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DoDPandemicPaper.R
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DoDPandemicPaper.R
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rm(list=ls())
library(curl)
library(readxl)
library(tidyverse)
library(HMDHFDplus)
library(paletteer)
library(ragg)
library(extrafont)
library(geomtextpath)
library(cowplot)
library(scales)
library(ggtext)
#Mortality smooth is currently off CRAN, so take mirrored version from Tim Riffe's GitHub
#remotes::install_github("timriffe/MortalitySmooth")
library(MortalitySmooth)
#Set common font for all plots
font <- "Lato"
theme_custom <- function() {
theme_classic() %+replace%
theme(plot.title.position="plot", plot.caption.position="plot",
strip.background=element_blank(), strip.text=element_text(face="bold", size=rel(1)),
plot.title=element_text(face="bold", size=rel(1.5), hjust=0,
margin=margin(0,0,5.5,0)),
text=element_text(family="Lato"),
plot.subtitle=element_text(colour="Grey40", hjust=0, vjust=1),
plot.caption=element_text(colour="Grey40", hjust=1, vjust=1, size=rel(0.8)),
axis.text=element_text(colour="Grey40"),
axis.title=element_text(colour="Grey20"),
legend.text=element_text(colour="Grey40"),
legend.title=element_text(colour="Grey20"))
}
options(scipen=10000)
#HMD credentials here
username <- ""
password <- ""
#################
#England & Wales#
#################
#Read in England & Wales data from
#https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/the21stcenturymortalityfilesdeathsdataset
temp <- tempfile()
url <- "https://www.ons.gov.uk/file?uri=/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/the21stcenturymortalityfilesdeathsdataset/current/21stcmortality.xlsx"
rawfile <- curl_download(url=url, destfile=temp, quiet=FALSE, mode="wb")
data.01 <- read_excel(rawfile, sheet="1", range="A5:E21265")
data.02 <- read_excel(rawfile, sheet="2", range="A5:E20880")
data.03 <- read_excel(rawfile, sheet="3", range="A5:E21251")
data.04 <- read_excel(rawfile, sheet="4", range="A5:E20959")
data.05 <- read_excel(rawfile, sheet="5", range="A5:E20928")
data.06 <- read_excel(rawfile, sheet="6", range="A5:E20866")
data.07 <- read_excel(rawfile, sheet="7", range="A5:E20657")
data.08 <- read_excel(rawfile, sheet="8", range="A5:E20660")
data.09 <- read_excel(rawfile, sheet="9", range="A5:E20792")
data.10 <- read_excel(rawfile, sheet="10", range="A5:E20784")
data.11 <- read_excel(rawfile, sheet="11", range="A5:E20380")
data.12 <- read_excel(rawfile, sheet="12", range="A5:E20211")
data.13 <- read_excel(rawfile, sheet="13", range="A5:E20439")
data.14 <- read_excel(rawfile, sheet="14", range="A5:E20426")
data.15 <- read_excel(rawfile, sheet="15", range="A5:E20198")
data.16 <- read_excel(rawfile, sheet="16", range="A5:E20280")
data.17 <- read_excel(rawfile, sheet="17", range="A5:E20193")
data.18 <- read_excel(rawfile, sheet="18", range="A5:E20481")
data.19 <- read_excel(rawfile, sheet="19", range="A5:E20305")
data.20 <- read_excel(rawfile, sheet="20", range="A5:E19032")
data.21 <- read_excel(rawfile, sheet="21", range="A5:E19157")
ewdata <- bind_rows(data.01, data.02, data.03, data.04, data.05, data.06, data.07,
data.08, data.09, data.10, data.11, data.12, data.13, data.14,
data.15, data.16, data.17, data.18, data.19, data.20, data.21) %>%
set_names("ICD10", "Year", "Sex", "Age", "Deaths") %>%
#Allocate causes to code groups
mutate(code1=substr(ICD10, 1, 1), code2=as.numeric(substr(ICD10,2,3)),
code3=as.numeric(substr(ICD10,4,4)),
Cause=case_when(
code1=="K" & code2 %in% c(70, 73, 74) ~ "Alcohol",
code1=="F" & code2==10 ~ "Alcohol",
code1=="X" & code2==45 ~ "Alcohol", #Difference from the Masters defns as X45 is clearly alcohol-related
code1=="Y" & code2==15 ~ "Alcohol", #Difference from the Masters defns as Y15 is clearly alcohol-related
code1=="X" & code2 %in% c(40:44, 85) ~ "Drugs",
code1=="Y" & code2 %in% c(10:14) ~ "Drugs",
code1=="F" & code2 %in% c(11:16, 18, 19) ~ "Drugs", #Including F18 here to align with Scottish data
code1=="U" & code2==3 ~ "Suicide",
code1=="X" & code2 %in% c(60:84) ~ "Suicide",
code1=="Y" & code2 ==87 ~ "Suicide",
TRUE ~ "Other"),
Age=case_when(
Age %in% c("neonates", "neonatal", "Neonates", "<1") ~ "<1",
TRUE ~ Age)) %>%
#Collapse into cause groups
group_by(Year, Age, Sex, Cause) %>%
summarise(Dx=sum(Deaths), .groups="drop")
#Set up framework
years <- length(unique(ewdata$Year))
ages <- length(unique(ewdata$Age))
causes <- length(unique(ewdata$Cause))
frame <- data.frame(Year=rep(2001:(2001+years-1), times=1, each=2*ages*causes),
Sex=rep(1:2, times=years, each=ages*causes),
Age=rep(unique(ewdata$Age), times=2*years, each=causes),
Cause=rep(unique(ewdata$Cause), times=2*years*ages, each=1))
#Widen for smoothing
ewdata.wide <- ewdata %>%
merge(frame, all.y=TRUE) %>%
mutate(Dx=replace_na(Dx, 0)) %>%
spread(Year, Dx)
#Add all-cause deaths group
ewdata.wide <- ewdata.wide %>%
group_by(Age, Sex) %>%
summarise(across(c(`2001`:`2021`), sum)) %>%
ungroup() %>%
mutate(Cause="Total") %>%
bind_rows(ewdata.wide)%>%
#Initiate start of age groups
mutate(agestart=case_when(
Age %in% c("neonatal", "neonates", "Neonates", "<1") ~ 0,
Age=="01-04" ~ 1, Age=="05-09" ~ 5, Age=="10-14" ~ 10, Age=="15-19" ~ 15,
Age=="20-24" ~ 20, Age=="25-29" ~ 25, Age=="30-34" ~ 30, Age=="35-39" ~ 35,
Age=="40-44" ~ 40, Age=="45-49" ~ 45, Age=="50-54" ~ 50, Age=="55-59" ~ 55,
