Methods Inf Med 2017; 56(05): 391-400
DOI: 10.3414/ME16-01-0135
Schattauer GmbH

Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions[*]

Adel Alaeddini
1   Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX, USA
Carlos A. Jaramillo
2   South Texas Veterans Healthcare System, San Antonio, TX, USA
Syed H. A. Faruqui
1   Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX, USA
Mary J. Pugh
2   South Texas Veterans Healthcare System, San Antonio, TX, USA
› Author Affiliations
Funding Supported by National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health under award number 1SC2GM118266-01, and VA Health Services Research and Development under award number VA HSR&D; DHI-09-237-2.
Further Information

Publication History

received: 22 November 2016

accepted in revised form: 15 August 2017

Publication Date:
24 January 2018 (online)


Objectives: Evolution of multiple chronic conditions (MCC) follows a complex stochastic process, influenced by several factors including the inter-relationship of existing conditions, and patient-level risk factors. Nearly 20% of citizens aged 18 years and older are burdened with two or more (multiple) chronic conditions (MCC). Treatment for people living with MCC currently accounts for an estimated 66% of the Nation’s healthcare costs. However, it is still not known precisely how MCC emerge and accumulate among individuals or in the general population. This study investigates major patterns of MCC transitions in a diverse population of patients and identifies the risk factors affecting the transition process.

Methods: A Latent regression Markov clustering (LRMCL) algorithm is proposed to identify major transitions of four MCC that include hypertension (HTN), depression, Post- Traumatic Stress Disorder (PTSD), and back pain. A cohort of 601,805 individuals randomly selected from the population of Iraq and Afghanistan war Veterans (IAVs) who received VA care during three or more years between 2002-2015, is used for training the proposed LRMCL algorithm.

Results: Two major clusters of MCC transition patterns with 78% and 22% probability of membership respectively were identified. The primary cluster demonstrated the possibility of improvement when the number of MCC is small and an increase in probability of MCC accumulation as the number of co- morbidities increased. The second cluster showed stability (no change) of MCC overtime as the major pattern. Age was the most significant risk factor associated with the most probable cluster for each IAV.

Conclusions: These findings suggest that our proposed LRMCL algorithm can be used to describe and understand MCC transitions, which may ultimately allow healthcare systems to support optimal clinical decision- making. This method will be used to describe a broader range of MCC transitions in this and non-VA populations, and will add treatment information to see if models including treatments and MCC emergence can be used to support clinical decision-making in patient care.

