Lifestyle Effects on the Risk of Transmission of COVID-19 in the United States: Evaluation of Market Segmentation Systems

被引:7
作者
Ozdenerol, Esra [1 ]
Seboly, Jacob [2 ]
机构
[1] Univ Memphis, Dept Earth Sci, Spatial Anal & Geog Educ Lab, Memphis, TN 38152 USA
[2] Mississippi State Univ, Dept Geosci, Starkville, MS 39762 USA
关键词
geographic information systems; lifestyle segment; Lifemodes; market segmentation; market intelligence; transmission risk; COVID-19; infection; risk mapping; ALL-CAUSE; MORTALITY; INEQUALITIES;
D O I
10.3390/ijerph18094826
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The aim of this study was to associate lifestyle characteristics with COVID-19 infection and mortality rates at the U.S. county level and sequentially map the impact of COVID-19 on different lifestyle segments. We used analysis of variance (ANOVA) statistical testing to determine whether there is any correlation between COVID-19 infection and mortality rates and lifestyles. We used ESRI Tapestry LifeModes data that are collected at the U.S. household level through geodemographic segmentation typically used for marketing purposes to identify consumers' lifestyles and preferences. According to the ANOVA analysis, a significant association between COVID-19 deaths and LifeModes emerged on 1 April 2020 and was sustained until 30 June 2020. Analysis of means (ANOM) was also performed to determine which LifeModes have incidence rates that are significantly above/below the overall mean incidence rate. We sequentially mapped and graphically illustrated when and where each LifeMode had above/below average risk for COVID-19 infection/death on specific dates. A strong northwest-to-south and northeast-to-south gradient of COVID-19 incidence was identified, facilitating an empirical classification of the United States into several epidemic subregions based on household lifestyle characteristics. Our approach correlating lifestyle characteristics to COVID-19 infection and mortality rate at the U.S. county level provided unique insights into where and when COVID-19 impacted different households. The results suggest that prevention and control policies can be implemented to those specific households exhibiting spatial and temporal pattern of high risk.
引用
收藏
页数:19
相关论文
共 25 条
  • [1] [Anonymous], 2020, UPDATED TIMELINE COR
  • [2] [Anonymous], 2020, HLTH PEOPL 2020
  • [3] [Anonymous], TIMELINE COVID 19 DE
  • [4] Bai S., MAPPING INTERCOUNTY
  • [5] Chen Y., 2020, Modeling the spatial factors of COVID-19 in new York City, DOI DOI 10.2139/SSRN.3606719
  • [6] The Association Between Income and Life Expectancy in the United States, 2001-2014
    Chetty, Raj
    Stepner, Michael
    Abraham, Sarah
    Lin, Shelby
    Scuderi, Benjamin
    Turner, Nicholas
    Bergeron, Augustin
    Cutler, David
    [J]. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2016, 315 (16): : 1750 - 1766
  • [7] Cyrus E, 2020, HEALTH EQUITY, V4, P476, DOI [10.1089/heq.2020.0030, 10.1101/2020.05.15.20096552]
  • [8] An interactive web-based dashboard to track COVID-19 in real time
    Dong, Ensheng
    Du, Hongru
    Gardner, Lauren
    [J]. LANCET INFECTIOUS DISEASES, 2020, 20 (05) : 533 - 534
  • [9] Social determinants of COVID-19 mortality at the county level
    Fielding-Miller, Rebecca K.
    Sundaram, Maria E.
    Brouwer, Kimberly
    [J]. PLOS ONE, 2020, 15 (10):
  • [10] Social Determinants of Health Caveats and Nuances
    Fuchs, Victor R.
    [J]. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2017, 317 (01): : 25 - 26