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

Arthritis in the Regional Cultures of the American Nations: An Overlooked Component of a Larger Unhealthy Lifestyle Syndemic

Introduction: Health indices vary widely within the United States (U.S.), with clear "belts and epicenters" where the prevalence of unhealthy lifestyle behaviors, chronic disease, and disability are all high. Arthritis is a significant, well-established risk factor for developing chronic disease. In this paper, we hypothesize that the prevalence and patterns of arthritis in the U.S. will mirror that of other leading health issues across the country. Methods: We merged county-level data on arthritis, disability, and depression prevalence, the Lifestyle Health Index (LHI), the Social Vulnerability Index (SVI) and the American Nations regional cultures schematic. Results: Data was available from 3073 U.S. counties. We found age-adjusted arthritis prevalence has statistically significant and generally strong Pearson product correlations (r ≈ 0.70, P < 0.001) with (1) LHI sub scores and overall score; (2) Depression prevalence; (3) Disability prevalence; and (4) SVI. The geographic distribution of age-adjusted arthritis prevalence according to the American Nations regional cultures model demonstrated clear heterogeneity across regions. Conclusions: The status of population health in the U.S. is disconcerting and the current model of health care delivery, which is often approached from a siloed (condition-specific vs holistic care), reactionary, secondary prevention model, is inadequate to the challenge.

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.

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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MESH Headings

• Humans
• Life Expectancy
• United States/epidemiology
• Aged, 80 and over
• Male
• Female
• Risk Factors
• Health Behavior
• Smoking/epidemiology
• Smoking/adverse effects
• Healthy Lifestyle
• Obesity/epidemiology
• Obesity/mortality
• Sedentary Behavior
• Diet, Healthy
• Residence Characteristics
• Aged
• Risk Reduction Behavior
• Exercise

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Grants Collapse

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 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.

The role of education and information to prevent trends towards increase of cardiovascular mortality rates in Europe from 2015 TO 2019

BACKGROUND

In Europe Cardio Vascular Disease (CVD) mortality rates decreased significantly in the last 25 years, with less decline in the last 5 years. The aim of our study was to analyze trends of risk factors which may explain plateauing of CVD mortality rates in the period 2015-2019 in Europe.

METHODS

We analyzed data from the Global Burden Disease and EUROSTAT concerning trends of CVD mortality rates for 25 European countries and simultaneous changes of exposure to risk factors of the population RESULTS: CVD related mortality decreased significantly in the analyzed countries in the period 2000-2015; in the period 2015-2019 there was a trend towards plateauing of CVD related mortality rates, which was associated with an increased exposure to several established risk factors including cigarette smoking, obesity and arterial hypertension. A decrease in expenditure for information, education and counseling programs was documented in most countries in the same period. Level of exposure to risk factors was correlated with educational attainment . Exposure to risk factors in the interval 2014-2019 increased for people with lower education, whereas decreased in people with higher education (p<0.001).

CONCLUSIONS

Organized information about risk factors for CVD have the potential to reduce mortality and burden, with diminished total health expenses. Education and information in this setting should consider the cultural and social level of the public.

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.