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Telzak A, Levano S, Haughton J, Chambers EC, Fiori KP. Understanding individual health-related social needs in the context of area-level social determinants of health: The case for granularity. J Clin Transl Sci 2024; 8:e78. [PMID: 38745875 PMCID: PMC11091925 DOI: 10.1017/cts.2024.519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 03/31/2024] [Accepted: 04/09/2024] [Indexed: 05/16/2024] Open
Abstract
Introduction Screening for health-related social needs (HRSNs) within health systems is a widely accepted recommendation, however challenging to implement. Aggregate area-level metrics of social determinants of health (SDoH) are easily accessible and have been used as proxies in the interim. However, gaps remain in our understanding of the relationships between these measurement methodologies. This study assesses the relationships between three area-level SDoH measures, Area Deprivation Index (ADI), Social Deprivation Index (SDI) and Social Vulnerability Index (SVI), and individual HRSNs among patients within one large urban health system. Methods Patients screened for HRSNs between 2018 and 2019 (N = 45,312) were included in the analysis. Multivariable logistic regression models assessed the association between area-level SDoH scores and individual HRSNs. Bivariate choropleth maps displayed the intersection of area-level SDoH and individual HRSNs, and the sensitivity, specificity, and positive and negative predictive values of the three area-level metrics were assessed in relation to individual HRSNs. Results The SDI and SVI were significantly associated with HRSNs in areas with high SDoH scores, with strong specificity and positive predictive values (∼83% and ∼78%) but poor sensitivity and negative predictive values (∼54% and 62%). The strength of these associations and predictive values was poor in areas with low SDoH scores. Conclusions While limitations exist in utilizing area-level SDoH metrics as proxies for individual social risk, understanding where and how these data can be useful in combination is critical both for meeting the immediate needs of individuals and for strengthening the advocacy platform needed for resource allocation across communities.
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Affiliation(s)
- Andrew Telzak
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Samantha Levano
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jessica Haughton
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Earle C. Chambers
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kevin P. Fiori
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
- Office of Community and Population Health, Montefiore Health System, Bronx, NY, USA
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Hatef E, Chang HY, Richards TM, Kitchen C, Budaraju J, Foroughmand I, Lasser EC, Weiner JP. Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study. JMIR Form Res 2024; 8:e54732. [PMID: 38470477 DOI: 10.2196/54732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/02/2024] [Accepted: 02/08/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Patients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide-scale basis within most US-based health care organizations. OBJECTIVE We aimed to retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs. METHODS We conducted a retrospective study using EHR data (2016-2021) and data from the US Census American Community Survey. We developed a prospective model using current year-1 risk factors to predict future year-2 outcomes within four 2-year cohorts. Predictors of interest included demographics, previous health care use, comorbidity, previously identified social needs, and neighborhood characteristics as reflected by the area deprivation index. The outcome variable was a binary indicator reflecting the likelihood of the presence of a patient with social needs. We applied a generalized estimating equation approach, adjusting for patient-level risk factors, the possible effect of geographically clustered data, and the effect of multiple visits for each patient. RESULTS The study population of 1,852,228 patients included middle-aged (mean age range 53.76-55.95 years), White (range 324,279/510,770, 63.49% to 290,688/488,666, 64.79%), and female (range 314,741/510,770, 61.62% to 278,488/448,666, 62.07%) patients from neighborhoods with high socioeconomic status (mean area deprivation index percentile range 28.76-30.31). Between 8.28% (37,137/448,666) and 11.55% (52,037/450,426) of patients across the study cohorts had at least 1 social need documented in their EHR, with safety issues and economic challenges (ie, financial resource strain, employment, and food insecurity) being the most common documented social needs (87,152/1,852,228, 4.71% and 58,242/1,852,228, 3.14% of overall patients, respectively). The model had an area under the curve of 0.702 (95% CI 0.699-0.705) in predicting prospective social needs in the overall study population. Previous social needs (odds ratio 3.285, 95% CI 3.237-3.335) and emergency department visits (odds ratio 1.659, 95% CI 1.634-1.684) were the strongest predictors of future social needs. CONCLUSIONS Our model provides an opportunity to make use of available EHR data to help identify patients with high social needs. Our proposed social risk score could help identify the subset of patients who would most benefit from further social needs screening and data collection to avoid potentially more burdensome primary data collection on all patients in a target population of interest.
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Affiliation(s)
- Elham Hatef
- Division of General Internal Medicine, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Hsien-Yen Chang
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Thomas M Richards
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Christopher Kitchen
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Janya Budaraju
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Iman Foroughmand
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Elyse C Lasser
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Jonathan P Weiner
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
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Craven CK, Highfield L, Basit M, Bernstam EV, Choi BY, Ferrer RL, Gelfond JA, Pruitt SL, Kannan V, Shireman PK, Spratt H, Morales KJT, Wang CP, Wang Z, Zozus MN, Sankary EC, Schmidt S. Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration. J Clin Transl Sci 2024; 8:e17. [PMID: 38384919 PMCID: PMC10880009 DOI: 10.1017/cts.2024.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/12/2023] [Accepted: 12/31/2023] [Indexed: 02/23/2024] Open
Abstract
Introduction The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients. Methods Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub's EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis. Results One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%-98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation. Conclusion Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.
