cadre

When considering the staff types across the seven cadres, there are five specific positions that are currently operating with 20% or less of the optimal staffing required (see Table 2). These include: Senior Dental officer, Dental Therapist, Dental Chair Side Assistant, Lab Technologist and Pharmacy Assistant. These gaps are explored more fully below:

While the absolute number of staff is relatively low in comparison to other cadres, the dental cadre as a whole is currently operating with less than half of the optimal staff required to meet existing demands (current gap of 65.5% to reach optimal staffing). When comparing this cadre across all cadres, it has three of the five positions that will require the most significant percentage growth from its current government-funded staffing levels to optimal staffing levels. An additional 10 Senior Dental Officers are required (1000% growth),15 additional Dental Therapists are required (750% growth), and 16 Dental Chair Side Assistants are required (400% growth).

There is currently a gap of 80.5% in the currently government-funded lab technologists compared to the optimal staffing levels for that position. Reaching the optimal levels would require an additional 66 positions (412.5% growth).

Pharmacy assistants comprise the other position that currently has less than 20% of the government-funded staff needed to meet patient demand. The country requires an additional 153 pharmacy assistants to meet its optimal staffing levels (90.0% gap between current government-funded to optimal required staff). This gap currently exists because the model of care employed in clinics does not staff pharmacy assistants at the primary care level except in areas within Shiselweni. This analysis projects the introduction of the pharmacy assistant position in primary health care facilities based on the EHCP.

A more detailed review of the cadre-specific findings will be presented in separate sections below, starting with the medical cadre.

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Table 2. All health workers by type

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Modeling Results: Medical Cadre

Key takeaways

- Swaziland has 52.2% of the medical cadre it requires to meet the existing demands for patient services

- Current-to-optimal staffing gap is 50.6% for Medical Officers and 35.2% for Medical Specialists

The medical cadre represents 8.0% of Swaziland’s current government-funded workforce among the relevant cadres for this analysis. An additional 120 Medical Officers are required to meet optimal staff requirements, a 102.6% growth from the current number of government-funded staff to the optimal staffing level, and an additional 19 Medical Specialists are required, a 54.3% growth. Figure 8. Medical cadre by type

As seen in Table 3, Manzini and Shiselweni have especially large Medical Specialist needs with current government-funded to optimal staffing gaps of 77.8% and 66.7%, respectively. Furthermore, Shiselweni also has the largest Medical Officer current-to-optimal gap at 68.4%.

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Table 3. Medical staff by region

As seen in Table 4, the absolute number of Medical staff required is relatively small when compared to some other cadres, but it is important to note the trends within how the current staff is distributed across urban and rural areas. For instance, Manzini’s urban areas have 11 facilities that currently have 35 government-funded medical staff, but will require an additional 54 medical staff to meet the optimal number required. Table 4. Medical staff by urban versus rural areas

According to the EHCP, Medical Officers and Medical Specialists are meant to be located at hospitals and health centers (see Table 5 and Annex IV for a description of where different staff types are positioned across the facility types). These facilities are currently facing relatively similar staffing gaps with the exception of sub-regional hospitals for Medical

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Specialists, which have a high current-to-optimal gap of 70.0% for Medical Specialists, and the national referral hospital for Medical Officers, which has a low gap of 19.3% for Medical Officers. Table 5. Medical staff by facility type

Modeling Results: Nursing Cadre

Key takeaways

- Swaziland has 82.0% of the nursing cadre it requires to meet the existing demands for patient services, but will

require the largest absolute number of new staff to meet the optimal number of staff required (n = 333

additional government-funded nurses)

- The greatest demand for nursing staff is at government-operated clinics (47.6% gap between current

government-funded filled positions and optimal number of staff required) and mission-operated sub-regional

hospitals (36.9% gap)

- The national referral hospital and regional hospitals appear to have more nursing staff than what is optimally

required, suggesting opportunities for reallocation of existing staff to ease workload pressures at clinics

The nursing cadre represents 79.9% of the country’s current government-funded health workforce, and requires the greatest absolute increase in the number of staff (n = 333) to meet the optimal staffing level required (See Figure 99). The staffing norms findings for the nursing cadre also present a number of opportunities to further optimize the current nursing workforce (i.e. distribution of tasks and workflow in the facilities, allocation of current workforce across facility types).

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In the results, the staff nurses (double-qualified) and general nurses (single-qualified) were combined into one registered nurse category. This was done because available data was ambiguous on the qualifications of nurses currently staffed at Swaziland healthcare facilities and as a result, it was impossible to accurately determine the number of single- versus double-qualified nurses at each facility. Of the 1,300 nurses prescribed in the optimal staffing level, the results recommended that at least 209 of the nurses be double-qualified. Figure 9. Nursing cadre by type

As seen in(36.8% and 30.4% respectively). Table 6, Manzini and Shiselweni have the largest gaps between the current government-funded and optimal nursing workforce required (36.8% and 30.4% respectively). Table 6. Nursing staff by region

When considering the distribution of nurses across facility types (see Table 7 and Table 8), the greatest demand for nurses is found at the government clinics (47.6% gap between current government-funded filled positions and optimal staff required) and the sub-regional hospitals (36.9% gap) while the national referral and regional hospitals appear to have a surplus of 98 and 72 nurses respectively when compared to the optimal staff required to meet the existing

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demands. This finding suggests that there may be opportunities to redistribute the current nursing workforce to alleviate shortages at the clinics. For example, those 170 additional nurses would help relieve the staffing shortages currently faced at the government-operated clinics and sub-regional hospitals. However, it is important to note that these overstaffing projections assume that the other cadres at these facilities are appropriately staffed. For example, the national referral hospital is only overstaffed by 98 nurses if the gaps in the other cadres such as the large gap in medical staff are fully addressed. As a result, the 170 additional nurses cannot all be redistributed immediately but the process can be considered and potentially initiated immediately. As the country works towards rolling out the EHCP, it is expected that the demands on nurses at the rural, decentralized health facilities will increase. This consideration is important when examining the findings below, as Swaziland will most likely continue to require the greatest absolute number of this cadre in the rural areas, which highlights the needs for a scale-up strategy that maximizes efficiencies and potential interventions to attract and retain nurses in the rural areas. Table 7. Nursing staff by facility type

Table 8. Nursing cadre by staff type

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Modeling Results: Dental Cadre

Key takeaways

- Swaziland has 34.5% of the dental cadre it requires to meet the existing demands for patient services

- Dentists are most needed at health centers, which only have 13.2% of the dental cadre required to meet current

demand for services

While the absolute number of dentists in Swaziland is small (representing 2.1% of the current workforce for the relevant cadres), the relative demand is high. The greatest demands are seen among the Senior Dental Officers and Dental Therapists, as the gap between current government-funded staff and the staff optimally required to meet current demand for services is 90.9% and 88.2%, respectively (See Figure 1010). Figure 10. Dental cadre by type

There is an unbalanced distribution of dentists across the regions as well. Lubombo has a gap of 76.5% of the dental required to meet the current demand for services, while Manzini has a gap of only 15.4% (See

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Table 9).

Table 9. Dental staff by region

When examining the distribution of the dental cadre across facility types, the government hospitals and health centers have the most significant gaps between the current government-funded filled positions and the optimal staffing levels (See Table 10 and Table 11). Within the dental cadre, there may be opportunities to examine task allocations and resource distribution across the facility types to optimize the capacity of the existing workforce. Table 10. Dental staff by facility type

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Table 11. Dental cadre by staff type

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Modeling Results: Medical Imaging Cadre

Key takeaways

- Swaziland has 39.7% of the medical imaging cadre it requires to meet the existing demands for patient services

- The country requires an additional 12 sonographers to meet its optimal staffing levels (75.0% gap between

current government-funded to optimal required staff)

The findings for medical imaging, as a key clinical support cadre, must be considered alongside the existing resource constraints for the equipment required for this cadre to conduct its work. Therefore, when considering that Swaziland has only 39.7% of the medical imaging staff it requires to meet existing demand (see Figure 1111), it is important to understand that each additional staff will also increase demands for the currently available medical imaging equipment. In total, Swaziland will need to hire an additional 47 medical imaging staff to meet the current demand for services. The greatest demands within the medical imaging cadre are seen among Sonographers, where the country is currently facing a gap of 75.0% between the current government-funded Sonographers and the optimal required. Sonographers also require ultrasonic imaging devices to do their work. Targeted and significant investments may be needed to rapidly scale up the number of Sonographers to meet the pressing demand for these health workers. Given these considerations, one approach would be to further examine the potential to expand and enhance public-private partnerships within Swaziland to alleviate some of the current demand on the existing medical imaging staff while also determining the most efficient way to scale-up the cadre to meet future demands. Figure 11. Medical imagining cadre by type

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When considering the distribution of medical imaging staff, the greatest demands are found in Manzini (77.3% gap between current filled government-funded positions and the optimal staff required) and Lubombo (73.3% gap). See Table 12. Table 12. Medical imaging staff by region

Medical imaging staff work only at hospitals and health centers (see Table 13 and Table 14). The national referral hospital and mission-operated sub-regional hospitals have the greatest demand for additional medical imaging staff. Table 13. Medical imaging staff by facility type

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Table 14. Medical imaging cadre by staff type

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Modeling Results: Laboratory Services Cadre

Key takeaways

- Swaziland has 26.8% of the laboratory services cadre it requires to meet the existing demands for patient

services

- Absorption of donor-funded positions should be a priority in working to bridge the staffing gap in this cadre as

the cadre staff is currently 66.5% donor-funded

In total, Swaziland will need to hire an additional 150 laboratory staff to meet the current demand for services. The key position within the laboratory cadre given its current working model and work streams is the Laboratory Technologist position, which is facing a gap of 80.5% between the current government-funded sonographers available and the optimal required. Furthermore, the Senior Laboratory Technologist and Lab Assistant/Phlebotomist are also presenting large gaps of 73.7% and 67.3% respectively, leaving the cadre overall with a current-to-optimal gap of 73.2% (See Figure 12. Laboratory cadre by type (adjusted results)12 ). These figures show that there is a severe human resources shortage in the laboratory cadre, but they also present an overestimate of the shortage of laboratory staff currently in facilities. Only 55 of the cadre’s 164 current total staff are government-funded, leaving 109 positions or 66.4% of the cadre to donors. This current staffing situation makes government absorption of donor-funded positions a viable and necessary solution that could cover the majority of the staffing gap in the laboratory cadre. This process should be careful and deliberate though as not all donor-funded positions in the cadre should necessarily be absorbed. Specifically, there are 73 donor-funded Lab Assistants and Phlebotomists and 34 government-funded staff in the position for a total of 107 Lab Assistants and Phlebotomists while the optimal staffing level for the position is only 104 workers. As a result, at most only 70 of the 73 donor-funded Lab Assistant/Phlebotomist positions should be absorbed by the government. The absorption should instead focus more of the Laboratory Technologist position where the optimal staffing level of 82 workers allows for all 36 donor-funded Laboratory Technologists to be potentially absorbed (See Figure 12). Like the medical imaging cadre, the findings for the laboratory cadre must also be considered alongside the existing resource constraints for the equipment required for this cadre to conduct its work. Therefore, when considering that Swaziland has only 26.8% of the laboratory staff it requires to meet existing demand (see Figure 11), it is important to understand that each additional staff may also increase demands for the currently available medical imaging equipment. Finally, additional considerations will also need to be considered when examining laboratory staffing moving forward. The roll out of the EHCP and referral process across the country will impact where clients are currently requesting lab services, which, in turn, will affect staffing requirements. Further, the results based on the current staffing arrangement and work processes also highlighted potential opportunities to examine task allocations and work flow processes to identify potential areas for greater efficiencies. Standardizing how the staff within the laboratory services cadre share tasks and the process for results validation, for example, will impact how many of each type of lab staff are required. Building on these findings, a more in-depth workload assessment of the laboratory cadre will need to be conducted to identify areas for potential efficiency gains through changes in work processes and guidelines, task allocation, patient flows, and test frequencies and turnarounds. New technologies that may change the volume and types of demands on lab staff should also be considered (i.e., point of care equipment, rapid diagnostic tests).

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Figure 12. Laboratory cadre by type (adjusted results)

Across the regions, Lubombo is the region with the least severe current-to-optimal staffing gap and reliance on donor-funded positions, with a gap of only 42.9% and a laboratory staff that is only 36.0% donor-funded (see Table 15). The other three regions all have severe current-to-optimal staffing gaps for the cadre and donor-funded position proportions above 65% (82.0% for Hhohho, 80.0% for Manzini, and 65.8% for Shiselweni). Table 15. Laboratory services staff by region (adjusted results)

As presented in Table 16 and Table 17, the hospitals have the largest staffing gaps with the national referral hospital, sub-regional hospitals, and regional hospitals having staffing gaps of 85.7%, 77.3%, and 82.6% respectively. Additionally, government-operated clinics and the national referral hospital had the highest proportions of the donor-funded positions in the country, both at 30.3% (n = 33) of the total number of donor-funded filled positions.

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Table 16. Laboratory services staff by facility type (adjusted results)

Table 17. Laboratory services cadre by staff type (adjusted results)

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Modeling Results: Pharmacy Cadre

Key takeaways

- Swaziland has 19.6% of the pharmacy cadre it requires to meet the existing demands for patient services

- This substantial staffing gap is primarily driven by the Pharmacy Assistant position, which should be bolstered in

the medium-term by recently established Pharmacy Assistant training programs in country

In Swaziland, the pharmacy cadre is one of the more understaffed cadres with only 19.6% of the government-funded staff needed and the country will need to hire or absorb an additional 218 pharmacy staff to meet the current demand for services. Of the three positions in the cadre, the greatest gap is seen among Pharmacy Assistants, where the country is currently facing a gap of 90.0% between the current government-funded Pharmacy Assistants available and the optimal required. This is largely due to the fact that Pharmacy Assistants are not yet officially part of the model of care at primary health care facilities where most of the Pharmacy Assistant gap can be accounted for. The roll out of the Pharmacy Assistant position to the government clinics in the country has not yet been completed and as a result, Pharmacy Assistants drive 70.2% of the pharmacy cadre staffing gap that can be expected to be significantly covered in the medium-term future, especially with the recent establishment of Pharmacy Assistant training programs within the country. Figure 13. Pharmacy cadre by type

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Examining regional distribution, the pharmacy cadre is found to have high needs across all four regions as none of the regions has a current-to-optimal gap below 75%. Among the four regions, Shiselweni has the largest gap at 88.2%, followed by Hhohho at 81.9% (See Table 18). Table 18. Pharmacy staff by region

Among different facility types, public health units and government clinics have particularly high current-to-optimal staffing gaps at 100% with both gaps being driven entirely by Pharmacy Assistant needs. Additionally, hospitals across the board have high Pharmacy Technician gaps and health centers have a 100% Pharmacist current-to-optimal gap (see Table 139 and Table 1420). Table 19. Pharmacy staff by facility type

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Table 20. Pharmacy cadre by staff type

The above is a comprehensive analysis of the pharmacy cadre staffing condition in the country but it could be further improved with better data availability and additional data collection and aggregation. Due to the inherent differences between the pharmacy cadre and some other cadres, the cadre was modeled somewhat differently from the other cadres (details in Annex I) and this analysis was potentially limited by data challenges experienced by the team. For example, a number of assumptions were necessary in the analysis because facility-level prescription data was not collected at the national level and because the team was unable to procure all of the necessary commodity issues data from Central Medical Stores and other sources. Additionally, Pharmacy Assistants should be integrated into the pharmacy cadre. Currently, the nursing cadre is managing the tasks of a Pharmacy Technician at the clinic level. To mitigate the existing pharmacy gaps found at clinics, Nazarene University has started training Pharmacy Assistants. The university has been working with MOPS to create the Establishment posts needed to formally integrate these new health workers into the formal health workforce. Placed at the clinics, these new personnel will be trained to fill and dispense the prescriptions that are currently being managed in many cases by overburdened nurses. Finally, the decentralization of the pharmacy workforce should continue as this will position pharmacy personnel closer to communities. As highlighted earlier, more than three quarters of Swaziland’s population lives in rural areas. Clinics will be their first point of facility based contact with the health system. Positioning one to two roving Pharmacists in each region will ensure that guidance, support, and oversight are provided clinics that currently lack pharmacy personnel.

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Modeling Results: Adherence Support Cadre

Key takeaways

- Swaziland has 23.1% of the adherence support staff it requires to meet the existing demands for patient services

- The country requires an additional 173 Expert Clients and/or HCT Counselors to meet its optimal staffing levels

As part of the formal and informal task shifting that has been occurring across Swaziland’s health system, a number of low-level positions have been introduced into the health workforce in recent years. These less highly trained positions are often supported by donors in an effort to ease the workloads and maximize the impact of more skilled health workers, specifically nurses. Many of these low-level positions support the nursing cadre and perform tasks related to TB and HIV/AIDS prevention, care, and treatment. These positions form the adherence support cadre and include facility expert clients, HTC counselors, community expert clients, mentor mothers, and TB treatment supporters. In this analysis, the facility-based positions, namely the facility expert clients, HTC counselors, mentor mothers, and TB treatment supporters, among these roles were modeled. In the analysis, the various relevant roles in the adherence support cadre were modeled together as one hybrid position, a HTC counselor/expert client position that was responsible for HIV, TB, PMTCT, and other counseling in addition to HIV rapid testing and sputum collection for send-away TB tests. This aggregation was done because if no task shifting is assumed, there is not the scale to justify separate workers for the expert client and HTC counselor roles in the primary health care facilities where these workers are most needed. Considering the current clinical demand and system, training up expert clients to fill a hybrid HTC counselor and expert client role responsible for counseling and HIV/TB testing seems like a natural solution in addition to formally inducting the adherence support cadre into the government establishment. With these parameters, Swaziland overall has 23.1% of the adherence support staff needed in filled government-funded positions and will need to hire or absorb an additional 173 adherence support staff to meet the current demand for services. Additionally, it is important to note that the cadre is strongly donor-funded with 288 of the 340 total positions in the cadre (84.7%) coming from donors. This natural as the cadre is not yet formally a part of the government establishment, a driving factor behind the gaps in the cadre. As a result, the gaps present in the analysis might be addressed relatively easily than the numbers indicate initially if the government does decide to incorporate a hybrid HTC counselor/expert client position into its payroll. In fact, if the donor-funded positions in this cadre are taken into account, the adherence support cadre is potentially currently somewhat overstaffed as a whole. Assuming no task shifting and that adherence support staff only serve to conduct relevant testing and provide counseling, Swaziland is projected to only need 225 staff in the cadre while it currently has 340 total government- and donor-funded staff in adherence support. Consequently, the staffing gaps between currently filled government-funded positions and the optimal staffing level could be bridged through absorption of donor-funded positions. Similar to the laboratory cadre, this would need to be a careful and deliberate process as not all donor-funded positions again need to be absorbed as evidenced by the potential overstaffing. However, the cadre is likely not as overstaffed as the projection numbers initially suggest. The results presented in the following figures assume no task shifting but there is task shifting happening between adherence support staff and other cadres currently happening in facilities across the country and there is opportunity for the government to explore task shifting as an intervention to potentially reduce HRH needs and costs overall. This analyzed in greater detail in the recommendations section of the report and has the potential to increase the adherence support optimal staffing level. Moreover, this optimal staffing level could be further increased by potential changes to the model of care such as an increase in ARV treatment for up to CD4 500 patients and by increases in HIV and TB patient loads over time.

