Quantification of Neighborhood-Level Social Determinants of Health in the Continental United States

Toward informatics-enabled preparedness for natural hazards to minimize health impacts of climate change

Natural hazards (NHs) associated with climate change have been increasing in frequency and intensity. These acute events impact humans both directly and through their effects on social and environmental determinants of health. Rather than relying on a fully reactive incident response disposition, it is crucial to ramp up preparedness initiatives for worsening case scenarios. In this perspective, we review the landscape of NH effects for human health and explore the potential of health informatics to address associated challenges, specifically from a preparedness angle. We outline important components in a health informatics agenda for hazard preparedness involving hazard-disease associations, social determinants of health, and hazard forecasting models, and call for novel methods to integrate them toward projecting healthcare needs in the wake of a hazard. We describe potential gaps and barriers in implementing these components and propose some high-level ideas to address them.

Substance use disorders and social determinants of health from electronic medical records obtained during Kentucky's "triple wave"

Social determinants of health (SDOH) play a critical role in the risk of harmful drug use. Examining SDOH as a means of differentiating populations with multiple co-occurring substance use disorders (SUDs) is particularly salient in the era of prevalent opioid and stimulant use known as the "Third Wave". This study uses electronic medical records (EMRs) from a safety net hospital system from 14,032 patients in Kentucky from 2017 to 2019 in order to 1) define three types of SUD cohorts with shared/unique risk factors, 2) identify patients with unstable housing using novel methods for EMRs and 3) link patients to their residential neighborhood to obtain quantitative perspective on social vulnerability. We identified patients in three cohorts with statistically significant unique risk factors that included race, biological sex, insurance type, smoking status, and urban/rural residential location. Adjusting for these variables, we found a statistically significant, increasing risk gradient for patients experiencing unstable housing by cohort type: opioid-only (n = 7385, reference), stimulant-only (n = 4794, odds ratio (aOR) 1.86 95 % confidence interval (CI): 1.66-2.09), and co-diagnosed (n = 1853, aOR = 2.75, 95 % CI: 2.39 to 3.16). At the neighborhood-level, we used 8 different measures of social vulnerability and found that, for the most part, increasing proportions of patients with stimulant use living in a census tract was associated with more social vulnerability. Our study identifies potentially modifiable factors that can be tailored by substance type and demonstrates robust use of EMRs to meet national goals of enhancing research on social determinants of health.

Improving patient well-being in the United States through care coordination interventions informed by social determinants of health

Health and well-being are promoted when primary care teams partner with patients and provide care coordination to mitigate risks and promote optimal health. Identification of patients for care coordination is typically based on claim-driven risk assessments. Evidence shows that social determinants of health (SDOH) drive risk for adverse health outcomes but are omitted from existing risk tools. Missed opportunities for care coordination contribute to increased healthcare costs, poorer health outcomes and reduced patient well-being. To address the gap of risk-informed care coordination that includes SDOH, the aim of this project was to implement process improvement of a system's care coordination program through refined patient selection and customised engagement in intensive care coordination. A non-randomised care coordination quality improvement project was conducted at a community health centre in 2020. Inclusion criteria (i.e. presence of risk attribution score, SDOH questionnaire completed) resulted in 540 patients being offered care coordination services; Patients having at least one month of care coordination were included in the analysis (N = 216). Analysis included the 216 patients that chose participation and the 324 patients that maintained usual care. Descriptive statistics were generated to distinguish patient demographics, frequency of care coordination contact, and specific SDOH insecurities for both the study and comparison groups. Paired t-tests were incorporated to evaluate statistical significance of the intervention group. Impact on well-being, SDOH barriers, appointment adherence and health outcomes were assessed in both conditions. Intervention condition patients reported improvement in well-being [feeling anxious (t = 4.051; p < 0.000)] and reduced SDOH barriers [food access (t = 4.662; p < 0.000); housing (t = 2.203; p = 0.008)] that were significantly different from the usual care condition in the expected directions. Care coordination based on factors including SDOH risks shows promise in improving patient well-being. Future research should refine this approach for comprehensive risk assessment to intervene and support patient health and well-being.

Relationship Between Neighborhood Socioeconomic Disadvantage and Severe Maternal Morbidity and Maternal Mortality

Background

Rates of severe maternal morbidity and maternal mortality (SMM/MM) in the United States are rising. Disparities in SMM/MM persist by race, ethnicity and geography, and could partially be attributed to social determinants of health.

Purpose

Utilizing data from the largest, statewide referral hospital in Alabama, we investigated the relationship between residence in disadvantaged neighborhoods and SMM/MM.

Methods

Data on all pregnancies between 2010 and 2020 were included; SMM/MM cases were identified using CDC definitions. Area deprivation index (ADI) available at the census-block group was geographically linked to individual records and categorized using quintile cutoffs; higher ADI score indicated higher socioeconomic disadvantage. Generalized estimating equation models were used to adjust for spatial autocorrelation and ORs were computed to evaluate the relationship between ADI and SMM/MM, adjusted for covariates including age, race, insurance, residence in medically underserved areas/population (MUAP), and urban/rural residence.

Results

Overall, 32,909 live-birth deliveries were identified, with a prevalence of 9.8% deliveries with SMM/MM with blood transfusion and 5.3% without blood transfusion, respectively. Increased levels of ADI were associated with increased odds of SMM/MM. Compared to women in the lowest quintile, the adjusted OR for SMM/MM among women in highest quintile was 1.78 (95%CI, 1.22-2.59, P=.0027); increasing age, non-Hispanic Black, government insurance and residence in MUAP were also significantly associated with increased odds of SMM/MM.

Conclusion

Our results suggest that residence within disadvantaged neighborhoods may contribute to SMM/MM even after adjusting for patient-level factors. Measures such as ADI can help identify the most vulnerable populations and provide points to intervene.

From Genes to Geography, from Cells to Community, from Biomolecules to Behaviors: The Importance of Social Determinants of Health

Much scientific work over the past few decades has linked health outcomes and disease risk to genomics, to derive a better understanding of disease mechanisms at the genetic and molecular level. However, genomics alone does not quite capture the full picture of one’s overall health. Modern computational biomedical research is moving in the direction of including social/environmental factors that ultimately affect quality of life and health outcomes at both the population and individual level. The future of studying disease now lies at the hands of the social determinants of health (SDOH) to answer pressing clinical questions and address healthcare disparities across population groups through its integration into electronic health records (EHRs). In this perspective article, we argue that the SDOH are the future of disease risk and health outcomes studies due to their vast coverage of a patient’s overall health. SDOH data availability in EHRs has improved tremendously over the years with EHR toolkits, diagnosis codes, wearable devices, and census tract information to study disease risk. We discuss the availability of SDOH data, challenges in SDOH implementation, its future in real-world evidence studies, and the next steps to report study outcomes in an equitable and actionable way.

Community distress as a predictor of early hernia recurrence for older adults undergoing ventral hernia repair (VHR)

Background

Social cohesion and neighborhood support have been linked to improved health in a variety of fields, but is not well-studied among the elderly population. This is particularly evident in surgical populations. Therefore, this study sought to assess the potential role of community distress in predicting early hernia recurrence among older adults.

Methods

The Abdominal Core Health Quality Collaborative (ACHQC) was used to identify patients aged 65 or older undergoing elective ventral hernia repair with zip code data available. Patients were linked to the Distressed Communities Index (DCI), which is a national database that assigns a score of 0–100 to each zip code based on 7 measures of neighborhood prosperity. Quintiles were used to compare groups: prosperous (0–20), comfortable (21–40), mid-tier (41–60), at-risk (61–80), and distressed (81–100). Distressed (0–20), at-risk (21–40), mid-tier (41–60), comfortable (61–80), and prosperous (81–100). Time to recurrence for neighborhood distress quintiles was examined using a Cox proportional hazards model.

Results

In total, 9819 patients were included in the study, including 3056 (31.1%) prosperous, 2307 (23.5%) comfortable, 1795 (18.2%) mid-tier, 1390 (14.2%) at-risk, and 1271 (12.9%) distressed. Distressed communities had lower mean age and greater proportion of racial minorities (p < 0.001). Open repairs were significantly more common among the distressed group (66.7%), as were all comorbidities (p < 0.001). Recurrence-free survival was shorter for distressed communities compared to prosperous after adjusting for baseline characteristics (HR 1.3, 95% CI 1.07–1.67, p = 0.01). Mean time to recurrence was lowest for patients living in distressed communities, indicating the worst recurrence rates, while mean time to recurrence was greatest for those in prosperous zip codes (p < 0.001).

Conclusion

Older VHR patients presenting from distressed zip codes, as identified by the Distressed Communities Index, experience hernia recurrence significantly sooner as compared to patients from prosperous zip codes. This study may provide evidence of the role of neighborhood and environmental factors in caring for older patients following VHR.

