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First published online October 5, 2020

Racial Disparities in Patient Activation: The Role of Economic Diversity

Abstract

The Patient Activation Measure (PAM) assesses a person’s level of knowledge, skills, and confidence to self-manage their day-to-day health. We conducted a mediation analysis to examine potential direct effects of race on significantly lower baseline PAM scores in Black than in White participants (p<0.001) who were a subset of 184 adults who participated in a randomized controlled trial. In the mediation analysis, using natural indirect effects, the continuous outcome was the PAM score. The mediators were income, education, ability to pay bills, and health literacy; race (Black or White) was the “exposure.” The results indicate that income (p=0.025) and difficulty paying monthly bills (p=0.04) mediated the relationship between race and baseline PAM score, whereas health literacy (p=0.301) and education (p=0.436) did not. Researchers must further investigate the role of economic diversity as an underlying mechanism of patient activation and differences in outcomes.
Clinical Trial Registration: Avoiding Health Disparities When Collecting Patient Contextual Data for Clinical Care and Pragmatic Research: NCT03766841

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References

Ahmad F., Lou W., Shakya Y., Ginsburg L., Ng P. T., Rashid M., Dinca-Panaitescu S., Ledwos C., McKenzie K. (2017). Preconsult interactive computer-assisted client assessment survey for common mental disorders in a community health centre: A randomized controlled trial. CMAJ Open, 5(1), E190–E197. https://doi.org/10.9778/cmajo.20160118
Alexander J. A., Hearld L. R., Mittler J. N., Harvey J. (2012). Patient-physician role relationships and patient activation among individuals with chronic illness. Health Services Research, 47(3 Pt. 1), 1201–1223. https://doi.org/10.1111/j.1475-6773.2011.01354.x
Baron R. M., Kenny D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.orgg/10.1037//0022-3514.51.6.1173
Bennett J. A. (2000). Mediator and moderator variables in nursing research: Conceptual and statistical differences. Research in Nursing & Health, 23(5), 415–420. https://www.ncbi.nlm.nih.gov/pubmed/11052395
Bourgois P., Holmes S. M., Sue K., Quesada J. (2017). Structural vulnerability: Operationalizing the concept to address health disparities in clinical care. Academic Medicine, 92(3), 299–307. https://doi.org/10.1097/ACM.0000000000001294
Canedo J. R., Miller S. T., Schlundt D., Fadden M. K., Sanderson M. (2018). Racial/ethnic disparities in diabetes quality of care: The role of healthcare access and socioeconomic status. Journal of Racial and Ethnic Health Disparities, 5(1), 7–14. https://doi.org/10.1007/s40615-016-0335-8
Cantor M. N., Thorpe L. (2018). Integrating data on social determinants of health into electronic health records. Health Affairs, 37(4), 585–590. https://doi.org/10.1377/hlthaff.2017.1252
Centers for Disease Control and Prevention. (2019, November 6). Nutrition, physical activity, and obesity: Data, trends and maps. https://www.cdc.gov/nccdphp/dnpao/data-trends-maps/index.html
Centers for Medicare & Medicaid Services. (2020). Medicare telemedicine healthcare provider fact sheet. Medicare Coverage and Payment of Virtual Services. https://www.cms.gov/newsroom/fact-sheets/medicare-telemedicine-health-care-provider-fact-sheet
Chew L. D., Griffin J. M., Partin M. R., Noorbaloochi S., Grill J. P., Snyder A., Bradley K. A., Nugent S. M., Baines A. D., Vanryn M. (2008). Validation of screening questions for limited health literacy in a large VA outpatient population. Journal of General Internal Medicine, 23(5), 561–566. https://doi.org/10.1007/s11606-008-0520-5
County Health Rankings & Roadmaps. (2018). What is health? County Health Rankings & Roadmaps. http://www.countyhealthrankings.org/what-is-health
