Consider socio-economic aspects in the attributable health impacts

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

output_attribute: List containing the outputs of the healthiar::attribute_health() assessments for each age group (each list element should be an age group-specific assessment).
age_group: String vector with the age groups included in the age standardization. The vector refers to age-dependent data in this function and to output_attribute (if provided).
geo_id_micro,: Numeric vector or string vector specifying the unique ID codes of each geographic area considered in the assessment (geo_id_micro).
social_indicator: Numeric vector showing the social indicator used for the analysis, e.g. a deprivation score (indicator of economic wealth) for each geographic unit. The length and the values must correspond with geo_id_micro. If geo_id_micro is not entered when using argument output_attribute, social_indicator must correspond to the column geo_id_micro in results_by_age_group of output_attribute.
increasing_deprivation: Boolean variable (TRUE/FALSE) specifying whether an increase in social_indicator corresponds to an increase (TRUE) or decrease FALSE in deprivation. Default: TRUE.
n_quantile: Integer value specifying the number of quantiles in the analysis.
social_quantile: Integer vector showing the values from 1 to the number of quantiles assigned to each geographic unit. Either enter social_indicator and n_quantile or social_quantile
population: Numeric vector specifying the population by age group and geographic unit.
ref_prop_pop: Numeric vector specifying with the reference proportion of population for each age group. If this argument is empty, the proportion of population by age group in the provided data will be used.
impact: (only if output_attribute not specified) Numeric vector containing the attributable health impacts by both age group and geo id.
exp: (only if output_attribute not specified) Numeric vector specifying the exposure level(s) to the environmental stressor.
bhd: (only if output_attribute not specified) Numeric vector specifying the baseline health data of the health outcome of interest per age group.
pop_fraction: (only if output_attribute not specified) Numeric vector specifying the population attributable fraction by age group and geographic unit.

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.

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

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 .

Author

Alberto Castro & Axel Luyten

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)
#> # A tibble: 1 × 5
#>   first  last difference_type difference_value comment                          
#>   <dbl> <dbl> <chr>                      <dbl> <chr>                            
#> 1  70.9  59.4 relative                 -0.0143 It can be interpreted as fractio…