Comparison of Community-Level and Patient-Level Social Risk Data in a Network of Community Health Centers

Patients with diabetes struggling to afford food and control their HbA1c in food-insecure areas in Bronx, NY

OBJECTIVE

To characterise the association between risk of poor glycaemic control and self-reported and area-level food insecurity among adult patients with type 2 diabetes.

DESIGN

We performed a retrospective, observational analysis of cross-sectional data routinely collected within a health system. Logistic regressions estimated the association between glycaemic control and the dual effect of self-reported and area-level measures of food insecurity.

SETTING

The health system included a network of ambulatory primary and speciality care sites and hospitals in Bronx County, NY.

PARTICIPANTS

Patients diagnosed with type 2 diabetes who completed a health-related social need (HRSN) assessment between April 2018 and December 2019.

RESULTS

5500 patients with type 2 diabetes were assessed for HRSN with 7·1 % reporting an unmet food need. Patients with self-reported food needs demonstrated higher odds of having poor glycaemic control compared with those without food needs (adjusted OR (aOR): 1·59, 95 % CI: 1·26, 2·00). However, there was no conclusive evidence that area-level food insecurity alone was a significant predictor of glycaemic control (aOR: 1·15, 95 % CI: 0·96, 1·39). Patients with self-reported food needs residing in food-secure (aOR: 1·83, 95 % CI: 1·22, 2·74) and food-insecure (aOR: 1·72, 95 % CI: 1·25, 2·37) areas showed higher odds of poor glycaemic control than those without self-reported food needs residing in food-secure areas.

CONCLUSIONS

These findings highlight the importance of utilising patient- and area-level social needs data to identify individuals for targeted interventions with increased risk of adverse health outcomes.

Association Between Medicaid Coverage Continuity and Survival in Patients With Newly Diagnosed Pediatric and Adolescent Cancers

PURPOSE

Many patients with cancer do not gain Medicaid coverage until a cancer diagnosis, which can reduce access to early cancer detection and timely treatment, potentially driving inferior survival. Little is known about whether continuous Medicaid coverage prediagnosis through postdiagnosis (v gaining Medicaid at/after diagnosis) provides survival benefits for pediatric/adolescent oncology patients.

MATERIALS AND METHODS

We identified patients newly diagnosed with cancer at age 21 years or younger in a large pediatric health system between 2007 and 2016. Electronic medical records (EMRs) were linked to Medicaid administrative data to differentiate insurance continuity patterns during the 6 months preceding through the 6 months after cancer diagnosis (assessment window): continuous Medicaid, newly gained Medicaid (at or after diagnosis), and other Medicaid enrollment patterns. For patients not linked to Medicaid data, we used EMR-reported insurance types at diagnosis. We followed patients from 6 months postdiagnosis up to 5 years, death, or December 2020, whichever came first. Multivariable regressions estimated all-cause and cancer-specific survival, controlling for sociodemographic and cancer-related factors.

RESULTS

Among 1,800 patients included in the analysis, 1,293 (71.8%) had some Medicaid enrollment during the assessment window; among them, 47.6% had continuous Medicaid and 36.3% had newly gained Medicaid. Patients not linked with Medicaid data had private (26.9%) or other/no insurance (1.2%) at diagnosis. Compared with patients with continuous Medicaid, those with newly gained Medicaid had higher risks of all-cause death (hazard ratio [HR], 1.41 [95% CI, 1.10 to 1.81]; P = .008) and cancer-specific death (HR, 1.46 [95% CI, 1.12 to 1.90]; P = .005).

CONCLUSION

Continuous Medicaid coverage throughout cancer diagnosis is associated with survival benefits for pediatric/adolescent patients. This finding has critical implications as millions of American individuals have been losing coverage since the unwinding of the Medicaid Continuous Enrollment Provision.

Establishing a Regional Health System and Community-Based Organization Social Care Coordination Network: An Application of Geospatial Analysis

INTRODUCTION

Adverse social determinants of health have been shown to be associated with a greater chance of developing chronic conditions. Although there has been increased focus on screening for health-related social needs (HRSNs) in health care delivery systems, it is seldom examined if the provision of needed services to address HRSNs is sufficiently available in communities where patients reside.

METHODS

The authors used geospatial analysis to determine how well a newly formed health system and community-based organizations (CBOs) social care coordination network covered the areas in which a high number of patients experiencing HRSNs live. Geospatial clusters (hotspots) were constructed for Kaiser Permanente Northwest members experiencing any of the following 4 HRSNs: transportation needs, housing instability, food insecurity, or financial strain. Next, a geospatial polygon was calculated indicating whether a member could reach a social care provider within 30 minutes of travel time.

RESULTS

A total of 185,535 Kaiser Permanente Northwest members completed a HRSN screener between April 2022 and April 2023. Overall, the authors found that among Kaiser Permanente Northwest members experiencing any of the 4 HRSNs, 97% to 98% of them were within 30 minutes of a social care provider. A small percentage of members who lived greater than 30 minutes to a social care provider were primarily located in rural areas.

DISCUSSION AND CONCLUSION

This study demonstrates the importance of health system and community-based organization partnerships and investment in community resources to develop social care coordination networks, as well as how patient-level HRSN can be used to assess the coverage and representativeness of the network.

Capturing the care of complex community-based health center patients: A comparison of multimorbidity indices and clinical classification software

OBJECTIVE

To compare morbidity burden captured from multimorbidity indices and aggregated measures of clinically meaningful categories captured in primary care community-based health center (CBHC) patients.

DATA SOURCES AND STUDY SETTING

Electronic health records of patients seen in 2019 in OCHIN's national network of CBHCs serving patients in rural and underserved communities.

STUDY DESIGN

Age-stratified analyses comparing the most common conditions captured by the Charlson, Elixhauser, and Multimorbidity Weighted (MWI) indices, and Classification Software Refined (CCSR) and Chronic Condition Indicator (CCI) algorithms.

DATA COLLECTION/EXTRACTION METHODS

Active ICD-10 conditions on patients' problem list in 2019.

PRINCIPAL FINDINGS

Approximately 35%-56% of patients with at least one condition are not captured by the Charlson, Elixhauser, and MWI indices. When stratified by age, this range broadens to 9%-90% with higher percentages in younger patients. The CCSR and CCI reflect a broader range of acute and chronic conditions prevalent among CBHC patients.

CONCLUSION

Three commonly used indices to capture morbidity burden reflect conditions most prevalent among older adults, but do not capture those on problem lists for younger CBHC patients. An index with an expanded range of care conditions is needed to understand the complex care provided to primary care populations across the lifespan.

Model-based estimation of individual-level social determinants of health and its applications in All of Us

OBJECTIVES

We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program.

MATERIALS AND METHODS

Our approach utilizes aggregated SDoH datasets to estimate individual-level SDoH, demonstrated with examples of no high school diploma (NOHSDP) and no health insurance (UNINSUR) variables. Models are estimated using American Community Survey data and applied to derive individual-level estimates for All of Us participants. We assess concordance between model-based SDoH estimates and self-reported SDoHs in All of Us and examine associations with undiagnosed hypertension and diabetes.

RESULTS

Compared to self-reported SDoHs, the area under the curve for NOHSDP is 0.727 (95% CI, 0.724-0.730) and for UNINSUR is 0.730 (95% CI, 0.727-0.733) among the 329 074 All of Us participants, both significantly higher than aggregated SDoHs. The association between model-based NOHSDP and undiagnosed hypertension is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.649. Similarly, the association between model-based NOHSDP and undiagnosed diabetes is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.900.

DISCUSSION AND CONCLUSION

The model-based SDoH estimation method offers a scalable and easily standardized approach for estimating individual-level SDoHs. Using the All of Us dataset, we demonstrate reasonable concordance between model-based SDoH estimates and self-reported SDoHs, along with consistent associations with health outcomes. Our findings also underscore the critical role of geographic contexts in SDoH estimation and in evaluating the association between SDoHs and health outcomes.

Neighborhood-level social determinants of health and cardioprotective behaviors among church members in New Orleans, Louisiana

BACKGROUND

Favorable neighborhood-level social determinants of health (SDoH) are associated with lower cardiovascular disease risk. Less is known about their influence on cardioprotective behaviors. We evaluated the associations between neighborhood-level SDoH and cardioprotective behaviors among church members in Louisiana.

METHODS

Participants were surveyed between November 2021 to February 2022, and were asked about health behaviors, aspects of their neighborhood, and home address (to link to census tract and corresponding social deprivation index [SDI] data). Logistic regression models were used to assess the relation of neighborhood factors with the likelihood of engaging in cardioprotective behaviors: 1) a composite of healthy lifestyle behaviors [fruit and vegetable consumption, physical activity, and a tobacco/nicotine-free lifestyle], 2) medication adherence, and 3) receipt of routine medical care within the past year.

