The objective of this paper is to model and study the impact of high temperature on mortality in Pakistan. For this purpose, we have used mortality and climate data consisting of maximum temperature, variation in monthly temperature, average rainfall, humidity, dewpoint, as well as average air pressure in the country over the period from 2000 to 2019. We have used the Generalized Linear Model with Quasi-Poisson link function to model the number of deaths in the country and to assess the impact of maximum temperature on mortality. We have found that the maximum temperature in the country has a significant impact on mortality. The number of deaths in Pakistan increases as the maximum temperature increases. We found that, as the maximum temperature increase beyond 30 °C, mortality increases significantly. Our results indicate that mortality increases by 27% when the maximum temperature in the country increases from medium category to a very high level. Similarly, the number of deaths in the country increases by 11% when the temperature increases from medium temperature to high level. Furthermore, our study found that when the maximum temperature in the country decreases from a medium level to a low level, the number of deaths in the country decreases by 23%. This study does not consider the impact of other factors on mortality, such as age, medical conditions, gender, geographical location, as well as variability of temperature across the country.

This study examine twenty-nine parametric mortality models and assess their suitability for graduating mortality rates of urban and rural areas in Pakistan. Grouped age specific mortality rates of rural and urban populations for the year 2019 are used. The data is collected from the website of National Institute of Population Studies which conduct Maternal Mortality Survey in Pakistan on regular basis. The parametric mortality models were applied to rural and urban mortality data. We used R software to estimate the model’s parameters and assess their suitability for urban and rural populations. The suitability of these models was assessed by using 3 different loss functions. Our analyses found that the fourth type of Heligman-Polard’s model with loss function 3 provides reliable results for graduating the mortality of rural population while second type of Carriere model with loss function 3 produce best results for graduating the urban mortality of Pakistan. Based on two models, mortality rates of urban and rural population have been graduated over age range 0-85. We suggest the use the graduated mortality rates of urban and rural areas for pricing life insurance products in rural and urban areas respectively. In addition, graduated mortality rates are also suggested for use in calculation of life insurance liabilities.

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