Load national IMD data

Source: https://opendatacommunities.org/data/societal-wellbeing/imd2019/indices

imd <- fread("M:/Analytics/CYP MH/Data for NDL briefing/Liverpool and Wirral/imd2019lsoa.csv",header=TRUE, sep=",", check.names=T)
#SOURCE: https://opendatacommunities.org/data/societal-wellbeing/imd2019/indices
#1 is most deprived

imd <- imd %>%
  filter(.,Measurement=="Decile",
         Indices.of.Deprivation=="a. Index of Multiple Deprivation (IMD)",
         DateCode=="2019") %>%
  select(.,FeatureCode,Value) %>%
  rename(.,`LSOA Code`=FeatureCode,
         IMD19=Value) %>% 
  mutate(.,imd_quintile=case_when(IMD19 %in% c("1","2") ~ "1",
                                 IMD19 %in% c("3","4")  ~ "2",
                                 IMD19 %in% c("5","6")  ~ "3",
                                 IMD19 %in% c("7","8")  ~ "4",
                                 IMD19 %in% c("9","10") ~ "5",
                                 TRUE ~ "NA")) %>%
  select(.,-"IMD19")

#Display snippet

imd %>%
  head(.,n=5) %>% 
  knitr::kable(., align = "lccrr")
LSOA Code imd_quintile
E01006616 1
E01007367 1
E01007376 1
E01006594 5
E01007489 2

Load population data by LSOA

Source: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/lowersuperoutputareamidyearpopulationestimates

#2019 population

boys_pop_19 <- read_excel("M:/Analytics/CYP MH/Data for NDL briefing/Liverpool and Wirral/SAPE22DT2-mid-2019-lsoa-syoa-estimates-unformatted.xlsx",sheet = "Mid-2019 Males", skip = 4) %>%
  mutate(.,sex="M",year="2019")
girls_pop_19 <- read_excel("M:/Analytics/CYP MH/Data for NDL briefing/Liverpool and Wirral/SAPE22DT2-mid-2019-lsoa-syoa-estimates-unformatted.xlsx",sheet = "Mid-2019 Females", skip = 4) %>%
  mutate(.,sex="F",year="2019")
pop19 <- plyr::rbind.fill(boys_pop_19,girls_pop_19)
rm(boys_pop_19,girls_pop_19)

#Use this data to select LSOAs that are in Liverpool or Wirral (based on 2019 boundaries)
LW_lsoas <- pop19 %>%
  filter(`LA name (2019 boundaries)` %in% c("Liverpool","Wirral")) %>%
  pull(`LSOA Code`) %>%
  unique(.)

#2020 population

boys_pop_20 <- read_excel("M:/Analytics/CYP MH/Data for NDL briefing/Liverpool and Wirral/sape23dt2mid2020lsoasyoaestimatesunformatted.xlsx",sheet = "Mid-2020 Males", skip = 4) %>%
  mutate(.,sex="M",year="2020")
girls_pop_20 <- read_excel("M:/Analytics/CYP MH/Data for NDL briefing/Liverpool and Wirral/sape23dt2mid2020lsoasyoaestimatesunformatted.xlsx",sheet = "Mid-2020 Females", skip = 4) %>%
  mutate(.,sex="F",year="2020")
pop20 <- plyr::rbind.fill(boys_pop_20,girls_pop_20)
rm(boys_pop_20,girls_pop_20)

#Combine into a single object for both years

pop <- plyr::rbind.fill(pop19,pop20)
rm(pop19,pop20)

Add up population for each IMD quintile, age group and sex strata (2019 and 2020)

agg_pop <- pop %>%
  filter(`LSOA Code` %in% LW_lsoas) %>%
  select(., -c(starts_with("LA"),"LSOA Name")) %>%
  left_join(.,imd,by="LSOA Code") %>% 
  pivot_longer(!c(`LSOA Code`,"imd_quintile","sex","year"), names_to = "ages", values_to = "count") %>%
  filter(.,ages!="All Ages",ages!="90+") %>%
  mutate(.,ages=as.numeric(ages)) %>% 
  mutate(.,age_band=case_when(
    ages>=0&ages<=14 ~ "0 to 14",
    ages>=15&ages<=25 ~ "15 to 25",
    ages>25 ~ "older than 25",
    TRUE ~ "NA"
  )) %>%
  group_by(sex,age_band,imd_quintile,year) %>%
  summarise(.,pop=sum(count)) %>% 
  ungroup() %>%
  filter(.,age_band %in% c("0 to 14","15 to 25")) %>%
  pivot_wider(names_from = year,
              names_sep = ".",
              values_from = c(pop))

#Rename to match CCG naming conventions

agg_pop_renamed <- agg_pop %>%
  dplyr::rename(.,`pop19`=`2019`,
         `pop20`=`2020`,
         quantile=imd_quintile,
         age_group2=age_band) %>%
  mutate(.,pop21=pop20,
         age_group2=case_when(
           age_group2=="0 to 14" ~ "[0,15)",
           age_group2=="15 to 25" ~ "[15,25]",
    TRUE ~ "NA"
  ),sex=case_when(
    sex=="F" ~ "Female",
    sex=="M" ~ "Male",
    TRUE ~ "NA"
  )) %>%
  mutate(pyears_19to21=pop19+pop20+0.5*pop21) #Compute 'person-years' - 2021 pop data not available yet so we use 2020 as a substitute
rm(agg_pop)

#Display snippet

agg_pop_renamed %>%
  knitr::kable(., align = "lccrr")
sex age_group2 quantile pop19 pop20 pop21 pyears_19to21
Female [0,15) 1 39706 39954 39954 99637.0
Female [0,15) 2 9169 9213 9213 22988.5
Female [0,15) 3 7376 7370 7370 18431.0
Female [0,15) 4 7795 7691 7691 19331.5
Female [0,15) 5 3534 3521 3521 8815.5
Female [15,25] 1 30894 30616 30616 76818.0
Female [15,25] 2 14728 15168 15168 37480.0
Female [15,25] 3 9830 10038 10038 24887.0
Female [15,25] 4 5832 5927 5927 14722.5
Female [15,25] 5 2727 2508 2508 6489.0
Male [0,15) 1 42279 42621 42621 106210.5
Male [0,15) 2 9845 9815 9815 24567.5
Male [0,15) 3 7597 7552 7552 18925.0
Male [0,15) 4 8246 8152 8152 20474.0
Male [0,15) 5 3539 3578 3578 8906.0
Male [15,25] 1 30174 30211 30211 75490.5
Male [15,25] 2 14383 14646 14646 36352.0
Male [15,25] 3 9820 10000 10000 24820.0
Male [15,25] 4 6597 6666 6666 16596.0
Male [15,25] 5 2955 2729 2729 7048.5

Import analysis by NDL Liverpool and Wirral, and merge in denominators

#Load CCG data

CCG_data_admissions <- fread("M:/Analytics/CYP MH/Data for NDL briefing/Liverpool and Wirral/national_imd_Figure_6_adm_by_reason_and_imd.csv",header=TRUE, sep=",", check.names=T) %>%
  mutate(.,quantile=as.character(natn_qunt),
         type="Admissions",variable=paste0("Adm_",variable)) %>%
      select(-c("an_pop","natn_qunt"))


CCG_data_AE <- fread("M:/Analytics/CYP MH/Data for NDL briefing/Liverpool and Wirral/national_imd_Figure_5_ae_by_reason_and_imd.csv",header=TRUE, sep=",", check.names=T) %>%
  mutate(.,quantile=as.character(natn_qunt),
         type="A&E",variable=paste0("AE_",variable)) %>%
          select(-c("an_pop","natn_qunt"))

