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Analysis of HIV/AIDS DRG in Portugal: a hierarchical finite mixture model

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  • Sara Dias
  • Valeska Andreozzi
  • Rosário Martins

Abstract

Inpatient length of stay (LOS) is an important measure of hospital activity, but its empirical distribution is often positively skewed, representing a challenge for statistical analysis. Taking this feature into account, we seek to identify factors that are associated with HIV/AIDS through a hierarchical finite mixture model. A mixture of normal components is applied to adult HIV/AIDS diagnosis-related group data (DRG) from 2008. The model accounts for the demographic and clinical characteristics of the patients, as well the inherent correlation of patients clustered within hospitals. In the present research, a normal mixture distribution was fitted to the logarithm of LOS and it was found that a model with two-components had the best fit, resulting in two subgroups of LOS: a short-stay subgroup and a long-stay subgroup. Associated risk factors for both groups were identified as well as some statistical differences in the hospitals. Our findings provide important information for policy makers in terms of discharge planning and the efficient management of LOS. The presence of “atypical” hospitals also suggests that hospitals should not be viewed or treated as homogenous bodies. Copyright Springer-Verlag 2013

Suggested Citation

  • Sara Dias & Valeska Andreozzi & Rosário Martins, 2013. "Analysis of HIV/AIDS DRG in Portugal: a hierarchical finite mixture model," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(5), pages 715-723, October.
    • Handle: RePEc:spr:eujhec:v:14:y:2013:i:5:p:715-723
    DOI: 10.1007/s10198-012-0416-5
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    References listed on IDEAS

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    1. Chungkham Singh & Laishram Ladusingh, 2010. "Inpatient length of stay: a finite mixture modeling analysis," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(2), pages 119-126, April.
    2. Xiao, Jianguo & Lee, Andy H. & Vemuri, Siva Ram, 1999. "Mixture distribution analysis of length of hospital stay for efficient funding," Socio-Economic Planning Sciences, Elsevier, vol. 33(1), pages 39-59, March.
    3. Leung, K.-M. & Elashoff, R.M. & Rees, K.S. & Hasan, M.M. & Legorreta, A.P., 1998. "Hospital- and patient-related characteristics determining maternity length of stay: A hierarchical linear model approach," American Journal of Public Health, American Public Health Association, vol. 88(3), pages 377-381.
    4. Martin, Stephen & Smith, Peter, 1996. "Explaining variations in inpatient length of stay in the National Health Service," Journal of Health Economics, Elsevier, vol. 15(3), pages 279-304, June.
    5. Grun, Bettina & Leisch, Friedrich, 2007. "Fitting finite mixtures of generalized linear regressions in R," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5247-5252, July.
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    Cited by:

    1. Marcello Montefiori & Michela Pasquarella & Paolo Petralia, 2020. "The effectiveness of the neonatal diagnosis-related group scheme," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-12, August.

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    More about this item

    Keywords

    Length of stay; Diagnosis related group; Mixture regression; Hierarchical modelling; I18; I10;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I10 - Health, Education, and Welfare - - Health - - - General

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