RESULTS

Participants (n = 302, mean age: 63 years, 77% female, 99% Black) were recruited from 12 churches in New Orleans. After adjusting for demographic and clinical factors, perceived neighborhood walkability or conduciveness to exercise (odds ratio [OR]=1.25; 95% CI: 1.03, 1.53), availability of fruits and vegetables (OR=1.23; 95% CI: 1.07, 1.42), and social cohesion (OR=1.55; 95% CI: 1.22, 1.97) were positively associated with the composite of healthy lifestyle behaviors. After multivariable adjustment, SDI was in the direction of association with all three cardioprotective behavior outcomes, but associations were not statistically significant.

CONCLUSIONS

In this predominantly Black, church-based population, neighborhood-level SDoH including the availability of fruits and vegetables, walkability or conduciveness to exercise, and social cohesion were associated with cardioprotective behaviors. Findings reiterate the need to address adverse neighborhood-level SDoH in the design and implementation of health interventions.

Place-Based Opportunity and Well Child Visit Attendance in Early Childhood

BACKGROUND

Lower neighborhood opportunity, measured by the Child Opportunity Index [COI], is associated with increased pediatric morbidity, but is less frequently used to examine longitudinal well child care. We aimed to evaluate associations between the COI and well child visit [WCV] attendance from birth - <36 months of age.

METHODS

The Upstate KIDS population-based birth cohort includes children born 2008-2010 in New York state. The exposure, 2010 census tract COI (very low [VL] to very high [VH]), was linked to children's geocoded residential address at birth. The outcome was attended WCVs from birth - <36 months of age. Parents reported WCVs and their child's corresponding age on questionnaires every 4-6 months. These data were applied to appropriate age ranges for recommended WCVs to determine attendance. Associations were modeled longitudinally as odds of attending visits and as mean differences in proportions of WCVs by COI.

RESULTS

Among 4650 children, 21% (n = 977) experienced VL or low COI. Children experiencing VL (adjusted OR [aOR] 0.68, 95% CI 0.61, 0.76), low (aOR 0.81, 95% CI 0.73, 0.90), and moderate COI (aOR 0.88, 95% CI 0.81, 0.96), compared to VH COI, had decreased odds of attending any WCV. The estimated, adjusted mean proportions of WCV attendance were lower among children experiencing VL (0.45, P < .01), low (0.53, P = .02), moderate (0.53, P = .05), and high (0.54, P = .03) compared to VH COI (0.56).

CONCLUSIONS

Lower COI at birth was associated with decreased WCV attendance throughout early childhood. Reducing barriers to health care access for children experiencing lower COI may advance equitable well child care.

Neighborhood-level social determinants of health measures independently predict receipt of living donor liver transplantation in the United States

Disparities exist in the access to living donor liver transplantation (LDLT) in the United States. However, the association of neighborhood-level social determinants of health (SDoH) on the receipt of LDLT is not well-established. This was a retrospective cohort study of adult liver transplant recipients between January 1, 2005 and December 31, 2021 at centers performing LDLT using the United Network for Organ Sharing database, which was linked through patients' ZIP code to a set of 24 neighborhood-level SDoH measures from different data sources. Temporal trends and center differences in neighborhood Social Deprivation Index (SDI), a validated scale of socioeconomic deprivation ranging from 0 to 100 (0=least disadvantaged), were assessed by transplant type. Multivariable logistic regression evaluated the association of increasing SDI on receipt of LDLT [vs. deceased donor liver transplantation (DDLT)]. There were 51,721 DDLT and 4026 LDLT recipients at 59 LDLT-performing centers during the study period. Of the 24 neighborhood-level SDoH measures studied, the SDI was most different between the 2 transplant types, with LDLT recipients having lower SDI (ie, less socioeconomic disadvantage) than DDLT recipients (median SDI 37 vs. 47; p < 0.001). The median difference in SDI between the LDLT and DDLT groups significantly decreased from 13 in 2005 to 3 in 2021 ( p = 0.003). In the final model, the SDI quintile was independently associated with transplant type ( p < 0.001) with a threshold SDI of ~40, above which increasing SDI was significantly associated with reduced odds of LDLT (vs. reference SDI 1-20). As a neighborhood-level SDoH measure, SDI is useful for evaluating disparities in the context of LDLT. Center outreach efforts that aim to reduce disparities in LDLT could preferentially target US ZIP codes with SDI > 40.

Assessing associations between individual-level social determinants of health and COVID-19 hospitalizations: Investigating racial/ethnic disparities among people living with human immunodeficiency virus (HIV) in the U.S. National COVID Cohort Collaborative (N3C)

Background

Leveraging the National COVID-19 Cohort Collaborative (N3C), a nationally sampled electronic health records repository, we explored associations between individual-level social determinants of health (SDoH) and COVID-19-related hospitalizations among racialized minority people with human immunodeficiency virus (HIV) (PWH), who have been historically adversely affected by SDoH.

Methods

We retrospectively studied PWH and people without HIV (PWoH) using N3C data from January 2020 to November 2023. We evaluated SDoH variables across three domains in the Healthy People 2030 framework: (1) healthcare access, (2) economic stability, and (3) social cohesion with our primary outcome, COVID-19-related hospitalization. We conducted hierarchically nested additive and adjusted mixed-effects logistic regression models, stratifying by HIV status and race/ethnicity groups, accounting for age, sex, comorbidities, and data partners.

Results

Our analytic sample included 280,441 individuals from 24 data partner sites, where 3,291 (1.17%) were PWH, with racialized minority PWH having higher proportions of adverse SDoH exposures than racialized minority PWoH. COVID-19-related hospitalizations occurred in 11.23% of all individuals (9.17% among PWH, 11.26% among PWoH). In our initial additive modeling, we observed that all three SDoH domains were significantly associated with hospitalizations, even with progressive adjustments (adjusted odds ratios [aOR] range 1.36-1.97). Subsequently, our HIV-stratified analyses indicated economic instability was associated with hospitalization in both PWH and PWoH (aOR range 1.35-1.48). Lastly, our fully adjusted, race/ethnicity-stratified analysis, indicated access to healthcare issues was associated with hospitalization across various racialized groups (aOR range 1.36-2.00).

Conclusion

Our study underscores the importance of assessing individual-level SDoH variables to unravel the complex interplay of these factors for racialized minority groups.

Understanding individual health-related social needs in the context of area-level social determinants of health: The case for granularity

Introduction

Screening for health-related social needs (HRSNs) within health systems is a widely accepted recommendation, however challenging to implement. Aggregate area-level metrics of social determinants of health (SDoH) are easily accessible and have been used as proxies in the interim. However, gaps remain in our understanding of the relationships between these measurement methodologies. This study assesses the relationships between three area-level SDoH measures, Area Deprivation Index (ADI), Social Deprivation Index (SDI) and Social Vulnerability Index (SVI), and individual HRSNs among patients within one large urban health system.

Methods

Patients screened for HRSNs between 2018 and 2019 (N = 45,312) were included in the analysis. Multivariable logistic regression models assessed the association between area-level SDoH scores and individual HRSNs. Bivariate choropleth maps displayed the intersection of area-level SDoH and individual HRSNs, and the sensitivity, specificity, and positive and negative predictive values of the three area-level metrics were assessed in relation to individual HRSNs.

Results

The SDI and SVI were significantly associated with HRSNs in areas with high SDoH scores, with strong specificity and positive predictive values (∼83% and ∼78%) but poor sensitivity and negative predictive values (∼54% and 62%). The strength of these associations and predictive values was poor in areas with low SDoH scores.

Conclusions

While limitations exist in utilizing area-level SDoH metrics as proxies for individual social risk, understanding where and how these data can be useful in combination is critical both for meeting the immediate needs of individuals and for strengthening the advocacy platform needed for resource allocation across communities.

Differences in up-to-date colorectal and cervical cancer screening rates by ethnicity and preferred language: An analysis across patient-, clinic-, and area-level data sources

Research objective

There is interest in using clinic- and area-level data to inform cancer control, but it is unclear what value these sources may add in combination with patient-level data sources. This study aimed to investigate associations of up-to-date colorectal and cervical cancer screenings at community health centers (CHCs) with ethnicity and language variables at patient-, clinic-, and area-levels, while exploring whether patient-level associations differed based on clinic-level patient language and ethnicity distributions.

Study design

This was a cross-sectional study using data from multiple sources, including electronic health records, clinic patient panel data, and area-level demographic data. The study sample included English-preferring Hispanic, Spanish-preferring Hispanic, English-preferring non-Hispanic, and non-English-preferring non-Hispanic patients eligible for either colorectal cancer (N = 98,985) or cervical cancer (N = 129,611) screenings in 2019 from 130 CHCs in the OCHIN network in CA, OR, and WA.

Population studied

The study population consisted of adults aged 45+ eligible for colorectal cancer screening and adults with a cervix aged 25-65 eligible for cervical cancer screening.