Graphical abstract

Associations of four indexes of social determinants of health and two community typologies with new onset type 2 diabetes across a diverse geography in Pennsylvania

Evaluation of geographic disparities in type 2 diabetes (T2D) onset requires multidimensional approaches at a relevant spatial scale to characterize community types and features that could influence this health outcome. Using Geisinger electronic health records (2008–2016), we conducted a nested case-control study of new onset T2D in a 37-county area of Pennsylvania. The study included 15,888 incident T2D cases and 79,435 controls without diabetes, frequency-matched 1:5 on age, sex, and year of diagnosis or encounter. We characterized patients’ residential census tracts by four dimensions of social determinants of health (SDOH) and into a 7-category SDOH census tract typology previously generated for the entire United States by dimension reduction techniques. Finally, because the SDOH census tract typology classified 83% of the study region’s census tracts into two heterogeneous categories, termed rural affordable-like and suburban affluent-like, to further delineate geographies relevant to T2D, we subdivided these two typology categories by administrative community types (U.S. Census Bureau minor civil divisions of township, borough, city). We used generalized estimating equations to examine associations of 1) four SDOH indexes, 2) SDOH census tract typology, and 3) modified typology, with odds of new onset T2D, controlling for individual-level confounding variables. Two SDOH dimensions, higher socioeconomic advantage and higher mobility (tracts with fewer seniors and disabled adults) were independently associated with lower odds of T2D. Compared to rural affordable-like as the reference group, residence in tracts categorized as extreme poverty (odds ratio [95% confidence interval] = 1.11 [1.02, 1.21]) or multilingual working (1.07 [1.03, 1.23]) were associated with higher odds of new onset T2D. Suburban affluent-like was associated with lower odds of T2D (0.92 [0.87, 0.97]). With the modified typology, the strongest association (1.37 [1.15, 1.63]) was observed in cities in the suburban affluent-like category (vs. rural affordable-like–township), followed by cities in the rural affordable-like category (1.20 [1.05, 1.36]). We conclude that in evaluating geographic disparities in T2D onset, it is beneficial to conduct simultaneous evaluation of SDOH in multiple dimensions. Associations with the modified typology showed the importance of incorporating governmentally, behaviorally, and experientially relevant community definitions when evaluating geographic health disparities.

Classification of Deprivation Indices That Applied to Detect Health Inequality: A Scoping Review

INTRODUCTION

Many studies around the world are undertaken to establish the association between deprivation and public health indicators. Both separate indicators (e.g., income, education, occupation, public security and social support) and complex models (indices) include several indicators. Deprivation indices are actively used in public health since the mid 1980s. There is currently no clear classification of indices.

METHODS

In the current review, data related to deprivation indices are combined and analyzed in order to create a taxonomy of indices based on the results obtained. The search was carried out using two bibliographic databases. After conducting a full-text review of the articles and searching and adding relevant articles from the bibliography, and articles that were already known to the authors, sixty studies describing the use of sixty deprivation indices in seventeen countries were included in the narrative synthesis, resulting in development of a taxonomy of indices. When creating the taxonomy, an integrative approach was used that allows integrating new classes and sub-classes in the event that new information appears.

RESULTS

In the review, 68% (41/60) of indices were classified as socio-economic, 7% (4/60) of indices as material deprivation, 5% (3/60) of indices as environmental deprivation and 20% (12/60) as multidimensional indices.

CONCLUSIONS

The data stimulates the use of a competent approach, and will help researchers and public health specialist in resolving conflicts or inconsistencies that arise during the construction and use of indices.

Effect of social disparities on 10 year survival in pediatric patients with Wilms' tumor

BACKGROUND

To stratify 10-year survival outcomes by degree of social disparities in pediatric Wilms' tumor patients. We applied the Social Deprivation Index (SDI) to survival outcomes from the national SEER database to elucidate the effects of lower socioeconomics on cancer survival.

METHODS

A retrospective cohort study was performed using the national Surveillance, Epidemiology, and End Results (SEER) oncology registry from 1975 to 2016 based on county-level data. Pediatric patients (<18 years old) with a diagnosis of WT (C64.9) and confirmed based on histology codes (8960/8963) were included. SDI scores were calculated for each patient and initially divided into quintiles. Patients were delineated into high-risk (>60th percentile/more deprived) or low-risk (<60th percentile/less deprived) groups. Statistics were assessed using Fisher's exact test, Student's t-test, and Kaplan-Meier assessed survival differences with log-rank test for trend.

RESULTS

A total of 3406 patients were included with 1366 patients reported in the high-risk group and 2040 patients in the low-risk group. Quintile data demonstrated a stratification in survival based on socioeconomic status. Patients in more socially deprived counties were significantly (p = 0.035) more likely to have worse overall survival compared with those living in less deprived areas at 10-year (87.3% vs 89.3%) follow-up.

CONCLUSIONS

10-year overall and cancer-specific survival data for patients with Wilms' tumor stratify by socioeconomic lines. This represents an area that needs to be addressed in this pediatric oncologic population. Patients from more socially deprived areas have significantly worse 10-year overall survival rates and noticeably different 10-year cancer-specific survival rates.

Social Determinants of Health are Associated With Postoperative Outcomes in Children With Complicated Appendicitis

INTRODUCTION

Socioeconomic disadvantage has been associated with increased complicated appendicitis rates. Our purpose was to analyze the complex interactions between social determinants of health (SDOH) and postoperative outcomes in pediatric appendicitis.

MATERIALS AND METHODS

Children who underwent appendectomy at our institution (1/2015-12/2020) were retrospectively reviewed. We used home addresses to determine composite measures of neighborhood/area-level socioeconomic advantage (Area Deprivation Index [ADI] and Social Deprivation Index [SDI]), and other area-level indicators. We created a novel, composite outcome score computed as a weighted average of eight outcome measures. Feature selection and exploratory factor analysis were used to create a multivariate model predictive of outcomes.

RESULTS

Of 1117 children with appendicitis, 20.59% had complicated (perforated) appendicitis. Factor analysis identified two multivariate latent factors; Factor 1 contained SDI, ADI, and % unemployed in the population, and Factor 2 contained % Hispanic and % foreign-born in the population. Low Factor 2 scores (communities with more Hispanic/foreign-born residents) were associated with increased length of stay, more frequent postoperative percutaneous drainage, and increased postoperative imaging.

CONCLUSIONS

Interactions between SDOH and pediatric surgical care go beyond the individual patient and suggest that vulnerable populations are exposed to contextual conditions that may impact outcomes. Specifically, neighborhood-level factors, including the prevalence of Hispanic ethnicity and foreign-born individuals, are associated with outcomes in pediatric patients with complicated appendicitis. Reducing disparities in complicated appendicitis outcomes may involve addressing neighborhood-level SDOH through strategic reallocation of healthcare resources and developing targeted interventions to improve access to pediatric surgical care in underserved communities.

Association of Rurality and Neighborhood Level Socioeconomic Deprivation with Perioperative Health Status in Total Joint Arthroplasty Patients: Analysis from a Large, Tertiary Care Hospital

BACKGROUND

Individual socioeconomic status (SES) is associated with disparities in access to care and worse outcomes in total joint arthroplasty (TJA). Neighborhood-level SES measures are sometimes used as a proxy for individual-level SES, but the validity of this approach is unknown. We examined neighborhood level SES and rurality on perioperative health status in TJA.

METHODS

The study population comprised 46,828 TJA surgeries performed at a tertiary care hospital. Community area deprivation index (ADI) was derived from the 2015 American Census Survey. Logistic regression was used to examine perioperative characteristics by ADI and rurality.

RESULTS

Compared to patients from the least deprived neighborhoods, patients from the most deprived neighborhoods were likely to be female (odds ratioOR 1.46, 95% confidence interval CI: 1.33-1.61), non-white (OR 1.36, 95% CI: 1.13-1.64), with education high school or less (OR 4.85, 95% CI: 4.35-5.41), be current smokers (OR 2.20, 95% CI: 1.61-2.49), have BMI>30 kg/m² (OR 1.43, 95% CI: 1.30-1.57), more limitation on instrumental activities of daily living (OR 1.75, 95% CI: 1.55-1.97) and American Society of Anesthesiologists (ASA) score > II (OR 2.0, 95% CI: 1.11-1.37). There was a progressive association between the degree of area level deprivation with preexisting comorbidities. Patients from rural communities were more likely to be male, white, have body mass index (BMI)>30 kg/m² and lower education levels. However, rurality was either not associated or negatively associated with comorbidities.

CONCLUSION

TJA patients from lower SES neighborhoods have worse behavioral risk factors and higher comorbidity burden than patients from higher SES neighborhoods. Patients from rural communities have worse behavioral risk factors but not comorbidities.

Evaluation of associations between estimates of particulate matter exposure and new onset type 2 diabetes in the REGARDS cohort

BACKGROUND

Studies of PM_2.5 and type 2 diabetes employ differing methods for exposure assignment, which could explain inconsistencies in this growing literature. We hypothesized associations between PM_2.5 and new onset type 2 diabetes would differ by PM_2.5 exposure data source, duration, and community type.

METHODS

We identified participants of the US-based REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort who were free of diabetes at baseline (2003-2007); were geocoded at their residence; and had follow-up diabetes information. We assigned PM_2.5 exposure estimates to participants for periods of 1 year prior to baseline using three data sources, and 2 years prior to baseline for two of these data sources. We evaluated adjusted odds of new onset diabetes per 5 µg/m³ increases in PM_2.5 using generalized estimating equations with a binomial distribution and logit link, stratified by community type.

RESULTS

Among 11,208 participants, 1,409 (12.6%) had diabetes at follow-up. We observed no associations between PM_2.5 and diabetes in higher and lower density urban communities, but within suburban/small town and rural communities, increases of 5 µg/m³ PM_2.5 for 2 years (Downscaler model) were associated with diabetes (OR [95% CI] = 1.65 [1.09, 2.51], 1.56 [1.03, 2.36], respectively). Associations were consistent in direction and magnitude for all three PM_2.5 sources evaluated.

SIGNIFICANCE

1- and 2-year durations of PM_2.5 exposure estimates were associated with higher odds of incident diabetes in suburban/small town and rural communities, regardless of exposure data source. Associations within urban communities might be obfuscated by place-based confounding.

Socioeconomic determinants of respiratory health in patients with cystic fibrosis: implications for treatment strategies

INTRODUCTION

Great variation exists in the progression and outcomes of cystic fibrosis (CF) lung disease, due to both genetic and environmental influences. Social determinants mediate environmental exposures and treatment success; people with CF from socioeconomically disadvantaged backgrounds have worse health and die younger than those in more advantaged positions.

