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1.
J Infect Dis ; 2022 Apr 16.
Article in English | MEDLINE | ID: covidwho-1886440

ABSTRACT

INTRODUCTION: This study aims to examine the worldwide prevalence of post 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: 50 studies were included, and 41 were meta-analyzed. Global estimated pooled prevalence of post COVID-19 condition was 0.43 (95% CI: 0.39,0.46). Hospitalized and non-hospitalized patients have estimates of 0.54 (95% CI: 0.44,0.63) and 0.34 (95% CI: 0.25,0.46), respectively. Regional prevalence estimates were Asia- 0.51 (95% CI: 0.37,0.65), Europe- 0.44 (95% CI: 0.32,0.56), and North America- 0.31 (95% CI: 0.21,0.43). Global prevalence for 30, 60, 90, and 120 days after infection were estimated to be 0.37 (95% CI: 0.26,0.49), 0.25 (95% CI: 0.15,0.38), 0.32 (95% CI: 0.14,0.57) and 0.49 (95% CI: 0.40,0.59), respectively. Fatigue was the most common symptom reported with a prevalence of 0.23 (95% CI: 0.17,0.30), followed by memory problems (0.14 [95% CI: 0.10,0.19]). DISCUSSION: This study finds post COVID-19 condition prevalence is substantial; the health effects of COVID-19 appear to be prolonged and can exert stress on the healthcare system.

2.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-312175

ABSTRACT

Objectives: Serological surveys were used to infer the infection attack rate in different populations. The sensitivity of the testing assay, Abbott, drops fast over time since infection which make the serological data difficult to interpret. In this work, we aim to solve this issue. Methods. We collect longitudinal serological data of Abbott to construct a sensitive decay function. We use the reported COVID-10 deaths to infer the infections, and use the decay function to simulate the seroprevalence and match to the reported seroprevalence in 12 Indian cities. Results. Our model simulated seroprevalence match the reported seroprevalence in most (but not all) of the 12 Indian cities we considered. We obtain reasonable infection attack rate and infection fatality rate for most of the 12 Indian cities. Conclusions. Using both reported COVID-19 deaths data and serological survey data, we infer the infection attack rate and infection fatality rate with increased confidence.

3.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-306299

ABSTRACT

The COVID-19 is sweeping the world with deadly consequences. Its contagious nature and clinical similarity to other pneumonias make separating subjects contracted with COVID-19 and non-COVID-19 viral pneumonia a priority and a challenge. However, COVID-19 testing has been greatly limited by the availability and cost of existing methods, even in developed countries like the US. Intrigued by the wide availability of routine blood tests, we propose to leverage them for COVID-19 testing using the power of machine learning. Two proven-robust machine learning model families, random forests (RFs) and support vector machines (SVMs), are employed to tackle the challenge. Trained on blood data from 208 moderate COVID-19 subjects and 86 subjects with non-COVID-19 moderate viral pneumonia, the best result is obtained in an SVM-based classifier with an accuracy of 84%, a sensitivity of 88%, a specificity of 80%, and a precision of 92%. The results are found explainable from both machine learning and medical perspectives. A privacy-protected web portal is set up to help medical personnel in their practice and the trained models are released for developers to further build other applications. We hope our results can help the world fight this pandemic and welcome clinical verification of our approach on larger populations.

4.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-326400

ABSTRACT

Streaming data routinely generated by mobile phones, social networks, e-commerce, and electronic health records present new opportunities for near real-time surveillance of the impact of an intervention on an outcome of interest via causal inference methods. However, as data grow rapidly in volume and velocity, storing and combing data become increasingly challenging. The amount of time and effort spent to update analyses can grow exponentially, which defeats the purpose of instantaneous surveillance. Data sharing barriers in multi-center studies bring additional challenges to rapid signal detection and update. It is thus time to turn static causal inference to online causal learning that can incorporate new information as it becomes available without revisiting prior observations. In this paper, we present a framework for online estimation and inference of treatment effects leveraging a series of datasets that arrive sequentially without storing or re-accessing individual-level raw data. We establish estimation consistency and asymptotic normality of the proposed framework for online causal inference. In particular, our framework is robust to biased data batches in the sense that the proposed online estimator is asymptotically unbiased as long as the pooled data is a random sample of the target population regardless of whether each data batch is. We also provide an R package for analyzing streaming observational data that enjoys great computation efficiency compared to existing software packages for offline analyses. Our proposed methods are illustrated with extensive simulations and an application to sequential monitoring of adverse events post COVID-19 vaccine.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325402

