The Memphis Pandemic Health Informatics System (MEMPHI-SYS)-Creating a Metropolitan COVID-19 Data Registry Linked Directly to Community Testing to Enhance Population Health Surveillance.

The current coronavirus disease (COVID-19) pandemic has placed unprecedented strain on underfunded public health resources in the Southeastern United States. The Memphis, TN, metropolitan region has lacked infrastructure for health data exchange.This manuscript describes a multidisciplinary initiative to create a community-focused COVID-19 data registry, the Memphis Pandemic Health Informatics System (MEMPHI-SYS). MEMPHI-SYS leverages test result data updated directly from community-based testing sites, as well as a full complement of public health data sets and knowledge-based informatics. It has been guided by relationships with community stakeholders and is managed alongside the largest publicly funded community-based COVID-19 testing response in the Mid-South. MEMPHI-SYS has supported interactive Web-based analytic resources and informs federally funded COVID-19 outreach directed toward neighborhoods most in need of pandemic support.MEMPHI-SYS provides an instructive case study of how to collaboratively establish the technical scaffolding and human relationships necessary for data-driven, health equity-focused pandemic surveillance, and policy interventions.

History Repeating-How Pandemics Collide with Health Disparities in the United States.

Across the United States, public health responses to the COVID-19 pandemic have fallen short. COVID-19 has exacerbated longstanding public health shortfalls in disadvantaged communities. Was this predestined? Understanding where we are today requires reflection on our longer journey. Disparities cataloged during COVID-19 reflect the same unequal host exposure and susceptibility risks that shaped previous pandemics. In this review, we provide historical context to better understand current events and to showcase forgotten lessons which may motivate future action to protect our most vulnerable citizens.

The Effects of Lifestyle on COVID-19 Vaccine Hesitancy in the United States: An Analysis of Market Segmentation.

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.

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

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.

Implementing an Urban Public Health Observatory for (Near) Real-Time Surveillance for the COVID-19 Pandemic.

The COVID-19 pandemic is broadly undercutting global health and economies, while disproportionally impacting socially disadvantaged populations. An impactful pandemic surveillance solution must draw from multi-dimensional integration of social determinants of health (SDoH) to contextually inform traditional epidemiological factors. In this article, we describe an Urban Public Health Observatory (UPHO) model which we have put into action in a mid-sized U.S. metropolitan region to provide near real-time analysis and dashboarding of ongoing COVID-19 conditions. Our goal is to illuminate associations between SDoH factors and downstream pandemic health outcomes to inform specific policy decisions and public health planning.