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Calculating population-averaged years of life lost

population_YLL.Rd

Compute population-averaged years of life lost (YLL) due to radiation exposure, aggregating over a population age distribution and sex-specific baseline rates. The function combines user-specified excess risk models (ERR/EAR) with site-specific baseline incidence/mortality and all-cause mortality to produce age-category summaries and overall totals, with uncertainty from Monte Carlo sampling.

Usage

population_YLL(dsGy, reference, riskmodel, agex = 1:8 * 10 - 5, nmc = 10000)

Arguments

dsGy

Numeric scalar. Radiation dose in Gy (or Sv). Must be nonnegative.

reference

List describing the target reference population, with components:

  • baseline — data frame of site-specific baseline incidence or mortality rates (per person-year) over ages 1–100, with columns age, male, female.

  • mortality — data frame of all-cause mortality (same structure/age grid).

  • agedist — data frame or vector with the population age distribution used to average risks across ages (e.g., by single year or grouped ages).

All three components must refer to the same region/population and share consistent sex coding.

riskmodel

List defining the radiation risk model. Typically contains sublists for ERR and/or EAR:

  • err / ear — each is a list with:

    • para — numeric vector of model parameters;

    • var — parameter variance–covariance matrix (for multi-parameter models), or ci — confidence interval for one-parameter models;

    • f — function of the form f(beta, data, lag) returning age-specific excess risk given parameters and data (dose, ages, sex).

See package examples (e.g., LSS_mortality$allsolid$L, LSS_incidence$leukaemia$LQ).

agex

Numeric vector of ages at exposure (midpoints for grouped categories). Default is 1:8 * 10 - 5 (i.e., 5, 15, …, 75) representing 0–10, 10–20, …, 70–80.

nmc

Integer Monte Carlo sample size for uncertainty propagation. Default 10000.

Value

A list with components for each transfer model present (typically $err and $ear). Each component is a data frame with one row per exposure age category (rows named by agex) plus an all row, and columns:

  • male, male_lo, male_up

  • female, female_lo, female_up

  • all, all_lo, all_up

Values are YLL.

Details

Let agedist denote the population age distribution (grouped ages) and baseline/mortality denote site-specific baseline rates and all-cause mortality for the same region/population and sex coding. For each age-at-exposure category in agex, population_YLL() evaluates YLL under the supplied risk model(s) and then averages across the age structure, returning sex-specific estimates and an all-sex average. Uncertainty is obtained by drawing model parameters either from a variance–covariance matrix (var) or, in one-parameter models, from 95% confidence bounds (ci) when provided.

The default agex = 1:8 * 10 - 5 corresponds to midpoint ages 5, 15, …, 75, i.e., exposure categories 0–10, 10–20, …, 70–80 years, which should match the categories of agedist.

Units & Alignment

Doses must be in Gy (or consistent with your model). Ensure baseline and mortality share the same age grid (typically 1–100) and sex coding, and that agedist corresponds to the same population.

See also

YLL, plot_agedist, population_CER, CER

Examples

set.seed(100)
# The following examples use default data provided in CanEpiRisk package
# for riskmodels (LSS_mortality) derived from Life Span Study
#     baseline rates and age distribution for WHO riskmodels (Mortality, agedist_rgn)
# Example: allsolid mortality, Region-1, exposed to 0.1 Gy, followed up to age 100, LSS linear ERR
ref1 <- list(  baseline=Mortality[[1]]$allsolid,     # baseline rates
mortality=Mortality[[1]]$allcause,     # allcause mortality
agedist=agedist_rgn[[1]] )           # age distribution
mod1 <- LSS_mortality$allsolid$L                     # risk model
population_YLL( dsGy=0.1, reference=ref1, riskmodel=mod1 )
#> $err
#>            male     male_lo     male_up      female   female_lo  female_up
#> 5   0.172093332 0.115054187 0.251928543 0.347325544 0.246518841 0.48922054
#> 15  0.120103006 0.084205848 0.164594216 0.241732716 0.188422239 0.30906550
#> 25  0.083974983 0.058456033 0.115108413 0.168027734 0.134188358 0.20943433
#> 35  0.058333605 0.038162859 0.085848411 0.112428208 0.083850680 0.14938280
#> 45  0.038426386 0.022879981 0.062888388 0.069750070 0.046646521 0.10317765
#> 55  0.021432108 0.011446513 0.039560089 0.037504206 0.022144657 0.06300306
#> 65  0.009341936 0.004452949 0.019444989 0.016196526 0.008357390 0.03098127
#> 75  0.002900235 0.001232958 0.006865364 0.004813645 0.002173955 0.01055622
#> all 0.066061937 0.046211884 0.089938716 0.122187713 0.097842813 0.15247074
#>             all      all_lo      all_up
#> 5   0.258461625 0.190515615 0.351867131
#> 15  0.179695903 0.146646728 0.221528993
#> 25  0.125271008 0.103982952 0.151551642
#> 35  0.085321209 0.064913699 0.111754703
#> 45  0.054272143 0.036484548 0.080522327
#> 55  0.029739716 0.017595310 0.050301611
#> 65  0.013048353 0.006737108 0.025101124
#> 75  0.003995321 0.001788337 0.008811119
#> all 0.094655007 0.079077809 0.113988108
#> 
#> $ear
#>            male     male_lo    male_up      female   female_lo  female_up
#> 5   0.180991413 0.122464921 0.26521423 0.280928669 0.208319078 0.37917933
#> 15  0.145671293 0.103712020 0.20037124 0.225743123 0.182005184 0.27817512
#> 25  0.114078178 0.080903922 0.15644111 0.177243542 0.145031848 0.21589577
#> 35  0.084801146 0.057078481 0.12117249 0.132892978 0.102336908 0.17162829
#> 45  0.057506510 0.035995649 0.08941087 0.091672552 0.063786725 0.13028683
#> 55  0.034070636 0.019410638 0.05825693 0.055614271 0.034629985 0.08819714
#> 65  0.016666232 0.008590505 0.03177567 0.027746277 0.015417861 0.04936936
#> 75  0.005878352 0.002685266 0.01247551 0.009852212 0.004853038 0.01972337
#> all 0.083709520 0.059624009 0.11489726 0.124576903 0.102115845 0.15191113
#>             all      all_lo     all_up
#> 5   0.230527318 0.174563760 0.30513382
#> 15  0.184994650 0.153053809 0.22427950
#> 25  0.145300155 0.121361676 0.17432024
#> 35  0.108838660 0.084640990 0.13973037
#> 45  0.074955600 0.052345410 0.10666407
#> 55  0.045241776 0.028286413 0.07204407
#> 65  0.022642128 0.012592451 0.04051401
#> 75  0.008132142 0.003999695 0.01641731
#> all 0.104612606 0.087724086 0.12591100
#>