AREAS COVERED

This paper reviews the literature on the mechanisms that are responsible for generating and sustaining disparities in CF health, and the ways by which social determinants translate into health advantages or disadvantages in people with CF. The authors make recommendations for addressing social risk factors in CF clinical practice.

EXPERT OPINION

Socioeconomic factors are not dichotomous and their impact is felt at every step of the social ladder. CF care programs need to adopt a systematic protocol to screen for health-related social risk factors, and then connect patients to available resources to meet individual needs. Considerations such as daycare, schooling options, living and working conditions, and opportunities for physical exercise and recreation as well as promotion of self-efficacy are often overlooked. In addition, advocacy for changes in public policies on health insurance, environmental regulations, social welfare, and education would all help address the root causes of CF health inequities.

Social Determinants of Health in Oncology: Towards a More Personalized and Equitable Delivery of Cancer Care

Social determinants of health (SDOH) are defined as the set of modifiable social and physical risk factors that affect health. It is known that SDOH directly influence the population's overall health, but their effects on patients with cancer are considerably less elucidated. Here, we review the literature describing the effects of SDOH outlined by the Healthy People 2020 framework on patients diagnosed with cancer. We have found that while some SDOH are well-defined in cancer patients, evidence surrounding several variables is scarce. In addition, we have found that many SDOH are associated with disparities at the screening stage, indicating that upstream interventions are necessary before addressing the clinical outcomes themselves. Further investigation is warranted to understand how SDOH affect screenings and outcomes in multiple disciplines of oncology and types of cancers as well as explore how SDOH affect the treatments sought by these vulnerable patients.

Association Between Neighborhood-Level Poverty and Incident Atrial Fibrillation: a Retrospective Cohort Study

BACKGROUND

Atrial fibrillation (AF) is a leading cause of cardiovascular morbidity and mortality. While neighborhood-level factors, such as poverty, have been related to prevalence of AF risk factors, the association between neighborhood poverty and incident AF has been limited.

OBJECTIVE

Using a large cohort from a health system serving the greater Chicago area, we sought to determine the association between neighborhood-level poverty and incident AF.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Adults, aged 30 to 80 years, without baseline cardiovascular disease from January 1, 2005, to December 31, 2018.

MAIN MEASURES

We geocoded and matched residential addresses of all eligible patients to census-level poverty estimates from the American Community Survey. Neighborhood-level poverty (low, intermediate, and high) was defined as the proportion of residents in the census tract living below the federal poverty threshold. We used generalized linear mixed effects models with a logit link function to examine the association between neighborhood poverty and incident AF, adjusting for patient demographic and clinical AF risk factors.

KEY RESULTS

Among 28,858 in the cohort, patients in the high poverty group were more often non-Hispanic Black or Hispanic and had higher rates of AF risk factors. Over 5 years of follow-up, 971 (3.4%) patients developed incident AF. Of these, 502 (51.7%) were in the low poverty, 327 (33.7%) in the intermediate poverty, and 142 (14.6%) in the high poverty group. The adjusted odds ratio (aOR) of AF was higher for the intermediate poverty compared with that for the low poverty group (aOR 1.23 [95% CI 1.01-1.48]). The point estimate for the aOR of AF incidence was similar, but not statistically significant, for the high poverty compared with the low poverty group (aOR 1.25 [95% CI 0.98-1.59]).

CONCLUSION

In adults without baseline cardiovascular disease managed in a large, integrated health system, intermediate neighborhood poverty was significantly associated with incident AF. Understanding neighborhood-level drivers of AF disparities will help achieve equitable care.

Inequalities in Life Expectancy Across North Carolina: A Spatial Analysis of the Social Determinants of Health and the Index of Concentration at Extremes

Health inequalities are characterized by spatial patterns of social, economic, and political factors. Life expectancy (LE) is a commonly used indicator of overall population health and health inequalities that allows for comparison across different spatial and temporal regions. The objective of this study was to examine geographic inequalities in LE across North Carolina census tracts by comparing the performance of 2 popular geospatial health indices: Social Determinants of Health (SDoH) and the Index of Concentration at Extremes (ICE). A principal components analysis (PCA) was used to address multicollinearity among variables and aggregate data into components to examine SDoH, while the ICE was constructed using the simple subtraction of geospatial variables. Spatial regression models were employed to compare both indices in relation to LE to evaluate their predictability for population health. For individual SDoH and ICE components, poverty and income had the strongest positive correlation with LE. However, the common spatial techniques of adding PCA components together for a final SDoH aggregate measure resulted in a poor relationship with LE. Results indicated that both metrics can be used to determine spatial patterns of inequities in LE and that the ICE metric has similar success to the more computationally complex SDoH metric. Public health practitioners may find the ICE metric's high predictability matched with lower data requirements to be more feasible to implement in population health monitoring.

Neighborhood Socioeconomic Environment and Risk of Type 2 Diabetes: Associations and Mediation Through Food Environment Pathways in Three Independent Study Samples

OBJECTIVE

We examined whether relative availability of fast-food restaurants and supermarkets mediates the association between worse neighborhood socioeconomic conditions and risk of developing type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS

As part of the Diabetes Location, Environmental Attributes, and Disparities Network, three academic institutions used harmonized environmental data sources and analytic methods in three distinct study samples: 1) the Veterans Administration Diabetes Risk (VADR) cohort, a national administrative cohort of 4.1 million diabetes-free veterans developed using electronic health records (EHRs); 2) Reasons for Geographic and Racial Differences in Stroke (REGARDS), a longitudinal, epidemiologic cohort with Stroke Belt region oversampling (N = 11,208); and 3) Geisinger/Johns Hopkins University (G/JHU), an EHR-based, nested case-control study of 15,888 patients with new-onset T2D and of matched control participants in Pennsylvania. A census tract-level measure of neighborhood socioeconomic environment (NSEE) was developed as a community type-specific z-score sum. Baseline food-environment mediators included percentages of 1) fast-food restaurants and 2) food retail establishments that are supermarkets. Natural direct and indirect mediating effects were modeled; results were stratified across four community types: higher-density urban, lower-density urban, suburban/small town, and rural.

RESULTS

Across studies, worse NSEE was associated with higher T2D risk. In VADR, relative availability of fast-food restaurants and supermarkets was positively and negatively associated with T2D, respectively, whereas associations in REGARDS and G/JHU geographies were mixed. Mediation results suggested that little to none of the NSEE-diabetes associations were mediated through food-environment pathways.

CONCLUSIONS

Worse neighborhood socioeconomic conditions were associated with higher T2D risk, yet associations are likely not mediated through food-environment pathways.

Predicting health-related social needs in Medicaid and Medicare populations using machine learning

Providers currently rely on universal screening to identify health-related social needs (HRSNs). Predicting HRSNs using EHR and community-level data could be more efficient and less resource intensive. Using machine learning models, we evaluated the predictive performance of HRSN status from EHR and community-level social determinants of health (SDOH) data for Medicare and Medicaid beneficiaries participating in the Accountable Health Communities Model. We hypothesized that Medicaid insurance coverage would predict HRSN status. All models significantly outperformed the baseline Medicaid hypothesis. AUCs ranged from 0.59 to 0.68. The top performance (AUC = 0.68 CI 0.66–0.70) was achieved by the “any HRSNs” outcome, which is the most useful for screening prioritization. Community-level SDOH features had lower predictive performance than EHR features. Machine learning models can be used to prioritize patients for screening. However, screening only patients identified by our current model(s) would miss many patients. Future studies are warranted to optimize prediction of HRSNs.

Assessment of Structural Barriers and Racial Group Disparities of COVID-19 Mortality With Spatial Analysis

IMPORTANCE

Although social determinants of health (SDOH) are important factors in health inequities, they have not been explicitly associated with COVID-19 mortality rates across racial and ethnic groups and rural, suburban, and urban contexts.

OBJECTIVES

To explore the spatial and racial disparities in county-level COVID-19 mortality rates during the first year of the pandemic.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study analyzed data for all US counties in 50 states and the District of Columbia for the first full year of the COVID-19 pandemic (January 22, 2020, to February 28, 2021). Counties with a high concentration of a single racial and ethnic population and a high level of COVID-19 mortality rate were identified as concentrated longitudinal-impact counties. The SDOH that may be associated with mortality rate across these counties and in urban, suburban, and rural contexts were examined. The 3 largest racial and ethnic groups in the US were selected: Black or African American, Hispanic or Latinx, and non-Hispanic White populations.

EXPOSURES

County-level characteristics and community health factors (eg, income inequality, uninsured rate, primary care physicians, preventable hospital stays, severe housing problems rate, and access to broadband internet) associated with COVID-19 mortality.

MAIN OUTCOMES AND MEASURES

Data on county-level COVID-19 mortality rates (deaths per 100 000 population) reported by the US Centers for Disease Control and Prevention were analyzed. Four indexes were used to measure multiple dimensions of SDOH: socioeconomic advantage index, limited mobility index, urban core opportunity index, and mixed immigrant cohesion and accessibility index. Spatial regression models were used to examine the associations between SDOH and county-level COVID-19 mortality rate.

