Artificial Intelligence (AI)-based approaches for extracting Social Drivers of Health (SDoH) from clinical notes offer healthcare systems an efficient way to identify patients' social needs, yet we know little about the acceptability of this approach to patients and clinicians. We investigated patient and clinician acceptability through interviews.

We interviewed primary care patients experiencing social needs (n = 19) and clinicians (n = 14) about their acceptability of "SDoH autosuggest," an AI-based approach for extracting SDoH from clinical notes. We presented storyboards depicting the approach and asked participants to rate their acceptability and discuss their rationale.

Participants rated SDoH autosuggest moderately acceptable (mean = 3.9/5 patients; mean = 3.6/5 clinicians). Patients' ratings varied across domains, with substance use rated most and employment rated least acceptable. Both groups raised concern about information integrity, actionability, impact on clinical interactions and relationships, and privacy. In addition, patients raised concern about transparency, autonomy, and potential harm, whereas clinicians raised concern about usability.

Despite reporting moderate acceptability of the envisioned approach, patients and clinicians expressed multiple concerns about AI systems that extract SDoH. Participants emphasized the need for high-quality data, non-intrusive presentation methods, and clear communication strategies regarding sensitive social needs. Findings underscore the importance of engaging patients and clinicians to mitigate unintended consequences when integrating AI approaches into care.

Although AI approaches like SDoH autosuggest hold promise for efficiently identifying SDoH from clinical notes, they must also account for concerns of patients and clinicians to ensure these systems are acceptable and do not undermine trust.

To examine the impact of two interventions aimed at increasing the adoption of social risk screening in community health centers (CHCs).

Intervention CHCs were in one of three groups, which received either: (1) tailored practice facilitation-focused social risk screening implementation supports; (2) financial incentives for screening; and (3) both practice facilitation and financial incentives in staggered order. A group of control clinics was identified through propensity score matching and a difference-in-difference analysis compared effects across groups.

Using electronic health record data, we calculated monthly rates of social risk screening (per 100 adult patients) at 32 intervention clinics (19 practice facilitation supports only, 6 financial incentives only, 7 both financial incentives and practice facilitation supports), and 32 control clinics.

Compared to control clinics, clinics in any intervention group had a greater increase in average monthly social risk screenings from pre- to post-intervention that was maintained over the 24 months following intervention (difference-in-difference: 4.66, 95% CI: 0.89, 8.43). In the primary analysis, clinics engaged in both interventions increased screening rates when practice facilitation implementation supports were added to financial incentives (12 months 3.70, 95% CI: 0.34, 7.07; 24 months 4.18, 95% CI: -0.01, 8.87); adding financial incentives to practice facilitation supports resulted in increased screening rates but did not reach statistical significance.

This study is the first to compare different interventions intended to bolster CHCs' social risk screening activities. As social risk screening becomes increasingly tied to US policy and payment structures, it is critical to identify strategies that can support implementation in settings serving underserved populations. Our findings suggest modest impacts of both financial incentives and practice facilitation supports.

To measure hospital engagement in interoperable exchange of health-related social needs (HRSN) data.

This study combined national data from the 2022 American Hospital Association (AHA) Annual Survey, AHA IT Supplement, and the Centers for Medicare and Medicaid Services Impact File. Multivariable logistic regression was used to identify hospital characteristics associated with receiving HRSN data from external organizations.

Of 2502 hospitals, 61.4% reported electronically receiving HRSN data from external sources, most commonly from health information exchange organizations. Hospitals participating in accountable care organizations or patient-centered medical homes and hospitals using Epic or Cerner electronic health records (EHRs) were more likely to receive external HRSN data. In contrast, for-profit hospitals and public hospitals were less likely to participate in HRSN data exchange.

Hospital ownership, participation in value-based care models, and EHR vendor capabilities are important drivers in advancing HRSN data exchange.

Additional policy and technological support may be needed to enhance HRSN data interoperability.

Children with new-onset epilepsy often experience co-morbid cognitive and behavioral challenges, which can be influenced by Social Determinants Of Health (SDOH) such as household income and parental education level. Although unsupervised machine learning has identified distinct cognitive and behavioral phenotypes at or near diagnosis, the relationship between these clusters remains underexplored. This study aims to examine the relationship between cognitive and behavioral clusters and the impact of SDOH among children with new-onset seizures.

We recruited 312 children (ages 6-16) within six weeks of their first recognized seizure. Each participant underwent a comprehensive neuropsychological assessment, from which factor analysis identified four primary domains: language, processing speed, executive function, and verbal memory. Parents also completed the Child's Behavior Checklist (CBCL). K-means cluster analysis was applied to the mean factor scores and CBCL T-scores to identify unique clusters. We assessed SDOH factors, including maternal education level, child's race, household income, and parental marital status, along with clinical epilepsy characteristics such as age at seizure onset, seizure frequency/intensity, seizure syndrome, MRI/EEG abnormalities, and neurologic examination findings to distinguish these clusters.

We identified two primary clusters within both cognitive and behavioral scores: Resilient and At-Risk. Children in the Resilient Cognitive Cluster exhibited fewer behavioral problems, while those in the Resilient Behavior Cluster demonstrated higher cognitive performance. Conversely, the At-Risk Cognitive Cluster was associated with greater behavioral problems, and the At-Risk Behavior Cluster correlated with lower cognitive performance. Notably, almost two-thirds of participants showed congruence in clustering, either displaying resilience in both cognition and behavior or vulnerability in both domains. Resilient children exhibited lower levels of sociodemographic disadvantage, whereas those in the At-Risk Clusters faced significant disadvantages. Sociodemographic factors were more pronounced in differentiating clusters compared to traditional clinical epilepsy characteristics.

Among children with new-onset seizures, some display significant resilience to multimorbidities, while others are particularly vulnerable to neurobehavioral challenges, often linked to sociodemographic disadvantages. Future research should explore whether early interventions targeting SDOH can mitigate these risks and improve outcomes for children with new-onset epilepsy.

Neighborhood-level social determinants of health (N-SDoH) impact cancer survival. However, the relationship between N-SDoH and epithelial ovarian cancer (EOC) survival remains understudied.

We used data on all Pennsylvania residents diagnosed with EOC from 2000 to 2023 throughout the University of Pittsburgh Medical Center to assess the impact of N-SDoH on survival. We used the Social Vulnerability Index (SVI) to characterize four N-SDoH themes and overall N-SDoH vulnerability based on each case's census tract at diagnosis. High-SVI overall and by N-SDoH theme was defined as being in the 75th percentile in Pennsylvania for that metric. Cox proportional hazard models assessed the association between high-SVI and overall mortality.

