Social Determinants Of Health Variables Research Articles

The Influence of Patient Characteristics and Social Determinants of Health on Postoperative Complications Following Achilles Tendon Rupture.

The influence of social determinants of health (SDH) on postoperative complications has been investigated in several studies, although correlation with Achilles tendon rupture (ATR) repair remains uninvestigated. SDH encompasses several factors, including insurance status and area-based measurements, including the Area Deprivation Index (ADI) and Social Vulnerability Index (SVI), which ranks neighborhoods by social disadvantage. This study investigated the correlation between patient demographics, SDH, and complications following ATR repair. A retrospective cohort study was conducted on 521 patients who presented with acute ATR and met the inclusion criteria, including age ≥18 years, a minimum of 30-day follow-up, and repair within 28 days of rupture. We reviewed patient demographics, time to surgery (TTS), and postoperative complications, including venous thromboembolism (VTE), rerupture, surgical site infection (SSI), wound dehiscence, and sural nerve injury. SDH variables included race, smoking status, insurance status, level of education, ADI, and SVI. Univariate regression tested the correlation between complications and SDH indicators. Significant variables (P < .05) were included in a multivariate regression. Sixty-eight complications occurred in 59 patients (11.3%). Multivariate regression showed that a higher ADI, that is, socially deprived individuals, was associated with lower rates of VTE (OR = 0.41, P = .04). Higher body mass index (BMI) was associated with rerupture (OR = 8.73, P < .01). Male patients had lower rates of wound dehiscence (OR = 0.31, P = .03) and VTE (OR = 0.32, P = .02) compared with women. Longer TTS correlated with sural nerve injuries (OR = 2.23, P < .01) and shorter TTS with reruptures (OR = 0.02, P = .02). Some measures of SDH were associated with postoperative complications. Gender also may have an effect, with male sex associated with lower rates of wound dehiscence and VTE. BMI was associated with higher rates of reruptures and overall general complications. Level IV, case series.

Prediction of Readmission Following Sepsis Using Social Determinants of Health.

To determine the predictive value of social determinants of health (SDoH) variables on 30-day readmission following a sepsis hospitalization as compared with traditional clinical variables. Multicenter retrospective cohort study using patient-level data, including demographic, clinical, and survey data. Thirty-five hospitals across the United States from 2017 to 2021. Two hundred seventy-one thousand four hundred twenty-eight individuals in the AllofUs initiative, of which 8909 had an index sepsis hospitalization. None. Unplanned 30-day readmission to the hospital. Multinomial logistic regression models were constructed to account for survival in determination of variables associate with 30-day readmission and are presented as adjusted odds rations (aORs). Of the 8909 sepsis patients in our cohort, 21% had an unplanned hospital readmission within 30 days. Median age (interquartile range) was 54 years (41-65 yr), 4762 (53.4%) were female, and there were self-reported 1612 (18.09%) Black, 2271 (25.49%) Hispanic, and 4642 (52.1%) White individuals. In multinomial logistic regression models accounting for survival, we identified that change to nonphysician provider type due to economic reasons (aOR, 2.55 [2.35-2.74]), delay of receiving medical care due to lack of transportation (aOR, 1.68 [1.62-1.74]), and inability to afford flow-up care (aOR, 1.59 [1.52-1.66]) were strongly and independently associated with a 30-day readmission when adjusting for survival. Patients who lived in a ZIP code with a high percentage of patients in poverty and without health insurance were also more likely to be readmitted within 30 days (aOR, 1.26 [1.22-1.29] and aOR, 1.28 [1.26-1.29], respectively). Finally, we found that having a primary care provider and health insurance were associated with low odds of an unplanned 30-day readmission. In this multicenter retrospective cohort, several SDoH variables were strongly associated with unplanned 30-day readmission. Models predicting readmission following sepsis hospitalization may benefit from the addition of SDoH factors to traditional clinical variables.

Social Determinants of Long-Term Opioid Use Following Total Knee Arthroplasty.

