Estimating the Population Size of Males Who Inject Drugs in Myanmar: Methods for Obtaining Township and National Estimates

Leveraging Routinely Collected Program Data to Inform Extrapolated Size Estimates for Key Populations in Namibia: Small Area Estimation Study

BACKGROUND

Estimating the size of key populations, including female sex workers (FSW) and men who have sex with men (MSM), can inform planning and resource allocation for HIV programs at local and national levels. In geographic areas where direct population size estimates (PSEs) for key populations have not been collected, small area estimation (SAE) can help fill in gaps using supplemental data sources known as auxiliary data. However, routinely collected program data have not historically been used as auxiliary data to generate subnational estimates for key populations, including in Namibia.

OBJECTIVE

To systematically generate regional size estimates for FSW and MSM in Namibia, we used a consensus-informed estimation approach with local stakeholders that included the integration of routinely collected HIV program data provided by key populations' HIV service providers.

METHODS

We used quarterly program data reported by key population implementing partners, including counts of the number of individuals accessing HIV services over time, to weight existing PSEs collected through bio-behavioral surveys using a Bayesian triangulation approach. SAEs were generated through simple imputation, stratified imputation, and multivariable Poisson regression models. We selected final estimates using an iterative qualitative ranking process with local key population implementing partners.

RESULTS

Extrapolated national estimates for FSW ranged from 4777 to 13,148 across Namibia, comprising 1.5% to 3.6% of female individuals aged between 15 and 49 years. For MSM, estimates ranged from 4611 to 10,171, comprising 0.7% to 1.5% of male individuals aged between 15 and 49 years. After the inclusion of program data as priors, the estimated proportion of FSW derived from simple imputation increased from 1.9% to 2.8%, and the proportion of MSM decreased from 1.5% to 0.75%. When stratified imputation was implemented using HIV prevalence to inform strata, the inclusion of program data increased the proportion of FSW from 2.6% to 4.0% in regions with high prevalence and decreased the proportion from 1.4% to 1.2% in regions with low prevalence. When population density was used to inform strata, the inclusion of program data also increased the proportion of FSW in high-density regions (from 1.1% to 3.4%) and decreased the proportion of MSM in all regions.

CONCLUSIONS

Using SAE approaches, we combined epidemiologic and program data to generate subnational size estimates for key populations in Namibia. Overall, estimates were highly sensitive to the inclusion of program data. Program data represent a supplemental source of information that can be used to align PSEs with real-world HIV programs, particularly in regions where population-based data collection methods are challenging to implement. Future work is needed to determine how best to include and validate program data in target settings and in key population size estimation studies, ultimately bridging research with practice to support a more comprehensive HIV response.

Patients' experience of accessing hepatitis C treatment through the Myanmar national hepatitis C treatment program: a qualitative evaluation

BACKGROUND

Globally, 56.8 million people are living with hepatitis C and over three-quarters of those reside in low and middle-income countries (LMICs). Barriers and enablers to hepatitis C care among people who inject drugs in high-income countries are well documented. However, there is scant literature describing the patient experience in LMICs. Understanding the barriers and enablers to care from the patient perspective is important to inform service refinements to improve accessibility and acceptability of hepatitis C care.

METHODS

We conducted a qualitative evaluation of the patient experience of accessing the national hepatitis C program at eight hospital sites in Myanmar. Semi-structured interviews were conducted with four to five participants per site. Interview data were analysed thematically, with deductive codes from Levesque et al.'s (2013) Framework on patient-centred access to healthcare.

RESULTS

Across the eight sites, 38 participants who had completed treatment were interviewed. Barriers to accessing care were mostly related to attending for care and included travel time and costs, multiple appointments, and wait times. Some participants described how they did not receive adequate information on hepatitis C, particularly its transmission routes, and on the level of cirrhosis of their liver and what they were required to do after treatment (i.e. reduce alcohol consumption, liver cirrhosis monitoring). Many participants commented that they had few or no opportunities to ask questions. Provision of treatment at no cost was essential to accessibility, and gratitude for free treatment led to high acceptability of care, even when accessing care was inconvenient.

CONCLUSIONS

These findings highlight the importance of streamlining and decentralising health services, adequate human resourcing and training, and affordable treatment in maximising the accessibility and acceptability of hepatitis C care in LMICs. Findings from this work will inform future service delivery refinements for national program and other decentralised programs to improve accessibility and acceptability of hepatitis C care in Myanmar.

