PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Med Care. Author manuscript; available in PMC Dec 1, 2013.
Published in final edited form as:
PMCID: PMC3494793
NIHMSID: NIHMS399978
Who Participates in Chronic Disease Self-Management (CDSM) Programs? Differences between Participants and Non-Participants in a Population of Multi-Morbid Older Adults
Melissa Dattalo, MPH, MD,corresponding author1,2 Erin R. Giovannetti, PhD,2 Daniel Scharfstein, ScD,1 Chad Boult, MD, MPH, MBA,1,2 Stephen Wegener, PhD,3 Jennifer L. Wolff, PhD,1 Bruce Leff, MD,2 Kevin D. Frick, PhD, MA,1 Lisa Reider, MHS,1 Katherine Frey, RN, MPH,1 Gary Noronha, MD,4 and Cynthia Boyd, MD, MPH1,2
Melissa Dattalo, Johns Hopkins Bayview Internal Medicine Residency Program, 4940 Eastern Avenue, Baltimore, MD 21224, P: 630-921-1715, F: 410-550-0491;
corresponding authorCorresponding author.
Melissa Dattalo: dattalo/at/jhmi.edu; Erin R. Giovannetti: erandgi1/at/jhmi.edu; Daniel Scharfstein: dscharf/at/jhsph.edu; Chad Boult: cboult/at/jhsph.edu; Stephen Wegener: swegener/at/jhmi.edu; Jennifer L. Wolff: jwolff/at/jhsph.edu; Bruce Leff: bleff/at/jhmi.edu; Kevin D. Frick: kfrick/at/jhsph.edu; Lisa Reider: lsemanic/at/jhsph.edu; Katherine Frey: kfrey/at/jhsph.edu; Gary Noronha: gnoronh3/at/jhmi.edu; Cynthia Boyd: cboyd/at/jhmi.edu
1Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
2Johns Hopkins University School of Medicine, Baltimore, Maryland.
3Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, Maryland.
4Johns Hopkins Community Physicians, Baltimore, Maryland.
BACKGROUND
Self-care management is recognized as a key component of care for multi-morbid older adults, but the characteristics of those most likely to participate in Chronic Disease Self-Management (CDSM) programs and how to maximize participation in such programs are unknown.
OBJECTIVES
To identify individual factors associated with attending CDSM programs in a sample of multi-morbid older adults.
RESEARCH DESIGN
Participants in the intervention arm of a matched-pair cluster-randomized controlled trial of the Guided Care model were invited to attend a six-session CDSM course. Logistic regression was used to identify factors independently associated with attendance.
SUBJECTS
All subjects (N=241) were 65 years or older, were at high risk for health care utilization, and were not homebound.
MEASURES
Baseline information on demographics, health status, health activities, and quality of care was available for CDSM participants and non-participants. Participation was defined as attendance at five or more CDSM sessions.
RESULTS
22.8% of multi-morbid older adults who were invited to CDSM courses participated in five or more sessions. Having better physical health (OR[95% CI] = 2.3[1.1–4.8]) and rating one’s physician poorly on support for patient activation (OR[95% CI] = 2.8[1.3–6.0]) were independently associated with attendance.
CONCLUSIONS
Multi-morbid older adults who are in better physical health and who are dissatisfied with their physicians’ support for patient activation are more likely to participate in CDSM courses.
