An HIV-1 risk assessment tool for women aged 15-49 in African countries: A pooled analysis across 15 nationally representative surveys.

BACKGROUND: Women in Africa disproportionately acquire HIV-1. Understanding which women are most likely to acquire HIV-1 can guide focused prevention with pre-exposure prophylaxis (PrEP). Our objective is to identify women at highest risk of HIV-1 and estimate PrEP efficiency at different sensitivity levels. METHODS: Nationally representative data were collected from 2015-2019 from 15 population-based household surveys. This analysis included women aged 15-49 who tested HIV-1 sero-negative 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 one infection. RESULTS: Among 209,012 participants 248 had recent HIV-1 infection, representing 118 million women and 402,000 (95% CI: 309,000-495,000) new annual infections. Two variables were retained in the model: living in a subnational area with high HIV-1 viremia and having a sexual partner living outside the home. Full-population AUC was 0.80 (95% CI: 0.76-0.84); cross-validated AUC was 0.79 (95% CI: 0.75-0.84). At a sensitivity of 33%, up to 130,000 cases could be averted if 7.9 million women were perfectly adherent to PrEP; NNT would be 61. At a sensitivity of 67%, up to 260,000 cases could be averted if 25.1 million women were perfectly adherent to PrEP; the NNT would be 96. CONCLUSIONS: This risk assessment tool was generalizable, predictive, and parsimonious with tradeoffs between reach and efficiency.

Assessing Etiologic Heterogeneity for Multinomial Outcome with Two-Phase Outcome-Dependent Sampling Design.

Etiologic heterogeneity occurs when distinct sets of events or exposures give rise to different subtypes of disease. Inference about subtype-specific exposure effects from two-phase outcome-dependent sampling data requires adjustment for both confounding and the sampling design. Common approaches to inference for these effects do not necessarily appropriately adjust for these sources of bias, or allow for formal comparisons of effects across different subtypes. Herein, using inverse probability weighting (IPW) to fit a multinomial model is shown to yield valid inference with this sampling design for subtype-specific exposure effects and contrasts thereof. The IPW approach is compared to common regression-based methods for assessing exposure effect heterogeneity using simulations. The methods are applied to estimate subtype-specific effects of various exposures on breast cancer risk in the Carolina Breast Cancer Study.

Quantifying the HIV reservoir with dilution assays and deep viral sequencing.

People living with HIV on antiretroviral therapy often have undetectable virus levels by standard assays, but "latent" HIV still persists in viral reservoirs. Eliminating these reservoirs is the goal of HIV cure research. The quantitative viral outgrowth assay (QVOA) is commonly used to estimate the reservoir size, that is, the infectious units per million (IUPM) of HIV-persistent resting CD4+ T cells. A new variation of the QVOA, the ultra deep sequencing assay of the outgrowth virus (UDSA), was recently developed that further quantifies the number of viral lineages within a subset of infected wells. Performing the UDSA on a subset of wells provides additional information that can improve IUPM estimation. This paper considers statistical inference about the IUPM from combined dilution assay (QVOA) and deep viral sequencing (UDSA) data, even when some deep sequencing data are missing. Methods are proposed to accommodate assays with wells sequenced at multiple dilution levels and with imperfect sensitivity and specificity, and a novel bias-corrected estimator is included for small samples. The proposed methods are evaluated in a simulation study, applied to data from the University of North Carolina HIV Cure Center, and implemented in the open-source R package SLDeepAssay.

G-formula for observational studies under stratified interference, with application to bed net use on malaria.

Assessing population-level effects of vaccines and other infectious disease prevention measures is important to the field of public health. In infectious disease studies, one person's treatment may affect another individual's outcome, that is, there may be interference between units. For example, the use of bed nets to prevent malaria by one individual may have an indirect effect on other individuals living in close proximity. In some settings, individuals may form groups or clusters where interference only occurs within groups, that is, there is partial interference. Inverse probability weighted estimators have previously been developed for observational studies with partial interference. Unfortunately, these estimators are not well suited for studies with large clusters. Therefore, in this paper, the parametric g-formula is extended to allow for partial interference. G-formula estimators are proposed for overall effects, effects when treated, and effects when untreated. The proposed estimators can accommodate large clusters and do not suffer from the g-null paradox that may occur in the absence of interference. The large sample properties of the proposed estimators are derived assuming no unmeasured confounders and that the partial interference takes a particular form (referred to as 'weak stratified interference'). Simulation studies are presented demonstrating the finite-sample performance of the proposed estimators. The Demographic and Health Survey from the Democratic Republic of the Congo is then analyzed using the proposed g-formula estimators to assess the effects of bed net use on malaria.