Principal findings

Spanish-preferring Hispanic patients were significantly more likely to be up-to-date with colorectal and cervical cancer screenings than other groups. Patients seen at clinics with higher concentrations of Spanish-preferring Hispanics were significantly more likely to be up-to-date, as were individuals residing in areas with higher percentages of Spanish-speaking residents. Differential associations between patient ethnicity and language and up-to-date colorectal cancer screenings were greater among patients seen at clinics with higher concentrations of Spanish-preferring Hispanics.

Conclusions

The findings highlight that Spanish-speaking Hispanics seen in CHCs have higher rates of up-to-date cervical and colorectal cancer screenings than other groups and that this relationship is stronger at clinics with higher percentages of Spanish-preferring Hispanic patients. Our findings suggest area-level variables are not good substitutions for patient-level data, but variables at the clinic patient panel-level are more informative.

Intersecting social determinants of health among patients with childcare needs: a cross-sectional analysis of social vulnerability

INTRODUCTION

Access to childcare is an understudied social determinant of health (SDOH). Our health system established a childcare facility for patients to address childcare barriers to healthcare. Recognizing that social risk factors often co-exist, we sought to understand intersecting social risk factors among patients with childcare needs who utilized and did not utilize the childcare facility and identify residual unmet social needs alongside childcare needs.

METHODS

We conducted a cross-sectional analysis of patients who enrolled in the childcare facility from November 2020 to October 2022 to compare parameters of the Social Vulnerability Index (SVI) associated with the census tract extracted from electronic medical record (EMR) data among utilizers and non-utilizers of the facility. Overall SVI and segmentation into four themes of vulnerability (socioeconomic status, household characteristics, racial/ethnic minority status, and housing type/transportation) were compared across utilizers and utilizers. Number of 90th percentile indicators were also compared to assess extreme levels of vulnerability. A sample of utilizers additionally received a patient-reported social needs screening questionnaire administered at the childcare facility.

RESULTS

Among 400 enrollees in the childcare facility, 70% utilized childcare services and 30% did not. Utilizers and non-utilizers were demographically similar, though utilizers were more likely to speak Spanish (34%) compared to non-utilizers (22%). Mean SVI was similar among utilizers and non-utilizers, but the mean number of 90th percentile indicators were higher for non-utilizers compared to utilizers (4.3 ± 2.7 vs 3.7 ± 2.7, p = 0.03), primarily driven by differences in the housing type/transportation theme (p = 0.01). Non-utilizers had a lower rate of healthcare utilization compared to utilizers (p = 0.02). Among utilizers who received patient-reported screening, 84% had one unmet social need identified, of whom 62% agreed for additional assistance. Among social work referrals, 44% were linked to social workers in their medical clinics, while 56% were supported by social work integrated in the childcare facility.

CONCLUSIONS

This analysis of SDOH approximated by SVI showed actionable differences, potentially transportation barriers, among patients with childcare needs who utilized a health system-integrated childcare facility and patients who did not utilize services. Furthermore, residual unmet social needs among patients who utilized the facility demonstrate the multifactorial nature of social risk factors experienced by patients with childcare needs and opportunities to address intersecting social needs within an integrated intervention. Intersecting social needs require holistic examination and multifaceted interventions.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Background

Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality.

Methods

We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes.

Discussion

Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Association of state Medicaid expansion policies with pediatric liver transplant outcomes

Children from minoritized/socioeconomically deprived backgrounds suffer disproportionately high rates of uninsurance and graft failure/death after liver transplant. Medicaid expansion was developed to expand access to public insurance. Our objective was to characterize the impact of Medicaid expansion policies on long-term graft/patient survival after pediatric liver transplantation. All pediatric patients (<19 years) who received a liver transplant between January 1, 2005, and December 31, 2020 in the US were identified in the Scientific Registry of Transplant Recipients (N = 8489). Medicaid expansion was modeled as a time-varying exposure based on transplant and expansion dates. We used Cox proportional hazards models to evaluate the impact of Medicaid expansion on a composite outcome of graft failure/death over 10 years. As a sensitivity analysis, we conducted an intention-to-treat analysis from time of waitlisting to death (N = 1 1901). In multivariable analysis, Medicaid expansion was associated with a 30% decreased hazard of graft failure/death (hazard ratio, 0.70; 95% confidence interval, 0.62, 0.79; P < .001) after adjusting for Black race, public insurance, neighborhood deprivation, and living in a primary care shortage area. In intention-to-treat analyses, Medicaid expansion was associated with a 72% decreased hazard of patient death (hazard ratio, 0.28; 95% confidence interval, 0.23-0.35; P < .001). Policies that enable broader health insurance access may help improve outcomes and reduce disparities for children undergoing liver transplantation.

Variation in multimorbidity by sociodemographics and social drivers of health among patients seen at community-based health centers

Purpose

Understanding variation in multimorbidity across sociodemographics and social drivers of health is critical to reducing health inequities.

Methods

From the multi-state OCHIN network of community-based health centers (CBHCs), we identified a cross-sectional cohort of adult (> 25 years old) patients who had a visit between 2019-2021. We used generalized linear models to examine the relationship between the Multimorbidity Weighted Index (MWI) and sociodemographics and social drivers of health (Area Deprivation Index [ADI] and social risks [e.g., food insecurity]). Each model included an interaction term between the primary predictor and age to examine if certain groups had a higher MWI at younger ages.

Results

Among 642,730 patients, 28.2% were Hispanic/Latino, 42.8% were male, and the median age was 48. The median MWI was 2.05 (IQR: 0.34, 4.87) and was higher for adults over the age of 40 and American Indians and Alaska Natives. The regression model revealed a higher MWI at younger ages for patients living in areas of higher deprivation. Additionally, patients with social risks had a higher MWI (3.16; IQR: 1.33, 6.65) than those without (2.13; IQR: 0.34, 4.89) and the interaction between age and social risk suggested a higher MWI at younger ages.

Conclusions

Greater multimorbidity at younger ages and among those with social risks and living in areas of deprivation shows possible mechanisms for the premature aging and disability often seen in community-based health centers and highlights the need for comprehensive approaches to improving the health of vulnerable populations.

Association between neighborhood-based material deprivation and liver transplant waitlist registrants demographics and mortality

BACKGROUND & AIMS

Liver transplantation for alcohol-related liver disease (ARLD) has increased. We examined temporal trends in ARLD listing practices by neighborhood deprivation and evaluated the impact of neighborhood deprivation on waitlist mortality.

METHODS

We included all adults > 18 years listed 2008-2019 in the UNOS registry. Our primary exposure was the neighborhood socioeconomic deprivation index based on patients' listing zip codes. We determined temporal trends in an ARLD listing diagnosis. We modeled ARLD listing diagnosis using logistic regression and waitlist mortality using Cox proportional hazards models.

RESULTS

The waitlist contained an increasing proportion of patients listed with ARLD over the study period; however, this rate increased the least for patients from the most deprived tertile (p < .001). Patients from the most deprived tertile were the least likely to be listed with ARLD (OR: .97, 95CI: .95-.98). In our adjusted model, patients from the most deprived tertile had an increased hazard of waitlist mortality (OR: 1.10, 95CI: 1.06-1.14).

CONCLUSION

Neighborhood deprivation was associated with a decreased likelihood of being listed with ARLD, suggesting that transplant for ARLD is inequitably available. The increased mortality associated with neighborhood deprivation demands future work to uncover the underlying reasons for this disparity.

Assessing Area-Level Deprivation as a Proxy for Individual-Level Social Risks

INTRODUCTION

Concerns about the opportunity costs of social screening initiatives have led some healthcare organizations to consider using social deprivation indices (area-level social risks) as proxies for self-reported needs (individual-level social risks). Yet, little is known about the effectiveness of such substitutions across different populations.

METHODS

This analysis explores how well the highest quartile (cold spot) of three different area-level social risk measures-the Social Deprivation Index, Area Deprivation Index, and Neighborhood Stress Score-corresponds with six individual-level social risks and three risk combinations among a national sample of Medicare Advantage members (N=77,503). Data were derived from area-level measures and cross-sectional survey data collected between October 2019 and February 2020. Agreement between individual and individual-level social risks, sensitivity values, specificity values, positive predictive values, and negative predictive values was calculated for all measures in summer/fall 2022.

RESULTS

Agreement between area and individual-level social risks ranged from 53% to 77%. Sensitivity for each risk and risk category never exceeded 42%; specificity values ranged from 62% to 87%. Positive predictive values ranged from 8% to 70%, and negative predictive values ranged from 48% to 93%. There were modest performance discrepancies across area-level measures.

CONCLUSIONS

These findings provide additional evidence that area-level deprivation indices may be inconsistent indicators of individual-level social risks, supporting policy efforts to promote individual-level social screening programs in healthcare settings.

