<section begin=glossary />
- Health impact assessment is an assessment method that is used to estimate the health impacts of a particular event or policy. In Europe, it is most widely used in UK, Finland, and the Netherlands.
<section end=glossary />
Question
How to calculate health impacts based on information about exposure, population, disease, and exposure-response function?
Answer
For simple calculations, you can use the concept of attributable fraction. This is presented here. For more complex and comprehensive methods, you may want to consider these:
An example model run by the model below [1].
Inputs
If you are able to describe your data in the format similar to the tables below, you can use ready-made tools in Opasnet and things are quite straightforward. The example tables show data about radon in indoor air.
Exposure
- The table has an index Observation with four locations: Exposed fraction, Background, Exposure, and Description.
Pollutant |
Exposure route |
Exposure metric |
Exposure parameter |
Population |
Exposure unit |
Exposed fraction |
Background |
Exposure |
Description
|
Radon |
Inhalation |
Annual average concentration |
Population average |
Finland |
Bq/m3 |
1 |
5 |
100 |
Kurttio Päivi, 2006: STUK otantatutkimus 100 (95 – 105); background 5 (4 – 9)
|
Radon |
Inhalation |
Annual average concentration |
Guidance value for new apartments |
Finland |
Bq/m3 |
1 |
0 |
200 |
STM decision 944/92 for new apartments [2]
|
Radon |
Inhalation |
Annual average concentration |
Guidance value for old apartments |
Finland |
Bq/m3 |
1 |
0 |
400 |
STM decision 944/92 for old apartments [3]
|
Disease response
- The table has index Observation with two locations: Response and Description.
Disease |
Response metric |
Population |
Unit |
Response |
Description
|
Lung cancer |
Incidence |
Finland |
1/100000 py |
38.058 |
2020/5307690*100000 [4]
|
Lung cancer |
Burden of disease |
Finland |
DALY |
14000 |
Olli Leino 2010: Includes trachea, bronchus, and lung cancers. [5]
|
Exposure-response function
Pollutant |
Disease |
Response metric |
Exposure route |
Exposure metric |
Exposure unit |
Threshold |
ERF parameter |
ERF |
Description
|
Radon |
Lung cancer |
Incidence |
Inhalation |
Annual average concentration |
Bq/m3 |
0 |
RR |
1.0016 |
Darby 2004: 1.0016 (1.0005 – 1.0031)
|
Population
Population |
Year |
Sex |
Age |
Amount |
Description
|
Finland |
2010 |
Total |
All |
5307690 |
[6]
|
Rationale
These are the equations you should use:
RR for exposure
= EXP(LN(RR)*(Exposure Result - MAX(Exposure Background, Exposure-response function threshold)))
Attributable fraction in the whole population
= Exposed fraction * (RR for exposure – 1) / (Exposed fraction *(RR for exposure – 1)+1)
Extra cases per year
=Disease incidence * Population * attributable fraction
Burden of disease of exposure
= Burden of disease of the disase * attributable fraction
Personal lifetime risk
= Extra cases per year * life expectancy * population
Calculations
See also Seturi: Excel file, [7]
+ Show code- Hide code
library(OpasnetUtils)
### ESTIMATES OF ATTRIBUTABLE CASES BASED ON ATTRIBUTABLE FRACTION
# We estimate the number of cases and their attributable causes based on [[Population attributable fractions]].
#popfraction <- 1 # We don't need the fraction of exposed population, because exposure distribution is
# calculated for each population subroup separately. If there are unexposed people, they are already included
# in the exposure distribution.
dose <- Ovariable("dose", # This calculates the body-weight-scaled exposure or "dose" to be used with ERFs.
dependencies = data.frame(Name = c(
"ERF", # Exposure-response function of the pollutants or agents (RR for unit exposure)
"exposure", # Exposure to the pollutants
"BW" # body weight
)),
formula = function(...) {
#################################################################33
######### Body weight scaling: In some cases, exposure is given as per body weight and in some cases as absolute amounts.
# Here we add one index to define for this difference.
scaling <- unique(ERF@output[c("ERF_parameter", "Exposure_agent")])
scaling <- Ovariable("scaling", data = data.frame(
scaling,
Result = 1 * grepl("bw", scaling$ERF_parameter) # if ERF parameter contains bw, scaling is TRUE, i.e. 1.
