Estimating the sizes of populations at high risk for HIV: a comparison study

Size estimation of key populations and 'bridge populations' based on the network scale-up method in Ukraine

INTRODUCTION

Correct estimation of the size of key and bridge populations is crucial for an efficient HIV/AIDS response in resource-limited settings, enabling efficient program planning and resource allocation. The hidden nature of these groups poses challenges to traditional methods, leading to the adoption of innovative approaches like the network scale-up method (NSUM). In this article we present the results of a NSUM study conducted in 2020 in Ukraine, focusing on four key populations and three bridge populations, highlighting challenges and contributions to development of the method.

METHODS

From July to September 2020, we conducted a nationally representative survey in Ukraine via computer-assisted telephone interviews, and applied the known population method and summation method to estimate social networks sizes. Results were weighted based on individual sampling probability and adjusted for social respect and visibility factors to address potential limitations.

RESULTS

Our study achieved a 20% response rate with 10,000 completed interviews. The social network size, using the known population method, was 213 people, and 125 using the summation method. Adjusting for the social respect and visibility, estimated key populations sizes were 295,857 [248,714-343,001] people who inject drugs, 152,267 [109,960-194,573] men who have sex with men, 78,385 [57,146-99,619] sex workers, and 9,963 [7,352-12,571] transgender people, detailed by age and gender. Bridge populations were estimated at 62,162 [50,445-73,879] sexual partners of people who inject drugs, 284,348 [233,113-335,583] clients of sex workers, and 13,697 [7,370-20,026] female partners of men who have sex with men.

CONCLUSIONS

NSUM proves reliable for estimating key populations size with appropriate corrections. It shows promise for further use in Ukraine, considering limited geographic coverage of the integrated bio-behavioral studies to use multiplier-based methods. However, the validity concerns persist for estimating bridge populations size, emphasizing the need for further method refinement and addressing implementation issues, particularly those related to data collection.

Consistently estimating network statistics using aggregated relational data

Collecting complete network data is expensive, time-consuming, and often infeasible. Aggregated Relational Data (ARD), which ask respondents questions of the form "How many people with trait X do you know?" provide a low-cost option when collecting complete network data is not possible. Rather than asking about connections between each pair of individuals directly, ARD collect the number of contacts the respondent knows with a given trait. Despite widespread use and a growing literature on ARD methodology, there is still no systematic understanding of when and why ARD should accurately recover features of the unobserved network. This paper provides such a characterization by deriving conditions under which statistics about the unobserved network (or functions of these statistics like regression coefficients) can be consistently estimated using ARD. We first provide consistent estimates of network model parameters for three commonly used probabilistic models: the beta-model with node-specific unobserved effects, the stochastic block model with unobserved community structure, and latent geometric space models with unobserved latent locations. A key observation is that cross-group link probabilities for a collection of (possibly unobserved) groups identify the model parameters, meaning ARD are sufficient for parameter estimation. With these estimated parameters, it is possible to simulate graphs from the fitted distribution and analyze the distribution of network statistics. We can then characterize conditions under which the simulated networks based on ARD will allow for consistent estimation of the unobserved network statistics, such as eigenvector centrality, or response functions by or of the unobserved network, such as regression coefficients.

Size Estimation of high-risk groups for hiv infection in india based on data from national integrated bio-behavioral surveillance and targeted interventions

Background

Targeted interventions (TIs) are one of the most effective strategies to control HIV/AIDS transmission, especially among the high-risk groups (HRGs). Implementation of HIV/AIDS control strategies relies heavily on estimation of the size of HRG population. Size estimation for key populations such as female sex workers (FSWs), men who have sex with men (MSM), and injecting drug users (IDUs) is a crucial component of national HIV strategic planning.

Objective

The objective of this study was to estimate the size of FSWs, MSM, and IDUs in various states of India.

