Mapping Cumulative Risk in Delaware: Approach and Implications for Health Equity.

BACKGROUND: Addressing health equity requires attention to upstream determinants of health, including environmental and social factors that act in tandem to increase communities' exposure to and vulnerability to toxicants. Cumulative risk assessment, which evaluates combined risks from environmental and social factors, is a useful approach for estimating potential drivers of health disparities. We developed a cumulative risk score of multiple indices of environmental and social conditions and assessed block group-level differences in New Castle County, Delaware. METHODS: This cross-sectional study used choropleth maps to visualize the distribution of environmental, social, and cumulative risks and Moran's I statistics to assess spatial clustering of cumulative risk across the county and among individual block groups. RESULTS: Findings indicate that environmental risk rarely occurs without social risk and that environmental and social risks co-occur in distinct areas, resulting in large-scale clustering of cumulative risk. Areas of higher cumulative risk had more Black residents and people of lower socioeconomic status. CONCLUSIONS: Replicable measures of cumulative risk can show how environmental and social risks are inequitably distributed by race and socioeconomic status, as seen here in New Castle County. Such measures can support upstream approaches to reduce health disparities resulting from histories of environmental racism.

Racial disparities in triple negative breast cancer: toward a causal architecture approach.

BACKGROUND: Triple negative breast cancer (TNBC) is an aggressive subtype of invasive breast cancer that disproportionately affects Black women and contributes to racial disparities in breast cancer mortality. Prior research has suggested that neighborhood effects may contribute to this disparity beyond individual risk factors. METHODS: The sample included a cohort of 3316 breast cancer cases diagnosed between 2012 and 2020 in New Castle County, Delaware, a geographic region of the US with elevated rates of TNBC. Multilevel methods and geospatial mapping evaluated whether the race, income, and race/income versions of the neighborhood Index of Concentration at the Extremes (ICE) metric could efficiently identify census tracts (CT) with higher odds of TNBC relative to other forms of invasive breast cancer. Odds ratios (OR) and 95% confidence intervals (CI) were reported; p-values < 0.05 were significant. Additional analyses examined area-level differences in exposure to metabolic risk factors, including unhealthy alcohol use and obesity. RESULTS: The ICE-Race, -Income-, and Race/Income metrics were each associated with greater census tract odds of TNBC on a bivariate basis. However, only ICE-Race was significantly associated with higher odds of TNBC after adjustment for patient-level age and race (most disadvantaged CT: OR = 2.09; 95% CI 1.40-3.13), providing support for neighborhood effects. Higher counts of alcohol and fast-food retailers, and correspondingly higher rates of unhealthy alcohol use and obesity, were observed in CTs that were classified into the most disadvantaged ICE-Race quintile and had the highest odds of TNBC. CONCLUSION: The use of ICE can facilitate the monitoring of cancer inequities and advance the study of racial disparities in breast cancer.

Caregiving During the COVID-19 Pandemic: A Cross-Sectional Study with Older Cisgender Sexual Minority Women in the United States.

Purpose: This study describes prevalence of caregiving before and after the onset of the COVID-19 pandemic among racially diverse older cisgender sexual minority women, examines factors associated with caregiving, and assesses relationships between caregiving and health. Methods: A convenience sample of participants aged ≥50 years completed self-administered online surveys assessing sociodemographic characteristics, caregiver status, self-rated health, and depressive symptoms. Bivariate statistics compared response variables by race, caregiver status, and timing of caregiving relative to the pandemic. Results: Of 365 participants, 82.7% identified as lesbian or gay and 41.1% as Black/African American; 40% were caregivers before (n = 32), during (n = 34), or both before and during (n = 80) the pandemic. A greater proportion of caregivers lived with a partner (45.9% vs. 35.6%, p = 0.06), were unemployed (37.7% vs. 29.7%, p = 0.07), and had high school or lower education (11.6% vs. 5%, p = 0.09). No differences were found in self-rated health by caregiver status; however, a higher proportion of Black (vs. White) caregivers reported good to excellent physical health (77.9% vs. 62.9%, p = 0.05). Caregivers more frequently reported depressive symptoms (28.1% vs. 17.8%, p = 0.03). Caregivers both before and during the pandemic had lower educational attainment than those who provided care only before or only during the pandemic (p = 0.04). Conclusion: Caregiving was common among older sexual minority women during the pandemic and experiences varied by race and other social factors. Consideration of these intersecting experiences is important for fully understanding caregiver experiences during COVID-19. Overall, caregiving was associated with depressive symptoms, underscoring the importance of psychosocial support for all caregivers.

