The geographic distribution of unhealthy living characteristics according to the American Nations model: Cultural factors warranting attention

The lifestyle health index in the context of COVID-19 mortality and vaccination in the United States: A syndemic not to be repeated

Public health, personal/community health behaviors, health care delivery, and the scientific community have all been impacted by the COVID-19 pandemic and are consequently poised to consider substantial paradigm shifts that will enhance disease prevention and public health resilience. The current analysis compares the newly developed Lifestyle Health Index (LHI) to U.S. county-level COVID-19 vaccination, infection, and mortality rates. We linked Centers of Disease Control PLACES, the U.S. Community Profile Report, and Nationhood lab databases through common zip-code identifiers to determine the association between county-level LHI scores and COVID-19 outcomes and vaccination status against the backdrop of U.S. regions with distinct cultural phenotypes. There was a statistically significant relationship between a poor LHI, lower COVID-19 vaccination rates and higher COVID-19 infection and mortality rates. There were clear differences in outcomes across the U.S. regions, suggesting distinct regional cultural characteristics may significantly influence health behaviors and outcomes. In the U.S., a syndemic comprising unhealthy lifestyle, chronic disease, and COVID-19 resulted in unnecessary hospitalizations and deaths. Politicization of the pandemic, socioeconomic inequity and regional cultural values meaningfully contributed to the uneven distribution of poor outcomes during this syndemic. Components of the syndemic were avoidable and should not be repeated. Condensed Abstract: The unhealthy lifestyle - chronic disease - COVID-19 U.S. syndemic resulted in unnecessary hospitalizations and deaths. Politicization of the pandemic, socioeconomic inequity and regional cultural values meaningfully contributed to the uneven distribution of poor outcomes during this syndemic. Components of the syndemic were avoidable and should not be repeated.

The Potential Influence of Firearm Violence on Physical Inactivity in the United States

BACKGROUND

Despite broad recognition of the physical inactivity pandemic, little to no progress has been made in the past decade in mitigating the problem. The current analysis builds upon previous research into the drivers of physical inactivity to assess the potential interactions with firearm violence in the United States.

METHODS

We merged county-level data on firearm fatality rates, physical inactivity prevalence, the Social Vulnerability Index, and the American nations regional cultures schematic.

RESULTS

Counties with a physical inactivity prevalence currently above the federal government's 2030 goal (ie, ≥21.8%) had a significantly higher firearm fatality rate per 100,000 population. This finding was consistent for both the overall rate and race-based subgroups. The overall White, Hispanic, and Black firearm fatality rates were also significantly higher in the American nations group comprising Greater Appalachia, Deep South, El Norte, New France, and First Nation. Stepwise linear regression analysis revealed that the Social Vulnerability Index, American nations dichotomous grouping, and firearm fatality rate were all retained (P < .001) in predicting physical inactivity prevalence as a continuous variable.

CONCLUSION

In conclusion, the United States faces myriad health and societal challenges. Unhealthy lifestyles and gun violence are two of the leaders. The current analysis in conjunction with previous findings demonstrates that solving these challenges by interacting, create complexity to finding solutions that has not been thoroughly considered.

County-level socio-environmental factors and obesity prevalence in the United States

AIMS

To investigate high-risk sociodemographic and environmental determinants of health (SEDH) potentially associated with adult obesity in counties in the United States using machine-learning techniques.

MATERIALS AND METHODS

We performed a cross-sectional analysis of county-level adult obesity prevalence (body mass index ≥30 kg/m2) in the United States using data from the Diabetes Surveillance System 2017. We harvested 49 county-level SEDH factors that were used in a classification and regression trees (CART) model to identify county-level clusters. The CART model was validated using a 'hold-out' set of counties and variable importance was evaluated using Random Forest.

RESULTS

Overall, we analysed 2752 counties in the United States, identifying a national median (interquartile range) obesity prevalence of 34.1% (30.2%, 37.7%). The CART method identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, seven key SEDH variables were identified by CART to guide the categorization of clusters, including Physically Inactive (%), Diabetes (%), Severe Housing Problems (%), Food Insecurity (%), Uninsured (%), Population over 65 years (%) and Non-Hispanic Black (%).

CONCLUSION

There is significant county-level geographical variation in obesity prevalence in the United States, which can in part be explained by complex SEDH factors. The use of machine-learning techniques to analyse these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo-specific strategic interventions and optimize resource allocation to help battle the obesity pandemic.

Qualitative systems mapping in promoting physical activity and cardiorespiratory fitness: Perspectives and recommendations

The purpose of this report is to provide a perspective on the use of qualitative systems mapping, provide examples of physical activity (PA) systems maps, discuss the role of PA systems mapping in the context of iterative learning to derive breakthrough interventions, and provide actionable recommendations for future work. Systems mapping methods and applications for PA are emerging in the scientific literature in the study of complex health issues and can be used as a prelude to mathematical/computational modeling where important factors and relationships can be elucidated, data needs can be prioritized and guided, interventions can be tested and (co)designed, and metrics and evaluations can be developed. Examples are discussed that describe systems mapping based on Group Model Building or literature reviews. Systems maps are highly informative, illustrate multiple components to address PA and physical inactivity issues, and make compelling arguments against single intervention action. No studies were identified in the literature scan that considered cardiorespiratory fitness the focal point of a systems maps. Recommendations for future research and education are presented and it is concluded that systems mapping represents a valuable yet underutilized tool for visualizing the complexity of PA promotion.