ABSTRACT

Background: Coronavirus disease-19 (COVID-19) has spread rapidly, with a growing number of cases confirmed around the world. This study explores the relationship of fasting blood glucose (FBG) at admission with mortality. Methods In this retrospective, single-center study, we analyzed the clinical characteristics of confirmed cases of COVID-19 in Wu Han from 29 January 2020 to 23 February 2020. Cox proportional hazard regression analysis was performed to evaluate the relationship between FBG and mortality. Results A total of 107 patients were enrolled in our study. The average age was 59.49 ± 13.33 and the FBG at admission was 7.35 ± 3.13 mmol/L. There were 16 people died of COVID-19 with an average age 68.1 ± 9.5 and the FBG was 8.94 ± 4.76 mmol/L. Regression analysis showed that there were significant association between FBG and death (HR = 1.13, 95%CI: 1.02-1.24). After adjusting for covariables, the significance still exists. In addition, our result showed that FBG > 7.0 mmol/L or diabetic mellitus can significantly increase mortality after adjusting for the age and gender. Conclusions This study suggests that FBG at admission is an effective and reliable indicator for disease prognosis in COVID-19 patients.

6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-292881

ABSTRACT

Importance As SARS-CoV-2 pervades worldwide, considerable focus has been placed on the longer lasting health effects of the virus on the human host and on the anticipated healthcare needs. Objective The primary aim of this study is to examine the prevalence of post-acute sequelae of COVID-19 (PASC), commonly known as long COVID, across the world and to assess geographic heterogeneities through a systematic review and meta-analysis. A second aim is to provide prevalence estimates for individual symptoms that have been commonly reported as PASC, based on the existing literature. Data Sources PubMed, Embase, and iSearch for preprints from medRxiv, bioRxiv, SSRN, and others, were searched on July 5, 2021 with verification extending to August 12, 2021. Study Selection Studies written in English that consider PASC (indexed as ailments persisting at least 28 days after diagnosis or recovery for SARS-CoV-2 infection) and that examine corresponding prevalence, risk factors, duration, or associated symptoms were included. A total of 40 studies were included with 9 from North America, 1 from South America, 17 from Europe, 11 from Asia, and 2 from other regions. Data Extraction and Synthesis Data extraction was performed and separately cross-validated on the following data elements: title, journal, authors, date of publication, outcomes, and characteristics related to the study sample and study design. Using a random effects framework for meta-analysis with DerSimonian-Laird pooled inverse-variance weighted estimator, we provide an interval estimate of PASC prevalence, globally, and across regions. This meta-analysis considers variation in PASC prevalence by hospitalization status during the acute phase of infection, duration of symptoms, and specific symptom categories. Main Outcomes and Measures Prevalence of PASC worldwide and stratified by regions. Results Global estimated pooled PASC prevalence derived from the estimates presented in 29 studies was 0.43 (95% confidence interval [CI]: 0.35, 0.63), with a higher pooled PASC prevalence estimate of 0.57 (95% CI: 0.45, 0.68), among those hospitalized during the acute phase of infection. Females were estimated to have higher pooled PASC prevalence than males (0.49 [95% CI: 0.35, 0.63] versus 0.37 [95% CI: 0.24, 0.51], respectively). Regional pooled PASC prevalence estimates in descending order were 0.49 (95% CI: 0.21, 0.42) for Asia, 0.44 (95% CI: 0.30, 0.59) for Europe, and 0.30 (95% CI: 0.32, 0.66) for North America. Global pooled PASC prevalence for 30, 60, 90, and 120 days after index test positive date were estimated to be 0.36 (95% CI: 0.25, 0.48), 0.24 (95% CI: 0.13, 0.39), 0.29 (95% CI: 0.12, 0.57) and 0.51 (95% CI: 0.42, 0.59), respectively. Among commonly reported PASC symptoms, fatigue and dyspnea were reported most frequently, with a prevalence of 0.23 (95% CI: 0.13, 0.38) and 0.13 (95% CI: 0.09, 0.19), respectively. Conclusions and Relevance The findings of this meta-analysis suggest that, worldwide, PASC comprises a significant fraction (0.43 [95% CI: 0.35, 0.63]) of COVID-19 tested positive cases and more than half of hospitalized COVID-19 cases, based on available literature as of August 12, 2021. Geographic differences appear to exist, as lowest to highest PASC prevalence is observed for North America (0.30 [95% CI: 0.32, 0.66]) to Asia (0.49 [95% CI: 0.21, 0.42]). The case-mix across studies, in terms of COVID-19 severity during the acute phase of infection and variation in the clinical definition of PASC, may explain some of these differences. Nonetheless, the health effects of COVID-19 appear to be prolonged and can exert marked stress on the healthcare system, with 237M reported COVID-19 cases worldwide as of October 12, 2021.