Keywords: health promotion, self-management, geriatrics, Guided Care, chronic disease, selection bias
There is growing interest in providing community-based self-management programs for older persons with multiple chronic conditions as an approach to preserving health and reducing health care costs.(1) The Chronic Disease Self-Management (CDSM) program equips patients with a set of skills (problem solving, decision making, resource utilization, building patient-provider partnership, self tailoring, and action planning) to manage a variety of chronic illnesses.(2) Studies have demonstrated the intervention’s ability to decrease disability, improve self-rated health and health behaviors,(37) and healthcare utilization.(35) When offered through an integrated healthcare system, one study estimated cost-savings to be $180 per patient.(6) The American Recovery and Reinvestment Act of 2009 allocated $27 million toward disseminating CDSM programs, which are now being offered in 45 states.(8)
Although research on the efficacy of self-management programs is promising, its impact is limited to those who participate. Older adults’ participation in health promotion programs is generally low.(912) Those who self-select to participate may be more likely to possess favorable health behaviors than non-participants.(13, 14) Because surveys administered to non-participants have low response rates,(15) factors associated with non-participation have not been well described. Factors previously found to be associated with participation in health promotion programs include higher education level,(14, 1621) higher socio-economic status,(11, 14, 16, 22, 23) younger age,(9, 16, 20, 24, 25) female gender,(11, 18, 26, 27) Caucasian race,(23, 26) better physical health,(9, 20, 22, 28) being married,(9, 16) practicing favorable health behaviors, (1214, 28, 29) and enrollment in an HMO insurance plan.(30)
Little is known about the characteristics of people who attend self-management programs. Prior work is limited by incomplete data on non-participants,(15, 31) and is focused on disease-specific self-management programs (17, 24, 26, 32) that do not fulfill the needs of older adults with multiple comorbid illnesses. This study compares multi-morbid older adult participants and non-participants in a non-disease-specific CDSM course. Our objective was to determine the characteristics of the multi-morbid older adults who are most likely to participate in CDSM programs and, thereby, to inform future strategies for maximizing participation by these populations. In addition to the variables previously shown to predict participation in health promotion programs, we hypothesized the level of patient activation, patient perception of quality of chronic illness care, and difficulty with health activities would also predict participation in CDSM programs.
To identify factors independently associated with CDSM participation, we utilized baseline data on CDSM participants and non-participants from the intervention arm of a matched-pair cluster-randomized controlled trial of the Guided Care model.
Guided Care
The Guided Care RCT was approved by all relevant institutional review boards and is previously described in detail.(33, 34) Guided Care Nurses (GCN) worked in primary care practices to provide comprehensive chronic care to 50–60 multi-morbid older patients. The intervention arm involved 7 primary care practice sites: 3 HMO sites, 3 community-based practice sites, and 1 community hospital-based site.(35) Six-session CDSM courses were offered twice over the course of one year at each intervention practice site or at local libraries at no cost to participants. The study team recruited lay leaders for the Guided Care CDSM courses, which were not affiliated with any existing community health programs.
Subjects
Guided Care patients were aged 65 or older, had been seen by a participating physician within the previous year, and were predicted to be in the highest 25% of Medicare service utilizers using the hierarchical condition categories (HCC) predictive model.(36) Eligible patients who provided informed consent completed baseline interviews (N=485).(33) A total of 293 Guided Care participants were deemed eligible for the CDSM course by the GCNs based on requirements that they were cognitively intact and not homebound. Invitations were mailed to eligible participants and followed-up by the GCNs through telephone and in-person conversations. One practice site had no participation and was dropped from the analysis (N=42). We suspected the lack of participation at this site was due to contextual factors and would therefore bias the analysis focused on individual predictors of attendance. Ten additional patients for whom CDSM attendance data were not available were also excluded from the analysis, yielding a sample size of 241 patients for the analysis.
Measurements
The baseline patient interview collected information regarding demographics, health status, health activities, patient activation, and patient perceptions of the quality of their health care. Patients reported financial strain as having “not enough money to make ends meet” at the end of each month (compared to some money left over or just enough to make ends meet). Health status measures included self-rated health (poor=1 to excellent=5), receiving assistance with activities of daily living (ADL: dressing, eating, using the toilet, bathing, and transferring between a bed and chair) and instrumental activities of daily living (IADL: using the telephone, heavy housework, light housework, managing money and shopping), functional health (SF-36)(37), and number of self-reported chronic conditions (diabetes, hypertension, heart attack, angina, congestive heart failure, other heart condition, asthma/COPD/emphysema, arthritis, sciatica, cancer, osteoporosis, hip fracture, and Alzheimer’s). Health activities included difficulty getting to medical appointments, exercise in the past week, missed medications in the past week and sufficiency of emotional support (received sufficient emotional support, versus needed a lot or some more emotional support). Patient activation was measured using the Patient Activation Measure (PAM), a validated scale (0–100) that measures how confident, knowledgeable, and active patients are in managing their own health conditions.(38) Quality of chronic illness care was measured using the Patient Assessment of Chronic Illness Care (PACIC), a validated instrument which asks patients about the frequency with which they received care congruent with the chronic care model in five domains: goal setting, coordination of care, decision support, problem solving, and patient activation.(39) Missing values for baseline interview questions were imputed as previously described.(33)
CDSM participation was defined as documented attendance at ≥5 CDSM sessions. This was chosen as a clinically relevant outcome because the average number of sessions attended by subjects in the original CSDM randomized trial(4) and the Kasier Permanente prospective study(6) were 5.5 and 5.3 respectively. Sensitivity analysis was conducted with a threshold of ≥4 CDSM sessions.
