Poly Chronic Disease Epidemiology: A Global View

  • Thomas T. H. Wan


The delivery and quality of health care for patients with poly chronic conditions can be improved through a comprehensive understanding of the patterns and trends of disease occurrence. Epidemiological studies examine the trilogy of agent, host, and environmental relationships to health or illness. Applying fundamental epidemiologic principles to the study of poly chronic diseases provides the opportunity to identify the influential individual and contextual factors that need to be addressed in order to improve the health care and outcomes for patients with multiple chronic conditions. One promising analytical strategy is to leverage the available massive data from varying sources, develop predictive analytical models, and formulate clinical and administrative decision support systems to improve patient-centered care and self-care management of chronic disease. Prevention of poly chronic conditions is a highly feasible option to realize optimal health of the population.


Epidemiological trilogy Agent Host Environment Predictive analytics Data science Prevention Patient-centered care Self-care management 


  1. Barnett, K., Mercer, S. W., Norbury, M., Watt, G., Wyke, S., & Guthrie, B. (2012). Epidemiology of multimorbidity and implications for health care, research, and medical education: A cross-sectional study. The Lancet, 380, 37–43.CrossRefGoogle Scholar
  2. Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117(3), 497.CrossRefGoogle Scholar
  3. Centers for Disease Control and Prevention. (2014). National diabetes statistics report: Estimates of diabetes and its burden in the United States, 2014. Atlanta: US Department of Health and Human Services.Google Scholar
  4. Centers for Disease Control Division of Diabetes Translation. (2015, January). Maps of trends in diagnosed diabetes and obesity.
  5. Centers for Medicare and Medicaid Services. (2012). Chronic conditions among medicare beneficiaries, chartbook, 2012 edition. Baltimore: Centers for Medicare and Medicaid Services.Google Scholar
  6. Cheng, J., Tasi, W. C., Lin, C. L., Chen, L. K., Lang, H. C., Hsieh, H. M., Shin, S. J., Chen, T., Huang, C. F., & Hsu, C. C. (2015). Trends and factors associated with healthcare use and costs in type 2 diabetes mellitus: A decade experience of a universal health insurance program. Medical Care, 53(2), 116–124.CrossRefGoogle Scholar
  7. Conn, V. S., Hafdahl, A. R., & Mehr, D. R. (2011). Interventions to increase physical activity among healthy adults: Meta-analysis of outcomes. American Journal of Public Health, 101(4), 751–758.CrossRefGoogle Scholar
  8. deCharms, R. (1981). Personal causation and locus of control: Two different traditions and two uncorrelated measures. Research with the Locus of Control Construct, 1, 337–358.CrossRefGoogle Scholar
  9. Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determination in personality. Journal of Research in Personality, 19(2), 109–134.CrossRefGoogle Scholar
  10. Deci, E. L., & Ryan, R. M. (2002). Handbook of Self Determination Theory. Rochester, NY: University of Rochester Press.Google Scholar
  11. Deci, E. L., & Ryan, R. M. (Eds.). (2004). Handbook of self-determination research. Rochester: University of Rochester Press.Google Scholar
  12. Dekker, J. M., Girman, C., Rhodes, T., Nijpels, G., Stehouwer, C. D., Bouter, L. M., & Heine, R. J. (2005). Metabolic syndrome and 10-year cardiovascular disease risk in the Hoorn Study. Circulation, 112(5), 666–673.CrossRefGoogle Scholar
  13. Frederick-Recascino, C. M. (2004). Self-determination theory and participation motivation research in the sport and exercise domain. In E. L. Deci & R. M. Ryan (Eds.), Handbook of Self-determination Research. Rochester: University of Rochester Press.Google Scholar
  14. Friis, R. H., & Sellers, T. A. (2014). Epidemiology for public health practice. Sudbury: Jones and Bartlett Publishers.Google Scholar
  15. Gerteis, J., Izrael, D., Deitz, D., LeRoy, L., Ricciardi, R., Miller, T., & Basu, J. (2014, April). Multiple chronic conditions chartbook (AHRQ Publications No, Q14-0038). Rockville: Agency for Healthcare Research and Quality.Google Scholar
  16. Hood, C. R., Jr., Kragt, L. L., & Badaczewski, A. J. (2017). Diabetes watch. Understanding the relationship of metabolic syndrome and pre-diabetes. Podiatry Today, 30(3), 16.Google Scholar
  17. Hsu, C. C., Almulaifi, A., Chen, J. C., Ser, K. H., Chen, S. C., Hsu, K. C., … & Lee, W. J. (2015). Effect of bariatric surgery vs medical treatment on type 2 diabetes in patients with body mass index lower than 35: Five-year outcomes. JAMA Surgery, 150(12), 1117–1124.Google Scholar
  18. Kaur, G. (2014). Improved J48 classification algorithm for the prediction of diabetes. International Journal of Computer Applications 98(22): 13–17.Google Scholar
  19. Krickeberg, K., Pham, V. T., & Pham, T. H. (2012). Epidemiology: Key to prevention (p. 2012). New York: Springer.CrossRefGoogle Scholar
