Vignette: Intro to healthiar

This vignette introduces healthiar and shows how to use it with the help of examples. In addition, the descriptions of the healthiar functions include short examples for every function, also for helper functions that this vignette only mentions (e.g. get_risk(), get_paf() or discount()). You can execute them (with healthiar loaded) by running example("function_name"), e.g. example("get_risk").

Before using healthiar, please read the README or the package website. By using healthiar, you agree to its disclaimer and license.

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About healthiar

The healthiar functions allow you to quantify and monetize the health impacts (or burden of disease) attributable to exposure. The main focus of the EU project that initiated the development of healthiar (BEST-COST) has been two environmental exposures: air pollution and noise. However, healthiar could be used for other exposures such as green spaces, chemicals, physical activity…

See below an overview of healthiar, which is the first page of the cheat sheet. The whole list of functions included in healthiar is linked there and available in the reference.

Figure: Overview of healthiar

Input & output data

Input

You can enter data in healthiar functions using: - hard coded values or - columns inside pre-loaded data frames or tibbles.

Let’s see some examples calling the most important function in healthiar: attribute_health().

Hard coded vs. columns

Hard coded

Depending on the function argument, you will need to enter numeric or character values.

results_pm_copd <- attribute_health(
  exp_central = 8.85, 
  rr_central = 1.369, 
  rr_increment = 10,  
  erf_shape = "log_linear",
  cutoff_central = 5,
  bhd_central = 30747 
)

Columns

healthiar comes with some example data that start with exdat_ that allow you to test functions. Some of these example data will be used in some examples in this vignette.

Now let’s call attribute_health() with input data from the healthiar example data. Note that you can easily provide input data to the function argument using the $ operator.

results_pm_copd <- attribute_health(
  erf_shape = "log_linear",
  rr_central = exdat_pm$relative_risk, 
  rr_increment = 10, 
  exp_central = exdat_pm$mean_concentration,
  cutoff_central = exdat_pm$cut_off_value,
  bhd_central = exdat_pm$incidence
)

Tidy data

Be aware that healthiar functions are easier to use if your data is prepared in a tidy format, i.e.:

• Each variable is a column; each column is a variable.

• Each observation is a row; each row is an observation.

• Each value is a cell; each cell is a single value.

To know more about the concept of tidy format, see the article by (Wickham 2014).

For example, in attribute_health() the length of the input vectors to be entered in the arguments must be either 1 or the result of the combinations of the different values of:

• geo_id_micro

• exp_...

• sex

• age

• (info for further sub-group analysis)

Output

Structure

The output of the healthiar functions attribute_health() and attribute_lifetable() consists of two lists (“folders”):

• health_main contains the main results

• health_detailed contains detailed results and additional info about the assessment.

In other healthiar functions you can find a similar output structure but using different prefixes. E.g., social_ in socialize() and monetization_ in monetize().

Access

A similar structure can be found in other large functions in healthiar, e.g., attribute_lifetable(), compare(), socialize() or monetize(). In some functions, different elements are available in the output. For instance, attribute_lifetable() creates additional output that is specific to life table calculations.

There exist different, equivalent ways of accessing the output:

• With $ operator: results_pm_copd$health_main$impact_rounded (as in the example above)

• By mouse: in the pane of your editor that lists the variables (e.g. Environment in RStudio), click on the variable you want to inspect, and then open the health_main results table

• With [[]] operator results_pm_copd[["health_main"]]

• With pluck() & pull(): use the purrr::pluck function to select a list and then the dplyr::pull function extract values from a specified column, e.g. results_pm_copd |> purrr::pluck("health_main") |> dplyr::pull("impact_rounded")

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Relative risk

Goal

To quantify the health impacts attributable to an exposure using a relative risk and baseline health data.

Methods

The comparative risk assessment approach (C. J. Murray et al. 2003) is applied obtaining the population attributable fraction (percent of cases that are attributable to the exposure) based on the relative risk. The exposure scenario is compared with a counterfactual scenario.

This approach has been extensively documented and applied (e.g., WHO 2003; Steenland and Armstrong 2006; Soares et al. 2022; Pozzer et al. 2023; GBD 2019 Risk Factors Collaborators 2020; Lehtomäki et al. 2025).

Figure: Relative risk approach

Population attributable fraction

General integral form for the population attributable fraction (PAF):

$$PAF = \frac{\int rr\_at\_exp(x) \times prop\_pop\_exp(x)dx - 1}{\int rr\_at\_exp(x) \times prop\_pop\_exp(x)dx}$$

Where:

• $x$ = exposure level

• $prop\_pop\_exp(x)$ = proportion of the population exposed at exposure level $x$

• $rr\_at\_exp(x)$ = relative risk at exposure level compared to the reference level

Simplified for categorical exposure distribution

If exposure is categorical, the integrals are converted to sums:

$$PAF = \frac{\sum rr\_at\_exp_i \times prop\_pop\_exp_i - 1}{\sum rr\_at\_exp_i \times prop\_pop\_exp_i}$$

Alternatively, an equivalent form is:

$$PAF = \frac{\sum prop\_pop\_exp_i \times (rr\_at\_exp_i - 1)}{\sum prop\_pop\_exp_i\times (rr\_at\_exp_i - 1) + 1}$$

healthiar uses this second form, which is the one published as Formula 3 in ETC HE Report 2023/11. Note that $prop\_pop\_exp_i$ is the proportion of the total population in the exposure category $i$, so the $prop\_pop\_exp_i$ do not have to add up to 1: the part of the population that they do not cover is treated as unexposed, i.e. it gets the relative risk of the reference level. In air pollution assessments everybody is usually exposed and the $prop\_pop\_exp_i$ add up to 1, whereas in noise assessments they typically add up to less than 1, because exposure is only reported above a given level. Accordingly, the baseline health data entered in bhd_... must always refer to the total population. The first form above divides by $\sum rr\_at\_exp_i \times prop\_pop\_exp_i$ instead and is therefore only equivalent when $\sum prop\_pop\_exp_i = 1$.

Simplified for single exposure value

If there is one single exposure value, corresponding to the population weighted mean concentration, the equation can be simplified as follows:

$$PAF = \frac{rr\_at\_exp - 1}{rr\_at\_exp }$$ #### Scaling relative risk How to get this relative risk at exposure level (rr_at_exp)? This is normally different to the relative risk published in the epidemiological literature (rr) together with the (concentration/dose) increment that corresponds to this relative risk. The equations used for scaling relative risk depend on the chosen exposure-response function shapes:

• linear (Lehtomäki et al. 2025) $$RRexp = 1 + \frac{rr - 1}{increment} \times (exp - cutoff)$$

• log-linear (Lehtomäki et al. 2025) $$RRexp = e^{\frac{\log(rr)}{increment} \times (exp - cutoff)}$$

• log-log (Lehtomäki et al. 2025) $$RRexp = \left( \frac{exp + 1}{cutoff + 1} \right)^{\frac{\log(rr)}{\log(increment + cutoff + 1) - \log(cutoff + 1)}}$$

• linear-log (Pozzer et al. 2023) $$RRexp = 1 + \frac{rr - 1}{\log(increment + cutoff + 1) - \log(cutoff + 1)} \times \log\left( \frac{exp + 1}{cutoff + 1} \right)$$

The relative risk at exposure level (rr_at_exp) is part of the output of attribute_health() and attribute_lifetable(). rr_at_exp can also be calculated using get_risk().

For conversion of hazard ratios and/or odds ratios to relative risks refer to (VanderWeele 2019) and/or use the conversion tools developed by the Teaching group in EBM in 2022 for hazard ratios (https://ebm-helper.cn/en/Conv/HR_RR.html) and/or odds ratios (https://ebm-helper.cn/en/Conv/OR_RR.html).

Example

In the example below, we quantify the COPD cases attributable to PM2.5 exposure in a country, using the example data exdat_pm.

Function call

results_pm_copd <- attribute_health(
  approach_risk = "relative_risk", # If you do not call this argument, "relative_risk" will be assigned by default.
  erf_shape = "log_linear",
  rr_central = exdat_pm$relative_risk, 
  rr_increment = 10, 
  exp_central = exdat_pm$mean_concentration,
  cutoff_central = exdat_pm$cut_off_value,
  bhd_central = exdat_pm$incidence
)

Main results

results_pm_copd$health_main
#> # A tibble: 1 × 24
#>   geo_id_micro erf_ci  exp_ci  bhd_ci  cutoff_ci exp_category sex   age_group
#>   <chr>        <chr>   <chr>   <chr>   <chr>            <int> <chr> <chr>    
#> 1 a            central central central central              1 all   all      
#> # ℹ 16 more variables: impact <dbl>, impact_rounded <dbl>, approach_risk <chr>,
#> #   rr_increment <dbl>, erf_shape <chr>, prop_pop_exp <dbl>, exp_length <int>,
#> #   exp_type <chr>, exp <dbl>, bhd <dbl>, cutoff <dbl>, rr <dbl>,
#> #   is_lifetable <lgl>, pop_fraction_type <chr>, rr_at_exp <dbl>,
#> #   pop_fraction <dbl>

It is a table of the format tibble of 3 rows and 23 columns. Be aware that this main output contains input data, some intermediate steps and the final results in different formats.

Let’s zoom in on some relevant aspects.

results_pm_copd$health_main |> 
  dplyr::select(exp, bhd, rr, erf_ci, pop_fraction, impact_rounded) |> 
  knitr::kable() # For formatting reasons only: prints tibble in nice layout

exp	bhd	rr	erf_ci	pop_fraction	impact_rounded
8.85	30747	1.369	central	0.1138961	3502

Interpretation: this table shows us that exposure was 8.85 $\mu g/m^3$, the baseline health data (bhd_central) was 30747 (COPD incidence in this instance). The 1st row further shows that the impact attributable to this exposure using the central relative risk (rr_central) estimate of 1.369 is 3502 COPD cases, or ~11% of all baseline cases.

Some of the most relevant results columns include:

• impact_rounded rounded attributable health impact/burden
• impact raw impact/burden
• pop_fraction population attributable fraction (PAF) or population impact fraction (PIF)

Absolute risk

Goal

To quantify the health impacts attributable to an exposure using an absolute risk, i.e. based on the exposed population instead of baseline health data.

Methods

In the absolute risk calculation pathway, estimates are based on the size and distribution of the exposed population, rather than on baseline health data, as is the case in the relative risk pathway (WHO 2011).

Figure: Absolute risk approach

$$impact = \sum \frac{absolute\_risk\_as\_percent_i}{100} \times pop\_exp_i$$

Where:

• $impact$ = attributable cases

• $absolute\_risk\_as\_percent_i$ = absolute risk (in %) at exposure category $i$, obtained from the exposure-response function entered in erf_eq_...

• $pop\_exp_i$ = absolute population exposed at exposure category $i$

healthiar consistently handles cut-off values across both relative and absolute risk approaches. When a cutoff_ argument is specified alongside an exposure–response function (erf_eq_), the function evaluates exposure as excess exposure relative to the cut-off (c as $\text{exp} - \text{cutoff}$), effectively shifting the function by the cut-off. However, many absolute risk curves published in the literature are parameterized on raw exposure levels that already incorporate the cut-off directly into the function definition. Therefore, healthiar issues an informative warning if both erf_eq_ and a cutoff_ are specified when using attribute_health(). If your absolute risk function is parameterized on raw exposure levels rather than on the exposure above the cut-off, see chapter Shifting the ERF.

Example 1: several exposure categories

In the example below, we quantify the cases of high annoyance attributable to road traffic noise in five noise exposure bands.

Function call

results_noise_ha <- attribute_health(
  approach_risk = "absolute_risk", # default is "relative_risk"
  exp_central = c(57.5, 62.5, 67.5, 72.5, 77.5), # mean of the exposure categories
  pop_exp = c(387500, 286000, 191800, 72200, 7700), # population exposed per exposure category
  erf_eq_central = "78.9270-3.1162*c+0.0342*c^2" # exposure-response function
)

The erf_eq_central argument can digest other types of functions (see section on user-defined ERF).

