External Validation of and Factors Associated with the Overuse Index: a Nationwide Population-Based Study from Taiwan

Abstract

Background

The Overuse Index (OI), previously called the Johns Hopkins Overuse Index, is developed and validated as a composite measure of systematic overuse/low-value care using United States claims data. However, no information is available concerning whether the external validation of the OI is sustained, especially for international application. Moreover, little is known about which supply and demand factors are associated with the OI.

Objective

We used nationwide population-based data from Taiwan to externally validate the OI and to examine the association of regional healthcare resources and socioeconomic factors with the OI.

Design and Participants

We analyzed 1,994,636 beneficiaries randomly selected from all people enrolled in the Taiwan National Health Insurance in 2013.

Main Measures

The OI was calculated for 2013 to 2015 for each of 50 medical regions. Spearman correlation analysis was applied to examine the association of the OI with total medical costs per capita and mortality rate. Generalized estimating equation linear regression analysis was conducted to examine the association of regional healthcare resources (number of hospital beds per 1000 population, number of physicians per 1000 population, and proportion of primary care physicians [PCPs]) and socioeconomic factors (proportion of low-income people and proportion of population aged 20 and older without a high school diploma) with the OI.

Results

Higher scores of the OI were associated with higher total medical costs per capita (ρ = 0.48, P < 0.001) and not associated with total mortality (ρ = − 0.01, P = 0.882). Higher proportions of PCPs and higher proportions of low-income people were associated with lower scores of the OI (β = − 0.022, P = 0.016 and β = − 0.224, P < 0.001, respectively).

Conclusions

Our study supported the external validation of the OI by demonstrating a similar association within a universal healthcare system, and it showed the association of a higher proportion of PCPs and a higher proportion of low-income people with less overuse/low-value care.

This is a preview of subscription content, log in to check access.

Figure 1
Figure 2
Figure 3

References

  1. 1.

    Saini V, Brownlee S, Elshaug AG, Glasziou P, Heath I. Addressing overuse and underuse around the world. Lancet. 2017;390:105-107.

    Article  Google Scholar 

  2. 2.

    Porter ME. What is value in health care? N Engl J Med 2010;363:2477-2481.

    CAS  Article  Google Scholar 

  3. 3.

    Elshaug AG, Rosenthal MB, Lavis JN, et al. Levers for addressing medical underuse and overuse: achieving high-value health care. Lancet. 2017;390:191-202.

    Article  Google Scholar 

  4. 4.

    Chassin MR, Galvin RW. The urgent need to improve health care quality. Institute of Medicine National Roundtable on Health Care Quality. JAMA. 1998;280:1000-1005.

    CAS  Article  Google Scholar 

  5. 5.

    Chan KS, Chang E, Nassery N, Chang HY, Segal JB. The state of overuse measurement: a critical review. Med Care Res Rev 2013;70:473-496.

    Article  Google Scholar 

  6. 6.

    Segal JB, Nassery N, Chang HY, Chang E, Chan K, Bridges JF. An index for measuring overuse of health care resources with Medicare claims. Med Care 2015;53:230-236.

    Article  Google Scholar 

  7. 7.

    Brownlee S, Chalkidou K, Doust J, et al. Evidence for overuse of medical services around the world. Lancet. 2017;390:156-168.

    Article  Google Scholar 

  8. 8.

    Oakes AH, Chang HY, Segal JB. Systemic overuse of health care in a commercially insured US population, 2010-2015. BMC Health Serv Res 2019;19:280.

    Article  Google Scholar 

  9. 9.

    Koehlmoos TP, Madsen CK, Banaag A, Haider AH, Schoenfeld AJ, Weissman JS. Assessing low-value health care services In the Military Health System. Health Aff (Millwood) 2019;38:1351-1357.

    Article  Google Scholar 

  10. 10.

    Berwick DM, Hackbarth AD. Eliminating waste in US health care. JAMA. 2012;307:1513-1516.

    CAS  Article  Google Scholar 

  11. 11.

    Wennberg JE, Fisher ES, Skinner JS. Geography and the debate over Medicare reform. Health Aff (Millwood). 2002;Suppl Web Exclusives:W96-114.