7.
Prim Care Diabetes ; 16(1): 57-64, 2022 02.
Article in English | MEDLINE | ID: covidwho-1487917

ABSTRACT

AIMS: The purpose of this study was to examine whether pandemic exposure impacted unmet social and diabetes needs, self-care behaviors, and diabetes outcomes in a sample with diabetes and poor glycemic control. METHODS: This was a cross-sectional analysis of participants with diabetes and poor glycemic control in an ongoing trial (n = 353). We compared the prevalence of unmet needs, self-care behaviors, and diabetes outcomes in successive cohorts of enrollees surveyed pre-pandemic (prior to March 11, 2020, n = 182), in the early stages of the pandemic (May-September, 2020, n = 75), and later (September 2020-January 2021, n = 96) stratified by income and gender. Adjusted multivariable regression models were used to examine trends. RESULTS: More participants with low income reported food insecurity (70% vs. 83%, p < 0.05) and needs related to access to blood glucose supplies (19% vs. 67%, p < 0.05) during the pandemic compared to pre-pandemic levels. In adjusted models among people with low incomes, the odds of housing insecurity increased among participants during the early pandemic months compared with participants pre-pandemic (OR 20.2 [95% CI 2.8-145.2], p < 0.01). A1c levels were better among participants later in the pandemic than those pre-pandemic (ß = -1.1 [95% CI -1.8 to -0.4], p < 0.01), but systolic blood pressure control was substantially worse (ß = 11.5 [95% CI 4.2-18.8, p < 0.001). CONCLUSION: Adults with low-incomes and diabetes were most impacted by the pandemic. A1c may not fully capture challenges that people with diabetes are facing to manage their condition; systolic blood pressures may have worsened and problems with self-care may forebode longer-term challenges in diabetes control.


Subject(s)
COVID-19 , Diabetes Mellitus , Adult , Cross-Sectional Studies , Diabetes Mellitus/diagnosis , Diabetes Mellitus/epidemiology , Diabetes Mellitus/therapy , Glycemic Control , Humans , Pandemics , SARS-CoV-2 , Self Care
8.
J Clin Med ; 10(19)2021 Sep 24.
Article in English | MEDLINE | ID: covidwho-1438639

ABSTRACT

Testing for SARS-CoV-2 antibodies is commonly used to determine prior COVID-19 infections and to gauge levels of infection- or vaccine-induced immunity. Michigan Medicine, a primary regional health center, provided an ideal setting to understand serologic testing patterns over time. Between 27 April 2020 and 3 May 2021, characteristics for 10,416 individuals presenting for SARS-CoV-2 antibody tests (10,932 tests in total) were collected. Relative to the COVID-19 vaccine roll-out date, 14 December 2020, the data were split into a pre- (8026 individuals) and post-vaccine launch (2587 individuals) period and contrasted with untested individuals to identify factors associated with tested individuals and seropositivity. Exploratory analysis of vaccine-mediated seropositivity was performed in 347 fully vaccinated individuals. Predictors of tested individuals included age, sex, smoking, neighborhood variables, and pre-existing conditions. Seropositivity in the pre-vaccine launch period was 9.2% and increased to 46.7% in the post-vaccine launch period. In the pre-vaccine launch period, seropositivity was significantly associated with age (10 year; OR = 0.80 (0.73, 0.89)), ever-smoker status (0.49 (0.35, 0.67)), respiratory disease (4.38 (3.13, 6.12)), circulatory disease (2.09 (1.48, 2.96)), liver disease (2.06 (1.11, 3.84)), non-Hispanic Black race/ethnicity (2.18 (1.33, 3.58)), and population density (1.10 (1.03, 1.18)). Except for the latter two, these associations remained statistically significant in the post-vaccine launch period. The positivity rate of fully vaccinated individual was 296/347(85.3% (81.0%, 88.8%)).