))
out <- exposure / (((BW - 1) * scaling) + 1) # If scaling is 0, BW cancels out.
out <- out * Ovariable( # Create alternative scenario with zero exposure.
output = data.frame(Exposcen = c("BAU", "No exposure"), Result = c(1, 0)),
marginal = c(TRUE, FALSE)
)
return(out)
}
)
RR <- Ovariable("RR", # This calculates the total number of cases in each population subgroup.
# The cases are calculated for specific (combinations of) causes. However, these causes are NOT visible in the result.
dependencies = data.frame(Name = c(
"ERF", # Exposure-response function of the pollutants or agents (RR for unit exposure)
"dose", # Exposure to the pollutants
"frexposed", # fraction of population that is exposed
"bgexposure", # Background exposure (a level below which you cannot get in practice)
"threshold" # exposure level below which the agent has no impact.
)),
formula = function(...) {
####################################################################
####### This part is about risks relative to background.
# Calcualte the risk ratio to each subgroup based on the exposure in that subgroup.
# Combine pollutant-specific RRs by multiplying. For description, see [[Exposure-response function]].
test <- list()
marginals <- character()
#First take the relative risk estimates
ERFrr <- ERF
ERFrr@output <- ERF@output[ERF@output$ERF_parameter %in% c("RR", "RR bw") , ]
if(nrow(ERFrr@output) > 0 & nrow((ERFrr * dose)@output) > 0) { # If an ovariable whose nrow(ova@output) == 0
# is used in Ops, it is re-EvalOutput'ed, and therefore ERFrr*dose may have rows even if ERFrr doesn't.
bigger <- (threshold > bgexposure) * threshold + (1 - (threshold > bgexposure)) * bgexposure # Choose bigger
RRrr <- exp(log(ERFrr) * (dose - bigger)) # Actual function
RRrr <- (RRrr - 1) * (dose > threshold) + 1 # RR is 1 below threshold
test <- c(test, RRrr)
marginals <- c(marginals, colnames(RRrr@output)[RRrr@marginal])
}
# Then take the relative Hill estimates
ED50 <- threshold
ED50@output <- ED50@output[ED50@output$ERF_parameter %in% c("Relative Hill", "Relative Hill bw") , ]
if(nrow(ED50@output) > 0 & nrow((ED50 * dose)@output) > 0) { # See ERFrr for explanation
RRrhill <- 1 + (dose * ERF) / (dose + ED50) # ERF has parameter value for Imax. If Imax < 0, risk reduces.
test <- c(test, RRrhill)
marginals <- c(marginals, colnames(RRrhill@output)[RRrhill@marginal])
}
# We need OR but not yet crucial, so let's postpone this. See [[:op_en:Converting between exposure-response parameters]]
# #Then take the odds ratio estimates
# OR <- ERF
# OR@output <- ERF@output[ERF@output$ERF_parameter %in% c("OR", "OR bw") , ]
# if(nrow(OR@output) > 0 & nrow((OR * dose)@output) > 0) { # See ERFrr for explanation
# bigger <- (threshold > bgexposure) * threshold + (1 - (threshold > bgexposure)) * bgexposure # Choose bigger
# OR <- RR = OR/( 1-PX0+OR*PX0 ) # Actual function where PX0 is the background incidence. How to write a code?
# RRor <- (RRrr - 1) * (dose > threshold) + 1 # RR is 1 below threshold
# test <- c(test, RRor)
# marginals <- c(marginals, colnames(OR@output)[OR@marginal]
# }
if(length(test) == 0) return(data.frame())
if(length(test) == 1) out <- test[[1]]@output
if(length(test) == 2) out <- orbind(test[[1]], test[[2]])
if(length(test) == 3) out <- orbind(orbind(test[[1]]), test[[2]], test[3])
out <- Ovariable(output = out, marginal = colnames(out) %in% marginals)
# Dilute the risk in the population if not all are exposed i.e. frexposed < 1.
out <- frexposed * (out - 1) + 1
out <- unkeep(out, prevresults = TRUE, sources = TRUE)
out <- oapply(out, cols = "Exposure_agent", FUN = prod)
return(out)
}
)
totcases <- Ovariable("totcases", # This calculates the total number of cases in each population subgroup.
