State-Level Firearm Laws and Firearm Homicide in US Cities: Heterogenous Associations by City Characteristics.

Despite well-studied associations of state firearm laws with lower state- and county-level firearm homicide, there is a shortage of studies investigating differences in the effects of distinct state firearm law categories on various cities within the same state using identical methods. We examined associations of 5 categories of state firearm laws-pertaining to buyers, dealers, domestic violence, gun type/trafficking, and possession-with city-level firearm homicide, and then tested differential associations by city characteristics. City-level panel data on firearm homicide cases of 78 major cities from 2010 to 2020 was assessed from the Centers for Disease Control and Prevention's National Vital Statistics System. We modeled log-transformed firearm homicide rates as a function of firearm law scores, city, state, and year fixed effects, along with time-varying city-level confounders. We considered effect measure modification by poverty, unemployment, vacant housing, and income inequality. A one z-score increase in state gun type/trafficking, possession, and dealer law scores was associated with 25% (95% confidence interval [CI]:-0.37,-0.1), 19% (95% CI:-0.29,-0.07), and 17% (95% CI:-0.28, -0.4) lower firearm homicide rates, respectively. Protective associations were less pronounced in cities with high unemployment and high housing vacancy, but more pronounced in cities with high income inequality. In large US cities, state-level gun type/trafficking, possession, and dealer laws were associated with lower firearm homicide rates, but buyers and domestic violence laws were not. State firearm laws may have differential effects on firearm homicides based on city characteristics, and city-wide policies to enhance socioeconomic drivers may add benefits of firearm laws.

Associations between a Novel Measure of Census Tract-Level Credit Insecurity and Frequent Mental Distress in US Urban Areas, 2020.

Access to and utilization of consumer credit remains an understudied social determinant of health. We examined associations between a novel, small-area, multidimensional credit insecurity index (CII), and the prevalence of self-reported frequent mental distress across US cities in 2020. The census tract-level CII was developed by the Federal Reserve Bank of New York using Census population information and a nationally representative sample of anonymized Equifax credit report data. The CII was calculated for tracts in 766 cities displayed on the City Health Dashboard at the time of analysis, predominantly representing cities with over 50,000 residents. The CII combined data on tract-level participation in the formal credit economy with information on the percent of individuals without revolving credit, percent with high credit utilization, and percent with deep subprime credit scores. Tracts were classified as credit-assured, credit-likely, mid-tier, at-risk, or credit-insecure. We used linear regression to examine associations between the CII and a modeled tract-level measure of frequent mental distress, obtained from the CDC PLACES project. Regression models were adjusted for neighborhood economic and demographic characteristics. We examined effect modification by US region by including two-way interaction terms in regression models. In adjusted models, credit-insecure tracts had a modestly higher prevalence of frequent mental distress (prevalence difference = 0.38 percentage points; 95% CI = 0.32, 0.44), compared to credit-assured tracts. Associations were most pronounced in the Midwest. Local factors impacting credit access and utilization are often modifiable. The CII, a novel indicator of community financial well-being, may be an independent predictor of neighborhood health in US cities and could illuminate policy targets to improve access to desirable credit products and downstream health outcomes.

Validation of a geospatial aggregation method for congressional districts and other US administrative geographies.

Stakeholders need data on health and drivers of health parsed to the boundaries of essential policy-relevant geographies. US Congressional Districts are an example of a policy-relevant geography which generally lack health data. One strategy to generate Congressional District heath data metric estimates is to aggregate estimates from other geographies, for example, from counties or census tracts to Congressional Districts. Doing so requires several methodological decisions. We refine a method to aggregate health metric estimates from one geography to another, using a population weighted approach. The method's accuracy is evaluated by comparing three aggregated metric estimates to metric estimates from the US Census American Community Survey for the same years: Broadband Access, High School Completion, and Unemployment. We then conducted four sensitivity analyses testing: the effect of aggregating counts vs. percentages; impacts of component geography size and data missingness; and extent of population overlap between component and target geographies. Aggregated estimates were very similar to estimates for identical metrics drawn directly from the data source. Sensitivity analyses suggest the following best practices for Congressional district-based metrics: utilizing smaller, more plentiful geographies like census tracts as opposed to larger, less plentiful geographies like counties, despite potential for less stable estimates in smaller geographies; favoring geographies with higher percentage population overlap.

