California Healthy Places Index: Frames Matter

Patient Location and Disparities in Access to Fertility Preservation for Women With Gynecologic or Breast Cancer

OBJECTIVE

To assess the effect of geographic factors on fertility-sparing treatment or assisted reproductive technology (ART) utilization among women with gynecologic or breast cancers.

METHODS

We conducted a cohort study of reproductive-aged patients (18-45 years) with early-stage cervical, endometrial, or ovarian cancer or stage I-III breast cancer diagnosed between January 2000 and December 2015 using linked data from the California Cancer Registry, the California Office of Statewide Health Planning and Development, and the Society for Assisted Reproductive Technology. Generalized linear mixed models were used to evaluate associations between distance from fertility and gynecologic oncology clinics, as well as California Healthy Places Index score (a Census-level composite community health score), and ART or fertility-sparing treatment receipt.

RESULTS

We identified 7,612 women with gynecologic cancer and 35,992 women with breast cancer. Among all patients, 257 (0.6%) underwent ART. Among patients with gynecologic cancer, 1,676 (22.0%) underwent fertility-sparing treatment. Stratified by quartiles, residents who lived at increasing distances from gynecologic oncology or fertility clinics had decreased odds of undergoing fertility-sparing treatment (gynecologic oncology clinics: Q2, odds ratio [OR] 0.76, 95% CI, 0.63-0.93, P =.007; Q4, OR 0.72, 95% CI, 0.56-0.94, P =.016) (fertility clinics: Q3, OR 0.79, 95% CI, 0.65-0.97, P =.025; Q4, OR 0.67, 95% CI, 0.52-0.88, P =.004), whereas this relationship was not observed among women who resided within other quartiles (gynecologic oncology clinics: Q3, OR 0.81 95% CI, 0.65-1.01, P =.07; fertility clinics: Q2, OR 0.87 95% CI, 0.73-1.05, P =.15). Individuals who lived in communities with the highest (51 st -100 th percentile) California Healthy Places Index scores had greater odds of undergoing fertility-sparing treatment (OR 1.29, 95% CI, 1.06-1.57, P =.01; OR 1.66, 95% CI, 1.35-2.04, P =.001, respectively). The relationship between California Healthy Places Index scores and ART was even more pronounced (Q2 OR 1.9, 95% CI, 0.99-3.64, P =.05; Q3 OR 2.86, 95% CI, 1.54-5.33, P <.001; Q4 OR 3.41, 95% CI, 1.83-6.35, P <.001).

CONCLUSION

Geographic disparities affect fertility-sparing treatment and ART rates among women with gynecologic or breast cancer. By acknowledging geographic factors, health care systems can ensure equitable access to fertility-preservation services.

Does socioeconomic and environmental burden affect vulnerability to extreme air pollution and heat? A case-crossover study of mortality in California

BACKGROUND

Extreme heat and air pollution is associated with increased mortality. Recent evidence suggests the combined effects of both is greater than the effects of each individual exposure. Low neighborhood socioeconomic status ("socioeconomic burden") has also been associated with increased exposure and vulnerability to both heat and air pollution. We investigated if neighborhood socioeconomic burden or the combination of socioeconomic and environmental exposures ("socioenvironmental burden") modified the effect of combined exposure to extreme heat and particulate air pollution on mortality in California.

METHODS

We used a time-stratified case-crossover design to assess the impact of daily exposure to extreme particulate matter <2.5 μm (PM2.5) and heat on cardiovascular, respiratory, and all-cause mortality in California 2014-2019. Daily average PM2.5 and maximum temperatures based on decedent's residential census tract were dichotomized as extreme or not. Census tract-level socioenvironmental and socioeconomic burden was assessed with the CalEnviroScreen (CES) score and a social deprivation index (SDI), and individual educational attainment was derived from death certificates. Conditional logistic regression was used to estimate associations of heat and PM2.5 with mortality with a product term used to evaluate effect measure modification.

RESULTS

During the study period 1,514,292 all-cause deaths could be assigned residential exposures. Extreme heat and air pollution alone and combined were associated with increased mortality, matching prior reports. Decedents in census tracts with higher socioenvironmental and socioeconomic burden experienced more days with extreme PM2.5 exposure. However, we found no consistent effect measure modification by CES or SDI on combined or separate extreme heat and PM2.5 exposure on odds of total, cardiovascular or respiratory mortality. No effect measure modification was observed for individual education attainment.

CONCLUSION

We did not find evidence that neighborhood socioenvironmental- or socioeconomic burden significantly influenced the individual or combined impact of extreme exposures to heat and PM2.5 on mortality in California.

IMPACT

We investigated the effect measure modification by socioeconomic and socioenvironmental of the co-occurrence of heat and PM2.5, which adds support to the limited previous literature on effect measure modification by socioeconomic and socioenvironmental burden of heat alone and PM2.5 alone. We found no consistent effect measure modification by neighborhood socioenvironmental and socioeconomic burden or individual level SES of the mortality association with extreme heat and PM2.5 co-exposure. However, we did find increased number of days with extreme PM2.5 exposure in neighborhoods with high socioenvironmental and socioeconomic burden. We evaluated multiple area-level and an individual-level SES and socioenvironmental burden metrics, each estimating socioenvironmental factors differently, making our conclusion more robust.