Social Determinants of Health, Quality of Public Health Data, and Health Equity in the United States

In recent years, increasing attention has been paid to the impact social determinants of health (SDOH) can have on health equity in the United States. In this essay, we provide a framework for considering the upstream structural factors that affect the distribution of SDOH as well as the downstream consequences for individuals and groups. Improving health equity in the United States will require multiple policy streams, each requiring comprehensive data for policy development, implementation, and evaluation. Although much progress has been made in improving these data, there remain considerable gaps and opportunities for improvement. (Am J Public Health. 2023;113(12):1301-1308. https://doi.org/10.2105/AJPH.2023.307423).

Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association

Cardiovascular-kidney-metabolic health reflects the interplay among metabolic risk factors, chronic kidney disease, and the cardiovascular system and has profound impacts on morbidity and mortality. There are multisystem consequences of poor cardiovascular-kidney-metabolic health, with the most significant clinical impact being the high associated incidence of cardiovascular disease events and cardiovascular mortality. There is a high prevalence of poor cardiovascular-kidney-metabolic health in the population, with a disproportionate burden seen among those with adverse social determinants of health. However, there is also a growing number of therapeutic options that favorably affect metabolic risk factors, kidney function, or both that also have cardioprotective effects. To improve cardiovascular-kidney-metabolic health and related outcomes in the population, there is a critical need for (1) more clarity on the definition of cardiovascular-kidney-metabolic syndrome; (2) an approach to cardiovascular-kidney-metabolic staging that promotes prevention across the life course; (3) prediction algorithms that include the exposures and outcomes most relevant to cardiovascular-kidney-metabolic health; and (4) strategies for the prevention and management of cardiovascular disease in relation to cardiovascular-kidney-metabolic health that reflect harmonization across major subspecialty guidelines and emerging scientific evidence. It is also critical to incorporate considerations of social determinants of health into care models for cardiovascular-kidney-metabolic syndrome and to reduce care fragmentation by facilitating approaches for patient-centered interdisciplinary care. This presidential advisory provides guidance on the definition, staging, prediction paradigms, and holistic approaches to care for patients with cardiovascular-kidney-metabolic syndrome and details a multicomponent vision for effectively and equitably enhancing cardiovascular-kidney-metabolic health in the population.

Area-level social determinants of health and individual-level social risks: Assessing predictive ability and biases in social risk screening

Introduction

Area-level social determinants of health (SDoH) and individual-level social risks are different, yet area-level measures are frequently used as proxies for individual-level social risks. This study assessed whether demographic factors were associated with patients being screened for individual-level social risks, the percentage who screened positive for social risks, and the association between SDoH and patient-reported social risks in a nationwide network of community-based health centers.

Methods

Electronic health record data from 1,330,201 patients with health center visits in 2021 were analyzed using multilevel logistic regression. Associations between patient characteristics, screening receipt, and screening positive for social risks (e.g., food insecurity, housing instability, transportation insecurity) were assessed. The predictive ability of three commonly used SDoH measures (Area Deprivation Index, Social Deprivation Index, Material Community Deprivation Index) in identifying individual-level social risks was also evaluated.

Results

Of 244,155 (18%) patients screened for social risks, 61,414 (25.2%) screened positive. Sex, race/ethnicity, language preference, and payer were associated with both social risk screening and positivity. Significant health system-level variation in both screening and positivity was observed, with an intraclass correlation coefficient of 0.55 for social risk screening and 0.38 for positivity. The three area-level SDoH measures had low accuracy, sensitivity, and area under the curve when used to predict individual social needs.

Conclusion

Area-level SDoH measures may provide valuable information about the communities where patients live. However, policymakers, healthcare administrators, and researchers should exercise caution when using area-level adverse SDoH measures to identify individual-level social risks.

Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study

BACKGROUND

More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis.

METHODS

This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC.

RESULTS

Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls.

CONCLUSIONS

This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.

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.

Health-Related Social Needs: Which Patients Respond to Screening and Who Receives Resources?

BACKGROUND

Health systems are screening patients for health-related social needs (HRSN) but the optimal approach is unknown.

OBJECTIVE

To describe the variation in responding to an HRSN questionnaire delivered via patient portal, and whether referral to and resources provided by social workers differed by response status.

DESIGN

Retrospective observational study.

PARTICIPANTS

Primary care patients with a visit between June 2020 and January 2022.

INTERVENTION

HRSN questionnaire MAIN MEASURES: We identified each patient's index visit (e.g., date of their first questionnaire response for responders or their first visit within the study period for non-responders). Through the EHR, we identified patients' demographic characteristics. We linked the area deprivation index (ADI) to each patient and grouped patients into quintiles. We used multilevel logistic regressions to identify characteristics associated with responding to the questionnaire and, for responders, reporting a need. We also determined if responder status was associated with receiving a social worker referral or receiving a resource. We included patient demographics and ADI quintile as fixed variables and practice site as a random variable.

KEY RESULTS

Our study included 386,997 patients, of which 51% completed at least one HRSN questionnaire question. Patients with Medicaid insurance (AOR: 0.62, 95%CI: 0.61, 0.64) and those who lived in higher ADI neighborhoods had lower adjusted odds of responding (AOR: 0.76, 95% CI: 0.75, 0.78 comparing quintile 5 to quintile 1). Of responders, having Medicaid insurance (versus private) increased the adjusted odds of reporting each of the HRSN needs by two- to eightfold (p < 0.01). Patients who completed a questionnaire (versus non-responders) had similar adjusted odds of receiving a referral (AOR: 0.91, 95% CI: 0.80, 1.02) and receiving a resource from a SW (AOR: AOR: 1.18, 95%CI: 0.79, 1.77).

CONCLUSION

HRSN questionnaire responses may not accurately represent the needs of patients, especially when delivered solely via patient portal.

Changes in cancer diagnoses and stage distribution during the first year of the COVID-19 pandemic in the USA: a cross-sectional nationwide assessment

BACKGROUND

The emergence of COVID-19 disrupted health care, with consequences for cancer diagnoses and outcomes, especially for early stage diagnoses, which generally have favourable prognoses. We aimed to examine nationwide changes in adult cancer diagnoses and stage distribution during the first year of the COVID-19 pandemic by cancer type and key sociodemographic factors in the USA.

METHODS

In this cross-sectional study, adults (aged ≥18 years) newly diagnosed with a first primary malignant cancer between Jan 1, 2018, and Dec 31, 2020, were identified from the US National Cancer Database. We included individuals across 50 US states and the District of Columbia who were treated in hospitals that were Commission on Cancer-accredited during the study period. Individuals whose cancer stage was 0 (except for bladder cancer), occult, or without an applicable American Joint Committee on Cancer staging scheme were excluded. Our primary outcomes were the change in the number and the change in the stage distribution of new cancer diagnoses between 2019 (Jan 1 to Dec 31) and 2020 (Jan 1 to Dec 31). Monthly counts and stage distributions were calculated for all cancers combined and for major cancer types. We also calculated annual change in stage distribution from 2019 to 2020 and adjusted odds ratios (aORs) using multivariable logistic regression, adjusted for age group, sex, race and ethnicity, health insurance status, comorbidity score, US state, zip code-level social deprivation index, and county-level age-adjusted COVID-19 mortality in 2020. Separate models were stratified by sociodemographic and clinical factors.

FINDINGS

We identified 2 404 050 adults who were newly diagnosed with cancer during the study period (830 528 in 2018, 849 290 in 2019, and 724 232 in 2020). Mean age was 63·5 years (SD 13·5) and 1 287 049 (53·5%) individuals were women, 1 117 001 (46·5%) were men, and 1 814 082 (75·5%) were non-Hispanic White. The monthly number of new cancer diagnoses (all stages) decreased substantially after the start of the COVID-19 pandemic in March, 2020, although monthly counts returned to near pre-pandemic levels by the end of 2020. The decrease in diagnoses was largest for stage I disease, leading to lower odds of being diagnosed with stage I disease in 2020 than in 2019 (aOR 0·946 [95% CI 0·939-0·952] for stage I vs stage II-IV); whereas, the odds of being diagnosed with stage IV disease were higher in 2020 than in 2019 (1·074 [1·066-1·083] for stage IV vs stage I-III). This pattern was observed in most cancer types and sociodemographic groups, although was most prominent among Hispanic individuals (0·922 [0·899-0·946] for stage I; 1·110 [1·077-1·144] for stage IV), Asian American and Pacific Islander individuals (0·924 [0·892-0·956] for stage I; 1·096 [1·052-1·142] for stage IV), uninsured individuals (0·917 [0·875-0·961] for stage I; 1·102 [1·055-1·152] for stage IV), Medicare-insured adults younger than 65 years (0·909 [0·882-0·937] for stage I; 1·105 [1·068-1·144] for stage IV), and individuals living in the most socioeconomically deprived areas (0·931 [0·917-0·946] for stage I; 1·106 [1·087-1·125] for stage IV).

INTERPRETATION

Substantial cancer underdiagnosis and decreases in the proportion of early stage diagnoses occurred during 2020 in the USA, particularly among medically underserved individuals. Monitoring the long-term effects of the pandemic on morbidity, survival, and mortality is warranted.

