| Title: | The DALY Calculator - Graphical User Interface for Probabilistic DALY Calculation in R |
|---|---|
| Description: | The DALY Calculator is a free, open-source Graphical User Interface (GUI) for probabilistic disability-adjusted life year (DALY) calculation. |
| Authors: | Brecht Devleesschauwer [aut, cre], Scott McDonald [aut], Juanita Haagsma [aut], Nicolas Praet [aut], Arie Havelaar [aut], Niko Speybroeck [aut] |
| Maintainer: | Brecht Devleesschauwer <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.5.1 |
| Built: | 2025-03-07 03:02:58 UTC |
| Source: | https://github.com/brechtdv/daly |
The DALY Calculator is a free, open-source Graphical User Interface (GUI)
for probabilistic disability-adjusted life year (DALY) calculation,
developed in the R environment for statistical computing.
The main goal of the DALY Calculator is to provide a flexible and
easy-to-use tool for DALY calculation, and to promote consistency in the
uncertainty analysis of DALYs.
| Package: | DALY |
| Type: | Package |
| Version: | 1.5.0 |
| Date: | 2016-11-25 |
| Depends: | R (>= 3.3.0) |
| License: | GPL (>= 2) |
| SystemRequirements: | Tcl/Tk (>= 8.5), Tktable (>= 2.9) |
| URL: | http://daly.cbra.be |
Brecht Devleesschauwer, Scott McDonald, Juanita Haagsma, Nicolas Praet, Arie Havelaar, Niko Speybroeck
Maintainer: [email protected]
Devleesschauwer B, et al. (2014). Calculating Disability-Adjusted Life Years to quantify burden of disease. International Journal of Public Health 59, 565-569. http://dx.doi.org/10.1007/s00038-014-0552-z
Murray CJL (1994). Quantifying the burden of disease: the technical basis for disability-adjusted life years. Bulletin of the World Health Organization 72, 429-445.
Praet N, et al. (2009). The disease burden of Taenia solium cysticercosis in Cameroon. PLoS Neglected Tropical Diseases 3, e406.
Kortbeek LM, et al. (2009). Congenital toxoplasmosis and DALYs in the Netherlands. Memorias de Instituto Oswaldo Cruz 104, 370-373.
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
Aggregate DALY Calculator output by outcome, age/sex class, or both.
## S3 method for class 'DALY' aggregate(x, by = c("total", "class", "outcome"), ...)## S3 method for class 'DALY' aggregate(x, by = c("total", "class", "outcome"), ...)
x |
Object of class |
by |
Any of |
... |
Additional arguments to be passed to |
For by = "total":
DALY |
Vector of DALYs aggregated by outcome and age/sex class |
YLD |
Vector of YLDs aggregated by outcome and age/sex class |
YLL |
Vector of YLLs aggregated by outcome and age/sex class |
cases |
Vector of cases aggregated by outcome and age/sex class |
deaths |
Vector of deaths aggregated by outcome and age/sex class |
pop |
Population matrix |
name |
Name of the disease |
For by = "class":
DALY |
Vector of DALYs aggregated by age/sex class |
YLD |
Vector of YLDs aggregated by age/sex class |
YLL |
Vector of YLLs aggregated by age/sex class |
cases |
Vector of cases aggregated by age/sex class |
deaths |
Vector of deaths aggregated by age/sex class |
pop |
Population matrix |
name |
Name of the disease |
For by = "outcome":
\emph{i} |
List containing vectors of DALYs, YLDs, YLLs, cases and
deaths for each outcome |
pop |
Population matrix |
name |
Name of the disease |
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
## Not run: ##= load the NCC example ================================== setDALYexample(1) setStdLE("GBD1990") ##= perform DALY calculation, store results in 'x' ======== x <- getDALY() ##= store simulations aggregated by ======================= ##= outcome and age/sex class ========================= y <- aggregate(x, by = "total") summary(y$DALY) # default summary of total DALYs ========= hist(y$deaths) # default histogram of total deaths ====== ## End(Not run)## Not run: ##= load the NCC example ================================== setDALYexample(1) setStdLE("GBD1990") ##= perform DALY calculation, store results in 'x' ======== x <- getDALY() ##= store simulations aggregated by ======================= ##= outcome and age/sex class ========================= y <- aggregate(x, by = "total") summary(y$DALY) # default summary of total DALYs ========= hist(y$deaths) # default histogram of total deaths ====== ## End(Not run)
This function produces a list of DALY objects and assigns class
'DALY_list' to it. Methods are available to print and plot the
resulting object.
DALY_list(...) ## S3 method for class 'DALY_list' print(x, ...) ## S3 method for class 'DALY_list' plot(x, prob = 0.95, sort = TRUE, names = NULL, bars = TRUE, col = c("grey90", "white"), error_bars = TRUE, eb_col = "black", grid = TRUE, ...)DALY_list(...) ## S3 method for class 'DALY_list' print(x, ...) ## S3 method for class 'DALY_list' plot(x, prob = 0.95, sort = TRUE, names = NULL, bars = TRUE, col = c("grey90", "white"), error_bars = TRUE, eb_col = "black", grid = TRUE, ...)