California's COVID-19 Vaccine Equity Policy: Cases, Hospitalizations, And Deaths Averted In Affected Communities

In March 2021, California implemented a vaccine equity policy that prioritized COVID-19 vaccine allocation to communities identified as least advantaged by an area-based socioeconomic measure, the Healthy Places Index. We conducted quasi-experimental and counterfactual analyses to estimate the effect of this policy on COVID-19 vaccination, case, hospitalization, and death rates. Among prioritized communities, vaccination rates increased 28.4 percent after policy implementation. Furthermore, an estimated 160,892 COVID-19 cases, 10,248 hospitalizations, and 679 deaths in the least-advantaged communities were averted by the policy. Despite these improvements, the share of COVID-19 cases, hospitalizations, and deaths in prioritized communities remained elevated. These estimates were robust in sensitivity analyses that tested exchangeability between prioritized communities and those not prioritized by the policy; model specifications; and potential temporal confounders, including prior infections. Correcting for disparities by strategically allocating limited resources to the least-advantaged or most-affected communities can reduce the impacts of COVID-19 and other diseases but might not eliminate health disparities.

Exploring the Relationship Between Medication Adherence and Diabetes Disparities among Hispanic Patients in a Large Health System

BACKGROUND

Sub-optimal HbA1c control is a driver of disparities in diabetes outcomes among Hispanic patients. Differences in medication adherence may underlie racial/ethnic differences in HbA1c level.

OBJECTIVE

To examine the relationship between medication adherence and disparities in HbA1c level among Hispanic patients, relative to other racial/ethnic groups, obtaining care in the University of California Health System (UC Health).

DESIGN

This study used clinical, administrative, and prescription dispensing data (January-December 2021) derived from the electronic health records of 5 Academic Medical Centers in UC Health, and linear regression models (LRMs) to conduct a cross-sectional analysis of the association between medication adherence, race/ethnicity, and HbA1c level. Adjusted LRMs were run with and without the measure of medication adherence to assess this relationship.

PARTICIPANTS

Patients with a UC Health primary care physician (PCP), with ≥ 1 PCP visit within the last 3 years, ages 18-75, reporting Asian, Hispanic, or White race/ethnicity, and who had ≥ 2 encounters with an ICD diagnosis of diabetes or had a prescription for a diabetes medication within the last 2 years, as of 12/31/21 (N = 27, 542; Asian = 6253, Hispanic = 7216, White = 14,073).

MAIN MEASURES

Our measure of medication adherence was the proportion of days covered (PDC) for diabetes medications in 2021. Our outcome was the most recent HbA1c value.

KEY RESULTS

In the LRM excluding the PDC, Hispanic ethnicity was positively associated with HbA1c level (β = 0.31, p =  < 0.001). In the LRM model including PDC, PDC was negatively associated with HbA1c level (β =  - 0.18, p =  < 0.001). However, the positive relationship between Hispanic ethnicity and HbA1c level did not change (β = 0.31, p =  < 0.001).

CONCLUSIONS

The findings of this study suggest that the relationship between Hispanic ethnicity, HbA1c level, and factors outside of medication adherence should be explored among primary care patients receiving care in Academic Medical Centers.

Spatial Heterogeneity of the Respiratory Health Impacts of Wildfire Smoke PM2.5 in California

Wildfire smoke fine particles (PM2.5) are a growing public health threat as wildfire events become more common and intense under climate change, especially in the Western United States. Studies assessing the association between wildfire PM2.5 exposure and health typically summarize the effects over the study area. However, health responses to wildfire PM2.5 may vary spatially. We evaluated spatially-varying respiratory acute care utilization risks associated with short-term exposure to wildfire PM2.5 and explored community characteristics possibly driving spatial heterogeneity. Using ensemble-modeled daily wildfire PM2.5, we defined a wildfire smoke day to have wildfire-specific PM2.5 concentration ≥15 μg/m3. We included daily respiratory emergency department visits and unplanned hospitalizations in 1,396 California ZIP Code Tabulation Areas (ZCTAs) and 15 census-derived community characteristics. Employing a case-crossover design and conditional logistic regression, we observed increased odds of respiratory acute care utilization on wildfire smoke days at the state level (odds ratio [OR] = 1.06, 95% confidence interval [CI]: 1.05, 1.07). Across air basins, ORs ranged from 0.88 to 1.57, with the highest effect estimate in San Diego. A within-community matching design and spatial Bayesian hierarchical model also revealed spatial heterogeneity in ZCTA-level rate differences. For example, communities with a higher percentage of Black or Pacific Islander residents had stronger wildfire PM2.5-outcome relationships, while more air conditioning and tree canopy attenuated associations. We found an important heterogeneity in wildfire smoke-related health impacts across air basins, counties, and ZCTAs, and we identified characteristics of vulnerable communities, providing evidence to guide policy development and resource allocation.

Neighborhood Health and Outcomes in Idiopathic Pulmonary Fibrosis

Rationale: Living in a disadvantaged neighborhood has been associated with worse survival in people with idiopathic pulmonary fibrosis (IPF), however, prior studies have only examined the impact of neighborhood health on outcomes in IPF as a composite measure. Objectives: To investigate the association between neighborhood health and disease severity, measured by pulmonary function at presentation, and death in follow-up, with an additional focus on the contributions of the neighborhood's underlying physical and social factors to these outcomes. Methods: In a retrospective study of participants from the University of California, San Francisco, IPF Cohort (2001-2020), geocoded home addresses were matched to the California Healthy Places Index (HPI), a census-tract measure of neighborhood health. The HPI comprises 25 indicators of neighborhood health that are organized into eight physical and social domains, each of which is weighted and summed to provide a composite HPI score. Regression models were used to examine associations between the HPI as a continuous variable, in quartiles, and across each physical and social domain of the HPI (higher values indicate greater advantage) and forced vital capacity (FVC) percent predicted (% predicted), diffusing capacity of the lung for carbon monoxide (DlCO) % predicted, and death, adjusting for demographic and clinical covariates. We also studied the interaction between disease severity at presentation and neighborhood health in our time-to-event models. Results: In 783 participants with IPF, each 10% increase in HPI was associated with a 1% increase in FVC % predicted and DlCO % predicted (95% confidence intervals [CIs] = 0.55, 1.72; and 0.49, 1.49, respectively). This association appeared primarily driven by the economic, education, access, and social HPI domains. We also observed differences in the associations of HPI with mortality depending on disease severity at presentation. In participants with normal to mildly impaired FVC % predicted (⩾70%) and DlCO % predicted (⩾60%), decreased HPI was associated with higher mortality (hazard ratio = 2.91 Quartile 1 vs. Quartile 4; 95% CI = 1.20, 7.05). No association was observed between the HPI and death for participants with moderate to severely impaired FVC % predicted and DlCO % predicted. Conclusions: Living in disadvantaged neighborhoods was associated with worse pulmonary function in participants with IPF and was independently associated with increased mortality in participants with normal to mild physiological impairment at presentation.

