JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Comorbidity Adjustment in Health Insurance Claim Database
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Health Policy and Management
  • Volume 26, Issue 1,  2016, pp.71-78
  • Publisher : The Korean Society of Health Policy and Administration
  • DOI : 10.4332/KJHPA.2016.26.1.71
 Title & Authors
Comorbidity Adjustment in Health Insurance Claim Database
Kim, Kyoung Hoon;
  PDF(new window)
 Abstract
The value of using health insurance claim database is continuously rising in healthcare research. In studies where comorbidities act as a confounder, comorbidity adjustment holds importance. Yet researchers are faced with a myriad of options without sufficient information on how to appropriately adjust comorbidity. The purpose of this study is to assist in selecting an appropriate index, look back period, and data range for comorbidity adjustment. No consensus has been formed regarding the appropriate index, look back period and data range in comorbidity adjustment. This study recommends the Charlson comorbidity index be selected when predicting the outcome such as mortality, and the Elixhauser`s comorbidity measures be selected when analyzing the relations between various comorbidities and outcomes. A longer look back period and inclusion of all diagnoses of both inpatient and outpatient data led to increased prevalence of comorbidities, but contributed little to model performance. Limited data range, such as the inclusion of primary diagnoses only, may complement limitations of the health insurance claim database, but could miss important comorbidities. This study suggests that all diagnoses of both inpatients and outpatients data, excluding rule-out diagnosis, be observed for at least 1 year look back period prior to the index date. The comorbidity index, look back period, and data range must be considered for comorbidity adjustment. To provide better guidance to researchers, follow-up studies should be conducted using the three factors based on specific diseases and surgeries.
 Keywords
Health insurance claim database;Comorbidity adjustment;Comorbidity index;Look back period;Data range;
 Language
Korean
 Cited by
 References
1.
Iezzoni LI. Risk adjustment for measuring health care outcomes. 3rd ed. Chicago (IL): Health Administration Press; 2003.

2.
Kim KH, Ahn LS. A comparative study on comorbidity measurements with Lookback period using health insurance database: focused on patients who underwent percutaneous coronary intervention. J Prev Med Public Health 2009;42(4):267-273. DOI: http://dx.doi.org/10.3961/jpmph.2009.42.4.267. crossref(new window)

3.
Klabunde CN, Potosky AL, Legler JM, Warren JL. Development of a comorbidity index using physician claims data. J Clin Epidemiol 2000;53(12):1258-1267. DOI: http://dx.doi.org/10.1016/s0895-4356(00)00256-0. crossref(new window)

4.
Jang S, Park C, Jang S, Yoon HK, Shin CS, Kim DY, et al. Medical service utilization with osteoporosis. Endocrinol Metab 2010;25(4):326-339. DOI: http://dx.doi.org/10.3803/enm.2010.25.4.326. crossref(new window)

5.
Im JH, Lee KS, Kim KY, Hong NS, Lee SW, Bae HJ. Follow-up study on mortality in Korean stroke patients. J Korean Med Assoc 2011;54(11):1199-1208. DOI: http://dx.doi.org/10.5124/jkma.2011.54.11.1199. crossref(new window)

6.
Seo EW, Lee KS. Difference in healthcare utilization for percutaneous transluminal coronary angioplasty inpatients by insurance types: propensity score matching analysis. Health Policy Manag 2015;25(1):3-10. DOI: http://dx.doi.org/10.4332/kjhpa.2015.25.1.3. crossref(new window)

7.
Cho SJ, Chung SH, Oh JY. Differences between diabetic patients' tertiary hospital and non-tertiary hospital utilization according to comorbidity score. Health Policy Manag 2011;21(4):527-540. DOI: http://dx.doi.org/10.4332/kjhpa.2011.21.4.527. crossref(new window)

8.
Kim KH. Comparative study on three algorithms of the ICD-10 Charlson comorbidity index with myocardial infarction patients. J Prev Med Public Health 2010;43(1):42-49. DOI: http://dx.doi.org/10.3961/jpmph.2010.43.1.42. crossref(new window)

9.
Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005;43(11):1130-1139. DOI: http://dx.doi.org/10.1097/01.mlr.0000182534.19832.83. crossref(new window)

10.
Von Korff M, Wagner EH, Saunders K. A chronic disease score from automated pharmacy data. J Clin Epidemiol 1992;45(2):197-203. DOI: http://dx.doi.org/10.1016/0895-4356(92)90016-g. crossref(new window)

11.
Sloan KL, Sales AE, Liu CF, Fishman P, Nichol P, Suzuki NT, et al. Construction and characteristics of the RxRisk-V: a VA-adapted pharmacy-based case-mix instrument. Med Care 2003;41(6):761-774. DOI: http://dx.doi.org/10.1097/01.mlr.0000064641.84967.b7.