# The cases are calculated for specific (combinations of) causes. However, these causes are NOT visible in the result.
dependencies = data.frame(Name = c(
"population", # Population divided into subgroups as necessary
"disincidence", # Incidence of the disease of interest
"RR", # Relative risks for the given exposure
"ERF", # Other ERFs than those that are relative to background.
"dose", # Exposure to the pollutants
"frexposed", # fraction of population that is exposed
"threshold" # exposure level below which the agent has no impact.
)),
formula = function(...) {
test <- list()
marginals <- character()
############### First look at the relative risks based on RR
if(nrow(RR@output) > 0 & nrow((RR * dose)@output) > 0) { # If an ovariable whose nrow(ova@output) == 0
# is used in Ops, it is re-EvalOutput'ed, and therefore ERFrr*dose may have rows even if ERFrr doesn't.
# if(class(population) != "ovariable") population <- EvalOutput(Ovariable("population", data = data.frame(Result = population)))
# # Remove redundant columns and locations.
# population@output <- dropall(population@output)
# population <- unkeep(population, sources = TRUE, prevresults = TRUE)
# disincidence <- unkeep(disincidence, sources = TRUE, prevresults = TRUE)
# takeout is a vector of column names of indices that ARE in population but NOT in the disease incidence.
# However, populationSource is kept because oapply does not run if there are no indices.
if(class(population) == "ovariable") {
takeout <- setdiff(colnames(population@output), c(colnames(disincidence@output), "populationSource"))
pop <- oapply(population, cols = takeout, FUN = sum) # Aggregate to larger subgroups.
} else {
takeout <- character()
pop <- population
}
# pci is the proportion of cases across different population subroups based on differential risks and
# population sizes. pci sums up to 1 for each larger subgroup found in disincidence.
# See [[Population attributable fraction]].
pci <- population * RR
# Divide pci by the values of the actually exposed group (discard nonexposed)
temp <- pci
temp@output <- temp@output[temp@output$Exposcen == "BAU" , ]
temp <- unkeep(temp, cols = "Exposcen", prevresults = TRUE, sources = TRUE)
pci <- pci / oapply(temp, cols = takeout, FUN = sum)
# The cases are divided into smaller subgroups based on weights in pci.
# This is why the larger groups of population are used (pop instead of population).
out1 <- disincidence * pop * unkeep(pci, prevresults = TRUE, sources = TRUE)
out1 <- unkeep(out1, cols = "populationResult") # populationResult comes from pop and not from pci that actually contains
# the population weighting for takeout indices. Therefore it would be confusing to leave it there.
test <- c(test, out1)
marginals <- c(marginals, colnames(out1@output)[out1@marginal])
}
##########################################################################
############# This part is about absolute risks (i.e., risk is not affected by background rates).
# Unit risk (UR), cancer slope factor (CSF), and Exposure-response slope (ERS) estimates.
UR <- ERF
UR@output <- UR@output[UR@output$ERF_parameter %in% c("UR", "CSF", "ERS", "UR bw", "CSF bw", "ERS bw") , ]
if(nrow(UR@output) > 0 & nrow((UR * dose)@output) > 0) { # See RR for explanation.
UR <- threshold + UR * dose * frexposed # Actual equation
# threshold is here interpreted as the baseline response (intercept of the line). It should be 0 for
# UR and CSF but it may have meaningful values with ERS
UR <- unkeep(UR, prevresults = TRUE, sources = TRUE)
UR <- oapply(UR, cols = "Exposure_agent", FUN = sum)
UR@output <- UR@output[!is.na(result(UR)) , ] # Remove empty rows
UR <- population * UR
test <- c(test, UR)
marginals <- c(marginals, colnames(UR)[UR@marginal])
}
# Step estimates: value is 1 below threshold and above ERF, and 0 in between.
# frexposed cannot be used with Step because this may be used at individual and maybe at population level.
Step <- ERF
Step@output <- Step@output[Step@output$ERF_parameter %in% c(
"Step", "Step bw", "ADI", "ADI bw", "TDI", "TDI bw", "RDI", "RDI bw", "NOAEL", "NOAEL bw") , ]
if(nrow(Step@output) > 0 & nrow((Step * dose)@output) > 0) { # See RR for explanation.
