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1.
BMC Public Health ; 22(1): 81, 2022 01 13.
Article in English | MEDLINE | ID: covidwho-1736373

ABSTRACT

BACKGROUND: Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known. METHODS: We conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population. RESULTS: Analysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90th and 10th percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties. CONCLUSIONS: County-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level.


Subject(s)
COVID-19 , Humans , Risk Factors , Rural Population , SARS-CoV-2 , Social Vulnerability , United States/epidemiology
2.
J Food Prot ; 85(3): 518-526, 2022 03 01.
Article in English | MEDLINE | ID: covidwho-1560770

ABSTRACT

ABSTRACT: There is limited examination about coronavirus disease 19 (COVID-19)-related food handling concerns and practices that cause chemical or microbial contamination and illness, particularly among those with food insecurity. We investigated consumer food handling concerns and practices during the COVID-19 pandemic and whether they differed by food insecurity status. An online survey was distributed among Chicago, IL, residents between 15 July and 21 August 2020 (n = 437). Independent t tests and Fisher's exact tests were used to identify differences in food handling concerns and practices between those with and without food insecurity (alpha = 0.05). Survey items included questions about food handling practices that were considered safe or neutral (i.e., washing hands and produce with water, sanitizing food packaging) and unsafe (i.e., using cleaning agents to wash foods, leaving perishable foods outside) by using 5-point Likert-style scales or categorical responses (i.e., yes, no). Participant responses fell between "slightly" and "somewhat" concerned about contracting COVID-19 from food and food packaging (mean ± standard error [SE]: 2.7 ± 0.1). Although participants reported washing their hands before eating and before preparing foods at least "most of the time" (mean ± SE: 4.4 ± 0.0 and 4.5 ± 0.0, respectively), only one-third engaged in unsafe practices. The majority of participants (68%) indicated that they altered food handling practices due to the COVID-19 pandemic and received information about food safety from social media (61%). When investigating differences in concerns and practices by food insecurity status, food insecure participants were more concerned about COVID-19 foodborne transmission for all food items (all P < 0.001) and more frequently performed unsafe food handling practices than those with food security (all P < 0.001). Results from this study suggest more investigation is needed to understand barriers to safe food handling knowledge and practices, particularly among those with food insecurity.


Subject(s)
COVID-19 , Food Handling , Food Security , Humans , Pandemics , SARS-CoV-2
3.
Health Place ; 68: 102540, 2021 03.
Article in English | MEDLINE | ID: covidwho-1101241

ABSTRACT

Epidemiological studies have highlighted the disparate impact of coronavirus disease 2019 (COVID-19) on racial and ethnic minority and socioeconomically disadvantaged populations, but data at the neighborhood-level is sparse. The objective of this study was to investigate the disparate impact of COVID-19 on disadvantaged neighborhoods and racial/ethnic minorities in Chicago, Illinois. Using data from the Cook County Medical Examiner, we conducted a neighborhood-level analysis of COVID-19 decedents in Chicago and quantified age-standardized years of potential life lost (YPLL) due to COVID-19 among demographic subgroups and neighborhoods with geospatial clustering of high and low rates of COVID-19 mortality. We show that age-standardized YPLL was markedly higher among the non-Hispanic (NH) Black (559 years per 100,000 population) and the Hispanic (811) compared with NH white decedents (312). We demonstrate that geomapping using residential address data at the individual-level identifies hot-spots of COVID-19 mortality in neighborhoods on the Northeast, West, and South areas of Chicago that reflect a legacy of residential segregation and persistence of inequality in education, income, and access to healthcare. Our results may contribute to ongoing public health and community-engaged efforts to prevent the spread of infection and mitigate the disproportionate loss of life among these communities due to COVID-19 as well as highlight the urgent need to broadly target neighborhood disadvantage as a cause of pervasive racial inequalities in life and health.


Subject(s)
COVID-19 , Ethnicity/statistics & numerical data , Minority Groups/statistics & numerical data , Quality-Adjusted Life Years , Racial Groups , Residence Characteristics/statistics & numerical data , Aged , COVID-19/epidemiology , COVID-19/mortality , Chicago/epidemiology , Female , Humans , Male
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