Total knee arthroplasty (TKA) risks persistent pain and long-term opioid use (LTO). The role of social determinants of health (SDoH) in LTO is not well established. We hypothesized that SDoH would be associated with postsurgical LTO after controlling for relevant demographic and clinical variables. This study utilized data from the Veterans Affairs Surgical Quality Improvement Program, VA Corporate Data Warehouse, and Centers for Medicare and Medicaid Services, including Veterans aged ≥ 65 who underwent elective TKA between 2013 and 2019 with no postsurgical complications or history of significant opioid use. LTO was defined as > 90 days of opioid use beginning within 90 days postsurgery. SDoH variables included the Area Deprivation Index, rurality, and housing instability in the last 12 months identified via medical record screener or International Classification of Diseases, Tenth Revision codes. Multivariable risk adjustment models controlled for demographic and clinical characteristics. Of the 9,064 Veterans, 97% were male, 84.2% white, mean age was 70.6 years, 46.3% rural, 11.2% living in highly deprived areas, and 0.9% with a history of homelessness/housing instability. Only 3.7% (n = 336) developed LTO following TKA. In a logistic regression model of only SDoH variables, housing instability (odds ratio [OR] = 2.38, 95% confidence interval [CI]: 1.09-5.22) and rurality conferred significant risk for LTO. After adjusting for demographic and clinical variables, LTO was only associated with increasing days of opioid supply in the year prior to surgery (OR = 1.52, 95% CI: 1.43-1.63 per 30 days) and the initial opioid fill (OR = 1.07; 95% CI: 1.06-1.08 per day). Our primary hypothesis was not supported; however, our findings do suggest that patients with housing instability may present unique challenges for postoperative pain management and be at higher risk for LTO.

Social Determinants of Cardiovascular Risk Factors Among Asian American Subgroups.

Social determinants of health (SDOH) play a significant role in the development of cardiovascular risk factors. We investigated SDOH associations with cardiovascular risk factors among Asian American subgroups. We utilized the National Health Interview Survey, a nationally representative survey of US adults, years 2013 to 2018. SDOH variables were categorized into economic stability, neighborhood and social cohesion, food security, education, and health care utilization. SDOH score was created by categorizing 27 SDOH variables as 0 (favorable) or 1 (unfavorable). Self-reported cardiovascular risk factors included diabetes, high cholesterol, high blood pressure, obesity, insufficient physical activity, suboptimal sleep, and nicotine exposure. Among 6395 Asian adults aged ≥18 years, 22.1% self-identified as Filipino, 21.6% as Asian Indian, 21.0% as Chinese, and 35.3% as other Asian. From multivariable-adjusted logistic regression models, each SD increment of SDOH score was associated with higher odds of diabetes among Chinese (odds ratio [OR], 1.45; 95% CI, 1.04-2.03) and Filipino (OR, 1.24; 95% CI, 1.02-1.51) adults; high blood pressure among Filipino adults (OR, 1.28; 95% CI, 1.03-1.60); insufficient physical activity among Asian Indian (OR, 1.42; 95% CI, 1.22-1.65), Chinese (OR, 1.58; 95% CI, 1.33-1.88), and Filipino (OR, 1.24; 95% CI, 1.06-1.46) adults; suboptimal sleep among Asian Indian adults (OR, 1.20; 95% CI, 1.01-1.42); and nicotine exposure among Chinese (OR, 1.56; 95% CI, 1.15-2.11) and Filipino (OR, 1.50; 95% CI, 1.14-1.97) adults. Unfavorable SDOH are associated with higher odds of cardiovascular risk factors in Asian American subgroups. Culturally specific interventions addressing SDOH may help improve cardiovascular health among Asian Americans.

The Social Determinants of Health and Athletic Trainer Availability in Indiana Secondary Schools.

The social determinants of health (SDOH) are circumstances under which individuals are born, work, and live that influence health outcomes. Previous researchers have examined 1 determinant of economic stability and found disparities in socioeconomic status and athletic trainer (AT) availability. To examine the SDOH characteristics of Indiana secondary schools and AT availability. Cross-sectional study. Database secondary analysis. A total of 426 secondary schools. All data were collected from publicly available databases. The independent variable was AT availability, and schools were classified as having a full-time AT, a part-time AT only, or no AT. The SDOH variables were gathered for each school (at the school and county levels). Data were summarized using measures of central tendencies, 1-way analysis of variance, and Kruskal-Wallis tests. School enrollment was larger in schools with greater AT availability (P< .001). The proportion of non-White students was greater in schools with more AT availability (P= .002). Greater AT availability was present in counties with higher graduation rates (P= .03). Post hoc comparisons revealed differences in graduation rates between schools with a part-time AT and those with no AT (P= .04). Schools with less AT availability were located in counties with a slightly higher percentage of the population uninsured (P= .02). Schools with greater AT availability were located in counties with a higher ratio of population to primary care physicians (P= .03). Schools with less AT availability were located in counties with a higher population experiencing severe housing problems (P= .02). No differences were found in AT availability based on the 3 social and community context variables (P> .05). Differences were noted in AT availability and SDOH characteristics at the secondary school level. We observed less AT availability where high school graduation rates and the population of primary care providers were lower. Strategies should be implemented to improve access to athletic health care in underresourced communities.