Among 4970 EOC cases, high-SVI overall was associated with later stage at diagnosis, greater residual disease, and a lower likelihood of receiving standard-of-care platinum-based therapy. High-SVI was also associated with a 13 % increased mortality hazard (adjusted-HR:1.13 95 %CI:1.02-1.25). The Household Characteristics, Racial and Ethnic Minority Status, and Housing Type and Transportation themes were also associated with increased mortality hazards (adjusted-HR[95 %CI]: 1.10[1.01-1.21], 1.23[1.08-1.39], 1.09[1.00-1.18], respectively). The Socioeconomic Status theme was associated with an increased mortality hazard of borderline significance (adjusted-HR 1.10, 95 %CI:0.99-1.23). The overall high-SVI association appeared similar when stratifying by race, although the number of Black cases was small (n = 168).

Higher neighborhood social vulnerability is associated with worse EOC survival. Replicating study findings in more diverse populations can help illuminate the neighborhood factors most influencing survival and support the design and testing of programs to reduce poor EOC outcome, especially within marginalized communities.

Hospital and health system initiatives addressing Social Determinants of Health (SDOH) are essential for achieving whole-person care and advancing health equity. Building on prior research characterizing these efforts (Part 1), this scoping review (Part 2) evaluates the effectiveness of these initiatives, with a focus on SDOH data integration, EHR utilization, and the broader scope of hospital efforts in addressing individual- and system-level determinants of health. Using an integrated conceptual framework combining the DeVoe & Cottrell framework for operational assessment and the National Academy of Medicine (NAM) 5A framework for systemic evaluation, this study provides a multidimensional assessment of hospital-based SDOH interventions.

Guided by PRISMA-ScR criteria, this review analyzed 41 U.S.-based studies published between 2018 and 2023, identified through three academic databases. Eligible studies examined hospital initiatives addressing SDOH with measurable outcomes. Analyses assessed SDOH data collection, integration into care practices, EHR use, and overall initiative effectiveness.

Most studies (66%) were randomized controlled trials in urban settings (68%), targeting patients with chronic or mental health conditions (39%) or high-risk healthcare users (20%). Nearly half of initiatives (49%) addressed multiple SDOH domains, focusing on Social & Community Context, Economic Stability, and Neighborhood & Built Environment. Only 24% of initiatives utilized EHRs for SDOH data collection. EHR-based initiatives demonstrated significantly higher adherence to evidence-based practices, including use of community resource guides for referrals (90% vs. 45%, p = 0.013). Across all outcome measures, 79% demonstrated improvement, with no instances of worsening outcomes. However, 85% of initiatives lacked community-level SDOH data integration, and few employed upstream, universal strategies. Process, clinical, and social outcomes were unevenly prioritized, with only 10% of studies addressing all three outcome types.

While these initiatives reflect progress in integrating SDOH into care workflows and improving whole-person care at the individual level, progress toward health equity remains insufficient. Persistent gaps in EHR use, community-level data integration, and upstream strategies hinder systemic impact, potentially perpetuating disparities. Strengthening SDOH-EHR integration, fostering community partnerships, and supporting policy advocacy are critical to bridging individual and community needs. Future research should emphasize long-term, sustainable, and community-level impacts of hospital-led SDOH interventions.

Mortality after heart transplantation can be influenced by multiple factors. This study analysed its variation across four regions of the USA.

Analyse the differences in mortality among patients receiving a heart transplant across four regions of the USA.

Organ Procurement and Transplantation Network/United Network for Organ Sharing registry was analysed for adult heart transplant recipients from 1987 to 2023. They were divided into four regions according to heart transplant recipients' residence: the Northeast, Midwest, South, and West. The endpoint was all-cause mortality. A total of 33 482 heart transplant recipients were included in the analysis. Baseline characteristics differed by region. The median survival (years) was lower in the South [Northeast 12.9 (6.1-17.9), Midwest 13.1 (6.5-18.1), South 11.6 (5.3-16.8), and West 13.6 (7.0-18.6); P < 0.0001]. Mortality incidence rate was greater in the South. When compared to the Northeast, in the unadjusted analysis, mortality was higher in the South {hazard ratio (HR) 1.13 [95% confidence interval (CI) 1.07-1.19], P < 0.001} and lower in the West [HR 0.89 (95% CI 0.83-0.94), P < 0.001]. After adjusting for demographic and clinical variables, only the South retained significant differences [HR 1.17 (95% CI 1.10-1.24), P < 0.001]. Mortality significantly increased in all regions after 2018.

Mortality of heart transplant recipients varies across region of residence in the USA. A significant increase in adjusted mortality was observed in the South. These findings suggest that there are regional disparities in the mortality rates of heart transplant recipients.

Health-related social needs (HRSN) influence health outcomes and health care utilization. Clinicians face challenges addressing HRSN due to limited skills, expertise, and time. Further insight is needed on how patients and clinicians navigate HRSN in clinical encounters.

This study examines outpatient primary care encounters predating widespread HRSN screening to identify how discussions on HRSN are initiated and addressed.

This qualitative analysis was conducted on transcripts of 97 audiotaped English-speaking patient encounters from 3 clinics in New York City within a municipal health care system from January 2011 through April 2015. Patients were eligible if they were older than 18 years, self-identified as Black or White, had a diagnosis of hypertension, and had at least one prior encounter with the participating clinician. Codes were developed from social needs domains addressed by the Accountable Health Communities HRSN Screening Tool. Codes were added for further social needs identified, whether a patient or clinician initiated the HRSN discussion, and how a social need was addressed, if at all. Encounters were analyzed between June 2023 and February 2024.

Characterization of the content and nature of HRSN discussions during clinical encounters within safety-net clinics.

A total of 97 patients (55 [56.7%] women, 58 [59.8%] Black, mean [SD] age, 59.7 [10.6] years) had audiotaped encounters with 27 clinicians (18 [66.7%] women, 15 [55.6%] White, mean [SD] age, 36 [5.8] years). Physical activity (36% of encounters), financial strain (35%), mental health (34%), and substance use (28%) were the most discussed HRSN domains across the 97 encounters. Patients introduced financial strain most often (70% of the time), while clinicians led substance use (75%), physical activity (51%) and mental health (51%) discussions. Patients initiated conversations on employment (77%), food insecurity (62%), and housing instability (52%). Interventions included prescriptions, forms, counseling, and referrals. Domains frequently intervened on included health care navigation needs (85% of discussions), substance use (33%), and mental health (27%).

In this qualitative study of HRSN discussions in primary care encounters, clinicians were more likely to initiate discussions on substance use, physical activity, and mental health, behaviors routinely assessed in primary care, but different from topics introduced by patients. Findings underscore the need for standardized screening to improve identification of domains less frequently addressed by clinicians. Additional interventions are also needed, including clinician training for how to address HRSN in resource-constrained settings and integration of other health care team members, to enhance HRSN identification and intervention.

The purpose of this study was to pilot test a patient-reported social risk factor (SRF) screening tool among rheumatology outpatients and to examine the distribution of SRFs in this population.

A SRF screening tool was completed electronically by patients. Patients were screened for four core SRF domains (financial strain, housing instability, food insecurity, and transportation needs) and four supplemental SRF domains (physical inactivity, social isolation, stress, and depression). Data from the electronic health record were extracted for patients with at least one domain screened who were seen by participating rheumatology providers between January 1 and October 31, 2023. Descriptive statistics and multivariable logistic regressions were used to examine trends and associations of SRFs in this population.

