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10.3961/jpmph.20.256 http://scihub22266oqcxt.onion/10.3961/jpmph.20.256 32752590!7411251!32752590     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       J+Prev+Med+Public+Health 2020 ; 53 (4): 220-227 Nephropedia Template TP {{tp|p=32752590|t=2020. Social Determinants of COVID-19 in Massachusetts, United States: An Ecological Study |pdf=|usr=}}{{32752590}} gab.com Text Social Determinants of COVID-19 in Massachusetts, United States: An Ecological Study JO: J Prev Med Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32752590 #FOAvip OBJECTIVES: The aim of this study was to assess how different social determinants of health (SDoH) may be related to variability in coronavirus disease 2019 (COVID-19) rates in cities and towns in Massachusetts (MA). METHODS: Data about the total number of cases, tests, and rates of COVID-19 as of June 10, 2020 were obtained for cities and towns in MA. The data on COVID-19 were matched with data on various SDoH variables at the city and town level from the American Community Survey. These variables included information about income, poverty, employment, renting, and insurance coverage. We compared COVID-19 rates according to these SDoH variables. RESULTS: There were clear gradients in the rates of COVID-19 according to SDoH variables. Communities with more poverty, lower income, lower insurance coverage, more unemployment, and a higher percentage of the workforce employed in essential services, including healthcare, had higher rates of COVID-19. Most of these differences were not accounted for by different rates of testing in these cities and towns. CONCLUSIONS: SDoH variables may explain some of the variability in the risk of COVID-19 across cities and towns in MA. Data about SDoH should be part of the standard surveillance for COVID-19. Efforts should be made to address social factors that may be putting communities at an elevated risk. Twit Text FOAVip Social Determinants of COVID-19 in Massachusetts, United States: An Ecological Study ????J Prev Med Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32752590 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32752590 #FOAvip English Wikipedia <ref>{{Cite pmid|32752590}}</ref> Social Determinants of COVID-19 in Massachusetts, United States: An Ecological Study #MMPMID32752590 Hawkins D J Prev Med Public Health 2020[Jul]; 53 (4): 220-227 PMID32752590show ga OBJECTIVES: The aim of this study was to assess how different social determinants of health (SDoH) may be related to variability in coronavirus disease 2019 (COVID-19) rates in cities and towns in Massachusetts (MA). METHODS: Data about the total number of cases, tests, and rates of COVID-19 as of June 10, 2020 were obtained for cities and towns in MA. The data on COVID-19 were matched with data on various SDoH variables at the city and town level from the American Community Survey. These variables included information about income, poverty, employment, renting, and insurance coverage. We compared COVID-19 rates according to these SDoH variables. RESULTS: There were clear gradients in the rates of COVID-19 according to SDoH variables. Communities with more poverty, lower income, lower insurance coverage, more unemployment, and a higher percentage of the workforce employed in essential services, including healthcare, had higher rates of COVID-19. Most of these differences were not accounted for by different rates of testing in these cities and towns. CONCLUSIONS: SDoH variables may explain some of the variability in the risk of COVID-19 across cities and towns in MA. Data about SDoH should be part of the standard surveillance for COVID-19. Efforts should be made to address social factors that may be putting communities at an elevated risk. |*Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Massachusetts/epidemiology[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH] |Poverty/statistics & numerical data[MESH] |SARS-CoV-2[MESH] |Social Determinants of Health/*statistics & numerical data[MESH]Social Determinants of COVID-19 in Massachusetts, United States: An Ec(epmc).pdf DeepDyve Pubget Overpricing