Life Expectancy and Built Environments in the U.S.: A Multilevel Analysis.

INTRODUCTION: The purpose of this study is to examine the associations between built environments and life expectancy across a gradient of urbanicity in the U.S. METHODS: Census tract‒level estimates of life expectancy between 2010 and 2015, except for Maine and Wisconsin, from the U.S. Small-Area Life Expectancy Estimates Project were analyzed in 2022. Tract-level measures of the built environment included: food, alcohol, and tobacco outlets; walkability; park and green space; housing characteristics; and air pollution. Multilevel linear models for each of the 4 urbanicity types were fitted to evaluate the associations, adjusting for population and social characteristics. RESULTS: Old housing (built before 1979) and air pollution were important built environment predictors of life expectancy disparities across all gradients of urbanicity. Convenience stores were negatively associated with life expectancy in all urbanicity types. Healthy food options were a positive predictor of life expectancy only in high-density urban areas. Park accessibility was associated with increased life expectancy in all areas, except rural areas. Green space in neighborhoods was positively associated with life expectancy in urban areas but showed an opposite association in rural areas. CONCLUSIONS: After adjusting for key social characteristics, several built environment characteristics were salient risk factors for decreased life expectancy in the U.S., with some measures showing differential effects by urbanicity. Planning and policy efforts should be tailored to local contexts.

Health and Equity Data to Teach the Next Generation of Public Health Leaders.

The COVID-19 pandemic restructured university learning environments while also underscoring the need for granular local health data. We describe how the University of Memphis School of Public Health used the City Health Dashboard, an online resource providing data at the city and neighborhood level for more than 35 measures of health outcomes, health drivers, and health equity for all US cities with populations >50 000, to enrich students' learning of applying data to community health policy. By facilitating students' engagement with population needs, assets, and capacities that affect communities' health-key components of the master of public health accreditation process-the Dashboard supports in-person and virtual learning at undergraduate and graduate levels and is recommended as a novel and rigorous data source for public health trainees.

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.

Social and Economic Differences in Neighborhood Walkability Across 500 U.S. Cities.

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.

Health and Health Determinant Metrics for Cities: A Comparison of County and City-Level Data.

We evaluated whether using county-level data to characterize public health measures in cities biases the characterization of city populations. We compared 4 public health and sociodemographic measures in 447 US cities (percent of children living in poverty, percent of non-Hispanic Black population, age-adjusted cardiovascular disease mortality, life expectancy at birth) to the same measures calculated for counties that contain those cities. We found substantial and highly variable city-county differences within and across metrics, which suggests that use of county data to proxy city measures could hamper accurate allocation of public health resources and appreciation of the urgency of public health needs in specific locales.

Leveraging Population Health Expertise to Enhance Community Benefit.

As the Internal Revenue Service strengthens the public health focus of community benefit regulations, and many states do the same with their tax codes, hospitals are being asked to look beyond patients in their delivery system to understand and address the needs of geographic areas. With the opportunities this affords come challenges to be addressed. The regulations' focus on population health is not limited to a defined clinical population-and the resulting emphasis on upstream determinants of health and community engagement is unfamiliar territory for many healthcare systems. At the same time, for many community residents and community-based organizations, large medical institutions can feel complicated to engage with or unwelcoming. And for neighborhoods that have experienced chronic underinvestment in upstream determinants of health-such as social services, housing and education-funds made available by hospitals through their community health improvement activities may seem insufficient and unreliable. Despite these regulatory requirements, many hospitals, focused as they are on managing patients in their delivery system, have not yet invested significantly in community health improvement. Moreover, although there are important exceptions, community health improvement projects have often lacked a strong evidence base, and true health system-community collaborations are relatively uncommon. This article describes how a large academic medical center tapped into the expertise of its population health research faculty to partner with local community-based organizations to oversee the community health needs assessment and to design, implement and evaluate a set of geographically based community-engaged health improvement projects. The resulting program offers a paradigm for health system investment in area-wide population health improvement.

Community Health Worker Intervention in Subsidized Housing: New York City, 2016-2017.

From April 2016 to June 2017, the Health + Housing Project employed four community health workers who engaged residents of two subsidized housing buildings in New York City to address individuals' broadly defined health needs, including social and economic risk factors. Following the intervention, we observed significant improvements in residents' food security, ability to pay rent, and connection to primary care. No immediate change was seen in acute health care use or more narrowly defined health outcomes.