Main results

results_noise_ha |>
  purrr::pluck("health_main") |>
  dplyr::select(erf_eq, erf_ci, impact_rounded) |> 
  knitr::kable() # Prints tibble in a minimal layout

erf_eq	erf_ci	impact_rounded
78.9270-3.1162c+0.0342c^2	central	174232

Results per noise exposure band

results_noise_ha[["health_detailed"]][["results_raw"]] |>
  dplyr::select(exp_category, exp, pop_exp, impact) |>
  knitr::kable()

exp_category	exp	pop_exp	impact
1	57.5	387500	49674.594
2	62.5	286000	50788.595
3	67.5	191800	46813.105
4	72.5	72200	23657.232
5	77.5	7700	3298.314

Example 2: one exposure category

Alternatively, it’s also possible to only assess the absolute risk impacts for one exposure category (e.g., a single noise exposure band).

Function call

results_noise_ha <- attribute_health(
  approach_risk = "absolute_risk",
  exp_central = 57.5,
  pop_exp = 387500,
  erf_eq_central = "78.9270-3.1162*c+0.0342*c^2"
)

Main results

results_noise_ha[["health_main"]] |>
  dplyr::select(exp_category, impact) |> 
  knitr::kable()

exp_category	impact
1	49674.59

Notes

Important: If the equation of the exposure-response function (erf_eq_...) requires taking a maximum in a vectorised context, pmax() must be used instead of max(). pmax() should be used whenever an element-wise maximum is required (the output will be a vector), while max() returns a single global maximum for the entire vector. For example:

erf_eq_central <-
  "exp(0.2969*log((pmax(0,c-2.4)/1.9+1))/(1+exp(-(pmax(0,c-2.4)-12)/40.2)))"

In the following chapters, only the code to access the results is shown. The tables are formatted as above, i.e. selecting some columns with dplyr::select() and printing them with knitr::kable().

Multiple geographic units

using relative risk

Goal

To quantify health impacts using a relative risk in several geographic units with one single function call and, optionally, aggregate them into larger units.

Example

In the example below, we quantify the disease cases attributable to PM2.5 exposure in five Swiss cantons and aggregate them by language region.

Function call

• Enter unique ID’s as a vector (numeric or character) to the geo_id_micro argument (e.g., municipality names or province abbreviations)

• Optional: aggregate unit-specific results by providing higher-level ID’s (e.g., region names or country abbreviations) as a vector (numeric or character) to the geo_id_macro argument

Input to the other function arguments is specified as usual, either as a vector or a single values (which will be recycled to match the length of the other input vectors).

results_iteration <- attribute_health(
    # Names of Swiss cantons
    geo_id_micro = c("Zurich", "Basel", "Geneva", "Ticino", "Jura"),
    # Names of languages spoken in the selected Swiss cantons
    geo_id_macro = c("German","German","French","Italian","French"),
    rr_central = 1.369,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = c(11, 11, 10, 8, 7),
    bhd_central = c(4000, 2500, 3000, 1500, 500)
)

Main results

The main output contains aggregated results

results_iteration$health_main

geo_id_macro	impact_rounded	erf_ci	exp_ci	bhd_ci
German	1116	central	central	central
French	466	central	central	central
Italian	135	central	central	central

In this case health_main contains the number of disease cases attributable to PM2.5 exposure summed by language region. In total, the 5 cantons have 1717 attributable cases (using a relative risk of 1.369).

Detailed results

The geo unit-specific information and results are stored under health_detailed>results_raw .

results_iteration$health_detailed$results_raw

geo_id_micro	impact_rounded	geo_id_macro
Zurich	687	German
Basel	429	German
Geneva	436	French
Ticino	135	Italian
Jura	30	French

health_detailed also contains impacts obtained through all combinations of input data central, lower and upper estimates (as usual), besides the results per geo unit (not shown above).

using absolute risk

Goal

To quantify health impacts using an absolute risk in several geographic units with one single function call.

Example

In the example below, we quantify the cases of high annoyance attributable to noise exposure in a rural and an urban area.

Function call

data <- exdat_noise |> 
  ## Filter for urban and rural regions
  dplyr::filter(region == "urban" | region == "rural")

results_iteration_ar <- attribute_health( 
    # Both the rural and urban areas belong to the higher-level "total" region
    geo_id_macro = "total",
    geo_id_micro = data$region,
    approach_risk = "absolute_risk",
    exp_central = data$exposure_mean,
    pop_exp = data$exposed,
    erf_eq_central = "78.9270-3.1162*c+0.0342*c^2"
)

Important: The length of the input vectors fed to geo_id_micro, exp_central, pop_exp must match and must be

(number of geo units) x (number of exposure categories) = 2 x 5 = 10,

because we have 2 geo units ("rural" and "urban") and 5 exposure categories.

Main results

health_main contains the aggregated results (i.e. sum of impacts in rural and urban areas).

results_iteration_ar$health_main

geo_id_macro	impact_rounded	erf_ci	exp_ci
total	174232	central	central

Detailed results

Impact by geo unit, in this case impact in the rural and in the urban area.

results_iteration_ar$health_detailed$results_by_geo_id_micro

geo_id_micro	geo_id_macro	impact
urban	total	150904.00
rural	total	23327.84

Uncertainty

Confidence interval

Goal

To quantify health impacts taking into account the uncertainty (confidence intervals) of the input data.

Example

In the example below, we quantify the COPD cases attributable to PM2.5 exposure with lower and upper bounds for the relative risk, the exposure and the baseline health data.

Function call

results_pm_copd <- attribute_health(
    erf_shape = "log_linear",
    rr_central = 1.369, 
    rr_lower = 1.124, # lower 95% confidence interval (CI) bound of RR
    rr_upper = 1.664, # upper 95% CI bound of RR
    rr_increment = 10, 
    exp_central = 8.85, 
    exp_lower = 8, # lower 95% CI bound of exposure
    exp_upper = 10, # upper 95% CI bound of exposure
    cutoff_central = 5,
    bhd_central = 30747, 
    bhd_lower = 28000, # lower 95% confidence interval estimate of BHD
    bhd_upper = 32000 # upper 95% confidence interval estimate of BHD
) 

Detailed results

Let’s inspect the detailed results:

results_pm_copd$health_detailed$results_raw

erf_ci	exp_ci	bhd_ci	impact_rounded
central	central	central	3502
lower	central	central	1353
upper	central	central	5474
central	central	lower	3189
lower	central	lower	1232
upper	central	lower	4985
central	central	upper	3645
lower	central	upper	1408
upper	central	upper	5697

Each row contains the estimated attributable cases (impact_rounded) obtained by the input data specified in the columns ending in “_ci” and the other calculation pathway specifications in that row (not shown).

• The 1st contains the estimated attributable impact when using the central estimates of relative risk, exposure and baseline health data.

• The 2nd row shows the impact when using the central estimates of the relative risk, exposure in combination with the lower estimate of the baseline health data.

• …

Only 9 of the 27 possible combinations and a selection of columns are shown.

Monte Carlo simulation

Goal

To summarize the uncertainty of several input data into one single confidence interval of the attributable health impacts.

Methods

General concepts

A Monte Carlo simulation is a statistical method that generates repeated random sampling (Robert and Casella 2004; Rubinstein and Kroese 2016). In healthiar, you can use the function summarize_uncertainty() to simulate values in the arguments with uncertainty and estimate a single confidence interval in the results.

For each entered input argument that includes a (95%) confidence interval (i.e. _lower and _upper bound value) a distribution is fitted (see distributions below). The median of the simulated attributable impacts is reported as the central estimate. The 2.5th and 97.5th percentiles of these simulated impacts define the lower and upper bounds of the 95% summary uncertainty interval. Aggregated estimates (e.g. for a geo_id_macro covering several geo_id_micro) are obtained by first summing the impacts of all lower level units within each simulation and only then taking the median and the 2.5th and 97.5th percentiles of these sums. Summing instead the central, lower and upper estimates of each lower level unit would assume that the uncertainty is perfectly correlated across the units and would therefore overestimate the width of the confidence interval.

Distributions used for simulation

summarize_uncertainty() assumes the following shapes of the distributions in the simulations:

• Relative risk: The values are simulated based on an optimized gamma distribution, which fits well as relative risks are positive and their distributions are usually right-skewed. The gamma distribution is parametrized such that its mean is equal to the central relative risk estimate (rate= shape/rr_central). The shape parameter is then optimized using stats::optimize() to match the inputed 95% confidence interval bounds, with stats::qgamma() used to evaluate candidate distributions. Finally, n_sim relative risk values are simulated using stats::rgamma().

• Exposure, cutoff, baseline health data and duration: The values are simulated based on a normal distribution using stats::rnorm() with mean = exp_central, mean = cutoff_central, mean = bhd_central or mean = duration_central and a standard deviation based on corresponding lower and upper 95% exposure confidence interval values. The standard deviation is calculated as $$(upper-lower)/(2*1.96)$$, since for a normal distribution the 95% CI spans approximately two standard deviations on either side of the mean. As these four variables cannot be negative, the distribution is truncated at zero, i.e. negative values are discarded and drawn again until they are positive.

• Disability weights: The values are simulated based on a beta distribution, as both the disability weights and the beta distribution are bounded by 0 and 1. The beta distribution best fitting the inputted central disability weight estimate and corresponding lower and upper 95% confidence interval values is fitted using stats::qbeta() (the best fitting distribution parameters shape1 and shape2 are determined using stats::optimize()). For this purpose, we partly adapted the R function prevalence::beta_expert with permission of one of the authors (Devleesschauwer et al. 2022). Finally, n_sim disability weight values are simulated using stats::rbeta().

For stability of the 95% confidence interval, a large number of simulations (e.g., 10,000) is recommended in practice. The example below uses n_sim = 100 for brevity.

Example

In the example below, we summarize the uncertainty of the COPD cases from the chapter Confidence interval.

Function call

results_pm_copd_summarized <- 
  summarize_uncertainty(
    output_attribute = results_pm_copd,
    n_sim = 100
)

Main results

The outcome of the Monte Carlo analysis is added to the variable entered as the output_attribute argument, which is results_pm_copd in our case.

Two lists (“folders”) are added:

• uncertainty_main contains the central estimate and the corresponding 95% confidence intervals obtained through the Monte Carlo assessment and

• uncertainty_detailed contains all n_sim simulations of the Monte Carlo assessment.

results_pm_copd_summarized$uncertainty_main

geo_id_micro	impact_ci	impact	impact_rounded
a	central_estimate	3530.588	3531
a	lower_estimate	1394.460	1394
a	upper_estimate	6027.056	6027

Detailed results

The folder uncertainty_detailed contains all single simulations. Let’s look at the impact of the first 10 simulations.

The columns erf_ci, exp_ci, bhd_ci, and cutoff_ci indicate the source of uncertainty component used for that simulation (in the first 10 simulations, all use central estimates).

results_pm_copd_summarized$uncertainty_detailed$impact_by_sim

geo_id_micro	erf_ci	exp_ci	bhd_ci	cutoff_ci	exp_category	sex	age_group	sim_id	impact	impact_rounded	approach_risk	rr_increment	erf_shape	prop_pop_exp	exp_length	exp_type	cutoff	is_lifetable	rr	exp	bhd	pop_fraction_type	rr_at_exp	pop_fraction
a	central	central	central	central	1	all	all	1	1935.156	1935	relative_risk	10	log_linear	1	1	population_weighted_mean	5	FALSE	1.251826	7.933793	30345.91	paf	1.068113	0.0637699
a	central	central	central	central	1	all	all	2	4698.352	4698	relative_risk	10	log_linear	1	1	population_weighted_mean	5	FALSE	1.430184	9.609612	30900.10	paf	1.179315	0.1520498
a	central	central	central	central	1	all	all	3	3722.356	3722	relative_risk	10	log_linear	1	1	population_weighted_mean	5	FALSE	1.414326	8.826146	29967.05	paf	1.141833	0.1242150
a	central	central	central	central	1	all	all	4	6061.313	6061	relative_risk	10	log_linear	1	1	population_weighted_mean	5	FALSE	1.570019	9.745453	31454.46	paf	1.238699	0.1927012
a	central	central	central	central	1	all	all	5	4045.348	4045	relative_risk	10	log_linear	1	1	population_weighted_mean	5	FALSE	1.479048	8.715480	29889.41	paf	1.156529	0.1353439
a	central	central	central	central	1	all	all	6	1756.592	1757	relative_risk	10	log_linear	1	1	population_weighted_mean	5	FALSE	1.143280	9.464298	30272.57	paf	1.061600	0.0580259
a	central	central	central	central	1	all	all	7	5447.089	5447	relative_risk	10	log_linear	1	1	population_weighted_mean	5	FALSE	1.728913	8.440255	31728.81	paf	1.207258	0.1716765
a	central	central	central	central	1	all	all	8	2354.822	2355	relative_risk	10	log_linear	1	1	population_weighted_mean	5	FALSE	1.202089	9.210268	31579.59	paf	1.080576	0.0745678
a	central	central	central	central	1	all	all	9	1699.985	1700	relative_risk	10	log_linear	1	1	population_weighted_mean	5	FALSE	1.169202	8.728108	30028.39	paf	1.060010	0.0566126
a	central	central	central	central	1	all	all	10	3659.699	3660	relative_risk	10	log_linear	1	1	population_weighted_mean	5	FALSE	1.345132	9.222098	31103.05	paf	1.133355	0.1176637

Notes

Important: The normal distribution used for exposure, cutoff, baseline health data and duration is symmetric. Therefore, the simulated values reproduce the entered confidence interval only if the entered _lower and _upper values are symmetric around the _central value. Only the width of the entered confidence interval is used, not its shape. The more asymmetric the entered confidence interval, the more the simulated values will depart from it. Moreover, if the entered confidence interval is so wide that a relevant share of the distribution falls below zero, the truncation at zero shifts the simulated central estimate above the entered _central value.

