Article Text
Abstract
BACKGROUND As the definitional formula for population attributable fraction is not usually directly usable in applications, separate estimation formulas are required. However, most epidemiology textbooks limit their coverage to Levin's formula, based on the (dichotomous) distribution of the exposure of interest in the population. Few present or explain Miettinen's formula, based on the distribution of the exposure in the cases; and even fewer present the corresponding formulas for situations with more than two levels of exposure. Thus, many health researchers and public health practitioners are unaware of, or are not confident in their use of, these formulas, particularly when they involve several exposure levels, or confounding factors.
METHODS/RESULTS A heuristic approach, coupled with pictorial representations, is offered to help understand and interconnect the structures behind the Levin and Miettinen formulas. The pictorial representation shows how to deal correctly with several exposure levels, and why a commonly used approach is incorrect. Correct and incorrect approaches are also presented for situations where estimates must be aggregated over strata of a confounding factor.
 attributable risk epidemiology
 regression
 casecontrol study
 aetiological fraction
 statistics
 confounding
 matching stratified data
Statistics from Altmetric.com
 attributable risk epidemiology
 regression
 casecontrol study
 aetiological fraction
 statistics
 confounding
 matching stratified data
The population attributable fraction (AF_{p}) is defined1 (page 295) as “the fraction of all cases (exposed and unexposed) that would not have occurred if exposure had not occurred.” This fraction can be estimated using two equivalent formulas, based on the distribution of exposure in the population,2 or in the cases.3 Most textbooks deal only with the exposure in the population and do not consider confounding factors. Many of them focus on deriving the formula algebraically or minimising the number of computational steps, thereby providing limited insight into the structure of the formula. Only a few texts elaborate on the “casebased” formula. As a result, although it is increasingly used to derive estimates of AF_{p}s from complex data,48 the casebased formula is less widely known and less well understood.
Likewise, despite the long existence3 9 of the corresponding AF_{p} formulas for more than two levels of an exposure of interest, and despite the fact that three advanced textbooks1012 do present and even illustrate them, many authors seem to be unaware of them. This author has recently encountered three prepublication examples where, with multiple exposure levels, the AF_{p} was calculated incorrectly. Table1 shows a published example13 of this same error.
Stratification and—increasingly—regression models are used to provide confounder adjusted rate ratio (RR) estimates as inputs to the calculation of AF_{p}s. As textbooks do not discuss such situations, and understanding of first principles is limited, the AF_{p} is often miscalculated in such instances too.14 Indeed, in addition to mishandling a trichotomous exposure, the above cited report13 also fails to correctly incorporate the adjusted RR into the AF_{p} calculation.
The primary aim of this article is to promote understanding of the AF_{p} formulas for a polytomous exposure. To do so, the article begins with the more familiar all or none exposure. A numerical example and a diagram allow the Levin and the Miettinen formulas to be understood directly from first principles, without algebra. This heuristic approach provides a foundation from which to extend the AF_{p} formulas correctly to polytomous exposure data, and to data stratified on a confounding variable.
Population (or population time) at risk and cases will be denoted by the letters P and C respectively. The fractions of the population (or population time) in the various exposure categories are denoted by “population fractions” (PFs), while the distribution of exposure in the cases is denoted by “case fractions” (CFs).3 The terms overall and population attributable fraction are used interchangeably. Given the difficulties15 of interpreting it as a true “aetiological” fraction, particularly when a long time span and competing risks can substantially change denominators, the AF_{p} is simply regarded as an “excess” fraction.15
All or none exposure
The exposed and unexposed categories are denoted by 1 and 0 and the ratio of the event rates in these two categories as RR: 1.
FORMULAS
Classic (Levin) structure for AF_{p}, based on distribution of exposure in population
Denote by PF_{1} the proportion (or fraction) of the total population time in the exposed category, and by PF_{0}the proportion in the unexposed category. The most popular1
11
1618 formula for AF_{p} is Levin's original version. Levin began by defining the AF_{p}: its denominator is the rate (or number of cases) in the overall population, and its numerator is the difference between this and the one that would prevail if all of the person time were in the unexposed category. From this, he algebraically derived the estimating formula
Attributable fractions for specific exposure categories.
