Danish 1994-2008 all-cause mortality data for eight age groups

Weekly number of all cause mortality from 1994-2008 in each of the eight age groups <1, 1-4, 5-14, 15-44, 45-64, 65-74, 75-84 and 85+ years, see Höhle and Mazick (2010).

Usage

data(momo)

Format

An object of class "sts" containing the weekly number of all-cause deaths in Denmark, 1994-2008 (782 weeks), for each of the eight age groups <1, 1-4, 5-14, 15-44, 45-64, 65-74, 75-84 and 85+ years. A special feature of the EuroMOMO data is that weeks follow the ISO 8601 standard, which can be handled by the "sts" class.

The population slot of the momo object contains the population size in each of the eight age groups. These are yearly data obtained from the StatBank Denmark.

Source

European monitoring of excess mortality for public health action (EuroMOMO) project. https://www.euromomo.eu/.

Department of Epidemiology, Statens Serum Institute, Copenhagen, Denmark StatBank Denmark, Statistics Denmark, https://www.statistikbanken.dk/

References

Höhle, M. and Mazick, A. (2010). Aberration detection in R illustrated by Danish mortality monitoring. In T. Kass-Hout and X. Zhang (eds.), Biosurveillance: A Health Protection Priority, chapter 12. Chapman & Hall/CRC.
Preprint available at https://staff.math.su.se/hoehle/pubs/hoehle_mazick2009-preprint.pdf

Examples

data("momo")
momo

## show the period 2000-2008 with customized x-axis annotation
## (this is Figure 1 in Hoehle and Mazick, 2010)
oopts <- surveillance.options("stsTickFactors" = c("%G" = 1.5, "%Q"=.75))
plot(momo[year(momo) >= 2000,], ylab = "", xlab = "Time (weeks)",
     par.list = list(las = 1), col = c(gray(0.5), NA, NA),
     xaxis.tickFreq = list("%G"=atChange, "%Q"=atChange),
     xaxis.labelFreq = list("%G"=atChange), xaxis.labelFormat = "%G")
surveillance.options(oopts)


if (surveillance.options("allExamples")) {

## stratified monitoring from 2007-W40 using the Farrington algorithm
phase2 <- which(epoch(momo) >= "2007-10-01")
momo2 <- farrington(momo, control = list(range=phase2, alpha=0.01, b=5, w=4))
print(colSums(alarms(momo2)))
plot(momo2, col = c(8, NA, 4), same.scale = FALSE)

## stripchart of alarms (Figure 5 in Hoehle and Mazick, 2010)
plot(momo2, type = alarm ~ time, xlab = "Time (weeks)", main = "",
     alarm.symbol = list(pch=3, col=1, cex=1.5))

}