Statistical Analysis
Logistic regression was used to identify individual factors associated with participation in CDSM programs. The regression model was adjusted for practice site using a fixed effect model to control for variation within practice sites.
We built a multivariate logistic regression model using any borderline significant variables (p-value <0.20) from bivariate relationships (adjusted for practice site) in addition to all theoretically relevant variables previously shown to influence participation health promotion programs (age,(9, 16, 20, 24, 25) gender,(11, 18, 26, 27) race,(23, 26) marital status,(9, 16) education,(14, 1621) financial strain,(11, 14, 16, 22, 23) ADL or IADL impairment, SF-36 physical heath score,(9, 20, 22, 28) and exercise behavior(28)). Analyses were conducted using STATA 10.0.(40)
CDSM Attendees vs. Non-Attendees
Of the 485 participants enrolled in the intervention arm of Guided Care, 293 (60.4%) were eligible and invited to participate in the CDSM program. Ten patients had missing attendance records and 42 patients were excluded from the analysis due to a 0% participation rate at their practice site. Of the 241 remaining patients, 55 (22.8%) attended at least five CDSM sessions and were defined as attendees (Table 1). CDSM attendees reported higher SF-36 physical health scores (p=0.005), had fewer IADL impairments (p=0.028), and exercised more often (p=0.052) than patients who did not attend the program (Table 2).
TABLE 1
TABLE 1
CDSM Attendance Pattern
TABLE 2
TABLE 2
Characteristics of CDSM Program Attendees and Non-Attendees
Multivariate Model
All theoretically relevant variables previously shown to influence participation in other types of health promotion programs and any variable with a p-value <0.20 from the bivariate analyses were used to build a multivariate logistic regression model, adjusted for primary care practice ownership (Table 3). Having higher SF-36 physical health scores (OR[95% CI] = 2.3[1.1–4.8]) and rating one’s physician as poor on the PACIC patient activation subscale (OR[95% CI] = 2.8[1.3–6.0])were independently associated with CDSM attendance in the final model. Both SF-36 physical health score and the PACIC patient activation subscore remain independently associated with participation in the final model when the attendance threshold was changed to 4 sessions.
TABLE 3
TABLE 3
Multivariate Logistic Regression Model of Factors Influencing Participation in CDSM Programs among Multi-Morbid Older Adults*
Self-management is recognized as a priority for multi-morbid older adults, yet little information is available about who is most likely to participate in CDSM programs or how to maximize participation.(41) Prior studies have been limited by incomplete information on non-participants and a focus on disease-specific programs. This study offers insights regarding factors associated with CDSM participation among a diverse population of multi-morbid older adults.
Almost one-quarter of eligible multi-morbid older adults attended at least five CDSM classes. This rate is high relative to other many health promotion programs, where participation has ranged from 4.3% to 33%.(912) Unlike findings from other health promotion programs, age,(9, 16, 20, 24) gender,(11, 18, 26) race,(26) socio-economic status,(11, 14, 16, 22, 23) and education(14, 1620, 24) did not demonstrate a statistically significant association with attendance in our study. CDSM programs may appeal to a broader demographic range because they are led by lay persons rather than health care professionals. Personalized recruitment by GCNs may have also allowed this CDSM program to reach a more diverse range of individuals.(13, 42, 43)
Consistent with other studies of health promotion programs,(9, 20, 22, 28) individuals who had higher physical function were twice as likely to attend CDSM classes compared to those with lower physical function. Healthier individuals face fewer physical barriers to participation in community classes and may be less likely to miss sessions.
Contrary to our hypothesis, our study did not find a significant association between patient activation measure (PAM) score and participation in CDSM.(13, 14) Likewise health behaviors, such as exercise, were not significant predictors of participation in the final model. Since the CDSM curriculum promotes patient activation, it is advantageous that the course reaches patients who are less engaged in their health care and presumably have the most to benefit. In this multi-morbid population, however, self-care motivation may be limited by physical capacity to follow-through with intended behaviors.
