Central Annals of Public Health and Research

Cite this article: Nawa M, Hangoma P, Michelo C (2019) Forecasting Malaria Reduction in a High Malaria Endemic Country; Investigating Programme Interventions for Malaria Elimination in Zambia. Ann Public Health Res 6(1): 1086.

Abstract

Background: Zambia has a high prevalence of malaria; in 2015 during the peak season, about 20% of the children less than five years old were infected by Malaria parasites. Cognizant of this high burden, the government aims to eliminate the disease by 2021 using adopted guidelines from the World Health Organization Global Technical Strategy (GTS).The technical strategy allows countries to deploy generalized high impact interventions such as Insecticide Treated Nets (ITNs), Indoor Residual Spraying (IRS) and prompt treatment of malaria cases among others to bring down malaria burden to low levels. After which, they can apply targeted interventions on malaria foci in low burden settings to eliminate it altogether and then prevent reintroduction of new infections. This study aimed to model how different types of malaria interventions and at what levels of deployment would bring malaria down to low levels suitable for focal interventions.

Methods: We obtained secondary data from three waves of the most recent nationally representative Malaria Indicator Surveys (MIS) from Zambia for 2010, 2012 and 2015 and applied multivariable logistic regression modeling to identify significant predictor variables on malaria prevalence. We then conducted post-estimation margins on the model to determine what type of interventions and at what levels of deployment it would take to bring malaria burden down to low levels suitable for focal interventions.

Results: Increasing IRS and ITNs from the 2015 levels of coverage of 28.9% and 58.9% to at least 80% would bring malaria down from 2015 levels of 19.4% to 16.3%. Including standard housing in the anti-malaria arsenal would augment the fight; increasing standard housing to 20% from the 13.4% in 2015 would bring malaria down to below 15% where accelerator interventions may be implemented for elimination. If the rate of standard housing is increased further to 90%, malaria prevalence can decrease to 10%.

Conclusion: The impact of current mainstay interventions against malaria may not bring down prevalence to at least moderate levels required for accelerator interventions’ deployment for focal malaria elimination. Inclusion and increasing of standard housing would augment the fight and bring malaria down to the levels needed for focal malaria elimination.

*Corresponding authorMukumbuta Nawa, School of Public Health, University of Zambia, Ridgeway Campus, P. O. Box 50110, Lusaka 1010, Zambia, Email:

Submitted: 18 December 2018

Accepted: 13 February 2019

Published: 15 February 2019

Copyright© 2019 Nawa et al.

OPEN ACCESS

Keywords•Indoor residual spraying•Insecticide treated nets•Standard housing•Elimination

Research Article

Forecasting Malaria Reduction in a High Malaria Endemic Country; Investigating Programme Interventions for Malaria Elimination in ZambiaMukumbuta Nawa*, Peter Hangoma, and Charles MicheloSchool of Public Health, University of Zambia, Zambia

ABBREVIATIONSCSA: Census Supervisory Area, CSO: Central Statistical

Office, GTS: Global Technical Strategy, IRS: Indoor Residual Spraying, ITN: Insecticide Treated Net, IVM: Integrated Vector Management, MERG: Monitoring and Evaluation Reference Group, MIS: Malaria Indicator Survey, MoH: Ministry of Health, NMCP: National Malaria Control Programme, NMEP: National Malaria Elimination Plan, RBM: Roll Back Malaria

INTRODUCTIONMalaria is endemic to Zambia throughout the year but highest

during the rainy season from December to April [1]. As of 2015, the national malaria prevalence was at 19.4% in children aged below five years; however, in some parts of the country such as Luapula province in the north, the prevalence was as high as 32%

[2]. This implies that on average, one in every three children aged below the age of five years had malaria as opposed to the national average where one in every five children had the disease. High malaria prevalence is associated with increased morbidity and mortality among children, chronic anaemia, chronic stunting and wasting [3-5]. Since the year 2000, the government and its cooperating partners have been fighting malaria among other health priorities. They do this through the implementation of various strategic plans such as the National Malaria Strategic Plans 2001 – 2005, 2006- 2010, 2011-2016 and lately the National Malaria Elimination Plan 2017-2021 [1,6]. During these two decades, key achievements have been attained including reduced malaria prevalence from 33% in 2006 to 19.4% in 2015. However, this achievement is still a far cry from the elimination of the disease which is now the long term goal [6].

