As of 1 November 2020, estimated case-fatality rates associated with coronavirus disease 2019 are not uniformly patterned across the world and differ substantially in magnitude. Given the global spatial heterogeneity in case-fatality rates, we applied the Blinder-Oaxaca regression decomposition technique to identify how putative sociodemographic, structural, and environmental sources influence variation in case-fatality rates. We show that compositional and associational differences in country-level risk factors explain a substantial proportion of the coronavirus disease 2019-related case-fatality rate gap across nations. Asian countries fair better vis-à-vis case-fatality rate differences mainly due to variation in returns to sociodemographic, structural, and environmental sources among their citizens, relative to those who share similar attributes but live in Europe or North America. The variation in case-fatality rate is driven by Asian populations being better able to buffer the harmful effects of the very risk factors purported to exacerbate the risk of coronavirus disease 2019-related death. The dire circumstances in which we find ourselves demand better understanding of how preexisting conditions across countries contribute to observed disparities in case-fatality rates.
With new cases of Covid-19 surging in the United States, we need to better understand how the spread of novel coronavirus varies across all segments of the population. We use hierarchical exponential growth curve modeling techniques to examine whether community social and economic characteristics uniquely influence the incidence of Covid-19 cases in the urban built environment. We show that, as of May 3, 2020, confirmed coronavirus infections are concentrated along demographic and socioeconomic lines in New York City and surrounding areas, the epicenter of the Covid-19 pandemic in the United States. Furthermore, we see evidence that, after the onset of the pandemic, timely enactment of physical distancing measures such as school closures is imperative in order to limit the extent of the coronavirus spread in the population. Public health authorities must impose nonpharmaceutical measures early on in the pandemic and consider community-level factors that associate with a greater risk of viral transmission.
More than a century of research has shown that sociodemographic conditions affect infectious disease transmission. In the late spring and early summer of 2020, reports of the effects of sociodemographic variables on the spread of COVID-19 were used in the media with minimal scientific proof attached. With new cases of COVID-19 surging in the United States at that time, it became essential to better understand how the spread of COVID-19 was varying across all segments of the population. We used hierarchical exponential growth curve modeling techniques to examine whether community socioeconomic characteristics uniquely influence the incidence of reported COVID-19 cases in the urban built environment. We show that as of July 19, 2020, confirmed coronavirus infections in New York City and surrounding areas—one of the early epicenters of the COVID-19 pandemic in the United States—were concentrated along demographic and socioeconomic lines. Furthermore, our data provides evidence that after the onset of the pandemic, timely enactment of physical distancing measures such as school closures was essential to limiting the extent of the coronavirus spread in the population. We conclude that in a pandemic, public health authorities must impose physical distancing measures early on as well as consider community-level factors that associate with a greater risk of viral transmission.
Objective: Through geocoding the physical residential address included in the electronic medical record to the census tract level, we present a novel model for concomitant examination of individual patient-related and residential context-related factors that are associated with patient-reported experience scores. Summary Background Data: When assessing patient experience in the surgical setting, researchers need to examine the potential influence of neighborhood-level characteristics on patient experience-of-care ratings. Methods: We geocoded the residential address included in the electronic medical record (EMR) from a tertiary care facility to the census tract level of Orange County, CA. We then linked each individual record to the matching census tract and use hierarchical regression analyses to test the impact of distinct neighborhood conditions on patient experience. This approach allows us to estimate how each neighborhood characteristic uniquely influences Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores. Results: Individuals residing in communities characterized by high levels of socioeconomic disadvantage have the highest experience ratings. Accounting for individual patient’s characteristics such as age, gender, race/ethnicity, primary language spoken at home, length of stay, and average pain levels during their hospital stay, neighborhood-level characteristics such as proportions of people receiving public assistance influence the ratings of hospital experience (0.01, P < 0.05) independent of, and beyond, these individual-level factors. Conclusions: This manuscript is an example of how geocoding could be used to analyze surgical patient experience scores. In this analysis, we have shown that neighborhood-level characteristics influence the ratings of hospital experience independent of, and beyond, individual-level factors
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