Health impact assessment

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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.

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How to calculate health impacts based on information about exposure, population, disease, and exposure-response function?


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].

Number of iterations:

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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.


  • 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 Year Sex Age Amount Description
Finland 2010 Total All 5307690 [6]


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

Attributable fraction is (RR-1)/RR=1-1/RR if RR>1. If smaller, you must compare the other way round: control group is considered an exposure to lack of a protective agent and thus the exposure group is the reference. In this comparison, the attributable fraction of lack of protection (AFlp) is calculated from a new rate ratio RRlp = 1/RR and

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

When multiplied by the number of cases, we get the number of excess cases (that would not have occurred if the population had not been exposed to lack of protection). This comparison is symmetric and we can use either counterfactual situation as the reference just by calculating the difference the other way round, i.e. changing the sign of the value. Therefore, the number of cases avoided with exposure to a protective agent is -AFlp = RR - 1. So, AF is calculated as 1-1/RR or RR-1 depending on whether RR>1 or not, respectively.


Depreciated code

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Ovariables for calculating RR

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Ovariables for calculating cases

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Totcases (old version)

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NOTE! These ovariables used to utilise ooapply function, but it was archived after improved oapply.

The codes above are based on these input variables:

See also related page: ISTE EBD.

See also

Assessments that use this HIA model

Helsinki energy decision 2015
In English
Assessment Main page | Helsinki energy decision options 2015
Helsinki data Building stock in Helsinki | Helsinki energy production | Helsinki energy consumption | Energy use of buildings | Emission factors for burning processes | Prices of fuels in heat production | External cost
Models Building model | Energy balance | Health impact assessment | Economic impacts
Related assessments Climate change policies in Helsinki | Climate change policies and health in Kuopio | Climate change policies in Basel
In Finnish
Yhteenveto Helsingin energiapäätös 2015 | Helsingin energiapäätöksen vaihtoehdot 2015 | Helsingin energiapäätökseen liittyviä arvoja | Helsingin energiapäätös 2015.pptx

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