12.
Woo HK, Park JH, Kang HS, Kim SY, Lee SI, Nam HH. Charlson comorbidity index as a predictor of long-term survival after surgery for breast cancer: a nationwide retrospective cohort study in South Korea. J Breast Cancer 2010;13(4):409-417. DOI: http://dx.doi.org/10.4048/jbc.2010.13.4.409. crossref(new window)

13.
Kim KH, Lee SM, Paik JW, Kim NS. The effects of continuous antidepressant treatment during the first 6 months on relapse or recurrence of depression. J Affect Disord 2011;132(1-2):121-129. DOI: http://dx.doi.org/10.1016/j.jad.2011.02.016. crossref(new window)

14.
Kim MG, Kim K. Factors affecting health care utilization in patient with lung cancer. Perspect Nurs Sci 2013;10(1):52-64.

15.
Lee CH, Hyun MK, Jang EJ, Lee NR, Kim K, Yim JJ. Inhaled corticosteroid use and risks of lung cancer and laryngeal cancer. Respir Med 2013;107(8):1222-1233. DOI: http://dx.doi.org/10.1016/j.rmed.2012.12.002. crossref(new window)

16.
Lee S, Ryu JH, Kim H, Kim KH, Ahn HS, Hann HJ, et al. An assessment of survival among Korean elderly patients initiating dialysis: a national population-based study. PLoS One 2014;9(1):e86776. DOI: http://dx.doi.org/10.1371/journal.pone.0086776. crossref(new window)

17.
Shin DW, Cho J, Yang HK, Park JH, Lee H, Kim H, et al. Impact of continuity of care on mortality and health care costs: a nationwide cohort study in Korea. Ann Fam Med 2014;12(6):534-541. DOI: http://dx.doi.org/10.1370/afm.1685. crossref(new window)

18.
Kim DY, Lee KS. A study on the effects of percutaneous transluminal coronary angioplasty and pediatric heart surgery on the differences of risk-adjusted length of stay and in-hospital death for coronary artery bypass graft patients. Korean J Health Serv Manag 2014;8(4):47-55. DOI: http://dx.doi.org/10.12811/kshsm.2014.8.4.047. crossref(new window)

19.
Suh HS, Kang HY, Kim J, Shin E. Effect of health insurance type on health care utilization in patients with hypertension: a national health insurance database study in Korea. BMC Health Serv Res 2014;14(1):570. DOI: http://dx.doi.org/10.1186/s12913-014-0570-9. crossref(new window)

20.
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40(5):373-383. DOI: http://dx.doi.org/10.1016/0021-9681(87)90171-8. crossref(new window)

21.
Sundararajan V, Quan H, Halfon P, Fushimi K, Luthi JC, Burnand B, et al. Cross-national comparative performance of three versions of the ICD-10 Charlson index. Med Care 2007;45(12):1210-1215. DOI: http://dx.doi.org/10.1097/mlr.0b013e3181484347. crossref(new window)

22.
Halfon P, Eggli Y, van Melle G, Chevalier J, Wasserfallen JB, Burnand B. Measuring potentially avoidable hospital readmissions. J Clin Epidemiol 2002;55(6):573-587. DOI: http://dx.doi.org/10.1016/s0895-4356(01)00521-2. crossref(new window)

23.
Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J Clin Epidemiol 2004;57(12):1288-1294. DOI: http://dx.doi.org/10.1016/j.jclinepi.2004.03.012. crossref(new window)

24.
Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45(6):613-619. DOI: http://dx.doi.org/10.1016/0895-4356(92)90133-8. crossref(new window)

25.
Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol 1993;46(10):1075-1079. DOI: http://dx.doi.org/10.1016/0895-4356(93)90103-8. crossref(new window)

26.
Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol 2011;173(6):676-682. DOI: http://dx.doi.org/10.1093/aje/kwq433. crossref(new window)

27.
Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care 1998;36(1):8-27. DOI: http://dx.doi.org/10.1097/00005650-199801000-00004. crossref(new window)

28.
Dominick KL, Dudley TK, Coffman CJ, Bosworth HB. Comparison of three comorbidity measures for predicting health service use in patients with osteoarthritis. Arthritis Rheum 2005;53(5):666-672. DOI: http://dx.doi.org/10.1002/art.21440. crossref(new window)

29.
Van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care 2009;47(6):626-633. DOI: http://dx.doi.org/10.1097/MLR.0b013e31819432e5. crossref(new window)

30.
Stukenborg GJ, Wagner DP, Connors AF Jr. Comparison of the performance of two comorbidity measures, with and without information from prior hospitalizations. Med Care 2001;39(7):727-739. DOI: http://dx.doi.org/10.1097/00005650-200107000-00009. crossref(new window)

31.
Southern DA, Quan H, Ghali WA. Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care 2004;42(4):355-360. DOI: http://dx.doi.org/10.1097/01.mlr.0000118861.56848.ee. crossref(new window)

32.
Kurichi JE, Stineman MG, Kwong PL, Bates BE, Reker DM. Assessing and using comorbidity measures in elderly veterans with lower extremity amputations. Gerontology 2007;53(5):255-259. DOI: http://dx.doi.org/10.1159/000101703. crossref(new window)

33.
Chu YT, Ng YY, Wu SC. Comparison of different comorbidity measures for use with administrative data in predicting short- and long-term mortality. BMC Health Serv Res 2010;10(1):140. DOI: http://dx.doi.org/10.1186/1472-6963-10-140. crossref(new window)

