Methodology and user guide. Economic assessment of transport infrastructure and policies. 2014 UpdateDownload - English (PDF, 2.4 MB) By
Sonja Kahlmeier, Paul Kelly, Charlie Foster, Thomas Götschi, Nick
Cavill, Hywell Dinsdale, James Woodcock, Christian Schweizer, Harry
Rutter, Christoph Lieb, Pekka Oja and Francesca Racioppi 2011, updated reprint 2014, viii + 39 pages The
promotion of cycling and walking for everyday physical activity not
only promotes health but can also have positive effects on the
environment. This booklet summarizes
the tools and guidance developed to facilitate this shift: the
methodology for the economic assessment of transport infrastructure and
policies in relation to the health effects of walking and cycling;
systematic reviews of the economic and health literature; and guidance
on applying the health economic assessment tools and the principles
underlying it. This methodology and
user guide will be of key interest to professionals at both national and
local levels: transport planners, traffic engineers, and special
interest groups working on transport, walking, cycling or the
environment, as well as health economists, physical activity experts and
health promotion experts. The development of HEAT for walking and cycling was supported by the European Union in the framework of the Health Programme 2008–2013 (Grant agreement 2009 52 02), the Austrian Federal Ministry of Agriculture, Forestry, Environment and Water Management, the Swiss Federal Office of Public Health, the Swedish Expertise Fund and a consortium of donors from the United Kingdom under the leadership of Natural England. Methodological guidance on economic Appraisal of health effects related to walking and cycling 2.3. Interactions between
transportrelated physical activity, air pollution and road traffic
injuries Transport-related health effects include possible negative
effects from exposure to ambient air pollution or road traffic injuries.
Possible interactions between the positive effects of exercise through
active transport and such negative effects need to
be considered. To date, no comprehensive review of active transport and
physical activity is available that takes the possible negative effects
of ambient air pollution into account. Two
recent scenario analyses showed that, in most cases, especially in
western Europe, the positive health effects of cycling are likely to
greatly outweigh the negative effects of air pollution and road traffic
accidents suffered by cyclists (11,12). Also, the use of all-cause
mortality estimates (see also below) rather than
cause- specific ones has the advantage of incorporating the possible
detrimental effects associated with walking or cycling. 2.4. Mortality or morbidity? Physical
activity has beneficial effects on many aspects of morbidity such as
coronary heart disease, stroke, diabetes, some types of cancer,
musculoskeletal health, energy balance and aspects of mental health
(including anxiety and depression) and improving functional health in
elderly people (13). From a public health point of view, these benefits
materialize more rapidly than reductions in mortality. They can also be
important in motivating individuals to walk and/or cycle, as people may
be more likely to increase their physical activity to improve their
immediate health and well-being than to prolong their life. The
consensus meetings therefore recommended, for the time being, focusing
only on all-cause mortality for HEAT for walking and for cycling. It
should be noted that this method is likely to produce conservative
estimates, since it does not account for diseaserelated benefits. 2.5. The nature of the relationship between
physical activity and health Epidemiological studies report
relationships between different categories or levels of exposure and
health outcomes. For example, a comparison of sedentary people with
people who are active beyond a specific threshold (such as 150 minutes
of activity per week) may show that active people are healthier. The
international advisory group concluded that, overall, a linear
dose–response function is the most suitable one to use for HEAT. In this
way, users do not have to know the baseline level of physical activity
of their subjects, and a constant absolute risk reduction can be applied
for all HEAT applications within the range of exposure for which an
incremental reduction of mortality risk can be observed. HEAT
does not take into account differences in the pace (or intensity) of