  12. 12.

    Schwartz AL, Landon BE, Elshaug AG, Chernew ME, McWilliams JM. Measuring low-value care in Medicare. JAMA Intern Med 2014;174:1067-1076.

    Article  Google Scholar 

  13. 13.

    Organisation for Economic Co-operation and Development. Geographic Variations in Health Care: What Do We Know and What Can Be Done to Improve Health System Performance? Paris: OECD Publishing; 2014.

  14. 14.

    Coronini-Cronberg S, Bixby H, Laverty AA, Wachter RM, Millett C. English National Health Service’s savings plan may have helped reduce the use of three ‘low-value’ procedures. Health Aff (Millwood). 2015;34:381-389.

    Article  Google Scholar 

  15. 15.

    McAlister FA, Lin M, Bakal J, Dean S. Frequency of low-value care in Alberta, Canada: a retrospective cohort study. BMJ Qual Saf 2018;27:340-346.

    Article  Google Scholar 

  16. 16.

    Shi L. Health services research methods. Clifton Park: Thomson/Delmar Learning; 2008.

    Google Scholar 

  17. 17.

    Kemper CJ. External Validity. In: Zeigler-Hill V, Shackelford TK, eds. Encyclopedia of Personality and Individual Differences. Cham: Springer International Publishing; 2017:1-4.

    Google Scholar 

  18. 18.

    Colla CH, Morden NE, Sequist TD, Schpero WL, Rosenthal MB. Choosing wisely: prevalence and correlates of low-value health care services in the United States. J Gen Intern Med 2015;30:221-228.

    Article  Google Scholar 

  19. 19.

    Colla CH, Morden NE, Sequist TD, Mainor AJ, Li Z, Rosenthal MB. Payer type and low-value care: comparing Choosing Wisely services across commercial and Medicare populations. Health Serv Res 2018;53:730-746.

    Article  Google Scholar 

  20. 20.

    Zhou M, Oakes AH, Bridges JFP, Padula WV, Segal JB. Regional supply of medical resources and systemic overuse of health care among medicare beneficiaries. J Gen Intern Med 2018;33:2127-2131.

    Article  Google Scholar 

  21. 21.

    Oakes AH, Sen AP, Segal JB. Understanding geographic variation in systemic overuse among the privately insured. Med Care 2020;58:257-264.

    Article  Google Scholar 

  22. 22.

    Health and Welfare Data Science Center. Database User Manual. Available at: https://dep.mohw.gov.tw/DOS/lp-3147-113.html. Accessed 29 March 2020.

  23. 23.

    Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45:613-619.

    CAS  Article  Google Scholar 

  24. 24.

    Organisation for Economic Co-operation and Development. Health at a Glance 2017. Available at: https://www.oecd-ilibrary.org/content/publication/health_glance-2017-en. Accessed 31 November 2017.

  25. 25.

    Kravet SJ, Shore AD, Miller R, Green GB, Kolodner K, Wright SM. Health care utilization and the proportion of primary care physicians. Am J Med 2008;121:142-148.

    Article  Google Scholar 

  26. 26.

    Mossialos E, Djordjevic A, Osborn R, Sarnak D. International Profiles of Health Care Systems. Available at: http://www.commonwealthfund.org/publications/fund-reports/2017/may/international-profiles. Accessed 31 May 2017.

  27. 27.

    Dailey AB, Kasl SV, Holford TR, Calvocoressi L, Jones BA. Neighborhood-level socioeconomic predictors of nonadherence to mammography screening guidelines. Cancer Epidemiol Biomark Prev 2007;16:2293-2303.

    Article  Google Scholar 

  28. 28.

    Prasad SM, Gu X, Lipsitz SR, Nguyen PL, Hu JC. Inappropriate utilization of radiographic imaging in men with newly diagnosed prostate cancer in the United States. Cancer. 2012;118:1260-1267.

    Article  Google Scholar 

  29. 29.

    Falchook AD, Salloum RG, Hendrix LH, Chen RC. Use of bone scan during initial prostate cancer workup, downstream procedures, and associated Medicare costs. Int J Radiat Oncol Biol Phys 2014;89:243-248.

