Social Determinants of Health Impacting the Experience of Young Adults With Cancer at a Single Community Urban Hospital: A Retrospective Cohort Study.

Adolescent and young adult (AYA) cancer patients receive palliative medicine consultation at a late stage and face diagnostic delays. Failure to address social determinants of health (SDOH) and AYA-specific needs can adversely impact patient experience. This retrospective observational cohort study used data from chart review to assess the frequency of SDOH impacting AYA patients and setting of initial diagnosis at a US urban safety-net hospital. The association of SDOH variables with delays in treatment, loss of follow-up, and no-shows was tested using Chi-square and t-tests. One hundred seventy five patient charts were reviewed. Sixty-two percent were diagnosed in acute care settings. Substance use disorders, financial, employment, and insurance issues were associated with delayed treatment, with weak to moderate effect sizes. Mental health diagnoses, substance use disorder, homelessness, and financial burdens were associated with patient no-shows, with moderate to large effect sizes. Twenty-five percent of patients received palliative medicine consultation; 70% of these occurred at end of life. This study demonstrates the impact of SDOH on AYA cancer care and the need for policy allowing for intervention on SDOH.

Searching for the social determinants of health: observations from evidence synthesis publications.

BACKGROUND: The social determinants of health (SDOH) are the focus of an exponentially increasing number of publications, including evidence syntheses. However, there is not an established standard for searching for SDOH literature. This study seeks to identify published evidence syntheses pertaining to the SDOH, analyzing the search strategies used and the studies included within these reviews. The primary objectives are to compare search strategies and create a test set of SDOH publications. METHODS: We searched PubMed, Embase, and Scopus for evidence syntheses that mentioned the SDOH in their research questions and included an SDOH search strategy. Relevant data extracted from each review included databases searched; search terms used for the SDOH; conceptual frameworks referenced; and the citations of primary studies included in the reviews, which were compiled to form a test set of cited papers. The relative recall of the respective search strategies was tested by documenting the total number of MEDLINE results each retrieved and the number of test set papers retrieved. RESULTS: Sixty-four evidence syntheses were identified and included in the analysis, and 2750 cited papers were extracted. Findings indicate few commonalities across search strategies in search terms used, the total number of results retrieved, and the number of test set cited papers retrieved. One hundred and ninety-three unique MeSH terms and 1385 unique keywords and phrases were noted among the various search strategies. The number of total results retrieved by the SDOH search strategies ranged from 21,793 to over 16 million. The percentage of cited papers retrieved by the search strategies ranged from 2.46 to 97.9%. Less than 3% of the cited papers were indexed with the Social Determinants of Health MeSH. CONCLUSIONS: There has been little consistency across evidence syntheses in approaches to searching for SDOH literature. Differences in these strategies could have a significant impact on what literature is retrieved, included in reviews, and, consequently, incorporated into evidence-based practice. By documenting these differences and creating a set of papers relevant to SDOH, this research provides a snapshot of the current challenges in searching for SDOH content and lays the groundwork for the creation of a standardized search approach for SDOH literature.

Social determinants of health Z-code documentation practices in mental health settings: a scoping review.

Mental health remains an urgent global priority, alongside efforts to address underlying social determinants of health (SDoH) that contribute to the onset or exacerbate mental illness. SDoH factors can be captured in the form of International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM), SDoH Z codes. In this scoping review, we describe current SDoH Z-code documentation practices, with a focus on mental health care contexts. Among 2 743 061 374 health care encounters noted across 12 studies in the United States, SDoH Z-code documentation rates ranged from 0.5% to 2.4%. Documentation often involved patients under 64 years of age who are publicly insured and experience comorbidities, including depression, bipolar disorder and schizophrenia, chronic pulmonary disease, and substance abuse disorders. Documentation varied across hospital types, number of beds per facility, patient race/ethnicity, and geographic region. Variation was observed regarding patient sex/gender, although SDoH Z codes were more frequently documented for males. Documentation was most observed in government, nonfederal, and private not-for-profit hospitals. From these insights, we offer policy and practice recommendations, as well as considerations for patient data privacy, security, and confidentiality, to incentivize more routine documentation of Z codes to better assist patients with complex mental health care needs.

Hospital use of common Z-codes for Medicare fee-for-service beneficiaries, 2017-2021.