walking or cycling, or the possibility that less well-trained
individuals may benefit more and better-trained individuals may benefit
less from the same amount of walking or cycling. 2.8. Time needed for health benefits to build up It is important to recognize that there will be a delay between increases in physical activity and measurable benefits to health. Based on the best available evidence, it was concluded that five years was a reasonable assumption to use for such “newly induced physical activity” to reach full effect, with an increment of 20% in benefits each year. 2.12. Costs applied To conduct an economic appraisal of walking and cycling, it is necessary to agree on a method of valuing health (or life). There are a number of ways in which this can be done. • A standard value of a statistical life (VSL) This is often used in transport appraisals. It is most commonly derived using a method called willingness to pay. The willingness to pay shows how much a representative sample of the population (who, in this instance, are potential victims) would be willing to pay (in monetary terms) for example for a policy that would reduce their annual risk of dying from 3 in 10 000 to 2 in 10 000. • Cost of illness This applies costs (for example costs to the national health service or loss of earnings) to each specific disease. • Years of life lost (or gained) This allows a more comprehensive assessment of health effects, as it takes the life expectancy of the participants into account. • Quality-adjusted life-years (QALYs) These are derived from years of life spent in ill health, multiplied by a weight measuring the relative undesirability of the illness state. • Disability-adjusted life-years” (DALYs) These measure the overall disease burden, expressed as the number of years lost due to ill health, disability or early death. As this project was aimed primarily at transport appraisals, the VSL approach was used, as this is more common in transport appraisals. It is recommended to use either a current, internationally agreed VSL or a local VSL, where available. 2.13. Discounting Since benefits occurring in the future are generally considered less valuable than those occurring in the present, economists apply a so called “discount rate” to future benefits. In many cases, the economic appraisal of health effects related to walking and cycling will be included as one component into a more comprehensive cost–benefit analysis of transport interventions or infrastructure projects. The final result of the comprehensive assessment would then be discounted to allow a calculation of the net present value. 2.14. Sensitivity analysis It is strongly recommended that the uncertainties around an assessment are made explicit, and that the calculations are carried out with high and low estimates of the main variables in order to gain a better understanding of the possible range of the final results. 3.1 Economic Literature Generally,
the economic analyses showed positive benefit–cost ratios, the median
being 5:1 with a range from –0.4 to 32.5. However, owing to the
different methods applied in the studies, this value has to be viewed
with caution. Some studies estimated the value attributed to each new
walker or cyclist; these ranged from about €120 to €1300. As
explained in section 2.12, HEAT uses the VSL method to economically
quantify the health benefits of reduced mortality from walking or
cycling. Due to the dearth of official VSL studies, HEAT previously
suggested using either a default value of €1.574 million (5,24) or a
national VSL. The
Organisation for Economic Co-operation and Development (OECD) recently
published a comprehensive review of VSL studies (23). Studies were only
included if they were based on a representative population sample of at
least 200 subjects (or 100 for subsamples of larger studies) and
provided information on the size of
the risk change in question. About 400 values were selected to
calculate the VSL for adults in 38 countries around the world. For the
EU27 countries, an average VSL of US$ 3.6 million with a range from US$
1.8 million to US$ 5.4 million (2005 US dollars). Since
the available studies used a range of different exposures, to conduct
the metaanalysis it was necessary to estimate for each study the reduced
risk at a common exposure level. For this purpose, the different
cycling exposures used in the studies were converted into MET-hours per
week (assuming a linear dose-response relationship and an average
