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The American Journal of Managed Care August 2018
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Impact of a Medical Home Model on Costs and Utilization Among Comorbid HIV-Positive Medicaid Patients
Paul Crits-Christoph, PhD; Robert Gallop, PhD; Elizabeth Noll, PhD; Aileen Rothbard, ScD; Caroline K. Diehl, BS; Mary Beth Connolly Gibbons, PhD; Robert Gross, MD, MSCE; and Karin V. Rhodes, MD, MS
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Impact of a Medical Home Model on Costs and Utilization Among Comorbid HIV-Positive Medicaid Patients

Paul Crits-Christoph, PhD; Robert Gallop, PhD; Elizabeth Noll, PhD; Aileen Rothbard, ScD; Caroline K. Diehl, BS; Mary Beth Connolly Gibbons, PhD; Robert Gross, MD, MSCE; and Karin V. Rhodes, MD, MS
Among HIV-positive Medicaid patients with comorbid medical and psychiatric disorders, there was increased outpatient service utilization, yet relative cost savings, for patients who were treated in patient-centered medical homes.
DISCUSSION

Among HIV-positive Medicaid patients with comorbid medical conditions (asthma, COPD, CHF, and/or diabetes) and psychiatric and/or substance use disorders, our data indicate that Pennsylvania’s Chronic Care medical home initiative resulted in substantial cost savings compared with non-CCI treatment in the state. Relative decreases in cost were apparent for outpatient substance abuse treatment, inpatient medical, and total inpatient services. Decreases in utilization of any inpatient medical services were evident. These decreases in costs and utilization occurred with concomitant increases in the cost and utilization of outpatient medical services and outpatient non-HIV pharmacy claims for CCI-treated patients relative to non-CCI patients. The CCI intervention apparently was successful in shifting inpatient costs and utilization to outpatient care and use of non-HIV medications.

Studies examining the PCMH model in general (non-HIV) medical populations have reported mixed effects in terms of both costs/utilization and clinical outcomes,18-20 although many of these studies have had methodological weaknesses.45 Initial smaller-sample reports on the PCMH model implemented in the Pennsylvania CCI have also been mixed.24-26 However, the largest study of the CCI focused on Medicaid patients and found considerable cost savings and utilization reductions among patients with complicating conditions requiring more healthcare utilization than the general population.28 Similarly, a study of privately insured patients in 15 of the CCI practices reported reductions in costs and utilization only among patients with multiple comorbidities.27 A PCMH model implemented at the University of Texas, Houston, also appeared to improve outcomes and reduce costs in a high-risk predominantly Medicaid-insured pediatric population with chronic illnesses.46 The current report extends these cost and utilization findings to an HIV-positive Medicaid population with both chronic medical and psychiatric and/or substance use comorbidities.

These findings have important implications for the future of HIV care. HIV-positive individuals are more likely than the general population to have psychiatric and/or substance use disorders, and their comorbidities may be more difficult to manage.47 Whether HIV itself causes this complexity or characteristics that predispose people to HIV interfere with managing comorbid illnesses, the burden on providers to coordinate and implement effective care is greater for HIV-positive populations than for most other patient groups. The magnitude of the CCI versus non-CCI relative cost reduction ($214.10 per month) found here was smaller than that previously found ($345.44 per month) in a report targeting predominately non-HIV patients with the same set of comorbidities as specified in the current analyses.28 It should be noted that the current sample, compared with the previous study, was older (45 vs 34 years), was more often male (51% vs 38%), and had a higher comorbidity index (mean [SD], 2.5 [1.9] vs 1.5 [1.5]). Whether these sample differences, the difficulty of managing comorbidities within an HIV-positive population, or the continuous use of expensive HIV medications is responsible for the lower cost savings found here is not clear. Nevertheless, the cost savings in the current sample were meaningful, and our findings suggest that coordination of behavioral and medical care is a skill that can be taught to providers and practices and results in more efficient care. The CCI model appears to empower medical providers to be better managers of this complex population by emphasizing integrated, rather than siloed, care.