The casebased version uses as one of its inputs the “attributable fraction in the exposed”, namely
This is a specific AF, as it restricts attention to exposed cases. To emphasise this specificity, we label it AF_{1}. The underappreciated fact that the “attributable fraction in theunexposed” is 0 becomes important later, and so we label the AF specific to cases in that category as AF_{0} = 0.
(Miettinen) structure for AF_{p}, based on distribution of exposure in cases
The casebased version uses as its other input the number of exposed cases, expressed as a fraction of the overall number of cases. Denote this fraction as the “case fraction”,3CF_{1}. Then the casebased formula for AF_{p}is
or in the notation used here,
AF_{p} = AF_{1} × CF_{1}. [1C]
NUMERICAL EXAMPLE
Suppose, as is depicted in figure 1, that PF_{1} = 2/5th of the population time (PT) is in the exposed category. Although the AF_{p} involves relative rather than absolute rates, suppose—for concreteness—that the event rates in the exposed and unexposed categories are 1.5 and 1.0 cases per 10^{4} PT units, so that the RR = 1.5, and the rate difference = 0.5 cases per 10^{4} PT units. Suppose further that the total population time is 10^{6} PT units.
Substitution of PF_{1} = 2/5 and RR—1 = 0.5 into formula [1P] yields
THE NUMBERS AND STRUCTURES BEHIND THE FORMULAS
Conceptually, the Levin formula directly divides the total number of cases into “expected” cases—those that would occur even if all of the PT were in the unexposed category—and “excess” cases. With a total of 10^{6} units of PT, there are 10^{6} × (1.0 × 10^{–4}) = 100 “expected” cases. Some 2/5th of the overall 10^{6} PT units are in the exposed category, where the excess rate is 0.5 per 10^{4} PT units. The product of this PT and the excess rate in this category is 20 “excess” cases. These 20 represent 1/6th of the overall total of 100 + 20 = 120 cases. Note that the 20 can also be represented as the “observed−expected” number, while the 120 represent the “observed” number, in keeping with the structure in Miettinen's 1985 text (page 254–256),3 and Levin's2original conceptualisation.
The casebased formula begins with the same total of 120 cases and immediately “rules out” all unexposed cases, as, by definition, none of them are “excess” cases. Based on the amount of unexposed PT, they number 60, or 3/5th of the 100 “expected” cases discussed above. This leaves 60 exposed cases (1/2 of the overall total); if RR > 1 and finite, then only a fraction of these 60 exposed cases (or of the 1/2) are excess cases (that is, the maximum possible AF_{p} is 1/2). What fraction of these 60 represent excess cases? The RR=1.5 implies that of every 1.5 exposed cases, 1 is “expected”, while 0.5 of the 1.5, or 1/3rd, are excess. Thus, of the subtotal of exposed 60 cases, 20 are “excess” cases. As the subtotal of 60 exposed cases constitute 1/2 of all cases, and as only 1/3rd of the 60 are excess, then 1/3rd of 1/2, that is, 1/6 of all cases are excess cases. This 1/6th is thus a “fraction of a fraction”. The one fraction (1/2) is simply what fraction of all cases are exposed cases—which we have denoted by CF_{1}. The other, RR—1 as a fraction of RR, that is, 1/3, is the AF specific to the exposed category, namely AF_{1}.
THESE NUMBERS AND FORMULAS REPRESENTED PICTORIALLY
Figure 1 begins “at the base” with the denominators—the PT distribution—that generated the cases. Some 3/5th (= PF_{0}) of the PT are in the unexposed category (empty area) and 2/5th (= PF_{1}) are in the exposed category (shaded area). The cases that arise from these are represented by empty or shaded circles respectively; “excess” cases are marked with an “X” (for “excess”), while “expected” cases are not.