intensity of 6.8 METs
for cycling if not otherwise stated). The common exposure level was set
at 11.25 MET-hours per week. This value was derived from the global
physical activity recommendations as corresponding to the recommended
level of at least 150 minutes of moderate-intensity physical activity
per week (14) using 4.5 METs as an average for moderate-intensity
physical activity. Using 6.8 METs as an average intensity for cycling,
this exposure represents about 100 minutes of cycling per week. 4. The HEAT for walking and for cycling: introduction The
principles and guidance set out in Chapter 2 have been developed into a
practical tool for walking and for cycling, known as HEAT (6). The tool
estimates the maximum and the mean annual benefit in terms of reduced
mortality as a result of walking or cycling. It can be applied in a number of situations, as further described in section 4.4, such as: • when planning a new piece of cycling or walking infrastructure, helping to make the case for investment; •
It will help to answer the following question: If x people cycle or
walk for y minutes on most days, what is the economic value of the
health benefits that occur as a result of the reduction in mortality due
to their physical activity? 4.2. Who is the tool for? The
tool is based on the best available evidence and transparent
assumptions. It is intended to be simple to use by a wide variety of
professionals at both national and local levels. These include
primarily: • transport planners; • traffic engineers; and • special interest groups working on transport, walking, cycling or the environment. 4.3. What can the tool be used for? The
tool provides an estimate of the economic benefits accruing from
walking or cycling as a result of lower death rates. Ideally, for a
comprehensive assessment, it would be supplemented with data on other
potential health outcomes from walking or cycling (morbidity) and
combined with other transport-related outcomes such as less congestion,
reduced journey times or fewer road traffic injuries. These and other
enhancements will be considered for inclusion in future versions of the
tool. 4.5. Basic functioning of the tool The
tool is based on relative risk data from published studies. The
included studies controlled for leisure-time physical activity as well
as the usual socioeconomic variables (age, sex, smoking, etc.). This
means that the relative risks reported for walking or cycling and
mortality were independent of other forms of physical activity. The
tool uses these relative risks and applies them to the amount of
walking or cycling entered by the user, assuming a linear relationship
between walking or cycling and mortality. To illustrate this, the
relative risk from the meta-analysis used for the updated version of
HEAT for cycling is 0.90 for regular commuter cycling for 100 minutes
per week for 52 weeks of the year (equivalent to 87 hours of cycling per
year). Thus,
in any given year, regular cyclists receive a protective benefit of 10%
(1.00 minus 0.90) – that is, they are 10% less likely to die from any
cause than non-cyclists. If the user enters a cycling volume equivalent
to 29 hours per year (i.e. three times less), the protective benefit of
this amount of cycling will be roughly 3%. If the user enters 174 hours
(twice the time cycled in the reference population), the resulting
protective benefit is 20%. This is twice the protective benefit of the
reference population. To
avoid inflated values at the upper end of the range, the risk reduction
available from the HEAT is capped. Inspection of the data points of the
new meta-analyses suggested that, after about 45% risk reduction for
cycling and 30% for walking, no significant further risk reductions were
achieved. HEAT will apply a maximum 45% risk reduction in the risk of
mortality for cycling (corresponding to 450 minutes per week) and a
maximum 30% risk reduction (corresponding to 458 minutes per week) for
walking. HEAT
then uses population-level mortality data to estimate the number of
adults who would normally be expected to die in any given year in the
target population. Next, it calculates the reduction in expected deaths
in this population that cycle or walk at the level specified by the
user, using the adjusted relative risk. Finally, the tool produces an
estimate of economic savings from this calculated reduction in deaths,
as well as discounted and average savings. The basic functioning of the tool is shown in fig. 1. 4.5.1.
