Building Public Health Surveillance 3.0: Emerging Timely Measures of Physical, Economic, and Social Environmental Conditions Affecting Health.

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. 2022;112(10):1436-1445. https://doi.org/10.2105/AJPH.2022.306917).

Validation of a neighborhood-level COVID Local Risk Index in 47 large U.S. cities.

OBJECTIVES: To present the COVID Local Risk Index (CLRI), a measure of city- and neighborhood-level risk for SARS COV-2 infection and poor outcomes, and validate it using sub-city SARS COV-2 outcome data from 47 large U.S. cities. METHODS: Cross-sectional validation analysis of CLRI against SARS COV-2 incidence, percent positivity, hospitalization, and mortality. CLRI scores were validated against ZCTA-level SARS COV-2 outcome data gathered in 2020-2021 from public databases or through data use agreements using a negative binomial model. RESULTS: CLRI was associated with each SARS COV-2 outcome in pooled analysis. In city-level models, CLRI was positively associated with positivity in 11/14 cities for which data were available, hospitalization in 6/6 cities, mortality in 13/14 cities, and incidence in 33/47 cities. CONCLUSIONS: CLRI is a valid tool for assessing sub-city risk of SARS COV-2 infection and illness severity. Stronger associations with positivity, hospitalization and mortality may reflect differential testing access, greater weight on components associated with poor outcomes than transmission, omitted variable bias, or other reasons. City stakeholders can use the CLRI, publicly available on the City Health Dashboard (www.cityhealthdashboard.com), to guide SARS COV-2 resource allocation.

Neighborhood-level Asian American Populations, Social Determinants of Health, and Health Outcomes in 500 US Cities.

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.

Benzene emissions from gas station clusters: a new framework for estimating lifetime cancer risk.

PURPOSE: During gas station operation, unburned fuel can be released to the environment through distribution, delivery, and storage. Due to the toxicity of fuel compounds, setback distances have been implemented to protect the general population. However, these distances treat gasoline sales volume as a categorical variable and only account for the presence of a single gas station and not clusters, which frequently occur. This paper introduces a framework for recommending setback distances for gas station clusters based on estimated lifetime cancer risk from benzene exposure. METHODS: Using the air quality dispersion model AERMOD, we simulated levels of benzene released to the atmosphere from single and clusters of generic gas stations and the associated lifetime cancer risk under meteorological conditions representative of Albany, New York. RESULTS: Cancer risk as a function of distance from gas station(s) and as a continuous function of total sales volume can be estimated from an equation we developed. We found that clusters of gas stations have increased cancer risk compared to a single station because of cumulative emissions from the individual gas stations. For instance, the cancer risk at 40 m for four gas stations each dispensing 1 million gal/year is 9.84 × 10-6 compared to 2.45 × 10-6 for one gas station. CONCLUSION: The framework we developed for estimating cancer risk from gas station(s) could be adopted by regulatory agencies to make setback distances a function of sales volume and the number of gas stations in a cluster, rather than on a sales volume category. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40201-020-00601-w.