Triangulating Truth and Reaching Consensus on Population Size, Prevalence, and More: Modeling Study.

BACKGROUND: Population size, prevalence, and incidence are essential metrics that influence public health programming and policy. However, stakeholders are frequently tasked with setting performance targets, reporting global indicators, and designing policies based on multiple (often incongruous) estimates of these variables, and they often do so in the absence of a formal, transparent framework for reaching a consensus estimate. OBJECTIVE: This study aims to describe a model to synthesize multiple study estimates while incorporating stakeholder knowledge, introduce an R Shiny app to implement the model, and demonstrate the model and app using real data. METHODS: In this study, we developed a Bayesian hierarchical model to synthesize multiple study estimates that allow the user to incorporate the quality of each estimate as a confidence score. The model was implemented as a user-friendly R Shiny app aimed at practitioners of population size estimation. The underlying Bayesian model was programmed in Stan for efficient sampling and computation. RESULTS: The app was demonstrated using biobehavioral survey-based population size estimates (and accompanying confidence scores) of female sex workers and men who have sex with men from 3 survey locations in a country in sub-Saharan Africa. The consensus results incorporating confidence scores are compared with the case where they are absent, and the results with confidence scores are shown to perform better according to an app-supplied metric for unaccounted-for variation. CONCLUSIONS: The utility of the triangulator model, including the incorporation of confidence scores, as a user-friendly app is demonstrated using a use case example. Our results offer empirical evidence of the model's effectiveness in producing an accurate consensus estimate and emphasize the significant impact that the accessible model and app offer for public health. It offers a solution to the long-standing problem of synthesizing multiple estimates, potentially leading to more informed and evidence-based decision-making processes. The Triangulator has broad utility and flexibility to be adapted and used in various other contexts and regions to address similar challenges.

Modeling of networked populations when data is sampled or missing.

Networked populations consist of inhomogeneous individuals connected via relational ties. The individuals typically vary in multivariate attributes. In some cases primary interest focuses on individual attributes and in others the understanding of the social structure of the ties. In many circumstances both are of interest, as is their relationship. In this paper we consider this last, most general, case. We model the joint distribution of social ties and individual attributes when the population is only partially observed. Of central interest is when the population is surveyed using a network sampling design. A second situation is when data about a subset of the ties and/or the individual attributes is unintentionally missing. Exponential-family random network models (ERNM)s are capable of specifying a joint statistical representation of both the ties of a network and individual attributes. This class of models allow the nodal attributes to be modeled as stochastic processes, expanding the range and realism of exponential-family approaches to network modeling. In this paper we develop a theory of inference for ERNMs when only part of the network is observed, as well as specific methodology for partially observed networks, including non-ignorable mechanisms for network-based sampling designs. In particular, we consider data collected via contact tracing, of considerable importance to infectious disease epidemiology and public health.

The HIV care continuum for sexually active transgender women in three metropolitan municipalities in South Africa: findings from a biobehavioural survey 2018-19.