FUNDING

None.

Evaluating the comparability of patient-level social risk data extracted from electronic health records: A systematic scoping review

Objective: To evaluate how and from where social risk data are extracted from EHRs for research purposes, and how observed differences may impact study generalizability. Methods: Systematic scoping review of peer-reviewed literature that used patient-level EHR data to assess 1 ± 6 social risk domains: housing, transportation, food, utilities, safety, social support/isolation. Results: 111/9022 identified articles met inclusion criteria. By domain, social support/isolation was most often included (N = 68/111), predominantly defined by marital/partner status (N = 48/68) and extracted from structured sociodemographic data (N = 45/48). Housing risk was defined primarily by homelessness (N = 39/49). Structured housing data was extracted most from billing codes and screening tools (N = 15/30, 13/30, respectively). Across domains, data were predominantly sourced from structured fields (N = 89/111) versus unstructured free text (N = 32/111). Conclusion: We identified wide variability in how social domains are defined and extracted from EHRs for research. More consistency, particularly in how domains are operationalized, would enable greater insights across studies.

Comparison of associations of household-level and neighbourhood-level poverty markers with paediatric asthma care utilisation by race/ethnicity in an open cohort of community health centre patients

OBJECTIVE

The objective of this research was to examine how different measurements of poverty (household-level and neighborhood-level) were associated with asthma care utilisation outcomes in a community health centre setting among Latino, non-Latino black and non-Latino white children.

DESIGN, SETTING AND PARTICIPANTS

We used 2012-2017 electronic health record data of an open cohort of children aged <18 years with asthma from the OCHIN, Inc. network. Independent variables included household-level and neighborhood-level poverty using income as a percent of federal poverty level (FPL). Covariate-adjusted generalised estimating equations logistic and negative binomial regression were used to model three outcomes: (1) ≥2 asthma visits/year, (2) albuterol prescription orders and (3) prescription of inhaled corticosteroids over the total study period.

RESULTS

The full sample (n=30 196) was 46% Latino, 26% non-Latino black, 31% aged 6-10 years at first clinic visit. Most patients had household FPL <100% (78%), yet more than half lived in a neighbourhood with >200% FPL (55%). Overall, neighbourhood poverty (<100% FPL) was associated with more asthma visits (covariate-adjusted OR 1.26, 95% CI 1.12 to 1.41), and living in a low-income neighbourhood (≥100% to <200% FPL) was associated with more albuterol prescriptions (covariate-adjusted rate ratio 1.07, 95% CI 1.02 to 1.13). When stratified by race/ethnicity, we saw differences in both directions in associations of household/neighbourhood income and care outcomes between groups.

CONCLUSIONS

This study enhances understanding of measurements of race/ethnicity differences in asthma care utilisation by income, revealing different associations of living in low-income neighbourhoods and households for Latino, non-Latino white and non-Latino black children with asthma. This implies that markers of family and community poverty may both need to be considered when evaluating the association between economic status and healthcare utilisation. Tools to measure both kinds of poverty (family and community) may already exist within clinics, and can both be used to better tailor asthma care and reduce disparities in primary care safety net settings.

Community-level social vulnerability and individual socioeconomic status on liver transplant referral outcome

BACKGROUND

Recent endeavors emphasize the importance of understanding early barriers to liver transplantation (LT) by consistently collecting data on patient demographics, socioeconomic factors, and geographic social deprivation indices.

METHODS

In this retrospective single-center cohort study of 1657 adults referred for LT evaluation, we assessed the association between community-level vulnerability and individual socioeconomic status measures on the rate of waitlisting and transplantation. Patients' addresses were linked to Social Vulnerability Index (SVI) at the census tract-level to characterize community-level vulnerability. Descriptive statistics were used to describe patient characteristics. Multivariable cause-specific HRs were used to assess the association between community-level vulnerability, individual measures of the socioeconomic status, and LT evaluation outcomes (waitlist and transplantation).

RESULTS

Among the 1657 patients referred for LT during the study period, 54% were waitlisted and 26% underwent LT. A 0.1 increase in overall SVI correlated with an 8% lower rate of waitlisting (HR 0.92, 95% CI 0.87-0.96, p < 0.001), with socioeconomic status, household characteristics, housing type and transportation, and racial and ethnic minority status domains contributing significantly to this association. Patients residing in more vulnerable communities experienced a 6% lower rate of transplantation (HR 0.94, 95% CI 0.91- 0.98, p = 0.007), with socioeconomic status and household characteristic domain of SVI significantly contributing to this association. At the individual level, both government insurance and employment status were associated with lower rates of waitlisting and transplantation. There was no association with mortality prior to waitlisting or mortality while on the waitlist.

CONCLUSION

Our findings indicate that both individual and community measures of the socioeconomic status (overall SVI) are associated with LT evaluation outcomes. Furthermore, we identified individual measures of neighborhood deprivation associated with both waitlisting and transplantation.

Racial and Ethnic Disparities in Opioid Access and Urine Drug Screening Among Older Patients With Poor-Prognosis Cancer Near the End of Life

PURPOSE

To characterize racial and ethnic disparities and trends in opioid access and urine drug screening (UDS) among patients dying of cancer, and to explore potential mechanisms.

METHODS

Among 318,549 non-Hispanic White (White), Black, and Hispanic Medicare decedents older than 65 years with poor-prognosis cancers, we examined 2007-2019 trends in opioid prescription fills and potency (morphine milligram equivalents [MMEs] per day [MMEDs]) near the end of life (EOL), defined as 30 days before death or hospice enrollment. We estimated the effects of race and ethnicity on opioid access, controlling for demographic and clinical factors. Models were further adjusted for socioeconomic factors including dual-eligibility status, community-level deprivation, and rurality. We similarly explored disparities in UDS.

RESULTS

Between 2007 and 2019, White, Black, and Hispanic decedents experienced steady declines in EOL opioid access and rapid expansion of UDS. Compared with White patients, Black and Hispanic patients were less likely to receive any opioid (Black, -4.3 percentage points, 95% CI, -4.8 to -3.6; Hispanic, -3.6 percentage points, 95% CI, -4.4 to -2.9) and long-acting opioids (Black, -3.1 percentage points, 95% CI, -3.6 to -2.8; Hispanic, -2.2 percentage points, 95% CI, -2.7 to -1.7). They also received lower daily doses (Black, -10.5 MMED, 95% CI, -12.8 to -8.2; Hispanic, -9.1 MMED, 95% CI, -12.1 to -6.1) and lower total doses (Black, -210 MMEs, 95% CI, -293 to -207; Hispanic, -179 MMEs, 95% CI, -217 to -142); Black patients were also more likely to undergo UDS (0.5 percentage points; 95% CI, 0.3 to 0.8). Disparities in EOL opioid access and UDS disproportionately affected Black men. Adjustment for socioeconomic factors did not attenuate the EOL opioid access disparities.

CONCLUSION

There are substantial and persistent racial and ethnic inequities in opioid access among older patients dying of cancer, which are not mediated by socioeconomic variables.

Association of Health-Related Social Needs With Quality and Utilization Outcomes in a Medicare Advantage Population With Diabetes

Importance

Recent research highlights the association of social determinants of health with health outcomes of patients with type 2 diabetes (T2D).

Objective

To examine associations between health-related social needs (HRSNs) and health care quality and utilization outcomes in a Medicare Advantage population with T2D.

Design, Setting, and Participants

This cross-sectional study used medical and pharmacy claims data from 2019. An HRSN survey was given between October 16, 2019, and February 29, 2020, to Medicare Advantage beneficiaries. Inclusion criteria were diagnosis of T2D, age of 20 to 89 years, continuous Medicare Advantage enrollment in 2019, and response to the HRSN survey. Data were analyzed between June 2021 and January 2022.

Exposures

Enrollment in Medicare Advantage, diagnosis of T2D, and completion of a survey on HRSNs.

Main Outcomes and Measures

Quality outcomes included diabetes medication adherence, statin adherence, completion of a glycated hemoglobin (HbA1c) laboratory test in the past 12 months, and controlled HbA1c. Utilization outcomes included all-cause hospitalization, potentially avoidable hospitalization, emergency department discharge, and readmission.

Results

Of the 21 528 Medicare Advantage beneficiaries with T2D included in the study (mean [SD] age, 71.0 [8.3] years; 55.4% women), most (56.9%) had at least 1 HRSN. Among the population with T2D reporting HRSNs, the most prevalent were financial strain (73.6%), food insecurity (47.5%), and poor housing quality (39.1%). In adjusted models, loneliness (odds ratio [OR], 0.85; 95% CI, 0.73-0.99), lack of transportation (OR, 0.80; 95% CI, 0.69-0.92), utility insecurity (OR, 0.86; 95% CI, 0.76-0.98), and housing insecurity (OR, 0.78; 95% CI, 0.67-0.91) were each associated with lower diabetes medication adherence. Loneliness and lack of transportation were associated with increased emergency visits (marginal effects of 173.0 [95% CI, 74.2-271.9] and 244.6 [95% CI, 150.4-338.9] emergency visits per 1000 beneficiaries for loneliness and transportation, respectively). Food insecurity was the HRSN most consistently associated with higher acute care utilization (marginal effects of 84.6 [95% CI, 19.8-149.4] emergency visits, 30.4 [95% CI, 9.5-51.3] inpatient encounters, and 17.1 [95% CI, 4.7-29.5] avoidable hospitalizations per 1000 beneficiaries).

