Attribute health impacts to an environmental stressor

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

approach_risk: String value specifying the risk method. Options: "relative_risk" (default) or "absolute_risk".
exp_central, exp_lower, exp_upper: Numeric value or numeric vector specifying the exposure level(s) to the environmental stressor and (optionally) the corresponding lower and upper bound of the 95% confidence interval. See Details for more info.
cutoff_central, cutoff_lower, cutoff_upper: Numeric value specifying the exposure cut-off value and (optionally) the corresponding lower and upper 95% confidence interval bounds. Default: 0. See Details for more info.
pop_exp: Numeric vector specifying the absolute size of the population(s) exposed to each exposure category. See Details for more info. Only applicable in AR pathways; always required.
erf_eq_central, erf_eq_lower, erf_eq_upper: String or function specifying the exposure-response function and (optionally) the corresponding lower and upper 95% confidence interval functions. See Details for more info. Required in AR pathways; in RR pathways required only if rr_... argument(s) not specified.
rr_central, rr_lower, rr_upper: Numeric value specifying the central relative risk estimate and (optionally) the corresponding lower and upper 95% confidence interval bounds. Only applicable in RR pathways; not required if erf_eq_... argument(s) already specified.
rr_increment: Numeric value specifying the exposure increment for which the provided relative risk is valid. See Details for more info. Only applicable in RR pathways; not required if erf_eq_... argument(s) already specified.
erf_shape: String value specifying the exposure-response function shape to be assumed. Options (no default): "linear", log_linear", "linear_log", "log_log". Only applicable in RR pathways; not required if erf_eq_... argument(s) already specified.
bhd_central, bhd_lower, bhd_upper: Numeric value or numeric vector providing the baseline health data of the health outcome of interest in the study population and (optionally) the corresponding lower bound and the upper 95% confidence interval bounds. See Details for more info. Only applicable in RR pathways; always required.
prop_pop_exp: Numeric value or numeric vector specifying the population fraction(s) exposed for each exposure (category). Default: 1. See Details for more info. Only applicable in RR pathways.
geo_id_micro, geo_id_macro: Numeric vector or string vector providing unique IDs of the geographic area considered in the assessment (geo_id_micro) and (optionally) providing higher-level IDs (geo_id_macro) to aggregate the geographic areas at. See Details for more info. Only applicable in assessments with multiple geographic units.
age_group: Numeric vector or string vector providing the age groups considered in the assessment. In case of use in attribute_lifetable)(), it must be a numeric and contain single year age groups. See Details for more info. Optional argument for attribute_health(); needed for attribute_lifetable().
sex: Numeric vector or string vector specifying the sex of the groups considered in the assessment.Optional argument.
dw_central, dw_lower, dw_upper: Numeric value or numeric vector providing the disability weight associated with the morbidity health outcome of interest and (optionally) the corresponding lower bound and the upper 95% confidence interval bounds. Only applicable in assessments of YLD (years lived with disability).
duration_central, duration_lower, duration_upper: Numeric value or numeric vector providing the duration associated with the morbidity health outcome of interest in years and (optionally) the corresponding lower and upper bounds of the 95% confidence interval. Default: 1. See Details for more info. Only applicable in assessments of YLD (years lived with disability).
info: String, data frame or tibble providing information about the assessment. See Details for more info. Optional argument.
population: Numeric vector For attribute_lifetable(), it is an obligatory argument specifying the mid-year populations per age (i.e. age group size = 1 year) for the (first) year of analysis. For attribute_health() it is an optional argument which specifies the population used to calculate attributable impacts rate per 100 000 population. See Details for more info.

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.

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

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

References

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. https://iris.who.int/server/api/core/bitstreams/3ebe7c55-be17-4ebe-89b9-8871fd287acd/content.

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.

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

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.

Author

Alberto Castro & Axel Luyten

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
#> [1] 3502


# 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
#> [1] 174232


# 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
#> [1] 1116  436  135

# Attributable cases (disaggregated)
results$health_detailed$results_raw |> dplyr::select(
  geo_id_micro,
  geo_id_macro,
  impact_rounded
)
#> # A tibble: 4 × 3
#>   geo_id_micro geo_id_macro impact_rounded
#>   <chr>        <chr>                 <dbl>
#> 1 Zurich       Ger                     687
#> 2 Basel        Ger                     429
#> 3 Geneva       Fra                     436
#> 4 Ticino       Ita                     135


# 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
#> [1] 3602.934