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.

Prevalence of HIV-1 drug resistance in Eastern European and Central Asian countries

Background

Eastern Europe and Central Asia (EECA) is one of the regions where the HIV epidemic continues to grow at a concerning rate. Antiretroviral therapy (ART) coverage in EECA countries has significantly increased during the last decade, which can lead to an increase in the risk of emergence, transmission, and spread of HIV variants with drug resistance (DR) that cannot be controlled. Because HIV genotyping cannot be performed in these countries, data about HIV DR are limited or unavailable.

Objectives

To monitor circulating HIV-1 genetic variants, assess the prevalence of HIV DR among patients starting antiretroviral therapy, and reveal potential transmission clusters among patients in six EECA countries: Armenia, Azerbaijan, Belarus, Russia, Tajikistan, and Uzbekistan.

Materials and methods

We analyzed 1071 HIV-1 pol-gene fragment sequences (2253–3369 bp) from patients who were initiating or reinitiating first-line ART in six EECA counties, i.e., Armenia (n = 120), Azerbaijan (n = 96), Belarus (n = 158), Russia (n = 465), Tajikistan (n = 54), and Uzbekistan (n = 178), between 2017 and 2019. HIV Pretreatment DR (PDR) and drug resistance mutation (DRM) prevalence was estimated using the Stanford HIV Resistance Database. The PDR level was interpreted according to the WHO standard PDR survey protocols. HIV-1 subtypes were determined using the Stanford HIV Resistance Database and subsequently confirmed by phylogenetic analysis. Transmission clusters were determined using Cluster Picker.

Results

Analyses of HIV subtypes showed that EECA, in general, has the same HIV genetic variants of sub-subtype A6, CRF63_02A1, and subtype B, with different frequencies and representation for each country.

The prevalence of PDR to any drug class was 2.8% in Uzbekistan, 4.2% in Azerbaijan, 4.5% in Russia, 9.2% in Armenia, 13.9% in Belarus, and 16.7% in Tajikistan. PDR to protease inhibitors (PIs) was not detected in any country. PDR to nucleoside reverse-transcriptase inhibitors (NRTIs) was not detected among patients in Azerbaijan, and was relatively low in other countries, with the highest prevalence in Tajikistan (5.6%). The prevalence of PDR to nonnucleoside reverse-transcriptase inhibitors (NNRTIs) was the lowest in Uzbekistan (2.8%) and reached 11.1% and 11.4% in Tajikistan and Belarus, respectively.

Genetic transmission network analyses identified 226/1071 (21.1%) linked individuals, forming 93 transmission clusters mainly containing two or three sequences. We found that the time since HIV diagnosis in clustered patients was significantly shorter than that in unclustered patients (1.26 years vs 2.74 years). Additionally, the K103N/S mutation was mainly observed in clustered sequences (6.2% vs 2.8%).

Conclusions

Our study demonstrated different PDR prevalence rates and DR dynamics in six EECA countries, with worrying levels of PDR in Tajikistan and Belarus, where prevalence exceeded the 10% threshold recommended by the WHO for immediate public health action.

Because DR testing for clinical purposes is not common in EECA, it is currently extremely important to conduct surveillance of HIV DR in EECA due to the increased ART coverage in this region.

Factors associated with unfavourable treatment outcomes in people with HIV-associated tuberculosis in Armenia, 2015 to 2019

To evaluate factors associated with tuberculosis (TB) treatment outcomes in human Immunodeficiency Virus-Associated (HIV) TB patients in Armenia, we conducted a nation-wide cohort study using routine programmatic data of all HIV-associated TB patients receiving TB treatment with first- or second-line drugs from 2015 to 2019. Data were obtained from the TB and HIV electronic databases. We analysed occurrence of the combined unfavourable outcome (failure, lost to follow-up, death and not evaluated) and death separately, and factors associated with both outcomes using Cox regression. There were 320 HIV-associated TB patients who contributed a total of 351 episodes of TB treatment. An unfavourable TB treatment outcome was registered in 155 (44.2%) episodes, including 85 (24.2%) due to death, 38 (10.8%) lost to follow up, 13 (3.7%) failure and 19 (5.4%) not evaluated. Multivariable analysis showed that receipt of Antiretroviral Treatment (ART) [ART start before TB treatment: adjusted hazard ratio (aHR)=0.3, 95% confidence interval (CI): 0.2-0.5, aHR=, 95% CI:, 95% CI:, 95% CI:TB meningitis (aHR=4.4, 95% CI: 1.6-11.9) increased the risk. The risk of death was affected by the same factors as above in addition to the low BMI (aHR=2.5, 95% CI: 1.3-4.5) and drug resistance (aHR=2.3, 95% CI: 1.0-5.4). In the subsample of episodes receiving ART, history of interruption of ART during TB treatment increased the risk of unfavourable outcome (aHR=2.1 95% CI: 1.2-3.9), while ART start during TB treatment was associated with lower risk of both unfavourable outcome (within first 8 weeks: aHR: 0.5, 95% CI: 0.3-0.9; after 8 weeks: aHR: 0.4, 95% CI: 0.2-1.0) and death (within first 8 weeks: aHR: 0.2, 95% CI: 0.1-0.4; after 8 weeks: aHR: 0.1, 95% CI: 0.01-0.3). The rates of unfavourable TB treatment outcomes, and death in particular, among HIV-associated TB patients in Armenia are high. Our findings emphasize the protective effect of ART and the importance of proper management of cases complicated by drug resistance or meningitis.

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.