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Figure 14. Adherence support cadre by type

Examining regional adherence support needs, all the regions besides Lubombo had substantial current-to-optimal staffing gaps above 80% (See Table 1221). Table 21. Adherence support staff by region

Among facility types, the national referral hospital, regional hospitals, and public health units currently have no government-funded adherence support staff and therefore 100% current-to-optimal staffing gaps. Conversely, sub-regional hospitals are the only facilities without substantial staffing gaps and as a category, they are actually in excess in the adherence support cadre with 8 additional government adherence staff (see Table 1322).

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Table 22. Adherence support staff by facility type

Overall, adherence support workers play an integral role in providing comprehensive care to clients with HIV and TB and, currently, most positions are not government-funded. To achieve strong adherence and retention among HIV and TB clients, Swaziland should recognize the vital role of these health workers by integrating them into the country’s Funded Establishment and adherence support workers should also be formally integrated into the staffing plans for clinics, ART units, and wherever HIV-positive clients are followed up in care in larger health facilities (e.g., PHUs, TB clinics). Where not already covered, their roles and responsibilities should be expanded to include HTC. These health workers can also be trained to provide MDR-TB support to clients, especially those co-infected with HIV. Training Facility Expert Clients in HIV, TB, and maternal and child health will ensure that the clients they interact with receive a more comprehensive set of health services.

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Modeling Results: Costing Swaziland’s Health Workforce Gap

Key takeaways

- To close the existing gap between the current government-funded filled positions and the optimal staff required, Swaziland would need to invest an additional Swazi Emalangeni (SZL) 185.5M in its workforce. It is important to note that this is a high-level costing analysis; different approaches to partially close this gap remain to be explored

- Across the regions, Hhohho and Manzini represent 62.7% (25.5% and 37.2%, respectively) of the total spending gap required to meet optimal staffing levels

- The medical and nursing cadres represent 58.8% of the spending required to meet optimal staffing levels - Further costing analyses should be conducted to closely examine the funds required, and determine how to

most effectively prioritize the investments needed to scale up Swaziland’s workforce

To provide a high-level understanding of the financial resources needed to close the gap between Swaziland’s current and optimal workforce requirements, a costing analysis was conducted. As presented in Figure 15, Swaziland will require SZL 185.5M to fund the additional 983 establishment health workers required to meet the optimal staffing requirements. To bridge the gap between currently filled government-funded positions and optimal workforce requirements, an additional SZL 207.7M will be require to fund the 1,136 positions needed. Figure 15. National health workforce costing requirements (in SZL millions)

Across the regions, Hhohho and Manzini represent 62.7% (25.5% and 37.2%, respectively) of the total spending gap required to meet optimal staffing levels (see

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Figure 16).

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Figure 16. National health workforce costing requirements by region (in SZL millions)

When examining the costs by facility type in Figure 17, sub-regional hospitals represent the largest cost gap by a substantial margin at SZL 67.5M. The next largest gap is found at government clinics at SZL 43.1M. Figure 17. National health workforce costing requirements by facility type (in SZL millions)

As presented in Figure 18, medical and nursing cadres represent 58.8% of the spending required to meet optimal staffing levels with the medical cadre presenting the largest cost need by far at 39.4% (SZL 81.9). Finally, a summary of all cost analysis findings is provided in Table 2323.

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Figure 18. National health workforce costing requirements by cadre (in SZL millions)

Table 23. National health workforce costing requirements (in SZL millions)

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Case Study: Environmental Health Cadre

Key takeaways

- Unlike most health workers, Environmental Health Workers (EHWs) spend on average 80% of their time in the

community with one day per week on average in a health facility

- There are currently 83 filled positions within environmental health cadre (or 0.08 environmental health workers

per 1, 000 population)

- Redistributing EHWs away from Manzini and into Shiselweni (5 additional) and Lubombo (1 additional) would

result in a distribution of existing resources that is tailored to the regional population size and their unique

environmental health needs

Background Swaziland’s environmental health cadre plays a critical role in reducing morbidity and mortality caused by environmental conditions and zoonotic diseases that are transmissible to humans. These activities include water sanitation and hygiene promotion, occupational hygiene and safety, the control of pests and rodents of public health importance, food hygiene and safety, communicable disease control, and provision of services during disease outbreaks and emergencies, among others. Unlike the other health cadres considered in this analysis, Environmental Health Workers (EHWs) spend on average 80% of their time in the community with one day per week on average in a facility. Based on the facility-level observations and interviews with key experts, Table 2424 outlines the tasks that EHWs are responsible for and the approximate time each task requires per week. The cells highlighted in yellow represent the tasks that require the greatest proportion of time spent by each type of EHW. Table 24. Responsibilities of environmental health cadre by activity type and percentage of time per week required

Activity Chief EHO (F1)

Deputy Chief

EHO (D7)

Principal/Senior

EHO (D5)

Regional EHO (D4)

EHO (C4)

Senior EHA (A6)

EHA (A5)

Total

Water Supply 9.0% 7.9% 10.8% 11.4% 17.0% 31.0% 34.1% 22.8%

Sanitation 6.7% 5.9% 12.1% 13.6% 16.0% 32.9% 35.2% 23.2%

Food Hygiene and Safety 0.0% 0.0% 2.7% 4.5% 4.7% 0.9% 0.0% 2.2%

Environmental Management 0.0% 0.0% 0.0% 0.0% 4.0% 0.0% 0.0% 1.3%

General Waste Management 0.0% 0.0% 0.0% 4.5% 5.0% 0.0% 4.1% 3.1%

Pollution Control 0.0% 0.0% 0.0% 0.0% 4.0% 0.0% 1.8% 1.8%

Housing 9.0% 7.9% 5.4% 4.5% 3.5% 0.0% 0.0% 2.5%

Port Health 4.5% 7.9% 8.1% 4.5% 4.5% 0.0% 0.0% 2.8%

Insect and Rodent Control 0.0% 7.9% 2.7% 6.8% 4.5% 0.0% 4.1% 3.6%

Occupational Health and Safety 0.0% 0.0% 0.0% 0.0% 6.5% 1.9% 0.0% 2.5%

Communicable Disease Control and Surveillance

0.0% 0.0% 8.1% 13.6% 6.5% 5.6% 3.5% 5.6%

Active and Passive Case Detection/Surveillance

0.0% 0.0% 5.4% 9.1% 7.4% 7.5% 3.5% 5.8%

Environmental Health Risk Assessment 17.9% 15.8% 10.8% 9.1% 2.0% 0.0% 0.0% 3.3%

Mitigation Measures During Communicable Disease Outbreaks

9.0% 7.9% 5.4% 2.3% 5.5% 10.3% 7.6% 6.9%

Laboratory Investigation 0.0% 0.0% 5.4% 0.0% 3.0% 0.0% 0.0% 1.3%

Children's Environmental Health (School Health)

6.7% 5.9% 5.4% 4.5% 4.1% 7.8% 4.9% 5.3%

Collaboration with Other Stakeholders 37.3% 32.9% 17.9% 11.4% 2.0% 1.9% 1.2% 6.0%

Total 100% 100% 100% 100% 100% 100% 100% 100%

*EHO = Environmental Health Officer; **EHA = Environmental Health Assistant

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These workers are vital for the health of the country; 26% of all patient admissions and diagnoses at Swaziland’s health facilities were attributable to environmental causes. Additionally, diarrheal and respiratory diseases, both of which can be attributed to environmental causes, are among the top five causes of morbidity and mortality among children under 5 in Swaziland, representing 54.2% of the disease burden in that age group (MOH 2012). A strategic distribution of EHWs across Swaziland would allow for more targeted population-based interventions to prevent and control risk factors for illnesses that result from environment-related issues.

Staffing Levels and Roles

According to the most recent MOPS Establishment Register (2011/2012), Swaziland has 83 available posts for Environmental Health Workers (80 filled positions and 3 vacant); this translates to a ratio of 0.08 environmental health workers per 1,000 population. There is a relatively even distribution of both Environmental Health Assistants (EHAs) and Senior Environmental Health Assistants across the four regions of Swaziland (Manzini, Hhohho, Lubombo, and Shiselweni). See Table 255. EHAs usually work in two to three communities, and are directly supported and supervised by Senior EHAs in their region. Senior EHAs are directly supervised by Environmental Health Officers (EHOs), who are supervised by the Regional EHOs. Strategic placement of EHWs is challenging, as more than three-quarters of the country’s approximately one million people live in rural areas and can be difficult to reach (World Bank 2011). Placement of EHWs at all levels is based almost entirely on the availability of accommodations for workers in the communities, and less so on other metrics like population size or disease burden. The EHOs for preventative programs (malaria, tuberculosis, and bilharzia) are actually based in the Manzini region, but serve the entire country. Table 25. Current distribution of Environmental Health staff by region

Location Chief EHO (F1)

Deputy Chief EHO(D7)

Principal/ Senior EHO (D5)

Regional EHO (D4)

EHO (C4)

Senior EHA (A6)

EHA (A5) Total

Manzini Region 0 0 0 1 0 2 10 (1 vacant)

13 (1 vacant)

Hhohho Region 0 0 0 1 0 1 12 14

Lubombo Region 0 0 0 1 0 1 12 (1 vacant)

14 (1 vacant)

Shiselweni Region 0 0 0 0 (1 vacant)

0 2 9 11 (1 vacant)

National Preventative Staff (Central-level)

0 0 2 0 2 4 12 20

National Staff (Central-level)

1 1 3 0 0 0 0 5

Total 1 1 5 3 (1 vacant)

2 10 57 (2 vacant)

77 (3 vacant)

When considering how to best allocate the EHW workforce across the country, it is interesting to note that Swaziland’s neighboring countries have a much lower ratio of EHWs per capita. Swaziland has a ratio of 0.08 per 1,000 population, while Lesotho, Mozambique, and South Africa have ratios of 0.03, 0.03, and 0.06 EHWs per 1,000 population, respectively. Although Lesotho is most easily comparable to Swaziland in size, population, and disease burden, South Africa’s health system may provide a better benchmark for EHWs. Although South Africa is much large it has roughly half as many EHWs per capita as Swaziland. This suggests that Swaziland may not need more EHWs, but rather may need to consider how to optimize those that already exist in an efficient and effective manner. Recommendations Based on the staffing data available, observations at the facilities, and interviews with key experts, three recommendations for the environmental health cadre are presented below:

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1. Apply a population-based proportional approach to optimize distribution of existing environmental health staff Given that much of their time is spent in the community and few metrics exist to track their exact workload, a workload-based demand analysis is not practical for setting staffing requirements for environmental health workers. As such, a simple population-based approach can be used to build staffing recommendations that will ensure an efficient, impactful EHW workforce for Swaziland. Based on the available Establishment positions for EHWs in Swaziland, Table 266 presents a population-based proportional distribution of EHWs that would place 17 EHWs in Manzini, 15 in Hhohho, 12 in Lubombo, and 11 in Shiselweni regions, respectively. Redistributing EHWs away from Manzini and into Shiselweni (5 additional) and Lubombo (1 additional) would result in a distribution of existing resources that is tailored to the regional population size and their unique environmental health needs. Table 26. Recommended population-based proportional staffing levels for environmental health cadre

Region Population

(2010)

Health Facilities per 100,000 Population

(2010)

Current Number of

EHWs

Recommended Redistribution

of Current EHWs

Hhohho 299,549 24.5 14 15

Lubombo 224,980 33.3 15 12

Manzini 325,296 31.7 14 17

Shiselweni 217948 17.3 12 11

Total 1,067,773 25.3 55 55

2. Ensure allocation of environmental health staff is responsive to shifts in population’s health burden

Swaziland has the highest HIV prevalence in the world, and is experiencing a rise in cases of individuals who are co-infected with HIV and tuberculosis (TB). More than 25% of Swaziland’s sexually active population is HIV-positive, and new TB cases have more than quadrupled in the last 15 years (MOH 2012). Yet, only three EHS officers are currently working in the TB Program, while there are 17 in the Malaria Control & Bilharzia Program despite the fact that Swaziland is nearing elimination of malaria (MOH 2012). Additional EHWs, either newly hired or redistributed, may have greater impact in the TB Program than in other disease programs, given the current and potential magnitude of the country’s TB burden. Within each disease program, it is also essential that EHWs are distributed to the regions where they are needed most. The 2011 HMIS data for Swaziland reveals that the Shiselweni region bears a greater burden of respiratory diseases than the other regions, and thus should receive proportionally more EHWs working in the TB program and on other respiratory diseases. Conversely, Shiselweni, and Manzini have a lower malaria burden (0.36 cases per 1,000 population and 0.67/1,000 respectively) than Hhohho and Lubombo (0.88/1,000 and 0.85/1,000), suggesting EHWs in the Malaria Control Program should be diverted from Shiselweni and Manzini to Hhohho and Lubombo regions. Clean water and sanitation are also areas where EHWs have an important health impact. In Swaziland, more than a third of the population does not have access to safe water, and almost half do not have access to sanitation (MOH 2012). By increasing the capacity of the environmental health cadre in areas where clean water and sanitation are lacking, EHWs can reduce the burden of preventable communicable disease. Shiselweni, for example, has a proportionately higher burden of diarrheal disease than the other regions, but Lubombo had the highest rate of blood/dysentery among cases of diarrhea (24.8% as compared to a range of 7.9-17.9% in the other regions). In concert with an assessment of the EHW distribution as compared to the country’s disease patterns, the MOH should also clearly define the EHW’s expected roles and responsibilities. Safeguarding the population of Swaziland against environmental health hazards encompasses an inherently broad range of activities, and new

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services are already on deck to be included in the EHW’s arsenal of preventative interventions (i.e., health care waste management, infection prevention and control, occupational health and safety). An estimation of the time required to conduct each EHW activity and a measurement for how often those activities should be occurring to meet specific health targets is needed to determine EHW staffing levels and ensure that there are sufficient staff available to meet Swaziland’s needs.

3. Assess transportation capacity needed for environmental health cadre to travel to underserved areas EHWs need to travel from facility-to-facility and household-to-household to effectively deliver their health services. Currently, the limited transportation capacity for EHWs is a severe limiting factor to expanding access to environmental health interventions to rural areas, especially in Lubombo and Shiselweni. Expanding transportation capacity for the environmental health cadre would allow these staff to use their time more effectively, and broaden and enhance the impact of their work.

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V. Recommendations

An effective and efficient mobilization of human resources for health is essential to improve the performance of their health systems in resource-limited settings. A well-functioning and rational distribution system for health workers can improve efficiency in the use of limited dollars, enhance access to care, particularly in underserved communities, and strengthen capacity to project future health staff requirements. Central- and facility-level coordination of the recruitment and deployment of health workers will benefit from evidence-based tools and strategies that allow the MOH to explore alternative interventions and facilitate a rational and equitable allocation of limited resources. The results from the workload-based demand model used to conduct this analysis equips the MOH with a foundational plan from which to base targeted, impactful staffing decisions designed to broaden access to health care across Swaziland. Recommendations Building on the findings presented above, this section will present five recommendations for working towards the achievement of optimal staffing norms. This can be achieved through two primary channels: changing facility staffing levels and affecting optimal facility staffing requirements. In the following section, recommendations 1 and 2 will focus on the former by positing solutions that will increase staffing in gap facilities closer to optimal levels and recommendations 3, 4, and 5 will implement the latter by presenting solutions designed to decrease the overall optimal staffing needs in Swazi healthcare facilities. Recommendation 1

Use staffing norms findings to determine and prioritize the short-, medium-, and long-term investments required to expand the country’s health workforce

The rapid scale up of production and deployment of well-trained health workers will require a sizable investment of time and resources. The additional health workers required to meet Swaziland’s optimal staffing levels will either need to be trained in-country or hired from other countries. Training health workers takes a focused investment of time and resources: three years on average for a nurse and up to ten years for a doctor. Supplemental interventions to target attrition, graduate, and public sector entry rates could also be used to help close staffing gaps. Yet, as presented in the Results section, there are a number of potentially competing priority areas for intervention, and Swaziland’s fiscal realities will challenge efforts to reach optimal staffing targets in the short-term. Crucial choices, therefore, must be made about how and where to intervene with targeted investments that will ensure an effective and efficient scale-up plan. As outlined in Figure 1919, this analysis offers the evidence to better understand Swaziland’s staffing requirements, but the country will then need to determine how to build its current workforce to the optimal levels, and this will require a better understanding of the current and future staff supply.

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Figure 19. Conceptual approach for linking health workforce requirements and supply projections

To close the gap between the current government-funded establishment positions and the optimal staff required, Swaziland will need to invest an additional SZL 185.5M in its workforce and create 983 more positions. Further costing analyses should be conducted to closely examine the funds required, and determine how to most effectively prioritize the investments needed to scale up Swaziland’s workforce. A detailed and strategic investment plan resulting from this analysis could be used to advocate for the increased budget support required to add new funded positions to the country’s Establishment. The results from the HRH optimization analysis could be used as a starting point for this effort. As a facility-level exercise, the staffing norms not only presents a high-level assessment of the HRH situation and needs in Swaziland but can also provide actionable information at a facility-specific level, identifying the gaps and surpluses that exist in facilities today. For example, using the staffing norms results, the country’s highest priority clinics for additional HRH investment can be easily identified and are shown below in Figure 20.