CCG_data <- plyr::rbind.fill(CCG_data_admissions,CCG_data_AE)
rm(CCG_data_admissions,CCG_data_AE)

#Charts data (by sex and age band)

agg_pop_both <- CCG_data %>%
  left_join(.,agg_pop_renamed,by=c("quantile","age_group2","sex")) %>%
  select(.,type,quantile,age_group2,sex,variable,value,pop19,pyears_19to21) %>%
  mutate(.,age_group2=case_when(
    age_group2=="[0,15)" ~ "0 to 14",
    age_group2=="[15,25]" ~ "15 to 25",
    TRUE ~ "NA"
  )) %>%
  mutate(.,age_sex=paste(age_group2,sex,sep=", ")) %>% 
  filter(.,variable!="Other") %>%
  mutate(.,rate_per_pop19=value/pop19*100,
         rate_per_person_year=value/pyears_19to21*100)

#Charts data (age band)

agg_pop_both_MF <- CCG_data %>%
    left_join(.,agg_pop_renamed,by=c("quantile","age_group2","sex")) %>%
    select(.,type,quantile,age_group2,sex,variable,value,pop19,pyears_19to21) %>%
    mutate(.,age_group2=case_when(
        age_group2=="[0,15)" ~ "0 to 14",
        age_group2=="[15,25]" ~ "15 to 25",
        TRUE ~ "NA"
    )) %>%
    filter(.,variable!="Other") %>%
    group_by(type,quantile,age_group2,variable) %>%
    summarise(pop19=sum(pop19,na.rm=TRUE),
                        value=sum(value,na.rm=TRUE)) %>% 
    ungroup() %>%
    mutate(.,rate_per_pop19=value/pop19*100)

Deprivation gradient of hospital admissions

Raw value

#Chart - raw value

figure_adm_raw <- agg_pop_both %>%
  filter(.,type=="Admissions") %>% 
  ggplot(., aes(x=quantile, y=value, fill=variable)) +
  facet_wrap(~age_sex) +
  geom_bar(position="dodge", stat="identity")+
  theme_ipsum() +
  xlab("IMD quantile (national) - 1 (worst) to 5 (best)") +
  ylab("value (count)") +
  ggtitle("Number of events (no denominator)")

ggplotly(figure_adm_raw)
#Display snippet

agg_pop_both %>%
  filter(.,type=="Admissions") %>%
    select(.,type,variable,quantile,age_group2,sex,value) %>% 
    arrange(.,variable,sex,age_group2,quantile) %>%
    knitr::kable(., align = "lccrr")
type variable quantile age_group2 sex value
Admissions Adm_Alcohol 1 0 to 14 Female 12
Admissions Adm_Alcohol 2 0 to 14 Female NA
Admissions Adm_Alcohol 3 0 to 14 Female NA
Admissions Adm_Alcohol 4 0 to 14 Female NA
Admissions Adm_Alcohol 5 0 to 14 Female NA
Admissions Adm_Alcohol 1 15 to 25 Female 231
Admissions Adm_Alcohol 2 15 to 25 Female 88
Admissions Adm_Alcohol 3 15 to 25 Female 45
Admissions Adm_Alcohol 4 15 to 25 Female 22
Admissions Adm_Alcohol 5 15 to 25 Female 13
Admissions Adm_Alcohol 1 0 to 14 Male NA
Admissions Adm_Alcohol 2 0 to 14 Male NA
Admissions Adm_Alcohol 3 0 to 14 Male NA
Admissions Adm_Alcohol 4 0 to 14 Male NA
Admissions Adm_Alcohol 5 0 to 14 Male NA
Admissions Adm_Alcohol 1 15 to 25 Male 228
Admissions Adm_Alcohol 2 15 to 25 Male 46
Admissions Adm_Alcohol 3 15 to 25 Male 30
Admissions Adm_Alcohol 4 15 to 25 Male 19
Admissions Adm_Alcohol 5 15 to 25 Male 10
Admissions Adm_Eating Disorders 1 0 to 14 Female 6
Admissions Adm_Eating Disorders 2 0 to 14 Female NA
Admissions Adm_Eating Disorders 3 0 to 14 Female NA
Admissions Adm_Eating Disorders 4 0 to 14 Female NA
Admissions Adm_Eating Disorders 5 0 to 14 Female NA
Admissions Adm_Eating Disorders 1 15 to 25 Female 99
Admissions Adm_Eating Disorders 2 15 to 25 Female 33
Admissions Adm_Eating Disorders 3 15 to 25 Female 15
Admissions Adm_Eating Disorders 4 15 to 25 Female 14
Admissions Adm_Eating Disorders 5 15 to 25 Female 11
Admissions Adm_Eating Disorders 1 0 to 14 Male NA
Admissions Adm_Eating Disorders 2 0 to 14 Male NA
Admissions Adm_Eating Disorders 3 0 to 14 Male NA
Admissions Adm_Eating Disorders 4 0 to 14 Male NA
Admissions Adm_Eating Disorders 5 0 to 14 Male NA
Admissions Adm_Eating Disorders 1 15 to 25 Male 28
Admissions Adm_Eating Disorders 2 15 to 25 Male NA
Admissions Adm_Eating Disorders 3 15 to 25 Male NA
Admissions Adm_Eating Disorders 4 15 to 25 Male NA
Admissions Adm_Eating Disorders 5 15 to 25 Male NA
Admissions Adm_Other 1 0 to 14 Female 276
Admissions Adm_Other 2 0 to 14 Female 47
Admissions Adm_Other 3 0 to 14 Female 46
Admissions Adm_Other 4 0 to 14 Female 46
Admissions Adm_Other 5 0 to 14 Female 22
Admissions Adm_Other 1 15 to 25 Female 1688
Admissions Adm_Other 2 15 to 25 Female 351
Admissions Adm_Other 3 15 to 25 Female 282
Admissions Adm_Other 4 15 to 25 Female 188
Admissions Adm_Other 5 15 to 25 Female 60
Admissions Adm_Other 1 0 to 14 Male 494
Admissions Adm_Other 2 0 to 14 Male 145
Admissions Adm_Other 3 0 to 14 Male 63
Admissions Adm_Other 4 0 to 14 Male 67
Admissions Adm_Other 5 0 to 14 Male 23
Admissions Adm_Other 1 15 to 25 Male 725
Admissions Adm_Other 2 15 to 25 Male 201
Admissions Adm_Other 3 15 to 25 Male 130
Admissions Adm_Other 4 15 to 25 Male 87
Admissions Adm_Other 5 15 to 25 Male 45
Admissions Adm_Self Harm 1 0 to 14 Female 103
Admissions Adm_Self Harm 2 0 to 14 Female 15
Admissions Adm_Self Harm 3 0 to 14 Female 19
Admissions Adm_Self Harm 4 0 to 14 Female 10
Admissions Adm_Self Harm 5 0 to 14 Female 5
Admissions Adm_Self Harm 1 15 to 25 Female 734
Admissions Adm_Self Harm 2 15 to 25 Female 185
Admissions Adm_Self Harm 3 15 to 25 Female 139
Admissions Adm_Self Harm 4 15 to 25 Female 74
Admissions Adm_Self Harm 5 15 to 25 Female 29
Admissions Adm_Self Harm 1 0 to 14 Male 14
Admissions Adm_Self Harm 2 0 to 14 Male 6
Admissions Adm_Self Harm 3 0 to 14 Male NA
Admissions Adm_Self Harm 4 0 to 14 Male NA
Admissions Adm_Self Harm 5 0 to 14 Male NA
Admissions Adm_Self Harm 1 15 to 25 Male 299
Admissions Adm_Self Harm 2 15 to 25 Male 59
Admissions Adm_Self Harm 3 15 to 25 Male 28
Admissions Adm_Self Harm 4 15 to 25 Male 20
Admissions Adm_Self Harm 5 15 to 25 Male NA
Admissions Adm_Substance Misuse 1 0 to 14 Female 11
Admissions Adm_Substance Misuse 2 0 to 14 Female NA
Admissions Adm_Substance Misuse 3 0 to 14 Female NA
Admissions Adm_Substance Misuse 4 0 to 14 Female NA
Admissions Adm_Substance Misuse 5 0 to 14 Female NA
Admissions Adm_Substance Misuse 1 15 to 25 Female 1691
Admissions Adm_Substance Misuse 2 15 to 25 Female 292
Admissions Adm_Substance Misuse 3 15 to 25 Female 210
Admissions Adm_Substance Misuse 4 15 to 25 Female 81
Admissions Adm_Substance Misuse 5 15 to 25 Female 27
Admissions Adm_Substance Misuse 1 0 to 14 Male 6
Admissions Adm_Substance Misuse 2 0 to 14 Male NA
Admissions Adm_Substance Misuse 3 0 to 14 Male NA
Admissions Adm_Substance Misuse 4 0 to 14 Male NA
Admissions Adm_Substance Misuse 5 0 to 14 Male NA
Admissions Adm_Substance Misuse 1 15 to 25 Male 1187
Admissions Adm_Substance Misuse 2 15 to 25 Male 207
Admissions Adm_Substance Misuse 3 15 to 25 Male 160
Admissions Adm_Substance Misuse 4 15 to 25 Male 87
Admissions Adm_Substance Misuse 5 15 to 25 Male 30