Age=="60-64" ~ 60, Age=="65-69" ~ 65, Age=="70-74" ~ 70, Age=="75-79" ~ 75,
Age=="80-84" ~ 80, TRUE ~ 85),
Age=case_when(
Age=="<1" ~ "0", Age=="01-04" ~ "1-4", TRUE ~ Age)) %>%
arrange(agestart) %>%
relocate(Cause, agestart)
#Download populations/exposures from HMD
ewpop <- readHMDweb(CNTRY="GBRTENW", "Population", username, password, fixup=FALSE) %>%
mutate(Age=as.numeric(Age), Age=if_else(is.na(Age), 110, Age)) %>%
filter(Year>=2001) %>%
gather(Sex, Ex, c("Male", "Female")) %>%
select(c("Age", "Sex", "Year", "Ex")) %>%
spread(Year, Ex) %>%
mutate(Sex=if_else(Sex=="Male", 1, 2))
#Group populations to match deaths age groups
ewpop.grouped <- ewpop %>%
mutate(agestart=case_when(
Age==0 ~ 0, Age<5 ~ 1, Age<10 ~ 5, Age<15 ~ 10, Age<20 ~ 15,
Age<25 ~ 20, Age<30 ~ 25, Age<35 ~ 30, Age<40 ~ 35, Age<45 ~ 40, Age<50 ~ 45,
Age<55 ~ 50, Age<60 ~ 55, Age<65 ~ 60, Age<70 ~ 65, Age<75 ~ 70, Age<80 ~ 75,
Age<85 ~ 80, TRUE ~ 85)) %>%
group_by(Sex, agestart) %>%
summarise(across(`2001`:`2021`, sum, na.rm=TRUE)) %>%
ungroup()
rm(list=setdiff(ls(), c("ewdata.wide", "ewpop", "ewpop.grouped", "username", "password",
"font", "theme_custom")))
##########
#Scotland#
##########
#Get Scottish data
scotfile.2021 <- tempfile()
scoturl.2021 <- "https://www.nrscotland.gov.uk/files//statistics/vital-events-ref-tables/2021/vital-events-21-ref-tabs-6.xlsx"
scotfile.2021 <- curl_download(url=scoturl.2021, destfile=scotfile.2021, quiet=FALSE, mode="wb")
scotdata.2021 <- read_excel(scotfile.2021, sheet="6.04", range=c("B10:Y1726"), col_names=FALSE) %>%
mutate(Year=2021)
scotfile.2020 <- tempfile()
scoturl.2020 <- "https://www.nrscotland.gov.uk/files//statistics/vital-events-ref-tables/2020/vital-events-20-ref-tabs-6.xlsx"
scotfile.2020 <- curl_download(url=scoturl.2020, destfile=scotfile.2020, quiet=FALSE, mode="wb")
scotdata.2020 <- read_excel(scotfile.2020, sheet="6.04", range=c("A9:X1789"), col_names=FALSE) %>%
mutate(Year=2020)
scotfile.2019 <- tempfile()
scoturl.2019 <- "https://www.nrscotland.gov.uk/files//statistics/vital-events-ref-tables/2019/vital-events-19-ref-tabs-6.xlsx"
scotfile.2019 <- curl_download(url=scoturl.2019, destfile=scotfile.2019, quiet=FALSE, mode="wb")
scotdata.2019 <- read_excel(scotfile.2019, sheet="6.04", range=c("A9:X1739"), col_names=FALSE) %>%
mutate(Year=2019)
scotfile.2018 <- tempfile()
scoturl.2018 <- "https://www.nrscotland.gov.uk/files//statistics/vital-events-ref-tables/2018/vital-events-18-ref-tabs-6.xlsx"
scotfile.2018 <- curl_download(url=scoturl.2018, destfile=scotfile.2018, quiet=FALSE, mode="wb")
scotdata.2018 <- read_excel(scotfile.2018, sheet="6.04", range=c("A9:X1698"), col_names=FALSE) %>%
mutate(Year=2018)
scotfile.2017 <- tempfile()
scoturl.2017 <- "https://www.nrscotland.gov.uk/files//statistics/vital-events-ref-tables/2017/vital-events-17-ref-tabs-6-corrected.xlsx"
scotfile.2017 <- curl_download(url=scoturl.2017, destfile=scotfile.2017, quiet=FALSE, mode="wb")
scotdata.2017 <- read_excel(scotfile.2017, sheet="6.04", range=c("A9:X1767"), col_names=FALSE) %>%
mutate(Year=2017)
scotfile.2016 <- tempfile()
scoturl.2016 <- "https://www.nrscotland.gov.uk/files//statistics/vital-events-ref-tables/16/6-d-cause/ve-ref-tabs-16-tab6.04.xlsx"
scotfile.2016 <- curl_download(url=scoturl.2016, destfile=scotfile.2016, quiet=FALSE, mode="wb")
scotdata.2016 <- read_excel(scotfile.2016, sheet="6.04", range=c("A9:X1776"), col_names=FALSE) %>%
mutate(Year=2016)
scotfile.2015 <- tempfile()
scoturl.2015 <- "https://www.nrscotland.gov.uk/files//statistics/vital-events-ref-tables/2015/section6/15-vital-events-ref-tabs-6-4.xlsx"
scotfile.2015 <- curl_download(url=scoturl.2015, destfile=scotfile.2015, quiet=FALSE, mode="wb")
scotdata.2015 <- read_excel(scotfile.2015, sheet="6.4", range=c("A9:W1784"), col_names=FALSE) %>%
mutate(Year=2015)
scotfile.2014 <- tempfile()
scoturl.2014 <- "https://www.nrscotland.gov.uk/files//statistics/vital-events-ref-tables/2014/section-6/14-vital-events-ref-tabs-6-4.xlsx"
scotfile.2014 <- curl_download(url=scoturl.2014, destfile=scotfile.2014, quiet=FALSE, mode="wb")
scotdata.2014 <- read_excel(scotfile.2014, sheet="6.4", range=c("A9:W1683"), col_names=FALSE) %>%
mutate(Year=2014)
scotfile.2013 <- tempfile()
scoturl.2013 <- "https://www.nrscotland.gov.uk/files//statistics/ve-ref-tables-2013/2013-ref-tabs-6-4.xls"
scotfile.2013 <- curl_download(url=scoturl.2013, destfile=scotfile.2013, quiet=FALSE, mode="wb")
scotdata.2013 <- read_excel(scotfile.2013, sheet="6.4", range=c("A9:W1741"), col_names=FALSE) %>%
mutate(Year=2013)
scotfile.2012 <- tempfile()
scoturl.2012 <- "https://www.nrscotland.gov.uk/files/statistics/ve-ref-tables-2012/ve-12-t6-4.xls"
scotfile.2012 <- curl_download(url=scoturl.2012, destfile=scotfile.2012, quiet=FALSE, mode="wb")
scotdata.2012 <- read_excel(scotfile.2012, sheet="6.4", range=c("A9:W1736"), col_names=FALSE) %>%
mutate(Year=2012)
scotfile.2011 <- tempfile()
scoturl.2011 <- "https://www.nrscotland.gov.uk/files/statistics/ve-reftables-2011/ve-2011-t6.4.xls"
scotfile.2011 <- curl_download(url=scoturl.2011, destfile=scotfile.2011, quiet=FALSE, mode="wb")
scotdata.2011 <- read_excel(scotfile.2011, sheet="6.4", range=c("A9:W1791"), col_names=FALSE) %>%
mutate(Year=2011)
scotfile.2010 <- tempfile()
scoturl.2010 <- "https://www.nrscotland.gov.uk/files/statistics/ve-reftables-2010/ve10-t6-4.xls"