The Emergence of Population Health in US Academic Medicine: A Qualitative Assessment.

Importance: In response to rapidly growing interest in population health, academic medical centers are launching department-level initiatives that focus on this evolving discipline. This trend, with its potential to extend the scope of academic medicine, has not been well characterized. Objective: To describe the emergence of departments of population health at academic medical centers in the United States, including shared areas of focus, opportunities, and challenges. Design, Setting, and Participants: This qualitative study was based on a structured in-person convening of a working group of chairs of population health-oriented departments on November 13 and 14, 2017, complemented by a survey of core characteristics of these and additional departments identified through web-based review of US academic medical centers. United States medical school departments with the word population in their name were included. Centers, institutes, and schools were not included. Main Outcomes and Measures: Departments were characterized by year of origin, areas of focus, organizational structure, faculty size, teaching programs, and service engagement. Opportunities and challenges faced by these emerging departments were grouped thematically and described. Results: Eight of 9 population health-oriented departments in the working group were launched in the last 6 years. The 9 departments had 5 to 97 full-time faculty. Despite varied organizational structures, all addressed essential areas of focus spanning the missions of research, education, and service. Departments varied significantly in their relationships with the delivery of clinical care, but all engaged in practice-based and/or community collaboration. Common attributes include core attention to population health-oriented research methods across disciplines, emphasis on applied research in frontline settings, strong commitment to partnership, interest in engaging other sectors, and focus on improving health equity. Tensions included defining boundaries with other academic units with overlapping areas of focus, identifying sources of sustainable extramural funding, and facilitating the interface between research and health system operations. Conclusions and Relevance: Departments addressing population health are emerging rapidly in academic medical centers. In supporting this new framing, academic medicine affirms and strengthens its commitment to advancing population health and health equity, to improving the quality and effectiveness of care, and to upholding the social mission of medicine.

City-Level Measures of Health, Health Determinants, and Equity to Foster Population Health Improvement: The City Health Dashboard.

OBJECTIVES: To support efforts to improve urban population health, we created a City Health Dashboard with area-specific data on health status, determinants of health, and equity at city and subcity (census tract) levels. METHODS: We developed a Web-based resource that includes 37 metrics across 5 domains: social and economic factors, physical environment, health behaviors, health outcomes, and clinical care. For the largest 500 US cities, the Dashboard presents metrics calculated to the city level and, where possible, subcity level from multiple data sources, including national health surveys, vital statistics, federal administrative data, and state education data sets. RESULTS: Iterative input from city partners shaped Dashboard development, ensuring that measures can be compared across user-selected cities and linked to evidence-based policies to spur action. Reports from early deployment indicate that the Dashboard fills an important need for city- and subcity-level data, fostering more granular understanding of health and its drivers and supporting associated priority-setting. CONCLUSIONS: By providing accessible city-level data on health and its determinants, the City Health Dashboard complements local surveillance efforts and supports urban population health improvement on a national scale.

Measuring Population Health in a Large Integrated Health System to Guide Goal Setting and Resource Allocation: A Proof of Concept.

In integrated health care systems, techniques that identify successes and opportunities for targeted improvement are needed. The authors propose a new method for estimating population health that provides a more accurate and dynamic assessment of performance and priority setting. Member data from a large integrated health system (n = 96,246, 73.8% female, mean age = 44 ± 0.01 years) were used to develop a mechanistic mathematical simulation, representing the top causes of US mortality in 2014 and their associated risk factors. An age- and sex-matched US cohort served as comparator group. The simulation was recalibrated and retested for validity employing the outcome measure of 5-year mortality. The authors sought to estimate potential population health that could be gained by improving health risk factors in the study population. Potential gains were assessed using both average life years (LY) gained and average quality-adjusted life years (QALYs) gained. The simulation validated well compared to integrated health system data, producing an AUC (area under the curve) of 0.88 for 5-year mortality. Current population health was estimated as a life expectancy of 84.7 years or 69.2 QALYs. Comparing potential health gain in the US cohort to the Kaiser Permanente cohort, eliminating physical inactivity, unhealthy diet, smoking, and uncontrolled diabetes resulted in an increase of 1.5 vs. 1.3 LY, 1.1 vs. 0.8 LY, 0.5 vs. 0.2 LY, and 0.5 vs. 0.5 LY on average per person, respectively. Using mathematical simulations may inform efforts by integrated health systems to target resources most effectively, and may facilitate goal setting.