BACKGROUND: Despite high HIV prevalence in transgender women in sub-Saharan Africa, to our knowledge no study presents data across the HIV care continuum for this population in the region. The aim of this study was to estimate HIV prevalence and present data to develop the HIV care continuum indicators for transgender women in three South African metropolitan municipalities. METHODS: Biobehavioural survey data were collected among sexually active transgender women in the metropolitan municipalities of Johannesburg, Buffalo City, and Cape Town, South Africa. Transgender women (aged ≥18 years, self-reporting consensual sex with a man in the 6 months before the survey) were recruited using respondent-driven sampling (RDS). An interviewer-administered questionnaire was used to determine awareness of HIV status; blood specimens were collected on dried blood spots to test for HIV antibodies, antiretroviral treatment (ART) exposure, and viral load suppression. Population-based estimates of HIV 95-95-95 cascade indicators were derived by use of individualised RDS weights with RDS Analyst software. Multivariate stepwise backward logistic regression modelling was used to determine factors associated with each cascade indicator. All eligible participants were included in the final analysis. FINDINGS: Between July 26, 2018, and March 15, 2019, we enrolled 887 sexually active transgender women: 323 in Johannesburg, 305 in Buffalo City, and 259 in Cape Town. HIV prevalence was highest in Johannesburg where 229 (74·1%) of 309 tests were positive (weighted prevalence estimate 63·3%, 95% CI 55·5-70·5), followed by Buffalo City where 121 (43·7%) of 277 were positive (46·1%, 38·7-53·6), and then Cape Town where 122 (48·4%) of 252 were positive (45·6%, 36·7-54·7). In Johannesburg, an estimated 54·2% (95% CI 45·8-62·4) of transgender women with HIV knew their positive status, in Cape Town this was 24·2% (15·4-35·8), and in Buffalo City this was 39·5% (27·1-53·4). Among those who knew their status, 82·1% (73·3-88·5) in Johannesburg, 78·2% (57·9-90·3) in Cape Town, and 64·7% (45·2-80·2) in Buffalo City were on ART. Of those on ART, 34·4% (27·2-42·4) in Johannesburg, 41·2% (30·7-52·6) in Cape Town, and 55·0% (40·7-68·4) in Buffalo City were virally suppressed. INTERPRETATION: Innovative strategies are needed to inform efforts to diagnose and to treat transgender women living with HIV promptly to achieve viral load suppression. Differentiated HIV services tailored to transgender women of race groups other than Black South African, and those with low education attainment and low outreach exposure, innovative testing, and adherence strategies should be developed to improve the HIV cascade for South African transgender women. FUNDING: The US President's Emergency Plan For AIDS Relief and US Centers for Disease Control and Prevention.

Improving Biomarker-based HIV Incidence Estimation in the Treatment Era.

BACKGROUND: Estimating HIV-1 incidence using biomarker assays in cross-sectional surveys is important for understanding the HIV pandemic. However, the utility of these estimates has been limited by uncertainty about what input parameters to use for false recency rate (FRR) and mean duration of recent infection (MDRI) after applying a recent infection testing algorithm (RITA). METHODS: This article shows how testing and diagnosis reduce both FRR and mean duration of recent infection compared to a treatment-naive population. A new method is proposed for calculating appropriate context-specific estimates of FRR and mean duration of recent infection. The result of this is a new formula for incidence that depends only on reference FRR and mean duration of recent infection parameters derived in an undiagnosed, treatment-naive, nonelite controller, non-AIDS-progressed population. RESULTS: Applying the methodology to eleven cross-sectional surveys in Africa results in good agreement with previous incidence estimates, except in 2 countries with very high reported testing rates. CONCLUSIONS: Incidence estimation equations can be adapted to account for the dynamics of treatment and recent infection testing algorithms. This provides a rigorous mathematical foundation for the application of HIV recency assays in cross-sectional surveys.

Estimation of HIV-1 Incidence Using a Testing History-Based Method; Analysis From the Population-Based HIV Impact Assessment Survey Data in 12 African Countries.

BACKGROUND: Estimating HIV incidence is essential to monitoring progress in sub-Saharan African nations toward global epidemic control. One method for incidence estimation is to test nationally representative samples using laboratory-based incidence assays. An alternative method based on reported HIV testing history and the proportion of undiagnosed infections has recently been described. METHODS: We applied an HIV incidence estimation method which uses history of testing to nationally representative cross-sectional survey data from 12 sub-Saharan African nations with varying country-specific HIV prevalence. We compared these estimates with those derived from laboratory-based incidence assays. Participants were tested for HIV using the national rapid test algorithm and asked about prior HIV testing, date and result of their most recent test, and date of antiretroviral therapy initiation. RESULTS: The testing history-based method consistently produced results that are comparable and strongly correlated with estimates produced using a laboratory-based HIV incidence assay (ρ = 0.85). The testing history-based method produced incidence estimates that were more precise compared with the biomarker-based method. The testing history-based method identified sex-, age-, and geographic location-specific differences in incidence that were not detected using the biomarker-based method. CONCLUSIONS: The testing history-based method estimates are more precise and can produce age-specific and sex-specific incidence estimates that are informative for programmatic decisions. The method also allows for comparisons of the HIV transmission rate and other components of HIV incidence among and within countries. The testing history-based method is a useful tool for estimating and validating HIV incidence from cross-sectional survey data.