34.
Lieffers JR, Baracos VE, Winget M, Fassbender K. A comparison of Charlson and Elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data. Cancer 2011;117(9):1957-1965. DOI: http://dx.doi.org/10.1002/cncr.25653. crossref(new window)

35.
Mnatzaganian G, Ryan P, Norman PE, Hiller JE. Accuracy of hospital morbidity data and the performance of comorbidity scores as predictors of mortality. J Clin Epidemiol 2012;65(1):107-115. DOI: http://dx.doi.org/10.1016/j.jclinepi.2011.03.014. crossref(new window)

36.
Sharabiani MT, Aylin P, Bottle A. Systematic review of comorbidity indices for administrative data. Med Care 2012;50(12):1109-1118. DOI: http://dx.doi.org/10.1097/MLR.0b013e31825f64d0. crossref(new window)

37.
Austin PC, van Walraven C, Wodchis WP, Newman A, Anderson GM. Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada. Med Care 2011;49(10):932-939. DOI: http://dx.doi.org/10.1097/MLR.0b013e318215d5e2. crossref(new window)

38.
Machnicki G, Pinsky B, Takemoto S, Balshaw R, Salvalaggio PR, Buchanan PM, et al. Predictive ability of pretransplant comorbidities to predict long-term graft loss and death. Am J Transplant 2009;9(3):494-505. DOI: http://dx.doi.org/10.1111/j.1600-6143.2008.02486.x. crossref(new window)

39.
Gagne JJ, Glynn RJ, Avorn J, Levin R, Schneeweiss S. A combined comorbidity score predicted mortality in elderly patients better than existing scores. J Clin Epidemiol 2011;64(7):749-759. DOI: http://dx.doi.org/10.1016/j.jclinepi.2010.10.004. crossref(new window)

40.
Li P, Kim MM, Doshi JA. Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality. BMC Health Serv Res 2010;10(1):245. DOI: http://dx.doi.org/10.1186/1472-6963-10-245. crossref(new window)

41.
Preen DB, Holman CD, Spilsbury K, Semmens JB, Brameld KJ. Length of comorbidity lookback period affected regression model performance of administrative health data. J Clin Epidemiol 2006;59(9):940-946. DOI: http://dx.doi.org/10.1016/j.jclinepi.2005.12.013. crossref(new window)

42.
Zhang JX, Iwashyna TJ, Christakis NA. The performance of different lookback periods and sources of information for Charlson comorbidity adjustment in Medicare claims. Med Care 1999;37(11):1128-1139. DOI: http://dx.doi.org/10.1097/00005650-199911000-00005. crossref(new window)

43.
Lee DS, Donovan L, Austin PC, Gong Y, Liu PP, Rouleau JL, et al. Comparison of coding of heart failure and comorbidities in administrative and clinical data for use in outcomes research. Med Care 2005;43(2):182-188. DOI: http://dx.doi.org/10.1097/00005650-200502000-00012. crossref(new window)

44.
Wang PS, Walker A, Tsuang M, Orav EJ, Levin R, Avorn J. Strategies for improving comorbidity measures based on Medicare and Medicaid claims data. J Clin Epidemiol 2000;53(6):571-578. DOI: http://dx.doi.org/10.1016/s0895-4356(00)00222-5. crossref(new window)

45.
Chen JS, Roberts CL, Simpson JM, Ford JB. Use of hospitalisation history (lookback) to determine prevalence of chronic diseases: impact on modelling of risk factors for haemorrhage in pregnancy. BMC Med Res Methodol 2011;11(1):68. DOI: http://dx.doi.org/10.1186/1471-2288-11-68. crossref(new window)

46.
Dobbins TA, Creighton N, Currow DC, Young JM. Look back for the Charlson Index did not improve risk adjustment of cancer surgical outcomes. J Clin Epidemiol 2015;68(4):379-386. DOI: http://dx.doi.org/10.1016/j.jclinepi.2014.12.002. crossref(new window)

47.
Health Insurance Review and Assessment Service. Health insurance claim data analysis manual for evidence-based health care. Wonju: Health Insurance Review and Assessment Service; 2015.

48.
Baldwin LM, Klabunde CN, Green P, Barlow W, Wright G. In search of the perfect comorbidity measure for use with administrative claims data: does it exist? Med Care 2006;44(8):745-753. DOI: http://dx.doi.org/10.1097/01.mlr.0000223475.70440.07. crossref(new window)

49.
Bang JH, Hwang SH, Lee EJ, Kim Y. The predictability of claim-data-based comorbidity-adjusted models could be improved by using medication data. BMC Med Inform Decis Mak 2013;13(1):128. DOI: http://dx.doi.org/10.1186/1472-6947-13-128. crossref(new window)

50.
Kil SR, Lee SI, Khang YH, Lee MS, Kim HJ, Kim SO, et al. Development and validation of comorbidity index in South Korea. Int J Qual Health Care 2012;24(4):391-402. DOI: http://dx.doi.org/10.1093/intqhc/mzs027. crossref(new window)