Step <- 1 - (dose >= threshold) * (dose <= Step) # Actual equation
Step <- unkeep(Step, prevresults = TRUE, sources = TRUE)
Step <- oapply(Step, cols = "Exposure_agent", FUN = sum)
# out3 <- (population * 0 + 1) * Step # This is to maintain the ovariable structure # Does not work because population may be numeric.
test <- c(test, Step)
marginals <- c(marginals, colnames(Step@output)[Step@marginal])
}
#####################################################################
# Combining effects
if(length(test) == 0) return(data.frame())
if(length(test) == 1) out <- test[[1]]@output
if(length(test) == 2) out <- orbind(test[[1]], test[[2]])
if(length(test) == 3) out <- orbind(orbind(test[[1]], test[[2]]), test[[3]])
X <- Ovariable(output = out, marginal = colnames(out) %in% marginals)
if("Exposcen" %in% colnames(out)) {
X <- X * Ovariable(data = data.frame(Exposcen = c("BAU", "No exposure"), Result = c(1, -1)))
X <- oapply(X, cols = "Exposcen", FUN = sum)
}
return(X)
}
)
AF <- Ovariable("AF", # Cases attributed to specific (combinations of) causal exposures.
dependencies = data.frame(Name = c(
"ERF", # Exposure-response function
"exposure", # Total exposure to an agent or pollutant
"frexposed", # fraction of population that is exposed
"bgexposure" # Background exposure to an agent (a level below which you cannot get in practice)
)),
formula = function(...) {
# First calculate risk ratio and remove redundant columns because they cause harm when operated with itself.
RR <- frexposed * exp(log(ERF) * (exposure - bgexposure)) - frexposed + 1
PAF <- (RR - 1) / unkeep(RR, sources = TRUE, prevresults = TRUE)
# pollutants is a vector of pollutants considered.
pollutants <- unique(exposure@output$Pollutant)
pollutants <- levels(pollutants)[pollutants]
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$Temp1 <- NULL
}
}
return(out)
}
)
########### HIA ovariable is outdated and should not be used.
HIA <- new("ovariable",
name = "HIA",
dependencies = data.frame(
Name = c(
"diseaseRisk",
"Exposure",
"Exposed.Fraction",
"Background.Exposure",
"ERF",
"BoD"
),
Ident = c(
NA, # "Op_en5917/initiate", # Disease risk
NA, # "Op_en5918/initiate", # Exposures in Finland
NA, # "Op_en5918/initiate", # Exposures in Finland
NA, # "Op_en5918/initiate", # Exposures in Finland
"Op_en5827/initiate",
"Op_en5453/initiate"
)
),
formula = function(dependencies, ...){
ComputeDependencies(dependencies, ...)
ERF@output$ERFResult <- as.numeric(as.character(ERF@output$ERFResult))
Exposed.Fraction@output$Exposed.FractionResult <-
as.numeric(as.character(Exposed.Fraction@output$Exposed.FractionResult))
Exposure@output$ExposureResult <- as.numeric(as.character(Exposure@output$ExposureResult))
BoD@output$BoDResult <- as.numeric(as.character(BoD@output$BoDResult))
ERF@output <- ERF@output[ERF@output$ERF.Parameter == "RR" , ]
if(verbose) {
cat("ERF\n")
oprint(summary(ERF), digits = 4)
cat("Exposure\n")
oprint(summary(Exposure))
cat("Exposed.Fraction\n")
oprint(summary(Exposed.Fraction))
cat("Background.Exposure\n")
oprint(summary(Background.Exposure))
cat("diseaseRisk\n")
oprint(summary(diseaseRisk))
cat("BoD\n")
oprint(summary(BoD))
}
RR <- exp(log(ERF) * (Exposure - Background.Exposure)) # Relative risk with given exposure
PAF <- Exposed.Fraction * (RR-1)/(Exposed.Fraction*(RR-1)+1) #Population attributable fraction
out <- PAF *BoD # DALYs
#out <- (RR - 1) / RR * diseaseRisk * Exposed.Fraction # Number of cases.
