Module 3: Health impacts

Source: vignettes/Module3_health.Rmd

Description

The functions in this module estimate adverse health effects attributable to fine particulate matter (PM2.5) and ozone (O3; M6M) exposure, measured as premature mortalities, years of life lost (YLLs), and disability adjusted life years (DALYs). The following code shows as an example of two functions to estimate premature mortalities attributable to PM2.5 and the DALYs associated with ozone exposure (M6M).All the functions associated to this module can be found in the References page.

library(rfasst)
library(magrittr)

 db_path<-"path_to_your_gcam_database"
 query_path<-"path_to_your_gcam_queries_file"
 db_name<-"name of the database"
 prj_name <- "Name of the rgcam project" # This can be an existing project, or, if not, this will be the name
 rdata_name <- "Name of the RData file." #It must contain the queries in a list
 scen_name<-"name of the GCAM scenario"
 queries<-"Name of the query file" # (the package includes a default query file that includes all the queries required in every function in the package, "queries_rfasst.xml")
 final_db_year <- "Final year in the GCAM database" # This allows to process databases with user-defined "stop periods"
 saveOutput <- T # Writes the files.By default = T
 map <- T # Produce the maps. By default = F
 recompute <- F  # If set to T, recomputes the function output. Otherwise, if the output was already computed once, it uses that value and avoids repeating computations. By default = F
 ssp <- "SSP2" # Set the ssp narrative associated to the GCAM scenario. c("SSP1","SSP2","SSP3","SSP4","SSP5"). By default is SSP2

     m3_get_mort_pm25(db_path = db_path, db_name = db_name, prj_name = prj_name, scen_name = scen_name, rdata_name = rdata_name, 
                                      query_path = query_path, queries = queries, ssp = ssp, saveOutput = F, final_db_year = final_db_year, 
                                      recompute = recompute)

     m3_get_daly_o3(db_path = db_path, db_name = db_name, prj_name = prj_name, scen_name = scen_name, rdata_name = rdata_name, 
                    query_path = query_path, queries = queries, ssp = ssp, saveOutput = F, final_db_year = final_db_year, recompute = recompute)

The calculation of premature mortalities is based on the population-attributable fraction approach as described in Murray et al. (2003). As explained in the TM5-FASST documentation paper (Van Dingenen et al 2018), cause-specific deaths are estimated following the following equation: \[Mort_{t,r,c,j}=mo_{r,c,j} \cdot ((RR_{c,j}-1)/RR_{c,j})\cdot Pop_{t,r}\] So premature mortality in period \(t\), region \(r\), for cause \(c\), associated with exposure to pollutant \(j\) is calculated as the product between the baseline mortality rate, the change in the RR relative risk of death attributable to a change in population-weighted mean pollutant concentration, and the population exposed.

Mortalities are estimated for six causes, namely stroke, ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), acute lower respiratory illness diseases ALRI), lung cancer (LC), and diabetes Mellitus Type II (DM). IHD and STROKE are calculated for different age groups, using “age-group-specific” parameters (i.e., mortality rates and RR), while COPD, ALRI, LC and DM are estimated for the whole population exposed (pop > 25 years).

The parameters come from different sources:

  • Population exposed is cause-specific. Population fractions are calculated from the from the SSP database.

  • Cause-specific baseline mortality rates are computed using absolute mortality from the Global Burden of Disease database and population statistics (to get exposed population and fractions). The projected changes of these rates over time are taken from the World Health Organization projections WHO 2013.

  • For O3, relative risk is based on the ERFs from Jerret et al 2009 and the Global Burden of Disease 2016 (Turner et al 2016). For PM2.5, we use three different models to calculate RR:

  • Global Burden of Disease (2018): This method is based on the Integrated Exposure-Response functions (ERFs) from Burnett et al 2014, with updated and age-group-spefici paraters and the addition of diabetes mellitus type II.

  • GEMM: The Global Exposure Mortality Model (Burnett et al 2018) is a PM2.5-mortality hazard ratio function based 41 cohort studies of outdoor air pollution that covers the global exposure range (16 countries).

  • FUSION: FUSION (Burnett et al 2022) is an improved RR model that addresses limitations in previous models.

YLLs and DALYs

Years of life lost and DALYs are estimated by transforming premature mortalities into either YLLs or DALYs using constant ratios (i.e., YLL-to-Mortalities and DALY-to-Mortalities ratios).

These ratios are estimated using the latest available data on mortailities, YLLs, and DALYs from the Institute for Health Metrics and Evaluation (IHME). These ratios (both for YLLs and for DALYs) are calculated regionally so they differ across regions. For IHD and STROKE, ratios are computed for specific age groups, while they are computed for the whole exposed population for the rest of causes considered (COPD, DM, LRI, and LC)

Outputs:

The complete list of outputs generated by the suite of functions that form this module is listed below:

  • PM25_MORT_[scenario]_[year].csv: Premature mortalities attributable to PM2.5 by region, age-group, disease and model (GBD, GEMM, and FUSION)
  • PM25_MORT_AGG_[scenario]_[year].csv: PM25_MORT aggregated to region level
  • PM25_YLL_[scenario]_[year].csv: Years of life lost attributable to PM2.5 by region, age-group, disease and model (GBD, GEMM, and FUSION)
  • PM25_YLL_TOT_[scenario]_[year].csv: PM25_YLL aggegated to region level
  • PM25_DALY_[scenario]_[year].csv: Disability-adjusted life years attributable to PM2.5 by region, age-group, disease and model (GBD, GEMM, and FUSION)
  • PM25_DALY_TOT_[scenario]_[year].csv: PM25_DALY aggregated to region level
  • O3_MORT_[scenario]_[year].csv: Premature mortalities attributable to O3 exposure by region, disease, and model (Jerret2009 and GBD2016)
  • O3_YLL_[scenario]_[year].csv: Years of life lost attributable to O3 exposure by region, disease, and model (Jerret2009 and GBD2016)
  • O3_DALY_[scenario]_[year].csv: Disability-adjusted life years attributable to O3 exposure by region, disease, and model (Jerret2009 and GBD2016)

As in Module 2, for all these functions, the package allows to produce different figures and/or animations, generated using the rmap package documented in the following page. To generate these maps, the user needs to include the map = T parameter, and they will be generated and stored in the corresponding output sub-directory. As an example for this module, the following map shows the premature mortalities attributable to PM2.5 exposure by cause in 2050. By default, GBD values are taken from the production of the maps.

Premature Mortalities attributable to PM2.5 concentration by cause in 2050 (#)