The Evidence Base for Social Determinants of Health as Risk Factors for Infant Mortality: A Systematic Scoping Review

2018 ◽  
Vol 29 (4) ◽  
pp. 1188-1208 ◽  
Author(s):  
Rebecca Reno ◽  
Ayaz Hyder
Keyword(s):  
Risk Factors ◽  
Infant Mortality ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Scoping Review ◽  
Evidence Base ◽  
Determinants Of Health

scholarly journals Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee

2021 ◽  
Vol 28 (1) ◽  
pp. e100439
Author(s):  
Lukasz S Wylezinski ◽  
Coleman R Harris ◽  
Cody N Heiser ◽  
Jamieson D Gray ◽  
Charles F Spurlock
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Learning Models ◽  
Vaccination Rates ◽  
Data Framework ◽  
The Impact ◽  
Machine Learning Models

IntroductionThe SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the USA, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities.MethodsWe combined county-level COVID-19 testing data, COVID-19 vaccination rates and SDOH information in Tennessee. Between February and May 2021, we trained machine learning models on a semimonthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance.ResultsOur results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased.ConclusionIncorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policy-makers with additional data resources to improve health equity and resilience to future public health emergencies.

scholarly journals Meaningful Information in the Age of Big Data: A Scoping Review on Social Determinants of Health Data Collection for Electronic Health Records

2019 ◽  
Author(s):  
Kelsey Berg ◽  
Chelsea Doktorchik ◽  
Hude Quan ◽  
Vineet Saini
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Electronic Health Records ◽  
Social Determinants Of Health ◽  
Health Systems ◽  
Social Determinants ◽  
Scoping Review ◽  
Determinants Of Health ◽  
Health Records ◽  
Automated Technology

Abstract Background: Electronic Health Records (EHRs) are key tools for integrating patient data into health information systems (IS). Advances in automated data collection methodology, particularly the collection of social determinants of health (SDOH), provide opportunities to advance health promotion and illness prevention through advanced analytics (i.e. “Big Data” techniques). We ask how current data collection processes in EHRs permit SDOH data to flow throughout health systems. Methods: Using a scoping review framework, we searched through medical literature to identify current practices in SDOH data collection within EHR systems. We extracted relevant information on data collection methodology, specifically focusing on uses of automated technology. We discuss our findings in the context of research methodology and potential for health equity. Results: Practitioners collect a variety of SDOH data at point of care through EHR, predominantly via embedded screening tools and clinical notes, and primarily capturing data on financial security, housing status, and social support. Health systems are increasingly using digital technology in data collection, including natural language processing algorithms. However overall use of automated technology is limited to date. End uses of data pertain to improving system efficiency, patient care-coordination, and addressing health disparities. Discussion & Conclusion: EHRs can realistically promote collection and meaningful use of SDOH data, although EHRs have not extensively been used to collect and manage this type of information. Future applied research on systems-level application of SDOH data is necessary, and should incorporate a range of stakeholders and interdisciplinary teams of researchers and practitioners in fields of health, computing, and social sciences.

scholarly journals Teaching the Social Determinants of Health in Undergraduate Medical Education: a Scoping Review

2019 ◽  
Vol 34 (5) ◽  
pp. 720-730 ◽  
Author(s):  
Ashti Doobay-Persaud ◽  
Mark D. Adler ◽  
Tami R. Bartell ◽  
Natalie E. Sheneman ◽  
Mayra D. Martinez ◽  
...  
Keyword(s):  
Medical Education ◽  
Social Determinants Of Health ◽  
Undergraduate Medical Education ◽  
Social Determinants ◽  
Scoping Review ◽  
Determinants Of Health ◽  
The Social

scholarly journals SOCIAL DETERMINANTS OF HEALTH DRIVE EMERGENCY ROOM AND HOSPITAL USE BY DUAL ELIGIBLE AND DISABLED PENNSYLVANIANS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S964-S964
Author(s):  
Sih-Ting Cai ◽  
Howard Degenholtz ◽  
Hayley Germack
Keyword(s):  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Physical Dependence ◽  
Health Hazards ◽  
Determinants Of Health ◽  
Economic Insecurity ◽  
Potential Health ◽  
Icd 10 ◽  
Dual Eligible