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Affiliation(s)
- Catherine K. Craven
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Division of Clinical Research Informatics, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Linda Highfield
- University of Texas Health Science Center at Houston, School of Public Health, San Antonio, TX, USA
| | - Mujeeb Basit
- Department of Internal Medicine, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Elmer V. Bernstam
- D. Bradley McWilliams School of Biomedical Informatics and Division of General Internal Medicine, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA
| | - Byeong Yeob Choi
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Biostatistics Division, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Robert L. Ferrer
- Department of Community and Family Medicine, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Jonathan A. Gelfond
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Biostatistics Division, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Sandi L. Pruitt
- University of Texas Southwestern Medical Center, Harold C. Simmons Comprehensive Cancer Center, Dallas, TX, USA
| | | | - Paula K. Shireman
- Department of Surgery, Division of Vascular and Endovascular Surgery, Texas A&M University School of Medicine, Bryan, TX, USA
- Departments of Primary Care & Rural Medicine and Medical Physiology, University of Texas Health Science Center San Antonio, San Antonio, TX, USA
| | - Heidi Spratt
- Department of Biostatistics and Data Science, University of Texas Medical Branch Galveston, Galveston, TX, USA
| | - Kayla J. Torres Morales
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Division of Clinical Research Informatics, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Chen-Pin Wang
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Biostatistics Division, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Zhan Wang
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Division of Clinical Research Informatics, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Meredith N. Zozus
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Division of Clinical Research Informatics, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Edward C. Sankary
- University of Texas Health Science Center San Antonio, UT Health Physicians, San Antonio, TX, USA
| | - Susanne Schmidt
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
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Jung D, Pollack HA, Konetzka RT. Predicting Hospitalization among Medicaid Home- and Community-Based Services Users Using Machine Learning Methods. J Appl Gerontol 2023; 42:241-251. [PMID: 36164857 PMCID: PMC10069559 DOI: 10.1177/07334648221129548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
We compare multiple machine learning algorithms and develop models to predict future hospitalization among Home- and Community-Based Services (HCBS) Users. Furthermore, we calculate feature importance, the score of input variables based on their importance to predict the outcome, to identify the most relevant variables to predict hospitalization. We use the 2012 national Medicaid Analytic eXtract data and Medicare Provider Analysis and Review data. Predicting any hospitalization, Random Forest appears to be the most robust approach, though XGBoost achieved similar predictive performance. While the importance of features varies by algorithm, chronic conditions, previous hospitalizations, as well as use of services for ambulance, personal care, and durable medical equipment were generally found to be important predictors of hospitalization. Utilizing prediction models to identify those who are prone to hospitalization could be useful in developing early interventions to improve outcomes among HCBS users.
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Affiliation(s)
- Daniel Jung
- Department of Health Policy and Management, 1355University of Georgia, Athens, USA
| | - Harold A Pollack
- School of Social Service Administration, 278762University of Chicago, IL, USA
| | - R Tamara Konetzka
- Division of Biological Sciences, Department of Public Health Sciences, University of Chicago, IL, USA
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5
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Phuong J, Riches NO, Calzoni L, Datta G, Duran D, Lin AY, Singh RP, Solomonides AE, Whysel NY, Kavuluru R. Toward informatics-enabled preparedness for natural hazards to minimize health impacts of climate change. J Am Med Inform Assoc 2022; 29:2161-2167. [PMID: 36094062 PMCID: PMC9667167 DOI: 10.1093/jamia/ocac162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/21/2022] [Accepted: 08/30/2022] [Indexed: 09/14/2023] Open
Abstract
Natural hazards (NHs) associated with climate change have been increasing in frequency and intensity. These acute events impact humans both directly and through their effects on social and environmental determinants of health. Rather than relying on a fully reactive incident response disposition, it is crucial to ramp up preparedness initiatives for worsening case scenarios. In this perspective, we review the landscape of NH effects for human health and explore the potential of health informatics to address associated challenges, specifically from a preparedness angle. We outline important components in a health informatics agenda for hazard preparedness involving hazard-disease associations, social determinants of health, and hazard forecasting models, and call for novel methods to integrate them toward projecting healthcare needs in the wake of a hazard. We describe potential gaps and barriers in implementing these components and propose some high-level ideas to address them.
