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Analysis of Survival Probabilities Using Nigerian Mortality Data

Abstract

The study examines the analysis of survival probabilities using Nigerian mortality data, focusing on the estimation of the likelihood that individuals in different age groups will survive to specified future ages. Survival probabilities are important components of actuarial analysis because they provide a basis for estimating life expectancy, insurance benefits, pension obligations, and other financial commitments that depend on mortality patterns. Nigerian mortality data can therefore provide useful information for developing survival estimates relevant to local actuarial applications. The World Health Organization provides life-table indicators for Nigeria, including age-specific mortality rates, probabilities of dying, numbers surviving, and expectation of life. The study will investigate survival probabilities across selected age groups using Nigerian mortality data and examine variations in survival patterns by age and sex. It will assess how mortality experience affects the probability of surviving from one age interval to another and identify the implications of these probabilities for actuarial applications. WHO mortality estimates for Nigeria draw on multiple data sources and modelling approaches because complete and reliable mortality data are not consistently available, particularly for adult and older-age mortality. The analysis will focus on age-specific mortality rates, probabilities of dying, survival probabilities, life expectancy, age groups, and sex-specific mortality patterns. Life-table techniques will be applied to derive survival probabilities from mortality information, while comparative analysis will be used to examine differences across selected age groups and demographic categories. The study will also consider how changes in mortality rates are reflected in the resulting survival estimates. A quantitative research approach will be adopted for the study. Secondary Nigerian mortality and life-table data will be obtained from reliable sources such as the World Health Organization and relevant international demographic databases. Descriptive statistics, life-table methods, mortality-rate analysis, survival probability calculations, and comparative analysis will be employed to examine the data. WHO life tables include measures such as the probability of dying between specified ages and the number of people remaining alive at a given age, which are directly relevant to the proposed analysis. The study is expected to reveal clear variations in survival probabilities across different age groups, with survival generally changing as age-specific mortality patterns change. It may also reveal differences between male and female survival patterns and variations across mortality-data periods. The analysis is expected to demonstrate that reliable mortality information can provide a useful basis for estimating survival probabilities and understanding the demographic patterns relevant to actuarial applications. The findings are expected to provide useful information for actuaries, life insurance companies, pension administrators, researchers, and other users of mortality information. Improved analysis of Nigerian survival probabilities may support more appropriate assumptions for life insurance valuation, annuity calculations, pension planning, and demographic forecasting. The study may also help highlight the importance of using locally relevant mortality information when developing actuarial models for Nigerian populations. The study concludes that Nigerian mortality data provide an important basis for analysing survival probabilities and understanding age-specific patterns of mortality. It is therefore recommended that actuaries and other users of demographic data regularly update survival estimates using reliable Nigerian mortality information and clearly account for the limitations associated with modelled mortality estimates and incomplete mortality registration.

Keywords: Survival probabilities, Nigerian mortality data, mortality rates, life tables, actuarial analysis, survival analysis, life expectancy, age-specific mortality, mortality experience, demographic analysis, actuarial modelling, mortality estimation, population survival, life insurance, pension valuation.