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

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.

A Neighborhood-level analysis of mental health distress and income inequality as quasi-longitudinal risk of reported COVID-19 infection and mortality outcomes in Chicago

Extant literature investigates the impact of COVID-19 on mental health outcomes, however there is a paucity of work examining mental health distress as a risk factor for COVID-19 outcomes. While systemic variables like income inequality relate to both mental health and COVID-19, more work is needed to test theoretically informed models including such variables. Using a social-ecological framework, we aimed to address these gaps in the literature by conducting a neighborhood-level analysis of potential mental health distress and systemic- (income inequality) level predictors of reported COVID-19 infection and mortality over time in Chicago. Neighborhood-level comparisons revealed differences in mental health distress, income inequality, and reported COVID-19 mortality, but not reported COVID-19 infection. Specifically, Westside and Southside neighborhoods generally reported higher levels of mental health distress and greater concentration of poverty. The Central neighborhood showed a decline in reported mortality rates over time. Multi-level negative binomial models established that Zip-codes with greater mental health distress were at increased reported COVID-19 infection risk, yet lower mortality risk; Zip-codes with more poverty were at increased reported COVID-19 infection risk, yet lower mortality risk; and Zip-codes with the highest percentage of People of Color were at decreased risk of reported COVID-19 mortality. Taken together, these findings substantiate Chicago neighborhood-level disparities in mental health distress, income inequality, and reported COVID-19 mortality; identify unique differential associations of mental health distress and income inequality to reported COVID-19 infection and reported mortality risk; and, offer an alternative lens towards understanding COVID-19 outcomes in terms of race/ethnicity.

Pre-pandemic trends and Black:White inequities in life expectancy across the 30 most populous U.S. cities: a population-based study

BACKGROUND

Racial inequities in life expectancy, driven by structural racism, have been documented at the state and county levels; however, less information is available at the city level where local policy change generally happens. Furthermore, an assessment of life expectancy during the decade preceding COVID-19 provides a point of comparison for life expectancy estimates and trends post COVID-19 as cities recover.

METHODS

Using National Vital Statistics System mortality data and American Community Survey population estimates, we calculated the average annual city-level life expectancies for the non-Hispanic Black (Black), non-Hispanic White (White), and total populations. We then calculated the absolute difference between the Black and White life expectancies for each of the 30 cities and the U.S. We analyzed trends over four time periods (2008-2010, 2011-2013, 2014-2016, and 2017-2019).

RESULTS

In 2017-2019, life expectancies ranged from 72.75 years in Detroit to 83.15 years in San Francisco (compared to 78.29 years for the U.S.). Black life expectancy ranged from 69.94 years in Houston to 79.04 years in New York, while White life expectancy ranged from 75.18 years in Jacksonville to 86.42 years in Washington, DC. Between 2008-2010 and 2017-2019, 17 of the biggest cities experienced a statistically significant improvement in life expectancy, while 9 cities experienced a significant decrease. Black life expectancy increased significantly in 14 cities and the U.S. but decreased significantly in 4 cities. White life expectancy increased significantly in 17 cities and the U.S. but decreased in 8 cities. In 2017-2019, the U.S. and all but one of the big cities had a significantly longer life expectancy for the White population compared to the Black population. There was more than a 13-year difference between Black and White life expectancies in Washington, DC (compared to 4.18 years at the national level). From 2008-2010 to 2017-2019, the racial gap decreased significantly for the U.S. and eight cities, while it increased in seven cities.

CONCLUSION

Urban stakeholders and equity advocates need data on mortality inequities that are aligned with city jurisdictions to help guide the allocation of resources and implementation of interventions.

AddREssing Social Determinants TO pRevent hypErtension (The RESTORE Network): Overview of the Health Equity Research Network to Prevent Hypertension

BACKGROUND

The American Heart Association funded a Health Equity Research Network on the prevention of hypertension, the RESTORE Network, as part of its commitment to achieving health equity in all communities. This article provides an overview of the RESTORE Network.

METHODS

The RESTORE Network includes five independent, randomized trials testing approaches to implement non-pharmacological interventions that have been proven to lower blood pressure (BP). The trials are community-based, taking place in churches in rural Alabama, mobile health units in Michigan, barbershops in New York, community health centers in Maryland, and food deserts in Massachusetts. Each trial employs a hybrid effectiveness-implementation research design to test scalable and sustainable strategies that mitigate social determinants of health (SDOH) that contribute to hypertension in Black communities. The primary outcome in each trial is change in systolic BP. The RESTORE Network Coordinating Center has five cores: BP measurement, statistics, intervention, community engagement, and training that support the trials. Standardized protocols, data elements and analysis plans were adopted in each trial to facilitate cross-trial comparisons of the implementation strategies, and application of a standard costing instrument for health economic evaluations, scale up, and policy analysis. Herein, we discuss future RESTORE Network research plans and policy outreach activities designed to advance health equity by preventing hypertension.

