The recent development and regulatory approval of a variety of serological assays indicating the presence of antibodies against severe acute respiratory syndrome coronavirus 2 has led to rapid and widespread implementation of seroprevalence studies. Accurate estimates of seroprevalence are needed to model transmission dynamics and estimate mortality rates. Furthermore, seroprevalence levels in a population help guide policy surrounding reopening efforts. The literature to date has focused heavily on issues surrounding the quality of seroprevalence tests and less on the sampling methods that ultimately drive the representativeness of resulting estimates. Seroprevalence studies based on convenience samples are being reported widely and extrapolated to larger populations for the estimation of total coronavirus disease 2019 (COVID-19) infections, comparisons of prevalence across geographic regions, and estimation of mortality rates. In this viewpoint, we discuss the pitfalls that can arise with the use of convenience samples and offer guidance for moving towards more representative and timely population estimates of COVID-19 seroprevalence.

Missing data are pandemic and a central problem for epidemiology. Missing data reduce precision and can cause notable bias. There remain too few simple published examples detailing types of missing data and illustrating their possible impact on results. Here we take an example randomized trial that was not subject to missing data and induce missing data to illustrate 4 scenarios in which outcomes are 1) missing completely at random, 2) missing at random with positivity, 3) missing at random without positivity, and 4) missing not at random. We demonstrate that accounting for missing data is generally a better strategy than ignoring missing data, which unfortunately remains a standard approach in epidemiology.

Background: Women in Africa disproportionately acquire human immunodeficiency virus type 1 (HIV-1). Understanding which women are most likely to acquire HIV-1 can guide focused prevention with preexposure prophylaxis (PrEP). Our objective was to identify women at the highest risk of HIV-1 and estimate PrEP efficiency at different sensitivity levels. Methods: Nationally representative data were collected from 2015 through 2019 from 15 population-based household surveys. This analysis included women aged 15–49 who tested HIV-1 seronegative or had recent HIV-1. Least absolute shrinkage and selection operator regression models were fit with 28 variables to predict recent HIV-1. Models were trained on the full population and internally cross-validated. Performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, and number needed to treat (NNT) with PrEP to avert 1 infection. Results: Among 209 012 participants, 248 had recent HIV-1 infection, representing 118 million women and 402 000 (95% confidence interval [CI], 309 000–495 000) annual infections. Two variables were retained: living in a subnational area with high HIV-1 viremia and having a sexual partner living outside the home. The full-population AUC was 0.80 (95% CI, .76–.84); cross-validated AUC was 0.79 (95% CI, .75–.84). At 33% sensitivity, 130 000 cases could be averted if 7.9 million women were perfectly adherent to PrEP; NNT would be 61. At 67% sensitivity, 260 000 cases could be averted if 25.1 million women were perfectly adherent; NNT would be 96. Conclusions: This risk assessment tool was generalizable, predictive, and parsimonious with trade-offs between reach and efficiency.

This paper presents methods to estimate the number of persons with HIV in North Carolina jails by applying finite population inferential approaches to data collected using web scraping and record linkage techniques. Administrative data are linked with web-scraped rosters of incarcerated persons in a non-random subset of counties. Outcome regression and calibration weighting are adapted for state-level estimation. Methods are compared in simulations and are applied to data from the US state of North Carolina. Outcome regression yielded more precise inference and allowed for county-level estimates, an important study objective, while calibration weighting exhibited double robustness under misspecification of the outcome or weight model.

The effect of SARS-CoV-2 infection on response to mRNA-based SARS-CoV-2 vaccines is not well-described. We assessed longitudinal SARS-CoV-2-specific antibody responses pre- and post-vaccination among individuals with and without prior infection. The antibody response to the first vaccine dose was almost two-fold higher in individuals who were seropositive before vaccination compared to those who were seronegative, suggesting that prior infection primes the immune response to the first dose of mRNA-based vaccine.