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Affiliation(s)
- Jimmy Phuong
- University of Washington, School of Medicine, Research Information Technologies, Seattle, Washington, USA
- University of Washington, Harborview Injury Prevention and Research Center, Seattle, Washington, USA
| | - Naomi O Riches
- University of Utah School of Medicine, Obstetrics and Gynecology Research Network, Salt Lake City, Utah, USA
| | - Luca Calzoni
- National Institute on Minority Health and Health Disparities (NIMHD), National Institutes of Health, Bethesda, Maryland, USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Gora Datta
- Department of Civil & Environmental Engineering, University of California at Berkeley, Berkeley, California, USA
| | - Deborah Duran
- National Institute on Minority Health and Health Disparities (NIMHD), National Institutes of Health, Bethesda, Maryland, USA
| | - Asiyah Yu Lin
- National Human Genome Research Institute (NHGRI), National Institutes of Health, Bethesda, Maryland, USA
| | - Ramesh P Singh
- School of Life and Earth Sciences, Schmid College of Science and Technology, Chapman University, Orange, California, USA
| | - Anthony E Solomonides
- Department of Communication Design, NorthShore University Health System, Outcomes Research Network, Research Institute, Evanston, Illinois, USA
| | - Noreen Y Whysel
- New York City College of Technology, CUNY, Brooklyn, New York, USA
| | - Ramakanth Kavuluru
- Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, Kentucky, USA
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6
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Patil SJ, Golzy M, Johnson A, Wang Y, Parker JC, Saper RB, Haire-Joshu D, Mehr DR, Foraker RE, Kruse RL. Individual-Level and Neighborhood-Level Factors Associated with Longitudinal Changes in Cardiometabolic Measures in Participants of a Clinic-Based Care Coordination Program: A Secondary Data Analysis. J Clin Med 2022; 11:2897. [PMID: 35629024 PMCID: PMC9145991 DOI: 10.3390/jcm11102897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Identifying individual and neighborhood-level factors associated with worsening cardiometabolic risks despite clinic-based care coordination may help identify candidates for supplementary team-based care. Methods: Secondary data analysis of data from a two-year nurse-led care coordination program cohort of Medicare, Medicaid, dual-eligible adults, Leveraging Information Technology to Guide High Tech, High Touch Care (LIGHT2), from ten Midwestern primary care clinics in the U.S. Outcome Measures: Hemoglobin A1C, low-density-lipoprotein (LDL) cholesterol, and blood pressure. Multivariable generalized linear regression models assessed individual and neighborhood-level factors associated with changes in outcome measures from before to after completion of the LIGHT2 program. Results: 6378 participants had pre-and post-intervention levels reported for at least one outcome measure. In adjusted models, higher pre-intervention cardiometabolic measures were associated with worsening of all cardiometabolic measures. Women had worsening LDL-cholesterol compared with men. Women with pre-intervention HbA1c > 6.8% and systolic blood pressure > 131 mm of Hg had worse post-intervention HbA1c and systolic blood pressure compared with men. Adding individual’s neighborhood-level risks did not change effect sizes significantly. Conclusions: Increased cardiometabolic risks and gender were associated with worsening cardiometabolic outcomes. Understanding unresolved gender-specific needs and preferences of patients with increased cardiometabolic risks may aid in tailoring clinic-community-linked care planning.
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Affiliation(s)
- Sonal J. Patil
- Department of Wellness and Preventive Medicine, Cleveland Clinic Community Care Institute, Cleveland, OH 44104, USA;
- Department of Family and Community Medicine, University of Missouri, Columbia, MO 65212, USA; (Y.W.); (D.R.M.); (R.L.K.)
| | - Mojgan Golzy
- Biostatistics and Research Design Unit, School of Medicine, University of Missouri, Columbia, MO 65211, USA;
| | - Angela Johnson
- Center for Applied Research and Engagement Systems (CARES), University of Missouri, Columbia, MO 65211, USA;
| | - Yan Wang
- Department of Family and Community Medicine, University of Missouri, Columbia, MO 65212, USA; (Y.W.); (D.R.M.); (R.L.K.)
| | - Jerry C. Parker
- Department of Physical Medicine and Rehabilitation, University of Missouri, Columbia, MO 65211, USA;
| | - Robert B. Saper
- Department of Wellness and Preventive Medicine, Cleveland Clinic Community Care Institute, Cleveland, OH 44104, USA;
| | - Debra Haire-Joshu
- Brown School, Washington University in St. Louis, St. Louis, MO 63130, USA;
| | - David R. Mehr
- Department of Family and Community Medicine, University of Missouri, Columbia, MO 65212, USA; (Y.W.); (D.R.M.); (R.L.K.)
| | - Randi E. Foraker
- Division of General Medical Sciences, School of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA;
| | - Robin L. Kruse
- Department of Family and Community Medicine, University of Missouri, Columbia, MO 65212, USA; (Y.W.); (D.R.M.); (R.L.K.)
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