ZIP Code-Level Estimates from a Local Health Survey: Added Value and Limitations

Neighborhood built environment, obesity, and diabetes: A Utah siblings study

Background

This study utilizes innovative computer vision methods alongside Google Street View images to characterize neighborhood built environments across Utah.

Methods

Convolutional Neural Networks were used to create indicators of street greenness, crosswalks, and building type on 1.4 million Google Street View images. The demographic and medical profiles of Utah residents came from the Utah Population Database (UPDB). We implemented hierarchical linear models with individuals nested within zip codes to estimate associations between neighborhood built environment features and individual-level obesity and diabetes, controlling for individual- and zip code-level characteristics (n = 1,899,175 adults living in Utah in 2015). Sibling random effects models were implemented to account for shared family attributes among siblings (n = 972,150) and twins (n = 14,122).

Results

Consistent with prior neighborhood research, the variance partition coefficients (VPC) of our unadjusted models nesting individuals within zip codes were relatively small (0.5%-5.3%), except for HbA1c (VPC = 23%), suggesting a small percentage of the outcome variance is at the zip code-level. However, proportional change in variance (PCV) attributable to zip codes after the inclusion of neighborhood built environment variables and covariates ranged between 11% and 67%, suggesting that these characteristics account for a substantial portion of the zip code-level effects. Non-single-family homes (indicator of mixed land use), sidewalks (indicator of walkability), and green streets (indicator of neighborhood aesthetics) were associated with reduced diabetes and obesity. Zip codes in the third tertile for non-single-family homes were associated with a 15% reduction (PR: 0.85; 95% CI: 0.79, 0.91) in obesity and a 20% reduction (PR: 0.80; 95% CI: 0.70, 0.91) in diabetes. This tertile was also associated with a BMI reduction of -0.68 kg/m2 (95% CI: -0.95, -0.40).

Conclusion

We observe associations between neighborhood characteristics and chronic diseases, accounting for biological, social, and cultural factors shared among siblings in this large population-based study.

Neighborhood Condition Prevalence Rates Correlate With COVID-19 Mortality in Milwaukee County, Wisconsin

Purpose

We sought to determine if census tract-level (ie, neighborhood) COVID-19 death rates in Milwaukee County correlated with the census tract-level condition prevalence rates (CPRs) for individual COVID-19 mortality risk.

Methods

This study used Milwaukee County-reported COVID-19 death rates per 100,000 lives for the 296 census tracts within the county to perform a linear regression with individual COVID-19 mortality risk CPR, mean age, racial composition of census tract (by percentage of non-White residents), and poverty (by percentage within census tract), followed by multiple regression with all 7 CPRs as well as the 7 CPRs combined with the additional demographic variables. CPR estimates were accessed from the Centers for Disease Control and Prevention 500 Cities Project. Demographics were accessed from the U.S. Census. The Milwaukee County Medical Examiner's office identified 898 deaths from COVID-19 in Milwaukee County from March 2020 to June 2021.

Results

Among the variables included, crude death rate demonstrated a statistically significant association with the 7 COVID-19 mortality risk CPRs (as analyzed collectively), census tract mean age, and several of the CPRs individually. The addition of census tract age, race, and poverty in multiple regression did not improve the association of the 7 CPRs with crude death rate.

Conclusions

Results from this population-level study indicated that census tracts with high COVID-19 mortality correlated with high-risk condition prevalence estimates within those census tracts, illustrating how health data collection and analysis at a census tract level could be helpful when planning pandemic-mitigating public health efforts.

Co-designing to advance community health and health equity in Wisconsin: Building the Neighborhood Health Partnerships Program

Engaging communities can increase the speed of translating health and health equity research into practice. Effective engagement requires a shared understanding of the health of a community. This can be challenging without timely and accurate local health data, or ways to provide that data, that are directly applicable to improving community health outcomes. The University of Wisconsin Institute for Clinical and Translational Research formed the Neighborhood Health Partnerships Program (NHP) to overcome this challenge, making sub-county health data available to researchers and community stakeholders while incorporating community voice into data delivery processes. The NHP team used a human-centered design approach to facilitate community engagement. Through co-design, the team created NHP reports and data-to-action tools to maximize accessibility and utility for a diverse set of community stakeholders. Early indicators show that the final co-designed NHP reports and data-to-action tools will be immediately useful in promoting community–academic partnerships and in planning, implementing, and evaluating research and other initiatives in communities. The NHP program demonstrates that an effective co-design strategy can lead to increased usability and adoption of Clinical and Translational Science Award resources, enabling a shared understanding of community health and ultimately leading to the successful translation of research into practice.