    Article  Google Scholar 

  30. 30.

    Falchook AD, Hendrix LH, Chen RC. Guideline-discordant use of imaging during work-up of newly diagnosed prostate cancer. J Oncol Pract 2015;11:e239-e246.

    Article  Google Scholar 

  31. 31.

    Ministry of Health and Welfare. Hospital establishment or expansion act. Available at: https://law.moj.gov.tw/LawClass/LawHistory.aspx?pcode = L0020163. Accessed 1 Nov 2018.

  32. 32.

    Chang HY, Bodycombe DP, Huang WF, Weiner JP. Risk-adjusted resource allocation: using Taiwan’s National Health Insurance as an example. Asia Pac J Public Health. 2015;27:Np958-971.

    Article  Google Scholar 

  33. 33.

    Tung YC, Chang GM, Chang HY, Yu TH. Relationship between early physician follow-up and 30-day readmission after acute myocardial infarction and heart failure. PLoS One 2017;12:e0170061.

    Article  Google Scholar 

  34. 34.

    Chou YY, Yu TH, Tung YC. Do hospital and physician volume thresholds for the volume-outcome relationship in heart failure exist? Med Care 2019;57:54-62.

    Article  Google Scholar 

  35. 35.

    Segal JB, Bridges JF, Chang HY, et al. Identifying possible indicators of systematic overuse of health care procedures with claims data. Med Care 2014;52:157-163.

    Article  Google Scholar 

  36. 36.

    ABIM Foundation. Choosing Wisely. Available at: http://www.choosingwisely.org/clinician-lists/american-academy-allergy-asthma-immunology-uncomplicated-acute-rhinosinusitis/. Accessed 10 March 2019.

  37. 37.

    Cui J, Qian G. Selection of working correlation structure and best model in GEE analyses of longitudinal data. Commun Stat Simul Comput 2007;36:987-996.

    Article  Google Scholar 

  38. 38.

    Forrest CB, Starfield B. The effect of first-contact care with primary care clinicians on ambulatory health care expenditures. J Fam Pract 1996;43:40-48.

    CAS  PubMed  Google Scholar 

  39. 39.

    Xu WY, Jung JK. Socioeconomic differences in use of low-value cancer screenings and distributional effects in Medicare. Health Serv Res 2017;52:1772-1793.

    Article  Google Scholar 

  40. 40.

    Choi WW, Williams SB, Gu X, Lipsitz SR, Nguyen PL, Hu JC. Overuse of imaging for staging low risk prostate cancer. J Urol 2011;185:1645-1649.

    Article  Google Scholar 

  41. 41.

    Dunnick NR, Applegate KE, Arenson RL. The inappropriate use of imaging studies: a report of the 2004 Intersociety Conference. J Am Coll Radiol 2005;2:401-406.

    Article  Google Scholar 

  42. 42.

    O’Malley AS, Forrest CB, Feng S, Mandelblatt J. Disparities despite coverage: gaps in colorectal cancer screening among Medicare beneficiaries. Arch Intern Med 2005;165:2129-2135.

    Article  Google Scholar 

  43. 43.

    Agency for Healthcare Research and Quality (2010) National Healthcare Quality and Disparities Reports. Available at: http://archive.ahrq.gov/research/findings/nhqrdr/nhqrdr10/qrdr10.html. Accessed 7 Sept 2019.

Download references

Acknowledgments

The authors would like to thank Dr. Jodi B. Segal for her comments on drafts.

Funding

The study was supported by grants from the Ministry of Science and Technology (MOST107-2410-H-002-227-MY3).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yu-Chi Tung PhD.

Ethics declarations

This study was approved by the Institutional Review Board of the National Taiwan University Hospital.

Conflict of Interest

The authors declare that they do not have a conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Prior Presentations

None.

Electronic supplementary material

ESM 1

(DOCX 81 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tung, Y., Li, G. & Chang, H. External Validation of and Factors Associated with the Overuse Index: a Nationwide Population-Based Study from Taiwan. J GEN INTERN MED (2020). https://doi.org/10.1007/s11606-020-06293-0

Download citation

KEY WORDS

  • overuse
  • low-value care
  • measurement
  • regional variation