The sample included 483 patients, and 84% of patients screened positive for at least one SRF. Physical inactivity (73%, 311 of 429) and social isolation (58%, 212 of 367) were the most common SRFs. Prevalence of core SRF domains was 35% for financial strain (145 of 412), 22% for housing instability (82 of 370), 23% for food insecurity (96 of 420), and 11% for transportation needs (48 of 427). Sociodemographic groups with higher prevalence of SRFs included patients reporting Medicaid insurance, younger age, Black race, or unmarried status. Multivariate analyses showed that Medicaid insurance increased odds of reporting SRFs in all core domains.

Our SRF screening identified a high burden of SRFs among rheumatology outpatients, giving a new level of detail into patients' barriers and possible needs. Future work will uncover associations between SRFs and clinical outcomes and evaluate the impact of interventions to address SRFs.

Marginalized populations face significant barriers to mental health care, such as stigma, poverty, and limited access to adapted services, with conventional psychiatric approaches often falling short. This study aimed to explore how psychiatric care can be adapted to better meet the needs of vulnerable populations. Data were collected from psychiatry residents, psychiatrists, and community organization staff during a course on vulnerable populations, using semi-structured discussions and analyzed through grounded theory with iterative coding. Seven main themes emerged: (1) barriers and needs of vulnerable populations, highlighting challenges like homelessness and stigma; (2) psychiatric interventions and flexible approaches, emphasizing tailored care; (3) collaboration with community organizations, focusing on partnerships to improve care access; (4) ethical approach and respect for rights, ensuring dignity in treatment; (5) specific populations and associated challenges, addressing the needs of groups like LGBTQ+ youth and migrants; (6) intervention and support models, such as proximity-based care and post-hospitalization follow-up; (7) innovation and evolution of practices, focusing on research and institutional adaptations. This study emphasizes the need for personalized, intersectoral care, recommending improved collaboration, flexible models, and greater clinical exposure, with future research exploring how psychiatric education can better prepare clinicians to work with marginalized groups.

Independence in everyday functioning has been associated with successful aging and declines in functioning may be indicative of pathological cognitive decline. Social determinants of health, like economic status and access to health care, a]lso play a role in everyday functioning. Understanding these factors are of particular importance for older Black adults who have had long-standing disparate access to care, education, and treatments. The current study aimed to evaluate social determinants of health, more specifically social engagement, as moderators of the association between cognition and everyday functioning.

A sample of 930 older Black adults from Rush University: The Memory and Aging Project, African American Clinical Core, and Minority Adult Research Study were used. Participants completed a battery of neuropsychological testing as well as questionnaires about their everyday functioning and social behaviors. Hierarchical linear regressions were utilized to determine to what extent social factors moderated the relationship between cognition and everyday functioning.

Late life social activity reduced the effect of global cognition on everyday functioning and was independently associated with everyday functioning. Social network size was associated with increased impairment.

Results from the current study provide novel information regarding the role of social interaction on cognition in an older Black adult sample. Future interventions may benefit from an emphasis on increasing social engagement.

Social determinants of health (SDOH) are vital components to helping address health equity and the social and economic factors that impact health and health outcomes. Two important aspects of SDOH to consider are informational access and accuracy. In an era increasingly influenced by digital platforms, this study delves into the significant role of social media as an SDOH. Through an exploratory sequential mixed-methods design, this study captures mental health professionals' perspectives regarding the professional use of social media. The findings of this study reveal a need for further guidance on how to professionally utilize social media and address boundary and confidentiality concerns. Additionally, the findings demonstrate that social media could be a valuable tool for health education, community support, and public health messaging, and highlights a necessity for accurate informational accessibility. This study underscores the need for a nuanced understanding of social media's role in health, advocating for informed and strategic use in mental health practice.

Thirty-five years after the Institute of Medicine report, The Future of Public Health (1988), multiple authors have considered what needs to be achieved to build a robust public health system that improves health for all. A central theme is the renewed emphasis on the role of evidence-based policy. This study provides clear evidence using a ranking methodology of how important policy is to achieving better health outcomes across the United States. The purpose of this study is to assess the association of state health rankings with well-established state health outcomes. Specifically, we calculate the strength of relationship of the rankings used in a previous study to six widely referenced health outcomes. Utilizing 88 predominantly health policy-oriented data elements found online for each of the United States, a state ranking was calculated across all actual, or behavioral, causes of death using the percent weightings attributable to each actual cause. These state rankings were correlated to state health outcomes. Findings indicated that state rankings correlated highly with five of the six health outcomes, excluding disability. States having supportive health policies ranked better in terms of the actual causes of death and population health outcomes. This finding is important to the field because it quantifies the degree that robust health policies matter to population health status and how these policies may be predictive of positive health outcomes. For practitioners, this finding supports greater emphasis on policy advocacy skills and encourages increased advocacy training in schools and programs of public health.

The increasing prevalence of non-communicable diseases (NCDs) among adolescents in South Africa is a growing public health concern.

To examine the clustering of NCD risk factors, with a focus on gender, socio-economic status (SES) and perceived stress among adolescents.

The study involved adolescents from eight schools in Buffalo City Metropolitan Municipality and Amatole district, Eastern Cape.

A stratified random sample of 266 adolescents (aged 12 years-18 years) was assessed for anthropometric, physiological, and perceived stress measures. Data included body mass index (BMI), blood pressure (BP), cholesterol, glucose levels, and perceived stress (via the Perceived Stress Scale). Analysis involved descriptive statistics, Chi-square tests, t-tests, and k-means clustering.

Females (n = 155) showed a higher prevalence of NCD risk factors, with 80.2% classified as overweight or obese compared to 19.8% of males, and 77.2% had elevated cholesterol versus 22.8% of males. Perceived stress was higher in affluent schools (59.8%) despite fewer metabolic risks. K-means analysis identified four health profiles with significant SES and health differences (p < 0.001). Cluster 1 (high SES) showed elevated BP, BMI, and stress, while Clusters 2-4 (low SES) varied in risks, with Cluster 4 showing the highest BP and metabolic risks despite low stress.

These results highlight unique health profiles and risk factors across socio- economic contexts, with female adolescents from lower socio-economic backgrounds facing greater metabolic risks.

This Study provides original insights into the interplay between gender, SES and perceived stress in shaping NCD risk among South African adolescents.

Social determinants of health (SDOH) impact cancer outcomes. The CDC Social Vulnerability Index (SVI) integrates scores for four neighborhood-based SDOH domains (socioeconomic status, household characteristics, minority status, and housing type/transportation) to assess neighborhood social vulnerability (NSV). While NSV has been associated with overall cancer mortality and lung, breast, colon, and endometrial cancer-specific mortality, the relationship between NSV as defined by the SVI and ovarian cancer outcomes remains unknown.