Fusing trial data for treatment comparisons: Single vs multi-span bridging.

While randomized controlled trials (RCTs) are critical for establishing the efficacy of new therapies, there are limitations regarding what comparisons can be made directly from trial data. RCTs are limited to a small number of comparator arms and often compare a new therapeutic to a standard of care which has already proven efficacious. It is sometimes of interest to estimate the efficacy of the new therapy relative to a treatment that was not evaluated in the same trial, such as a placebo or an alternative therapy that was evaluated in a different trial. Such dual-study comparisons are challenging because of potential differences between trial populations that can affect the outcome. In this article, two bridging estimators are considered that allow for comparisons of treatments evaluated in different trials, accounting for measured differences in trial populations. A "multi-span" estimator leverages a shared arm between two trials, while a "single-span" estimator does not require a shared arm. A diagnostic statistic that compares the outcome in the standardized shared arms is provided. The two estimators are compared in simulations, where both estimators demonstrate minimal empirical bias and nominal confidence interval coverage when the identification assumptions are met. The estimators are applied to data from the AIDS Clinical Trials Group 320 and 388 to compare the efficacy of two-drug vs four-drug antiretroviral therapy on CD4 cell counts among persons with advanced HIV. The single-span approach requires weaker identification assumptions and was more efficient in simulations and the application.

Higher-order evidence.

Higher-order evidence is evidence about evidence. Epidemiologic examples of higher-order evidence include the settings where the study data constitute first-order evidence and estimates of misclassification comprise the second-order evidence (e.g., sensitivity, specificity) of a binary exposure or outcome collected in the main study. While sampling variability in higher-order evidence is typically acknowledged, higher-order evidence is often assumed to be free of measurement error (e.g., gold standard measures). Here we provide two examples, each with multiple scenarios where second-order evidence is imperfectly measured, and this measurement error can either amplify or attenuate standard corrections to first-order evidence. We propose a way to account for such imperfections that requires third-order evidence. Further illustrations and exploration of how higher-order evidence impacts results of epidemiologic studies is warranted.

Barriers to Cervical Cancer Screening by Sexual Orientation Among Low-Income Women in North Carolina.

We sought to examine cervical cancer screening barriers by sexual orientation among low-income women in North Carolina. The MyBodyMyTest-3 Trial recruited low-income women (< 250% of federal poverty level) aged 25-64 years who were 1+ year overdue for cervical cancer screening. We compared perceptions of cervical cancer screening among those who self-identified as lesbian, gay, bisexual, or queer (LGBQ; n = 70) to straight/heterosexual women (n = 683). For both LGBQ and straight respondents, the greatest barriers to screening were lack of health insurance (63% and 66%) and cost (49% and 50%). LGBQ respondents were more likely than straight respondents to report forgetting to screen (16% vs. 8%, p = .05), transportation barriers (10% vs. 2%, p = .001), and competing mental or physical health problems (39% vs. 27%, p = .10). Addressing access remains important for improving cervical cancer screening among those under-screened. For LGBQ women, additional attention may be needed for reminders, co-occurring health needs, and transportation barriers.

Combined oral contraceptive utilization and uterine fibroid incidence: A prospective study in a cohort of African-American women.

