Author:
Ozdenerol Esra,Bingham-Byrne Rebecca Michelle,Seboly Jacob Daniel
Abstract
The aim of this study was to investigate lifestyles at risk of Lyme disease, and to geographically identify target populations/households at risk based on their lifestyle preferences. When coupled with geographically identified patient health information (e.g., incidence, diagnostics), lifestyle data provide a more solid base of information for directing public health objectives in minimizing the risk of Lyme disease and targeting populations with Lyme-disease-associated lifestyles. We used an ESRI Tapestry segmentation system that classifies U.S. neighborhoods into 67 unique segments based on their demographic and socioeconomic characteristics. These 67 segments are grouped within 14 larger “LifeModes” that have commonalities based on lifestyle and life stage. Our dataset contains variables denoting the dominant Tapestry segments within each U.S. county, along with annual Lyme disease incidence rates from 2000 through 2017, and the average incidence over these 18 years. K-means clustering was used to cluster counties based on yearly incidence rates for the years 2000–2017. We used analysis of variance (ANOVA) statistical testing to determine the association between Lyme disease incidence and LifeModes. We further determined that the LifeModes Affluent Estates, Upscale Avenues, GenXurban, and Cozy Country Living were associated with higher Lyme disease risk based on the results of analysis of means (ANOM) and Tukey’s post hoc test, indicating that one of these LifeModes is the LifeMode with the greatest Lyme disease incidence rate. We further conducted trait analysis of the high-risk LifeModes to see which traits were related to higher Lyme disease incidence. Due to the extreme regional nature of Lyme disease incidence, we carried out our national-level analysis at the regional level. Significant differences were detected in incidence rates and LifeModes in individual regions. We mapped Lyme disease incidence with associated LifeModes in the Northeast, Southeast, Midcontinent, Rocky Mountain, and Southwest regions to reflect the location-dependent nature of the relationship between lifestyle and Lyme disease.
Subject
Health, Toxicology and Mutagenesis,Public Health, Environmental and Occupational Health
Reference45 articles.
1. Lifestyle Effects on the Risk of Transmission of COVID-19 in the United States: Evaluation of Market Segmentation Systems
2. Esri—Tapestry
http://www.esri.com/landing-pages/tapestry
3. The small world of global health;Koplan;Mt. Sinai J. Med.,2002
4. Lyme Disease Therapeutics Market: Rise in the Rate of Incidence of Lyme Disease across the Globe to Drive the Market
https://www.biospace.com/article/lyme-disease-market-rise-in-the-rate-of-incidence-of-lyme-disease-across-the-globe-to-drive-the-market/
5. The Application of Remote Sensing and GIS Tools in the Study of Lyme Disease Risk Prediction
http://www.edc.uri.edu/nrs/classes/NRS409509/509_2008/Berger.pdf
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献