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Mortality analysis examines the patterns, trends, causes, and differentials in death across populations. Key measures include the infant mortality rate (IMR), under-five mortality rate, maternal mortality ratio, and cause-specific death rates. The epidemiologic transition describes the historical shift from infectious and parasitic diseases as leading causes of death to chronic and degenerative diseases — a shift driven by improvements in nutrition, sanitation, public health, and medical care. Mortality differentials by sex, socioeconomic status, race/ethnicity, and geography reveal the social determinants of health. Decomposition techniques allow analysts to attribute changes in life expectancy to specific age groups or causes of death.
Compare cause-of-death profiles and age-specific mortality curves for a low-income and a high-income country. The contrast between a mortality regime dominated by infectious disease in childhood versus one dominated by chronic disease in old age makes the epidemiologic transition concrete.
With life tables providing the framework for converting age-specific mortality into survivorship and life expectancy, mortality analysis adds the dimensions of cause, trend, and differential. The life table tells you *how much* mortality there is at each age; mortality analysis asks *why* — what kills people, how those causes have changed over time, and who is at greatest risk.
The infant mortality rate (IMR) is the most commonly cited single indicator of population health, calculated as deaths under age 1 per 1,000 live births. Despite its name, it is technically a ratio rather than a rate, because the denominator is a flow (births) rather than a stock (mid-year population). This technicality has practical consequences: some deaths counted in the numerator for a calendar year occurred to babies born the previous year, and some babies born this year will die in the next. Neonatal mortality (deaths in the first 28 days) and postneonatal mortality (deaths from 28 days to 1 year) have different cause profiles — neonatal deaths are dominated by congenital conditions and birth complications, while postneonatal deaths are more sensitive to nutrition, sanitation, and infectious disease.
The epidemiologic transition, conceptualized by Abdel Omran in 1971, describes the shift in cause-of-death patterns that accompanies development. In the first stage ("age of pestilence and famine"), infectious diseases, malnutrition, and maternal complications dominate, and life expectancy is low. In the second stage ("age of receding pandemics"), public health improvements reduce infectious mortality, and life expectancy rises. In the third stage ("age of degenerative and man-made diseases"), chronic conditions — heart disease, cancer, stroke — become the leading killers as populations age. Later scholars added a fourth stage of delayed degenerative diseases and a possible fifth stage of re-emerging infections. The model is useful but not deterministic: HIV/AIDS reversed the transition in several African countries, demonstrating that progress is contingent, not guaranteed.
Mortality differentials reveal how death is socially patterned. In virtually every studied population, mortality varies by sex (women live longer), socioeconomic status (higher income and education predict lower mortality), race/ethnicity (reflecting structural inequality rather than biology), and geography (urban-rural gaps, regional variation). Decomposition methods — particularly Arriaga's and Pollard's techniques — allow analysts to attribute a change in life expectancy to contributions from specific age groups and causes, answering questions like "how much of the gap in life expectancy between men and women is due to cardiovascular mortality in ages 50-69?"
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