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10.1097/PHH.0000000000001236 http://scihub22266oqcxt.onion/10.1097/PHH.0000000000001236 32969952!ä!32969952 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32969952&cmd=llinks): Failed to open stream: HTTP request failed! 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Predictors of State-Level Stay-at-Home Orders in the United States and Their Association With Mobility of Residents |pdf=|usr=}}{{32969952}} gab.com Text Predictors of State-Level Stay-at-Home Orders in the United States and Their Association With Mobility of Residents JO: J Public Health Manag Pract http://www.kidney.de/pget.php?&tw=1&pmid=32969952 #FOAvip OBJECTIVE: To evaluate predictors of stay-at-home order adoption among US states, as well as associations between order enactment and residents' mobility. DESIGN: We assess associations between state characteristics and adoption timing. We also assess associations between enactment and aggregate state-level measures of residents' mobility (Google COVID-19 Community Mobility Reports). SETTING: The United States. PARTICIPANTS: Adoption population: 50 US states and District of Columbia. Mobility population: state residents using devices with GPS tracking accessible by Google. INTERVENTION AND EXPOSURES: State characteristics: COVID-19 diagnoses per capita, 2016 Trump vote share, Republican governor, Medicaid expansion status, hospital beds per capita, public health funding per capita, state and local tax revenue per capita, median household income, population, percent residents 65 years or older, and percent urban residents. Mobility exposure: indicator of order enactment by March 29, 2020 (date of mobility data collection). MAIN OUTCOME MEASURES: Order adoption timing: days since adoption of first order. Mobility: changes in mobility to 6 locations from February 6 to March 29, 2020. RESULTS: In bivariate models, order adoption was associated with COVID-19 diagnoses (hazard ratio [HR] = 1.01; 95% confidence interval [CI], 1.00 to 1.01), Republican governor (HR = 0.24; 95% CI, 0.13 to 0.44), Medicaid expansion (HR = 2.50; 95% CI, 1.40 to 4.48), and hospital capacity (HR = 0.43; 95% CI, 0.26 to 0.70), consistent with findings in the multivariate models. Order enactment was positively associated with time at home (beta (B) = 1.31; 95% CI, 0.35 to 2.28) and negatively associated with time at retail and recreation (B = -7.17; 95% CI, -10.89 to -3.46) and grocery and pharmacy (B = -8.28; 95% CI, -11.97 to -4.59) locations. Trump vote share was associated with increased mobility for 4 of 6 mobility measures. CONCLUSIONS AND RELEVANCE: While politics influenced order adoption, public health considerations were equally influential. While orders were associated with decreased mobility, political ideology was associated with increased mobility under social distancing policies. Twit Text FOAVip Predictors of State-Level Stay-at-Home Orders in the United States and Their Association With Mobility of Residents ????J Public Health Manag Pract http://www.kidney.de/pget.php?&tw=1&pmid=32969952 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32969952 #FOAvip English Wikipedia <ref>{{Cite pmid|32969952}}</ref> Predictors of State-Level Stay-at-Home Orders in the United States and Their Association With Mobility of Residents #MMPMID32969952 Gigliotti P; Martin EG J Public Health Manag Pract 2020[Nov]; 26 (6): 622-631 PMID32969952show ga OBJECTIVE: To evaluate predictors of stay-at-home order adoption among US states, as well as associations between order enactment and residents' mobility. DESIGN: We assess associations between state characteristics and adoption timing. We also assess associations between enactment and aggregate state-level measures of residents' mobility (Google COVID-19 Community Mobility Reports). SETTING: The United States. PARTICIPANTS: Adoption population: 50 US states and District of Columbia. Mobility population: state residents using devices with GPS tracking accessible by Google. INTERVENTION AND EXPOSURES: State characteristics: COVID-19 diagnoses per capita, 2016 Trump vote share, Republican governor, Medicaid expansion status, hospital beds per capita, public health funding per capita, state and local tax revenue per capita, median household income, population, percent residents 65 years or older, and percent urban residents. Mobility exposure: indicator of order enactment by March 29, 2020 (date of mobility data collection). MAIN OUTCOME MEASURES: Order adoption timing: days since adoption of first order. Mobility: changes in mobility to 6 locations from February 6 to March 29, 2020. RESULTS: In bivariate models, order adoption was associated with COVID-19 diagnoses (hazard ratio [HR] = 1.01; 95% confidence interval [CI], 1.00 to 1.01), Republican governor (HR = 0.24; 95% CI, 0.13 to 0.44), Medicaid expansion (HR = 2.50; 95% CI, 1.40 to 4.48), and hospital capacity (HR = 0.43; 95% CI, 0.26 to 0.70), consistent with findings in the multivariate models. Order enactment was positively associated with time at home (beta (B) = 1.31; 95% CI, 0.35 to 2.28) and negatively associated with time at retail and recreation (B = -7.17; 95% CI, -10.89 to -3.46) and grocery and pharmacy (B = -8.28; 95% CI, -11.97 to -4.59) locations. Trump vote share was associated with increased mobility for 4 of 6 mobility measures. CONCLUSIONS AND RELEVANCE: While politics influenced order adoption, public health considerations were equally influential. While orders were associated with decreased mobility, political ideology was associated with increased mobility under social distancing policies. |*Geographic Information Systems[MESH] |*Public Policy[MESH] |*Quarantine[MESH] |*Travel[MESH] |Betacoronavirus[MESH] |COVID-19/*epidemiology[MESH] |Coronavirus Infections/*epidemiology[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Pandemics[MESH] |Physical Distancing[MESH] |Pneumonia, Viral/*epidemiology[MESH] |SARS-CoV-2[MESH]Predictors of State-Level Stay-at-Home Orders in the United States and(epmc).pdf DeepDyve Pubget Overpricing