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Figure 20. Swaziland’s highest priority clinics for HRH investment

Furthermore, evidence from the staffing norms analysis could be used to conduct further analyses to supplement the work already done to determine the relative priority and level of investment needed to expand Swaziland’s health workforce in an effective and efficient way. “What-if” policy scenario modeling, for example, provide a valuable tool to help policy-makers examine how a range of HRH training and hiring investments would impact Swaziland’s HRH supply over time (Tjoa 2010). While a useful tool, the HRH optimization analysis presented here should also be considered with a few caveats. First, as a high-level analysis, the staffing norms results do not account for all factors that might influence decisions on HRH investment. For example, the staffing norms results presented here should be considered in conjunction with infrastructure analyses as many facilities currently do not have the infrastructure (i.e. space, consultation rooms, equipment) to absorb the necessary staff and will require concurrent infrastructure investments. Additionally, patient loads are dynamic. As healthcare staffing is increased to adequate levels in facilities across Swaziland, patient loads can be expected to increase. In Zambia, a similar HRH optimization analysis was done and substantial patient volume increases were recorded in facilities where additional staff members were added as quality of care in these facilities increased (see Figure 21). While this is a positive and desirable outcome for the country’s quality of care, it also necessitates revisions in the optimal staffing level for these facilities experiencing changes. Figure 21. Effect of additional staffing on patient load in Zambian facilities

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As a result, in coordination with Swaziland’s National HRH Strategic Plan, the workload-based demand methodology should be integrated into the current health worker planning processes. Building on the models used for this analysis, the HRIS and HMIS could be bridged with a unique tool that pulls together patient volume and facility-level staffing data to assess the optimal staffing needs and current gaps in “real time.” If this methodology were integrated into the current HRH planning systems, the MOH would be able to quickly identify staffing gaps and adjust the distribution and allocation of its health workers in response to the always-evolving health demands of the population. Recommendation 2

Use staffing norms findings to identify immediate optimization opportunities within the existing workforce

The staffing norms analysis was designed to answer the day-to-day questions faced by Swaziland’s health planners around how and where to allocate their health workforce. The findings from this analysis will enable the MOH to deploy health workers equitably and rationally to facilities that are most in need of staff, which will have a tremendous impact on the health service delivery across Swaziland. A number of opportunities to further optimize the existing workforce were revealed during the observations at the facility-level and confirmed through the analysis. One of these opportunities may be to consider redistributing the current healthcare staff in country to ease the acute staffing shortages currently faced by specific facilities in the short- and medium-term. For example, the nursing cadre represents a prime opportunity for distribution optimization due to excess staffing in referral facilities and substantial gaps in primary care facilities (see Figure 22). Capitalizing on these opportunities to redistribute urgently-needed health workers to facilities with severe staffing shortages would facilitate broader access to care in Swaziland, especially as health model changes such as increased decentralization of health services threaten to exacerbate already existing imbalances. Figure 22. Facility type breakdown of nursing gaps between optimal staffing and current staffing total

It is important, however, to note that stakeholders must ensure that redeployment and redistribution decisions are holistic. All cadres and positions exist as part of a larger system and the figures in Figure 22 above, for instance, are dependent on the rest of the cadres being staffed at optimal levels. For example, the analysis suggests that Mbabane Government Hospital is currently overstaffed by 51 registered nurses but that only holds true if the rest of the cadres present at the facilities are staffed appropriately, which is not currently true. In practical terms, this means that the redistribution process can be started in the short-term but must proceed gradually in phases and concurrently with other interventions bring facilities closer to optimal staffing levels across the board.

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Recommendation 3

Reduce staffing costs by implementing appropriate task shifting for key cadres

Another potential opportunity to further optimize the existing resources for the health workforce lies in task shifting. When structuring the roles and responsibilities of each type of health worker during the expert workshop and subsequent interviews, it was clear that there are a number of tasks that are shared across different types of health workers. For example, there are some health service activities that both a Medical Officer and a General Nurse could provide, and, in a busy work environment, workloads are heavy and clients are often seen by the health worker who is first available. From a case management perspective, this approach is clearly and rightly intended to prioritize the care and treatment of the patient. From a human resources management viewpoint, however, this highlights the need for a close assessment of how all the different health service activities are being managed by each type of health worker. The model can then be used to further investigate the potential impact of different task allocations, patient flows, and staffing models, such as, for example, a clearer demarcation of responsibilities between Staff and General Nurses. To support Swaziland’s upcoming roll out of its EHCP and efforts to achieve an efficient and effective health delivery system, this type of task shifting assessment needs to be comprehensive and examine task allocations for services beyond HIV care and treatment. If appropriate and well-implemented, task shifting could generate cost efficiencies and reduce need for certain higher cadres by shifting tasks to lower cadres without adversely affecting quality of care in the process. As an example, one form of task shifting was examined: task shifting between nursing staff (Registered Nurses and Nursing Assistants) and adherence support (Expert Clients and HTC Counselors). This task shifting was observed in the field in various levels so the following four scenarios were examined as potential options:

Scenario 1: No task shifting – HTC/EC handles only HIV testing and patient counseling

Scenario 2: HIV check-ins shifted – HTC/EC handles TB screens and check-in for HIV patients

Scenario 3: All OPD check-ins shifted – HTC/EC handles TB screens, and check-in for all out-patients

Scenario 4: No adherence support – No HTC/EC staffed, nurses handle all HIV testing and counseling

Figure 23 below shows that task shifting affects the optimal staffing levels for the various cadres involved. Here, as task shifting increases, the optimal staffing level increases for adherence support but decreases for the nursing cadre. Figure 23. Optimal nursing and adherence support staffing in different task shifting scenarios

Since adherence support positions are currently not included in the government establishment, it is uncertain which salary grade they would be introduced at if they were added to the establishment. Consequently, Table 27 shows the total cost of the nursing and adherence support cadres under the four different scenarios analyzed at three different salary grades for adherence support positions (A2, A3, and A4). Compared to no adherence support (Scenario 4), full

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task shifting of outpatient check-ins and TB screens from nurses to adherence support (Scenario 3) could reduce HRH costs by up to 25M SZL. Full task shifting of OPD patient check-ins (Scenario 3) compared to no task shifting (Scenario 1) could reduce costs by up to 8.8M SZL. Table 27. Required nursing and adherence support costs in different task shifting scenarios (M SZL)

HTC/EC Salary Grade

S1: No task shifting

S2: HIV check-ins shifted

S3: All OPD check-ins shifted

S4: No adherence

support

A2 $ 231.3 $ 230.3 $ 222.5 $ 247.8

A3 $ 235.7 $ 235.1 $ 230.1 $ 247.8

A4 $ 238.8 $ 238.3 $ 235.3 $ 247.8

In fact, field interviews and observations suggest that task shifting is happening almost universally in facilities. In a validation exercise for these staffing norms, 93% of interviewees (65 interviewees across 17 facilities) confirmed that task shifting is already happening in their respective facilities. Interviewees also often expressed that task shifting is being pushed as far as appropriate and sometimes farther to the detriment of quality of care. Consequently, these results demonstrate a need to standardize task shifting across facilities, which could start with the further development and roll-out of the EHCP task shifting framework. This would serve to capitalize on an opportunity to reduce HRH costs required for the country while maintaining high standards for quality of care in facilities. Recommendation 4

Address quality of care impact from staff absences through greater coordination and structured substitution

Another opportunity that emerged is in staff absences. Overall, staff absences are a frequent occurrence and driver of quality of care challenges at all levels of the Swaziland health system. These absences can be due to personal leave, illness, workshops, meetings, or trainings. In interviews and facility visits conducted in the validation exercise, 16 out of 17 facilities visited had at least one staff member missing and on average (n = 65 interviews), interviewees expressed that staff members are missing from the facility on about 50% of working weekdays. As facility quality of care drops substantially with staff absences (especially in smaller primary care facilities), these absences represent an opportunity to produce high leverage impact with minimal staffing and resources. This area could be addressed through two avenues. First, there is a need to ensure greater coordination between partners and government organizations that organize trainings and workshops. Interviewees mentioned frequently that certain months are light for trainings (Jan – Jul) while others are heavy (Sep – Nov) and greater communication between groups could more evenly spread out the necessary staff shortage burden caused by these absences. Additionally, the MOH could institute a structured substitution program to cover for absent staff members. This could take the form of shifting staff members from more well-staffed facilities at first or of hiring roaming staff to shift between facilities in the longer-term. If implemented, this would allow facilities to avoid the acute temporary staff shortages that result from staff absences and allow staff members in understaffed facilities to attend meetings and workshops and take personal leave when needed, which would have the benefits of improving consistency of quality of care, increasing the skills of health workers, and reducing potential staff burnout. Recommendation 5

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Increase community outreach in facilities to improve their quality of care and ability to meet patient demand

Finally, another potential intervention that emerged from field visit observations and interviews is increasing community outreach run through healthcare facilities, especially primary clinics. Increasing outreach would theoretically improve quality of care in two ways. First, increased community outreach would directly improve quality of care as increased convenience for patients would likely increase adoption of health services and home-based care would allow non-ambulatory individuals to receive the care that they require. Secondly, increased outreach would decrease daily in-facility patient load as more patients are seen closer to their communities. This would improve quality of care for patients in facilities as well because lower patient loads would lead to shorter waiting times and longer consultations for patients with cases urgent enough to visit facilities. Patient load at all primary healthcare facilities is disproportionally high during mornings and low during afternoons. As a result, community outreach could be scheduled each week for a few afternoons and would allow staff to see patients during afternoons, providing an opportunity to more efficiently use health worker time if feasible. However, logistics, such as transport availability, would likely pose the largest challenge to implementation.

VI. Next Steps A natural immediate next step would be to institutionalize the HRH optimization analysis exercise as a tool for HRH investment decision making within the MOH. The model used for this analysis could be streamlined and developed a tool to be used by the MOH HR department to update and use as needed for a sustainable, dynamic, and evidence-driven HRH investment decision making process. Additionally, further evaluation of the recommendations for working towards optimal staffing levels presented in this report for feasibility and prioritization should be conducted. This would include prioritization, infrastructure analysis and other due diligence for potential investment in additional healthcare staff; feasibility analyses for redistribution, staff substitution, and community outreach; and standardization and roll-out for the EHCP task shifting framework. Looking forward, several components of the staffing norms would benefit from further investigation as well. Further analysis could be conducted to validate pharmacy results as the least data was available for that analysis. Additionally, it was not possible to create case studies for the operations support staff (ambulance drivers, orderlies, kitchen staff, and laundry staff) with the information available. Further interviews and data collection is needed to assess the metrics that are unique to these types of critical support staff, including but not limited to the number of ambulance transports provided by facility; total number of square footage for each facility and the time required to clean each square footage per person per day; total number of meals served per facility; and the total pounds of clothes and linens cleaned per facility. After this information is collected, benchmarks and staffing requirements for the operations support staff can be analyzed.

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Annex I. Technical Methods The following technical appendix will map out the logic of the model, including the conceptual framework, data sources, and step-by-step methodology. Timeline for Staffing Norms Analysis The staffing norms analysis was conducted over the course of four months in 2012 and the validation and refining process was conducted over a similar time period in 2013. The timelines are presented below. Table 28. Timeline for staffing norms analysis (2012)

Table 29. Timeline for staffing norms validation and refinement (2013)

Model Logic The HRH workforce optimization analysis was based on a rigorous analytical framework that was structured to maximize the country’s existing data sources. The model logic is outlined in Figure 24 below.

Activity Completion Date

Approval of Study Protocol September 10

Pre-Test and Roll-Out of Facility-based Observations September 12 - October 5

HCW Activity Times Presented to HRH TWG for Review October 23

All Inputs Finalized and Model Developed October 17 - November 26

Preliminary Results Presented to HRH TWG for Review December 4

Preliminary Results Presented to Medical Imaging & Dental Experts for Review December 7

Preliminary Results Presented to MoH Senior Staff for Review December 10

Refined Results for Selected Cadres and Preliminary Results for Special Cases

Presented to HRH TWG for Review

December 11

Preliminary Results Presented to Medical & Nursing Experts for Review December 11

Final Results and Report Presented December 17

Activity Completion Date

Model update for 2013 SAM and 2012 HMIS data October 15

MOH and key stakeholder approval of way forward October 31

Healthcare facility field visits December 2

Validation of laboratory results December 2

Refinement of HRH optimization model December 13

Development of costing model December 13

Development of pharmacy and adherence support models December 16

Final results presentation December 18

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Figure 24. Workload-based demand methodology used in staffing norms analysis

Ethical Approval A protocol for the staffing norms analysis was submitted to the Swaziland Scientific and Ethics Review Committee on August 23, 2012. Permission was obtained to conduct observations of health workers and clients to determine activity times and workloads at a representative sample of 31 health facilities. Per the protocol approval, HMIS and staffing data were also provided by the MOH for this analysis. Data Sources for Model Data was synthesized from a comprehensive range of sources, and organized as follows: Model inputs

Health activities: Defined by the Essential Health Care Package (EHCP)

Activity times: Defined using time-motion observations (n = 526) and validated through interviews with clinical experts (n = 42 experts), further validated using additional time-motion observations (n = 1,258) and interviews with clinical experts and staff (n = 65 interviews)

Incidence: Used Health Management Information System (HMIS) service volume data (2012)

Productivity: Defined using staff entitlements per the Ministry of Public Service (MOPS) General Orders

Model parameters

Health facilities: Represents 88% of all Swaziland public and mission health facilities (n = 132 facilities). See Annex I for further detail on the facility types. As explained in Annex I, 18 facilities were removed from the analysis either because: a) they were missing HMIS or staffing data or b) the specialized types of services offered by their health staff did not fit into the modeling parameters, which are structured to calculate the staff required to meet the demands of an “average” patient.

Activities and Times

Data collected from facility observations

and interviews

Services offered at hospitals, health

centers, clinics, and PHUs

Time spent on each activity by each of the priority health

worker cadres

Proportion of time different cadres

perform each activity

Incidence Data

Data collected from HMIS

Incidence should match activity areas

outlined in EHCP

Monthly incidence converted to yearly incidence for each

activity

Total Time to Meet

Demand for Services

Aggregate activities and times by HCW cadre to get total time needed to

meet demand for services

Multiply aggregated activity times by

incidence for each activity to get total time needed for all activities for each

cadre

Health Worker

Productivity

Scheduling and availability of health

worker cadres

Number of working days per year, hours

available per day, etc.

Optimal Workforce

for Each Health

Worker Cadre

Optimal workforce for each health worker cadre

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Health worker cadres: Analyzes seven priority cadres (Medical, Nursing, Dental, Medical Imaging, Laboratory, Pharmacy, and Adherence Support). Data available for Environmental Health Services so this cadre was presented as a case study. An overview of the staffing model for each facility type is presented in Annex IV.

Number of staff currently employed: Used current staffing data from the Service Availability Mapping (2013) exercise to calculate the number of staff currently employed in each facility.

Number of funded positions: Used staffing data from MOPS Establishment Register (2012/2013) to define government-funded positions and data from the Donor Supported Positions in Health Sector Swaziland Report (2013) to define donor-funded positions.

Facilities Eligible for Modeling 98 government-managed facilities and 34 mission facilities were included in the analysis (see Table 3030). 15 facilities did not have the data required to be included in the modeling exercise. Table 30. Types of health facilities in Swaziland

Special Cases Excluded from Modeling Models cannot account for all of the many complexities of a real health system, and, inevitably, it is difficult to apply one methodology to every type of health worker and health facility. Health workers, such as environmental health specialists, for example, spend the majority of their working time outside of the health facility in the community, and, as such, their workload would not be represented in the data captured in the reported HMIS data – a key input for a workload-based demand model. After assessing the availability of data and the job characteristics of Swaziland’s health workforce, two of the occupational categories within the workforce were excluded from the modeling approach and considered as “special cases” (environmental health specialists and operations support staff). The two national specialty hospitals were also categorized as special cases so as to ensure the analysis could be structured to capture the unique characteristics of the staffing for these types of facilities. The methodology used to assess the special cases is described in Table 3131 below. Other cadres and activities were analyzed with an adjusted version of the standard modeling methodology. These included the pharmacy and adherence support cadres and the treatment of inpatients for the nursing cadre. These adjustments are detailed later in this annex.

Health facility type Description

Number

eligible for

analysis

Total number

of facilities

National Referral Hospitals Refers to public, non-mission owned hospitals that provide tertiary level and specialty care. They

are Mbabane Government Hospital, the National Tuberculosis Hospital and the National Mental

Health Hospital 1 3

Regional Referral Hospitals All hospitals who receive referrals from lower facilities (health centers and clinics) regardless of

ownership. These facilities provide tertiary level care. There are five in total, including sub-

regional hospitals: Good Sherpherd Hospital, Hlathikhulu Government Hospital, Raleigh Fitkin

Memorial Hospital, Mankayane Government Hospital and Piggs Peak Government Hospital 5 5

Health Center These are secondary health care facilities that provide curative services and inpatient care. They

are five: Dvokolwako Health Center, Matsanjeni Health Center, Mkhuzweni Health Center,

Nhlangano Health Center, and Sithobela Rural Health Centre 5 5

Public Health Unit Public Health Units focus on public health services and community outreach. There are six:

Hlathikhulu Public Health Unit, King Sobhuza II Public Health Unit, Mankayane Public Health Unit,

Mbabane Public Health Unit, Piggs Peak Public Health Unit, and Siteki Public Health Unit 6 6

Clinic A These facilities provide preventive and basic curative services at the community level 101 110

Clinic B These facilities provide preventive and basic curative services at the community level and also

have facility provisions for maternity services 14 14

132 143

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Table 31. Rationale and methodology for cadres and facilities categorized as special cases

Step-by-Step Process Behind Workload-based Demand Analysis Each step taken in the staffing norms analysis is outlined below, and the model logic used to calculate the optimal staffing targets is detailed at the end.

A. Categorize numbers of health workers

As a first step, seven clinical cadres were prioritized by the MOH’s Human Resources and Administration directorate for this analysis—physicians, nurses, medical imaging staff, laboratory staff, pharmacy staff, environmental health specialists, and dentists. See Annex IV for an overview of the health worker types and roles included in the analysis. Per Swaziland’s Human Resources for Health Strategic Plan, these cadres are responsible for managing 80% of the disease burden in the country. Health worker staffing data provided by MOPS and human resources data from the 2013 Service Availability Mapping were organized to obtain a count of the current number of health staff in each of these seven cadres across all government and mission health facilities throughout the country. The MOPS staffing register used to determine the current facility-level staffing does not have detailed staff counts for each government-managed clinic. Therefore, the staffing requirements calculated through this analysis for government clinics were summarized at the regional level. Upon review of the available data and work processes, environmental health specialists and operations support staff (i.e. orderlies, cooks) were excluded from the modeling exercise and investigated through an in-depth case study.

B. Determine health worker roles and responsibilities Focus group discussions with key informants were used to determine the roles and responsibilities of the different types of health workers included in this analysis. Convenience and snowball sampling techniques were used to identify the key informants from the MOH, health facilities, MOPS, and other stakeholders to participate in the focus group discussions during the workshop. These key informants were selected based on their clinical and public health knowledge of Swaziland, and all key informants have worked in various capacities of the national health system ranging from the clinic to referral hospital level. Over the course of two 2-day workshops, semi-structured focus group discussions were conducted with this sample of clinical and public health experts to achieve the following objectives:

1) Define the specific roles and responsibilities of each health cadre based on existing job descriptions when compared to the day-to-day realities of their workday in facilities,

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2) Identify the specific activities undertaken by each selected health cadre to provide essential health care services per the Essential Health Care Package,

3) Gather preliminary time range estimates for the amount of time required to complete each health service activity, and

4) Gather preliminary time range estimates for health worker time needed for non-patient-facing activities (i.e., administrative tasks).