scotfile.2010 <- curl_download(url=scoturl.2010, destfile=scotfile.2010, quiet=FALSE, mode="wb")
scotdata.2010 <- read_excel(scotfile.2010, range=c("A9:W1779"), col_names=FALSE) %>%
mutate(Year=2010)
scotfile.2009 <- tempfile()
scoturl.2009 <- "https://www.nrscotland.gov.uk/files/statistics/ve-reftables-09/ve09-t6-4.xls"
scotfile.2009 <- curl_download(url=scoturl.2009, destfile=scotfile.2009, quiet=FALSE, mode="wb")
scotdata.2009 <- read_excel(scotfile.2009, range=c("A9:W1794"), col_names=FALSE) %>%
mutate(Year=2009)
scotfile.2008 <- tempfile()
scoturl.2008 <- "https://www.nrscotland.gov.uk/files/statistics/vital-events-ref-tables-2008/ve-2008-t6-4.xls"
scotfile.2008 <- curl_download(url=scoturl.2008, destfile=scotfile.2008, quiet=FALSE, mode="wb")
scotdata.2008 <- read_excel(scotfile.2008, range=c("A9:W1811"), col_names=FALSE) %>%
mutate(Year=2008)
scotfile.2007 <- tempfile()
scoturl.2007 <- "https://www.nrscotland.gov.uk/files/statistics/07t6-4.xls"
scotfile.2007 <- curl_download(url=scoturl.2007, destfile=scotfile.2007, quiet=FALSE, mode="wb")
scotdata.2007 <- read_excel(scotfile.2007, range=c("A9:W1867"), col_names=FALSE) %>%
mutate(Year=2007)
scotfile.2006 <- tempfile()
scoturl.2006 <- "https://www.nrscotland.gov.uk/files/statistics/06t6-4%20rev.xls"
scotfile.2006 <- curl_download(url=scoturl.2006, destfile=scotfile.2006, quiet=FALSE, mode="wb")
scotdata.2006 <- read_excel(scotfile.2006, range=c("A8:W1984"), col_names=FALSE) %>%
mutate(Year=2006)
scotfile.2005 <- tempfile()
scoturl.2005 <- "https://www.nrscotland.gov.uk/files/statistics/old/05t6-4.xls"
scotfile.2005 <- curl_download(url=scoturl.2005, destfile=scotfile.2005, quiet=FALSE, mode="wb")
scotdata.2005 <- read_excel(scotfile.2005, range=c("A9:X2017"), col_names=FALSE) %>%
mutate(Year=2005)
scotfile.2004 <- tempfile()
scoturl.2004 <- "https://www.nrscotland.gov.uk/files/statistics/old/04t6-4.xls"
scotfile.2004 <- curl_download(url=scoturl.2004, destfile=scotfile.2004, quiet=FALSE, mode="wb")
scotdata.2004 <- read_excel(scotfile.2004, range=c("A9:X2033"), col_names=FALSE) %>%
mutate(Year=2004)
scotfile.2003 <- tempfile()
scoturl.2003 <- "https://www.nrscotland.gov.uk/files/statistics/old/03t6-4.xls"
scotfile.2003 <- curl_download(url=scoturl.2003, destfile=scotfile.2003, quiet=FALSE, mode="wb")
scotdata.2003 <- read_excel(scotfile.2003, range=c("A9:X2081"), col_names=FALSE) %>%
mutate(Year=2003)
scotfile.2002 <- tempfile()
scoturl.2002 <- "https://www.nrscotland.gov.uk/files/statistics/old/02t6-4.xls"
scotfile.2002 <- curl_download(url=scoturl.2002, destfile=scotfile.2002, quiet=FALSE, mode="wb")
scotdata.2002 <- read_excel(scotfile.2002, range=c("A9:X2067"), col_names=FALSE) %>%
mutate(Year=2002)
scotfile.2001 <- tempfile()
scoturl.2001 <- "https://www.nrscotland.gov.uk/files/statistics/old/01t6_4.xls"
scotfile.2001 <- curl_download(url=scoturl.2001, destfile=scotfile.2001, quiet=FALSE, mode="wb")
scotdata.2001 <- read_excel(scotfile.2001, range=c("A9:X2056"), col_names=FALSE) %>%
mutate(Year=2001)
#Bring together older data, with infuriatingly slightly different formatting
scotdata <- bind_rows(scotdata.2001, scotdata.2002, scotdata.2003, scotdata.2004, scotdata.2005) %>%
filter(!is.na(`...4`)) %>%
rename(ICD10=`...1`, Sex=`...4`) %>%
gather(Age, Dx, c(6:24)) %>%
fill(ICD10) %>%
filter(nchar(ICD10)<=3) %>%
#Stupid faff because of *horrible* formatting choices in the data
mutate(`...5`=as.numeric(`...5`)) %>%
arrange(Year, Sex, Age, ICD10, `...5`) %>%
distinct(Year, Sex, Age, ICD10, .keep_all=TRUE) %>%
select(-c(`...2`, `...3`, `...5`)) %>%
mutate(Dx=as.numeric(if_else(Dx %in% c("-", ".", NA), "0", Dx)),
Age=case_when(Age=="...6" ~ "0", Age=="...7" ~ "1-4",Age=="...8" ~ "5-9",
Age=="...9" ~ "10-14", Age=="...10" ~ "15-19", Age=="...11" ~ "20-24",
Age=="...12" ~ "25-29", Age=="...13" ~ "30-34", Age=="...14" ~ "35-39",
Age=="...15" ~ "40-44", Age=="...16" ~ "45-49", Age=="...17" ~ "50-54",
Age=="...18" ~ "55-59", Age=="...19" ~ "60-64", Age=="...20" ~ "65-69",
Age=="...21" ~ "70-74", Age=="...22" ~ "75-79", Age=="...23" ~ "80-84",
Age=="...24" ~ "85+"),
Sex=if_else(Sex=="M", 1, 2))
#Age groups are 90+ in 2016 onwards, but 85+ before then
scotdata <- bind_rows(scotdata.2006, scotdata.2007, scotdata.2008, scotdata.2009, scotdata.2010,
scotdata.2011, scotdata.2012, scotdata.2013, scotdata.2014, scotdata.2015,
scotdata.2016, scotdata.2017, scotdata.2018, scotdata.2019, scotdata.2020) %>%
filter(!is.na(`...3`)) %>%
rename(ICD10=`...1`, Sex=`...3`) %>%
gather(Age, Dx, c(5:23, 25)) %>%
fill(ICD10) %>%
filter(nchar(ICD10)<=3) %>%
#Stupid faff because of *horrible* formatting choices in the data
mutate(`...4`=as.numeric(`...4`)) %>%
arrange(Year, Sex, Age, ICD10, `...4`) %>%
distinct(Year, Sex, Age, ICD10, .keep_all=TRUE) %>%
select(-c(`...2`, `...4`)) %>%
mutate(Dx=as.numeric(if_else(Dx %in% c("-", ".", NA), "0", Dx)),
Age=case_when(Age=="...5" ~ "0", Age=="...6" ~ "1-4",Age=="...7" ~ "5-9",
Age=="...8" ~ "10-14", Age=="...9" ~ "15-19", Age=="...10" ~ "20-24",
Age=="...11" ~ "25-29", Age=="...12" ~ "30-34", Age=="...13" ~ "35-39",
Age=="...14" ~ "40-44", Age=="...15" ~ "45-49", Age=="...16" ~ "50-54",
Age=="...17" ~ "55-59", Age=="...18" ~ "60-64", Age=="...19" ~ "65-69",
Age=="...20" ~ "70-74", Age=="...21" ~ "75-79", Age=="...22" ~ "80-84",
