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Healthy Longevity Initiative

The economic value associated with avoidable mortality: a systematic assessment by cause of death across world regions

Stéphane Verguet1, Sarah Bolongaita1, Angela Y. Chang2, Diego S. Cardoso3, Gretchen A. Stevens4

1 Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA
2 Danish Institute for Advanced Study, University of Southern Denmark, Denmark
3 Department of Agricultural Economics, Purdue University, USA
4 Independent Researcher, Los Angeles, CA, USA

Description

This Git Hub repository contains the analysis code for Chapter 4 of the World Bank report Unlocking the Power of Healthy Longevity: Demographic Change, Non-communicable Diseases, and Human Capital, entitled The economic value associated with avoidable mortality: a systematic assessment by cause of death across world regions. A shortened version of this paper, entitled The economic value of reducing mortality due to non-communicable diseases and injury, also based on this analysis, was published in Nature Medicine. Supplementary figures for the Nature Medicine article can be found here.

The input data for this project can be found on Dropbox.

The following text provides an outline of the analysis steps and guides users through the accompanying code stored in this repository.

1. Data Cleaning

Scripts with prefix 1- clean the data by filtering it to the countries, years, and causes of death that are relevant for the analysis.

Note: Input data for this project can be found on Dropbox. These input data were pre-processed at basic, perfunctory level. The raw data will not be provided due to size constraints, however the code to pre-process the raw data can be reviewed here: scr/raw-data-cleaning.R.

1-1. Country eligibility

Script: 1-1_country-eligibility.R

Objective: Define and apply the eligibility criteria for inclusion in the analysis

This script defines and applies the eligibility criteria for country inclusion in the analysis. Countries were eligible for inclusion if they had populations of at least five million in 2019 and available income data (i.e., GNI per capita) for 2019. Of those analysis-eligible countries, a smaller subset were eligible for the frontier analysis: those with high-quality vital registration data and which were not excluded from the GHE 2019 analysis.

Input:

  • data/input/ghe_*.csv
  • data/input/gni.csv
  • data/input/population.csv
  • data/input/quality.csv
  • data/input/region.csv

Output:

  • data/processed/country_info.Rda
  • data/processed/ghe.Rda

1-2. Population

Script: 1-2_population.R

Objective: Filter the input population data to countries of interest

This script filters the input population data to the countries of interest based on the inclusion criteria applied in 1-1_country-eligibility.R.

Input:

  • data/processed/country_info.Rda
  • data/input/population.csv

Output:

  • data/processed/population.Rda

1-3. GHE recode

Script: 1-3_ghe-recode.R

Objective: Recode the GHE cause of death data so that causes of death are relevant for this analysis, mutually exclusive, and collectively exhaustive

This script recodes the processed GHE cause of death data (i.e., limited to analysis countries in 1-1_country-eligibility.R) so that causes of death are relevant for this analysis, mutually exclusive, and collectively exhaustive. This script relies a cause recode map created in scr/non-init/ghe_recode.R.

Input:

  • data/processed/ghe.Rda
  • data/processed/cause_recode_map.Rda

Output:

  • data/processed/ghe_recoded.Rda

2. Frontier Analysis

Scripts with prefix 2- analyze and develop the age- and cause-specific mortality frontiers.

2-1. Frontier definition

Script: 2-1_frontier-definition.R

Objective: Extract three definitions of the frontier: the minimum, the 10th percentile, and the 20th percentile of age-cause-specific mortality

This script takes the recoded GHE data for frontier-eligible countries, calculates age-cause-specific mortality rates, and the extracts the frontier using three definitions: the minimum, the 10th percentile, and the 20th percentile. It creates a data/processed/frontier_info folder, in which the 'frontier' is tracked throughout each subsequent step of its calculation (i.e., scripts with prefix 2-).

Input:

  • data/processed/cause_hierarchy.Rda
  • data/processed/country_info.Rda
  • data/processed/ghe_recoded.Rda
  • data/processed/population.Rda

Output:

  • data/processed/frontier_base.Rda
  • data/processed/frontier_info/frontier_info_1.Rda

2-2. Frontier harmonization

Script: 2-2_frontier-harmonization.R

Objective: Harmonize (scale) the frontier, such that mortality rates of lower level causes sum to mortality rates of higher level causes

This script harmonizes (or scales) the frontier using a level-wise approach, such that mortality rates of lower level causes of death sum to mortality rates of higher level causes of death. This script relies a function (harmonize) created in scr/harmonize.R.