Applicable age range HEAT for cycling is designed for analyses of adult
populations aged about 20–65 years. This is because HEAT should be used
for regular behaviour such as commuting, and the retirement age is
about 65 years in most countries. If
the age distribution in the assessed population is significantly
different (much younger, much older), HEAT may underestimate or
overestimate the resulting benefits. HEAT should not be applied to
populations of children, very young adults or older people. 4.6. What input data are needed? To use HEAT, the following data are needed: •
an estimate of how many people are walking or cycling, which might come
from route user surveys, population surveys or roadside counts, or
could be estimates from scenario analyses •
an estimate of the average time spent walking or cycling in the study
population, which can again come from surveys or estimates and can be
entered in a number of ways: • duration (average
time walked or cycled per person, e.g. 30 minutes walked on average per
day), which is the most direct data entry route; • distance (average distance walked or cycled per person, e.g. 10 km cycled on average per day); • trips (average per person or total observed across a population, e.g. 250 bicycle trips per year); or • steps (average number of steps taken per person, e.g. 9000 steps per day). A
number of default values are provided in HEAT; these have been derived
from the literature and agreed on as part of the expert consensus
process. They should be used unless more relevant data are available
that more accurately reflect the situation under study, for the
following variables: • mortality rate (a European
average can be used or a national rate from the WHO European Detailed
Mortality Database (30) for an average population (about 20–74 years
old), a younger average population (about 20–45 years old) or a
predominantly older average population (about 46–74 years old), or the
local mortality rate can be entered); •
VSL (values commonly used across Europe are provided in the model but
users may adapt this value by, for example, adopting agreed values for
their own country; for more information see section 2.12); • the period of time over which average benefits are to be calculated; and • a discount rate, if so wished; the default value supplied can be used or an alternative rate can be entered. In
addition, details of the cost of promoting cycling or walking can be
entered, which can be used to calculate a benefit–cost ratio. Along
the way, some assumptions may need to be taken where no data are
available, such as on the supposed impact of an intervention on newly
induced levels of walking and cycling. Input is provided for such
assumptions, wherever possible with default values (and their sources).
Explanations and further information on the different steps of the tool
as well as a section with frequently asked questions are provided on the
web site 4.7. Data sources Input data for the model may come from a number of sources, including: • route user surveys; • population-level travel behaviour surveys; • destination-based behaviour travel surveys (e.g. commuter behaviour); and • traffic counts. Alternatively,
informed estimates may serve as surrogates for empirical data, such as
in scenario calculations. In all cases, it is important to use the most
reliable data possible and to validate these with secondary sources
where available. A few considerations will help to make the best use of the available data and avoid mistakes. 4.7.1. Use of short-term counts and surveys The
main concern with short-term counts is that they do not accurately
capture variations in walking or cycling over time (i.e. time of the
day, day of the week, season or weather). Since HEAT assumes that the
entered data reflect long-term average levels of walking or cycling,
data from short-term counts may distort the results. This issue will
affect single-site evaluations (such as a footpath or a bridge) where
counts are conducted at the site itself, or community- wide evaluations
that are based on surveys conducted only during a certain time of the
year. 4.7.2. Use of data from a few locations The
choice of location may strongly influence the count numbers, which may
not be representative of the wider level of walking (or cycling).
Results need to be interpreted carefully, and should in general not be extrapolated beyond the locations where actual data were collected. 4.7.3.
Use of trip or count data In HEAT, trip or count data need to be
combined with an estimate of average trip length in order to calculate
the volume of walking or cycling. An example is provided by counts
conducted on a bridge, where it remains unknown how far people walk or
cycle beyond the bridge. Average trip distance estimates may be derived
from user surveys on a specific facility or from travel surveys. There are several methods of estimating cycling and walking distances. • Cyclists or pedestrians can be asked to draw their route on a map and to measure the distance. •
Cyclists and pedestrians can be asked to provide their starting and
finishing points and to multiply the straight-line distance between the
two points with a correction factor. One study has suggested a factor of
1.26. • Another method is based on subjective
estimates of distance travelled, although this has been shown to lead to
distances being overestimated and not to be always reliable. Thus, if
subjective measures are used, it is recommended that a correction be
made for overestimation; a correction factor of 0.88 has been suggested. •
Making use of global positioning systems (GPS) has been shown to
overestimate the distance; a correction factor of 0.95 has therefore
been suggested. • Making use of shortest- or
fastest-route algorithms in geographical information systems has been
shown to overestimate distance by between 12% and 21%, depending on the
algorithm used. This corresponds to correction factors of 0.89 and 0.83,
respectively. 4.8. What data will the tool produce? The tool will produce an estimate of the following outputs: • maximum annual benefit; • mean annual benefit; and • net present value of mean annual benefit. The
maximum annual benefit is the total value of reduced mortality due to
the level of walking or cycling entered by the user. This is a maximum
value, as it assumes that the maximum possible benefits to health will
have occurred as a result of the entered level of walking or cycling. In
reality, the health benefits are likely to accrue over time. The
mean annual benefit is therefore the key output of the model. It
adjusts the maximum annual benefit (total value of lives saved due to
the level of walking or cycling entered by the user) by three main
factors: • an estimate of the time it takes for the health benefits from regular walking or cycling to occur; •
a build-up period for uptake of walking or cycling, which allows the
user to vary the projections in uptake if valuing a specific
intervention such as for a new cycle path, and varies for full usage
occurring between 1 and 50 years; and • the net
present value of mean annual benefit, which adjusts the above outputs to
take the diminishing value of current savings over time into account
(the model suggests a discount rate of 5% but this can be varied). 6. HEAT for cycling: instructions for users The tool is available on the WHO Regional Office for Europe web site at www.euro.who.int/HEAT or directly from the HEAT site www.heatwalkingcycling.org. 6.2. How to use the tool: five simple steps 6.2.1.