Recognizing the impact of the social determinants of health (SDOH) on health outcomes, in 2016, the Centers for Medicare and Medicaid Services recommended the use of International Classification of Diseases, 10th Revision (ICD-10), Z-codes to capture patients' health-related social needs. We examined changes in Z-code utilization to document health-related social needs for Medicare fee-for-service recipients among US hospitals between 2017 and 2021 across 5 common SDOH domains. We found that, while 56.9% of hospitals had at least 1 Z-code recorded in at least 1 patient per year, apart from those referring to housing needs, rates of Z-code adoption were low. Additionally, hospitals that were general medical, part of a teaching institution, affiliated with larger health systems, and of medium to large size had greater odds of utilizing Z-codes. Findings from this study highlight the need for continued efforts in promoting the consistent use of standardized SDOH capturing methods like Z-code documentation, such as provider training.

Recruiting a prospective community cohort to study Alzheimer's disease and structural and social determinants of health among adults racialized as Black: The ARCHES cohort.

INTRODUCTION: This ongoing, prospective study examines the effectiveness of methods used to successfully recruit and retain 238 Black older adults in a longitudinal, observational Alzheimer's disease (AD) study. METHODS: Recruitment strategies included traditional media, established research registries, speaking engagements, community events, and snowball sampling. Participants were asked to complete an annual office testing session, blood-based biomarker collection, optional one-time magnetic resonance imaging (MRI) scan, and community workshop. RESULTS: Within the first 22 months of active recruitment, 629 individuals expressed interest in participating, and 238 enrolled in the ongoing study. Of the recruitment methods used, snowball sampling, community events, and speaking engagements were the most effective. DISCUSSION: The systemic underrepresentation of Black participants in AD research impacts the ability to generalize research findings and determine the effectiveness and safety of disease-modifying treatments. Research to slow, stop, or prevent AD remains a top priority but requires diversity in sample representation. Highlights: Provide flexible appointments in the evening or weekends, offering transportation assistance, and allowing participants to complete study visits at alternative locations, such as senior centers or community centers.Continuously monitor and analyze recruitment data to identify trends, challenges, and opportunities for improvement.Implement targeted strategies to recruit participants who are underrepresented based on sex, gender, or education to increase representation.Diversify the research team to include members who reflect the racial and cultural backgrounds of the target population, to enhance trust and rapport with prospective participants.

Measuring value in health care: lessons from accountable care organizations.

Accountable care organizations (ACOs) were created to promote health care value by improving health outcomes while curbing health care expenditures. Although a decade has passed, the value of care delivered by ACOs is yet to be fully understood. We proposed a novel measure of health care value using data envelopment analysis and examined its association with ACO organizational characteristics and social determinants of health (SDOH). We observed that the value of care delivered by ACOs stagnated in recent years, which may be partially attributed to challenges in care continuity and coordination across providers. ACOs that were solely led by physicians and included more participating entities exhibited lower value, highlighting the role of coordination across ACO networks. Furthermore, SDOH factors, such as economic well-being, healthy food consumption, and access to health resources, were significant predictors of ACO value. Our findings suggest a "skinny in scale, broad in scope" approach for ACOs to improve the value of care. Health care policy should also incentivize ACOs to work with local communities and enhance care coordination of vulnerable patient populations across siloed and disparate care delivery systems.

American clusters: using machine learning to understand health and health care disparities in the United States.

Health and health care access in the United States are plagued by high inequality. While machine learning (ML) is increasingly used in clinical settings to inform health care delivery decisions and predict health care utilization, using ML as a research tool to understand health care disparities in the United States and how these are connected to health outcomes, access to health care, and health system organization is less common. We utilized over 650 variables from 24 different databases aggregated by the Agency for Healthcare Research and Quality in their Social Determinants of Health (SDOH) database. We used k-means-a non-hierarchical ML clustering method-to cluster county-level data. Principal factor analysis created county-level index values for each SDOH domain and 2 health care domains: health care infrastructure and health care access. Logistic regression classification was used to identify the primary drivers of cluster classification. The most efficient cluster classification consists of 3 distinct clusters in the United States; the cluster having the highest life expectancy comprised only 10% of counties. The most efficient ML clusters do not identify the clusters with the widest health care disparities. ML clustering, using county-level data, shows that health care infrastructure and access are the primary drivers of cluster composition.

Socioeconomic Disadvantage in Pregnancy and Postpartum Risk of Cardiovascular Disease.

