Human mortality is unanticipated and unavoidable, particularly in light of the recent COVID-19 pandemic. Insurance companies, actuaries, financial institutions, demographers, and the government may suffer catastrophic losses as a result of imprecise mortality estimates. Understanding the factors that contribute to mortality at the population level can help the government improve its efforts to promote health and reduce health inequalities. Consequently, the present study utilizes an econometrics model to estimate Malaysia’s mortality rate, with macroeconomic factors as explanatory variables. The present study employed the unemployment rate, pension liabilities, gross domestic product, education expenditure, and healthcare expenditure as explanatory variables. The empirical results imply that the fixed effects model is feasible when using panel data across specific age groups. Moreover, the fixed effects model is devoid of cross-sectional dependency, heteroscedasticity, and serial correlation. The findings reveal that the unemployment rate, gross domestic product, and education expenditure all have a significant influence on the mortality rate. However, pension liabilities and health expenditure have an insignificant relationship with the mortality rate. The fixed effects model is demonstrated to be a robust model that fits the Malaysian scenario with an R-squared of approximately 84.69%. The present study is novel due to the fact that the model established between explanatory variables and the mortality rate shows a significant relationship, which can be helpful in forecasting the mortality at population level as a preparation for the post-COVID-19 mortality. The present study aims to contribute to the development of an effective support mechanism by rectifying Malaysia’s socioeconomic inequalities in order to mitigate the COVID-19 increase in mortality rate. Therefore, the Malaysian government is strongly encouraged to examine its expenditure on education and gross domestic product in order to improve the mortality rate, particularly among the adult and older population.

Malaysia has been experiencing longevity risk since the last decade due to improvements of mortality rates. Longevity risk refers to the probability of a person living longer than expected. According to the Department of Statistics Malaysia, a baby born in the year 2018 is predicted to live an average life of 75 years. Since the minimum retirement age policy of 60 years had come into force in 2012, the 2018 baby would live approximately 15 more years after retirement. Therefore, this study aims to compare the Heligman-Pollard and P-splines smoothing for fitting the Malaysian mortality rate. This model fitting will give a clear picture of the mortality pattern in predicting the mortality rate accurately, especially for the baby boomer generation. The data obtained from the Department of Statistics Malaysia are split into groups of five years, from 0 to 75 years old, and time ranges from 1995 to 2018. The data set from 1995 to 2010, known as the training set is used to fit the mortality rate. After fitting the mortality rate for both methods, this study will measure the performance in the testing set from 2011 until 2018. This study uses the mean absolute percentage error (MAPE) to identify the better method to fit the Malaysian mortality rate. Based on the MAPE values, P-splines smoothing gives a relatively smaller value compared to the Heligman-Pollard. For overall performance from 1995 to 2018, P-spline smoothing has proven to fit the Malaysian data well.

SARS-CoV-2, known as COVID-19, has affected the entire world, resulting in an unexpected death rate as compared to the death probability before the pandemic. Prior to the COVID-19 pandemic, death probability has been assessed in a normal context that is different from those anticipated during the pandemic, particularly for the older population cluster. However, there is no such evidence of excess mortality in Malaysia to date. Therefore, this study determines the excess mortality rate for specific age groups during the pandemic outbreak in Malaysia. Before determining the excess mortality rate, this study aims to establish the efficiency of various parametrized mortality models in reference to the data set before the pandemic. This study employs the hold-out, repeated hold-out, and leave-one-out cross-validation procedures to identify the optimal mortality law for fitting the mortality data. Based on the goodness-of-fit measures (mean absolute percentage error, mean absolute error, sum square error, and mean square error), the Heligman-Pollard model for men and Rogers Planck model for women are considered as the optimal models. In assessing the excess mortality, both models favour the hold-out technique. When the COVID-19 mortality data are incorporated to forecast the mortality rate for people aged 60 and above, there is an excess mortality rate. However, the men’s mortality rate appears to be delayed and more prolonged than the women’s mortality rate. Consequently, the government is recommended to amend the existing policy to reflect the post COVID-19 mortality forecast.