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Malaria elimination in Africa is not an unachievable pipe dream [7]. Several countries which were once malaria endemichave managed to eliminate the disease over time through deliberate interventions [8]. As late as 1945, malaria transmission was almost worldwide including North America, Western Europe, continental Europe, the whole of Africa and the Asian Pacific Region [9]. By 2015, the World Health Organization reported transmission remaining in 91 countries located mainly along the equator and the tropics [8]. Countries in temperate and sub-tropics have eliminated malaria while the disease remains concentrated in the tropics. The main measures used by malaria-free countries were Dichlorodiphenyltrichloroethane (DDT) spraying widely, prophylaxis on potential infected populations and treatment of infected cases using quinine and Chloroquine [10]. While individual countries were fighting malaria prior; concerted malaria elimination efforts were spearheaded by the Global Malaria Elimination Program (GMEP).Between 1955 and 1970, the GMEP was established by a resolution of the World Health Assembly (WHA) and coordinated by the WHO. The GMEP helped eliminate malaria in most developed countries; however, that was not the case in Africa. In Africa, efforts were only carried out to a limited extent in three countries as it was considered not feasible to extend the campaign to the rest of the continent [11].

In South America, the GMEP had significant impacts between the 1950s and 60s.For example, Brazil managed to reduce malaria from 4-6 million cases a year among 45 million Brazilians in the 1940s to below 1% by 1970 [12]. Following the discontinuation of the GMEP, malaria increased in Brazil between 1970 and 1990 and to date, the country is still battling the disease due to lack of sustainability of the earlier gains [13]. Another country that lies near the equator with a hot and wet climate that benefited from the GMEP activities in the 1950s and 60s is Sri Lanka. It was once a very malarias country with reports of over three million cases out of 5.5 million populations in 1934/5. Sri Lanka implemented full coverage of Indoor Residual Spraying (IRS) with DDT, intensified prompt diagnosis and treatment of cases, surveillance and reporting of cases through a well-coordinated system [14]. By 1963, only 17 cases were reported nationwide; this led to discontinuation of IRS with DDT. The number of cases increased steadily after that and by 1969 over half a million cases were reported, and there were further increases into the 1980s [15,16]. Since 2000, renewed efforts through integrated vector management, case management and targeted focal interventions led to a steady decline until Sri Lanka was declared Malaria free by WHO in September 2016 [17,18].

Other than Sri Lanka which lies near the equator (5-10 degrees North), most other countries that have eliminated malaria lie away from the equator at least 23 degrees north or south. While countries that still have malaria lie between the Tropics of Capricorn and Cancer; suffice to say that the climate and environment in the tropics favor the breeding of mosquito vectors. The other reason for malaria persistence in the tropics is not only environmental factors but economic as demonstrated by near elimination in Brazil and Sri Lanka during the GMEP period. Following its suspension in 1969, developing countries (incidentally located in the tropics) could not sustain the interventions using their national resources resulting in the reestablishment of malaria [11,19].

Countries in Africa that are certified malaria-free are Libya (1980), Morocco (2010) and Tunisia (1979). Two other countries Egypt and Algeria have had no recent indigenous malaria cases and are in the process of being declared malaria-free [8]. Incidentally, these are countries in the semi-arid region of Africa. They had regional or focal malaria in oases and river basins mainly. They also lie in the subtropics along the tropic of Cancer, on the same latitude as other countries which have been declared malaria-free or about to be declared so such as Iraq and Uzbekistan. In the southern hemisphere, countries that lie in the subtropics such as Chile, Uruguay have also been or about to be declared malaria-free. Our interpretation of this in the context of the GMEP and what happened after that is that irrespective of where a country is situated, malaria is possible to be eliminated given the right tools and level of resources. Countries with unstable or low endemicity of the disease may with their country resources be able to eliminate malaria. High burden malaria countries, however, require substantial and sustained additional investments such as was availed during the GMEP. These ideas were echoed by the WHO Executive Director in the 2017 World Malaria Report “The WHO African Region continues to account for about 90% of malaria cases and deaths worldwide. Clearly, if we are to get the global malaria response back on track, supporting the most heavily affected countries in this region must be our primary focus” [8]. Similar sentiments were echoed in the Global Fund to fight AIDS, Tuberculosis and Malaria (GTATM) 2018 Report “Countries tend to fall into one of two categories: those progressing toward malaria elimination and those with a high burden that are slipping backwards in their response. Nearly all countries in the second category are in Africa” [20].

