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Create the BEST-COST Multidimensional Deprivation Index (MDI)

Source: R/prepare_mdi.R
prepare_mdi.Rd

This function creates the BEST-COST Multidimensional Deprivation Index (MDI) and checks internal consistency of the single deprivation indicators using Cronbach's coefficient \(\alpha\) and other internal consistency checks

Usage

prepare_mdi(
  geo_id_micro,
  edu,
  unemployed,
  single_parent,
  pop_change,
  no_heating,
  n_quantile,
  verbose = TRUE
)

Arguments

geo_id_micro

Numeric vector or string vector specifying the unique ID codes of each geographic area considered in the assessment (geo_id_micro).

edu

Numeric vector indicating educational attainment as % of individuals (at the age 18 or older) without a high school diploma (ISCED 0-2) per geo unit

unemployed

Numeric vector containing % of unemployed individuals in the active population (18-65) per geo unit

single_parent

Numeric vector containing single-parent households as % of total households headed by a single parent per geo unit

pop_change

Numeric vector containing population change as % change in population over the previous 5 years (e.g., 2017-2021) per geo unit

no_heating

Numeric vector containing % of households without central heating per geo unit

n_quantile

Integer value specifying the number of quantiles in the analysis.

verbose

Boolean indicating whether function output is printed to console. Default: TRUE.

Value

This function returns a list containing

1) mdi_main (tibble) with the columns (selection);

  • geo_id_micro containing the numeric geo id's

  • MDI containing the numeric BEST-COST Multidimensional Deprivation Index values

  • MDI_index numeric decile based on values in the column MDI

  • additional columns containing the function input data

2) mdi_detailed (list) with several elements for the internal consistency check of the BEST-COST Multidimensional Deprivation Index.

  • boxplot (language) containing the code to reproduce the boxplot of the single indicators

  • histogram (language) containing the code to reproduce a histogram of the BEST-COST Multidimensional Deprivation Index (MDI) values with a normal distribution curve

  • descriptive_statistics (list table of descriptive statistics (mean, SD, min, max) of the normalized input data and the MDI

  • cronbachs_alpha_value (numeric value See the Details section for the reliability rating this value indicates

  • pearsons_corr_coeff (numeric vector) Person's correlation coefficient (pairwise-comparisons)

Details

Methodology

This function condenses socio-economic indicators into a multiple deprivation index (MDI) (Mogin et al. 2025) . The reliability of the MDI is assessed using Cronbach's alpha (Cronbach 1951) .

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

Data completeness and imputation

Ensure the data set is as complete as possible. Otherwise, you can try to impute missing data, but R^2 should be greater than or equal to 0.7.

Plots

See the example below for how to reproduce the box plots and the histogram after the prepare_mdi function call.

References

Cronbach LJ (1951). “Coefficient alpha and the internal structure of tests.” Psychometrika, 16(3), 297–334. doi:10.1007/BF02310555 .

Mogin G, Gorasso V, Idavain J, Lepnurm M, Delaunay-Havard S, Kocbach Bølling A, Buekers J, Luyten A, Devleesschauwer B, Baravelli CM (2025). “A scoping review of multiple deprivation indices in Europe.” European Journal of Public Health, 35(6), 1122-1128. doi:10.1093/eurpub/ckaf190 , https://academic.oup.com/eurpub/article-pdf/35/6/1122/65042936/ckaf190.pdf.

See also

Author

Carl Baravelli, Vanessa Gorasso, Alberto Castro & Axel Luyten

Examples

# Goal: create the BEST-COST Multidimensional Deprivation Index for
# a selection of geographic units

results <- prepare_mdi(
  geo_id_micro = exdat_prepare_mdi$id,
  edu = exdat_prepare_mdi$edu,
  unemployed = exdat_prepare_mdi$unemployed,
  single_parent = exdat_prepare_mdi$single_parent,
  pop_change = exdat_prepare_mdi$pop_change,
  no_heating = exdat_prepare_mdi$no_heating,
  n_quantile = 10,
  verbose = TRUE
)
#> [1] "CRONBACH'S α : 0.746"
#> [1] "Acceptable reliability: 0.7 ≤ α < 0.8"
#> [1] "DESCRIPTIVE STATISTICS"
#>      norm_edu norm_unemployed norm_single_parent norm_pop_change
#> MEAN 0.542    0.289           0.315              0.338          
#> SD   0.171    0.198           0.2                0.09           
#> MIN  0        0               0                  0              
#> MAX  1        1               1                  1              
#>      norm_no_heating MDI      
#> MEAN 0.303           0.357    
#> SD   0.186           0.122    
#> MIN  0               0.1313045
#> MAX  1               0.7855948
#> [1] "PEARSON'S CORRELATION COEFFICIENTS"
#>                     norm_edu norm_unemployed norm_single_parent norm_pop_change
#> norm_edu           1.0000000       0.3773418          0.2526394       0.1494546
#> norm_unemployed    0.3773418       1.0000000          0.9212899       0.2287402
#> norm_single_parent 0.2526394       0.9212899          1.0000000       0.1737842
#> norm_pop_change    0.1494546       0.2287402          0.1737842       1.0000000
#> norm_no_heating    0.5215904       0.3248140          0.3327336       0.2115399
#>                    norm_no_heating
#> norm_edu                 0.5215904
#> norm_unemployed          0.3248140
#> norm_single_parent       0.3327336
#> norm_pop_change          0.2115399
#> norm_no_heating          1.0000000



results$mdi_main |>
  dplyr::select(geo_id_micro, MDI, MDI_index) |>
  dplyr::slice(1:15)
#> # A tibble: 15 × 3
#>    geo_id_micro   MDI MDI_index
#>           <int> <dbl>     <int>
#>  1        11001 0.212         1
#>  2        11002 0.432         8
#>  3        11004 0.185         1
#>  4        11005 0.379         7
#>  5        11007 0.312         5
#>  6        11008 0.257         2
#>  7        11009 0.225         1
#>  8        11013 0.214         1
#>  9        11016 0.266         3
#> 10        11018 0.357         6
#> 11        11021 0.175         1
#> 12        11022 0.211         1
#> 13        11023 0.222         1
#> 14        11024 0.223         1
#> 15        11025 0.248         2

# Reproduce plots after the function call
eval(results$mdi_detailed$boxplot)

eval(results$mdi_detailed$histogram)