IF 2.3 3区 生物学Q2 MULTIDISCIPLINARY SCIENCESPeerJPub Date : 2025-01-16eCollection Date: 2025-01-01DOI:10.7717/peerj.18537
Nirmalendu Deb Nath, Agricola Odoi
{"title":"美国佛罗里达州县级糖尿病相关死亡风险的预测因素:一项回顾性生态学研究。","authors":"Nirmalendu Deb Nath, Agricola Odoi","doi":"10.7717/peerj.18537","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Diabetes is an increasingly important public health problem due to its socioeconomic impact, high morbidity, and mortality. Although there is evidence of increasing diabetes-related deaths over the last ten years, little is known about the population level predictors of diabetes-related mortality risks (DRMR) in Florida. Identifying these predictors is important for guiding control programs geared at reducing the diabetes burden and improving population health. Therefore, the objective of this study was to identify geographic disparities and predictors of county-level DRMR in Florida.</p><p><strong>Methods: </strong>The 2019 mortality data for the state of Florida were obtained from the Florida Department of Health. The 10th International Classification of Disease codes E10-E14 were used to identify diabetes-related deaths which were then aggregated to the county-level. County-level DRMR were computed and presented as number of deaths per 100,000 persons. Geographic distribution of DRMR were displayed in choropleth maps and ordinary least squares (OLS) regression model was used to identify county-level predictors of DRMR.</p><p><strong>Results: </strong>There was a total 6,078 diabetes-related deaths in Florida during the study time period. County-level DRMR ranged from 9.6 to 75.6 per 100,000 persons. High mortality risks were observed in the northern, central, and southcentral parts of the state. Relatively higher mortality risks were identified in rural counties compared to their urban counterparts. Significantly high county-level DRMR were observed in counties with high percentages of the population that were: 65 year and older (<i>p</i> < 0.001), current smokers (<i>p</i> = 0.032), and insufficiently physically active (<i>p</i> = 0.036). Additionally, percentage of households without vehicles (<i>p</i> = 0.022) and percentage of population with diabetes (<i>p</i> < 0.001) were significant predictors of DRMR.</p><p><strong>Conclusion: </strong>Geographic disparities of DRMR exist in Florida, with high risks being observed in northern, central, and southcentral counties of the state. The study identified county-level predictors of these identified DRMR disparities in Florida. The findings are useful in guiding health professionals to better target intervention efforts.</p>","PeriodicalId":19799,"journal":{"name":"PeerJ","volume":"13 ","pages":"e18537"},"PeriodicalIF":2.3000,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11742252/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predictors of county-level diabetes-related mortality risks in Florida, USA: a retrospective ecological study.\",\"authors\":\"Nirmalendu Deb Nath, Agricola Odoi\",\"doi\":\"10.7717/peerj.18537\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Diabetes is an increasingly important public health problem due to its socioeconomic impact, high morbidity, and mortality. Although there is evidence of increasing diabetes-related deaths over the last ten years, little is known about the population level predictors of diabetes-related mortality risks (DRMR) in Florida. Identifying these predictors is important for guiding control programs geared at reducing the diabetes burden and improving population health. Therefore, the objective of this study was to identify geographic disparities and predictors of county-level DRMR in Florida.</p><p><strong>Methods: </strong>The 2019 mortality data for the state of Florida were obtained from the Florida Department of Health. The 10th International Classification of Disease codes E10-E14 were used to identify diabetes-related deaths which were then aggregated to the county-level. County-level DRMR were computed and presented as number of deaths per 100,000 persons. Geographic distribution of DRMR were displayed in choropleth maps and ordinary least squares (OLS) regression model was used to identify county-level predictors of DRMR.</p><p><strong>Results: </strong>There was a total 6,078 diabetes-related deaths in Florida during the study time period. County-level DRMR ranged from 9.6 to 75.6 per 100,000 persons. High mortality risks were observed in the northern, central, and southcentral parts of the state. Relatively higher mortality risks were identified in rural counties compared to their urban counterparts. Significantly high county-level DRMR were observed in counties with high percentages of the population that were: 65 year and older (<i>p</i> < 0.001), current smokers (<i>p</i> = 0.032), and insufficiently physically active (<i>p</i> = 0.036). Additionally, percentage of households without vehicles (<i>p</i> = 0.022) and percentage of population with diabetes (<i>p</i> < 0.001) were significant predictors of DRMR.</p><p><strong>Conclusion: </strong>Geographic disparities of DRMR exist in Florida, with high risks being observed in northern, central, and southcentral counties of the state. The study identified county-level predictors of these identified DRMR disparities in Florida. The findings are useful in guiding health professionals to better target intervention efforts.</p>\",\"PeriodicalId\":19799,\"journal\":{\"name\":\"PeerJ\",\"volume\":\"13 \",\"pages\":\"e18537\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-01-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11742252/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PeerJ\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.7717/peerj.18537\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PeerJ","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.7717/peerj.18537","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Predictors of county-level diabetes-related mortality risks in Florida, USA: a retrospective ecological study.
Background: Diabetes is an increasingly important public health problem due to its socioeconomic impact, high morbidity, and mortality. Although there is evidence of increasing diabetes-related deaths over the last ten years, little is known about the population level predictors of diabetes-related mortality risks (DRMR) in Florida. Identifying these predictors is important for guiding control programs geared at reducing the diabetes burden and improving population health. Therefore, the objective of this study was to identify geographic disparities and predictors of county-level DRMR in Florida.
Methods: The 2019 mortality data for the state of Florida were obtained from the Florida Department of Health. The 10th International Classification of Disease codes E10-E14 were used to identify diabetes-related deaths which were then aggregated to the county-level. County-level DRMR were computed and presented as number of deaths per 100,000 persons. Geographic distribution of DRMR were displayed in choropleth maps and ordinary least squares (OLS) regression model was used to identify county-level predictors of DRMR.
Results: There was a total 6,078 diabetes-related deaths in Florida during the study time period. County-level DRMR ranged from 9.6 to 75.6 per 100,000 persons. High mortality risks were observed in the northern, central, and southcentral parts of the state. Relatively higher mortality risks were identified in rural counties compared to their urban counterparts. Significantly high county-level DRMR were observed in counties with high percentages of the population that were: 65 year and older (p < 0.001), current smokers (p = 0.032), and insufficiently physically active (p = 0.036). Additionally, percentage of households without vehicles (p = 0.022) and percentage of population with diabetes (p < 0.001) were significant predictors of DRMR.
Conclusion: Geographic disparities of DRMR exist in Florida, with high risks being observed in northern, central, and southcentral counties of the state. The study identified county-level predictors of these identified DRMR disparities in Florida. The findings are useful in guiding health professionals to better target intervention efforts.
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