General features of the HEAT web site HEAT is composed of 16 questions
in total; depending on the route you take, some questions will be
skipped. On the left-hand side of the screen you will see the flow chart
of questions to help you orientate where you are in the assessment
process. Click
on “next question” or “back” to move between questions; do not use the
back-button of your internet browser. You can also go back to a previous
question by clicking on it in the flow chart of questions on the
left-hand side of the screen. If you make changes, click on “save
changes” before you continue. Step 1: entering cycling data First of all, the scope for the use of HEAT needs to be considered to make sure that is applicable for an
assessment. If HEAT is right for the study in question, a decision
needs to be taken as to which of the two possible data types is going to
be used for the assessment. • Data from a single
point in time are used when assessing the status quo, such as valuing
current levels of cycling in a city, or if data on the results of an
intervention only are available (no “before” data). •
Before and after data are used when assessing the impact of an actual
intervention or hypothetical scenarios. Before and after data are
required, and the tool evaluates the difference in levels of cycling
between the two. All assessments require two main parameters to be entered: 1. the amount of cycling done in the study area as duration (the most direct entry route), distance or trips per day; and 2. the number of people benefiting from this amount of cycling. Amount of cycling: select the desired option for input data Duration Enter the average time spent cycling per person per day. Distance Enter the average time spent cycling per person per day. המשפט דלעיל שגוי. צריך להיות נתון הקשור למרחק Trips If
data are entered as trips, the average number of trips per person per
day can be entered or the total number of trips observed in the study
area (e.g. from a count of cyclists passing a sample point). If the
total number of trips includes trips by modes of transport other than
cycling, the mode share option can be used to take account of this by
specifying the proportion of these trips that are cycling trips. Then,
either the total number of people taking these cycling trips or the
proportion of these trips that are return journeys needs to be entered.
For example, if 1000 trips a day are observed at a sample point, this
could correspond to 1000 individuals each counted once or 500
individuals each counted twice (as they make a return
journey), or some combination of the two. Whenever possible, it is
strongly recommended to use the actual number of people cycling. This is
because alternative methods involve a number of assumptions, which
would reduce the accuracy of the results. If the total number of people
taking these trips is unknown, the tool will use the proportion of
return journeys to estimate the number of individuals taking the trips.