Challenges with non-descriptive compliance labeling of end-stage renal disease patients in accessibility for renal transplantation.

Non-descriptive and convenient labels are uninformative and unfairly project blame onto patients. The language clinicians use in the Electronic Medical Record, research, and clinical settings shapes biases and subsequent behaviors of all providers involved in the enterprise of transplantation. Terminology such as noncompliant and nonadherent serve as a reason for waitlist inactivation and limit access to life-saving transplantation. These labels fail to capture all the circumstances surrounding a patient's inability to follow their care regimen, trivialize social determinants of health variables, and bring unsubstantiated subjectivity into decisions regarding organ allocation. Furthermore, insufficient Medicare coverage has forced patients to ration or stop taking medication, leading to allograft failure and their subsequent diagnosis of noncompliant. We argue that perpetuating non-descriptive language adds little substantive information, increases subjectivity to the organ allocation process, and plays a major role in reduced access to transplantation. For patients with existing barriers to care, such as racial/ethnic minorities, these effects may be even more drastic. Transplant committees must ensure thorough documentation to correctly encapsulate the entirety of a patient's position and give voice to an already vulnerable population.

Abstract P287: Incorporating Behavioral and Social Determinants of Health Variables Into a Machine Learning Model Predicting Cardiometabolic Disease

Introduction: Machine learning models hold potential for improved cardiometabolic disease prediction and more efficacious prevention and intervention efforts. Current models are limited by use of cross-sectional datasets and variables (features) consisting of diagnostic criteria (such as blood glucose or hemoglobin A1c in type 2 diabetes models) and overlook health behaviors and social determinants of health (HB-SDH). Hypothesis: Machine learning models built with HB-SDH variables will accurately predict type 2 diabetes. Methods: We used data from 2,493 participants without diabetes in the Adolescent to Adult Health study (mean age = 15.5 years at baseline (1994), 40.9% male) who contributed blood samples and anthropometric measures in wave 4 (2008) or 5 (2016-2018). We used an 80/20 split to form separate training and test datasets; the training dataset was used to build random forest and XGBoost models, and performance with respect to classifying diabetes was evaluated in the test dataset. We first built a model using 15 demographic and biomarker features as a comparator for our second model, which included 34 HB-SDH features measured during adulthood but no biomarkers. Results: Five-hundred sixteen participants developed type 2 diabetes. In XGBoost, the comparator model was found to predict diabetes with 92.6% accuracy (95% CI: 89.8%, 94.8%) and area under the curve (AUC) = 0.8165. The HB-SDH model had 88.7% accuracy (95% CI: 85.4, 91.4%) and AUC = 0.7631. Using random forest, the comparator model had 93.0% accuracy (95% CI: 90.3, 95.2%) and AUC = 0.8376, while the HB-SDH model had 89.8% (95% CI: 88.6, 92.4%) accuracy and AUC = 0.7650. The top 10 variables in ascertaining classifications in the random forest model included self-reported general health, waist circumference, self-reported anxiety diagnosis, self-reported hypertension diagnosis, income, BMI, education level, change in weight, financial scarcity, and experienced stigma, while in the XGBoost model these were self-reported general health, experienced stigma, financial scarcity, self-reported anxiety diagnosis, experienced weight stigma, waist circumference, income, self-reported hypertension diagnosis, sugar-sweetened beverage intake, and BMI. Conclusions: Machine learning models built with behavioral and social determinants of health variables perform comparably to models with biomarkers and identify salient features for diabetes risk. The features that factored into classification performance were similar between random forest and XGBoost models. In this population, HB-SDH of relevance included socioeconomic indicators, sugary beverages, anxiety, and experiencing stigma, in addition to adiposity and hypertension. This finding needs to be replicated in diverse cohorts that may experience different HB-SDH.