... |
In |
x |
Object of class |
prob |
Probability span by DALY error bar;
defaults to |
sort |
Should values be sorted from high to low?
defaults to |
names |
Vector of names to be plotted on the y-axis. If this argument
is |
bars |
Should YLL/YLD bars be plotted?
defaults to |
col |
Color of YLL and YLD barplot;
defaults to |
error_bars |
Should DALY error bars be plotted?
defaults to |
eb_col |
Color of DALY error bars; defaults to black |
grid |
Should a vertical grid be plotted?
defaults to |
An object of S3 class 'DALY_list'.
getDALYprint.DALYplot.DALYscatterplot
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
## Not run: ##= load NCC example ====================================== setDALYexample(1) setStdLE("GBD1990") ##= calculate DALYs under different scenarios ============= ncc_00 <- getDALY(aw = FALSE, dr = 0) ncc_03 <- getDALY(aw = FALSE, dr = 0.03) ncc_13 <- getDALY(aw = TRUE, dr = 0.03) ##= store results as 'DALY_list' ========================== ncc <- DALY_list(ncc_00, ncc_03, ncc_13) ##= barplot of different scenarios ======================== par(mar = c(4, 4, 1, 1) + .5) plot(ncc, names = c("DALY[0,0]", "DALY[0,0.03]", "DALY[1,0.03]")) ## End(Not run)## Not run: ##= load NCC example ====================================== setDALYexample(1) setStdLE("GBD1990") ##= calculate DALYs under different scenarios ============= ncc_00 <- getDALY(aw = FALSE, dr = 0) ncc_03 <- getDALY(aw = FALSE, dr = 0.03) ncc_13 <- getDALY(aw = TRUE, dr = 0.03) ##= store results as 'DALY_list' ========================== ncc <- DALY_list(ncc_00, ncc_03, ncc_13) ##= barplot of different scenarios ======================== par(mar = c(4, 4, 1, 1) + .5) plot(ncc, names = c("DALY[0,0]", "DALY[0,0.03]", "DALY[1,0.03]")) ## End(Not run)
The Neurocysticercosis dataset consists of a nested list
containing data needed to calculate DALYs due to
Neurocysticercosis in West Cameroon, according to Praet et al. (2009).
data(DALY_Neurocysticercosis)data(DALY_Neurocysticercosis)
List of 3 $ model : List of 2 ..$ diseaseName : chr "Neurocysticercosis" ..$ outcomeNames : List of 8 $ settings : List of 4 ..$ pop : num [1:5, 1:2] 397229 686600 1073342 210474 129081 ... ..$ LE : num [1:21, 1:2] 80 79.4 75.4 70.4 65.4 ... ..$ aw : chr "Yes" ..$ dr : num 3 $ data : List of 8 ..$ : List of 8 .. ..$ inc : List of 3 .. .. ..$ dist : chr "Gamma" .. .. ..$ strat : chr "Age and Sex" .. .. ..$ param : num [1:5, 1:4] 47.3 47.3 47.3 47.3 47.3 ... .. ..$ trt : List of 3 .. .. ..$ dist : chr "Beta" .. .. ..$ strat : chr "None" .. .. ..$ param : num [1, 1:2] 267 733 .. ..$ ons : List of 3 .. .. ..$ dist : chr "Fixed" .. .. ..$ strat : chr "Age" .. .. ..$ param : num [1:5, 1] 2.5 9.95 26.99 51.94 73.6 .. ..$ dur : List of 3 .. .. ..$ dist : chr "Fixed" .. .. ..$ strat : chr "Age and Sex" .. .. ..$ param : num [1:5, 1:2] 1.4 2 3.6 2.8 1.6 1.6 3.1 5.9 6 2.8 .. ..$ DWt : List of 3 .. .. ..$ dist : chr "Beta" .. .. ..$ strat : chr "Age" .. .. ..$ param : num [1:5, 1:2] 1.5 1.5 1.5 1.5 1.5 35 21.6 21.6 21.6 21.6 .. ..$ DWn : List of 3 .. .. ..$ dist : chr "Beta" .. .. ..$ strat : chr "Age" .. .. ..$ param : num [1:5, 1:2] 3 3 3 3 3 27.3 17 17 17 17 .. ..$ mrt : List of 3 .. .. ..$ dist : chr "Gamma" .. .. ..$ strat : chr "None" .. .. ..$ param : num [1, 1:2] 3.05 12.32 .. ..$ lxp : List of 3 .. .. ..$ dist : chr "Fixed" .. .. ..$ strat : chr "Age" .. .. ..$ param : num [1:5, 1] 2.5 10 30 52.5 77.5 [...]
Taenia solium cysticercosis is a zoonotic disease occurring in many developing countries. A relatively high prevalence in humans and pigs has been reported in several parts of the world, but insufficient data are available on the disease burden. Disease impact assessment needs detailed information on well-defined epidemiological and economic parameters. Our work conducted in West Cameroon over several years allowed us to collect the necessary information to estimate the impact of the parasite on the human and animal populations in this area using both cost and Disability Adjusted Life Year (DALY) estimations. This study identified the professional inactivity caused by the disease as the major loss factor in comparison to the cost of health care and losses due to infected pigs. These findings should allow a simpler estimation of the global disease burden based on information on salary levels and human cysticercosis prevalence in endemic areas of the world. In addition, the number of DALYs was higher than estimates already available for some other neglected tropical diseases in sub-Saharan Africa.
The average number of DALYs was 9.0 per thousand persons per year (95% CR 2.8-20.4).
Praet N, Speybroeck N, Manzanedo R, Berkvens D, Nforninwe DN, Zoli A, Quet F, Preux P-M, Carabin H, Geerts S (2009). The disease burden of Taenia solium cysticercosis in Cameroon. PLoS Neglected Tropical Diseases 3(3), e406.