Conclusions and Relevance

In this cross-sectional study of Medicare Advantage beneficiaries with T2D, some HRSNs were associated with care quality and utilization. The results of the study may be used to direct interventions to the social needs most associated with T2D health outcomes and inform policy decisions at the insurance plan and community level.

Association Between Community-Level Social Risk and Spending Among Medicare Beneficiaries: Implications for Social Risk Adjustment and Health Equity

Importance

Payers are increasingly using approaches to risk adjustment that incorporate community-level measures of social risk with the goal of better aligning value-based payment models with improvements in health equity.

Objective

To examine the association between community-level social risk and health care spending and explore how incorporating community-level social risk influences risk adjustment for Medicare beneficiaries.

Design, Setting, and Participants

Using data from a Medicare Advantage plan linked with survey data on self-reported social needs, this cross-sectional study estimated health care spending health care spending was estimated as a function of demographics and clinical characteristics, with and without the inclusion of Area Deprivation Index (ADI), a measure of community-level social risk. The study period was January to December 2019. All analyses were conducted from December 2021 to August 2022.

Exposures

Census block group-level ADI.

Main Outcomes and Measures

Regression models estimated total health care spending in 2019 and approximated different approaches to social risk adjustment. Model performance was assessed with overall model calibration (adjusted R2) and predictive accuracy (ratio of predicted to actual spending) for subgroups of potentially vulnerable beneficiaries.

Results

Among a final study population of 61 469 beneficiaries (mean [SD] age, 70.7 [8.9] years; 35 801 [58.2%] female; 48 514 [78.9%] White; 6680 [10.9%] with Medicare-Medicaid dual eligibility; median [IQR] ADI, 61 [42-79]), ADI was weakly correlated with self-reported social needs (r = 0.16) and explained only 0.02% of the observed variation in spending. Conditional on demographic and clinical characteristics, every percentile increase in the ADI (ie, more disadvantage) was associated with a $11.08 decrease in annual spending. Directly incorporating ADI into a risk-adjustment model that used demographics and clinical characteristics did not meaningfully improve model calibration (adjusted R2 = 7.90% vs 7.93%) and did not significantly reduce payment inequities for rural beneficiaries and those with a high burden of self-reported social needs. A postestimation adjustment of predicted spending for dual-eligible beneficiaries residing in high ADI areas also did not significantly reduce payment inequities for rural beneficiaries or beneficiaries with self-reported social needs.

Conclusions and Relevance

In this cross-sectional study of Medicare beneficiaries, the ADI explained little variation in health care spending, was negatively correlated with spending conditional on demographic and clinical characteristics, and was poorly correlated with self-reported social risk factors. This prompts caution and nuance when using community-level measures of social risk such as the ADI for social risk adjustment within Medicare value-based payment programs.

"Why Do They Want to Know?": A Qualitative Assessment of Caregiver Perspectives on Social Drivers of Health Screening in Pediatric Primary Care

OBJECTIVE

Despite strong evidence that social factors have a large influence on child health, systematic screening for social needs is not performed universally in pediatric primary care. This is due to multiple barriers, including concerns about acceptability to families. This study sought to assess family acceptability of social needs screening in pediatric primary care.

METHODS

Eight semi-structured focus groups were performed with English and Spanish-speaking caregivers of pediatric patients from a diverse academic medical center. Focus groups explored the acceptability of social domains including housing, education, finances, food access, and safety. Focus group transcripts were qualitatively analyzed to identify themes.

RESULTS

Four salient themes emerged: 1) the acceptability of social determinants of health screening questions was tied to participants' understanding of the connection between the topic and child health, 2) families preferred a warm handoff to community services, 3) families feared child protective services intervention as a result of sharing unmet social needs, and 4) positive provider rapport was an important factor in choosing to share social needs.

CONCLUSIONS

Pediatric primary care providers should feel comfortable implementing social needs screening when they can clearly explain the connection to child health. They should become knowledgeable about organizations and partners within their communities and feel empowered to connect patients to these resources.

A comparison of deprivation indices and application to transplant populations

The choice of deprivation index can influence conclusions drawn regarding the extent of deprivation within a community and the identification of the most deprived communities in the United States. This study aimed to determine the degree of correlation among deprivation indices commonly used to characterize transplant populations. We used a retrospective cohort consisting of adults listed for liver or kidney transplants between 2008 and 2018 to compare 4 deprivation indices: neighborhood deprivation index, social deprivation index (SDI), area deprivation index, and social vulnerability index. Pairwise correlation between deprivation indices by transplant referral regions was measured using Spearman correlations of population-weighted medians and upper quartiles. In total, 52 individual variables were used among the 4 deprivation indices with 25% overlap. For both organs, the correlation between the population-weighted 75th percentile of the deprivation indices by transplant referral region was highest between SDI and social vulnerability index (liver and kidney, 0.93) and lowest between area deprivation index and SDI (liver, 0.19 and kidney, 0.15). The choice of deprivation index affects the applicability of research findings across studies examining the relationship between social risk and clinical outcomes. Appropriate application of these measures to transplant populations requires careful index selection based on the intended use and included variable relevance.

Utilizing Publicly Available Community Data to Address Social Determinants of Health: A Compendium of Data Sources

To compile a compendium of data sources representing different areas of social determinants of health (SDOH) in New York City. We conducted a PubMed search of the peer-reviewed and gray literature using the terms "social determinants of health" and "New York City," with the Boolean operator "AND." We then conducted a search of the "gray literature," defined as sources outside of standard bibliographic databases, using similar terms. We extracted publicly available data sources containing NYC-based data. In defining SDOH, we used the framework outlined by the CDC's Healthy People 2030, which uses a place-based framework to categorize 5 domains of SDOH: (1) healthcare access and quality; (2) education access and quality; (3) social and community context; (4) economic stability; and (5) neighborhood and built environment. We identified 29 datasets from the PubMed search, and 34 datasets from the gray literature, resulting in 63 datasets related to SDOH in NYC. Of these, 20 were available at the zip code level, 18 at the census tract-level, 12 at the community-district level, and 13 at the census block or specific address level. Community-level SDOH data are readily attainable from many public sources and can be linked with health data on local geographic-levels to assess the effect of social and community factors on individual health outcomes.

Examining How Social Risk Factors Are Integrated Into Clinical Settings Using Existing Data: A Scoping Review

PURPOSE

Integrating social care into clinical care requires substantial resources. Use of existing data through a geographic information system (GIS) has the potential to support efficient and effective integration of social care into clinical settings. We conducted a scoping literature review characterizing its use in primary care settings to identify and address social risk factors.

METHODS

In December 2018, we searched 2 databases and extracted structured data for eligible articles that (1) described the use of GIS in clinical settings to identify and/or intervene on social risks, (2) were published between December 2013 and December 2018, and (3) were based in the United States. Additional studies were identified by examining references.

RESULTS

Of the 5,574 articles included for review, 18 met study eligibility criteria: 14 (78%) were descriptive studies, 3 (17%) tested an intervention, and 1 (6%) was a theoretical report. All studies used GIS to identify social risks (increase awareness); 3 studies (17%) described interventions to address social risks, primarily by identifying relevant community resources and aligning clinical services to patients' needs.

CONCLUSIONS

Most studies describe associations between GIS and population health outcomes; however, there is a paucity of literature regarding GIS use to identify and address social risk factors in clinical settings. GIS technology may assist health systems seeking to address population health outcomes through alignment and advocacy; its current application in clinical care delivery is infrequent and largely limited to referring patients to local community resources.

Adjusting for Patient Economic/Access Issues in a Hypertension Quality Measure

INTRODUCTION

The American Heart Association and American College of Cardiology have proposed adjusting hypertension-related care quality measures by excluding patients with economic/access issues from the denominator of rate calculations. No research to date has explored the methods to operationalize this recommendation or how to measure economic/access issues. This study applied and compared different approaches to populating these denominator exceptions.

METHODS

Electronic health record data from 2019 were used in 2021 to calculate hypertension control rates in 84 community health centers. A total of 10 different indicators of patient economic/access barriers to care were used as denominator exclusions to calculate and then compare adjusted quality measure performance. Data came from a nonprofit health center‒controlled network that hosts a shared electronic health record for community health centers located in 22 states.

