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Development and validation of a Medicines Comorbidity Index for older people

  • Pharmacoepidemiology and Prescription
  • Published:
European Journal of Clinical Pharmacology Aims and scope Submit manuscript

Abstract

Purpose

An index for estimating multimorbidity based on prescription claims data is important for predicting health outcomes for older people in pharmacoepidemiological studies. We aimed to develop a Medicines Comorbidity Index (MCI) based on nationwide prescription claims data and evaluate its performance in predicting adverse outcomes in older individuals.

Methods

The index was developed on a retrospective cohort comprising of all individuals aged ≥ 65 years old, captured in the claims dataset from 1st January to 31st December 2012. The cohort was followed for 1 year to identify an event of hospitalisation or mortality. A list of medications for 20 comorbidities based on the Chronic Disease Score framework was collated. Predictive performance of the MCI was evaluated against the Charlson Comorbidity Index (CCI) using measures of discrimination (Receiver Operating Characteristic curves), sensitivity and specificity (c-statistic) and calibration (Brier scores) for regression models.

Results

The MCI was validated for an outcome of mortality (n = 161,461) and hospitalisation (n = 149,729). For mortality, MCI had a marginally lower c-statistic in comparison to CCI (0.70, 95% CI 0.70–0.71 vs 0.72, 95% CI 0.71–0.72 at p < 0.05) with Brier scores of 0.07 at p < 0.05. For hospitalisation, the Hazard Ratio was higher with MCI (1.08, 95% CI 1.08–1.08, p < 0.001) compared to CCI (0.92, 95% CI 0.91–0.92, p < 0.001).

Conclusion

Initial testing indicates that the MCI is a valid and appropriate tool for measuring multimorbidity and predicting health outcomes for older individuals, and can be an important index for adjusting comorbidity in pharmacoepidemiological studies.

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References

  1. Narayan SW, Tordoff JM, Nishtala PS (2016) Temporal trends in the utilisation of preventive medicines by older people: a 9-year population-based study. Arch Gerontol Geriatr 62:103–11

  2. Nishtala PS, Salahudeen MS (2015) Temporal trends in polypharmacy and hyperpolypharmacy in older New Zealanders over a 9-year period: 2005–2013. Gerontology 61:195–202

    Article  PubMed  Google Scholar 

  3. Katz JN, Chang LC, Sangha O et al (1996) Can comorbidity be measured by questionnaire rather than medical record review? Med Care 34:73–84

    Article  CAS  PubMed  Google Scholar 

  4. Dong YH, Chang CH, Shau WY et al (2013) Development and validation of a pharmacy-based comorbidity measure in a population-based automated health care database. Pharmacotherapy 33:126–136

    Article  PubMed  Google Scholar 

  5. Sharabiani MT, Aylin P, Bottle A (2012) Systematic review of comorbidity indices for administrative data. Med Care 50:1109–1118

    Article  PubMed  Google Scholar 

  6. Schneeweiss S, Maclure M (2000) Use of comorbidity scores for control of confounding in studies using administrative databases. Int J Epidemiol 29:891–898

    Article  CAS  PubMed  Google Scholar 

  7. Schneeweiss S, Seeger JD, Maclure M et al (2001) Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. Am J Epidemiol 154:854–864

    Article  CAS  PubMed  Google Scholar 

  8. Klabunde CN, Harlan LC, Warren JL (2006) Data sources for measuring comorbidity: a comparison of hospital records and medicare claims for cancer patients. Med Care 44:921–928

    Article  PubMed  Google Scholar 

  9. Sarfati D, Gurney J, Stanley J et al (2014) Cancer-specific administrative data-based comorbidity indices provided valid alternative to Charlson and National Cancer Institute indices. J Clin Epidemiol 67:586–595

    Article  PubMed  Google Scholar 

  10. Kan WC, Wang JJ, Wang SY et al (2013) The new comorbidity index for predicting survival in elderly dialysis patients: a long-term population-based study. PLoS One 8:e68748

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Charlson ME, Pompei P, Ales KL et al (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373–383

    Article  CAS  PubMed  Google Scholar 

  12. Charlson M, Szatrowski TP, Peterson J et al (1994) Validation of a combined comorbidity index. J Clin Epidemiol 47:1245–1251