Limitations

It is important to note several limitations of the current study. First, this study was restricted to Medicaid patients. We do not know whether the cost savings found for HIV-positive patients with Medicaid treated in the CCI would generalize to privately insured HIV-positive patients with or without the comorbidities examined here. Second, because participation in the CCI was voluntary for each practice, the results reported here could be a function of selection bias rather than the intervention per se. Third, we were not able to control for practice in the analyses, because individual comparison (non-CCI) practices with adequate numbers of HIV-positive patients were not available. Fourth, healthcare costs and utilization were only evaluated for 1 year post the index episode. Fifth, outcome measures were restricted to utilization and cost variables obtainable through a claims database. We did not have data on the quality of implementation of the medical home model; such implementation variables may be important for utilization and costs. We also did not have data on clinical outcomes. Without such data, it is difficult to know if cost reductions, for example, in outpatient substance abuse treatment costs, represent appropriate (eg, elimination of treatment that is not evidence based, improvement in substance use outcomes) versus inappropriate (eg, early termination of treatment) care. Future research needs to assess both clinical outcomes and costs to fully understand the basis for reductions in costs. Sixth, our focus was on 4 chronic medical conditions (COPD, heart failure, asthma, diabetes) that were highlighted in the CCI model. The impact of a PCMH on costs and clinical outcomes when other chronic medical conditions (eg, hypertension) are comorbid with HIV and psychiatric or substance use disorders needs to be examined in future research.

CONCLUSIONS

The current study found that among HIV-positive Medicaid patients with medical and psychiatric comorbidities, the Pennsylvania CCI was associated with overall cost savings and relative decreases in inpatient healthcare utilization. The CCI model should be considered for permanent implementation in this population and adapted and tested for other complex medical conditions as well. 

Acknowledgments

The authors wish to acknowledge the support and encouragement of the chief medical officer of Pennsylvania’s Office of Medical Assistance Programs, David Kelley, MD; Trevor Hadley, PhD, for facilitating access to the Pennsylvania Medicaid claims database; and Marcela Myers, MD, at the Pennsylvania Department of Human Services for providing the list of CCI practices.

Author Affiliations: Perelman School of Medicine, University of Pennsylvania (PCC, EN, AR, CKD, MBCG, RGr, KVR), Philadelphia, PA; West Chester University (RGa), West Chester, PA; University of Pennsylvania School of Social Policy and Practice (AR), Philadelphia, PA; Leonard Davis Institute of Health Economics (AR, RGr), Philadelphia, PA.

Source of Funding: Research reported in this publication was supported by the Robert Wood Johnson Foundation State Health Access Reform Evaluation Grant #70165, the University of Pennsylvania Center for AIDS Research (CFAR, NIH Grant P30-AI045008), and the University of Pennsylvania Mental Health AIDS Research Center (PMHARC, NIH Grant P30-MH097488).

The content is solely the responsibility of the authors and does not necessarily represent the official views of the Robert Wood Johnson Foundation, the National Institutes of Health, the Perelman School of Medicine, Leonard Davis Institute of Health Economics, or the University of Pennsylvania School of Social Policy and Practice.

Author Disclosures: Dr Gross serves on a Data Safety Monitoring Board for a Pfizer drug unrelated to HIV. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (PCC, AR, RGr, KVR); acquisition of data (PCC, EN, AR); analysis and interpretation of data (PCC, RGa, EN, AR, MBCG, RGr, KVR); drafting of the manuscript (PCC, RGa, CKD, MBCG, KVR); critical revision of the manuscript for important intellectual content (PCC, AR, CKD, MBCG, RGr, KVR); statistical analysis (RGa, EN, AR); obtaining funding (PCC, KVR); administrative, technical, or logistic support (PCC, EN, CKD); and supervision (PCC).

Address Correspondence to: Paul Crits-Christoph, PhD, 3535 Market St, Rm 650, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104. Email: crits@mail.med.upenn.edu.
REFERENCES

1. Ciesla JA, Roberts JE. Meta-analysis of the relationship between HIV infection and risk for depressive disorders. Am J Psychiatry. 2001;158(5):725-730. doi: 10.1176/appi.ajp.158.5.725.