User-defined ERF

Goal

To quantify health impacts with an exposure-response function of any shape, defined by the user instead of one of the predefined shapes.

Example

In the example below, we quantify the COPD cases attributable to PM2.5 exposure with an exposure-response function defined by points, such as the MR-BRT curves of the Global Burden of Disease study.

Function call

In this case, the function arguments erf_eq_... require a function as input, so we use an auxiliary function (splinefun()) to transform the points on the ERF into type function.

results_pm_copd_mr_brt <- attribute_health(
  exp_central = 8.85,
  bhd_central = 30747,
  cutoff_central = 0,
  # Specify the function based on x-y point pairs that lie on the ERF
  erf_eq_central = splinefun(
    x = c(0, 5, 10, 15, 20, 25, 30, 50, 70, 90, 110),
    y = c(1.00, 1.04, 1.08, 1.12, 1.16, 1.20, 1.23, 1.35, 1.45, 1.53, 1.60),
    method = "natural")
)

The ERF curve created looks as follows

Alternatively, other functions (e.g. approxfun()) can be used to create the ERF

Sub-group analysis

Every sub-group stratifies the assessment: by age group, by sex, or by anything entered in info, such as education level. What differs is what happens to that stratification in the main results.

For the sub-groups in this chapter the impacts are summed, so the stratification disappears from health_main and only the total per geographic unit is shown. It is still available in the results_by_... tables of the detailed output, and that is usually what you want: the total across education levels, or across age groups, is a meaningful number.

Not every dimension of an assessment is a sub-group in this sense, though. A stratification splits one population into parts that add up to the whole, which is exactly why summing them is meaningful. Different exposure-outcome pairs do not: PM2.5 and mortality and NO2 and asthma cover the same whole population, so they are parallel analyses rather than parts of one. Adding them up counts the same people twice.

Those dimensions therefore have to be kept in the main results, which is what the argument main_results_by does. See the chapter Multiple exposure-outcome pairs.

by age group

Goal

To quantify health impacts by age group.

Example

In the example below, we quantify the health impacts attributable to air pollution in two age groups (below 65 and 65+).

Function call

To obtain age-group-specific results, the baseline health data (and possibly exposure) must be available by age group.

If the age argument was specified, age-group-specific results are available under health_detailed in the sub-folder results_by_age_group.

results_age_group <- attribute_health(
        approach_risk = "relative_risk",
        age = c("below_65", "65_plus"),
        exp_central = c(8, 7),
        cutoff_central = c(5, 5),
        bhd_central = c(1000, 5000),
        rr_central = 1.06,
        rr_increment = 10,
        erf_shape = "log_linear"
      )

Results by age group

results_age_group$health_detailed$results_by_age_group |> 
  dplyr::select(age_group, impact_rounded, exp, bhd) |> 
  knitr::kable()

age_group	impact_rounded	exp	bhd
below_65	17	8	1000
65_plus	58	7	5000

by sex

Goal

To quantify health impacts by sex.

Example

In the example below, we quantify the health impacts attributable to air pollution in females and males.

Function call

The baseline health data (and possibly exposure) must be entered by sex.

results_sex <- attribute_health(
        approach_risk = "relative_risk",
        sex = c("female", "male"),
        exp_central = c(8, 8),
        cutoff_central = c(5, 5),
        bhd_central = c(1000, 1100),
        rr_central = 1.06,
        rr_increment = 10,
        erf_shape = "log_linear"
      )

Results by sex

If the sex argument was specified, sex-specific results are available under health_detailed in the sub-folder results_by_sex.

results_sex$health_detailed$results_by_sex |> 
  dplyr::select(sex, impact_rounded, exp, bhd) |> 
  knitr::kable()

sex	impact_rounded	exp	bhd
female	17	8	1000
male	19	8	1100

by other sub-groups

Goal

To quantify health impacts by any other sub-group, such as education level.

Example

In the example below, we quantify the health impacts attributable to air pollution by education level in four geographic units.

Function call

A single vector (or a data frame / tibble with multiple columns) to group the results by can be entered to the info argument.

In a second step one can group the results based on one or more columns and so summarize the results by the preferred sub-groups.

If the argument info is a data frame or tibble, the columns in the info argument are renamed adding the prefix info_. If the argument info is a vector, the column in the results is called info.

output_attribute <- healthiar::attribute_health(
    rr_central = 1.063,
    rr_increment = 10,
    erf_shape = "log_linear",
    cutoff_central =  0,
    exp_central = c(6, 7, 8,
                    7, 8, 9,
                    8, 9, 10,
                    9, 10, 11),
    bhd_central = c(600, 700, 800,
                    700, 800, 900,
                    800, 900, 1000,
                    900, 1000, 1100),
    geo_id_micro = rep(c("a", "b", "c", "d"), each = 3),
    info = data.frame(
      education = rep(c("secondary", "bachelor", "master"), times = 4)) # education level
  )

Results by other sub-group

output_stratified <- output_attribute$health_detailed$results_raw |>
      dplyr::group_by(info_education) |>
      dplyr::summarize(mean_impact = mean(impact))|>
      dplyr::pull(mean_impact) |>
      print()
#> [1] 43.72087 54.26844 34.30332

by age, sex and other sub-groups

Goal

To quantify health impacts by several sub-groups at the same time, such as age, sex and education level.

Example

In the example below, we quantify the health impacts attributable to air pollution by age group, sex and education level in two geographic units.

Function call

output_attribute <- healthiar::attribute_health(
    rr_central = 1.063,
    rr_increment = 10,
    erf_shape = "log_linear",
    cutoff_central =  0,
    age_group = rep(c("50_and_younger", "50_plus"), each = 4, times= 2),
    sex = rep(c("female", "male"), each = 2, times = 4),
    exp_central = c(6, 7, 8, 7, 8, 9, 8, 9,
                    10, 9, 10, 11, 10, 11, 12, 13),
    bhd_central = c(600, 700, 800, 700, 800, 900, 800, 900,
                    1000, 900, 1000, 1100, 1000, 1100, 1200, 1000),
    geo_id_micro = rep(c("a", "b"), each = 8),
    info = data.frame(
      education = rep(c("without_master", "with_master"), times = 8)) # education level
  )

Results by all sub-groups

output_stratified <- output_attribute$health_detailed$results_raw |>
      dplyr::group_by(info_education) |>
      dplyr::summarize(mean_impact = mean(impact))|>
      dplyr::pull(mean_impact) |>
      print()
#> [1] 52.80090 49.83826

Multiple exposure-outcome pairs

Goal

To quantify the health impacts of several exposure-outcome pairs, or of the same pair with different relative risks, in one single function call.

Methods

As explained in the chapter Sub-group analysis, the impacts of the sub-groups are summed by default, so only the total per geographic unit is shown in health_main.

That is not what you want here. Premature deaths attributable to PM2.5 and asthma cases attributable to NO2 are not two parts of one population: they are two analyses of the same population, so adding them up counts the same people twice. The same applies to a sensitivity analysis in which one pair is quantified with the relative risks of two different studies: the two results are alternatives to each other, not summands.

Enter therefore in main_results_by the names of the dimensions that the main results have to be reported by. They stay as separate rows in health_main and in all results_by_... tables. Note that main_results_by does not create those tables, which are available in the detailed output in any case: it determines which dimensions survive in them and in the main results.

Example 1: several exposure-outcome pairs

In the example below, we quantify the COPD cases attributable to PM2.5 and the asthma cases attributable to NO2 in two geographic units.

Function call

The pairs are identified in a column of info, and that column is named in main_results_by. All other arguments are entered as parallel vectors, exactly as for multiple geographic units. Note that the pairs can have different exposure-response functions, here log-linear for PM2.5 and linear for NO2.

output_attribute <- healthiar::attribute_health(
    info = data.frame(
      pair = rep(c("pm2.5_copd", "no2_asthma"), each = 2)),
    main_results_by = "pair",
    exp_central    = c(8.85, 9.20, 22.1, 24.5),
    cutoff_central = c(5, 5, 10, 10),
    rr_central     = c(1.369, 1.369, 1.041, 1.041),
    rr_increment   = 10,
    erf_shape      = c("log_linear", "log_linear", "linear", "linear"),
    bhd_central    = c(30747, 31500, 12000, 12500),
    geo_id_micro   = rep(c("a", "b"), times = 2)
  )

Main results

One row per exposure-outcome pair and geographic unit. Without main_results_by the four impacts would have been added up into one single number per geographic unit.

output_attribute$health_main |>
  dplyr::select(info_pair, geo_id_micro, erf_ci, impact_rounded) |>
  print()
#> # A tibble: 4 × 4
#>   info_pair  geo_id_micro erf_ci  impact_rounded
#>   <chr>      <chr>        <chr>            <dbl>
#> 1 pm2.5_copd a            central           3502
#> 2 pm2.5_copd b            central           3893
#> 3 no2_asthma a            central            567
#> 4 no2_asthma b            central            701

Example 2: sensitivity analysis with several exposure-response functions

The same mechanism compares one exposure-outcome pair quantified with the relative risks of two different studies.

Function call

output_sensitivity <- healthiar::attribute_health(
    info = data.frame(rr_source = c("Liu 2020", "WHO 2021")),
    main_results_by = "rr_source",
    exp_central = c(8.85, 8.85),
    cutoff_central = 5,
    rr_central = c(1.369, 1.080),
    rr_increment = 10,
    erf_shape = "log_linear",
    bhd_central = c(30747, 30747)
  )

Main results

output_sensitivity$health_main |>
  dplyr::select(info_rr_source, erf_ci, impact_rounded) |>
  print()
#> # A tibble: 2 × 3
#>   info_rr_source erf_ci  impact_rounded
#>   <chr>          <chr>            <dbl>
#> 1 Liu 2020       central           3502
#> 2 WHO 2021       central            898

Notes

Which dimensions can be entered

main_results_by is available in both attribute_health() and attribute_lifetable(). It accepts:

• the columns of info, using the names that you gave them (e.g. "pair"), or "info" itself if you entered a vector instead of a data frame;
• the arguments sex, age_group, exp_category, geo_id_micro and geo_id_macro;
• in attribute_lifetable() also year, i.e. the years of the time horizon, which are otherwise added up.

For example, main_results_by = "sex" shows one row per sex in health_main instead of the total across sexes. This changes nothing in the calculation, only which rows are kept apart in the results, and the rows still add up to the total that you would get without the argument.

A name that is not available in the assessment produces an error listing the options, so a typo in a column name of info cannot pass unnoticed.