BACKGROUND: Pregnancy is an educable and actionable life stage to address social determinants of health (SDOH) and lifelong cardiovascular disease (CVD) prevention. But the link between a risk score that combines multiple neighborhood-level social determinants in pregnancy and the risk of long-term CVD remains to be evaluated. OBJECTIVE: To examine whether neighborhood-level socioeconomic disadvantage measured by the Area Deprivation Index (ADI) in early pregnancy is associated with a higher 30-year predicted risk of CVD postpartum, as measured by the Framingham Risk Score. METHODS: An analysis of data from the prospective Nulliparous Pregnancy Outcomes Study-Monitoring Mothers-to-Be (nuMoM2b) Heart Health Study longitudinal cohort. Participant home addresses during early pregnancy were geocoded at the Census-block level. The exposure was neighborhood-level socioeconomic disadvantage using the 2015 ADI by tertile (least deprived [T1], reference; most deprived [T3]) measured in the first trimester. Outcomes were the predicted 30-year risks of atherosclerotic CVD (ASCVD, composite of fatal and non-fatal coronary heart disease and stroke) and total CVD (composite of ASCVD plus coronary insufficiency, angina pectoris, transient ischemic attack, intermittent claudication, and heart failure) using the Framingham Risk Score measured 2-to-7 years after delivery. These outcomes were assessed as continuous measures of absolute estimated risk in increments of 1%, and, secondarily, as categorical measures with high-risk defined as an estimated probability of CVD >10%. Multivariable linear regression and modified Poisson regression models adjusted for baseline age and individual-level social determinants, including health insurance, educational attainment, and household poverty. RESULTS: Among 4,309 nulliparous individuals at baseline, the median age was 27 years (IQR: 23-31) and the median ADI was 43 (IQR: 22-74). At 2-to-7 years postpartum (median: 3.1 years, IQR: 2.5, 3.7), the median 30-year risk of ASCVD was 2.3% (IQR: 1.5, 3.5) and of total CVD was 5.5% (IQR: 3.7, 7.9); 2.2% and 14.3% of individuals had predicted 30-year risk >10%, respectively. Individuals living in the highest ADI tertile had a higher predicted risk of 30-year ASCVD % (adj. ß: 0.41; 95% CI: 0.19, 0.63) compared with those in the lowest tertile; and those living in the top two ADI tertiles had higher absolute risks of 30-year total CVD % (T2: adj. ß: 0.37; 95% CI: 0.03, 0.72; T3: adj. ß: 0.74; 95% CI: 0.36, 1.13). Similarly, individuals living in neighborhoods in the highest ADI tertile were more likely to have a high 30-year predicted risk of ASCVD (aRR: 2.21; 95% CI: 1.21, 4.02) and total CVD ≥10% (aRR: 1.35; 95% CI: 1.08, 1.69). CONCLUSIONS: Neighborhood-level socioeconomic disadvantage in early pregnancy was associated with a higher estimated long-term risk of CVD postpartum. Incorporating aggregated SDOH into existing clinical workflows and future research in pregnancy could reduce disparities in maternal cardiovascular health across the lifespan, and requires further study.

Analysis of Social Determinants of Health in the Pediatric General Surgical Population of a Northeastern Urban Health System.

BACKGROUND: Social determinants of health (SDOH) have been identified as factors that influence patient health outcomes. These are well described in adult and pediatric general populations, however, there is a paucity of data for surgical patients. This study compares the prevalence of health-related social needs (HRSN) among pediatric surgery and general pediatric patients. METHODS: We retrospectively assessed electronic health record data to identify patients who completed a standardized HRSN screener within our health system and underwent surgery by a pediatric surgeon between January 2019 and December 2021. We compared this population to non-surgical pediatric patients during this time. Bivariate and multivariate logistic regressions were conducted to estimate the likelihood of having 1 or more HRSN given a patient's surgery status. Logistic and linear regressions were conducted to estimate healthcare utilization in pediatric surgery patients given their HRSN status. RESULTS: 33,989 general pediatric and pediatric surgery patients (age <21 years) were screened for HRSNs, and 2112 operations were performed during the study period. 343 (20%) of the surgical patients operated on by pediatric surgeons were screened for HRSNs. Surgical patients were more likely to be younger, Latinx, Spanish-speaking, and non-commercially insured (p < 0.0001). Surgical patients were 50% more likely to report one or more HRSN, when adjusting for demographic characteristics (aOR 1.50, 95% CI 1.16, 1.94). CONCLUSION: Pediatric surgery patients are more likely to report HRSNs compared to the general pediatric population. Surgical patients may represent an at-risk group, and universal HRSN screening and support should be considered to improve outcomes. LEVEL OF EVIDENCE: Level III.