2. Pence BW, Miller WC, Whetten K, Eron JJ, Gaynes BN. Prevalence of DSM-IV-defined mood, anxiety, and substance use disorders in an HIV clinic in the Southeastern United States. J Acquir Immune Defic Syndr. 2006;42(3):298-306. doi: 10.1097/01.qai.0000219773.82055.aa.

3. Justice AC, Lasky E, McGinnis KA, et al; VACS 3 Project Team. Medical disease and alcohol use among veterans with human immunodeficiency infection: a comparison of disease measurement strategies. Med Care. 2006;44(8 suppl 2):S52-S60. doi: 10.1097/01.mlr.0000228003.08925.8c.

4. Strathdee SA, Stockman JK. Epidemiology of HIV among injecting and non-injecting drug users: current trends and implications for interventions. Curr HIV/AIDS Rep. 2010;7(2):99-106. doi: 10.1007/s11904-010-0043-7.

5. Butt AA, McGinnis K, Rodriguez-Barradas MC, et al; Veterans Aging Cohort Study. HIV infection and the risk of diabetes mellitus. AIDS. 2009;23(10):1227-1234. doi: 10.1097/QAD.0b013e32832bd7af.

6. Hernández-Vázquez LR, Martinez JH, Rivera-Anaya C, et al. Prevalence of diabetes mellitus in human immunodeficency virus positive patients in Puerto Rico—San Juan City Hospital experience. Bol Asoc Med P R. 2015;107(3):5-8.

7. Butt AA, Chang CC, Kuller L, et al. Risk of heart failure with human immunodeficiency virus in the absence of prior diagnosis of coronary heart disease. Arch Intern Med. 2011;171(8):737-743. doi: 10.1001/archinternmed.2011.151.

8. White JR, Chang CC, So-Armah KA, et al. Depression and human immunodeficiency virus infection are risk factors for incident heart failure among veterans: Veterans Aging Cohort Study. Circulation. 2015;132(17):1630-1638. doi: 10.1161/CIRCULATIONAHA.114.014443.

9. Crothers K, Butt AA, Gibert CL, Rodriguez-Barradas MC, Crystal S, Justice AC; Veterans Aging Cohort 5 Project Team. Increased COPD among HIV-positive compared to HIV-negative veterans. Chest. 2006;130(5):1326-1333. doi: 10.1378/chest.130.5.1326.

10. Drummond MB, Kirk GD, Astemborski J, et al. Prevalence and risk factors for unrecognized obstructive lung disease among urban drug users. Int J Chron Obstruct Pulmon Dis. 2011;6:89-95. doi: 10.2147/COPD.S15968.

11. Hasse B, Tarr PE, Marques-Vidal P, et al. Strong impact of smoking on multimorbidity and cardiovascular risk among human immunodeficiency virus-infected individuals in comparison with the general population. Open Forum Infect Dis. 2015;2(3):ofv108. doi: 10.1093/ofid/ofv108.

12. Gijsen R, Hoeymans N, Schellevis FG, Schellevis FG, Ruwaard D, Satariano WA, van den Bos GA. Causes and consequences of comorbidity: a review. J Clin Epidemiol. 2001;54(7):661-674. doi: 10.1016/S0895-4356(00)00363-2.

13. Wolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med. 2002;162(20):2269-2276. doi: 10.1001/archinte.162.20.2269.

14. Mijch A, Burgess P, Judd F, et al. Increased health care utilization and increased antiretroviral use in HIV-infected individuals with mental health disorders. HIV Med. 2006;7(4):205-212. doi: 10.1111/j.1468-1293.2006.00359.x.

15. Asch SM, Kilbourne AM, Gifford AL, et al; HCSUS Consortium. Underdiagnosis of depression in HIV: who are we missing? J Gen Intern Med. 2003;18(6):450-460. doi: 10.1046/j.1525-1497.2003.20938.x.