# Based on PAF * incidence * population size, where diseaseRisk = incidence * total population size
return(out)
}
)
objects.store(dose, RR, totcases, AF, HIA)
cat("Ovariables dose, RR, totcases, AF, HIA saved. page: Op_en2261, code_name: initiate.\n")
| |
The code above is based on these input variables:
See also
- WHO Health impact assessment tools
- WHO: HIA tools
- DALY calculator in R
- Converting between exposure-response parameters
- The effectiveness of health impact assessment. WHO 2007
- Ihmisiin kohdistuvien vaikutusten arviointi (käsikirja) (in Finnish)
- Ihmisiin kohdistuvien vaikutusten arviointi (sivusto) (in Finnish)
- An article about HIA in Finland
- Health impact assessment suggested to be released in Intarese
- Intarese Health Effects Methodology (D13 Final, July 2007)
- Health impact assessment in Wikipedia
- Impact assessment in Wikipedia
- IngentaConnect: Impact assessment and Project Appraisal ISSN 1471-5465 21: 4 (2003).
- Scott-Samuel: Health impact assessment BMJ.1996; 313: 183-184
- Assessing health impact assessment: multidisciplinary and international perspectives. J Epidemiol Community Health 2003;57:659-662
- Health Impact Assessment in Urban Settings New South Wales Public Health Bulletin 18: 9 & 10, 2007.
- Health Impact Assessment. Bulletin of the World Health Organization (BLT): 81: 6: 387-472, 2003.
- Health Impact Assessment. Community knowledge wiki
- IMPACT - International Health Impact Assessment Consortium
- Health Impact Assessment page by WHO
- ISO 31000:2009 Risk management -- Principles and guidelines
- Risk Observatory, based in the European Agency for Safety and Health at Work
- Health-EU Portal
- STM: Hyvinvointi (Well-being, in Finnish)
- John Kemm, Jayne Parry, Stephen Palmer: Health impact assessment: concepts, theory, techniques, and applications
- Interdepartmental Liaison Group on Risk Assessment in UK
- Life cycle assessment
- Four-step impact assessment by HSPH.
Further reading
- The text below is a description of HIA by A. Knol and B. Staatsen from RIVM. It was originally written for use in Intarese project.
Health Impact Assessment
One way to compare different policy options is by carrying out a health impact assessment (HIA). HIA is a combination of procedures, methods and instruments used for assessing the potential health impacts of certain matters. These can vary from a single environmental factor to a more complicated set of factors, for instance in an infrastructural or industrial project. For quantifying health impacts, the following steps can be distinguished (Hertz-Picciotto, 1998):
- Selection of health endpoints with sufficient proof (based on expert judgements) of a causal relationship with the risk factor
- Assessment of population exposure (combination of measurements, models and demographic data)
- Identification of exposure-response relations (relative risks, threshold values) based on (meta) analyses and epidemiological and toxicological research.
- Estimation of the (extra) number of cases with the specific health state, attributable to exposure to the risk factor. This is a function of the population distribution, exposure-response relation and base prevalence of the health state in the population.
- Computation of the total health burden, or costs to society of all risk factors (if wanted/necessary)
A common problem is that the health effects of environmental factors can vary considerably with regard to their severity, duration and magnitude. These differences hamper the comparison of policies (comparative risk assessment) or the costs of policy measures (cost effectiveness analysis). An integrated health measure, using the same denominator for all health effects, can help with interpretation and comparison of health problems and policies.
Integrated health measures
Common health measures include mortality, morbidity, healthy life expectancy, attributable burden of disease measures, and monetary valuation. Some of these measures will be further described below. All methods have several associated difficulties, such as imprecision of the population exposure assessment; uncertain shapes of the exposure-response curves for the low environmental exposure levels; insufficient (quality of) epidemiological data; extrapolation from animal to man or from occupational to the general population; generalisation of exposure-response relations from locally collected data for use on regional, national or global scale; combined effects in complex mixtures, etc.
