Difference between revisions of "Attributable risk"

Revision as of 11:50, 17 March 2016

Population attributable fraction (PAF) of an exposure agent is the fraction of disease that would disappear if the exposure to that agent would disappear.

Question

How to calculate population attributable fraction?

Answer

Probably this is the most useful form of population attributable fraction (PAF or AF_p) for impact assessment:

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): AF_p = \Sigma_i p_{ci} \frac{p_i(RR_i - 1)}{p_i(RR_i - 1) + 1},

where

• p_ci is the proportion of cases falling in subgroup i (so that Σ_ip_ci = 1),
• p_i is the fraction of exposed people within subgroup i (and 1-p_i is the fraction of unexposed),
• RR_i is the risk ratio for subgroup i due to the subgroup-specific exposure level (assuming that everyone in that subgroup is exposed to that level or none). --# : Does the equation hold if exposure level varies between groups? --Jouni (talk) 15:43, 25 April 2014 (EEST)

p_ci can be calculated for each subgroup with the following equation if the background risk of disease is equal in all subgroups (and thus cancels out):

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): p_{ci} = \frac{N_i \Pi_j RR_{i,j}}{\Sigma_i N_i \Pi_j RR_{i,j}},

where

• N_i is the number of people in each subgroup i,
• RR_i,j is the risk ratio in subgroup i due to pollutant j (accounting for the estimated exposure in the subgroup). Note that this assumes that multiplicative assumption holds between different pollutant effects.

This page does not contain R code. Instead, it is written as part of the model in Health impact assessment.

library(OpasnetUtils)
AF <- function(x) {return(data.frame(RR = x, AF = (x-1)/x, EF_lower = (x-1)/x^(x/(x-1)), EF_upper = 1))}

oprint(AF(RR))

Rationale

WHO approach

^[1] PAF is

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): PAF = \frac{\Sigma_{i=1}^n P_i RR_i - \Sigma_{i=1}^n P'_i RR_i}{\Sigma_{i=1}^n P_i RR_i}

where i is a certain exposure level, P is the fraction of population in that exposure level, RR is the relative risk at that exposure level, and P' is the fraction of population in a counterfactual ideal situation (where the exposure is typically lower).

Based on this, we can limit our examination to a situation where there are only two population groups, one exposed to background level (with relative risk 1) and the other exposed to a higher level (with relative risk RR). In the counterfactual situation nobody is exposed. Thus, we get

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): PAF = \frac{(P RR + (1-P)*1) - (0*RR + 1*1)}{P RR + (1-P)*1}

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): PAF = \frac{P RR - P}{P RR + 1 - P}

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): PAF = \frac{P(RR - 1)}{P(RR -1) + 1}

See also Attributable risk, although it is a stub.

This equation is used in e.g. Health impact assessment.

Rothman approach

Modern Epidemiology ^[2] is the authoritative source of epidemiology. They first define attributable fraction AF for a cohort of people (pages 295-297). It is the fraction of cases among the exposed that would not have occurred if the exposure would not have taken place:

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): AF = \frac{RR - 1}{RR},

where RR is the causal risk ratio.

The population attributable fraction AF_p is that fraction among the whole cohort:

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): AF_p = \frac{N_1 (R_1 - R_0)}{N_1 R_1 + N_0 R_0} = \frac{N_1 (R_1 - R_0)/R_0}{N_1 R_1/R_0 + N_0 R_0/R_0} = \frac{N_1 (RR - 1)}{N_1 RR + N_0}

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): = \frac{ \frac{N_1 (RR - 1)}{N_1 + N_0} }{ \frac{N_1 RR + N_0}{N_1 + N_0}} = \frac{ p (RR - 1) }{ \frac{N_1 RR - N_1 + (N_1 + N_0)}{N_1 + N_0}} = \frac{p (RR - 1)}{p RR - p + 1} = \frac{p (RR - 1)}{p (RR - 1) + 1},

where

• N₁ and N₀ are the numbers of exposed and unexposed people, respectively,
• R₁ and R₀ are the risks of disease in the exposed and unexposed group, respectively, and RR = R₁ / R₀,
• p is the fraction of exposed people among the whole cohort.

Note that there is a typo in the Modern Epidemiology book: the denominator should be p(RR-1)+1, not p(RR-1)-1.

Population attributable fraction can be calculated as a weighted average based on subgroup data:

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): AF_p = \Sigma_i p_{ci} AF_{pi},

where

• p_ci is the proportion of cases falling in stratum (subgroup) i,
• AF_pi is the population attributable fraction calculated for the subgroup.

