A country reports 8,000 infant deaths and 200,000 live births in a year. What is its infant mortality rate, and why is this measure technically not a 'rate' in the strict demographic sense?
A40 per 1,000; it is technically a rate because it uses the mid-year infant population as the denominator
B40 per 1,000; it is technically a ratio because the denominator (live births) is not the mid-year population at risk but rather a flow measure
C4 per 1,000; the denominator should be the total population, making it a true rate
D40 per 1,000; the distinction between rates and ratios is semantic and has no analytical consequence
IMR = 8,000 / 200,000 x 1,000 = 40 per 1,000. Strictly, a rate uses a mid-year population denominator (a stock measure); the IMR uses live births (a flow measure). This matters analytically because the births in the denominator and the deaths in the numerator do not refer to exactly the same cohort — some deaths in a calendar year are to babies born the previous year, and some babies born this year will die next year.
Question 2 True / False
The epidemiologic transition describes a permanent, irreversible shift from infectious to chronic disease as the dominant cause of death.
TTrue
FFalse
Answer: False
While the epidemiologic transition describes a general historical pattern, it is not necessarily permanent or irreversible. HIV/AIDS caused a reversal in several sub-Saharan African countries, with life expectancy declining substantially in the 1990s and 2000s as an infectious disease became the leading killer. Some scholars identify a 'fourth stage' of re-emerging infectious diseases and antimicrobial resistance. The transition is a useful model, not an iron law.
Question 3 Short Answer
Explain what a cause-deleted life table shows and why it can overestimate the gain in life expectancy from eliminating a cause of death.
Think about your answer, then reveal below.
Model answer: A cause-deleted life table recalculates survival probabilities after removing deaths from a specific cause, showing how much life expectancy would increase if that cause were eliminated. It overestimates the actual gain because it assumes independence of causes — that eliminating one cause does not affect the probability of dying from others. In reality, causes compete: a person 'saved' from heart disease remains at risk of cancer, stroke, and other causes, and many of these share common risk factors. The gain from eliminating one cause is therefore less than the cause-deleted table suggests.
Competing risks are fundamental to mortality analysis. The assumption of cause independence is mathematically convenient but biologically unrealistic — most people who die of one cause had elevated risk of several others. This is why cause-deleted life tables provide an upper bound on the life expectancy gain rather than a precise estimate.