scholarly journals EPID-09. QUANTIFYING SOCIAL DETERMINANTS OF HEALTH AMONG GLIOMA PATIENTS

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii80-ii80
Author(s):  
Angelica Hutchinson ◽  
Alexandria Marshall ◽  
Fang-Chi Hsu ◽  
Kathryn Weaver ◽  
Alisha DeTroye ◽  
...  
Keyword(s):  
Health Risk ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Active Treatment ◽  
Economic Education ◽  
Neighborhood Environment ◽  
Determinants Of Health ◽  
Risk Scores ◽  
Diffuse Astrocytoma ◽  
Modifiable Factors

Abstract BACKGROUND Social determinants of health (SDH) are modifiable factors that contribute to health outcomes. Despite studies linking SDHs with cervical, ovarian, and prostate cancer outcomes, few studies have explored SDHs in glioma patients. We conducted a cross-sectional survey to characterize and contextualize SDHs in glioma patients by community income, rural/urban residence, and treatment status. METHODS Two validated instruments: PRAPARE (Protocol for responding to and assessing patents’ assets risks and experiences) and AHC (accountable health communities instrument) quantified SDHs; along with study-specific supplemental questions. Risk scores were calculated and combined into an overall and domain-specific (economic, education, neighborhood environment, social context, and healthcare) SDH risk, with a higher score being indicative of higher SDH risk. Scores were compared between low-income (LIC) vs high-income (HIC) communities (defined by median household income), urban vs rural (defined by zip code), and active treatment vs surveillance (determined by patient medical record) using Wilcoxon rank-sum test. RESULTS 100 glioma patients were enrolled: mean age 53 years (range: 22–78); 49% male; 18% oligodendroglioma, 34% diffuse astrocytoma, 38% glioblastoma, 10% other glioma; 68% resided in LICs, 27% in rural zip codes, and 51% were on active treatment. Overall, SDH risk scores were low (mean= 4.43-out-of-38). Scores in the healthcare domain were the highest. Compared to patients from LICs, patients from HICs had higher healthcare risk scores (p< 0.05). Surveillance patients had higher overall SDH risk on the AHC than patients in active treatment (p< 0.05), with age being a confounder. In multivariable analysis, younger age, and astrocytoma histology were associated with higher social health risk. CONCLUSION Glioma patients report relatively few SDH risk factors on standardized instruments designed for general clinic populations. The higher health risk observed in patients in HICs and higher AHC risk for those in surveillance will be further explored in planned qualitative analysis.

Polysocial Risk Scores for Assessing Social Determinants of Health

JAMA ◽  
2020 ◽  
Vol 324 (16) ◽  
pp. 1680
Author(s):  
Stuart A. Kinner ◽  
Rohan Borschmann
Keyword(s):  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Risk Scores

scholarly journals Leveraging Electronic Health Records and Administrative Datasets to Understand Social Determinants of Health: Opportunities and Challenges

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Jonathan Tan ◽  
Jack Wasey ◽  
Olivia Nelson ◽  
Vicky Tam ◽  
Allan Simpao ◽  
...  
Keyword(s):  
Health Care ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Neighborhood Environment ◽  
Determinants Of Health ◽  
Research Quality ◽  
Patient Privacy ◽  
Clinical Factors ◽  
Health Records ◽  
Administrative Datasets

BackgroundElectronic health records (EHRs) are ubiquitous in modern health care systems and contain granular detail about an individual’s medical care. EHRs, however can be extremely limited in the amount of information about the patient’s socioeconomic status and other non-clinical factors that relate to social determinants of health (SDOH). As increasing evidence supports the role of socioeconomic and environmental factors having a greater impact on overall health than health care services, it is important to create novel data analysis methods that can improve our understanding of SDOH factors for each patient and the greater population a health system cares for. Main AimTo demonstrate the feasibility and value of linking EHRs with administrative datasets to produce SDOH insights and better support research, quality improvement initiatives and operational decision making. Methods/ApproachWe linked EHR’s at the Children’s Hospital of Philadelphia, between 2015-2019, with administrative datasets including the US Census and the American Community Survey Datasets for over 40,000 pediatric patients. This required the validation of geocoding addresses and spatial join techniques to provide clinical insight into the individual patient along with SDOH insights into their neighborhood environment. We then conducted several studies that combined individual clinical factors and neighborhood socioeconomic risk factors to improve our understanding and care pathways for patients. ResultsLinking EHR data and administrative datasets required novel methods with unique challenges. Challenges included conducting such analysis with concern for the protection of patient privacy, overcoming technical data linkage challenges, and understanding the limitations of administrative dataset granularity when compared to detailed granularity of individual medical records. Presentation of results to further ensure patient confidentiality and privacy was also critical to our analysis. ConclusionEHRs and administrative datasets can be successfully linked together to leverage the strength of both traditional clinical data and SDOH factors. While there can be technical and privacy related challenges, there are many benefits of using administrative datasets to provide unique multidimensional insights into socioeconomic and environmental impacts of health.