The Roll Back Malaria (RBM) initiative is one such international collaborative platforms in the post GMEP period that has been in existence since 1998. In its 2018 – 2020 strategic plan, it advocates for malaria awareness on the global agenda, regional fight against malaria and advocacy for increasing malaria financing. During its time, key results include a reduction of 60% of malaria mortality between 2000 and 2015 and reduction of cases by 75% in 57 countries [21]. Another critical global collaboration is the World Health Organization Global Technical Strategy (GTS) which aims among other specific targets to reduce the global mortality and incidence by 40% of the 2015 baseline by 2020 and 90% by 2030 [21]. The GTS recommends a phased approach of bringing down the malaria burden using Indoor Residual Spraying (IRS), Insecticide Treated Nets (ITNs) and facility case management in high burden areas. Further, it recommends elimination using Mass drug administration, community and facility case management, enhanced vector control and focal investigations in low burden areas [6,21].

Our impression of the progress in the fight against malaria post-GMEP is that, other than Sri Lanka, countries that have eliminated malaria or have reported zero cases are either in temperate or subtropics and are not the ‘hotbeds’ of malaria. It is more like the ‘low hanging fruits’ are being picked. In Sub-Saharan Africa (SSA) which has at least 90% of the global burden of malaria morbidity and mortality, it is countries at the tips of Africa that have eliminated or set to eliminate malaria. Countries such as South Africa, Botswana and Swaziland in the southern hemisphere and Egypt and Algeria in the northern hemisphere

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are not high burden malaria countries. Whilst 12 of the 13 countries said to have even increased their burden of malaria between 2010 and 2015 lie in the middle of Africa with typical tropical ecological conditions and also poor economically.

Similarly, on the South American continent, it is countries at the tips of the continent like Argentina, Chile, Uruguay and Paraguay in the south and Guatemala, Costa Rica and Mexico in the north that are tipped to be on track to elimination. High burden tropical countries like Brazil, Peru, Venezuela and Colombia which together account for 70% of malaria in the Americas are still grappling with the disease despite some notable successes in reducing the burden [22]. As noted in the World Malaria Report 2017, 90% of the reported 216 million cases of malaria reported in 2016 were in Africa, 7% from South East Asia and 2% from the East Mediterranean. Fifteen countries had 80% of all these cases, and all but one (India) were in the middle of Sub-Saharan Africa [8]. Zambia is also one of the 13 countries that recorded an increase in malaria burden despite a global decrease in 2015.

In 2017, Zambia launched its 2017- 2021 Malaria Elimination Strategic Plan whose main aim to eliminate local transmission of malaria by 2021. It plans to follow the WHO Global Technical Strategy (GTS) to malaria elimination and intends to employ key tools in the fight including vector control, case management, malaria in pregnancy, parasite clearance and health promotion. It plans to reduce its malaria incidence from the baseline of 336 cases per 1000 population in 2016 to 5 per 1000 in 2019 and zero local transmission by 2021 [6]. Zambia is also part of the Elimination Eight group; a group of eight southern African countries that are committed to eliminating malaria in their territories. It is against the backdrop that malaria prevalence increased in Zambia from 16% in 2010 to 19.4% in 2015 despite increases in intervention coverage like IRS and ITN that this study was conducted. We aimed to use statistical modeling to find out if current tools available in Zambia using the phased GTS approach can bring down malaria prevalence from high prevalence (2015 level of 19.4% among children aged below five years) to low prevalence (below 5% in the same age group).