Exploring spatial heterogeneity in synergistic effects of compound climate hazards: Extreme heat and wildfire smoke on cardiorespiratory hospitalizations in California

Extreme heat and wildfire smoke events are increasingly co-occurring in the context of climate change, especially in California. Extreme heat and wildfire smoke may have synergistic effects on population health that vary over space. We leveraged high-resolution satellite and monitoring data to quantify spatially varying compound exposures to extreme heat and wildfire smoke in California (2006-2019) at ZIP Code Tabulation Area (ZCTA) level. We found synergistic effects between extreme heat and wildfire smoke on daily cardiorespiratory hospitalizations at the state level. We also found spatial heterogeneity in such synergistic effects across ZCTAs. Communities with lower education attainment, lower health insurance coverage, lower income, lower proportion of automobile ownership, lower tree canopy coverage, higher population density, and higher proportions of racial/ethnic minorities experienced higher synergistic effects. This study highlights the need to incorporate compound hazards and environmental justice considerations into evidence-based policy development to protect populations from increasingly prevalent compound hazards.

Life Expectancy and Causes of Premature Death by Subgroup for Community-Based Action in Marin County, California, 2017-2021

INTRODUCTION

Marin is a medium-sized county in California's San Francisco Bay Area. Despite its historically higher-than-average life expectancy and socioeconomic level, known economic and health disparities by race, ethnicity, and geography became more visible during the COVID-19 pandemic.  Methods: We calculated life expectancy, measured years of potential life lost (YPLLs), and described premature mortality for the five years of 2017-2021 by race, ethnicity, census tract, and resource level (as measured by Healthy Places Index [HPI]) to provide data on inequities to guide community-centered action to reduce premature mortality.  Results: Life expectancy for the county was 85.2 years. The non-Hispanic African American/Black population experienced the lowest life expectancy of 77.1 years, 11.6 years lower than the non-Hispanic Asian population which had the highest life expectancy (88.7 years). There was a 14.9-year difference in life expectancy between the census tracts with the lowest (77.1 years) and highest (92.0 years) estimates. We found a moderate, positive association between census tract resource level (HPI) and life expectancy (r=0.58, p<0.01). The leading causes of premature death were cancer, diseases of the circulatory system, and accidental overdoses, with variation by subgroup.  Conclusion: These data highlight health disparities that persist in Marin County and can inform data-driven public health strategies to narrow gaps in longevity between communities.

A Novel Approach to Developing Disease and Outcome-Specific Social Risk Indices

INTRODUCTION

A variety of industry composite indices are employed within health research in risk-adjusted outcome measures and to assess health-related social needs. During the COVID-19 pandemic, the relationships among risk adjustment, clinical outcomes, and composite indices of social risk have become relevant topics for research and healthcare operations. Despite the widespread use of these indices, composite indices are often comprised of correlated variables and therefore may be affected by information duplicity of their underlying risk factors.

METHODS

A novel approach is proposed to assign outcome- and disease group-driven weights to social risk variables to form disease and outcome-specific social risk indices and apply the approach to the county-level Centers for Disease Control and Prevention social vulnerability factors for demonstration. The method uses a subset of principal components reweighed through Poisson rate regressions while controlling for county-level patient mix. The analyses use 6,135,302 unique patient encounters from 2021 across seven disease strata.

RESULTS

The reweighed index shows reduced root mean squared error in explaining county-level mortality in five of the seven disease strata and equivalent performance in the remaining strata compared with the reduced root mean squared error using the current Centers for Disease Control and Prevention Social Vulnerability Index as a benchmark.

CONCLUSIONS

A robust method is provided, designed to overcome challenges with current social risk indices, by accounting for redundancy and assigning more meaningful disease and outcome-specific variable weights.

Demographic Disparities in Mpox Vaccination Series Completion, by Route of Vaccine Administration - California, August 9, 2022-March 31, 2023

In August 2022, the Food and Drug Administration authorized JYNNEOS vaccine (modified vaccinia Ankara vaccine, Bavarian Nordic), a 2-dose series used for the prevention of Monkeypox virus infection, to be administered via a dose-sparing intradermal route, in addition to the previously authorized subcutaneous route. The California Department of Public Health investigated whether demographic disparities in vaccination series completion varied by route of administration of the recipient's first dose. Among California residents who received their first dose during August 9, 2022-March 31, 2023, a total of 59.8% received a second dose. Series completion was highest among non-Hispanic White persons (64.1%), persons aged ≥65 years (72.6%), and adults with male sex assignment at birth (62.1%); series completion was lowest among non-Hispanic Black or African American persons (51.3%), persons aged 18-24 years (42.9%), and adults assigned female sex at birth (42.8%). When the first dose was received by subcutaneous administration, overall series completion was 58.8% compared with 60.2% when the first dose was administered intradermally. Odds of series completion across all race and ethnicity groups, persons aged 18-64 years, community health conditions, and persons assigned male sex at birth were not greater when the first dose was administered subcutaneously compared with intradermally. Intradermal use of JYNNEOS vaccine did not lower overall 2-dose series completion rates. Continued efforts are needed to ensure persons at risk for Monkeypox virus infection receive both recommended doses.