Couture É. M., Chouinard M.-C., Fortin M., Hudon C. (2018). The relationship between health literacy and patient activation among frequent users of healthcare services: A cross-sectional study. BMC Family Practice, 19(1), 38. https://doi.org/10.1186/s12875-018-0724-7
Coylewright M., Branda M., Inselman J. W., Shah N., Hess E., LeBlanc A., Montori V. M., Ting H. H. (2014). Impact of sociodemographic patient characteristics on the efficacy of decision AIDS: A patient-level meta-analysis of 7 randomized trials. Circulation. Cardiovascular Quality and Outcomes, 7(3), 360–367. https://doi.org/10.1161/HCQ.0000000000000006
Cusatis R., Holt J. M., Williams J., Nukuna S., Asan O., Flynn K. E., Neuner J., Moore J., Makoul G., Crotty B. H. (2020). The impact of patient-generated contextual data on communication in clinical practice: A qualitative assessment of patient and clinician perspectives. Patient Education and Counseling, 103(4), 734–740. https://doi.org/10.1016/j.pec.2019.10.020
Cutler R. L., Fernandez-Llimos F., Frommer M., Benrimoj C., Garcia-Cardenas V. (2018). Economic impact of medication non-adherence by disease groups: A systematic review. BMJ Open, 8(1), e016982. https://doi.org/10.1136/bmjopen-2017-016982
Dyer N., Sorra J. S., Smith S. A., Cleary P. D., Hays R. D. (2012). Psychometric properties of the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) clinician and group adult visit survey. Medical Care, 50(Suppl.), S28–S34. https://doi.org/10.1097/MLR.0b013e31826cbc0d
Emsley R., Liu H. (2013). PARAMED: Stata module to perform causal mediation analysis using parametric regression models. https://EconPapers.repec.org/RePEc:boc:bocode:s457581
Eneanya N. D., Winter M., Cabral H., Waite K., Henault L., Bickmore T., Hanchate A., Wolf M., Paasche-Orlow M. K. (2016). Health literacy and education as mediators of racial disparities in patient activation within an elderly patient cohort. Journal of Health Care for the Poor and Underserved, 27(3), 1427–1440. https://doi.org/10.1353/hpu.2016.0133
Evans G. W., Cassells R. C. (2014). Childhood poverty, cumulative risk exposure, and mental health in emerging adults. Clinical Psychological Science, 2(3), 287–296. https://doi.org/10.1177/2167702613501496
Fisher R. A. (1922). On the interpretation of χ2 from contingency tables, and the calculation of P. Journal of the Royal Statistical Society, 85(1), 87–94. https://doi.org/10.2307/2340521
Fowles J. B., Terry P., Xi M., Hibbard J., Bloom C. T., Harvey L. (2009). Measuring self-management of patients’ and employees’ health: Further validation of the Patient Activation Measure (PAM) based on its relation to employee characteristics. Patient Education and Counseling, 77(1), 116–122. https://doi.org/10.1016/j.pec.2009.02.018
Greenberg-Worisek A. J., Kurani S., Finney Rutten L. J., Blake K. D., Moser R. P., Hesse B. W. (2019). Tracking Healthy People 2020 internet, broadband, and mobile device access goals: An update using data from the Health Information National Trends Survey. Journal of Medical Internet Research, 21(6), e13300. https://doi.org/10.2196/13300
Greene J., Hibbard J. H. (2012). Why does patient activation matter? An examination of the relationships between patient activation and health-related outcomes. Journal of General Internal Medicine, 27(5), 520–526. https://doi.org/10.1007/s11606-011-1931-2
Greene J., Hibbard J. H., Sacks R., Overton V., Parrotta C. D. (2015). When patient activation levels change, health outcomes and costs change, too. Health Affairs, 34(3), 431–437. https://doi.org/10.1377/hlthaff.2014.0452
Green T. L. (2018). Unpacking racial/ethnic disparities in prenatal care use: The role of individual-, household-, and area-level characteristics. Journal of Women’s Health, 27(9), 1124–1134. https://doi.org/10.1089/jwh.2017.6807