Hidden population size estimation and diagnostics using two respondent-driven samples with applications in Armenia

Estimating the size of hidden populations is essential to understand the magnitude of social and healthcare needs, risk behaviors, and disease burden. However, due to the hidden nature of these populations, they are difficult to survey, and there are no gold standard size estimation methods. Many different methods and variations exist, and diagnostic tools are needed to help researchers assess method-specific assumptions as well as compare between methods. Further, because many necessary mathematical assumptions are unrealistic for real survey implementation, assessment of how robust methods are to deviations from the stated assumptions is essential. We describe diagnostics and assess the performance of a new population size estimation method, capture-recapture with successive sampling population size estimation (CR-SS-PSE), which we apply to data from 3 years of studies from three cities and three hidden populations in Armenia. CR-SS-PSE relies on data from two sequential respondent-driven sampling surveys and extends the successive sampling population size estimation (SS-PSE) framework by using the number of individuals in the overlap between the two surveys and a model for the successive sampling process to estimate population size. We demonstrate that CR-SS-PSE is more robust to violations of successive sampling assumptions than SS-PSE. Further, we compare the CR-SS-PSE estimates to population size estimations using other common methods, including unique object and service multipliers, wisdom of the crowd, and two-source capture-recapture to illustrate volatility across estimation methods.

Deriving and interpreting population size estimates for adolescent and young key populations at higher risk of HIV transmission: Men who have sex with men and females who sell sex

Population sizes of adolescent (15- to 19-years) and young (20 to 24-years) key populations at risk for HIV transmission are essential for developing effective national HIV control strategies. We present new population size estimates of adolescent and young men who have sex with men and females who sell sex from 184 countries in nine UNICEF regions using UNAIDS published population size estimations submitted by national governments to derive 15-24-year-old population proportions based on the size of equivalent adult general populations. Imputed sizes based on regional estimates were used for countries or regions where adult proportion estimates were unavailable. Proportions were apportioned to adolescents and young adults based on age at sexual debut, by adjusting for the cumulative percentage of the sexually active population at each age for sex. Among roughly 69.5 million men who have sex with men, 12 million are under the age of 24 years, of whom 3 million are adolescents. There are an estimated 1.4 million adolescent and 3.7 million young females who sell sex. Roughly four and a half million adolescent men who have sex with men and females who sell sex would benefit from early HIV interventions. These population size estimates suggest there are roughly 17 million adolescent and young men who have sex with men and females who sell sex who need HIV prevention services and social support. These data provide evidence for national and international programs to determine how many adolescent and young key populations need essential health services and are living with HIV and other infections. Age disaggregated population sizes inform epidemic models, which increasingly use age-sex structures and are often used to obtain and allocate resources and human capacity and to plan critical prevention, treatment, and infection control programs.

Summarizing methods for estimating population size for key populations: a global scoping review for human immunodeficiency virus research

BACKGROUND

Estimating the population sizes of key populations(people who inject drugs, men who have sex with men, transgender persons, and commercial sex workers) is critical for understanding the overall Human Immunodeficiency Virus burden. This scoping review aims to synthesize existing methods for population size estimation among key populations, and provide recommendations for future application of the existing methods.

METHODS

Relevant studies published from 1st January 2000 to 4th August 2020 and related to key population size estimation were retrieved and 120 of 688 studies were assessed. After reading the full texts, 81 studies were further excluded. Therefore, 39 studies were included in this scoping review. Estimation methods included five digital methods, one in-person method, and four hybrid methods.

FINDING

We summarized and organized the methods for population size estimateion into the following five categories: methods based on independent samples (including capture-recapture method and multiplier method), methods based on population counting (including Delphi method and mapping method), methods based on the official report (including workbook method), methods based on social network (including respondent-driven sampling method and network scale-up method) and methods based on data-driven technologies (Bayesian estimation method, Stochastic simulation method, and Laska, Meisner, and Siegel estimation method). Thirty-six (92%) articles were published after 2010 and 23 (59%) used multiple methods. Among the articles published after 2010, 11 in high-income countries and 28 in low-income countries. A total of 10 estimated the size of commercial sex workers, 14 focused on men who have sex with men, and 10 focused on people who inject drugs.

CONCLUSIONS

There was no gold standard for population size estimation. Among 120 studies that were related to population size estimation of key populations, the most commonly used population estimation method is the multiplier method (26/120 studies). Every method has its strengths and biases. In recent years, novel methods based on data-driven technologies such as Bayesian estimation have been developed and applied in many surveys.

