attribute_lifetable: 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
)

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.

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: Numberic value specifying the minimum and maximum age for which the exposure will affect the exposed population, respectively. Default min_age: 30. Default max_age: none. 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.
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.
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.
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.
info: String, data frame or tibble providing information about the assessment. See Details for more info. Optional argument.

Author

Alberto Castro & Axel Luyten

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+ WHO2020_reporthealthiar, which is described in previous literature Miller2003_jechhealthiar.

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().

References

Examples

Run this code

# 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

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