attribute_lifetable: Attribute premature deaths or YLL to an environmental...
In healthiar: Quantifying and Monetizing Health Impacts Attributable to Exposure

attribute_lifetable

R Documentation

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,
  fraction_lived = 0.5,
  exp_central = NULL,
  exp_lower = NULL,
  exp_upper = NULL,
  cutoff_central = 0,
  cutoff_lower = NULL,
  cutoff_upper = NULL,
  threshold = 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,
  main_results_by = NULL
)

Arguments

`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.
`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.
`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.
`health_outcome`	`String` specifying the desired result of the life table assessment. Options: `"deaths"` (premature deaths), `"yll"` (years of life lost).
`min_age`, `max_age`	`Numeric value` specifying the minimum and maximum age for which the exposure will affect the exposed population, respectively. See Details for more info.
`approach_exposure`	`String` specifying whether exposure is constant or only in one year. Options: `"single_year"` (default), `"constant"`.
`approach_newborns`	`String` specifying whether newborns are to be considered in the years after the year of analysis or not. Options: `"without_newborns"` (default), `"with_newborns"`. See Details for more info.
`year_of_analysis`	`Numeric value` providing the first with exposure to the environmental stressor.
`time_horizon`	`Numeric value` specifying the time horizon (number of years) for which the attributable YLL or premature deaths are to be considered. See Details for more info. Optional argument.
`fraction_lived`	`Numeric vector` Numeric vector or `single numeric scalar` referring to the average fraction of the age interval lived by individuals who die within that interval. Default is 0.5.
`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, i.e. the exposure level below which no health impacts are quantified, and (optionally) the corresponding lower and upper 95% confidence interval bounds. Default: 0, or same value as `threshold`, if it is entered. If `cutoff` is higher than `threshold`, the exposure-response function is truncated at the cut-off value. Expressed in the same unit as the exposure. See the vignette chapter Cut-off vs. threshold.
`threshold`	`Numeric value` specifying the effect threshold, i.e. the exposure level from which the exposure-response function starts to show an effect. It is the anchor of the curve and is therefore subtracted from the exposure. Default: same value as the cut-off. Expressed in the same unit as the exposure. See the vignette chapter Cut-off vs. threshold.
`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"`. Input exposure values must be expressed in same unit as the increment of the relative risk. The re-scale of the relative risk is unbounded above and users are responsible for the plausible range. Only applicable in RR pathways; not required if `erf_eq_...` argument(s) already specified.
`prop_pop_exp`	`Numeric value` or `numeric vector` specifying the population fraction(s) exposed for each exposure (category), i.e. the proportion of the total population that falls in each exposure category. Default: 1. The fractions do not have to add up to 1: the part of the population that they do not cover is treated as unexposed, i.e. it gets the relative risk of the reference level. E.g, in air pollution assessments everybody is usually exposed and the fractions add up to 1, whereas in noise assessments they typically add up to less than 1, because exposure is only reported above a given level. Accordingly, `bhd_...` must always refer to the total population. 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.
`info`	`String`, `data frame` or `tibble` providing information about the assessment. This will be added to the results table as column(s) keeping the name(s) that you entered with the prefix `info_` (e.g. a column `education` becomes `info_education`) if a `data frame` is entered, or as one single column called `info` if a vector is entered. Different values define sub-groups (e.g. education levels or exposure-outcome pairs), which are assessed separately and summed in the main results unless named in `main_results_by`. Values that only differ by age or sex (e.g. age bands) do not define additional sub-groups. Optional argument.
`main_results_by`	`Character vector` naming the dimensions that the main results are reported by, i.e. the dimensions whose impacts must never be added together, e.g. different exposure-outcome pairs. By default all dimensions except the geographic units and the uncertainty (`_ci`) columns are summed in the main results. Names entered here are kept as separate rows instead. Options: the columns of `info` (named as you named them, or `"info"` if you entered a vector instead of a data frame), `"sex"`, `"age_group"`, `"exp_category"`, `"geo_id_micro"`, `"geo_id_macro"` and, in `attribute_lifetable()`, `"year"`. Note that this argument does not create the `results_by_...` tables of the detailed output, which are available anyway: it determines which dimensions survive in `health_main` and in all of them. See the vignette chapter Multiple exposure-outcome pairs. Optional argument.

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).

main_results_by Optional argument. By default the impacts of all subgroups, and of all the years of the time horizon, are added up in the main results. That is not meaningful for subgroups that quantify overlapping people in different ways, e.g. different exposure-outcome pairs. Enter their names here to keep them as separate rows. Entering "year" shows the impacts per year of the time horizon. See the vignette chapter Multiple exposure-outcome pairs.

Last age group The life table is closed at the last age group, i.e. its survivors are not projected into a further age. Therefore, the last age group must be one in which essentially all remaining deaths occur, as in most national life tables. Otherwise the attributable health impacts are underestimated. If needed, condensate the last age group (i.e. sum the populations and the deaths of the highest ages into it) instead of adding age groups beyond the data. More information in the vignette.

bhd_central,bhd_lower,bhd_upper The life table approach is only applicable to all-cause mortality: the deaths must be all-cause deaths and the exposure-response function must refer to all-cause mortality. The deaths are used to project how the population survives over the years, which requires the deaths of all causes. Cause-specific deaths (e.g. lung cancer) would lead to unrealistically high survival and therefore to overestimated impacts. For cause-specific mortality, use attribute_health() instead. See the vignette chapter YLL and deaths with life table.

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.

approach_exposure Strategy for modeling exposure over time:

"single_year": Air pollution exposure is evaluated for a single year (year of analysis). Attributable premature deaths are calculated for this year only.
"constant": Air pollution exposure is sustained across the full projection horizon. Attributable premature deaths and YLLs are accumulated across all years within time_horizon.

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: the number of age groups entered in the age_group argument.

min_age, max_age Both bounds are inclusive, e.g. min_age = 30 implies that all adults aged 30 or older will be affected by the exposure and max_age = 69 that no health effects are considered above the age of 69. By default the exposure affects all age groups, i.e. min_age is the youngest and max_age the oldest age group entered in age_group.

fraction_lived is by default 0.5 for all age groups, i.e. the value that AirQ+ assumes. It determines the survival probabilities and matters most in the age groups with the highest mortality, i.e. the last ones. See the vignette for how to choose it.

If the single-year age groups were obtained with prepare_lifetable(), enter here the same fraction_lived that was used there, which is available in the output column fraction_lived_for_attribute. Otherwise the life table would be built with an assumption that differs from the one used for the conversion.

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+ \insertCiteWHO2020_reporthealthiar, which is described in previous literature \insertCiteMiller2003_jechhealthiar.

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

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

\insertAllCited

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

healthiar documentation built on Oct. 12, 2026, 5:07 p.m.