size estimation of most at risk populations

Size estimation of most at-risk populations

Dr. Don Ajith KarawitaMBBS, PGD VEN, MD Venereology

National STD/AIDS Control Programme, Sri Lanka

Overview of the presentation

1. Overview of population size estimation methodologies

2. Survey-based methods3. Mapping-based methods4. Mapping of MARPs in Sri Lanka

1. Objective2. Specific objective3. Methodology

Population size estimation overview

Introduction to size estimation of High Risk Groups (HRG)

(A). Survey-Based methods

1. Study of individuals with high risk (Survey-Based methods)1. Comparing two independent sources of data on high risk

groups (multiplier method)1. Program data compare with a probability survey data

(Programmatic multiplier)2. Distribution of unique object data compare with survey data

(Unique object multiplier)

2. Capture-Recapture method3. Network scale up

MSM Registered in NGO

X

MSM Registered in NGO

X

Probability survey of MSM

MSM population whose size is going to be estimated

Source 1: Programme data

Use counts from a pre-existing programme listing during a specific time periode.g. No registered during last 3 mNo attended for STI screening

Source 2: probability survey data

Survey data should come from a representative survey of the population whose size is being estimatedThe survey area must encompass the program listing area i.e. people in the list have to be eligible for the survey

Multiplier method: Programmatic multiplier

MSM Registered in NGO X

e.g. 80

MSM Registered in NGO X

e.g. 80

Probability survey of MSM

MSM population whose size is going to be estimated

Population size = ?

If we want an estimate of the size of the MSM populationSource 1: Programme data

NGO X reports that there were 80 MSM registered in their programme as of May 2007

Source 2: probability survey data

In the survey, conducted in May 2007, 40% of the respondents report receiving service from NGO X in the past year.

Use of multiplier formula:Popu=NGO X No/ proportion of MSM found in the survey

Population=80/40/100 = 200

Example of how the programatic multiplier works

Proportion of MSM visited NGO X = 40%

Problems with field implementation (Bias)

• Source 1: Programme dataSource 1: Programme data– Failure to record beneficiaries– Failure to remove inactive beneficiaries– Duplication of data– Failure to include appropriate beneficiaries (e.g. mixing up

DU and IDU)

• Source 2: probability survey dataSource 2: probability survey data– Recall Bias - Failure to recall the service delivery

point/service (Multiple NGOs providing services)– Respondents who are in contact with interventions more

likely to be identified and sampled (survey is not representative).

No of unique objects

distributed

No of unique objects

distributed

Probability survey of MSM

MSM population whose size is going to be estimated

Source 1: Unique object method data

A known number of “Unique objects” (T-shirt, Key tag, Purse etc.) are handed out to eligible individuals prior to the probability survey usually 1-2 wks before the survey.

Source 2: probability survey data

Survey data should come from a representative survey of the population whose size is being estimatedThe survey area must encompass distribution of unique object recipients

Multiplier method: Unique object method

No of unique objects

distributed 150

No of unique objects

distributed 150

Probability survey of MSM

MSM population whose size is going to be estimated

Population size = ?

If we want an estimate of the size of the MSM populationSource 1: Unique object data

The survey team distribute 150 special key tags to MSW 2wks before the survey starts.

Source 2: probability survey data

In the survey, respondents are asked whether they received a key tag & are shown an example of the object.10% of the survey respondents reported receiving the key tag

Use of multiplier formula:Popu=No of objects/ proportion of MSM found received the object in the survey

Population=150/10/100 = 1500

Example of how the unique object works

Proportion of MSM received the unique object = 10%

No of unique objects

distributed

No of unique objects

distributed

Probability survey of MSM

MSM population whose size is going to be estimated

Source 1: Unique object method data

A known number of “Unique objects” (T-shirt, Key tag, Purse etc.) are handed out to eligible individuals prior to the probability survey usually 1-2 wks before the survey.

Source 2: probability survey data

Survey data should come from a representative survey of the population whose size is being estimatedThe survey area must encompass distribution of unique object recipients

Multiplier method: Unique object method

Problems with field implementation (Bias)

• Source 1: Unique object dataSource 1: Unique object data– Object related – Object is not unique if commonly available in shops,

not a memorable one .– Object distribution related - Giving to non-MSW, MSW outside the

geographic area, non distribution without reporting or keeping by peer educators, giving more than one object, passing the object to others, objects are given to people who are more likely to participate in the survey or giving the object as they are recruited for the survey.

• Source 2: probability survey dataSource 2: probability survey data– Recall Bias - Failure to recall the object if recall period is long or object

is not memorable, not showing the object during the survey.– Respondents who are known to peer educators are more likely to be

given the object and identified and sampled for the survey (survey is not representative)

1. Study of spots with high risk activity (Mapping-Based methods)1. Studying the whole list of spots (Census/Geographic

mapping)2. Studying hidden networks (Network mapping)3. Studying a sample of spots (Enumeration)

(B). Mapping-Based methods

Mapping Vs Survey-based methods

Mapping Multiplier

Types of HRG Good for HRGs that are found in accessible or public area. Areas can be listed

Listing or counts of HRG available. HRG can be sampled

Main data applications

Site specific sizes and site profiles Overall population estimates for large geographic area

Resources requirment

Field workers familiar with HRG areas (NGO, Intervention teams etc)

Existing service records and / or probability sample survey

Key challange Obtaining a comprehensive list of sites

Achieving independence of the two sources of data

Thank youThank you