RESULTS

Of the 3142 counties included in the study, 531 were identified as concentrated longitudinal-impact counties. Of these counties, 347 (11.0%) had a large Black or African American population compared with other counties, 198 (6.3%) had a large Hispanic or Latinx population compared with other counties, and 33 (1.1%) had a large non-Hispanic White population compared with other counties. A total of 489 254 COVID-19-related deaths were reported. Most concentrated longitudinal-impact counties with a large Black or African American population compared with other counties were spread across urban, suburban, and rural areas and experienced numerous disadvantages, including higher income inequality (297 of 347 [85.6%]) and more preventable hospital stays (281 of 347 [81.0%]). Most concentrated longitudinal-impact counties with a large Hispanic or Latinx population compared with other counties were located in urban areas (114 of 198 [57.6%]), and 130 (65.7%) of these counties had a high percentage of people who lacked health insurance. Most concentrated longitudinal-impact counties with a large non-Hispanic White population compared with other counties were in rural areas (23 of 33 [69.7%]), included a large group of older adults (26 of 33 [78.8%]), and had limited access to quality health care (24 of 33 [72.7%]). In urban areas, the mixed immigrant cohesion and accessibility index was inversely associated with COVID-19 mortality (coefficient [SE], -23.38 [6.06]; P < .001), indicating that mortality rates in urban areas were associated with immigrant communities with traditional family structures, multiple accessibility stressors, and housing overcrowding. Higher COVID-19 mortality rates were also associated with preventable hospital stays in rural areas (coefficient [SE], 0.008 [0.002]; P < .001) and higher socioeconomic status vulnerability in suburban areas (coefficient [SE], -21.60 [3.55]; P < .001). Across all community types, places with limited internet access had higher mortality rates, especially in urban areas (coefficient [SE], 5.83 [0.81]; P < .001).

CONCLUSIONS AND RELEVANCE

This cross-sectional study found an association between different SDOH measures and COVID-19 mortality that varied across racial and ethnic groups and community types. Future research is needed that explores the different dimensions and regional patterns of SDOH to address health inequity and guide policies and programs.

Association between law enforcement seizures of illicit drugs and drug overdose deaths involving cocaine and methamphetamine, Ohio, 2014-2019

BACKGROUND

The United States continues to experience a crisis of mounting opioid overdose deaths involving cocaine and methamphetamine (hereafter illicit stimulants). Law enforcement drug seizure data present a unique opportunity to examine the association between illicit-stimulant-involved overdose deaths (ISODs) and the illicit drug supply. Our objective is to better understand correlations between illicit drug market trends and increases in ISODs in Ohio in 2014-2019.

METHODS

This observational study analyzes the universe of ISODs and drug seizures in Ohio from 2014 to 2019. We use graphs and descriptive statistics to characterize trends over time and estimate a time series model of their association. ISODs were summed to yield monthly statewide counts of seizures containing methamphetamine, cocaine, illicitly manufactured fentanyl (IMF), and other non-IMF opioids (e.g., heroin). All rates were calculated per 100,000 persons.

RESULTS

Roughly 80% of ISODs in Ohio from 2014 to 2019 involved an opioid, with IMF co-occurring in 90% of ISODs by 2019. Methamphetamine and cocaine seizures containing IMF were associated with 0.439 (p < .01) and 0.457 (p < .01) additional deaths per 100,000 persons per month, respectively. IMF seizures not containing cocaine nor methamphetamine were also associated with additional ISODs (0.119, p < .01) and seizures of illicit stimulants not containing IMF were not associated with ISODs.

CONCLUSIONS

The number of ISODs was extremely high when IMF was co-involved and relatively low without IMF involvement. By demonstrating how supply-side trends correspond with ISOD rates, the current study bolsters the analytical utility of law enforcement seizures and complements growing literature in the field.

How Should We Measure Social Deprivation in Orthopaedic Patients?

BACKGROUND

Social deprivation negatively affects a myriad of physical and behavioral health outcomes. Several measures of social deprivation exist, but it is unclear which measure is best suited to describe patients with orthopaedic conditions.

QUESTIONS/PURPOSES

(1) Which measure of social deprivation, defined as "limited access to society's resources due to poverty, discrimination, or other disadvantage," is most strongly and consistently correlated with patient-reported physical and behavioral health in patients with orthopaedic conditions? (2) Compared with the use of a single measure alone, how much more variability in patient-reported health does the simultaneous use of multiple social deprivation measures capture?

METHODS

Between 2015 and 2017, a total of 79,818 new patient evaluations occurred within the orthopaedic department of a single, large, urban, tertiary-care academic center. Over that period, standardized collection of patient-reported health measures (as described by the Patient-reported Outcomes Measurement Information System [PROMIS]) was implemented in a staged fashion throughout the department. We excluded the 25% (19,926) of patient encounters that did not have associated PROMIS measures reported, which left 75% (59,892) of patient encounters available for analysis in this cross-sectional study of existing medical records. Five markers of social deprivation were collected for each patient: national and state Area Deprivation Index, Medically Underserved Area Status, Rural-Urban Commuting Area code, and insurance classification (private, Medicare, Medicaid, or other). Patient-reported physical and behavioral health was measured via PROMIS computer adaptive test domains, which patients completed as part of standard care before being evaluated by a provider. Adults completed the PROMIS Physical Function version 1.2 or version 2.0, Pain Interference version 1.1, Anxiety version 1.0, and Depression version 1.0. Children ages 5 to 17 years completed the PROMIS Pediatric Mobility version 1.0 or version 2.0, Pain Interference version 1.0 or version 2.0, Upper Extremity version 1.0, and Peer Relationships version 1.0. Age-adjusted partial Pearson correlation coefficients were determined for each social deprivation measure and PROMIS domain. Coefficients of at least 0.1 were considered clinically meaningful for this purpose. Additionally, to determine the percentage of PROMIS score variability that could be attributed to each social deprivation measure, an age-adjusted hierarchical regression analysis was performed for each PROMIS domain, in which social deprivation measures were sequentially added as independent variables. The model coefficients of determination (r2) were compared as social deprivation measures were incrementally added. Improvement of the r2 by at least 10% was considered clinically meaningful.

RESULTS

Insurance classification was the social deprivation measure with the largest (absolute value) age-adjusted correlation coefficient for all adult and pediatric PROMIS physical and behavioral health domains (adults: correlation coefficient 0.40 to 0.43 [95% CI 0.39 to 0.44]; pediatrics: correlation coefficient 0.10 to 0.19 [95% CI 0.08 to 0.21]), followed by national Area Deprivation Index (adults: correlation coefficient 0.18 to 0.22 [95% CI 0.17 to 0.23]; pediatrics: correlation coefficient 0.08 to 0.15 [95% CI 0.06 to 0.17]), followed closely by state Area Deprivation Index. The Medically Underserved Area Status and Rural-Urban Commuting Area code each had correlation coefficients of 0.1 or larger for some PROMIS domains but neither had consistently stronger correlation coefficients than the other. Except for the PROMIS Pediatric Upper Extremity domain, consideration of insurance classification and the national Area Deprivation Index together explained more of the variation in age-adjusted PROMIS scores than the use of insurance classification alone (adults: r2 improvement 32% to 189% [95% CI 0.02 to 0.04]; pediatrics: r2 improvement 56% to 110% [95% CI 0.01 to 0.02]). The addition of the Medically Underserved Area Status, Rural-Urban Commuting Area code, and/or state Area Deprivation Index did not further improve the r2 for any of the PROMIS domains.

CONCLUSION

To capture the most variability due to social deprivation in orthopaedic patients' self-reported physical and behavioral health, insurance classification (categorized as private, Medicare, Medicaid, or other) and national Area Deprivation Index should be included in statistical analyses. If only one measure of social deprivation is preferred, insurance classification or national Area Deprivation Index are reasonable options. Insurance classification may be more readily available, but the national Area Deprivation Index stratifies patients across a wider distribution of values. When conducting clinical outcomes research with social deprivation as a relevant covariate, we encourage researchers to consider accounting for insurance classification and/or national Area Deprivation Index, both of which are freely available and can be obtained from data that are typically collected during routine clinical care.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Categorizing community type for epidemiologic evaluation of community factors and chronic disease across the United States

Existing classifications of community type do not differentiate urban cores from surrounding non-rural areas, an important distinction for analyses of community features and their impact on health. Inappropriately classified community types can introduce serious methodologic flaws in epidemiologic studies and invalid inferences from findings. To address this, we evaluate a modification of the United States Department of Agriculture's Rural Urban Commuting Area codes at the census tract, propose a four-level categorization of community type, and compare this with existing classifications for epidemiologic analyses. Compared to existing classifications, our method resulted in clearer geographic delineations of community types within urban areas.

Association Between Acute Exposure to Crime and Individual Systolic Blood Pressure

INTRODUCTION

Hypertension is associated with adverse cardiovascular outcomes and is geographically concentrated in urban underserved neighborhoods. This study examines the temporal-spatial association between individual exposure to violent crime and blood pressure.

METHODS

A retrospective observational cohort study analyzed 39,211 patients with 227,595 blood pressure measurements from 2014 to 2016 at 3 outpatient clinics at an academic medical center in Chicago. Patients were included in the study if they had documentation of blood pressure in the medical record and resided in census tracts with >1,000 observations. Geocoded violent crime events were obtained from the Chicago Police Department. Individual-level exposure was defined on the basis of spatial and temporal buffers around each patient's home. Spatial buffers included 100-, 250-, 500-, and 1,000-meter disc radii, and temporal buffers included 7, 30, and 60 days preceding each outpatient appointment. Systolic blood pressure measurements (mmHg) were abstracted from the electronic health record. Analysis was performed in 2019-2020.

RESULTS

For each violent crime event within 100 meters from home, systolic blood pressure increased by 0.14 mmHg within 7 days of exposure compared with 0.08 mmHg at 30 days of exposure. In analyses stratified by neighborhood cluster, systolic blood pressure increased by 0.37 mmHg among patients in the suburban affluent cluster relative to that among those in an extreme poverty cluster for the same spatial and temporal buffer.

CONCLUSIONS

Exposure to a violent crime event was associated with increased blood pressure, with gradient effects by both distance and time from exposure.