Hanmer J., Cherepanov D. (2016). A single question about a respondent’s perceived financial ability to pay monthly bills explains more variance in health utility scores than absolute income and assets questions. Quality of Life Research, 25(9), 2233–2237. https://doi.org/10.1007/s11136-016-1269-7
Harris P. A., Taylor R., Thielke R., Payne J., Gonzalez N., Conde J. G. (2009). Research electronic data capture (REDCap)–A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42(2), 377–381. https://doi.org/10.1016/j.jbi.2008.08.010
Hays R. D., Bjorner J. B., Revicki D. A., Spritzer K. L., Cella D. (2009). Development of physical and mental health summary scores from the patient-reported outcomes measurement information system (PROMIS) global items. Quality of Life Research, 18(7), 873–880. https://doi.org/10.1007/s11136-009-9496-9
Hibbard J. H. (2008). Increasing patient activation to improve health and reduce costs. Institute for Policy Research and Innovation. https://nam.edu/wp-content/uploads/2017/11/Judith-Hibbard2.pdf
Hibbard J. H., Greene J. (2013). What the evidence shows about patient activation: Better health outcomes and care experiences; fewer data on costs. Health Affairs, 32(2), 207–214. https://doi.org/10.1377/hlthaff.2012.1061
Hibbard J. H., Greene J., Becker E. R., Roblin D., Painter M. W., Perez D. J., Burbank-Schmitt E., Tusler M. (2008). Racial/ethnic disparities and consumer activation in health. Health Affairs, 27(5), 1442–1453. https://doi.org/10.1377/hlthaff.27.5.1442
Hibbard J. H., Greene J., Overton V. (2013). Patients with lower activation associated with higher costs; delivery systems should know their patients’ “scores.” Health Affairs, 32(2), 216–222. https://doi.org/10.1377/hlthaff.2012.1064
Hibbard J. H., Greene J., Sacks R. M., Overton V., Parrotta C. (2017). Improving population health management strategies: Identifying patients who are more likely to be users of avoidable costly care and those more likely to develop a new chronic disease. Health Services Research, 52(4), 1297–1309. https://doi.org/10.1111/1475-6773.12545
Hibbard J. H., Greene J., Shi Y., Mittler J., Scanlon D. (2015). Taking the long view: How well do patient activation scores predict outcomes four years later? Medical Care Research and Review: MCRR, 72(3), 324–337. https://doi.org/10.1177/1077558715573871
Hibbard J. H., Greene J., Tusler M. (2009). Improving the outcomes of disease management by tailoring care to the patient’s level of activation. The American Journal of Managed Care, 15(6), 353–360. https://www.ncbi.nlm.nih.gov/pubmed/19514801
Hibbard J. H., Mahoney E. R., Stockard J., Tusler M. (2005). Development and testing of a short form of the patient activation measure. Health Services Research, 40(6 Pt. 1), 1918–1930. https://doi.org/10.1111/j.1475-6773.2005.00438.x
Hibbard J. H., Stockard J., Mahoney E. R., Tusler M. (2004). Development of the Patient Activation Measure (PAM): Conceptualizing and measuring activation in patients and consumers. Health Services Research, 39(4 Pt 1), 1005–1026. https://doi.org/10.1111/j.1475-6773.2004.00269.x
Hobson K. (2017, September 8). Why do people stop taking their meds? Cost is just one reason. NPR. https://www.npr.org/sections/health-shots/2017/09/08/549414152/why-do-people-stop-taking-their-meds-cost-is-just-one-reason
Holt J. M., Cusatis R., Asan O., Williams J., Nukuna S., Flynn K. E., Moore J., Crotty B. H. (2019). Incorporating patient-generated contextual data into care: Clinician perspectives using the Consolidated Framework for Implementation Science. Healthcare, 100369. https://doi.org/10.1016/j.hjdsi.2019.100369
Holt J. M., Cusatis R., Winn A., Asan O., Spanbauer C., Williams J. S., Flynn K. E., Somai M., Laud P., Crotty B. H. (2020). The impact of pre-visit contextual data collection on patient-provider communication and patient activation: Study protocol for a randomized control trial (Preprint). JMIR Research Protocols. https://doi.org/10.2196/20309