As the HEAT web site assumes that the trip data you have entered relates
to a regular (i.e. daily or near-daily) pattern of cycling, the number
of individual cyclists is calculated from the proportion of return
journeys, using the daily average number of trips. On the HEAT for
cycling web site, input is given to derive the best proportion of return
journeys for different types of count data. Finally, the duration or distance of the cycling trips has to be entered. For
all entry options, the user also has to enter how many days per year
this amount of cycling is done. If this amount is done every day (or
represents an average value per year, e.g. from a travel survey), 365
should be entered. However, most individuals do not cycle every day. If
no long-term data are used and users are unsure how
many days they cycled in a year, 124 is recommended as a default, which
is the observed number of days in Stockholm. This is a conservative
value, which should be changed only if reliable local data are
available, as it will influence the final calculation. If
data from a single point in time are assessed, the user can then enter
the general parameters. Otherwise, users will be asked to enter the
after-intervention data. They can choose to use a different metric for
the after data (e.g. duration for the before data and distance for the
after data). Number of people benefiting The
tool requires information on the number of individuals doing the amount
of cycling entered in the previous questions. In many cases, this
figure will be the number of cyclists in the study area, city or
country, or the number of people who stand to benefit from the reported
levels of cycling entered if the data were entered as cycling trips (see
above). In
some cases, cycling data may have been derived from a survey based on a
representative sample of a larger population, where the findings apply
to the whole population. For example, in the case of a national travel
survey that is representative of the whole population, the total
population should be used here rather than the sample size of the travel
survey. It
is important to ensure that the correct population figure is entered,
as this can substantially affect the resulting calculations. Step
2: checking the cycling summary HEAT will now show a summary of the
entries, allowing you to make corrections or to change entries. HEAT
will also show the likely reduction in the risk of mortality in the
study population, based on the entries. Warning
messages will appear here in two cases: (a) if levels of cycling have
been entered that are above the suggested scope of HEAT for cycling of
about 1 hour of cycling per day; and (b) if levels of cycling have been
entered that would theoretically lead to very high reductions in the
mortality rate. Specifically,
if an equivalent of 120 minutes or more of cycling per day is entered,
users are requested to consider whether their entered volume of cycling
truly represents long-term behaviour in an average adult population, as
this is what HEAT is designed for. To avoid inflated values, the risk
reduction available from the HEAT is capped at 45%. Step 3: Impact of an intervention or all current cycling? In
this step, users can decide whether they want to quantify the benefits
of a current situation (or a scenario analysis) in a country, in a
community or on a specific infrastructure. This means that HEAT will
provide an estimate of the value of all the cycling data entered (and no
build-up period for the health benefits to accrue will be applied). If
instead “impact of an intervention” is selected, the tool will ask for
an estimate of the proportion of the cycling that can be attributed to
the intervention. When assessing the impact of an intervention, it is
prudent to assume that not all the cycling or increase in cycling
observed is newly induced. Data
to estimate the proportion of newly induced cycling are rarely
available. Therefore, the proportion of cycling to be attributed to the
intervention (i.e. to be evaluated) needs to be estimated to the best of
the user’s knowledge. It
is strongly advised to calculate various scenarios with higher and
lower percentages, as this number significantly affects your results. Note
that if users wish to assess the value of an increase of cycling over
time without a particular intervention, 100% should be entered. Time needed to reach full level of cycling This
allows adjustment for the estimated time it will take to reach the full
level of cycling entered. This can be particularly useful when
assessing interventions. For example, if a new cycle path is built and
it is estimated it will take 5 years for usage to reach a steady state,
this figure should be changed to 5. The default value has been set at 1
year. Step 4: checking the parameters The
parameters in step 4 have been set by the expert advisory group
according to the best information currently available. They should be
changed only if reliable local data are available, as changes to these
parameters can have a significant impact on the final values. Nevertheless, local values for the following two parameters should be used where available: •
For the value of a statistical life (in local currency), the standard
value of a statisticallife used in the country of study should be
entered; the preferred currency can be chosen. This will form the basis
of the cost savings in the model. Whenever possible, enter a
country-specific value or use a country value from the drop-down menu.