We used data from 177 patients enrolled in an observational ovarian cancer cohort study from October 2012 through September 2022. All patients underwent debulking surgery and completed an entire course of standard-of-care platinum-based chemotherapy. Follow-up was completed through May 2024. SVI was calculated using census tract at diagnosis. High NSV was defined as SVI in the top quartile of the cohort. Cox proportional hazard models assessed the association between NSV and progression-free (PFS) and overall (OS) survival.

After accounting for demographic and clinical factors, high NSV was associated with significantly worse PFS (HR:2.31 [95% CI:1.48-3.61]; P < 0.001) and OS (HR:1.79 [95% CI:1.10-2.92]; P = 0.02), with neighborhood socioeconomic status associated with significantly worse PFS (HR:2.29 [95% CI:1.47-3.56]; P < 0.001) and OS (HR:1.71 [95% CI:1.04-2.80]; P = 0.03). Neighborhood housing type/transportation was also associated with significantly worse PFS (HR:1.65 [95% CI:1.07-2.55]; P = 0.02) and trended towards worse OS (HR:1.43 [95% CI: 0.80-2.33]).

Higher neighborhood social vulnerability is associated with worse outcomes among ovarian cancer patients. Validating these results in a population-based cohort and assessing programs to reduce neighborhood social vulnerability to improve ovarian cancer outcomes is warranted.

Evidence-informed population health initiatives often leverage data from various sources, such as epidemiologic surveillance data and administrative datasets. Recent interest has arisen in using area-level composite measures describing a community's social risks to inform the development and implementation of health policies, including payment reform initiatives. Our objective was to capture the breadth of available area-level composite measures that describe social determinants of health (SDH) and have potential for application in population health and policy work.

We conducted a scoping review of the scientific literature from 2010 to 2022 to identify multifactorial indices and rankings reflected in peer-reviewed literature that estimate SDH and that have publicly accessible data sources. We discovered several additional composite measures incidental to the scoping review process. Literature searches for each composite measure aimed to contextualize common applications in public health investigations.

From 491 studies, we identified 31 composite measures and categorized them into 8 domains: environmental conditions and pollution, opportunity and infrastructure, deprivation and well-being, COVID-19, rurality, food insecurity, emergency response and community resilience, and health. Composite measures are applied most often as an independent variable associated with disparities, risk factors, and/or outcomes affecting individuals, populations, communities, and health systems.

Area-level composite measures describing SDH have been applied to wide-ranging population health work. Social risk indicators may enable policy makers, evaluators, and researchers to better assess community risks and needs, thereby facilitating the evidence-informed development, implementation, and study of initiatives that aim to improve population health.

Concussion evaluations include a multifaceted approach; however, individual differences can influence test score interpretations and validity. Social determinants of health (SDoH) differentially affect disease risk and outcomes based upon social and environmental characteristics. Efforts to better define, diagnose, manage, and treat concussion have increased, but minimal efforts have focused on examining SDoH that may affect concussion recovery.

This review examined previous research that examined the effect of SDoH on concussion recovery of athletes.

CINAHL, MEDLINE, PsycInfo, and SPORTDiscus databases were used to search the terms "concussion" AND "recovery," "youth, adolescent, teen and/or adult," and "social determinants of health" and variations of these terms. The evidence level for each study was evaluated using the 2011 Oxford Center for Evidence-Based Medicine Guide.

Seven thousand nine hundred and twenty-one articles were identified and screened for inclusion. Five studies met the inclusion criteria and were included in this systematic review. Using the Downs and Black Quality Index, the studies included in this review were deemed high quality.

Though limited literature exists, there is preliminary evidence to suggest that SDoH (specifically, economic stability, education access and quality, and social and community context) may have an impact on the clinical recovery from concussion. The dimensions evaluated varied between studies and the results were inconsistent. No single factor consistently affected clinical recovery; however, private insurance and race appear to have an association with the speed of recovery. Unfortunately, the potential intersection of these variables and other preinjury factors limits the ability to make clear recommendations. While most of the studies in this review are retrospective in nature, future efforts should focus on training clinicians to prospectively evaluate the effect of SDoH on concussion recovery and injury outcomes. Funding and registration for this systematic review were not obtained nor required.

To determine the differential impact of Medicaid expansion on all-cause mortality between Black, Latino/a, and White populations in rural and urban areas, and assess how expansion impacted mortality disparities between these groups.

We employ a county-level time-varying heterogenous treatment effects difference-in-difference analysis of Medicaid expansion on all-cause age-adjusted mortality for those 64 years of age or younger from 2009 to 2019. For all counties within the 50 US States and the District of Columbia, we use restricted-access vital statistics data to estimate Average Treatment Effect on the Treated (ATET) for all combinations of racial and ethnic group (Black, Latino/a, White), rurality (rural, urban), and sex. We then assess aggregate ATET, as well as how the ATET changed as time from expansion increased.

Medicaid expansion led to a reduction in all-cause age-adjusted mortality for urban Black populations, but not rural Black populations. Urban White populations experienced mixed effects dependent on years after expansion. Latino/a populations saw no appreciable impact. While no effect was observed for rural Black and Latino/a populations, rural White all-cause age-adjusted mortality unexpectedly increased due to Medicaid expansion. These effects reduced rural- and urban-specific Black-White mortality disparities but did not shrink the rural-urban mortality gap.

The mortality-reducing impact of Medicaid expansion has been uneven across racial and ethnic groups and rural-urban status; suggesting that many populations-particularly rural individuals-are not seeing the same benefits as others. It is imperative that states work to ensure Medicaid expansion is being appropriately implemented in rural areas.

Social determinants of health (SDOH) have been shown to be important predictors of health outcomes. Here we developed methods to extract them from inpatient electronic medical record (EMR) data using techniques compatible with current EMR systems.

Four social determinants were targeted: patient language barriers, employment status, education, and whether the patient lives alone. Inpatients aged 18 and older with records in the Calgary-wide EMR system were studied. Algorithms were developed on the January 2019 hospital admissions (n=8,999) and validated on the January 2018 hospital admissions (n=8,839). SDOH documented as structured data were compared against those extracted from unstructured free-text notes.

More than twice as many patients had a note documenting a language barrier in EMR data than in structured data; 12 % of patients indicated by EMR notes to be living alone had a partner noted in their structured marital status. The Positive Predictive Value (PPV) of the elements extracted from notes was high, at 99 % (95 % CI 94.0 %-100.0 %) for language barriers, 98 % (95 % CI 92.6 %-99.9 %) for living alone, 96 % (95 % CI 89.8 %-98.8 %) for unemployment, and 88 % (95 % CI 80.0 %-93.1 %) for retirement.

All SDOH elements were extracted with high PPV. SDOH documentation was largely missing in structured data and sometimes misleading.