Communities organizing to promote equity: engaging local communities in public health responses to health inequities exacerbated by COVID-19-protocol paper.

Background: The COVID-19 pandemic has disproportionately impacted rural and under-resourced urban communities in Kansas. The state's response to COVID-19 has relied on a highly decentralized and underfunded public health system, with 100 local health departments in the state, few of which had prior experience engaging local community coalitions in a coordinated response to a public health crisis. Methods: To improve the capacity for local community-driven responses to COVID-19 and other public health needs, the University of Kansas Medical Center, in partnership with the Kansas Department of Health and Environment, will launch Communities Organizing to Promote Equity (COPE) in 20 counties across Kansas. COPE will establish Local Health Equity Action Teams (LHEATs), coalitions comprised of community members and service providers, who work with COPE-hired community health workers (CHWs) recruited to represent the diversity of the communities they serve. CHWs in each county are tasked with addressing unmet social needs of residents and supporting their county's LHEAT. LHEATs are charged with implementing strategies to improve social determinants of health in their county. Monthly, LHEATs and CHWs from all 20 counties will come together as part of a learning collaborative to share strategies, foster innovation, and engage in peer problem-solving. These efforts will be supported by a multilevel communications strategy that will increase awareness of COPE activities and resources at the local level and successes across the state. Our mixed methods evaluation design will assess the processes and impact of COPE activities as well as barriers and facilitators to implementation using aspects of both the Consolidated Framework for Implementation Research (CFIR) and Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) models. Discussion: This protocol is designed to expand community capacity to strategically partner with local public health and social service partners to prioritize and implement health equity efforts. COPE intentionally engages historically resilient communities and those living in underserved rural areas to inform pragmatic strategies to improve health equity.

Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data.

PURPOSE: To develop a machine learning algorithm, using patient-reported data from early pregnancy, to predict later onset of first time moderate-to-severe depression. METHODS: A sample of 944 U.S. patient participants from a larger longitudinal observational cohortused a prenatal support mobile app from September 2019 to April 2022. Participants self-reported clinical and social risk factors during first trimester initiation of app use and completed voluntary depression screenings in each trimester. Several machine learning algorithms were applied to self-reported data, including a novel algorithm for causal discovery. Training and test datasets were built from a randomized 80/20 data split. Models were evaluated on their predictive accuracy and their simplicity (i.e., fewest variables required for prediction). RESULTS: Among participants, 78% identified as white with an average age of 30 [IQR 26-34]; 61% had income ≥ $50,000; 70% had a college degree or higher; and 49% were nulliparous. All models accurately predicted first time moderate-severe depression using first trimester baseline data (AUC 0.74-0.89, sensitivity 0.35-0.81, specificity 0.78-0.95). Several predictors were common across models, including anxiety history, partnered status, psychosocial factors, and pregnancy-specific stressors. The optimal model used only 14 (26%) of the possible variables and had excellent accuracy (AUC = 0.89, sensitivity = 0.81, specificity = 0.83). When food insecurity reports were included among a subset of participants, demographics, including race and income, dropped out and the model became more accurate (AUC = 0.93) and simpler (9 variables). CONCLUSION: A relatively small amount of self-report data produced a highly predictive model of first time depression among pregnant individuals.

A 12-year follow up of principles learned in a pediatric dentistry Master of Public Health dual degree program applied to professional practice.

