WHO / World Bank Health-Expenditure Mediation Data (Benchmark Data Set)

A benchmark data set for high-dimensional mediation methods: the World Health Organization (WHO) and World Bank panel analyzed in Yu and Kelley (in press). It is a country-by-year panel used to ask how health-care expenditure mediates the relationship between economic growth and population health outcomes. The exposure is economic growth, the candidate mediators are 57 health-expenditure indicators, and five health outcomes are available. With 57 intercorrelated mediators this is a genuinely high-dimensional mediation problem of the kind the power-enhanced tests are built for, and the mediation effects are heterogeneous (different kinds of spending push different outcomes in different directions), which is exactly where the benchmark test loses power and the PE test does not.

Usage

WHO_health_mediation

Format

WHO_health_mediation is a data.frame with 2002 rows (91 WHO member states times the years 2000–2021) and 68 columns:

code: ISO-3166 alpha-3 country code.
country: Country name.
region: WHO region: AFR (Africa, 32 members), AMR (Americas, 28), EMR (Eastern Mediterranean, 8), EUR (Europe, 9), SEAR (South-East Asia, 5), WPR (Western Pacific, 9).
income: World Bank income group, a factor (not an ordered one) whose levels run from low to high: Low (16 members), Lower-middle (27), Upper-middle (26), High (22). The article abbreviates these Low, LM, UM, High.
year: Calendar year, 2000–2021.
gdp_growth: The exposure \(X\): annual percentage growth of GDP per capita (World Bank Open Data), a common proxy for economic growth.
imr: Infant mortality rate (deaths per 1000 live births).
u5mr: Under-five mortality rate (deaths per 1000 live births).
leb: Life expectancy at birth (years).
lbw: Prevalence of low birthweight (percent of live births; covers 73 members, 2000–2020, so other rows are NA).
pou: Prevalence of undernourishment (percent of population; covers 79 members, 2001–2021, so other rows are NA).
che_gdp, ..., ext_usd2021_pc: The 57 candidate mediators \(M\): health-expenditure indicators from the WHO Global Health Expenditure Database. Their codes and full names are in WHO_indicator_codebook. They are complete (no missing values).

Source

Exposure and health outcomes: World Bank Open Data, World Development Indicators (https://data.worldbank.org/), licensed by the World Bank under the Creative Commons Attribution 4.0 International license (CC BY 4.0, https://datacatalog.worldbank.org/public-licenses), which permits redistribution and adaptation with attribution and a note of the changes made. Health-expenditure mediators and the region and income labels: World Health Organization, Global Health Expenditure Database (https://apps.who.int/nha/database), made available under WHO's terms and conditions for the use of its data (https://www.who.int/about/policies/publishing/data-policy/terms-and-conditions): non-commercial use for public health purposes, with attribution to WHO and to the countries that provided the underlying data, and no suggestion that WHO endorses any product or service. Attribution: World Health Organization, Global Health Expenditure Database, accessed November 2024; World Bank, World Development Indicators, accessed November 2024. Changes made: the two sources were merged by country and year, restricted to the 91 WHO member states with complete health-expenditure data for 2000 to 2021, and the outcome series were attached; no value was altered. The script that builds the data set, data-raw/WHO_health_mediation.R, is in the package's GitHub repository (https://github.com/yelleKneK/POEMED), not in the package itself. The package's LICENSE.note file restates these terms.

Details

It is shipped as a benchmark so that a method, old or new, can be evaluated on a real, published high-dimensional mediation problem (see WHO_mediation_analysis(), whose output is the package's benchmark fit). The World Health Organization (WHO) is the source of the health-expenditure mediators and the region and income labels; the World Bank is the source of the economic-growth exposure and the health outcomes.

Three of the five health outcomes (imr, u5mr, leb) cover all 91 members for 2000–2021; lbw and pou have narrower coverage, so they carry NA where unavailable. The mediators are intercorrelated by construction, since many are different normalizations of the same underlying spending (per capita, percent of GDP, percent of current health expenditure, constant versus current currency, and so on), which is precisely the high-dimensional, correlated-mediator regime the methods target.

Use WHO_mediation_design() to assemble the exposure, outcome, mediator, and confounder matrices for a given outcome (and optionally a region or income subset) ready for pe_mediation(). In the article's global model the confounders are the region, income group, and year.

References

Yu, X., & Kelley, K. (in press). Power Enhancement in High-Dimensional Heterogeneous Mediation Analysis. Journal of the American Statistical Association.

Examples

data(WHO_health_mediation)
table(WHO_health_mediation$region[WHO_health_mediation$year == 2010])
#> 
#>  AFR  AMR  EMR  EUR SEAR  WPR 
#>   32   28    8    9    5    9 

# A first test on the global infant-mortality design with the package
# defaults, which standardize X, M, and Z and center Y.
# WHO_mediation_analysis() instead applies the preprocessing and the
# tuning grid behind the article's tables, which leaves the
# confounders unscaled (see ?WHO_mediation_design for the difference).
# HBIC chooses 0.05, the smallest value of the default grid, so this fit
# warns; WHO_mediation_analysis() searches the article's grid instead.
des <- WHO_mediation_design("imr")
pe_mediation(des$X, des$Y, des$M, Z = des$Z, outcome = "continuous")
#> Warning: HBIC selected lambda = 0.05, the smallest value of `lambda_grid`, for the full model and the reduced model of the benchmark test. The criterion is minimized over the grid alone, so its minimum may lie beyond that end or between the end and its neighbor. Consider extending the grid past it and using a finer partition (more values), and compare the active mediators identified and the p-values across grids.
#>  term                  value    
#>  stat_hdmm             8.966e-06
#>  pval_hdmm             0.9976   
#>  stat_pe               238.6    
#>  j_pe                  238.6    
#>  pval_pe               < 0.0001 
#>  total_indirect_effect 0.0005369
#>  n_active_mediators    2        
#>  df                    1        
#>  n_candidate_mediators 57       
#>  n_observations        2002     
#> 
#> Outcome model: continuous (linear)
#> Active mediators identified (2): gge_gdp, ext_usd2021
#> Tuning parameter (HBIC): lambda = 0.05 from 100 values in [0.05, 10] (the grid's lower end)