Disparities in COVID-19 Disease Incidence by Income and Vaccination Coverage - 81 Communities, Los Angeles, California, July 2020-September 2021

COVID-19 has disproportionately affected socially vulnerable communities characterized by lower income, lower education attainment, and higher proportions of minority populations, among other factors (1-4). Disparities in COVID-19 incidence and the impact of vaccination on incidence disparities by community income were assessed among 81 communities in Los Angeles, California. Median community vaccination coverage and COVID-19 incidence were calculated across household income strata using a generalized linear mixed effects model with Poisson distribution during three COVID-19 surge periods: two before vaccine availability (July 2020 and January 2021) and the third after vaccines became widely available in April 2021 (September 2021). Adjusted incidence rate ratios (aIRRs) during the peak month of each surge were compared across communities grouped by median household income percentile. The aIRR between communities in the lowest and highest median income deciles was 6.6 (95% CI = 2.8-15.3) in July 2020 and 4.3 (95% CI = 1.8-9.9) in January 2021. However, during the September 2021 surge that occurred after vaccines became widely availabile, model estimates did not identify an incidence disparity between the highest- and lowest-income communities (aIRR = 0.80; 95% CI = 0.35-1.86). During this surge, vaccination coverage was lowest (59.4%) in lowest-income communities and highest (71.5%) in highest-income communities (p<0.001). However, a significant interaction between income and vaccination on COVID-19 incidence (p<0.001) indicated that the largest effect of vaccination on disease incidence occured in the lowest-income communities. A 20% increase in community vaccination was estimated to have resulted in an additional 8.1% reduction in COVID-19 incidence in the lowest-income communities compared with that in the highest-income communities. These findings highlight the importance of improving access to vaccination and reducing vaccine hesitancy in underserved communities in reducing disparities in COVID-19 incidence.

Use of the Improvement Index to Evaluate Equitable COVID-19 Vaccine Allocation in the San Francisco Bay Area

Introduction

A social determinants of health index score or Vaccine Equity Metric was used to prioritize resources and address geographic disparities in California's vaccination coverage. We calculated the improvement index or percentage of the vaccination disparity gap closed to evaluate the impacts of this vaccination strategy in the San Francisco Bay Area during the SARS-CoV-2 Delta variant surge.

Methods

We conducted a cross-sectional study on San Francisco Bay Area ZIP codes during the Delta surge (July 6-October 5, 2021). Data came from the California Immunization Registry and the 2019 5-year American Community Survey. We used Spearman correlations to examine the relationships between Vaccine Equity Metric category and vaccine coverage and Kruskal-Wallis tests to compare vaccination improvement index across Vaccine Equity Metric categories.

Results

We studied 248 ZIP codes in the San Francisco Bay Area. Those with the lowest resources (Vaccine Equity Metric Level 1) had the highest absolute increase in vaccination coverage (14.3 vs 5.4 percentage points in Vaccine Equity Metric Level 4), although a contribution was higher starting vaccination rates in Level 4 ZIP codes with the greatest resources. The ratio of vaccination coverage between the lowest- and highest-resourced ZIP codes increased from 0.79 to 0.9, suggesting reduced disparity. However, it is difficult to interpret given wide differences in n (Level 1 n=8 vs Level 4 n=151). In contrast, the vaccination improvement index accounts for each Vaccine Equity Metric category's baseline vaccination; all were statistically similar (grand mean=41.5%, p=0.367), implying comparable improvement across all ZIP codes.

Conclusions

Using a Vaccine Equity Metric to identify and prioritize resources to vulnerable communities contributed to equitable vaccine allocation in the San Francisco Bay Area. Our study shows an example of the improvement index's advantages over conventional health equity metrics, such as absolute differences and relative effect measures, which can overestimate an intervention's impact.

The role of the California tier system in controlling population mobility during the COVID-19 pandemic

BACKGROUND

Policies to restrict population mobility are a commonly used strategy to limit the transmission of contagious diseases. Among measures implemented during the COVID-19 pandemic were dynamic stay-at-home orders informed by real-time, regional-level data. California was the first state in the U.S. to implement this novel approach; however, the effectiveness of California's four-tier system on population mobility has not been quantified.

METHODS

Utilizing data from mobile devices and county-level demographic data, we evaluated the impact of policy changes on population mobility and explored whether demographic characteristics explained variability in responsiveness to policy changes. For each California county, we calculated the proportion of people staying home and the average number of daily trips taken per 100 persons, across different trip distances and compared this to pre-COVID-19 levels.

RESULTS

We found that overall mobility decreased when counties moved to a more restrictive tier and increased when moving to a less restrictive tier, as the policy intended. When placed in a more restrictive tier, the greatest decrease in mobility was observed for shorter and medium-range trips, while there was an unexpected increase in the longer trips. The mobility response varied by geographic region, as well as county-level median income, gross domestic product, economic, social, and educational contexts, the prevalence of farms, and recent election results.

CONCLUSIONS

This analysis provides evidence of the effectiveness of the tier-based system in decreasing overall population mobility to ultimately reduce COVID-19 transmission. Results demonstrate that socio-political demographic indicators drive important variability in such patterns across counties.