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
The Toxoplasmosis dataset consists of a nested list
containing data needed to calculate DALYs due to
congenital toxoplasmosis in the Netherlands, according to Kortbeek et al. (2009).
data(DALY_Toxoplasmosis)data(DALY_Toxoplasmosis)
List of 3 $ model : List of 2 ..$ diseaseName : chr "Toxoplasmosis" ..$ outcomeNames : List of 8 $ settings : List of 4 ..$ pop : num [1:5, 1:2] 97000 NA NA NA NA 97000 NA NA NA NA ..$ LE : num [1:21, 1:2] 79 0 0 0 0 0 0 0 0 0 ... ..$ aw : chr "No" ..$ dr : num 0 $ data : List of 8 ..$ : List of 8 .. ..$ inc : List of 3 .. .. ..$ dist : chr "Beta-Pert" .. .. ..$ strat : chr "Age" .. .. ..$ param : logi [1:5, 1:3] NA NA NA NA NA NA ... .. ..$ trt : List of 3 .. .. ..$ dist : chr "Fixed" .. .. ..$ strat : chr "Age" .. .. ..$ param : logi [1:5, 1] NA NA NA NA NA .. ..$ ons : List of 3 .. .. ..$ dist : chr "Fixed" .. .. ..$ strat : chr "Age" .. .. ..$ param : logi [1:5, 1] NA NA NA NA NA .. ..$ dur : List of 3 .. .. ..$ dist : chr "Fixed" .. .. ..$ strat : chr "Age" .. .. ..$ param : logi [1:5, 1] NA NA NA NA NA .. ..$ DWt : List of 3 .. .. ..$ dist : chr "Fixed" .. .. ..$ strat : chr "Age" .. .. ..$ param : logi [1:5, 1] NA NA NA NA NA .. ..$ DWn : List of 3 .. .. ..$ dist : chr "Fixed" .. .. ..$ strat : chr "Age" .. .. ..$ param : logi [1:5, 1] NA NA NA NA NA .. ..$ mrt : List of 3 .. .. ..$ dist : chr "Beta-Pert" .. .. ..$ strat : chr "Age" .. .. ..$ param : num [1:5, 1:3] 0.0323 NA NA NA NA ... .. ..$ lxp : List of 3 .. .. ..$ dist : chr "Fixed" .. .. ..$ strat : chr "Age" .. .. ..$ param : num [1:5, 1] 0 NA NA NA NA [...]
The calculation of disability-adjusted life years (DALYs) enables public health policy makers to compare the burden of disease of a specific disease with that of other (infectious) diseases. The incidence of a disease is important for the calculation of DALYs. To estimate the incidence of congenital toxoplasmosis (CT), a random sample of 10,008 dried blood spot filter paper cards from babies born in 2006 in the Netherlands were tested for Toxoplasma gondii-specific IgM antibodies. Eighteen samples were confirmed as positive for IgM, resulting in an observed birth incidence of CT of 1.8 cases per 1,000 live-born children in 2006 and an adjusted incidence of 2.0 cases per 1,000. This means that 388 infected children were born in 2006. The most likely burden of disease is estimated to be 2,300 DALYs (range 820-6,710 DALYs). In the previous calculations, using data from a regional study from 1987, this estimate was 620 DALYs (range 220-1,900 DALYs). The incidence of CT in the Netherlands is much higher than previously reported; it is 10 times higher than in Denmark and 20 times higher than in Ireland, based on estimates obtained using the same methods. There is no screening program in the Netherlands; most children will be born asymptomatic and therefore will not be detected or treated.
Kortbeek LM, Hofhuis A, Nijhuis CDM, Havelaar AH (2009). Congenital toxoplasmosis and DALYs in the Netherlands. Memorias de Instituto Oswaldo Cruz 104(2), 370-373.
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
This function opens the main window of the DALY Calculator.
After initiating the DALY Calculator main window,
the following steps have to be followed to calculate DALYs:
Set the ‘Population’ table
Set the ‘Life Expectancy’ table - the default is the standard life expectancy table introduced in the GBD2010 study
Set the input parameters and corresponding distributions and stratification levels
Set the age weighting and time discounting rates
Calculate DALYs !
In order to demonstrate the calculation process of the DALY Calculator, two examples from the foodborne disease burden literature are built-in:
The burden of Taenia solium cysticercosis in Cameroon (Praet et al., 2009)
Congenital toxoplasmosis and DALYs in the Netherlands (Kortbeek et al., 2009)
In the following sections, a brief summary will be given of the functionalities
of the DALY Calculator.
A more comprehensive overview is provided in the DALY Calculator manual,
which is available through the DALYmanual function.
DALYcalculator()DALYcalculator()
The DALY Calculator provides the following three menu options:
File
Load DALY data from file... reads population and epidemiological
data from an .RData file (through readDALYdata)
Save DALY data to file... saves the population and
epidemiological data to an .RData file
(through saveDALYdata)
Reset DALY calculator clears all entered data,
and resets the DALY Calculator to its default settings,
except for the life expectancy table (through reset)
Exit exits the DALY Calculator, without saving the entered data
Settings
Life Expectancy Table... opens the life expectancy table window
(through setLifeExp)
Options... opens the options window
(through DALYoptions)
Help
Load examples loads one of the two built-in examples
(through setDALYexample)
The burden of Neurocysticercosis in West-Cameroon
The burden of Congenital Toxoplasmosis in the Netherlands
Html help opens the HTML help file for the DALY Calculator
(cf ?DALYcalculator)
DALY Calculator manual (PDF) opens the DALY Calculator Manual
(through DALYmanual)
Package description opens the HTML help file for the DALY
package (cf ?DALY)
DALY Calculator Info shows current version and developers core team
Devleesschauwer B, et al. (2014). Calculating Disability-Adjusted Life Years to quantify burden of disease. International Journal of Public Health 59, 565-569. http://dx.doi.org/10.1007/s00038-014-0552-z
Praet N, Speybroeck N, Manzanedo R, Berkvens D, Nforninwe DN, Zoli A, Quet F, Preux P-M, Carabin H, Geerts S (2009). The disease burden of Taenia solium cysticercosis in Cameroon. PLoS Neglected Tropical Diseases 3(3), e406.