CONCLUSIONS

The RESTORE Network was designed to promote health equity in the US by testing effective and sustainable implementation strategies focused on addressing SDOH to prevent hypertension among Black adults.

Digital health for all: How digital health could reduce inequality and increase universal health coverage

Digital transformation in health care has a lot of opportunities to improve access and quality of care. However, in reality not all individuals and communities are benefiting equally from these innovations. People in vulnerable conditions, already in need of more care and support, are often not participating in digital health programs. Fortunately, numerous initiatives worldwide are committed to make digital health accessible to all citizens, stimulating the long-cherished global pursuit of universal health coverage. Unfortunately initiatives are not always familiar with each other and miss connection to jointly make a significant positive impact. To reach universal health coverage via digital health it is necessary to facilitate mutual knowledge exchange, both globally and locally, to link initiatives and apply academic knowledge into practice. This will support policymakers, health care providers and other stakeholders to ensure that digital innovations can increase access to care for everyone, leading towards Digital health for all.

COVID-19 Outcomes Among the Hispanic Population of 27 Large US Cities, 2020-2021

Objectives. To examine racial/ethnic disparities in COVID-19 outcomes between Hispanics and Whites across 27 US jurisdictions whose health departments are members of the Big Cities Health Coalition (BCHC). Methods. Using surveillance data from the BCHC COVID-19 dashboard as of mid-June 2021, we computed crude incidence, age-adjusted hospitalization and mortality, and full vaccination coverage rates for Hispanics and Whites by city. We estimated relative and absolute disparities cumulatively and for 2020 and 2021 and explored associations between city-level social vulnerability and the magnitude of disparities. Results. In most of the cities with available COVID-19 incidence data, rates among Hispanics were 2.2 to 6.7 times higher than those among Whites. In all cities, Hispanics had higher age-adjusted hospitalization (1.5-8.6 times as high) and mortality (1.4-6.2 times as high) rates. Hispanics had lower vaccination coverage in all but 1 city. Disparities in incidence and hospitalizations narrowed in 2021, whereas disparities in mortality remained similar. Disparities in incidence, hospitalization, mortality, and vaccination rates were wider in cities with lower social vulnerability. Conclusions. A deeper exploration of racial/ethnic disparities in COVID-19 outcomes is essential to understand and prevent disparities among marginalized communities. (Am J Public Health. 2022;112(7): 1034-1044. https://doi.org/10.2105/AJPH.2022.306809).

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.

Tracking COVID-19 Inequities Across Jurisdictions Represented in the Big Cities Health Coalition (BCHC): The COVID-19 Health Inequities in BCHC Cities Dashboard

Objectives. To describe the creation of an interactive dashboard to advance the understanding of the COVID-19 pandemic from an equity and urban health perspective across 30 large US cities that are members of the Big Cities Health Coalition (BCHC). Methods. We leveraged the Drexel‒BCHC partnership to define the objectives and audience for the dashboard and developed an equity framework to conceptualize COVID-19 inequities across social groups, neighborhoods, and cities. We compiled data on COVID-19 trends and inequities by race/ethnicity, neighborhood, and city, along with neighborhood- and city-level demographic and socioeconomic characteristics, and built an interactive dashboard and Web platform to allow interactive comparisons of these inequities across cities. Results. We launched the dashboard on January 21, 2021, and conducted several dissemination activities. As of September 2021, the dashboard included data on COVID-19 trends for the 30 cities, on inequities by race/ethnicity in 21 cities, and on inequities by neighborhood in 15 cities. Conclusions. This dashboard allows public health practitioners to contextualize racial/ethnic and spatial inequities in COVID-19 across large US cities, providing valuable insights for policymakers. (Am J Public Health. 2022;112(6):904-912. https://doi.org/10.2105/AJPH.2021.306708).

Black-White Inequities in Kidney Disease Mortality Across the 30 Most Populous US Cities

OBJECTIVES

To examine city-level kidney disease mortality rates and Black:White racial inequities for the USA and its largest cities, and to determine if these measures changed over the past decade.

METHODS

We used National Vital Statistics System mortality data and American Community Survey population estimates to calculate age-standardized kidney disease mortality rates for the non-Hispanic Black (Black), non-Hispanic White (White), and total populations for the USA and the 30 most populous US cities. We examined two time points, 2008-2013 (T1) and 2014-2018 (T2), and assessed changes in rates and inequities over time. Racial inequities were measured with Black:White mortality rate ratios and rate differences.