Introduction: Dental public health professionals play a critical role in preventing and controlling oral diseases. The purpose of this study was to assess the application of public health principles learned in a pediatric dentistry Master of Public Health (MPH) dual degree program to professional practice upon graduation. Methods: Semi-structured interviews were conducted with pediatric dentistry/MPH dual degree alumni who graduated from the program between 2012 and 2023. Interview questions inquired about characteristics of patient population, location of providers' clinic/organization, whether the program was worthwhile to their practice and application of principles learned in the program to their professional practice. Results: Twenty of the 22 program alumni agreed to be interviewed. All alumni thought the program was extremely worthwhile to their practice. They felt the MPH component of the program gave them the public health background and tools they needed to provide comprehensive and holistic care to their patients. Additionally, all alumni reported applying the public health principles they learned in the program to their professional practice through leadership roles, research and teaching that focuses on oral disease prevention and the promotion of dental health. Discussion: Given the importance of a dental public health professionals' role in reducing oral health disparities at the population level, more pediatric dentistry MPH dual degree programs are urgently needed. Additionally, more research is necessary to demonstrate the effectiveness of these programs, which will be critical to helping ensure the value of a dual degree in dentistry and public health is recognized and promoted worldwide.

Addressing food insecurity in rural primary care: a mixed-methods evaluation of barriers and facilitators.

BACKGROUND: Food insecurity (FI) is associated with negative health outcomes and increased healthcare utilization. Rural populations face increased rates of FI and encounter additional barriers to achieving food security. We sought to identify barriers and facilitators to screening and interventions for FI in rural primary care practices. METHODS: We conducted a mixed-methods study using surveys and semi-structured interviews of providers and staff members from rural primary care practices in northern New England. Survey data were analyzed descriptively, and thematic analysis was used to identify salient interview themes. RESULTS: Participants from 24 rural practices completed the survey, and 13 subsequently completed an interview. Most survey respondents (54%) reported their practices systematically screen for FI and 71% reported food needs were "very important" for their patients and communities. Time and resource constraints were the most frequently cited barriers to screening for and addressing FI in practices based on survey results. Interview themes were categorized by screening and intervention procedures, community factors, patient factors, external factors, practice factors, process and implementation factors, and impact of FI screening and interventions. Time and resource constraints were a major theme in interviews, and factors attributed to rural practice settings included geographically large service areas, stigma from loss of privacy in small communities, and availability of food resources through farming. CONCLUSIONS: Rural primary care practices placed a high value on addressing food needs but faced a variety of barriers to implementing and sustaining FI screening and interventions. Strategies that utilize practice strengths and address time and resource constraints, stigma, and large service areas could promote the adoption of novel interventions to address FI.

Examining health apps and wearable use in improving physical and mental well-being across U.S., China, and Singapore.

Health apps and wearables are touted to improve physical health and mental well-being. However, it is unclear from existing research the extent to which these health technologies are efficacious in improving physical and mental well-being at a population level, particularly for the underserved groups from the perspective of health equity and social determinants. Also, it is unclear if the relationship between health apps and wearables use and physical and mental well-being differs across individualistic, collectivistic, and a mix of individual-collectivistic cultures. A large-scale online survey was conducted in the U.S. (individualist culture), China (collectivist culture), and Singapore (mix of individual-collectivist culture) using quota sampling after obtaining ethical approval from the Institutional Review Board (IRB-2021-262) of Nanyang Technological University (NTU), Singapore. There was a total of 1004 respondents from the U.S., 1072 from China, and 1017 from Singapore. Data were analyzed using multiple regression and negative binomial regression. The study found that income consistently had the strongest relationship with physical and mental well-being measures in all three countries, while the use of health apps and wearables only had a moderate association with psychological well-being only in the US. Health apps and wearables were associated with the number of times people spent exercising and some mental health outcomes in China and Singapore, but they were only positively associated with psychological well-being in the US. The study emphasizes the importance of considering the social determinants, social-cultural context of the population, and the facilitating conditions for the effective use of digital health technologies. The study suggests that the combined use of both health apps and wearables is most strongly associated with better physical and mental health, though this association is less pronounced when individuals use only apps or wearables.

The relationship between social determinants of health and neurocognitive and mood-related symptoms in the primary brain tumor population: A systematic review.