Finally, this study showed an interesting relationship between patient perceived quality of patient-activation support by a health care provider and participation in CDSM. Patients were more likely to participate if they rated their primary care providers as less supportive of patient activation (measured by the PACIC patient activation sub-score). The perception that one’s physician is less supportive of patient activation may motivate that person to seek such support through other venues, such as CDSM courses. Quality of care has not previously been studied as a predictor of health program participation. These results should be interpreted with caution as recent studies do not support the validity of the PACIC subscales,(44, 45) suggesting that the total PACIC score is the most reliable measure.
Limitations
Sample size was limited. One site was removed from the analysis due to 0% participation at that site. We believe lack of participation at this site was more likely the result of contextual factors than individual factors. Generalizability may be limited by similar motivations influencing both consent to participate in the Guided Care trial and CDSM attendance. Although we controlled for practice site variation through the inclusion of fixed effects in our model, our small sample size per site hampered our ability to explore whether the effects of individual factors are heterogeneous across sites. Finally, the small number of sites hampered our ability to evaluate the effect of site-level contextual factors on participation.
Applicability
This study suggests CDSM programs appeal to a diverse demographic range of older adults and can garner reasonable participation by older-adults with multi-morbidity. It is critical that future CDSM courses reach this older, multi-morbid population in order to maximize the potential impact on their health and health-care expenditures.(1, 6)
As federally-funded CDSM courses are disseminated nationally, it will be important to reach underrepresented populations such as older adults with poor physical health. It may be beneficial to advertise CDSM as a program that can fulfill an unmet need for patient activation support. Further work is needed to explore the influence of quality of care on participation rates and to investigate strategies to increase recruitment among older adults with multiple chronic conditions.
Acknowledgments
Funding Sources: Grant R-01HS14580 from the Agency for Healthcare Research and Quality (AHRQ)and the National Institute on Aging (NIA), Grant 2004-0335 from The John A. Hartford Foundation, NIMH 5K01MH82885-2, The Jacob and Valeria Langeloth Foundation, Kaiser-Permanente of the Mid-Atlantic States, Johns Hopkins HealthCare, Roger C. Lipitz Center for Integrated Health Care, National Institute of Health (NIH) Predoctoral Clinical Research Training Program. Dr. Boyd was supported by the Johns Hopkins Bayview Center for Innovative Medicine, The Robert Wood Johnson Foundation Physician Faculty Scholars Program, and the Paul Beeson Career Development Award Program (NIA K23 AG032910, AFAR, The John A. Hartford Foundation, The Atlantic Philanthropies, The Starr Foundation and an anonymous donor).
Contributor Information
Melissa Dattalo, Johns Hopkins Bayview Internal Medicine Residency Program, 4940 Eastern Avenue, Baltimore, MD 21224, P: 630-921-1715, F: 410-550-0491.
Erin R. Giovannetti, 5200 Eastern Ave, Mason F. Lord Building, 7th Floor, Center Tower, Baltimore, MD 21224 P: 410-274-8824, F: 410-550-8701.
Daniel Scharfstein, 615 N. Wolfe Street, E3547, Baltimore, MD 21117, P: 410-955-2420, F: 410-955-0958.
Chad Boult, 624 N. Broadway, Hampton House Room 693, Baltimore, MD 21205, P: 410-955-6546, F: 410-955-0470.
Stephen Wegener, 600 N. Wolfe Street, Phipps 174, Baltimore, MD 21297, P: 410-502-2438, F: 410-502-2419.
Jennifer L. Wolff, 624 N. Broadway, Hampton House, Room 692, Baltimore, MD 21205, P: 410-502-0458, F: 410-955-0470.
Bruce Leff, 5505 Hopkins Bayview Circle, Beacham Center, Baltimore, MD 21224, P: 410-550-2652, F: 410-550-8701.
Kevin D. Frick, 624 N. Broadway, Hampton House, Room 606, Baltimore, MD 21205, P: 410-614-4018, F: 410-955-0470.
Lisa Reider, 624 N. Broadway, Hampton House Room 355, Baltimore, MD 21205, P: 410-502-3962, F: 410-955-0470.
Katherine Frey, 624 N. Broadway, Hampton House Room 350, Baltimore, MD 21205, P: 410-502-9109, F: 410-955-0470.
Gary Noronha, 3100 Wyman Park Dr, Baltimore, MD 21211, P: 410-338-3421, F: 410-338-3498.
Cynthia Boyd, 5200 Eastern Ave, Mason F. Lord Building, 7th Floor, Center Tower, Baltimore, MD 21224, P: 410-550-8676, F: 410-550-8701.
