Dying at Home Due to Coronavirus Disease 2019

Background. Coronavirus disease 2019 (COVID-19) is a leading cause of US deaths and when severe requires admission to a hospital; however, 9% of US COVID-19 deaths before 2022 occurred at home. Methods. Death certificate data were used to examine the cumulative probability of dying at home from COVID-19 and from any cause in North Carolina, including by race and ethnicity. Results. Between March 1, 2020 and December 31, 2021, 22 646 COVID-19 deaths were recorded in North Carolina; of these, 1771 (7.8%) occurred at home. Cumulative risk of dying at home with COVID-19 increased from 3.3/100 000 on December 31, 2020 to 13.0/ 100 000 on December 31, 2021. After standardizing each racial/ethnic group, cumulative at-home COVID-19 mortality among Hispanic people compared to White people was 9.9/100 000 versus 2.3/100 000, respectively, at year-end 2020 (difference, 7.6/100 000; 95% confidence interval [CI], 5.6–9.6) and 19.0/100 000 versus 11.4/100 000 at year-end 2021 (difference, 7.6; 95% CI, 4.9– 10.4). At-home mortality among Black people was also elevated compared to White people (difference, 5.6/100 000; 95% CI, 3.7– 7.4) at year-end 2021. Rates of dying at home from any cause increased overall but were greatest among Hispanic people. Conclusions. By the end of 2021, the risk of dying at home from COVID-19 increased, especially for persons of color. The risk of dying at-home from any cause also increased for all but more so for Hispanic persons. These findings suggest perennial barriers to care prevent those with progressive COVID-19 from accessing medical attention and the need for initiatives that extend healthcare access for those disproportionately impacted by COVID-19 to prevent avoidable deat

The Metropolis algorithm: A useful tool for epidemiologists

The Metropolis algorithm is a Markov chain Monte Carlo (MCMC) algorithm used to simulate from parameter distributions of interest, such as generalized linear model parameters. The "Metropolis step" is a keystone concept that underlies classical and modern MCMC methods and facilitates simple analysis of complex statistical models. Beyond Bayesian analysis, MCMC is useful for generating uncertainty intervals, even under the common scenario in causal inference in which the target parameter is not directly estimated by a single, fitted statistical model. We demonstrate, with a worked example, pseudo-code, and R code, the basic mechanics of the Metropolis algorithm. We use the Metropolis algorithm to estimate the odds ratio and risk difference contrasting the risk of childhood leukemia among those exposed to high versus low level magnetic fields. This approach can be used for inference from Bayesian and frequentist paradigms and, in small samples, offers advantages over large-sample methods like the bootstrap.Comment: 26 pages, 3 figure

All your data are always missing: incorporating bias due to measurement error into the potential outcomes framework

Epidemiologists often use the potential outcomes framework to cast causal inference as a missing data problem. Here, we demonstrate how bias due to measurement error can be described in terms of potential outcomes and considered in concert with bias from other sources. In addition, we illustrate how acknowledging the uncertainty that arises due to measurement error increases the amount of missing information in causal inference. We use a simple example to show that estimating the average treatment effect requires the investigator to perform a series of hidden imputations based on strong assumptions

Parametric assumptions equate to hidden observations: comparing the efficiency of nonparametric and parametric models for estimating time to AIDS or death in a cohort of HIV-positive women

Abstract Background When conducting a survival analysis, researchers might consider two broad classes of models: nonparametric models and parametric models. While nonparametric models are more flexible because they make few assumptions regarding the shape of the data distribution, parametric models are more efficient. Here we sought to make concrete the difference in efficiency between these two model types using effective sample size. Methods We compared cumulative risk of AIDS or death estimated using four survival models – nonparametric, generalized gamma, Weibull, and exponential – and data from 1164 HIV patients who were alive and AIDS-free in 1995. We added pseudo-observations to the sample until the spread of the 95% confidence limits for the nonparametric model became less than that for the parametric models. Results We found the 3-parameter generalized gamma to be a good fit to the nonparametric risk curve, but the 1-parameter exponential both underestimated and overestimated the risk at different times. Using two year-risk as an example, we had to add 354, 593, and 3960 observations for the nonparametric model to be as efficient as the generalized gamma, Weibull, and exponential models, respectively. Conclusions These added observations represent the hidden observations underlying the efficiency gained through parametric model form assumptions. If the model is correctly specified, the efficiency gain may be justified, as appeared to be the case for the generalized gamma model. Otherwise, precision will be improved, but at the cost of specification bias, as was the case for the exponential model

Opportunities for Enhanced Strategic Use of Surveys, Medical Records, and Program Data for HIV Surveillance of Key Populations: Scoping Review.

BACKGROUND: Normative guidelines from the World Health Organization recommend tracking strategic information indicators among key populations. Monitoring progress in the global response to the HIV epidemic uses indicators put forward by the Joint United Nations Programme on HIV/AIDS. These include the 90-90-90 targets that require a realignment of surveillance data, routinely collected program data, and medical record data, which historically have developed separately. OBJECTIVE: The aim of this study was to describe current challenges for monitoring HIV-related strategic information indicators among key populations ((men who have sex with men [MSM], people in prisons and other closed settings, people who inject drugs, sex workers, and transgender people) and identify future opportunities to enhance the use of surveillance data, programmatic data, and medical record data to describe the HIV epidemic among key populations and measure the coverage of HIV prevention, care, and treatment programs. METHODS: To provide a historical perspective, we completed a scoping review of the expansion of HIV surveillance among key populations over the past three decades. To describe current efforts, we conducted a review of the literature to identify published examples of SI indicator estimates among key populations. To describe anticipated challenges and future opportunities to improve measurement of strategic information indicators, particularly from routine program and health data, we consulted participants of the Third Global HIV Surveillance Meeting in Bangkok, where the 2015 World Health Organization strategic information guidelines were launched. RESULTS: There remains suboptimal alignment of surveillance and programmatic data, as well as routinely collected medical records to facilitate the reporting of the 90-90-90 indicators for HIV among key populations. Studies (n=3) with estimates of all three 90-90-90 indicators rely on cross-sectional survey data. Programmatic data and medical record data continue to be insufficiently robust to provide estimates of the 90-90-90 targets for key populations. CONCLUSIONS: Current reliance on more active data collection processes, including key population-specific surveys, remains warranted until the quality and validity of passively collected routine program and medical record data for key populations is optimized

Remdesivir and COVID-19

The Panel on Antiretroviral Guidelines for Adults and Adolescents with HIV and the American Association for the Study of Liver Diseases guidelines for hepatitis C virus treatment suggest that combination therapy for severe acute respiratory syndrome coronavirus 2 infection will outperform single drugs

Occupational Radon Exposure and Lung Cancer Mortality: Estimating Intervention Effects Using the Parametric g-Formula

Traditional regression analysis techniques used to estimate associations between occupational radon exposure and lung cancer focus on estimating the effect of cumulative radon exposure on lung cancer, while public health interventions are typically based on regulating radon concentration rather than workers’ cumulative exposure. Moreover, estimating the direct effect of cumulative occupational exposure on lung cancer may be difficult in situations vulnerable to the healthy worker survivor bias