9.
PLoS ONE ; 16(2), 2021.
Article in English | CAB Abstracts | ID: covidwho-1410723

ABSTRACT

COVID-19 has had a substantial impact on clinical care and lifestyles globally. The State of Michigan reports over 80,000 positive COVID-19 tests between March 1, 2020 and July 29, 2020. We surveyed 8,041 Michigan Medicine biorepository participants in late June 2020. We found that 55% of COVID-19 cases reported no known exposure to family members or to someone outside the house diagnosed with COVID-19. A significantly higher rate of COVID-19 cases were employed as essential workers (45% vs 19%, p = 9x10-12). COVID-19 cases reporting a fever were more likely to require hospitalization (categorized as severe;OR = 4.4 [95% CI: 1.6-12.5, p = 0.005]) whereas respondents reporting rhinorrhea was less likely to require hospitalization (categorized as mild-to-moderate;OR = 0.16 [95% CI: 0.04-0.73, p = 0.018]). African-Americans reported higher rates of being diagnosed with COVID-19 (OR = 4.0 [95% CI: 2.2-7.2, p = 5x10-6]), as well as higher rates of exposure to family or someone outside the household diagnosed with COVID-19, an annual household income < $40,000, living in rental housing, and chronic diseases. During the Executive Order in Michigan, African Americans, women, and the lowest income group reported worsening health behaviors and higher overall concern for the potential detrimental effects of the pandemic. The higher risk of contracting COVID-19 observed among African Americans may be due to the increased rates of working as essential employees, lower socioeconomic status, and exposure to known positive cases. Continued efforts should focus on COVID-19 prevention and mitigation strategies, as well as address the inequality gaps that result in higher risks for both short-term and long-term health outcomes.

10.
Huan Jing Ke Xue ; 42(7): 3099-3106, 2021 Jul 08.
Article in Chinese | MEDLINE | ID: covidwho-1332912

ABSTRACT

This study analyzed the impacts of meteorological conditions and changes in air pollutant emissions on PM2.5 across the country during the first quarter of 2020 based on the WRF-CMAQ model. Results showed that the variations in meteorological conditions led to a national PM2.5 concentration decreased of 1.7% from 2020-01 to 2020-03, whereas it increased by 1.6% in January and decreased by 1.3% and 7.9% in February and March, respectively. The reduction of pollutants emissions led to a decrease of 14.1% in national PM2.5 concentration during the first quarter of 2020 and a decrease of 4.0%, 25.7%, and 15.0% in January, February, and March, respectively. Compared to the same period last year, the PM2.5 concentration measured in Wuhan City decreased more than in the entire country. This was caused by improved meteorological conditions and a higher reduction of pollutant emissions in Wuhan City. PM2.5 in Beijing increased annually before the epidemic outbreak and during the strict control period, mainly due to unfavorable meteorological conditions. However, the decrease in PM2.5 in Beijing compared to March 2019 was closely related to the substantial reduction of emissions. The measured PM2.5 in the "2+26" cities, the Fenwei Plain and the Yangtze River Delta (YRD) decreased during the first quarter of 2020, with the largest drop occurring in the Yangtze River Delta due to higher YRD emissions reductions. The meteorological conditions of "2+26" cities and Fenwei Plain were unfavorable before the epidemic outbreak and greatly improved during the strict control period, whereas the Yangtze River Delta had the most favorable meteorological conditions in March. The decrease in PM2.5 concentration caused by the reduction of pollutant emissions in the three key areas was highest during the strict control period.


Subject(s)
Air Pollutants , Air Pollution , Epidemics , Air Pollutants/analysis , Air Pollution/analysis , Beijing , China , Cities , Environmental Monitoring , Meteorology , Particulate Matter/analysis
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