Methods

The program multiplier method was used to estimate the size of FSWs, MSM, and IDUs across the country using two distinct but overlapping data sources - Integrated Bio-Behavioral Surveillance and TI program from the same geographical area at the same time period.

Results

In India, as on 2018-2019, there were nearly 18.2 lakhs estimated FSWs accounting to 0.53% among female population aged 15-49 years, with a highest in West Bengal (4.5 lakhs); 5.7 lakhs estimated MSM accounting to 0.16% among male population aged 15-49 years, with a highest in Gujarat (0.7 lakh); and 3.9 lakhs estimated IDUs accounting to 0.11% among male population aged 15-49 years, with a highest in Uttar Pradesh (0.5 lakh).

Conclusions

The current size estimates on HRGs will support the development of projections and estimations of the HIV epidemic at national and state levels. These estimates also help in framing national guidelines such as HIV strategic planning, program design, allocation of resources, prioritizing the interventions, and monitoring and evaluation.

Mapping and size estimates of female sex workers in Cameroon: Toward informed policy for design and implementation in the national HIV program

Background

Due to high HIV prevalence among Female Sex Workers (FSWs) in Cameroon (36.5%), this population is especially vulnerable to HIV acquisition and transmission nationwide. Though being prioritized in the national HIV response, it would be relevant to generate statistics on the number of FSWs in order to guide HIV interventions among FSWs. Our objective was to estimate the size of FSWs within hotspots of Cameroon.

Methods

A cross-sectional study was conducted from September-November 2015 in selected cities in Cameroon: Bafoussam, Bamenda, Bertoua, Buea, Douala, Kribi, Limbé, and Yaoundé. A programmatic mapping was used, consisting of interviews with secondary key informants (KI) to identify hotspots of FSWs and their respective estimated numbers. Validation of size estimates was done by interviews with FSW at each hotspot. Size estimations in the councils mapped were extended to others not mapped using a Poisson regression model.

Results

A total of 2,194 hotspots were identified: Douala (760), Yaoundé (622), Bamenda (263), Bafoussam (194), Kribi (154), Bertoua (140), Limbé (35), and Buea (26). The estimated total number (range) of FSWs was 21,124 (16,079–26,170), distributed per city as follows: Douala 7,557 (5,550–9,364), Yaoundé 6,596 (4,712–8,480), Bafoussam 2,458 (1,994–2,923), Bamenda 1,975 (1,605–2,345), Kribi 1,121 (832–1,408), Bertoua 1,044 (891–1,198), Buea 225 (185–266), and Limbé 148 (110–148). The variability of estimates among cities was also observed within the councils of each city. The national predicted estimate of FSW population was 112,580 (103,436–121,723), covering all councils of Cameroon. An estimate of 1.91% (112,580/5,881,526; 0.47%-3.36%) adult female population in Cameroon could be sex workers.

Conclusion

There are considerable numbers of FSW in major cities in Cameroon. There is a need to prioritize interventions for HIV prevention toward this population in order to limit the burden of HIV sexual transmission nationwide.

A review of methods to estimate the visibility factor for bias correction in network scale-up studies

Network scale-up is an indirect size estimation method, in which participants are questioned on sensitive behaviors of their social network members. Therefore, the visibility of the behavior affects the replies and estimates. Many attempts to estimate visibility have been made. The aims of this study were to review the main methods used to address visibility and to provide a summary of reported visibility factors (VFs) across populations. We systematically searched relevant databases and Google. In total, 15 studies and reports that calculated VFs were found. VF calculation studies have been applied in 9 countries, mostly in East Asia and Eastern Europe. The methods applied were expert opinion, comparison of NSU with another method, the game of contacts, social respect, and the coming-out rate. The VF has been calculated for heavy drug users, people who inject drugs (PWID), female sex workers (FSWs) and their clients, male who have sex with male (MSM), alcohol and methamphetamine users, and those who have experienced extra-/pre-marital sex and abortion. The VF varied from 1.4% in Japan to 52.0% in China for MSM; from 34.0% in Ukraine to 111.0% in China for FSWs; and from 12.0% among Iranian students to 57.0% in Ukraine for PWID. Our review revealed that VF estimates were heterogeneous, and were not available for most settings, in particular the Middle East and North Africa region, except Iran. More concrete methodologies to estimate the VF are required.