Limitations

All pairs in one call must use the same way of defining the exposure-response function, i.e. either rr_... or erf_eq_... for all of them, and the same approach_risk.

standardize() keeps the pairs apart as well, so each pair gets its own age-standardized impacts. Nothing has to be done for that: its results simply carry the columns of main_results_by.

socialize() and summarize_uncertainty() analyse one pair at a time and stop with an error otherwise, so for them attribute_health() has to be called once per pair. The reason is different in each case. socialize() groups the impacts only by geographic unit, age group and social quantile, and its arguments have no way of identifying the pairs when the input is entered directly instead of as an attribute_health() output. summarize_uncertainty() simulates one value per variable and geographic unit, assuming that e.g. the relative risk is the same everywhere, which is not the case when each pair has its own.

YLL & deaths with life table

The life table approach of attribute_lifetable() is only applicable to all-cause mortality. The baseline deaths (bhd_central) are used for two purposes at the same time: to project how the population survives over the years and to obtain the deaths that are attributable to the exposure. The projection of the population needs the deaths of all causes, so bhd_central must contain all-cause deaths and the exposure-response function must refer to all-cause mortality.

Cause-specific mortality (e.g. lung cancer) cannot be assessed with attribute_lifetable():

• If only the cause-specific deaths are entered, the projected population hardly dies of any other cause and reaches unrealistically high ages. The YLL and the attributable deaths are then overestimated.
• If all-cause deaths are entered with a cause-specific exposure-response function, the relative risk is applied to all causes of death, which overestimates the impacts as well.

For cause-specific mortality, the attributable deaths can be obtained with attribute_health().

Data preparation

Goal

To convert population and death data by larger age groups into the one-year age groups required by the life table approach.

Methods

The life table approach to obtain YLL and deaths requires population and baseline mortality data to be stratified by one year age groups. However, in some cases these data are only available for larger age groups (e.g., 5-year data: 0-4 years old, 5-9 years old, …). What to do?

• If your population and mortality data are not available by one-year age group, data must be prepared by interpolating values. The healthiar function prepare_lifetable() makes this conversion using the same approach as the WHO tool AirQ+ (WHO 2020). In standard AirQ+ life table disaggregations, deaths are assumed to be distributed uniformly across age intervals with a fraction of the age interval lived of 0.5. The argument fraction_lived allows other assumptions, see the section on the fraction of the age interval lived below.

• If your population and death data are stratified by one-year age group, you are lucky, you can ignore this initial step. prepare_lifetable() passes single-year data on unchanged, so it can also be used for a life table that mixes already available single-year data with converted larger age groups.

Fraction of the age interval lived

The argument fraction_lived, available in both prepare_lifetable() and attribute_lifetable(), is the average fraction of an age interval that is lived by those who die in it. It corresponds to $_na_x$ in the life table literature (Chiang 1984; Preston et al. 2001). The default of 0.5 for all age groups means that deaths are evenly distributed over the age interval, which is the value that AirQ+ assumes (WHO 2020). It can be entered as a single value applying to all age groups or as one value per age group.

What it determines

With $_nm_x$ the death rate of an age interval of width $n$, the probability of dying in that interval is

$$_nq_x = \frac{n \times {}_nm_x}{1 + n \times (1 - {}_na_x) \times {}_nm_x}$$

and the person-years lived at a single year of age, i.e. its mid-year population, is one year for each person reaching the next birthday plus $fraction\_lived$ years for each person dying at that age:

$$midyear\_population = entry\_population - (1 - fraction\_lived) \times deaths$$

In prepare_lifetable() these two relations fix the conversion. The entry population of the first single year of age of an age group follows from the condition that the conversion may neither create nor lose population, i.e. that the single-year mid-year populations have to add up to the mid-year population of the age group that was entered:

$$entry\_population_{first} = \frac{population + (1 - fraction\_lived) \times bhd}{\sum_{k = 0}^{n - 1} prob\_survival^{\,k}}$$

For $fraction\_lived = 0.5$ this reproduces the values published in the AirQ+ manual, and unlike the formula given there it holds for any age interval width and any value of fraction_lived.

Both relations are checked in internal tests in healthiar against a published life table, the abridged life table for Austrian males in 1992 (Preston et al. 2001), in which the fraction of the age interval lived ranges from 0.48 to 0.63 in the 5-year age groups and is 0.068 at age 0. Applying the probability of dying obtained by prepare_lifetable() to the published survivors reproduces the published deaths of that life table for every age group, and the entry population it determines from the published mid-year population and deaths at age 0 reproduces the published number of survivors.

What it does not determine

fraction_lived is not a way to control how the deaths of an age group are distributed over the single years of age it contains. That distribution follows from the survival probabilities, as in AirQ+, so fraction_lived influences it only indirectly and far too weakly to be used for it. Both quantities are distinct in the life table literature and the distribution of the deaths cannot be derived from $_na_x$(Preston et al. 2001).

prepare_lifetable() can therefore not reproduce the concentration of deaths at age 0 that is observed in low-mortality settings. If the deaths at age 0 matter for your assessment, obtain single-year population and death data for the youngest ages instead of relying on the conversion.

How to choose it

The sensitivity of the survival probability to fraction_lived is

$$\frac{\partial \; prob\_survival}{\partial \; fraction\_lived} = - \frac{hazard\_rate^2}{(1 + (1 - fraction\_lived) \times hazard\_rate)^2}$$

i.e. it grows with the square of the hazard rate. fraction_lived therefore matters little in the age groups with a low mortality and most in those with the highest mortality, which are the last ones. With the example data of this vignette, entering 0.1 for age 0 changes the YLL by 0.01%, whereas adapting the last age group changes them by 0.25%.

Values of $_na_x$ other than 0.5 are usually derived from the mortality level itself. For age 0 and for the ages 1 to 4, Preston et al. (2001) report rules of thumb going back to Coale and Demeny, e.g. $_1a_0 = 0.045 + 2.684 \times {}_1m_0$ for males with $_1m_0 < 0.107$. For an open-ended last age group, $_na_x$ is the remaining life expectancy $1 / {}_nm_x$, which can exceed the width of the age interval.

Whichever fraction_lived is used in prepare_lifetable() has to be passed on to attribute_lifetable(), which needs the same assumption to obtain the survival probabilities. The output column fraction_lived_for_attribute is provided for that purpose.

Example

In the example below, we convert the population and deaths of four 5-year age groups into one-year age groups, once with the default and once with an age-specific fraction of the age interval lived.

Function call

age_groups <- c(0, 5, 10, 15)
pop <- c(438200, 445100, 439800, 421500)
bhd_counts <- c(1420, 45, 50, 125)

# Standard uniform disaggregation (fraction_lived = 0.5)
prepared_data <- healthiar::prepare_lifetable(
  age_group = age_groups,
  population = pop,
  bhd = bhd_counts
  # fraction_lived = 0.5 (default)
)

# Age-group specific fraction of the age interval lived (first fraction_lived = 0.1)
prepared_data_with_age_specific_fraction_lived <- healthiar::prepare_lifetable(
  age_group = age_groups,
  population = pop,
  bhd = bhd_counts,
  fraction_lived = c(0.1, 0.5, 0.5, 0.5)
)

YLL

Goal

To quantify the years of life lost (YLL) attributable to an exposure using a life table.

Methods

General concept

The life table methodology of attribute_lifetable() follows that of the WHO tool AirQ+ (WHO 2020), which is described in more detail by Miller and Hurley (2003). The generalized formulas, which do not assume a fixed fraction of life lived (fraction_lived = 0.5), are based on the foundational work by Chiang (1984). In short, two scenarios are compared:

1. a scenario with the exposure level specified in the function (“exposed scenario”) and

2. a scenario with no exposure (“unexposed scenario”).

First, the entry and mid-year populations of the (first) year of analysis in the unexposed scenario are determined using modified survival probabilities. Second, age-specific population projections using scenario-specific survival probabilities are done for both scenarios. Third, by subtracting the populations in the unexposed scenario from the populations in the exposed scenario the premature deaths/years of life lost attributable to the exposure are determined. An extensive life table case study is available in a report by Miller (2010).

Use the two arguments approach_exposure and approach_newborns to modify the life table calculation:

• approach_exposure

  • "single_year" (default): Population is exposed for only one year (year of analysis). Applicable for both deaths and YLL. The attributable deaths refer to the year of analysis only, while the YLL caused by the exposure in that year are accumulated across the year of analysis and the following years (see argument time_horizon).

  • "constant": Population is exposed every year, i.e. air pollution exposure is sustained across the full projection horizon and attributable health impacts are projected and cumulated across the full lifetime period (see argument time_horizon). Applicable for both deaths and YLL.

  Both options give the same attributable deaths and YLL in the year of analysis, because they only differ from the following year on. By default, AirQ+ assumes that air pollution remains stable over time (WHO 2020): its results over several years (e.g. “YLL over 10 years”) correspond to "constant", while the attributable deaths that it reports for the first year are the same with both options. A single-year exposure can only be obtained in AirQ+ with its advanced scenario “Change in pollution level” (WHO 2020), set to reduce the pollution level by 100% in the years after the year of analysis.

• approach_newborns

  • "without_newborns" (default): Population in the year of analysis is followed over time, without considering newborns being born.

  • "with_newborns": For each year after the year of analysis n babies are born, with n being equal to the (male and female) population aged 0 that is provided in the argument population.

Last age group

The life table is closed at the last age group, i.e. the survivors of the last age group are not projected into a further age. This follows the usual practice in the life table method, which extends the life table up to an age past which the probability of survival is negligible and then sets the survival above it to zero (Miller and Hurley 2003).

The data must therefore reach an age at which survival is negligible, i.e. the last age group should be one in which essentially all remaining deaths occur. National life tables usually already close in this way, with a probability of dying of 1 in the last age group. If the last age group still has an appreciable number of survivors, those survivors are dropped from the projection: neither the life years they would still live, nor the difference in those life years between the exposed and the unexposed scenario, are counted. The attributable health impacts are then underestimated. With the example data used in this vignette, which end at age 99 and still have a survival probability of about 0.34 there, the underestimation is around 0.7%.

If that is the case, condense the last age group, i.e. sum the populations and the deaths of the highest ages into it, so that all remaining deaths fall within it. Do not add extra age groups beyond the data instead: the added ages would need a population greater than 0, that population would be considered exposed and would generate attributable health impacts of its own, and the results would then grow with the number of added age groups instead of converging.

Comparison with AirQ+

healthiar aligns with AirQ+ in the equations of the life table. The survival probability of AirQ+, $S_i = (2 - h_i) / (2 + h_i)$, is identical to the one used here with the default fraction_lived = 0.5, and the modified survival probability is obtained in both tools by dividing the hazard rate by the relative risk (which is equivalent to multiplying it by $1 - PAF$) (WHO 2020).

healthiar reproduces the results of AirQ+. For the same input data, the attributable YLL and premature deaths of both tools agree to the last decimal that AirQ+ prints, for an exposure of one single year as well as for a constant exposure, year by year and accumulated over several years, and so do the life expectancies with and without the exposure.

The comparison has to be made by sex, though, because the “All genders” results of AirQ+ are not the sum of its male and female results as in healthiar: AirQ+ obtains them from a life table in which the populations and the deaths of both sexes are pooled, which has to be entered and run separately (WHO 2020). With the data of the example below, the pooled life table for all genders in AirQ+ gives 1.6% more YLL than the sum of two sex-specific ones in healthiar. healthiar returns that pooled value too, when the populations and the deaths of both sexes are entered as one single life table, i.e. passing the same value in sex for every age group.