Advancing Population Health at Academic Medical Centers: A Case Study and Framework for an Emerging Field.

The Triple Aim framework for advancing health care transformation elevated population health improvement as a central goal, together with improving patient experiences and reducing costs. Though population health improvement is often viewed in the context of clinical care delivery, broader-reaching approaches that bridge health care delivery, public health, and other sectors to foster area-wide health gains are gathering momentum. Academic medical centers (AMCs) across the United States are poised to play key roles in advancing population health and have begun to structure themselves accordingly. Yet, few frameworks exist to guide these efforts. Here, the authors offer a generalizable approach for AMCs to promote population health across the domains of research, education, and practice. In 2012, NYU School of Medicine, a major AMC dedicated to high-quality care of individual patients, launched an academic Department of Population Health with a strongly applied approach. A rigorous research agenda prioritizes scalable initiatives to improve health and reduce inequities in populations defined by race, ethnicity, geography, and/or other factors. Education targets population-level thinking among future physicians and research leadership among graduate trainees. Four key mission-bridging approaches offer a framework for population health departments in AMCs: engaging community, turning information into insight, transforming health care, and shaping policy. Challenges include tensions between research, practice, and evaluation; navigating funding sources; and sustaining an integrated, interdisciplinary approach. This framework of discipline-bridging, partnership-engaging inquiry, as it diffuses throughout academic medicine, holds great promise for realigning medicine and public health.

Evaluation of the Substance Abuse Research and Education Training (SARET) program: Stimulating health professional students to pursue careers in substance use research.

BACKGROUND: We developed and implemented the Substance Abuse Research Education and Training (SARET) program for medical, dental, nursing, and social work students to address the dearth of health professionals pursuing research and careers in substance use disorders (SUD). SARET has 2 main components: (1) a novel online curriculum addressing core SUD research topics, to reach a large number of students; (2) a mentored summer research experience for in-depth exposure. METHODS: Modules were integrated into the curricula of the lead institution, and of 5 external schools. We assessed the number of Web modules completed and their effect on students' interest in SUD research. We also assessed the impact of the mentorship experience on participants' attitudes and early career trajectories, including current involvement in SUD research. RESULTS: Since 2008, over 24,000 modules have been completed by approximately 9700 individuals. In addition to integration of the modules into curricula at the lead institution, all 5 health-professional partner schools integrated at least 1 module and approximately 5500 modules were completed by individuals outside the lead institution. We found an increase in interest in SUD research after completion of the modules for students in all 4 disciplines. From 2008 to 2015, 76 students completed summer mentorships; 8 students completed year-long mentorships; 13 published in SUD-related journals, 18 presented at national conferences, and 3 are actively engaged in SUD-related research. Mentorship participants reported a positive influence on their attitudes towards SUD-related clinical care, research, and interprofessional collaboration, leading in some cases to changes in career plans. CONCLUSIONS: A modular curriculum that stimulates clinical and research interest in SUD can be successfully integrated into medical, dental, nursing, and social work curricula. The SARET program of mentored research participation fostered early research successes and influenced career choice of some participants. Longer-term follow-up will enable us to assess more distal careers of the program.

Addiction Research Training Programs: Four Case Studies and Recommendations for Evaluation.

: The presence of structured addiction research training programs helps to ensure that the scientific workforce includes well-trained, diverse scientists necessary to reduce the negative impact of alcohol, drug, and tobacco use disorders. Although the field has made significant progress in the development of standards for clinical training in addiction medicine, there remains significant room for improvement in the training of addiction researchers, and also opportunities to synergize across addiction research training programs. The purpose of this commentary is to describe 4 National Institutes of Health (NIH)-sponsored addiction research training programs, highlight critical components, and provide recommendations for more comprehensive and effective program evaluation. Moving forward, evaluation of addiction research training programs would be enhanced by the use of conceptual models to inform process and outcome evaluations, the application of innovative methods to ensure long-term data collection, the improvement of mentorship evaluation measures, and the integration of training methods from other fields of study. We encourage NIH and others in the field to be proactive in establishing core metrics for evaluation across programs. Furthermore, centralized tracking of NIH-funded addiction research trainees, analysis of aggregate data across programs, and innovative methods to effectively disseminate program materials and processes are recommended.