An illustration of and programs estimating attributable fractions in large scale surveys considering multiple risk factors

Background: Attributable fractions (AF) assess the proportion of
cases in a population attributable to certain risk factors but are
infrequently reported and mostly calculated without considering
potential confounders. While logistic regression for adjusted
individual estimates of odds ratios (OR) is widely used, similar
approaches for AFs are rarely applied. Methods: Different methods
for calculating adjusted AFs to risk factors of cardiovascular
disease (CVD) were applied using data from the National Health and
Nutrition Examination Survey (NHANES). We compared AFs from the
unadjusted approach using Levin's formula, from Levin's formula
using adjusted OR estimates, from logistic regression according to
Bruzzi's approach, from logistic regression with sequential removal
of risk factors ('sequential AF') and from logistic regression with
all possible removal sequences and subsequent averaging ('average
AF'). Results: AFs following the unadjusted and adjusted (using
adjusted ORs) Levin's approach yielded clearly higher estimates
with a total sum of more than 100% compared to adjusted approaches
with sums < 100%. Since AFs from logistic regression were
related to the removal sequence of risk factors, all possible
sequences were considered and estimates were averaged. These
average AFs yielded plausible estimates of the population impact of
considered risk factors on CVD with a total sum of 90%. The average
AFs for total and HDL cholesterol levels were 17%, for hypertension
16%, for smoking 11%, and for diabetes 5%. Conclusion: Average AFs
provide plausible estimates of population attributable risks and
should therefore be reported at least to supplement unadjusted
estimates. We provide functions/macros for commonly used
statistical programs to encourage other researchers to calculate
and report average AFs.