Developing a Community-Oriented and Place-Based Strategy to Improve COVID-19 Vaccine Accessibility

Objective To address the challenges of inequitable access to the COVID-19 vaccines, Kaiser Permanente Southern California developed a community-oriented and geographic vaccine strategy combining clinical data, community data, and predictive models to identify ZIP codes requiring increased resources to achieve equitable vaccine receipt. Study Design This is a quality-improvement implementation study. Methods The authors developed hot-spot maps for southern California service areas to assist clinicians in identifying specific ZIP codes to increase vaccination efforts. Data inputs for these hot spots included COVID-19 incidence, hospitalization, ecologic variables of social determinants of health, and predictive models of vaccine penetrance. Partnering with community organizations, vaccine penetrance was improved by targeting hot spots with pop-up clinics, mobile health vehicle visits, extending facility hours, and sending tailored text messages. Results By the end of 2021, Kaiser Permanente Southern California achieved a 70% vaccination rate in 83% of 670 ZIP codes it serves, resulting in a total vaccination rate of 81% in 2021. Further, more than 2 out of 3 individuals receiving a vaccine through the hot-spot guided mobile health vehicle were Hispanic or Black. The hot-spotting approach produced a refreshed monthly dashboard of hot spots in 7 counties covering over 670 ZIP codes to help decision makers better understand and improve vaccination in targeted communities. Conclusion The hot-spot methodology produced monthly lists of ZIP codes requiring additional health-care resources and vaccination strategies. This was a feasible place-based approach to mitigating disparities in vaccine uptake in historically disinvested communities that may be readily applied to other areas of care.

Inclusion of Race and Ethnicity With Neighborhood Socioeconomic Deprivation When Assessing COVID-19 Hospitalization Risk Among California Veterans Health Administration Users

IMPORTANCE

Despite complexities of racial and ethnic residential segregation (hereinafter referred to as segregation) and neighborhood socioeconomic deprivation, public health studies, including those on COVID-19 racial and ethnic disparities, often rely on composite neighborhood indices that do not account for residential segregation.

OBJECTIVE

To examine the associations by race and ethnicity among California's Healthy Places Index (HPI), Black and Hispanic segregation, Social Vulnerability Index (SVI), and COVID-19-related hospitalization.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included veterans with positive test results for COVID-19 living in California who used Veterans Health Administration services between March 1, 2020, and October 31, 2021.

MAIN OUTCOMES AND MEASURES

Rates of COVID-19-related hospitalization among veterans with COVID-19.

RESULTS

The sample available for analysis included 19 495 veterans with COVID-19 (mean [SD] age, 57.21 [17.68] years), of whom 91.0% were men, 27.7% were Hispanic, 16.1% were non-Hispanic Black, and 45.0% were non-Hispanic White. For Black veterans, living in lower-HPI (ie, less healthy) neighborhoods was associated with higher rates of hospitalization (odds ratio [OR], 1.07 [95% CI, 1.03-1.12]), even after accounting for Black segregation (OR, 1.06 [95% CI, 1.02-1.11]). Among Hispanic veterans, living in lower-HPI neighborhoods was not associated with hospitalization with (OR, 1.04 [95% CI, 0.99-1.09]) and without (OR, 1.03 [95% CI, 1.00-1.08]) Hispanic segregation adjustment. For non-Hispanic White veterans, lower HPI was associated with more frequent hospitalization (OR, 1.03 [95% CI, 1.00-1.06]). The HPI was no longer associated with hospitalization after accounting for Black (OR, 1.02 [95% CI, 0.99-1.05]) or Hispanic (OR, 0.98 [95% CI, 0.95-1.02]) segregation. Hospitalization was higher for White (OR, 4.42 [95% CI, 1.62-12.08]) and Hispanic (OR, 2.90 [95% CI, 1.02-8.23]) veterans living in neighborhoods with greater Black segregation and for White veterans in more Hispanic-segregated neighborhoods (OR, 2.81 [95% CI, 1.96-4.03]), adjusting for HPI. Living in higher SVI (ie, more vulnerable) neighborhoods was associated with greater hospitalization for Black (OR, 1.06 [95% CI, 1.02-1.10]) and non-Hispanic White (OR, 1.04 [95% CI, 1.01-1.06]) veterans.

CONCLUSIONS AND RELEVANCE

In this cohort study of US veterans with COVID-19, HPI captured neighborhood-level risk for COVID-19-related hospitalization for Black, Hispanic, and White veterans comparably with SVI. These findings have implications for the use of HPI and other composite neighborhood deprivation indices that do not explicitly account for segregation. Understanding associations between place and health requires ensuring composite measures accurately account for multiple aspects of neighborhood deprivation and, importantly, variation by race and ethnicity.

Unmet needs for food, medicine, and mental health services among vulnerable older adults during the COVID-19 pandemic

OBJECTIVE

To examine sociodemographic factors associated with having unmet needs in medications, mental health, and food security among older adults during the COVID-19 pandemic.

DATA SOURCES AND STUDY SETTING

Primary data and secondary data from the electronic health records (EHR) in an age-friendly academic health system in 2020 were used.

STUDY DESIGN

Observational study examining factors associated with having unmet needs in medications, food, and mental health.

DATA COLLECTING/EXTRACTION METHODS

Data from a computer-assisted telephone interview and EHR on community-dwelling older patients were analyzed.

PRINCIPLE FINDINGS

Among 3400 eligible patients, 1921 (53.3%) (average age 76, SD 11) responded, with 857 (45%) of respondents having at least one unmet need. Unmet needs for medications were present in 595 (31.0%), for food in 196 (10.2%), and for mental health services in 292 (15.2%). Racial minorities had significantly higher probabilities of having unmet needs for medicine and food, and of being referred for services related to medications, food, and mental health. Patients living in more resource-limited neighborhoods had a higher probability of being referred for mental health services.