TRUE ~ "85+"),
Sex=if_else(Sex=="M", 1, 2)) %>%
#Add in 2021 data, which was formatted differently (yay)
bind_rows(scotdata.2021 %>%
#Sort out first 26 rows which are extra borked for some reason
slice_head(n=26) %>%
mutate(lag1=lag(`...1`, n=1), lag2=lag(`...2`, n=1),
`...1`=coalesce(`...1`, lag1), `...2`=coalesce(`...2`, lag2)) %>%
bind_rows(scotdata.2021 %>% slice_tail(n=(nrow(.)-26))) %>%
filter(!is.na(`...2`)) %>%
mutate(ICD10=substr(`...2`, 1, 3)) %>%
select(-`...4`) %>%
gather(Age, Dx, c(4:23)) %>%
select(Year, `...3`, Age, ICD10, Dx) %>%
rename(Sex=`...3`) %>%
mutate(Dx=replace_na(as.numeric(Dx),0),
Age=case_when(Age=="...5" ~ "0", Age=="...6" ~ "1-4",Age=="...7" ~ "5-9",
Age=="...8" ~ "10-14", Age=="...9" ~ "15-19", Age=="...10" ~ "20-24",
Age=="...11" ~ "25-29", Age=="...12" ~ "30-34", Age=="...13" ~ "35-39",
Age=="...14" ~ "40-44", Age=="...15" ~ "45-49", Age=="...16" ~ "50-54",
Age=="...17" ~ "55-59", Age=="...18" ~ "60-64", Age=="...19" ~ "65-69",
Age=="...20" ~ "70-74", Age=="...21" ~ "75-79", Age=="...22" ~ "80-84",
TRUE ~ "85+"),
Sex=if_else(Sex=="M", 1, 2))) %>%
#Collapse into age groups we actually want
group_by(Age, Sex, Year, ICD10) %>%
summarise(Dx=sum(Dx), .groups="drop") %>%
bind_rows(scotdata) %>%
#Remove stuborn codes that remain due to bad formatting in the data
filter(!ICD10 %in% c("E90", "F99", "G99", "J99", "K93", "L99", "M99", "N99", "Y98")) %>%
filter(!(ICD10=="I99" & Year==2006)) %>%
mutate(code1=substr(ICD10, 1, 1), code2=as.numeric(substr(ICD10,2,3)),
Cause=case_when(
code1=="K" & code2 %in% c(70, 73, 74) ~ "Alcohol",
code1=="F" & code2==10 ~ "Alcohol",
code1=="X" & code2==45 ~ "Alcohol", #Difference from the Masters defns as X45 is clearly alcohol-related
code1=="Y" & code2==15 ~ "Alcohol", #Difference from the Masters defns as Y15 is clearly alcohol-related
code1=="X" & code2 %in% c(40:44, 85) ~ "Drugs",
code1=="Y" & code2 %in% c(10:14) ~ "Drugs",
code1=="F" & code2 %in% c(11:16, 18, 19) ~ "Drugs", #Including F18 here to align with Scottish data
code1=="U" & code2==3 ~ "Suicide",
code1=="X" & code2 %in% c(60:84) ~ "Suicide",
code1=="Y" & code2 ==87 ~ "Suicide",
TRUE ~ "Other")) %>%
group_by(Sex, Age, Cause, Year) %>%
summarise(Dx=sum(Dx)) %>%
ungroup()
#Add all-cause deaths group
scotdata.wide <- scotdata %>%
spread(Year, Dx) %>%
group_by(Age, Sex) %>%
summarise(across(c(`2001`:`2021`), sum)) %>%
ungroup() %>%
mutate(Cause="Total") %>%
bind_rows(scotdata %>% spread(Year, Dx) )%>%
#Initiate start of age groups
mutate(agestart=case_when(
Age=="0" ~ 0,
Age=="1-4" ~ 1, Age=="5-9" ~ 5, Age=="10-14" ~ 10, Age=="15-19" ~ 15,
Age=="20-24" ~ 20, Age=="25-29" ~ 25, Age=="30-34" ~ 30, Age=="35-39" ~ 35,
Age=="40-44" ~ 40, Age=="45-49" ~ 45, Age=="50-54" ~ 50, Age=="55-59" ~ 55,
Age=="60-64" ~ 60, Age=="65-69" ~ 65, Age=="70-74" ~ 70, Age=="75-79" ~ 75,
Age=="80-84" ~ 80, TRUE ~ 85)) %>%
arrange(agestart) %>%
relocate(Cause, agestart)
#Download populations
scotpop <- readHMDweb(CNTRY="GBR_SCO", "Population", username, password, fixup=FALSE) %>%
mutate(Age=as.numeric(Age), Age=if_else(is.na(Age), 110, Age)) %>%
filter(Year>=2001) %>%
gather(Sex, Ex, c("Male", "Female")) %>%
select(c("Age", "Sex", "Year", "Ex")) %>%
spread(Year, Ex) %>%
mutate(Sex=if_else(Sex=="Male", 1, 2))
#Group populations to match deaths age groups
scotpop.grouped <- scotpop %>%
mutate(agestart=case_when(
Age==0 ~ 0, Age<5 ~ 1, Age<10 ~ 5, Age<15 ~ 10, Age<20 ~ 15,
Age<25 ~ 20, Age<30 ~ 25, Age<35 ~ 30, Age<40 ~ 35, Age<45 ~ 40, Age<50 ~ 45,
Age<55 ~ 50, Age<60 ~ 55, Age<65 ~ 60, Age<70 ~ 65, Age<75 ~ 70, Age<80 ~ 75,
Age<85 ~ 80, TRUE ~ 85)) %>%
group_by(Sex, agestart) %>%
summarise(across(`2001`:`2021`, sum, na.rm=TRUE)) %>%
ungroup()
rm(list=setdiff(ls(), c("ewdata.wide", "ewpop", "ewpop.grouped", "scotdata.wide", "scotpop",
"scotpop.grouped", "username", "password", "font", "theme_custom",
"cleanedpop")))
##################
#Northern Ireland#
##################
#Get NI data
nifile.2021 <- tempfile()
niurl.2021 <- "https://www.nisra.gov.uk/system/files/statistics/Section%206%20-%20Cause_Death_Tables_2021_Final.xlsx"
nifile.2021 <- curl_download(url=niurl.2021, destfile=nifile.2021, quiet=FALSE, mode="wb")
nidata.2021 <- read_excel(nifile.2021, sheet="Table 6.4", range=c("A7:V1944"), col_names=FALSE) %>%
mutate(Year=2021,
#Formatting of various conditions is wonky and unhelpful
`...1`=gsub("\\(G60) ", "\\(G60):", `...1`)) %>%
separate(`...1`, into=c("Condition", "Sex"), sep=":")%>%
mutate(Condition=gsub("\\(chickenpox", "chickenpox", Condition)) %>%
mutate(Condition=gsub("\\(HIV", "HIV", Condition)) %>%
mutate(Condition=gsub("\\(nodular", "nodular", Condition)) %>%
mutate(Condition=gsub("\\(primary", "primary", Condition)) %>%
mutate(Condition=gsub("\\(hyperthyroidism", "hyperthyroidism", Condition)) %>%
mutate(Condition=gsub("\\(affective", "affective", Condition)) %>%
mutate(Condition=gsub("\\(suicide", "suicide", Condition)) %>%
mutate(Condition=gsub("I06", "\\(I06", Condition)) %>%
mutate(Condition=gsub(" K20", "\\(K20)", Condition)) %>%
mutate(Condition=gsub("M00-M25", "\\(M00-M25", Condition),
Sex=str_trim(Sex, side="left")) %>%
separate(Condition, c("Cause", "ICD10"), sep="\\(") %>%
mutate(ICD10=gsub("\\)", "", ICD10))
niallcause.2021 <- nidata.2021 %>% slice_head(n=2)
nidata.2021 <- nidata.2021 %>% slice(3:nrow(.))