    Article  CAS  PubMed  Google Scholar 

  13. Romano PS, Roos LL, Jollis JG (1993) Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol 46:1075–1079 discussion 81-90

    Article  CAS  PubMed  Google Scholar 

  14. de Groot V, Beckerman H, Lankhorst GJ et al (2003) How to measure comorbidity. A critical review of available methods. J Clin Epidemiol 56:221–229

    Article  PubMed  Google Scholar 

  15. International Classification of Diseases (ICD) 2016 Accessed in January. Available at http://www.who.int/classifications/icd/en/.

  16. Bannay A, Chaignot C, Blotière P-O et al (2016) The best use of the Charlson Comorbidity Index with electronic health care database to predict mortality. Med Care 54:188–194

    Article  PubMed  Google Scholar 

  17. Kurbasic I, Pandza H, Masic I et al (2008) The advantages and limitations of international classification of diseases, injuries and causes of death from aspect of existing health care system of Bosnia and Herzegovina. Acta Inf Medica 16:159–161

    Article  Google Scholar 

  18. George J, Vuong T, Bailey MJ et al (2006) Development and validation of the medication-based disease burden index. Ann Pharmacother 40:645–650

    Article  PubMed  Google Scholar 

  19. Walker J, Halbesma N, Lone N et al (2016) Socioeconomic status, comorbidity and mortality in patients with type 2 diabetes mellitus in Scotland 2004–2011: a cohort study. J Epidemiol Community Health 70:596–601

    Article  PubMed  Google Scholar 

  20. Brilleman SL, Salisbury C (2013) Comparing measures of multimorbidity to predict outcomes in primary care: a cross sectional study. Fam Pract 30:172–178

    Article  PubMed  Google Scholar 

  21. Narayan SW, Nishtala PS (2016) Decade-long temporal trends in the utilization of preventive medicines by centenarians. J Clin Pharm Ther 42:165–169 https://10.1111/jcpt.12487

  22. Von Korff M, Wagner EH, Saunders K (1992) A chronic disease score from automated pharmacy data. J Clin Epidemiol 45:197–203

    Article  Google Scholar 

  23. Clark DO, Von Korff M, Saunders K et al (1995) A chronic disease score with empirically derived weights. Med Care 33:783–795

    Article  CAS  PubMed  Google Scholar 

  24. Sloan KL, Sales AE, Liu CF et al (2003) Construction and characteristics of the RxRisk-V: a VA-adapted pharmacy-based case-mix instrument. Med Care 41:761–774

    PubMed  Google Scholar 

  25. Farley JF, Harley CR, Devine JW (2006) A comparison of comorbidity measurements to predict healthcare expenditures. Am J Manag Care 12:110–119

    PubMed  Google Scholar 

  26. Sarfati D, Gurney J, Stanley J et al (2014) Development of a pharmacy-based comorbidity index for patients with cancer. Med Care 52:586–593

    Article  PubMed  Google Scholar 

  27. Nishtala PS, Narayan SW, Wang T et al (2014) Associations of drug burden index with falls, general practitioner visits, and mortality in older people. Pharmacoepidemiol Drug Saf 23:753–758

    Article  PubMed  Google Scholar 

  28. Narayan SW, Nishtala PS (2015) Associations of potentially inappropriate medicine use with fall-related hospitalisations and primary care visits in older New Zealanders: a population-level study using the updated 2012 Beers Criteria. Drugs—Real World Outcomes 2:137–141

    Article  PubMed  PubMed Central  Google Scholar 

  29. Quail JM, Lix LM, Osman BA et al (2011) Comparing comorbidity measures for predicting mortality and hospitalization in three population-based cohorts. BMC Health Serv Res 11:146

    Article  PubMed  PubMed Central  Google Scholar 

  30. Salmond CE, Crampton P (2012) Development of New Zealand’s deprivation index (NZDep) and its uptake as a national policy tool. Can J Public Health 103:S7–11

    PubMed  Google Scholar 

  31. Lepa T, Norris P, Horsburgh S et al (2013) Accuracy of National Health Index numbers for Pacific people in NZ. Aust N Z J Public Health 37:189–190

