Title:	Quantifying and Monetizing Health Impacts Attributable to Exposure
Version:	0.2.3
Description:	This R package has been developed with a focus on air pollution and noise but can be applied to other exposures. The initial development has been funded by the European Union project BEST-COST. Disclaimer: It is work in progress and the developers are not liable for any calculation errors or inaccuracies resulting from the use of this package. Selection of relevant references (in chronological order): WHO (2003) https://www.who.int/publications/i/item/9241546204, Murray et al. (2003) <doi:10.1186/1478-7954-1-1>, Miller & Hurley (2003) <doi:10.1136/jech.57.3.200>, Steenland & Armstrong (2006) <doi:10.1097/01.ede.0000229155.05644.43>, WHO (2011) https://iris.who.int/items/723ab97c-5c33-4e3b-8df1-744aa5bc1c27, GBD 2019 Risk Factors Collaborators (2020) <doi:10.1016/S0140-6736(20)30752-2>.
License:	GPL (≥ 3)
URL:	https://swisstph.github.io/healthiar/, https://github.com/SwissTPH/healthiar, https://doi.org/10.5281/zenodo.18220361
BugReports:	https://github.com/SwissTPH/healthiar/issues
Encoding:	UTF-8
RoxygenNote:	7.3.3
Imports:	dplyr, tidyr, purrr, tibble, Rdpack
RdMacros:	Rdpack
Depends:	R (≥ 4.2.0)
LazyData:	true
Config/testthat/edition:	3
VignetteBuilder:	knitr
Suggests:	sf, terra, exactextractr, testthat, devtools, knitr, rmarkdown
Config/Needs/website:	rmarkdown
NeedsCompilation:	no
Packaged:	2026-02-19 18:15:03 UTC; castal
Author:	Alberto Castro [cre, aut], Axel Luyten [aut], Arno Pauwels [ctb], Liliana Vazquez Fernandez [ctb], Gianni Ardielli [ctb], Iracy Pimenta [ctb], Susanne Breitner [ctb], Carl Baravelli [ctb], Vanessa Gorasso [ctb], Maria Lepnurm [ctb], Andreia Novais [ctb], Maria Jose Rueda Lopez [ctb], Minh Nhat Pham [ctb], Ana Barbosa [ctb], Joao Vasco Santos [ctb], Anette Kocbach Bolling [ctb]
Maintainer:	Alberto Castro <alberto.castrofernandez@swisstph.ch>
Repository:	CRAN
Date/Publication:	2026-02-19 18:40:02 UTC

healthiar: Quantifying and Monetizing Health Impacts Attributable to Exposure

Description

This R package has been developed with a focus on air pollution and noise but can be applied to other exposures. The initial development has been funded by the European Union project BEST-COST. Disclaimer: It is work in progress and the developers are not liable for any calculation errors or inaccuracies resulting from the use of this package. Selection of relevant references (in chronological order): WHO (2003) https://www.who.int/publications/i/item/9241546204, Murray et al. (2003) doi:10.1186/1478-7954-1-1, Miller & Hurley (2003) doi:10.1136/jech.57.3.200, Steenland & Armstrong (2006) doi:10.1097/01.ede.0000229155.05644.43, WHO (2011) https://iris.who.int/items/723ab97c-5c33-4e3b-8df1-744aa5bc1c27, GBD 2019 Risk Factors Collaborators (2020) doi:10.1016/S0140-6736(20)30752-2.

Author(s)

Maintainer: Alberto Castro alberto.castrofernandez@swisstph.ch (ORCID)

Authors:

• Axel Luyten (ORCID)

Other contributors:

• Arno Pauwels (ORCID) [contributor]

• Liliana Vazquez Fernandez (ORCID) [contributor]

• Gianni Ardielli (ORCID) [contributor]

• Iracy Pimenta (ORCID) [contributor]

• Susanne Breitner (ORCID) [contributor]

• Carl Baravelli (ORCID) [contributor]

• Vanessa Gorasso (ORCID) [contributor]

• Maria Lepnurm (ORCID) [contributor]

• Andreia Novais (ORCID) [contributor]

• Maria Jose Rueda Lopez (ORCID) [contributor]

• Minh Nhat Pham (ORCID) [contributor]

• Ana Barbosa (ORCID) [contributor]

• Joao Vasco Santos (ORCID) [contributor]

• Anette Kocbach Bolling (ORCID) [contributor]

See Also

Useful links:

• https://swisstph.github.io/healthiar/

• https://github.com/SwissTPH/healthiar

• doi:10.5281/zenodo.18220361

• Report bugs at https://github.com/SwissTPH/healthiar/issues

Add meta-information to the data frame containing the input data

Description

This function adds meta-information of the input data within the data frame containing the input data.

Usage

add_info(df, info)

Arguments

Value

This function returns a data frame with binding the input data with the info columns (info_ is added to the column names)

Author(s)

Alberto Castro & Axel Luyten

Attribute health impacts to an environmental stressor

Description

This function calculates the attributable health impacts (mortality or morbidity) due to exposure to an environmental stressor (air pollution or noise), using either relative risk (RR) or absolute risk (AR).

Arguments for both RR & AR pathways

• approach_risk

• exp_central, exp_lower, exp_upper

• cutoff_central, cutoff_lower, cutoff_upper

• erf_eq_central, erf_eq_lower, erf_eq_upper

Arguments only for RR pathways

• rr_central, rr_lower, rr_upper

• rr_increment

• erf_shape

• bhd_central, bhd_lower, bhd_upper

• prop_pop_exp

Argument for AR pathways

• pop_exp

Optional arguments for both RR & AR pathways

• geo_id_micro, geo_id_macro,

• age_group, sex, info, population

• dw_central, dw_lower, dw_upper

• duration_central, duration_lower, duration_upper

Usage

attribute_health(
  approach_risk = "relative_risk",
  exp_central,
  exp_lower = NULL,
  exp_upper = NULL,
  cutoff_central = 0,
  cutoff_lower = NULL,
  cutoff_upper = NULL,
  pop_exp = NULL,
  erf_eq_central = NULL,
  erf_eq_lower = NULL,
  erf_eq_upper = NULL,
  rr_central = NULL,
  rr_lower = NULL,
  rr_upper = NULL,
  rr_increment = NULL,
  erf_shape = NULL,
  bhd_central = NULL,
  bhd_lower = NULL,
  bhd_upper = NULL,
  prop_pop_exp = 1,
  geo_id_micro = "a",
  geo_id_macro = NULL,
  age_group = "all",
  sex = "all",
  dw_central = NULL,
  dw_lower = NULL,
  dw_upper = NULL,
  duration_central = NULL,
  duration_lower = NULL,
  duration_upper = NULL,
  info = NULL,
  population = NULL
)

Arguments

Details

What you put in is what you get out

The health metric you put in (e.g. absolute disease cases, deaths per 100 000 population, DALYs, prevalence, incidence, ...) is the one you get out.

Exception: if you enter a disability weight (via the dw_... arguments) the attributable impact will be in YLD.

Function arguments

exp_central, exp_lower, exp_upper In case of exposure bands enter only one exposure value per band (e.g. the means of the lower and upper bounds of the exposure bands).

cutoff_central, cutoff_lower, cutoff_upper The cutoff level refers to the exposure level below which no health effects occur in the same unit as the exposure. If exposure categories are used, the length of this input must be the same as in the exp_... argument(s).

pop_exp Only applicable in AR pathways; always required. In AR pathways the population exposed per exposure category is multiplied with the corresonding category-specific risk to obtain the absolute number of people affected by the health outcome.

erf_eq_central, erf_eq_lower, erf_eq_upper Required in AR pathways; in RR pathways required only if rr_... arguments not specified. Enter the exposure-response function as a function, e.g. output from stats::splinefun() or stats::approxfun(), or as a string formula, e.g. "3+c+c^2" (with the c representing the concentration/exposure). If you have x-axis (exposure) and y-axis (relative risk) value pairs of multiple points lying on the the exposure-response function, you could use e.g. stats::splinefun(x, y, method="natural") to derive the exposure-response function (in this example using a cubic spline natural interpolation). rr_increment Only applicable in RR pathways. Relative risks from the literature are valid for a specific increment in the exposure, in case of air pollution often 10 or 5 \mu g/m^3).

bhd_central, bhd_lower, bhd_upper Only applicable in RR pathways. Baseline health data for each exposure category must be entered.

prop_pop_exp Only applicable in RR pathways. In RR pathways indicates the fraction(s) (value(s) from 0 until and including 1) of the total population exposed to the exposure categories. See equation of the population attributable fraction for categorical exposure below.

geo_id_macro, geo_id_micro Only applicable in assessments with multiple geographic units. For example, if you provide the names of the municipalities under analysis to geo_id_micro, you might provide to geo_id_macro the corresponding region / canton / province names. Consequently, the vectors fed to geo_id_micro and geo_id_macro must be of the same length.

age_group Can be either numeric or character. If it is numeric, it refers to the first age of the age group. E.g. c(0, 40, 80) means age groups [0, 40), [40, 80), >=80].

info Optional argument. Information entered to this argument will be added as column(s) names info_1, info_2, info_... to the results table. These additional columns can be used to further stratify the analysis in a secondary step (see example below).

population Optional argument. The population entered here is used to determine impact rate per 100 000 population. Note the requirement for the vector length in the paragraph Assessment of multiple geographic units below.

duration_central, duration_lower, duration_upper Only applicable in assessments of YLD (years lived with disability). Measured in years. A value of 1 (year) refers to the prevalence-based approach, while values above 1 to the incidence-based approach.

Methodology

This function can quantify the attributable health impacts by means of a relative risk or an absolute risk (depending on the health outcome).