pop19 as denominator

#Chart - raw value

figure_adm_pop19 <- agg_pop_both %>%
  filter(.,type=="Admissions") %>% 
  ggplot(., aes(x=quantile, y=rate_per_pop19, fill=variable)) +
  facet_wrap(~age_sex) +
  geom_bar(position="dodge", stat="identity")+
  theme_ipsum() +
  xlab("IMD quantile (national) - 1 (worst) to 5 (best)") +
  ylab("rate") +
  ggtitle("Number of events, divided by pop19")

ggplotly(figure_adm_pop19)
#Display snippet

agg_pop_both %>%
  filter(.,type=="Admissions") %>%
    select(.,type,variable,quantile,age_group2,sex,value,pop19,rate_per_pop19) %>% 
    arrange(.,variable,sex,age_group2,quantile) %>%
    knitr::kable(., align = "lccrr")
type variable quantile age_group2 sex value pop19 rate_per_pop19
Admissions Adm_Alcohol 1 0 to 14 Female 12 39706 0.0302221
Admissions Adm_Alcohol 2 0 to 14 Female NA 9169 NA
Admissions Adm_Alcohol 3 0 to 14 Female NA 7376 NA
Admissions Adm_Alcohol 4 0 to 14 Female NA 7795 NA
Admissions Adm_Alcohol 5 0 to 14 Female NA 3534 NA
Admissions Adm_Alcohol 1 15 to 25 Female 231 30894 0.7477180
Admissions Adm_Alcohol 2 15 to 25 Female 88 14728 0.5975014
Admissions Adm_Alcohol 3 15 to 25 Female 45 9830 0.4577823
Admissions Adm_Alcohol 4 15 to 25 Female 22 5832 0.3772291
Admissions Adm_Alcohol 5 15 to 25 Female 13 2727 0.4767143
Admissions Adm_Alcohol 1 0 to 14 Male NA 42279 NA
Admissions Adm_Alcohol 2 0 to 14 Male NA 9845 NA
Admissions Adm_Alcohol 3 0 to 14 Male NA 7597 NA
Admissions Adm_Alcohol 4 0 to 14 Male NA 8246 NA
Admissions Adm_Alcohol 5 0 to 14 Male NA 3539 NA
Admissions Adm_Alcohol 1 15 to 25 Male 228 30174 0.7556174
Admissions Adm_Alcohol 2 15 to 25 Male 46 14383 0.3198220
Admissions Adm_Alcohol 3 15 to 25 Male 30 9820 0.3054990
Admissions Adm_Alcohol 4 15 to 25 Male 19 6597 0.2880097
Admissions Adm_Alcohol 5 15 to 25 Male 10 2955 0.3384095
Admissions Adm_Eating Disorders 1 0 to 14 Female 6 39706 0.0151111
Admissions Adm_Eating Disorders 2 0 to 14 Female NA 9169 NA
Admissions Adm_Eating Disorders 3 0 to 14 Female NA 7376 NA
Admissions Adm_Eating Disorders 4 0 to 14 Female NA 7795 NA
Admissions Adm_Eating Disorders 5 0 to 14 Female NA 3534 NA
Admissions Adm_Eating Disorders 1 15 to 25 Female 99 30894 0.3204506
Admissions Adm_Eating Disorders 2 15 to 25 Female 33 14728 0.2240630
Admissions Adm_Eating Disorders 3 15 to 25 Female 15 9830 0.1525941
Admissions Adm_Eating Disorders 4 15 to 25 Female 14 5832 0.2400549
Admissions Adm_Eating Disorders 5 15 to 25 Female 11 2727 0.4033737
Admissions Adm_Eating Disorders 1 0 to 14 Male NA 42279 NA
Admissions Adm_Eating Disorders 2 0 to 14 Male NA 9845 NA
Admissions Adm_Eating Disorders 3 0 to 14 Male NA 7597 NA
Admissions Adm_Eating Disorders 4 0 to 14 Male NA 8246 NA
Admissions Adm_Eating Disorders 5 0 to 14 Male NA 3539 NA
Admissions Adm_Eating Disorders 1 15 to 25 Male 28 30174 0.0927951
Admissions Adm_Eating Disorders 2 15 to 25 Male NA 14383 NA
Admissions Adm_Eating Disorders 3 15 to 25 Male NA 9820 NA
Admissions Adm_Eating Disorders 4 15 to 25 Male NA 6597 NA
Admissions Adm_Eating Disorders 5 15 to 25 Male NA 2955 NA
Admissions Adm_Other 1 0 to 14 Female 276 39706 0.6951091
Admissions Adm_Other 2 0 to 14 Female 47 9169 0.5125968
Admissions Adm_Other 3 0 to 14 Female 46 7376 0.6236443
Admissions Adm_Other 4 0 to 14 Female 46 7795 0.5901219
Admissions Adm_Other 5 0 to 14 Female 22 3534 0.6225241
Admissions Adm_Other 1 15 to 25 Female 1688 30894 5.4638441
Admissions Adm_Other 2 15 to 25 Female 351 14728 2.3832156
Admissions Adm_Other 3 15 to 25 Female 282 9830 2.8687691
Admissions Adm_Other 4 15 to 25 Female 188 5832 3.2235940
Admissions Adm_Other 5 15 to 25 Female 60 2727 2.2002200
Admissions Adm_Other 1 0 to 14 Male 494 42279 1.1684288
Admissions Adm_Other 2 0 to 14 Male 145 9845 1.4728288
Admissions Adm_Other 3 0 to 14 Male 63 7597 0.8292747
Admissions Adm_Other 4 0 to 14 Male 67 8246 0.8125152
Admissions Adm_Other 5 0 to 14 Male 23 3539 0.6499011
Admissions Adm_Other 1 15 to 25 Male 725 30174 2.4027308
Admissions Adm_Other 2 15 to 25 Male 201 14383 1.3974831
Admissions Adm_Other 3 15 to 25 Male 130 9820 1.3238289
Admissions Adm_Other 4 15 to 25 Male 87 6597 1.3187813
Admissions Adm_Other 5 15 to 25 Male 45 2955 1.5228426
Admissions Adm_Self Harm 1 0 to 14 Female 103 39706 0.2594066
Admissions Adm_Self Harm 2 0 to 14 Female 15 9169 0.1635947
Admissions Adm_Self Harm 3 0 to 14 Female 19 7376 0.2575922
Admissions Adm_Self Harm 4 0 to 14 Female 10 7795 0.1282874
Admissions Adm_Self Harm 5 0 to 14 Female 5 3534 0.1414827
Admissions Adm_Self Harm 1 15 to 25 Female 734 30894 2.3758659
Admissions Adm_Self Harm 2 15 to 25 Female 185 14728 1.2561108
Admissions Adm_Self Harm 3 15 to 25 Female 139 9830 1.4140387
Admissions Adm_Self Harm 4 15 to 25 Female 74 5832 1.2688615
Admissions Adm_Self Harm 5 15 to 25 Female 29 2727 1.0634397
Admissions Adm_Self Harm 1 0 to 14 Male 14 42279 0.0331134
Admissions Adm_Self Harm 2 0 to 14 Male 6 9845 0.0609446
Admissions Adm_Self Harm 3 0 to 14 Male NA 7597 NA
Admissions Adm_Self Harm 4 0 to 14 Male NA 8246 NA
Admissions Adm_Self Harm 5 0 to 14 Male NA 3539 NA
Admissions Adm_Self Harm 1 15 to 25 Male 299 30174 0.9909193
Admissions Adm_Self Harm 2 15 to 25 Male 59 14383 0.4102065
Admissions Adm_Self Harm 3 15 to 25 Male 28 9820 0.2851324
Admissions Adm_Self Harm 4 15 to 25 Male 20 6597 0.3031681
Admissions Adm_Self Harm 5 15 to 25 Male NA 2955 NA
Admissions Adm_Substance Misuse 1 0 to 14 Female 11 39706 0.0277036
Admissions Adm_Substance Misuse 2 0 to 14 Female NA 9169 NA
Admissions Adm_Substance Misuse 3 0 to 14 Female NA 7376 NA
Admissions Adm_Substance Misuse 4 0 to 14 Female NA 7795 NA
Admissions Adm_Substance Misuse 5 0 to 14 Female NA 3534 NA
Admissions Adm_Substance Misuse 1 15 to 25 Female 1691 30894 5.4735547
Admissions Adm_Substance Misuse 2 15 to 25 Female 292 14728 1.9826181
Admissions Adm_Substance Misuse 3 15 to 25 Female 210 9830 2.1363174
Admissions Adm_Substance Misuse 4 15 to 25 Female 81 5832 1.3888889
Admissions Adm_Substance Misuse 5 15 to 25 Female 27 2727 0.9900990
Admissions Adm_Substance Misuse 1 0 to 14 Male 6 42279 0.0141914
Admissions Adm_Substance Misuse 2 0 to 14 Male NA 9845 NA
Admissions Adm_Substance Misuse 3 0 to 14 Male NA 7597 NA
Admissions Adm_Substance Misuse 4 0 to 14 Male NA 8246 NA
Admissions Adm_Substance Misuse 5 0 to 14 Male NA 3539 NA
Admissions Adm_Substance Misuse 1 15 to 25 Male 1187 30174 3.9338503
Admissions Adm_Substance Misuse 2 15 to 25 Male 207 14383 1.4391991
Admissions Adm_Substance Misuse 3 15 to 25 Male 160 9820 1.6293279
Admissions Adm_Substance Misuse 4 15 to 25 Male 87 6597 1.3187813
Admissions Adm_Substance Misuse 5 15 to 25 Male 30 2955 1.0152284

