A Theoretical Framework for Clinical Implementation of Social Determinants of Health

The role of public health in rare diseases: hemophilia as an example

Introduction

The role of public health has evolved from addressing infectious diseases to encompass non-communicable diseases. Individuals with genetic disorders and rare diseases constitute a particularly vulnerable population, requiring tailored public health policies, practical implementation strategies, and a long-term vision to ensure sustainable support. Given the prolonged duration and significant costs often associated with these conditions, comprehensive, patient-centered, and cost-effective approaches are essential to safeguard their physical and mental well-being.

Aims

To summarize definitions and concepts related to health, public health, rare diseases, and to highlight the role of integrating public health interventions into routine care in improving patient outcomes. Hemophilia was selected as an exemplary rare disease due to its significant lifetime treatment costs and the recent approval and pricing of its gene therapy as the world's most expensive drug, highlighting the critical importance of public health policies in ensuring equitable access to care and treatment.

Methods

A narrative literature review was conducted between July 2023 and December 2024, searching PubMed, Google Scholar, and Google for various topics related to rare diseases, public health, and hemophilia.

Results

Public health can play an important role in improving the health outcomes of people with rare diseases by implementing conceptual and applied models to accomplish a set of objectives. Over the past two decades, legislative and regulatory support in high income countries (HICs) has facilitated the development and approval of diagnostics and treatments for several rare diseases leading to important advancements. In contrast, many low- and middle-income countries (LMICs) face obstacles in enacting legislation, developing regulations, and implementing policies to support rare disease diagnosis and treatment. More investment and innovation in drug discovery and market access pathways are still needed in both LMICs and HICs. Ensuring the translation of public health policies into regulatory measures, and in turn implementing, and regularly evaluating these measures to assess their effectiveness is crucial. In the case of hemophilia, public health can play a pivotal role.

Conclusion

Enhancing public health surveillance, policies, and interventions in hemophilia and other rare diseases can bridge data gaps, support access to equitable treatment, promote evidence-based care, and improve outcomes across the socioeconomic spectrum.

Achievement of guideline-based lipid goals among very-high-risk patients with atherosclerotic cardiovascular disease and type 2 diabetes: results in 213,380 individuals from the cvMOBIUS2 registry

Objective

Lowering lipid to reach guideline-indicated goals significantly reduces cardiovascular outcomes in very-high-risk (VHR) patients with atherosclerotic cardiovascular disease (ASCVD) and type 2 diabetes (DM2). How well VHR patients currently achieve these goals in community practice is unknown.

Methods

VHR patients with ASCVD and DM2 were identified across 14 US healthcare systems using electronic health records between 1/1/2021-12/31/2022. Achievement of guideline-based lipid goals was determined according to the 2018 AHA/ACC/Multisociety guideline, defined as either having a low-density lipoprotein-cholesterol <70 mg/dL or receiving maximal lipid-lowering therapy (i.e., on a PCSK9i monoclonal antibody). Multivariable logistic regression was used to evaluate factors associated with the achievement of these goals.

Results

Among 213,380 eligible patients (median age 71.0 years, 42 % women), 51.8 % achieved guideline-based lipid goals. Female sex (odds ratio [OR], 0.64; 95 % confidence interval [CI], 0.61-0.66), Black race (OR, 0.67; 95 % CI, 0.63-0.72 vs white race), and those on Medicaid (OR, 0.92; 95 % CI, 0.86-0.97 vs Medicare) were associated with a lower likelihood of achieving guideline-based lipid goals. Overall, 76.0 % of patients were on statin, 40.5 % were on a high-intensity statin and only 5.8 % were on a statin in combination with ezetimibe or a PCSK9i monoclonal antibody.

Conclusion

Almost half of all VHR patients with ASCVD and DM2 do not achieve current guideline lipid goals. Women, Black individuals, and those on Medicaid were significantly less likely to achieve these goals relative to their counterparts. Further targeted quality improvement interventions are needed to improve the equitable achievement of guideline-based lipid goals.