• Relative risk: The comparative risk assessment approach (Murray et al. 2003) is applied by obtaining the population attributable fraction (percent of cases that are attributable to the exposure) based on the relative risk (WHO 2003; Steenland and Armstrong 2006; GBD 2019 Risk Factors Collaborators 2020; Soares et al. 2022; Pozzer et al. 2023; Lehtomäki et al. 2025).

• Absolute risk: The attributable cases are directly derived from population exposed (WHO 2011).

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• Relative risk

• Absolute risk

Value

This function returns a list containing:

1) health_main (tibble) containing the main results;

• impact (numeric column) attributable health burden/impact

• pop_fraction (numeric column) population attributable fraction; only applicable in relative risk assessments

• And many more

2) health_detailed (list) containing detailed (and interim) results.

• input_args (list) containing all the argument inputs used in the background

• input_table (tibble) containing the inputs after preparation

• results_raw (tibble) containing results for all combinations of input (geo units, uncertainty, age and sex specific data...)

• results_by_... (tibble) containing results stratified by each geographic unit, age or sex.

Author(s)

Alberto Castro & Axel Luyten

References

GBD 2019 Risk Factors Collaborators (2020). “Global burden of 87 risk factors in 204 countries and territories, 1990–2019.” The Lancet. doi:10.1016/S0140-6736(20)30752-2.

Lehtomäki H, Aasvang GM, Sulo G, Denby BR, Hänninen OO, Brauer M, Pereira G, Dadras O, Bølling AK (2025). “Burden of disease attributable to PM2.5 at low exposure levels: impact of methodological choices.” Environmental Health, 25(1), 4. ISSN 1476-069X, doi:10.1186/s12940-025-01250-y.

Murray CJ, Ezzati M, Lopez AD, Rodgers A, Hoorn SV (2003). “Comparative risk assessment: conceptual framework and design.” Epidemiology, 14(4), 447-458. doi:10.1186/1478-7954-1-1.

Pozzer A, Anenberg SC, Dey S, Haines A, Lelieveld J, Chowdhury S (2023). “Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates.” GeoHealth, 7(1), e2022GH000711. doi:10.1029/2022GH000711, e2022GH000711 2022GH000711, https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022GH000711, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022GH000711.

Soares J, González Ortiz A, Gsella A, Horálek J, Plass D, Kienzler S (2022). “Health risk assessment of air pollution and the impact of the new WHO guidelines (Eionet Report – ETC HE 2022/10).” European Topic Centre on Human Health and the Environment. doi:10.5281/zenodo.7405988.

Steenland K, Armstrong B (2006). “An overview of methods for calculating the burden of disease due to specific risk factors.” Epidemiology, 17(5), 512–519. doi:10.1097/01.ede.0000229155.05644.43.

WHO (2003). “Introduction and methods: Assessing the environmental burden of disease at national and local levels.” World Health Organization. https://www.who.int/publications/i/item/9241546204.

WHO (2011). “Burden of disease from environmental noise: Quantification of healthy life years lost in Europe.” World Health Organization. https://www.who.int/publications/i/item/burden-of-disease-from-environmental-noise-quantification-of-healthy-life-years-lost-in-europe.

See Also

• Upstream: prepare_exposure (only if no exposure data available)

• Alternative: attribute_lifetable, get_paf, get_risk

• Downstream: attribute_mod, compare, daly, multiexpose, standardize, monetize, socialize

Examples

# Goal: attribute lung cancer cases to population-weighted PM2.5 exposure
# using relative risk

results <- attribute_health(
  erf_shape = "log_linear",
  rr_central = 1.369,            # Central relative risk estimate
  rr_increment = 10,             # per \mu g / m^3 increase in PM2.5 exposure
  exp_central = 8.85,            # Central exposure estimate in \mu g / m^3
  cutoff_central = 5,            # \mu g / m^3
  bhd_central = 30747            # Baseline health data: lung cancer incidence
 )

results$health_main$impact_rounded # Attributable cases


# Goal: attribute cases of high annoyance to (road traffic) noise exposure
# using absolute risk

results <- attribute_health(
  approach_risk = "absolute_risk",
  exp_central = c(57.5, 62.5, 67.5, 72.5, 77.5),
  pop_exp = c(387500, 286000, 191800, 72200, 7700),
  erf_eq_central = "78.9270-3.1162*c+0.0342*c^2"
)

results$health_main$impact_rounded # Attributable high annoyance cases


# Goal: attribute disease cases to PM2.5 exposure in multiple geographic
# units, such as municipalities, provinces, countries, ...

results <- attribute_health(
  geo_id_micro = c("Zurich", "Basel", "Geneva", "Ticino"),
  geo_id_macro = c("Ger","Ger","Fra","Ita"),
  rr_central = 1.369,
  rr_increment = 10,
  cutoff_central = 5,
  erf_shape = "log_linear",
  exp_central = c(11, 11, 10, 8),
  bhd_central = c(4000, 2500, 3000, 1500)
)

# Attributable cases (aggregated)
results$health_main$impact_rounded

# Attributable cases (disaggregated)
results$health_detailed$results_raw |> dplyr::select(
  geo_id_micro,
  geo_id_macro,
  impact_rounded
)


# Goal: determine attributable YLD (years lived with disability)
results  <- attribute_health(
  exp_central = 8.85,
  prop_pop_exp = 1,
  cutoff_central = 5,
  bhd_central = 1000,
  rr_central = 1.1,
  rr_increment = 10,
  erf_shape = "log_linear",
  duration_central = 100,
  dw_central = 1,
  info = "pm2.5_yld"
)

results$health_main$impact

Attribute premature deaths or YLL to an environmental stressor using a life table approach

Description

This function assesses premature deaths or years of life lost (YLL) attributable to exposure to an environmental stressor using a life table approach.

Usage

attribute_lifetable(
  age_group,
  sex,
  bhd_central,
  bhd_lower = NULL,
  bhd_upper = NULL,
  population,
  health_outcome = NULL,
  min_age = NULL,
  max_age = NULL,
  approach_exposure = "single_year",
  approach_newborns = "without_newborns",
  year_of_analysis,
  time_horizon = NULL,
  exp_central = NULL,
  exp_lower = NULL,
  exp_upper = NULL,
  cutoff_central = 0,
  cutoff_lower = NULL,
  cutoff_upper = NULL,
  erf_eq_central = NULL,
  erf_eq_lower = NULL,
  erf_eq_upper = NULL,
  rr_central = NULL,
  rr_lower = NULL,
  rr_upper = NULL,
  rr_increment = NULL,
  erf_shape = NULL,
  prop_pop_exp = 1,
  geo_id_micro = "a",
  geo_id_macro = NULL,
  info = NULL
)

Arguments

Details

Function arguments

age_group The numeric values must refer to 1 year age groups, e.g. c(0:99). To convert multi-year/larger age groups to 1 year age groups use the function prepare_lifetable() (see its function documentation for more info).

bhd_central,bhd_lower,bhd_upper Deaths per age must be inputted with 1 value per age (i.e. age group size = 1 year). There must be greater than or equal to 1 deaths per age to avoid issues during the calculation of survival probabilities. If zeros show up in the last ages (e.g. age 98 = 0 deaths, 99 years old = 1), please sum the values and condensate last category (e.g. age 98 = 1).

population The population data must be inputted with 1 value per age (i.e. age group size = 1 year). The values must be greater than or equal to 1 per age to avoid issues during the calculation of survival probabilities. Mid-year population of year x can be approximated as the mean of either end-year populations of years x-1 and x or start-of-year populations of years x and x+1. For each age, the inputted values must be greater than or equal to 1 to avoid issues during the calculation of survival probabilities.

approach_newborns If "with_newborns" is selected, it is assumed that for each year after the year of analysis n babies (population aged 0) are born.

time_horizon Applicable for the following cases:

• YLL: single_year or constant exposure

• premature deaths: constant exposure

For example, if 10 is entered one is interested in the impacts of exposure during the year of analysis and the next 9 years (= 10 years in total). Default value: length of the numeric vector specified in the age_group argument.

min_age, max_age The min_age default value 30 implies that all adults aged 30 or older will be affected by the exposure; max_age analogeously specifies the age above which no health effects of the exposure are considered.

Methodology

The life table approach to obtain YLL and deaths requires population and baseline mortality data to be stratified by one year age groups. This function applies the same approach as the on applied in the WHO tool AirQ+ (WHO 2020), which is described in previous literature (Miller and Hurley 2003).

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• YLL and deaths with life table

Conversion of multi-year to single year age groups

To convert multi-year/larger age groups to 1 year age groups, use the healthiar function prepare_lifetable().

Value

This function returns a list containing:

1) health_main (tibble) containing the main results;

• impact (numeric column) attributable health burden/impact

• pop_fraction (numeric column) population attributable fraction; only applicable in relative risk assessments

• And many more

2) health_detailed (list) containing detailed (and interim) results.

• input_args (list) containing all the argument inputs used in the background

• input_table (tibble) containing the inputs after preparation

• results_raw (tibble) containing results for all combinations of input (geo units, uncertainty, age and sex specific data...)

• results_by_... (tibble) containing results stratified by each geographic unit, age or sex.

Note

For this specific function, the return object health_detailed also contains intermediate_calculations. This is a nested tibble containing intermediate results, such as population projections and impact by age/year.

Author(s)

Alberto Castro & Axel Luyten

References

Miller BG, Hurley JF (2003). “Life table methods for quantitative impact assessments in chronic mortality.” Journal of Epidemiology & Community Health, 57(3), 200–206. ISSN 0143-005X, doi:10.1136/jech.57.3.200.