Mainstreaming in parallel with ovarian cancer tumor testing to improve genetic testing uptake

ObjectivesAlthough genetic testing (GT) is universally recommended for patients with epithelial ovarian cancer (EOC), rates are low (34%). In 1/2019, we implemented mainstreaming—GT in parallel with tumor testing via MSK-IMPACT within oncology clinics. We sought to determine GT rates pre/post-mainstreaming and patient characteristics associated with GT. MethodsPatients with newly diagnosed EOC seen at our institution from 7/1/2015–3/31/2022 were included. Clinical data were abstracted including social determinants of health (SDOH) variables, race/ethnicity, marital status, insurance, language, comorbidities, employment, and Yost index, a measure of socioeconomic status. GT rates were calculated overall and pre−/post-mainstreaming (1/2019). Logistic regression models were fit to identify variables associated with GT. ResultsOf 1742 patients with EOC, 1591 (91%) underwent GT. Rates of GT increased from 87% to 95% after mainstreaming (p < 0.001). Among 151 patients not undergoing GT, major reasons were lack of provider recommendation (n = 76, 50%) and logistical issues (n = 38, 25%) with few declining (n = 14, 9%) or having medical complications preventing GT (n = 7, 4.6%). High-grade serous histology, advanced stage (III/IV), and having a spouse/partner were associated with increased GT uptake (p < 0.01). Among SDOH variables, there were no differences by insurance, Yost score, language, comorbidities, employment, or race/ethnicity. In multivariable models, likelihood of GT increased with mainstreaming, even after adjustment for histology, stage, and marital status (OR 3.77; 95% CI: 2.56–5.66). ConclusionsMainstreaming increased the likelihood of GT in patients with EOC. We found lower testing rates in patients without partners/spouses, non–high-grade serous histology, and early-stage disease, representing potential areas for future interventions.

Reporting of social determinants of health in randomized controlled trials conducted in the pediatric intensive care unit.

The influence of social determinants of health (SDOH) on access to care and outcomes for critically ill children remains an understudied area with a paucity of high-quality data. Recent publications have highlighted the importance of incorporating SDOH considerations into research but the frequency with which this occurs in pediatric intensive care unit (PICU) research is unclear. Our objective was to determine the frequency and categories of SDOH variables reported and how these variables were defined in published PICU randomized controlled trials (RCTs). We searched Medline, Embase, Lilacs, and Central from inception to Dec 2022. Inclusion criteria were randomized controlled trials of any intervention on children or their families in a PICU. Data related to study demographics and nine WHO SDOH categories were extracted, and descriptive statistics and qualitative data generated. 586 unique RCTs were included. Studies had a median sample size of 60 patients (IQR 40-106) with 73.0% of studies including ≤100 patients and 41.1% including ≤50 patients. A total of 181 (181/586, 30.9%) studies reported ≥1 SDOH variable of which 163 (163/586, 27.8%) reported them by randomization group. The most frequently reported categories were food insecurity (100/586, 17.1%) and social inclusion and non-discrimination (73/586, 12.5%). Twenty-five of 57 studies (43.9%) investigating feeding or nutrition and 11 of 82 (13.4%) assessing mechanical ventilation reported baseline nutritional assessments. Forty-one studies investigated interventions in children with asthma or bronchiolitis of which six reported on smoking in the home (6/41, 14.6%). Reporting of relevant SDOH variables occurs infrequently in PICU RCTs. In addition, when available, categorizations and definitions of SDOH vary considerably between studies. Standardization of SDOH variable collection along with consistent minimal reporting requirements for PICU RCT publications is needed.

Abstract TMP40: The Impact of Social Determinants of Health on Stroke Readmissions and Recovery

Introduction: The consequences of increased morbidity and increased socioeconomic strain, for stroke survivors and their families, is well documented in stroke literature worldwide. Variables that determine readmission rates of stroke survivors may be complicated by the intersection of social determinants of health (SDOH). SDOH include race/ethnicity, insecurities, living conditions, social isolation, health literacy, income and education. This single center, prospective, observational study explores SDOH that lead to higher risk of readmission (within 30-days to 30-90 days) after discharge from the hospital once diagnosed with stroke or transient ischemic attack (TIA). Methods: A questionnaire was distributed to 100 patients admitted with acute stroke or TIA per the study inclusion criteria. Additional variables of interest collected included race, ethnicity, income, insurance status, treatment, baseline and discharge National Institutes of Health Stroke Scale score and modified Rankin scale (mRS) score, comorbidities, zip code and discharge disposition. Results: Univariate analysis found prior stroke/TIA and baseline mRS score of 0-2 to be statistically significant at predicting readmission within 30-days, p=0.07 and p=0.01, respectively. Multivariate analysis showed patients who find it difficult to eat healthy and participate in activities to improve their health, have history of prior TIA/stroke, coronary artery disease, diabetes and baseline mRS score were highly predictive for readmission. Discussion: SDOH are thought to contribute to readmission rates for patients who experienced stroke or TIA. In conclusion, our preliminary study found that patients who admitted to difficulties maintaining a healthy lifestyle had a correlation with both 30-day and 30-90 day readmission. Further studies are needed to identify the SDOH variables that most accurately predict readmission after stroke/TIA. This will further the mandate for increased precision medicine, tailoring targeted interventions to improve the quality of life for stroke survivors.

