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Instituto Nacional de Salud Pública

2009, Number 5

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salud publica mex 2009; 51 (5)

Statistical analysis for count data: Use of healthcare services applications.

Salinas-Rodríguez A, Manrique-Espinoza B, Sosa-Rubí SG

Language: Spanish
References: 53
Page: 397-406
PDF size: 164.75 Kb.


ABSTRACT

Objective. To describe some of the statistical models for the study of count variables in the context of the use of health services. Material and Methods. We used the Seguro Popular Evaluation Survey to estimate the effect of Seguro Popular on the frequency of use of outpatient health services, using Poisson regression models and negative binomial, zeroinflated negative binomial and the hurdle negative binomial models. We used the Akaike Information Criterion (AIC) to define the best model. Results. Results show that the best statistical approach to model the use of health services is the hurdle model, taking into account both the main theoretical assumptions and the statistical results of the AIC. Discussion. The modelling of count data requires the application of statistical models to model data dispersion; in the presence of an excess of zeros, the hurdle model is an appropriate statistical option.


REFERENCES

  1. Pohlmeier W, Ulrich V. An econometric model of the two-part decision process in the demand for health. J Hum Res 1995;30(2):339-361.

  2. Santos-Silva JMC, Windmeijer F. Two-part multiple spell models for health care demand. J Econo 2001;104(1):67-89.

  3. Gerdtham UG. Equity in health care utilization: further tests based on Hurdle models and swedish micro data. Health Econo 1997;6:303-319.

  4. Grootendorst PV. A comparison of alternative models of prescription drug utilization. Health Econo 1995;4:183-198.

  5. Deb P, Trivedi PK. Demand for medical care by the elderly: a finite mixture approach. J Appl Econo 1997;12:313-336.

  6. Hardin J, Hilbe J. Generalized linear models and extensions. 2nd ed. Texas: Stata Press, 2007.

  7. McCullagh P, Nelder JA. Generalized linear models. 2nd ed. New York: Chapman & Hall, 1989.

  8. Dobson A, Barnett A. An introduction to generalized linear models. 3rd ed. New York: Chapman & Hall/CRC, 2008.

  9. Hilbe J. Negative binomial regression. Cambridge: Cambridge University Press, 2007.

  10. Congdon P, Almog M, Curtis S, Ellerman R. A spatial structural equation modelling framework for health count responses. Stat Med 2007;26:5267-5284.

  11. Cameron AC, Trivedi PK. Regression analysis of count data. Cambridge: Cambridge University Press, 1998.

  12. Efron B, Tibshirani R. An introduction to the bootstrap. New York: Chapman & Hall/CRC, 1993.

  13. Jones AM. Applied health economics. Oxford: Routledge, 2007.

  14. Lambert D. Zero-inflated poisson regression with an application to defects in manufacturing. Technometrics 1992;34:1-14.

  15. Mullahy J. Specification and testing of some modified count data models. J Econo 1986;33:341-365.

  16. King G. A “politically robust” experimental design for public policy

  17. evaluation, with application to the Mexican Universal Health Insurance Program. J Pol Anal Manag 2007;26(3):479-506.

  18. Ashenfelter O, Card D. Using the longitudinal structure of earnings to estimate the effect of training programs. Rev Econo Stat 1985;67(4):648-660.

  19. R Development Core Team. [sitio de internet]. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2008. ISBN 3-900051-07-0. [Consultado 2009 feb 10]. Disponible en http://www.R-project.org.

  20. Cameron AC, Trivedi PK. Econometric models based on count data: comparisons and applications of some estimators and tests. J Appl Econo 1986;(1):29-53.

  21. Cameron AC, Trivedi PK. Regression-based tests for overdispersion in the poisson model. J Econo 1990;46:347-364.

  22. Dean C, Lawless JF. Tests for detecting overdispersion in poisson regression models. J Am Stat Ass 1989;84:467-472.

  23. Ellencweig AY, Pagliccia N. Utilization patterns of cohorts of elderly clients: a structural equation model. Health Serv Res 1994;29:225-245.

