Background: This study aims to examine the worldwide prevalence of post-coronavirus disease 2019 (COVID-19) condition, through a systematic review and meta-analysis. Methods: PubMed, Embase, and iSearch were searched on July 5, 2021 with verification extending to March 13, 2022. Using a random-effects framework with DerSimonian-Laird estimator, we meta-analyzed post-COVID-19 condition prevalence at 28+ days from infection. Results: Fifty studies were included, and 41 were meta-analyzed. Global estimated pooled prevalence of post-COVID-19 condition was 0.43 (95% confidence interval [CI], .39–.46). Hospitalized and nonhospitalized patients had estimates of 0.54 (95% CI, .44–.63) and 0.34 (95% CI, .25–.46), respectively. Regional prevalence estimates were Asia (0.51; 95% CI, .37–.65), Europe (0.44; 95% CI, .32–.56), and United States of America (0.31; 95% CI, .21–.43). Global prevalence for 30, 60, 90, and 120 days after infection were estimated to be 0.37 (95% CI, .26–.49), 0.25 (95% CI, .15–.38), 0.32 (95% CI, .14–.57), and 0.49 (95% CI, .40–.59), respectively. Fatigue was the most common symptom reported with a prevalence of 0.23 (95% CI, .17–.30), followed by memory problems (0.14; 95% CI, .10–.19). Conclusions: This study finds post-COVID-19 condition prevalence is substantial; the health effects of COVID-19 seem to be prolonged and can exert stress on the healthcare system.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has underscored racial disparities in the United States.^1-25 In the state of Michigan in particular, there have been 113 820 confirmed COVID-19 cases and 6767 deaths as of September 1, 2020, which makes Michigan one of the most affected states in the US.²⁶ While Black residents represent 14% of the Michigan population,²⁷ they account for 21% of COVID-19 cases and 37% of deaths attributed to COVID-19.²⁶ Similar trends have been observed in New York⁹ and other states, where there is an overrepresentation of Black and Latinx people in COVID-19 cases and deaths.²³

As public awareness of consequences of environmental exposures has grown, estimating the adverse health effects due to simultaneous exposure to multiple pollutants is an important topic to explore. The challenges of evaluating the health impacts of environmental factors in a multipollutant model include, but are not limited to: identification of the most critical components of the pollutant mixture, examination of potential interaction effects, and attribution of health effects to individual pollutants in the presence of multicollinearity. In this paper, we reviewed five methods available in the statistical literature that are potentially helpful for constructing multipollutant models. We conducted a simulation study and presented two data examples to assess the performance of these methods on feature selection, effect estimation and interaction identification using both cross-sectional and time-series designs. We also proposed and evaluated a two-step strategy employing an initial screening by a tree-based method followed by further dimension reduction/variable selection by the aforementioned five approaches at the second step. Among the five methods, least absolute shrinkage and selection operator regression performs well in general for identifying important exposures, but will yield biased estimates and slightly larger model dimension given many correlated candidate exposures and modest sample size. Bayesian model averaging, and supervised principal component analysis are also useful in variable selection when there is a moderately strong exposure-response association. Substantial improvements on reducing model dimension and identifying important variables have been observed for all the five statistical methods using the two-step modeling strategy when the number of candidate variables is large. There is no uniform dominance of one method across all simulation scenarios and all criteria. The performances differ according to the nature of the response variable, the sample size, the number of pollutants involved, and the strength of exposure-response association/interaction. However, the two-step modeling strategy proposed here is potentially applicable under a multipollutant framework with many covariates by taking advantage of both the screening feature of an initial tree-based method and dimension reduction/variable selection property of the subsequent method. The choice of the method should also depend on the goal of the study: risk prediction, effect estimation or screening for important predictors and their interactions.

Background: This mega-systematic review evaluated the global prevalence of long COVID and its subtypes and symptoms, and assessed the effects of risk factors for long COVID. Methods: Studies published from 5 July 2021 to 29 May 2024 were searched in PubMed, Embase, and Web of Science, with supplemental updates on 23 July 2024. Data were pooled using a random-effects framework with DerSimonian-Laird estimator. Risk of bias analysis was conducted. Results: A total of 429 studies were meta-analyzed. The global pooled long COVID prevalence was 36% (95% confidence interval [CI], 33%–40%) with 144 contributing studies. The highest prevalence rates were observed in South America (51% [95% CI, 35%–66%]). The prevalence of long COVID persisted over time, with 35% (95% CI, 31%–39%) at <1 year of follow-up and 46% (95% CI, 37%–57%) at 1–2 years. The most prevalent subtypes were respiratory (20% [95% CI, 14%–28%]) estimated from 31 studies, general fatigue (20% [95% CI, 18%–23%]) from 119 studies, psychological (18% [95% CI, 11%–28%]) from 10 studies, and neurological (16% [95% CI, 8%–30%]) from 23 studies. The 3 strongest risk factors were being unvaccinated for COVID-19 (pooled odds ratio [OR], 2.09 [95% CI, 1.55–2.81]) meta-analyzed from 7 studies, infections from pre-Omicron variants (OR, 1.74 [95% CI, 1.40–2.17]) from 6 studies, and female sex (OR, 1.56 [95% CI, 1.32–1.84]) from 33 studies. Conclusions: Long COVID is globally prevalent after a severe acute respiratory syndrome coronavirus 2 infection, highlighting a continuing health challenge. The heterogeneity of estimates across populations argues the need for well-designed follow-up studies that use consistent measures and are globally representative.