"I Think There's Only Two Fields for That": Hospital Registrar Attitudes and Practices for Collecting Patient Gender Identity Data.

Purpose: This study aimed to understand the experiences of hospital registrars in collecting gender identity data. Methods: A qualitative study that thematically analyzed key informant interviews with 37 registrars regarding their attitudes and practices in collecting gender identity data. Results: Collection of gender identity is influenced by (1) system-level barriers, (2) discrepancies in source of truth for documentation, and (3) registrars' underlying attitudes and behaviors. Conclusions: Findings demonstrate that person- and system-level barriers can interfere with the accurate and respectful collection of gender identity data, which is critical for tracking and addressing lesbian, gay, bisexual, transgender, and queer health disparities.

Neighborhood factors and triple negative breast cancer: The role of cumulative exposure to area-level risk factors.

BACKGROUND: Despite similar incidence rates among Black and White women, breast cancer mortality rates are 40% higher among Black women. More than half of the racial difference in breast cancer mortality can be attributed to triple negative breast cancer (TNBC), an aggressive subtype of invasive breast cancer that disproportionately affects Black women. Recent research has implicated neighborhood conditions in the etiology of TNBC. This study investigated the relationship between cumulative neighborhood-level exposures and TNBC risk. METHODS: This single-institution retrospective study was conducted on a cohort of 3316 breast cancer cases from New Castle County, Delaware (from 2012 to 2020), an area of the country with elevated TNBC rates. Cases were stratified into TNBC and "Non-TNBC" diagnosis and geocoded by residential address. Neighborhood exposures included census tract-level measures of unhealthy alcohol use, metabolic dysfunction, breastfeeding, and environmental hazards. An overall cumulative risk score was calculated based on tract-level exposures. RESULTS: Univariate analyses showed each tract-level exposure was associated with greater TNBC odds. In multivariate analyses that controlled for patient-level race and age, tract-level exposures were not associated with TNBC odds. However, in a second multivariate model that included patient-level variables and considered tract-level risk factors as a cumulative exposure risk score, each one unit increase in cumulative exposure was significantly associated with a 10% increase in TNBC odds. Higher cumulative exposure risk scores were found in census tracts with relatively high proportions of Black residents. CONCLUSIONS: Cumulative exposure to neighborhood-level risk factors that disproportionately affect Black communities was associated with greater TNBC risk.

Policy Recommendations for Reducing Tobacco Exposure for Youth and Adults in Wilmington, Delaware.

Objective: To highlight and recommend policies that can be projected to reduce disproportionate tobacco exposure for youth and adults in Wilmington, Delaware's densest and most disadvantaged neighborhoods. Four policy options were drawn from the literature: pharmacy tobacco bans, zoning-based tobacco retailer reductions, residential density caps, and buffers around K-12 schools. Method: Changes in tobacco retailer density and resident-to-retailer distance in Wilmington's medium- and high- density residentially zoned neighborhoods were projected using GIS analysis of current conditions and projections for each of the four policies. Results: Banning tobacco sales in pharmacies was found to be least effective, while 500-meter buffers around K-12 schools was projected to have the greatest impact on both retailer density and resident-to-retailer distance. Policy Implications: As a result of these findings, the authors recommend a ban of tobacco sales with a 500-meter radius of all K-12 schools in the City of Wilmington.

Reducing Exposure to Tobacco Retailers with Residential Zoning Policy: Insights from a Geospatial Analysis of Wilmington, Delaware.

Cigarette use remains the leading preventable cause of premature mortality in the US, with declines in smoking rates slowing in recent years. One promising target for improved tobacco control is the expanded regulation of tobacco retailers. Evaluations of such policy attempts have largely produced mixed results to date. The objective of this study was to the assess the potential of using a novel, residentially-focused zoning approach to produce a more targeted and equitable reduction in tobacco retailers in high-risk urban settings. We focused on Wilmington, Delaware, a city characterized by high poverty rates, a majority Black population, a disparate number of tobacco retailers, and an elevated smoking prevalence. Through the use of geospatial analyses, we observed disproportionately higher counts of convenience store tobacco retailers in medium- and high-density residential zones in Wilmington relative to the surrounding county. By linking electronic health record (EHR) data from a local health care system and US Census Bureau data, we further found that approximately 80% of Wilmington smokers and 60% of Wilmington youth lived in these residential zones. These findings highlight the potential to more equitably reduce tobacco retailer exposure through a residentially-focused zoning approach. Tobacco control policy and research implications are considered.