The number of excess cases as a fraction of all cases can be seen from two different views. In the first (Levin), the total number of cases is directly subdivided into two arrays—the (bottom) square array of expected cases, and the (upper) rectangular array of excess cases. With its height (representing the rate in category 0) arbitrarily scaled to 1, and with its entire base of 1, the square array of “expected” cases has an area of 1, representing one expected case. The width of the rectangle of excess cases is PF_{1} = 2/5 and its height is RR—1 = 1.5—1 = 0.5, so that its area (the number of “excess cases per one expected case”) is (RR—1) × PF_{1} = 0.5 × 2/5 = 0.2, yielding AF_{p} = 0.2/(1 + 0.2) = 0.2/1.2 = 1/6.
In the other view (Miettinen), the total number of cases is first subdivided into two arrays (and thus “casefractions”) on the basis of exposure. Only the exposed (shaded) cases (a fraction CF_{1} of all cases) are “eligible” to be excess cases. These exposed cases are then further subdivided into subarrays of excess and expected cases, yielding the attributable fraction AF_{1} specific to the exposed cases. An attraction of this “fraction of a fraction” structure of the overall AF_{p}is that it does not explicitly involve the 3/5 : 2/5 exposure distribution in the source PT—a distribution that is not always easy to estimate—but rather the 60:60 split of the cases themselves.
For those who prefer algebra to pictures, an algebraic derivation of the casebased formula is given in the .
POPULATION ATTRIBUTABLE FRACTION AS A WEIGHTED AVERAGE
Unfortunately, immediately “eliminating” the unexposed cases, and focusing on the exposed ones, distracts from the fact that the casebased AF_{p} can also be viewed as a weighted average of the two category specific attributable fractions AF_{0}(= 0) and AF_{1}. Naturally, as the focus is on all cases, the weights are given by the relative numbers of cases in exposure categories 0 and 1—that is, by the proportions CF_{0} and CF_{1}. Thus the weighted average of the two category specific fractions AF_{0} and AF_{1} across both categories of cases is 0 × CF_{0} + AF_{1} × CF_{1} = AF_{1} × CF_{1}. This representation of AF_{p} as a weighted average3 is key to understanding the casebased formula for the polytomous exposure situation considered next.
Polytomous exposure
Figure 2 depicts the data, and illustrates the correct AF_{p} calculations, for the “three exposure levels” example given in table 1.
CLASSIC STRUCTURE, BASED ON DISTRIBUTION OF EXPOSURE IN POPULATION
Again, the expected cases are shown in the square array of unmarked circles. Now, there are two sets of excess cases, denoted by the lightly shaded and more heavily shaded rectangular arrays of cases marked with an “X”. The scaled heights, {RR_{1}—1} and {RR_{2}—1}, of these rectangles, multiplied by their widths, PF_{1} and PF_{2} , yield excess “areas” of {RR_{1}—1} × PF_{1} and {RR_{2}—1} × PF_{2} respectively. These products represent the number of excess cases for every one “expected”. This “expected” versus “excess” partition of the cases leads immediately to the formula
For every one expected case, there are 0.12 + 0.14 = 0.26 “excess” cases, yielding an AF_{p} of 0.26/1.26 = 20.6% (last column of table 1). Note that applying formula 1P [all or none exposure] twice13 (second last column of table 1), overestimates the overall fraction of excess cases.
STRUCTURE BASED ON DISTRIBUTION OF EXPOSURE IN CASES
In this view, the AF_{p} is a sum of 2 “fractions of fractions”, that is,
as originally given in reference 3. As AF_{0}=0, the AF_{p} can also be seen as a weighted average of the category specific AFs over all 3 levels 0, 1 and 2
AF_{p} = CF_{0} × AF_{0} + CF_{1}× AF_{1} + CF_{2} × AF_{2}[2C']
For the data in table 1 and figure 2, the appropriate calculation is
(50/126) × 0 + (42/126) × (0.4/1.4) + (34/126) × (0.7/1.7),
yielding the “CF weighed” average, AF_{p} = 20.6%.
The explains a version that is useful when there are several strata or covariate patterns.
