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Effects of competition on hospital quality: an examination using hospital administrative data

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Abstract

This paper investigates the effects of competition on hospital quality using hospital administration data from the State of Victoria, Australia. Hospital quality is measured by 30-day mortality rates and 30-day unplanned readmission rates. Competition is measured by Herfindahl–Hirschman index and the numbers of competing public and private hospitals. The paper finds that hospitals facing higher competition have lower unplanned admission rates. However, competition is related negatively to hospital quality when measured by mortality, albeit the effects are weak and barely statistically significant. The paper also finds that the positive effect of competition on quality as measured by unplanned readmission differs greatly depending on whether the hospital is publicly or privately owned.

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Notes

  1. However, a recent theoretical contribution that incorporates semi-altruistic care providers [7], show that an ambiguous relationship exists between competition and quality.

  2. See also [6, 28, 29] and the review by Gaynor [19].

  3. The discussion in this paragraph is based mainly on Productivity Commission [39].

  4. See Harper et al. [24] as cited in Jensen et al. [27].

  5. The DRG classification follows that of the Australian refined diagnosis related groups (AR-DRGs) version 5.1. These seven DRG codes are: F65 (peripheral vascular disorders), F66 (coronary atherosclerosis), F67 (hypertension), F69 (valvular disorder), F71 (non-major arrhythmia and conduction disorders), F72 (unstable angina), F73 (syncope and collapse).

  6. A measure of the complexity of an episode, the Charson comorbidity index [9] is a good indicator of the complexity of an episode and is a strong predictor of mortality. We compute the Charlson comorbidity index by making use of the diagnosis information coded in ICD-10 codes in the data and follow the procedure outlined in Sundararajan et al. [48].

  7. Although distance-based approaches of defining hospital markets are considerably more popular than other approaches, they are not without problems; see Propper et al. [41] for a discussion.

  8. We fit the model in Eq. 5 using the Stata command xtmelogit, which uses the numerical method known as adaptive Gaussian quadrature.

  9. We use (1 − HHI) in the estimation to facilitate the interpretation of the results. An increase in competition is represented by a rise in (1 − HHI), meaning that a positive coefficient on (1 − HHI) implies quality is affected adversely by competition.

  10. Full listings of coefficient estimates can be found in the “Appendix”.

  11. Due to the random effects term in the logistic regression, it is not possible to obtain the SE via analytical methods such as the delta method.

  12. One possibility is that critically ill patients in private hospitals may be readmitted to public hospitals such that readmission rates of private hospitals might be understated ([2, 43 as cited in Fasken et al. [18]). However, as explained earlier, our readmission rates take into account both readmission to the same hospital as well as to other hospitals.

  13. Detailed coefficient estimates are available from the authors upon request.

  14. A complete listing of coefficient estimates for both steps is available from the authors upon request.

  15. Unfortunately there is no analytical solution for the SE, a bootstrap approach would be computationally intensive and has not been attempted.

  16. A complete listing of all coefficient estimates are available from the authors upon request.

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Acknowledgements

This research is supported by the Australian Research Council Linkage Grant LP0455325. We are grateful to our linkage partner the Victorian Department of Human Services for providing the data. We are indebted to Richard Bolitho, Kaye Brown, Phyllis Rosendale, Tony Scott, Vijaya Sundararajan, Christine Stone, Beth Webster, John Creedy, and seminar participants at the Department of Human Services and the Melbourne Institute for providing valuable input. All responsibility for the model specification and outcomes of this work lies with the authors and all questions should be directed to them.

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Correspondence to Jongsay Yong.

Appendix: main estimation results

Appendix: main estimation results

See Tables 8 and 9.

Table 8 Random intercept logistic regression—mortality
Table 9 Random intercept logistic regression—unplanned readmission

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Palangkaraya, A., Yong, J. Effects of competition on hospital quality: an examination using hospital administrative data. Eur J Health Econ 14, 415–429 (2013). https://doi.org/10.1007/s10198-012-0386-7

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