Kortbeek LM, Hofhuis A, Nijhuis CDM, Havelaar AH (2009). Congenital toxoplasmosis and DALYs in the Netherlands. Memorias de Instituto Oswaldo Cruz 104(2), 370-373.
A command-line interface to the DALY Calculator
is available through the following functions:
setPop opens the ‘Population’ window
setLifeExp opens the ‘Life Expectancy’ window
setStdLE sets the standard life expectancy
setData opens a ‘Data’ window
getDALY initiates the DALY calculation process and returns the simulated results
sensitivity performs DALY sensitivity analysis
DALY_list merges different DALY objects
See DALYmanual
for a more comprehensive overview of the DALY Calculator
This function opens the "DALY Calculator Manual", which is included as PDF file in the DALY package.
DALYmanual()DALYmanual()
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
This function opens the ‘Options’ window of the DALY Calculator.
DALYoptions()DALYoptions()
The following settings can be modified in the ‘options’ window:
Iterations: the default number of iterations is 20,000
Numerical output: the standard output contains a summary of DALYs, YLDs, YLLs, incident cases and deaths. Different options are available:
“Summed over age/sex classes” OR “Per age/sex class”
“Summed over outcomes” OR “Per outcome”
“Absolute” OR “Relative (per 1000 pop)”
Graphical output: the user can specficy whether or not to view a
standardized DALY histogram (see hist.DALY).
The numerical output methods for class 'DALY': print.DALY, summary.DALY
The graphical output method for class 'DALY': hist.DALY
The aggregator method for class 'DALY': aggregate.DALYDALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
This function initiates the Monte Carlo DALY calculation process, based on the entered data, and returns the simulated results.
getDALY(button.call = FALSE, aw, dr)getDALY(button.call = FALSE, aw, dr)
button.call |
Gives information on the origin of the call
(i.e., through the GUI button ( |
aw |
Logical flag, indicating if age weighting should be applied;
if |
dr |
Time discount rate (numeric value between 0 and 1);
if |
getDALY returns an object of class 'DALY',
which inherits from class 'list'.
The generic functions print and summary may be used to
obtain and print the DALY calculation results.
The generic function aggregate extracts from the 'DALY' object
results by outcome, age/sex class, or both.
An object of class 'DALY' is a list containing the following elements:
\emph{i} |
For each outcome |
pop |
Population |
name |
Name of the disease |
For each outcome i, DALY[[i]] is a list
containing the following elements:
DALY |
Vector of simulated DALYs |
YLD |
Vector of simulated YLDs |
YLL |
Vector of simulated YLLs |
INC |
Vector of simulated incident cases |
MRT |
Vector of simulated deaths |
name |
Name of the health outcome |
The vectors containing simulated DALYs, YLDs, YLLs, incident cases and deaths each have three dimensions:
1 |
Iterations |
2 |
Age groups |
3 |
Sex |
The numerical output methods for 'DALY': print.DALY, summary.DALY
The graphical output method for 'DALY': hist.DALY
The aggregator method for 'DALY': aggregate.DALY
DALY sensitivity analysis : sensitivityDALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
## Not run: ##= load the NCC example ================================== setDALYexample(1) setStdLE("GBD1990") ##= perform DALY calculation ============================= ##= without age weighting and time discounting ============ getDALY(aw = FALSE, dr = 0) ##= perform DALY calculation, store results in 'x' ======== ##= (with age weighting and a 3% time discount rate) ====== x <- getDALY(aw = TRUE, dr = 0.03) ##= view the structure of 'x' ============================= str(x) ##= view the DALY calculation results ===================== print(x) # absolute, total ============== print(x, relative = TRUE) # relative (ie, per 1000 pop) == print(x, outcomes = TRUE) # outcome-wise ================= ##= obtain minimum & maximum simulated DALY =============== min(x[[1]]$DALY) max(x[[1]]$DALY) ##= standardized DALY histogram =========================== hist(x) ##= DALY sensitivity analysis ============================= sensitivity(x) ## End(Not run)## Not run: ##= load the NCC example ================================== setDALYexample(1) setStdLE("GBD1990") ##= perform DALY calculation ============================= ##= without age weighting and time discounting ============ getDALY(aw = FALSE, dr = 0) ##= perform DALY calculation, store results in 'x' ======== ##= (with age weighting and a 3% time discount rate) ====== x <- getDALY(aw = TRUE, dr = 0.03) ##= view the structure of 'x' ============================= str(x) ##= view the DALY calculation results ===================== print(x) # absolute, total ============== print(x, relative = TRUE) # relative (ie, per 1000 pop) == print(x, outcomes = TRUE) # outcome-wise ================= ##= obtain minimum & maximum simulated DALY =============== min(x[[1]]$DALY) max(x[[1]]$DALY) ##= standardized DALY histogram =========================== hist(x) ##= DALY sensitivity analysis ============================= sensitivity(x) ## End(Not run)
Plot a standardized histogram of DALY Calculator output.