nifile.2020 <- tempfile()
niurl.2020 <- "https://www.nisra.gov.uk/system/files/statistics/Section%206%20-%20Cause_Death_Tables_2020_Revised%20June%202022.xlsx"
nifile.2020 <- curl_download(url=niurl.2020, destfile=nifile.2020, quiet=FALSE, mode="wb")
nidata.2020 <- read_excel(nifile.2020, sheet="Table 6.4", range=c("A7:V1904"), col_names=FALSE) %>%
mutate(Year=2020,
#Formatting of various conditions is wonky and unhelpful
`...1`=gsub("\\(G60) ", "\\(G60):", `...1`)) %>%
separate(`...1`, into=c("Condition", "Sex"), sep=":")%>%
mutate(Condition=gsub("\\(chickenpox", "chickenpox", Condition)) %>%
mutate(Condition=gsub("\\(HIV", "HIV", Condition)) %>%
mutate(Condition=gsub("\\(nodular", "nodular", Condition)) %>%
mutate(Condition=gsub("\\(primary", "primary", Condition)) %>%
mutate(Condition=gsub("\\(hyperthyroidism", "hyperthyroidism", Condition)) %>%
mutate(Condition=gsub("\\(affective", "affective", Condition)) %>%
mutate(Condition=gsub("\\(suicide", "suicide", Condition)) %>%
mutate(Condition=gsub("I06", "\\(I06", Condition)) %>%
mutate(Condition=gsub(" K20", "\\(K20)", Condition)) %>%
mutate(Condition=gsub("M00-M25", "\\(M00-M25", Condition),
Sex=str_trim(Sex, side="left")) %>%
separate(Condition, c("Cause", "ICD10"), sep="\\(")%>%
mutate(ICD10=gsub("\\)", "", ICD10))
niallcause.2020 <- nidata.2020 %>% slice_head(n=2)
nidata.2020 <- nidata.2020 %>% slice(3:nrow(.))
nifile.2019 <- tempfile()
niurl.2019 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/Cause_Death_Tables_2019.xlsx"
nifile.2019 <- curl_download(url=niurl.2019, destfile=nifile.2019, quiet=FALSE, mode="wb")
nidata.2019 <- read_excel(nifile.2019, sheet="Table 6.4", range=c("A8:X2950"), col_names=FALSE) %>%
mutate(Year=2019)
niallcause.2019 <- nidata.2019 %>% slice_head(n=2)
nidata.2019 <- nidata.2019 %>% slice(4:nrow(.))
temp <- tempfile()
nifile.2018 <- tempfile()
niurl.2018 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/RG_tables_2018.zip"
temp <- curl_download(url=niurl.2018, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2018)
nidata.2018 <- read_excel(file.path(nifile.2018, "Section 6 - Cause of Death.xlsx"), sheet="Table 6.4",
range=c("A8:X2882"), col_names=FALSE) %>%
mutate(Year=2018)
niallcause.2018 <- nidata.2018 %>% slice_head(n=2)
nidata.2018 <- nidata.2018 %>% slice(4:nrow(.))
nifile.2017 <- tempfile()
niurl.2017 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/RG_tables_2017.zip"
temp <- curl_download(url=niurl.2017, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2017)
nidata.2017 <- read_excel(file.path(nifile.2017, "CauseOfDeath_2017.xls"), sheet="Table 6.4",
range=c("A8:X2817"), col_names=FALSE) %>%
mutate(Year=2017)
niallcause.2017 <- nidata.2017 %>% slice_head(n=2)
nidata.2017 <- nidata.2017 %>% slice(3:nrow(.))
nifile.2016 <- tempfile()
niurl.2016 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/RG_tables_2016.zip"
temp <- curl_download(url=niurl.2016, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2016)
nidata.2016 <- read_excel(file.path(nifile.2016, "CauseOfDeath_2016.xls"), sheet="Table 6.4",
range=c("A8:X2150"), col_names=FALSE) %>%
mutate(Year=2016)
niallcause.2016 <- nidata.2016 %>% slice_head(n=2)
nidata.2016 <- nidata.2016 %>% slice(4:nrow(.))
nifile.2015 <- tempfile()
niurl.2015 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/Tables_2015.zip"
temp <- curl_download(url=niurl.2015, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2015)
nidata.2015 <- read_excel(file.path(nifile.2015, "Chpt6_2015.xls"), sheet="Table6.4",
range=c("A6:X5623"), col_names=FALSE) %>%
mutate(Year=2015)
niallcause.2015 <- nidata.2015 %>% slice_head(n=2)
nidata.2015 <- nidata.2015 %>% slice(4:nrow(.))
nifile.2014 <- tempfile()
niurl.2014 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/Tables_2014.zip"
temp <- curl_download(url=niurl.2014, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2014)
nidata.2014 <- read_excel(file.path(nifile.2014, "Chpt6_2014.xls"), sheet="Table6.4",
range=c("A6:X5607"), col_names=FALSE) %>%
mutate(Year=2014)
niallcause.2014 <- nidata.2014 %>% slice_head(n=2)
nidata.2014 <- nidata.2014 %>% slice(4:nrow(.))
nifile.2013 <- tempfile()
niurl.2013 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/Tables_2013.zip"
temp <- curl_download(url=niurl.2013, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2013)
nidata.2013 <- read_excel(file.path(nifile.2013, "Chpt6_2013.xls"), sheet="Table 6.4",
range=c("A8:X2540"), col_names=FALSE) %>%
mutate(Year=2013)
niallcause.2013 <- nidata.2013 %>% slice_head(n=2)
nidata.2013 <- nidata.2013 %>% slice(4:nrow(.))
nifile.2012 <- tempfile()
niurl.2012 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/Tables_2012.zip"
temp <- curl_download(url=niurl.2012, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2012)
nidata.2012 <- read_excel(file.path(nifile.2012, "Chpt6_2012.xls"), sheet="Table 6.4",
range=c("A8:X2292"), col_names=FALSE) %>%
mutate(Year=2012)
niallcause.2012 <- nidata.2012 %>% slice_head(n=2)
nidata.2012 <- nidata.2012 %>% slice(4:nrow(.))
nifile.2011 <- tempfile()
niurl.2011 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/Tables_2011.zip"
temp <- curl_download(url=niurl.2011, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2011)
nidata.2011 <- read_excel(file.path(nifile.2011, "Chpt6_2011.xls"), sheet="Table 6.4",
range=c("A8:X2289"), col_names=FALSE) %>%
mutate(Year=2011)
niallcause.2011 <- nidata.2011 %>% slice_head(n=2)
nidata.2011 <- nidata.2011 %>% slice(4:nrow(.))
nifile.2010 <- tempfile()
niurl.2010 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/Tables_2010.zip"
temp <- curl_download(url=niurl.2010, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2010)
nidata.2010 <- read_excel(file.path(nifile.2010, "Chpt6_2010.xls"), sheet="Table 6.4",
range=c("A9:X2075"), col_names=FALSE) %>%
mutate(Year=2010)
niallcause.2010 <- nidata.2010 %>% slice_head(n=2)
nidata.2010 <- nidata.2010 %>% slice(4:nrow(.))
nifile.2009 <- tempfile()
niurl.2009 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/Tables_2009.zip"
temp <- curl_download(url=niurl.2009, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2009)
nidata.2009 <- read_excel(file.path(nifile.2009, "Chpt6_2009.xls"), sheet="Table 6.4",
range=c("A9:X2075"), col_names=FALSE) %>%
mutate(Year=2009)
niallcause.2009 <- nidata.2009 %>% slice_head(n=2)
nidata.2009 <- nidata.2009 %>% slice(4:nrow(.))
