Abstract
The burden of chronic diseases and multimorbidity is continue to increase, especially in developing countries, but little comprehensive population-based data exist related to their epidemiology and associated healthcare utilization and costs. Our aim is to estimate incidence rate, prevalence, and trend of common chronic diseases and clusters of multimorbidity among East Azerbaijan’s Health Insurance Organization (EAHIO) enrollees applying adopted and updated pharmacoepidemiological approach and to analyze outpatient health service utilization patterns and associated costs among this population using outpatient claims database. Other objective is to identify challenges in healthcare service management in chronic diseases and multimorbidity from the perspectives of experts, benefiting from quantitatively extracted evidences as real-world evidence. It’s time to appreciate the value of, and make use of, available administrative individual-level datasets, such as claims databases, for management of current epidemiological phenomenon, i.e., multimorbidity. The database of our study can be supplemented through linkage with other possible sources of data with more demographical, socio-economical and clinical data to provide more comprehensive picture of chronic diseases and multimorbidity and associated healthcare utilization and expenditure. Counterparts of this kind of dataset may be found in other countries. Results of replication of our project in other countries, even developed nations, could be highly beneficial for key healthcare decision makers in managing CCs and multimorbidities.
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Notes
Iran Health Insurance Organization. Available from: http://en.ihio.gov.ir.
Abbreviations
- NCD:
-
Non-Communicable-Diseases
- CC:
-
Chronic conditions
- WHO:
-
World Health Organization
- EAHIO:
-
East Azerbaijan’s Health Insurance Organization
- ATC:
-
The Anatomical Therapeutic Chemical
- CoC:
-
Continuity of Care
- COCI:
-
Continuity of Care Index
- UPC:
-
Usual Provider Index
- SECON:
-
Sequential Continuity Index
- FGD:
-
Focus Group Discussion
References
Abegunde, D.O., Mathers, C.D., Adam, T., Ortegon, M., Strong, K.: The burden and costs of chronic diseases in low-income and middle-income countries. Lancet 370(9603), 1929–1938 (2007)
Becketti, S.: Introduction to Time Series Using STATA. STATA Press, College Station (2013)
Benjamin, E.J., Virani, S.S., Callaway, C.W., Chamberlain, A.M., Chang, A.R., Cheng, S., et al.: Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation 137(12), e67–e492 (2018)
Berg, B.L., Lune, H., Lune, H.: Qualitative Research Methods for the Social Sciences. Pearson, Boston (2004)
Bhardwaj, N., Wodajo, B., Spano, A., Neal, S., Coustasse, A.: The impact of big data on chronic disease management. Health Care Manag. 37(1), 90–98 (2018)
Boutayeb, A., Boutayeb, S.: The burden of non communicable diseases in developing countries. Int. J. Equity Health 4(1), 2 (2005)
Chini, F., Pezzotti, P., Orzella, L., Borgia, P., Guasticchi, G.: Can we use the pharmacy data to estimate the prevalence of chronic conditions? A comparison of multiple data sources. BMC Public Health 11(1), 688 (2011)
Corti, M.C., Guralnik, J.M., Sartori, L., Baggio, G., Manzato, E., Pezzotti, P., et al.: The effect of cardiovascular and osteoarticular diseases on disability in older Italian men and women: rationale, design, and sample characteristics of the Progetto Veneto Anziani (PRO. VA) study. J. Am. Geriatr. Soc. 50(9), 1535–1540 (2002)
Eriksson, E.A., Mattsson, L.-G.: Quantitative measurement of continuity of care: measures in use and an alternative approach. Med. Care 21, 858–875 (1983)
Forouzanfar, M.H., Sepanlou, S.G., Shahraz, S., Dicker, D., Naghavi, P., Pourmalek, F., Mokdad, A., Lozano, R., et al.: Evaluating causes of death and morbidity in Iran, global burden of diseases, injuries, and risk factors study 2010. Arch. Iran. Med. 17(5), 304 (2014)
Fortin, M., Lapointe, L., Hudon, C., Vanasse, A., Ntetu, A.L., Maltais, D.: Multimorbidity and quality of life in primary care: a systematic review. Health Qual. Outcomes 2(1), 51 (2004)
Fusch, P.I., Ness, L.R.: Are we there yet? Data saturation in qualitative research. Qual. Rep. 20(9), 1408–1416 (2015)
Gavrielov-Yusim, N., Friger, M.: Use of administrative medical databases in population-based research. J. Epidemiol. Community Health 68(3), 283–287 (2014)
Gijsen, R., Hoeymans, N., Schellevis, F.G., Ruwaard, D., Satariano, W.A., van den Bos, G.A.: Causes and consequences of comorbidity: a review. J. Clin. Epidemiol. 54(7), 661–674 (2001)
Graneheim, U.H., Lundman, B.: Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Educ. Today 24(2), 105–112 (2004)
