Combining the randomized response technique and the network scale-up method to estimate the female sex worker population size: an exploratory study

Population size estimation of female sex workers using network scale-up method in Kermanshah city

BACKGROUND

Appropriate estimate of size of the female sex workers as well as the other hard-to-reach-populations plays a crucial role in reaching them with effective preventive measures. This study aimed to estimate the population size of female sex workers in Kermanshah City using the network scale-up method.

METHOD

In this cross-sectional study, of the 1000 pedestrians aged between 18 and 65 years, selected from the population of Kermanshah City via a multistage sampling, were recruited in the study. Face-to-face interview using a questionnaire including the number of FSWs in the social network of the respondent was used to collect the data.

RESULTS

The overall estimated number of female sex workers in the general population was 4848(UI 95%: 4597, 5074). Accordingly, the prevalence of FSWs among women 18 years and older in Kermanshah in 2018 was about 11.92 (UI 95%: 11.30, 12.47) in one thousand. More than half of the female sex workers in the respondent's social network were unmarried and were in the age range of 18 to 29, and had a high school or university degree. Among high-risk behaviors, a history of alcohol consumption accounted for the highest percentage.

CONCLUSION

The number of female sex workers estimated in this study is considerable, which highlights the importance of planning harm reduction interventions in order to alleviate the burden of HIV infection in the population.

Measure of Personal Network Size Using the Known Population Method: A Methodological Guide

The network scale-up method (NSUM) has shown promise in measuring the prevalence of hidden public health problems and at-risk populations. The technique involves asking survey respondents how many people they know with the health problem or characteristic of interest and extrapolating this information to the population level. An important component of the NSUM estimate is the size of each respondent's network, which is determined by asking respondents about the number of people they know who belong to populations of known size. There is little systematic discussion, however, to guide selection of these questions. Furthermore, many of the most commonly used known population questions are appropriate only in countries with a robust data infrastructure. Here, we draw from the NSUM literature to present a set of best practices in the selection of NSUM known population questions. Throughout, we address the unique situations that many researchers face in collecting prevalence data in the developing world, where innovative prevalence estimation techniques, such as NSUM, are most needed. (Am J Public Health. 2022;112(5):747-753. https://doi.org/10.2105/AJPH.2022.306731).

Thirty Years of The Network Scale-up Method

Estimating the size of hard-to-reach populations is an important problem for many fields. The Network Scale-up Method (NSUM) is a relatively new approach to estimate the size of these hard-to-reach populations by asking respondents the question, "How many X's do you know," where X is the population of interest (e.g. "How many female sex workers do you know?"). The answers to these questions form Aggregated Relational Data (ARD). The NSUM has been used to estimate the size of a variety of subpopulations, including female sex workers, drug users, and even children who have been hospitalized for choking. Within the Network Scale-up methodology, there are a multitude of estimators for the size of the hidden population, including direct estimators, maximum likelihood estimators, and Bayesian estimators. In this article, we first provide an in-depth analysis of ARD properties and the techniques to collect the data. Then, we comprehensively review different estimation methods in terms of the assumptions behind each model, the relationships between the estimators, and the practical considerations of implementing the methods. We apply many of the models discussed in the review to one canonical data set and compare their performance and unique features, presented in the supplementary materials. Finally, we provide a summary of the dominant methods and an extensive list of the applications, and discuss the open problems and potential research directions in this area.

Using Social Networks to Estimate the Number of COVID-19 Cases: The Incident (Hidden COVID-19 Cases Network Estimation) Study Protocol

Recent literature has reported a high percentage of asymptomatic or paucisymptomatic cases in subjects with COVID-19 infection. This proportion can be difficult to quantify; therefore, it constitutes a hidden population. This study aims to develop a proof-of-concept method for estimating the number of undocumented infections of COVID-19. This is the protocol for the INCIDENT (Hidden COVID-19 Cases Network Estimation) study, an online, cross-sectional survey with snowball sampling based on the network scale-up method (NSUM). The original personal network size estimation method was based on a fixed-effects maximum likelihood estimator. We propose an extension of previous Bayesian estimation methods to estimate the unknown network size using the Markov chain Monte Carlo algorithm. On 6 May 2020, 1963 questionnaires were collected, 1703 were completed except for the random questions, and 1652 were completed in all three sections. The algorithm was initialized at the first iteration and applied to the whole dataset. Knowing the number of asymptomatic COVID-19 cases is extremely important for reducing the spread of the virus. Our approach reduces the number of questions posed. This allows us to speed up the completion of the questionnaire with a subsequent reduction in the nonresponse rate.

Use of the multiplier method to estimate the size of populations most at risk of human immunodeficiency virus infection in China: a systematic review

OBJECTIVE

United Nations Programme on human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome and World Health Organization believe that some of the benchmark numbers collected may be inaccurate when using the multiplier method to estimate the size of populations most at risk of acquiring HIV. Here, study data have been evaluated to characterize the inaccurate benchmark numbers.

STUDY DESIGN

The study design used is a systematic review.

METHODS

Studies published from 1 January 2004 to 1 December 2019 using the multiplier method to estimate the population proportions of men who have sex with men (MSM) and female sex workers (FSWs) in China were reviewed. Five electronic bibliographic databases were searched: Medline, the China National Knowledge Infrastructure, VIP Database for Chinese Technical Periodicals, Wanfang Data, and the Chinese BioMedical Literature Database.

RESULTS

In all eight studies of FSW, six of the estimated population proportions fell within the range of national estimates. However, the estimated MSM population proportions of all 18 studies fell outside the range of national estimates.

CONCLUSIONS

When estimating the MSM population, the use of benchmark numbers from homosexual websites or MSM-frequented sites usually led to an inaccurate estimation. Therefore, benchmark numbers from services/programs that meet fundamental needs, such as those dealing with health and wellness, should be used.

Estimating the size of substance using street children in Delhi using Respondent-Driven Sampling (RDS)

Street children as a population sub-group exist in significant numbers in the developing world and have been reported to be vulnerable to adverse health and risk behaviours that include physical and sexual risk behaviour. However, the estimation of prevalence for various psychoactive substances among the street children in a representative sample has not been attempted in the developing country like India. This is challenging due to the absence of an appropriate sampling frame, the population living in isolation, hiding their identity and concealing their behaviour. Use of psychoactive substances in street children is riddled with these challenges. The present investigation describes the findings from size estimation study on a representative sample of street child (n = 766) in Delhi, by implementing the Respondent Driven Sampling Methodology (RDS). The weighted prevalence for three commonest substances was found as Tobacco (31.1 %), alcohol (13.5 %) and inhalants (11.3 %) used during the last one year with the estimated number as -Tobacco (21,770), Alcohol (9450) and Inhalants (5600). Almost one third of street children were using some substance. Also, a large majority of ever users were also using substances currently. The study demonstrates successfully the implementation of RDS for the estimation of the prevalence of psychoactive substances in a representative manner. A large majority of street children use psychoactive substances, and there is a need for more of such studies in other metropolitan cities so that local level interventions and planning can be done for treatment and prevention of psychoactive substances among the street children. The important public health implications had been discussed.