EFFECT OF COLLAPSING CATEGORIES OF HIGHER RISK INTO ONE
Several authors have noted11 19 20 or shown7 that the AF_{p} involving an exposure with levels 0, 1, ..., k is the same as if one first combined categories 1 to k and used the formula for the “all or none” situation. This is easy to see from figure 2, where the RR for the “moderate or high” category, relative to low, is 1.52, and PF_{moderate/high} = 0.5. Thus, for every one expected case, there are 0.5 × 0.52 = 0.26 excess cases, yielding AF_{p} = 0.26/1.26 = 20.6%.
The “distributive”21 property of the AF_{p}is useful in multiple regression if, instead of aggregating exposure categories, one subdivides them, to the point that each case defines its own exposure category. Details are given in the .
Stratified data
To correctly understand how to aggregate stratum specific AF_{p}s, first write
Then, with Σ denoting summation over the strata that form the aggregate, disaggregate the numbers of cases so that
Finally, rewrite this as
that is, as a weighted average of stratum specific AF_{p}s, with the numbers of cases in the each stratum as weights.
Figure 3 illustrates the correct calculation. Whether one arrives at the stratum specific AF_{p}s “by P or by C”, one must average them using the stratum specific numbers (or proportions) of cases as weights. The figure also illustrates the commonly used, but incorrect practice of coupling adjusted (RR1)s with the marginal distribution of exposure in the overall source.
Discussion
The primary aim of this article is to promote understanding of the AF_{p} formulas for a polytomous exposure, and for stratified data. To this end, you must begin with the simpler and more familiar, but not fully understood, representations for an all or none exposure. The article also shows how the two seemingly very different representations can both be derived—without algebraic manipulations—directly from the same diagram. A third aim is to increase awareness of the casebased formulas.
There are a number of possible explanations for the limited awareness and understanding of the casebased formulas. Some textbooks focus on the overall AF before (or without ever) dealing with the specific AFs that are aggregated to create the overall AF. Also, the casebased formula is given in fewer textbooks, usually without a complete derivation. The usually cited source3 does not explicitly derive it; instead it cites another source,22 which in turn cites an unpublished source. It was acknowledged (page 331)3 that the basis for the formula “may not be immediately obvious” and a cryptic explanation was offered. The “fraction of a fraction” formula is derived 11 years later (equation A.2.17, page 256)10 but in a seemingly different context, and using a purely algebraic manoeuvre that does not reveal the logic behind it (see ). The lengthy way in which the formula continues to be algebraically derived in subsequent articles and textbooks4 5 7 11 23 suggests that the simplicity and “immediate obviousness” of its structure have not been fully or widely understood.
The most important practical benefit of the casebased version is AF_{p} estimation from stratified, or individually matched, casecontrol studies22 where the classic formula is inappropriate.19 24 25 Variations on this version (see) are also increasingly used to derive—and quantify the sampling variability of—estimators of AF_{p} from stratified data or multiple logistic regression.48
The casebased structure also has conceptual benefits. Firstly, it emphasises that AFs refer to cases, and that the observed numbers of cases are the denominators of these AFs. This is in contrast with most epidemiological calculations, where numbers of cases serve as the numerators of statistics. As exemplified in figure 3, this difference has important implications for how to correctly aggregate stratum specific AF_{p}s—no matter which version of the formula (classic or casebased) is used to calculate the stratum specific AF_{p}s. Failure to appreciate this focus on cases may explain why authors, such as those of reference 13, incorrectly couple adjusted (RR1)s with the marginal distribution of exposure in the source via formulas [1P] and [2P]. This is a common mistake.14 It is of note, and testimony to the naturalness of the casebased representation, that in the example in figure 3, the weighted average of the stratum specific AF_{p}s (the AF_{p}s having been derived from adjusted RRs), using the stratum specific numbers (or proportions) of cases as weights, yields the correct AF_{p}for the aggregate.
Secondly, although originally derived for empirical estimates from casecontrol studies, the versatility of the casebased representation can be used in a broader context—for example, to structure the very AF_{p} parameter itself.3 8 (section 2, page 866). For these practical and conceptual reasons, the casebased representation needs to be better understood, and not presented in textbooks and articles simply as an algebraic fact.