Ivey S. L., Shortell S. M., Rodriguez H. P., Wang Y. E. (2018). Patient engagement in ACO practices and patient-reported outcomes among adults with co-occurring chronic disease and mental health conditions. Medical Care, 56(7), 551–556. https://doi.org/10.1097/MLR.0000000000000927
Khullar D., Chokshi D. A. (2018). Health, income, & poverty: Where we are & what could help. Health Affairs Policy Brief. https://www.healthaffairs.org/do/10.1377/hpb20180817.901935/full/
Kim C., Tamborini C. R. (2014). Response error in earnings: An analysis of the survey of income and program participation matched with administrative data. Sociological Methods & Research, 43(1), 39–72. https://doi.org/10.1177/0049124112460371
Kirzinger A., Lopes L., Wu B., Brodie M. F. (2019, March 1). KFF Health Tracking Poll – February 2019: Prescription drugs. The Henry J. Kaiser Family Foundation. https://www.kff.org/health-reform/poll-finding/kff-health-tracking-poll-february-2019-prescription-drugs/
Krieger N., Williams D. R., Moss N. E. (1997). Measuring social class in US public health research: Concepts, methodologies, and guidelines. Annual Review of Public Health, 18, 341–378. https://doi.org/10.1146/annurev.publhealth.18.1.341
Kruse C. S., Stein A., Thomas H., Kaur H. (2018). The use of electronic health records to support population health: A systematic review of the literature. Journal of Medical Systems, 42(11), 1–1. https://doi.org/10.1007/s10916-018-1075-6
Lindsay A., Hibbard J. H., Boothroyd D. B., Glaseroff A., Asch S. M. (2018). Patient activation changes as a potential signal for changes in health care costs: Cohort study of US high-cost patients. Journal of General Internal Medicine, 33(12), 2106–2112. https://doi.org/10.1007/s11606-018-4657-6
Long A. S., Hanlon A. L., Pellegrin K. L. (2018). Socioeconomic variables explain rural disparities in US mortality rates: Implications for rural health research and policy. SSM - Population Health, 6, 72–74. https://doi.org/10.1016/j.ssmph.2018.08.009
Lubetkin E. I., Lu W.-H., Gold M. R. (2010). Levels and correlates of patient activation in health center settings: Building strategies for improving health outcomes. Journal of Health Care for the Poor and Underserved, 21(3), 796–808. https://doi.org/10.1353/hpu.0.0350
Lurie N., Somers S. A., Fremont A., Angeles J., Murphy E. K., Hamblin A. (2008). Challenges to using a business case for addressing health disparities. Health Affairs, 27(2), 334–338. https://doi.org/10.1377/hlthaff.27.2.334
Moore J. C., Stinson L. L., Welniak E. J. (2000). Income measurement error in surveys: A review. Journal of Official Statistics, 16, 331–361.
National Center for Health Statistics. (2016). Health, United States, 2015: With special feature on racial and ethnic health disparities. https://www.ncbi.nlm.nih.gov/pubmed/27308685
Newman J. C., Des Jarlais D. C., Turner C. F., Gribble J., Cooley P., Paone D. (2002). The differential effects of face-to-face and computer interview modes. American Journal of Public Health, 92(2), 294–297. https://doi.org/10.2105/ajph.92.2.294
O’Malley D., Dewan A. A., Ohman-Strickland P. A., Gundersen D. A., Miller S. M., Hudson S. V. (2018). Determinants of patient activation in a community sample of breast and prostate cancer survivors. Psycho-Oncology, 27(1), 132–140. https://doi.org/10.1002/pon.4387
Robert Wood Johnson Foundation. (2008). Overcoming obstacles to health. http://www.commissiononhealth.org/PDF/ObstaclesToHealth-Report.pdf
Robins J. M., Greenland S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3(2), 143–155. https://doi.org/10.1097/00001648-199203000-00013
Sacks R. M., Greene J., Hibbard J., Overton V., Parrotta C. D. (2017). Does patient activation predict the course of type 2 diabetes? A longitudinal study. Patient Education and Counseling, 100(7), 1268–1275. https://doi.org/10.1016/j.pec.2017.01.014