Aggregated spatial intensity as a method for estimating point-level exposures within area-level units: The case of tobacco retailer exposure in census tracts.

BACKGROUND: Aggregating point-level events to area-level units can produce misleading interpretations when displayed via choropleth maps. We developed the aggregated intensity method to share point-level location information across unit boundaries prior to aggregation. This method was applied to tobacco retailers among census tracts in New Castle County, DE. METHODS: Aggregated intensity uses kernel density estimation to generate spatially continuous expected counts of events per unit area, then aggregates these results to area-level units. We calculated a relative difference measure to compare aggregated intensity to observed counts. RESULTS: Aggregated intensity produces estimates of event exposure unconstrained by boundaries. The relative difference between aggregated intensity and counts is greater for units with many events proximal to their borders. The appropriateness of aggregated intensity depends on events' spatial influence and proximity to unit boundaries, as well as computational inputs. CONCLUSIONS: Aggregated intensity may facilitate more spatially realistic estimates of exposure to point-level events.

A Population Health Assessment in a Community Cancer Center Catchment Area: Triple-Negative Breast Cancer, Alcohol Use, and Obesity in New Castle County, Delaware.

BACKGROUND: The NCI requires designated cancer centers to conduct catchment area assessments to guide cancer control and prevention efforts designed to reduce the local cancer burden. We extended and adapted this approach to a community cancer center catchment area with elevated rates of triple-negative breast cancer (TNBC). METHODS: Cancer registry data for 462 TNBC and 2,987 "Not-TNBC" cases diagnosed between 2012 and 2020 at the Helen F. Graham Cancer Center & Research Institute (HFGCCRI), located in New Castle County, Delaware, were geocoded to detect areas of elevated risk (hot spots) and decreased risk (cold spots). Next, electronic health record (EHR) data on obesity and alcohol use disorder (AUD) and catchment area measures of fast-food and alcohol retailers were used to assess for spatial relationships between TNBC hot spots and potentially modifiable risk factors. RESULTS: Two hot and two cold spots were identified for TNBC within the catchment area. The hot spots accounted for 11% of the catchment area but nearly a third of all TNBC cases. Higher rates of unhealthy alcohol use and obesity were observed within the hot spots. CONCLUSIONS: The use of spatial methods to analyze cancer registry and other secondary data sources can inform cancer control and prevention efforts within community cancer center catchment areas, where limited resources can preclude the collection of new primary data. IMPACT: Targeting community outreach and engagement activities to TNBC hot spots offers the potential to reduce the population-level burden of cancer efficiently and equitably.

Operationalizing the Population Health Framework: Clinical Characteristics, Social Context, and the Built Environment.

As a framework, population health emphasizes health outcomes for entire populations, the broad range of determinants of these outcomes, and the comparative effectiveness of medical and public health interventions. In practice, however, many contemporary population health programs instead focus on small subsets of patients who account for a disproportionate share of health care utilization, often with disappointing results. The authors proposed a new approach to operationalize population health in clinical settings, with the example of tobacco use. Electronic health record (EHR) data from a mid-Atlantic health system were used to: (1) define and describe a hospital-based population of current smokers, (2) analyze the demographic characteristics of the population to consider how the social context may impact treatment, and (3) join EHR data with public licensing data on tobacco retail locations to assess the relationship between the built environment and smoking status. Out of a total of 20,310 unique adult admissions to the health system, 3749 (18.5%) were current smokers. Compared to never smokers, current smokers were significantly younger, more likely to be male, more likely to be Black/African American, less likely to be Hispanic/Latino/a, and more likely to be on Medicaid or be self-pay. Current vs. former smokers had significantly higher exposure to tobacco retail locations, even after adjusting for demographic and other covariates. By defining populations around leading modifiable medical determinants of health, and accounting for the larger context of sociodemographic factors and the built environment, health systems can invest in comprehensive programs designed to produce the greatest population health returns.