The Social Risk ACTIONS Framework: Characterizing Responses to Social Risks by Health Care Delivery Organizations

Social risks refer to individuals' social and economic conditions shaped by underlying social determinants of health. Health care delivery organizations increasingly screen patients for social risks given their potential impact on health outcomes. However, it can be challenging to meaningfully address patients' needs. Existing frameworks do not comprehensively describe and classify ways in which health care delivery organizations can address social risks after screening. Addressing this gap, the authors developed the Social Risk ACTIONS framework (Actionability Characteristics To Inform Organizations' Next steps after Screening) describing 4 dimensions of actionability: Level of action, Actor, Purpose of action, and Action. First, social risk actions can occur at 3 organizational levels (ie, patient encounter, clinical practice/institution, community). Second, social risk actions are initiated by different staff members, referred to as "actors" (ie, clinical care professionals with direct patient contact, clinical/institutional leaders, and researchers). Third, social risk actions can serve one or more purposes: strengthening relationships with patients, tailoring care, modifying the social risk itself, or facilitating population health, research, or advocacy. Finally, specific actions on social risks vary by level, actor, and purpose. This article presents the Social Risk ACTIONS framework, applies its concepts to 2 social risks (food insecurity and homelessness), and discusses its broader applications and implications. The framework offers an approach for leaders of health care delivery organizations to assess current efforts and identify additional opportunities to address social risks. Future work should validate this framework with patients, clinicians, and health care leaders, and incorporate implementation challenges to social risk action.

Social Needs Screening in Academic Health Systems: A Landscape Assessment

Screening for social needs has gained traction as an approach to addressing social determinants of health, but it faces challenges regarding standardization, resource allocation, and follow-up care. The year-long study, conducted by the Association of American Medical Colleges, integrated data from conferences, surveys, and key informant interviews to examine the integration of social needs screening into health care services within Academic Health Systems (AHS). The authors' analysis unveiled eight key themes, showcasing AHS's active involvement in targeted social needs screening alongside persistent resource allocation obstacles. AHS are dedicated to efficiently identifying high-risk populations, fostering partnerships with community-based organizations, and embracing technology for closed-loop referrals. However, concerns endure about the utilization of reimbursement codes for social needs and regulatory compliance. AHS confront staffing issues, resource allocation intricacies, and the imperative for seamless integration across clinical and nonclinical departments. Notably, opportunities arise in standardized training, alignment of AHS priorities, exploration of social investment models, and engagement with state-level health information exchanges. Aligning clinical care, research pursuits, and community engagement endeavors holds promise for AHS in effectively addressing social needs.

Patterns of Social Determinants of Health and Child Mental Health, Cognition, and Physical Health

Importance

Social determinants of health (SDOH) influence child health. However, most previous studies have used individual, small-set, or cherry-picked SDOH variables without examining unbiased computed SDOH patterns from high-dimensional SDOH factors to investigate associations with child mental health, cognition, and physical health.

Objective

To identify SDOH patterns and estimate their associations with children's mental, cognitive, and physical developmental outcomes.

Design, Setting, and Participants

This population-based cohort study included children aged 9 to 10 years at baseline and their caregivers enrolled in the Adolescent Brain Cognitive Development (ABCD) Study between 2016 and 2021. The ABCD Study includes 21 sites across 17 states.

Exposures

Eighty-four neighborhood-level, geocoded variables spanning 7 domains of SDOH, including bias, education, physical and health infrastructure, natural environment, socioeconomic status, social context, and crime and drugs, were studied. Hierarchical agglomerative clustering was used to identify SDOH patterns.

Main Outcomes and Measures

Associations of SDOH and child mental health (internalizing and externalizing behaviors) and suicidal behaviors, cognitive function (performance, reading skills), and physical health (body mass index, exercise, sleep disorder) were estimated using mixed-effects linear and logistic regression models.

Results

Among 10 504 children (baseline median [SD] age, 9.9 [0.6] years; 5510 boys [52.5%] and 4994 girls [47.5%]; 229 Asian [2.2%], 1468 Black [14.0%], 2128 Hispanic [20.3%], 5565 White [53.0%], and 1108 multiracial [10.5%]), 4 SDOH patterns were identified: pattern 1, affluence (4078 children [38.8%]); pattern 2, high-stigma environment (2661 children [25.3%]); pattern 3, high socioeconomic deprivation (2653 children [25.3%]); and pattern 4, high crime and drug sales, low education, and high population density (1112 children [10.6%]). The SDOH patterns were distinctly associated with child health outcomes. Children exposed to socioeconomic deprivation (SDOH pattern 3) showed the worst health profiles, manifesting more internalizing (β = 0.75; 95% CI, 0.14-1.37) and externalizing (β = 1.43; 95% CI, 0.83-2.02) mental health problems, lower cognitive performance, and adverse physical health.

