Female sex workers population size estimation in Rwanda using a three-source capture-recapture method.
G Musengimana1, E Tuyishime2, E Remera1
1Rwanda Biomedical Center, Ministry of Health, Kigali, Rwanda.
Epidemiology and Infection
|March 18, 2021
Summary
Accurate population size estimates (PSE) for female sex workers (FSW) in Rwanda were established using the three-source capture-recapture (3S-CRC) method. The study estimated between 8,328 and 22,806 FSW, providing crucial data for public health services.
Area of Science:
- Epidemiology
- Public Health
- Biostatistics
Background:
- Accurate population size estimates (PSE) are crucial for effective service delivery and resource allocation.
- The multiple-source capture-recapture (MS-CRC) method enhances the accuracy and reliability of PSE, particularly for hard-to-reach populations.
- Previous estimates for female sex worker (FSW) populations in Rwanda were limited, impacting public health program planning.
Purpose of the Study:
- To estimate the population size of street and venue-based female sex workers (FSW) in Rwanda using the three-source capture-recapture (3S-CRC) method.
- To assess the feasibility and cost-effectiveness of the 3S-CRC technique for estimating populations in challenging contexts.
- To provide a reliable denominator for evaluating HIV program performance and epidemic control strategies targeting FSW in Rwanda.
Main Methods:
- The three-source capture-recapture (3S-CRC) method was implemented in August 2018, sampling street and venue-based FSW across Rwanda's provinces.
- FSW were sampled at identified hotspots, tagged with unique gifts, and captures were conducted within one week to minimize migration and recall bias.
- Data from three captures (n=1042, 1204, 1488) with specific overlap counts (recaptures) were analyzed to derive the population size estimate.
Main Results:
- The 3S-CRC method yielded a population size estimate for street and venue-based FSW in Rwanda within a 95% credible interval of 8,328 to 22,806.
- The median estimated population size was 13,716 FSW.
- The 3S-CRC technique proved to be a low-cost and user-friendly approach for generating PSE in this population.
Conclusions:
- The 3S-CRC method provides a robust and cost-effective approach for estimating the size of hard-to-reach populations like FSW in Rwanda.
- The generated PSE is essential for accurately assessing HIV program effectiveness and guiding epidemic control efforts.
- Further research is recommended to include home- and cyber-based FSW in future population size estimations.
Related Concept Videos
00:30What are Populations and Communities?
35.8K
Overview
35.8K
01:20Sample Proportion and Population Proportion
6.0K
Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
6.0K
01:26Estimating Population Standard Deviation
3.2K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.2K
01:19Sample Size Calculation
5.4K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
5.4K
01:24Testing a Claim about Population Proportion
3.6K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.6K
01:20Cluster Sampling Method
13.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.6K