CONCLUSIONS

Age-friendly health systems (AFHS) and their recognition should include assessing and addressing social risk factors among older adults. Proactive efforts to address unmet needs should be integral to AFHS.

The impact of social determinants of health on obesity and diabetes disparities among Latino communities in Southern California

BACKGROUND

Social determinants of health (SDoH) describe the complex network of circumstances that impact an individual before birth and across the lifespan. SDoH contextualize factors in a community that are associated with chronic disease risk and certain health disparities. The main objective of this study was to explore the impact of SDoH on the prevalence of obesity and diabetes, and whether these factors explain disparities in these health outcomes among Latinos in Southern California.

METHODS

We utilized three composite indices that encompass different SDoH: the Healthy Places Index (HPI), Social Vulnerability Index (SVI), and CalEnviroScreen (CES). Univariate linear regression models explored the associations between index scores with adult obesity, adult diabetes, and childhood obesity.

RESULTS

Communities with lower HPI scores were associated with higher prevalence of metabolic disease and a greater proportion of Latino residents. Cities in the lowest decile of HPI scores had 71% of the population identifying as Latino compared to 12% in the highest decile. HPI scores explained 61% of the variability in adult obesity (p < 0.001), 41% of the variability in childhood obesity (p < 0.001), and 47% of the variability in adult diabetes (p < 0.001). Similar results were observed when examining SVI and CES with these health outcomes.

CONCLUSIONS

These results suggest that Latinos in Southern California live in communities with adverse SDoH and face a greater burden of adult obesity, diabetes, and childhood obesity.

Characterization of COVID-19 Vaccine Hesitancy Among Essential Workforce Members of a Large Safety Net Urban Medical Center

OBJECTIVES

Vaccine hesitancy among essential workers remains a significant public health challenge. We examined psychological constructs of perceived susceptibility, threat, and self-efficacy and their associations with COVID-19 vaccine hesitancy among a racially and ethnically diverse essential workforce population.

METHODS

We performed a cross-sectional survey of essential workers from September-December 2020 at a large Los Angeles safety-net medical center as part of a program offering free COVID-19 serology testing. Program participants completed a standardized survey at the time of phlebotomy. Hierarchical logistic regression was utilized to determine factors independently associated with vaccine hesitancy.

RESULTS

Among 1327 persons who had serology testing, 1235 (93%) completed the survey. Of these, 958 (78%) were healthcare workers. Based on expressed intent, 22% were vaccine-hesitant 78% were vaccine acceptors. In our multivariate model, vaccine hesitancy was associated with female gender [aOR = 2.09; 95% CI (1.44-3.05)], African American race [aOR = 4.32; (2.16-8.62)], LatinX ethnicity [aOR = 2.47; 95% CI (1.51-4.05)] and history of not/sometimes receiving influenza vaccination [aOR = 4.39; 95% CI (2.98-6.48)]. Compared to nurses, vaccine hesitancy was lower among physicians [aOR = 0.09; 95% CI (0.04-0.23)], non-nursing/non-physician healthcare workers [aOR = 0.55; 95% CI (0.33-0.92)], and non-healthcare care workers [aOR = 0.53; 95% CI (0.36-0.78)].

CONCLUSIONS

Among a racially/ethnically diverse group of safety net medical center essential workers, COVID-19 vaccine hesitancy was associated with racial/ethnic minority groups, employment type, and prior influenza vaccination hesitancy. Interestingly, we found no association with the Health Belief Model construct measures of perceived susceptibility, threat, and self-efficacy. Psychological constructs not assessed may be drivers of vaccine hesitancy in our population.

Primary Mpox and Secondary Syphilis in an HIV Patient: A Community Hospital Experience

Mpox was an emerging outbreak in California in 2022, primarily in major metropolitan areas, as part of the global mpox outbreak declared by World Health Organization in July 2022. Community hospitals outside of major metropolitan areas have seen fewer cases to date, so they may be less equipped to diagnose and treat patients with mpox. They may have limited public health resources commensurate with the area's population density. Mpox may also be superimposed on ongoing local outbreaks of other sexually transmitted infections. We present a case of a person with HIV who contracted mpox and also developed secondary syphilis. Early detection can be beneficial for prompt treatment, decreased burden of disease for the individual, and prevention of further spread of the infection.

Association of Area-Based Socioeconomic Measures with Tuberculosis Incidence in California

We assessed the association of area-based socio-economic status (SES) measures with tuberculosis (TB) incidence in California. We used TB disease data for 2012-2016 (n = 9901), population estimates, and SES measures to calculate incidence rates, rate ratios, and 95% confidence intervals (95% CI) by SES and birth country. SES was measured by census tract and was categorized by quartiles for education, crowding, and the California Healthy Places Index (HPI)and by specific cutoffs for poverty. The lowest SES areas defined by education, crowding, poverty, and HPI had 39%, 40%, 41%, and 33% of TB cases respectively. SES level was inversely associated with TB incidence across all SES measures and birth countries. TB rates were 3.2 (95% CI 3.0-3.4), 2.1 (95% CI 1.9-2.2), 3.6 (95% CI 3.3-3.8), and 2.0 (95% CI 1.9-2.1) times higher in lowest SES areas vs. highest SES areas as defined by education, crowding, poverty and HPI respectively. Area-based SES measures are associated with TB incidence in California. This information could inform TB prevention efforts in terms of materials, partnerships, and prioritization.