Pragmatic Precision Psychiatry-A New Direction for Optimizing Treatment Selection

IMPORTANCE

Clinical trials have identified numerous prescriptive predictors of mental disorder treatment response, ie, predictors of which treatments are best for which patients. However, none of these prescriptive predictors is strong enough alone to guide precision treatment planning. This has prompted growing interest in developing precision treatment rules (PTRs) that combine information across multiple prescriptive predictors, but this work has been much less successful in psychiatry than some other areas of medicine. Study designs and analysis schemes used in research on PTR development in other areas of medicine are reviewed, key challenges for implementing similar studies of mental disorders are highlighted, and recent methodological advances to address these challenges are described here.

OBSERVATIONS

Discovering prescriptive predictors requires large samples. Three approaches have been used in other areas of medicine to do this: conduct very large randomized clinical trials, pool individual-level results across multiple smaller randomized clinical trials, and develop preliminary PTRs in large observational treatment samples that are then tested in smaller randomized clinical trials. The third approach is most feasible for research on mental disorders. This approach requires working with large real-world observational electronic health record databases; carefully selecting samples to emulate trials; extracting information about prescriptive predictors from electronic health records along with other inexpensive data augmentation strategies; estimating preliminary PTRs in the observational data using appropriate methods; implementing pragmatic trials to validate the preliminary PTRs; and iterating between subsequent observational studies and quality improvement pragmatic trials to refine and expand the PTRs. New statistical methods exist to address the methodological challenges of implementing this approach.

CONCLUSIONS AND RELEVANCE

Advances in pragmatic precision psychiatry will require moving beyond the current focus on randomized clinical trials and adopting an iterative discovery-confirmation process that integrates observational and experimental studies in real-world clinical populations.

Community versus individual risk of SARS-CoV-2 infection in two municipalities of Louisiana, USA: An assessment of Area Deprivation Index (ADI) paired with seroprevalence data over time

OBJECTIVE

Determine whether an individual is at greater risk of severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2) infection because of their community or their individual risk factors.

STUDY DESIGN AND SETTING

4,752 records from two large prevalence studies in New Orleans and Baton Rouge, Louisiana were used to assess whether zip code tabulation areas (ZCTA)-level area deprivation index (ADI) or individual factors accounted for risk of infection. Logistic regression models assessed associations of individual-level demographic and socioeconomic factors and the zip code-level ADI with SARS-CoV-2 infection.

RESULTS

In the unadjusted model, there were increased odds of infection among participants residing in high versus low ADI (both cities) and high versus mid-level ADI (Baton Rouge only) zip codes. When individual-level covariates were included, the odds of infection remained higher only among Baton Rouge participants who resided in high versus mid-level ADI ZCTAs. Several individual factors contributed to infection risk. After adjustment for ADI, race and age (Baton Rouge) and race, marital status, household size, and comorbidities (New Orleans) were significant.

CONCLUSIONS

While higher ADI was associated with higher risk of SARS-CoV-2 infection, individual-level participant characteristics accounted for a significant proportion of this association. Additionally, stage of the pandemic may affect individual risk factors for infection.

Your neighborhood matters: A machine-learning approach to the geospatial and social determinants of health in 9-1-1 activated chest pain

Healthcare disparities in the initial management of patients with acute coronary syndrome (ACS) exist. Yet, the complexity of interactions between demographic, social, economic, and geospatial determinants of health hinders incorporating such predictors in existing risk stratification models. We sought to explore a machine-learning-based approach to study the complex interactions between the geospatial and social determinants of health to explain disparities in ACS likelihood in an urban community. This study identified consecutive patients transported by Pittsburgh emergency medical service for a chief complaint of chest pain or ACS-equivalent symptoms. We extracted demographics, clinical data, and location coordinates from electronic health records. Median income was based on US census data by zip code. A random forest (RF) classifier and a regularized logistic regression model were used to identify the most important predictors of ACS likelihood. Our final sample included 2400 patients (age 59 ± 17 years, 47% Females, 41% Blacks, 15.8% adjudicated ACS). In our RF model (area under the receiver operating characteristic curve of 0.71 ± 0.03) age, prior revascularization, income, distance from hospital, and residential neighborhood were the most important predictors of ACS likelihood. In regularized regression (akaike information criterion = 1843, bayesian information criterion = 1912, χ²  = 193, df = 10, p < 0.001), residential neighborhood remained a significant and independent predictor of ACS likelihood. Findings from our study suggest that residential neighborhood constitutes an upstream factor to explain the observed healthcare disparity in ACS risk prediction, independent from known demographic, social, and economic determinants of health, which can inform future work on ACS prevention, in-hospital care, and patient discharge.

Geographic, racial/ethnic, and socioeconomic inequities in broadband access

INTRODUCTION

Broadband access is a "super determinant of health." Understanding the spatial distribution and predictors of access may help target government programs and telehealth applications. Our aim was to examine broadband access across geography and sociodemographic characteristics using American Community Survey (ACS) data.

METHODS

We used 5-year ACS estimates from 2014 to 2018 to evaluate broadband access across contiguous US census tracts. Rural-Urban Commuting Area (RUCA) codes were categorized as metropolitan, micropolitan, small town, and isolated rural. We performed bivariate analyses to determine differences by RUCA categories and meeting the Healthy People 2020 (HP2020) objective (83.2% broadband access) or not. We conducted spatial statistics and spatial regression analyses to identify clusters of broadband access and sociodemographic factors associated with broadband access.

RESULTS

No RUCA grouping met the HP2020 objective; 80.6% of households had broadband access, including 82.0% of metropolitan, 73.9% of micropolitan, 70.7% of small town, and 70.0% of isolated rural households. Areas with high percentages of Black residents had lower broadband access, particularly in isolated rural tracts (54.9%). Low access was spatially clustered in the Southeast, Southwest, and northern plains. In spatial regression models, poverty and education were most strongly associated with broadband access, while the proportion of American Indian/Alaska Native population was the strongest racial/ethnic factor.

CONCLUSIONS

Rural areas had less broadband access with the greatest disparities experienced among geographically isolated areas with larger Black and American Indian/Alaska Native populations, more poverty, and lower educational attainment, following well-known social gradients in health. Resources and initiatives should target these areas of greatest need.

Poverty, Deprivation, and Mortality Risk Among Women With HIV in the United States

BACKGROUND

Prior studies suggest neighborhood poverty and deprivation are associated with adverse health outcomes including death, but evidence is limited among persons with HIV, particularly women. We estimated changes in mortality risk from improvement in three measures of area-level socioeconomic context among participants of the Women's Interagency HIV Study.

METHODS

Starting in October 2013, we linked geocoded residential census block groups to the 2015 Area Deprivation Index (ADI) and two 2012-2016 American Community Survey poverty variables, categorized into national tertiles. We used parametric g-computation to estimate, through March 2018, impacts on mortality of improving each income or poverty measure by one and two tertiles maximum versus no improvement.

RESULTS

Of 1596 women with HIV (median age 49), 91 (5.7%) were lost to follow-up and 83 (5.2%) died. Most women (62%) lived in a block group in the tertile with the highest proportions of individuals with income:poverty <1; 13% lived in areas in the tertile with the lowest proportions. Mortality risk differences comparing a one-tertile improvement (for those in the two highest poverty tertiles) in income:poverty <1 versus no improvement increased over time; the risk difference was -2.2% (95% confidence interval [CI] = -3.7, -0.64) at 4 years. Estimates from family income below poverty level (-1.0%; 95% CI = -2.7, 0.62) and ADI (-1.5%; 95% CI = -2.8, -0.21) exposures were similar.

CONCLUSIONS

Consistent results from three distinct measures of area-level socioeconomic environment support the hypothesis that interventions to ameliorate neighborhood poverty or deprivation reduce mortality risk for US women with HIV. See video abstract at, http://links.lww.com/EDE/B863.

Racial Disparities in Stillbirth Risk Factors among non-Hispanic Black Women and non-Hispanic White Women in the United States

INTRODUCTION

Historically, stillbirth risk factors are more prevalent among non-Hispanic Black women than non-Hispanic White women, including age < 20, lower formal educational attainment, prepregnancy obesity, smoking, hypertension, diabetes, short interpregnancy interval, small for gestational age newborn, late prenatal care, and previous cesarean birth. We examined whether these disparities have changed since 2011 and identified a group of risk factors that differed between Black women and White women when accounting for correlations among variables.

METHODS

In a random sample of 315 stillbirths from the National Center for Health Statistics' 2016 fetal death data, Black women and White women were compared for each risk factor using t-tests or chi-square tests. Variables with p ≤ .20 were analyzed using multivariate analysis of variance.

RESULTS

In this sample, Black women experiencing stillbirth were less likely to have a Bachelor's degree (12.94% vs. 28.49%, p = .04), and more likely to be obese (44.5% vs. 29.1%, p = .01) than White women. Multivariate analysis accounting for correlations among variables showed a group of risk factors that differed between Black women and White women: age < 20, lower education, prepregnancy obesity, hypertension (chronic and pregnancy-associated), nulliparity before stillbirth, and earlier gestation.

CLINICAL IMPLICATIONS

Less formal education, obesity, age <20, hypertension, chronic and pregnancy-associated, nulliparity, and earlier gestation are important to consider in multilevel stillbirth prevention interventions to decrease racial disparity in stillbirth. Respectfully listening to women and taking their concerns seriously is one way nurses and other health care providers can promote equity in health outcomes for childbearing women.

Analysis of Suicide After Cancer Diagnosis by US County-Level Income and Rural vs Urban Designation, 2000-2016

IMPORTANCE

Studies suggest the risk of suicide among people with cancer diagnosis is higher compared with the general population. However, little is known about how suicide risk among people diagnosed with cancer might vary according to area-level income and rurality.

