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Estimates of Age-Specific Mortality Rates from Sequential Cross-Sectional Data in Malawi

DOI: 10.1155/2012/194187

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Abstract:

This paper uses a method for estimating age-specific event rates for adults (15–49 years) in Malawi between 1977 and 1998. This method, which is based on the development of unstable populations, is similar to the “variable-r” methods. Data from Malawi demonstrate mortality reduction nearly for all age groups between 1977 and 1987 for males whereas for females the reduction was observed for age groups 15–19 and 40–44. Contrary to this finding, the 1987–1998 intercensal period shows that mortality increased at a higher rate in the ages 20 and above for males than females. However, the increase for the females is much higher in the 1987–1998 intercensal period than in the 1977–1987 intercensal period. These findings may be related to the onset and effect of the AIDS epidemic. Implications for future research are discussed. 1. Introduction The availability of cross-sectional data to demographers provides an opportunity to learn about event rates by comparing changes between the two periods of interest. In the case of Malawi, the available census data between 1987 and 1998 can be used to investigate, among other things, age-specific mortality rates over the 1987–1998 inter-ensal period for males and females. This means that we can compare the “before” and “after” schedules of mortality. In this case, the “before” schedule may be associated with the 1987 census as the period characterized by low mortality compared with the “after” schedule as this period is before 1998 is characterized by high mortality. Schmertmann [1] provides a simple formula for estimating event rates from cross sectional data. The formula may also be used in general applications such as learning about young adults’ moves in and out of parental homes by comparing age schedules of the proportion living with parents from consecutive censuses or to study prevalence of smoking by age. Thus, rates can be inferred from cross-sections in many fields. 2. Background Malawi lies in the eastern and southern African “AIDS-belt” with a population of about 10 million according to the 1998 census. (The estimated population in 2011 was 15 million (http://www.countrystat.org/mwi/en; accessed on 1 November 2011).) Malawi is a poor country, overwhelmingly agrarian, and about 90% of the population reside in rural areas [2]. Malawi’s agrarian economy accounts for more than 90% of its export earnings, contributes 45% of GDP, and supports 90% of the population. The country’s export trade is dominated by tobacco, tea, cotton, coffee, and sugar. In general, Malawi is very poor, even by African standards: its

References

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