A tool to capture time estimates from the clinical and public health experts was used during the focus group discussions. An example is seen below in Figure 2525 that captures outpatient admissions for children under age 5. In this example, the health worker positions have been verified with the MOH and MOPS, as well as with clinical and public health experts. The activities required to complete out-patient under admissions have been determined in consultation with the clinical experts. Clinical and public health experts can enter their estimate of the amount of time required to complete outpatient screening for an “average” under 5 client.

Figure 25. Screenshot of data collection tool used to define health service activities and record activity times

Each focus group included 5-7 members, and was segmented by cadre type (i.e., 5-7 participants for the Medical focus group). The focus group discussions were conducted over a period of approximately 2 hours, for 3 sessions per day and were moderated by a facilitator. A facilitator guide was developed, and included several open-ended questions and probes to guide a free-flowing discussion and avoid the interference of facilitator bias. A note-taker also attended the focus group to document the proceedings; all documentation was hand-written since the discussions were not tape-recorded. Immediately upon completion of the focus group discussion, the facilitator and the note-taker reviewed the notes and documented any missing information.

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C. Determine activity times for health services Time-motion observations at health facilities In order to validate data gathered in the focus group discussions and determine the activity times required for each type of health service delivery task, facility-based observations of provider-patient encounters (timed with a stop watch) were conducted across a sample of health facilities and health workers (by type and geography) over a three week period. The data collection tool described in Section B was adjusted with feedback and input gathered during the expert workshop, and used to document the timed observations at the facilities. Given this timeline, 21% (n = 31) of government and mission-based health facilities included in this exercise was determined to be a feasible number of facilities to visit with a data collection team of eight people. This stratified proportion was agreed upon after discussions with the MOH’s Human Resources for Health Technical Working Group (HRH TWG). Data from the Service Availability Mapping (SAM) Report, Health Management Information System (HMIS) and the Human Resources Information System (HRIS) was used to inform the sampling. For the sampling, all 140 facilities included in this modeling exercise were ranked based on their outpatient volume (highest volume to lowest volume of outpatient visits over a one-year period). The facilities with the median volume of outpatient visits were considered. From this subset, the final sample of facilities was purposively selected to ensure a broad representation of geography and facility types. Thirty-two facilities were sampled for the facility-based observations: 21 Clinic A, 3 Clinic B, 1 Health Center, 2 Public Health Units, 2 Regional Referral Hospitals, and all 3 National Referral Hospitals. Two Regional Referral Hospitals were selected to allow for the diversity of experiences between a government and mission facility. In 2013, these time estimates were further refined through similar waiting room observations at the facilities visited for the field validation of the staffing norms. A total of 17 facilities were visited: 3 hospitals, 2 PHUs, 1 health center, and 11 clinics. This facility sample was chosen to be representative across a number of variables, including geography, ownership, and size. In these visits, 1,258 additional time observations were made from waiting rooms for various categories of clinical visits and used to benchmark activity times used for modeling. Follow-up expert interviews to validate time estimates Three rounds of follow-up interviews were held in 2012 with the clinical and public health experts to validate time estimates and other demand data. Round one of the follow-up interviews were conducted one-on-one and in small groups. These interviews were scheduled to be convenient for the interviewees. During rounds 2 and 3 of the interviews, the clinical and public health experts were brought back together to validate preliminary staffing norms results and determine the proportion of time spent by each of the cadres on interchangeable activities (i.e., tasks that can be managed by both a Senior Dental Officer and a Dental Officer). In the 2013 field validation process, 65 staff interviews were also conducted at each facility visited to further validate time estimates used. The time estimate perspectives from these staff members were used to refine the inputs used for modeling that were found to be most influential in the model based on a sensitivity analysis conducted. The specific model inputs reevaluated are included in section E of this annex. Sensitivity analysis was conducted by setting each model time estimate input to zero and evaluating the result this change had on optimal staffing level projections. The final time estimates used in the model are provided in Annex V.

D. Determine available annual working time per staff

Next, expert interviews were conducted with the MOH and MOPS to determine the number of working days per year based on what absences are allowable per the staff entitlements outlined in MOPS’ General Orders. On average, a health worker in Swaziland’s public services is expected to spend 6.5 hours per day conducting patient care activities (8 paid hours minus 1 hour for lunch break and 0.5 hour for tea breaks = 6.5 hours per day). The number of productive hours available per year for each type of health worker was derived by subtracting the number of public holidays, vacation days, casual leave (for nurses only), sick days, maternity days (for female health workers only), and study leave from the number of workdays per year. Health workers are entitled to up to 6 months of sick leave if necessary, but, for

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the purposes of this analysis, an average of seven days was applied. The entitlements used to calculate the productive working time for each type of health worker included in this analysis are outlined below in Table 3232. Table 32. Entitlements used to calculate health worker productivity

Staff Type

Medical Officers (E4), Medical

Specialists (E6), Senior Dental

Officer (E5), Dental Officer (E4)

Staff Nurse (C5), General Nurse (C3), Nursing Assistant (C2), Dental Laboratory

Technologist (C6), Dental Therapist (C5), Dental Hygienist (C3), Senior Radiographer (C4), Sonographer (C5), Radiographer (C3),

Senior Lab Technologist (C6), Lab Technologist (C3, C4, C5), Pharmacy

Technician

Dental Side Chair Assistant (A3), Dark

Room Attendant (A3), Lab Assistant &

Phlebotomist (A3, A4), Pharmacy

Assistant, HTC/EC

Workdays / Year 260 260 260

Public holidays 13 13 13

Vacation days 25 20 15

Casual leave 0 7 (for nurses only) 0

Sick days 7 7 7

Maternity leave 84 84 84

Study leave 21 21 21

E. Categorize health service activities

The health service activities provided by health workers included in the analysis were defined by the MOH’s Essential Health Care Package and categorized as follows:

Clinical Categories (Medical, Nursing, Adherence Support) - Inpatient admissions - Inpatient ongoing monitoring (per number of bed days) - Normal deliveries - Complicated deliveries - Caesarean sections - Under 5 outpatient admissions - Over 5 outpatient admissions - Accidents and emergencies - Antenatal care – First visit - Antenatal care – Follow-up visit - Nutrition program - EPI - Client-initiated counseling and testing program - HIV/AIDS program – New patient - HIV/AIDS program – Follow-up patient - TB program – New patient - TB program – Follow-up patient - STI clinic - Family planning

Dental Categories - Inpatient dental admissions - Inpatient dental ongoing monitoring (per number of bed days) - Dental accidents and emergencies - Dental surgical procedures - Under 5 dental outpatient

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- Over 5 dental outpatient

Medical Imaging Categories - Diagnostic radiographic services - Ultrasounds

Laboratory Categories - POC collection and tests - Haemotology (including CD4 tests) - Parasitology - Biochemistry - Microbiology, TB cultures, and molecular - TB microscopy - Serology - Cytology/histology - Blood transfusion lab analysis - Send aways preparation

Pharmacy Categories - Outpatient script - Inpatient script - Issue received

The following health service activities were determined to be the most influential time estimates based on sensitivity analysis. The time estimates for these influential inputs were further validated during the field validation process for the staffing norms: Table 33. Time Estimate Inputs Validated in Field Visits

Hospital Health Center Public Health Unit Clinic

Medical

IPD Normal Admissions IPD Ongoing monitoring

IPD Ongoing monitoring Over 5 OPD

Over 5 OPD HIV Follow-up

Under 5 OPD Other Daily Tasks

Other Daily Tasks

Nursing

IPD Normal Admissions IPD Ongoing monitoring Under 5 OPD Under 5 OPD

IPD Ongoing monitoring Over 5 OPD Over 5 OPD Over 5 OPD

Over 5 OPD EPI Nutrition Program EPI

HIV Follow-up HIV Follow-up EPI Other Daily Tasks

Other Daily Tasks Other Daily Tasks Other Daily Tasks

Dental

Over 5 Dental Over 5 Dental Over 5 Dental

Other Daily Tasks IPD Dental Other Daily Tasks

Other Daily Tasks

Medical Imaging

Diagnostic Radiographic Services Diagnostic Radiographic Services

Ultrasounds Other Daily Tasks

Other Daily Tasks

Laboratory

Haematology Haematology POC Tests

Biochemistry Biochemistry Send Away Prep

Cytology/Histology Other Daily Tasks Other Daily Tasks

Other Daily Tasks

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F. Ascertain annual volume of health services delivered

Monthly HMIS data from 2012 was aggregated to calculate the annual incidence or the number of activities performed over the course of a year, which is one of the key components of the demand estimates. The activity categories described above served as model inputs and were matched to facility-based data from the HMIS to determine the number of times each health activity is performed in each facility per year.

G. Determine number of health facilities eligible for analysis

98 government-managed facilities and 34 mission facilities were included in the analysis. Mission based facilities were included in the analysis as they receive Government subventions. The following 18 facilities did not have the data required to be included in the modeling exercise:

The National TB Hospital, the Mental Health Hospital and the TB Centre were removed because they provide specialty services.

Ngonini Royal clinic was removed because it is a specialty facility that exclusively serves members of the Royal family.

Bhalekane Correctional Services Clinic, Ekululameni Clinic, Hope House Clinic, Ka Zondwako, Kwaluseni Campus Clinic, Limkokwing University Clinic, Luyengo Campus Clinic, Manzana Clinic, Mbabane Campus Clinic, Mbabane City Council Clinic, Nkwalini Clinic, Mdzimba USDF Clinic, Sidwashini Correctional Services Clinic, Wellness Centre Clinic, and Young Person Correctional Facility Clinic were removed because they did not report data in 2012.

H. Calculate optimal health worker staffing requirements

The workforce optimization model uses Excel (Microsoft Office, Microsoft; 2010) to calculate the optimal number of health workers required to meet health demands. To calculate the optimal workforce for each health worker cadre for each facility, let:

a = Number of minutes health workers spend on each activity b = Number of minutes required to meet demand for services at each facility per year c = Number of minutes male health workers available each year d = Number of minutes female health workers available each year e = Optimal number of health workers required to meet current demand for services at each facility

Where,

a = [ (Number of minutes spent on each activity per health worker for the given facility type) * (Proportion of time different cadres perform each activity in the given facility type)

b = a * (Number of times each activity is performed at each facility per year) c = [(Percentage of male health workers) * [(Number days per year) – (Public holidays) – (Vacation days) –

(Sick days) – (Study leave days)] * [(Productive hours per day) * Number of minutes in an hour]] d = [(Percentage of female health workers)* [(Number days per year) – (Public holidays) – (Vacation days) –

(Sick days) – (Study leave days) - (Maternity days)] * [(Productive hours per day) * Number of minutes in an hour]]

e = b / (c + d)

The activity time data was input into the HRH optimization model to calculate the health worker time required per category of health service and the HMIS data was input to determine the demand frequencies at the health facilities. The human resources requirements were then calculated according to the above algorithms to determine the number of staff demanded at each type of health facility across the country.

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Data Sources for Costing The costing analysis of the staffing norms results provides a broad estimate of the funds required to close the gap between the current filled positions and the optimal staff required. This costing exercise utilized salary data from the latest Swaziland government salary circular (“Establishment Circular Number 5 of 2013: Cost of Living Adjustment for 2013/14”, Ref: MSD 543 VOL X) and current employee allowances data from the Ministry of Public Service. Compensation by position was then multiplied against the difference between the CHAI optimal number and current staffing levels for government and donor funded positions. Total compensation data includes both salary and allowances and is represented by the following categories: 1) Average salary per year based on the highest and lowest grades; salary is not broken down by grade levels; and 2) Average allowance per year including housing, transport and other. Methodology Adjustments for Specific Cadres and Activities Several cadres and situations analyzed within the scope of this analysis required a more tailored approach. Adjustments were made to the standard methodology for the pharmacy cadre, adherence support cadre, and inpatient monitoring for nurses.

A. Pharmacy cadre The pharmacy cadre modeling was relatively similar to the standard methodology used for all the cadres but two major adjustments were made to tailor the process to fit the cadre in question more appropriately. First, due to the complex and dynamic nature of the role pharmacists play in the Swaziland health system, it was not appropriate to include pharmacists in the modeling in a manner similar to most other positions because there were no drivers, patient-linked or otherwise, that adequately described their role. Instead, the Chief Pharmacist and Director of Central Medical Stores were asked to provide estimates on adequate staffing norm figures for pharmacists. Furthermore, a framework was used for the pharmacy cadre analysis that divided pharmacy tasks into “front of shop” activities such as prescription filling and “back of shop” activities like procurement, receiving, and pre-packing. Front of shop activities were allocated to pharmacy technicians or nurses and back of shop activities were allocated to pharmacy assistants or nursing assistants. Back of shop activities were treated particularly differently from most other aspects of the model. Back of shop activities are comprised mostly of administrative tasks carried out at regular time interviews, and as a result, the time needed to complete these activities is calculated using time spent per week or per month inputs rather than driven by patient load. The one back of shop activity not calculated in this manner is receiving, which is also not driven by patient load but by the number of issues received by the facility. Activity allocations to front of shop or back of shop, time estimates, and task responsibility inputs can be found in Annex V.

B. Adherence support cadre Analysis for the adherence support cadre was also mostly similar to the standard methodology. The two major differences revolved around scenario analysis. First, scenario analysis was used to model four different scenarios of task shifting to examine the potential of task shifting between cadres to reduce the cost burden of meeting staffing norms and to provide proof-of-concept of the “What-If” analysis potential with the model built. The four scenarios considered examined task shifting between the nursing and adherence support cadres and were as follows:

Scenario 1: No task shifting – HTC/EC handles only HIV testing and patient counseling

Scenario 2: HIV check-ins shifted – HTC/EC handles TB screens and check-in for HIV patients

Scenario 3: All OPD check-ins shifted – HTC/EC handles TB screens, and check-in for all out-patients

Scenario 4: No adherence support – No HTC/EC staffed, nurses handle all HIV testing and counseling The time estimate and task responsibility inputs used to model each scenario are included in Annex V. The adherence support results in the main section of the report assume no task shifting (Scenario 1).

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Additionally, since the adherence support cadre is not yet integrated into the government establishment, the HTC counselor/expert client position modeled does not currently have a salary grade. The costing for the adherence support cadre was therefore modeled using a range of potential salary grades (A2, A3, and A4). The costing results in the main section of the report assume a salary grade of A3.

C. Inpatient ongoing monitoring for the nursing cadre Finally, the modeling approach for inpatient ongoing monitoring was also modified for the nursing cadre. This was done because IPD ongoing monitoring responsibilities for nurses are not structured similarly to other activities. In inpatient wards at hospitals and health centers, patients need to be supervised 24 hours a day and nurses need to be able to respond to emergencies and other needs immediately when they occur. Consequently, time demands for inpatient ongoing monitoring for nurses are not predictable and a standard HRH optimization modeling approach for this activity was found to be underestimating the optimal number of nurses needed in ward shift coverage. The analysis was therefore adjusted to consider coverage ratios and nursing shifts in wards. In this approach, the coverage for each ward was split into three shifts (morning, afternoon, and evening) and the optimal nursing staffing level for the inpatient wards was calculated using two methods for each shift:

1. Patient load approach – Inpatient bed days were used from the HMIS database to calculate the average daily inpatient load on each relevant facility. This figure was then divided by the number of wards in the corresponding facility to arrive at an average daily load per ward figure. This was then multiplied by the optimal nurse-to-patient ratio for each shift to arrive at the optimal number of nurses to be staffed during each shift for inpatient ongoing monitoring. For this method, the optimal nurse-to-patient ratio inputs were developed through expert interviews of staff at each of the inpatient facilities (hospitals and health centers) in Swaziland.

2. Minimum staffing approach – To ensure minimal coverage of all wards during all shifts, the number of nurses needed to ensure minimal coverage of each ward in facilities as mandated by facility policies was also calculated for each ward during each shift. The minimal coverage requirements were also aggregated through expert interviews of staff at each of the inpatient facilities in Swaziland.

The optimal nurse staffing level was calculated by taking the greater result between the two methods for each shift at each relevant facility to ensure both minimal coverage requirements and recommended nurse-to-patient ratios. The optimal nurse staffing numbers for inpatient ongoing monitoring were directly added to the optimal staffing totals for nurses derived from the rest of the relevant activities, including inpatient normal admissions. Ward data, recommended nurse-to-patient ratios, and minimum ward coverage requirements used are included in Annex V.

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Annex II. Considerations for Selecting a Workload-Based Demand Model Staffing models can help program managers make informed decisions about how to allocate limited financial and human resources to better suit the needs of their populations given their country’s unique context. In order to maximize the benefits of human resource modeling, it is crucial that policymakers understand the scope and limitations of modeling, as well as key inputs needed to ensure accurate staffing outputs. The aim of this document is to provide high-level guidance for Swaziland’s MOH staff, policymakers, and program managers at the national level who are considering using either the CHAI Optimization Model or the WHO’s Workload Indicators of Staffing Needs (WISN) model. The following technical overview will provide a high-level comparison of the two workload-based demand models used in Swaziland’s staffing norms analysis. Workload-based demand models offer useful analyses that can be used to inform HRH planning. However, several limitations of any modeling approach should be kept in mind. Workload-based demand models can only calculate the current staffing requirements based on the current situation, and should not be used to predict future staffing needs. The model outputs are only as strong as the data inputs; Bad data will lead to incorrect outputs. Decision-makers should interrogate and validate the inputs used in HRH modeling based on their knowledge of the local context to verify the outputs of the models. In addition to inputs provided by the country team or policymakers, models operate on certain key assumptions that may not always be true in the real world. Policymakers should be aware of the assumptions being made in the model, and should take them into consideration when interpreting the model outputs. While these types of models can calculate the optimal staffing levels required to deliver health services, decision-makers will ultimately need to determine where human resources should be deployed based not only the available human resources, but also on cost, feasibility, political priorities, existing resource gaps, and other factors. A comparison of the inputs and capabilities of the two models used in this staffing norms analysis can be found in Table 3434. It is important to note that both staffing models were built using similar inputs and assumptions, and the same Swaziland-specific data was entered into both models with the exception of the productivity estimates. A main difference in functionality of both models is that the HRH Optimization Model allows the user the functionality to manipulate the productivity categories (i.e. entitlements) used to calculate the available working time for each type of health worker. Consequently, country-specific productivity adjustments can be made, such as maternity leave which should only be deducted from female health worker’s available working time. Productivity adjustments for maternity leave are important because it means either maternity leave can’t be accounted for in the productivity assessment (therefore overestimating the total available working time for the health workforce) or maternity leave is applied to all health workers (therefore underestimating the total available working time for the male health workforce who do not take maternity leave).However, as described below, the validation process suggests that this discrepancy had only had a minor impact on this analysis since the outputs of the two models were not significantly different.

