Air Pollution and Respiratory-Related Restricted Activity Days in Tunisia

Authors: Mokhtar Kouki Inès Rekik

Abstract:

This paper focuses on the assessment of the air pollution and morbidity relationship in Tunisia. Air pollution is measured by ozone air concentration and the morbidity is measured by the number of respiratory-related restricted activity days during the 2-week period prior to the interview. Socioeconomic data are also collected in order to adjust for any confounding covariates. Our sample is composed by 407 Tunisian respondents; 44.7% are women, the average age is 35.2, near 69% are living in a house built after 1980, and 27.8% have reported at least one day of respiratory-related restricted activity. The model consists on the regression of the number of respiratory-related restricted activity days on the air quality measure and the socioeconomic covariates. In order to correct for zero-inflation and heterogeneity, we estimate several models (Poisson, negative binomial, zero inflated Poisson, Poisson hurdle, negative binomial hurdle and finite mixture Poisson models). Bootstrapping and post-stratification techniques are used in order to correct for any sample bias. According to the Akaike information criteria, the hurdle negative binomial model has the greatest goodness of fit. The main result indicates that, after adjusting for socioeconomic data, the ozone concentration increases the probability of positive number of restricted activity days.

Keywords: Bootstrapping, hurdle negbin model, overdispersion, ozone concentration, respiratory-related restricted activity days.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1098990

References:

[1] J. Mullahy, P. Portney, “Air pollution, cigarette smoking, and the production of respiratory health”, J Health Econ 9, 1990, pp. 193-205.
[2] B.D. Ostro, “Air pollution and morbidity revisited: A specification test”. J Environ Econ Manage 14, 1987, pp. 87-98.
[3] P. Portney, J. Mullahy, Urban air quality and acute respiratory illness. Resources for the future/Washington, D.C., 1986.
[4] E. Samakovlis, A. Huhtala, T. Bellander, et al. “Air quality and morbidity: Concentration-response relationships for Sweden”. NIER Stockholm Working Paper No 87, 2004.
[5] J. Mullahy, Heterogeneity, “Excess Zeros, and the Structure of Count Data Models”, J Appl Econometr 12, 1997, pp. 337-350.
[6] J. Mullahy, “Specification and testing of some modified count data models”. J Appl Econometr 12, 1986, pp. 337-350.
[7] M.L. Dalrymple, I.L. Hudson, RPK Ford, “Finite Mixture, Zero-inflated Poisson and Hurdle models with application to SIDS”, Comput Stat Data Anal 41, 2003, pp. 491-504.
[8] S. Gurmu and P. Trivedi, “Excess Zeros in count models for recreational trips”, J Bus Econom Statist 14 No 4, 1996, pp. 469-477.
[9] A.C. Cameron, P.K. Trivedi, Regression analysis of count data. Cambridge University Press. 1998.
[10] A.P. Dempster, N.M. Laird, D.B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm (with discussion)”, J R Statist Soc B 39 No1, 1977, pp. 1-38.
[11] G. McLachlan and D. Peel, Finite mixture models. A Wiley-Interscience Publication, 2000.
[12] A.J. Izenman and C.J. Sommer, “Philatelic mixtures and multimodal densities”, J Amer Statist Assoc 83, 1988, pp. 941-953.
[13] P. Wang, M.L. Puterman, I. Cockburn et al. “Mixed Poisson models with covariate dependent rates”, Biometrics 52 No2, 1996, pp. 381-400.
[14] D. Holt D, TMF Smith, “Post stratification”, J R Statist Soc A 142 No1, 1979, pp. 33-46.
[15] J. Hausman, B. Hall, Z. Griliches, “Econometric models for count data with an application to the patent R&D relationship”, Econometrica 52 No 4, 1984, pp. 909-938.
[16] N. Künzli, R. Kaiser, S. Medina et al. “Public health impact of outdoor and traffic-related air pollution: A European assessment” The Lancet 356, 2000, pp. 795-801.
[17] B. Efron and R. Tibshirani, An introduction to the bootstrap. Chapman & Hall. 1993.