* Supplementary material published on our website

  • References

  • 1 Elsayed EA. Reliability engineering. 2nd ed. Hoboken, NJ: John Wiley & Sons; 2012
  • 2 WHO. Diet, nutrition, and the prevention of chronic diseases: report of a WHO Study Group: World Health Organization; 1990.
  • 3 Schaefer AJ, Bailey MD, Shechter SM, Roberts MS. Modeling medical treatment using Markov decision processes. Handbook of Operations Research/Management Science Applications in Health Care.. Boston, MA: Kluwer Academic Publishers; 2004: 593-612.
  • 4 Hauskrecht M, Fraser H. Planning treatment of ischemic heart disease with partially observable Markov decision processes. Artificial Intelligence in Medicine 2000; 18 (03) 221-244.
  • 5 Alagoz O, Maillart LM, Schaefer AJ, Roberts MS. Choosing among living-donor and cadaveric livers. Management Science 2007; 53 (11) 1702-1715.
  • 6 Shechter SM, Bailey MD, Schaefer AJ, Roberts MS. The optimal time to initiate HIV therapy under ordered health states. Operations Research 2008; 56 (01) 20-33.
  • 7 Faissol DM, Griffin PM, Swann JL. Timing of testing and treatment of hepatitis C and other diseases. INFORMS International Meeting. 2007
  • 8 Maillart LM, Ivy JS, Ransom S, Diehl K. Assessing dynamic breast cancer screening policies. Operations Research 2008; 56 (06) 1411-1427.
  • 9 Denton BT, Kurt M, Shah ND, Bryant SC, Smith SA. Optimizing the start time of statin therapy for patients with diabetes. Medical Decision Making 2009; 29 (03) 351-567.
  • 10 Kurt M, Denton B, Schaefer AJ, Shah N, Smith S. At what lipid ratios should a patient with type 2 diabetes initiate statins. Available from:
  • 11 Kreke JE, Bailey MD, Schaefer AJ, Angus DC, Roberts MS. Modeling hospital discharge policies for patients with pneumonia-related sepsis. IIE Transactions 2008; 40 (09) 853-860.
  • 12 Kreke JE. Modeling disease management decisions for patients with pneumonia-related sepsis [dissertation].. Pittsburgh, PA: University of Pittsburgh; 2007
  • 13 Hu C, Lovejoy WS, Shafer SL. Comparison of some suboptimal control policies in medical drug therapy. Operations Research 1996; 44 (05) 696-709.
  • 14 Alterovitz R, Branicky M, Goldberg K. Constant- curvature motion planning under uncertainty with applications in image-guided medical needle steering. Akella S, Amato NM, Huang WH, Mishra B. Algorithmic Foundation of Robotics VII. Springer Tracts in Advanced Robotics vol 47.. Berlin, Heidelberg: Springer; 2008: 319-334.
  • 15 Zhao Y, Zeng D, Socinski MA, Kosorok MR. Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer. Biometrics 2011; 67 (04) 1422-1433.
  • 16 Waltz JA, Frank MJ, Robinson BM, Gold JM. Selective reinforcement learning deficits in schizophrenia support predictions from computational models of striatal-cortical dysfunction. Biological Psychiatry 2007; 62 (07) 756-764.
  • 17 Lighthall NR, Gorlick MA, Schoeke A, Frank MJ, Mather M. Stress modulates reinforcement learning in younger and older adults. Psychology and Aging 2013; 28 (01) 35.
  • 18 Laber EB, Linn K, Stefanski L. Interactive Q-learning. J Stat Softw 2015; 64 (01) i01.
  • 19 Kharoufeh JP, Cox SM. Stochastic models for degradation-based reliability. IIE Transactions 2005; 37 (06) 533-542.
  • 20 Centers for Medicare and Medicaid Services. Chronic Conditions among Medicare Beneficiaries. Chartbook, 2012 Edition.. Baltimore, MD: 2012. Available from:
  • 21 Prados-Torres A, Calderón-Larrañaga A, Hancco-Saavedra J, Poblador-Plou B, van den Akker M. Multimorbidity patterns: a systematic review. Journal of Clinical Epidemiology 2014; 67 (03) 254-266.
  • 22 Vogeli C, Shields AE, Lee TA, Gibson TB, Marder WD, Weiss KB. et al. Multiple chronic conditions: prevalence, health consequences, and implications for quality, care management, and costs. Journal of General Internal Medicine 2007; 22 (03) 391-395.
  • 23 Lehnert T, Heider D, Leicht H, Heinrich S, Corrieri S, Luppa M. et al. Review: health care utilization and costs of elderly persons with multiple chronic conditions. Medical Care Research and Review 2011; 68 (04) 387-420.
  • 24 Fried TR, Tinetti ME, Iannone L, O’Leary JR, Towle V, Van Ness PH. Health outcome prioritization as a tool for decision making among older persons with multiple chronic conditions. Archives of Internal Medicine 2011; 171 (20) 1856-1858.