Greater awareness and understanding of the formulas for polytomous exposure should also decrease computational errors. Even in the absence of confounding, the repeated application of formula [1P], once for each exposure category separately13 yields an overestimate of AF_{p}. As is made obvious by figure 2, a single application of formula [2P] yields the correct estimate.
Although its purpose was “multivariate” from the outset, the paper by Eide and Gefeller26 uses a graphical depiction similar to that presented here. It is helpful to think all of the covariate patterns shown in figure 1 of the Eide and Gefeller article as different levels of a single composite factor, in the spirit of the single polytomous factor in figure 2 of the present article.
The heuristic approach also gives insights into more realistic, and more complex, scenarios than are discussed in introductory textbooks. Indeed, it was questions from a colleague, in a study involving four levels of risk, and the consequences of switching, not to the lowest risk category, but to lower risk categories, that prompted the author to produce diagrams similar to figure 2. Readers are referred elsewhere10 (appendix 2.3, page 254–6)12 27 28 for more on this topic.
Technical details on estimating AFp from regression models can be found in papers by Benichou7 and Greenland and Dresher (page 1763).8 Benichou warns that his method for calculating the precision of the estimates is “complex”. The methods used by Greenland and Dresher are more tractable, but the matrix notation and associated calculations may still require the help of a statistician. The portion of the article by Ojaet al 29 that deals with conventional logistic regression modelling, and in particular the hand workable example in the , is a useful point of departure before tackling either of these papers. If you wish to avoid matrix calculations, then bootstrap confidence intervals are an attractive alternative.30
Acknowledgments
The author is grateful to Drs Robert Allard, JeanFrançois Boivin, Michael Kramer and Olli Miettinen for comments and advice on the various versions of this manuscript.
FORMAL ALGEBRAIC DERIVATION OF CASEBASED FORMULA 1C
Even for those who prefer algebra to pictures, the majority of the published derivations of formula 1C are much more tedious than they need be. The simplest algebraic derivation is found in Miettinen's text (page 256).10 It uses the same “fraction of a fraction” logic used to determine that the percentage of eligible subjects who respond to a survey is the percentage of eligible subjects contacted × the percentage of contacted subjects who respond.
A USEFUL REEXPRESSION OF THE CASEBASED FORMULAS
The version of the “casebased” structure that has become popular as a point of departure for multivariate applications in the past 15 years is, for the three exposure levels example (equation 12, page 327).3
where RR_{0} = 1. One can algebraically derive formula 2C* from formula 2C, by rewriting each specific AF in terms of the corresponding RR, and simplifying terms. However, it is more instructive to view the process as taking the complement of the “expected” fraction. In figure 2, the overall “expected” fraction is the sum of three fractions: Of the (50) cases in exposure category 0, the fraction of “expected” cases is 1; of the (42 and 34) cases in categories 1 and 2, the corresponding fractions are 1/RR_{1} = 10/14th, and 1/RR_{2} = 10/17th. Thus, the fraction of the overall cases that are “expected” is the weighted average of the fractions 1, 10/14, and 10/17, with weights given by the case fractions CF_{0} = 50/126, CF_{1} = 42/126, and CF_{2} = 34/126. Thus, the complement of AF_{p}
= (50/126) × 1 + (42/126) × 10/14 + (34/126) × 10/17
= 100/126,
leading immediately, by subtraction, to formula 2C*. Note, however, that unlike formula 2C, this “complement” method requires summation over all levels of the exposure.
EFFECT OF SUBDIVIDING (INDIVIDUALISING) CATEGORIES OF HIGHER RISK
Imagine that, instead of aggregating exposure categories, you continue to subdivide them, to the point that each case defines its own exposure category (this would happen if the exposure takes on values on a continuum, or is a multivariate “x” vector in a multiple regression. Then by the “distributive” property21 of the AF_{p},
where RR_{i} is the (unconfounded) RR for the covariate pattern of the ith case, and where the summation is over all of the individual cases. This structure is useful in complex designs30 and when constructing AF_{p} from a logistic regression in which each case has a unique covariate pattern.
References
Footnotes

Funding: this work was supported by an operating grant from the Natural Sciences and Engineering Research Council of Canada.

Conflicts of interest: none.