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Table 34. Descriptive comparison of Workload Indicator of Staffing Needs and Human Resources for Health Optimization Models

Tool Name Workload Indicator of Staffing Needs (WISN) Human Resources for Health Optimization Model

Too

l

Info

rmat

ion

Organization World Health Organization Clinton Health Access Initiative

Tool description Software package used to record, analyze, and

report facility-level staffing requirements

Excel-based dynamic model used to calculate the national-, regional-, and facility-level optimal

staffing requirements

Pro

gram

Inp

uts

Activity Times ✔

Based on expert input and facility observations

✔

Based on expert input and facility observations

Service Demand Data

✔ Annual patient volume data for a specific health service at a specific facility type based on HMIS

and other facility-level data

✔ Annual patient volume data for a specific health service at a specific facility type based on HMIS

and other facility level data

Productivity/Available Working Time*

✔

Total health worker time available in a specific time frame (1 year) based on Ministry of Public

Service General Orders

✔

Total health worker time available in a specific time frame (1 year) based on Ministry of Public

Service General Orders

Use

r Fu

nct

ion

alit

y User Able to Alter Inputs

Most features can be tailored, but the software package has some built-in pre-defined input

categories that cannot be altered (i.e., productivity categories)

All model inputs are customized to the relevant country-specific inputs required for the

calculations

Data Analysis Data analysis happens in the background after all

inputs are entered High comfort level with Excel is required to create

the model required to conduct the analysis

User Interface Simple layout for data entry and interpretation,

but data cannot be uploaded so each facility-level input has to be manually entered into the tool

Model is organized in Excel, and, therefore, is highly sensitive

Ou

tpu

ts Optimal Staffing Levels

✔ Provides numerical and graphical

facility-level outputs

✔ Provides numerical and graphical

facility-level outputs

Reporting Two available reporting formats to present

facility-level results. National summary results not available in reporting option

User creates reports to own specification

Model validation techniques can be used to strengthen confidence in the outputs. The validation exercise that was conducted as part of Swaziland’s staffing norms analysis has demonstrated that the WISN and the HRH Optimization Models produce very similar results. In fact, the total discrepancy between the optimal required number of staff calculated by the two models was only 53 positions (see Figure 2626). Figure 26. Validation of results between HRH Optimization Model and WISN Model (based on December 2012 staffing norms results which have since been refined)

126

1,346

44 28 35133

1,377

63 48 50

410

1,976

98 106

392408

1,996

100 109

421

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

Medical Nursing Dental Services Medical Imaging Laboratory

Current Filled Positions (Government-Funded Only) Available Establishment Posts

Optimal Required Staff (Optimization Model) Optimal Required Staff (WISN Model)

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Annex III. Participants in Swaziland Staffing Norms Analysis Below is a list of the experts and practitioners who provided invaluable input to inform and guide the staffing norms analysis.

Name Position Organization

Human Resources for Health Technical Working Group

Jabbin Malwanda CD JHPIEGO

Themba Motsa MA MoPS

Thoko Malaza Dlamini HRO MoH

Wisdom Chiviya GNCBEO ICAP

Russell Nxumalo HRO MoPS

Grace Masuku HICD PEPFAR

Dr. A S Shabangu SMO MoH

Blessing Dladla Senior Economist MEPD

Mandla Dlamini Training and Development MoH

Appolo Maphalala HSS Grant Manager MoH

Sanele Malambe PHRO (Acting US) MoET

Thulani Dlamini M & E Officer MoH

Zanela Simelane HMIS MoH

Sabelo Sifundza MA MoPS

Sebentile Dlamini HRO MoPS

Nyamile Manana AD/MSD MoPS

Nonhlanhla Hlophe MA MoPS

Khosi Mthethwa HSS Advisor WHO

Thembi Khumalo CNO MoH

Constance Vilakati PHRO MoH

Sibongile Mndzebele M&E MoH

Danicia Phiri Snr Comp Analyst MoH

Alison End Country Director CHAI

Maxwell Masuku Director, MSD MOPS

Nonhlanhla Dlamini Nurse Advisor ICAP

Kidwell Motshotjana Country Director MSH

Lungile Shongwe Director, NES MOLSS

Lindiwe Mkhatshwa HSS URC

Thembi Masuku HSS Program Manager EGPAF

Wenkhosi Mdzebele AMA MoPS

Sabelo Sifundza AMA MoPS

Patience N Hlophe AMA MoPS

Sabelo Masuku Applications Development Computer Services

Violet Buluma EHPS Coordinator MoH

Regional Administrators /MOH Managers

Happy Tsabedze Administrator MoH

Mcebo Xaba HR Manager Good Shepherd

Sipho Shongwe DCEHO MoH

Glory Msibi Registrar SNC

Wendy Shongwe SMA MoPS

Zanele Dlamini HRO MoPS

Thoko Ngubeni - Simelane RHA Shiselweni MoH

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Name Position Organization

Makinathi shongwe RHA Lubombo MoH

World Health Organization

Magda Awases WHO HRA

Stanley Midzi WHO MPN

Jennifer Nyoni WHO Technical Officer

Adam Ahmat WHO Technical Officer

Teena Kunjumen WHO Technical Officer

Ministry of Health

Thembisile Khumalo MoH CNO

Appolo Maphalala MoH HSS Coordinator

Constance Vilakati MoH PHRO

Zanela Simelane MoH HMIS

Thoko Malaza-Dlamini MoH HRO

Thoko Ngubeni-Simelane MoH RHA Shiselweni

Neliswa Mabaso MoH Admin

Ministry of Public Service

Nyamile Manana MoPS Ass Director (MSD)

Wenkhosi Mndzebele MA MOPS

Wendy Shongwe MoPS Snr MA

Themba Motsa MoPS MA

Nonhlanhla Hlophe MoPS MA

Mfanawenkhosi Mndzebele MoPS Analyst

Ministry of Education and Training

David Kunene MOET HRO

International Centre for AIDS Care Program

Wisdom Chiriya ICAP GNCB EO

Clinical Experts

Dr B. A Karumba MoH - Piggs Peak Doctor

Dr A. S Shabangu MoH - Hlatikulu Act SMO

Khanyisile Nkhabindze MoH RPH Matron

Sabelo Dlamini Nursing Sister MoH

Thandi Nsibandze MoH Matron I

Thandi Maphalala MoH Matron

Goodness Zwane MoH - RFMH Matron I

Constance Luhlanga MoH - Good Shepherd Matron

Dr Matekere Nimrod ICU Consultant Health

Dr VN Tsabedze SDO-MGH MoH

Mgcibelo Tsabedze MAT HGH

Bonakele Hlatshwayo Acting Deno MOH

Dr Makhosazana Mabuza S.D.O. MoH

Edmund Dlamini CEHO MoH

Dr Ncamile Mabuza SDO Health

Dr R. A Bitchong SMO RFM Hospital

Mdzebele Mathokoza REHO Health

Dr. Nomthandazo Lukhele Manzini Region - Clinical Advisor MoH

Lomini Dlamini HR Officer MSF

Simelane Erick HR manager MSF

Mapula Masuku Nursing sister MoH

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Name Position Organization

Bonisile Zikalala Nursing Sister MoH

Mgcibelo Dlamini MAT HGH

Makhosazana Dlamini SMO MGH

Philile Zulu Pharmacist MGH

Gay Mabilisa Senior Diagnostic Radiographer MGH

Nhlanhla Mnisi Hospital Administrator MGH

Leonard Dlamini Hospital Administrator RFM

George Radebe Radiographer MGH

Katungu Byakera Lab Technologist PP GH

Sizwe Shabangu Med Technologist SNBTS

Patrick Msibi Med Technologist SNBTS

Sindisiwe Dlamini Chief Technologist NCLS

Derrick Khumalo Senior lab Technologist NCLS

Fortunate Fakudze Senior Pharmacist CMS

Dumile Sibandze Principal Lab Technologist MoH - Lab

Gilbert Masona Lab Supervisor RFM

Kholiwe Shongwe Pharmacist RFM

Nkululeko Maphosa Acting Chief Radiographer MoH - Radiology

Thuli Magagula Senior Pharmacist MGH

Sibongile Mabuza Pharmacist Manzini Region- MoH

Philton Ndzinisa Senior Lab Technologist CLS

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Annex IV. Health Worker Types

Health worker type

National

Referral

Hospital

Regional

Referral

Hospital

Health

Centers

Public

Health

Units

Clinics

(Types A

and B)

Medical Specialist (E6)

Medical Officer (E4)

Staff Nurse (C5)

General Nurse (C3)

Nursing Assistant (C2)

Senior Dental Officer (E2)

Dental Officer (E4)

Dental Therapist (C5)

\Dental Lab Technologist (C6)

Dental Hygienist (C3)

Dental Chairside Assistant (A3)

Senior Radiographer (C4)

Radiographer (C3)

Sonographer (C5)

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Health Worker Types (cont.)

Source: MOH job descriptions and schemes of service

Health worker type

National

Referral

Hospital

Regional

Referral

Hospital

Health

Centers

Public

Health

Units

Clinics

(Types A

and B)

Senior Lab Technologist (C6)

Lab Technologist (I, II & III) (C3, C4, C5)*

Lab Assistant (I &II)/Phlebotomist (I & II) (A3 & A4)

Senior Pharmacist (Hospital) (E3)

Senior Pharmacist (Central Medical Stores) (E3)

Quality Control Pharmacists (Central Medical Stores) (E3)

Pharmacist (Health Facility) (E2)

Pharmacist (Central Medical Stores) (E2)

Senior Pharmacy Technician (Central Medical Stores) (C4)

Senior Pharmacy Technician (Health Facility) (C4)

Pharmacy Technician (Health Facility)Pharmacy Technician (Central Medical Stores) (C3)

Pharmacy Technician (Health Facility) (C3)

Regional Environmental Health Officer (D4)

Senior Environmental Health Officer (D5)

Environmental Health Officer (C4)

Senior Environmental Health Assistant (A6)

Environmental Health Assistant (A5)

Page | 75

Annex V. Service Delivery Activity Time Inputs for Modeling These time estimates were prepared based on facility-based observations of provider-patient encounters (timed with a stop watch) and were conducted across a sample of health facilities and health workers (by type and geography) over a three week period. The data was validated during interviews with clinical experts. Further detail on the methodology is provided in Annex I. Service delivery activity times (min): Hospitals

Service Delivery Activity Times (min) : Hospitals

Medical

Officer (E4)

Medical

Specialist

(E6)

Staff Nurse

(C5)

General

Nurse (C3)

Nursing

Assistant

(C2)

Inpatient Services

Normal Admissions Process 30 30 20 20 20

% of Time Spent of All Cases 80% 20% 80% 20% 100%

Inpatient Admissions (Ongoing Monitoring) 15 15 0 0 0

% of Time Spent of All Cases 70% 30% 0% 0% 0%

Normal Deliveries 30 0 30 0 0

% of Time Spent of All Cases 5% 0% 95% 0% 0%

Complicated Deliveries 55 37 60 0 0

% of Time Spent of All Cases 60% 40% 100% 0% 0%

Caesarean Sections 75 30 60 0 0

% of Time Spent of All Cases 60% 40% 100% 0% 0%

Minor Surgical Procedures 30 30 20 25 0

% of Time Spent of All Cases 90% 10% 80% 20% 0%

Major Surgical Procedures 90 90 90 90 0

% of Time Spent of All Cases 0.6 0 0.8 0 0

Medical Nursing

Page | 76

Service delivery activity times (min): Hospitals Service Delivery Activity Times (min) : Hospitals

Medical

Officer (E4)

Medical

Specialist

(E6)

Staff Nurse

(C5)

General

Nurse (C3)

Nursing

Assistant

(C2)

Outpatient Services

Under 5 Screening 10 15 0 20 8

% of Time Spent of All Cases 95% 5% 0% 100% 100%

Over 5 Screening 10 15 0 20 8

% of Time Spent of All Cases 95% 5% 0% 100% 100%

Accidents and Emergencies 30 30 0 35 15

% of Time Spent of All Cases 80% 20% 0% 100% 10%

Antental Care - First Visit 25 0 30 0 0

% of Time Spent of All Cases 5% 0% 100% 0% 0%

Antental Care - Follow up 15 0 15 0 0

% of Time Spent of All Cases 20% 0% 100% 0% 0%

Nutrition Program 0 0 0 10 15

% of Time Spent of All Cases 0% 0% 0% 20% 100%

EPI 0 0 0 0 10

% of Time Spent of All Cases 0% 0% 0% 0% 100%

Client-Initiated Counseling and Testing Program - Positive 0 0 0 30 0

% of Time Spent of All Cases 0% 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Negative 0 0 0 20 0

% of Time Spent of All Cases 0% 0% 0% 100% 0%

HIV/AIDS Program - New Medically Complex 18 0 0 12 10

% of Time Spent of All Cases 100% 0% 0% 100% 100%

HIV/AIDS Program - New Non Medically Complex 10 0 0 13 8

% of Time Spent of All Cases 70% 0% 0% 100% 100%

HIV/AIDS Program - Established Medically Complex 12 0 0 11 8

% of Time Spent of All Cases 75% 0% 0% 100% 100%

HIV/AIDS Program - Established Non Medically Complex 8 0 0 12 7

% of Time Spent of All Cases 10% 0% 0% 40% 100%

HIV/AIDS Program - New PMTCT 12 0 12 0 7

% of Time Spent of All Cases 15% 0% 100% 0% 100%

HIV/AIDS Program - Established PMTCT 8 0 0 12 7

% of Time Spent of All Cases 10% 0% 0% 40% 100%

HIV/AIDS Program - Pre-ART 12 0 0 13 8

% of Time Spent of All Cases 10% 0% 0% 75% 100%

HIV/AIDS Program - New Pediatric 12 0 0 13 8

% of Time Spent of All Cases 90% 0% 0% 100% 100%

HIV/AIDS Program - Established Pediatric 8 0 0 10 7

% of Time Spent of All Cases 25% 0% 0% 80% 100%

HIV/AIDS Program - Infant 15 0 0 14 10

% of Time Spent of All Cases 100% 0% 0% 100% 100%

TB Program - New Patient 10 0 0 40 3

% of Time Spent of All Cases 100% 0% 0% 100% 100%

TB Program - Follow-up Patient 8 0 0 30 3

% of Time Spent of All Cases 20% 0% 0% 100% 100%

STI Clinic 20 0 0 25 0

% of Time Spent of All Cases 10% 0% 0% 100% 0%

Family Planning 15 0 15 0 0

% of Time Spent of All Cases 10% 0% 100% 0% 0%

Administrative

Other Weekly Tasks 0 0 0 0 0

Other Daily Tasks 120 120 120 120 120

Individual Tasks 30 0 20 0 0

Number Completing Individual Tasks 7 0 3 0 0

Medical Nursing

Page | 77

Service delivery activity times (min): Hospitals

Service Delivery Activity Times : Hospitals

Senior

Dental

Officer (E5)

Dental

Officer (E4)

Dental

Laboratory

Technologist

(C6)

Dental

Therapist

(C5)

Dental

Hygienist

(C3)

Dental Chair

Side

Assistant

(A3)

Dental Services

Inpatient Dental Admissions 36 40 0 0 0 20

% of Time Spent of All Cases 50% 50% 0% 0% 0% 100%

Inpatient Dental Bed Days (Ongoing Monitoring) 36 66 0 0 0 20

% of Time Spent of All Cases 50% 50% 0% 0% 0% 100%

Dental Accidents and Emergencies 60 60 30 30 0 15

% of Time Spent of All Cases 50% 50% 50% 50% 0% 100%

Dental Surgical Procedures 90 90 0 0 0 20

% of Time Spent of All Cases 50% 50% 0% 0% 0% 100%

Under 5 Dental Admissions 40 40 15 30 30 10

% of Time Spent of All Cases 50% 50% 50% 50% 90% 100%

Over 5 Dental Admissions 30 30 15 30 30 15

% of Time Spent of All Cases 50% 50% 50% 50% 90% 100%

Administrative

Other Weekly Tasks 0 0 0 0 0 0

Other Daily Tasks 60 60 30 30 30 30

Individual Tasks 24 24 0 0 0 0

Number Completing Individual Tasks 1 1 0 0 0 0

Dental

Service Delivery Activity Times : Hospitals

Senior

Radiographer

(C4)

Sonographer

(C5)

Radiographer

(C3)

Dark Room

Attendant (A3)

Clinical Support Services

Medical Imaging - Diagnostic Radiographic Services 20 20 20 8

% of Time Spent of All Cases 15% 25% 75% 100%

Medical Imaging - Ultrasounds 20 20 20 0

% of Time Spent of All Cases 15% 85% 15% 0%

Administrative

Other Weekly Tasks 0 0 0 0

Other Daily Tasks 90 60 60 20

Individual Tasks 120 240 0 0

Number Completing Individual Tasks 1 1 0 0

Medical Imaging

Page | 78

Service delivery activity times (min): Hospitals

Service Delivery Activity Times : Hospitals

Senior Lab

Technologist (C6)

Lab Technologist (I,

II & III) (C3, C4 &

C5)

Lab Assistant (I &

II)/Phlebotomist (I

& II) (A3 & A4)

Clinical Support Services

Laboratory - POC Collection & Tests (RDT- Av for HIV, Syphilis, Malaria, Preg test, Glucose, Hb ,Urine biochem)0 5 10

% of Time Spent of All Cases 0% 10% 100%

Laboratory - Haematology 6 4 7

% of Time Spent of All Cases 15% 100% 75%

Laboratory - CD4 3 3 7

% of Time Spent of All Cases 20% 100% 75%

Laboratory - Parasitology (Urine and Stool Samples) 8 8 7

% of Time Spent of All Cases 15% 100% 75%

Laboratory - Biochemistry 6 6 7

% of Time Spent of All Cases 15% 100% 75%

Laboratory - Microbiology 15 15 7

% of Time Spent of All Cases 15% 100% 75%

Laboratory - TB Cultures 20 20 7

% of Time Spent of All Cases 15% 100% 75%

Laboratory - Molecular 10 10 7

% of Time Spent of All Cases 15% 100% 75%

Laboratory - TB Microscopy 0 10 10

% of Time Spent of All Cases 0% 10% 100%

Laboratory - Serology 10 10 7

% of Time Spent of All Cases 15% 50% 75%

Laboratory - Cytology 10 20 7

% of Time Spent of All Cases 20% 100% 75%

Laboratory - Histology 5 40 7

% of Time Spent of All Cases 20% 100% 75%

Laboratory - Lab Analysis of Donated Blood/Blood Transfusion Service 10 10 7

% of Time Spent of All Cases 15% 100% 75%

Laboratory - Send Aways Preparation 0 0 0

% of Time Spent of All Cases 0% 0% 0%

Administrative

Other Weekly Tasks 0 0 0

Other Daily Tasks 90 60 30

Individual Tasks 480 20 20

Number Completing Individual Tasks 3 2 1

Laboratory

Page | 79

Service delivery activity times (min): Hospitals (scenario analysis inputs)