  • 25 Hempstead K, DeLia D, Cantor JC, Nguyen T, Brenner J. The fragmentation of hospital use among a cohort of high utilizers: implications for emerging care coordination strategies for patients with multiple chronic conditions. Medical Care 2014; 52: S67-S74.
  • 26 Piette JD, Richardson C, Valenstein M. Addressing the needs of patients with multiple chronic illnesses: the case of diabetes and depression. American Journal of Managed Care 2004; 10 (02) PART 2 152-62.
  • 27 Pugh MJV, Finley EP, Copeland LA, Wang C-P, Noel PH, Amuan ME. et al. Complex comorbidity clusters in OEF/OIF veterans: the polytrauma clinical triad and beyond. Medical Care 2014; 52 (02) 172-181.
  • 28 Pujades-Rodriguez M, George J, Shah AD, Rapsomaniki E, Denaxas S, West R. et al. Heterogeneous associations between smoking and a wide range of initial presentations of cardiovascular disease in 1 937 360 people in England: lifetime risks and implications for risk prediction. International Journal of Epidemiology 2015; 44 (01) 129-141.
  • 29 Wu M-F, Wen C-Y. A novel shuttle walking model using networked sensing and control for chronic obstructive pulmonary disease: A preliminary study. Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2012 6th International Conference on. 2012. IEEE.;
  • 30 Ward BW, Schiller JS, Goodman RA. Peer Reviewed: Multiple Chronic Conditions Among US Adults: A 2012 Update. Prev Chronic Dis 2014; 11: E62.
  • 31 Health UDo, Services H. Multiple chronic conditions – a strategic framework: optimum health and quality of life for individuals with multiple chronic conditions. Washington, DC: US Department of Health and Human Services; 2010
  • 32 Tangri N, Stevens LA, Griffith J, Tighiouart H, Djurdjev O, Naimark D. et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 2011; 305 (15) 1553-1559.
  • 33 Xun L, Linsheng L, Li L, Tanqi L. A Markov model study on the hierarchical prognosis and risk factors in patients with chronic kidney disease. Computer Science and Electronics Engineering (ICCSEE), 2012 International Conference on. 2012. IEEE.;
  • 34 Boshuizen HC, Lhachimi SK, van Baal PH, Hoogenveen RT, Smit HA, Mackenbach JP. et al. The DYNAMO-HIA model: an efficient implementation of a risk factor/chronic disease Markov model for use in Health Impact Assessment (HIA). Demography 2012; 49 (04) 1259-1283.
  • 35 Neumann M, Wirtz M, Ernstmann N, Ommen O, Längler A, Edelhäuser F. et al. Identifying and predicting subgroups of information needs among cancer patients: an initial study using latent class analysis. Supportive Care in Cancer 2011; 19 (08) 1197-1209.
  • 36 Zhang B, Mitchell SL, Bambauer KZ, Jones R, Prigerson HG. Depressive symptom trajectories and associated risks among bereaved Alzheimer disease caregivers. The American Journal of Geriatric Psychiatry 2008; 16 (02) 145-155.
  • 37 Jaffe A, Shoptaw S, Stein JA, Reback CJ, Rotheram-Fuller E. Depression ratings, reported sexual risk behaviors, and methamphetamine use: latent growth curve models of positive change among gay and bisexual men in an outpatient treatment program. Experimental and Clinical Psychophar- macology 2007; 15 (03) 301.
  • 38 Wolfe F. A reappraisal of HAQ disability in rheumatoid arthritis. Arthritis & Rheumatism 2000; 43 (12) 2751-2761.
  • 39 Paulli M, Berti E, Rosso R, Boveri E, Kindl S, Klersy C. et al. CD30/Ki-1-positive lymphoprolife- rative disorders of the skin – clinicopathologic correlation and statistical analysis of 86 cases: a multicentric study from the European Organization for Research and Treatment of Cancer Cutaneous Lymphoma Project Group. Journal of Clinical Oncology 1995; 13 (06) 1343-1354.
  • 40 Kenter W. Exploring Markov modeling approaches for the health economic assessment of circulating tumor cells in the management of metastatic prostate cancer patients [dissertation]. Enschede: University of Twente; 2015
  • 41 Kemere C, Santhanam G, Byron MY, Afshar A, Ryu SI, Meng TH. et al. Detecting neural-state transitions using hidden Markov models for motor cortical prostheses. Journal of Neurophysiology 2008; 100 (04) 2441-2452.