Populations size estimations using SS-PSE among MSM in four European cities: how many MSM are living with HIV?

BACKGROUND

Although men who have sex with men (MSM) are considered at high risk for transmission of sexually transmitted infections, including HIV, there are few studies estimating the population size of MSM in Europe. We used network data from a survey of MSM in four cities to perform successive sampling-population size estimations (SS-PSE) to estimate MSM population sizes.

METHODS

Data were collected in 2013-14 in Bratislava, Bucharest, Verona and Vilnius using respondent-driven sampling (RDS). SS-PSE uses a Bayesian framework to approximate the RDS sampling structure via a successive sampling model and uses the selection order of the sample to provide information about the distribution of network sizes over the population members of MSM.

RESULTS

We estimate roughly 4600 MSM in Bratislava, 25 300 MSM in Bucharest, 7200 in Verona and 2900 in Vilnius. This represents 2.9% of the estimated adult male population in Bratislava, 2.3% in Bucharest, 2.7% in Verona and 1.5% in Vilnius. The number of MSM living with HIV would roughly be 200 in Bratislava, 4554 in Bucharest, 690 in Verona and 100 in Vilnius.

CONCLUSIONS

Benefits of this method are that no additional information from an RDS survey needs to be collected, that the sizes can be calculated ex post facto a survey and that there is a software programme that can run the SS-PSE models. However, this method relies on having reliable priors. Although many countries are estimating the sizes of their vulnerable populations, European countries have yet to incorporate similar and novel methods.

Systematic Review of Human Poisoning and Toxic Exposures in Myanmar

The International Health Regulations (2005) promote national capacity in core institutions so that countries can better detect, respond to and recover from public health emergencies. In accordance with the ‘all hazards’ approach to public health risk, this systematic review examines poisoning and toxic exposures in Myanmar. A systematic literature search was undertaken to find articles pertaining to poisoning in Myanmar published between 1998 and 2020. A number of poisoning risks are identified in this review, including snakebites, heavy metals, drugs of abuse, agrochemicals and traditional medicine. Patterns of poisoning presented in the literature diverge from poisoning priorities reported in other lower-middle income countries in the region. The experience of professionals working in a Yangon-based poison treatment unit also indicate that frequently observed poisoning as a result of pharmaceuticals, methanol, and petroleum products was absent from the literature. Other notable gaps in the available research include assessments of the public health burden of poisoning through self-harm, household exposures to chemicals, paediatric risk and women’s occupational risk of poisoning. There is a limited amount of research available on poisoning outcomes and routes of exposure in Myanmar. Further investigation and research are warranted to provide a more complete assessment of poisoning risk and incidence.

Estimating the prevalence of problem drug use from drug-related mortality data

BACKGROUND AND AIMS

Indirect estimation methods are required for estimating the size of populations where only a proportion of individuals are observed directly, such as problem drug users (PDUs). Capture-recapture and multiplier methods are widely used, but have been criticized as subject to bias. We propose a new approach to estimating prevalence of PDU from numbers of fatal drug-related poisonings (fDRPs) using linked databases, addressing the key limitations of simplistic 'mortality multipliers'.

METHODS

Our approach requires linkage of data on a large cohort of known PDUs to mortality registers and summary information concerning additional fDRPs observed outside this cohort. We model fDRP rates among the cohort and assume that rates in unobserved PDUs are equal to rates in the cohort during periods out of treatment. Prevalence is estimated in a Bayesian statistical framework, in which we simultaneously fit regression models to fDRP rates and prevalence, allowing both to vary by demographic factors and the former also by treatment status.

RESULTS

We report a case study analysis, estimating the prevalence of opioid dependence in England in 2008/09, by gender, age group and geographical region. Overall prevalence was estimated as 0.82% (95% credible interval = 0.74-0.94%) of 15-64-year-olds, which is similar to a published estimate based on capture-recapture analysis.

CONCLUSIONS

Our modelling approach estimates prevalence from drug-related mortality data, while addressing the main limitations of simplistic multipliers. This offers an alternative approach for the common situation where available data sources do not meet the strong assumptions required for valid capture-recapture estimation. In a case study analysis, prevalence estimates based on our approach were surprisingly similar to existing capture-recapture estimates but, we argue, are based on a much more objective and justifiable modelling approach.