WHO (2020). “Health impact assessment of air pollution: AirQ+ life table manual.” World Health Organization - Regional Office for Europe. https://www.who.int/europe/publications/i/item/WHO-EURO-2020-1559-41310-56212.

See Also

• Upstream: prepare_exposure (only if no exposure data available)

• Alternative: attribute_health, get_paf, get_risk

• Downstream: attribute_mod, compare, daly, multiexpose, standardize, monetize, socialize

Examples

# Goal: determine YLL attributable to air pollution exposure during one year
# using the life table approach
results <- attribute_lifetable(
  health_outcome = "yll",
  approach_exposure = "single_year",
  approach_newborns = "without_newborns",
  exp_central = 8.85,
  prop_pop_exp = 1,
  cutoff_central = 5,
  rr_central =  1.118,
  rr_increment = 10,
  erf_shape = "log_linear",
  age_group = exdat_lifetable$age_group,
  sex = exdat_lifetable$sex,
  bhd_central = exdat_lifetable$deaths,
  population = exdat_lifetable$midyear_population,
  year_of_analysis = 2019,
  min_age = 20
)
results$health_main$impact # Attributable YLL


# Goal: determine attributable premature deaths due to air pollution exposure
# during one year using the life table approach
results_pm_deaths <- attribute_lifetable(
  health_outcome = "deaths",
  approach_exposure = "single_year",
  exp_central = 8.85,
  prop_pop_exp = 1,
  cutoff_central = 5,
  rr_central =  1.118,
  rr_increment = 10,
  erf_shape = "log_linear",
  age_group = exdat_lifetable$age_group,
  sex = exdat_lifetable$sex,
  bhd_central = exdat_lifetable$deaths,
  population = exdat_lifetable$midyear_population,
  year_of_analysis = 2019,
  min_age = 20
)
results_pm_deaths$health_main$impact # Attributable premature deaths


# Goal: determine YLL attributable to air pollution exposure (exposure distribution)
# during one year using the life table approach
results <- attribute_lifetable(
  health_outcome = "yll",
  exp_central = rep(c(8, 9, 10), each = 100*2), # each = length of sex or age_group vector
  prop_pop_exp = rep(c(0.2, 0.3, 0.5), each = 100*2), # each = length of sex or age_group vector
  cutoff_central = 5,
  rr_central = 1.118,
  rr_lower = 1.06,
  rr_upper = 1.179,
  rr_increment = 10,
  erf_shape = "log_linear",
  age_group = rep(
    exdat_lifetable$age_group,
    times = 3), # times = number of exposure categories
  sex = rep(
    exdat_lifetable$sex,
    times = 3), # times = number of exposure categories
  population = rep(
    exdat_lifetable$midyear_population,
    times = 3), # times = number of exposure categories
  bhd_central = rep(
    exdat_lifetable$deaths,
    times = 3), # times = number of exposure categories
  year_of_analysis = 2019,
  min_age = 20
)
results$health_main$impact_rounded # Attributable YLL

Attributabe health impact to an environmental stressor

Description

This INTERNAL function calculates the health impacts, mortality or morbidity, of an environmental stressor using a single value for baseline heath data, i.e. without life table.

Usage

attribute_master(
  approach_risk = NULL,
  exp_central,
  exp_lower = NULL,
  exp_upper = NULL,
  cutoff_central = NULL,
  cutoff_lower = NULL,
  cutoff_upper = NULL,
  pop_exp = NULL,
  erf_eq_central = NULL,
  erf_eq_lower = NULL,
  erf_eq_upper = NULL,
  rr_central = NULL,
  rr_lower = NULL,
  rr_upper = NULL,
  rr_increment = NULL,
  erf_shape = NULL,
  bhd_central = NULL,
  bhd_lower = NULL,
  bhd_upper = NULL,
  prop_pop_exp = NULL,
  geo_id_micro = NULL,
  geo_id_macro = NULL,
  age_group = "all",
  sex = "all",
  population = NULL,
  info = NULL,
  dw_central = NULL,
  dw_lower = NULL,
  dw_upper = NULL,
  duration_central = NULL,
  duration_lower = NULL,
  duration_upper = NULL,
  is_lifetable = NULL,
  health_outcome = NULL,
  min_age = NULL,
  max_age = NULL,
  approach_newborns = NULL,
  approach_exposure = NULL,
  year_of_analysis = NULL,
  time_horizon = NULL,
  input_args = NULL
)

Arguments

Value

This function returns a list containing:

1) health_main (tibble) containing the main results;

• impact (numeric column) attributable health burden/impact

• pop_fraction (numeric column) population attributable fraction; only applicable in relative risk assessments

• And many more

2) health_detailed (list) containing detailed (and interim) results.

• input_args (list) containing all the argument inputs used in the background

• input_table (tibble) containing the inputs after preparation

• results_raw (tibble) containing results for all combinations of input (geo units, uncertainty, age and sex specific data...)

• results_by_... (tibble) containing results stratified by each geographic unit, age or sex.

Author(s)

Alberto Castro & Axel Luyten

Create a scenario 2 by modifying an existing scenario 1 and determine attributable health impacts in it

Description

This function quantifies the attributable health impacts in a new scenario 2 as follows:

• taking the input data of an existing scenario 1 (obtained using attribute_health()),

• modifying some of these input data of scenario 1 for the scenario 2 and

• calling in the background attribute_health() with the new data for scenario 2

Usage

attribute_mod(
  output_attribute,
  erf_shape = NULL,
  rr_central = NULL,
  rr_lower = NULL,
  rr_upper = NULL,
  rr_increment = NULL,
  erf_eq_central = NULL,
  erf_eq_lower = NULL,
  erf_eq_upper = NULL,
  exp_central = NULL,
  exp_lower = NULL,
  exp_upper = NULL,
  prop_pop_exp = NULL,
  pop_exp = NULL,
  cutoff_central = NULL,
  cutoff_lower = NULL,
  cutoff_upper = NULL,
  bhd_central = NULL,
  bhd_lower = NULL,
  bhd_upper = NULL,
  geo_id_micro = NULL,
  geo_id_macro = NULL,
  age_group = NULL,
  sex = NULL,
  population = NULL,
  info = NULL,
  min_age = NULL,
  max_age = NULL,
  approach_exposure = NULL,
  approach_newborns = NULL,
  year_of_analysis = NULL
)

Arguments

Details

Methodology

This function calls in the background attribute_health(), which can use the relative risk (Murray et al. 2003) and absolute risk (WHO 2011).

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• Modification of scenarios

• Relative risk

• Absolute risk

Value

This function returns a list containing:

1) health_main (tibble) containing the main results;

• impact (numeric column) attributable health burden/impact

• pop_fraction (numeric column) population attributable fraction; only applicable in relative risk assessments

• And many more

2) health_detailed (list) containing detailed (and interim) results.

• input_args (list) containing all the argument inputs used in the background

• input_table (tibble) containing the inputs after preparation

• results_raw (tibble) containing results for all combinations of input (geo units, uncertainty, age and sex specific data...)

• results_by_... (tibble) containing results stratified by each geographic unit, age or sex.

Author(s)

Alberto Castro & Axel Luyten

References

Murray CJ, Ezzati M, Lopez AD, Rodgers A, Hoorn SV (2003). “Comparative risk assessment: conceptual framework and design.” Epidemiology, 14(4), 447-458. doi:10.1186/1478-7954-1-1.

WHO (2011). “Burden of disease from environmental noise: Quantification of healthy life years lost in Europe.” World Health Organization. https://www.who.int/publications/i/item/burden-of-disease-from-environmental-noise-quantification-of-healthy-life-years-lost-in-europe.

See Also

• Upstream: attribute_health, attribute_lifetable

• Downstream: compare

Examples

# Goal: adjust an existing healthiar scenario and determine the health
# impacts in the modified scenario

## First create a scenario to be modified
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_A$health_main$impact # Attributable impact in scenario A

## Modify scenario (adjust exposure value)
scenario_B <- attribute_mod(
  output_attribute = scenario_A,
  exp_central = 6       # EXPOSURE 2
)

scenario_B$health_main$impact # Attributable impact in scenario B

Cost-benefit analysis

Description

This function performs a cost-benefit analysis. It is assumed that the benefit is caused by the positive health impacts of a policy intervention, which generates some costs.

Usage

cba(
  output_attribute = NULL,
  impact_benefit = NULL,
  valuation,
  cost,
  discount_rate_benefit = NULL,
  discount_rate_cost = NULL,
  inflation_rate = NULL,
  discount_shape = "exponential",
  n_years_benefit = 1,
  n_years_cost = 1
)

Arguments

Details

Methodology

This function provides as output three types of cost-benefit indicators (Boardman et al. 2018):

• net benefit

• cost-benefit ratio

• return of investment

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• cost-benefit analysis

• monetization

Value

This function returns a list containing:

1) cba_main (tibble) containing the main CBA results;

• net_benefit (numeric column) containing the difference between benefit and cost (i.e. benefit - cost)

• benefit (numeric column) containing discounted benefit (i.e. monetized attributable health impact)

• cost (numeric column) containing discounted cost

• And many more

2) cba_detailed (list) containing detailed (and interim) results.

• benefit (list)

• cost (tibble)

If the argument output_attribute was specified, then the two results elements are added to the existing output.

Author(s)

Alberto Castro & Axel Luyten

References

Boardman AE, Greenberg DH, Vining AR, Weimer DL (2018). Cost-Benefit Analysis: Concepts and Practice, 5th edition. Cambridge University Press, Cambridge, UK. ISBN 978-1108401296.

