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.

Understanding the Impact of General Vaccine Attitudes on the Intent for Early COVID-19 Vaccination

Despite relatively high rates of population spread, morbidity and mortality, the adoption of COVID-19 vaccines among the eligible populations was relatively slow. Some of the reasons for vaccination hesitancy and refusals have been attributed to unique aspects of this pandemic, including attitudes toward COVID-19 vaccines. However, little attention has been paid to the role of underlying vaccine beliefs in the likelihood of early vaccine adoption for COVID-19. This study provides a more comprehensive assessment of factors influencing willingness to get an early vaccination, and the relative contribution of general vaccine attitudes, compared to demographics, perceived threat and institutional trust. Monthly national surveys were conducted between June and November 2020 using a national consumer panel of U.S. adults (n = 6185). By late November, only 24% of respondents said they were very likely to get a Food and Drug Administration (FDA)-approved COVID-19 vaccine as soon as it became available. While COVID-19 risk perceptions, confidence and trust in key institutions and information sources, and some demographic variables, were predictive of early vaccination intent, general beliefs regarding vaccines played a significant role, even compared to demographics, perceived risk and institutional trust. This lesson from the COVID-19 experience could help inform public health communications in future epidemics.

Better Understanding Adult COVID-19 Vaccination Hesitancy and Refusal: The Influence of Broader Beliefs about Vaccines

Published surveys in the United States provide much evidence that COVID-19 vaccination is influenced by disease and vaccine-related risk perceptions. However, there has been little examination of whether individual’s general beliefs about vaccines are also related to COVID-19 vaccination, especially among unvaccinated adults. This study used an August 2021 national survey of 1000 U.S. adults to examine whether general beliefs about vaccines were associated with COVID-19 vaccination status. In addition, it used multivariate analyses to assess the relative contribution of individual vaccine beliefs to current vaccine status independently of COVID-19-specific attitudes and experiences, and demographics. The findings indicated that, collectively, general vaccine beliefs mattered more than demographics, COVID-19-specific risk perceptions, confidence in government, or trust in public health agencies in COVID-19 vaccination status. Overall, the findings affirm the importance of vaccine education and communication efforts that help people understand why vaccines are needed, how vaccine safety is established and monitored, and how vaccines provide protection from infectious diseases. To achieve success among vaccine-hesitant individuals, communication strategies should target vaccine beliefs that most influence vaccination outcomes.

Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification

Health research using electronic health records (EHR) has gained popularity, but misclassification of EHR-derived disease status and lack of representativeness of the study sample can result in substantial bias in effect estimates and can impact power and type I error. In this paper, we develop new strategies for handling disease status misclassification and selection bias in EHR-based association studies. We first focus on each type of bias separately. For misclassification, we propose three novel likelihood-based bias correction strategies. A distinguishing feature of the EHR setting is that misclassification may be related to patient-varying factors, and the proposed methods leverage data in the EHR to estimate misclassification rates without gold standard labels. For addressing selection bias, we describe how calibration and inverse probability weighting methods from the survey sampling literature can be extended and applied to the EHR setting. Addressing misclassification and selection biases simultaneously is a more challenging problem than dealing with each on its own, and we propose several new strategies. For all methods proposed, we derive valid standard error estimators and provide software for implementation. We provide a new suite of statistical estimation and inference strategies for addressing misclassification and selection bias simultaneously that is tailored to problems arising in EHR data analysis. We apply these methods to data from The Michigan Genomics Initiative, a longitudinal EHR-linked biorepository.

An analytic framework for exploring sampling and observation process biases in genome and phenome-wide association studies using electronic health records

Large-scale association analyses based on observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, challenges due to nonprobability sampling and phenotype misclassification associated with the use of these data sources are often ignored in standard analyses. The extent of the bias introduced by ignoring these factors is not well-characterized. In this paper, we develop an analytic framework for characterizing the bias expected in disease-gene association studies based on electronic health records when disease status misclassification and the sampling mechanism are ignored. Through a sensitivity analysis approach, this framework can be used to obtain plausible values for parameters of interest given summary results from standard analysis. We develop an online tool for performing this sensitivity analysis. Simulations demonstrate promising properties of the proposed method. We apply our approach to study bias in disease-gene association studies using electronic health record data from the Michigan Genomics Initiative, a longitudinal biorepository effort within The University Michigan health system.