If this is not known, European default values of €2.487 million (WHO
European Region), €3.387 million (EU-27 countries) or €3.371 million
(EU-28 countries including Croatia) can be used. •
The annual rate of the working-age population that dies each year
(deaths per 100,000 people per year in the respective age group) can be
derived from published mortality data for people of working age for the
study country. The default value is set at the last available average
for the WHO European Region according to the WHO European Detailed
Mortality Database. HEAT also provides national values as available in
the WHO European Detailed Mortality Database. Users have the option to
select default mortality rates for an average population (about 20–64
years old), a younger average population (about 20–45 years old) or a
predominantly older average population (about 46–64 years old). It is
suggested that the most recently available local rate be used wherever
possible. Users can also enter their own value. In this case, it is suggested to use the local crude annual death rate, as it reflects the age- and sex-specific mortality rates and the age and sex distribution of the population. Enter the number of deaths per year per 100 000 people aged 20–64 years. It must be noted that HEAT is not appropriate for populations consisting mainly of children, very young adults or older people, as the underlying relative risk would not be appropriate. The time frame for calculating mean annual benefit is the period over which the discounted mean annual benefit will be calculated. This is usually standardized within each country; the default value has been set at 10 years. If it is known how much it cost to promote cycling in a particular case (such as a specific promotionproject or new infrastructure), and the user would like the tool to calculate a benefit–cost ratio for the local data, costs can be entered here. The costs must include all relevant investments. For example, to assess the benefit–cost ratio of a promotion campaign for cycling, costs for the cycling infrastructure used by the target audience, which may be borne by the local administration, will also need to be included. The time frame entered to calculate the benefit-cost ratio can differ from the time frame entered to calculate the average annual benefit. For the discount rate, the rate to be used for calculating future benefits can be entered. Savings that occur in future years will be discounted by this percentage per year, and will be shown in the “present value” section of Step 5. A rate of 5% has been set as the default value. Common discount rates are usually available from government agencies; one option is to use interest rates on long-term government bonds. Step 5. Reading the economic savings resulting from reduced mortality Results are presented in three different ways. The average annual benefit is the value of lives saved (mortality only) per year. It averages the benefit over the time frame entered to calculate the benefits. This takes into account the time periods selected for uptake of cycling and the build up of health benefits (see also section 5.3). In addition, the total benefit accumulated over the time period entered for averaging the result is given as well as the maximum annual benefit achieved when both health benefits and uptake of cycling have reached the maximum levels. These should always be quoted as maximum rather than average values. The current value of the average annual benefit is the second main output of the model, using the discount rate from Step 4 to calculate the net present value, taking into account the reduced value of benefits over time. The current value of total benefit s accumulated over the time period entered is also shown. If costs are entered, HEAT also provides a benefit–cost ratio. 6.3. Assumptions The results of the assessment depend on a number of assumptions, which were agreed at the consensus meetings. •
The build-up of benefits is the estimated time it will take for
cyclists in the model to realize the benefits in terms of mortality of
the cycling entered at step 1. The default value is 5 years, based on
expert consensus. If a steady-state situation is assessed (selecting
“all current cycling”), no build-up period for the health benefits is
applied. • The average cycling speed is set at 14
km/h. This value is based on commuting time per week from a study in
Copenhagen, combined with data from the Stockholm commuting studies on
the number of trips per week over the year, distance and duration. Based
on an estimated average of 4 km per trip, the observed distance–speed
relationship produces an estimated average speed of 14 km/h. •
The relative risk data from the meta-analysis, which includes studies
from China and Europe, can be applied to cyclists in other settings. •
There is a linear relationship between risk of death and cycling
duration (assuming a constant average speed), in other words, each dose
of cycling leads to the same absolute risk reduction. • No thresholds have to be reached to achieve health benefits. • Men and women have approximately the same level of relative risk reduction. You
are reminded that the HEAT tools provide you with an approximation of
the level of health benefits. To get a better sense for the possible
range of the results, you are strongly advised to rerun the model,
entering slightly different values for variables where you have provided
a “best guess”, such as entering high and low estimates for such variables. Source: who.int |
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