Service Delivery Activity Times : Hospitals Adherence

Scenario 1: No Task ShiftingStaff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC/EC

Outpatient Services

Under 5 Screening 0 20 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Over 5 Screening 0 20 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Accidents and Emergencies 0 35 15 0

% of Time Spent of All Cases 0% 100% 10% 0%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 10 15 0

% of Time Spent of All Cases 0% 20% 100% 0%

EPI 0 0 10 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 0 30 0 45

% of Time Spent of All Cases 0% 100% 0% 100%

Client-Initiated Counseling and Testing Program - Negative 0 20 0 30

% of Time Spent of All Cases 0% 100% 0% 100%

HIV/AIDS Program - New Medically Complex 0 12 10 20

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - New Non Medically Complex 0 13 8 15

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - Established Medically Complex 0 11 8 15

% of Time Spent of All Cases 0% 100% 100% 80%

HIV/AIDS Program - Established Non Medically Complex 0 12 7 10

% of Time Spent of All Cases 0% 40% 100% 80%

HIV/AIDS Program - New PMTCT 12 0 7 15

% of Time Spent of All Cases 100% 0% 100% 100%

HIV/AIDS Program - Established PMTCT 0 12 7 10

% of Time Spent of All Cases 0% 40% 100% 80%

HIV/AIDS Program - Pre-ART 0 13 8 10

% of Time Spent of All Cases 0% 75% 100% 90%

HIV/AIDS Program - New Pediatric 0 13 8 20

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - Established Pediatric 0 10 7 15

% of Time Spent of All Cases 0% 80% 100% 80%

HIV/AIDS Program - Infant 0 14 10 20

% of Time Spent of All Cases 0% 100% 100% 100%

TB Program - New Patient 0 40 3 20

% of Time Spent of All Cases 0% 100% 100% 100%

TB Program - Follow-up Patient 0 30 3 10

% of Time Spent of All Cases 0% 100% 100% 50%

STI Clinic 0 25 0 0

% of Time Spent of All Cases 0% 100% 0% 0%

Family Planning 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nursing

Page | 80

Service delivery activity times (min): Hospitals (scenario analysis inputs) Service Delivery Activity Times : Hospitals Adherence

Scenario 2: HIV Patient Check-ins ShiftedStaff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC/EC

Outpatient Services

Under 5 Screening 0 20 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Over 5 Screening 0 20 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Accidents and Emergencies 0 35 15 0

% of Time Spent of All Cases 0% 100% 10% 0%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 10 15 0

% of Time Spent of All Cases 0% 20% 100% 0%

EPI 0 0 10 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 0 30 0 45

% of Time Spent of All Cases 0% 100% 0% 100%

Client-Initiated Counseling and Testing Program - Negative 0 20 0 30

% of Time Spent of All Cases 0% 100% 0% 100%

HIV/AIDS Program - New Medically Complex 0 12 7 25

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - New Non Medically Complex 0 13 5 20

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - Established Medically Complex 0 11 5 20

% of Time Spent of All Cases 0% 100% 100% 80%

HIV/AIDS Program - Established Non Medically Complex 0 12 4 15

% of Time Spent of All Cases 0% 40% 100% 80%

HIV/AIDS Program - New PMTCT 12 0 4 20

% of Time Spent of All Cases 100% 0% 100% 100%

HIV/AIDS Program - Established PMTCT 0 12 4 15

% of Time Spent of All Cases 0% 40% 100% 80%

HIV/AIDS Program - Pre-ART 0 13 5 15

% of Time Spent of All Cases 0% 75% 100% 90%

HIV/AIDS Program - New Pediatric 0 13 5 25

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - Established Pediatric 0 10 4 20

% of Time Spent of All Cases 0% 80% 100% 80%

HIV/AIDS Program - Infant 0 14 7 25

% of Time Spent of All Cases 0% 100% 100% 100%

TB Program - New Patient 0 40 0 25

% of Time Spent of All Cases 0% 100% 0% 100%

TB Program - Follow-up Patient 0 30 0 15

% of Time Spent of All Cases 0% 100% 0% 50%

STI Clinic 0 25 0 0

% of Time Spent of All Cases 0% 100% 0% 0%

Family Planning 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nursing

Page | 81

Service delivery activity times (min): Hospitals (scenario analysis inputs)

Service Delivery Activity Times : Hospitals Adherence

Scenario 3: OPD Patient Check-ins ShiftedStaff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC/EC

Outpatient Services

Under 5 Screening 0 20 5 5

% of Time Spent of All Cases 0% 100% 100% 100%

Over 5 Screening 0 20 5 5

% of Time Spent of All Cases 0% 100% 100% 100%

Accidents and Emergencies 0 35 10 8

% of Time Spent of All Cases 0% 100% 10% 100%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 10 15 0

% of Time Spent of All Cases 0% 20% 100% 0%

EPI 0 0 10 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 0 30 0 45

% of Time Spent of All Cases 0% 100% 0% 100%

Client-Initiated Counseling and Testing Program - Negative 0 20 0 30

% of Time Spent of All Cases 0% 100% 0% 100%

HIV/AIDS Program - New Medically Complex 0 12 7 25

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - New Non Medically Complex 0 13 5 20

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - Established Medically Complex 0 11 5 20

% of Time Spent of All Cases 0% 100% 100% 80%

HIV/AIDS Program - Established Non Medically Complex 0 12 4 15

% of Time Spent of All Cases 0% 40% 100% 80%

HIV/AIDS Program - New PMTCT 12 0 4 20

% of Time Spent of All Cases 100% 0% 100% 100%

HIV/AIDS Program - Established PMTCT 0 12 4 15

% of Time Spent of All Cases 0% 40% 100% 80%

HIV/AIDS Program - Pre-ART 0 13 5 15

% of Time Spent of All Cases 0% 75% 100% 90%

HIV/AIDS Program - New Pediatric 0 13 5 25

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - Established Pediatric 0 10 4 20

% of Time Spent of All Cases 0% 80% 100% 80%

HIV/AIDS Program - Infant 0 14 7 25

% of Time Spent of All Cases 0% 100% 100% 100%

TB Program - New Patient 0 40 0 25

% of Time Spent of All Cases 0% 100% 0% 100%

TB Program - Follow-up Patient 0 30 0 15

% of Time Spent of All Cases 0% 100% 0% 50%

STI Clinic 0 25 0 0

% of Time Spent of All Cases 0% 100% 0% 0%

Family Planning 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nursing

Page | 82

Service delivery activity times (min): Hospitals (scenario analysis inputs)

Service Delivery Activity Times : Hospitals Adherence

Scenario 4: No Adherence SupportStaff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC/EC

Outpatient Services

Under 5 Screening 0 20 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Over 5 Screening 0 20 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Accidents and Emergencies 0 35 15 0

% of Time Spent of All Cases 0% 100% 10% 0%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 10 15 0

% of Time Spent of All Cases 0% 20% 100% 0%

EPI 0 0 10 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 0 60 12 0

% of Time Spent of All Cases 0% 100% 100% 0%

Client-Initiated Counseling and Testing Program - Negative 0 40 8 0

% of Time Spent of All Cases 0% 100% 20% 0%

HIV/AIDS Program - New Medically Complex 0 12 30 0

% of Time Spent of All Cases 0% 100% 100% 0%

HIV/AIDS Program - New Non Medically Complex 0 13 24 0

% of Time Spent of All Cases 0% 100% 100% 0%

HIV/AIDS Program - Established Medically Complex 0 11 24 0

% of Time Spent of All Cases 0% 100% 100% 0%

HIV/AIDS Program - Established Non Medically Complex 0 12 18 0

% of Time Spent of All Cases 0% 40% 100% 0%

HIV/AIDS Program - New PMTCT 12 0 24 0

% of Time Spent of All Cases 100% 0% 100% 0%

HIV/AIDS Program - Established PMTCT 0 12 18 0

% of Time Spent of All Cases 0% 40% 100% 0%

HIV/AIDS Program - Pre-ART 0 13 18 0

% of Time Spent of All Cases 0% 75% 100% 0%

HIV/AIDS Program - New Pediatric 0 13 28 0

% of Time Spent of All Cases 0% 100% 100% 0%

HIV/AIDS Program - Established Pediatric 0 10 24 0

% of Time Spent of All Cases 0% 80% 100% 0%

HIV/AIDS Program - Infant 0 14 30 0

% of Time Spent of All Cases 0% 100% 100% 0%

TB Program - New Patient 0 40 25 0

% of Time Spent of All Cases 0% 100% 100% 0%

TB Program - Follow-up Patient 0 30 15 0

% of Time Spent of All Cases 0% 100% 50% 0%

STI Clinic 0 25 0 0

% of Time Spent of All Cases 0% 100% 0% 0%

Family Planning 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nursing

Page | 83

Service delivery activity times (min): Hospitals

Service Delivery Activity Times : Hospitals

Pharmacy

Technician

Pharmacy

Assistant

Front of Shop

OPD Dispensing 3 0

% of Time Spent of All Cases 100% 0%

IPD Dispensing 3 0

% of Time Spent of All Cases 100% 0%

Back of Shop

Pre-Packing 0 120

% of Time Spent of All Cases 0% 100%

Stock Inventory 0 300

% of Time Spent of All Cases 0% 100%

Procurement 180 0

% of Time Spent of All Cases 100% 0%

Receiving 0 0.05

% of Time Spent of All Cases 0% 100%

Inventory Management 0 20

% of Time Spent of All Cases 0% 100%

Expiration Management 0 60

% of Time Spent of All Cases 0% 100%

Ward Rounds/Issuing Ward Stock 120 0

% of Time Spent of All Cases 100% 0%

Storage Organization/Upkeep 0 45

% of Time Spent of All Cases 0% 100%

Reporting 30 0

% of Time Spent of All Cases 100% 0%

Pharmacy

Page | 84

Service delivery activity times (min): Health Centers

Service Delivery Activity Times : HC

Medical Officer

(E4)

Medical

Specialist (E6)

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)

Inpatient Services

Normal Admissions Process 30 0 20 20 20

% of Time Spent of All Cases 100% 0% 80% 20% 100%

Inpatient Admissions (Ongoing Monitoring) 15 0 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0% 0%

Normal Deliveries 30 0 30 0 0

% of Time Spent of All Cases 5% 0% 95% 0% 0%

Minor Surgical Procedures 30 0 20 25 0

% of Time Spent of All Cases 90% 0% 80% 20% 0%

Administrative

Other Weekly Tasks 0 0 0 0 0

Other Daily Tasks 120 0 80 80 80

Individual Tasks 0 0 20 0 0

Number Completing Individual Tasks 0 0 3 0 0

Medical Officer Nursing

Page | 85

Service delivery activity times (min): Health Centers

Service Delivery Activity Times : HC

Medical Officer

(E4)

Medical

Specialist (E6)

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)

Outpatient Services

Under 5 Screening 10 0 0 20 8

% of Time Spent of All Cases 30% 0% 0% 100% 100%

Over 5 Screening 10 0 0 20 8

% of Time Spent of All Cases 30% 0% 0% 100% 100%

Accidents and Emergencies 25 0 0 35 15

% of Time Spent of All Cases 30% 0% 0% 80% 100%

Antental Care - First Visit 25 0 30 0 0

% of Time Spent of All Cases 10% 0% 100% 0% 0%

Antental Care - Follow up Visit 15 0 15 0 0

% of Time Spent of All Cases 10% 0% 100% 0% 0%

Nutrition Program 0 0 0 0 15

% of Time Spent of All Cases 0% 0% 0% 0% 100%

EPI 0 0 0.0 0.0 10.0

% of Time Spent of All Cases 0% 0% 0% 0% 100%

Client-Initiated Counseling and Testing Program - Positive 0 0 0 30 0

% of Time Spent of All Cases 0% 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Negative 0 0 0 20 0

% of Time Spent of All Cases 0% 0% 0% 100% 0%

HIV/AIDS Program - New Medically Complex 18 0 0 12 10

% of Time Spent of All Cases 100% 0% 0% 100% 100%

HIV/AIDS Program - New Non Medically Complex 10 0 0 13 8

% of Time Spent of All Cases 70% 0% 0% 100% 100%

HIV/AIDS Program - Established Medically Complex 12 0 0 11 8

% of Time Spent of All Cases 75% 0% 0% 100% 100%

HIV/AIDS Program - Established Non Medically Complex 8 0 0 12 7

% of Time Spent of All Cases 10% 0% 0% 40% 100%

HIV/AIDS Program - New PMTCT 12 0 12 0 7

% of Time Spent of All Cases 15% 0% 100% 0% 100%

HIV/AIDS Program - Established PMTCT 8 0 0 12 7

% of Time Spent of All Cases 10% 0% 0% 40% 100%

HIV/AIDS Program - Pre-ART 12 0 0 13 8

% of Time Spent of All Cases 10% 0% 0% 75% 100%

HIV/AIDS Program - New Pediatric 12 0 0 13 8

% of Time Spent of All Cases 90% 0% 0% 100% 100%

HIV/AIDS Program - Established Pediatric 8 0 0 10 7

% of Time Spent of All Cases 25% 0% 0% 80% 100%

HIV/AIDS Program - Infant 15 0 0 14 10

% of Time Spent of All Cases 100% 0% 0% 100% 100%

TB Program - New Patient 10 0 0 40 3

% of Time Spent of All Cases 20% 0% 0% 100% 100%

TB Program - Follow-up Patient 8 0 0 25 3

% of Time Spent of All Cases 10% 0% 0% 100% 100%

STI Clinic 20 0 0 25 0

% of Time Spent of All Cases 30% 0% 0% 80% 0%

Family Planning 15 0 15 0 0

% of Time Spent of All Cases 10% 0% 100% 0% 0%

Medical Officer Nursing

Page | 86

Service delivery activity times (min): Health Centers

Service Delivery Activity Times : HC

Senior Dental

Officer (E5)

Dental Officer

(E4)

Dental

Laboratory

Technologist

(C6)

Dental

Therapist (C5)

Dental

Hygienist (C3)

Dental Chair

Side Assistant

(A3)

Dental Services

Inpatient Dental Admissions 0 80 0 0 30 0

% of Time Spent of All Cases 0% 100% 0% 0% 70% 0%

Inpatient Dental Bed Days (Ongoing Monitoring) 0 60 0 0 30 0

% of Time Spent of All Cases 0% 100% 0% 0% 70% 0%

Dental Accidents and Emergencies 0 80 30 30 20 0

% of Time Spent of All Cases 0% 90% 50% 50% 10% 0%

Dental Surgical Procedures 0 20 0 0 0 20

% of Time Spent of All Cases 0% 100% 0% 0% 0% 100%

Under 5 Dental Admissions 0 40 15 30 30 30

% of Time Spent of All Cases 0% 90% 50% 50% 10% 100%

Over 5 Dental Admissions 0 30 15 30 30 20

% of Time Spent of All Cases 0% 90% 50% 50% 10% 100%

Administrative

Other Weekly Tasks 0 0 0 0 0 0

Other Daily Tasks 0 60 30 30 30 30

Individual Tasks 0 0 0 0 0 0

Number Completing Individual Tasks 0 0 0 0 0 0

Dental

Service Delivery Activity Times : HC

Senior

Radiographer (C4)Sonographer (C5) Radiographer (C3)

Dark Room

Attendant (A3)

Clinical Support Services

Medical Imaging - Diagnostic Radiographic Services 20 0 20 8

% of Time Spent of All Cases 15% 0 100% 100%

Medical Imaging - Ultrasounds 0 0 0 0

% of Time Spent of All Cases 0% 0% 0% 0%

Administrative

Other Weekly Tasks 0 0 0 0

Other Daily Tasks 90 0 60 20

Individual Tasks 0 0 0 0

Number Completing Individual Tasks 0 0 0 0

Medical Imaging

Page | 87

Service delivery activity times (min): Health Centers

Service Delivery Activity Times : HC

Senior Lab

Technologist (C6)

Lab Technologist (I,

II & III) (C3, C4 &

C5)

Lab Assistant (I &

II)/Phlebotomist (I

& II) (A3 & A4)

Clinical Support Services

Laboratory - POC Collection & Tests (RDT- Av for HIV, Syphilis, Malaria, Preg test, Glucose, Hb ,Urine biochem)0 5 7

% of Time Spent of All Cases 0% 10% 100%

Laboratory - Haematology 6 4 7

% of Time Spent of All Cases 15% 100% 75%

Laboratory - CD4 3 3 7

% of Time Spent of All Cases 20% 100% 75%

Laboratory - Parasitology (Urine and Stool Samples) 8 8 7

% of Time Spent of All Cases 15% 100% 75%

Laboratory - Biochemistry 6 6 7

% of Time Spent of All Cases 15% 100% 75%

Laboratory - Serology 10 10 7

% of Time Spent of All Cases 15% 50% 75%

Laboratory - Lab Analysis of Donated Blood/Blood Transfusion Service10 10 7

% of Time Spent of All Cases 15% 100% 75%

Laboratory - Send Aways Preparation 0 0 0

% of Time Spent of All Cases 0% 0% 0%

Administrative

Other Weekly Tasks 0 0 0

Other Daily Tasks 90 60 30

Individual Tasks 0 20 0

Number Completing Individual Tasks 0 1 0

Laboratory

Page | 88

Service delivery activity times (min): Health Centers (scenario analysis inputs)

Service Delivery Activity Times : Health Centers Adherence

Scenario 1: No Task Shifting

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC Counselor

Outpatient Services

Under 5 Screening 0 20 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Over 5 Screening 0 20 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Accidents and Emergencies 0 35 15 0

% of Time Spent of All Cases 0% 80% 100% 0%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up Visit 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

EPI 0.0 0.0 10.0 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 0 30 0 45

% of Time Spent of All Cases 0% 100% 0% 100%

Client-Initiated Counseling and Testing Program - Negative 0 20 0 30

% of Time Spent of All Cases 0% 100% 0% 100%

HIV/AIDS Program - New Medically Complex 0 12 10 20

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - New Non Medically Complex 0 13 8 15

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - Established Medically Complex 0 11 8 15

% of Time Spent of All Cases 0% 100% 100% 80%

HIV/AIDS Program - Established Non Medically Complex 0 12 7 10

% of Time Spent of All Cases 0% 40% 100% 80%

HIV/AIDS Program - New PMTCT 12 0 7 15

% of Time Spent of All Cases 100% 0% 100% 100%

HIV/AIDS Program - Established PMTCT 0 12 7 10

% of Time Spent of All Cases 0% 40% 100% 80%

HIV/AIDS Program - Pre-ART 0 13 8 10

% of Time Spent of All Cases 0% 75% 100% 90%

HIV/AIDS Program - New Pediatric 0 13 8 20

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - Established Pediatric 0 10 7 15