Characterizing the spatial relationship between smoking status and tobacco retail exposure: Implications for policy development and evaluation.

Tobacco retail density and smoking prevalence remain elevated in marginalized communities, underscoring the need for strategies to address these place-based disparities. The spatial variation of smokers and tobacco retailers is often measured by aggregating them to area-level units (e.g., census tracts), but spatial statistical methods that use point-level data, such as spatial intensity and K-functions, can better describe their geographic patterns. We applied these methods to a case study in New Castle County, DE to characterize the cross-sectional spatial relationship between tobacco retailers and smokers, finding that current smokers experience greater tobacco retail exposure and clustering relative to former smokers. We discuss how analysis at different geographic scales can provide complementary insights for tobacco control policy.

The co-occurrence of smoking and alcohol use disorder in a hospital-based population: Applying a multimorbidity framework using geographic information system methods.

Tobacco and alcohol use are leading causes of premature mortality in the US and concurrent use is associated with even greater health risks. A cross-sectional study of 20,310 patients admitted to a Mid-Atlantic acute health care system between July 1, 2018 and June 30, 2019 were categorized according to smoking and alcohol use disorder (AUD) status. Of the total admissions, 1464 (7.2%) were current smokers with an AUD. These patients were younger (52.4 vs. 63.9), more likely to be male (64.1% vs. 38.0%) and covered by Medicaid (46.9% vs. 11.6%), and resided in proximity to higher counts of tobacco (10.3 vs. 4.72) and alcohol (2.24 vs. 1.14) retailers than never smokers without an AUD. Clinically, these patients had higher rates of other substance use disorders (60.4% vs. 6.1%), depression (64.6% vs. 34.8%), HIV/AIDS (3.3% vs. 0.6%), and liver disease (40.7% vs. 13.2%) than never smokers without an AUD. Patients who concurrently smoke and have an AUD face unique and serious health risks. A multimorbidity framework can guide clinical and community-based interventions for individuals with concurrent psychiatric and chronic medical conditions, complex social needs, and adverse environmental exposures.

Social Determinants of Health, From Assessment to Action:: A Review of 3 Studies from the Value Institute at ChristianaCare.

INTRODUCTION: The COVID-19 crisis highlights the importance of screening for and managing adverse social determinants of health (SDoH). Many of the same SDoH items that put individuals at increased risk of COVID-19 infection have increased dramatically due to the economic repercussions of slowing the viral spread. METHODS: This is a review of 3 studies conducted by the Health Services Research Core in the Value Institute at ChristianaCare. The studies had 3 overarching goals: 1) to conduct a survey of primary care providers in Delaware to determine their current methods for collection of social determinants data, 2) to validate a 2-item screening tool for food insecurity, and 3) to assess the geographic distribution of patients with food insecurity. RESULTS: Our studies have demonstrated the importance of screening for SDoH by highlighting the inconsistent data collection of SDoH items, examining the prevalence of food insecurity and validating a standardized instrument for rapid data collection, as well as displaying geospatial differences in food insecurity prevalence across New Castle County, DE. PUBLIC HEALTH IMPLICATIONS: The COVID-19 pandemic has increased the prevalence of these social determinants in our communities. Therefore, it is imperative to employ screening and geospatial strategies to address the SDoH implications of the novel coronavirus.

Mapping the ChristianaCare response to COVID-19:: Clinical insights from the Value Institute's Geospatial Analytics Core.

INTRODUCTION: COVID-19 exemplifies the spatial nature of infectious disease in both its mechanism of transmission and the community-level conditions that facilitate its spread. With a long history of use for infectious disease applications, maps and geographic information systems (GIS) have been widely used in recent months for surveillance and risk prediction mapping. The Value Institute's Geospatial Analytics Core applied spatial methodologies to inform ChristianaCare's pandemic response around telehealth, testing disparities, and test site prioritization. METHODS: Descriptive data related to disparities in telehealth utilization were mapped to identify areas in which intervention is needed to increase telehealth access. Cluster detection methodology was used to identify "hot" and "cold" spots for COVID-19 testing by place and race across New Castle County, DE. A composite risk score was created to prioritize communities for testing sites. All analyses took place in Delaware from March-June 2020, with particular emphasis on New Castle County. RESULTS: Parts of northeastern New Castle County and western Sussex County were highlighted for intervention to increase broadband internet access for telehealth utilization. "Cold" spots for COVID-19 testing were found in New Castle County, indicating neighborhoods in which testing levels were significantly lower than expected. Data for testing levels, disease positivity, and socioeconomic risk factors were used to identify communities in northeastern New Castle County that warranted new test sites to mitigate disease spread. PUBLIC HEALTH IMPLICATIONS: Geospatial methodologies can be used to combine electronic health record data and population-level spatial data for pandemic response efforts. This allows health systems to confidently identify areas of need while mitigating disparities in resource allocation.