pop19 as denominator (all 15-25 year olds)

#Chart - raw value

figure_adm_pop19_older <- agg_pop_both_MF %>%
    filter(.,type=="Admissions",age_group2=="15 to 25") %>% 
    ggplot(., aes(x=quantile, y=rate_per_pop19, fill=variable)) +
    facet_wrap(~variable) +
    geom_bar(position="dodge", stat="identity")+
    theme_ipsum() +
    xlab("IMD quantile (national) - 1 (worst) to 5 (best)") +
    ylab("events per 100 people") +
    ggtitle("Number of events (divided by pop19) among all 15-25 year olds")

ggplotly(figure_adm_pop19_older)
#Display snippet

agg_pop_both_MF %>%
    filter(.,type=="Admissions",age_group2=="15 to 25") %>%
    select(.,type,variable,quantile,age_group2,value,pop19,rate_per_pop19) %>% 
    arrange(.,variable,age_group2,quantile) %>%
    knitr::kable(., align = "lccrr")
type variable quantile age_group2 value pop19 rate_per_pop19
Admissions Adm_Alcohol 1 15 to 25 459 61068 0.7516211
Admissions Adm_Alcohol 2 15 to 25 134 29111 0.4603071
Admissions Adm_Alcohol 3 15 to 25 75 19650 0.3816794
Admissions Adm_Alcohol 4 15 to 25 41 12429 0.3298737
Admissions Adm_Alcohol 5 15 to 25 23 5682 0.4047870
Admissions Adm_Eating Disorders 1 15 to 25 127 61068 0.2079649
Admissions Adm_Eating Disorders 2 15 to 25 33 29111 0.1133592
Admissions Adm_Eating Disorders 3 15 to 25 15 19650 0.0763359
Admissions Adm_Eating Disorders 4 15 to 25 14 12429 0.1126398
Admissions Adm_Eating Disorders 5 15 to 25 11 5682 0.1935938
Admissions Adm_Other 1 15 to 25 2413 61068 3.9513329
Admissions Adm_Other 2 15 to 25 552 29111 1.8961904
Admissions Adm_Other 3 15 to 25 412 19650 2.0966921
Admissions Adm_Other 4 15 to 25 275 12429 2.2125674
Admissions Adm_Other 5 15 to 25 105 5682 1.8479409
Admissions Adm_Self Harm 1 15 to 25 1033 61068 1.6915570
Admissions Adm_Self Harm 2 15 to 25 244 29111 0.8381711
Admissions Adm_Self Harm 3 15 to 25 167 19650 0.8498728
Admissions Adm_Self Harm 4 15 to 25 94 12429 0.7562958
Admissions Adm_Self Harm 5 15 to 25 29 5682 0.5103837
Admissions Adm_Substance Misuse 1 15 to 25 2878 61068 4.7127792
Admissions Adm_Substance Misuse 2 15 to 25 499 29111 1.7141287
Admissions Adm_Substance Misuse 3 15 to 25 370 19650 1.8829517
Admissions Adm_Substance Misuse 4 15 to 25 168 12429 1.3516775
Admissions Adm_Substance Misuse 5 15 to 25 57 5682 1.0031679

pop19+pop20+0.5*pop21 as denominator

#Chart - raw value

figure_adm_pyear <- agg_pop_both %>%
  filter(.,type=="Admissions") %>%
  ggplot(., aes(x=quantile, y=rate_per_person_year, fill=variable)) +
  facet_wrap(~age_sex) +
  geom_bar(position="dodge", stat="identity")+
  theme_ipsum() +
  xlab("IMD quantile (national) - 1 (worst) to 5 (best)") +
  ylab("rate") +
  ggtitle("Number of events, divided by pop19+pop20+0.5*pop21")