Social and structural determinants of health include historical, social, political, and economic forces, many of which are rooted in racism and inequality, that shape the relationship between environmental conditions and individual health. Unmet social needs can increase the risk of many conditions treated by obstetrician-gynecologists (ob-gyns), including, but not limited to, preterm birth, unintended pregnancy, infertility, cervical cancer, breast cancer, and maternal mortality. An individual health care professional's biases (whether overt or unconscious) affect delivery of care and may exacerbate and reinforce health disparities through inequitable treatment. Obstetrician-gynecologists and other health care professionals should seek to understand patients' health care decision making not simply as patients' individual-level behavior, but rather as the result of intersecting sociopolitical conditions, structural inequities, and social needs that create and maintain inequalities in health and health care. Recognizing the importance of social and structural determinants of health can help ob-gyns and other health care professionals to better understand patients, effectively communicate about health-related conditions and behavior, and contribute to improved health outcomes, including patients' experience of care and their trust in the health care system.

The growing emphasis on reducing health disparities and addressing social determinants of health (SDH) has prompted many national and local health agencies to report population health data by SDH measures. However, many agencies rely on descriptive epidemiology methods for such reports and are susceptible to biased findings due to inadequate confounding control. In this brief analytic essay, using the data presented in an HIV Surveillance Report by the Centers for Disease Control and Prevention (CDC), we demonstrated an example of how reporting health outcomes by SDH with descriptive methods could bias the results and conclusions. SDH are causes of health disparities and SDH analysis requires analytic epidemiology methods to ensure valid research results and effective interventions.

Multiple state and national health care organizations have invested in activities to screen for and address the health-related social needs (HRSNs) of their patients. However, patient perspectives concerning HRSN screening discussions and facilitated referrals to supports are largely unexplored. The main objectives of this study were to explore the ways in which Massachusetts Medicaid (MassHealth) members engage with their health care clinicians to discuss HRSNs, to identify common needs discussed, and to describe whether members feel these needs are being addressed by health care clinicians and staff.

The study team performed a cross-sectional, qualitative research study that included in-depth, open-ended interviews with 44 adult MassHealth members. Interviews were conducted between June and October 2022. Interviews were recorded, transcribed, and systematically coded for analysis, and common themes were reported. The data collected for this study were part of a larger independent evaluation of MassHealth's 2017-2022 Section 1115 Demonstration that granted authority from CMS to implement health care delivery system reforms in Massachusetts.

In this qualitative study of Medicaid members, some reportedly felt comfortable freely discussing all of their clinical and social needs with their health care clinicians, while others noted feelings of apprehension. Several members recalled being asked about their HRSNs in various clinical or community settings, while others did not. The majority of members endorsed having an unmet HRSN, including housing, nutrition, financial, or transportation issues, and many barriers to addressing these HRSNs were discussed. Finally, many members cited a preference for discussing HRSNs with community-based care coordinators and social workers at the community partner organizations rather than with their health care clinicians. Community-based care coordinators were lauded as essential facilitators in making the connection to necessary resources to help address HRSNs.

Study results highlight an opportunity to increase the effectiveness of HRSN screening and referral practices within the health care setting through relationship building between Medicaid members and diverse interdisciplinary care teams that include staff such as community health workers. Continued investment in cross-sector partnerships, screening workflows, and patient-clinician relationships may contribute to establishing an environment in which members can comfortably discuss HRSNs and connect with needed services to improve their health.

Since January 2020, COVID-19 has affected more than 100 million people in the U.S. Previous studies on racial and ethnic disparities related to characteristics and outcomes of COVID-19 patients have been insightful. However, appropriate epidemiologic age-standardization of the disease burden and disparities for hospitalization data are lacking.

To identify and describe racial and ethnic disparities for primary COVID-19 hospitalizations in the U.S. in 2020.

In this nationally representative observational study, we use the National Inpatient Sample to quantify racial and ethnic disparities in COVID-19 hospitalizations. Descriptive statistics for patient characteristics, common comorbidities, age-standardized hospitalization rates, inpatient complications, and mortality among COVID-19 hospitalizations were contrasted by race and ethnicity.

There were 1,058,815 primary COVID-19 hospitalizations in 2020. Of those, 47.2% were female, with median age of 66 (IQR, 54, 77). Overall inpatient mortality rate was 11.1%. When compared to White patients, Black, Hispanic, and Native American patients had higher age-standardized hospitalization rate ratios of 2.42 (95% CI 2.40-2.43), 2.26 (2.25-2.28), and 2.51 (2.46-2.56), respectively. Non-White patients had increased age-adjusted rates for procedures and complications. Factors associated with inpatient mortality include age, male sex, Hispanic or Native American race or ethnicity, lower income, Medicaid, heart failure, arrhythmias, coagulopathy, and chronic liver disease.

Marginalized populations in the U.S. had over twice the COVID-19 hospitalization rate relative to White patients. Age-adjusted mortality rates were highest for Black, Hispanic, and Native American patients. Careful consideration for vulnerable populations is encouraged during highly communicable respiratory pandemics.

Polysocial risk scores were recently proposed as a strategy for improving the clinical relevance of knowledge about social determinants of health. Our objective in this study was to assess whether the polysocial risk score model improves prediction of cognition and all-cause mortality in middle-aged and older adults beyond simpler models including a smaller set of key social determinants of health. We used a sample of 13 773 individuals aged ≥50 years at baseline from the 2006-2018 waves of the Health and Retirement Study, a US population-based longitudinal cohort study. Four linear mixed models were compared: 2 simple models including a priori-selected covariates and 2 polysocial risk score models which used least absolute shrinkage and selection operator (LASSO) regularization to select covariates among 9 or 21 candidate social predictors. All models included age. Predictive accuracy was assessed via R2 and root mean-squared prediction error (RMSPE) using training/test split validation and cross-validation. For predicting cognition, the simple model including age, race, sex, and education had an R2 value of 0.31 and an RMSPE of 0.880. Compared with this, the most complex polysocial risk score selected 12 predictors (R2 = 0.35 and RMSPE = 0.858; 2.2% improvement). For all-cause mortality, the simple model including age, race, sex, and education had an area under the receiver operating characteristic curve (AUROC) of 0.747, while the most complex polysocial risk score did not demonstrate improved performance (AUROC = 0.745). Models built on a smaller set of key social determinants performed comparably to models built on a more complex set of social "risk factors."

Nurses have frequent opportunities to address social determinants of health (SDOH) in practice. However, many nurses graduate without completing coursework in SDOH, and there remain barriers to incorporating SDOH content into nursing curricula.

We propose the revision of nursing pre-requisites to include substantive, introductory coursework on SDOH.

We explored the history and professional context surrounding nursing's current pre-requisite course expectations. We also performed an assessment of the credit hour requirements and pre-requisite course titles for the nation's 100 top-ranked nursing programs.

Our assessment revealed that the allocation of credit hour requirements for most programs leaves little room for SDOH-focused credits once nursing coursework starts; also, based on title analyses, foundational coursework on SDOH may be missing from most programs' pre-requisite listings.

Nursing pre-requisites should include SDOH content for reasons including the limited availability of credit hours for SDOH-focused electives and the importance of students developing a strong foundation in SDOH before their nursing coursework begins.