Harrell, F.E.: Regression Modeling Strategies, with Applications to Linear Models, Survival Analysis and Logistic Regression. Springer, Berlin (2001)
Horton, R.: The neglected epidemic of chronic disease. Lancet 366(9496), 1514 (2005)
Huber, C.A., Szucs, T.D., Rapold, R., Reich, O.: Identifying patients with chronic conditions using pharmacy data in Switzerland: an updated mapping approach to the classification of medications. BMC Public Health 13(1), 1030 (2013)
Lo Siou, G., Yasui, Y., Csizmadi, I., McGregor, S.E., Robson, P.J.: Exploring statistical approaches to diminish subjectivity of cluster analysis to derive dietary patterns: the tomorrow project. Am. J. Epidemiol. 173(8), 956–967 (2011)
Lynn, J., Straube, B.M., Bell, K.M., Jencks, S.F., Kambic, R.T.: Using population segmentation to provide better health care for all: the “Bridges to Health” model. Milbank Q. 85(2), 185–208 (2007)
Olaya, B., Moneta, M.V., Caballero, F.F., Tyrovolas, S., Bayes, I., Ayuso-Mateos, J.L., et al.: Latent class analysis of multimorbidity patterns and associated outcomes in Spanish older adults: a prospective cohort study. BMC Geriatr. 17(1), 186 (2017)
Quinn, K.J., Shah, N.H.: A dataset quantifying polypharmacy in the United States. Sci. Data 4, 170167 (2017)
Rabe-Hesketh, S., Skrondal, A.: Multilevel and Longitudinal Modeling Using STATA. STATA Press, College Station (2008)
Ray, W.A.: Policy and program analysis using administrative databases. Ann. Intern. Med. 127(8_Part_2), 712–718 (1997)
Roos, N., Black, C., Roos, L., Frohlich, N., DeCoster, C., Mustard, C., et al.: Managing health services: how administrative data and population-based analyses can focus the agenda. Health Serv. Manag. Res. 11(1), 49–67 (1998)
Salisbury, C.: Multimorbidity: redesigning health care for people who use it. Lancet 380(9836), 7–9 (2012)
Sarrazin, M.S.V., Rosenthal, G.E.: Finding pure and simple truths with administrative data. JAMA 307(13), 1433–1435 (2012)
Schneeweiss, S., Avorn, J.: A review of uses of health care utilization databases for epidemiologic research on therapeutics. J. Clin. Epidemiol. 58(4), 323–337 (2005)
Struckmann, V., Leijten, F.R., van Ginneken, E., Kraus, M., Reiss, M., Spranger, A., et al.: Relevant models and elements of integrated care for multi-morbidity: results of a scoping review. Health Policy 122(1), 23–35 (2018)
Tinetti, M.E., Fried, T.R., Boyd, C.M.: Designing health care for the most common chronic condition—multimorbidity. JAMA 307(23), 2493–2494 (2012)
UN. United Nations World Population Ageing: 1950–2050, Countries of area: Iran. http://www.un.org/esa/population/publications/worldageing19502050/pdf/113iran%28.pdf. Accessed 22 Oct 2018
van Oostrom, S.H., Picavet, H.S.J., van Gelder, B.M., Lemmens, L.C., Hoeymans, N., van Dijk, C.E., et al.: Multimorbidity and comorbidity in the Dutch population–data from general practices. BMC Public Health 12(1), 715 (2012)
Von Korff, M., Wagner, E.H., Saunders, K.: A chronic disease score from automated pharmacy data. J. Clin. Epidemiol. 45(2), 197–203 (1992)
Wang, Y., Kung, L., Byrd, T.A.: Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Change 126, 3–13 (2018)
Whitson, H.E., Johnson, K.S., Sloane, R., Cigolle, C.T., Pieper, C.F., Landerman, L., et al.: Identifying patterns of multimorbidity in older Americans: application of latent class analysis. J. Am. Geriatr. Soc. 64(8), 1668–1673 (2016)
WHO Collaborating Centre for Drug Statistics Methodology. Guidelines for ATC classification and DDD assignment 2018. https://www.whocc.no/filearchive/publications/guidelines.pdf (2018)
Wolff, J.L., Starfield, B., Anderson, G.: Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch. Intern. Med. 162(20), 2269–2276 (2002)
World Health Organization: Global Health Risks-Mortality and Burden of Disease Attributable to Selected Major Risks. World Health Organization, Geneva (2009)
World Health Organization, Public Health Agency of Canada, Public Health Agency of Canada. Preventing chronic diseases: a vital investment. World Health Organization (2005)
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Funding was provided by Tabriz University of Medical Sciences as a doctoral thesis in healthcare management (Grant No. 60752).
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Ebrahimoghli, R., Janati, A., Sadeghi-Bazargani, H. et al. Chronic Diseases and Multimorbidity in Iran: A Study Protocol for the Use of Iranian Health Insurance Organization’s Claims Database to Understand Epidemiology, Health Service Utilization, and Patient Costs. Health Serv Outcomes Res Method 21, 407–418 (2021). https://doi.org/10.1007/s10742-020-00232-6
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DOI: https://doi.org/10.1007/s10742-020-00232-6