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).

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.

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.

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.

The state of population health surveillance using electronic health records: a narrative review.

Electronic health records (EHRs) are transforming the practice of clinical medicine, but the extent to which they are being harnessed to advance public health goals remains uncertain. Data extracted from integrated EHR networks offer the potential for almost real-time determination of the health status of populations in care, for targeting interventions to vulnerable populations, and for monitoring the impact of such initiatives over time. This is especially true in ambulatory care settings, which are uniquely suited for monitoring population health indicators including risk factors and disease management indicators associated with chronic diseases. As efforts gather steam to integrate health data across delivery systems, large networks of electronic patient information are increasingly emerging. Few of the national population health surveillance systems that rely on EHR data have progressed beyond laying groundwork to launch and maintain EHR-based surveillance, but a limited number of more focused or local efforts have demonstrated innovation in population health surveillance. Common challenges include incompleteness of population coverage, lack of interoperability across data systems, and variable data quality. This review defines progress, opportunities, and challenges in using EHR data for population health surveillance.

Population health and the academic medical center: the time is right.

Optimizing the health of populations, whether defined as persons receiving care from a health care delivery system or more broadly as persons in a region, is emerging as a core focus in the era of health care reform. To achieve this goal requires an approach in which preventive care is valued and "nonmedical" determinants of patients' health are engaged. For large, multimission systems such as academic medical centers, navigating the evolution to a population-oriented paradigm across the domains of patient care, education, and research poses real challenges but also offers tremendous opportunities, as important objectives across each mission begin to align with external trends and incentives. In clinical care, opportunities exist to improve capacity for assuming risk, optimize community benefit, and make innovative use of advances in health information technology. Education must equip the next generation of leaders to understand and address population-level goals in addition to patient-level needs. And the prospects for research to define strategies for measuring and optimizing the health of populations have never been stronger. A remarkable convergence of trends has created compelling opportunities for academic medical centers to advance their core goals by endorsing and committing to advancing the health of populations.

Monitoring the pulse of hospital activity: electronic health record utilization as a measure of care intensity.

BACKGROUND: Hospital care on weekends has been associated with reduced quality and poor clinical outcomes, suggesting that decreases in overall intensity of care may have important clinical effects. We describe a new measure of hospital intensity of care based on utilization of the electronic health record (EHR). METHODS: We measured global intensity of care at our academic medical center by monitoring the use of the EHR in 2011. Our primary measure, termed EHR interactions, was the number of accessions of a patient's electronic record by a clinician, adjusted for hospital census, per unit of time. Our secondary measure was percent of total available central processing unit (CPU) power used to access EHR servers at a given time. RESULTS: EHR interactions were lower on weekend days as compared to weekdays at every hour (P < 0.0001), and the daytime peak in intensity noted each weekday was blunted on weekends. The relative rate and 95% confidence interval (CI) of census-adjusted record accessions per patient on weekdays compared with weekends were: 1.76 (95% CI: 1.74-1.77), 1.52 (95% CI: 1.50-1.55), and 1.14 (95% CI: 1.12-1.17) for day, morning/evening, and night hours, respectively. Percent CPU usage correlated closely with EHR interactions (r = 0.90). CONCLUSIONS: EHR usage is a valid and easily reproducible measure of intensity of care in the hospital. Using this measure we identified large, hour-specific differences between weekend and weekday intensity. EHR interactions may serve as a useful measure for tracking and improving temporal variations in care that are common, and potentially deleterious, in hospital systems.

Engaging health professional students in substance abuse research: development and early evaluation of the SARET program.

OBJECTIVE: There is a need to build the ranks of health care professionals engaged in substance abuse (SA)-focused clinical research. The authors simultaneously developed and evaluated SARET, the Substance Abuse Research Education and Training program. The fundamental goal of this interprofessional program is to stimulate medical, dental, and nursing student interest and experience in SA research. Evaluation aims to understand program feasibility and acceptability and to assess short-term impact. METHODS: SARET comprises 2 main components: stipend-supported research mentorships and a Web-based module series, consisting of 6, interactive, multimedia modules addressing core SA research topics, delivered via course curricula and in the research mentorships. Authors assessed program feasibility and impact on student interest in conducting SA research by tracking participation and conducting participant focus groups and online surveys. RESULTS: Thirty early health care professional students completed mentorships (25 summer, 5 yearlong) and 1324 completed at least 1 Web-module. SARET was considered attractive for the opportunity to conduct clinically oriented research and to work with health care professionals across disciplines. Mentorship students reported positive impact on their vision of SA-related clinical care, more positive attitudes about research, and, in some cases, change in career plans. Web-based modules were associated with enhanced interest in SA (35% increase, P = 0.005, in those somewhat/very interested for neurobiology module) and SA research (+38%, P < 0.001 for activation, +45%, P < 0.001 for personal impact, +7%, P = 0.089 for neurobiology). CONCLUSIONS: The SARET program stimulates SA clinical and research interest among students of nursing, medicine, and dentistry and may lend itself to dissemination.

The challenge of attribution: responsibility for population health in the context of accountable care.

One of the 3 goals for accountable care organizations is to improve population health. This will require that accountable care organizations bridge the schism between clinical care and public health. But do health care delivery organizations and public health agencies share a concept of "population"? We think not: whereas delivery systems define populations in terms of persons receiving care, public health agencies typically measure health on the basis of geography. This creates an attribution problem, particularly in large urban centers, where multiple health care providers often serve any given neighborhood. We suggest potential innovations that could allow urban accountable care organizations to accept accountability, and rewards, for measurably improving population health.

Training physician investigators in medicine and public health research.

OBJECTIVES: We have described and evaluated the impact of a unique fellowship program designed to train postdoctoral, physician fellows in research at the interface of medicine and public health. METHODS: We developed a rigorous curriculum in public health content and research methods and fostered linkages with research mentors and local public health agencies. Didactic training provided the foundation for fellows' mentored research initiatives, which addressed real-world challenges in advancing the health status of vulnerable urban populations. RESULTS: Two multidisciplinary cohorts (6 per cohort) completed this 2-year degree-granting program and engaged in diverse public health research initiatives on topics such as improving pediatric care outcomes through health literacy interventions, reducing hospital readmission rates among urban poor with multiple comorbidities, increasing cancer screening uptake, and broadening the reach of addiction screening and intervention. The majority of fellows (10/12) published their fellowship work and currently have a career focused in public health-related research or practice (9/12). CONCLUSIONS: A fellowship training program can prepare physician investigators for research careers that bridge the divide between medicine and public health.

Factors associated with frequent utilization of crisis substance use detoxification services.

Previous research suggests that some substance users have multiple crisis detoxification visits and never access rehabilitation care. This care-seeking pattern leads to poorer outcomes and higher costs. The authors aimed to identify predictors of repeat detoxification visits by analyzing state-level data routinely collected at the time of substances use services admission. Repeat detoxification clients were more likely to be homeless, city-dwelling fee-for-service Medicaid recipients. Repeat detoxification clients were less likely than those with one admission to enter rehabilitation within 3 days. Treatment providers should aim for rapid transfer to rehabilitation and consider expanding detoxification intake data to improve risk stratification.