RESULTS

A total of 5 of 10 measures yielded an increase in adjusted hypertension control rates in ≥50% of clinics (average rate increases of 0.7-3.71 percentage points). A total of 3 of 10 measures yielded a decrease in adjusted hypertension control rates in >50% of clinics (average rate decreases of 1.33-13.82 percentage points). A total of 5 measures resulted in excluding >50% of the clinic's patient population from quality measure assessments.

CONCLUSIONS

Changes in clinic-level hypertension control rates after adjustment differed depending on the measure of economic/access issue. Regardless of the exclusion method, changes between baseline and adjusted rates were small. Removing community health center patients experiencing economic/access barriers from a hypertension control quality measure resulted in excluding a large proportion of patients, raising concerns about whether this calculation can be a meaningful measure of clinical performance.

Trends, Social Context, and Transplant Implications of Obesity Among Incident Dialysis Patients in the United States

BACKGROUND

Kidney transplant programs have variable thresholds to accept obese candidates. This study aimed to examine trends and the social context of obesity among United States dialysis patients and implications for kidney transplant access.

METHODS

We performed a retrospective cohort study of 1 084 816 adults who initiated dialysis between January 2007 and December 2016 using the United States Renal Data System data. We estimated national body mass index (BMI) trends and 1-y cumulative incidence of waitlisting and death without waitlisting by BMI category (<18.5 kg/m 2 , ≥18.5 and <25 kg/m 2 [normal weight], ≥25 and <30 kg/m 2 [overweight], ≥30 and <35 kg/m 2 [class 1 obesity], ≥35 and <40 kg/m 2 [class 2 obesity], and ≥40 kg/m 2 [class 3 obesity]). We then used Fine-Gray subdistribution hazard regression models to examine associations between BMI category and 1-y waitlisting with death as a competing risk and tested for effect modification by End Stage Renal Disease (ESRD) network, patient characteristics, and neighborhood social deprivation index.

RESULTS

The median age was 65 (interquartile range 54-75) y, 43% were female, and 27% were non-Hispanic Black. From 2007 to 2016, the adjusted prevalence of class 1 obesity or higher increased from 31.9% to 38.2%. Class 2 and 3 obesity but not class 1 obesity were associated with lower waitlisting rates relative to normal BMI, especially for younger individuals, women, those of Asian race, or those living in less disadvantaged neighborhoods ( pinteraction < 0.001 for all).

CONCLUSIONS

Obesity prevalence is rising among US incident dialysis patients. Relative to normal BMI, waitlisting rates with class 2 and 3 obesity were lower and varied substantially by region, patient characteristics, and socioeconomic context.

Community access to primary care is an important geographic disparity among ovarian cancer patients undergoing cytoreductive surgery

Objective

Given the importance of understanding neighborhood context and geographic access to care on individual health outcomes, we sought to investigate the association of community primary care (PC) access on postoperative outcomes and survival in ovarian cancer patients.

Methods

This was a retrospective cohort study of Stage III-IV ovarian cancer patients who underwent surgery at a single academic, tertiary care hospital between 2012 and 2015. PC access was determined using a Health Resources and Services Administration designation. Outcomes included 30-day surgical and medical complications, extended hospital stay, ICU admission, hospital readmission, progression-free and overall survival. Descriptive statistics and chi-squared analyses were used to analyze differences between patients from PC-shortage vs not PC-shortage areas.

Results

Among 217 ovarian cancer patients, 54.4 % lived in PC-shortage areas. They were more likely to have Medicaid or no insurance and live in rural areas with higher poverty rates, significantly further from the treating cancer center and its affiliated hospital. Nevertheless, 49.2 % of patients from PC-shortage areas lived in urban communities. Residing in a PC-shortage area was not associated with increased surgical or medical complications, ICU admission, or hospital readmission, but was linked to more frequent prolonged hospitalization (26.3 % vs 14.1 %, p = 0.04). PC-shortage did not impact progression-free or overall survival.

Conclusions

Patients from PC-shortage areas may require longer inpatient perioperative care in order to achieve the same 30-day postoperative outcomes as patients who live in non-PC shortage areas. Community access to PC is a critical factor to better understanding and reducing disparities among ovarian cancer patients.

Factors associated with federally qualified health center financial performance

OBJECTIVES

To understand factors associated with federally qualified health center (FQHC) financial performance.

STUDY DESIGN

We used multivariate linear regression to identify correlates of health center financial performance. We examined six measures of health center financial performance across four domains: margin (operating margin), liquidity (days cash on hand [DCOH], current ratio), solvency (debt-to-equity ratio), and others (net patient accounts receivable days, personnel-related expenses). We examined potential correlates of financial performance, including characteristics of the patient population, health center organization, and location/geography.

DATA SOURCES

We use 2012-2017 Uniform Data System (UDS) files, financial audit data from Capital link, and publicly available data.

DATA COLLECTION/EXTRACTION METHODS

We focused on health centers in the 50 US states and District of Columbia, which reported information to UDS for at least 1 year between 2012 and 2017 and had Capital link financial audit data.

PRINCIPAL FINDINGS

FQHC financial performance generally improved over the study period, especially from 2015 to 2017. In multivariate regression models, a higher percentage of Medicaid patients was associated with better margins (operating margin: 0.06, p < 0.001), liquidity (DCOH: 0.67, p < 0.001; current ratio: 0.28, p = 0.001), and solvency (debt-to equity ratio: -0.08, p = 0.004). Moreover, a staffing mix comprised of more nonphysician providers was associated with better margin (operating margin: 0.21, p = 0.001) and liquidity (current ratio: 1.12, p < 0.001) measures. Patient-centered medical home (PCMH) recognition was also associated with better liquidity (DCOH: 19.01, p < 0.001; current ratio: 4.68, p = 0.014) and solvency (debt-to-equity ratio: -2.03, p < 0.001).

CONCLUSIONS

The financial health of FQHCs improved with provisions of the Affordable Care Act, which included significant Medicaid expansion and direct funding support for health centers. FQHC financial health was also associated with key staffing and operating characteristics of health centers. Maintaining the financial health of FQHCs is critical to their ability to continuously provide affordable and high-quality care in medically underserved areas.

The Utility of the Brokamp Area Deprivation Index as a Prescreen for Social Risk in Primary Care

OBJECTIVES

To assess the relationship between an Area Deprivation Index (ADI) and a Social Determinant of Health (SDoH) measure within a diverse sample. A prescreening tool based on routinely collected information could reduce clinical burden by identifying patients impacted by SDoH for comprehensive assessment.

STUDY DESIGN

In total, 499 consented pediatric patient-families who spoke English, Spanish, or Arabic and had a child ≤12 years receiving primary care at a large academic institution were enrolled. Participants completed the Health Leads Social Needs (HLSN) survey. Residential address was extracted from the electronic health record to calculate Brokamp ADI at the census-tract level. The main outcome was the correlations between the total HLSN score and Brokamp ADI, overall and in each language subgroup. ADI distributions were also compared between participants with/without need for each of the 8 HLSN survey SDoH domains, using 2-sample t-tests and Pearson χ² tests.

RESULTS

In total, 54.9% of participants were English-speaking, 30.9% were Spanish-speaking, and 14.2% were Arabic-speaking. Spearman correlations between Brokamp ADI and total HLSN score were overall (r_s = 0.15; P = .001), English (r_s = 0.12; P = .04), Spanish (r_s = 0.03; P = .7), and Arabic (r_s = 0.24; P = .04). SDoH domain analyses found significant ADI differences between those with/without need in housing instability, childcare, transportation, and health literacy.

CONCLUSIONS

There were small but statistically significant associations between the Brokamp ADI and total HLSN score and SDoH domains of housing instability, childcare, transportation, and health literacy. These findings support testing the Brokamp ADI as a prescreening tool to help identify patients with social needs in an outpatient clinical setting.

Risk Factors Associated with Post-Acute Sequelae of SARS-CoV-2 in an EHR Cohort: A National COVID Cohort Collaborative (N3C) Analysis as part of the NIH RECOVER program

Background

More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID).

Objective

To identify risk factors associated with PASC/long-COVID.

Design

Retrospective case-control study.

Setting

31 health systems in the United States from the National COVID Cohort Collaborative (N3C).

Patients

8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system.

Measurements

Risk factors included demographics, comorbidities, and treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC.

Results

Among 8,325 individuals with PASC, the majority were >50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30+ days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls.

Conclusions

This national study identified important risk factors for PASC such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.

Repeatable enhancement of healthcare data with social determinants of health

Background

Social and behavioral aspects of our lives significantly impact our health, yet minimal social determinants of health (SDOH) data elements are collected in the healthcare system.

Methods

In this proof-of-concept study we developed a repeatable SDOH enrichment and integration process to incorporate dynamically evolving SDOH domain concepts from consumers into clinical data. This process included SDOH mapping, linking compiled consumer data to patient records in Electronic Health Records, data quality analysis and preprocessing, and storage.

