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

ABSTRACT: 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.

SUBMITTER: Ozdenerol E

PROVIDER: S-EPMC8125751 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The aim of this study is to correlate lifestyle characteristics to COVID-19 vaccination rates at the U.S. County level and provide where and when COVID-19 vaccination impacted different households. We grouped counties by their dominant LifeMode, and the mean vaccination rates per LifeMode are calculated. A 95% confidence interval for both the mean and median vaccination rate for each LifeMode is generated. The limits of this interval were compared to the nationwide statistics to determine whether each LifeMode's vaccine uptake differs significantly from the nationwide average. We used Environmental Systems Research Institute Inc. (ESRI) Tapestry LifeModes data that are collected at the U.S. household level through geodemographic segmentation typically used for marketing purposes. High risk Lifestyle segments and their locations are clearly the areas in the U.S. where the public might benefit from a COVID-19 vaccine. We then used logistic regression analysis to predict vaccination rates using ESRI's tapestry segmentation and other demographic variables. Our findings demonstrate that vaccine uptake appears to be highest in the urban corridors of the Northeast and the West Coast and in the retirement communities of Arizona and Florida and lowest in the rural areas of the Great Plains and Southeast. Looking closely at other parts of the West such as the Dakotas and Montana, counties that contain Native American reservations have higher vaccination rates. Racial/ethnic minorities also adopt the vaccine at higher rates. The most effective predictor of vaccination hesitancy was Republican voting habits, with Republican counties less likely to take the vaccine. The other predictors in order of importance were college education, minority race/ethnicity, median income, and median age. Our approach correlating lifestyle characteristics to COVID-19 vaccination rate at the U.S. County level provided unique insights into where and when COVID-19 vaccination impacted different households. The results suggest that prevention and control policies can be implemented to those specific households.

Project description:BackgroundAnticipating an initial shortage of vaccines for COVID-19, the Centers for Disease Control (CDC) in the United States developed priority vaccine allocations for specific demographic groups in the population. This study evaluates the performance of the CDC vaccine allocation strategy with respect to multiple potentially competing vaccination goals (minimizing mortality, cases, infections, and years of life lost (YLL)), under the same framework as the CDC allocation: four priority vaccination groups and population demographics stratified by age, comorbidities, occupation and living condition (congested or non-congested).Methods and findingsWe developed a compartmental disease model that incorporates key elements of the current pandemic including age-varying susceptibility to infection, age-varying clinical fraction, an active case-count dependent social distancing level, and time-varying infectivity (accounting for the emergence of more infectious virus strains). The CDC allocation strategy is compared to all other possibly optimal allocations that stagger vaccine roll-out in up to four phases (17.5 million strategies). The CDC allocation strategy performed well in all vaccination goals but never optimally. Under the developed model, the CDC allocation deviated from the optimal allocations by small amounts, with 0.19% more deaths, 4.0% more cases, 4.07% more infections, and 0.97% higher YLL, than the respective optimal strategies. The CDC decision to not prioritize the vaccination of individuals under the age of 16 was optimal, as was the prioritization of health-care workers and other essential workers over non-essential workers. Finally, a higher prioritization of individuals with comorbidities in all age groups improved outcomes compared to the CDC allocation.ConclusionThe developed approach can be used to inform the design of future vaccine allocation strategies in the United States, or adapted for use by other countries seeking to optimize the effectiveness of their vaccine allocation strategies.

Project description:The outbreak of the novel coronavirus disease (COVID-19) severely threatens the public health worldwide, but the transmission mechanism and the effectiveness of mitigation measures remain uncertain. Here we assess the role of airborne transmission in spreading the disease and the effectiveness of face covering in preventing inter-human transmission for the top-fifteen infected U.S. states during March 1 and May 18, 2020. For all fifteen states, the curve of total confirmed infections exhibits an initial sub-exponential growth and a subsequent linear growth after implementing social distancing/stay-at-home orders. The linearity extends one to two months for the six states without mandated face covering and to the onset of mandated face covering for the other nine states with this measure, reflecting a dynamic equilibrium between first-order transmission kinetics and intervention. For the states with mandated face covering, significant deviation from this linearity and curve flattening occur after the onset of this measure for seven states, with exceptions for two states. Most states exhibit persistent upward trends in the daily new infections after social distancing/stay-at-home orders, while reversed downward or slowing trends occur for eight states after implementing mandated face covering. The inadequacy of social distancing and stay-at-home measures alone in preventing inter-human transmission is reflected by the continuous linear growth in the total infection curve after implementing these measures, which is mainly driven by airborne transmission. We estimate that the number of the total infections prevented by face covering reaches ~252,000 on May 18 in seven states, which is equivalent to ~17% of the total infections in the nation. We conclude that airborne transmission and face covering play the dominant role in spreading the disease and flattening the total infection curve, respectively. Our findings provide policymakers and the public with compelling evidence that universal face covering, in conjunction with social distancing and hand hygiene, represents the maximal protection against inter-human transmission and the combination of these intervention measures with rapid and extensive testing as well as contact tracing is crucial in containing the COVID-19 pandemic.

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

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