ggplotly(figure_adm_pyear)
#Display snippet

agg_pop_both %>%
  filter(.,type=="Admissions") %>%
    select(.,type,variable,quantile,age_group2,sex,value,pyears_19to21,rate_per_person_year) %>% 
    arrange(.,variable,sex,age_group2,quantile) %>%
    knitr::kable(., align = "lccrr")
type variable quantile age_group2 sex value pyears_19to21 rate_per_person_year
Admissions Adm_Alcohol 1 0 to 14 Female 12 99637.0 0.0120437
Admissions Adm_Alcohol 2 0 to 14 Female NA 22988.5 NA
Admissions Adm_Alcohol 3 0 to 14 Female NA 18431.0 NA
Admissions Adm_Alcohol 4 0 to 14 Female NA 19331.5 NA
Admissions Adm_Alcohol 5 0 to 14 Female NA 8815.5 NA
Admissions Adm_Alcohol 1 15 to 25 Female 231 76818.0 0.3007108
Admissions Adm_Alcohol 2 15 to 25 Female 88 37480.0 0.2347919
Admissions Adm_Alcohol 3 15 to 25 Female 45 24887.0 0.1808173
Admissions Adm_Alcohol 4 15 to 25 Female 22 14722.5 0.1494311
Admissions Adm_Alcohol 5 15 to 25 Female 13 6489.0 0.2003390
Admissions Adm_Alcohol 1 0 to 14 Male NA 106210.5 NA
Admissions Adm_Alcohol 2 0 to 14 Male NA 24567.5 NA
Admissions Adm_Alcohol 3 0 to 14 Male NA 18925.0 NA
Admissions Adm_Alcohol 4 0 to 14 Male NA 20474.0 NA
Admissions Adm_Alcohol 5 0 to 14 Male NA 8906.0 NA
Admissions Adm_Alcohol 1 15 to 25 Male 228 75490.5 0.3020248
Admissions Adm_Alcohol 2 15 to 25 Male 46 36352.0 0.1265405
Admissions Adm_Alcohol 3 15 to 25 Male 30 24820.0 0.1208703
Admissions Adm_Alcohol 4 15 to 25 Male 19 16596.0 0.1144854
Admissions Adm_Alcohol 5 15 to 25 Male 10 7048.5 0.1418742
Admissions Adm_Eating Disorders 1 0 to 14 Female 6 99637.0 0.0060219
Admissions Adm_Eating Disorders 2 0 to 14 Female NA 22988.5 NA
Admissions Adm_Eating Disorders 3 0 to 14 Female NA 18431.0 NA
Admissions Adm_Eating Disorders 4 0 to 14 Female NA 19331.5 NA
Admissions Adm_Eating Disorders 5 0 to 14 Female NA 8815.5 NA
Admissions Adm_Eating Disorders 1 15 to 25 Female 99 76818.0 0.1288760
Admissions Adm_Eating Disorders 2 15 to 25 Female 33 37480.0 0.0880470
Admissions Adm_Eating Disorders 3 15 to 25 Female 15 24887.0 0.0602724
Admissions Adm_Eating Disorders 4 15 to 25 Female 14 14722.5 0.0950925
Admissions Adm_Eating Disorders 5 15 to 25 Female 11 6489.0 0.1695176
Admissions Adm_Eating Disorders 1 0 to 14 Male NA 106210.5 NA
Admissions Adm_Eating Disorders 2 0 to 14 Male NA 24567.5 NA
Admissions Adm_Eating Disorders 3 0 to 14 Male NA 18925.0 NA
Admissions Adm_Eating Disorders 4 0 to 14 Male NA 20474.0 NA
Admissions Adm_Eating Disorders 5 0 to 14 Male NA 8906.0 NA
Admissions Adm_Eating Disorders 1 15 to 25 Male 28 75490.5 0.0370908
Admissions Adm_Eating Disorders 2 15 to 25 Male NA 36352.0 NA
Admissions Adm_Eating Disorders 3 15 to 25 Male NA 24820.0 NA
Admissions Adm_Eating Disorders 4 15 to 25 Male NA 16596.0 NA
Admissions Adm_Eating Disorders 5 15 to 25 Male NA 7048.5 NA
Admissions Adm_Other 1 0 to 14 Female 276 99637.0 0.2770055
Admissions Adm_Other 2 0 to 14 Female 47 22988.5 0.2044501
Admissions Adm_Other 3 0 to 14 Female 46 18431.0 0.2495795
Admissions Adm_Other 4 0 to 14 Female 46 19331.5 0.2379536
Admissions Adm_Other 5 0 to 14 Female 22 8815.5 0.2495604
Admissions Adm_Other 1 15 to 25 Female 1688 76818.0 2.1974017
Admissions Adm_Other 2 15 to 25 Female 351 37480.0 0.9364995
Admissions Adm_Other 3 15 to 25 Female 282 24887.0 1.1331217
Admissions Adm_Other 4 15 to 25 Female 188 14722.5 1.2769570
Admissions Adm_Other 5 15 to 25 Female 60 6489.0 0.9246417
Admissions Adm_Other 1 0 to 14 Male 494 106210.5 0.4651141
Admissions Adm_Other 2 0 to 14 Male 145 24567.5 0.5902106
Admissions Adm_Other 3 0 to 14 Male 63 18925.0 0.3328930
Admissions Adm_Other 4 0 to 14 Male 67 20474.0 0.3272443
Admissions Adm_Other 5 0 to 14 Male 23 8906.0 0.2582529
Admissions Adm_Other 1 15 to 25 Male 725 75490.5 0.9603857
Admissions Adm_Other 2 15 to 25 Male 201 36352.0 0.5529269
Admissions Adm_Other 3 15 to 25 Male 130 24820.0 0.5237712
Admissions Adm_Other 4 15 to 25 Male 87 16596.0 0.5242227
Admissions Adm_Other 5 15 to 25 Male 45 7048.5 0.6384337
Admissions Adm_Self Harm 1 0 to 14 Female 103 99637.0 0.1033753
Admissions Adm_Self Harm 2 0 to 14 Female 15 22988.5 0.0652500
Admissions Adm_Self Harm 3 0 to 14 Female 19 18431.0 0.1030872
Admissions Adm_Self Harm 4 0 to 14 Female 10 19331.5 0.0517290
Admissions Adm_Self Harm 5 0 to 14 Female 5 8815.5 0.0567183
Admissions Adm_Self Harm 1 15 to 25 Female 734 76818.0 0.9555052
Admissions Adm_Self Harm 2 15 to 25 Female 185 37480.0 0.4935966
Admissions Adm_Self Harm 3 15 to 25 Female 139 24887.0 0.5585245
Admissions Adm_Self Harm 4 15 to 25 Female 74 14722.5 0.5026320
Admissions Adm_Self Harm 5 15 to 25 Female 29 6489.0 0.4469102
Admissions Adm_Self Harm 1 0 to 14 Male 14 106210.5 0.0131814
Admissions Adm_Self Harm 2 0 to 14 Male 6 24567.5 0.0244225
Admissions Adm_Self Harm 3 0 to 14 Male NA 18925.0 NA
Admissions Adm_Self Harm 4 0 to 14 Male NA 20474.0 NA
Admissions Adm_Self Harm 5 0 to 14 Male NA 8906.0 NA
Admissions Adm_Self Harm 1 15 to 25 Male 299 75490.5 0.3960763
Admissions Adm_Self Harm 2 15 to 25 Male 59 36352.0 0.1623019
Admissions Adm_Self Harm 3 15 to 25 Male 28 24820.0 0.1128122
Admissions Adm_Self Harm 4 15 to 25 Male 20 16596.0 0.1205110
Admissions Adm_Self Harm 5 15 to 25 Male NA 7048.5 NA
Admissions Adm_Substance Misuse 1 0 to 14 Female 11 99637.0 0.0110401
Admissions Adm_Substance Misuse 2 0 to 14 Female NA 22988.5 NA
Admissions Adm_Substance Misuse 3 0 to 14 Female NA 18431.0 NA
Admissions Adm_Substance Misuse 4 0 to 14 Female NA 19331.5 NA
Admissions Adm_Substance Misuse 5 0 to 14 Female NA 8815.5 NA
Admissions Adm_Substance Misuse 1 15 to 25 Female 1691 76818.0 2.2013070
Admissions Adm_Substance Misuse 2 15 to 25 Female 292 37480.0 0.7790822
Admissions Adm_Substance Misuse 3 15 to 25 Female 210 24887.0 0.8438140
Admissions Adm_Substance Misuse 4 15 to 25 Female 81 14722.5 0.5501783
Admissions Adm_Substance Misuse 5 15 to 25 Female 27 6489.0 0.4160888
Admissions Adm_Substance Misuse 1 0 to 14 Male 6 106210.5 0.0056492
Admissions Adm_Substance Misuse 2 0 to 14 Male NA 24567.5 NA
Admissions Adm_Substance Misuse 3 0 to 14 Male NA 18925.0 NA
Admissions Adm_Substance Misuse 4 0 to 14 Male NA 20474.0 NA
Admissions Adm_Substance Misuse 5 0 to 14 Male NA 8906.0 NA
Admissions Adm_Substance Misuse 1 15 to 25 Male 1187 75490.5 1.5723833
Admissions Adm_Substance Misuse 2 15 to 25 Male 207 36352.0 0.5694322
Admissions Adm_Substance Misuse 3 15 to 25 Male 160 24820.0 0.6446414
Admissions Adm_Substance Misuse 4 15 to 25 Male 87 16596.0 0.5242227
Admissions Adm_Substance Misuse 5 15 to 25 Male 30 7048.5 0.4256225