Predicting life expectancy in the united states: The importance of healthy living behaviors and residential geography

According to the World Health Organization, 30 countries currently have a life expectancy of ≥80 years: the United States (U.S.) is not among this group of countries. The current analysis assesses the ability of key lifestyle behaviors and characteristics to predict a life expectancy of ≥80 years. Only 577 (19%) of the 3066 U.S. Counties assessed had a life expectancy ≥80 years. These counties had significantly higher life expectancy (81 ± 3 vs. 76 ± 2 years) and lower percent of the population who are physically inactive (20.7 ± 3.9 vs. 27.0 ± 4.7%), actively smoke (15.9 ± 3.1 vs. 21.1 ± 3.6%), obese (31.7 ± 4.7 vs. 37.3 ± 3.9%) and have limited access to healthy food (7.1 ± 6.8 vs. 8.4 ± 6.6%) (all p < 0.001). Binary logistic regression revealed percent adults who currently smoke, percent obese, percent physically inactive, and percent with limited access to healthy food were all significant univariate predictors of Collapse

Key Words

• Chronic disease risk
• Culture
• Healthy living
• Sedentary lifestyle
• Social justice

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Affiliation(s)

Ross Arena Department of Physical Therapy, College of Applied Science, University of Illinois, Chicago, IL, USA; Healthy Living for Pandemic Event Protection (HL - PIVOT) Network, Chicago, IL, USA.
Nicolaas P Pronk Healthy Living for Pandemic Event Protection (HL - PIVOT) Network, Chicago, IL, USA; HealthPartners Institute, Minneapolis, MN, USA; Department of Health Policy and Management, University of Minnesota, Minneapolis, MN, USA
Colin Woodard Nationhood Lab, Pell Center for International Relations and Public Policy, Salve Regina University, Newport, RI, USA

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The Influence of Social Vulnerability and Culture on Physical Inactivity in the United States - Identifying Hot Spots in Need of Attention

BACKGROUND

In the United States, ongoing efforts to increase individual- and population-level physical activity have made little to no progress over the past decade in reducing the percentage of individuals who report no leisure-time physical activity. The purpose of the current study is to further assess the relationship between social vulnerability and the prevalence of physical inactivity at the county level.

METHODS

We merged county-level data on physical inactivity prevalence, the Social Vulnerability Index (SVI), and the American Nations regional cultures schematic.

RESULTS

Physical inactivity significantly correlated (P < .001) with the overall SVI score and all subtheme scores to varying levels of strength. Clear and statistically significant heterogeneity in the SVI scores was apparent across distinct regions using the American Nations model, consistent with previously demonstrated patterns of physical inactivity prevalence.

CONCLUSIONS

On a national level, physical inactivity prevalence is unacceptably high and has not appreciably improved over the past decade. Within the United States, high levels of social vulnerability and physical inactivity are concentrated within specific geographic regions that need tailored solutions to resolve health disparities.

Physical Inactivity and Obesity in the United States Through the Lens of the 2012 and 2016 Presidential Elections

The physical inactivity (PI) and obesity pandemics in the United States (U.S.) have undauntingly persisted in recent history. We have previously demonstrated differences in PI, obesity, socioeconomics, race, and regional culture according to county-level results for the 2020 presidential election. This commentary extends this analysis by considering if the 2020 trend is consistent with the 2 previous presidential election cycles. On a national level, during both the 2012 and 2016 presidential elections, counties where the Democratic candidate received more votes than the Republican one had a significantly lower PI and obesity prevalence. Counties where the Democratic candidate received more votes also had higher median national incomes, a higher proportion of the population who identify as Black, and a higher percentage of people who had completed at least some college. However, at a U.S. regional level, unique, region-specific cultural identities and partisan coalition demographics were apparent and showed some variation between election cycles. In most of the distinct U.S. cultural regions defined by the American Nations model, PI and obesity prevalence were lower in democratic-majority counties, although there were exceptions. These results support our previous findings demonstrating PI and obesity trends in the U.S. are influenced by cultural and political factors that are likely interrelated and which warrant further attention.

Physical Inactivity and Obesity in the United States: At the Intersection of Politics, Socioeconomics, Race, and Culture

Efforts to reverse the physical inactivity (PI) and obesity pandemics in the United States (U.S.) have been unsuccessful. This commentary provides a view of PI and obesity in the U.S. from the intersection of politics, socioeconomics, race, and culture. On a national level, counties where the Democratic presidential candidate received more votes in 2020 than the Republican candidate had a lower PI and obesity prevalence. The percentage completing some college and Black individuals as well as median household income were higher in counties where the Democratic candidate received more votes. Regionally, unique, region-specific cultural identities and partisan coalition demographics were apparent and serve as potential explanations for inconsistencies in PI and obesity prevalence across the U.S. Identifying the driving forces of PI and obesity within specific U.S. regions and crafting messaging that has optimal efficacy on a local level is essential to reducing the burden of cardiovascular disease and other chronic conditions.