Input:

  • data/processed/cause_hierarchy.Rda
  • data/processed/frontier_base.Rda
  • data/processed/frontier_info/frontier_info_1.Rda

Output:

  • data/processed/frontier_harmonized.Rda
  • data/processed/frontier_info/frontier_info_2.Rda

2-3. Frontier projection

Script: 2-3_frontier-projection.R

Objective: Project the age-cause-specific mortality frontiers into the future (2050)

This script takes the harmonized frontier mortality rates for 2010-2019 and projects them into the future using ordinary least squares (OLS) linear regression, unless there are stochastic concerns or a positive trend line from the regression, in which case the average mortality rate from 2010-2019 is used.

Input:

  • data/processed/frontier_base.Rda
  • data/processed/frontier_harmonized.Rda
  • data/processed/frontier_info/frontier_info_2.Rda
  • data/processed/ghe_recoded.Rda
  • data/processed/population.Rda

Output:

  • data/processed/frontier_projected.Rda
  • data/processed/frontier_projection_info.Rda
  • data/processed/frontier_info/frontier_info_3.Rda

2-4. Frontier scaling

Script: 2-4_frontier_scaling.R

Objective: Scale the age-cause-specific mortality frontiers with Chang et al. (2023) all-cause frontiers

This script takes the harmonized and projected frontier mortality rates and scales them with the all cause frontiers of the accompanying HLI paper, Chang et al. (2023), based on UN Population data.

Input:

  • data/processed/cause_hierarchy.Rda
  • data/processed/frontier_harmonized.Rda
  • data/processed/frontier_projected.Rda
  • data/processed/frontier_info/frontier_info_4.Rda
  • data/input/chang_frontier.csv

Output:

  • data/processed/chang_scaling_factors.Rda
  • data/processed/frontier_scaled.Rda
  • data/processed/frontier_info/frontier_info_4.Rda

3. Country and Region Analyses

3-1. Country projection

Script: 3-1_country-projection.R

Objective: Project the country age-cause-specific mortality rates into the future (2050)

This script takes country mortality rates for 2000-2019 and projects them into the future using ordinary least squares linear regression, unless there are stochastic concerns or a positive trend line from the regression, in which case an average is used. This method is the same as the method used for the frontier projections.

Input:

  • data/processed/country_info.Rda
  • data/processed/ghe_recoded.Rda
  • data/processed/population.Rda

Output:

  • data/processed/country_projected.Rda
  • data/processed/country_projection_info/country_projection_info_1.Rda

3-2. Country scaling

Script: 3-2_country-scaling.R

Objective: Scale country mortality rates with Chang et al. (2022) longevity frontiers

This script takes the projected country mortality rates and scales them with the demographic longevity frontiers of the accompanying HLI paper, Chang et al. (2023).

Input:

  • data/processed/country_info.Rda
  • data/processed/country_projected.Rda
  • data/processed/country_projection_info/country_projection_info_1.Rda
  • data/input/chang_country.csv

Output:

  • data/processed/country_scaled.Rda
  • data/processed/country_projection_info/country_projection_info_2.Rda

4. Calculations

4-1. Calculations

Script: 4-calculations.Rda

Objective: Calculate the value of eliminating avoidable mortality by cause

This script takes the 10th percentile frontier and the scaled country projections and calculates the value of eliminating avoidable mortality by cause and country/region.

Input:

  • data/processed/country_info.Rda
  • data/processed/country_scaled.Rda
  • data/processed/frontier_scaled.Rda
  • data/processed/population.Rda
  • data/input/chang_envelope.csv

Output:

  • output/data/country_calculations.Rda
  • output/data/country_calculations.csv
  • output/data/region_calculations.Rda
  • output/data/region_calculations.csv
  • output/data/roc.Rda
  • output/data/roc.xlsx

4-2. Sensitivity analyses

Script: 4-sensitivity-analyses.Rda

Objective: Conduct sensitivity analyses

This script runs several sensitivity analyses: (1) using the minimum of cause-specific mortality rates as the frontier (instead of the 10th percentile); (2) varying income elasticities, to either 1.0 or 1.5; (3) varying discount rates, to either 1 or 5% per year; and using (4) using an alternative baseline VSL-to-income ratio of 100 relative to the average income among OECD countries (instead of the US VSL-to-income ratio of 160).

Input:

  • data/processed/country_info.Rda
  • data/processed/country_scaled.Rda
  • data/processed/frontier_scaled.Rda
  • data/processed/population.Rda
  • data/input/chang_envelope.csv

Output:

  • output/data/SA_country_calculations.Rda
  • output/data/SA_country_calculations.csv
  • output/data/SA_region_calculations.Rda
  • output/data/SA_region_calculations.csv
  • output/data/SA_roc.Rda
  • output/data/SA_roc.xlsx