Deprivation gradient of A&E attendances

Raw value

#Chart - raw value

figure_AE_raw <- agg_pop_both %>%
  filter(.,type=="A&E") %>% 
  ggplot(., aes(x=quantile, y=value, fill=variable)) +
  facet_wrap(~age_sex) +
  geom_bar(position="dodge", stat="identity")+
  theme_ipsum() +
  xlab("IMD quantile (national) - 1 (worst) to 5 (best)") +
  ylab("value (count)") +
  ggtitle("Number of events (no denominator)")

ggplotly(figure_AE_raw)
#Display snippet

agg_pop_both %>%
  filter(.,type=="A&E") %>% 
    select(.,type,variable,quantile,age_group2,sex,value) %>% 
    arrange(.,variable,sex,age_group2,quantile) %>%
    knitr::kable(., align = "lccrr")
type variable quantile age_group2 sex value
A&E AE_Alcohol 1 0 to 14 Female 32
A&E AE_Alcohol 2 0 to 14 Female 6
A&E AE_Alcohol 3 0 to 14 Female 9
A&E AE_Alcohol 4 0 to 14 Female NA
A&E AE_Alcohol 5 0 to 14 Female NA
A&E AE_Alcohol 1 15 to 25 Female 237
A&E AE_Alcohol 2 15 to 25 Female 117
A&E AE_Alcohol 3 15 to 25 Female 58
A&E AE_Alcohol 4 15 to 25 Female 25
A&E AE_Alcohol 5 15 to 25 Female 16
A&E AE_Alcohol 1 0 to 14 Male NA
A&E AE_Alcohol 2 0 to 14 Male NA
A&E AE_Alcohol 3 0 to 14 Male NA
A&E AE_Alcohol 4 0 to 14 Male NA
A&E AE_Alcohol 5 0 to 14 Male NA
A&E AE_Alcohol 1 15 to 25 Male 186
A&E AE_Alcohol 2 15 to 25 Male 63
A&E AE_Alcohol 3 15 to 25 Male 40
A&E AE_Alcohol 4 15 to 25 Male 28
A&E AE_Alcohol 5 15 to 25 Male 12
A&E AE_Eating Disorders 1 0 to 14 Female 21
A&E AE_Eating Disorders 2 0 to 14 Female NA
A&E AE_Eating Disorders 3 0 to 14 Female NA
A&E AE_Eating Disorders 4 0 to 14 Female NA
A&E AE_Eating Disorders 5 0 to 14 Female NA
A&E AE_Eating Disorders 1 15 to 25 Female 15
A&E AE_Eating Disorders 2 15 to 25 Female NA
A&E AE_Eating Disorders 3 15 to 25 Female NA
A&E AE_Eating Disorders 4 15 to 25 Female NA
A&E AE_Eating Disorders 5 15 to 25 Female NA
A&E AE_Eating Disorders 1 0 to 14 Male 13
A&E AE_Eating Disorders 2 0 to 14 Male 8
A&E AE_Eating Disorders 3 0 to 14 Male NA
A&E AE_Eating Disorders 4 0 to 14 Male NA
A&E AE_Eating Disorders 5 0 to 14 Male NA
A&E AE_Eating Disorders 1 15 to 25 Male NA
A&E AE_Eating Disorders 2 15 to 25 Male NA
A&E AE_Eating Disorders 3 15 to 25 Male NA
A&E AE_Eating Disorders 4 15 to 25 Male NA
A&E AE_Eating Disorders 5 15 to 25 Male NA
A&E AE_Self Harm 1 0 to 14 Female 63
A&E AE_Self Harm 2 0 to 14 Female 12
A&E AE_Self Harm 3 0 to 14 Female 10
A&E AE_Self Harm 4 0 to 14 Female 5
A&E AE_Self Harm 5 0 to 14 Female NA
A&E AE_Self Harm 1 15 to 25 Female 314
A&E AE_Self Harm 2 15 to 25 Female 96
A&E AE_Self Harm 3 15 to 25 Female 94
A&E AE_Self Harm 4 15 to 25 Female 35
A&E AE_Self Harm 5 15 to 25 Female 18
A&E AE_Self Harm 1 0 to 14 Male 13
A&E AE_Self Harm 2 0 to 14 Male 7
A&E AE_Self Harm 3 0 to 14 Male NA
A&E AE_Self Harm 4 0 to 14 Male NA
A&E AE_Self Harm 5 0 to 14 Male NA
A&E AE_Self Harm 1 15 to 25 Male 117
A&E AE_Self Harm 2 15 to 25 Male 31
A&E AE_Self Harm 3 15 to 25 Male 23
A&E AE_Self Harm 4 15 to 25 Male 18
A&E AE_Self Harm 5 15 to 25 Male 7
A&E AE_Substance Misuse 1 0 to 14 Female 21
A&E AE_Substance Misuse 2 0 to 14 Female 6
A&E AE_Substance Misuse 3 0 to 14 Female 13
A&E AE_Substance Misuse 4 0 to 14 Female NA
A&E AE_Substance Misuse 5 0 to 14 Female NA
A&E AE_Substance Misuse 1 15 to 25 Female 283
A&E AE_Substance Misuse 2 15 to 25 Female 68
A&E AE_Substance Misuse 3 15 to 25 Female 78
A&E AE_Substance Misuse 4 15 to 25 Female 45
A&E AE_Substance Misuse 5 15 to 25 Female 14
A&E AE_Substance Misuse 1 0 to 14 Male 6
A&E AE_Substance Misuse 2 0 to 14 Male NA
A&E AE_Substance Misuse 3 0 to 14 Male NA
A&E AE_Substance Misuse 4 0 to 14 Male NA
A&E AE_Substance Misuse 5 0 to 14 Male NA
A&E AE_Substance Misuse 1 15 to 25 Male 177
A&E AE_Substance Misuse 2 15 to 25 Male 42
A&E AE_Substance Misuse 3 15 to 25 Male 28
A&E AE_Substance Misuse 4 15 to 25 Male 18
A&E AE_Substance Misuse 5 15 to 25 Male 14

pop19 as denominator

#Chart - raw value

figure_AE_pop19 <- agg_pop_both %>%
  filter(.,type=="A&E") %>% 
  ggplot(., aes(x=quantile, y=rate_per_pop19, fill=variable)) +
  facet_wrap(~age_sex) +
  geom_bar(position="dodge", stat="identity")+
  theme_ipsum() +
  xlab("IMD quantile (national) - 1 (worst) to 5 (best)") +
  ylab("rate") +
  ggtitle("Number of events, divided by pop19")

