Introduction: Neighborhood walkability has been established as a potentially important determinant of various health outcomes that are distributed inequitably by race/ethnicity and sociodemographic status. The objective of this study is to assess the differences in walkability across major urban centers in the U.S.Methods: City-and census tract−level differences in walkability were assessed in 2020 using the 2019 Walk Score across 500 large cities in the U.S.Results: At both geographic levels, high-income and majority White geographic units had the lowest walkability overall. Walkability was lower with increasing tertile of median income among majority White, Latinx, and Asian American and Native Hawaiian and Pacific Islander neighborhoods. However, this association was reversed within majority Black neighborhoods, where tracts in lower-income tertiles had the lowest walkability. Associations varied substantially by region, with the strongest differences observed for cities located in the South.Conclusions: Differences in neighborhood walkability across 500 U.S. cities provide evidence that both geographic unit and region meaningfully influence associations between sociodemographic factors and walkability. Structural interventions to the built environment may improve equity in urban environments, particularly in lower-income majority Black neighborhoods.

Introduction: The US Asian American (AA) population is projected to double by 2050, reaching ~43 million, and currently resides primarily in urban areas. Despite this, the geographic distribution of AA subgroup populations in US cities is not well-characterized, and social determinants of health (SDH) and health measures in places with significant AA/AA subgroup populations have not been described. Our research aimed to: 1) map the geographic distribution of AAs and AA subgroups at the city- and neighborhood- (census tract) level in 500 large US cities (population ≥66,000); 2) characterize SDH and health outcomes in places with significant AA or AA subgroup populations; and 3) compare SDH and health outcomes in places with significant AA or AA subgroup populations to SDH and health outcomes in places with significant non-Hispanic White (NHW) populations.Methods: Maps were generated using 2019 Census 5-year estimates. SDH and health outcome data were obtained from the City Health Dashboard, a free online data platform providing more than 35 measures of health and health drivers at the city and neighborhood level. T-tests compared SDH (unemployment, high-school completion, childhood poverty, income inequality, racial/ ethnic segregation, racial/ethnic diversity, percent uninsured) and health outcomes (obesity, frequent mental distress, cardiovas­cular disease mortality, life expectancy) in cities/neighborhoods with significant AA/AA subgroup populations to SDH and health outcomes in cities/neighborhoods with sig­nificant NHW populations (significant was defined as top population proportion quin­tile). We analyzed AA subgroups including Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, and Other AA.Results: The count and proportion of AA/ AA subgroup populations varied sub­stantially across and within cities. When comparing cities with significant AA/AA subgroup populations vs NHW populations, there were few meaningful differences in SDH and health outcomes. However, when comparing neighborhoods within cities, areas with significant AA/AA subgroup vs NHW populations had less favorable SDH and health outcomes.Conclusion: When comparing places with significant AA vs NHW populations, city-level data obscured substantial variation in neighborhood-level SDH and health outcome measures. Our findings empha­size the dual importance of granular spatial and AA subgroup data in assessing the influence of SDH in AA populations.Ethn Dis. 2021;31(3):433-444; doi:10.18865/ed.31.3.433

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What is already known on this topic? Many local health departments develop city-level public health policies but lack city-level health data. This lack causes reliance on county-level data, which may misrepresent city populations. What is added by this report? We found substantial and highly variable city-county differences within and across 4 public health metrics, suggesting use of county-level data may mischaracterize health metrics in cities. What are the implications for public health practice? Use of county data to proxy city measures could hamper municipal public health policymaking. Public health officials concerned with cities should use city-level data whenever possible.

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The purpose of this research was to examine whether the local food environment, specifically the distance to the nearest sugar sweetened beverage (SSB) vendor, a measure of SSB availability and accessibility, was correlated with the likelihood of self-reported SSB consumption among a sample of fast food consumers. As part of a broader SSB behavior study in 2013–2014, respondents were surveyed outside of major chain fast food restaurants in New York City (NYC). Respondents were asked for the intersection closest to their home and how frequently they consume SSBs. Comprehensive, administrative food outlet databases were used to geo-locate the SSB vendor closest to the respondents’ home intersections. We then used a logistic regression model to estimate the association between the distance to the nearest SSB vendor (overall and by type) and the likelihood of daily SSB consumption. Our results show that proximity to the nearest SSB vendor was not statistically significantly associated with the likelihood of daily SSB consumption, regardless of type of vendor. Our results are robust to alternative model specifications, including replacing the linear minimum distance measure with count of the total number of SSB vendors or presence of a SSB vendor within a buffer around respondents’ home intersections. We conclude that there is not a strong relationship between proximity to nearest SSB vendor, or proximity to a specific type of SSB vendor, and frequency of self-reported SSB consumption among fast food consumers in NYC. This suggests that policymakers focus on alternative strategies to curtail SSB consumption, such as improving the within-store food environment or taxing SSBs.

In response to rapidly changing societal conditions stemming from the COVID-19 pandemic, we summarize data sources with potential to produce timely and spatially granular measures of physical, economic, and social conditions relevant to public health surveillance, and we briefly describe emerging analytic methods to improve small-area estimation. To inform this article, we reviewed published systematic review articles set in the United States from 2015 to 2020 and conducted unstructured interviews with senior content experts in public heath practice, academia, and industry. We identified a modest number of data sources with high potential for generating timely and spatially granular measures of physical, economic, and social determinants of health. We also summarized modeling and machine-learning techniques useful to support development of time-sensitive surveillance measures that may be critical for responding to future major events such as the COVID-19 pandemic. (Am J Public Health. Published online ahead of print August 4, 2022:e1–e10. https://doi.org/10.2105/AJPH.2022.306917 )

Why do childhood obesity interventions produce such tepid results? In 2006, Helen Thomas, a faculty member at McMaster University in Ontario, Canada, attempted to answer this question through a review of childhood obesity interventions. 1 Planet Health, a two-year classroom intervention targeting sixth-and seventh-graders, was among the interventions Thomas reviewed and is a prime example of the lackluster results these interventions tend to produce. Planet Health's goals were to reduce participants' television viewing time, improve their diet, and increase the amount they exercised. The intervention's results were mixed: it reduced obesity prevalence and increased healthy eating in girls; however, it did not have any effect on obesity status or healthy eating among boys and had no effect on exercise for either sex. 2 These results were clinically modest, and the reduction in obesity among girls was not substantial enough to indicate that the intervention would have a meaningful effect on childhood obesity at the population level. Unfortunately, Planet Health is one of the most successful childhood obesity interventions Thomas reviewed.Like Planet Health, many other childhood obesity interventions returned results that were clinically modest and only effective for one subgroup. Some programs had an effect only on boys, others only on girls, some only on white children, and still others only on individuals of a certain socioeconomic status. 3-6 Some interventions had a paradoxical effect, increasing participants' body mass index. 5,7 High-impact positive outcomes have been so elusive that one review recommended limiting intervention groups to participants who volunteer, essentially encouraging selection bias to ensure results. 8 Thomas was not the first, nor the last, to find that many childhood obesity interventions produce at best underwhelming results. [9][10][11][12] Each of these childhood obesity interventions failed for a reason. That reason could be some feature of the intervention's design or implementation, the food environment in which the intervention took place, a lack of accessible physical activity opportunities for intervention participants, or various other factors.