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.
Highlights
We tested four research questions: “Is there a difference in average Lyme disease incidence rates among different LifeModes?”, “Which LifeModes have incidence rates that are higher/lower than average?”, “Which pairs of LifeModes have significantly different incidence rates?”, and “Which LifeModes could potentially have the maximum incidence rate?” We focused on comparing each LifeMode’s mean to the national mean in order to ascertain spatial and temporal patterns of high-risk households and the effects of lifestyle on the risk of Lyme disease infection in the United States
This study demonstrated that social survey data are a valuable but underutilized source of information for understanding regional variation in Lyme disease exposure, and for integrating this information into risk maps
The risk of Lyme disease is very low throughout the rest of the United States
Summary
The ability to profile a target audience in terms of its morbidity (i.e., sickness) characteristics and its health service needs is becoming increasingly critical for successful marketing initiatives [1]. A wide variety of healthcare entities—whether providers of care, producers of medical supplies and drugs, or organizations providing goods or services to the healthcare industry—are required to market themselves to their prospective customers. The basic questions start with who gets sick, what they get sick from, and where they get sick. Since morbid conditions are not randomly distributed within the population, but are concentrated within certain segments of it, this baseline information can be expanded by segmenting the population based on
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