Social determinants of health (SDOH) impact cancer-related health outcomes, including survival, but their impact on symptoms is less understood among the primary brain tumor (PBT) population. We conducted a systematic review to examine the relationships between SDOH and neurocognitive and mood-related symptoms among the PBT population. PubMed, EMBASE, and CINAHL were searched using PROGRESS criteria (place of residence, race/ethnicity, occupation, gender/sex, religion, education, socioeconomic status, and social capital) on March 8th, 2022. Two individuals screened and assessed study quality using the NHLBI Assessment Tool for Observational Cohort and Cross-sectional Studies. Of 3006 abstracts identified, 150 full-text articles were assessed, and 48 were included for a total sample of 28 454 study participants. Twenty-two studies examined 1 SDOH; none examined all 8. Four studies measured place of residence, 2 race/ethnicity, 13 occupation, 42 gender, 1 religion, 18 education, 4 socioeconomic status, and 15 social capital. Fifteen studies assessed neurocognitive and 37 mood-related symptoms. While higher education was associated with less neurocognitive symptoms, and among individuals with meningioma sustained unemployment after surgery was associated with depressive symptoms, results were otherwise disparate among SDOH and symptoms. Most studies were descriptive or exploratory, lacking comprehensive inclusion of SDOH. Standardizing SDOH collection, reducing bias, and recruiting diverse samples are recommended in future interventions.

Measuring concordance and discordance between selected individual characteristics and corresponding neighborhood-level social determinants of health.

Background: Individuals reside within communities influenced by various social determinants impacting health, which may harmonize or conflict at individual and neighborhood levels. While some experience concordant circumstances, discordance is prevalent, yet poorly understood due to the lack of a universally accepted method for quantifying it. This paper proposes a methodology to address this gap. Methods: We propose a systematic approach to operationalize concordance and discordance between individual and neighborhood social determinants, using household income (HHI) (continuous) and race/ethnicity (categorical) as examples for individual social determinants. We demonstrated our method with a small dataset that combines self-reported individual data with geocoded neighborhood level. We anticipate that the risk profiles created by either self-reported individual data or neighborhood-level data alone will differ from patterns demonstrated by typologies based on concordance and discordance. Results: In our cohort, it was revealed that 20% of patients experienced discordance between their HHIs and neighborhood characteristics. Additionally, 38% reside in racially/ethnically concordant neighborhoods, 23% in discordant ones, and 39% in neutral ones. Conclusion: Our study introduces an innovative approach to defining and quantifying the notions of concordance and discordance in individual attributes concerning neighborhood-level social determinants. It equips researchers with a valuable tool to conduct more comprehensive investigations into the intricate interplay between individuals and their environments. Ultimately, this methodology facilitates a more accurate modeling of the true impacts of social determinants on health, contributing to a deeper understanding of this complex relationship.

The Tensegrity Curriculum: A Comprehensive Curricular Structure Supporting Cultural Humility in Undergraduate Medical Education.

Due to growing health disparities in underserved communities, a comprehensive approach is needed to train physicians to work effectively with patients who have cultures and belief systems different from their own. To address these complex healthcare inequities, Rowan-Virtua SOM implemented a new curriculum, The Tensegrity Curriculum, designed to expand beyond just teaching skills of cultural competence to include trainees' exploration of cultural humility. The hypothesis is that this component of the curriculum will mitigate health inequity by training physicians to recognize and interrupt the bias within themselves and within systems. Early outcomes of this curricular renewal process reveal increased student satisfaction as measured by course evaluations. Ongoing course assessments examine deeper understanding of the concepts of implicit bias, social determinants of health, systemic discrimination and oppression as measured by performance on graded course content, and greater commitment to continual self-evaluation and critique throughout their careers as measured by course feedback. Structured research is needed to understand the relationship between this longitudinal and integrated curricular design, and retainment or enhancement of empathy during medical training, along with its impact on health disparities and community-based outcomes.

Disparities in postpartum readmission by patient- and hospital-level social risk factors in the United States: a retrospective multistate analysis, 2015-2020.

BACKGROUND: Postpartum readmission is an area of focus for improving obstetric care and reducing costs. We examined disparities in all-cause 30-day postpartum readmission by patient- and hospital-level factors in the United States. METHODS: We conducted a retrospective cohort study using 2015-2020 records from the State Inpatient Databases from four states. Generalized linear mixed models were constructed to estimate the effects of individual patient- and hospital-level factors on adjusted odds of 30-day readmission after controlling for confounders. Stratified analyses by delivery and anesthesia type (New York only) and interaction models were performed. RESULTS: Black mothers were more likely than White mothers to be readmitted within 30-days postpartum (aOR 1.57, 95% CI 1.52 to 1.61). Mothers with public insurance had increased odds of readmission compared with those with private insurance (Medicare: aOR 2.13, 95% CI 1.95 to 2.32; Medicaid: aOR 1.14, 95% CI 1.11 to 1.17). Compared with mothers in the lowest income quartile, those in the highest quartile experienced a 14% lower odds of readmission (aOR 0.86, 95% CI 0.83 to 0.89). There were no significant associations between hospital-level characteristics and readmission. Black mothers were more likely to be readmitted regardless of delivery type and most combinations of delivery and anesthesia type. Black mothers from the highest income quartile were more likely to be readmitted than White mothers from the lowest income quartile. CONCLUSION: Substantial disparities in 30-day postpartum readmissions by patient-level social factors were observed, particularly amongst Black mothers. Action is needed to address and mitigate disparities in postpartum readmission.