nifile.2008 <- tempfile()
niurl.2008 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/Tables_2008.zip"
temp <- curl_download(url=niurl.2008, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2008)
nidata.2008 <- read_excel(file.path(nifile.2008, "Chpt6_2008.xls"), sheet="Table 6.4",
range=c("A9:X2100"), col_names=FALSE) %>%
mutate(Year=2008)
niallcause.2008 <- nidata.2008 %>% slice_head(n=2)
nidata.2008 <- nidata.2008 %>% slice(4:nrow(.))
nifile.2007 <- tempfile()
niurl.2007 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/Tables_2007.zip"
temp <- curl_download(url=niurl.2007, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2007)
nidata.2007 <- read_excel(file.path(nifile.2007, "Chpt6_2007.xls"), sheet="Table 6.4",
range=c("A8:X2200"), col_names=FALSE) %>%
mutate(Year=2007)
niallcause.2007 <- nidata.2007 %>% slice_head(n=2)
nidata.2007 <- nidata.2007 %>% slice(4:nrow(.))
nifile.2006 <- tempfile()
niurl.2006 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/Tables_2006.zip"
temp <- curl_download(url=niurl.2006, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2006)
nidata.2006 <- read_excel(file.path(nifile.2006, "Chpt6_2006.xls"), sheet="Table 6.4",
range=c("A8:X2200"), col_names=FALSE) %>%
mutate(Year=2006)
niallcause.2006 <- nidata.2006 %>% slice_head(n=2)
nidata.2006 <- nidata.2006 %>% slice(4:nrow(.))
nifile.2005 <- tempfile()
niurl.2005 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/Tables_2005.zip"
temp <- curl_download(url=niurl.2005, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2005)
nidata.2005 <- read_excel(file.path(nifile.2005, "Chpt6_2005.xls"), sheet="Table 6.4",
range=c("A8:X2300"), col_names=FALSE) %>%
mutate(Year=2005)
niallcause.2005 <- nidata.2005 %>% slice_head(n=2)
nidata.2005 <- nidata.2005 %>% slice(4:nrow(.))
nifile.2004 <- tempfile()
niurl.2004 <- "https://www.nisra.gov.uk/sites/nisra.gov.uk/files/publications/Tables_2004.zip"
temp <- curl_download(url=niurl.2004, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=nifile.2004)
nidata.2004 <- read_excel(file.path(nifile.2004, "AnnRep6_2004.xls"), sheet="Table 6.4",
range=c("A8:X2300"), col_names=FALSE) %>%
mutate(Year=2004)
niallcause.2004 <- nidata.2004 %>% slice_head(n=2)
nidata.2004 <- nidata.2004 %>% slice(4:nrow(.))
#2003 data and earlier is only available as pdfs
#https://www.nisra.gov.uk/publications/registrar-general-annual-reports-2001-2010
#Many thanks to Irina Kolegova for digitising these
niurl.0103 <- "https://github.com/VictimOfMaths/DeathsOfDespair/raw/master/Paper/NITables6_4_2001_2003.xlsx"
temp <- curl_download(url=niurl.0103, destfile=temp, quiet=FALSE, mode="wb")
nidata.2003 <- read_excel(temp, sheet="2003_no_sum", range=c("A6:X1295"), col_names=FALSE) %>%
mutate(Year=2003)
niallcause.2003 <- nidata.2003 %>% slice_head(n=2)
nidata.2003 <- nidata.2003 %>% slice(3:nrow(.))
nidata.2002 <- read_excel(temp, sheet="2002_no_sum", range=c("A6:X1295"), col_names=FALSE) %>%
mutate(Year=2002)
niallcause.2002 <- nidata.2002 %>% slice_head(n=2)
nidata.2002 <- nidata.2002 %>% slice(3:nrow(.))
nidata.2001 <- read_excel(temp, sheet="2001_no_sum", range=c("A6:X1258"), col_names=FALSE) %>%
mutate(Year=2001,
across(c(4:24), as.character))
niallcause.2001 <- nidata.2001 %>% slice_head(n=2)
nidata.2001 <- nidata.2001 %>% slice(3:nrow(.))
#Stitch them all together
nidata <- bind_rows(nidata.2001, nidata.2002, nidata.2003, nidata.2004, nidata.2005, nidata.2006,
nidata.2007, nidata.2008, nidata.2009, nidata.2010, nidata.2011) %>%
fill(`...1`) %>%
filter(!is.na(`...4`)) %>%
select(-4) %>%
gather(Age, Dx, c(4:23)) %>%
mutate(Age=case_when(
Age=="...5" ~ "0", Age=="...6" ~ "1-4", Age=="...7" ~ "5-9", Age=="...8" ~ "10-14",
Age=="...9" ~ "15-19", Age=="...10" ~ "20-24", Age=="...11" ~ "25-29", Age=="...12" ~ "30-34",
Age=="...13" ~ "35-39", Age=="...14" ~ "40-44", Age=="...15" ~ "45-49", Age=="...16" ~ "50-54",
Age=="...17" ~ "55-59", Age=="...18" ~ "60-64", Age=="...19" ~ "65-69", Age=="...20" ~ "70-74",
Age=="...21" ~ "75-79", Age=="...22" ~ "80-84", Age=="...23" ~ "85-89", Age=="...24" ~ "90+"),
Dx=as.numeric(Dx), Dx=if_else(is.na(Dx), 0, Dx)) %>%
set_names(c("ICD10", "Cause", "Sex", "Year", "Age", "Dx")) %>%
bind_rows(bind_rows(nidata.2012, nidata.2013, nidata.2014, nidata.2015, nidata.2016,
nidata.2017 ,nidata.2018 ,nidata.2019) %>%
fill(`...1`) %>%
filter(!is.na(`...4`)) %>%
select(-4) %>%
gather(Age, Dx, c(4:23)) %>%
mutate(Age=case_when(
Age=="...5" ~ "0", Age=="...6" ~ "1-4", Age=="...7" ~ "5-9", Age=="...8" ~ "10-14",
Age=="...9" ~ "15-19", Age=="...10" ~ "20-24", Age=="...11" ~ "25-29", Age=="...12" ~ "30-34",
Age=="...13" ~ "35-39", Age=="...14" ~ "40-44", Age=="...15" ~ "45-49", Age=="...16" ~ "50-54",
Age=="...17" ~ "55-59", Age=="...18" ~ "60-64", Age=="...19" ~ "65-69", Age=="...20" ~ "70-74",
Age=="...21" ~ "75-79", Age=="...22" ~ "80-84", Age=="...23" ~ "85-89", Age=="...24" ~ "90+"),
Dx=as.numeric(Dx), Dx=if_else(is.na(Dx), 0, Dx)) %>%
set_names(c("ICD10", "Cause", "Sex", "Year", "Age", "Dx"))) %>%
bind_rows(bind_rows(nidata.2020, nidata.2021) %>% gather(Age, Dx, c(5:24)) %>%
select(-`...4`) %>%
mutate(Age=case_when(
Age=="...5" ~ "0", Age=="...6" ~ "1-4", Age=="...7" ~ "5-9", Age=="...8" ~ "10-14",
Age=="...9" ~ "15-19", Age=="...10" ~ "20-24", Age=="...11" ~ "25-29", Age=="...12" ~ "30-34",
Age=="...13" ~ "35-39", Age=="...14" ~ "40-44", Age=="...15" ~ "45-49", Age=="...16" ~ "50-54",
Age=="...17" ~ "55-59", Age=="...18" ~ "60-64", Age=="...19" ~ "65-69", Age=="...20" ~ "70-74",
Age=="...21" ~ "75-79", Age=="...22" ~ "80-84", Age=="...23" ~ "85-89", Age=="...24" ~ "90+"),
ICD10=gsub("\\)", "", ICD10))) %>%
#Fill down ICD-10 codes and remove rows we don't want (e.g. summary rows)
filter(nchar(ICD10)<=3) %>%
filter(!Sex %in% c("P", "All")) %>%
filter(!ICD10 %in% c("B99", "D48", "D89", "E90", "F99", "G99", "I99", "J99", "K93", "L99", "M99",
"N99", "O99", "P96", "Q99", "R99", "Y98", "X59", "Y09")) %>%
#Assign codes to DoD groupings
mutate(code1=substr(ICD10, 1, 1), code2=as.numeric(substr(ICD10,2,3)),
Cause=case_when(
code1=="K" & code2 %in% c(70, 73, 74) ~ "Alcohol",
code1=="F" & code2==10 ~ "Alcohol",
code1=="X" & code2==45 ~ "Alcohol", #Difference from the Masters defns as X45 is clearly alcohol-related
code1=="Y" & code2==15 ~ "Alcohol", #Difference from the Masters defns as Y15 is clearly alcohol-related
code1=="X" & code2 %in% c(40:44, 85) ~ "Drugs",
code1=="Y" & code2 %in% c(10:14) ~ "Drugs",
code1=="F" & code2 %in% c(11:16, 18, 19) ~ "Drugs", #Including F18 here to align with Scottish data
code1=="U" & code2==3 ~ "Suicide",
code1=="X" & code2 %in% c(60:84) ~ "Suicide",
code1=="Y" & code2 ==87 ~ "Suicide",
TRUE ~ "Other"),
#FOR THE LOVE OF GOD HOW MANY WAYS TO WRITE "Female" ARE THERE?