Published associations between combined oral contraceptive use and uterine fibroid development have lacked prospective imaging with ultrasound to distinguish between incident and prevalent fibroids. The Study of Environment, Lifestyle, and Fibroids prospectively followed fibroid-free, African-American women (the group with the highest disease burden in the U.S.) to identify incident cases. We examined associations between combined oral contraceptive use and the 40-month cumulative risk of fibroids. History of hormonal contraceptive use was collected via telephone interview at enrollment. Fibroid identification was performed using transvaginal ultrasonography at enrollment, and at 20 and 40-months of follow-up. Inverse probability weights for exposures and censoring were used to construct weighted risk ratios (wRR) and weighted risk different (wRD) estimators which control for differences in fibroid risk factors between exposure groups. In addition, unweighted fully adjusted log-binomial regression models (aRR) were run for comparison. Of the 1,308 participants in the analysis sample, 70% had used combined oral contraceptives and 17% developed fibroids by 40 months. We observed an inverse association between ever use of combined oral contraceptives and cumulative fibroid incidence (wRR: 0.78; 95% Confidence Interval (CI): 0.60, 1.00; wRD: -0.05, 95% CI: -0.11, 0; aRR: 0.76, 95% CI: 0.60, 0.98). Fibroid incidence was greater in participants who started using combined oral contraceptives after age 17 years than among younger initiators, though the restriction to ever-users made this estimate less precise (wRR: 1.25; 95% CI: 0.89, 1.76; wRD: 0.04, 95% CI: -0.02, 0.10). No consistent patterns of fibroid incidence were seen among ever-users for duration of, or years since, last combined oral contraceptives use.

Semiparametric g-computation for survival outcomes with time-fixed exposures: An illustration.

PURPOSE: Generalized (g-) computation is a useful tool for causal inference in epidemiology. However, in settings when the outcome is a survival time subject to right censoring, the standard pooled logistic regression approach to g-computation requires arbitrary discretization of time, parametric modeling of the baseline hazard function, and the need to expand one's dataset. We illustrate a semiparametric Breslow estimator for g-computation with time-fixed treatments and survival outcomes that is not subject to these limitations. METHODS: We compare performance of the Breslow g-computation estimator to the pooled logistic g-computation estimator in simulations and illustrate both approaches to estimate the effect of a 3-drug vs 2-drug antiretroviral therapy regimen among people with HIV. RESULTS: In simulations, both approaches performed well at the end of follow-up. The pooled logistic approach was biased at times between the endpoints of the discrete time intervals used, while the Breslow approach was not. In the example, both approaches estimated a 1-year risk difference of about 6 % in favor of the 3-drug regimen, but the shape of the survival curves differed. CONCLUSIONS: The Breslow g-computation estimator of counterfactual risk functions does not rely on strong parametric assumptions about the time-to-event distribution or onerous dataset expansions.

Estimating the Effects of Cancer Screening in Clinical Practice Settings: The Role of Selective Uptake and Suboptimal Adherence along the Cancer Screening Continuum.

Randomized controlled trials (RCT) are the gold standard in determining efficacy of cancer screening tests. Yet, systematic differences between RCT and the general populations eligible for screening raise concerns about the generalizability and relevance of RCT findings to guide the development and dissemination of cancer screening programs. Observational studies from clinical practice settings have documented selective uptake in screening-i.e., variation across subgroups regarding who is screened and not screened-as well as suboptimal adherence to screening recommendations, including follow-up of positive findings with subsequent imaging studies and diagnostic invasive procedures. When the effectiveness of a screening intervention varies across subgroups, and there is selective uptake and suboptimal adherence to screening in clinical practice relative to that in the RCT, the effects of screening reported in RCTs are not expected to generalize to clinical practice settings. Understanding the impacts of selective uptake and suboptimal adherence on estimates of the effectiveness of cancer screening in clinical practice will generate evidence that can be used to inform future screening recommendations and enhance shared decision-making tools.

Estimating SARS-CoV-2 seroprevalence.

Governments and public health authorities use seroprevalence studies to guide responses to the COVID-19 pandemic. Seroprevalence surveys estimate the proportion of individuals who have detectable SARS-CoV-2 antibodies. However, serologic assays are prone to misclassification error, and non-probability sampling may induce selection bias. In this paper, non-parametric and parametric seroprevalence estimators are considered that address both challenges by leveraging validation data and assuming equal probabilities of sample inclusion within covariate-defined strata. Both estimators are shown to be consistent and asymptotically normal, and consistent variance estimators are derived. Simulation studies are presented comparing the estimators over a range of scenarios. The methods are used to estimate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in New York City, Belgium, and North Carolina.