Social Vulnerability Association with Thyroid Cancer Disparities in the United States.

Background: As thyroid cancer incidence rises, it is increasingly valuable to recognize disparities in treatment and diagnosis. Prior investigations into social determinants of health (SDoH) are limited to pediatric populations or studies looking at single factors such as race or environmental influences. Utilizing the CDC-social vulnerability index and SEER-patient database to assess the amalgamated, real-world influence of varied SDoH and their quantifiable impact on thyroid cancer disparities across the United States. Methods: In a retrospective cohort study, 199,340 adult thyroid cancer patients from 1975 to 2017 were assessed for significant regression trends in months of follow-up/surveillance, survival, late staging, and treatment receipt across thyroid cancer-subtypes with increasing overall social vulnerability, as well as in 15 SDoH variables regarding socioeconomic status, minority-language status, household composition, and housing-transportation across all the U.S. counties while accounting for sociodemographic regional differences. Results: With increasing overall social vulnerability, decreases in months of follow-up were observed with patients with papillary, follicular, medullary, oncocytic, and anaplastic thyroid cancer (p = 0.001). Comparing lowest with highest vulnerability cohorts, relative decreases in months of surveillance ranged from 55.6% (14.5-6.5 months) with anaplastic to 17% (108.6-90.2) with oncocytic. Socioeconomic status vulnerabilities, followed by vulnerabilities in household composition and housing-transportation type, contributed to these overall trends. Similar survival decreases occurred across all thyroid cancer patients, ranging from 55.9% (9.6-4.2) with anaplastic to 28.3% (97-69.5) with oncocytic. Minority-language status vulnerabilities and housing-transportation types largely contributed to these trends. Increasing overall vulnerability was associated with increased odds of advanced staging for papillary (odds ratio [OR] = 1.07 [confidence interval, CI 1.03-1.12]) and decreased odds of indicated treatment via surgery (lowest, medullary: 0.91 [CI 0.84-0.99]), radiation therapy (lowest, anaplastic: 0.88 [CI 0.82-0.93]), and chemotherapy (lowest, oncocytic: 0.81 [CI 0.67-0.98]) were observed. Vulnerabilities in minority-language status and housing-transportation, followed by socioeconomic status vulnerabilities, were differential contributors to these overall vulnerability trends. Conclusions: Our results show significant detriments in thyroid cancer care and prognosis in the United States with increasing overall social vulnerability while identifying which SDoH quantifiably contribute more to disparities in inter-relational, real-world-like contexts.

Exploring the Relation between Contextual Social Determinants of Health and COVID-19 Occurrence and Hospitalization

It is prudent to take a unified approach to exploring how contextual social determinants of health (SDoH) relate to COVID-19 occurrence and outcomes. Poor geographically represented data and a small number of contextual SDoH examined in most previous research studies have left a knowledge gap in the relationships between contextual SDoH and COVID-19 outcomes. In this study, we linked 199 contextual SDoH factors covering 11 domains of social and built environments with electronic health records (EHRs) from a large clinical research network (CRN) in the National Patient-Centered Clinical Research Network (PCORnet) to explore the relation between contextual SDoH and COVID-19 occurrence and hospitalization. We identified 15,890 COVID-19 patients and 63,560 matched non-COVID-19 patients in Florida between January 2020 and May 2021. We adopted a two-phase multiple linear regression approach modified from that in the exposome-wide association (ExWAS) study. After removing the highly correlated SDoH variables, 86 contextual SDoH variables were included in the data analysis. Adjusting for race, ethnicity, and comorbidities, we found six contextual SDoH variables (i.e., hospital available beds and utilization, percent of vacant property, number of golf courses, and percent of minority) related to the occurrence of COVID-19, and three variables (i.e., farmers market, low access, and religion) related to the hospitalization of COVID-19. To our best knowledge, this is the first study to explore the relationship between contextual SDoH and COVID-19 occurrence and hospitalization using EHRs in a major PCORnet CRN. As an exploratory study, the causal effect of SDoH on COVID-19 outcomes will be evaluated in future studies.