Examining the relationship between food environment and gestational diabetes.

BACKGROUND: Studies have shown an association between the incidence of gestational diabetes and living in neighborhoods oversaturated with unhealthy foods. OBJECTIVE: This study sought to determine if the food environment also affects the management of gestational diabetes. We hypothesized that living in areas with a higher quality of food decreased the risk of requiring medication to treat gestational diabetes. STUDY DESIGN: This was a retrospective cohort study of singleton births at the Christiana Care Health System between 2015 and 2018. Patients with gestational diabetes who live in Delaware (N=1327) were geocoded and classified according to their census tract food environment. The food environment was assessed using the modified Retail Food Environment Index, which measures the percentage of healthy food retailers among all food retailers within a half-mile radius of the census tract boundaries. The modified Retail Food Environment Index scores were divided into 3 categories: poor (modified Retail Food Environment Index score, 0-3), average (modified Retail Food Environment Index score, 4-10), and good or above average (modified Retail Food Environment Index score, ≥11) food environments. The primary outcome was the prevalence of A2 gestational diabetes mellitus. Several neonatal and obstetrical outcomes were also examined including type II diabetes mellitus (defined as a 2-hour glucose tolerance test with at least 1 value above the threshold), cesarean delivery, shoulder dystocia, admission to the neonatal intensive care unit, neonatal hypoglycemia, neonatal hyperbilirubinemia, neonatal respiratory distress syndrome, and macrosomia. RESULTS: A total of 689 (52%) women were diagnosed as having A2 gestational diabetes mellitus. Women in the average or good or above average food environment groups had a lower prevalence of A2 gestational diabetes mellitus than women in the poor food environment group (modified Retail Food Environment Index score, 4-10 [adjusted odds ratio, 0.58; 95% confidence interval, 0.37-0.92] and modified Retail Food Environment Index score, ≥11 [adjusted odds ratio, 0.56; 95% confidence interval, 0.40-0.82]). They also had a lower prevalence of type II diabetes mellitus (modified Retail Food Environment Index score, 4-10 [adjusted odds ratio, 0.25; 95% confidence interval, 0.09-0.72] and modified Retail Food Environment Index score, ≥11 [adjusted odds ratio, 0.48; 95% confidence interval, 0.27-0.86]). There were no differences in the other secondary outcomes of interest. CONCLUSION: The food environment affects the requirement for medication to obtain glycemic levels that are within the target range for those with gestational diabetes.

Mapping the Burden of Chronic Diseases in Delaware for Public Health Decision Making.

Public health decision-makers need to consider geographic differences in rates of chronic disease risk factors and outcomes in order to focus intervention efforts on populations exhibiting the greatest burden of disease. Increasingly, public health agencies are using geographic information systems (GIS) to analyze area-based variations and identify geographic priority areas for health promoting interventions. The articles in this issue are descriptive studies presenting the geographic distribution of select chronic disease risk factors and outcomes among Delaware communities. These studies emerged from a collaboration between the Christiana Care Value Institute and the Jefferson College of Population Health. These studies show that the burden of chronic diseases is not distributed evenly among communities in Delaware. The results of these studies add to the evidence base about public health in Delaware, and should inform public health practitioners working to improve the health of Delaware communities.

A Retrospective Analysis of Nursing Students' Clinical Experience in an All-Male Maximum Security Prison.

Prisons provide an ideal learning experience to prepare prelicensure students with the knowledge and skill set needed for practice in the 21st century. Beginning descriptive evidence demonstrates that correctional health is an innovative community resource to educate nursing students in today's changing model of health care delivery and practice. This article shares results from a retrospective analysis of the perceptions and experiences of nursing students during their community clinical rotation in an all-male maximum security prison.