Conclusions

This study shows that an unbiased quantitative analysis of multidimensional SDOH can permit the determination of how SDOH patterns are associated with child developmental outcomes. Children exposed to socioeconomic deprivation showed the worst outcomes relative to other SDOH categories. These findings suggest the need to determine whether improvement in socioeconomic conditions can enhance child developmental outcomes.

Impact of Social Support on Cardiovascular Risk Prediction Models: A Systematic Review

Cardiovascular diseases (CVD) stand as the primary causes of both mortality and morbidity on a global scale. Social factors such as low social support can increase the risk of developing heart diseases and have shown poor prognosis in cardiac patients. Resources such as PubMed and Google Scholar were searched using a boolean algorithm for articles published between 2003 and 2023. Eligible articles showed an association between social support and cardiovascular risks. A systematic review was conducted using the guidance published in the Cochrane Prognosis Method Group and the PRISMA checklist, for reviews of selected articles. A total of five studies were included in our final analysis. Overall, we found that participants with low social support developed cardiovascular events, and providing a good support system can decrease the risk of readmission in patients with a history of CVD. We also found that integrating social determinants in the cardiovascular risk prediction model showed improvement in accessing the risk. Population with good social support showed low mortality and decreased rate of readmission. There are various prediction models, but the social determinants are not primarily included while calculating the algorithms. Although it has been proven in multiple studies that including the social determinants of health (SDOH) improves the accuracy of cardiovascular risk prediction models. Hence, the inclusion of SDOH should be highly encouraged.

2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines

AIM

The "2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease" provides an update to and consolidates new evidence since the "2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease" and the corresponding "2014 ACC/AHA/AATS/PCNA/SCAI/STS Focused Update of the Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease."

METHODS

A comprehensive literature search was conducted from September 2021 to May 2022. Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants were identified that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline.

STRUCTURE

This guideline provides an evidenced-based and patient-centered approach to management of patients with chronic coronary disease, considering social determinants of health and incorporating the principles of shared decision-making and team-based care. Relevant topics include general approaches to treatment decisions, guideline-directed management and therapy to reduce symptoms and future cardiovascular events, decision-making pertaining to revascularization in patients with chronic coronary disease, recommendations for management in special populations, patient follow-up and monitoring, evidence gaps, and areas in need of future research. Where applicable, and based on availability of cost-effectiveness data, cost-value recommendations are also provided for clinicians. Many recommendations from previously published guidelines have been updated with new evidence, and new recommendations have been created when supported by published data.

2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines

AIM

The "2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease" provides an update to and consolidates new evidence since the "2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease" and the corresponding "2014 ACC/AHA/AATS/PCNA/SCAI/STS Focused Update of the Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease."

METHODS

A comprehensive literature search was conducted from September 2021 to May 2022. Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants were identified that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline.

STRUCTURE

This guideline provides an evidenced-based and patient-centered approach to management of patients with chronic coronary disease, considering social determinants of health and incorporating the principles of shared decision-making and team-based care. Relevant topics include general approaches to treatment decisions, guideline-directed management and therapy to reduce symptoms and future cardiovascular events, decision-making pertaining to revascularization in patients with chronic coronary disease, recommendations for management in special populations, patient follow-up and monitoring, evidence gaps, and areas in need of future research. Where applicable, and based on availability of cost-effectiveness data, cost-value recommendations are also provided for clinicians. Many recommendations from previously published guidelines have been updated with new evidence, and new recommendations have been created when supported by published data.

A Concept Development for the Symptom Science Model 2.0

BACKGROUND

The National Institute of Nursing Research developed the National Institutes of Health symptom science model (SSM) in 2015 as a parsimonious conceptual model to guide symptom science research.

OBJECTIVES

This concept development paper synthesizes justifications to strengthen the original model.

METHODS

A literature review was performed, discussions with symptom science content expert stakeholders were held, and opportunities for expanding the current model were identified. Concept elements for a revised conceptual model-the SSM 2.0-were developed.

RESULTS

In addition to the four original concept elements (complex symptom presentation, phenotypic characterization, biobehavioral factors [previously biomarker discovery], and clinical applications), three new concept elements are proposed, including social determinants of health, patient-centered experience, and policy/population health.