Results

Consumer compilers data coverage ranged from ~90 to ~54% and the percentage match rate between compilers was between ~21 and 64%. Our preliminary analysis showed that apart from demographic factors, several SDOH factors like home-ownership, marital-status, presence of children, number of members per household, economic stability and education were significantly different between the COVID-19 positive and negative patient groups while estimated family-income and home market-value were not.

Conclusion

Our preliminary analysis shows commercial consumer data can be a viable source of SDOH factor at an individual-level for clinical data thus providing a path for clinicians to improve patient treatment and care.

Associations Between Different Self-reported Social Risks and Neighborhood-level Resources in Medicaid Patients

BACKGROUND

Adverse social conditions are a key contributor to health disparities. Improved understanding of how social risk factors interact with each other and with neighborhood characteristics may inform efforts to reduce health disparities.

DATA

A questionnaire of 29,281 patients was collected through the enrollment of Medicaid beneficiaries in a large Northern California integrated health care delivery system between May 2016 and February 2020.

EXPOSURES

Living in the least resourced quartile of neighborhoods as measured by a census-tract level Neighborhood Deprivation Index score.

MAIN OUTCOMES

Five self-reported social risk factors: financial need, food insecurity, housing barriers, transportation barriers, and functional limitations.

RESULTS

Nearly half (42.0%) of patients reported at least 1 social risk factor; 22.4% reported 2 or more. Mean correlation coefficient between social risk factors was ρ=0.30. Multivariable logistic models controlling for age, race/ethnicity, sex, count of chronic conditions, and insurance source estimated that living in the least resourced neighborhoods was associated with greater odds of food insecurity (adjusted odds ratio=1.07, 95% confidence interval: 1.00-1.13) and transportation barriers (adjusted odds ratio=1.20, 95% confidence interval: 1.11-1.30), but not financial stress, housing barriers, or functional limitations.

CONCLUSIONS AND RELEVANCE

We found that among 5 commonly associated social risk factors, Medicaid patients in a large Northern California health system typically reported only a single factor and that these factors did not correlate strongly with each other. We found only modestly greater social risk reported by patients in the least resourced neighborhoods. These results suggest that individual-level interventions should be targeted to specific needs whereas community-level interventions may be similarly important across diverse neighborhoods.

Association of Primary Care Shortage Areas with Adverse Outcomes after Pediatric Liver Transplant

OBJECTIVE

To characterize associations between living in primary care shortage areas and graft failure/death for children after liver transplantation.

STUDY DESIGN

This was an observational study of all pediatric patients (aged <19 years) who received a liver transplant between January 1, 2005, and December 31, 2015 in the US, with follow-up through January 2019 (N = 5964). One hundred ninety-five patients whose home ZIP code could not be matched to primary care shortage area status were excluded. The primary outcome was a composite endpoint of graft failure or death. We used Cox proportional hazards to model the associations between health professional shortage area (HPSA) and graft failure/death.

RESULTS

Children living in HPSAs had lower estimated graft survival rates at 10 years compared with those not in HPSAs (76% vs 80%; P < .001). In univariable analysis, residence in an HPSA was associated with a 22% higher hazard of graft failure/death than non-residence in an HPSA (hazard ratio [HR], 1.22; 95% CI, 1.09-1.36; P < .001). Black children from HPSAs had a 67% higher hazard of graft failure/death compared with those not in HPSAs (HR, 1.67; 95% CI, 1.29 to 2.16; P = .006); the effect of HPSA status was less pronounced for White children (HR, 1.11; 95% CI, 0.98-1.27; P = .10).

CONCLUSIONS

Children living in primary care shortage areas are at increased risk of graft failure and death after liver transplant, and this risk is particularly salient for Black children. Future work to understand how living in these regions contributes to adverse outcomes may enable teams to mitigate this risk for all children with chronic illness.

Impact of Social Needs in Electronic Health Records and Claims on Health Care Utilization and Costs Risk-Adjustment Models Within Medicaid Population

Patients enrolled in Medicaid have significantly higher social needs (SNs) than others. Using claims and electronic health records (EHRs) data, managed care organizations (MCOs) could systemically identify high-risk patients with SNs and develop population health management interventions. Impact of SNs on models predicting health care utilization and costs was assessed. This retrospective study included claims and EHRs data on 39,267 patients younger than age 65 years who were continuously enrolled during 2018-2019 in a Medicaid-managed care plan. SN marker was developed suggesting presence of International Classification of Diseases, 10th revision codes in any of the 5 SN domains. Impact of SN marker was compared across demographic and 2 diagnosis-based (ie, Charlson and Adjusted Clinical Groups risk score) prediction models of emergency department (ED) visit and hospitalizations, and total, medical, and pharmacy costs. After combining data sources, prevalence of documented SN marker increased from 11% and 13% to 18% of the study population across claims, EHRs, and both combined, respectively. SN marker improved predictions of demographic models for all utilization and total costs outcomes (area under the curve [AUC] of ED model increased from 0.57 to 0.61 and R² of total cost model increased from 10.9 to 12.2). In both diagnosis-based models, adding SN marker marginally improved outcomes prediction (AUC of ED model increased from 0.65 to 0.66). This study demonstrated feasibility of using claims and EHRs data to systematically capture SNs and incorporate in prediction models that could enable MCOs and policy makers to adjust and develop effective population health interventions.

Health equity research in obstetric anesthesia

PURPOSE OF REVIEW

Health equity is an important priority for obstetric anesthesia, but describing disparities in perinatal care process and health outcome is insufficient to achieve this goal. Conceptualizing and framing disparity is a prerequisite to pose meaningful research questions. We emphasize the need to hypothesize and test which mechanisms and drivers are instrumental for disparities in perinatal processes and outcomes, in order to target, test and refine effective countermeasures.

RECENT FINDINGS

With an emphasis on methodology and measurement, we sketch how health systems and disparity research may advance maternal health equity by narrating, conceptualizing, and investigating social determinants of health as key drivers of perinatal disparity, by identifying the granular mechanism of this disparity, by making the economic case to address them, and by testing specific interventions to advance obstetric health equity.

SUMMARY

Measuring social determinants of health and meaningful perinatal processes and outcomes precisely and accurately at the individual, family, community/neighborhood level is a prerequisite for healthcare disparity research. A focus on elucidating the precise mechanism driving disparity in processes of obstetric care would inform a more rational effort to promote health equity. Implementation scientists should rigorously investigate in prospective trials, which countermeasures are most efficient and effective in mitigating perinatal outcome disparities.

Social Determinants of Health Factors for Gene-Environment COVID-19 Research: Challenges and Opportunities

The characteristics of a person's health status are often guided by how they live, grow, learn, their genetics, as well as their access to health care. Yet, all too often, studies examining the relationship between social determinants of health (behavioral, sociocultural, and physical environmental factors), the role of demographics, and health outcomes poorly represent these relationships, leading to misinterpretations, limited study reproducibility, and datasets with limited representativeness and secondary research use capacity. This is a profound hurdle in what questions can or cannot be rigorously studied about COVID-19. In practice, gene-environment interactions studies have paved the way for including these factors into research. Similarly, our understanding of social determinants of health continues to expand with diverse data collection modalities as health systems, patients, and community health engagement aim to fill the knowledge gaps toward promoting health and wellness. Here, a conceptual framework is proposed, adapted from the population health framework, socioecological model, and causal modeling in gene-environment interaction studies to integrate the core constructs from each domain with practical considerations needed for multidisciplinary science.

Individual-Level and Neighborhood-Level Factors Associated with Longitudinal Changes in Cardiometabolic Measures in Participants of a Clinic-Based Care Coordination Program: A Secondary Data Analysis

Background: Identifying individual and neighborhood-level factors associated with worsening cardiometabolic risks despite clinic-based care coordination may help identify candidates for supplementary team-based care. Methods: Secondary data analysis of data from a two-year nurse-led care coordination program cohort of Medicare, Medicaid, dual-eligible adults, Leveraging Information Technology to Guide High Tech, High Touch Care (LIGHT2), from ten Midwestern primary care clinics in the U.S. Outcome Measures: Hemoglobin A1C, low-density-lipoprotein (LDL) cholesterol, and blood pressure. Multivariable generalized linear regression models assessed individual and neighborhood-level factors associated with changes in outcome measures from before to after completion of the LIGHT2 program. Results: 6378 participants had pre-and post-intervention levels reported for at least one outcome measure. In adjusted models, higher pre-intervention cardiometabolic measures were associated with worsening of all cardiometabolic measures. Women had worsening LDL-cholesterol compared with men. Women with pre-intervention HbA1c > 6.8% and systolic blood pressure > 131 mm of Hg had worse post-intervention HbA1c and systolic blood pressure compared with men. Adding individual’s neighborhood-level risks did not change effect sizes significantly. Conclusions: Increased cardiometabolic risks and gender were associated with worsening cardiometabolic outcomes. Understanding unresolved gender-specific needs and preferences of patients with increased cardiometabolic risks may aid in tailoring clinic-community-linked care planning.