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10.1016/j.annepidem.2021.05.006 http://scihub22266oqcxt.onion/10.1016/j.annepidem.2021.05.006 34048904!8451980!34048904     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Ann+Epidemiol 2021 ; 62 (ä): 51-58 Nephropedia Template TP {{tp|p=34048904|t=2021. Dynamics of Covid-19 mortality and social determinants of health: a spatiotemporal analysis of exceedance probabilities |pdf=|usr=}}{{34048904}} gab.com Text Dynamics of Covid-19 mortality and social determinants of health: a spatiotemporal analysis of exceedance probabilities JO: Ann Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=34048904 #FOAvip PURPOSE: To determine the association of social factors with Covid-19 mortality and identify high-risk clusters. METHODS: Data on Covid-19 deaths across 3,108 contiguous U.S. counties from the Johns Hopkins University and social determinants of health (SDoH) data from the County Health Ranking and the Bureau of Labor Statistics were fitted to Bayesian semi-parametric spatiotemporal Negative Binomial models, and 95% credible intervals (CrI) of incidence rate ratios (IRR) were used to assess the associations. Exceedance probabilities were used for detecting clusters. RESULTS: As of October 31, 2020, the median mortality rate was 40.05 per 100, 000. The monthly urban mortality rates increased with unemployment (IRR(adjusted):1.41, 95% CrI: 1.24, 1.60), percent Black population (IRR(adjusted):1.05, 95% CrI: 1.04, 1.07), and residential segregation (IRR(adjusted):1.03, 95% CrI: 1.02, 1.04). The rural monthly mortality rates increased with percent female population (IRR(adjusted): 1.17, 95% CrI: 1.11, 1.24) and percent Black population (IRR(adjusted):1.07 95% CrI:1.06, 1.08). Higher college education rates were associated with decreased mortality rates in rural and urban counties. The dynamics of exceedance probabilities detected the shifts of high-risk clusters from the Northeast to Southern and Midwestern counties. CONCLUSIONS: Spatiotemporal analyses enabled the inclusion of unobserved latent risk factors and aid in scientifically grounded decision-making at a granular level. Twit Text FOAVip Dynamics of Covid-19 mortality and social determinants of health: a spatiotemporal analysis of exceedance probabilities ????Ann Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=34048904 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34048904 #FOAvip English Wikipedia <ref>{{Cite pmid|34048904}}</ref> Dynamics of Covid-19 mortality and social determinants of health: a spatiotemporal analysis of exceedance probabilities #MMPMID34048904 Paul R; Adeyemi O; Ghosh S; Pokhrel K; Arif AA Ann Epidemiol 2021[Oct]; 62 (ä): 51-58 PMID34048904show ga PURPOSE: To determine the association of social factors with Covid-19 mortality and identify high-risk clusters. METHODS: Data on Covid-19 deaths across 3,108 contiguous U.S. counties from the Johns Hopkins University and social determinants of health (SDoH) data from the County Health Ranking and the Bureau of Labor Statistics were fitted to Bayesian semi-parametric spatiotemporal Negative Binomial models, and 95% credible intervals (CrI) of incidence rate ratios (IRR) were used to assess the associations. Exceedance probabilities were used for detecting clusters. RESULTS: As of October 31, 2020, the median mortality rate was 40.05 per 100, 000. The monthly urban mortality rates increased with unemployment (IRR(adjusted):1.41, 95% CrI: 1.24, 1.60), percent Black population (IRR(adjusted):1.05, 95% CrI: 1.04, 1.07), and residential segregation (IRR(adjusted):1.03, 95% CrI: 1.02, 1.04). The rural monthly mortality rates increased with percent female population (IRR(adjusted): 1.17, 95% CrI: 1.11, 1.24) and percent Black population (IRR(adjusted):1.07 95% CrI:1.06, 1.08). Higher college education rates were associated with decreased mortality rates in rural and urban counties. The dynamics of exceedance probabilities detected the shifts of high-risk clusters from the Northeast to Southern and Midwestern counties. CONCLUSIONS: Spatiotemporal analyses enabled the inclusion of unobserved latent risk factors and aid in scientifically grounded decision-making at a granular level. |*COVID-19[MESH] |*Social Determinants of Health[MESH] |Bayes Theorem[MESH] |Female[MESH] |Humans[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH] |Spatio-Temporal Analysis[MESH]Dynamics of Covid-19 mortality and social determinants of health: a s(epmc).pdf DeepDyve Pubget Overpricing