Determination of populations in the (first) year of analysis

Entry population

The entry (i.e. start of year) populations in both scenarios (exposed and unexposed) is determined as follows:

$$entry\_population_{year_1} = midyear\_population_{year_1} + (1 - fraction\_lived) \times deaths_{year_1}$$

Survival probabilities

Exposed scenario

The survival probabilities in the exposed scenario from start of year $i$ to start of year $i+1$ are calculated as follows:

$$prob\_survival = \frac{midyear\_population_i - fraction\_lived \times deaths_i}{midyear\_population_i + (1 - fraction\_lived) \times deaths_i}$$

Analogously, the probability of survival from start of year $i$ to mid-year $i$:

$$prob\_survival\_until\_midyear = 1 - (1 - fraction\_lived) \times (1 - prob\_survival)$$

Unexposed scenario

The survival probabilities in the unexposed scenario are calculated as follows:

First, the age-group specific hazard rate in the exposed scenario is calculated using the inputted age-specific mid-year populations and deaths.

$$hazard\_rate = \frac{deaths}{mid\_year\_population}$$

Second, the hazard rate is multiplied by the modification factor ($= 1 - PAF$) to obtain the age-specific hazard rate in the unexposed scenario.

$$hazard\_rate\_mod = hazard\_rate \times modification\_factor$$

Third, the age-specific survival probabilities (from the start until the end in a given age group) in the unexposed scenario are calculated as follows:

$$prob\_survival\_mod = \frac{1 - fraction\_lived \times hazard\_rate\_mod}{1 + (1 - fraction\_lived) \times hazard\_rate\_mod}$$

Mid-year population

The mid-year population of the (first) year of analysis (year_1) in the unexposed scenario are determined as follows:

First, the survival probabilities from start of year $i$ to mid-year $i$ in the unexposed scenario is calculated as:

$$prob\_survival\_until\_midyear_{mod} = 1 - (1 - fraction\_lived) \times (1 - prob\_survival\_mod)$$

Second, the mid-year populations of the (first) year of analysis (year_1) in the unexposed scenario is calculated:

$$midyear\_population\_unexposed_{year_1} = entry\_population_{year_1} \times prob\_survival\_until\_midyear_{mod}$$

Population projection

Using the age group-specific and scenario-specific survival probabilities calculated above, future populations of each age-group under each scenario are calculated.

Exposed scenario

The population projections for the two possible options of approach_exposure ("single_year" and "constant") for the exposed scenario are different. In the case of "single_year" exposure, the population projection for the years after the year of exposure uses the same (modified) survival probabilities as the unexposed scenario.

In the case of "constant" the population projection is done as follows:

First, the entry population of year $i+1$ is calculated (which is the same as the end of year population of year $i$) using the entry population of year $i$.

$$entry\_population_{i+1} = entry\_population_i \times prob\_survival$$

Second, the mid-year population of year $i+1$ is calculated.

$$midyear\_population_{i+1} = entry\_population_{i+1} \times prob\_survival\_until\_midyear$$

Unexposed scenario

The entry and mid-year population projections in the unexposed scenario are done as follows:

First, the entry population of year $i+1$ is calculated (which is the same as the end of year population of year $i$) by multiplying the entry population of year $i$ and the modified survival probabilities.

$$entry\_population_{i+1} = entry\_population_i \times prob\_survival\_mod$$

Second, the mid-year population of year $i+1$ is calculated.

$$midyear\_population_{i+1} = entry\_population_{i+1} \times prob\_survival\_until\_midyear_{mod}$$

Example

In the example below, we quantify the YLL from all-cause mortality attributable to PM2.5 exposure, using the example life table exdat_lifetable.

Function call

results_pm_yll <- attribute_lifetable(
  year_of_analysis = 2019, 
  health_outcome = "yll",
  rr_central =  1.118, 
  rr_increment = 10,
  erf_shape = "log_linear",
  exp_central = 8.85,
  cutoff_central = 5,
  min_age = 20, # age from which population is affected by the exposure
  # Life table information
  age_group = exdat_lifetable$age_group,
  sex = exdat_lifetable$sex,
  population = exdat_lifetable$midyear_population,
  # In the life table case, BHD refers to deaths
  bhd_central = exdat_lifetable$deaths
) 

Main results

Total YLL attributable to exposure (sum of sex-specific impacts).

results_pm_yll$health_main

impact_rounded	erf_ci	exp_ci	bhd_ci
28810	central	central	central

Detailed results

Attributable YLL results

• per year

• per age (group)

• per sex (if sex-specific life table data entered)

are available.

Below, the results per age and the population projections are shown for females; the results for males are also available.

YLL per year

Only a selection of years is shown.

results_pm_yll$health_detailed$results_raw |>
  dplyr::summarize(
    .by = year,
    impact = sum(impact, na.rm = TRUE)
  )

year	impact
2019	1299.683
2020	2421.604
2021	2221.148
2022	2032.978
2023	1857.582
2024	1694.959
2025	1545.430
2026	1408.650
2027	1284.054
2028	1170.668

YLL per age

results_pm_yll$health_detailed$intermediate_calculations |> 
  dplyr::filter(sex == "female") |>
  dplyr::pull(impact_by_age_and_year) |>
  purrr::pluck(1)

age_start	age_end	impact_2019
91	92	29.480668
92	93	27.542091
93	94	25.166285
94	95	22.111703
95	96	18.514777
96	97	14.505077
97	98	11.222461
98	99	8.170093
99	100	31.772534

Population (exposed scenario)

The exposed scenario is the scenario with the exposure specified in the assessment.

results_pm_yll$health_detailed$intermediate_calculations|> 
  dplyr::filter(sex == "female") |>
  dplyr::pull(projection_if_exposed_by_age_and_year) |>
  purrr::pluck(1)

age_start	midyear_population_2019	midyear_population_2020	midyear_population_2021	midyear_population_2022
91	10560	10980.4178	11536.8448	11815.045
92	8728	9105.4297	9498.3206	9979.643
93	7140	7377.6106	7725.1173	8058.449
94	5655	5910.7546	6133.2128	6422.105
95	4332	4582.9334	4813.1037	4994.250
96	3118	3436.4582	3654.9171	3838.479
97	2234	2419.2261	2682.1499	2852.657
98	1520	1695.7730	1848.4164	2049.304
99	2246	879.8714	988.6583	1077.651

Population (unexposed scenario)

The unexposed scenario is the scenario without exposure.

results_pm_yll$health_detailed$intermediate_calculations|> 
  dplyr::filter(sex == "female") |>
  dplyr::pull(projection_if_unexposed_by_age_and_year) |>
  purrr::pluck(1)

age_start	midyear_population_2019	midyear_population_2020	midyear_population_2021	midyear_population_2022
91	10589.481	11037.9003	11589.268	11861.323
92	8755.542	9160.1507	9548.044	10024.990
93	7165.166	7428.2700	7771.543	8100.635
94	5677.112	5956.6019	6175.327	6460.700
95	4350.515	4622.8492	4850.437	5028.544
96	3132.505	3469.5619	3686.750	3868.253
97	2245.222	2444.9150	2707.987	2877.502
98	1528.170	1715.4685	1868.044	2069.045
99	2277.773	890.9436	1000.141	1089.095

Deaths

Goal

To quantify the premature deaths attributable to an exposure using a life table.

Example

In the example below, we quantify the all-cause premature deaths attributable to PM2.5 exposure, using the same life table as for YLL.

Function call

See example on YLL for additional info on attribute_lifetable() calculations and its output.

results_pm_deaths <- attribute_lifetable(
  health_outcome = "deaths",
  year_of_analysis = 2019,
  rr_central =  1.118, 
  rr_increment = 10,
  erf_shape = "log_linear",
  exp_central = 8.85,
  cutoff_central = 5,
  min_age = 20, # age from which population is affected by the exposure   
  # Life table information
  age_group = exdat_lifetable$age_group,   
  sex = exdat_lifetable$sex,
  population = exdat_lifetable$midyear_population, 
  bhd_central = exdat_lifetable$deaths
)

Main results

Total premature deaths attributable to exposure (sum of sex-specific impacts).

results_pm_deaths$health_main$impact

impact_rounded	erf_ci	exp_ci	bhd_ci
2599	central	central	central

Detailed results

Attributable premature deaths results

• per year (if argument approach_exposure = "constant")

• per age (group)

• per sex (if sex-specific life table data entered)

are available.

Below, the results for females are shown; the results for males are also available.

Because the function argument approach_exposure is not specified in this example, the default approach_exposure = "single_year" is used and the premature deaths refer to one year (the year of analysis). If you set approach_exposure = "constant", results are projected and accumulated across the full time horizon.

results_pm_deaths$health_detailed$intermediate_calculations|> 
  dplyr::filter(sex == "female") |>
  dplyr::pull(projection_if_unexposed_by_age_and_year) |>
  purrr::pluck(1)

yoa	age_group	age_start	age_end	bhd	deaths	population	fraction_lived	modification_factor	prob_survival	prob_survival_until_midyear	hazard_rate	is_exposed_age	prob_survival_mod	prob_survival_until_midyear_mod	hazard_rate_mod	midyear_population_yoa	entry_population_yoa	end_population_yoa	deaths_yoa	entry_population_yoa_plus_1
2019	91	91	92	1498	1498	10560	0.5	0.9579656	0.8675391	0.9337696	0.1418561	TRUE	0.8727528	0.9363764	0.1358932	10589.481	11309.0	9869.961	1439.0387	11787.888
2019	92	92	93	1412	1412	8728	0.5	0.9579656	0.8503286	0.9251643	0.1617782	TRUE	0.8561675	0.9280837	0.1549779	8755.542	9434.0	8077.084	1356.9158	9869.961
2019	93	93	94	1302	1302	7140	0.5	0.9579656	0.8328841	0.9164420	0.1823529	TRUE	0.8393444	0.9196722	0.1746878	7165.166	7791.0	6539.333	1251.6674	8077.084
2019	94	94	95	1155	1155	5655	0.5	0.9579656	0.8146811	0.9073406	0.2042440	TRUE	0.8217767	0.9108884	0.1956588	5677.112	6232.5	5121.723	1110.7766	6539.333
2019	95	95	96	976	976	4332	0.5	0.9579656	0.7975104	0.8987552	0.2253001	TRUE	0.8051929	0.9025964	0.2158297	4350.515	4820.0	3881.030	938.9704	5121.723
2019	96	96	97	772	772	3118	0.5	0.9579656	0.7796804	0.8898402	0.2475946	TRUE	0.7879595	0.8939798	0.2371871	3132.505	3504.0	2761.010	742.9898	3881.030
2019	97	97	98	603	603	2234	0.5	0.9579656	0.7621771	0.8810885	0.2699194	TRUE	0.7710294	0.8855147	0.2585735	2245.222	2535.5	1954.945	580.5551	2761.010
2019	98	98	99	443	443	1520	0.5	0.9579656	0.7456216	0.8728108	0.2914474	TRUE	0.7550044	0.8775022	0.2791965	1528.170	1741.5	1314.840	426.6598	1954.945
2019	99	99	100	2231	2231	2246	0.5	0.9579656	0.3363082	0.6681541	0.9933215	TRUE	0.3552120	0.6776060	0.9515678	2277.773	3361.5	1194.045	2167.4549	1314.840

YLD

Goal

To quantify the years lived with disability (YLD) attributable to an exposure.

Methods

To quantify the YLDs, you can use a prevalence-based or an incidence-based approach (Kim et al. 2022).

• Prevalence-based: Enter 1 (year) in the argument(s) duration_... (default) and prevalence cases in bhd_....

• Incidence-based: Enter the duration of the disease in years (value above 1) in duration_... and incidence cases in bhd_....

In both approaches, enter the disability weight (between 0 and 1) in dw_....

Example

In the example below, we quantify the YLD attributable to PM2.5 exposure for a disease with 1,000 incidence cases, a duration of 10 years and a disability weight of 0.2.

Function call

results_pm_copd_yld  <- attribute_health(
  rr_central = 1.1, 
  rr_increment = 10, 
  erf_shape = "log_linear",  
  exp_central = 8.85,
  cutoff_central = 5,
  bhd_central = 1000,
  duration_central = 10,
  dw_central = 0.2
)

Main results

results_pm_copd_yld$health_main

erf_ci	impact
central	72.05868

DALY

Goal

To quantify the disability-adjusted life years (DALY) attributable to an exposure as the sum of YLL and YLD.

Methods

To obtain the attributable DALY, its two components, i.e. years of life lost (YLL) and years lived with disability (YLD), must be summed (GBD 2019 Risk Factors Collaborators 2020).

$$DALY = YLL + YLD$$

Example 1: DALY of one scenario

In the example below, we add up the YLL and the YLD from the previous chapters, using the function daly().