Utilizing social determinants of health to identify most vulnerable neighborhoods-Latent class analysis and GIS map.

OBJECTIVES: Public Health officials are often challenged to effectively allocate limited resources. Social determinants of health (SDOH) may cluster in areas to cause unique profiles related to various adverse life events. The authors use the framework of unintended teen pregnancies to illustrate how to identify the most vulnerable neighborhoods. METHODS: This study used data from the U.S. American Community Survey, Princeton Eviction Lab, and Connecticut Office of Vital Records. Census tracts are small statistical subdivisions of a county. Latent class analysis (LCA) was employed to separate the 832 Connecticut census tracts into four distinct latent classes based on SDOH, and GIS mapping was utilized to visualize the distribution of the most vulnerable neighborhoods. GEE Poisson regression model was used to assess whether latent classes were related to the outcome. Data were analyzed in May 2021. RESULTS: LCA's results showed that class 1 (non-minority non-disadvantaged tracts) had the least diversity and lowest poverty of the four classes. Compared to class 1, class 2 (minority non-disadvantaged tracts) had more households with no health insurance and with single parents; and class 3 (non-minority disadvantaged tracts) had more households with no vehicle available, that had moved from another place in the past year, were low income, and living in renter-occupied housing. Class 4 (minority disadvantaged tracts) had the lowest socioeconomic characteristics. CONCLUSIONS: LCA can identify unique profiles for neighborhoods vulnerable to adverse events, setting up the potential for differential intervention strategies for communities with varying risk profiles. Our approach may be generalizable to other areas or other programs. KEY MESSAGES: What is already known on this topic Public health practitioners struggle to develop interventions that are universally effective. The teen birth rates vary tremendously by race and ethnicity. Unplanned teen pregnancy rates are related to multiple social determinants and behaviors. Latent class analysis has been applied successfully to address public health problems. What this study adds While it is the pregnancy that is not planned rather than the birth, access to pregnancy intention data is not available resulting in a dependency on teen birth data for developing public health strategies. Using teen birth rates to identify at-risk neighborhoods will not directly represent the teens at risk for pregnancy but rather those who delivered a live birth. Since teen birth rates often fluctuate due to small numbers, especially for small neighborhoods, LCA may avoid some of the limitations associated with direct rate comparisons. The authors illustrate how practitioners can use publicly available SDOH from the Census Bureau to identify distinct SDOH profiles for teen births at the census tract level. How this study might affect research, practice or policy These profiles of classes that are at heightened risk potentially can be used to tailor intervention plans for reducing unintended teen pregnancy. The approach may be adapted to other programs and other states to prioritize the allocation of limited resources.

Social Determinants of Health and College Food Insecurity.

In recent years, many students have faced economic hardship and experienced food insecurity, even as universities strive to create more equitable pathways to college. There is a need for a more holistic perspective that addresses the complexity of food insecurity amongst college students. To this end, we examined the relationship between the social determinants of health, including college food insecurity (CoFI) and childhood food insecurity (ChFI), and their relationship with well-being measures. The study sample was a convenience sample that included 372 students at a public university who responded to an online survey in fall 2021. Students were asked to report their food security status in the previous 30 days. We used the following analytical strategies: chi-square tests to determine differences between food secure (FS) and food insecure (FI) students; binary logistic regression of CoFI on student demographics and ChFI; and ordinal or binary logistic regression for well-being measures. Black students, off-campus students, first-generation students, in-state students, and humanities/behavioral/social/health sciences majors were more likely to report CoFI. FI students were more likely to have experienced ChFI and to have lower scores on all well-being measures. ChFI was associated with four well-being measures and its effects were mediated by CoFI. College student health initiatives would benefit from accounting for SDOH, including ChFI experiences and its subsequent cumulative disadvantages experienced during college.