Salgado T. M., Mackler E., Severson J. A., Lindsay J., Batra P., Petersen L., Farris K. B. (2017). The relationship between patient activation, confidence to self-manage side effects, and adherence to oral oncolytics: A pilot study with Michigan oncology practices. Supportive Care in Cancer, 25(6), 1797–1807.
Saunders P. (2002). The direct and indirect effects of unemployment on poverty and inequality. Australian Journal of Labour Economics, 5(4), 507–529. https://search.informit.com.au/documentSummary;dn=148088576583204;res=IELAPA
Shaw K. M., Theis K. A., Self-Brown S., Roblin D. W., Barker L. (2016). Chronic disease disparities by county economic status and metropolitan classification, behavioral risk factor surveillance system, 2013. Preventing Chronic Disease, 13, E119. https://doi.org/10.5888/pcd13.160088
Singh-Manoux A., Adler N. E., Marmot M. G. (2003). Subjective social status: Its determinants and its association with measures of ill-health in the Whitehall II study. Social Science & Medicine, 56(6), 1321–1333. https://doi.org/10.1016/s0277-9536(02)00131-4
Smeeding T. M., Thornton K. A. (2019). Wisconsin poverty report: Treading water in 2017. Institute for Research on Poverty University of Wisconsin–Madison. https://www.irp.wisc.edu/category/wisconsin-poverty-reports/
University of Wisconsin Population Health Institute. (2014). County health rankings model: What is health? County Health Rankings & Roadmaps. http://www.countyhealthrankings.org/explore-health-rankings/measures-data-sources/county-health-rankings-model
U.S. Bureau of Labor Statistics. (2020). The employment situation—April 2020 (No. USDL-20-0815). https://www.bls.gov/news.release/pdf/empsit.pdf
United States Census Bureau. (2019a). American Community Survey Data. https://www.census.gov/programs-surveys/acs/data.html
United States Census Bureau. (2019b). Income and poverty in the United States: 2018. https://www.census.gov/data/tables/2019/demo/income-poverty/p60-266.html
VanderWeele T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press. https://play.google.com/store/books/details?id=gBYoBgAAQBAJ
Vila P. M., Swain G. R., Baumgardner D. J., Halsmer S. E., Remington P. L., Cisler R. A. (2007). Health disparities in Milwaukee by socioeconomic status. WMJ: Official Publication of the State Medical Society of Wisconsin, 106(7), 366–372. https://www.ncbi.nlm.nih.gov/pubmed/18030822
Weller S. C., Baer R. D., Garcia de, Alba Garcia J., Salcedo Rocha A. L. (2012). Explanatory models of diabetes in the U.S. and Mexico: The patient-provider gap and cultural competence. Social Science & Medicine, 75(6), 1088–1096. https://doi.org/10.1016/j.socscimed.2012.05.003
Wisconsin Department of Health Services. (2018, December 12). Chronic disease prevention data and reports: Quick facts. https://www.dhs.wisconsin.gov/disease/facts-chronic.htm
World Health Organization. (2003). Social determinants of health: The solid facts (R. Wilkinson, Marmot M., Eds.; 2nd ed). http://www.euro.who.int/__data/assets/pdf_file/0005/98438/e81384.pdf

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Article first published online: October 5, 2020
Issue published: June 2021

Keywords

  1. Patient participation
  2. income
  3. education
  4. self-management
  5. vulnerable population
  6. socioeconomic factors

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History

Published online: October 5, 2020
Issue published: June 2021
PubMed: 33012264

Authors

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Jeana M. Holt
Aaron Winn
Medical College of Wisconsin, Milwaukee, WI, USA
Rachel Cusatis
Medical College of Wisconsin Department of Medicine, Milwaukee, WI, USA
AkkeNeel Talsma
University of Wisconsin Milwaukee, Milwaukee, WI, USA
Bradley H. Crotty
Medical College of Wisconsin Department of Medicine, Milwaukee, WI, USA

Notes

Jeana M. Holt, University of Wisconsin-Milwaukee College of Nursing, 2901 E Hartford Ave, Milwaukee, WI 53201, USA. Email: [email protected]

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