Function call

results_daly <- daly(
     output_attribute_yll = results_pm_yll,
     output_attribute_yld = results_pm_copd_yld
)

Main results

YLL, YLD & DALY

results_daly$health_main |> 
  dplyr::select(impact_yll_rounded, impact_yld_rounded, impact_rounded)

impact_yll_rounded	impact_yld_rounded	impact_rounded
28810	72	28882

Example 2: DALY difference between two scenarios

daly() also accepts the outputs of compare() (see chapter Comparison of two health scenarios). Compare first the YLL and the YLD of the two scenarios separately and then enter both comparisons in daly() to obtain the difference in DALY between the scenarios.

Function call

results_daly_comparison <- daly(
  output_attribute_yll = comparison_yll, # compare() output with YLL
  output_attribute_yld = comparison_yld  # compare() output with YLD
)

Modification of scenarios

Goal

To create a new scenario that differs from an existing assessment only in some input data, without entering all input data again.

Example

In the example below, we change the exposure of an existing assessment from 8.85 to 6 $\mu g/m^3$.

Function call

scenario_A <- attribute_health(
    exp_central = 8.85,   # EXPOSURE 1
    cutoff_central = 5, 
    bhd_central = 25000,
    approach_risk = "relative_risk",
    erf_shape = "log_linear",
    rr_central = 1.118,
    rr_increment = 10)

The function attribute_mod() can be used to modify one or multiple arguments of attribute_health() in an existing scenario, e.g. scenario_A.

scenario_B <- attribute_mod(
  output_attribute = scenario_A, 
  exp_central = 6
)

This is equivalent to building the whole scenario again (see below), but more time and code efficient.

scenario_B <- attribute_health(
    exp_central = 6,     # EXPOSURE 2
    cutoff_central = 5, 
    bhd_central = 25000,
    approach_risk = "relative_risk",
    erf_shape = "log_linear",
    rr_central = 1.118,
    rr_increment = 10)

Comparison of two health scenarios

Goal

To quantify the difference in health impacts between two scenarios, e.g. before and after an intervention.

Methods

Two approaches can be used for the comparison of scenarios:

• Delta: Subtraction of health impact in scenario 1 minus in scenario 2 (i.e. two PAF) (WHO Regional Office for Europe 2014)

• Population impact fraction (PIF) (WHO 2003; C. J. L. Murray et al. 2003; Askari and Namayandeh 2020).

Note that the PIF comparison approach assumes same baseline health data for scenario 1 and 2 (e.g., comparison of two scenarios at the same time point), while the delta comparison approach, the difference between two scenarios is obtained by subtraction. Therefore, the delta approach is suited for comparison of scenarios in different time points.

Population Impact Fraction (PIF)

The Population Impact Fraction (PIF) is defined as the proportional change in disease or mortality when exposure to a risk factor is changed, for instance due to an intervention.

General Integral Form

The most general equation describing this mathematically is an integral form (WHO 2003; C. J. L. Murray et al. 2003):

$$PIF = \frac{\int rr\_at\_exp(x) \times prop\_pop\_exp\_1(x)dx - \int rr\_at\_exp(x) \times prop\_pop\_exp\_2(x)dx}{\int rr\_at\_exp(x) \times prop\_pop\_exp\_1(x)dx}$$

Where:

• $x$ = exposure level
• $prop\_pop\_exp\_1(x)$ = proportion of the population exposed at exposure level $x$ in scenario 1
• $prop\_pop\_exp\_2(x)$ = proportion of the population exposed at exposure level $x$ in scenario 2 (alternative scenario)
• $rr\_at\_exp(x)$ = relative risk at exposure level compared to the reference level

Categorical Exposure Form

If the population exposure is described as a categorical rather than continuous exposure, the integrals may be converted to sums (WHO 2003; C. J. L. Murray et al. 2003):

$$PIF = \frac{\sum rr\_at\_exp_{i} \times prop\_pop\_exp\_1_{i} - \sum rr\_at\_exp_{i} \times prop\_pop\_exp\_2_{i}}{\sum rr\_at\_exp_{i} \times prop\_pop\_exp\_1_{i}}$$

Where:

• $i$ = the exposure category (e.g., in bins of 1 $\mu g/m^3$$PM_{2.5}$ or 5 dB noise exposure)
• $prop\_pop\_exp\_1_i$ = proportion of the population in exposure category $i$ in scenario 1
• $prop\_pop\_exp\_2_i$ = proportion of the population in exposure category $i$ in scenario 2 (alternative scenario)
• $rr\_at\_exp_i$ = relative risk for exposure category level $i$ compared to the reference level

Population weighted mean concentration form

Finally, if the exposure is provided as the population weighted mean concentration, the equation for the PIF is reduced to:

$$PIF = \frac{rr\_at\_exp\_1 - rr\_at\_exp\_2}{rr\_at\_exp\_1}$$

Where:

• $rr\_at\_exp\_1$ = relative risk at the exposure level in scenario 1
• $rr\_at\_exp\_2$ = relative risk at the exposure level in scenario 2 (alternative scenario)

Example

In the example below, we compare the health impacts of a PM2.5 exposure of 8.85 $\mu g/m^3$ with those of 6 $\mu g/m^3$.

Function call

1. Use attribute_health() to calculate burden of scenarios A & B.

scenario_A <- attribute_health(
    exp_central = 8.85,   # EXPOSURE 1
    cutoff_central = 5, 
    bhd_central = 25000,
    approach_risk = "relative_risk",
    erf_shape = "log_linear",
    rr_central = 1.118,
    rr_increment = 10)

scenario_B <- attribute_mod(
  output_attribute = scenario_A, 
  exp_central = 6
)

2. Use compare() to compare scenarios A & B.

results_comparison <- healthiar::compare(
  approach_comparison = "delta", # or "pif" (population impact fraction)
  output_attribute_scen_1 = scenario_A,
  output_attribute_scen_2 = scenario_B
)

The default value for the argument approach_comparison is "delta". The alternative is "pif" (population impact fraction). See the function documentation of compare() for more details.

Main results

results_comparison$health_main

impact	impact_rounded	impact_scen_1	impact_scen_2	bhd	exp_category	exp_length	exp_type	exp_scen_1	exp_scen_2
773.5564	774	1050.86	277.304	25000	1	1	population_weighted_mean	8.85	6

Detailed results

The compare() results contain two additional outputs in addition to those we have already seen:

• health_detailed

  • scen_1 contains results of scenario 1 (scenario A in our case)

  • scen_2 contains results of scenario 2 (scenario B in our case)

Notes

Important: If your aim is to quantify health impacts from a policy intervention, you should use the same year of analysis and therefore same health baseline data in both scenarios. The only variable that should change in the second scenario is the exposure (change as a result of the intervention).

Two correlated exposures

Goal

To quantify the total health impact attributable to two correlated exposures.

Methods

A methodological report of the EU project BEST-COST (Strak et al. 2024) identified three approaches to add up attributable health impacts from correlated exposures:

• Additive approach (Steenland and Armstrong 2006):

$$PAF_{additive} = PAF_{exposure1} + PAF_{exposure2}$$

• Multiplicative approach (Jerrett et al. 2013):

$$PAF_{multiplicative} = \frac{\sum prop\_pop\_exp \times (rr\_at\_exp_{multiplicative} - 1)}{\sum prop\_pop\_exp \times (rr\_at\_exp_{multiplicative}-1) + 1}$$

$$rr\_at\_exp_{multiplicative} = rr\_at\_exp_{exposure1} * rr\_at\_exp_{exposure2}$$

• Combined approach (Steenland and Armstrong 2006):

$$PAF_{combined} = 1-[(1-PAF_{exposure1}) \times (1-PAF_{exposure2})]$$

Important: To apply any of these approaches, the relative risks for one exposure must be adjusted for the second exposure and vice versa.

Example

In the example below, we combine the impacts of PM2.5 and NO2 with the multiplicative approach, using the function multiexpose().

Function call

results_pm <- attribute_health(
  erf_shape = "log_linear",
  rr_central = 1.369, 
  rr_increment = 10,
  exp_central = 8.85,
  cutoff_central = 5,
  bhd_central = 30747
) 

results_no2 <- attribute_mod(
  output_attribute = results_pm,
  exp_central = 10.9,
  rr_central = 1.031
)

results_multiplicative <- multiexpose(
  output_attribute_exp_1 = results_pm,
  output_attribute_exp_2 = results_no2,
  exp_name_1 = "pm2.5",
  exp_name_2 = "no2",
  approach_multiexposure = "multiplicative"
)

Main results

results_multiplicative$health_main

impact_rounded
3988

Standardization

Goal

To obtain age-standardized health impacts, which allow comparing populations with different age structures.

Methods

Age standardization involves adjusting the observed rates of a particular outcome to a standard population with a specific age structure. This is a technique used to allow the comparison of populations with different age structures (GBD 2019 Demographics Collaborators 2020; Ahmad et al. 2001). In healthiar, the function standardize() applies the direct method, where the age-specific rates observed in a study population are applied to a standard (reference) population distribution.

The standardized health impact rate is computed as $$impact\_per\_100k\_inhab_{std} = \sum_{i=1}^{k} (impact\_per\_100k\_inhab_i \times ref\_prop\_pop_i)$$

where:

• $impact\_per\_100k\_inhab_{std}$ is the age-standardized health impact rate.
• $impact\_per\_100k\_inhab_i$ is the impact rate observed in age group $i$ (e.g., impact per 100,000 inhabitants).
• $ref\_prop\_pop_i$ is the proportion of the reference population in age group $i$ .
• $k$ is the number of age groups.

Example

In the example below, we standardize the health impacts of two age groups (below and above 40) with a reference population split equally between them.

Function call

output_attribute <- attribute_health(
  rr_central = 1.063,
  rr_increment = 10,
  erf_shape = "log_linear",
  cutoff_central =  0,
  age_group = c("below_40", "above_40"),
  exp_central = c(8.1, 10.9),
  bhd_central = c(1000, 4000),
  population = c(100000, 500000)
  )

results <- standardize(
  output_attribute = output_attribute,
  age_group = c("below_40", "above_40"),
  ref_prop_pop = c(0.5, 0.5)
  )

Main results

Age-standardized impact rate:

print(results$health_main$impact_per_100k_inhab)  
#> [1] 49.91113

Age group-specific impact rate:

print(results$health_detailed$results_raw$impact_per_100k_inhab)  
#> [1] 48.28250 51.53977

Preparation of exposure data

Goal

To obtain the population-weighted mean exposure of geographic units from gridded concentration data and population data.

Methods

The healthiar function prepare_exposure() helps users that do not have the exposure data (needed for healthiar functions), but only spatial concentration and population data. The function calculates an average concentration value in each geographic unit, weighted with population at each location.

$$exp = \frac{\sum_{i=1}^{n} (C_i \times population_i)}{\sum_{i=1}^{n} population_i}$$

where:

• $exp$ = population-weighted mean exposure for the geographic unit.
• $C_i$ = pollutant concentration in grid cell $i$.
• $population_i$ = population count in grid cell $i$.
• $n$ = total number of grid cells contained by the geographic unit.

In case population is entered as count by geographic sub-unit, the function calculates the mean concentration in each sub-unit and aggregates it to higher-level geographic units. If no population data is entered, the function calculates a simple spatial mean concentration as exposure value.

The output of prepare_exposure() can be entered in the arguments exp_central, exp_lower and/or exp_upper in healthiar functions such as attribute_health().

Example

In the example below, we obtain the population-weighted mean PM2.5 exposure of neighbourhoods in Brussels (Belgium), aggregated by region.

Function call

# exdat_pwm_1 = Pollution grid data
exdat_pwm_1 <- terra::rast(system.file("extdata", "exdat_pwm_1.tif", package = "healthiar"))

# exdat_pwm_2 = Data with the geo units and population data. This is pre-loaded in healthiar.
# If your raw data are in .gpkg format, you can use e.g.  sf::st_read() 

pwm <- healthiar::prepare_exposure(
  poll_grid = exdat_pwm_1, # Formal class SpatRaster,
  geo_units = exdat_pwm_2, # sf of the geographic sub-units
  population = sf::st_drop_geometry(exdat_pwm_2$population), # population per geographic sub-unit
  geo_id_macro = sf::st_drop_geometry(exdat_pwm_2$region)) # higher-level IDs to aggregate

Main results

Within the function output, the list exposure_main contains the population-weighted mean exposures for the (higher-level) geographic units in the column exposure_mean and the total population in each unit in column population_total.

 knitr::kable(as.data.frame(pwm$exposure_main))

geo_id_macro	exposure_mean	population_total
Center	11.47271	203105
East	11.10716	187907
North	11.48670	257573
South	11.09940	308860
West	11.38849	298802

Cut-off vs. threshold

Goal

To quantify health impacts when the exposure data only cover exposures above a level (cut-off) that is higher than the effect threshold of the exposure-response function.