16. Salter ML, Lau B, Go VF, Mehta SH, Kirk GD. HIV infection, immune suppression, and uncontrolled viremia are associated with increased multimorbidity among aging injection drug users. Clin Infect Dis. 2011;53(12):1256-1264. doi: 10.1093/cid/cir673.

17. American Academy of Family Physicians; American Academy of Pediatrics; American College of Physicians; American Osteopathic Association. Joint principles of the patient-centered medical home. American College of Physicians website. acponline.org/system/files/documents/running_practice/delivery_and_payment_models/pcmh/demonstrations/jointprinc_05_17.pdf. Published March 7, 2007. Accessed July 5, 2018.

18. Fifield J, Forrest DD, Burleson JA, Martin-Peele M, Gillespie W. Quality and efficiency in small practices transitionng to patient centered medical homes: a randomized trial. J Gen Intern Med. 2013;28(6):778-786. doi: 10.1007/s11606-013-2386-4.

19. Rosenthal MB, Friedberg MW, Singer SJ, Eastman D, Li Z, Schneider EC. Effect of a multipayer patient-centered medical home on health care utilization and quality: the Rhode Island chronic care sustainability initiative pilot program. JAMA Intern Med. 2013;173(20):1907-1913. doi: 10.1001/jamainternmed.2013.10063.

20. Werner RM, Duggan M, Duey K, Zhu J, Stuart EA. The patient-centered medical home: an evaluation of a single private payer demonstration in New Jersey. Med Care. 2013;51(6):487-493. doi: 10.1097/MLR.0b013e31828d4d29.

21. Liss DT, Fishman PA, Rutter CM, et al. Outcomes among chronically ill adults in a medical home prototype. Am J Manag Care. 2013;19(10):e348-e358.

22. Gabbay RA, Bailit MH, Mauger DT, Wagner EH, Siminerio L. Multipayer patient-centered medical home implementation guided by the chronic care model. Jt Comm J Qual Patient Saf. 2011;37(6):265-273. doi: 10.1016/S1553-7250(11)37034-1.

23. Wagner EH, Austin BT, Von Korff M. Organizing care for patients with chronic illness. Milbank Q. 1996;74(4):511-544.

24. Friedberg MW, Schneider EC, Rosenthal MB, Volpp KG, Werner RM. Association between participation in a multipayer medical home intervention and changes in quality, utilization, and costs of care. JAMA. 2014;311(8):815-825. doi: 10.1001/jama.2014.353.

25. Friedberg MW, Rosenthal MB, Werner RM, Volpp KG, Schneider EC. Effects of a medical home and shared savings intervention on quality and utilization of care. JAMA Intern Med. 2015;175(8):1362-1368. doi: 10.1001/jamainternmed.2015.2047.

26. David G, Gunnarsson C, Saynisch PA, Chawla R, Nigam S. Do patient-centered medical homes reduce emergency department visits? Health Serv Res. 2015;50(2):418-439. doi: 10.1111/1475-6773.12218.

27. Higgins S, Chawla R, Colombo C, Snyder R, Nigam S. Medical homes and cost and utilization among high-risk patients. Am J Manag Care. 2014;20(3):e61-e71.

28. Rhodes KV, Basseyn S, Gallop R, Noll E, Rothbard A, Crits-Christoph P. Pennsylvania’s medical home initiative: reductions in healthcare utilization and cost among Medicaid patients with medical and psychiatric comorbidities. J Gen Intern Med. 2016;31(11):1373-1381. doi: 10.1007/s11606-016-3734-y.

29. Thomas MR, Waxmonsky JA, Gabow PA, Flanders-McGinnis G, Socherman R, Rost K. Prevalence of psychiatric disorders and costs of care among adult enrollees in a Medicaid HMO. Psychiatr Serv. 2005;56(11):1394-1401. doi: 10.1176/appi.ps.56.11.1394.

30. Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality. Treatment Episode Data Set (TEDS): 2000-2010. National Admissions to Substance Abuse Treatment Services. DASIS Series S-61, HHS Publication No. (SMA) 12-4701. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2012. samhsa.gov/data/sites/default/files/2010_Treatment_Episode_Data_Set_National/2010_Treatment_Episode_Data_Set_National.html. Accessed February 15, 2016.

31. Blount A, Kathol R, Thomas M, et al. The economics of behavioral health services in medical settings: a summary of the evidence. Prof Psychol Res Pr. 2007;38(3):290-297. psycnet.apa.org/doiLanding?doi=10.1037/0735-7028.38.3.290. Accessed September 10, 2014.

32. Druss BG, Walker ER. Mental disorders and medical comorbidity [Research Synthesis Report no. 21]. Robert Wood Johnson Foundation website. rwjf.org/content/dam/farm/reports/issue_briefs/2011/rwjf69438/subassets/rwjf69438_1. Published February 2011. Accessed September 5, 2014.

33. Chronic health conditions in Pennsylvania: diabetes, asthma, COPD, heart failure. Pennsylvania Health Care Cost Containment Council website. phc4.org/reports/chroniccare/10/docs/chroniccare2010report.pdf. Published June 2010. Accessed September 7, 2014.

34. Guo JJ, Keck PE Jr, Li H, Jang R, Kelton CM. Treatment costs and health care utilization for patients with bipolar disorder in a large managed care population. Value Health. 2008;11(3):416-423. doi: 10.1111/j.1524-4733.2007.00287.x.

35. Zhang Q, Menditto L. Incremental cost savings 6 months following initiation of insulin glargine in a Medicaid fee-for-service sample. Am J Ther. 2005;12(4):337-343.

36. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399-424. doi: 10.1080/00273171.2011.568786

37. Curtis LH, Hammill BG, Eisenstein EL, Kramer JM, Anstrom KJ. Using inverse probability-weighted estimators in comparative effectiveness analyses with observational databases. Med Care. 2007;45(10 suppl 2):S103-S107. doi: 10.1097/MLR.0b013e31806518ac.

38. SAS Institute Inc. SAS 9.4 Language Reference: Concepts. 5th ed. Cary, NC: SAS Institute Inc; 2015.

39. Hilbe JM. Negative Binomial Regression. New York, NY: Cambridge University Press; 2007.

40. Vuong QH. Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica. 1989;57(2):307-333. doi: 10.2307/1912557.

41. Stokes ME, Davis CS, Koch GG. Categorical Data Analysis Using the SAS System. 1st ed. Cary, NC: SAS Institute Inc; 1995.

42. Austin PC. Goodness-of-fit diagnostics for the propensity score model when estimating treatment effects using covariate adjustment with the propensity score. Pharmacoepidemiol Drug Saf. 2008;17(12):1202-1217. doi: 10.1002/pds.1673.

43. Faries D, Leon AC, Haro JM, Obenchain RL, eds. Analysis of Observational Health Care Data Using SAS. Cary, NC: SAS Institute Inc; 2010.

44. Austin PC, Mamdani MM. A comparison of propensity score methods: a case study estimating the effectiveness of post-AMI statin use. Stat Med. 2006;25(12):2084-2106. doi: 10.1002/sim.2328.

45. Jackson GL, Powers BJ, Chatterjee R, et al. The patient-centered medical home: a systematic review. Ann Intern Med. 2013;158(3):169-178. doi: 10.7326/0003-4819-158-3-201302050-00579.

46. Mosquera RA, Avritchser EBC, Samuels CL, et al. Effects of an enhanced medical home on serious illness and cost of care among high-risk children with chronic illness: a randomized clinical trial. JAMA. 2014;312(24):2640-2648. doi: 10.1001/jama.2014.16419.

47. Han JH, Crane HM, Bellamy SL, Frank I, Cardillo S, Bisson GP; Centers for AIDS Research Network of Integrated Clinical Systems (CNICS). HIV infection and glycemic response to newly initiated diabetic medical therapy. AIDS. 2012;26(16):2087-2095. doi: 10.1097/QAD.0b013e328359a8e5. 
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