Which is a critical assumption of cumulative incidence
A. Time is an intrinsic part of the denominator
B. The entire population is followed for the entire period
C. Time heals all wounds.
D. The population must not be at risk for the disease
The correct answer and explanation is:
The correct answer is B. The entire population is followed for the entire period.
Cumulative incidence, also known as incidence proportion, measures the probability or risk that an individual in a defined population will develop a disease or outcome during a specified time period. It is calculated by dividing the number of new cases of the disease during the period by the number of individuals initially at risk in the population.
A critical assumption of cumulative incidence is that the entire population at risk is followed for the full duration of the study period. This means that no individuals are lost to follow-up, and everyone is observed for the entire time frame. This assumption is important because the calculation relies on knowing the true number of new cases occurring over the period among all at-risk individuals.
If some individuals are lost to follow-up, or if the observation time varies among participants, the denominator may not accurately represent the population at risk throughout the entire period. This can bias the estimate of cumulative incidence, often underestimating or overestimating the true risk.
In contrast, the other options are not correct assumptions related to cumulative incidence. Option A, “Time is an intrinsic part of the denominator,” is incorrect because time is part of the calculation for incidence rate, not cumulative incidence. Option C, “Time heals all wounds,” is a phrase unrelated to epidemiologic assumptions. Option D, “The population must not be at risk for the disease,” is incorrect because cumulative incidence only applies to populations that are initially disease-free but at risk.
In summary, following the entire population for the complete time period is essential to correctly calculate cumulative incidence and accurately measure disease risk. Without this assumption, the measure can lose validity.