Information CycleData Handling in Information Cycle:

Collection and Collation

University of OsloDepartment of Informatics

Oslo - 2007Facilitator: Gertrudes Macueve

11th April 2007

Learning objectives (1)

• Define what is data and what is information

• Identify the different stages of the information cycle

• Explain how to handle data• Recognize the difference between

collecting data and gathering data• Identify data collection tools

Learning objectives (2)

• Explain the need for flexibility and standardization in data collection

• Explain the rationale for use of an essential dataset

• Explain the correlation between data elements and indicators

• Define what is data collation • Indicate common data collation methods

and problems

Data and information• Data

– observations and measurements about the world, e.g.

– Representation of observations or concepts suitable for communication, interpretation, and processing by humans or machines.

– May or may be not useful to a particular task.

• Information– facts extracted from a set of data (interpreted data),

Meaningful and useful – Data brought together in aggregate to demonstrate

facts; – It is useful to a particular task.

Information CycleInformation Cycle

What do we collect?

What do we do with it?

How do we present it?

How do we use it?

Quality information

Information Cycle

Data converted to information

What do we do with What do we do with it?it?

How do we present How do we present it?it?

How do we use it?How do we use it?

data sources & tools

Process & Analysis

Reports & graphs

Interpretation of information

Good quality data

What do we collect?What do we collect?Decision-making

for effective management

feedbackfeedback

Stages Tools Outputs

Quality at Quality at every stageevery stage

EDSEDS

Data Handling in the Information Cycle:

1. Data collection

The starting point…Feeding the information cycle

Collection

InputRaw data

PresentingInterpreting

USEANALYSIS Processing

OutputINFORMATION

Data collection• Two ways to obtain data

1. Collect data: Physical counting of elements

2. Gather data: if data have already been collected; Requirements:• The definitions of the data are the same as

ours • The format in which the data are collected, is

the same • Data are collected reasonably accurately • We are able to negotiate access to the data

Data collection/gathering guiding principles

• WHO health care workers at all levels • WHAT Essential Data Set

• WHEN daily – collated weekly & processed monthly

• WHERE work sites, facilities, districts (info filter)

• HOW data sources (tally sheets, registers etc)• WHY To monitor progress towards goals & targets

To Plan new policies and changes

To evaluate current services

To assist health management processes

What data elements should be collected?

• Can provide useful information (affecting the management decisions)

• Cannot be obtained elsewhere • Are easy to collect • Do not require much work or time• Can be collected relatively accurately

ESSENTIAL DATA SET based on indicators reflecting the health status of the community

Essential data set

MUST KNOW

The % of children under one year who are fully immunised

Drop out rate DPT 1-3; measles coverage The % of children

under two years who are fully immunised

Other programme vaccines given

Essential data set at each level

• Standardised• Usefulness• Address the needs

of all stakeholders• User-friendly• Dynamic

Where do we get data from?• Routine data collection

– Routine health unit and community data• Activity data about patients seen and

programmes run, routine services and epidemiological surveillance; e.g.

• Semi-permanent data about the population served, the facility itself and staff that run it

– Civil registration

Where do we get data from?

• Non-routine data collection– Surveys– Population census (headcounts

proportion/facility catchment’s area)– Quantitative or qualitative rapid assessment

methods

Example: data collected at PHC facilitiesSpecial programme activities

• Mental & reproductive health• Child health & nutrition• HIV/AIDS, STI and TB• Chronic diseases

Routine Service Activities

• Minor ailments• Non-priority activities

Epidemiological surveillance

• Notifiable diseases• Environmental health

Administrative Systems

• Infrastructure, equipment• Human resources• Drugs, transport, labs, finances, budget, staff

Population • Census: age, sex, place• Births & deaths registration

Requirement for data collection:Standardised definitions

• Essential standardised definitions of both data elements and indicators:– To ensure comparability between different

facilities, districts and provinces– To ensure comparability across years

Data collection tools

A. Client Record Cards

B. Tally Sheets

C. Registers

A. Client Record Cards

• Record details of the client’s interaction with the health service, e.g.:– Health facility record system (traditional)

Associated with misfiling and loss vs– Client-held record system (Road to Health

Card, Child Health Booklet, Women’s Health Book, TB patient treatment card);

Associated with efficiency of the individual concern, suitable for mobile community

Road to Health card

Family planning consultation card

B. Tally sheets• Easy way of counting identical events that do not

have to be followed-up (e.g. headcounts, children weighed)

C. Registers• Record data that need follow-up over long periods

such as ANC, immunisation, FP, TB

Assessment of data collection Assessment of data collection toolstools

(Using SOURCE criteria)(Using SOURCE criteria) conduct an information audit of all tools – type & number

S simple – ease of use (layout)

O overlap – duplication (no overlap)

U useful for – indicators (relevance)

R relevance

C clear – ease of use (layout)

E effective – decisions used for (purpose)

Data collection ToolsData collection Toolscriteria for appropriatenesscriteria for appropriateness

TOOL PURPOSE LAYOUT

RELEVANCE OVERLAP

How many?

•client cards• tally sheets• registers • reports

Effective decision-making for:•Public health• Management• Supervision/support•monitoring • evaluation

Simple,Clear,Easy to understand•Priority actions•No useless data•Missing actions evident

Useful for: • Output/ Outcome/imput/ Process • coverage/ Quality• incidence/ prevalence

• no Overlap with other forms• What • When• Where• Why• How

Data Collation

1. summarising data from the same data elements but from different sources

2. summarising data from the same source but over a period of time.

Ways of collating data

Common collation problems

• Incorrect grouping of data

• Data are incorrectly added

• Missing data forms

• Double counting of data

Data collation practical methodsUnities method

Disease Cases Frequency

Cholera III 3Accidents I 1

Malaria IIII IIII IIII 15

Diarrhea 12IIII IIII II

Data collation practical methodsRectangles method

Disease Cases Frequency

Cholera 3Accidents 1

15

12

Malaria

Diarrea

Data collation practical methodsZeros Method (Tally sheet)

Disease Cases FrequencyMalaria 00000 00000 00000 00000 00000 15Diarrea 00000 00000 00000 00000 00000 12Cholera 00000 00000 00000 00000 00000 3Accidents 00000 00000 00000 00000 00000 1