## S3 method for class 'DALY' hist(x, xval = c("DALY", "YLD", "YLL", "cases", "deaths"), prob = 0.95, central = c("mean", "median"), breaks = 25, fill = "grey95", ...)## S3 method for class 'DALY' hist(x, xval = c("DALY", "YLD", "YLL", "cases", "deaths"), prob = 0.95, central = c("mean", "median"), breaks = 25, fill = "grey95", ...)
x |
Object of class |
xval |
Value to plot on x-axis;
must be any of |
prob |
Probability span by plotted credibility interval;
defaults to |
central |
Plotted central value of distribution;
must be any of |
breaks |
See |
fill |
The colour to be used to fill the bars |
... |
Additional arguments to be passed to |
This function plots a standardized histogram of DALY Calculator output. The histogram contains by default 25 bars. The limits of the credible interval are denoted by vertical lines; the credible interval limits and central tendency are printed above the histogram.
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
## Not run: ##= load NCC example ====================================== setDALYexample(1) setStdLE("GBD1990") ##= perform DALY calculation, store results in 'x' ======= x <- getDALY() ##= plot histogram of total deaths ======================== ##= + show 90% credible interval & median ================= hist(x, xval = "deaths", prob = 0.90, central = "median") ## End(Not run)## Not run: ##= load NCC example ====================================== setDALYexample(1) setStdLE("GBD1990") ##= perform DALY calculation, store results in 'x' ======= x <- getDALY() ##= plot histogram of total deaths ======================== ##= + show 90% credible interval & median ================= hist(x, xval = "deaths", prob = 0.90, central = "median") ## End(Not run)
Plot a stacked barplot of YLLs/YLDs with a DALY error bar.
## S3 method for class 'DALY' plot(x, prob = 0.95, sort = TRUE, names = NULL, bars = TRUE, col = c("grey90", "white"), error_bars = TRUE, eb_col = "black", grid = TRUE, ...)## S3 method for class 'DALY' plot(x, prob = 0.95, sort = TRUE, names = NULL, bars = TRUE, col = c("grey90", "white"), error_bars = TRUE, eb_col = "black", grid = TRUE, ...)
x |
Object of class |
prob |
Probability span by DALY error bar;
defaults to |
sort |
Should values be sorted from high to low?
defaults to |
names |
Vector of names to be plotted on the y-axis. If this argument
is |
bars |
Should YLL/YLD bars be plotted?
defaults to |
col |
Color of YLL and YLD barplot;
defaults to |
error_bars |
Should DALY error bars be plotted?
defaults to |
eb_col |
Color of DALY error bars; defaults to black |
grid |
Should a vertical grid be plotted?
defaults to |
... |
Additional arguments to be passed to |
This function plots the results of the DALY calculation aggregated by outcome. By default, both a stacked barplot of YLLs/YLDs and an error bar of total DALYs are shown.
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
## Not run: ##= load toxoplasmosis example ============================ setDALYexample(2) ##= perform DALY calculation, store results in 'x' ======= x <- getDALY() ##= plot results ========================================== par(mar = c(4, 7, 4, 1)) plot(x) plot(x, bars = FALSE) # only error bars plot(x, error_bars = FALSE) # only barplots ## End(Not run)## Not run: ##= load toxoplasmosis example ============================ setDALYexample(2) ##= perform DALY calculation, store results in 'x' ======= x <- getDALY() ##= plot results ========================================== par(mar = c(4, 7, 4, 1)) plot(x) plot(x, bars = FALSE) # only error bars plot(x, error_bars = FALSE) # only barplots ## End(Not run)
Print summary of DALY Calculator output aggregated by age/sex class.
## S3 method for class 'DALY' print(x, relative = FALSE, outcomes = FALSE, prob = 0.95, digits = 0, ...)## S3 method for class 'DALY' print(x, relative = FALSE, outcomes = FALSE, prob = 0.95, digits = 0, ...)
x |
Object of class |
relative |
Show results per 1000 population? See details below |
outcomes |
Show results per outcome? See details below |
prob |
Probability span by printed credibility intervals;
defaults to |
digits |
Number of decimals to be printed; defaults to |
... |
Additional arguments to be passed to |
The standard print method gives the absolute number of DALYs, YLDs,
YLLs, Cases and Deaths. By specifying relative = TRUE, the number of
DALYs, YLDs, YLLs, cases and deaths per 1,000 population will be shown.
The standard print method gives the number of DALYs, YLDs, YLLs,
cases and deaths aggregated over all health outcomes. By specifying
outcomes = TRUE, the number of DALYs, YLDs, YLLs, cases and deaths
for each outcome will be shown as well.
print.DALY returns a list containg the following elements:
total |
Matrix containing mean, median and |
outcomes |
Named list, containing matrices of DALYs, YLDs, YLLs, cases and deaths for each health outcome |
pct |
Vector of contribution of YLDs and YLLs to overall DALYs |
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
## Not run: ##= load the NCC example ================================== setDALYexample(1) setStdLE("GBD1990") ##= perform DALY calculation, store results in 'x' ======== x <- getDALY() ##= view the DALY calculation results ===================== print(x) # absolute, total ============== print(x, prob = .90) # absolute, total (90% CrI) ==== print(x, relative = TRUE) # relative (ie, per 1000 pop) == print(x, outcomes = TRUE) # outcome-wise ================= ## End(Not run)## Not run: ##= load the NCC example ================================== setDALYexample(1) setStdLE("GBD1990") ##= perform DALY calculation, store results in 'x' ======== x <- getDALY() ##= view the DALY calculation results ===================== print(x) # absolute, total ============== print(x, prob = .90) # absolute, total (90% CrI) ==== print(x, relative = TRUE) # relative (ie, per 1000 pop) == print(x, outcomes = TRUE) # outcome-wise ================= ## End(Not run)
This function loads data from an .RData image file into the
DALY Calculator. The file can be an external file, previously saved
through saveDALYdata(), or one of the two built-in example datasets.
