Demographics, Socioeconomic Context, and the Spread of Infectious Disease: The Case of COVID-19

Abstract

Importance: Due to the evolving variants of coronavirus disease 2019 (COVID-19), it is important to understand the relationship between the disease condition and socioeconomic, demographic, and health indicators across regions. Background: Studies examining the relationships between infectious disease and socioeconomic variables are not yet well established. Design: A total of 3042 counties in the United States are included as the observation unit in the study. Two outcome variables employed in the study are the control of disease spread and infection prevalence rates in each county. Method: Data are submitted to quantile regression, hierarchical regression, and random forest analyses to understand the extent to which health outcomes are affected by demographics, socioeconomics, and health indicators. Results: Counties with better control of the disease spread tend to have lower infection rates, and vice versa. When measuring different outcome variables, the common risk factors for COVID-19 with a 5% level of statistical significance include employment ratio, female labor ratio, young population ratio, and residents’ average health risk factors, while protective factors include land size, housing value, travel time to work, female population ratio, and ratio of residents who identify themselves as mixed race. Conclusions: The implications of the findings are that the ability to maintain social distancing and personal hygiene habits are crucial in deterring disease transmission and lowering incidence rates, especially in the early stage of disease formation. Relevant authorities should identify preventive factors and take early actions to fight infectious diseases in the future.

Impact of the COVID-19 pandemic on low back pain management in commercially insured and Medicare Advantage cohorts. A retrospective cohort study

“…The copyright holder for this preprint this version posted May 21, 2023. ; https://doi.org/10.1101/2023.05.15.23289993 doi: medRxiv preprint additional or amplified existing well known confounders and limitations for analyzing the type of HCP initially contacted by an individual with LBP. 19 Variation in the rate of COVID infections by date and geography 56 and corresponding local COVID public health policies 5,6,[57][58][59] , coupled with the analysis not being a representative sample of the U.S. introduced numerous potential confounders beyond the scope of the analysis. The impact of avoidance of elective spine surgery 60 , individuals reluctance to seek in-person hands on care, reduced capacity of primary care 61 and emergency departments 62 , and reluctance to use of public transportation 63,64 This study corroborated and contrasted with the findings of other studies exploring the impact of COVID on the management of LBP.…”

The Impact of Socioeconomic and Demographic Factors on COVID-19 Forecasting Model

“…In this study, we calculate the daily average as the representative of data in a cluster. The calculation process follows (10)…”

“…According to [9], social structures such as income and poverty play an essential role in determining COVID-19 cases in several European countries. The study from [10] used machine learning techniques such as Random Forest, Quantile Regression, and Hierarchical Regression to understand how socioeconomic and demographic factors affect COVID-19. They found that population density, the proportion of women, and commuting time were the three main factors controlling the spread of COVID-19.…”

Accuracy and Performance of Machine Learning Methodologies: Novel Assessments of Country Pandemic Vulnerability Based on Non-Pandemic Predictors

“…Data used for analyses varied from a single data source to multiple sources supplementing the main databases, from a single data point to longitudinal data collected over time, and from several weeks up to 15 months. For example, Johns Hopkins University (January to July 2020), Oxford COVID-19 Government Response Tracker (January to December 2022), Research and Development data for overall Information Value scores, and World Health Organization-Joint External Evaluation data for Ready Score and four sub scores; Our World in Data repository (2021); and data from 3042 counties in the United States (January 2020 to March 2021) [13], [14], [16], [17].…”