Sex=as.numeric(if_else(Sex %in% c("Female", "F", "Fales", "females", "FEMALES", "Females"), "2", "1")),
Age=if_else(Age %in% c("85-89", "90+"), "85+", Age)) %>%
group_by(Sex, Age, Cause, Year) %>%
summarise(Dx=sum(Dx)) %>%
ungroup()
#Add all-cause total
niallcause <- bind_rows(niallcause.2001, niallcause.2002, niallcause.2003, niallcause.2004,
niallcause.2005, niallcause.2006, niallcause.2007, niallcause.2008,
niallcause.2009, niallcause.2010, niallcause.2011) %>%
select(-c(`...1`,`...2`, `...4`)) %>%
gather(Age, Dx, c(2:21)) %>%
mutate(Age=case_when(
Age=="...5" ~ "0", Age=="...6" ~ "1-4", Age=="...7" ~ "5-9", Age=="...8" ~ "10-14",
Age=="...9" ~ "15-19", Age=="...10" ~ "20-24", Age=="...11" ~ "25-29", Age=="...12" ~ "30-34",
Age=="...13" ~ "35-39", Age=="...14" ~ "40-44", Age=="...15" ~ "45-49", Age=="...16" ~ "50-54",
Age=="...17" ~ "55-59", Age=="...18" ~ "60-64", Age=="...19" ~ "65-69", Age=="...20" ~ "70-74",
Age=="...21" ~ "75-79", Age=="...22" ~ "80-84", Age=="...23" ~ "85-89", Age=="...24" ~ "90+"),
Dx=as.numeric(Dx), Dx=if_else(is.na(Dx), 0, Dx),
Cause="Total") %>%
set_names(c("Sex", "Year", "Age", "Dx", "Cause")) %>%
bind_rows(bind_rows(niallcause.2012, niallcause.2013, niallcause.2014, niallcause.2015,
niallcause.2016, niallcause.2017, niallcause.2018, niallcause.2019) %>%
select(-c(`...1`,`...2`, `...4`)) %>%
gather(Age, Dx, c(2:21)) %>%
mutate(Age=case_when(
Age=="...5" ~ "0", Age=="...6" ~ "1-4", Age=="...7" ~ "5-9", Age=="...8" ~ "10-14",
Age=="...9" ~ "15-19", Age=="...10" ~ "20-24", Age=="...11" ~ "25-29", Age=="...12" ~ "30-34",
Age=="...13" ~ "35-39", Age=="...14" ~ "40-44", Age=="...15" ~ "45-49", Age=="...16" ~ "50-54",
Age=="...17" ~ "55-59", Age=="...18" ~ "60-64", Age=="...19" ~ "65-69", Age=="...20" ~ "70-74",
Age=="...21" ~ "75-79", Age=="...22" ~ "80-84", Age=="...23" ~ "85-89", Age=="...24" ~ "90+"),
Dx=as.numeric(Dx), Dx=if_else(is.na(Dx), 0, Dx),
Cause="Total") %>%
set_names(c("Sex", "Year", "Age", "Dx", "Cause"))) %>%
bind_rows(bind_rows(niallcause.2020, niallcause.2021) %>% select(-c(ICD10, Cause, `...4`)) %>%
gather(Age, Dx, c(2:21)) %>%
mutate(Age=case_when(
Age=="...5" ~ "0", Age=="...6" ~ "1-4", Age=="...7" ~ "5-9", Age=="...8" ~ "10-14",
Age=="...9" ~ "15-19", Age=="...10" ~ "20-24", Age=="...11" ~ "25-29", Age=="...12" ~ "30-34",
Age=="...13" ~ "35-39", Age=="...14" ~ "40-44", Age=="...15" ~ "45-49", Age=="...16" ~ "50-54",
Age=="...17" ~ "55-59", Age=="...18" ~ "60-64", Age=="...19" ~ "65-69", Age=="...20" ~ "70-74",
Age=="...21" ~ "75-79", Age=="...22" ~ "80-84", Age=="...23" ~ "85-89", Age=="...24" ~ "90+"),
Cause="Total")) %>%
mutate(Sex=as.numeric(if_else(Sex %in% c("M", "Male", "Males"), "1", "2")),
Age=if_else(Age %in% c("85-89", "90+"), "85+", Age)) %>%
group_by(Sex, Age, Cause, Year) %>%
summarise(Dx=sum(Dx)) %>%
ungroup()
nidata.wide <- nidata %>%
bind_rows(niallcause) %>%
spread(Year, Dx) %>%
#Initiate start of age groups
mutate(agestart=case_when(
Age=="0" ~ 0,
Age=="1-4" ~ 1, Age=="5-9" ~ 5, Age=="10-14" ~ 10, Age=="15-19" ~ 15,
Age=="20-24" ~ 20, Age=="25-29" ~ 25, Age=="30-34" ~ 30, Age=="35-39" ~ 35,
Age=="40-44" ~ 40, Age=="45-49" ~ 45, Age=="50-54" ~ 50, Age=="55-59" ~ 55,
Age=="60-64" ~ 60, Age=="65-69" ~ 65, Age=="70-74" ~ 70, Age=="75-79" ~ 75,
Age=="80-84" ~ 80, TRUE ~ 85)) %>%
arrange(agestart) %>%
relocate(Cause, agestart)
#Download populations
nipop <- readHMDweb(CNTRY="GBR_NIR", "Population", username, password, fixup=FALSE) %>%
mutate(Age=as.numeric(Age), Age=if_else(is.na(Age), 110, Age)) %>%
filter(Year>=2001) %>%
gather(Sex, Ex, c("Male", "Female")) %>%
select(c("Age", "Sex", "Year", "Ex")) %>%
spread(Year, Ex) %>%
mutate(Sex=if_else(Sex=="Male", 1, 2))
#Group populations to match deaths age groups
nipop.grouped <- nipop %>%
mutate(agestart=case_when(
Age==0 ~ 0, Age<5 ~ 1, Age<10 ~ 5, Age<15 ~ 10, Age<20 ~ 15,
Age<25 ~ 20, Age<30 ~ 25, Age<35 ~ 30, Age<40 ~ 35, Age<45 ~ 40, Age<50 ~ 45,
Age<55 ~ 50, Age<60 ~ 55, Age<65 ~ 60, Age<70 ~ 65, Age<75 ~ 70, Age<80 ~ 75,
Age<85 ~ 80, TRUE ~ 85)) %>%
group_by(Sex, agestart) %>%
summarise(across(`2001`:`2021`, sum, na.rm=TRUE)) %>%
ungroup()
rm(list=setdiff(ls(), c("ewdata.wide", "ewpop", "ewpop.grouped", "scotdata.wide", "scotpop",