DISCUSSION

There have been several calls to revise the original SSM from the nursing scientific community to expand its utility to other healthcare settings. Incorporating three additional concept elements can facilitate a broader variety of translational nursing research symptom science collaborations and applications, support additional scientific domains for symptom science activities, and produce more translatable symptom science to a wider audience of nursing research scholars and stakeholders during recovery from the COVID-19 pandemic. The revised SSM 2.0 with newly incorporated social determinants of health, patient-centered experience, and policy/population health components now empowers nursing scientists and scholars to address specific symptom science public health challenges particularly faced by vulnerable and underserved populations.

Food insecurity and housing instability as determinants of cardiovascular health outcomes: A systematic review

AIMS

The primary objective of this study is to conduct a systematic review of existing literature on the association between food insecurity and housing instability with CVD and its subtypes-related outcomes. Summarizing the comprehensive evidence for independent/interchangeable relationship of food and housing instability with CVD outcomes may inform specific interventions strategies to reduce CVD-risk.

DATA SYNTHESIS

The search focused on English-language articles in PubMed/Medline, from January 1, 2010, to June 1, 2021, with restriction to the US adult population. We included studies estimating the association between food insecurity or/and housing instability(exposure) and CVD-subtypes-related health outcomes (outcome). The study methodological quality was assessed using the Study Quality Assessment Tools (SQAT). Nineteen studies met eligibility criteria, consisted of 15 cross-sectional and 4 cohort studies. Of total studies, 7 examined housing instability, 11 studies focused on food insecurity, and one examined both. Food insecurity/housing instability was associated with increased overall CVD-mortality rate and greater healthcare cost utilization, while evidence were mixed for hospital readmission rate. By subtype, stroke mortality was greater with food insecurity but not with housing instability. The likelihood of myocardial infarction, coronary heart disease, and congestive heart failure was greater with food insecurity. Although mortality with MI was higher with housing instability, readmission and surgical procedure rates were significantly lower than housing stable adults.

CONCLUSION

Findings from this review suggest an urgent need to test the impact of screening for food and housing insecurities, referral services, and community engagement for CV health, within clinical and public health settings.

PROTOCOL REGISTRATION

Prospero CRD4202123352.

Evaluating How Safety-Net Hospitals Are Identified: Systematic Review and Recommendations

Objective:

To systematically review how safety-net hospitals' status is identified and defined, discuss current definitions' limitations, and provide recommendations for a new classification and evaluation framework.

Data Sources:

Safety-net hospital-related studies in the MEDLINE database published before May 16, 2019.

Study Design:

Systematic review of the literature that adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Data Collection/Extraction Methods:

We followed standard selection protocol, whereby studies went through an abstract review followed by a full-text screening for eligibility. For each included study, we extracted information about the identification method itself, including the operational definition, the dimension(s) of disadvantage reflected, study objective, and how safety-net status was evaluated.

Principal Findings:

Our review identified 132 studies investigating safety-net hospitals. Analysis of identification methodologies revealed substantial heterogeneity in the ways disadvantage is defined, measured, and summarized at the hospital level, despite a 4.5-fold increase in studies investigating safety-net hospitals for the past decade. Definitions often exclusively used low-income proxies captured within existing health system data, rarely incorporated external social risk factor measures, and were commonly separated into distinct safety-net status categories when analyzed.

Conclusions:

Consistency in research and improvement in policy both require a standard definition for identifying safety-net hospitals. Yet no standardized definition of safety-net hospitals is endorsed and existing definitions have key limitations. Moving forward, approaches rooted in health equity theory can provide a more holistic framework for evaluating disadvantage at the hospital level. Furthermore, advancements in precision public health technologies make it easier to incorporate detailed neighborhood-level social determinants of health metrics into multidimensional definitions. Other countries, including the United Kingdom and New Zealand, have used similar methods of identifying social need to determine more accurate assessments of hospital performance and the development of policies and targeted programs for improving outcomes.

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

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

Frailty and Determinants of Health Among Older Adults in the United States 2011-2016

OBJECTIVE

To characterize frailty phenotype in a representative cohort of older Americans and examine determinants of health factors.