Social, demographic and morbimortality characteristics of the cases treated for COVID-19 at the Ignacio Chávez National Institute of Cardiology. A descriptive cross-sectional study

The COVID-19 pandemic brought with it a large number of adverse consequences for public health with serious socioeconomic repercussions. In this study we characterize the social, demographic, morbidity and mortality conditions of individuals treated for COVID-19 in one of the SARS-CoV-2 reference hospitals in Mexico City. A descriptive cross-sectional study was carried out in 259 patients discharged from the Instituto Nacional de Cardiología Ignacio Chávez, between April 11, 2020 and March 14, 2021. A multivariate logistic regression model was used to identify the association between sociodemographic and clinical variables. An optimization was performed using maximum likelihood calculations to choose the best model compatible with the data. The maximum likelihood model was evaluated using ROC curves, goodnessof-fit estimators, and multicollinearity analysis. Statistically significant patterns of comorbidities were inferred by evaluating a hypergeometric test over the frequencies of co-occurrence of pairs of conditions. A network analysis was implemented to determine connectivity patterns based on degree centrality, between comorbidities and outcome variables. The main social disadvantages of the studied population are related to the lack of social security (96.5%) and the lag in housing conditions (81%). Variables associated with the probability of survival were being younger (p < 0.0001), having more durable material goods (p = 0.0034) and avoiding: pneumonia (p = 0.0072), septic shock (p < 0.0001) and acute respiratory failure (p < 0.0001); (AUROC: 91.5%). The comorbidity network for survival cases has a high degree of connectivity between conditions such as cardiac arrhythmias and essential arterial hypertension (Degree Centrality = 90 and 78, respectively). Given that among the factors associated with survival to COVID-19 there are clinical, sociodemographic and social determinants of health variables, in addition to age; It is imperative to consider the various factors that may affect or modify the health status of a population, especially when addressing emerging epidemic phenomena such as the current COVID-19 pandemic.

Examination of Social Determinants of Health Characteristics Influencing Maternal Postpartum Symptom Experiences.

Postpartum women experience multiple, co-occurring postpartum symptoms. It is unknown if social determinants of health (SDOH) influence postpartum symptom typologies. This secondary analysis used the Community and Child Health Network study data. Participants included for analysis varied depending on the availability of the SDOH data (N = 851 to 1784). Bivariate and multiple regression analyses were conducted to examine the association between SDOH and previously identified postpartum symptom typologies. Area under the receiver operating characteristics curve (AUROC) was calculated to examine if adding SDOH variables contributes to predicting postpartum symptom typologies. The adjusted odds (aOR) of being in high symptom severity or occurrence typologies were greater for participants who had less than high school education (aOR = 2.29), experienced healthcare discrimination (aOR = 2.21), used governmental aid (aOR = 2.11), or were food insecure (aOR = 2.04). AUROC improved after adding SDOH. Considering experiences of different social-economic hardships influence postpartum symptom typologies, future practice and research should address SDOH to improve postpartum symptom experiences.

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.

A SCOPING REVIEW OF SOCIAL DETERMINANTS OF HEALTH IN CHARLES BONNET SYNDROME RESEARCH

Abstract Charles Bonnet Syndrome (CBS) is a neurological condition featuring pseudohallucinations that affects previously sighted individuals, many of whom are older adults. The CBS literature primarily concerns prevalence estimates, neurological studies, and experiences of people living with the condition; it is not clear what is known concerning social determinants of health (SDoH). We conducted a scoping review guided by Healthy People 2030 SDoH categories including Economic Stability, Education Access &amp; Quality, Health Care Access &amp; Quality, Neighbourhood &amp; Built Environment, and Social &amp; Community Context. Research articles included in this study represent 5 countries on 5 different continents. Sample size ranged from 5 to 88 across all included studies (M = 40.25, Median = 33). Two studies did not report enough info to extract mean age; Mean of mean age across the remaining 6 studies is 63.25 (Range: 27.8-79.47). Across the 8 included articles, 9 different SDoH variables were identified, representing 4 of the 5 Healthy People 2030 SDoH categories, with the exception of Health Care Access &amp; Quality. All studies reported age and gender, most reported education (n = 6, 75%) and living arrangement (n = 5, 62.5%), and half (n = 4) reported marital status. Few studies, however, reported race (n = 2, 25%), loneliness/social life (n = 2, 25%), occupational status (n = 1, 12.5%), and living environment (n = 1, 12.5%). This study highlights the need for expanded inclusion of SDoH variables in CBS research to fill gaps in the knowledge base in this understudied condition.

