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

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,
  threshold = 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

output_attribute

List containing the output of the function attribute() for scenario 1.

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.

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_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.

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.

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.

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.

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.

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.

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.

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.

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. These additional columns can be used to further stratify the analysis in a secondary step. Optional argument.

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.

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

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

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

Author

Alberto Castro & Axel Luyten

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
#> [1] 1050.86

## 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
#> [1] 277.304