METHODS

Retrospective analysis of data from 5,553 adults ≥60 years old in the 2011-2016 cross-sectional National Health and Nutrition Examination Survey (NHANES). World Health Organization "Determinants of Health" conceptual model was used to prioritize variables for multinomial logistic regression for the outcome of modified Fried frailty phenotype.

RESULTS

482 participants (9%) were frail and 2432 (44%) prefrail. Four factors were highly associated with frailty: difficulty with ≥1 activity of daily living (77%; OR 24.81 p < 0.01), ≥2 hospitalizations in the previous year (17%, OR 3.94 p < 0.01), having >2 comorbidities (27%; OR 3.33 p < 0.01), and polypharmacy (66%; OR 2.38 p < 0.01).

DISCUSSION

A modified Fried frailty assessment incorporating five self-reported criteria may be useful as a rapid nursing screen in low-resource settings. These assessments can streamline nursing care coordination and case management activities, thereby facilitating targeted frailty interventions to support healthy aging in vulnerable populations.

Effects of Neighborhood-level Data on Performance and Algorithmic Equity of a Model That Predicts 30-day Heart Failure Readmissions at an Urban Academic Medical Center

BACKGROUND

Socioeconomic data may improve predictions of clinical events. However, owing to structural racism, algorithms may not perform equitably across racial subgroups. Therefore, we sought to compare the predictive performance overall, and by racial subgroup, of commonly used predictor variables for heart failure readmission with and without the area deprivation index (ADI), a neighborhood-level socioeconomic measure.

METHODS AND RESULTS

We conducted a retrospective cohort study of 1316 Philadelphia residents discharged with a primary diagnosis of congestive heart failure from the University of Pennsylvania Health System between April 1, 2015, and March 31, 2017. We trained a regression model to predict the probability of a 30-day readmission using clinical and demographic variables. A second model also included the ADI as a predictor variable. We measured predictive performance with the Brier Score (BS) in a held-out test set. The baseline model had moderate performance overall (BS 0.13, 95% CI 0.13-0.14), and among White (BS 0.12, 95% CI 0.12-0.13) and non-White (BS 0.13, 95% CI 0.13-0.14) patients. Neither performance nor algorithmic equity were significantly changed with the addition of the ADI.

CONCLUSIONS

The inclusion of neighborhood-level data may not reliably improve performance or algorithmic equity.

Association of Social Network Characteristics With Cardiovascular Health and Coronary Artery Calcium in South Asian Adults in the United States: The MASALA Cohort Study

Background

South Asian adults have worse cardiovascular health (CVH) and more coronary artery calcium compared with other race/ethnicities. The impact of the social environment has not been examined as a potential driver of CVH or coronary artery calcium in this population. We evaluated associations of social network characteristics with CVH and coronary artery calcium in South Asian American adults to inform strategies for CVH promotion in this at‐risk population.

Methods and Results

Using data from the MASALA (Mediators of Atherosclerosis in South Asians Living in America) cohort study, multinomial and multivariable logistic regression were used to evaluate associations of participant social network size and density, proportion of network who are kin or South Asian ethnicity and reported health of participant's identified social network members (“alters”), with participant CVH and presence of coronary artery calcium. The 699 MASALA participants included were mean age 59.2 (SD, 9.2) years and 42.9% women. After adjustment, a 1‐person larger social network size was associated with 13% higher odds of ideal CVH (odds ratio [OR], 1.13; 95% CI, 1.01–1.27). Reporting an alter with high blood pressure was associated with lower odds of ideal CVH (OR, 0.51; 95% CI, 0.29–0.88), and reporting an alter with high cholesterol was associated with lower odds of ideal CVH (OR, 0.54; 95% CI, 0.30–0.94).

Conclusions

Social network characteristics are associated with CVH in South Asian American adults. Engaging social networks may help promote CVH in this population.

Social and economic influences on disparities in the health of racial and ethnic group Canadian immigrants

OBJECTIVE

To examine social, economic, and migratory influences on the health of racial and ethnic minority groups in Canada, with a special focus on Caribbean immigrants.

METHODS

Combined annual cycles (2011-2016) of the Canadian Community Health Survey (CCHS) data totaling over 300,000 adult Canadian residents were aggregated. Descriptive statistics and multivariable logistic regression models were used to examine the prevalence and associated factors of (1) cardiovascular disease diagnosed by a healthcare professional, and (2) self-rated general health among racial and ethnic groups.

