County-level correlation between adult obesity rates and prevalence of dentists

Creative Approaches for Assessing Long-term Outcomes in Children

Advances in new technologies, when incorporated into routine health screening, have tremendous promise to benefit children. The number of health screening tests, many of which have been developed with machine learning or genomics, has exploded. To assess efficacy of health screening, ideally, randomized trials of screening in youth would be conducted; however, these can take years to conduct and may not be feasible. Thus, innovative methods to evaluate the long-term outcomes of screening are needed to help clinicians and policymakers make informed decisions. These methods include using longitudinal and linked-data systems to evaluate screening in clinical and community settings, school data, simulation modeling approaches, and methods that take advantage of data available in the digital and genomic age. Future research is needed to evaluate how longitudinal and linked-data systems drawing on community and clinical settings can enable robust evaluations of the effects of screening on changes in health status. Additionally, future studies are needed to benchmark participating individuals and communities against similar counterparts and to link big data with natural experiments related to variation in screening policies. These novel approaches have great potential for identifying and addressing differences in access to screening and effectiveness of screening across population groups and communities.

The Hispanic paradox in the prevalence of obesity at the county-level

Objective

The percentage of Hispanics in a county has a negative association with prevalence of obesity. Because Hispanic individuals are unevenly distributed in the United States, this study examined whether this protective association persists when stratifying counties into quartiles based on the size of the Hispanic population and after adjusting for county‐level demographic, socioeconomic, healthcare, and environmental factors.

Methods

Data were extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings. Counties were categorized into quartiles based on their percentage of Hispanics, 0%–5% (n = 1794), 5%–20% (n = 962), 20%–50% (n = 283), and >50% (n = 99). For each quartile, univariate and multivariate regression models were used to evaluate the association between prevalence of obesity and demographic, socioeconomic, healthcare, and environmental factors.

Results

Counties with the top quartile of Hispanic individuals had the lowest prevalence of obesity compared to counties at the bottom quartile (28.4 ± 3.6% vs. 32.7 ± 4.0%). There was a negative association between county‐level percentage of Hispanics and prevalence of obesity in unadjusted analyses that persisted after adjusting for all county‐level factors.

Conclusions

Counties with a higher percentage of Hispanics have lower levels of obesity, even after controlling for demographic, socioeconomic, healthcare, and environmental factors. More research is needed to elucidate why having more Hispanics in a county may be protective against county‐level obesity.

Relationships between County Health Rankings and child overweight and obesity prevalence: a serial cross-sectional analysis

Background

The County Health Rankings (CHR) system provides health rankings for U.S. counties. These factors may have utility for evaluating and predicting health outcomes. This study examined the association between CHR factors and the prevalence of child overweight/obesity (OWOB) in the state of Pennsylvania over 3 years.

Methods

The prevalence of childhood OWOB was obtained for all Pennsylvania school districts for the 2009-10 through 2011-12 school years. Correlational and inferential statistical analyses were used to examine the associations between the prevalence of OWOB in grades K-6 (OWOB1) and 7-12 (OWOB2) and z-score for the overall CHR Health Factors rank, as well as for individual predictive factors (Health Behaviors, Clinical Care, Social and Economic Factors and Physical Environment).

Results

Low to moderate correlations (0.29–0.43) were found between OWOB1 and CHR factors. Weaker and less consistent correlations were found for adolescents. There was a significantly higher prevalence of OWOB in counties with poorer CHR scores.

Conclusions

County-level adult indicators of health are significantly associated with levels of child obesity. Future studies should examine the relationship between CHR and other health outcomes.

Associations of Provider-to-Population Ratios and Population Health by County-Level Rurality

PURPOSE

To explore the relationship between provider-to-population ratios, rurality and population health in the United States using counties as the unit of analysis.

METHOD

Population ratios for registered nurses (RNs), primary care physicians, and dentists were included in multivariable regression analyses. Population health indices assessed were premature death rate, self-rated health, teen birth rate, and mammography screening rate.

FINDINGS

County levels of health and health care providers per capita declined as rurality increased. In adjusted regression models, the highest RN-to-population ratio was associated with significantly better health measures in most urban/rural categories, with the magnitude of these associations generally increasing as rurality increased. In the smallest rural counties, the highest RN-to-population quartile was associated with 1,655 fewer years of potential life lost (YPLL), 2% lower rates of poor or fair health, 11/1,000 fewer teen births, and 6% more mammography screening relative to the lowest quartile. For primary care physicians, more significant associations were found in medium and small rural counties where the highest quartile was associated with 1,482 fewer YPLL, 3% lower rates of poor or fair health, 7/1,000 fewer teen births, and 4% more mammography screening. The highest quartile of dentist-to-population ratio was generally associated with lower rates of premature death and poor or fair health in urban, large-, and medium-sized rural counties, but not in small rural counties.

CONCLUSIONS

The consistency of the results by provider type suggests that the supply of health care professionals, particularly in rural areas, positively impacts the health of the population.