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Predictors of Death Rate during the COVID-19 Pandemic.

ABSTRACT: Coronavirus (COVID-19) is a potentially fatal viral infection. This study investigates geography, demography, socioeconomics, health conditions, hospital characteristics, and politics as potential explanatory variables for death rates at the state and county levels. Data from the Centers for Disease Control and Prevention, the Census Bureau, Centers for Medicare and Medicaid, Definitive Healthcare, and USAfacts.org were used to evaluate regression models. Yearly pneumonia and flu death rates (state level, 2014-2018) were evaluated as a function of the governors' political party using a repeated measures analysis. At the state and county level, spatial regression models were evaluated. At the county level, we discovered a statistically significant model that included geography, population density, racial and ethnic status, three health status variables along with a political factor. A state level analysis identified health status, minority status, and the interaction between governors' parties and health status as important variables. The political factor, however, did not appear in a subsequent analysis of 2014-2018 pneumonia and flu death rates. The pathogenesis of COVID-19 has a greater and disproportionate effect within racial and ethnic minority groups, and the political influence on the reporting of COVID-19 mortality was statistically relevant at the county level and as an interaction term only at the state level.

SUBMITTER: Feinhandler I

PROVIDER: S-EPMC7551935 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Predictors of Death Rate during the COVID-19 Pandemic.

Feinhandler Ian I Cilento Benjamin B Beauvais Brad B Harrop Jordan J Fulton Lawrence L

Healthcare (Basel, Switzerland) 20200914 3

Coronavirus (COVID-19) is a potentially fatal viral infection. This study investigates geography, demography, socioeconomics, health conditions, hospital characteristics, and politics as potential explanatory variables for death rates at the state and county levels. Data from the Centers for Disease Control and Prevention, the Census Bureau, Centers for Medicare and Medicaid, Definitive Healthcare, and USAfacts.org were used to evaluate regression models. Yearly pneumonia and flu death rates (st ...[more]

PMID: 32937804

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Project description:Social contexts (e.g., family, friends) are important in predicting and preventing loneliness in middle childhood (MC) and adolescence; however, these social contexts were disrupted during the COVID-19 pandemic. Comparison of social context factors that may differentially contribute to loneliness at each developmental stage (MC vs. adolescence) during the COVID-19 pandemic have been overlooked. This study examined longitudinal predictors of loneliness, including social contexts and COVID-19 impact, within MC (8-12y) and adolescence (13-17y). Parents reported on demographic information, and their children completed surveys on COVID-19 impact, loneliness, and family functioning using the COVID-19 Exposure and Family Impact Survey (CEFIS), the NIH Toolbox Loneliness (Ages 8-17) measure, and the PROMIS Family Relationships Short Form 4a measure, respectively. Regression models examined time one (T1; May-June 2020) predictors of time two (T2; November 2020-January 2021) MC child (n=92, Mage=10.03) and adolescent (n=56, Mage=14.66) loneliness. For the MC child model, significant predictors of higher loneliness included worse family functioning as well as higher COVID-19 impact and lower family income. On the other hand, higher adolescent loneliness was significantly predicted by not having married/partnered parents and was marginally significantly predicted by higher COVID-19 impact. The regression model with the full sample and interaction terms revealed no significant interactions, but that lower family functioning and higher COVID-19 impact were significant predictors of higher loneliness. Lower family income and lower in-person communication were marginally significant predictors of higher loneliness in the combined interaction model. Lastly, further exploratory mediation analyses displayed that family functioning significantly mediated the relationship between COVID-19 impact and T2 loneliness only for MC children and the full sample. Results support future interventions focused on optimizing family functioning to help mitigate MC loneliness in the context of adversity, such as a global pandemic.

Dataset Information

Predictors of Death Rate during the COVID-19 Pandemic.

Publications

Predictors of Death Rate during the COVID-19 Pandemic.

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