RESULTS

Caribbeans in general, Black and other non-White Canadians had significantly higher odds (adjusted for age/sex) of reporting any cardiovascular disease compared with White Canadians. Only non-Caribbean Blacks had higher odds of self-rated fair or poor general health compared with White Canadians. Multivariate logistic regression models revealed that after controlling for social and demographic factors, immigration status and years since migration, Caribbean non-Blacks and Black Caribbeans were at higher odds of having a doctor-reported cardiovascular health condition compared with White Canadians. Caribbean non-Blacks also had higher odds of fair or poor self-rated health than White Canadians.

CONCLUSION

The results of this study highlight the need for additional investigations of other potential influences on physical health statuses, especially among migrants and those of African ancestry who might be more prone to adverse health outcomes.

Social determinants of health and outcomes for children and adults with congenital heart disease: a systematic review

BACKGROUND

Social determinants of health (SDH) can substantially impact health outcomes. A systematic review, however, has never been conducted on associations of SDH with congenital heart disease (CHD) outcomes. The aim, therefore, was to conduct such a systematic review.

METHODS

Seven databases were searched through May 2020 to identify articles on SDH associations with CHD. SDH examined included poverty, uninsurance, housing instability, parental educational attainment, immigration status, food insecurity, and transportation barriers. Studies were independently selected and coded by two researchers based on the PICO statement.

RESULTS

The search generated 3992 citations; 88 were included in the final database. SDH were significantly associated with a lower likelihood of fetal CHD diagnosis, higher CHD incidence and prevalence, increased infant mortality, adverse post-surgical outcomes (including hospital readmission and death), decreased healthcare access (including missed appointments, no shows, and loss to follow-up), impaired neurodevelopmental outcomes (including IQ and school performance) and quality of life, and adverse outcomes for adults with CHD (including endocarditis, hospitalization, and death).

CONCLUSIONS

SDH are associated with a wide range of adverse outcomes for fetuses, children, and adults with CHD. SDH screening and referral to appropriate services has the potential to improve outcomes for CHD patients across the lifespan.

IMPACT

Social determinants of health (SDH) are associated with a wide range of adverse outcomes for fetuses, children, and adults with congenital heart disease (CHD). This is the first systematic review (to our knowledge) on associations of SDH with congenital heart disease CHD outcomes. SDH screening and referral to appropriate services has the potential to improve outcomes for CHD patients across the lifespan.

Blood pressure management in an ecosystem context

The Hippocratic text On Airs, Waters, Places advises physicians to attend to all aspects of the environment-the seasons, the wind direction, and the soil and water quality, i.e., the ecosystem-when addressing people's health. Hippocrates emphasizes that the ecosystem influences health, disease, and therapeutic choices. Now is the time to consider how this medical wisdom can be integrated into healthcare systems and utilized for people's health. This review discusses how the ecosystem can affect blood pressure (BP) in humans and provides a synthesis of the related resources available in the literature to inform the actions of healthcare providers.

Defragmenting Heart Failure Care: Medical Records Integration

Heart failure management requires intensive care coordination. Guideline-directed medical therapies have been shown to save lives but are practically challenging to implement because of the fragmented care that heart failure patients experience. Electronic health record adoption has transformed the collection and storage of clinical data, but accessing these data often remains prohibitively difficult. Current legislation aims to increase the interoperability of software systems so that providers and patients can easily access the clinical information they desire. Novel heart failure devices and technologies leverage patient-generated data to manage heart failure patients, whereas new data standards make it possible for this information to guide clinical decision-making.

Social Determinants of Health Improve Predictive Accuracy of Clinical Risk Models for Cardiovascular Hospitalization, Annual Cost, and Death

BACKGROUND

Risk models in the private insurance setting may systematically underpredict in the socially disadvantaged. In this study, we sought to determine whether US minority Medicare beneficiaries had disproportionately low costs compared with their clinical outcomes and whether adding social determinants of health (SDOH) into risk prediction models improves prediction accuracy.