OBJECTIVE

To examine whether the risks and patterns of suicide mortality among people with a cancer diagnosis differ by US county-level median income and rural or urban status.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective, population-based cohort study following up individuals who were diagnosed with cancer between January 1, 2000, and December 31, 2016, was conducted. The Surveillance, Epidemiology, and End Results Program 18 registries (SEER 18) database was used to obtain data on persons diagnosed with a first primary malignant tumor. Comparisons with the general US population were based on mortality data collected by the National Center for Health Statistics. Analyses were conducted from February 22 to October 14, 2020.

EXPOSURES

County-level median household income and urban or rural status.

MAIN OUTCOMES AND MEASURES

Standardized mortality ratios (SMRs) of suicide deaths and annual percentage changes (APCs) of SMRs.

RESULTS

The SEER 18 database included 5 362 782 persons with cancer diagnoses living in 635 counties. Most study participants were men (51.2%), White (72.2%), and older than 65 years (49.7%). Among them, 6357 persons died of suicide (SMR, 1.41; 95% CI, 1.38-1.44). People with cancer living in the lowest-income counties had a significantly higher risk (SMR, 1.94; 95% CI, 1.76-2.13) than those in the highest-income counties (SMR, 1.30; 95% CI, 1.26-1.34). Those living in rural counties also had significantly higher SMR than those in urban counties (SMR, 1.81; 95% CI, 1.70-1.92 vs SMR, 1.35; 95% CI, 1.32-1.39). For all county groups, the SMRs were the highest within the first year following cancer diagnosis. However, among people living in the lowest-income counties, the risk remained significantly high even after 10 or more years following cancer diagnosis (SMR, 1.83; 95% CI, 1.31-2.48). The comparative risk of suicide mortality within 1 year following cancer diagnosis significantly decreased over the years but then plateaued in the highest-income (2005-2015: APC, 2.03%; 95% CI, -0.97% to 5.13%), lowest-income (2010-2015: APC, 4.80%; 95% CI, -19.97% to 37.24%), and rural (2004-2015: APC, 1.83; 95% CI, -1.98% to 5.79%) counties.

CONCLUSIONS AND RELEVANCE

This cohort study showed disparities in suicide risks and their patterns among people diagnosed with cancer by county-level income and rural or urban status. The findings suggest that additional research and effort to provide psychological services addressing these disparities among people with cancer may be beneficial.

The uneven impacts of avoiding public transit on riders' access to healthcare during COVID-19

BACKGROUND

During the COVID-19 pandemic, many urban residents stopped riding public transit despite their reliance on it to reach essential services like healthcare. Few studies have examined the implications of public transit reliance on riders' ability to reach healthcare when transit is disrupted. To understand how shocks to transportation systems impact healthcare access, this study measures the impact of avoiding public transit on the ability of riders to access healthcare and pharmacy services during lockdowns.

METHODS

We deployed a cross-sectional survey of residents of Toronto and Vancouver in May 2020 through Facebook advertisements and community list-serves. Eligibility criteria included riding transit at least weekly prior to the pandemic and subsequent cessation of transit use during the pandemic. We applied multivariable modified Poisson models to identify socio-demographic, transportation, health-related, and neighborhood predictors of experiencing increased difficulty accessing healthcare and getting prescriptions while avoiding public transit. We also predicted which respondents reported deferring medical care until they felt comfortable riding transit again.

RESULTS

A total of 4367 former transit riders were included (64.2% female, 56.1% Toronto residents). Several factors were associated with deferring medical care including: being non-White (Toronto, APR, 1.14; 95% CI, 1.00-1.29; Vancouver, APR, 1.52; 95% CI, 1.26-1.84), having a physical disability (Toronto, APR, 1.20; 95% CI, 1.00-1.45; Vancouver, APR, 1.42; 95% CI, 1.08-1.87), having no vehicle access (Toronto, APR, 1.74; 95% CI, 1.51-2.00; Vancouver, APR, 2.74; 95% CI, 2.20-3.42), and having low income (Toronto, APR, 1.77; 95% CI, 1.44-2.17; Vancouver, APR, 1.51; 95% CI, 1.06-2.14).

DISCUSSION

During COVID-19 in two major Canadian cities, former transit riders from marginalized groups were more likely to defer medical care than other former riders. COVID-19 related transit disruptions may have imposed a disproportionate burden on the health access of marginalized individuals. Policymakers should consider prioritizing healthcare access for vulnerable residents during crises.

Analysis of Social Determinants and the Utilization of Pediatric Tele-Urgent Care During the COVID-19 Pandemic: Cross-sectional Study

BACKGROUND

Telehealth is increasingly used to provide specialty consultations to infants and children receiving care. However, there is uncertainty if the COVID-19 pandemic has influenced the use of telehealth among vulnerable populations.

OBJECTIVE

This research aims to compare the overall use of tele-urgent care visits for pediatric patients before and after the pandemic, especially among vulnerable populations.

METHODS

We conducted a cross-sectional analysis of pediatric tele-urgent care visits at a virtual care center at a southeastern health care center. The main outcome of this study was the use of pediatrics tele-urgent visits across geographical regions with different levels of social disparities and between 2019 and 2020.

RESULTS

Of 584 tele-urgent care visits, 388 (66.4%) visits occurred in 2020 during the pandemic compared to 196 (33.6%) visits in 2019. Among 808 North Carolina zip codes, 181 (22%) consisted of a high concentration of vulnerable populations, where 17.7% (56/317) of the tele-urgent care visits originated from. The majority (215/317, 67.8%) of tele-urgent care visits originated from zip codes with a low concentration of vulnerable populations. There was a significant association between the rate of COVID-19 cases and the concentration level of social factors in a given Zip Code Tabulation Area.

CONCLUSIONS

The use of tele-urgent care visits for pediatric care doubled during the COVID-19 pandemic. The majority of the tele-urgent care visits after COVID-19 originated from regions where there is a low presence of vulnerable populations. In addition, our geospatial analysis found that geographic regions with a high concentration of vulnerable populations had a significantly higher rate of COVID-19-confirmed cases and deaths compared to regions with a low concentration of vulnerable populations.

Racial differences in health and cognition as a function of HIV among older adults

The present study investigated the contribution of health risk factors (using the Charlson Comorbidity Index [CCI]) on cognitive outcomes in a sample of 380 HIV-positive (HIV+; n = 221) and HIV-seronegative (HIV-; n = 159) African American and European American adults aged 50+.

Participants were recruited from HIV clinics and community advertisements. HIV status was confirmed by serological testing. Self-report and chart history review was used to gather information about medical ssscomorbidities. The Charlson Comorbidity Index (CCI) was used to create a comorbidity score. Participants were administered a brief cognitive test battery.

As expected, health risks were greater among those with HIV. There was a HIV × Race interaction on CCI scores, such that in the HIV + group, European Americans had significantly higher CCI scores (M = 3.74; SD = 2.1) than African American HIV + participants (M = 2.70; SD = 1.9). However, in the HIV - group, African Americans had significantly higher CCI scores (M = 2.20; SD = 1.1) than HIV - European American participants (M = 1.80; SD = 1.2). Also, consistent with hypotheses, across the entire sample CCI score was significantly associated with global cognition (β = -.24, p = .02).

Study results underscore the importance of considering HIV serostatus in studies examining racial disparities in health, and how multiple medical risks relate to cognitive outcomes. Neuropsychologists evaluating patients living with HIV should consider how the presence of multiple medical comorbidities may contribute to the course of cognitive decline as people age.

Associations Between Social Determinants and Hypertension, Stage 2 Hypertension, and Controlled Blood Pressure Among Men and Women in the United States

BACKGROUND

Social determinants influence the development and control of hypertension.

METHODS

National Health and Nutrition Examination Survey (2011-2018) data for adults aged ≥18 included education, income, employment, race/ethnicity, healthcare access, marital status, and nativity status. Outcomes were hypertension (blood pressure [BP] ≥130/80 mm Hg or self-reported hypertension medication use), stage 2 hypertension (BP ≥140/90 mm Hg), and controlled BP (BP <130/80 mm Hg among those with hypertension). Poisson regression with robust variance estimates was used to examine associations between social determinants and outcomes, by sex.

RESULTS

The analysis included 21,664 adults (mean age 47.1 years), of whom 51% were women. After adjustment, hypertension and stage 2 hypertension prevalence remained higher among Black and Asian than White adults, regardless of sex. Blacks had lower prevalence of controlled BP than Whites. Compared with college graduates, men and women with less education had a higher prevalence of hypertension and stage 2 hypertension. Men (prevalence ratio [PR]: 0.28, 95% confidence interval: 0.16-0.49) and women (PR: 0.44, 0.24-0.78) with no routine place for healthcare had lower prevalence of controlled BP than those who had a routine place for healthcare. Uninsured men (PR: 0.66, 0.44-0.99) and women (PR: 0.67, 0.51-0.88) had lower prevalence of controlled BP than those insured. Unemployed or unmarried women were more likely to have controlled BP than employed or married women.

CONCLUSIONS

Social determinants were independently associated with hypertension outcomes in US adults. Policy interventions are urgently needed to address healthcare access and education, and eliminate racial disparities.

Can social vulnerability indices predict county trauma fatality rates?

BACKGROUND

Social vulnerability indices were created to measure resiliency to environmental disasters based on socioeconomic and population characteristics of discrete geographic regions. They are composed of multiple validated constructs that can also potentially identify geographically vulnerable populations after injury. Our objective was to determine if these indices correlate with injury fatality rates in the US.