readDALYdata(file = NULL, example = NULL)readDALYdata(file = NULL, example = NULL)
file |
Character string, denoting the name of the .RData file |
example |
Integer, denoting the example to be loaded
(any of |
NA
saveDALYdata,
setDALYexample,
DALY_Neurocysticercosis,
DALY_Toxoplasmosis
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
This function resets the DALY Calculator to its default values,
*except* for the ‘Life Expectancy’ table and the options
(cf DALYoptions).
reset()reset()
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
This function saves the population and epidemiological data to an .RData image file.
This file can be loaded into the DALY Calculator through readDALYdata().
saveDALYdata()saveDALYdata()
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
Generate a scatterplot of population versus patient level burden. The method is available for objects of class 'DALY' and 'DALY_list'.
## S3 method for class 'DALY' scatterplot(x, plot = c("DALY", "YLD", "YLL"), outcomes = TRUE, per = 1000, samples = 1000, pch = 16, col = NULL, legend = NULL, legend_pos = c("topright", "topleft", "bottomright", "bottomleft"), ...) ## S3 method for class 'DALY_list' scatterplot(x, plot = c("DALY", "YLD", "YLL"), per = 1000, samples = 1000, pch = 16, col = NULL, legend = NULL, legend_pos = c("topright", "topleft", "bottomright", "bottomleft"), ...)## S3 method for class 'DALY' scatterplot(x, plot = c("DALY", "YLD", "YLL"), outcomes = TRUE, per = 1000, samples = 1000, pch = 16, col = NULL, legend = NULL, legend_pos = c("topright", "topleft", "bottomright", "bottomleft"), ...) ## S3 method for class 'DALY_list' scatterplot(x, plot = c("DALY", "YLD", "YLL"), per = 1000, samples = 1000, pch = 16, col = NULL, legend = NULL, legend_pos = c("topright", "topleft", "bottomright", "bottomleft"), ...)
x |
Object of class |
plot |
Which estimates should be plotted? Must be either |
outcomes |
Should different outcomes be plotted? Defaults to |
per |
Denominator for population level burden. Defaults to 1000 |
samples |
Number of samples to plotted. Defaults to 1000 |
pch |
Plotting symbol. Defaults to 16 (= solid circle).
See |
col |
Plotting color. Defaults to |
legend |
A character or |
legend_pos |
The position of the legend, either |
... |
Additional arguments to be passed to |
This function generates a scatterplot of the estimated burden at the
population level (x-axis) versus the patient level (y-axis). Scatterplots can
be generated of 'DALY' objects, to visualize the overall or
outcome-specific burden; and of 'DALY_list' objects, to compare the
overall burden of, e.g., different diseases or different DALY calculation
scenarios.
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
## Not run: ##= load toxoplasmosis example ============================ setDALYexample(2) ##= perform DALY calculation for different scenarios ====== toxo_00 <- getDALY(aw = FALSE, dr = 0) toxo_03 <- getDALY(aw = FALSE, dr = 0.03) toxo_13 <- getDALY(aw = TRUE, dr = 0.03) ##= combine scenarios in 'DALY_list' ====================== toxo <- DALY_list(toxo_00, toxo_03, toxo_13) ##= plot YLL and YLL for toxo_00 ========================== par(mar = c(4, 4, 1, 1) + .5) par(mfrow = c(1, 2)) scatterplot(toxo_00, plot = "YLL", outcomes = FALSE, legend = FALSE) scatterplot(toxo_00, plot = "YLD", outcomes = FALSE, legend = FALSE) ##= plot all three scenarios ============================== par(mfrow = c(1, 1)) scatterplot(toxo, legend = c("DALY[0,0]", "DALY[0,0.03]", "DALY[1,0.03]"), legend_pos = "topleft", log = "xy", main = "Scenario analysis") ## End(Not run)## Not run: ##= load toxoplasmosis example ============================ setDALYexample(2) ##= perform DALY calculation for different scenarios ====== toxo_00 <- getDALY(aw = FALSE, dr = 0) toxo_03 <- getDALY(aw = FALSE, dr = 0.03) toxo_13 <- getDALY(aw = TRUE, dr = 0.03) ##= combine scenarios in 'DALY_list' ====================== toxo <- DALY_list(toxo_00, toxo_03, toxo_13) ##= plot YLL and YLL for toxo_00 ========================== par(mar = c(4, 4, 1, 1) + .5) par(mfrow = c(1, 2)) scatterplot(toxo_00, plot = "YLL", outcomes = FALSE, legend = FALSE) scatterplot(toxo_00, plot = "YLD", outcomes = FALSE, legend = FALSE) ##= plot all three scenarios ============================== par(mfrow = c(1, 1)) scatterplot(toxo, legend = c("DALY[0,0]", "DALY[0,0.03]", "DALY[1,0.03]"), legend_pos = "topleft", log = "xy", main = "Scenario analysis") ## End(Not run)
This function performs a probabilistic global sensitivity analysis of the
overall DALY estimate, based on standardized regression coefficients
(method src) or partial correlation coefficients (method pcc).