See Also

• Upstream: attribute_health

• Alternative: monetize

Examples

# Goal: performs a cost-benefit analysis using an existing output
# of a attribute_... function

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

results <- cba(
  output_attribute = output_attribute,
  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
)

results$cba_main |>
  dplyr::select(benefit, cost, net_benefit)

Check if arguments are identical before stop

Description

This function checks if two different sets of arguments are identical

Usage

check_if_args_identical(args_a, args_b, names_to_check)

Arguments

Author(s)

Alberto Castro & Axel Luyten

Collapse rows by grouping columns

Description

This function paste rows with different values and keep only unique rows following grouping columns

Usage

collapse_df_by_group(
  df,
  group_col_names,
  multi_value_col_names = NULL,
  ci_col_names = NULL,
  only_unique_rows = TRUE
)

Arguments

Value

This function returns a data frame or tibble with lower number rows after collapsing

Author(s)

Alberto Castro & Axel Luyten

Compare the attributable health impacts between two scenarios

Description

This function calculates the health impacts between two scenarios (e.g. before and after a intervention in a health impact assessments) using either the delta or pif approach.

Usage

compare(
  output_attribute_scen_1,
  output_attribute_scen_2,
  approach_comparison = "delta"
)

Arguments

Details

Methodology This function compares the attributable health impacts in scenario 1 with scenario 2. It can use two approaches:

• Delta: Subtraction of health impacts in the two scenarios (two PAF) (WHO Regional Office for Europe 2014)

• Potential impact fraction (PIF): Single PIF for both scenarios (WHO 2003; Murray et al. 2003; Askari and Namayandeh 2020)

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• comparison of two health scenarios

Specifications of the comparison approach

Please, 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). With the delta comparison approach, the difference between two scenarios is obtained by subtraction. The delta approach is suited for all comparison cases, allowing a comparison of a situation now with a situation in the future.

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

Comparing DALY

If you want to use compare() DALY with daly(), do not enter the output of daly() in compare(). Instead, follow these steps:

1) use compare() for YLL and YLD separately

2) use daly() inserting the output of both compare()

Alternatively, you can use attribute_health to quantify DALY entering DALY in the argument bhd_central and then use compare()

Value

This function returns a list containing:

1) health_main (tibble) containing the main results from the comparison;

• impact (numeric column) difference in attributable health burden/impact between scenario 1 and 2

• impact_scen_1 (numeric column) attributable health impact of scenario 1

• impact_scen_2 (numeric column) attributable health impact of scenario 2

• And many more

2) health_detailed (list) containing detailed (and interim) results from the comparison.

• results_raw (tibble) containing comparison results for each combination of input uncertainty for both scenario 1 and 2

• results_by_geo_id_micro (tibble) containing comparison results for each geographic unit under analysis (specified in geo_id_micro argument)

• results_by_geo_id_macro (tibble) containing comparison results for each aggregated geographic unit under analysis (specified in geo_id_macro argument))

• input_table (list) containing the inputs to each relevant argument for both scenario 1 and 2

• input_args (list) containing all the argument inputs for both scenario 1 and 2 used in the background

• scen_1 (tibble) containing results for scenario 1

• scen_2 (tibble) containing results for scenario 2

Author(s)

Alberto Castro & Axel Luyten

References

Askari M, Namayandeh SM (2020). “The Difference between the Population Attributable Risk (PAR) and the Potentioal Impact Fraction (PIF).” Iranian Journal of Public Health, 49(10), 2018–2019. doi:10.18502/ijph.v49i10.4713, https://pmc.ncbi.nlm.nih.gov/articles/PMC7719653/.

Murray CJL, Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S (2003). “Comparative quantification of health risks conceptual framework and methodological issues.” Popul. Health Metr., 1(1), 1.

WHO Regional Office for Europe (2014). WHO Expert Meeting: Methods and tools for assessing the health risks of air pollution at local, national and international level. Meeting report; 12-13 May 2014; Bonn, Germany. WHO Regional Office for Europe, Copenhagen. https://iris.who.int/handle/10665/142940.

WHO (2003). “Introduction and methods: Assessing the environmental burden of disease at national and local levels.” World Health Organization. https://www.who.int/publications/i/item/9241546204.

See Also

• Upstream: attribute_health, attribute_mod, standardize,

• Downstream: daly

Examples

# Goal: comparison of two scenarios with delta approach
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_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
)
results <- compare(
approach_comparison = "delta",
output_attribute_scen_1 = scenario_A,
output_attribute_scen_2 = scenario_B
)
# Inspect the difference, stored in the \code{impact} column
results$health_main |>
  dplyr::select(impact, impact_scen_1, impact_scen_2) |>
  print()

# Goal: comparison of two scenarios with potential impact fraction (pif) approach
output_attribute_scen_1 <- 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_lower = 1.060, rr_upper = 1.179,
  rr_increment = 10
)
output_attribute_scen_2 <- 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_lower = 1.060, rr_upper = 1.179,
  rr_increment = 10
)
results <- compare(
  output_attribute_scen_1 = output_attribute_scen_1,
  output_attribute_scen_2 = output_attribute_scen_2,
  approach_comparison = "pif"
)
# Inspect the difference, stored in the impact column
results$health_main$impact

Compile input

Description

This function compiles the input data of the main function and calculates the population attributable fraction based on the input data (all in one data frame)

Usage

compile_input(input_args, is_lifetable)

Arguments

Value

This function returns a data.frame with all input data together Moreover, the data frame includes columns such as:

• Attributable fraction

• Health impact

• Outcome metric

• And many more.

Author(s)

Alberto Castro & Axel Luyten

Attributable disability-adjusted life years

Description

This function quantifies the attributable disability-adjusted life years (DALY) by summing the two DALY components: years of life lost (YLL) and years lived with disability (YLD).

Usage

daly(output_attribute_yll, output_attribute_yld)

Arguments

Details

Methodology

This function sums the years of life lost (YLL) and years lived with disability (YLD) to obtain the disability-adjusted lost years (DALY) (GBD 2019 Risk Factors Collaborators 2020).

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• dalys

Value

This function returns a list containing:

1) health_main (tibble) containing the main results;

• impact (numeric column) attributable health burden/impact in DALY

• impact_yld (numeric column) attributable health burden/impact in YLD

• impact_yll (numeric column) attributable health burden/impact in YLL

• dw (numeric column) disability weight used for YLD calculation

• And many more

2) health_detailed (list) containing detailed (and interim) results.

• results_raw (tibble) containing results for each combination of input uncertainty

• results_by_geo_id_micro (tibble) containing results for each geographic unit under analysis (specified in geo_id_micro argument)

• input_args (list) containing all the argument inputs used in the background

Author(s)

Alberto Castro & Axel Luyten

References

GBD 2019 Risk Factors Collaborators (2020). “Global burden of 87 risk factors in 204 countries and territories, 1990–2019.” The Lancet. doi:10.1016/S0140-6736(20)30752-2.

See Also

• Upstream: attribute_health, attribute_lifetable

Examples

# Goal: obtain DALY (disability-adjusted life years) from two existing \code{attribute_...} outputs
# Step 1: Create YLL (years of life lost) assessment
results_yll <- attribute_lifetable(
  health_outcome = "yll",
  approach_exposure = "single_year",
  approach_newborns = "without_newborns",
  exp_central = 8.85,
  prop_pop_exp = 1,
  cutoff_central = 5,
  rr_central =  1.118,
  rr_increment = 10,
  erf_shape = "log_linear",
  age_group = exdat_lifetable$age_group,
  sex = exdat_lifetable$sex,
  bhd_central = exdat_lifetable$deaths,
  population = exdat_lifetable$midyear_population,
  year_of_analysis = 2019,
  min_age = 20
)
# Step 2: Create YLD (years lived with disability) assessment
results_yld  <- attribute_health(
  exp_central = 8.85,
  prop_pop_exp = 1,
  cutoff_central = 5,
  bhd_central = 1000,
  rr_central = 1.1,
  rr_increment = 10,
  erf_shape = "log_linear",
  duration_central = 100,
  dw_central = 0.5,
  info = "pm2.5_yld"
)
# Step 3: obtain DALY
results <- daly(
  output_attribute_yll = results_yll,
  output_attribute_yld = results_yld
)
# Attributable impact in DALY
results$health_main |>
  dplyr::select(impact, impact_yll, impact_yld)

Discount health impacts

Description

This function calculates discounted health impacts (without valuation).

Usage

discount(
  output_attribute = NULL,
  impact = NULL,
  discount_rate = NULL,
  n_years = NULL,
  discount_shape = NULL,
  inflation_rate = NULL
)

Arguments

Details

Methodology

This function applies a discount (Frederick et al. 2002; Harvey 1986; Mazur 1987), optionally with inflation (Brealey et al. 2023), to attributable health impacts into the future.

From an epidemiological perspective, the attributable health impacts cannot be discounted (or inflated), only economic costs/benefits can. However, in some economic analyses the attributable health impacts are discounted (and/or inflated) as a previous step to valuating them. For this specific purpose, this function is offered.

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• Monetization

Value

This function returns a list containing:

1) monetization_main (tibble) containing the main monetized results;

• monetized_impact (numeric column)

• discount_factor (numeric column) calculated based on the entered discount_rate

• And many more

2) monetization_detailed (list) containing detailed (and interim) results.

• results_by_year (tibble)

• health_raw (tibble) containing the monetized results for each for each combination of input uncertainty that were provided to the initial attribute_health() call

If the argument output_attribute was specified, then the two results elements are added to the existing output.

Author(s)

Alberto Castro & Axel Luyten

References

Brealey RA, Myers SC, Allen F, Benninga S, Read J (2023). Principles of Corporate Finance, 14th edition. McGraw-Hill Education, New York, NY. ISBN 978-1264117464.