Population Size Estimation From Capture-Recapture Studies Using shinyrecap: Design and Implementation of a Web-Based Graphical User Interface.

BACKGROUND: Population size estimates (PSE) provide critical information in determining resource allocation for HIV services geared toward those at high risk of HIV, including female sex workers, men who have sex with men, and people who inject drugs. Capture-recapture (CRC) is often used to estimate the size of these often-hidden populations. Compared with the commonly used 2-source CRC, CRC relying on 3 (or more) samples (3S-CRC) can provide more robust PSE but involve far more complex statistical analysis. OBJECTIVE: This study aims to design and describe the Shiny application (shinyrecap), a user-friendly interface that can be used by field epidemiologists to produce PSE. METHODS: shinyrecap is built on the Shiny web application framework for R. This allows it to seamlessly integrate with the sophisticated CRC statistical packages (eg, Rcapture, dga, LCMCR). Additionally, the application may be accessed online or run locally on the user's machine. RESULTS: The application enables users to engage in sample size calculation based on a simulation framework. It assists in the proper formatting of collected data by providing a tool to convert commonly used formats to that used by the analysis software. A wide variety of methodologies are supported by the analysis tool, including log-linear, Bayesian model averaging, and Bayesian latent class models. For each methodology, diagnostics and model checking interfaces are provided. CONCLUSIONS: Through a use case, we demonstrated the broad utility of this powerful tool with 3S-CRC data to produce PSE for female sex workers in a subnational unit of a country in sub-Saharan Africa.

On The Robustness Of Respondent-Driven Sampling Estimators To Measurement Error.

Respondent-driven sampling (RDS) is a popular method of conducting surveys in hard to reach populations where strong assumptions are required in order to make valid statistical inferences. In this paper we investigate the assumption that network degrees are measured accurately by the RDS survey and find that there is likely significant measurement error present in typical studies. We prove that most RDS estimators remain consistent under an imperfect measurement model with little to no added bias, though the variance of the estimators does increase.

A new method for estimating HIV incidence from a single cross-sectional survey.

Estimating incidence from cross-sectional data sources is both important to the understanding of the HIV epidemic and challenging from a methodological standpoint. We develop a new incidence estimator that measures the size of the undiagnosed population and the amount of time spent undiagnosed in order to infer incidence and transmission rates. The estimator is calculated using commonly collected information on testing history and HIV status and, thus, can be deployed in many HIV surveys without additional cost. If ART biomarker status and/or viral load information is available, the estimator can be adjusted for biases in self-reported testing history. The performance of the estimator is explored in two large surveys in Kenya, where we find our point estimates to be consistent with assay-derived estimates, with much smaller standard errors.

Respondent-driven sampling and the homophily configuration graph.

Respondent-Driven Sampling (RDS) is a popular method for surveying hard-to-reach populations, especially in the public health domain. Adjusting for the complex sampling mechanism of the RDS procedure is challenging. We propose a new model for the RDS mechanism motivated by a graph model, which we call the Homophily Configuration Graph. Under this model, we develop a new estimator for population proportions that is robust to seed bias, differential activity, differential recruitment and short recruitment chains. We also connect it to existing RDS theory by showing that, if the sample fraction is small, our estimator limits to the popular Salganik-Heckathorn estimator. We perform simulation studies on both empirically observed networks and networks with known statistical properties, suggesting that this new estimator has less bias than currently recommended estimators.

