Skip to main content
Intended for healthcare professionals
Restricted access
Research article
First published online October 5, 2020

Racial Disparities in Patient Activation: The Role of Economic Diversity


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

Get full access to this article

View all access and purchase options for this article.


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.
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.
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.
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.
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.
Cantor M. N., Thorpe L. (2018). Integrating data on social determinants of health into electronic health records. Health Affairs, 37(4), 585–590.
Centers for Disease Control and Prevention. (2019, November 6). Nutrition, physical activity, and obesity: Data, trends and maps.
Centers for Medicare & Medicaid Services. (2020). Medicare telemedicine healthcare provider fact sheet. Medicare Coverage and Payment of Virtual Services.
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.
County Health Rankings & Roadmaps. (2018). What is health? County Health Rankings & Roadmaps.
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.
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.
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.
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.
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.
Emsley R., Liu H. (2013). PARAMED: Stata module to perform causal mediation analysis using parametric regression models.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Hibbard J. H. (2008). Increasing patient activation to improve health and reduce costs. Institute for Policy Research and Innovation.
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.
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.
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.
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.
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.
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.
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.
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.
Hobson K. (2017, September 8). Why do people stop taking their meds? Cost is just one reason. NPR.
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.
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.
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.
Khullar D., Chokshi D. A. (2018). Health, income, & poverty: Where we are & what could help. Health Affairs Policy Brief.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Robert Wood Johnson Foundation. (2008). Overcoming obstacles to health.
Robins J. M., Greenland S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3(2), 143–155.
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.
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.;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.
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.
Smeeding T. M., Thornton K. A. (2019). Wisconsin poverty report: Treading water in 2017. Institute for Research on Poverty University of Wisconsin–Madison.
University of Wisconsin Population Health Institute. (2014). County health rankings model: What is health? County Health Rankings & Roadmaps.
U.S. Bureau of Labor Statistics. (2020). The employment situation—April 2020 (No. USDL-20-0815).
United States Census Bureau. (2019a). American Community Survey Data.
United States Census Bureau. (2019b). Income and poverty in the United States: 2018.
VanderWeele T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press.
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.
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.
Wisconsin Department of Health Services. (2018, December 12). Chronic disease prevention data and reports: Quick facts.
World Health Organization. (2003). Social determinants of health: The solid facts (R. Wilkinson, Marmot M., Eds.; 2nd ed).

Cite article

Cite article

Cite article


Download to reference manager

If you have citation software installed, you can download article citation data to the citation manager of your choice

Share options


Share this article

Share with email

Share access to this article

Sharing links are not relevant where the article is open access and not available if you do not have a subscription.

For more information view the Sage Journals article sharing page.

Information, rights and permissions


Published In

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


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

Rights and permissions

© The Author(s) 2020.
Request permissions for this article.


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



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


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

Metrics and citations


Journals metrics

This article was published in Western Journal of Nursing Research.


Article usage*

Total views and downloads: 434

*Article usage tracking started in December 2016


See the impact this article is making through the number of times it’s been read, and the Altmetric Score.
Learn more about the Altmetric Scores

Articles citing this one

Web of Science: 0

Crossref: 0

There are no citing articles to show.

Figures and tables

Figures & Media


View Options

Get access

Access options

If you have access to journal content via a personal subscription, university, library, employer or society, select from the options below:

MNRS members can access this journal content using society membership credentials.

MNRS members can access this journal content using society membership credentials.

Alternatively, view purchase options below:

Purchase 24 hour online access to view and download content.

Access journal content via a DeepDyve subscription or find out more about this option.

View options


View PDF/ePub

Full Text

View Full Text