Mortality figures
The annual mortality risk or the number of deaths related to a certain (environment-related) disease can be compared with this risk or number in another region or country, or with data from another period in time. Subsequently, different policies can be compared and policies that do or do not work can be identified. Within a country, time trends can be analyzed. This method is easy to comprehend. No ethical questions are attached; everyone is treated equal. Since this method only includes mortality, it is not suitable for assessing factors with less severe consequences (morbidity). Also, it is difficult to attribute mortality to specific environmental causes.
Morbidity figures
Similar to mortality figures, morbidity numbers (prevalences or incidences based on hospital admissions or doctor visits) can be used to evaluate a (population) health state. Advantages and drawbacks are comparable to those applying to using mortality figures. The use of morbidity numbers is therefore similarly limited, especially when (environmental) causes of the diseases vary.
Healthy life expectancy
Using mortality tables, one can calculate the total average life expectancy for different age groups in a population, subdivided into years with good and years with less-than-good health.
This measure is especially useful to review the generic health state in a country for the long term, but it doesn’t give insight into specific health effects, effects of specific policy interventions, or trends in certain subgroups.
Attributable burden of disease
Health impact assessments can also be executed by calculating the attributable burden of disease. There are several ways to assess the burden of disease attributable to an (environmental) factor, such as the QALY and the DALY.
Quality Adjusted Life Years, QALYs, capture both the quality and quantity elements of health in one indicator. Essentially, time spent in ill health (measured in years) is multiplied by a weight measuring the relative (un)desirability of the illness state. Thereby a number is obtained which represents the equivalent number of years with full health. QALYs are commonly used for cost-utility analysis and to appraise different forms of health care. To do that, QALYs combine life years gained as a result of these health interventions/health care programs with a judgment about the quality of these life years.
Disability adjusted life years, DALYs, are comparable to QALYs in that they both combine information on quality and quantity of life. However, contrary to QALYs, DALYs give an indication of the (potential) number of healthy life years lost due to premature mortality or morbidity and are estimated for particular diseases, instead of a health state. Morbidity is weighted for the severity of the disorder.
With QALY, the focus is on assessing individual preference for different non-fatal health outcomes that might result from a specific intervention, whereas the DALY was developed primarily to compare relative burdens among different diseases and among different populations (Morrow and Bryant, 1995). DALYs are suitable for analyzing particular disorders or specific factors that influence health. Problems associated with the DALY approach include the difficulty of estimating the duration of the effects (which have hardly been studied) and the severity of a disease; and allowing for combined effects in the same individual (first you have symptoms, then you go to a hospital and then you may die). The DALY concept, which has been used in our study, will be further described in the next chapter. More information on the drawbacks of the method can be found in Chapter 6.4.
Monetary valuation
Another approach to health impact assessment is monetary valuation. In this measure, money is used as a unit to express health loss or gain, thereby facilitating the comparison of policy costs and benefits. It can help policy makers in allocating limited (health care) resources and setting priorities. There are different approaches to monetary valuation such as ‘cost of illness’ and ‘willingness to pay/accept’.
The cost of illness (COI) approach estimates the material costs related to mortality and morbidity. It includes the costs for the whole society and considers loss of income, productivity and medical costs. This approach does not include immaterial costs, such as impact of disability (pain, fear) or decrease in quality of life. This could lead to an underestimation of the health costs. Furthermore, individual preferences are not considered.
The willingness to pay (WTP) approach measures how much money one would be willing to pay for improvement of a certain health state or for a reduction in health risk. The willingness to accept (WTA) approach measures how much money one wants to receive to accept an increased risk. WTP and WTA can be estimated by observing the individual’s behaviour and expenditures on related goods (revealed preference). For example, the extra amount of money people are willing to pay for safer or healthier products (e.g. cars with air bags), or the extra salary they accept for compensation of a risky occupation (De Hollander, 2004). Another similar method is contingent valuation (CV), in which people are asked directly how much money they would be willing to pay (under hypothetical circumstances) for obtaining a certain benefit (e.g. clean air or good health).
Source: Knol, A.B. en Staatsen, B.A.M. (2005). Trends in the environmental burden of disease in the Netherlands, 1980-2020. Rapport 500029001, RIVM, Bilthoven. Downloadable at http://www.rivm.nl/bibliotheek/rapporten/500029001.html