[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-283391-105":3,"detail-sidebar-cat-1-en-105":81,"doc-detail-283391-en":126},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","insurance-and-clinical-factors-in-a-mostly-hispanic-cohort-of-patients-hospitalized-for-covid-19-poster-summary","Insurance and Clinical Factors in a Mostly Hispanic Cohort of Patients Hospitalized for COVID-19 - Poster Summary","","Insurance status and clinical factors shape COVID-19 outcomes in a mostly Hispanic inpatient cohort. Retrospective EMR data from Doctor’s Hospital at Renaissance (March–July 2020) were analyzed using R. Obesity-related comorbidities such as type 2 diabetes, hypertension, and dyslipidemia were linked to higher odds of death. Patients with health insurance showed higher odds of death, with insured individuals appearing a median of 14 years older, reflecting combined age and comorbidity risk.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/template/","Template",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/template/posters/","Posters",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/template/insurance-and-clinical-factors-in-a-mostly-hispanic-cohort-of-patients-hospitalized-for-covid-19-poster-summary/283391/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/insurance-and-clinical-factors-in-a-mostly-hispanic-cohort-of-patients-hospitalized-for-covid-19-poster-summary/283391.png","ImageObject",442,249,{"name":42,"@type":43},"Levi","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-12","2026-09-16",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",11,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What data and analysis methods were used in this study?","Question",{"text":63,"@type":64},"The study used retrospective electronic medical record (EMR) data from March to July 2020 at Doctor’s Hospital at Renaissance. R and RStudio were used for data cleaning and statistical analysis including bivariate logistic regressions and hypothesis tests such as chi-square and t-tests.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which factors were associated with greater odds of death?",{"text":68,"@type":64},"Obesity-related comorbidities—type 2 diabetes, hypertension, and dyslipidemia—were significantly associated with greater odds for death. Health insurance status was also associated with higher odds of death in this cohort.",{"name":70,"@type":61,"acceptedAnswer":71},"How did insurance status relate to patient age and overall risk?",{"text":72,"@type":64},"Patients with insurance were a median of 14 years older than uninsured patients. The combination of older age and higher comorbidity-related death risk contributed to greater death risk among insured patients.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},283391,1790483821,{"code":4,"msg":82,"data":83},"success",[84,88,93,98,102,107,112,117,122],{"id":55,"doc_module":22,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Presentations",90,"presentations",{"id":89,"doc_module":22,"doc_module_name":25,"category_name":90,"show_sort_weight":91,"slug":92},12,"Resumes",80,"resumes",{"id":94,"doc_module":22,"doc_module_name":25,"category_name":95,"show_sort_weight":96,"slug":97},14,"Invoices",70,"invoices",{"id":99,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":100,"slug":101},15,60,"posters",{"id":103,"doc_module":22,"doc_module_name":25,"category_name":104,"show_sort_weight":105,"slug":106},16,"Social Media",50,"social-media",{"id":108,"doc_module":22,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},17,"Forms",40,"forms",{"id":113,"doc_module":22,"doc_module_name":25,"category_name":114,"show_sort_weight":115,"slug":116},18,"Letters",30,"letters",{"id":118,"doc_module":22,"doc_module_name":25,"category_name":119,"show_sort_weight":120,"slug":121},21,"Paper Templates",5,"papers-templates",{"id":123,"doc_module":22,"doc_module_name":25,"category_name":124,"show_sort_weight":4,"slug":125},158,"General","general-158",{"code":4,"msg":82,"data":127},{"doc_id":79,"user_id":128,"nickname":42,"user_avatar":129,"doc_module":22,"category_id":99,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":22,"language":135,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":12,"update_tm":139,"read_time":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Abstract  \nBackground:  \n• SARS-CoV-2 has had a worldwide impact, though to varying degrees amongst different communities and populations. A disparate incidence ofCOVID-19, as well as for severe illness resulting from COVID-19 infection, has been observed in minority populations in the  \nU.S. 1,4  \n• Several medical conditions including obesity have been linked to more severe COVID-19 and worse outcomes .2,8  \n• Additionally, insurance status has been associated with differential risk for obesity, diabetes, and other conditions that are potential risk factors for COVID-19.3  \nMethods:  \n• Patient data was retrospectively collected from the electronic medical record (EMR) at Doctor’s Hospital at Renaissance from March – July 2020.  \n• R programming (4.0.2) & RStudio (1.3.1056) were used to clean the patient data and conduct a statistical analysis involving:  \n• Bivariate logistic regressions  \n• T-tests  \n• Chi-square tests  \n• Fisher’s exact test  \nResults:  \n• Several comorbidities for obesity (type 2 diabetes, hypertension, and dyslipidemia) were significantly associated with greater odds for death.  \n• Patients with these comorbidities had significantly greater odds for having health insurance  \n• Patients with health insurance had significantly greater odds for death  \n• Patients with insurance were seen to be a median of 14 years older than uninsured patients.  \n• The combination of their more advanced age and their increased risk for death from several comorbidities contributed to the greater risk of death for insured patients in this cohort  \nPublic Health Implications  \n• Comorbidities of obesity increase the risk for death in Hispanic COVID-19 patients  \nInsurance and Clinical Factors in a Mostly Hispanic Cohort of Patients  \nHospitalized for COVID-19  \nNicolas Kaplan 1 , Harry Feng, PhD 1 , Peter Zimmerman, PhD 1  \n1 Case Western Reserve University School of Medicine, Department of Population and Quantitative Health  \nSciences  \nPopulation:  \n• The study cohort was recruited from patients admitted for in-patient, critical care services at Doctor’s Hospital at Renaissance between March 1 st and July 27th 2020.  \n• Doctor’s Hospital at Renaissance is the single largest hospital in Hidalgo County  \n• Hidalgo County’s population is mostly (>92%) Hispanic.7  \n• Hidalgo County has elevated prevalence of risk factors for COVID-19:  \n• 42.9% obesity prevalence (vs . 29.2% in Texas) .5  \n• ~1/3 individuals under 65 years are uninsured.7  \n• Hidalgo County contains a major urban center and is a major point of entry from Mexico  \n• ~100,000 Americans move South into the region as part of a yearly migration .6  \nLearning Objectives:  \n• Work with a team to prepare a protocol for submission to the hospital IRB  \n• Discuss how an electronic medical record can be applied towards data collection for population health  \n• Utilize software to clean and analyze patient data  \nActivities 􀃆 Deliverables:  \n• Assisted in IRB proposal 􀃆 IRB approval  \n• Studied electronic medical record & collected data 􀃆 Preliminary data set  \n• Familiarized with R software & used R to clean and analyze patient data 􀃆 Complete data set  \nLessons Learned:  \n• Gained an understanding of R software  \n• Learned to apply statistics towards data analysis  \n• Familiarized myself with electronic medical record prior to matriculating to medical school  \n• Prepared to work with and direct clinical/research teams  \nTables and Figures:  \nFigure 1: Speculative Population Movement into Hidalgo County, Texas  \n\n| Variable | Median | Mean | St. Dev |\n| --- | --- | --- | --- |\n| Age | 64 | 62.14347 | 16.82703 |\n| BMI | 30.8 | 32.10416 | 8.706785 |\n| Length of Stay | 7 | 10.48661 | 9.272805 |\n| Proportional Breakdown |  |  |  |\n| Variable | Proportion | \\# |  |\n| Sex |  |  |  |\n| Male | 0.543 | 378 |  |\n| Female | 0.457 | 318 |  |\n| Race |  |  |  |\n| Hispanic, Latino, or of Hispanic origin | 0.963 | 659 |  |\n| American Indian or Alaskan Native | 0.001 | 1 |  |\n| Black or A","cbCairiAULTSPSk7","https://ap.wps.com/l/cbCairiAULTSPSk7","pdf",444429,"English","# Abstract\n## Background\n## Methods\n## Results\n## Public Health Implications\n## Population and Learning Objectives\n## Activities and Deliverables\n## Tables and Figures\n## References","[{\"question\":\"What data and analysis methods were used in this study?\",\"answer\":\"The study used retrospective electronic medical record (EMR) data from March to July 2020 at Doctor’s Hospital at Renaissance. R and RStudio were used for data cleaning and statistical analysis including bivariate logistic regressions and hypothesis tests such as chi-square and t-tests.\"},{\"question\":\"Which factors were associated with greater odds of death?\",\"answer\":\"Obesity-related comorbidities—type 2 diabetes, hypertension, and dyslipidemia—were significantly associated with greater odds for death. Health insurance status was also associated with higher odds of death in this cohort.\"},{\"question\":\"How did insurance status relate to patient age and overall risk?\",\"answer\":\"Patients with insurance were a median of 14 years older than uninsured patients. The combination of older age and higher comorbidity-related death risk contributed to greater death risk among insured patients.\"}]","Insurance and Clinical Factors in a Mostly Hispanic Cohort of Patients Hospitalized for COVID-19 - Poster Summary | PDF",1789602760]