Frederick S, Loewenstein G, O'Donoghue T (2002). “Time Discounting and Time Preference: A Critical Review.” Journal of Economic Literature, 40(2), 351–401. doi:10.1257/002205102320161311.

Harvey CM (1986). “Value Functions for Infinite-Period Planning.” Management Science, 32(9), 1123–1139. doi:10.1287/mnsc.32.9.1123.

Mazur JE (1987). “An adjusting procedure for studying delayed reinforcement.” In Commons ML, Mazur JE, Nevin JA, Rachlin H (eds.), Quantitative Analyses of Behavior: Volume V. The Effect of Delay and of Intervening Events on Reinforcement Value, 55–73. Lawrence Erlbaum Associates, Hillsdale, NJ. ISBN 0-89859-800-1.

See Also

• Upstream: attribute_health, attribute_health

• Alternative: monetize

Examples

# Goal: discount attributable health impacts
results <- discount(
  impact = 20000,
  discount_shape = "exponential",
  discount_rate = 0.03,
  n_years = 20
)
results$monetization_main$monetized_impact

PM2.5 exposure and COPD incidence in Switzerland

Description

This tibble contains PM2.5 exposure and COPD incidence data from Switzerland.

Usage

data(exdat_cantons)

Format

exdat_cantons

year

year

canton

abbreviation of Swiss cantons

lung_cancer_incidence

lung cancer incidence

exposure

mean country-wide population-weighted exposure level

pollutant

PM2.5

exposure_type

exposure type

population

number of inhabitants per canton

rr

central relative risk estimate

rr_l

lower 95% confidence interval bound of the relative risk estimate

rr_u

upper 95% confidence interval bound of the relative risk estimate

increment

exposure increment in \mu g/m^3 for which the relative risk estimates are valid

function_shape

shape of the exposure-response function

cutoff

cutoff level below which no health effects are attributable to the exposure

language_main

language spoken by the majority of inhabitants in the canton

canton_long

full (English) name of the canton

Author(s)

Alberto Castro & Axel Luyten

Source

Real-world data

Population data per age and sex in Switzerland

Description

This tibble contains population per age and sex for Switzerland.

Usage

data(exdat_lifetable)

Format

exdat_lifetable

age_group

single year age groups

sex

female or male

midyear_population

mid-year populations

deaths

annual deaths

Author(s)

Alberto Castro & Axel Luyten

Source

Real-world data

Noise exposure in urban and rural regions in Norway

Description

This tibble contains noise exposure data from urban and rural regions in Norway.

Usage

data(exdat_noise)

Format

exdat_noise

exposure_category

noise exposure range of the exposure category

exposure_mean

mean noise exposure in the exposure category

region

region for which exposure is valid

exposed

number of exposed persons

Author(s)

Anette Kocbach Bolling & Vázquez Fernández

Source

Real-world data

PM2.5 exposure and COPD incidence in Switzerland

Description

This tibble contains modelled ozone (O_3) exposure and chronic obstructive pulmonary disease (COPD) incidence data from the Germany in 2016.

Usage

data(exdat_ozone)

Format

exdat_ozone

pollutant

O_3

exposure

mean exposure level in the exposure category

exp_unit

unit of the exposure

proportion_population_exposed

proportion of the total population exposed to each exposure category

mortality_copd_tota_yearl

mortality due to chronic obstructive pulmonary disease (ICD-10 J40-44)

rr_central

central relative risk estimate

rr_lower

lower 95% confidence interval bound of the relative risk estimate

rr_upper

upper 95% confidence interval bound of the relative risk estimate

rr_increment

exposure increment in \mu g/m^3 for which the relative risk estimates are valid

cutoff

cutoff level below which no health effects are attributable to the exposure

erf_shape

shape of the exposure-response function

exposure_type

exposure type

rr_source

source of the relative risk estimates

country

country

year

year of the data

Author(s)

Alberto Castro & Axel Luyten

Source

Real-world data

PM2.5 exposure and COPD incidence in Switzerland

Description

This tibble contains PM2.5 exposure and COPD incidence data from Switzerland.

Usage

data(exdat_pm)

Format

exdat_pm

year_of_analysis

year that the exposure and incidence data is from

mean_concentration

population-weighted annual mean concentration

relative_risk

central relative risk estimate

relative_risk_lower

lower 95% confidence interval bound of the relative risk estimate

relative_risk_upper

upper 95% confidence interval bound of the relative risk estimate

erf_shape

shape of the exposure-response function

incidence

COPD incidence in the year of analysis

cutoff_value

cut-off value

rr_increment

exposure increment in \mu g/m^3 for which the relative risk estimates are valid

rr_source

source of the relative risk

rr_doi

DOI linking to the publication from which the relative risk was taken

Author(s)

Alberto Castro & Axel Luyten

Source

Real-world data

Social indicators of the BEST-COST Multidimensional Deprivation Index (MDI)

Description

This tibble contains social indicators of the BEST-COST Multidimensional Deprivation Index (MDI) of geo units in Belgium.

Usage

data(exdat_prepare_mdi)

Format

exdat_prepare_mdi

id

id of the geographic unit

geo_name

name of the geographic unit

edu, unemployed, single_parent, no_heating, pop_change

single social indicators that make up the MDI

norm_...

normalized single social indicators of the MDI

MDI

BEST-COST Multidimensional Deprivation Index (MDI)

MDI_decile

decile of the MDI rankig

MDI_quartile

quartile of the MDI ranking

Author(s)

Arno Pauwels & Vanessa Gorasso

Source

Real-world data

Air pollution expsoure data of the Brussels-Capital region (Belgium)

Description

The data can be loaded using

exdat_pwm_1 <- terra::rast(

system.file("extdata", "exdat_pwm_1.tif", package = "healthiar")

)

(see Examples section below).

When loaded it is a variable of class SpatRaster, which contains air pollution exposure levels of municipalities in the Brussels-Capital region (Belgium).

Because it is a .tif file it is stored in the package's inst/extdata directory.

Format

GeoTIFF raster

Author(s)

Arno Pauwels

Source

Real-world data

Examples

path <- system.file("extdata", "exdat_pwm_1.tif", package = "healthiar")
exdat_pwm_1 <- terra::rast(path)

Geospatial outlines and populations of the Brussels-Capital region (Belgium)

Description

This variable of class sf and data.frame contains the geospatial outlines and populations of municipalities in the Brussels-Capital region (Belgium).

Usage

data(exdat_pwm_2)

Format

exdat_pwm_2

code

NIS-codes (i.e. unique statistical code assigned to each geographic area in Belgium)

name

Dutch names of the municipalities

population

municipality populations

region

geographic position of each municipality (North, East, West, South) relative to Brussels (Center)

geom

geospatial outlines of the municipalities (MULTIPOLYGON)

Author(s)

Arno Pauwels

Source

Real-world data

Municipalities in Belgium ranked by BEST-COST Multidimensional Deprivation Index (MDI)

Description

This tibble contains data for municipalities in Belgium ranked by BEST-COST Multidimensional Deprivation Index (MDI).

Usage

data(exdat_socialize)

Format

exdat_socialize

CS01012020

unique identifier of the geographic unit

NUTS1

NUTS1 region tag

PM25_MEAN

mean PM2.5 exposure

RR

relative risk estimate from the literature

score

BEST-COST Multidimensional Deprivation Index (MDI)

rank

rank of the observation based on column score; note that the rank is not continuous, as some observations are missing

deciles

deciles of the geo units based on the MDI

POPULATION_below_40

(fake) populations up until and including 39 years of age

POPULATION_40_plus

(fake) populations from 40 years of age onwards

MORTALITY_below_40

(fake) mortality up until and including 39 years of age

MORTALITY_40_plus

(fake) mortality from 40 years of age onwards

Author(s)

Arno Pauwels & Vanessa Gorasso

Source

Real-world data combined with fake population and mortality data

Find joining columns

Description

This function finds columns in two data frames that have same name and identical values (excluding exceptions).- The resulting joining columns are used to dplyr::x_join data frames and add the vector to by=.

Usage

find_joining_columns(df_1, df_2, except = NULL)

Arguments

Value

This function returns a vector of strings

Author(s)

Alberto Castro & Axel Luyten

Find columns with multiple values

Description

This function find data frame or tibble column names with different values in their rows (i.e. not a unique value)

Usage

find_multi_value_col_names(df, group_col_names = NULL)

Arguments

Value

This function returns a string vector with the names of the columns with multiple values

Author(s)

Alberto Castro & Axel Luyten

Get discount factor

Description

This function calculates the discount factor based on discount rate. If the argument inflation_rate is NULL (default), it is assumed that the discount rate is already corrected for inflation). Otherwise (if a value for inflation_rate is entered), the resulted discount factor is adjusted for inflation.

Usage

get_discount_factor(
  discount_rate,
  n_years,
  discount_shape = "exponential",
  inflation_rate = NULL
)

Arguments

Details

Methodology

This function is called inside monetize().

One of the following three discount shapes can be selected:

• Exponential (Frederick et al. 2002)

• Hyperbolic as Harvey (1986)

• Hyperbolic as Mazur (1987)

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• Monetization

Value

This function returns the numeric discount factor.

Author(s)

Alberto Castro & Axel Luyten

References

Frederick S, Loewenstein G, O'Donoghue T (2002). “Time Discounting and Time Preference: A Critical Review.” Journal of Economic Literature, 40(2), 351–401. doi:10.1257/002205102320161311.

Harvey CM (1986). “Value Functions for Infinite-Period Planning.” Management Science, 32(9), 1123–1139. doi:10.1287/mnsc.32.9.1123.