Neighborhood disadvantage is associated with KRAS-mutated non-small cell lung cancer risk

Purpose

It remains unclear why individuals living in disadvantaged neighborhoods have shorter non-small cell lung cancer (NSCLC) survival. It is possible that living in these deprived areas is linked with increased risk of developing aggressive NSCLC biology. Here, we explored the association of somatic KRAS mutations, which are associated with shorter survival in NSCLC patients, and 11 definitions of neighborhood disadvantage spanning socioeconomic and structural environmental elements.

Methods

We analyzed data from 429 NSCLC patients treated at a Comprehensive Cancer Center from 2015 to 2018. Data were abstracted from medical records and each patient’s home address was used to assign publicly available indices of neighborhood disadvantage. Prevalence Ratios (PRs) for the presence of somatic KRAS mutations were estimated using modified Poisson regression models adjusted for age, sex, smoking status, race/ethnicity, educational attainment, cancer stage, and histology.

Results

In the NSCLC cohort, 29% had KRAS mutation-positive tumors. We found that five deprivation indices of socioeconomic disadvantage were associated with KRAS mutation. A one decile increase in several of these socioeconomic disadvantage indices was associated with a 1.06 to 1.14 increased risk of KRAS mutation. Measures of built structural environment were not associated with KRAS mutation status.

Conclusion

Socioeconomic disadvantage at the neighborhood level is associated with higher risk of KRAS mutation while disadvantage related to built environmental structural measures was inversely associated. Our results indicate not only that neighborhood disadvantage may contribute to aggressive NSCLC biology, but the pathways linking biology to disadvantage are likely operating through socioeconomic-related stress.

Developing a granular scale environmental burden index (EBI) for diverse land cover types across the contiguous United States

Critical to identifying the risk of environmentally driven disease is an understanding of the cumulative impact of environmental conditions on human health. Here we describe the methodology used to develop an environmental burden index (EBI). The EBI is calculated at U.S. census tract level, a finer scale than many similar national-level tools. EBI scores are also stratified by tract land cover type as per the National Land Cover Database (NLCD), controlling for urbanicity. The EBI was developed over the course of four stages: 1) literature review to identify potential indicators, 2) data source acquisition and indicator variable construction, 3) index creation, and 4) stratification by land cover type. For each potential indicator, data sources were assessed for completeness, update frequency, and availability. These indicators were: (1) particulate matter (PM2.5), (2) ozone, (3) Superfund National Priority List (NPL) locations, (4) Toxics Release Inventory (TRI) facilities, (5) Treatment, Storage, and Disposal (TSD) facilities, (6) recreational parks, (7) railways, (8) highways, (9) airports, and (10) impaired water sources. Indicators were statistically normalized and checked for collinearity. For each indicator, we computed and summed percentile ranking scores to create an overall ranking for each tract. Tracts having the same plurality of land cover type form a 'peer' group. We re-ranked the tracts into percentiles within each peer group for each indicator. The percentile scores were combined for each tract to obtain a stratified EBI. A higher score reveals a tract with increased environmental burden relative to other tracts of the same peer group. We compared our results to those of related indices, finding good convergent validity between the overall EBI and CalEnviroScreen 4.0. The EBI has many potential applications for research and use as a tool to develop public health interventions at a granular scale.

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.

Protective Places: the Relationship between Neighborhood Quality and Preterm Births to Black Women in Oakland, California (2007-2011)

Black women have the highest incidence of preterm birth (PTB). Upstream factors, including neighborhood context, may be key drivers of this increased risk. This study assessed the relationship between neighborhood quality, defined by the Healthy Places Index, and PTB among Black women who lived in Oakland, California, and gave birth between 2007 and 2011 (N = 5418 women, N = 107 census tracts). We found that, compared with those living in lower quality neighborhoods, women living in higher quality neighborhoods had 20-38% lower risk of PTB, independent of confounders. Findings have implications for place-based research and interventions to address racial inequities in PTB.

Geospatial Social Determinants of Health Correlate with Disparities in Syphilis and Congenital Syphilis Cases in California

Syphilis and congenital syphilis (CS) are increasing in California (CA). From 2015 through 2019, for example, CA cases of early syphilis among reproductive-age females (15−44) and CS each increased by >200%. Certain populations—including people experiencing homelessness, using drugs, and/or belonging to certain racial/ethnic groups—have been disproportionately impacted. We hypothesized that geospatial social determinants of health (SDH) contribute to such health inequities. To demonstrate this, we geospatially described syphilis in CA using the Healthy Places Index (HPI). The HPI is a composite index that assigns a score to each CA census tract based on eight socioeconomic characteristics associated with health (education, housing, transportation, neighborhood conditions, clean environment, and healthcare access as well as economic and social resources). We divided CA census tracts into four quartiles based on HPI scores (with the lowest quartile having the least healthy socioeconomic and environmental conditions), then used 2013−2020 CA sexually transmitted diseases surveillance data to compare overall syphilis (among adults and adolescents) and CS case counts, incidence rates (per 100,000 population or live births), and incidence rate ratios (IRRs) among these quartiles. From 2013 to 2020, across all stages of syphilis and CS, disease burden was greatest in the lowest HPI quartile and smallest in the highest quartile (8308 cases (representing 33.2% of all incidents) versus 3768 (15.1%) for primary and secondary (P&S) syphilis; 5724 (31.6%) versus 2936 (16.2%) for early non-primary non-secondary (NPNS) syphilis; 11,736 (41.9%) versus 3026 (10.8%) for late/unknown duration syphilis; and 849 (61.9%) versus 57 (4.2%) for CS; all with p < 0.001). Using the highest HPI quartile as a reference, the IRRs in the lowest quartile were 17 for CS, 4.5 for late/unknown duration syphilis, 2.6 for P&S syphilis, and 2.3 for early NPNS syphilis. We thus observed a direct relationship between less healthy conditions (per HPI) and syphilis/CS in California, supporting our hypothesis that SDH correlate with disparities in syphilis, especially CS. HPI could inform allocation of resources to: (1) support communities most in need of assistance in preventing syphilis/CS cases and (2) reduce health disparities.