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

OBJECTIVES

Recall bias, barrier effects, transmission error, and response bias affecting the network scale-up method (NSUM) led the Joint United Nations Programme on HIV/AIDS and the World Health Organization to designate NSUM as a developing method for estimating the size of high-risk populations in 2010. The method has subsequently been adjusted for three of these biases. The present study, therefore, aimed to explore the combination of the randomized response technique (RRT) and NSUM to adjust the last remaining bias.

STUDY DESIGN

RRT was used in an NSUM survey to estimate the population proportion of female sex workers (FSWs) in Taiyuan, China, in 2012.

METHODS

Multiplier method estimates and national-level estimates of the population proportion of FSWs were used as criteria to assess the aforementioned results.

RESULTS

Successful interviews were completed with 96.4% of the respondents selected for the NSUM survey. The NSUM estimate fell within the range of the national-level estimates of the population proportion of FSWs in Asia and was close to the estimate yielded by the multiplier method.

CONCLUSIONS

In the present study, the combination of RRT and NSUM obtained a high response rate and produced a reliable estimate of the size of a high-risk population.

Underreporting in HIV-related high-risk behaviors: comparing the results of multiple data collection methods in a behavioral survey of prisoners in Iran

We explored the potentials of using three indirect methods including crosswise, proxy respondent method, and network scale-up (NSU) in comparison to direct questioning in collecting sensitive and socially stigmatized HIV-related risk behaviors information from prisoners (N=265). Participants reported more sexual contact in prison for their friends than they did for themselves (10.6% vs. 3.8% in men, 13.7% vs. 0% in women). In men, NSU provided lower estimates than direct questioning, while in women NSU estimates were higher. Different data collection methods provide different estimates, and collectively offer a more comprehensive picture of HIV-related risk behaviors in prisons.

Population size estimation of female sex workers in Iran: Synthesis of methods and results

INTRODUCTION

Estimating the number of key populations at risk of HIV is essential for planning, monitoring, and evaluating prevention, care, and treatment programmes. We conducted this study to estimate the number of female sex workers (FSW) in major cities of Iran.

METHODS

We used three population size estimation methods (i.e., wisdom of the crowds, multiplier method, and network scale-up) to calculate the number of FSW in 13 cities in Iran. The wisdom of the crowds and multiplier methods were integrated into a nationwide bio-behavioural surveillance survey in 2015, and the network scale-up method was included in a national survey of the general population in 2014. The median of the three methods was used to calculate the proportion of the adult female population who practice sex work in the 13 cities. These figures were then extrapolated to provide a national population size estimation of FSW across urban areas.

RESULTS

The population size of FSW was 91,500 (95% Uncertainty Intervals [UIs] 61,400-117,700), corresponding to 1.43% (95% UIs 0.96-1.84) of the adult (i.e., 15-49 year-old) female population living in these 13 cities. The projected numbers of FSW for all 31 provincial capital cities were 130,800 (95% UIs 87,800-168,200) and 228,700 (95% UIs 153,500-294,300) for all urban settings in Iran.

CONCLUSIONS

Using methods of comparable rigor, our study provided a data-driven national estimate of the population size of FSW in urban areas of Iran. Our findings provide vital information for enhancing HIV programme planning and lay a foundation for assessing the impact of harm reduction efforts within this marginalized population.