Social Determinants of Health in Pediatric Ophthalmology Patients: Availability of Data in the Electronic Health Record and Association With Clinic Attendance.

To characterize the availability of social determinants of health data in the electronic health record of pediatric ophthalmology patients and to examine the association of social determinants of health with attendance at scheduled operating room and clinic visits. This was a retrospective cohort study of pediatric ophthalmology patients seen at The Hospital for Sick Children between June 1, 2018, and May 23, 2022. Data were collected on demographics, diagnosis, and management-plan. The χ2 tests and multivariable regression were used to examine associations between social determinants of health and attendance at scheduled operating room and clinic visits. The cohort consisted of 26,102 study subjects with 31,288 unique eye-related diagnoses representing 57 unique ICD-10 codes. Availability of data in the electronic health record ranged from 100% for sex, age and postal code to 0.1% for ethnic group. Female sex (P = 0.004) and urbanicity (P = 0.05) were associated with higher operating room visit cancellations. Female sex (P = 0.002), age group 0-13 (P ≤ 0.001), low-medium neighborhood income quintile (P ≤ 0.001), residence of Northern Ontario (P ≤ 0.001), and urbanicity (P ≤ 0.001) were associated with higher clinic visit cancellations and no-shows. At a major tertiary-care hospital in Canada, key social determinant data such as ethnicity are not consistently available in the electronic health record of pediatric ophthalmology patients. Female sex, younger age, and living in a rural area or neighborhood with low-medium income quintile may be predictors of missed visits and require further study. This study highlights a need for improved documentation of social determinants of health variables in electronic health records.

Dietary Behaviors and Social Determinants of Health Among Adults Living with Sickle Cell Disease

Background: Social determinants of health (SDoH) may impact outcomes in sickle cell disease (SCD), and recent literature reports that particular SDoH, such as food insecurity and housing insecurity, may impact diet choices for individuals with SCD. Notably, health outcomes in SCD are linked to diet choices through increased protein and caloric needs, and through nutrients, such as polyphenols, omega fatty acids and micronutrients, which are involved in SCD pathophysiology. The connection between SDoH and diet quality may occur through multiple pathways including access to food, affordability, chronic pain and psychosocial factors at the individual and family level. Importantly, the impact of this connection may relate to outcomes such as pain frequency, hospitalizations, and SCD pathophysiologic biomarkers, such as inflammation. Study Design and Methods: The Diet and SCD Study (NCT: 05170412) is an observational mixed-methods study initiated in 2021 at the National Institutes of Health Clinical Center (NIH CC) in Bethesda MD with a target sample of 75 adults living with SCD in the United States. The purpose of the study is to evaluate associations between SDoH (food insecurity, neighborhood food access, education, employment, healthcare access), dietary intake, and population health related outcomes among adults living with SCD. Study inclusion criteria consists of adults (≥18 years of age) residing within the United States at the time of screening who have a confirmed diagnosis of sickle cell disease with any genotype, and are not hospitalized at the time of enrollment. To ensure participation from adults in diverse regions of the United States, the study has a hybrid design of in-person and telehealth procedures. In-person protocol visits are conducted at the NIH CC. The CC clinical visit includes a clinical assessment, anthropometric measurements, phlebotomy, as well as monitored/proctored electronic surveys that collect SDoH and disease severity variables. The telehealth protocol visit includes a virtual clinical assessment, phlebotomy at a local laboratory center, as well as the same monitored/proctored electronic surveys completed by CC clinical visit participants. A dietary behavior semi-structured interview is conducted at both the CC clinical visit and telehealth protocol visit to garner participants' personal stories related to food choices and diet. Laboratory variables include SCD disease specific markers, amino acid levels, and measurement of inflammatory cytokine markers. All participants undergo two unannounced phone-administered 24-hour dietary recall interviews with trained NIH nutrition staff to measure recent dietary intake. Data from 24-hour recall interviews are used to determine fluid, caloric, macro and micronutrient, and polyphenol intake. Usual dietary intake of foods of potentially related to SCD pathophysiology (cruciferous vegetables and thiocyanate-rich foods) is measured through self-administered electronic 12-month food frequency questionnaires. Primary objectives are to evaluate social determinants of health related to food security and access relevant to disease severity among adults living with sickle cell disease in the U.S. Secondary outcomes are to evaluate the association of dietary quality, specific dietary nutrient intake and serum levels, including amino acids and polyphenols, with disease severity. Tertiary outcomes include evaluating inflammatory and glucometabolic biomarkers with measures of social determinants of health. When completed, the Diet and SCD study will provide important information on the associations between SDoH, dietary quality, inflammatory biomarkers and disease severity for adults living with SCD and provide initial data for future cohort and intervention studies.