Mazur JE (1987). “An adjusting procedure for studying delayed reinforcement.” In Commons ML, Mazur JE, Nevin JA, Rachlin H (eds.), Quantitative Analyses of Behavior: Volume V. The Effect of Delay and of Intervening Events on Reinforcement Value, 55–73. Lawrence Erlbaum Associates, Hillsdale, NJ. ISBN 0-89859-800-1.

See Also

• Alternative: monetize

Examples

get_discount_factor(
  discount_rate = 0.07,
  n_years = 5
 )

Attributable health cases based on relative risk

Description

This function calculates the health impacts for each uncertainty and geo area.

Usage

get_impact(input_table, pop_fraction_type)

Arguments

Value

TBD. E.g. This function returns a data.frame with one row for each value of the concentration-response function (i.e. central, lower and upper bound confidence interval. Moreover, the data frame includes columns such as:

• Attributable fraction

• Health impact

• Outcome metric

• And many more.

Author(s)

Alberto Castro & Axel Luyten

Get population impact over time

Description

Get population impact over time

Usage

get_impact_with_lifetable(input_with_risk_and_pop_fraction)

Arguments

Value

This function returns a data.frame with one row for each value of the concentration-response function (i.e. central estimate, lower and upper bound confidence interval). Moreover, the data frame include columns such as:

• Attributable fraction

• Health impact

• Outcome metric

• And many more.

Author(s)

Alberto Castro & Axel Luyten

Get inflation factor

Description

This function calculates the inflation factor based on inflation rate.

Usage

get_inflation_factor(n_years, inflation_rate = NULL)

Arguments

Details

Methodology

This function is called inside monetize().

It calculates the inflation factor based on the inflation rate and the number of years into the future as described in Brealey et al. (2023).

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• Monetization

Value

This function returns the numeric inflation factor.

Author(s)

Alberto Castro & Axel Luyten

References

Brealey RA, Myers SC, Allen F, Benninga S, Read J (2023). Principles of Corporate Finance, 14th edition. McGraw-Hill Education, New York, NY. ISBN 978-1264117464.

See Also

• Alternative: monetize

Examples

get_inflation_factor(
  inflation_rate = 0.02,
  n_years = 5
)

get_input_args

Description

This function compiles the input data of the main function and calculates the population attributable fraction based on the input data (all in one data frame)

Usage

get_input_args(environment, call)

Value

This function returns a list with all input data together by argument

Author(s)

Alberto Castro & Axel Luyten

Obtain and store output

Description

This function distributes and store outputs by level of detail by aggregating or filtering impacts.

Usage

get_output(
  input_args = NULL,
  input_table = NULL,
  intermediate_calculations = NULL,
  results_raw
)

Arguments

Value

TBD. E.g. This function returns a data.frame with one row for each value of the concentration-response function (i.e. central, lower and upper bound confidence interval. Moreover, the data frame includes columns such as:

• Attributable fraction

• Health impact

• Outcome metric

• And many more.

Author(s)

Alberto Castro & Axel Luyten

Get population attributable fraction

Description

This function calculates the population attributable fraction (PAF) of a health outcome due to exposure to an environmental stressor

Usage

get_paf(rr_at_exp, prop_pop_exp)

Arguments

Details

Methodology

This function is called internally inside other healthiar functions, e.g. attribute_health(). The function calculates the population attributable fraction (i.e. the percent of health cases that are attributable to the exposure) based on the relative risk as described in the extensive existing literature (WHO 2003; Steenland and Armstrong 2006; Lehtomäki et al. 2025).

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• relative risk

Value

This function returns the population attributable fraction as a numeric value.

Author(s)

Alberto Castro & Axel Luyten

References

Lehtomäki H, Aasvang GM, Sulo G, Denby BR, Hänninen OO, Brauer M, Pereira G, Dadras O, Bølling AK (2025). “Burden of disease attributable to PM2.5 at low exposure levels: impact of methodological choices.” Environmental Health, 25(1), 4. ISSN 1476-069X, doi:10.1186/s12940-025-01250-y.

Steenland K, Armstrong B (2006). “An overview of methods for calculating the burden of disease due to specific risk factors.” Epidemiology, 17(5), 512–519. doi:10.1097/01.ede.0000229155.05644.43.

WHO (2003). “Introduction and methods: Assessing the environmental burden of disease at national and local levels.” World Health Organization. https://www.who.int/publications/i/item/9241546204.

See Also

• Alternative: attribute_health, attribute_lifetable

Examples

# Goal: calculate PAF based on RR and the proportion of population exposed
get_paf(rr = 1.062, prop_pop_exp = 1)

Get potential impact fraction (PIF)

Description

This function calculates the potential impact fraction of a health outcome due to exposure to an environmental stressor

Usage

get_pif(rr_at_exp_1, rr_at_exp_2, prop_pop_exp_1, prop_pop_exp_2)

Arguments

Details

Methodology

This function is internally used inside the healthiar function compare(). The potential impact fraction (PIF) is calculated as described in WHO (2003), Murray et al. (2003) and Askari and Namayandeh (2020).

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• comparison of two health scenarios

Value

This function returns the potential impact fraction as a numeric value.

Author(s)

Alberto Castro & Axel Luyten

References

Askari M, Namayandeh SM (2020). “The Difference between the Population Attributable Risk (PAR) and the Potentioal Impact Fraction (PIF).” Iranian Journal of Public Health, 49(10), 2018–2019. doi:10.18502/ijph.v49i10.4713, https://pmc.ncbi.nlm.nih.gov/articles/PMC7719653/.

Murray CJL, Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S (2003). “Comparative quantification of health risks conceptual framework and methodological issues.” Popul. Health Metr., 1(1), 1.

WHO (2003). “Introduction and methods: Assessing the environmental burden of disease at national and local levels.” World Health Organization. https://www.who.int/publications/i/item/9241546204.

See Also

• Alternative: compare

Examples

# Goal: calculate the potential impact fraction (PIF)
results <- get_pif(
  rr_at_exp_1 = 1.043879,
  rr_at_exp_2 = 1.011217,
  prop_pop_exp_1 = 1,
  prop_pop_exp_2 = 1
)
print(results)

Get population attributable or impact fraction

Description

This function calculates the population attributable fraction or potential impact fraction of a health outcome due to exposure to an environmental stressor

Usage

get_pop_fraction(rr_at_exp_1, rr_at_exp_2, prop_pop_exp_1, prop_pop_exp_2)

Arguments

Details

For more information about the equations used please see the function documentation of attribute_health.

Value

This function returns a value corresponding to the population attributable fraction

Author(s)

Alberto Castro & Axel Luyten

Calculates reference proportion of population

Description

This function calculates reference proportion of population. To be used in socialize() and standardize() in case that ref_prop_pop is not provided.

Usage

get_ref_prop_pop(df)

Arguments

Value

A tibble with the columns

• age_group containing numeric age values

• ref_prop_pop containing numeric values

Author(s)

Alberto Castro & Axel Luyten

Get the relative risk of an exposure level

Description

This function re-scales the relative risk from the increment value in the epidemiological study (e.g. for PM2.5 10 or 5 ug/m3) to the actual population exposure

Usage

get_risk(
  erf_shape = NULL,
  rr = NULL,
  rr_increment = NULL,
  erf_eq = NULL,
  cutoff = 0,
  exp
)

Arguments

Details

Function arguments erf_eq If the function is provided as string, it can only contain the variable c (exposure), e.g. "3+c+c^2". If the function is provided as a function, the object must be of the class function. If only the values of the x-axis (exposure) and y axis (relative risk) of the dots in the exposure-response function are available, a cubic spline natural interpolation can be assumed to get the function using, e.g., stats::splinefun(x, y, method="natural")

Methodology

This function is called internally inside other healthiar functions, e.g. attribute_health(). The function calculates the relative risk at the exposure level based on the relative risk available in the epidemiological literature and the assumed shape of the exposure-response function (Pozzer et al. 2023; Lehtomäki et al. 2025).

Detailed information about the methodology (including corresponding equations and literature) is available in the package vignette. More specifically, see chapters:

• relative risk

Value

This function returns the numeric risk value(s) at the specified exposure level(s), referred to as rr_at_exp in the relative risk equations above.

Author(s)

Alberto Castro & Axel Luyten

References

Lehtomäki H, Aasvang GM, Sulo G, Denby BR, Hänninen OO, Brauer M, Pereira G, Dadras O, Bølling AK (2025). “Burden of disease attributable to PM2.5 at low exposure levels: impact of methodological choices.” Environmental Health, 25(1), 4. ISSN 1476-069X, doi:10.1186/s12940-025-01250-y.

Pozzer A, Anenberg SC, Dey S, Haines A, Lelieveld J, Chowdhury S (2023). “Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates.” GeoHealth, 7(1), e2022GH000711. doi:10.1029/2022GH000711, e2022GH000711 2022GH000711, https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022GH000711, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022GH000711.

See Also

• Alternative: attribute_health, attribute_lifetable

Examples

# Goal: scale relative risk to observed exposure level
get_risk(
  rr = 1.05,
  rr_increment = 10,
  erf_shape = "linear",
  exp = 10,
  cutoff = 5
)

# Goal: determine the absolute risk for high annoyance at specific noise exposure levels
get_risk(
  erf_eq = "78.9270-3.1162*c+0.0342*c^2",
  exp = c(57.5, 62.5, 67.5, 72.5, 77.5)
)

# Goal: attribute COPD cases to air pollution exposure
# by applying a user-defined exposure response function,
# e.g. MR-BRT curves from Global Burden of Disease study.
get_risk(
  erf_eq = 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"),
  exp = c(8, 9, 10)
)

Get input data and PAF

Description

This function calculates the population attributable fraction (PAF) based on the input data and puts the results in additional columns joined to the input data frame.