"scotpop.grouped", "nidata.wide", "nipop", "nipop.grouped", "password",
"username", "font", "theme_custom")))
#Combine EW, Scotland & NI
UKdata <- ewdata.wide %>%
gather(Year, Dx, c(5:25)) %>%
select(Cause, agestart, Sex, Year, Dx) %>%
mutate(Country="England & Wales") %>%
merge(ewpop.grouped %>% gather(Year, pop, c(3:23))) %>%
mutate(mx=Dx*100000/pop) %>%
bind_rows(scotdata.wide %>%
gather(Year, Dx, c(5:25)) %>%
select(Cause, agestart, Sex, Year, Dx) %>%
mutate(Country="Scotland") %>%
merge(scotpop.grouped %>% gather(Year, pop, c(3:23))) %>%
mutate(mx=Dx*100000/pop)) %>%
bind_rows(nidata.wide %>%
gather(Year, Dx, c(5:25)) %>%
select(Cause, agestart, Sex, Year, Dx) %>%
mutate(Country="Northern Ireland") %>%
merge(nipop.grouped %>% gather(Year, pop, c(3:23))) %>%
mutate(mx=Dx*100000/pop))
#Apply smoothing based approach suggested by Tim Riffe
#Prediction models fall over if you include <10 year olds, so exclude them as not relevant to analysis
x <- seq(10,85, by=5)
UKdata <- UKdata %>% filter(agestart>=10)
y <- 2001:2021
z <- UKdata %>% select(-c(pop, mx)) %>%
filter(agestart>=10) %>%
spread(Year, Dx) %>%
arrange(agestart)
offset <- UKdata %>% select(-c(Dx, mx)) %>%
filter(agestart>=10) %>%
spread(Year, pop) %>%
arrange(agestart)
#Fit smoothing models within years only
mx_smoothed1D <- data.frame(Country=character(), Cause=character(), Sex=integer(), Age=integer(),
Year=integer(), mx_smt1D=double())
for(i in c("England & Wales", "Northern Ireland", "Scotland")){
for(j in c("Alcohol", "Drugs", "Suicide", "Total")){
for(k in 1:2){
for(l in 2001:2021){
y <- z %>% filter(Country==i & Cause==j & Sex==k) %>%
select(-c(agestart, Sex, Country, Cause, Sex)) %>%
select(c(l-2000)) %>%
unlist() %>%
as.vector()
offset_i <- offset %>% filter(Country==i & Cause==j & Sex==k& agestart>=10) %>%
select(-c(agestart, Sex, Country, Cause, Sex)) %>%
select(c(l-2000)) %>%
log() %>%
unlist() %>%
as.vector()
mod <- Mort1Dsmooth(x, y, offset=offset_i)
mx_smoothed1D <- predict(mod, newdata=c(10:85)) %>%
exp() %>%
as.data.frame() %>%
rename(mx_smt1D=1) %>%
mutate(Age=c(10:85), Country=i, Cause=j, Sex=k, Year=l) %>%
bind_rows(mx_smoothed1D)
}
}
}
}
UKsmoothed <- mx_smoothed1D %>%
merge(ewpop %>% gather(Year, pop.ew, c(3:23))) %>%
merge(scotpop %>% gather(Year, pop.s, c(3:23))) %>%
merge(nipop %>% gather(Year, pop.ni, c(3:23)), all.x=TRUE) %>%
mutate(pop=case_when(
Country=="Scotland" ~ pop.s,
Country=="England & Wales" ~ pop.ew,
TRUE ~ pop.ni)) %>%
select(-c(pop.s, pop.ew, pop.ni)) %>%
mutate(Dx_smt1D=mx_smt1D*pop, Sex=if_else(Sex==1, "Male", "Female"))
agg_png("Outputs/DoDPandemicLexis_Male.png", units="in", width=8, height=13, res=500)
UKsmoothed %>%
filter(Cause %in% c("Alcohol", "Drugs", "Suicide") & Sex=="Male" & Age<80) %>%
ggplot()+
geom_raster(aes(x=Year, y=Age, fill=mx_smt1D*100000))+
scale_x_continuous(breaks=c(2000,2010,2020))+
scale_y_continuous(name="Age", breaks=c(10, 20, 30, 40, 50, 60, 70, 80))+
scale_fill_paletteer_c("viridis::turbo", name="Deaths\nper 100,000")+
facet_grid(Cause~Country)+
theme_custom()+
theme(axis.text.x=element_text(angle=45, hjust=1, vjust=1))+
coord_equal()
dev.off()
agg_png("Outputs/DoDPandemicLexis_Female.png", units="in", width=8, height=13, res=500)
UKsmoothed %>%
filter(Cause %in% c("Alcohol", "Drugs", "Suicide") & Sex=="Female" & Age<80) %>%
ggplot()+
geom_raster(aes(x=Year, y=Age, fill=mx_smt1D*100000))+
scale_x_continuous(breaks=c(2000,2010,2020))+
scale_y_continuous(name="Age", breaks=c(10, 20, 30, 40, 50, 60, 70, 80))+
scale_fill_paletteer_c("viridis::turbo", name="Deaths\nper 100,000")+
facet_grid(Cause~Country)+
theme_custom()+
theme(axis.text.x=element_text(angle=45, hjust=1, vjust=1))+
coord_equal()
dev.off()
#Age-standardise
#Age-standardising within each age-group to European Standard Population
Combined_short <- UKsmoothed %>%
mutate(stdpop=case_when(
Age<15 ~ 5500/5, Age<20 ~ 5500/5, Age<25 ~ 6000/5, Age<30 ~ 6000/5, Age<35 ~ 6500/5,
Age<40 ~ 7000/5, Age<45 ~ 7000/5, Age<50 ~ 7000/5, Age<55 ~ 7000/5, Age<60 ~ 6500/5,
Age<65 ~ 6000/5, Age<70 ~ 5500/5, Age<75 ~ 5000/5, Age<80 ~ 4000/5, Age<85 ~ 2500/5,
Age<90 ~ 1500/5, TRUE ~ 1000
)) %>%
group_by(Country, Cause, Sex, Year) %>%
summarise(Dx_smt1D=sum(Dx_smt1D), pop=sum(pop),