Chronic obstructive pulmonary disease (COPD) ranks as the third leading cause of global illness and mortality. It is commonly triggered by exposure to respiratory irritants like cigarette smoke or biofuel pollutants. This multifaceted condition manifests through an array of symptoms and lung irregularities, characterized by chronic inflammation and reduced lung function. Present therapies primarily rely on maintenance medications to alleviate symptoms, but fall short in impeding disease advancement. COPD's diverse nature, influenced by various phenotypes, complicates diagnosis, necessitating precise molecular characterization. Omics-driven methodologies, including biomarker identification and therapeutic target exploration, offer a promising avenue for addressing COPD's complexity. This analysis underscores the critical necessity of improving molecular profiling to deepen our comprehension of COPD and identify potential therapeutic targets. Moreover, it advocates for tailoring treatment strategies to individual phenotypes. Through comprehensive exploration-based molecular characterization and the adoption of personalized methodologies, innovative treatments may emerge that are capable of altering the trajectory of COPD, instilling optimism for efficacious disease-modifying interventions.

To quantify inequalities in lifespan across multiple social determinants of health, how they act in tandem with one another, and to create a scoring system that can accurately identify subgroups of the population at high risk of mortality.

Comparison of life tables across 54 subpopulations defined by combinations of four social determinants of health: sex, marital status, education and race, using data from the Multiple Cause of Death dataset and the American Community Survey.

United States, 2015-2019.

We compared the partial life expectancies (PLEs) between age 30 and 90 years of all subpopulations. We also developed a scoring system to identify subgroups at high risk of mortality.

There is an 18.0-year difference between the subpopulations with the lowest and highest PLE. Differences in PLE between subpopulations are not significant in most pairwise comparisons. We visually illustrate how the PLE changes across social determinants of health. There is a complex interaction among social determinants of health, with no single determinant fully explaining the observed variation in lifespan. The proposed scoring system adds clarification to this interaction by yielding a single score that can be used to identify subgroups that might be at high risk of mortality. A similar scoring system by cause of death was also created to identify which subgroups could be considered at high risk of mortality from specific causes. Even if subgroups have similar mortality levels, they are often subject to different cause-specific mortality risks.

Having one characteristic associated with higher mortality is often not sufficient to be considered at high risk of mortality, but the risk increases with the number of such characteristics. Reducing inequalities is vital for societies, and better identifying individuals and subgroups at high risk of mortality is necessary for public health policy.

We aimed to examine the feasibility of applying natural language processing (NLP) to unstructured electronic health record (EHR) documents to detect the presence of financial insecurity among patients with rheumatologic disease enrolled in an integrated care management program (iCMP).

We incorporated supervised, rule-based NLP and statistical methods to identify financial insecurity among patients with rheumatic conditions enrolled in an iCMP (n = 20,395) in a multihospital EHR system. We constructed a lexicon for financial insecurity using data from available knowledge sources and then reviewed EHR notes from 538 randomly selected individuals (training cohort n = 366, validation cohort n = 172). We manually categorized records as having "definite," "possible," or "no" mention of financial insecurity. All available notes were processed using Narrative Information Linear Extraction, a rule-based version of NLP. Models were trained using the NLP features for financial insecurity using logistic, least absolute shrinkage operator (LASSO), and random forest performance characteristic and were compared with the reference standard.

A total of 245,142 notes were processed from 538 individual patient records. Financial insecurity was present among 100 (27%) individuals in the training cohort and 63 (37%) in the validation cohort. The LASSO and random forest models performed identically and slightly better than logistic regression, with positive predictive values of 0.90, sensitivities of 0.29, and specificities of 0.98.

The development of a context-driven lexicon used with rule-based NLP to extract data that identify financial insecurity is feasible for use and improved the capture for presence of financial insecurity with high accuracy. In the absence of a standard lexicon and construct definition for financial insecurity status, additional studies are needed to optimize the sensitivity of algorithms to categorize financial insecurity with construct validity.

There is no standardised approach to screening adults for social risk factors. The goal of this study was to develop mortality risk prediction models based on the social determinants of health (SDoH) for clinical risk stratification.

Data were used from REasons for Geographic And Racial Differences in Stroke (REGARDS) study, a national, population-based, longitudinal cohort of black and white Americans aged ≥45 recruited between 2003 and 2007. Analysis was limited to participants with available SDoH and mortality data (n=20 843). All-cause mortality, available through 31 December 2018, was modelled using Cox proportional hazards with baseline individual, area-level and business-level SDoH as predictors. The area-level Social Vulnerability Index (SVI) was included for comparison. All models were adjusted for age, sex and sampling region and underwent internal split-sample validation.

The baseline prediction model including only age, sex and REGARDS sampling region had a c-statistic of 0.699. An individual-level SDoH model (Model 1) had a higher c-statistic than the SVI (0.723 vs 0.708, p<0.001) in the testing set. Sequentially adding area-level SDoH (c-statistic 0.723) and business-level SDoH (c-statistics 0.723) to Model 1 had minimal improvement in model discrimination. Structural racism variables were associated with all-cause mortality for black participants but did not improve model discrimination compared with Model 1 (p=0.175).

In conclusion, SDoH can improve mortality prediction over 10 years relative to a baseline model and have the potential to identify high-risk patients for further evaluation or intervention if validated externally.

Despite the incentives and provisions created for hospitals by the US Affordable Care Act related to value-based payment and community health needs assessments, concerns remain regarding the adequacy and distribution of hospital efforts to address SDOH. This scoping review of the peer-reviewed literature identifies the key characteristics of hospital/health system initiatives to address SDOH in the US, to gain insight into the progress and gaps.

PRISMA-ScR criteria were used to inform a scoping review of the literature. The article search was guided by an integrated framework of Healthy People SDOH domains and industry recommended SDOH types for hospitals. Three academic databases were searched for eligible articles from 1 January 2018 to 30 June 2023. Database searches yielded 3,027 articles, of which 70 peer-reviewed articles met the eligibility criteria for the review.

Most articles (73%) were published during or after 2020 and 37% were based in Northeast US. More initiatives were undertaken by academic health centers (34%) compared to safety-net facilities (16%). Most (79%) were research initiatives, including clinical trials (40%). Only 34% of all initiatives used the EHR to collect SDOH data. Most initiatives (73%) addressed two or more types of SDOH, e.g., food and housing. A majority (74%) were downstream initiatives to address individual health-related social needs (HRSNs). Only 9% were upstream efforts to address community-level structural SDOH, e.g., housing investments. Most initiatives (74%) involved hot spotting to target HRSNs of high-risk patients, while 26% relied on screening and referral. Most initiatives (60%) relied on internal capacity vs. community partnerships (4%). Health disparities received limited attention (11%). Challenges included implementation issues and limited evidence on the systemic impact and cost savings from interventions.