METHODS

We evaluated three social vulnerability indices: The Hazards & Vulnerability Research Institute's Social Vulnerability Index (SoVI), the Center for Disease Control's Social Vulnerability Index (SVI), and the Economic Innovation Group's Distressed Community Index (DCI). We analyzed SVI subindices and common individual census variables as indicators of socioeconomic status. Outcomes included age-adjusted county-level overall, firearm, and motor vehicle collision deaths per 100,000 population. Linear regression determined the association of injury fatality rates with the SoVI, SVI, and DCI. Bivariate choropleth mapping identified geographic variation and spatial autocorrelation of overall fatality, SoVI, and DCI.

RESULTS

A total of 3,137 US counties were included. Only 24.6% of counties fell into the same vulnerability quintile for all three indices. Despite this, all indices were associated with increasing fatality rates for overall, firearm, and motor vehicle collision fatality. The DCI performed best by model fit, explanation of variance, and diagnostic performance on overall injury fatality. There is significant geographic variation in SoVI, DCI, and injury fatality rates at the county level across the United States, with moderate spatial autocorrelation of SoVI (Moran's I, 0.35; p < 0.01) and high autocorrelation of injury fatality rates (Moran's I, 0.77; p < 0.01) and DCI (Moran's I, 0.53; p < 0.01).

CONCLUSION

While the indices contribute unique information, higher social vulnerability is associated with higher injury fatality across all indices. These indices may be useful in the epidemiologic and geographic assessment of injury-related fatality rates. Further study is warranted to determine if these indices outperform traditional measures of socioeconomic status and related constructs used in trauma research.

LEVEL OF EVIDENCE

Epidemiological, level IV.

The Child Opportunity Index 2.0 and Hospitalizations for Ambulatory Care Sensitive Conditions

BACKGROUND AND OBJECTIVES

Hospitalizations for ambulatory care sensitive conditions (ACSCs) are thought to be avoidable with high-quality outpatient care. Morbidity related to ACSCs has been associated with socioeconomic contextual factors, which do not necessarily capture the complex pathways through which a child's environment impacts health outcomes. Our primary objective was to test the association between a multidimensional measure of neighborhood-level child opportunity and pediatric hospitalization rates for ACSCs across 2 metropolitan areas.

METHODS

This was a retrospective population-based analysis of ACSC hospitalizations within the Kansas City and Cincinnati metropolitan areas from 2013 to 2018. Census tracts were included if located in a county where Children's Mercy Kansas City or Cincinnati Children's Hospital Medical Center had >80% market share of hospitalizations for children <18 years. Our predictor was child opportunity as defined by a composite index, the Child Opportunity Index 2.0. Our outcome was hospitalization rates for 8 ACSCs.

RESULTS

We included 604 943 children within 628 census tracts. There were 26 977 total ACSC hospitalizations (46 hospitalizations per 1000 children; 95% confidence interval [CI]: 45.4-46.5). The hospitalization rate for all ACSCs revealed a stepwise reduction from 79.9 per 1000 children (95% CI: 78.1-81.7) in very low opportunity tracts to 31.2 per 1000 children (95% CI: 30.5-32.0) in very high opportunity tracts (P < .001). This trend was observed across cities and diagnoses.

CONCLUSIONS

Links between ACSC hospitalizations and child opportunity extend across metropolitan areas. Targeting interventions to lower-opportunity neighborhoods and enacting policies that equitably bolster opportunity may improve child health outcomes, reduce inequities, and decrease health care costs.

Predictive modeling of COVID-19 case growth highlights evolving racial and ethnic risk factors in Tennessee and Georgia

INTRODUCTION

The SARS-CoV-2 (COVID-19) pandemic has exposed the need to understand the risk drivers that contribute to uneven morbidity and mortality in US communities. Addressing the community-specific social determinants of health (SDOH) that correlate with spread of SARS-CoV-2 provides an opportunity for targeted public health intervention to promote greater resilience to viral respiratory infections.

METHODS

Our work combined publicly available COVID-19 statistics with county-level SDOH information. Machine learning models were trained to predict COVID-19 case growth and understand the social, physical and environmental risk factors associated with higher rates of SARS-CoV-2 infection in Tennessee and Georgia counties. Model accuracy was assessed comparing predicted case counts to actual positive case counts in each county.

RESULTS

The predictive models achieved a mean R2 of 0.998 in both states with accuracy above 90% for all time points examined. Using these models, we tracked the importance of SDOH data features over time to uncover the specific racial demographic characteristics strongly associated with COVID-19 incidence in Tennessee and Georgia counties. Our results point to dynamic racial trends in both states over time and varying, localized patterns of risk among counties within the same state. For example, we find that African American and Asian racial demographics present comparable, and contrasting, patterns of risk depending on locality.

CONCLUSION

The dichotomy of demographic trends presented here emphasizes the importance of understanding the unique factors that influence COVID-19 incidence. Identifying these specific risk factors tied to COVID-19 case growth can help stakeholders target regional interventions to mitigate the burden of future outbreaks.

Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health

The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic.

Neighborhood Conditions and Recurrent Emergency Department Utilization by Children in the United States

OBJECTIVE

To determine the associations of social and physical neighborhood conditions with recurrent emergency department (ED) utilization by children in the US.

STUDY DESIGN

This cross-sectional study was conducted with the National Survey of Children's Health from 2016 to 2018 to determine the associations of neighborhood characteristics of cohesion, safety, amenities, and detractors with the proportions of children aged 1-17 years with recurrent ED utilization, defined as 2 or more ED visits during the past 12 months. A multivariable regression model was used to determine the independent association of each neighborhood characteristic with recurrent ED utilization controlling for individual-level characteristics.

RESULTS

In this study of 98 711 children weighted to a population of 70 million nationally, children had significantly greater rates of recurrent ED utilization if they lived in neighborhoods that were not cohesive, were not safe, or had detractors present (all P < .001). With adjustment for individual-level covariates and the other neighborhood characteristics, only neighborhood detractors were independently associated with recurrent ED utilization (1 detractor: aOR 1.32, 95% CI 1.03-1.68; 2 or 3 detractors: aOR 1.37, 95% CI 1.04-1.81).

CONCLUSIONS

Among neighborhood characteristics, the presence of physical detractors such as rundown housing and vandalism was most strongly associated with recurrent ED utilization by children. Negative attributes of the built environment may be a potential target for neighborhood-level, place-based interventions to alleviate disparities in child healthcare utilization.

Local Health Department and Hospital Collaboration Around Community Health Needs Assessment to Improve Health Outcomes

The objectives of this study were to examine the relationships between local health department (LHD) and nonprofit hospital collaboration around community health needs assessment (CHNA), levels of collaboration, and selected community health outcomes. Data were obtained from multiple sources including the National Profile of Local Health Departments. Results showed that high levels of LHD-hospital collaboration around CHNA were associated with lower self-reported poor or fair health, lower years of potential life lost per 100 000 population, and lower premature age-adjusted mortality per 100 000 population. More research is needed to examine the influence of collaboration around CHNA on community health.

Profiles in social vulnerability: The association of social determinants of health with postoperative surgical outcomes

BACKGROUND

The association of different social vulnerability subthemes (ie, socioeconomic status, household composition and disability, minority status and language, and housing and transportation) with surgical outcomes remains poorly defined. The current study aimed to identify distinct profiles of social vulnerability among Medicare beneficiaries and define the association of these profiles with postoperative outcomes.

METHODS

The Medicare 100% Standard Analytic Files were used to identify patients undergoing lung resection, coronary artery bypass grafting, abdominal aortic aneurysm repair, and colectomy between 2013 and 2017. A cluster analysis was performed based on ranked scores across the 4 subthemes of the Centers for Disease Control and Prevention social vulnerability index. The likelihood of complications, extended length of stay, readmission, and mortality were assessed relative to cluster vulnerability profiles.

RESULTS

Among 852,449 Medicare beneficiaries undergoing surgery, median social vulnerability index among patients in the cohort was 49 (interquartile range: 24-74); cluster analysis revealed 5 vulnerability profiles that had heterogeneity in the vulnerability subthemes, even among patients with similar overall social vulnerability index scores. Postoperative outcomes differed across the 5 vulnerability profiles, with patients in the profiles characterized by higher overall vulnerability having worse postoperative outcomes. In particular, risk of complications (profile 1, 31.9% vs profile 5, 34.0%), extended length of stay (profile 1, 21.7% vs profile 5, 24.3%), 30-day readmission (profile 1, 12.6% vs profile 5, 13.2%), and 30-day mortality (profile 1, 4.0% vs profile 5, 4.7%) was greater among patients with the highest vulnerability (all P < .01). Of note, surgical outcomes varied among patients who resided in communities with similar average social vulnerability index scores (social vulnerability index 49-54). In particular, patients in social vulnerability profile 4 had 26% increased odds of 30-day mortality compared to social vulnerability profile 2 patients (odds ratio 1.26, 95% confidence interval 1.21-1.30). Additionally, profile 3 patients had 15% higher odds of 30-day mortality versus profile 2 patients (odds ratio 1.15, 95% confidence interval 1.10-1.20).

CONCLUSION

Postoperative outcomes differed across patients based on cluster vulnerability profiles. Despite similar overall aggregate social vulnerability index scores, cluster analysis was able to discriminate various social determinants of health subthemes among patients who resided in "average" vulnerability communities that stratified patients relative to risk of adverse postoperative events.