  • 42 Dumont J, Hernandez A, Fleureau J, Carrault G. Modelling temporal evolution of cardiac electrophysiological features using hidden semi-Markov models. Engineering in Medicine and Biology Society, 2008 EMBS 2008 30th Annual International Conference of the IEEE. 2008. IEEE.;
  • 43 Dobra A, Lenkoski A. Copula Gaussian graphical models and their application to modeling functional disability data. The Annals of Applied Statistics 2011; 5 2A 969-993.
  • 44 Acuna E, Rodriguez C. The treatment of missing values and its effect on classifier accuracy. Banks D, House L, McMorris FR, Arabie P, Gaul W. Classification, Clustering and Data Mining Applications.. Berlin, Heidelberg: Springer; 2004: 639-648.
  • 45 Pugh MJ, Finley EP, Wang C-P, Copeland LA, Jaramillo CA, Swan AA. et al. A retrospective cohort study of comorbidity trajectories associated with traumatic brain injury in veterans of the Iraq and Afghanistan wars. Brain Injury 2016; 30 (12) 1481-1490.
  • 46 Jaramillo CA, Cooper DB, Wang C-P, Tate DF, Eapen BC, York GE. et al. Subgroups of US IRAQ and Afghanistan veterans: associations with traumatic brain injury and mental health conditions. Brain Imaging and Behavior 2015; 9 (03) 445-455.
  • 47 Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Medical Care 1998; 36 (01) 8-27.
  • 48 Rajarshi M. Markov Chains and Their Extensions. Statistical Inference for Discrete Time Stochastic Processes.. India: Springer; 2013: 19-38.
  • 49 Agresti A, Kateri M. Categorical data analysis. Springer; 2011
  • 50 Fahrmeir L, Tutz G. Multivariate statistical modelling based on generalized linear models. Springer Science & Business Media; 2013
  • 51 Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B (Methodological). 1977: 1-38.
  • 52 Chung H, Loken E, Schafer JL. Difficulties in drawing inferences with finite-mixture models. The American Statistician. 2004 58. 02.
  • 53 Vlasblom J, Wodak SJ. Markov clustering versus affinity propagation for the partitioning of protein interaction graphs. BMC Bioinformatics 2009; 10 (01) 99.
  • 54 Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Research 2002; 30 (07) 1575-1584.
  • 55 Hospedales T, Gong S, Xiang T. A markov clustering topic model for mining behaviour in video. Computer Vision, 2009 IEEE 12th International Conference on. 2009. IEEE.;
  • 56 Copeland LA FE, Bollinger MJ, Amuan ME, Pugh MJ. Comorbidity Correlates of Death among New Veterans of Iraq and Afghanistan Deployment. Medical Care. 2016 forthcoming.
  • 57 Kibler JL, Tursich M, Ma M, Malcolm L, Greenbarg R. Metabolic, autonomic and immune markers for cardiovascular disease in posttrau- matic stress disorder. World J Cardiol 2014; 6 (06) 455-461.
  • 58 Leino-Arjas P, Solovieva S, Kirjonen J, Reunanen A, Riihimäki H. Cardiovascular risk factors and low-back pain in a long-term follow-up of industrial employees. Scandinavian Journal of Work, Environment & Health. 2006: 12-9.
  • 59 Strine TW, Hootman JM. US national prevalence and correlates of low back and neck pain among adults. Arthritis Care & Research 2007; 57 (04) 656-665.
  • 60 Sugiyama T, Kiraku J-i, Ashida T, Fujii J. Remission of hypertension: retrospective observations over a period of 20 years. Hypertension Research 1998; 21 (02) 103-108.
  • 61 Meng L, Chen D, Yang Y, Zheng Y, Hui R. Depression increases the risk of hypertension incidence: a meta-analysis of prospective cohort studies. Journal of Hypertension 2012; 30 (05) 842-851.
  • 62 Rubio-Guerra AF, Rodriguez-Lopez L, Vargas-Ayala G, Huerta-Ramirez S, Serna DC, Lozano-Nuevo JJ. Depression increases the risk for uncontrolled hypertension. Experimental & Clinical Cardiology 2013; 18 (01) 10.
  • 63 Licht CM, De Geus EJ, Seldenrijk A, Van Hout HP, Zitman FG, Van Dyck R. et al. Depression is associated with decreased blood pressure, but antidepressant use increases the risk for hypertension. Hypertension 2009; 53 (04) 631-638.
  • 64 Pugh MJ FE, Wang CP, Copeland LA, Jaramillo CA, Swan AA, Elnitsky CA, Leykum LK, Mortensen EM, Eapen BA, Noel PH, Pugh JA. A Retrospective Cohort Study of Comorbidity Trajectories Associated with Traumatic Brain Injury in Veterans of the Iraq and Afghanistan Wars. Brain Injury. under review.
  • 65 Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J. et al. Frailty in older adults evidence for a phenotype. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 2001; 56 (03) M146-M57.