MATERIALS AND METHODS

Study design

This study is secondary data analysis of cross-sectional surveys from the Malaria Indicator Surveys 2010, 2012 and 2015.

Study settings

The study used data from studies carried out in the whole of Zambia. Zambia is a country in Sub-Saharan Africa (SSA), it has a tropical climate and a high burden of malaria.

Sampling

The study included the whole household member MIS datasets without resampling for 2010, 2012 and 2015. The primary surveys the MISs are 2 stage cluster sampling surveys; the first stage is cluster sampling at the national level among 25 631 Standard Enumeration Areas (SEAs) by the Central Statistics Office from the 2010 Census of Population and Housing. For 2015, 150 clusters were sampled, 2012, 160 clusters and 2010, 180 clusters. The second stage was sampling 25 households without

replacement within sampled SEAs, and all household members were included in the survey [2,23,24]. The MISs are set at 95% confidence level, 80% power, the design effect of 2 and adjusted for a 20% non-response rate and are representative at national, provincial, urban and rural level.

Data collection

We obtained the datasets from the repository at the National Malaria Elimination Centre of the Ministry of Health in Stata 13 format for 2015 datasets and Microsoft Access 2010 for 2010 and 2012 datasets. We then extracted the data, appended it and cleaned it in Stata version 15 [25].

Data analysis

Basic descriptive statistics like mean, median, standard deviation and interquartile range for continuous variables were calculated respectively depending on whether the variable was normally distributed or not as determined by histogram and Shapiro-Wilk test. Categorical data were summarised using counts and percentages. We then fitted a multivariable logistic regression model in survey-set in Stata version 17 using apriori subject knowledge to establish the effect size measures between predictor and dependent variables. The model was then validated using baseline 2015 observed values of malaria prevalence at national and provincial levels against values predicted by the model using margins command. We then compared the predicted value against the observed in MIS 2015 value using one sample test of proportions. Further, a post-estimation marginal prediction sensitivity analysis was used to determine what magnitude of the predictor variables in coverage would bring the dependent variable to the desired level in line with this study’s objective.

Ethical considerations

This study was approved by ERES Converge IRB Ref No. 2017-Aug-005 and by the National Health Research Authority. The Ministry of Health also granted permission for us to access the datasets for this study. We did not deal with individual respondents but anonymized datasets, so there was no need for individual respondents’ consent.

RESULTS Basic demographic characteristics of respondents

The response rates for all the three (3) surveys were more than 95%. The demographics were comparable across the surveys. Figure 1 summary the demographic characteristics of the participants. Model Estimation of 2015 Malaria Prevalence Observed by Malaria Indicator Survey (Table 1) summaries the variable beta coefficients, confidence intervals and the respective P values; the beta coefficients were used in estimating malaria prevalence. Post-estimation margins were able to predict the 2015 Malaria Prevalence at national and provincial levels with precision for most values of malaria prevalence in 2015 except for values of malaria prevalence less than 10%. Table 2 shows observed values in 2015 Malaria Indicator Survey, the predicted value using the post-estimation margins of the model and respective P-values for comparisons of the observed 2015 MIS prevalence and predicted values.

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Figure 1 Baseline Characteristics of Respondents.

Table 1: The Predictive Model.

Variable (Xi) Coefficient 95% Conf. Interval P - Value

Age category (12 - 59 Months) 1.563 1.184 1.94 < 0.001

Sex (Females) -0.1 -0.278 0.08 0.27

Residence Location (Urban) -1.496 -1.95 -1 < 0.001

Altitude (meters) -0.001 -0.001 0 0.065

standard house (Yes) -0.693 -1.031 -0.4 < 0.001

Wealth status (not poorest quartile) -0.411 -0.64 -0.2 < 0.001

Indoor Residual Sprayed (Yes) -0.376 -0.673 -0.1 0.013

Slept Under a net (Yes) -0.196 -0.352 -0 0.015

Rainfall (mm) 0.001 -0.001 0 0.302

Temperature ( Degree Celsius) -0.323 -0.456 -0.2 < 0.001

_cons 5.239 1.242 9.24 0.01

Sensitivity analysis estimating malaria prevalence by varying levels of interventions

Sensitivity analysis was done using hypothetical values in the model to predict how malaria prevalence would respond to varying levels of coverage of malaria interventions. Figure 2 shows how malaria prevalence would vary with different levels

of coverage of IRS while keeping all other variables constant at 2015 levels. Malaria prevalence would reduce from 20.5% (95% CI 10.4 – 30.6%) when IRS coverage is at 10% to 16.0% (6.8- 25.3%) when IRS is at 90%.