Theoretical and Empirical Comparisons of Methods to Estimate the Size of Hard-to-Reach Populations: A Systematic Review

Worldwide, the HIV epidemic is concentrated among hidden populations (i.e., female sex workers, men who have sex with men, and people who inject drugs). To understand the true scope and scale of the HIV epidemic, estimates of the sizes of these populations are needed. Various methods are available to enumerate hidden populations, but the degree of agreement between these methods has not been formally evaluated. We systematically reviewed the peer-reviewed literature to assess the extent to which different population size estimation methods provide the same estimate of a target population. Of the 341 studies identified from our search, 25 met our eligibility criteria. Twenty-one unique methods were documented. The service multiplier method was the most common in the review. Eighty target populations were estimated, covering 16 countries. We observed variable population size estimates, with little agreement between methods. We note trends in the relative performance of individual methods.

Estimated Number of People Who Inject Drugs in San Francisco, 2005, 2009, and 2012

Allocation of resources to public health responses depends on having plausible estimates of the size of the population at risk. Unfortunately, the numbers of people who inject drugs (PWID) are difficult to estimate since injection drug use is highly stigmatized. Though estimation methods exist, the robustness of the methods to their assumptions is not well understood. Comparisons between methods are also lacking; information regarding the successive-sampling method is particularly scarce. The present study used several methods-including the successive-sampling method-to produce population size estimates from three rounds of cross-sectional surveys of PWID in San Francisco. It compares these estimates across time and across method. Our summary estimates are 10,158 for 2005, 15,554 for 2009, and 22,500 for 2012. Though the point summaries suggest an increasing population, considerable uncertainty is involved. Comparisons between and within methods reveal high variability, suggesting dependence on assumptions and analytic choices. We conclude that further research is needed to improve upon the estimation methods or develop entirely new ones. Meanwhile, plausible estimates can be achieved via multiple methods while avoiding the pitfall of relying on a single method that may be highly biased and highly imprecise.

Generalizing the Network Scale-Up Method: A New Estimator for the Size of Hidden Populations

The network scale-up method enables researchers to estimate the size of hidden populations, such as drug injectors and sex workers, using sampled social network data. The basic scale-up estimator offers advantages over other size estimation techniques, but it depends on problematic modeling assumptions. We propose a new generalized scale-up estimator that can be used in settings with non-random social mixing and imperfect awareness about membership in the hidden population. Further, the new estimator can be used when data are collected via complex sample designs and from incomplete sampling frames. However, the generalized scale-up estimator also requires data from two samples: one from the frame population and one from the hidden population. In some situations these data from the hidden population can be collected by adding a small number of questions to already planned studies. For other situations, we develop interpretable adjustment factors that can be applied to the basic scale-up estimator. We conclude with practical recommendations for the design and analysis of future studies.

Application of Network Scale Up Method in the Estimation of Population Size for Men Who Have Sex with Men in Shanghai, China

BACKGROUND

Men who have sex with men (MSM) are at high risk of HIV infection. For developing proper interventions, it is important to know the size of MSM population. However, size estimation of MSM populations is still a significant public health challenge due to high cost, hard to reach and stigma associated with the population.

OBJECTIVES

We aimed to estimate the social network size (c value) in general population and the size of MSM population in Shanghai, China by using the net work scale-up method.

METHODS

A multistage random sampling was used to recruit participants aged from 18 to 60 years who had lived in Shanghai for at least 6 months. The "known population method" with adjustment of backward estimation and regression model was applied to estimate the c value. And the MSM population size was further estimated using an adjusted c value taking into account for the transmission effect through social respect level towards MSM.

RESULTS

A total of 4017 participants were contacted for an interview, and 3907 participants met the inclusion criterion. The social network size (c value) of participants was 236 after adjustment. The estimated size of MSM was 36354 (95% CI: 28489-44219) for the male Shanghaies aged 18 to 60 years, and the proportion of MSM among the total male population aged 18 to 60 years in Shanghai was 0.28%.

CONCLUSIONS

We employed the network scale-up method and used a wide range of data sources to estimate the size of MSM population in Shanghai, which is useful for HIV prevention and intervention among the target population.