International journal of epidemiology and public health research最新文献

{"title":"Investigation of the Impact of Social Vulnerability and Racial Disparity on COVID-19 Infection and Death Rates Among Georgians (USA).","authors":"Mohamed Mubasher, Lynnette Ametewee, Reinetta Thompson Waldrop, Peter Baltrus, Sabrina Mobley, Rakale C Quarells, Michelle Nwagwu, Chanelle Harris, Kamaria Glover, Mekhi Hill, Brittany D Taylor, Tabia Henry Akintobi","doi":"10.61148/2836-2810/IJEPHR","DOIUrl":"10.61148/2836-2810/IJEPHR","url":null,"abstract":"<p><strong>Introduction: </strong>The novel coronavirus (COVID-19) continues to shed light on the disproportionately negative impact of public health pandemics among racial/ethnic minorities and other systematically marginalized communities persistency experiencing poorer health and health outcomes. Far less statistical investigation has been conducted to confirm the disease agnostic social determinants correlated with the intersection of emergent crisis, chronic health conditions and local contexts to inform proactive response strategies. This study investigated the influence of Social Vulnerability (SV), barriers to access to vaccination and Racial Disparities (RD) on COVID-19 infection and death rates among Georgian residents using Georgia Department of Public Health data and County Health Rankings & Roadmaps.</p><p><strong>Method: </strong>We adjusted analyses for other predictors of outcomes by using the Poisson Generalized Linear Mixed Models (with county as the unit of analysis). We iteratively modeled county-specific infection/death rates as a function of the Social Vulnerability Index (SVI, % Racial Population Gap (RPG) [(60+years % White - %African Americans/Blacks (AA)/Black)], education, %unemployed, %uninsured, % obese, % fully vaccinated, racial differences in respiratory infection discharge rates and %AA /Black residents (w/o RPG in the model).</p><p><strong>Results: </strong>Per adjusted models' results of COVID-19 related death,, I) AA/Blacks, relative to Whites, were 51% more likely to die (p-value <0.0001), 1) by age-specific and overall estimates(p-values <0.0001 and 2) at a younger mean age (p-value < 0.0001), II) 1% increase in SVI increases the risk of death by 25% (p-value <0.0001) and III) risk of death decreases by 2.3% for every % increase in 60+ years old Whites vs. Black males county residents (p-value <0.0001). The case infection rate a) decreased by a.1) 0.1% for every percent population increase in the racial gap (i.e., more Whites than AA/Blacks in a county) (p-values = 0.0122) and a.2) 27% for every % increase of those fully vaccinated(p-value < 0.0001). The rates also increased by a) 17% with every 1% increase in SV Index p-value <0.0001) b) 1% for every 0.1% increase in those a) obese (p-value < 0.0001) and b) uninsured (p-value < 0.0001).</p><p><strong>Conclusions: </strong>Attention to the Social Vulnerability Index (SVI) factors associated with COVID-19 illness and death signal the need for proactive prevention and mitigation interventions prior to and in the wake of public health pandemics thereby bridging the health disparity gap.</p>","PeriodicalId":520833,"journal":{"name":"International journal of epidemiology and public health research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12164599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulations-Based Least Required Sample Size and Power in Clinical Trials with Time-to-Event endpoint and Variable Hazard.","authors":"Mohamed Mubasher, Liang Shan, Fengxia Yan, Brian Rivers, Fan Wu, Muhammed Idris, Alexander Quarshie, Robert M Mayberry, Elizabeth Ofili, Tabia Henry Akintobi, Sejong Bae","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Two of the pivotal design parameters for planning clinical trials with time-to-event outcome(s) are sample size and power. Attention needs to be placed on the hazard function (which characterizes the rate at which events occur and can be constant, decreasing, and/or increasing in time). This work employs simulation(s) of real scenarios of randomized studies to generate time-to-event variables with specific hazard characterization, obeying the Weibull function which accommodates variable hazard situations. Our aim is to determine the least required sample size and power values, based on simulating two independent samples of Weibull distributed responses, differing by various postulated hazard patterns (constant, decreasing, or increasing in time), different scale parameter values, and follow-up periods.</p>","PeriodicalId":520833,"journal":{"name":"International journal of epidemiology and public health research","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12087582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144103509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}