Correction: A New Method for Estimating the Number of Undiagnosed HIV Infected Based on HIV Testing History, with an Application to Men Who Have Sex with Men in Seattle/King County, WA.

[This corrects the article DOI: 10.1371/journal.pone.0129551.].

A New Method for Estimating the Number of Undiagnosed HIV Infected Based on HIV Testing History, with an Application to Men Who Have Sex with Men in Seattle/King County, WA.

We develop a new approach for estimating the undiagnosed fraction of HIV cases, the first step in the HIV Care Cascade. The goal is to address a critical blindspot in HIV prevention and treatment planning, with an approach that simplifies data requirements and can be implemented with open-source software. The primary data required is HIV testing history information on newly diagnosed cases. Two methods are presented and compared. The first is a general methodology based on simplified back-calculation that can be used to assess changes in the undiagnosed fraction over time. The second makes an assumption of constant incidence, allowing the estimate to be expressed as a simple closed formula calculation. We demonstrate the methods with an application to HIV diagnoses among men who have sex with men (MSM) from Seattle/King County. The estimates suggest that 6% of HIV-infected MSM in King County are undiagnosed, about one-third of the comparable national estimate. A sensitivity analysis on the key distributional assumption gives an upper bound of 11%. The undiagnosed fraction varies by race/ethnicity, with estimates of 4.9% among white, 8.6% of African American, and 9.3% of Hispanic HIV-infected MSM being undiagnosed.

Do people with schizophrenia have differential impairment in episodic memory and/or working memory relative to other cognitive abilities?

Efforts to identify differential or core cognitive deficits in schizophrenia have been made for several decades, with limited success. Part of the difficulty in establishing a cognitive profile in schizophrenia is the considerable inter-patient heterogeneity in the level of cognitive impairment. Thus, it may be useful to examine the presence of relative cognitive weaknesses on an intra-person level. In the present study we examined the rates of significant intra-person differences between crystallized verbal ability versus five other cognitive abilities among 127 persons with schizophrenia or schizoaffective disorder and 127 demographically matched normal comparison (NC) subjects. We found that the rates of significant discrepancies above the NC group base-rates was significantly greater in reference to those discrepancies involving visual memory relative to those associated with auditory memory, working memory, processing speed, and perceptual organization. The findings conflict with prior suggestions that working memory or auditory episodic memory are differential or core deficits in schizophrenia, and highlight the importance of considering visual memory in characterizing the cognitive effects of this condition.

Worth the risk? Relationship of incentives to risk and benefit perceptions and willingness to participate in schizophrenia research.

OBJECTIVE: Providing incentives for research participation is widely practiced but minimally studied. In schizophrenia research, questions about capacity to consent and potential vulnerability may raise concerns when offering incentives for participation. Despite empirical attention focused on consent and decision-making capacity in schizophrenia, the issue of incentives has been essentially ignored. We examined willingness to participate in research, in relation to perceived risks and benefits, among people with schizophrenia and schizoaffective disorder. METHOD: Forty-six people with schizophrenia or schizoaffective disorder rated perceived risks and benefits of 5 hypothetical research vignettes. They also indicated whether they would be willing to participate at each of 5 incentive levels (including no compensation). Cognition was assessed with Mattis Dementia Rating Scale. RESULTS: Ratings of risk and potential personal benefit were inversely correlated. For all scenarios, significant correlations were found between perceived risk and willingness to participate for greater compensation. Conversely, lower perceived likelihood of benefit was associated with a higher compensation threshold for participation in each scenario. Even at the highest proffered payment level for each scenario, however, a substantial proportion of respondents were not willing to participate. Risk assessment and willingness to participate (at all levels of compensation) were not associated with demographic variables or cognitive status. CONCLUSIONS: Determining whether incentives impede voluntarism remains an important task for empirical ethics research. Assessing potential research participants' understanding and perceptions of risks, benefits, and alternatives to participation will help ensure that informed consent fulfills its mission--embodying the ethical principle of respect for persons.