  24. Andersen AS, Laake P. A causal model for physician utilization: analysis of Norwegian data. Med Care 1983;21:266-278.

  25. Deb P, Trivedi PK. The structure of demand for health care: latent class versus two-part models. J Health Econo 2002;21:601-625.

  26. Jimenez-Martin S. Latent class versus two-part models in the demand for physician services across the European Union. Health Econo 2002;11:301-321.

  27. Bago d’Uva T. Latent class models for health care utilization. Health Econo 2006;15:329-343.

  28. Pohlmeier W, Ulrich V. An econometric model of the two-part decision process in the demand for health. J Hum Res 1995;30(2):339-361.

  29. Santos-Silva JMC, Windmeijer F. Two-part multiple spell models for health care demand. J Econo 2001;104(1):67-89.

  30. Gerdtham UG. Equity in health care utilization: further tests based on Hurdle models and swedish micro data. Health Econo 1997;6:303-319.

  31. Grootendorst PV. A comparison of alternative models of prescription drug utilization. Health Econo 1995;4:183-198.

  32. Deb P, Trivedi PK. Demand for medical care by the elderly: a finite mixture approach. J Appl Econo 1997;12:313-336.

  33. Hardin J, Hilbe J. Generalized linear models and extensions. 2nd ed. Texas: Stata Press, 2007.

  34. McCullagh P, Nelder JA. Generalized linear models. 2nd ed. New York: Chapman & Hall, 1989.

  35. Dobson A, Barnett A. An introduction to generalized linear models. 3rd ed. New York: Chapman & Hall/CRC, 2008.

  36. Hilbe J. Negative binomial regression. Cambridge: Cambridge University Press, 2007.

  37. Congdon P, Almog M, Curtis S, Ellerman R. A spatial structural equation modelling framework for health count responses. Stat Med 2007;26:5267-5284.

  38. Cameron AC, Trivedi PK. Regression analysis of count data. Cambridge: Cambridge University Press, 1998.

  39. Efron B, Tibshirani R. An introduction to the bootstrap. New York: Chapman & Hall/CRC, 1993.

  40. Jones AM. Applied health economics. Oxford: Routledge, 2007.

  41. Lambert D. Zero-inflated poisson regression with an application to defects in manufacturing. Technometrics 1992;34:1-14.

  42. Mullahy J. Specification and testing of some modified count data models. J Econo 1986;33:341-365.

  43. King G. A “politically robust” experimental design for public policy evaluation, with application to the Mexican Universal Health Insurance Program. J Pol Anal Manag 2007;26(3):479-506.

  44. Ashenfelter O, Card D. Using the longitudinal structure of earnings to estimate the effect of training programs. Rev Econo Stat 1985;67(4):648-660.

  45. R Development Core Team. [sitio de internet]. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2008. ISBN 3-900051-07-0. [Consultado 2009 feb 10]. Disponible en http://www.R-project.org.

  46. Cameron AC, Trivedi PK. Econometric models based on count data: comparisons and applications of some estimators and tests. J Appl Econo 1986;(1):29-53.

  47. Cameron AC, Trivedi PK. Regression-based tests for overdispersion in the poisson model. J Econo 1990;46:347-364.

  48. Dean C, Lawless JF. Tests for detecting overdispersion in poisson regression models. J Am Stat Ass 1989;84:467-472.

  49. Ellencweig AY, Pagliccia N. Utilization patterns of cohorts of elderly clients: a structural equation model. Health Serv Res 1994;29:225-245.

  50. Andersen AS, Laake P. A causal model for physician utilization: analysis of Norwegian data. Med Care 1983;21:266-278.

  51. Deb P, Trivedi PK. The structure of demand for health care: latent class versus two-part models. J Health Econo 2002;21:601-625.

  52. Jimenez-Martin S. Latent class versus two-part models in the demand for physician services across the European Union. Health Econo 2002;11:301-321.

  53. Bago d’Uva T. Latent class models for health care utilization. Health Econo 2006;15:329-343.




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