Abstract The study examined correlates and consequences of social determinants of health risk factors (SDoH) among dual eligible aged and disabled individuals; Pennsylvania is transitioning this population into a managed care plan with responsibility for care coordination and incentives to prevent hospitalization and nursing home placement. Medicaid and Medicare claims were used to identify people with SDoH based on ICD-10 codes in 2016 in four domains: economic insecurity, life stressors, physical dependence, and potential health hazards. Of 281,918 people, 38.6% had one or more SDoH. Among people with severe mental illnesses (SMI; schizophrenia, psychosis, major depressive disorder, or bipolar disorder), the prevalence of SDoH was 57.9%. Of people with one or more SDoH, 42% visited the ED, compared to only 32% of people with no SDoH. Economic insecurity (OR 1.68; CI 1.59-1.78), life stressors (OR 1.39; CI 1.29-1.48), physical dependence, (OR 2.01; CI 1.97-2.06), and potential health hazards (OR 1.52; CI 1.47-1.56) were independently associated with risk of hospitalization, controlling for age, gender, race, SMI, chronic conditions and disability. The introduction of diagnosis codes for SDoH under ICD-10 has facilitated identifying individuals with deficits that might increase health care use above and beyond their underlying health status. Although the prevalence of these risk factors as captured in diagnosis data is likely an underestimate, the strong association with subsequent ED use and hospitalization lends credence to these indicators. Medicare and Medicaid claims data can be used to identify people with SDoH and target interventions to prevent downstream health services use.

scholarly journals Childhood obesity in Mexico: social determinants of health and other risk factors

2018 ◽  
pp. bcr-2017-223862
Author(s):  
David Avelar Rodriguez ◽  
Erick Manuel Toro Monjaraz ◽  
Karen Rubi Ignorosa Arellano ◽  
Jaime Ramirez Mayans
Keyword(s):  
Risk Factors ◽  
Childhood Obesity ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health

scholarly journals Social determinants of health associated with hemodialysis non-adherence and emergency department utilization: a pilot observational study

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Kamna S. Balhara ◽  
Lori Fisher ◽  
Naya El Hage ◽  
Rosemarie G. Ramos ◽  
Bernard G. Jaar
Keyword(s):  
Risk Factors ◽  
Health Literacy ◽  
Observational Study ◽  
Social Determinants Of Health ◽  
Resource Utilization ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Emergency Department Utilization ◽  
Convenience Sample ◽  
Physical Limitations

Abstract Background Dialysis patients who miss treatments are twice as likely to visit emergency departments (EDs) compared to adherent patients; however, prospective studies assessing ED use after missed treatments are limited. This interdisciplinary pilot study aimed to identify social determinants of health (SDOH) associated with missing hemodialysis (HD) and presenting to the ED, and describe resource utilization associated with such visits. Methods We conducted a prospective observational study with a convenience sample of patients presenting to the ED after missing HD (cases); patients at local dialysis centers identified as HD-compliant by their nephrologists served as matched controls. Patients were interviewed with validated instruments capturing associated risk factors, including SDOH. ED resource utilization by cases was determined by chart review. Chi-square tests and ANOVA were used to detect statistically significant group differences. Results All cases visiting the ED had laboratory and radiographic studies; 40% needed physician-performed procedures. Mean ED length of stay (LOS) for cases was 17 h; 76% of patients were admitted with average LOS of 6 days. Comparing 25 cases and 24 controls, we found no difference in economic stability, educational attainment, health literacy, family support, or satisfaction with nephrology care. However, cases were more dependent on public transport for dialysis (p = 0.03). Despite comparable comorbidity burdens, cases were more likely to have impaired mobility, physical limitations, and higher severity of pain and depression. (p < 0.05). Conclusions ED visits after missed HD resulted in elevated LOS and admission rates. Frequently-cited SDOH such as health literacy did not confer significant risk for missing HD. However, pain, physical limitations, and depression were higher among cases. Community-specific collaborations between EDs and dialysis centers would be valuable in identifying risk factors specific to missed HD and ED use, to develop strategies to improve treatment adherence and reduce unnecessary ED utilization.

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