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10.1016/j.healthplace.2021.102574 http://scihub22266oqcxt.onion/10.1016/j.healthplace.2021.102574 33895489!8631550!33895489     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Health+Place 2021 ; 69 (ä): 102574 Nephropedia Template TP {{tp|p=33895489|t=2021. Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes |pdf=|usr=}}{{33895489}} gab.com Text Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes JO: Health Place http://www.kidney.de/pget.php?&tw=1&pmid=33895489 #FOAvip We aim to understand the spatial inequality in Coronavirus disease 2019 (COVID-19) positivity rates across New York City (NYC) ZIP codes. Applying Bayesian spatial negative binomial models to a ZIP-code level dataset (N = 177) as of May 31st, 2020, we find that (1) the racial/ethnic minority groups are associated with COVID-19 positivity rates; (2) the percentages of remote workers are negatively associated with positivity rates, whereas older population and household size show a positive association; and (3) while ZIP codes in the Bronx and Queens have higher COVID-19 positivity rates, the strongest spatial effects are clustered in Brooklyn and Manhattan. Twit Text FOAVip Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes ????Health Place http://www.kidney.de/pget.php?&tw=1&pmid=33895489 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33895489 #FOAvip English Wikipedia <ref>{{Cite pmid|33895489}}</ref> Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes #MMPMID33895489 Yang TC; Kim S; Zhao Y; Choi SE Health Place 2021[May]; 69 (ä): 102574 PMID33895489show ga We aim to understand the spatial inequality in Coronavirus disease 2019 (COVID-19) positivity rates across New York City (NYC) ZIP codes. Applying Bayesian spatial negative binomial models to a ZIP-code level dataset (N = 177) as of May 31st, 2020, we find that (1) the racial/ethnic minority groups are associated with COVID-19 positivity rates; (2) the percentages of remote workers are negatively associated with positivity rates, whereas older population and household size show a positive association; and (3) while ZIP codes in the Bronx and Queens have higher COVID-19 positivity rates, the strongest spatial effects are clustered in Brooklyn and Manhattan. |*Health Status Disparities[MESH] |Bayes Theorem[MESH] |COVID-19/*epidemiology[MESH] |Ethnicity/*statistics & numerical data[MESH] |Geography[MESH] |Humans[MESH] |New York City/epidemiology[MESH] |Residence Characteristics/*statistics & numerical data[MESH] |Socioeconomic Factors[MESH] |Spatial Analysis[MESH]Examining spatial inequality in COVID-19 positivity rates across New Y(epmc).pdf DeepDyve Pubget Overpricing