% of Time Spent of All Cases 0% 80% 100% 80%

HIV/AIDS Program - Infant 0 14 10 20

% of Time Spent of All Cases 0% 100% 100% 100%

TB Program - New Patient 0 40 3 20

% of Time Spent of All Cases 0% 100% 100% 100%

TB Program - Follow-up Patient 0 25 3 10

% of Time Spent of All Cases 0% 100% 100% 50%

STI Clinic 0 25 0 0

% of Time Spent of All Cases 0% 80% 0% 0%

Family Planning 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nursing

Page | 89

Service delivery activity times (min): Health Centers (scenario analysis inputs)

Service Delivery Activity Times : Health Centers Adherence

Scenario 2: HIV Patient Check-ins Shifted

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC Counselor

Outpatient Services

Under 5 Screening 0 20 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Over 5 Screening 0 20 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Accidents and Emergencies 0 35 15 0

% of Time Spent of All Cases 0% 80% 100% 0%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up Visit 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

EPI 0.0 0.0 10.0 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 0 30 0 45

% of Time Spent of All Cases 0% 100% 0% 100%

Client-Initiated Counseling and Testing Program - Negative 0 20 0 30

% of Time Spent of All Cases 0% 100% 0% 100%

HIV/AIDS Program - New Medically Complex 0 12 7 25

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - New Non Medically Complex 0 13 5 20

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - Established Medically Complex 0 11 5 20

% of Time Spent of All Cases 0% 100% 100% 80%

HIV/AIDS Program - Established Non Medically Complex 0 12 4 15

% of Time Spent of All Cases 0% 40% 100% 80%

HIV/AIDS Program - New PMTCT 12 0 4 20

% of Time Spent of All Cases 100% 0% 100% 100%

HIV/AIDS Program - Established PMTCT 0 12 4 15

% of Time Spent of All Cases 0% 40% 100% 80%

HIV/AIDS Program - Pre-ART 0 13 5 15

% of Time Spent of All Cases 0% 75% 100% 90%

HIV/AIDS Program - New Pediatric 0 13 5 25

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - Established Pediatric 0 10 4 20

% of Time Spent of All Cases 0% 80% 100% 80%

HIV/AIDS Program - Infant 0 14 7 25

% of Time Spent of All Cases 0% 100% 100% 100%

TB Program - New Patient 0 40 0 25

% of Time Spent of All Cases 0% 100% 0% 100%

TB Program - Follow-up Patient 0 25 0 15

% of Time Spent of All Cases 0% 100% 0% 50%

STI Clinic 0 25 0 0

% of Time Spent of All Cases 0% 80% 0% 0%

Family Planning 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nursing

Page | 90

Service delivery activity times (min): Health Centers (scenario analysis inputs)

Service Delivery Activity Times : Health Centers Adherence

Scenario 3: OPD Patient Check-ins Shifted

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC Counselor

Outpatient Services

Under 5 Screening 0 20 5 5

% of Time Spent of All Cases 0% 100% 100% 100%

Over 5 Screening 0 20 5 5

% of Time Spent of All Cases 0% 100% 100% 100%

Accidents and Emergencies 0 35 10 8

% of Time Spent of All Cases 0% 80% 100% 100%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up Visit 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

EPI 0.0 0.0 10.0 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 0 30 0 45

% of Time Spent of All Cases 0% 100% 0% 100%

Client-Initiated Counseling and Testing Program - Negative 0 20 0 30

% of Time Spent of All Cases 0% 100% 0% 100%

HIV/AIDS Program - New Medically Complex 0 12 7 25

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - New Non Medically Complex 0 13 5 20

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - Established Medically Complex 0 11 5 20

% of Time Spent of All Cases 0% 100% 100% 80%

HIV/AIDS Program - Established Non Medically Complex 0 12 4 15

% of Time Spent of All Cases 0% 40% 100% 80%

HIV/AIDS Program - New PMTCT 12 0 4 20

% of Time Spent of All Cases 100% 0% 100% 100%

HIV/AIDS Program - Established PMTCT 0 12 4 15

% of Time Spent of All Cases 0% 40% 100% 80%

HIV/AIDS Program - Pre-ART 0 13 5 15

% of Time Spent of All Cases 0% 75% 100% 90%

HIV/AIDS Program - New Pediatric 0 13 5 25

% of Time Spent of All Cases 0% 100% 100% 100%

HIV/AIDS Program - Established Pediatric 0 10 4 20

% of Time Spent of All Cases 0% 80% 100% 80%

HIV/AIDS Program - Infant 0 14 7 25

% of Time Spent of All Cases 0% 100% 100% 100%

TB Program - New Patient 0 40 0 25

% of Time Spent of All Cases 0% 100% 0% 100%

TB Program - Follow-up Patient 0 25 0 15

% of Time Spent of All Cases 0% 100% 0% 50%

STI Clinic 0 25 0 0

% of Time Spent of All Cases 0% 80% 0% 0%

Family Planning 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nursing

Page | 91

Service delivery activity times (min): Health Centers (scenario analysis inputs)

Service Delivery Activity Times : Health Centers Adherence

Scenario 4: No Adherence Support

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC Counselor

Outpatient Services

Under 5 Screening 0 20 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Over 5 Screening 0 20 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Accidents and Emergencies 0 35 15 0

% of Time Spent of All Cases 0% 80% 10% 0%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up Visit 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

EPI 0.0 0.0 10.0 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 0 30 12 0

% of Time Spent of All Cases 0% 100% 100% 0%

Client-Initiated Counseling and Testing Program - Negative 0 20 8 0

% of Time Spent of All Cases 0% 100% 20% 0%

HIV/AIDS Program - New Medically Complex 0 12 30 0

% of Time Spent of All Cases 0% 100% 100% 0%

HIV/AIDS Program - New Non Medically Complex 0 13 24 0

% of Time Spent of All Cases 0% 100% 100% 0%

HIV/AIDS Program - Established Medically Complex 0 11 24 0

% of Time Spent of All Cases 0% 100% 100% 0%

HIV/AIDS Program - Established Non Medically Complex 0 12 18 0

% of Time Spent of All Cases 0% 40% 100% 0%

HIV/AIDS Program - New PMTCT 12 0 24 0

% of Time Spent of All Cases 100% 0% 100% 0%

HIV/AIDS Program - Established PMTCT 0 12 18 0

% of Time Spent of All Cases 0% 40% 100% 0%

HIV/AIDS Program - Pre-ART 0 13 18 0

% of Time Spent of All Cases 0% 75% 100% 0%

HIV/AIDS Program - New Pediatric 0 13 28 0

% of Time Spent of All Cases 0% 100% 100% 0%

HIV/AIDS Program - Established Pediatric 0 10 24 0

% of Time Spent of All Cases 0% 80% 100% 0%

HIV/AIDS Program - Infant 0 14 30 0

% of Time Spent of All Cases 0% 100% 100% 0%

TB Program - New Patient 0 40 25 0

% of Time Spent of All Cases 0% 100% 100% 0%

TB Program - Follow-up Patient 0 25 15 0

% of Time Spent of All Cases 0% 100% 50% 0%

STI Clinic 0 25 0 0

% of Time Spent of All Cases 0% 80% 0% 0%

Family Planning 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nursing

Page | 92

Service delivery activity times (min): Health Centers Service Delivery Activity Times : Health Centers

Pharmacy

TechnicianPharmacy Assistant

Front of Shop

OPD Dispensing 0 3

% of Time Spent of All Cases 0% 100%

IPD Dispensing 0 0

% of Time Spent of All Cases 0% 0%

Back of Shop

Pre-Packing 0 120

% of Time Spent of All Cases 0% 10%

Stock Inventory 0 300

% of Time Spent of All Cases 0% 100%

Procurement 0 180

% of Time Spent of All Cases 0% 100%

Receiving 0 0.05

% of Time Spent of All Cases 0% 100%

Inventory Management 0 20

% of Time Spent of All Cases 0% 100%

Expiration Management 0 60

% of Time Spent of All Cases 0% 100%

Ward Rounds/Issuing Ward Stock 0 0

% of Time Spent of All Cases 0% 0%

Storage Organization/Upkeep 0 45

% of Time Spent of All Cases 0% 100%

Reporting 0 30

% of Time Spent of All Cases 0% 100%

Pharmacy

Page | 93

Service delivery activity times (min): Public Health Units

Service Delivery Activity Times : Public Health Units

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)

Outpatient Services

Under 5 Screening 0 15 3

% of Time Spent of All Cases 0% 100% 100%

Over 5 Screening 0 15 3

% of Time Spent of All Cases 0% 100% 100%

Accidents and Emergencies 0 35 3

% of Time Spent of All Cases 0% 100% 100%

Antental Care - First Visit 30 0 0

% of Time Spent of All Cases 100% 0% 0%

Antental Care - Follow up Visit 15 0 0

% of Time Spent of All Cases 100% 0% 0%

Nutrition Program 0 0 15

% of Time Spent of All Cases 0% 0% 100%

EPI 0.0 0.0 10.0

% of Time Spent of All Cases 0% 0% 100%

Client-Initiated Counseling and Testing Program - Positive 30 0 0

% of Time Spent of All Cases 100% 0% 0%

Client-Initiated Counseling and Testing Program - Negative 20 0 0

% of Time Spent of All Cases 100% 0% 0%

HIV/AIDS Program - New Medically Complex 20 0 13

% of Time Spent of All Cases 100% 0% 80%

HIV/AIDS Program - New Non Medically Complex 13 0 12

% of Time Spent of All Cases 80% 0% 80%

HIV/AIDS Program - Established Medically Complex 16 0 11

% of Time Spent of All Cases 100% 0% 80%

HIV/AIDS Program - Established Non Medically Complex 12 0 10

% of Time Spent of All Cases 30% 0% 80%

HIV/AIDS Program - New PMTCT 12 0 7

% of Time Spent of All Cases 100% 0% 80%

HIV/AIDS Program - Established PMTCT 12 0 10

% of Time Spent of All Cases 30% 0% 80%

HIV/AIDS Program - Pre-ART 13 0 12

% of Time Spent of All Cases 60% 0% 80%

HIV/AIDS Program - New Pediatric 18 0 15

% of Time Spent of All Cases 100% 0% 80%

HIV/AIDS Program - Established Pediatric 12 0 12

% of Time Spent of All Cases 75% 0% 80%

HIV/AIDS Program - Infant 20 0 16

% of Time Spent of All Cases 100% 0% 80%

TB Program - New Patient 0 40 3

% of Time Spent of All Cases 0% 100% 100%

TB Program - Follow-up Patient 0 30 3

% of Time Spent of All Cases 0% 100% 100%

STI Clinic 18 0 0

% of Time Spent of All Cases 100% 0% 0%

Family Planning 15 0 0

% of Time Spent of All Cases 100% 0% 0%

Administrative

Other Weekly Tasks 0 0 0

Other Daily Tasks 180 180 180

Individual Tasks 20 0 0

Number Completing Individual Tasks 3 0 0

Nursing

Page | 94

Service delivery activity times (min): Public Health Units

Service Delivery Activity Times : Public Health Units

Senior Dental

Officer (E5)

Dental Officer

(E4)

Dental

Laboratory

Technologist

(C6)

Dental

Therapist (C5)

Dental

Hygienist (C3)

Dental Chair

Side Assistant

(A3)

Dental Services

Dental Accidents and Emergencies 0 0 0 0 30 30

% of Time Spent of All Cases 0% 0% 0% 0% 100% 100%

Under 5 Dental Admissions 0 0 0 40 40 40

% of Time Spent of All Cases 0% 0% 0% 30% 70% 100%

Over 5 Dental Admissions 0 0 0 30 30 30

% of Time Spent of All Cases 0% 0% 0% 30% 70% 100%

Administrative

Other Weekly Tasks 0 0 0 0 0 0

Other Daily Tasks 0 0 0 30 30 30

Individual Tasks 0 0 0 0 0 0

Number Completing Individual Tasks 0 0 0 0 0 0

Dental

Page | 95

Service delivery activity times (min): Public Health Units (scenario analysis inputs)

Service Delivery Activity Times : Public Health Units Adherence

Scenario 1: No Task Shifting

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC Counselor

Outpatient Services

Under 5 Screening 0 15 3 0

% of Time Spent of All Cases 0% 100% 100% 0%

Over 5 Screening 0 15 3 0

% of Time Spent of All Cases 0% 100% 100% 0%

Accidents and Emergencies 0 35 3 0

% of Time Spent of All Cases 0% 100% 100% 0%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up Visit 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

EPI 0.0 0.0 10.0 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 30 0 0 45

% of Time Spent of All Cases 100% 0% 0% 100%

Client-Initiated Counseling and Testing Program - Negative 20 0 0 30

% of Time Spent of All Cases 100% 0% 0% 100%

HIV/AIDS Program - New Medically Complex 20 0 13 20

% of Time Spent of All Cases 100% 0% 80% 100%

HIV/AIDS Program - New Non Medically Complex 13 0 12 15

% of Time Spent of All Cases 80% 0% 80% 100%

HIV/AIDS Program - Established Medically Complex 16 0 11 15

% of Time Spent of All Cases 100% 0% 80% 80%

HIV/AIDS Program - Established Non Medically Complex 12 0 10 10

% of Time Spent of All Cases 30% 0% 80% 80%

HIV/AIDS Program - New PMTCT 12 0 7 15

% of Time Spent of All Cases 100% 0% 80% 100%

HIV/AIDS Program - Established PMTCT 12 0 10 10

% of Time Spent of All Cases 30% 0% 80% 80%

HIV/AIDS Program - Pre-ART 13 0 12 10

% of Time Spent of All Cases 60% 0% 80% 90%

HIV/AIDS Program - New Pediatric 18 0 15 20

% of Time Spent of All Cases 100% 0% 80% 100%

HIV/AIDS Program - Established Pediatric 12 0 12 15

% of Time Spent of All Cases 75% 0% 80% 80%

HIV/AIDS Program - Infant 20 0 16 20

% of Time Spent of All Cases 100% 0% 80% 100%

TB Program - New Patient 0 40 3 20

% of Time Spent of All Cases 0% 100% 100% 100%

TB Program - Follow-up Patient 0 30 3 10

% of Time Spent of All Cases 0% 100% 100% 50%

STI Clinic 18 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Family Planning 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nursing

Page | 96

Service delivery activity times (min): Public Health Units (scenario analysis inputs)

Service Delivery Activity Times : Public Health Units Adherence

Scenario 2: HIV Patient Check-ins Shifted

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC Counselor

Outpatient Services

Under 5 Screening 0 15 3 0

% of Time Spent of All Cases 0% 100% 100% 0%

Over 5 Screening 0 15 3 0

% of Time Spent of All Cases 0% 100% 100% 0%

Accidents and Emergencies 0 35 3 0

% of Time Spent of All Cases 0% 100% 100% 0%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up Visit 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

EPI 0.0 0.0 10.0 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 30 0 0 45

% of Time Spent of All Cases 100% 0% 0% 100%

Client-Initiated Counseling and Testing Program - Negative 20 0 0 30

% of Time Spent of All Cases 100% 0% 0% 100%

HIV/AIDS Program - New Medically Complex 20 0 10 25

% of Time Spent of All Cases 100% 0% 80% 100%

HIV/AIDS Program - New Non Medically Complex 13 0 9 20

% of Time Spent of All Cases 80% 0% 80% 100%

HIV/AIDS Program - Established Medically Complex 16 0 8 20

% of Time Spent of All Cases 100% 0% 80% 80%

HIV/AIDS Program - Established Non Medically Complex 12 0 7 15

% of Time Spent of All Cases 30% 0% 80% 80%

HIV/AIDS Program - New PMTCT 12 0 4 20

% of Time Spent of All Cases 100% 0% 80% 100%

HIV/AIDS Program - Established PMTCT 12 0 7 15

% of Time Spent of All Cases 30% 0% 80% 80%

HIV/AIDS Program - Pre-ART 13 0 9 15

% of Time Spent of All Cases 60% 0% 80% 90%

HIV/AIDS Program - New Pediatric 18 0 12 25

% of Time Spent of All Cases 100% 0% 80% 100%

HIV/AIDS Program - Established Pediatric 12 0 9 20

% of Time Spent of All Cases 75% 0% 80% 80%

HIV/AIDS Program - Infant 20 0 13 25

% of Time Spent of All Cases 100% 0% 80% 100%

TB Program - New Patient 0 40 0 25

% of Time Spent of All Cases 0% 100% 0% 100%

TB Program - Follow-up Patient 0 30 0 15

% of Time Spent of All Cases 0% 100% 0% 50%

STI Clinic 18 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Family Planning 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nursing

Page | 97

Service delivery activity times (min): Public Health Units (scenario analysis inputs)

Service Delivery Activity Times : Public Health Units Adherence

Scenario 3: OPD Patient Check-ins Shifted

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC Counselor

Outpatient Services

Under 5 Screening 0 15 0 5

% of Time Spent of All Cases 0% 100% 0% 100%

Over 5 Screening 0 15 0 5

% of Time Spent of All Cases 0% 100% 0% 100%

Accidents and Emergencies 0 35 0 5

% of Time Spent of All Cases 0% 100% 0% 100%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up Visit 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

EPI 0.0 0.0 10.0 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 30 0 0 45

% of Time Spent of All Cases 100% 0% 0% 100%

Client-Initiated Counseling and Testing Program - Negative 20 0 0 30

% of Time Spent of All Cases 100% 0% 0% 100%

HIV/AIDS Program - New Medically Complex 20 0 10 25

% of Time Spent of All Cases 100% 0% 80% 100%

HIV/AIDS Program - New Non Medically Complex 13 0 9 20

% of Time Spent of All Cases 80% 0% 80% 100%

HIV/AIDS Program - Established Medically Complex 16 0 8 20

% of Time Spent of All Cases 100% 0% 80% 80%

HIV/AIDS Program - Established Non Medically Complex 12 0 7 15

% of Time Spent of All Cases 30% 0% 80% 80%

HIV/AIDS Program - New PMTCT 12 0 4 20

% of Time Spent of All Cases 100% 0% 80% 100%

HIV/AIDS Program - Established PMTCT 12 0 7 15

% of Time Spent of All Cases 30% 0% 80% 80%

HIV/AIDS Program - Pre-ART 13 0 9 15

% of Time Spent of All Cases 60% 0% 80% 90%

HIV/AIDS Program - New Pediatric 18 0 12 25

% of Time Spent of All Cases 100% 0% 80% 100%

HIV/AIDS Program - Established Pediatric 12 0 9 20

% of Time Spent of All Cases 75% 0% 80% 80%

HIV/AIDS Program - Infant 20 0 13 25

% of Time Spent of All Cases 100% 0% 80% 100%

TB Program - New Patient 0 40 0 25

% of Time Spent of All Cases 0% 100% 0% 100%

TB Program - Follow-up Patient 0 30 0 15

% of Time Spent of All Cases 0% 100% 0% 50%

STI Clinic 18 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Family Planning 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nursing

