Introducing Hurdle_T as a Count Regression Model with High Potency in Solving Problems

Count data regression models exhibit different strengths and weaknesses in their bids to solving problems. The study considers six count models namely Poisson Regression Model (PRM), Negative Regression Model (NBRM), Zero Inflated Poisson (ZIP), Zero Inflated Negative Binomial (ZINB), Zero Truncated Poisson (ZTP) and Zero Truncated Negative Binomial (ZTNB) and an additional model called hurdle_T. These models are used to analyze two health data sets. The data on male breast cancer reveals that male breast cancer cuts across all age brackets or categories but it is more prevalent between the ages of 50 and 60. The PRM yields a better result than the NBRM in the case of cancer data as shown by the information criteria. The analysis of the second data, which is on doctor’s visit reveals that ZINB yields a better result than the other five models, followed by NBRM, then the ZTNB before their Poisson counterparts. The hurdle_T model shows the propensity of each coefficient as reflected by the positive count in the Tobit (Binary) model. The study also shows that at 65 years and above, gender has significant effect on doctor’s visit. In particular, females, more than males attract more doctors’ visit in the said age range. Government policies should provide more funds in the health sector to accommodate cancer cases in terms of the provision of awareness, studies/ research and infrastructural development. Males should be encouraged to visit clinics especially in their late forties and above for breast cancer related checkup. At age 65 and above, doctors visit to patients are frequent, especially to females. Policy of government in the health sector should accommodate a favourable adjustment in the budget to take care of doctors’ visit.