Abstract 15779: Unfavorable Social Determinants of Health Profile and Financial Toxicity From Healthcare in Atherosclerotic Cardiovascular Disease

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Javier Valero Elizondo ◽  
Rohan Khera ◽  
Ramzi Dudum ◽  
Isaac Acquah ◽  
Adnan Hyder ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Cumulative Effect ◽  
Determinants Of Health ◽  
Risk Scores ◽  
Atherosclerotic Cardiovascular Disease ◽  
Financial Toxicity ◽  
Neighborhood Conditions ◽  
Increased Risk

Introduction: Healthcare expenses for atherosclerotic cardiovascular disease (ASCVD) are associated with substantial financial toxicity (FT). Whether certain social determinants of health (SDOH) portend an increased risk of FT in ASCVD is critical to refine care processes that may be most amenable to interventions. Methods: Using National Health Interview Survey (2013-17), we assessed patients with self-reported ASCVD. We identified 35 discrete SDOH items, rated as favorable/unfavorable, across 6 domains: economic stability, education, food access, neighborhood conditions, social context, and health systems. To capture the cumulative effect of SDOH, an aggregate score was computed as their sum, and divided into quartiles, the highest quartile containing the most unfavorable scores. FT included presence of: difficulty paying medical bills/inability to pay them at all, and/or delayed/foregone care due to cost, and/or cost-related medication non-adherence. Results: Of 164,696 surveyed adults, of which 15,758 (weighted: 8%) reported ASCVD. Mean (SE) age-adjusted SDOH risk scores among ASCVD vs non-ASCVD patients were 11.8 (0.12) vs 9.5 (0.03), respectively. Among individuals with ASCVD, age-adjusted prevalence of any FT was directly proportional to SDOH aggregate scores, with step-wise increases noted. Prevalence of ≥ 2 FT factors among SDOH quartiles 1 st vs 4 th was 8% vs 50% among non-elderly (< 65), and 2% vs 35% among elderly adults with ASCVD, respectively (Figure). Further, in adjusted models, patients in the most unfavorable quartile for SDOH had an 8-fold higher likelihood of FT compared to those in the most favorable group. Conclusions: Among ASCVD patients, a higher number of unfavorable SDOH features is strongly associated with the presence of FT, further calling for the development of interventions to alleviate FT among those with unfavorable SDOH profiles.

Addressing the Social Determinants of Health

2019 ◽  
Vol 101 (4) ◽  
pp. 357-395 ◽  
Author(s):  
Saty Satya-Murti ◽  
Jennifer Gutierrez
Keyword(s):  
Twentieth Century ◽  
Los Angeles ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Social Activism ◽  
Determinants Of Health ◽  
Medical Attention ◽  
Community Center ◽  
Early Twentieth Century ◽  
The Social

The Los Angeles Plaza Community Center (PCC), an early twentieth-century Los Angeles community center and clinic, published El Mexicano, a quarterly newsletter, from 1913 to 1925. The newsletter’s reports reveal how the PCC combined walk-in medical visits with broader efforts to address the overall wellness of its attendees. Available records, some with occasional clinical details, reveal the general spectrum of illnesses treated over a twelve-year span. Placed in today’s context, the medical care given at this center was simple and minimal. The social support it provided, however, was multifaceted. The center’s caring extended beyond providing medical attention to helping with education, nutrition, employment, transportation, and moral support. Thus, the social determinants of health (SDH), a prominent concern of present-day public health, was a concept already realized and practiced by these early twentieth-century Los Angeles Plaza community leaders. Such practices, although not yet nominally identified as SDH, had their beginnings in the late nineteenth- and early twentieth-century social activism movement aiming to mitigate the social ills and inequities of emerging industrial nations. The PCC was one of the pioneers in this effort. Its concerns and successes in this area were sophisticated enough to be comparable to our current intentions and aspirations.

648-P: Do Social Determinants of Health (SDoH) Influence Participant Outcomes of Those Enrolled in the Diabetes Prevention Program, Montana 2015-2018?

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 648-P
Author(s):  
DOROTA CARPENEDO ◽  
SONJA TYSK ◽  
MELISSA HOUSE ◽  
JESSIE FERNANDES ◽  
MARCI K. BUTCHER ◽  
...  
Keyword(s):  
Social Determinants Of Health ◽  
Social Determinants ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Diabetes Prevention Program ◽  
Determinants Of Health ◽  
Participant Outcomes
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