Figure 3 shows how malaria prevalence would vary with different levels of coverage of ITNs while keeping all other

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Figure 2 Varying Indoor Residual Spraying and Keeping all other variables constant at 2015 levels.

variables constant at 2015 levels. Malaria prevalence would reduce from 20.8% (95% CI 10.1 – 31.6%) when ITN utilization is at 10% to 18.4% (95%CI 9.1- 27.7%) when ITN utilization is at 90%. Figure 4 shows how malaria prevalence would vary with different levels of coverage of standard housing while keeping all other variables constant at 2015 levels. Prevalence would reduce from 19.6% (95% CI 9.8- 29.7%) when standard housing is at 10% to 12.4 (95% CI 4.6 – 20.2) when standard housing is at 90%. Figure 5 shows a best case scenario where IRS and ITNs were fixed at 90% coverage and standard housing was varied whilst keeping all other variables constant at 2015 levels. Prevalence reduced from 16.3% (95%CI 7.2- 25.4%) when standard housing was at 10% to 10.1% (96%CI 3.3 – 16.9%) when standard housing was at 90%. With this latter scenario, prevalence reduced to the moderate level of less than 15% when standard housing was at 25% where it is permissible to commence elimination activities. Finally, we assumed the worst case scenario where interventions like ITNs and IRS reduced to 10% and all other variables remained constant at 2015 levels; prevalence would increase from 2015’s 19.4 to 22.0 (95% CI 11.0- 33.1%).

DISCUSSIONThis study demonstrated that malaria prevalence might be

predicted based on the level of coverage of intervention variables and prevailing climatic conditions. Studies have shown that global malaria is declining mainly due to interventions even though not all the decline can be explained [26]. Models have been developed for forecasting malaria, some of which are based on climatic conditions like temperature, rainfall and vegetative indices [27-29]. This modeling approach is proactive particularly in unstable malaria environments where malaria epidemics can be predicted, and authorities can plan to address outbreaks ahead of time [30].

Other models use historical data from routine surveillance sources and apply some versions of time series. Such models have been shown to help in forecasting malaria burden including seasonal variations even in high burden areas so that programs can prepare for ongoing malaria transmission and adequately prepare inputs to avoid stock-outs of drugs, test kits, and other supplies [31-33]. Our model combines biological, environmental and sociodemographic variables and also includes a constant

which encompasses the unaccounted for residuals that have been generated using data from the recent three Malaria Indicator Surveys in Zambia. This model can be useful in strategic planning for target setting where multiple interventions are used in malaria programs. For example, if a malaria program wants to reduce prevalence by 50%, it can estimate the coverage in predictor variables that would result in the desired 50% reduction in prevalence by changing coverage levels in the model.

To remain realistic, only variables that can feasibly be changed using program implementation such as IRS, ITNs and Housing were varied while keeping variables that cannot be feasibly changed in a malaria program constant at 2015 levels. For example, /rural residence location; you cannot move people from rural areas to urban areas just because urban areas have less risk of malaria compared to rural areas. Similarly, you cannot change temperature, altitude or people’s wealth status in a malaria intervention program. A malaria program can spray people’s houses, distribute nets and promote policies for the building of standard houses and installation of fly screens in houses.