Usage

get_risk_and_pop_fraction(input_table, pop_fraction_type)

Arguments

Value

This function returns a data.frame with the input data adding a column for the population attributable fraction Moreover, the data frame includes columns such as:

• Attributable fraction

• Health impact

• Outcome metric

• And many more.

Author(s)

Alberto Castro & Axel Luyten

Monetize health impacts

Description

This function monetizes health impacts

Usage

monetize(
  output_attribute = NULL,
  impact = NULL,
  valuation,
  discount_rate = NULL,
  discount_shape = "exponential",
  n_years = NULL,
  inflation_rate = NULL,
  info = NULL
)

Arguments

Details

Methodology

This function monetize health impacts valuating them and applying discounting (Frederick et al. 2002; Harvey 1986; Mazur 1987) and/or inflation (Brealey et al. 2023).

One of the following three discount shapes can be selected:

• Exponential (Frederick et al. 2002)

• Hyperbolic as Harvey (1986)

• Hyperbolic as Mazur (1987)

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• Monetization

Value

This function returns a list containing:

1) monetization_main (tibble) containing the main monetized results;

• monetized_impact (numeric column)

• discount_factor (numeric column) calculated based on the entered discount_rate

• And many more

2) monetization_detailed (list) containing detailed (and interim) results.

• results_by_year (tibble)

• health_raw (tibble) containing the monetized results for each for each combination of input uncertainty that were provided to the initial attribute_health() call

If the argument output_attribute was specified, then the two results elements are added to the existing output.

Author(s)

Alberto Castro & Axel Luyten

References

Brealey RA, Myers SC, Allen F, Benninga S, Read J (2023). Principles of Corporate Finance, 14th edition. McGraw-Hill Education, New York, NY. ISBN 978-1264117464.

Frederick S, Loewenstein G, O'Donoghue T (2002). “Time Discounting and Time Preference: A Critical Review.” Journal of Economic Literature, 40(2), 351–401. doi:10.1257/002205102320161311.

Harvey CM (1986). “Value Functions for Infinite-Period Planning.” Management Science, 32(9), 1123–1139. doi:10.1287/mnsc.32.9.1123.

Mazur JE (1987). “An adjusting procedure for studying delayed reinforcement.” In Commons ML, Mazur JE, Nevin JA, Rachlin H (eds.), Quantitative Analyses of Behavior: Volume V. The Effect of Delay and of Intervening Events on Reinforcement Value, 55–73. Lawrence Erlbaum Associates, Hillsdale, NJ. ISBN 0-89859-800-1.

See Also

• Upstream: attribute_health, attribute_lifetable, compare

• Alternative: get_inflation_factor, get_discount_factor, cba

Examples

# Goal: monetize the attributable impacts of an existing healthiar
# assessment
output_attribute <- 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
)

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

# Attributable COPD cases its monetized impact
results$monetization_main |>
  dplyr::select(impact, monetized_impact)

Aggregate health impacts from multiple exposures

Description

This function aggregates health impacts from multiple exposures to environmental stressors.

Usage

multiexpose(
  output_attribute_exp_1,
  output_attribute_exp_2,
  exp_name_1,
  exp_name_2,
  approach_multiexposure = "additive"
)

Arguments

Details

Methodology

This function can add up the attributable health impacts from correlated exposures applying one of the following methods (Strak et al. 2024):

• Additive (Steenland and Armstrong 2006)

• Multiplicative (Jerrett et al. 2013)

• Combined (Steenland and Armstrong 2006)

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• Two correlated exposures

Value

This function returns a list containing:

1) health_main (tibble) containing the main results;

• impact (numeric column) attributable health burden/impact

• pop_fraction (numeric column) population attributable fraction; only applicable in relative risk assessments

• And many more

2) health_detailed (list) containing detailed (and interim) results.

• input_args (list) containing all the argument inputs used in the background

• input_table (tibble) containing the inputs after preparation

• results_raw (tibble) containing results for all combinations of input (geo units, uncertainty, age and sex specific data...)

• results_by_... (tibble) containing results stratified by each geographic unit, age or sex.

Author(s)

Alberto Castro & Axel Luyten

References

Jerrett M, Burnett RT, Beckerman BS, Turner MC, Krewski D, Thurston G, Martin RV, van Donkelaar A, Hughes E, Shi Y, Gapstur SM, Thun MJ, Pope 3CA (2013). “Spatial analysis of air pollution and mortality in California.” American Journal of Respiratory and Critical Care Medicine, 188(5), 593–599. doi:10.1164/rccm.201303-0609OC.

Steenland K, Armstrong B (2006). “An overview of methods for calculating the burden of disease due to specific risk factors.” Epidemiology, 17(5), 512–519. doi:10.1097/01.ede.0000229155.05644.43.

Strak M, Houthuijs D, Staatsen B (2024). “D1.2 Report on the methodology for assessing the burden of correlated exposures.” EU Project BEST-COST.

See Also

• Upstream: attribute_health, attribute_lifetable

Examples

# Goal: determine aggregated health impacts from multiple exposures
# Step 1: create assessment with exposure 1
output_attribute_exp_1 <- attribute_health(
  erf_shape = "log_linear",
  rr_central = 1.369,
  rr_increment = 10,
  exp_central = 8.85,
  cutoff_central = 5,
  bhd_central = 30747
)
output_attribute_exp_1$health_main$impact
# Step 2: create assessment with exposure 2
output_attribute_exp_2 <- attribute_mod(
  output_attribute = output_attribute_exp_1,
  exp_central = 10.9,
  rr_central = 1.031
)
output_attribute_exp_2$health_main$impact
# Step 3: aggregate impacts of the two assessments
results <- multiexpose(
  output_attribute_exp_1 = output_attribute_exp_1,
  output_attribute_exp_2 = output_attribute_exp_2,
  exp_name_1 = "pm2.5",
  exp_name_2 = "no2",
  approach_multiexposure = "multiplicative"
)
results$health_main$impact

Prepare exposure data

Description

This function prepares tabular population exposure data compatible with the attribute() and compare() functions, based on gridded pollution concentration data and polygon data representing geographic units. If population data is provided, the function calculates an average concentration value in each geographic unit that is weighted with the population number at each location. If no population data is provided, the function calculates the simple spatial average concentration in each geographic unit.

Usage

prepare_exposure(
  poll_grid,
  geo_units,
  population = NULL,
  pop_grid = NULL,
  geo_id_micro = NULL,
  geo_id_macro = NULL,
  bin_width = 0.1
)

Arguments

Value

This function returns a list containing:

1) main (list) containing the main results as vectors;

• geo_id_micro of geo_id_macro (string column) containing the (higher-level) geographic IDs of the assessment

• exposure_mean (numeric column) containing the (population-weighted) mean exposure

• population_total (integer column) containing the total population in each geographic unit, if population data was provided

2) detailed (list) containing detailed (and interim) results.

Author(s)

Arno Pauwels & Liliana Vazquez Fernandez

Examples

# Goal: determine population-weighted mean PM2.5 exposure for several
# neighborhoods of Brussels (Belgium)

path <- system.file("extdata", "exdat_pwm_1.tif", package = "healthiar")
exdat_pwm_1 <- terra::rast(path)

pwm <- 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 at
)

pwm$exposure_main # population-weighted mean exposures for the (higher-level) geographic units

Convert multi-year life table to single year life table

Description

This function determines populations and deaths by one year age groups.

Usage

prepare_lifetable(age_group, population, bhd)

Arguments

Details

Methodology

The conversion follows the methodology of the WHO tool. See the AirQ+ manual "Health impact assessment of air pollution: AirQ+ life table manual" for guidance on how to convert larger age groups to 1 year age groups, section "Estimation of yearly values" (WHO 2020).

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• YLL and deaths with life table

Value

This function returns a tibble containing the columns:

• population_for_attribute (numeric) containing population values for each age

• bhd_for_attribute (numeric) containing baseline health data values for each age

• and more columns containing input data or results

Author(s)

Alberto Castro & Axel Luyten

References

WHO (2020). “Health impact assessment of air pollution: AirQ+ life table manual.” World Health Organization - Regional Office for Europe. https://www.who.int/europe/publications/i/item/WHO-EURO-2020-1559-41310-56212.

See Also

• Downstream: attribute_lifetable

Examples

# Goal: Convert 5-year population and death data into single year life table
results <- prepare_lifetable(
  age_group = c(0, 5, 10, 15),
  population = c(3387900, 3401300, 3212300, 3026100),
  bhd = c(4727, 472, 557, 1323)
)

Create the BEST-COST Multidimensional Deprivation Index (MDI)

Description

This function creates the BEST-COST Multidimensional Deprivation Index (MDI) and checks internal consistency of the single deprivation indicators using Cronbach's coefficient \alpha and other internal consistency checks

Usage

prepare_mdi(
  geo_id_micro,
  edu,
  unemployed,
  single_parent,
  pop_change,
  no_heating,
  n_quantile,
  verbose = TRUE
)

Arguments

Details

Methodology

This function condenses socio-economic indicators into a multiple deprivation index (MDI) (Mogin et al. 2025). The reliability of the MDI is assessed using Cronbach's alpha (Cronbach 1951).

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• Multiple deprivation index

Data completeness and imputation

Ensure the data set is as complete as possible. Otherwise, you can try to impute missing data, but R^2 should be greater than or equal to 0.7.

Plots

See the example below for how to reproduce the box plots and the histogram after the prepare_mdi function call.

Value

This function returns a list containing

1) mdi_main (tibble) with the columns (selection);

• geo_id_micro containing the numeric geo id's

• MDI containing the numeric BEST-COST Multidimensional Deprivation Index values

• MDI_index numeric decile based on values in the column MDI

• additional columns containing the function input data

2) mdi_detailed (list) with several elements for the internal consistency check of the BEST-COST Multidimensional Deprivation Index.