ggplotly(figure_AE_pop19)
#Display snippet

agg_pop_both %>%
  filter(.,type=="A&E") %>%
    select(.,type,variable,quantile,age_group2,sex,value,pop19,rate_per_pop19) %>% 
    arrange(.,variable,sex,age_group2,quantile) %>%
    knitr::kable(., align = "lccrr")
type variable quantile age_group2 sex value pop19 rate_per_pop19
A&E AE_Alcohol 1 0 to 14 Female 32 39706 0.0805924
A&E AE_Alcohol 2 0 to 14 Female 6 9169 0.0654379
A&E AE_Alcohol 3 0 to 14 Female 9 7376 0.1220174
A&E AE_Alcohol 4 0 to 14 Female NA 7795 NA
A&E AE_Alcohol 5 0 to 14 Female NA 3534 NA
A&E AE_Alcohol 1 15 to 25 Female 237 30894 0.7671393
A&E AE_Alcohol 2 15 to 25 Female 117 14728 0.7944052
A&E AE_Alcohol 3 15 to 25 Female 58 9830 0.5900305
A&E AE_Alcohol 4 15 to 25 Female 25 5832 0.4286694
A&E AE_Alcohol 5 15 to 25 Female 16 2727 0.5867253
A&E AE_Alcohol 1 0 to 14 Male NA 42279 NA
A&E AE_Alcohol 2 0 to 14 Male NA 9845 NA
A&E AE_Alcohol 3 0 to 14 Male NA 7597 NA
A&E AE_Alcohol 4 0 to 14 Male NA 8246 NA
A&E AE_Alcohol 5 0 to 14 Male NA 3539 NA
A&E AE_Alcohol 1 15 to 25 Male 186 30174 0.6164247
A&E AE_Alcohol 2 15 to 25 Male 63 14383 0.4380171
A&E AE_Alcohol 3 15 to 25 Male 40 9820 0.4073320
A&E AE_Alcohol 4 15 to 25 Male 28 6597 0.4244353
A&E AE_Alcohol 5 15 to 25 Male 12 2955 0.4060914
A&E AE_Eating Disorders 1 0 to 14 Female 21 39706 0.0528887
A&E AE_Eating Disorders 2 0 to 14 Female NA 9169 NA
A&E AE_Eating Disorders 3 0 to 14 Female NA 7376 NA
A&E AE_Eating Disorders 4 0 to 14 Female NA 7795 NA
A&E AE_Eating Disorders 5 0 to 14 Female NA 3534 NA
A&E AE_Eating Disorders 1 15 to 25 Female 15 30894 0.0485531
A&E AE_Eating Disorders 2 15 to 25 Female NA 14728 NA
A&E AE_Eating Disorders 3 15 to 25 Female NA 9830 NA
A&E AE_Eating Disorders 4 15 to 25 Female NA 5832 NA
A&E AE_Eating Disorders 5 15 to 25 Female NA 2727 NA
A&E AE_Eating Disorders 1 0 to 14 Male 13 42279 0.0307481
A&E AE_Eating Disorders 2 0 to 14 Male 8 9845 0.0812595
A&E AE_Eating Disorders 3 0 to 14 Male NA 7597 NA
A&E AE_Eating Disorders 4 0 to 14 Male NA 8246 NA
A&E AE_Eating Disorders 5 0 to 14 Male NA 3539 NA
A&E AE_Eating Disorders 1 15 to 25 Male NA 30174 NA
A&E AE_Eating Disorders 2 15 to 25 Male NA 14383 NA
A&E AE_Eating Disorders 3 15 to 25 Male NA 9820 NA
A&E AE_Eating Disorders 4 15 to 25 Male NA 6597 NA
A&E AE_Eating Disorders 5 15 to 25 Male NA 2955 NA
A&E AE_Self Harm 1 0 to 14 Female 63 39706 0.1586662
A&E AE_Self Harm 2 0 to 14 Female 12 9169 0.1308758
A&E AE_Self Harm 3 0 to 14 Female 10 7376 0.1355748
A&E AE_Self Harm 4 0 to 14 Female 5 7795 0.0641437
A&E AE_Self Harm 5 0 to 14 Female NA 3534 NA
A&E AE_Self Harm 1 15 to 25 Female 314 30894 1.0163786
A&E AE_Self Harm 2 15 to 25 Female 96 14728 0.6518197
A&E AE_Self Harm 3 15 to 25 Female 94 9830 0.9562564
A&E AE_Self Harm 4 15 to 25 Female 35 5832 0.6001372
A&E AE_Self Harm 5 15 to 25 Female 18 2727 0.6600660
A&E AE_Self Harm 1 0 to 14 Male 13 42279 0.0307481
A&E AE_Self Harm 2 0 to 14 Male 7 9845 0.0711021
A&E AE_Self Harm 3 0 to 14 Male NA 7597 NA
A&E AE_Self Harm 4 0 to 14 Male NA 8246 NA
A&E AE_Self Harm 5 0 to 14 Male NA 3539 NA
A&E AE_Self Harm 1 15 to 25 Male 117 30174 0.3877510
A&E AE_Self Harm 2 15 to 25 Male 31 14383 0.2155322
A&E AE_Self Harm 3 15 to 25 Male 23 9820 0.2342159
A&E AE_Self Harm 4 15 to 25 Male 18 6597 0.2728513
A&E AE_Self Harm 5 15 to 25 Male 7 2955 0.2368866
A&E AE_Substance Misuse 1 0 to 14 Female 21 39706 0.0528887
A&E AE_Substance Misuse 2 0 to 14 Female 6 9169 0.0654379
A&E AE_Substance Misuse 3 0 to 14 Female 13 7376 0.1762473
A&E AE_Substance Misuse 4 0 to 14 Female NA 7795 NA
A&E AE_Substance Misuse 5 0 to 14 Female NA 3534 NA
A&E AE_Substance Misuse 1 15 to 25 Female 283 30894 0.9160355
A&E AE_Substance Misuse 2 15 to 25 Female 68 14728 0.4617056
A&E AE_Substance Misuse 3 15 to 25 Female 78 9830 0.7934893
A&E AE_Substance Misuse 4 15 to 25 Female 45 5832 0.7716049
A&E AE_Substance Misuse 5 15 to 25 Female 14 2727 0.5133847
A&E AE_Substance Misuse 1 0 to 14 Male 6 42279 0.0141914
A&E AE_Substance Misuse 2 0 to 14 Male NA 9845 NA
A&E AE_Substance Misuse 3 0 to 14 Male NA 7597 NA
A&E AE_Substance Misuse 4 0 to 14 Male NA 8246 NA
A&E AE_Substance Misuse 5 0 to 14 Male NA 3539 NA
A&E AE_Substance Misuse 1 15 to 25 Male 177 30174 0.5865977
A&E AE_Substance Misuse 2 15 to 25 Male 42 14383 0.2920114
A&E AE_Substance Misuse 3 15 to 25 Male 28 9820 0.2851324
A&E AE_Substance Misuse 4 15 to 25 Male 18 6597 0.2728513
A&E AE_Substance Misuse 5 15 to 25 Male 14 2955 0.4737733

pop19+pop20+0.5*pop21 as denominator

#Chart - raw value

figure_AE_pyear <- agg_pop_both %>%
  filter(.,type=="A&E") %>% 
  ggplot(., aes(x=quantile, y=rate_per_person_year, fill=variable)) +
  facet_wrap(~age_sex) +
  geom_bar(position="dodge", stat="identity")+
  theme_ipsum() +
  xlab("IMD quantile (national) - 1 (worst) to 5 (best)") +
  ylab("rate") +
  ggtitle("Number of events, divided by pop19+pop20+0.5*pop21")