Actual or ranked values may be used, and for method src, coefficients
or mapped values may be reported. See below for more details.
sensitivity(x, method = c("src", "pcc"), rank = FALSE, mapped = TRUE) ## S3 method for class 'DALY_sensitivity' print(x, digits = 3, signif_stars = getOption("show.signif.stars"), ...) ## S3 method for class 'DALY_sensitivity' plot(x, alpha = 0.05, main = "Sensitivity analysis", show_values = FALSE, value_digits = 3, value_cex = 0.6, ...)sensitivity(x, method = c("src", "pcc"), rank = FALSE, mapped = TRUE) ## S3 method for class 'DALY_sensitivity' print(x, digits = 3, signif_stars = getOption("show.signif.stars"), ...) ## S3 method for class 'DALY_sensitivity' plot(x, alpha = 0.05, main = "Sensitivity analysis", show_values = FALSE, value_digits = 3, value_cex = 0.6, ...)
x |
Object of class |
method |
Sensitivity analysis method: |
rank |
Should ranked values be used? Defaults to |
mapped |
Should mapped regression coefficients be presented?
Defaults to |
digits |
Number of decimals to be printed; defaults to |
signif_stars |
Should significance stars be printed? |
alpha |
Significance level for selecting significant variables; defaults to 0.05 |
main |
Main title of tornado plot; defaults to "Sensitivity analysis" |
show_values |
Should values be plotted next to bars?; defaults to FALSE |
value_digits |
Number of significant digits for plotted values; defaults to 3 |
value_cex |
Size of plotted values; defaults to 0.6 |
... |
Additional arguments to be passed to methods |
Sensitivity analysis studies how the uncertainty in the overall DALY estimate can be apportioned to the different sources of uncertainty in the input parameters. These results can therefore help to identify those input parameters that cause significant uncertainty in the overall DALY estimate and that therefore may be the focus of further research if one wishes to reduce the uncertainty in the overall estimate.
The sensitivity function implements a probabilistic global
sensitivity analysis, in which the analysis is conducted over the full range
of plausible input values (hence global), determined by the specified
uncertainty distributions (hence probabilistic).
Specifying method = "src" will perform a linear regression-based
sensitivity analysis. Here, the simulated overall DALY estimates will be
regressed against the simulated values for the probabilistic input parameters
(using lm). To facilitate comparison, the independent terms
are standardized such that they are normally distributed with mean zero and
standard deviation one (using scale). The resulting regression
coefficients are therefore referred to as standardized regression
coefficients.
Argument rank specifies whether the regression should be performed on
the actual values (rank = FALSE; default) or on the ranked values
(rank = TRUE). Rank-based regression may be preferred when the
relation between output and inputs is non-linear. R^2 values smaller than
0.60 may be indicative of a poor fit of the default linear regression model.
If mapped = TRUE, the dependent term is not standardized, such that
the resulting mapped regression coefficients correspond to the change
in overall DALY given one standard deviation change in the corresponding
input parameter. If mapped = FALSE, the dependent term is
standardized, such that the resulting standardized regression
coefficients correspond to the number of standard deviations change in
overall DALY given one standard deviation change in the corresponding input
parameter.
Specifying method = "pcc" will calculate partial correlation
coefficients for each of the input variables. Partial correlation
coefficients represent the correlation between two variables when adjusting
for other variables. In the presence of important interactions between input
variables, partial correlation coefficients may be preferred over
standardized regression coefficients.
Argument rank specifies whether the correlation should be calculated
between the actual values (rank = FALSE; default) or between the
ranked values (rank = TRUE).
Method plot can be used to generate a tornado plot of the significant
input variables.
An object of S3 class DALY_sensitivity, containing the following two
elements:
method |
List containing the specified values for |
out |
Either the output of |
Vose D (2000). Risk analysis: a quantitative guide. John Wiley & Sons.
Saltelli A, Chan K, Scott EM (2000). Sensitivity analysis. John Wiley & Sons.
Package sensitivity (https://cran.r-project.org/package=sensitivity), for more advanced sensitivity analysis methods.
## Not run: ##= load NCC example ====================================== setDALYexample(1) setStdLE("GBD1990") ##= perform DALY calculation, store results in 'x' ======== x <- getDALY() ##= perform DALY sensitivity analysis ===================== ## default sensitivity analysis sa <- sensitivity(x) print(sa) ## tornado plot of default sensitivity analysis plot(sa, show_values = TRUE) ## standardized regression coefficients ------------------- sensitivity(x, method = "src", rank = FALSE, mapped = TRUE) sensitivity(x, method = "src", rank = FALSE, mapped = FALSE) ## standardized rank regression coefficients -------------- sensitivity(x, method = "src", rank = TRUE, mapped = TRUE) sensitivity(x, method = "src", rank = TRUE, mapped = FALSE) ## partial correlation coefficients ----------------------- sensitivity(x, method = "pcc", rank = FALSE) ## partial rank correlation coefficients ------------------ sensitivity(x, method = "pcc", rank = FALSE) ## End(Not run)## Not run: ##= load NCC example ====================================== setDALYexample(1) setStdLE("GBD1990") ##= perform DALY calculation, store results in 'x' ======== x <- getDALY() ##= perform DALY sensitivity analysis ===================== ## default sensitivity analysis sa <- sensitivity(x) print(sa) ## tornado plot of default sensitivity analysis plot(sa, show_values = TRUE) ## standardized regression coefficients ------------------- sensitivity(x, method = "src", rank = FALSE, mapped = TRUE) sensitivity(x, method = "src", rank = FALSE, mapped = FALSE) ## standardized rank regression coefficients -------------- sensitivity(x, method = "src", rank = TRUE, mapped = TRUE) sensitivity(x, method = "src", rank = TRUE, mapped = FALSE) ## partial correlation coefficients ----------------------- sensitivity(x, method = "pcc", rank = FALSE) ## partial rank correlation coefficients ------------------ sensitivity(x, method = "pcc", rank = FALSE) ## End(Not run)
This function resets the DALY Calculator
(through reset),
and loads one of the two built-in DALY calculation examples
(through readDALYdata):
Neurocysticercosis in West-Cameroon
Congenital Toxoplasmosis in the Netherlands
setDALYexample(example)setDALYexample(example)
example |
This argument takes value ‘ |
DALY_Neurocysticercosis,
DALY_Toxoplasmosis,
readDALYdataDALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
This function opens a data window where the input parameters (with corresponding distributions and stratification levels) for one of the disease categories or outcomes can be entered.