Specifically, we can divide the cohort into subgroups based on exposure (in the simplest case exposed and unexposed), so we get

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): AF_p = p_c \frac{1(RR - 1)}{1(RR - 1) + 1} + (1 - p_c) \frac{0(RR - 1)}{0(RR - 1) +1} = p_c \frac{RR - 1}{RR},

where p_c is the proportion of cases in the exposed group among all cases; this is the same as exposure prevalence among cases.

p_c can be calculated by first calculating number of cases in each subgroup:

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): cases_i = N_i * background * \Pi_j e^{ln(ERF_{j}) exposure_{i,j}},

where

• cases_i is the number of cases in subgroup i,
• N_i is the number of people in subgroup i,
• background is the background risk of the disease in the unexposed; we assume that it is the same in all subgroups,
• ERF_j is the risk ratio for unit exposure for each pollutant j (if the exposure response function ERF assumes another form than relative risk, i.e. exponential, then another equations must be used),
• exposure_i,j is the amount of exposure in a subgroup i to pollutant j.

Therefore,

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): p_{ci} = \frac{cases_i}{\Sigma_i cases_i} = \frac{N_i * background * \Pi e^{ln(ERF_{j}) exposure_{i,j}}}{background \Sigma N_i \Pi e^{ln(ERF_{j}) exposure_{i,j}}}

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): p_{ci} = \frac{N_i \Pi_j RR_{i,j}}{\Sigma_i N_i \Pi_j RR_{i,j}},

where RR_i,j = exp(ln(ERF_j) exposure_i,j).

In addition, if only fraction p of the population is exposed, for the whole population we get

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): RR = \frac{p * N * background * RR_{exposed} + (1-p) * N * background * RR_{unexposed}}{N * background * RR_{unexposed}}

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): = \frac{p e^{ln(ERF)exposure} + (1-p)1}{1} = p e^{ln(ERF)exposure} -p + 1

Calculations

⇤# : UPDATE AF TO REFLECT THE CURRENT IMPLEMENTATION OF ERF Exposure-response function --Jouni (talk) 05:20, 13 June 2015 (UTC)

library(OpasnetUtils)

# UPDATE AF TO REFLECT THE CURRENT IMPLEMENTATION OF ERF [[Exposure-response function]]
### ESTIMATES OF ATTRIBUTABLE CASES BASED ON ATTRIBUTABLE FRACTION
# We estimate the number of cases and their attributable causes based on [[Population attributable fraction]].

AF <- Ovariable("AF", # Cases attributed to specific (combinations of) causal exposures.
	dependencies = data.frame(Name = c(
		"ERF", # Exposure-response function
		"exposure", # Total exposure to an agent or pollutant
		"frexposed", # fraction of population that is exposed
		"bgexposure" # Background exposure to an agent (a level below which you cannot get in practice)
	)),
	
	formula = function(...) {

		# First calculate risk ratio and remove redundant columns because they cause harm when operated with itself.
		RR <- frexposed * exp(log(ERF) * (exposure - bgexposure)) - frexposed + 1
		PAF <- (RR - 1) / unkeep(RR, sources = TRUE, prevresults = TRUE)

		# pollutants is a vector of pollutants considered.
		pollutants <- as.character(unique(exposure@output$Pollutant))

		expname <- paste(exposure@name, "Result", sep = "")

		out <- 1
		for(i in 1:length(pollutants)) {
			
			# Attributable fraction of a particular pollutant is combined with all pollutant AFs.
			# The combination has 2^n rows (n = number of pollutants). Pollutant is either + or - depending on
			# whether it caused the disease or not.
			temp <- Ovariable("temp", data = data.frame(
				Pollutant = pollutants[i], 
				Temp1 = c(paste(pollutants[i], "-", sep = ""), paste(pollutants[i], "+", sep = "")), 
				Result = c(-1, 1) # Non-causes are temporarily marked with negative numbers.
			))
			temp <- temp * PAF

			# Non-causes are given the remainder (1-AF) of temporary attributable fraction AF.
			result(temp) <- ifelse(result(temp) > 0, result(temp), 1 + result(temp))
			# Causes with 0 AF are marked 1. This must be corrected.
			result(temp) <- ifelse(result(temp) == 1 & grepl("\\+", temp@output$Temp1), 0, result(temp))

			#If exists, the exposureResult is renamed so that it can be kept without side effects.
			#These should not be marginals but there seems to be problems in this respect.
			if(expname != "Result"){
				colnames(temp@output)[colnames(temp@output) == expname] <- paste("expo", pollutants[i], sep = "")
			}
			
			out <- out * temp
			out <- unkeep(out, cols = "Pollutant", sources = TRUE, prevresults = TRUE)

			# Combine and rename columns.
			if(i == 1) {
				colnames(out@output)[colnames(out@output) == "Temp1"] <- "Causes"
			} else {
				out@output$Causes <- paste(out@output$Causes, out@output$Temp1)
				out@output$Temp1 <- NULL
			}
		}
		return(out)
	}
)

objects.store(AF)
cat("Ovariable AF stored.\n")

References