scholarly journals Response to letter to the Editor on “Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services”

2018 ◽  
Vol 25 (8) ◽  
pp. 1108-1108
Author(s):  
Suranga N Kasthurirathne ◽  
Joshua R Vest ◽  
Nir Menachemi ◽  
Paul K Halverson ◽  
Shaun J Grannis
Keyword(s):  
Social Determinants Of Health ◽  
Social Services ◽  
Social Determinants ◽  
Predictive Models ◽  
Health Data ◽  
Determinants Of Health ◽  
Letter To The Editor

scholarly journals Addressing Social Needs in Health Care Settings: Evidence, Challenges, and Opportunities for Public Health

2020 ◽  
Vol 42 (1) ◽  
Author(s):  
Matthew W. Kreuter ◽  
Tess Thompson ◽  
Amy McQueen ◽  
Rachel Garg
Keyword(s):  
Public Health ◽  
Health Care ◽  
Social Determinants Of Health ◽  
Social Services ◽  
Social Determinants ◽  
Safety Net ◽  
Social Needs ◽  
Determinants Of Health ◽  
Health Improvement ◽  
Health Care Settings

There has been an explosion of interest in addressing social needs in health care settings. Some efforts, such as screening patients for social needs and connecting them to needed social services, are already in widespread practice. These and other major investments from the health care sector hint at the potential for new multisector collaborations to address social determinants of health and individual social needs. This article discusses the rapidly growing body of research describing the links between social needs and health and the impact of social needs interventions on health improvement, utilization, and costs. We also identify gaps in the knowledge base and implementation challenges to be overcome. We conclude that complementary partnerships among the health care, public health, and social services sectors can build on current momentum to strengthen social safety net policies, modernize social services, and reshape resource allocation to address social determinants of health. Expected final online publication date for the Annual Review of Public Health, Volume 42 is April 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Assessing the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission or death

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0235064
Author(s):  
Yongkang Zhang ◽  
Yiye Zhang ◽  
Evan Sholle ◽  
Sajjad Abedian ◽  
Marianne Sharko ◽  
...  
Keyword(s):  
Social Determinants Of Health ◽  
Social Determinants ◽  
Predictive Models ◽  
Determinants Of Health ◽  
The Impact

Collecting Social Determinants of Health Data in the Clinical Setting: Findings from National PRAPARE Implementation

2020 ◽  
Vol 31 (2) ◽  
pp. 1018-1035 ◽  
Author(s):  
Rosy Chang Weir ◽  
Michelle Proser ◽  
Michelle Jester ◽  
Vivian Li ◽  
Carlyn M. Hood-Ronick ◽  
...  
Keyword(s):  
Social Determinants Of Health ◽  
Social Determinants ◽  
Clinical Setting ◽  
Health Data ◽  
Determinants Of Health

scholarly journals Using ICD-10-based Social Determinants of Health Categories to Assess Patients Risk for Acute Care Utilization

2021 ◽  
Author(s):  
Peter H. Nguyen ◽  
James Wang ◽  
Pamela Garcia-Filion ◽  
Deborah Dominick ◽  
Hamed Abbaszadegan ◽  
...  
Keyword(s):  
Social Determinants Of Health ◽  
Social Services ◽  
Acute Care ◽  
Social Determinants ◽  
Hospital Admissions ◽  
Medical Center ◽  
Determinants Of Health ◽  
Care Utilization ◽  
Icd 10 ◽  
Acute Care Utilization

ABSTRACTObjectiveSocial determinants of health (SDoH) play a pivotal role in health care utilization and adverse health outcomes. However, the optimal method to identify SDoH remains debatable. We ascertained SDoH based on International Classification of Disease 10 (ICD-10) codes in patient electronic health records (EHR) to assess the correlation with acute care utilization, and determine if social services interventions reduced care utilization.MethodsWe analyzed retrospective data for active patients at a Department of Veterans Affairs Medical Center (VAMC) from 2015-2017. Eleven categories of SDoH were developed based on existing literature of the social determinants; the relevant ICD-10 codes were divided among these categories. Emergency Room (ER) visits, hospital admissions, and social work visits were determined for each patient in the cohort.ResultsIn a cohort of 44,401 patients, the presence of ICD-10 codes within the EHR in the 11 SDoH categories was positively correlated with increased acute care utilization. Veterans with at least one SDoH risk factor were 71% (95%CI: 68% - 75%) more likely to use the ED and 71% (95%CI: 65%-77%) more likely to be admitted to the hospital. Utilization decreased with social service interventions.ConclusionThis project demonstrates a potentially meaningful method to capture patient social risk profiles through existing EHR data in the form of ICD-10 codes, which can be used to identify the highest risk patients for intervention with the understanding that not all SDoH codes are uniformly used and some SDoHs may not be captured.

scholarly journals Modeling the Impact of Social Determinants of Health on HIV

2021 ◽  
Author(s):  
Joseph W. Hogan ◽  
Noya Galai ◽  
Wendy W. Davis
Keyword(s):  
Social Determinants Of Health ◽  
Population Health ◽  
Social Services ◽  
Social Determinants ◽  
Critical Role ◽  
Individual Characteristics ◽  
Determinants Of Health ◽  
Modeling Approaches ◽  
The Impact

