摘要：Objectives. We decomposed the total effect of coexisting diseases on a timed occurrence of an adverse outcome into additive effects from individual diseases. Methods. In a cohort of older adults enrolled in the Precipitating Events Project in New Haven County, Connecticut, we assessed a longitudinal extension of the average attributable fraction method (LE-AAF) to estimate the additive and order-free contributions of multiple diseases to the timed occurrence of a health outcome, with right censoring, which may be useful when relationships among diseases are complex. We partitioned the contribution to death into additive LE-AAFs for multiple diseases. Results. The onset of heart failure and acute episodes of pneumonia during follow-up contributed the most to death, with the overall LE-AAFs equal to 13.0% and 12.1%, respectively. The contribution of preexisting diseases decreased over the years, with a trend of increasing contribution from new onset of diseases. Conclusions. LE-AAF can be useful for determining the additive and order-free contribution of individual time-varying diseases to a time-to-event outcome. Attributable fraction (AF), also called attributable risk, of a risk factor has been extensively used to measure how much of the burden of an adverse health outcome might be reduced if a risk factor were eliminated. 1–3 For example, calculation of the AF of hypertension on death is based on the death rate difference between the absence and the presence of hypertension. The AF is unadjusted for coexisting risk factors because it does not take into account other risk factors. When several risk factors exist simultaneously, the sum of the individual AFs usually exceeds the AF corresponding to eliminating all the involved risk factors. This is because the nonindependence or nonbalance of the coexisting risk factors leads to overlapping contributions to the occurrence of an adverse health outcome. Adjusted AF is a calculation of the AF of a risk factor that takes into account coexisting risk factors and assumes that they are removed before the risk factor of interest. Adjusted AFs of individual risk factors also do not sum to their combined AF. 4–7 Individual AFs derived by a sequential approach may sum to the total AF, 8 but the sequential AF of a risk factor varies greatly across different removal orders. Assuming a particular removal order is often not plausible because the relationships among the risk factors may be nondirectional and complex (e.g., reciprocal, mediational, synergetic, counteracting), which limits the utility of methods based on sequential partitioning. Broadly speaking, risk factors are the presence of a disease or nondisease exposure, and the outcome can be any adverse health event, including a disease that is different from any of the risk factors. We developed an approach to partitioning the combined effects of time-varying risk factors into additive, individual contributions to the occurrence of a timed or time-to-event outcome. In such longitudinal situations, a measure that quantifies a risk factor’s contribution to the outcome must account for possible overlapping effects among coexisting risk factors. An effective measure should be additive (the total contribution from the multiple risk factors combined equals the sum of the measures of contribution of the separate risk factors), should display symmetry (the measures of contribution should be independent of the order the coexisting risk factors were removed or added when the measures are calculated), and should account for the timing and duration of the risk factor exposures and the outcome. This third property is necessary because risk factors may be chronic, with long durations, or acute, with short durations prior to the occurrence of the adverse outcome over time. The methods of AF and adjusted AF lack all 3 properties. 5,9,10 The sequential methods lack the property of symmetry. By contrast, in a cross-sectional setting, the average attributable fraction (AAF) possesses the properties of additivity and symmetry. 11–14 We extended use of AAF to situations with time-varying risk factors and timed or time-to-event outcomes while preserving additivity and symmetry, creating the longitudinal extension of AAF (LE-AAF). The key to achieving additivity and symmetry is averaging the contributions of a disease in all the possible removal orders of the coexisting diseases (i.e., it is the average of the sequentially adjusted AFs). By contrast, an adjusted AF or an AF derived from the sequential method only accounts for one particular order. Samuelsen and Eide developed attributable hazard fractions for time-to-event outcomes, but this measure does not possess the properties of additivity or symmetry. 15 The LE-AAF of a risk factor reveals its fraction of contribution to the timed occurrence of the adverse outcome in the presence of multiple coexisting risk factors that may have a complex relationship. The LE-AAFs correctly allocate the overlapping effects among coexisting risk factors to individual risk factors. The LE-AAF method makes an important contribution by accurately assessing time-varying risk factors and timed occurrence of an outcome, including a time-to-event outcome. Five commonly occurring and prevalent diseases and their association with time to death in a cohort of older adults illustrate the calculation of LE-AAFs. We considered the diseases as risk factors and death as the adverse outcome. Most deaths in older adults occur in persons with multiple coexisting diseases. 16 These coexisting diseases have both separate and overlapping effects on death. 17–19 The contribution of these coexisting diseases is not captured in current methods that assign cause of death according to medical decision rules applied to death certificates. 20 The cause of death recorded on a death certificate thus reflects a medical decision on the overlapping effects of coexisting diseases rather than a quantitative allocation of overlapping effects.