Page | 98

Service delivery activity times (min): Public Health Units (scenario analysis inputs)

Service Delivery Activity Times : Public Health Units Adherence

Scenario 4: No Adherence Support

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC Counselor

Outpatient Services

Under 5 Screening 0 15 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Over 5 Screening 0 15 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Accidents and Emergencies 0 35 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up Visit 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

EPI 0.0 0.0 10.0 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 60 0 12 0

% of Time Spent of All Cases 100% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Negative 40 0 8 0

% of Time Spent of All Cases 100% 0% 20% 0%

HIV/AIDS Program - New Medically Complex 20 0 32 0

% of Time Spent of All Cases 100% 0% 100% 0%

HIV/AIDS Program - New Non Medically Complex 13 0 27 0

% of Time Spent of All Cases 80% 0% 100% 0%

HIV/AIDS Program - Established Medically Complex 16 0 25 0

% of Time Spent of All Cases 100% 0% 100% 0%

HIV/AIDS Program - Established Non Medically Complex 12 0 20 0

% of Time Spent of All Cases 30% 0% 100% 0%

HIV/AIDS Program - New PMTCT 12 0 22 0

% of Time Spent of All Cases 100% 0% 100% 0%

HIV/AIDS Program - Established PMTCT 12 0 20 0

% of Time Spent of All Cases 30% 0% 100% 0%

HIV/AIDS Program - Pre-ART 13 0 22 0

% of Time Spent of All Cases 60% 0% 100% 0%

HIV/AIDS Program - New Pediatric 18 0 35 0

% of Time Spent of All Cases 100% 0% 100% 0%

HIV/AIDS Program - Established Pediatric 12 0 27 0

% of Time Spent of All Cases 75% 0% 100% 0%

HIV/AIDS Program - Infant 20 0 35 0

% of Time Spent of All Cases 100% 0% 100% 0%

TB Program - New Patient 0 40 25 0

% of Time Spent of All Cases 0% 100% 100% 0%

TB Program - Follow-up Patient 0 30 15 0

% of Time Spent of All Cases 0% 100% 50% 0%

STI Clinic 18 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Family Planning 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nursing

Page | 99

Service delivery activity times (min): Public Health Units

Service Delivery Activity Times : Public Health Units

Pharmacy

TechnicianPharmacy Assistant

Front of Shop

OPD Dispensing 0 3

% of Time Spent of All Cases 0% 100%

IPD Dispensing 0 0

% of Time Spent of All Cases 0% 0%

Back of Shop

Pre-Packing 0 120

% of Time Spent of All Cases 0% 10%

Stock Inventory 0 300

% of Time Spent of All Cases 0% 100%

Procurement 0 180

% of Time Spent of All Cases 0% 100%

Receiving 0 0.05

% of Time Spent of All Cases 0% 100%

Inventory Management 0 20

% of Time Spent of All Cases 0% 100%

Expiration Management 0 60

% of Time Spent of All Cases 0% 100%

Ward Rounds/Issuing Ward Stock 0 0

% of Time Spent of All Cases 0% 0%

Storage Organization/Upkeep 0 45

% of Time Spent of All Cases 0% 100%

Reporting 0 30

% of Time Spent of All Cases 0% 100%

Pharmacy

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Service delivery activity times (min): Clinics A & B

Service Delivery Activity Times : Clinics

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)

Outpatient Services

Under 5 Screening 0 15 3

% of Time Spent of All Cases 0% 100% 100%

Over 5 Screening 0 15 3

% of Time Spent of All Cases 0% 100% 100%

Accidents and Emergencies 0 35 3

% of Time Spent of All Cases 0% 100% 100%

Antental Care - First Visit 30 0 0

% of Time Spent of All Cases 100% 0% 0%

Antental Care - Follow up Visit 15 0 0

% of Time Spent of All Cases 100% 0% 0%

Nutrition Program 0 0 15

% of Time Spent of All Cases 0% 0% 100%

EPI 0.0 0.0 10.0

% of Time Spent of All Cases 0% 0% 100%

Client-Initiated Counseling and Testing Program - Positive 0 30 0

% of Time Spent of All Cases 0% 100% 0%

Client-Initiated Counseling and Testing Program - Negative 0 20 0

% of Time Spent of All Cases 0% 100% 0%

HIV/AIDS Program - New Medically Complex 0 20 13

% of Time Spent of All Cases 0% 100% 80%

HIV/AIDS Program - New Non Medically Complex 0 13 12

% of Time Spent of All Cases 0% 80% 80%

HIV/AIDS Program - Established Medically Complex 0 16 11

% of Time Spent of All Cases 0% 100% 80%

HIV/AIDS Program - Established Non Medically Complex 0 12 10

% of Time Spent of All Cases 0% 30% 80%

HIV/AIDS Program - New PMTCT 12 0 7

% of Time Spent of All Cases 100% 0% 80%

HIV/AIDS Program - Established PMTCT 0 12 10

% of Time Spent of All Cases 0% 30% 80%

HIV/AIDS Program - Pre-ART 0 13 12

% of Time Spent of All Cases 0% 60% 80%

HIV/AIDS Program - New Pediatric 0 18 15

% of Time Spent of All Cases 0% 100% 80%

HIV/AIDS Program - Established Pediatric 0 12 12

% of Time Spent of All Cases 0% 75% 80%

HIV/AIDS Program - Infant 0 20 16

% of Time Spent of All Cases 0% 100% 80%

TB Program - New Patient 0 40 3

% of Time Spent of All Cases 0% 100% 100%

TB Program - Follow-up Patient 0 30 3

% of Time Spent of All Cases 0% 100% 100%

STI Clinic 0 18 0

% of Time Spent of All Cases 0% 100% 0%

Family Planning 0 0 15

% of Time Spent of All Cases 0% 0% 100%

Administrative

Other Weekly Tasks 0 0 0

Other Daily Tasks 120 120 120

Individual Tasks 20 0 0

Number Completing Individual Tasks 1 0 0

Nursing

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Service delivery activity times (min): Clinics A & B

Service Delivery Activity Times : Clinics

Senior

Dental

Officer (E5)

Dental

Officer (E4)

Dental

Laboratory

Technologist

(C6)

Dental

Therapist

(C5)

Dental

Hygienist

(C3)

Dental Chair

Side

Assistant

(A3)

Dental Services

Dental Accidents and Emergencies 0 0 0 0 60 0

% of Time Spent of All Cases 0% 0% 0% 0% 100% 0%

Under 5 Dental Admissions 0 0 0 0 40 0

% of Time Spent of All Cases 0% 0% 0% 0% 100% 0%

Over 5 Dental Admissions 0 0 0 0 30 0

% of Time Spent of All Cases 0% 0% 0% 0% 100% 0%

Administrative

Other Weekly Tasks 0 0 0 0 0 0

Other Daily Tasks 0 0 0 0 30 0

Individual Tasks 0 0 0 0 0 0

Number Completing Individual Tasks 0 0 0 0 0 0

Dental

Service Delivery Activity Times : Clinics

Senior Lab

Technologist (C6)

Lab Technologist (I,

II & III) (C3, C4 &

C5)

Lab Assistant (I &

II)/Phlebotomist (I

& II) (A3 & A4)

Clinical Support Services

Laboratory - POC Collection & Tests (RDT- Av for HIV, Syphilis, Malaria, Preg test, Glucose, Hb ,Urine biochem)0 0 10

% of Time Spent of All Cases 0% 0% 100%

Laboratory - Send Aways Preparation 0 0 8

% of Time Spent of All Cases 0% 0% 100%

Administrative

Other Weekly Tasks 0 0 0

Other Daily Tasks 0 0 45

Individual Tasks 0 0 0

Number Completing Individual Tasks 0 0 0

Laboratory

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Service delivery activity times (min): Clinics A & B (scenario analysis inputs)

Service Delivery Activity Times : Clinics Adherence

Scenario 1: No Task Shifting

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC Counselor

Outpatient Services

Under 5 Screening 0 15 3 0

% of Time Spent of All Cases 0% 100% 100% 0%

Over 5 Screening 0 15 3 0

% of Time Spent of All Cases 0% 100% 100% 0%

Accidents and Emergencies 0 35 3 0

% of Time Spent of All Cases 0% 100% 100% 0%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up Visit 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

EPI 0.0 0.0 10.0 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 0 30 0 45

% of Time Spent of All Cases 0% 100% 0% 100%

Client-Initiated Counseling and Testing Program - Negative 0 20 0 30

% of Time Spent of All Cases 0% 100% 0% 100%

HIV/AIDS Program - New Medically Complex 0 20 13 20

% of Time Spent of All Cases 0% 100% 80% 100%

HIV/AIDS Program - New Non Medically Complex 0 13 12 15

% of Time Spent of All Cases 0% 80% 80% 100%

HIV/AIDS Program - Established Medically Complex 0 16 11 15

% of Time Spent of All Cases 0% 100% 80% 80%

HIV/AIDS Program - Established Non Medically Complex 0 12 10 10

% of Time Spent of All Cases 0% 30% 80% 80%

HIV/AIDS Program - New PMTCT 12 0 7 15

% of Time Spent of All Cases 100% 0% 80% 100%

HIV/AIDS Program - Established PMTCT 0 12 10 10

% of Time Spent of All Cases 0% 30% 80% 80%

HIV/AIDS Program - Pre-ART 0 13 12 10

% of Time Spent of All Cases 0% 60% 80% 90%

HIV/AIDS Program - New Pediatric 0 18 15 20

% of Time Spent of All Cases 0% 100% 80% 100%

HIV/AIDS Program - Established Pediatric 0 12 12 15

% of Time Spent of All Cases 0% 75% 80% 80%

HIV/AIDS Program - Infant 0 20 16 20

% of Time Spent of All Cases 0% 100% 80% 100%

TB Program - New Patient 0 40 3 20

% of Time Spent of All Cases 0% 100% 100% 100%

TB Program - Follow-up Patient 0 30 3 10

% of Time Spent of All Cases 0% 100% 100% 50%

STI Clinic 0 18 0 0

% of Time Spent of All Cases 0% 100% 0% 0%

Family Planning 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

Nursing

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Service delivery activity times (min): Clinics A & B (scenario analysis inputs)

Service Delivery Activity Times : Clinics Adherence

Scenario 2: HIV Patient Check-ins Shifted

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC Counselor

Outpatient Services

Under 5 Screening 0 15 3 0

% of Time Spent of All Cases 0% 100% 100% 0%

Over 5 Screening 0 15 3 0

% of Time Spent of All Cases 0% 100% 100% 0%

Accidents and Emergencies 0 35 3 0

% of Time Spent of All Cases 0% 100% 100% 0%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up Visit 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

EPI 0.0 0.0 10.0 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 0 30 0 45

% of Time Spent of All Cases 0% 100% 0% 100%

Client-Initiated Counseling and Testing Program - Negative 0 20 0 30

% of Time Spent of All Cases 0% 100% 0% 100%

HIV/AIDS Program - New Medically Complex 0 20 13 20

% of Time Spent of All Cases 0% 100% 80% 100%

HIV/AIDS Program - New Non Medically Complex 0 13 12 15

% of Time Spent of All Cases 0% 80% 80% 100%

HIV/AIDS Program - Established Medically Complex 0 16 11 15

% of Time Spent of All Cases 0% 100% 80% 80%

HIV/AIDS Program - Established Non Medically Complex 0 12 10 10

% of Time Spent of All Cases 0% 30% 80% 80%

HIV/AIDS Program - New PMTCT 12 0 7 15

% of Time Spent of All Cases 100% 0% 80% 100%

HIV/AIDS Program - Established PMTCT 0 12 10 10

% of Time Spent of All Cases 0% 30% 80% 80%

HIV/AIDS Program - Pre-ART 0 13 12 10

% of Time Spent of All Cases 0% 60% 80% 90%

HIV/AIDS Program - New Pediatric 0 18 15 20

% of Time Spent of All Cases 0% 100% 80% 100%

HIV/AIDS Program - Established Pediatric 0 12 12 15

% of Time Spent of All Cases 0% 75% 80% 80%

HIV/AIDS Program - Infant 0 20 16 20

% of Time Spent of All Cases 0% 100% 80% 100%

TB Program - New Patient 0 40 0 25

% of Time Spent of All Cases 0% 100% 0% 100%

TB Program - Follow-up Patient 0 30 0 15

% of Time Spent of All Cases 0% 100% 0% 50%

STI Clinic 0 18 0 0

% of Time Spent of All Cases 0% 100% 0% 0%

Family Planning 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

Nursing

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Service delivery activity times (min): Clinics A & B (scenario analysis inputs)

Service Delivery Activity Times : Clinics Adherence

Scenario 3: OPD Patient Check-ins Shifted

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC Counselor

Outpatient Services

Under 5 Screening 0 15 0 5

% of Time Spent of All Cases 0% 100% 0% 100%

Over 5 Screening 0 15 0 5

% of Time Spent of All Cases 0% 100% 0% 100%

Accidents and Emergencies 0 35 0 5

% of Time Spent of All Cases 0% 100% 0% 100%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up Visit 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

EPI 0.0 0.0 10.0 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 0 30 0 45

% of Time Spent of All Cases 0% 100% 0% 100%

Client-Initiated Counseling and Testing Program - Negative 0 20 0 30

% of Time Spent of All Cases 0% 100% 0% 100%

HIV/AIDS Program - New Medically Complex 0 20 13 20

% of Time Spent of All Cases 0% 100% 80% 100%

HIV/AIDS Program - New Non Medically Complex 0 13 12 15

% of Time Spent of All Cases 0% 80% 80% 100%

HIV/AIDS Program - Established Medically Complex 0 16 11 15

% of Time Spent of All Cases 0% 100% 80% 80%

HIV/AIDS Program - Established Non Medically Complex 0 12 10 10

% of Time Spent of All Cases 0% 30% 80% 80%

HIV/AIDS Program - New PMTCT 12 0 7 15

% of Time Spent of All Cases 100% 0% 80% 100%

HIV/AIDS Program - Established PMTCT 0 12 10 10

% of Time Spent of All Cases 0% 30% 80% 80%

HIV/AIDS Program - Pre-ART 0 13 12 10

% of Time Spent of All Cases 0% 60% 80% 90%

HIV/AIDS Program - New Pediatric 0 18 15 20

% of Time Spent of All Cases 0% 100% 80% 100%

HIV/AIDS Program - Established Pediatric 0 12 12 15

% of Time Spent of All Cases 0% 75% 80% 80%

HIV/AIDS Program - Infant 0 20 16 20

% of Time Spent of All Cases 0% 100% 80% 100%

TB Program - New Patient 0 40 0 25

% of Time Spent of All Cases 0% 100% 0% 100%

TB Program - Follow-up Patient 0 30 0 15

% of Time Spent of All Cases 0% 100% 0% 50%

STI Clinic 0 18 0 0

% of Time Spent of All Cases 0% 100% 0% 0%

Family Planning 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

Nursing

Page | 105

Service delivery activity times (min): Clinics A & B (scenario analysis inputs)

Service Delivery Activity Times : Clinics Adherence

Scenario 4: No Adherence Support

Staff Nurse

(C5)

General Nurse

(C3)

Nursing

Assistant (C2)HTC Counselor

Outpatient Services

Under 5 Screening 0 15 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Over 5 Screening 0 15 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Accidents and Emergencies 0 35 8 0

% of Time Spent of All Cases 0% 100% 100% 0%

Antental Care - First Visit 30 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Antental Care - Follow up Visit 15 0 0 0

% of Time Spent of All Cases 100% 0% 0% 0%

Nutrition Program 0 0 15 0

% of Time Spent of All Cases 0% 0% 100% 0%

EPI 0.0 0.0 10.0 0

% of Time Spent of All Cases 0% 0% 100% 0%

Client-Initiated Counseling and Testing Program - Positive 0 30 12 0

% of Time Spent of All Cases 0% 100% 100% 0%

Client-Initiated Counseling and Testing Program - Negative 0 20 8 0

% of Time Spent of All Cases 0% 100% 20% 0%

HIV/AIDS Program - New Medically Complex 0 20 32 0

% of Time Spent of All Cases 0% 100% 100% 0%

HIV/AIDS Program - New Non Medically Complex 0 13 27 0

% of Time Spent of All Cases 0% 80% 100% 0%

HIV/AIDS Program - Established Medically Complex 0 16 25 0

% of Time Spent of All Cases 0% 100% 100% 0%

HIV/AIDS Program - Established Non Medically Complex 0 12 20 0

% of Time Spent of All Cases 0% 30% 100% 0%

HIV/AIDS Program - New PMTCT 12 0 22 0

% of Time Spent of All Cases 100% 0% 100% 0%

HIV/AIDS Program - Established PMTCT 0 12 20 0

% of Time Spent of All Cases 0% 30% 100% 0%

HIV/AIDS Program - Pre-ART 0 13 22 0

% of Time Spent of All Cases 0% 60% 100% 0%

HIV/AIDS Program - New Pediatric 0 18 35 0

% of Time Spent of All Cases 0% 100% 100% 0%

HIV/AIDS Program - Established Pediatric 0 12 27 0

% of Time Spent of All Cases 0% 75% 100% 0%

HIV/AIDS Program - Infant 0 20 35 0

% of Time Spent of All Cases 0% 100% 100% 0%

TB Program - New Patient 0 40 25 0

% of Time Spent of All Cases 0% 100% 100% 0%

TB Program - Follow-up Patient 0 30 15 0

% of Time Spent of All Cases 0% 100% 50% 0%

STI Clinic 0 18 0 0

% of Time Spent of All Cases 0% 100% 0% 0%

Family Planning 0 0 0 0

% of Time Spent of All Cases 0% 0% 0% 0%

Nursing

Page | 106

Service delivery activity times (min): Clinics A & B

Source: Time-motion observations at facilities and expert interviews

Service Delivery Activity Times : Clinics

Pharmacy

TechnicianPharmacy Assistant

Front of Shop

OPD Dispensing 0 3

% of Time Spent of All Cases 0% 100%

IPD Dispensing 0 0

% of Time Spent of All Cases 0% 0%

Back of Shop

Pre-Packing 0 120

% of Time Spent of All Cases 0% 10%

Stock Inventory 0 300

% of Time Spent of All Cases 0% 100%

Procurement 0 180

% of Time Spent of All Cases 0% 100%

Receiving 0 0.50

% of Time Spent of All Cases 0% 100%

Inventory Management 0 20

% of Time Spent of All Cases 0% 100%

Expiration Management 0 60

% of Time Spent of All Cases 0% 100%

Ward Rounds/Issuing Ward Stock 0 0

% of Time Spent of All Cases 0% 0%

Storage Organization/Upkeep 0 45

% of Time Spent of All Cases 0% 100%

Reporting 0 30

% of Time Spent of All Cases 0% 100%

Pharmacy

Page | 107

Annex VI. Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

Page | 109

Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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Facility-Level Optimal Staffing Requirements

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