1. U.S. Department of Health & Human Services. [Accessed April 29, 2012];Multiple Chronic Conditions: A Strategic Framework. 2010 Available at: http://www.hhs.gov/ash/initiatives/mcc/mcc_framework.pdf.
2. Lorig KR, Holman H. Self-management education: history, definition, outcomes, and mechanisms. Ann Behav Med. 2003;26:1–7. [PubMed]
3. Lorig KR, Ritter PL, Gonzalez VM. Hispanic chronic disease self-management: a randomized community-based outcome trial. Nurs Res. 2003;52:361–369. [PubMed]
4. Lorig KR, Sobel DS, Stewart AL, et al. Evidence suggesting that a chronic disease self-management program can improve health status while reducing hospitalization: a randomized trial. Med Care. 1999;37:5–14. [PubMed]
5. Dongbo F, Hua F, McGowan P, et al. Implementation and quantitative evaluation of chronic disease self-management programme in Shanghai, China: randomized controlled trial. Bulletin of the World Health Organization. 2003;81:174. [PubMed]
6. Lorig KR, Sobel DS, Ritter PL, et al. Effect of a self-management program on patients with chronic disease. Eff Clin Pract. 2001;4:256–262. [PubMed]
7. Dongbo F, Ding Y, McGowan P, et al. Qualitative evaluation of Chronic Disease Self Management Program (CDSMP) in Shanghai. Patient Educ Couns. 2006;61:389–396. [PubMed]
8. [Accessed May 10, 2011];American Recovery and Reinvestment Act Communities Putting Prevention to Work: Chronic Disease Self-Management Program. 2010 Available at: http://www.aoa.gov/AoARoot/AoA_Programs/HPW/ARRA/index.aspx.
9. Laliberte L, Mor V. Why was there low participation in a health promotion and disease prevention program offered to Medicare beneficiaries? R I Med. 1993;76:35–39. [PubMed]
10. Mills KM, Stewart AL, King AC, et al. Factors associated with enrollment of older adults into a physical activity promotion program. J Aging Health. 1996;8:96–113. [PubMed]
11. Kocken PL, Voorham AJ. Interest in participation in a peer-led senior health education program. Patient Educ Couns. 1998;34:5–14. [PubMed]
12. Elzen H, Slaets JPJ, Snijders TAB, et al. Do older patients who refuse to participate in a self-management intervention in the Netherlands differ from older patients who agree to participate? Aging Clinical and Experimental Research. 2008;20:266–271. [PubMed]
13. Carter WB, Elward K, Malmgren J, et al. Participation of older adults in health programs and research: a critical review of the literature. Gerontologist. 1991;31:584–592. [PubMed]
14. Wagner EH, Grothaus LC, Hecht JA, et al. Factors associated with participation in a senior health promotion program. Gerontologist. 1991;31:598–602. [PubMed]
15. Muhlenkamp AF, Brown NJ, Sands D. Determinants of health promotion activities in nursing clinic clients. Nurs Res. 1985;34:327–332. [PubMed]
16. Lave JR, Ives DG, Traven ND, et al. Participation in health promotion programs by the rural elderly. Am J Prev Med. 1995;11:46–53. [PubMed]
17. Thoolen B, de Ridder D, Bensing J, et al. Who participates in diabetes self-management interventions?: Issues of recruitment and retainment. Diabetes Educ. 2007;33:465–474. [PubMed]
18. Bode C, De Ridder DT. Investing in the future--identifying participants in an educational program for middle-aged and older adults. Health Educ Res. 2007;22:473–482. [PubMed]
19. Buchner DM, Pearson DC. Factors associated with participation in a community senior health promotion program: a pilot study. Am J Public Health. 1989;79:775–777. [PubMed]
20. Jong ZD, Munneke M, Jansen LM, et al. Differences between participants and nonparticipants in an exercise trial for adults with rheumatoid arthritis. Arthritis Care & Research. 2004;51:593–600. [PubMed]
21. Martinson BC, Crain AL, Sherwood NE, et al. Population reach and recruitment bias in a maintenance RCT in physically active older adults. J Phys Act Health. 2010;7:127–135. [PMC free article] [PubMed]
22. Watkins AJ, Kligman EW. Attendance patterns of older adults in a health promotion program. Public Health Rep. 1993;108:86–90. [PMC free article] [PubMed]
23. Saunders KW, Von Korff M, Grothaus LC. Predictors of participation in primary care group-format back pain self-care interventions. Clinical Journal of Pain. 2000;16:236–243. [PubMed]
24. Muntner P, Sudre P, Uldry C, et al. Predictors of participation and attendance in a new asthma patient self-management education program. Chest. 2001;120:778–784. [PubMed]
25. Blanch DC, Rudd RE, Wright E, et al. Predictors of refusal during a multi-step recruitment process for a randomized controlled trial of arthritis education. Patient Educ Couns. 2008;73:280–285. [PMC free article] [PubMed]
26. Bruce B, Lorig K, Laurent D. Participation in patient self-management programs. Arthritis Rheum. 2007;57:851–854. [PubMed]
27. Rowland RM, Fisher KJ, Green M, et al. Recruiting inactive older adults to a neighborhood walking trial: The SHAPE project. Journal of Aging Studies. 2004;18:353–368.