    Article  PubMed  Google Scholar 

  32. New Zealand Burden of Diseases, Injuries and Risk Factors Study, 2006–2016. Accessed on 17 July 2015. Available at: http://www.health.govt.nz/nz-health-statistics/health-statistics-and-data-sets/new-zealand-burden-diseases-injuries-and-risk-factors-study-2006-2016

  33. The New Zealand Formulary. Available at: http://nzformulary.org/. Accessed on 22 July 2016

  34. WHO Collaborating Centre for Drug Statistics Methodology Norwegian Institute of Public Health. Available at http://www.whocc.no/. Accessed January 2016

  35. Harrison DA, Patel K, Nixon E et al (2014) Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team. Resuscitation 85:993–1000

    Article  PubMed  PubMed Central  Google Scholar 

  36. Lumley T, Scott A (2015) AIC and BIC for modeling with complex survey data. J Surv Stat Methodol 3:1–18

    Article  Google Scholar 

  37. Austin PC, Steyerberg EW (2012) Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable. BMC Med Res Methodol 12:82

    Article  PubMed  PubMed Central  Google Scholar 

  38. Sundararajan V, Henderson T, Perry C et al (2004) New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J Clin Epidemiol 57:1288–1294

    Article  PubMed  Google Scholar 

  39. Ho TW, Tsai YJ, Ruan SY et al (2014) In-hospital and one-year mortality and their predictors in patients hospitalized for first-ever chronic obstructive pulmonary disease exacerbations: a nationwide population-based study. PLoS One 9:e114866

    Article  PubMed  PubMed Central  Google Scholar 

  40. Yourman LC, Lee SJ, Schonberg MA et al (2012) Prognostic indices for older adults: a systematic review. JAMA 307:182–192

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Calderón-Larrañaga A, Abrams C, Poblador-Plou B, et al. Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: the impact of a local calibration. BMC Health Serv Res, 2010; 10: 22

  42. Kuo RNC, Lai M-S (2010) Comparison of Rx-defined morbidity groups and diagnosis-based risk adjusters for predicting healthcare costs in Taiwan. BMC Health Serv Res 10:1–12

    Article  Google Scholar 

  43. Robusto F, Lepore V, D'Ettorre A et al (2016) The drug derived complexity index (DDCI) predicts mortality, unplanned hospitalization and hospital readmissions at the population level. PLoS One 11:e0149203

    Article  PubMed  PubMed Central  Google Scholar 

  44. Ou H-T, Mukherjee B, Erickson SR et al (2012) Comparative performance of comorbidity indices in predicting health care-related behaviors and outcomes among Medicaid enrollees with type 2 diabetes. Popul Health Manag 15:220–229

    Article  PubMed  PubMed Central  Google Scholar 

  45. Lu CY, Barratt J, Vitry A et al (2011) Charlson and Rx-Risk comorbidity indices were predictive of mortality in the Australian health care setting. J Clin Epidemiol 64:223–228

    Article  PubMed  Google Scholar 

Download references

Funding

The authors would like to thank the RiPE (Research in Pharmacoepidemiology) group, School of Pharmacy, University of Otago for providing support. Sujita Narayan is supported by a doctoral scholarship by the School of Pharmacy, University of Otago, Dunedin, New Zealand. The funding institution did not play any role in the study concept, data analysis or interpretation.

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Authors and Affiliations

  1. School of Pharmacy, University of Otago, P O Box 56, Dunedin, 9054, New Zealand

    Sujita W. Narayan & Prasad S. Nishtala

Authors
  1. Sujita W. Narayan
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  2. Prasad S. Nishtala
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Correspondence to Sujita W. Narayan.

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Conflicts of interest

Sujita Narayan and Prasad Nishtala declare that they have no conflicts of interest relevant to the content of this review.

Ethical considerations

All patient data evaluated in this review were de-identified.

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Narayan, S.W., Nishtala, P.S. Development and validation of a Medicines Comorbidity Index for older people. Eur J Clin Pharmacol 73, 1665–1672 (2017). https://doi.org/10.1007/s00228-017-2333-0

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  • DOI: https://doi.org/10.1007/s00228-017-2333-0

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