Methods

healthiar distinguishes two exposure levels:

• threshold is the effect threshold, i.e. the exposure level from which the exposure-response function starts to show an effect. It is the anchor of the curve and is therefore subtracted from the exposure. The term is used in this sense in health risk assessment guidance, e.g., for environmental noise (Engelmann et al. 2025). Because the risk is expressed relative to the threshold, the threshold is also the counterfactual exposure of the assessment, i.e. the exposure of the hypothetical scenario that the actual exposure is compared with (C. J. Murray et al. 2003).
• cutoff_... is the exposure level below which no health impacts are quantified. It determines which exposures are assessed, not the level that they are compared with. Usually, the cut-off is assumed to be the effect threshold, and then it is the counterfactual exposure as well. This is how the term is used in health risk assessment guidance, e.g., for air pollution (WHO 2025), where one single level plays both roles.

Accordingly, there are three possible combinations:

a) Cut-off equal to the effect threshold

This is the default situation. Since both threshold and cut-off are usually identical, healthiar assumes that the values are the same, if one of them is missing. Therefore, in that case, it is enough to enter only one value. The exposure-response function is anchored at that level, which is therefore subtracted from the exposure, and the exposures below it get the risk at the reference level:

$$rr_{at\_exp} = f(\max(exp, threshold) - threshold)$$

The same result is obtained by entering only cutoff_..., only threshold, or both with the same value.

b) Cut-off higher than the effect threshold

The exposure-response function is truncated at the cut-off. The exposures above the cut-off are assessed with the risk referring to the effect threshold, while the exposures below the cut-off get the risk at the reference level:

$$rr_{at\_exp} = \begin{cases} 1, & exp < cutoff \\ f(exp - threshold), & exp \ge cutoff \end{cases}$$

In the absolute risk approach, the exposures below the cut-off get an absolute risk of 0 instead of a relative risk of 1, i.e. in both approaches no health impacts are attributed to them.

This is the case, e.g., in noise assessments under the Environmental Noise Directive, in which exposure data are only reported above a given exposure level (cut-off) although the exposure-response functions show effects already at lower exposures (effect threshold). Be aware that this results in a step in the exposure-response function at the cut-off and that the people exposed between the effect threshold and the cut-off get no attributable health impacts (i.e. a conservative estimate).

c) Cut-off lower than the effect threshold

The cut-off has no effect on the results and healthiar shows a warning. The reason is that the exposure-response function is anchored at the effect threshold, so the exposures below the effect threshold already get the risk at the reference level. A cut-off below the effect threshold cannot remove any additional exposure and the results are the same as if only threshold had been entered.

Example

In the example below (case b above), we quantify the health impacts in the noise exposure groups above 55 dB (cut-off) with an effect threshold of 45 dB.

Function call

results_cutoff_above_threshold <- healthiar::attribute_health(
  approach_risk = "relative_risk",
  erf_shape = "log_linear",
  rr_central = 1.055,
  rr_increment = 10,
  prop_pop_exp = c(300000,200000,150000,120000,100000,70000,60000)/10000000,
  exp_central = c(47,52,57,62,67,72,77),
  # Effect threshold, i.e. the anchor of the exposure-response function
  threshold = 45,
  # Cut-off, i.e. the exposures below it are not quantified
  cutoff_central = 55,
  bhd_central = 50000)$health_main$impact_rounded

Alternatively, the same result can be obtained by entering a user-defined exposure-response function in erf_eq_... which already includes the case distinction. This is only needed if the shape of the curve above the cut-off is not covered by the erf_shape options. The function arguments erf_eq_... require a function as input. Instead of using a splinefun() this can also be fulfilled by using a ‘function(c)’ which is of type ‘function’.

#setting up function parameters
threshold <- 45
RR <- 1.055
cutoff <- 55
rr_increment <- 10

# define categorical function, the ifelse condition enables the case distinction
erf_function <- function(c){
  output <- ifelse(c<cutoff, 1, exp((log(RR)/rr_increment)*(c-threshold)))
  return(output)
}
# attribute_health
results_catERF_different_cutoff <- healthiar::attribute_health(
  approach_risk = "relative_risk",
  erf_eq_central = erf_function,
  prop_pop_exp = c(300000,200000,150000,120000,100000,70000,60000)/10000000,
  exp_central = c(47,52,57,62,67,72,77),
  cutoff_central=0,
  bhd_central=50000)$health_main$impact_rounded

The categorical ERF curve created looks as follows. The step at the cut-off is clearly visible.

Shifting the ERF

Goal

To enter an exposure-response function in erf_eq_... correctly, depending on whether it already incorporates the cut-off.

Methods

healthiar evaluates the exposure-response function entered in erf_eq_... at $c = exp - threshold$, and the threshold takes the value of the cut-off if it is not entered (see chapter Cut-off vs. threshold). The cut-off therefore moves the exposure-response function horizontally. There are two situations:

a) Unshifted exposure-response function

The function is parameterized on raw exposure levels, i.e. it already incorporates the cut-off in its definition. Do not enter a cutoff_... value, so that nothing is subtracted from the exposure, i.e. $c = exp$.

b) Shifted exposure-response function

The function is parameterized on the exposure above the cut-off, i.e. it does not incorporate the cut-off. Enter a value in the argument(s) cutoff_..., so that the function is moved horizontally by the cut-off, i.e. $c = exp - cutoff$.

In case b), healthiar shows a warning when a cutoff_... is entered alongside approach_risk = "absolute_risk", because many exposure-response functions published in the literature already incorporate the cut-off in the function definition. The warning can be ignored here.

Example

In the example below, the same absolute risk function is entered once unshifted and once shifted by a cut-off of 53 dB.

Function call

# Case A: Unshifted exposure-response function
# The function is parameterized on raw exposure levels
unshifted_erf <- healthiar::attribute_health(
  approach_risk = "absolute_risk",
  exp_central = c(50, 60, 70),
  pop_exp = c(300000,200000,150000),
  erf_eq_central = "78.9270 - 3.1162 * c + 0.0342 * c^2"
)

# Case B: Shifted exposure-response function
# The function is parameterized on the exposure above the cut-off
shifted_erf <- healthiar::attribute_health(
  approach_risk = "absolute_risk",
  exp_central = c(50, 60, 70),
  pop_exp = c(300000,200000,150000),
  cutoff_central = 53,
  erf_eq_central = "78.9270 - 3.1162 * (c + 53) + 0.0342 * (c + 53)^2"
)
#> Warning: You entered a value for: cutoff_central alongside absolute risk.
#> Be aware that healthiar shifts the exposure in 'erf_eq' as c = (exp - cutoff).

If you want the exposure-response function to stay unshifted but the exposures below a given level not to be quantified, enter a cutoff_... value higher than the threshold (see chapter Cut-off vs. threshold).

Economic dimension

Monetization

Goal

To express attributable health impacts in monetary terms, optionally with discounting, inflation and growth in valuation.

Methods

In health economic evaluations and economic burden of disease assessments, health impacts may need to be converted into monetary values. For this purpose, you can use monetize().

Several valuation metrics are available, depending on how outcomes are quantified in natural or health units (e.g. cases reduced, deaths prevented, reductions in mortality risk, life-years gained, QALYs gained, DALYs averted). Common metrics include the Value of a Statistical Life (VSL) (OECD 2025), the Value of a Life-Year (VOLY) (Hammitt 2007) and the Value of a Quality-Adjusted Life-Year (VAQALY) (Bobinac et al. 2010).

Discounting is the practice of converting future costs (or health impacts as previous step to valuating them) into their present value. The underlying rationale is that the value placed on outcomes declines as they occur further in the future. Therefore, future costs and effects are converted into present-value terms to make them comparable over time (Attema et al. 2018).

Discounting is implemented by selecting a discount rate, which is used to compute a discount factor for each time period. This factor is then multiplied by the corresponding future cost (or effect) to express it in present-value terms.

If you need the discounted values of a cost or health outcome, you can call the healthiar function discount(). If you just need the discount factor, you can alternatively call get_discount_factor(). If you just need the inflation factor, you can call get_inflation_factor(), or the deflation factor by entering is_deflation = TRUE in the same function.

Different functional forms can be used to apply discounting. The most common is exponential discounting, also referred to as constant discounting, since outcomes are discounted proportionally as time increases. An alternative is hyperbolic discounting, which tends to better capture human behavior by discounting the near present more heavily than outcomes further in the future (Lipman and Attema 2024)

See below the equations that are used behind these functions.

$$monetized\_impact = impact \times valuation \times discount\_factor \times deflator\_factor \times real\_growth\_factor$$ The arguments discount_factor, deflator_factor and real_growth_factor are only used if a value is entered in the arguments discount_rate, inflation_rate and real_growth_rate respectively
(otherwise ignored).

Discount factor

Exponential discounting

As suggested by Frederick et al. (2002)

$$discount\_factor = \frac{1}{(1 + discount\_rate)^{n\_years}}$$

Hyperbolic discounting Harvey

As suggested by Harvey (1986)

$$discount\_factor = \frac{1}{(1 + n\_years)^{discount\_rate}}$$

Hyperbolic discounting Mazur

As suggested by Mazur (1987)

$$discount\_factor = \frac{1}{1 + (discount\_rate \times n\_years)}$$

Deflation factor

Inflation can be handled in monetize() by applying a deflator on future values, projected in nominal terms, in order to convert them into real values (i.e., express these values in terms of constant prices of a single base year). Therefore, if the user of the function provides a value for the inflation_rate argument, a deflator factor (HM Treasury 2022; Brealey et al. 2023; Samuelson 1937) is applied according to the following formulas

$$deflator\_factor = \frac{1}{(1 + inflation\_rate)^{n\_years}}$$

The formula above assumes that inflation is constant over time. Because inflation usually varies from year to year, inflation_rate also accepts a vector of year-specific rates (at least as many values as years to be considered, the first value referring to the first year after the present). In that case, the deflator factor is calculated as the product of the year-specific factors

$$deflator\_factor = \frac{1}{\prod_{t=1}^{n\_years}(1 + inflation\_rate_t)}$$

Real valuation growth

If a rising societal value of health over time is required in the monetization, unlike inflation, this represents a “real” increase in value. Thus, as societies become wealthier, their willingness to pay to avoid mortality and morbidity risks tends to rise (OECD 2012).

For this use case, you have to enter a value in the argument real_growth_rate in monetize(), which allows you to project this growth by applying a valuation growth factor to base-year unit values:

$$real\_growth\_factor =(1 + real\_growth\_rate)^{n\_years}$$

Where $real\_growth\_rate$ represents the annual real growth rate in health valuation. As in inflation_rate, a vector of year-specific rates can be entered instead of one single value, in which case the factor is the product of the year-specific factors. This ensures that long-term environmental impacts are not undervalued.

Example 1: output of attribute_health()

In the example below, we monetize the COPD cases from the chapter Confidence interval with a value of 50,000 EUR per case, discounted at 3% over 5 years.

Function call

monetized_pm_copd <- monetize(
    output_attribute = results_pm_copd,
    discount_shape = "exponential",
    discount_rate = 0.03,
    n_years = 5,
    valuation = 50000 # E.g. EURO
)

Main results

The outcome of the monetization is added to the variable entered to the output_attribute argument, which is results_pm_copd in our case.

Two folders are added:

• monetization_main contains the central monetization estimate and the corresponding 95% confidence intervals obtained through the specified monetization.

• monetization_detailed contains the monetized results for each unique combination of the input variable estimates that were provided to the initial attribute_health() call.

monetized_pm_copd$monetization_main

erf_ci	monetized_impact
central	151041153
lower	58358321
upper	236091201

We see that the central estimate of the monetized impact (discounted) is about 151 million EURO.