No Equity without Data Equity: Data Reporting Gaps for Native Hawaiians and Pacific Islanders as Structural Racism

Data on the health and social determinants for Native Hawaiians and Pacific Islanders (NHPIs) in the United States are hidden, because data are often not collected or are reported in aggregate with other racial/ethnic groups despite decades of calls to disaggregate NHPI data. As a form of structural racism, data omissions contribute to systemic problems such as inability to advocate, lack of resources, and limitations on political power. The authors conducted a data audit to determine how US federal agencies are collecting and reporting disaggregated NHPI data. Using the COVID-19 pandemic as a case study, they reviewed how states are reporting NHPI cases and deaths. They then used California's neighborhood equity metric-the California Healthy Places Index (HPI)-to calculate the extent of NHPI underrepresentation in communities targeted for COVID-19 resources in that state. Their analysis shows that while collection and reporting of NHPI data nationally has improved, federal data gaps remain. States are vastly underreporting: more than half of states are not reporting NHPI COVID-19 case and death data. The HPI, used to inform political decisions about allocation of resources to combat COVID-19 in at-risk neighborhoods, underrepresents NHPIs. The authors make recommendations for improving NHPI data equity to achieve health equity and social justice.

County-Level Social Vulnerability Is Positively Associated with Cardiometabolic Disease in Colorado

Cardiometabolic diseases are a group of interrelated diseases that pose greater burden among socially vulnerable communities. The social vulnerability index (SVI) identifies communities vulnerable to emergencies and may also help determine communities at risk of adverse chronic health outcomes. However, no studies have examined the relationship between the SVI and cardiometabolic health outcomes in Colorado or focused on rural settings. The aim of this ecological study was to determine whether the county-level SVI is associated with county-level cardiometabolic health indicators with a particular focus on rurality and racial/ethnic diversity. We obtained 2014 SVI scores from the Centers for Disease Control and Prevention (scored 0–1; higher = more vulnerable) and 2013–2015 cardiometabolic health estimates from the Colorado Department of Public Health and Environment. The distribution of social determinants of health was spatially evaluated. Bivariate relationships between the SVI and cardiometabolic indicators were estimated using simple linear regression models. The highest SVI scores were observed in rural areas, including the San Luis Valley (mean: 0.78, median: 0.91), Southeast (mean: 0.72, median: 0.73), and Northeast (mean: 0.66, median: 0.76) regions. Across Colorado, the SVI accounted for 41% of the variability in overweight and obesity prevalence (p < 0.001), 17% of the variability in diabetes prevalence (p = 0.001), and 58% of the age-adjusted myocardial infarction hospitalization rate (p < 0.001). SVI values may be useful in determining a community’s burden of cardiometabolic diseases.

The Interplay of Environmental Exposures and Mental Health: Setting an Agenda

BACKGROUND

To date, health-effects research on environmental stressors has rarely focused on behavioral and mental health outcomes. That lack of research is beginning to change. Science and policy experts in the environmental and behavioral health sciences are coming together to explore converging evidence on the relationship-harmful or beneficial-between environmental factors and mental health.

OBJECTIVES

To organize evidence and catalyze new findings, the National Academy of Sciences, Engineering, and Medicine (NASEM) hosted a workshop 2-3 February 2021 on the interplay of environmental exposures and mental health outcomes.

METHODS

This commentary provides a nonsystematic, expert-guided conceptual review and interdisciplinary perspective on the convergence of environmental and mental health, drawing from hypotheses, findings, and research gaps presented and discussed at the workshop. Featured is an overview of what is known about the intersection of the environment and mental health, focusing on the effects of neurotoxic pollutants, threats related to climate change, and the importance of health promoting environments, such as urban green spaces.

DISCUSSION

We describe what can be gained by bridging environmental and psychological research disciplines and present a synthesis of what is needed to advance interdisciplinary investigations. We also consider the implications of the current evidence for a) foundational knowledge of the etiology of mental health and illness, b) toxicant policy and regulation, c) definitions of climate adaptation and community resilience, d) interventions targeting marginalized communities, and e) the future of research training and funding. We include a call to action for environmental and mental health researchers, focusing on the environmental contributions to mental health to unlock primary prevention strategies at the population level and open equitable paths for preventing mental disorders and achieving optimal mental health for all. https://doi.org/10.1289/EHP9889.

Public Protests and the Risk of Novel Coronavirus Disease Hospitalizations: A County-Level Analysis from California

The objective of this study was to assess the relationship between public protests and county-level, novel coronavirus disease (COVID-19) hospitalization rates across California. Publicly available data were included in the analysis from 55 of 58 California state counties (29 March–14 October 2020). Mixed-effects negative binomial regression models were used to examine the relationship between daily county-level COVID-19 hospitalizations and two main exposure variables: any vs. no protests and 1 or >1 protest vs. no protests on a given county-day. COVID-19 hospitalizations were used as a proxy for viral transmission since such rates are less sensitive to temporal changes in testing access/availability. Models included covariates for daily county mobility, county-level characteristics, and time trends. Models also included a county-population offset and a two-week lag for the association between exposure and outcome. No significant associations were observed between protest exposures and COVID-19 hospitalization rates among the 55 counties. We did not find evidence to suggest that public protests were associated with COVID-19 hospitalization within California counties. These findings support the notion that protesting during a pandemic may be safe, ostensibly, so long as evidence-based precautionary measures are taken.