Disparity in Access to Oncology Precision Care: A Geospatial Analysis of Driving Distances to Genetic Counselors in the U.S

In the US, the growing demand for precision medicine, particularly in oncology, continues to put pressure on the availability of genetic counselors to meet that demand. This is especially true in certain geographic locations due to the uneven distribution of genetic counselors throughout the US. To assess these disparities, access to genetic counselors of all specialties is explored by geography, cancer type, and social determinants of health. Geospatial technology was used to combine and analyze genetic counselor locations and cancer incidence at the county level across the US, with a particular focus on tumors associated with BRCA mutations including ovarian, pancreatic, prostate and breast. Access distributions were quantified, and associations with region, cancer type, and socioeconomic variables were investigated using correlational tests. Nationally, in 2020, there were 4,813 genetic counselors, or 1.49 genetic counselors per 100,000 people, varying between 0.17 to 5.7 per 100,000 at the state level. Seventy-one percent of U.S. residents live within a 30-minute drive-time to a genetic counselor. Drive-times, however, are not equally distributed across the country - while 82% of people in metropolitan areas are 30 minutes from a genetic counselor, only 6% of people in nonmetro areas live within 30 minutes' drive time. There are statistically significant differences in access across geographical regions, socioeconomics and cancer types. Access to genetic counselors for cancer patients differs across groups, including regional, socioeconomic, and cancer type. These findings highlight areas of the country that may benefit from increased genetic counseling provider supply, by increasing the number of genetic counselors in a region or by expanding the use of telegenetics a term used to describe virtual genetic counseling consults that occur via videoconference. Policy intervention to allow genetic counselors to bill for their services may be an effective route for increasing availability of genetic counselors' services However, genetic counselors in direct patient care settings also face other challenges such as salary, job satisfaction, job recognition, overwork/burnout, and appropriate administrative/clinical support, and addressing these issues should also be considered along with policy support. These results could support targeted policy reform and alternative service models to increase access to identified pockets of unmet need, such as telemedicine. Data and analysis are available to the public through an interactive dashboard.

Patient Social Vulnerability and Hospital Community Racial/Ethnic Integration: Do All Patients Undergoing Pancreatectomy Receive the Same Care Across Hospitals?

OBJECTIVE

The objective of the current study was to characterize the role of patient social vulnerability relative to hospital racial/ethnic integration on postoperative outcomes among patients undergoing pancreatectomy.

BACKGROUND

The interplay between patient- and community-level factors on outcomes after complex surgery has not been well-examined.

METHODS

Medicare beneficiaries who underwent a pancreatectomy between 2013 and 2017 were identified utilizing 100% Medicare inpatient files. P-SVI was determined using the Centers for Disease Control and Prevention criteria, whereas H-REI was estimated using Shannon Diversity Index. Impact of P-SVI and H-REI on "TO" [ie, no surgical complication/extended length-of-stay (LOS)/90-day mortality/90-day readmission] was assessed.

RESULTS

Among 24,500 beneficiaries who underwent pancreatectomy, 12,890 (52.6%) were male and median age was 72 years (Interquartile range: 68-77); 10,619 (43.3%) patients achieved a TO. The most common adverse postoperative outcome was 90-day readmission (n = 8,066, 32.9%), whereas the least common was 90-day mortality (n = 2282, 9.3%). Complications and extended LOS occurred in 30.4% (n = 7450) and 23.3% (n = 5699) of the cohort, respectively. Patients from an above average SVI county who underwent surgery at a below average REI hospital had 18% lower odds [95% confidence interval (CI): 0.74-0.95] of achieving a TO compared with patients from a below average SVI county who underwent surgery at a hospital with above average REI. Of note, patients from the highest SVI areas who underwent pancreatectomy at hospitals with the lowest REI had 30% lower odds (95% CI: 0.54-0.91) of achieving a TO compared with patients from very low SVI areas who underwent surgery at a hospital with high REI. Further comparisons of these 2 patient groups indicated 76% increased odds of 90-day mortality (95% CI: 1.10-2.82) and 50% increased odds of an extended LOS (95% CI: 1.07-2.11).

CONCLUSION

Patients with high social vulnerability who underwent pancreatectomy in hospitals located in communities with low racial/ethnic integration had the lowest chance to achieve an "optimal" TO. A focus on both patient- and community-level factors is needed to ensure optimal and equitable patient outcomes.

The impact of social vulnerability subthemes on postoperative outcomes differs by racial/ethnic minority status

INTRODUCTION

Social vulnerability is an important driver of disparate surgical outcomes, however the extent to which certain types of vulnerability impact outcomes is poorly understood.

METHODS

Medicare beneficiaries 65 years or older who underwent one of four operations were identified. Multivariable mixed-effects logistic regression was used to measure the association of four social vulnerability subthemes from the social vulnerability index (SVI) were assessed relative to the likelihood to achieve a textbook outcome (TO).

RESULTS

Among 579,846 Medicare beneficiaries, median age was 74 years and most patients (536,455,92.5%) were White/non-Hispanic. On multivariable analysis, the overall impact of the composite SVI metric on the odds to achieve a postoperative TO was lower among White/non-Hispanic patients (Δ25%ile SVI:OR:0.98,95%CI:0.97-0.98) compared with ethnic/minority patients (Δ25%ile SVI:OR:0.93,95%CI:0.91-0.94). Increasing vulnerability in the subthemes of socioeconomic status (Δ25%ile SVI:ethnic/minority:OR:0.92, 95%CI:0.91-0.94) and household composition (Δ25%ile SVI:ethnic/minority:OR:0.92,95%CI:0.91-0.94) was associated with a greater likelihood not to achieve a TO among minority patients.

CONCLUSIONS

Worsening SES and household compositions & disability had a detrimental effect on odds of TO following surgery with the most pronounced effect on non-White minority patients.

Neighborhood characteristics and psychiatric disorders in the aftermath of mass trauma: A representative study of New York City public school 4th-12th graders after 9/11

Studies of the relationship between neighborhood characteristics and childhood/adolescent psychopathology in large samples examined one outcome only, and/or general (e.g., 'psychological distress') or aggregate (e.g., 'any anxiety disorder') measures of psychopathology. Thus, in the only representative sample of New York City public school 4th-12th graders (N = 8202) surveyed after the attacks of 9/11/2001, this study examined whether (1) indices of neighborhood Socioeconomic Status, Quality, and Safety and (2) neighborhood disadvantage (defined as multidimensional combinations of SES, Quality and Safety indicators) are associated with eight psychiatric disorders: posttraumatic stress disorder, separation anxiety disorder (SAD), agoraphobia, generalized anxiety disorder (GAD), panic disorder, major depression, conduct disorder, and alcohol use disorder (AUD). (1) The odds ratios (OR) of psychiatric disorders were between 0.55 (AUD) and 1.55 (agoraphobia), in low and intermediate-low SES neighborhoods, respectively, between 0.50 (AUD) and 2.54 (agoraphobia) in low Quality neighborhoods, and between 0.52 (agoraphobia) and 0.65 (SAD) in low Safety neighborhoods. (2) In neighborhoods characterized by high disadvantage, the OR were between 0.42 (AUD) and 1.36 (SAD). This study suggests that neighborhood factors are important social determinants of childhood/adolescent psychopathology, even in the aftermath of mass trauma. At the community level, interventions on modifiable neighborhood characteristics and targeted resources allocation to high-risk contexts could have a cost-effective broad impact on children's mental health. At the individual-level, increased knowledge of the living environment during psychiatric assessment and treatment could improve mental health outcomes; for example, specific questions about neighborhood factors could be incorporated in DSM-5's Cultural Formulation Interview.

Lifestyle Effects on the Risk of Transmission of COVID-19 in the United States: Evaluation of Market Segmentation Systems

The aim of this study was to associate lifestyle characteristics with COVID-19 infection and mortality rates at the U.S. county level and sequentially map the impact of COVID-19 on different lifestyle segments. We used analysis of variance (ANOVA) statistical testing to determine whether there is any correlation between COVID-19 infection and mortality rates and lifestyles. We used ESRI Tapestry LifeModes data that are collected at the U.S. household level through geodemographic segmentation typically used for marketing purposes to identify consumers’ lifestyles and preferences. According to the ANOVA analysis, a significant association between COVID-19 deaths and LifeModes emerged on 1 April 2020 and was sustained until 30 June 2020. Analysis of means (ANOM) was also performed to determine which LifeModes have incidence rates that are significantly above/below the overall mean incidence rate. We sequentially mapped and graphically illustrated when and where each LifeMode had above/below average risk for COVID-19 infection/death on specific dates. A strong northwest-to-south and northeast-to-south gradient of COVID-19 incidence was identified, facilitating an empirical classification of the United States into several epidemic subregions based on household lifestyle characteristics. Our approach correlating lifestyle characteristics to COVID-19 infection and mortality rate at the U.S. county level provided unique insights into where and when COVID-19 impacted different households. The results suggest that prevention and control policies can be implemented to those specific households exhibiting spatial and temporal pattern of high risk.

COVID-19 Medical Vulnerability Indicators: A Predictive, Local Data Model for Equity in Public Health Decision Making

This article reports the outcome of a project to develop and assess a predictive model of vulnerability indicators for COVID-19 infection in Los Angeles County. Multiple data sources were used to construct four indicators for zip code tabulation areas: (1) pre-existing health condition, (2) barriers to accessing health care, (3) built environment risk, and (4) the CDC's social vulnerability. The assessment of the indicators finds that the most vulnerable neighborhoods are characterized by significant clustering of racial minorities. An overwhelming 73% of Blacks reside in the neighborhoods with the two highest levels of pre-existing health conditions. For the barriers to accessing health care indicator, 40% of Latinx reside in the highest vulnerability places. The built environment indicator finds that selected Asian ethnic groups (63%), Latinx (55%), and Blacks (53%) reside in the neighborhoods designated as high or the highest vulnerability. The social vulnerability indicator finds 42% of Blacks and Latinx and 38% of selected Asian ethnic group residing in neighborhoods of high vulnerability. The vulnerability indicators can be adopted nationally to respond to COVID-19. The metrics can be utilized in data-driven decision making of re-openings or resource distribution such as testing, vaccine distribution and other pandemic-related resources to ensure equity for the most vulnerable.