Example 2: impacts from other sources

Alternatively, you can also monetize (attributable) health impacts from a non-healthiar source.

Function call

results <- monetize(
  impact = 1151,
  valuation = 100
)

Cost-benefit analysis

Goal

To compare the monetized health benefits of an intervention with its costs.

Methods

The CBA is a type of economic evaluation that compares the costs and the benefits of an intervention, considering both measures expressed in monetary terms.

To perform a CBA, you can use the function cba(). This approach requires monetizing benefits so they can be directly compared with costs. Since interventions typically generate costs and benefits over multi-year time horizons, discounting is a common practice to obtain the present value of future costs and benefits. Depending on the reference guidelines, the discount rate can be specified as the same for costs and benefits or different across them. The outputs of a Cost-Benefit Analysis can be expressed as three main indicators (Boardman et al. 2018): - intervention’s net benefit: the difference between monetized benefits and costs - Cost-Benefit Ratio (CBR): monetized benefits divided by costs and - Return on Investment (ROI): return generated per unit of expenditure by relating net benefits to the intervention’s costs.

An intervention is recommended from a Cost-Benefit Analysis perspective, if it yields a positive net benefit or a positive ROI, or equivalently, a CBR greater than one, meaning that the intervention’s monetized benefits exceed its costs. These three outputs are available when running cba() and are calculated considering the following formulas.

Net Benefit $$net\_benefit = benefit - cost$$

Cost-Benefit Ratio (CBR) $$cbr = \frac{benefit}{cost}$$

Return on Investment (ROI) $$roi = \frac{benefit - cost}{cost} \times 100$$

Example

Let’s imagine we design a policy that would reduce air pollution to 5 $\mu g/m^3$, which is the concentration specified in the cutoff_central argument in the initial attribute_health() call. So we could avoid all COPD cases attributed to air pollution.

Function call

Considering the cost to implement the policy (estimated at 100 million EURO), what would be the monetary net benefit of such a policy? We can find out using the healthiar function cba().

cba <- cba(
    output_attribute = results_pm_copd,
    valuation = 50000,
    cost = 100000000,
    discount_shape = "exponential",
    discount_rate_benefit = 0.03,
    discount_rate_cost = 0.03,
    n_years_benefit = 5,
    n_years_cost = 5
)

Main results

The outcome of the CBA is contained in two folders, which are added to the existing assessment:

• cba_main contains the central estimate and the corresponding 95% confidence intervals obtained

• cba_detailed contains additional intermediate results for both cost and benefit

  • benefit contains results by_year and raw results health_raw

  • cost contains the costs of the policy at the end of the period specified in the n_years_cost argument

cba$cba_main |>  
  dplyr::select(benefit, cost, net_benefit) |> 
  knitr::kable()

benefit	cost	net_benefit
151041153	86260878	64780274
58358321	86260878	-27902557
236091201	86260878	149830323

We see that the central and upper 95% confidence interval estimates of avoided attributable COPD cases result in a net monetary benefit of the policy, while the lower 95% confidence interval estimate results in a net cost!

Social aspects

Health impact attributable to social indicator

Goal

To quantify the differences in attributable health impacts between more and less deprived areas.

Methods

Taking into account socio-economic indicators, e.g. a multiple deprivation index (Mogin et al. 2025), the differences in attributable health impacts across the study areas can be estimated (Renard et al. 2019; Otavova et al. 2022).

Social inequalities are quantified as the difference between the least deprived areas (the last n-quantile) and

• the most deprived areas or

• the population overall.

These differences can be

• absolute or

• relative.

Difference most deprived vs. least deprived

$$absolute\_quantile = first - last$$ Where:

• $absolute\_quantile$ = Absolute difference between quantiles.
• $first$ = Average health impacts in most deprived quantile.
• $last$ = Average health impacts in least deprived quantile.

$$relative\_quantile = \frac{absolute\_quantile}{last}$$

Difference overall vs. least deprived

$$absolute\_overall = overall - last$$ Where:

• $absolute\_overall$ = Absolute difference regarding the overall average.
• $overall$ = Overall average health impacts in the study area.
• $last$ = Average health impacts in least deprived quantile.

If you assume that the least deprived areas are similar to counterfactual cases (no exposure to deprivation), the relative difference regarding the overall average health impact could be interpreted as some kind of relative risk attributable to social inequalities.

Example

In the example below, we compare the mortality attributable to PM2.5 exposure across deciles of a deprivation score.

Function call

First, quantify health impacts.

 health_impact <- healthiar::attribute_health(
   age_group = exdat_socialize$age_group,
   exp_central = exdat_socialize$pm25_mean,
   cutoff_central = 0,
   rr_central = exdat_socialize$rr,
   erf_shape = "log_linear",
   rr_increment = 10,
   bhd_central = exdat_socialize$mortality,
   population = exdat_socialize$population,
   geo_id_micro = exdat_socialize$geo_unit)

Second, use the function socialize() entering the whole output of attribute_health() in the argument output_attribute.

social_t <- healthiar::socialize(
  output_attribute = health_impact,
  age_group = exdat_socialize$age_group, # They have to be the same in socialize() and in attribute_health()
  ref_prop_pop = exdat_socialize$ref_prop_pop, # Population already provided in output_attribute
  geo_id_micro = exdat_socialize$geo_unit,
  social_indicator = exdat_socialize$score,
  n_quantile = 10,
  increasing_deprivation = TRUE)
#> Warning: 194 geographic unit(s) have no value in social_indicator. They get no
#> social_quantile and are therefore not included in the comparison between the
#> quantiles (first and last), but they are still part of the overall values.

Alternatively, you can directly enter the health impact in the socialize() argument impact.

social <- healthiar::socialize(
  impact = health_impact$health_detailed$results_by_age_group$impact,
  age_group = exdat_socialize$age_group, # They have to be the same in socialize() and in attribute_health()
  ref_prop_pop = exdat_socialize$ref_prop_pop,
  geo_id_micro = exdat_socialize$geo_unit,
  social_indicator = exdat_socialize$score,
  population = exdat_socialize$population, # Population has to be provided because no output_attribute
  n_quantile = 10,
  increasing_deprivation = TRUE)
#> Warning: 194 geographic unit(s) have no value in social_indicator. They get no
#> social_quantile and are therefore not included in the comparison between the
#> quantiles (first and last), but they are still part of the overall values.

Main results

social$social_main |> 
  dplyr::select(c(parameter, difference_type, 
                  difference_compared_with, difference_value, 
                  comment)) |>
  print()
#> # A tibble: 4 × 5
#>   parameter      difference_type difference_compared_…¹ difference_value comment
#>   <chr>          <chr>           <chr>                             <dbl> <chr>  
#> 1 impact_rate_s… absolute        last_quantile                    26.5   NA     
#> 2 impact_rate_s… relative        last_quantile                     0.597 NA     
#> 3 impact_rate_s… absolute        overall                          14.2   It can…
#> 4 impact_rate_s… relative        overall                           0.242 It can…
#> # ℹ abbreviated name: ¹​difference_compared_with

Multiple deprivation index

Goal

To create a multiple deprivation index (MDI) from several socio-economic indicators, e.g. to enter it in the argument social_indicator of socialize().

Methods

Socio-economic indicators (e.g., education level, employment status and family structure) can be condensed into a multiple deprivation index (MDI) (Mogin et al. 2025). For this purpose, the indicators can be normalized using min-max scaling.

The reliability of the MDI can be assessed using Cronbach’s alpha (Cronbach 1951).

$$\alpha = \frac{k}{k - 1} \left( 1 - \frac{\sum_{i=1}^{k} \sigma^2_{y_i}}{\sigma^2_x} \right)$$ where:

• $k$ is the number of items/variables.
• $\sigma^2_{y_i}$ is the variance of the $i$-th item.
• $\sum_{i=1}^{k} \sigma^2_{y_i}$ is the sum of the variances of all items.
• $\sigma^2_x$ is the total variance of the observed total scores (the sum of all items).

To apply this approach, you should ensure that the data set is as complete as possible. Otherwise, you can try to impute missing data using: - Time-Based Imputation: Linear regression based on historical trends if prior years’ data is complete. - Indicator-Based Imputation: Multiple linear regression if the missing indicator correlates strongly with others.

Imputation models should have an R^2 greater than or equal to 0.7. If R^2 lower than 0.7, consider alternative data sources or methods.

Example

In the example below, we create an MDI from five indicators (education, unemployment, single parents, population change and lack of heating) and split it into deciles.

Function call

mdi <- prepare_mdi(
  geo_id_micro = exdat_prepare_mdi$id,
  edu = exdat_prepare_mdi$edu,
  unemployed = exdat_prepare_mdi$unemployed,
  single_parent = exdat_prepare_mdi$single_parent,
  pop_change = exdat_prepare_mdi$pop_change,
  no_heating = exdat_prepare_mdi$no_heating,
  n_quantile = 10,
  verbose = FALSE
)

verbose = FALSE suppresses any output to the console (default: verbose = TRUE, i.e. with printing turned on).

Main results

Function output includes:

• mdi_main, a tibble containing the BEST-COST MDI

mdi$mdi_main |> 
  dplyr::select(geo_id_micro, MDI, MDI_index)

geo_id_micro	MDI	MDI_index
11001	0.2117721	1
11002	0.4319924	8
11004	0.1847750	1
11005	0.3787937	7
11007	0.3121354	5
11008	0.2565185	2
11009	0.2245822	1
11013	0.2140148	1
11016	0.2656597	3
11018	0.3566141	6

The function assesses the reliability of the MDI based on the Cronbach’s alpha value as follows: - 0.9 and higher: Excellent reliability - between 0.8 (included) and 0.9: Good reliability - between 0.7 (included) and 0.8: Acceptable reliability - between 0.6 (included) and 0.7: Questionable reliability - lower than 0.6: Poor reliability

Detailed results

• mdi_detailed

  • DESCRIPTIVE STATISTICS

  • PEARSON’S CORRELATION COEFFICIENTS

  • CRONBACH’S α, including the reliability rating this value indicates

  • Code for boxplots of the single indicators

  • Code for histogram of the MDI’s for the geo units with a normal distribution curve

To reproduce the boxplots run

eval(mdi$mdi_detailed$boxplot)

Analogously, to reproduce the histogram run

eval(mdi$mdi_detailed$histogram)

---

Inside pipes

Pipe |>

healthiar can be used inside the native pipes |>. See the example below.

exdat_noise |>
  (\(df) {
    healthiar::attribute_health(
      approach_risk = df$risk_estimate_type,
      exp_central = df$exposure_mean,
      pop_exp = df$exposed,
      erf_eq_central = df$erf
      )$health_main$impact_rounded
    })()

Shorter, making use of the base R function with().

exdat_noise |>
      (\(df) {
        with(df, healthiar::attribute_health(
         approach_risk = risk_estimate_type,
         exp_central = exposure_mean,
         pop_exp = exposed,
         erf_eq_central = erf
         ))$health_main$impact_rounded
        })()
#> [1] 348464

Pipe %>%

healthiar can also be used inside magrittr pipes %>% as follows.

exdat_noise %>%
  {
    healthiar::attribute_health(
      approach_risk = .$risk_estimate_type,
      exp_central = .$exposure_mean,
      pop_exp = .$exposed,
      erf_eq_central = .$erf
    )$health_main$impact_rounded
  }

---

Export and visualize

Exporting and visualizing results is out of scope of healthiar. To export and visualize, you can make use of existing functions in other packages beyond healthiar as indicated below.

Export results

Export as .csv file

write.csv(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.csv")

Save as .Rdata file

save(results_pm_copd, file = "exported_results/results_pm_copd.Rdata")

Export to Excel (as .xlsx file)

openxlsx::write.xlsx(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.xlsx")

Visualize results

Visualization is out of scope of healthiar. You can visualize in:

• R using base programming or packages such as ggplot2 (Wickham 2016),
• Excel (export results first) or
• Other tools.

---

Abbreviations

BHD/bhd = baseline health data

CI = confidence interval

CBA/cba = cost-benefit analysis

exp = exposure

ERF = exposure-response function

RR/rr = relative risk

WHO = World Health Organization

YLL/yll = years of life lost

---

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