RESULTS

Kidney disease mortality rates varied from 2.5 (per 100,000) in San Diego to 24.6 in Houston at T2. The Black kidney disease mortality rate was higher than the White rate in the USA and all cities studied at both time points. In T2, the Black mortality rate ranged from 7.9 in New York to 45.4 in Charlotte, while the White mortality rate ranged from 2.0 in San Diego to 18.6 in Indianapolis. At T2, the Black:White rate ratio ranged from 1.79 (95% CI 1.62-1.99) in Philadelphia to 5.25 (95% CI 3.40-8.10) in Washington, DC, compared to the US rate ratio of 2.28 (95% CI 2.25-2.30). Between T1 and T2, only one city (Nashville) saw a significant decrease in the Black:White mortality gap.

CONCLUSIONS

The largest US cities experience widely varying kidney disease mortality rates and widespread racial inequities. These local data on racial inequities in kidney disease mortality can be used by city leaders and health stakeholders to increase awareness, guide the allocation of limited resources, monitor trends over time, and support targeted population health strategies.

Prediction of clinical trial enrollment rates

Clinical trials represent a critical milestone of translational and clinical sciences. However, poor recruitment to clinical trials has been a long standing problem affecting institutions all over the world. One way to reduce the cost incurred by insufficient enrollment is to minimize initiating trials that are most likely to fall short of their enrollment goal. Hence, the ability to predict which proposed trials will meet enrollment goals prior to the start of the trial is highly beneficial. In the current study, we leveraged a data set extracted from ClinicalTrials.gov that consists of 46,724 U.S. based clinical trials from 1990 to 2020. We constructed 4,636 candidate predictors based on data collected by ClinicalTrials.gov and external sources for enrollment rate prediction using various state-of-the-art machine learning methods. Taking advantage of a nested time series cross-validation design, our models resulted in good predictive performance that is generalizable to future data and stable over time. Moreover, information content analysis revealed the study design related features to be the most informative feature type regarding enrollment. Compared to the performance of models built with all features, the performance of models built with study design related features is only marginally worse (AUC = 0.78 ± 0.03 vs. AUC = 0.76 ± 0.02). The results presented can form the basis for data-driven decision support systems to assess whether proposed clinical trials would likely meet their enrollment goal.

HIV mortality across the 30 largest U.S. cities: assessing overall trends and racial inequities

BACKGROUND

Despite decreases in overall HIV mortality in the U.S., large racial inequities persist. Most previous analyses of HIV mortality and mortality inequities have utilized national- or state-level data.

METHODS

Using vital statistics mortality data and American Community Survey population estimates, we calculated HIV mortality rates and Black:White HIV mortality rate ratios (RR) for the 30 most populous U.S. cities at two time points, 2010-2014 (T1) and 2015-2019 (T2).

RESULTS

Almost all cities (28) had HIV mortality rates higher than the national rate at both time points. At T2, HIV mortality rates ranged from 0.8 per 100,000 (San Jose, CA) to 15.2 per 100,000 (Baltimore, MD). Across cities, Black people were approximately 2-8 times more likely to die from HIV compared to White people at both time points. Over the decade, these racial disparities decreased at the national level (T1: RR = 11.0, T2: RR = 9.8), and in one city (Charlotte, NC).

DISCUSSION

We identified large geographic and racial inequities in HIV mortality in U.S. urban areas. These city-specific data may motivate change in cities and can help guide city leaders and other health advocates as they implement, test, and support policies and programming to decrease HIV mortality.

Influenza and Pneumonia Mortality Across the 30 Biggest U.S. Cities: Assessment of Overall Trends and Racial Inequities

Influenza and pneumonia account for substantial morbidity in the United States and show a demonstrated racial inequity. Detailed race-specific analysis at the city level can be used to guide targeted prevention efforts within the most at-risk communities. The purpose of this study is to analyze city-level data of influenza/pneumonia mortality rates and racial disparities across the 30 biggest U.S. cities over time. We assess racial inequities in influenza/pneumonia mortality in the 30 biggest cities and compare city-level trends overtime through age-adjusted overall and race-specific mortality rates calculated from public death records for the years 2008–2017. The national influenza/pneumonia mortality rate significantly decreased as did 45% of the cities included in the study. Nationally, the Black mortality rate was 16% higher than White mortality rate, and a significant disparity was seen within about one-third of the biggest cities. Over half (56%) of the cities showed reductions in both Black and White mortality; however, there was no overall trend in racial equity with some cities reducing the inequities between the Blacks and Whites and others increasing the inequities. Elevated mortality rates in communities of color can be traced to structural racism, social factors, and access to treatment and prevention services. We recommend an approach utilizing community outreach administered through localized public health organizations and supported by data at the city level.