AbstractThere is growing evidence for the key role of social determinants of health (SDOH) in understanding morbidity and mortality outcomes globally. Factors such as stigma, racism, poverty or access to health and social services represent complex constructs that affect population health via intricate relationships to individual characteristics, behaviors and disease prevention and treatment outcomes. Modeling the role of SDOH is both critically important and inherently complex. Here we describe different modeling approaches and their use in assessing the impact of SDOH on HIV/AIDS. The discussion is thematically divided into mechanistic models and statistical models, while recognizing the overlap between them. To illustrate mechanistic approaches, we use examples of compartmental models and agent-based models; to illustrate statistical approaches, we use regression and statistical causal models. We describe model structure, data sources required, and the scope of possible inferences, highlighting similarities and differences in formulation, implementation, and interpretation of different modeling approaches. We also indicate further needed research on representing and quantifying the effect of SDOH in the context of models for HIV and other health outcomes in recognition of the critical role of SDOH in achieving the goal of ending the HIV epidemic and improving overall population health.

scholarly journals Linking Health and Social Data to Assess the Performance of High Dimensional Propensity Scores

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Naomi Hamm ◽  
Deepa Singal ◽  
Matthew Dahl ◽  
Dan Chateau ◽  
Marni Brownell
Keyword(s):  
School Readiness ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Social Factors ◽  
Propensity Scores ◽  
Health Data ◽  
Determinants Of Health ◽  
Income Quintile ◽  
Social Data ◽  
Income Assistance

IntroductionHigh dimensional propensity scores (HDPS) aim to account for unmeasured confounding. However, it is unclear to what extent HDPS are able to attain this. Objectives and ApproachThis study aimed to test how well HDPS can account for confounding due to social determinants of health when using only health data. A retrospective cohort study was used to examine the effect of exposure to prescription opioids in utero on childhood outcomes (ADHD, school readiness, NICU admission, and hospitalization within the first year of life). Administrative health and social data were linked at the individual level and HDPS for each outcome were calculated using the mothers’ health data. Exposed and unexposed mother-child dyads were then matched. Standardized differences of mothers’ social factors (history of teen birth, lowest income quintile, ever received income assistance (i.e., welfare), ever lived in social housing, history with child protection services, residential mobility, and contact with the justice system) were compared before and after matching to determine to what degree the HDPS could account for differences in social determinants of health. Additional HDPS analyses were performed with social factors included in the HDPS with the health data. ResultsBefore matching, standardized differences between exposed and unexposed groups for the social factors ranged between 0.40-0.75. Income assistance and lowest income quintile consistently had the greatest and smallest standardized difference for all outcomes, respectively. After matching, using health data only, standardized differences decreased considerably, ranging from 0.05-0.27. When including social factors into the HDPS, the addition of income assistance produced the smallest standardized differences with a range of 0.01-0.13 for all outcomes. ConclusionsUsing the HDPS with health data only can reduce confounding due to social factors. If data are available, including income assistance in the HDPS may further reduce confounding for all social determinants of health.

scholarly journals Integrating Ontario Health and Social Services Data to for Research and Policy Development

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Mandeep Flora ◽  
Sujitha Ratnasingham ◽  
Aki Tefera ◽  
J. Charles Victor ◽  
Michael Schull
Keyword(s):  
Health Care ◽  
Developmental Disabilities ◽  
Social Determinants Of Health ◽  
Data Sharing ◽  
Social Services ◽  
Social Determinants ◽  
Data Access ◽  
Health Data ◽  
Health And Social Services ◽  
Research And Policy

IntroductionIntegrating health and social services data is critical to understanding social determinants of health and responding to public expectations for evidence-based policies amidst changing demographics and fiscal constraint. While academia has long understood the importance of social determinants of health, real and perceived obstacles have slowed their evaluation in Ontario. Objectives and ApproachThis report describes how the Institute for Clinical Evaluative Sciences (ICES) and the Ministry and Community and Social Services (MCSS) have partnered to bring social services data and health data together to better understand the Ontario population and better support decision makers across various sectors. We present how ICES and MCSS tackled barriers to data access and cultural challenges to data sharing in the Ontario context, provide an overview of their unique data and research partnership - including the new collaboration research and data access platforms created, highlight research findings to date, and identify key topics of interest moving forward. ResultsOver the last decade, ICES and MCSS have led the way in Ontario linking health administrative and social services data. An initial single year linkage enabled the success of the Health Care Access Research and Developmental Disabilities project. This cross-sectoral initiative provided a clearer sense of how people with developmental disabilities experienced health care in Ontario. Building on this work, ICES and MCSS recently expanded their partnership bringing together 15 years of social services and health data through a broader data sharing agreement. This agreement allows greater data access to researchers. In addition, ICES and MCSS have been successful in creating a new integrated research platform that will increase the depth and quality of health and social services research and policy evaluation in Ontario. Conclusion/ImplicationsA broader collaborative research community will now be able to answer questions of interest, do self-directed integrated data analytics and leverage respective program data expertise to tackle joint research projects. Importantly, MCSS analytics teams will now also have access to linked data on this platform to conduct their own research.

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