Assessing Bias in Population Size Estimates Among Hidden Populations When Using the Service Multiplier Method Combined With Respondent-Driven Sampling Surveys: Survey Study

BACKGROUND

Population size estimates (PSEs) for hidden populations at increased risk of HIV, including female sex workers (FSWs), are important to inform public health policy and resource allocation. The service multiplier method (SMM) is commonly used to estimate the sizes of hidden populations. We used this method to obtain PSEs for FSWs at 9 sites in Zimbabwe and explored methods for assessing potential biases that could arise in using this approach.

OBJECTIVE

This study aimed to guide the assessment of biases that arise when estimating the population sizes of hidden populations using the SMM combined with respondent-driven sampling (RDS) surveys.

METHODS

We conducted RDS surveys at 9 sites in late 2013, where the Sisters with a Voice program (the program), which collects program visit data of FSWs, was also present. Using the SMM, we obtained PSEs for FSWs at each site by dividing the number of FSWs who attended the program, based on program records, by the RDS-II weighted proportion of FSWs who reported attending this program in the previous 6 months in the RDS surveys. Both the RDS weighting and SMM make a number of assumptions, potentially leading to biases if the assumptions are not met. To test these assumptions, we used convergence and bottleneck plots to assess seed dependence of RDS-II proportion estimates, chi-square tests to assess if there was an association between the characteristics of FSWs and their knowledge of program existence, and logistic regression to compare the characteristics of FSWs attending the program with those recruited to RDS surveys.

RESULTS

The PSEs ranged from 194 (95% CI 62-325) to 805 (95% CI 456-1142) across 9 sites from May to November 2013. The 95% CIs for the majority of sites were wide. In some sites, the RDS-II proportion of women who reported program use in the RDS surveys may have been influenced by the characteristics of selected seeds, and we also observed bottlenecks in some sites. There was no evidence of association between characteristics of FSWs and knowledge of program existence, and in the majority of sites, there was no evidence that the characteristics of the populations differed between RDS and program data.

CONCLUSIONS

We used a series of rigorous methods to explore potential biases in our PSEs. We were able to identify the biases and their potential direction, but we could not determine the ultimate direction of these biases in our PSEs. We have evidence that the PSEs in most sites may be biased and a suggestion that the bias is toward underestimation, and this should be considered if the PSEs are to be used. These tests for bias should be included when undertaking population size estimation using the SMM combined with RDS surveys.

Population Size Estimations Among Hidden Populations Using Respondent-Driven Sampling Surveys: Case Studies From Armenia

Background

Estimates of the sizes of hidden populations, including female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID), are essential for understanding the magnitude of vulnerabilities, health care needs, risk behaviors, and HIV and other infections.

Objective

This article advances the successive sampling-population size estimation (SS-PSE) method by examining the performance of a modification allowing visibility to be jointly modeled with population size in the context of 15 datasets. Datasets are from respondent-driven sampling (RDS) surveys of FSW, MSM, and PWID from three cities in Armenia. We compare and evaluate the accuracy of our imputed visibility population size estimates to those found for the same populations through other unpublished methods. We then suggest questions that are useful for eliciting information needed to compute SS-PSE and provide guidelines and caveats to improve the implementation of SS-PSE for real data.

Methods

SS-PSE approximates the RDS sampling mechanism via the successive sampling model and uses the order of selection of the sample to provide information on the distribution of network sizes over the population members. We incorporate visibility imputation, a measure of a person’s propensity to participate in the study, given that inclusion probabilities for RDS are unknown and social network sizes, often used as a proxy for inclusion probability, are subject to measurement errors from self-reported study data.

Results

FSW in Yerevan (2012, 2016) and Vanadzor (2016) as well as PWID in Yerevan (2014), Gyumri (2016), and Vanadzor (2016) had great fits with prior estimations. The MSM populations in all three cities had inconsistencies with expert prior values. The maximum low prior value was larger than the minimum high prior value, making a great fit impossible. One possible explanation is the inclusion of transgender individuals in the MSM populations during these studies. There could be differences between what experts perceive as the size of the population, based on who is an eligible member of that population, and what members of the population perceive. There could also be inconsistencies among different study participants, as some may include transgender individuals in their accounting of personal network size, while others may not. Because of these difficulties, the transgender population was split apart from the MSM population for the 2018 study.

Conclusions

Prior estimations from expert opinions may not always be accurate. RDS surveys should be assessed to ensure that they have met all of the assumptions, that variables have reached convergence, and that the network structure of the population does not have bottlenecks. We recommend that SS-PSE be used in conjunction with other population size estimations commonly used in RDS, as well as results of other years of SS-PSE, to ensure generation of the most accurate size estimation.