• boxplot (language) containing the code to reproduce the boxplot of the single indicators

• histogram (language) containing the code to reproduce a histogram of the BEST-COST Multidimensional Deprivation Index (MDI) values with a normal distribution curve

• descriptive_statistics (list table of descriptive statistics (mean, SD, min, max) of the normalized input data and the MDI

• cronbachs_alpha_value (numeric value See the Details section for the reliability rating this value indicates

• pearsons_corr_coeff (numeric vector) Person's correlation coefficient (pairwise-comparisons)

Author(s)

Carl Baravelli, Vanessa Gorasso, Alberto Castro & Axel Luyten

References

Cronbach LJ (1951). “Coefficient alpha and the internal structure of tests.” Psychometrika, 16(3), 297–334. doi:10.1007/BF02310555.

Mogin G, Gorasso V, Idavain J, Lepnurm M, Delaunay-Havard S, Kocbach Bølling A, Buekers J, Luyten A, Devleesschauwer B, Baravelli CM (2025). “A scoping review of multiple deprivation indices in Europe.” European Journal of Public Health, 35(6), 1122-1128. doi:10.1093/eurpub/ckaf190, https://academic.oup.com/eurpub/article-pdf/35/6/1122/65042936/ckaf190.pdf.

See Also

• Downstream: socialize

Examples

# Goal: create the BEST-COST Multidimensional Deprivation Index for
# a selection of geographic units

results <- 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 = TRUE
)

results$mdi_main |>
  dplyr::select(geo_id_micro, MDI, MDI_index) |>
  dplyr::slice(1:15)

# Reproduce plots after the function call
eval(results$mdi_detailed$boxplot)
eval(results$mdi_detailed$histogram)

Consider socio-economic aspects in the attributable health impacts

Description

This function analyzes differences in attributable health impacts across study areas looking at the value of a socio-economic indicator (e.g. multiple deprivation index). If nothing is entered in the argument output_attribute, it is assumed that all data come from a table and the argument refer to the columns of that table.

Usage

socialize(
  output_attribute = NULL,
  age_group,
  geo_id_micro,
  social_indicator = NULL,
  increasing_deprivation = TRUE,
  n_quantile = NULL,
  social_quantile = NULL,
  population = NULL,
  ref_prop_pop = NULL,
  impact = NULL,
  exp = NULL,
  bhd = NULL,
  pop_fraction = NULL
)

Arguments

Details

Methodology

This function estimates the absolute and relative differences in attributable health impacts comparing study areas with different values for a socio-economic indicator (Renard et al. 2019; Otavova et al. 2022).

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• Health impact attributable to social indicator

Value

This function returns a list containing the impact (absolute and relative) theoretically attributable to the difference in the social indicator (e.g. degree of deprivation) between the quantiles:

1) social_main (tibble) containing the main results;

• difference_value (numeric column) attributable health burden/impact due to differences in deprivation levels

• And more

2) social_detailed (list) containing detailed (and interim) results.

• input_data_with_quantile (tibble) containing input data and information about the social quantile

• results_all_parameters (tibble) containing deprivation-related results

• parameters_overall (tibble) containing overall results for different input variables

• parameters_per_quantile (tibble) containing quantile-specific results for different input variables

If the argument output_attribute was specified, then the two lists are added next to the existing attribute output.

Author(s)

Alberto Castro & Axel Luyten

References

Otavova M, Faes C, Bouland C, De Clercq E, Vandeninden B, Eggerickx T, Sanderson J, Devleesschauwer B, Masquelier B (2022). “Inequalities in mortality associated with housing conditions in Belgium between 1991 and 2020.” BMC Public Health, 22(1), 2397. ISSN 1471-2458, doi:10.1186/s12889-022-14819-w.

Renard F, Devleesschauwer B, Speybroeck N, Deboosere P (2019). “Monitoring health inequalities when the socio-economic composition changes: are the slope and relative indices of inequality appropriate? Results of a simulation study.” BMC Public Health, 19(1), 662. ISSN 1471-2458, doi:10.1186/s12889-019-6980-1.

See Also

• Upstream: attribute_health, attribute_lifetable, prepare_mdi,

Examples

# Goal: determine fraction of attributable health impact that can
# be attributed to differences in deprivation between the geographic
# units under analysis

## Create assessments for multiple geographic units for the age group
## 40 years and younger
results_age_groups <-
   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)

## Difference in attributable impacts between geographic units
## that is attributable to differences in deprivation
results <- socialize(
  output_attribute = results_age_groups,
  age_group = exdat_socialize$age_group, # The same as in attribute_health()
  ref_prop_pop = exdat_socialize$ref_prop_pop,
  geo_id_micro = exdat_socialize$geo_unit,
  social_indicator = exdat_socialize$score,
  n_quantile = 10,
  increasing_deprivation = TRUE)


results$social_main |>
  dplyr::filter(difference_type == "relative") |>
  dplyr::filter(difference_compared_with == "overall") |>
  dplyr::select(first, last, difference_type, difference_value, comment)

Obtain age-standardized health impacts

Description

This function obtains age-standardized health impacts based on multiple age-group specific assessments

Usage

standardize(output_attribute, age_group, ref_prop_pop = NULL)

Arguments

Details

Methodology

This function applies the direct method of standardization, where the age-specific rates observed in a study population are applied to a standard (reference) population distribution.

For age standardization in health impact assessments, the World Health Organization (Ahmad et al. 2001) and the Global Burden of Disease study (GBD 2019 Demographics Collaborators 2020) provide the relevant information on this topic.

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• Standardization

This function works after running attribute_health() or attribute_lifetable() functions. If you want to use it in combination with compare(), please standardize first the results of attribute functions and then compare.

Value

This function returns a list containing:

1) health_main (tibble) containing the age-standardized main results;

2) health_detailed (tibble) containing the results per age group.

Author(s)

Alberto Castro & Axel Luyten

References

Ahmad OB, Pinto CB, Lopez AD, Murray CJ, Lozano R, Inoue M (2001). “Age standardization of rates: a new WHO standard.” Technical Report GPE Discussion Paper Series: No. 31, World Health Organization, Geneva.

GBD 2019 Demographics Collaborators (2020). “Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019.” The Lancet, 396(10258), 1160-1203. doi:10.1016/S0140-6736(20)30977-6.

See Also

• Upstream: attribute_health, attribute_lifetable

Examples

# Goal: age-standardize two age group-specific impacts
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)
)
results$health_detailed$results_raw$impact_per_100k_inhab # age group-specific impact rate
results$health_main$impact_per_100k_inhab # age-standardized impact rate

Get Monte Carlo confidence intervals

Description

This function obtains a summary of uncertainty (based on central, lower and upper estimates of at least one input variable) using a Monte Carlo simulation.

Input variables that will be processed are:

• relative_risk (rr_...)

• exposure (exp_...)

• cutoff (cutoff_...)

• baseline health data (bhd_...)

• disability weight (dw_...)

• duration (duration_...)

Usage

summarize_uncertainty(output_attribute, n_sim, seed = NULL)

Arguments

Details

Function arguments seed If the seed argument is specified then the parallel package is used to generate independent L’Ecuyer random number streams. One stream is allocated per variable (or per variable–geography combination, as needed), ensuring reproducible and independent random draws across variables and scenarios.

Methodology

This function summarizes the uncertainty of the attributable health impacts (i.e.a single confidence interval instead of many combinations). For this purpose, it applies a Monte Carlo simulation (Robert and Casella 2004; Rubinstein and Kroese 2016).

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• Monte Carlo simulation

Value

This function returns a list containing:

1) uncertainty_main (tibble) containing the numeric summary uncertainty central estimate and corresponding lower and upper confidence intervals for the attributable health impacts obtained through Monte Carlo simulation;

2) uncertainty_detailed (list) containing detailed (and interim) results.

• impact_by_sim (tibble) containing the results for each simulation

• uncertainty_by_geo_id_micro (tibble) containing results for each geographic unit under analysis (specified in geo_id_micro argument in the preceding attribute_health call)

The two results elements are added to the existing output.

Author(s)

Alberto Castro & Axel Luyten

References

Robert CP, Casella G (2004). Monte Carlo Statistical Methods, Springer Texts in Statistics. Springer Science & Business Media. doi:10.1007/978-1-4757-4145-2.

Rubinstein RY, Kroese DP (2016). Simulation and the Monte Carlo Method. John Wiley & Sons. doi:10.1002/9781118631980.

See Also

• Upstream: attribute_health, attribute_lifetable, compare

Examples

# Goal: obtain summary uncertainty for an existing attribute_health() output
# First create an assessment
attribute_health_output <- attribute_health(
  erf_shape = "log_linear",
  rr_central = 1.369,
  rr_lower = 1.124,
  rr_upper = 1.664,
  rr_increment = 10,
  exp_central = 8.85,
  exp_lower = 8,
  exp_upper = 10,
  cutoff_central = 5,
  bhd_central = 30747,
  bhd_lower = 28000,
  bhd_upper = 32000
)
# Then run Monte Carlo simulation
results <- summarize_uncertainty(
  output_attribute = attribute_health_output,
  n_sim = 100
)
results$uncertainty_main$impact # Central, lower and upper estimates

Check the input_args data of attribute_master()

Description

Check the input_args data in attribute_master() and provides specific warnings or errors if needed.

Usage

validate_input_attribute(input_args, is_lifetable)

Arguments

Value

This function returns warning or error messages if needed.

Author(s)

Alberto Castro & Axel Luyten