28. Schweitzer SO, Atchison KA, Lubben JE, et al. Health promotion and disease prevention for older adults: opportunity for change or preaching to the converted? Am J Prev Med. 1994;10:223–229. [PubMed]
29. Dodge JA, Clark NM, Janz NK, et al. Nonparticipation of Older Adults in a Heart-Disease Self-Management Project - Factors Influencing Involvement. Research on Aging. 1993;15:220–237.
30. Musich S, Ignaczak A, McDonald T, et al. Self-reported utilization of preventive health services by retired employees age 65 and older. J Am Geriatr Soc. 2001;49:1665–1672. [PubMed]
31. Ives DG, Traven ND, Kuller LH, et al. Selection bias and nonresponse to health promotion in older adults. Epidemiology. 1994;5:456–461. [PubMed]
32. Carr JL, Moffett JA, Sharp DM, et al. Is the Pain Stages of Change Questionnaire (PSOCQ) a useful tool for predicting participation in a self-management programme? Further evidence of validity, on a sample of UK pain clinic patients. BMC Musculoskelet Disord. 2006;7:101. [PMC free article] [PubMed]
33. Boult C, Reider L, Frey K, et al. Early effects of “Guided Care” on the quality of health care for multimorbid older persons: a cluster-randomized controlled trial. J Gerontol A Biol Sci Med Sci. 2008;63:321–327. [PubMed]
34. Boyd CM, Boult C, Shadmi E, et al. Guided care for multimorbid older adults. Gerontologist. 2007;47:697–704. [PubMed]
35. Boult C, Reider L, Leff B, et al. The Effect of Guided Care Teams on the Use of Health Services Results From a Cluster-Randomized Controlled Trial. Archives of Internal Medicine. 2011;171:460–466. [PubMed]
36. Pope GC, Kautter J, Ellis RP, et al. Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financ Rev. 2004;25:119–141. [PubMed]
37. Ware J, Kosinski M, Dewey J. Version 2 of the SF-36 Health Survey. Lincoln, RI: Quality Metric; 2003.
38. Hibbard JH, Mahoney ER, Stockard J, et al. Development and testing of a short form of the patient activation measure. Health Serv Res. 2005;40:1918–1930. [PMC free article] [PubMed]
39. Glasgow RE, Wagner EH, Schaefer J, et al. Development and validation of the Patient Assessment of Chronic Illness Care (PACIC) Med Care. 2005;43:436–444. [PubMed]
40. Rabe-Hesketh S, Skrondal A. Multilevel and Longitudinal Modeling Using Stata. College Station, TX: Stata Press; 2005.
41. Services. USDoHaH. Multiple Chronic Conditions—A Strategic Framework: Optimum Health and Quality of Life for Individuals with Multiple Chronic Conditions. 2010.
42. Durham ML, Beresford S, Diehr P, et al. Participation of higher users in a randomized trial of Medicare reimbursement for preventive services. Gerontologist. 1991;31:603–606. [PubMed]
43. Groupp E, Haas M, Fairweather A, et al. Recruiting seniors with chronic low back pain for a randomized controlled trial of a self-management program. J Manipulative Physiol Ther. 2005;28:97–102. [PubMed]
44. Spicer J, Budge C, Carryer J. Taking the PACIC back to basics: the structure of the Patient Assessment of Chronic Illness Care. J Eval Clin Pract. 2010 [PubMed]
45. Gugiu C, Coryn CL, Applegate B. Structure and measurement properties of the Patient Assessment of Chronic Illness Care instrument. J Eval Clin Pract. 2010;16:509–516. [PubMed]