Abstract 14088: Impact of Social Determinants of Health on Blood Pressure and Cholesterol Control in a Smartphone-Based Cardiovascular Risk Self-Management Program

Background: Social determinants of health (SDOH) are environmental factors that have an impact on one's health, outcome, and quality of life. SDOH like language, income, healthcare access, race/ethnicity, and neighborhood characteristics can manifest in inequalities in cardiovascular disease (CVD) and its risk factor burden, resulting in inequitable CVD related morbidity and mortality. Limited data are available on effects of SDOH on CVD risk factor control among users of digital health technology. Aim: To study the impact of SDOH like primary language, household income, access to healthcare and urban/rural residential type on blood pressure (BP) and total cholesterol (TC) control in participants of a smartphone-based cardiovascular risk self-management program with AI-based digital coaching. Methods: Participants’ data were matched with Agency for Healthcare Research and Quality’s 2020 SDOH database by ZIP code. Changes in systolic BP (SBP), diastolic BP (DBP) and TC were evaluated from baseline to 6 months follow up. The SDOH variables- primary language, median household income, distance to healthcare facility and urban/rural residential type - were used to predict changes in BP and TC via regression models adjusted for age, gender, and baseline BP/TC. Results: In 27,553 participants (age 55 ± 0.1 y, 48.5% women), overall reduction in BP and TC were observed (p&lt;0.001). Of those with baseline stage 2 hypertension, 80.5% reduced SBP at follow up, and 82.9% of those with baseline high TC (≥240 mg/dL) reduced TC at follow up. Median household income was associated with change in SBP (β -1.6E-5, p &lt; 0.001) and DBP (β -4.8E-6, p &lt; 0.001), but not TC. This association was not clinically significant as each $62,535 increase in the median household income was associated with an additional 1 mm Hg in BP reduction. No difference in BP or TC change was found based on primary language, distance to healthcare facility, and urban/rural residential type (p &gt; 0.05). Conclusion: In a smartphone-based cardiovascular risk self-management program, the majority (&gt;80%) of high-risk participants reduce BP and TC across various SDOH factors. These results highlight the potential use of this technology to bridge health inequity gaps in prevention and control of CVD risk.

Abstract 15849: Studying the Effects of Race, Community Health, and Vascular Inequities on Outcomes for Peripheral Artery Disease: Principal Components Analysis of Community Health Metrics Using the Agency for Healthcare Research and Quality Social Determinants of Health Database

Introduction: Healthcare disparities associated with peripheral artery disease (PAD) are influenced by differences in healthcare quality and social determinants of health (SDOH). Hypothesis: We used principal components analysis (PCA) to summarize key themes of community-level SDOH variables. Methods: This retrospective analysis used data from a single health system quality improvement database of patients diagnosed with PAD between 2016-2019. Subjects were geocoded to census tracts and variables from the Agency for Healthcare Quality and Research SDOH database were obtained to examine small-area variability in social, economic, educational, physical infrastructure, and healthcare contexts. PCA was used to identify major themes in SDOH heterogeneity across communities. Relevant SDOH variables were normalized within a data matrix and those with a loading value &gt;0.2 were retained in a principal component (PC). PCs with an eigenvalue &gt; 1.0 were identified to parsimoniously exemplify key themes. Results: Among 7,187 subjects with PAD, the mean age was 72.2 11.1 years, 45.9% were female, 9.4% self-reported as non-Hispanic Black, and 86.6% as non-Hispanic White. PCA was applied to 29 SDOH variables relevant to vascular health. PCA retained 6 PCs to an SDOH matrix describing 73.8% of variance in census tract level data. The greatest variance in data was attributed to socioeconomic advantage (PC1) and urbanicity/migration context (PC2) with some overlap in Black neighborhood context (PC3), income inequality (PC4), multi-race cohesion (PC5), and underrepresented minority context (PC6). Conclusions: PCA transforms high-dimensional SDOH data into distinct typologies of community health while retaining complex features of the data. Future analysis will weight these results within a hierarchical model that accounts for patient-level characteristics and quality of care to inform a more complete social-ecological model of whole-person vascular care.