Hospital/health system initiatives have predominantly taken the form of downstream initiatives to address HRSNs through hot-spotting or screening-and-referral. The emphasis on clinical trials coupled with lower use of EHR to collect SDOH data, limits transferability to safety-net facilities. Policymakers must create incentives for hospitals to invest in integrating SDOH data into EHR systems and harnessing community partnerships to address SDOH. Future research is needed on the systemic impact of hospital initiatives to address SDOH.

The consistent and persuasive evidence illustrating the influence of social determinants on health has prompted a growing realization throughout the health care sector that enhancing health and health equity will likely depend, at least to some extent, on addressing detrimental social determinants. However, detailed social determinants of health (SDoH) information is often buried within clinical narrative text in electronic health records (EHRs), necessitating natural language processing (NLP) methods to automatically extract these details. Most current NLP efforts for SDoH extraction have been limited, investigating on limited types of SDoH elements, deriving data from a single institution, focusing on specific patient cohorts or note types, with reduced focus on generalizability. This study aims to address these issues by creating cross-institutional corpora spanning different note types and healthcare systems, and developing and evaluating the generalizability of classification models, including novel large language models (LLMs), for detecting SDoH factors from diverse types of notes from four institutions: Harris County Psychiatric Center, University of Texas Physician Practice, Beth Israel Deaconess Medical Center, and Mayo Clinic. Four corpora of deidentified clinical notes were annotated with 21 SDoH factors at two levels: level 1 with SDoH factor types only and level 2 with SDoH factors along with associated values. Three traditional classification algorithms (XGBoost, TextCNN, Sentence BERT) and an instruction tuned LLM-based approach (LLaMA) were developed to identify multiple SDoH factors. Substantial variation was noted in SDoH documentation practices and label distributions based on patient cohorts, note types, and hospitals. The LLM achieved top performance with micro-averaged F1 scores over 0.9 on level 1 annotated corpora and an F1 over 0.84 on level 2 annotated corpora. While models performed well when trained and tested on individual datasets, cross-dataset generalization highlighted remaining obstacles. To foster collaboration, access to partial annotated corpora and models trained by merging all annotated datasets will be made available on the PhysioNet repository.

To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different disease groups, and examine population-level extraction ratio.

We developed SDoH corpora using clinical notes identified at the University of Florida (UF) Health. We systematically compared 7 transformer-based large language models (LLMs) and developed an open-source package - SODA (i.e., SOcial DeterminAnts) to facilitate SDoH extraction from clinical narratives. We examined the performance and potential bias of SODA for different race and gender groups, tested the generalizability of SODA using two disease domains including cancer and opioid use, and explored strategies for improvement. We applied SODA to extract 19 categories of SDoH from the breast (n = 7,971), lung (n = 11,804), and colorectal cancer (n = 6,240) cohorts to assess patient-level extraction ratio and examine the differences among race and gender groups.

We developed an SDoH corpus using 629 clinical notes of cancer patients with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH, and another cross-disease validation corpus using 200 notes from opioid use patients with 4,342 SDoH concepts/attributes. We compared 7 transformer models and the GatorTron model achieved the best mean average strict/lenient F1 scores of 0.9122 and 0.9367 for SDoH concept extraction and 0.9584 and 0.9593 for linking attributes to SDoH concepts. There is a small performance gap (∼4%) between Males and Females, but a large performance gap (>16 %) among race groups. The performance dropped when we applied the cancer SDoH model to the opioid cohort; fine-tuning using a smaller opioid SDoH corpus improved the performance. The extraction ratio varied in the three cancer cohorts, in which 10 SDoH could be extracted from over 70 % of cancer patients, but 9 SDoH could be extracted from less than 70 % of cancer patients. Individuals from the White and Black groups have a higher extraction ratio than other minority race groups.

Our SODA package achieved good performance in extracting 19 categories of SDoH from clinical narratives. The SODA package with pre-trained transformer models is available at https://github.com/uf-hobi-informatics-lab/SODA_Docker.

Cancer and cardiovascular disease (CVD) are major causes of morbidity and mortality in the United States (US). Cancer survivors have increased risks for CVD and CVD-related mortality due to multiple factors including cancer treatment-related cardiotoxicity. Disparities are rooted in differential exposure to risk factors and social determinants of health (SDOH), including systemic racism. This review aimed to assess SDOH's role in disparities, document CVD-related disparities among US cancer survivors, and identify literature gaps for future research.

Following the Peer Review of Electronic Search Strategies (PRESS) guidelines, MEDLINE, PsycINFO, and Scopus were searched on March 15, 2021, with an update conducted on September 26, 2023. Articles screening was performed using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, a pre-defined Population, Exposure, Comparison, Outcomes, and Settings (PECOS) framework, and the Rayyan platform. A modified version of the Newcastle-Ottawa Scale was used to assess the risk of bias, and RAW Graphs for alluvial charts. This review is registered with PROSPERO under ID #CRD42021236460.

Out of 7,719 retrieved articles, 24 were included, and discussed diverse SDOH that contribute to CVD-related disparities among cancer survivors. The 24 included studies had a large combined total sample size (n=7,704,645; median=19,707). While various disparities have been investigated, including rural-urban, sex, socioeconomic status, and age, a notable observation is that non-Hispanic Black cancer survivors experience disproportionately adverse CVD outcomes when compared to non-Hispanic White survivors. This underscores historical racism and discrimination against non-Hispanic Black individuals as fundamental drivers of CVD-related disparities.

Stakeholders should work to eliminate the root causes of disparities. Clinicians should increase screening for risk factors that exacerbate CVD-related disparities among cancer survivors. Researchers should prioritise the investigation of systemic factors driving disparities in cancer and CVD and develop innovative interventions to mitigate risk in cancer survivors.

Social determinants of health (SDoH) have been associated with disparate outcomes among those with metabolic dysfunction-associated steatotic liver disease (MASLD) and its risk factors. To address SDoH among this population, real-time SDoH screening in clinical settings is required, yet optimal screening methods are unclear. We performed a scoping review to describe the current literature on SDoH screening conducted in the clinical setting among individuals with MASLD and MASLD risk factors.

Through a systematic literature search of MEDLINE, Embase, and CINAHL Complete databases through 7/2023, we identified studies with clinic-based SDoH screening among individuals with or at risk for MASLD that reported pertinent clinical outcomes including change in MASLD risk factors like diabetes and hypertension.

Ten studies (8 manuscripts, 2 abstracts) met inclusion criteria involving 148,151 patients: 89,408 with diabetes and 25,539 with hypertension. Screening was primarily completed in primary care clinics, and a variety of screening tools were used. The most commonly collected SDoH were financial stability, healthcare access, food insecurity and transportation. Associations between clinical outcomes and SDoH varied; overall, higher SDoH burden was associated with poorer outcomes including elevated blood pressure and hemoglobin A1c.

Despite numerous epidemiologic studies showing associations between clinical outcomes and SDoH, and guidelines recommending SDoH screening, few studies describe in-clinic SDoH screening among individuals with MASLD risk factors and none among patients with MASLD. Future research should prioritize real-time, comprehensive assessments of SDoH, particularly among patients at risk for and with MASLD, to mitigate disease progression and reduce MASLD health disparities.

