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The zero-inflated regression models such as zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB) or zero-inflated generalized Poisson (ZIGP) regression models can model the count data with excess zeros. The ZINB model can handle over-dispersed and the ZIGP model can handle the over or under-dispersed count data with excess zeros as well. Moreover, the count data may be correlated because of data collection procedure or special study design. The clustered sampling approach is one of the examples in which the correlation among subjects could be defined. In such situations, a marginal model using generalized estimating equation (GEE) approach can incorporate these correlations and lead up to the relationships at the population level. In this study, the GEE-based zero-inflated generalized Poisson regression model was proposed to fit over and under-dispersed clustered count data with excess zeros. [ABSTRACT FROM AUTHOR]

Background: Poverty and low socioeconomic status are the most important reasons of increasing the global burden of tuberculosis, not only in developing countries but also in developed countries for particular groups. The purpose of this study was to assess the association between socioeconomic factors and the number of tuberculosis patients using quantile regression for count data. Methods: This cross-sectional study was conducted on 11,320 tuberculosis patients from March 2010 to March 201 in Iran. Data was gathered from the 345 sections of Iran by Ministry of Health and Medical Education and Statistical Center of Iran. The jittering method was applied for smoothing, and then, the quantile regression for count data was fitted. The AIC was used to compare the fitness of quantile regression for count data model and Poisson log-linear model. The R (3.0.1) software and Quantreg and AER packages were used for all analysis and modeling of the data. Results: The results of fitting the quantile regression for count data showed that in all percentiles, the more increase in immigration rate, illiteracy rate, unemployment and urbanization rates, the more tuberculosis morbidity rate was increased. The maximum increase of tuberculosis due to immigration rate, urbanization rate, unemployment rate, and illiteracy rate was in 95th percentile (β ̂=0.315), 85'Th percentile (β ̂=0.162), 75'Th percentile (β ̂=0.114), and 95'Th percentile (β ̂=0.304), respectively. For 50th percentiles and higher percentiles, with increasing the sum of physicians to the number of population, the tuberculosis morbidity rate was decreased, and the maximum decrease was in 95'Th percentile (β ̂=-0.1). For all percentiles, the AIC showed that quantile regression for count data had been a better fit to data. Conclusion: With respect to the relationship between socioeconomic factors and TB rate, health care observers should pay close attention to improving these factors in Iran to reduce the TB mortality and morbidity. [ABSTRACT FROM AUTHOR]