ggplotly(figure_AE_pyear)
#Display snippet

agg_pop_both %>%
  filter(.,type=="A&E") %>%
    select(.,type,variable,quantile,age_group2,sex,value,pyears_19to21,rate_per_person_year) %>% 
    arrange(.,variable,sex,age_group2,quantile) %>%
    knitr::kable(., align = "lccrr")
type variable quantile age_group2 sex value pyears_19to21 rate_per_person_year
A&E AE_Alcohol 1 0 to 14 Female 32 99637.0 0.0321166
A&E AE_Alcohol 2 0 to 14 Female 6 22988.5 0.0261000
A&E AE_Alcohol 3 0 to 14 Female 9 18431.0 0.0488308
A&E AE_Alcohol 4 0 to 14 Female NA 19331.5 NA
A&E AE_Alcohol 5 0 to 14 Female NA 8815.5 NA
A&E AE_Alcohol 1 15 to 25 Female 237 76818.0 0.3085214
A&E AE_Alcohol 2 15 to 25 Female 117 37480.0 0.3121665
A&E AE_Alcohol 3 15 to 25 Female 58 24887.0 0.2330534
A&E AE_Alcohol 4 15 to 25 Female 25 14722.5 0.1698081
A&E AE_Alcohol 5 15 to 25 Female 16 6489.0 0.2465711
A&E AE_Alcohol 1 0 to 14 Male NA 106210.5 NA
A&E AE_Alcohol 2 0 to 14 Male NA 24567.5 NA
A&E AE_Alcohol 3 0 to 14 Male NA 18925.0 NA
A&E AE_Alcohol 4 0 to 14 Male NA 20474.0 NA
A&E AE_Alcohol 5 0 to 14 Male NA 8906.0 NA
A&E AE_Alcohol 1 15 to 25 Male 186 75490.5 0.2463886
A&E AE_Alcohol 2 15 to 25 Male 63 36352.0 0.1733055
A&E AE_Alcohol 3 15 to 25 Male 40 24820.0 0.1611604
A&E AE_Alcohol 4 15 to 25 Male 28 16596.0 0.1687154
A&E AE_Alcohol 5 15 to 25 Male 12 7048.5 0.1702490
A&E AE_Eating Disorders 1 0 to 14 Female 21 99637.0 0.0210765
A&E AE_Eating Disorders 2 0 to 14 Female NA 22988.5 NA
A&E AE_Eating Disorders 3 0 to 14 Female NA 18431.0 NA
A&E AE_Eating Disorders 4 0 to 14 Female NA 19331.5 NA
A&E AE_Eating Disorders 5 0 to 14 Female NA 8815.5 NA
A&E AE_Eating Disorders 1 15 to 25 Female 15 76818.0 0.0195267
A&E AE_Eating Disorders 2 15 to 25 Female NA 37480.0 NA
A&E AE_Eating Disorders 3 15 to 25 Female NA 24887.0 NA
A&E AE_Eating Disorders 4 15 to 25 Female NA 14722.5 NA
A&E AE_Eating Disorders 5 15 to 25 Female NA 6489.0 NA
A&E AE_Eating Disorders 1 0 to 14 Male 13 106210.5 0.0122398
A&E AE_Eating Disorders 2 0 to 14 Male 8 24567.5 0.0325633
A&E AE_Eating Disorders 3 0 to 14 Male NA 18925.0 NA
A&E AE_Eating Disorders 4 0 to 14 Male NA 20474.0 NA
A&E AE_Eating Disorders 5 0 to 14 Male NA 8906.0 NA
A&E AE_Eating Disorders 1 15 to 25 Male NA 75490.5 NA
A&E AE_Eating Disorders 2 15 to 25 Male NA 36352.0 NA
A&E AE_Eating Disorders 3 15 to 25 Male NA 24820.0 NA
A&E AE_Eating Disorders 4 15 to 25 Male NA 16596.0 NA
A&E AE_Eating Disorders 5 15 to 25 Male NA 7048.5 NA
A&E AE_Self Harm 1 0 to 14 Female 63 99637.0 0.0632295
A&E AE_Self Harm 2 0 to 14 Female 12 22988.5 0.0522000
A&E AE_Self Harm 3 0 to 14 Female 10 18431.0 0.0542564
A&E AE_Self Harm 4 0 to 14 Female 5 19331.5 0.0258645
A&E AE_Self Harm 5 0 to 14 Female NA 8815.5 NA
A&E AE_Self Harm 1 15 to 25 Female 314 76818.0 0.4087584
A&E AE_Self Harm 2 15 to 25 Female 96 37480.0 0.2561366
A&E AE_Self Harm 3 15 to 25 Female 94 24887.0 0.3777072
A&E AE_Self Harm 4 15 to 25 Female 35 14722.5 0.2377314
A&E AE_Self Harm 5 15 to 25 Female 18 6489.0 0.2773925
A&E AE_Self Harm 1 0 to 14 Male 13 106210.5 0.0122398
A&E AE_Self Harm 2 0 to 14 Male 7 24567.5 0.0284929
A&E AE_Self Harm 3 0 to 14 Male NA 18925.0 NA
A&E AE_Self Harm 4 0 to 14 Male NA 20474.0 NA
A&E AE_Self Harm 5 0 to 14 Male NA 8906.0 NA
A&E AE_Self Harm 1 15 to 25 Male 117 75490.5 0.1549864
A&E AE_Self Harm 2 15 to 25 Male 31 36352.0 0.0852773
A&E AE_Self Harm 3 15 to 25 Male 23 24820.0 0.0926672
A&E AE_Self Harm 4 15 to 25 Male 18 16596.0 0.1084599
A&E AE_Self Harm 5 15 to 25 Male 7 7048.5 0.0993119
A&E AE_Substance Misuse 1 0 to 14 Female 21 99637.0 0.0210765
A&E AE_Substance Misuse 2 0 to 14 Female 6 22988.5 0.0261000
A&E AE_Substance Misuse 3 0 to 14 Female 13 18431.0 0.0705333
A&E AE_Substance Misuse 4 0 to 14 Female NA 19331.5 NA
A&E AE_Substance Misuse 5 0 to 14 Female NA 8815.5 NA
A&E AE_Substance Misuse 1 15 to 25 Female 283 76818.0 0.3684032
A&E AE_Substance Misuse 2 15 to 25 Female 68 37480.0 0.1814301
A&E AE_Substance Misuse 3 15 to 25 Female 78 24887.0 0.3134166
A&E AE_Substance Misuse 4 15 to 25 Female 45 14722.5 0.3056546
A&E AE_Substance Misuse 5 15 to 25 Female 14 6489.0 0.2157497
A&E AE_Substance Misuse 1 0 to 14 Male 6 106210.5 0.0056492
A&E AE_Substance Misuse 2 0 to 14 Male NA 24567.5 NA
A&E AE_Substance Misuse 3 0 to 14 Male NA 18925.0 NA
A&E AE_Substance Misuse 4 0 to 14 Male NA 20474.0 NA
A&E AE_Substance Misuse 5 0 to 14 Male NA 8906.0 NA
A&E AE_Substance Misuse 1 15 to 25 Male 177 75490.5 0.2344666
A&E AE_Substance Misuse 2 15 to 25 Male 42 36352.0 0.1155370
A&E AE_Substance Misuse 3 15 to 25 Male 28 24820.0 0.1128122
A&E AE_Substance Misuse 4 15 to 25 Male 18 16596.0 0.1084599
A&E AE_Substance Misuse 5 15 to 25 Male 14 7048.5 0.1986238