The purpose of this study was to perform a cost-effectiveness analysis of the Koa Family Program, a community-based telewellness weight reduction intervention for overweight and obese women aged 21-45 years with low income. The Koa Family Program resulted in an approximately 8-pound weight loss as demonstrated in an RCT published previously.

Estimates for the cost-effectiveness were derived from the prospective 25-week RCT including 70 women (25 kg/m2≤BMI<40 kg/m2). The analysis was from a program-funder perspective. Base case costs, as well as low and high scenario costs, were estimated from the services provided to intervention participants. The incremental costs were compared with the incremental effectiveness, with weight loss being the outcome of interest. Costs were in 2021 U.S. dollars. Cost-effectiveness was assessed using the incremental cost-effectiveness ratio and the incremental net benefit. The statistical uncertainty was characterized using an incremental net benefit by willingness-to-pay plot and a cost-effectiveness acceptability curve.

The base case average cost per participant was $564.39. The low and high scenario average costs per participant were $407.34 and $726.22, respectively. Over the 25-week study timeframe, participants lost an average 7.7 pounds, yielding a base case incremental cost-effectiveness ratio of approximately $73 per extra pound lost. The probability that the Koa Family Program is cost-effective is 90%, assuming a willingness-to-pay of $115 for a 1-pound reduction, and is 95%, assuming a willingness-to-pay of $140.

The Koa Family Program provides good value with cost-effectiveness in line with other weight-loss interventions. This is a striking finding given that the Koa Family Program serves a more vulnerable population than is typically engaged in weight loss research studies.

Social determinants of health (SDOH)-education, transportation, housing, employment, health systems and services, economic status, and physical and social environments-influence patient outcomes; therefore, athletic trainers (ATs) need to understand and address these factors. However, little is known about how ATs perceive SDOH or how knowledgeable they are about social factors that contribute to patient health and well-being.

To evaluate ATs' familiarity and comfort with SDOH and their perceived knowledge and recognition of SDOH.

Cross-sectional study.

Online survey.

Our survey was distributed to 17 000 ATs; 1829 accessed it (access rate = 10.8%), and 1694 completed it (completion rate = 92.6%, AT experience = 15.2 ± 10.6 years, age = 36.6 ± 10.8 years).

The survey included multipart questions that evaluated ATs' perceptions of their familiarity, comfort, and knowledge about SDOH. Data were summarized using descriptive statistics.

Few respondents (4.1%, 70/1691) reported being extremely familiar with SDOH. Most indicated being moderately familiar (45.0%, 761/1691), minimally familiar (34.7%, 587/1691), or not familiar at all (16.1%, 273/1691). For questions about comfort, few described being extremely comfortable (3.5%, 59/1691) with SDOH, and most reported being moderately comfortable (35.4%, 598/1691), minimally comfortable (41.1%, 695/1691), or not comfortable at all (18.6%, 314/1691). For questions about knowledge, few indicated being extremely knowledgeable (2.7%, 46/1686) about SDOH, and the majority described being moderately (36.8%, 622/1686), minimally (41.8%, 704/1686), or not knowledgeable at all (18.6%, 314/1686). Over half of ATs accurately categorized 8 of the 9 SDOH listed in the survey, and 22% endorsed more correct than incorrect items.

A majority of ATs perceived their familiarity, comfort, and knowledge about SDOH to be moderate to low, which may reflect the relatively recent emphasis on SDOH in athletic health care. Because SDOH can have a major effect on patient health and well-being, strategies should be developed for educating ATs about SDOH. Developing strategies to increase comfort with SDOH in patient care is critical to ensure that those factors that can be addressed at the patient level are identified and managed.

To investigate whether the adverse impact of lower educational attainment on mortality risk in patients with coronary artery disease (CAD) is mediated by the activation of inflammatory and immune pathways, estimated as elevated soluble urokinase plasminogen activator receptor levels.

In 3164 patients undergoing coronary angiography, we investigated multivariable associations between suPAR and educational attainment and assessed the relationship between a lower educational level (defined as a high-school degree or less as the highest educational qualification) and outcomes using Cox proportional hazard and Fine and Gray's subdistribution competing risk models. The potential mediating effect through suPAR and high-sensitivity C-reactive protein (hs-CRP) was assessed using mediation analysis. A total of 1814 patients (57.3%) had achieved a higher (≥college) education level and 1350 patients (42.7%) a lower (≤high school) education level. Soluble urokinase plasminogen activator receptor levels were 9.0% [95% confidence interval (CI) 6.3-11.8, P ≤ 0.0001] higher in patients with lower educational qualifications than in those with higher educational qualifications after covariate adjustment. Lower educational attainment was associated with a higher risk of cardiovascular death after adjustment for demographic, clinical, and behavioural covariates, including CAD severity and heart failure history, medication use, and hs-CRP levels [hazard ratio 1.26 (95% CI 1.02-1.55, P = 0.03)]. However, after adjustment for suPAR levels, the effect of a lower educational level on cardiovascular death became insignificant. Values were similar for all-cause death. Soluble urokinase plasminogen activator receptor levels mediated 49% and hs-CRP levels 17% of the cardiovascular death risk attributable to lower educational attainment.

Circulating suPAR levels importantly mediate the effects of lower educational attainment on mortality, indicating the importance of systemic inflammation and immune dysregulation as biologic mediators of adverse social determinants of health.

Investments in public health - prevention of illnesses, and promotion, surveillance, and protection of population health - may improve population health, however, effects may only be observed over a long period of time.

To investigate the potential long-run relationship between expenditures on public health and avoidable mortality from preventable causes.

We focused on the country spending the most on public health in the OECD, Canada. We constructed a longitudinal dataset on mortality, health care expenditures and socio-demographic information covering years 1979-2017 for the ten Canadian provinces. We estimated error correction models for panel data to disentangle short-from long-run relationships between expenditures on public health and avoidable mortality from preventable causes. We further explored some specific causes of mortality to understand potential drivers. For comparison, we also estimated the short-run relationship between curative expenditures and avoidable mortality from treatable causes.

We find evidence of a long-run relationship between expenditures on public health and preventable mortality, and no consistent short-run associations between these two variables. Findings suggest that a 1% increase in expenditures on public health could lead to 0.22% decrease in preventable mortality. Reductions in preventable mortality are greater for males (-0.29%) compared to females (-0.09%). These results are robust to different specifications. Reductions in some cancer and cardiovascular deaths are among the probable drivers of this overall decrease. By contrast, we do not find evidence of a consistent short-run relationship between curative expenditures and treatable mortality, except for males.

This study supports the argument that expenditures on public health reap health benefits primarily in the long run, which, in this case, represents a reduction in avoidable mortality from preventable causes. Reducing public health expenditures on the premise that they have no immediate measurable benefits might thus harm population health outcomes in the long run.