Smoothed Temporal Atlases of Age-Gender All-Cause Mortality in South Africa

Abstract

Most mortality maps in South Africa and most contried of the sub-Saharan region are static, showing aggregated count data over years or at specific years. Lack of space and temporral dynamanics in these maps may adversely impact on their use and application for vigorous public health policy decisions and interventions. This study aims at describing and modeling sub-national distributions of age–gender specific all-cause mortality and their temporal evolutions from 1997 to 2013 in South Africa. Mortality information that included year, age, gender, and municipality administrative division were obtained from Statistics South Africa for the period. Individual mortality level data were grouped by three ages groups (0–14, 15–64, and 65 and over) and gender (male, female) and aggregated at each of the 234 municipalities in the country. The six age-gender all-cause mortality rates may be related due to shared common social deprivation, health and demographic risk factors. We undertake a joint analysis of the spatial-temporal variation of the six age-gender mortality risks. This is done within a shared component spatial model construction where age-gender common and specific spatial and temporal trends are estiamted using a hierarchical Bayesian spatial model. The results show municipal and temporal differentials in mortality risk profiles between age and gender groupings. High rates were seen in 2005, especially for the 15–64 years age group for both males and females. The dynamic geographical and time distributions of subnational age-gender all-cause mortality contribute to a better understanding of the temporal evolvement and geographical variations in the relationship between demographic composition and burden of diseases in South Africa. This provides useful information for effective monitoring and evaluation of public health policies and programmes targeting mortality reduction across time and sub-populations in the country.

Metropolitan age-specific mortality trends at borough and neighborhood level: The case of Mexico City

“…Regarding the description of spatial mortality trends, two main approaches are used: i) Allcause or ii) Cause-specific mortality studies. In the first case, spatial and temporal variability have been measured, using different levels of granularity [13][14][15][16][17]. For cause-specific mortality several models have been developed for diseases such as cancer, diabetes, hypertension, chronic obstructive pulmonary disease, cardiovascular disease, hepatitis C and HIV/AIDS [6,14,[17][18][19][20][21][22][23][24][25][26].…”

Metropolitan age-specific mortality trends at borough and neighbourhood level: The case of Mexico City

“…In regard to the description of spatial mortality trends, there exists basically two main opposite approaches: i) All-cause or ii) Cause-specific mortality studies. In the first case, all-cause mortality rates, including its spatial and temporal variability have been measured, using different levels of granularity [11,12,13,14,15]. Contrarily, several cause-specific mortality models have been developed for diseases such as cancer, diabetes, hypertension, chronic obstructive pulmonary disease, cardiovascular disease, hepatitis C and HIV/AIDS [16,12,15,17,18,19,20,21,22,6,23,24].…”

Multivariate Bayesian meta-analysis: joint modelling of multiple cancer types using summary statistics

“…For example, [18] applied multivariate disease mapping of seven prevalent cancer types in Iran using a shared component model. A joint-analysis of the spatio-temporal variation of the six age-gender (three ages groups (0-14, 15-64, and 65 and over) and gender (male, female)) mortality risks was performed by [19] using a shared component spatio-temporal model. Bayesian shared component spatio-temporal models for male and female lung cancer was applied to analyse the spatio-temporal variation of lung cancer diagnosis [20,21].…”