setData(n)setData(n)
n |
Integer, corresponding to the health outcome to be set |
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
This function opens the ‘Life Expectancy’ window, where the life expectancy table can be customized. The default life expectancy table is the GBD2010 standard life expectancy table. Alternative standard life expectancy tables may be selected via the ‘Life Expectancy’ window or via function setStdLE.
setLifeExp()setLifeExp()
The DALY Calculator provides three standard life expectancy tables:
GBD1990: the Coale and Demeny model life-table West, level 26 and 25, which has a life expectancy at birth of 80 for males and 82.5 for females (Murray, 1994);
GBD2010 (default): the synthetic standard life expectancy introduced for the GBD 2010 study, with a life expectancy at birth of 86 for both males and females;
WHO/GHE: the projected frontier life expectancy for the year 2050, used for the WHO Global Health Estimates, with a life expectancy at birth of 92 for both males and females.
setStdLEDALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
This function opens the ‘Population’ window, where the population under study can be entered, stratified by sex and age group.
setPop()setPop()
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
This function sets the ‘Life Expectancy’ table to a standard life expectancy for DALY calculation. The default standard life expectancy table is the synthetic life table introduced for the GBD 2010 study. Other options include the Coale and Demeny model life table West (introduced for GBD 1990 study), and the frontier life expectancy table used for the WHO Global Health Estimates.
setStdLE(table = NULL)setStdLE(table = NULL)
table |
The required standard life expectancy table – i.e., one of: |
The DALY Calculator provides three standard life expectancy tables:
GBD1990: the Coale and Demeny model life-table West, level 26 and 25, which has a life expectancy at birth of 80 for males and 82.5 for females (Murray, 1994);
GBD2010 (default): the synthetic standard life expectancy introduced for the GBD 2010 study, with a life expectancy at birth of 86 for both males and females;
WHO/GHE: the projected frontier life expectancy for the year 2050, used for the WHO Global Health Estimates, with a life expectancy at birth of 92 for both males and females.
Devleesschauwer B, et al. (2014). Calculating Disability-Adjusted Life Years to quantify burden of disease. International Journal of Public Health 59, 565-569. http://dx.doi.org/10.1007/s00038-014-0552-z
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
Print summary of DALY Calculator output per age/sex class.
## S3 method for class 'DALY' summary(object, relative = FALSE, outcomes = FALSE, digits = 0, ...)## S3 method for class 'DALY' summary(object, relative = FALSE, outcomes = FALSE, digits = 0, ...)
object |
Object of class |
relative |
Show results per 1000 population? See details below |
outcomes |
Show results per outcome? See details below |
digits |
Number of decimals to be printed; defaults to |
... |
Additional arguments to be passed to |
The standard summary method gives the absolute number of
DALYs, YLDs, YLLs, cases and deaths. By specifying relative = TRUE,
the number of DALYs, YLDs, YLLs, cases and deaths per 1,000 population will
be shown.
The standard summary method gives the number of DALYs, YLDs, YLLs,
cases and deaths aggregated over all health outcomes. By specifying
outcomes = TRUE, the number of DALYs, YLDs, YLLs, cases and deaths
for each outcome will be shown as well.
summary.DALY returns a list containg the following elements:
total |
Matrix containing mean, median and |
outcomes |
Named list, containing matrices of DALYs, YLDs, YLLs, cases and deaths for each health outcome |
pct |
Vector of contribution of YLDs and YLLs to overall DALYs |
DALYcalculator
(for a brief description of the DALY Calculator)DALYmanual
(for a more comprehensive overview)
## Not run: ##= load the NCC example ================================== setDALYexample(1) setStdLE("GBD1990") ##= perform DALY calculation, store results in 'x' ======== x <- getDALY() ##= view the DALY calculation results ===================== summary(x) # absolute, total ============ summary(x, relative = TRUE) # relative (ie, per 1000) ==== summary(x, outcomes = TRUE) # outcome-wise =============== ## End(Not run)## Not run: ##= load the NCC example ================================== setDALYexample(1) setStdLE("GBD1990") ##= perform DALY calculation, store results in 'x' ======== x <- getDALY() ##= view the DALY calculation results ===================== summary(x) # absolute, total ============ summary(x, relative = TRUE) # relative (ie, per 1000) ==== summary(x, outcomes = TRUE) # outcome-wise =============== ## End(Not run)