METHODS AND RESULTS

Retrospective observational cohort study of 2016 to 2017 Medicare Current Beneficiary Survey data (n=3614) linked to Medicare fee-for-service claims. Logistic and linear regressions were used to determine the relationship between race/ethnicity and annual costs of care, all-cause hospitalization, cardiovascular hospitalization, and death. We calculated the observed-to-expected (O:E) ratios for all outcomes under 4 risk models: (1) age+sex, (2) model 1+clinical comorbidity adjustment, (3) model 2+SDOH, and (4) SDOH alone. Our sample was 44% male and 11% black or Hispanic. Among minorities, adverse clinical outcomes were inversely related to cost. After multivariable adjustment, blacks/Hispanics had higher rates of cardiovascular hospitalization (incidence rate ratio, 1.78; P=0.012) but similar annual costs ($-336, P=0.77) compared with whites. Among whites, models 1 to 4 all showed similar O:E ratios, suggesting high accuracy in risk prediction using current models. Among minorities, adjustment for age, sex, and comorbidities underpredicted all-cause hospitalization by 20% (O:E, 1.20) and cardiovascular hospitalization by 70% (O:E, 1.70) and overpredicted death by 21% (O:E, 0.79); adding SDOH brought O:E near 1 for all outcomes. Among both groups, the SDOH risk model alone performed with equal or superior accuracy to the model based on clinical comorbidities.

CONCLUSIONS

A paradoxical relationship was observed between clinical outcomes and costs among racial and ethnic minorities. Because of systematic differences in access to care, cost may not be an appropriate surrogate for predicting clinical risk among vulnerable populations. Adjustment for SDOH improves the accuracy of risk models among racial and ethnic minorities and could guide use of prevention strategies.

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

IMPORTANCE

An association between social and neighborhood characteristics and health outcomes has been reported but remains poorly understood owing to complex multidimensional factors that vary across geographic space.

OBJECTIVES

To quantify social determinants of health (SDOH) as multiple dimensions across the continental United States (the 48 contiguous states and the District of Columbia) at a small-area resolution and to examine the association of SDOH with premature mortality within Chicago, Illinois.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study, census tracts from the US Census Bureau from 2014 were used to develop multidimensional SDOH indices and a regional typology of the continental United States at a small-area level (n = 71 901 census tracts with approximately 312 million persons) using dimension reduction and clustering machine learning techniques (unsupervised algorithms used to reduce dimensions of multivariate data). The SDOH indices were used to estimate age-adjusted mortality rates in Chicago (n = 789 census tracts with approximately 7.5 million persons) with a spatial regression for the same period, while controlling for violent crime.

MAIN OUTCOMES AND MEASURES

Fifteen variables, measured as a 5-year mean, were selected to characterize SDOH as small-area variations for demographic characteristics of vulnerable groups, economic status, social and neighborhood characteristics, and housing and transportation availability at the census-tract level. This SDOH data matrix was reduced to 4 indices reflecting advantage, isolation, opportunity, and mixed immigrant cohesion and accessibility, which were then clustered into 7 distinct multidimensional neighborhood typologies. The association between SDOH indices and premature mortality (defined as death before age 75 years) in Chicago was measured by years of potential life lost and aggregated to a 5-year mean. Data analyses were conducted between July 1, 2018, and August 30, 2019.

RESULTS

Among the 71 901 census tracts examined across the continental United States, a median (interquartile range) of 27.2% (47.1%) of residents had minority status, 12.1% (7.5%) had disabilities, 22.9% (7.6%) were 18 years and younger, and 13.6% (8.1%) were 65 years and older. Among the 789 census tracts examined in Chicago, a median (interquartile range) of 80.4% (56.3%) of residents had minority status, 10.2% (8.2%) had disabilities, 23.2% (10.9%) were 18 years and younger, and 9.5% (7.1%) were 65 years and older. Four SDOH indices accounted for 71% of the variance across all census tracts in the continental United States in 2014. The SDOH neighborhood typology of extreme poverty, which is of greatest concern to health care practitioners and policy advocates, comprised only 9.6% of all census tracts across the continental United States but characterized small areas of known public health crises. An association was observed between all SDOH indices and age-adjusted premature mortality rates in Chicago (R2 = 0.63; P < .001), even after accounting for violent crime and spatial structures.

CONCLUSIONS AND RELEVANCE

The modeling of SDOH as multivariate indices rather than as a singular deprivation index may better capture the complexity and spatial heterogeneity underlying SDOH. During a time of increased attention to SDOH, this analysis may provide actionable information for key stakeholders with respect to the focus of interventions.