Out of the five hypothetical scenarios we created in our sensitivity analysis where we varied ITNs, IRS and standard housing, no scenario was able to predict the desired prevalence of less than 5% even when ITNs and IRS were fixed at 80% and standard housing was varied up to 90% which predicted a lowest prevalence of 10%. Based on the WHO GTS for malaria elimination, level four (prevalence > 15%) where most of Zambia was as at 2015, elimination activities may not be implemented as there is need to bring down the prevalence first using vector control and facility case management. When prevalence gets to level three where it lies between 15% and 5%, accelerator activities such as Mass Drug Administration (MDA) and community case management may be implemented in areas that have the capacity. Otherwise, these are applied when prevalence is at levels two, one and zero where prevalence is below 5%. In Zambia, only two provinces had prevalence below 5% namely Lusaka (2.4%) and Southern (0.6%) province as at 2015. Accelerator interventions such as MDA and reactive case detection have been implemented in these areas to drive the elimination agenda at sub national level [34]. The primary challenge to sub national elimination efforts is

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Figure 3 Varying ITNs, Keeping All Other Variables Constant at 2015 Levels.

Figure 4 Varying Standard Housing, Keeping All Other Variables Constant at 2015 Levels.

Figure 5 Varying Standard Housing, Raising ITNs and IRS to 80% & Keeping all other Variables Constant at 2015 Levels.

contamination through importation of malaria cases because of increased mobility of persons across provinces as there are no restrictions or screening between regions within the country. For example, in Lusaka district; one study found that 94% of malaria cases at health facilities were imported from other provinces.

The frequency of travel and duration of stay in malaria areas were found to be significant risk factors among residents [35]. Due to the high risk of continual contamination because of human mobility within the country, we feel a sub national approach to elimination is not sustainable. Instead, bringing down prevalence

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in all provinces and implementing the accelerator interventions to drive down local transmission further while screening people entering at borders and airports may be a more feasible approach.

Our model was able to predict cover ages that would bring prevalence to at least level three. In one scenario; ITNs and IRS were kept constant at 2015 levels, standard housing had to reach at least 60% which in our view may not be very feasible. In 2015, standard housing was at only 13% of the surveyed households. On the other hand, when mainstream interventions such as ITNs and IRS are increased to 80% from 2015 levels of 49% and 29% respectively, level three was reached when standard housing reached only 20% which is more feasible from the 13% of recorded in 2015. Keeping other variables constant and varying either ITNs or IRS alone did bring prevalence below 15% neither did increase both ITN and IRS at the same time to 80%.

CONCLUSIONThe current malaria elimination progress seems to be

benefiting more the countries with unstable malaria burden where elimination activities are implemented. While heavy burden countries such as Zambia are even experiencing episodes of resurgence despite the declining global trends. Modeling prevalence using different levels of coverage of predictor interventions and environmental indicators, it was shown in this study that malaria prevalence can be predicted and the results were comparable to the actual field data from the National Malaria Indicator Survey.

Among key interventions, IRS, ITN and standard housing at appropriate coverage levels have been shown to have potential to reduce malaria prevalence from current high levels in most parts of the country to at least moderate levels where accelerator interventions for elimination may be implemented.

RECOMMENDATIONSThere is a need to increase coverage of mainstay interventions

such as ITNs and IRS to at least 80% from 2015 levels.

Current mainstay interventions such as ITN and IRS alone are effective but have not been shown to bring down malaria to at

least moderate levels. There is a need to complement them with housing infrastructure improvements from improvised housing structures that allow free entry of vector mosquito to standard housing structures for moderate burden levels to be reached. In line with the WHO GTS recommendations, it can be possible then to implement accelerator interventions such as enhanced vector control, Mass Drug administration, community case management and surveillance once prevalence is brought down to at least moderate levels.

The modeling approach used in this study can be used by malaria programs to inform target setting and intervention selection for locally effective programs.

Limitations

Due to inadequate numbers of respondents who received treatment in the two weeks preceding the surveys, the effect of prompt treatment on malaria prevalence could not be included in the model.

ACKNOWLEDGEMENTSWe want to acknowledge Dr John Miller and the team at the

National Malaria Elimination Centre of the Ministry of Health in Zambia for sharing the Malaria Indicator Survey datasets which were used for this secondary data analysis.

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Table 2: Observed Vs Predicted Malaria Prevalence.

Malaria Prevalence

Province Observed Predicted (95%CI) P-value No. of Children

1 Central 13.8 15.9 ( 7.5 - 24.2) 0.386 203

2 Copper-belt 15.2 17.8 ( 8.6 - 27.1) 0.198 316

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