Latent Class Analysis to Represent Social Determinant of Health Risk Groups in the Medicaid Cohort of the District of Columbia.
Cohort Studies
District of Columbia
Female
Health Equity
/ statistics & numerical data
Health Status
Housing
/ statistics & numerical data
Humans
Male
Medicaid
/ statistics & numerical data
Middle Aged
Poverty
/ statistics & numerical data
Social Determinants of Health
/ statistics & numerical data
United States
Journal
Medical care
ISSN: 1537-1948
Titre abrégé: Med Care
Pays: United States
ID NLM: 0230027
Informations de publication
Date de publication:
01 03 2021
01 03 2021
Historique:
pubmed:
5
12
2020
medline:
7
5
2021
entrez:
4
12
2020
Statut:
ppublish
Résumé
To develop distinct social risk profiles based on social determinants of health (SDH) information and to determine whether these social risk groups varied in terms of health, health care utilization, and costs. We prospectively enrolled 8943 beneficiaries insured by the District of Columbia Medicaid program between September 2017 and December 2018. Participants completed a SDH survey and we obtained their Medicaid claims data for a 2-year period before study enrollment. We used latent class analysis (LCA) to identify distinct social risk profiles based on their SDH responses. We assessed the relationship among different SDH as well as the relationship among the social risk classes and health, health care use and costs. The majority of SDH were moderately to strongly correlated with one another. LCA yielded 4 distinct social risk groups. Group 1 reported the least social risks with the most employed. Group 2 was distinguished by financial strain and housing instability with fewer employed. Group 3 were mostly unemployed with limited car and internet access. Group 4 had the most social risks and most unemployed. The social risk groups demonstrated meaningful differences in health, acute care utilization, and health care costs with group 1 having the best health outcomes and group 4 the worst (P<0.05). LCA is a practical method of aggregating correlated SDH data into a finite number of distinct social risk groups. Understanding the constellation of social challenges that patients face is critical when attempting to address their social needs and improve health outcomes.
Identifiants
pubmed: 33273298
pii: 00005650-202103000-00010
doi: 10.1097/MLR.0000000000001468
pmc: PMC7878329
mid: NIHMS1644266
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
251-258Subventions
Organisme : NIMHD NIH HHS
ID : R01 MD011607
Pays : United States
Informations de copyright
Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
Références
World Health Organization. Health in All Policies (HiAP): Framework for Country Action. 2014. Available at: https://www.who.int/cardiovascular_diseases/140120HPRHiAPFramework.pdf . Accessed September 16, 2018.
National Academies of Sciences Engineering, and Medicine. Integrating Social Care Into the Delivery of Health Care: Moving Upstream to Improve the Nation’s Health. Washington, DC: The National Academies Press; 2019.
Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records; Board on Population Health and Public Health Practice IoM. Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2. Washington, DC: National Academies Press; 2014. Available at: https://www.nap.edu/catalog/18951/capturing-social-and-behavioral-domains-and-measures-in-electronic-health-records . Accessed September 16, 2018.
Fichtenberg C, Delva J, Minyard K, et al. Health and human services integration: generating sustained health and equity improvements. Health Aff. 2020;39:567–573.
Solar OI. A Conceptual Framework of Action on the Social Determinants of Health. Geneva: World Health Organization; 2010.
American Community Survey (ACS). 2016. Available at: http://www2.census.gov/programs-surveys/acs/methodology/questionnaires/2016/quest16.pdf . Accessed September 29, 2017.
Dooley D. Unemployment, underemployment, and mental health: conceptualizing employment status as a continuum. Am J Community Psychol. 2003;32:9–20.
Hager ER, Quigg AM, Black MM, et al. Development and validity of a 2-item screen to identify families at risk for food insecurity. Pediatrics. 2010;126:e26–e32.
Ouellette T, Burstein N, Long D, et al. Measures of Material Hardship: Final Report. Washington, DC: US Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation; 2004.
Econometrica. 2015 American Housing Survey National and Metropolitan Area Public Use File Microdata and Codebook. Bethesda, MD: US Census Bureau; 2017.
Montgomery AE, Fargo JD, Byrne TH, et al. Universal screening for homelessness and risk for homelessness in the Veterans Health Administration. Am J Public Health. 2013;103(suppl 2):S210–S211.
Group GATSC. Tobacco Questions for Surveys: A Subset of Key Questions From the Global Adult Tobacco Survey (GATS). Atlanta, GA: Centers for Disease Control and Prevention; 2011.
Bush K, Kivlahan DR, McDonell MB, et al. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol Use Disorders Identification Test. Arch Intern Med. 1998;158:1789–1795.
Skinner HA. The drug abuse screening test. Addict Behav. 1982;7:363–371.
The accountable health communities health-related social needs screening tool. 2017. Available at: https://innovation.cms.gov/files/worksheets/ahcm-screeningtool.pdf . Accessed August 15, 2017.
Dubowitz H, Prescott L, Feigelman S, et al. Screening for intimate partner violence in a pediatric primary care clinic. Pediatrics. 2008;121:e85–e91.
Berwick DM, Murphy JM, Goldman PA, et al. Performance of a five-item mental health screening test. Med Care. 1991;29:169–176.
Kronick R, Gilmer T, Dreyfus T, et al. Improving health-based payment for Medicaid beneficiaries: CDPS. Health Care Financ Rev. 2000;21:29–64.
Gilmer T, Kronick R, Fishman P, et al. The Medicaid Rx model: pharmacy-based risk adjustment for public programs. Med Care. 2001;39:1188–1202.
White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30:377–399.
Weller BE, Bowen NK, Faubert SJ. Latent class analysis: a guide to best practice. J Black Psychol. 2020;46:287.
Lanza ST, Collins LM, Lemmon D, et al. PROC LCA: a SAS procedure for latent class analysis. Struct Equ Modeling. 2007;14:671–694.
Vilorio D. Education matters. Career Outlook . 2016. Available at: https://www.bls.gov/careeroutlook/2016/data-on-display/education-matters.htm#:~:text=According%20to%20data%20from%20the,than%20those%20with%20less%20education . Accessed June 16–20, 2020.
Chin MH. Creating the business case for achieving health equity. J Gen Intern Med. 2016;31:792–796.