  20. Lochner, K. A., Goodman, R. A., Posner, S., & Parekh, A. (2013). Multiple chronic conditions among medicare beneficiaries: State-level variations in prevalence, utilization, and cost, 2011. Medicare & Medicaid Research Review, 3(3), E1–E19.CrossRefGoogle Scholar
  21. Mayans, L. (2015). Metabolic syndrome: Insulin resistance and prediabetes. FP Essent, 435, 11–16.Google Scholar
  22. Mayo Clinic. Metabolic syndrome overview. Accessed 5 Apr 2017.
  23. Mujica-Mota, R. E., Roberts, M., Abel, G., Elliott, M., Lyratzopoulos, G., Roland, M., & Campbell, J. (2015). Common patterns of morbidity and multi-morbidity and their impact on health-related quality of life: Evidence from a national survey. Quality of Life Research, 24(4), 909.CrossRefGoogle Scholar
  24. Nathan, D. M., Davidson, M. B., DeFronzo, R. A., Heine, R. J., Henry, R. R., Pratley, R., & Zinman, B. (2007). Impaired fasting glucose and impaired glucose tolerance. Diabetes Care, 30(3), 753–759.CrossRefGoogle Scholar
  25. Oleske, D. M. (2009). Epidemiology and the delivery of health care services: Methods and applications (p. c2009). New York: Springer.Google Scholar
  26. Rao, D. P., Dai, S., Legace, C., & Krewski, D. (2014). Metabolic syndrome and chronic disease. Chronic Diseases and Injuries in Canada, 34(1), 36–35.Google Scholar
  27. Rich, M. L., Miller, A. C., Niyigena, P., Franke, M. F., Niyonzima, J. B., Socci, A., … & Epino, H. (2012). Excellent clinical outcomes and high retention in care among adults in a community-based HIV treatment program in rural Rwanda. JAIDS Journal of Acquired Immune Deficiency Syndromes, 59(3), e35–e42.Google Scholar
  28. Ryan, J. G., Brewster, C., DeMaria, P., Fedders, M., & Jennings, T. (2010). Metabolic syndrome and prevalence in an urban, medically underserved, community-based population. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 4(3), 137.CrossRefGoogle Scholar
  29. Samson, S. L., & Garber, A. J. (2014). Metabolic syndrome. Endocrinolgy and Metabolism Clinics of North America, 43(1), 1–23.CrossRefGoogle Scholar
  30. Skinner, H. G., Coffey, R., Jones, J., Heslin, K. C., & Moy, E. (2016). The effects of multiple chronic conditions on hospitalization costs and utilization for ambulatory care sensitive conditions in the United States: A nationally representative cross-sectional study. BMC Health Services Research, 16(1), 77.CrossRefGoogle Scholar
  31. Starfield, B. (2011). The hidden inequity in health care. International Journal for Equity in Health, 10(15), 1–3.Google Scholar
  32. Timmreck, T. C. (1998). An introduction to epidemiology. Boston: Jones & Bartlett Learning.Google Scholar
  33. Violan, C., Foguet-Boreu, Q., Flores-Mateo, G., Salisbury, C., Blom, J., Freitag, M., … & Valderas, J. M. (2014). Prevalence, determinants and patterns of multimorbidity in primary care: A systematic review of observational studies. PLoS One, 9(7), e102149.Google Scholar
  34. Wan, T. T. H. (2002). Evidence-based health care management: Multivariate modeling approaches. Boston: Kluwer Academic Publishers.CrossRefGoogle Scholar
  35. Wan, T. T. H., Terry, A., McKee, N. B., & Kattan, W. (2017). KMAP-O framework for care management research of patients with type 2 diabetes. World Journal of Diabetes 8(4): 165–171.Google Scholar
  36. Wang, H. H., Wang, J. J., Wong, S. Y., Wong, M. C., Li, F. J., Wang, P. X., … & Mercer, S. W. (2014). Epidemiology of multimorbidity in China and implications for the healthcare system: Cross-sectional survey among 162,464 community household residents in southern China. BMC Medicine, 12(1), 188.Google Scholar
  37. Westdahl, C., Milan, S., Magriples, U., Kershaw, T. S., Rising, S. S., & Ickovics, J. R. (2007). Social support and social conflict as predictors of prenatal depression. Obstetrics and Gynecology, 110(1), 134.CrossRefGoogle Scholar
  38. White, R. W. (1959). Motivation reconsidered: The concept of competence. Psychological Review, 66(5), 297.CrossRefGoogle Scholar
  39. Williams, C., & Wan, T. T. H. (2016). A remote monitoring program evaluation: A retrospective study. Journal of Evaluation in Clinical Practice, 22(6), 978–984. CrossRefGoogle Scholar
  40. Williams, G. G., Gagné, M., Ryan, R. M., & Deci, E. L. (2002). Facilitating autonomous motivation for smoking cessation. Health Psychology, 21(1), 40.CrossRefGoogle Scholar
  41. Wilson, K., & Brookfield, D. (2009). Effect of goal setting on motivation and adherence in a six-week exercise program. International Journal of Sport and Exercise Psychology, 7(1), 89–100.CrossRefGoogle Scholar
  42. Wilson, P. M., Rogers, W. T., Rodgers, W. M., & Wild, T. C. (2006). The psychological need satisfaction in exercise scale. Journal of Sport and Exercise Psychology, 28(3), 231–251.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Thomas T. H. Wan
    • 1
  1. 1.College of Health and Public AffairsUniversity of Central FloridaOrlandoUSA

Personalised recommendations