Flu Vaccincation Coverage - Fourth More Deprived Decile (IMD 2019)

Abstract

This report summarises flu vaccination coverage for individuals aged 65 years and over, at-risk populations and primary school aged children. These data are extracted from The Office for Health Improvment and Disparities (OHID) Fingertips API. The data presented covers local authorities in the fourth more deprived decile (IMD 2019) for the most recent data available as of 11/09/2022. This report is for demonstating the use of fingertips API only.

# Summary of code -----------------------------------------------------------------

# Extract Flu vaccination indicators from Fingertips API.
# Summarise data deprivation decile and sort by low > high while highlighting Enfield LA.
# Benchmark and RAG rate against vaccination targets.
# Include horizontal bar for England and London average.
# Export Bar charts as PDF and publish as HTML.


# Set up  -----------------------------------------------------------------

setwd("G:/My Drive/Work/R")

getwd()

#RTools setup instructions
write('PATH="${RTOOLS40_HOME}\\usr\\bin;${PATH}"', file = "~/.Renviron", append = TRUE)


# Enable repository from ropensci
options(repos = c(
  ropensci = 'https://ropensci.r-universe.dev',
  CRAN = 'https://cloud.r-project.org'))

# Download and install fingertipsR in R
install.packages('fingertipsR')



# Packages ----------------------------------------------------------------


library(fingertipsR)
library(dplyr)
library(ggplot2)



# Misc ------------------------------------------------------------


categories <- category_types()

respiratory_areas <- area_types(402)

area_types <- area_types()



# Data extract ------------------------------------------------------------

# find domain ID codes
prof <- profiles()

# find indicator ID codes
indicators(DomainID = 1938133210)

# Flu indicator vector
flu_indicators <- c(30314, 30315, 92325, 93691)

# Extract dataframe of flu indicators by County and UA area type.
respiratory <- fingertips_data(
  IndicatorID = flu_indicators,
  AreaTypeID = 402,
  ParentAreaTypeID = 10146)

# Extract and append IMD2019 Deciles rates to end of dataset


#IMD scores by LA code
IMD2019 <- deprivation_decile(AreaTypeID = 402,
                           Year = 2019)

# Data format -------------------------------------------------------------

#Join IMD scores to dataset
respiratory2 <- respiratory %>%
  left_join(IMD2019)


#Sum counts and denominators by indicator and decile and calculate decile rates

respiratory_deciles_count <- respiratory2 %>%
  group_by(IndicatorName) %>%
  filter(TimeperiodSortable == max(TimeperiodSortable)) %>%
  group_by(decile, IndicatorName)%>%
  summarise(Count = sum(Count, na.rm = T))

respiratory_deciles_denominator <- respiratory2 %>%
  group_by(IndicatorName) %>%
  filter(TimeperiodSortable == max(TimeperiodSortable)) %>%
  group_by(decile, IndicatorName)%>%
  summarise(Denominator = sum(Denominator))

respiratory_deciles_rate <- left_join(respiratory_deciles_count,
                                    respiratory_deciles_denominator,
                                    by = c("decile", "IndicatorName"))

respiratory_deciles_rate <- respiratory_deciles_rate%>%
  mutate(Value = Count/Denominator)
 
# Join decile names and bind rows so decile names fall under $AreaNames and decile rates under $Value

AreaName <- c("Most deprived decile", 
              "Second most deprived decile",
              "Third more deprived decile",
              "Fourth more deprived decile",
              "Fifth more deprived decile",
              "Fifth less deprived decile",
              "Fourth less deprived decile",
              "Third less deprived decile",
              "Second least deprived decile",
              "Least deprived deprived decile")

decile <- c(1,2,3,4,5,6,7,8,9,10)

decile_lookup <- data.frame(AreaName,decile)

respiratory_deciles_rate <- respiratory_deciles_rate%>%
  left_join(decile_lookup)

respiratory3 <- respiratory2 %>%
  bind_rows(respiratory2,
            respiratory_deciles_rate[,c("AreaName")])


# Charts ------------------------------------------------------------------

# horizontal bar data frames

yintercept75 <- data.frame(yi = 75)
yintercept55 <- data.frame(yi = 55)
yintercept65 <- data.frame(yi = 65)



# Over 65 Flu

# filter data frame for ggplot
Over65 <- 
  respiratory2 %>%
  filter(IndicatorID == 30314)%>%
  filter(decile == 4)%>%
  filter(TimeperiodSortable == max(TimeperiodSortable))%>%
  arrange(.,desc(Value))

#sort Area names based on value
Over65$AreaName <- 
  factor(Over65$AreaName,
         levels=Over65$AreaName[order(Over65$Value,
                                        decreasing=TRUE)])
 
# create plot
plot_over65 <- 
  ggplot(Over65,
       aes(x=AreaName,
           y=Value))+
  geom_bar(stat = "identity")+ 
  scale_x_discrete(limits = rev(levels(Over65$AreaName))) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x = "County and UA's (Fourth more deprived decile)", 
       y = "Proportion (%)", 
       title = "Flu Vaccination Coverage, Ages 65+, 2021/22")+
  geom_hline(data = yintercept75, aes(yintercept = yi, linetype = factor(yi)), show_guide = TRUE, size = 1.5) +
  scale_linetype_manual(name = "Goal >= 75%", values = "solid", labels = "")+
  theme(axis.text.x = element_text(face = c('plain', 'plain', 'bold', 'plain', 'plain', "plain","plain",'plain', "plain",'plain', "plain",'plain', "plain",'plain', "plain")))





# At risk Individuals Flu


at_risk <- 
  respiratory2 %>%
  filter(IndicatorID == 30315)%>%
  filter(decile == 4)%>%
  filter(TimeperiodSortable == max(TimeperiodSortable))%>%
  arrange(.,desc(Value))

at_risk$AreaName <- 
  factor(at_risk$AreaName,
         levels=at_risk$AreaName[order(at_risk$Value,
                                      decreasing=TRUE)])


plot_at_risk <-
  ggplot(at_risk,
       aes(x=AreaName,
           y=Value))+
  geom_bar(stat = "identity")+ 
  scale_x_discrete(limits = rev(levels(at_risk$AreaName))) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x = "County and UA's (Fourth more deprived decile)", 
       y = "Proportion (%)", 
       title = "Flu Vaccination Coverage,At Risk Individuals, 2021/22")+
  geom_hline(data = yintercept55, aes(yintercept = yi, linetype = factor(yi)), show_guide = TRUE, size = 1.5) +
  scale_linetype_manual(name = "Goal >= 55%", values = "solid", labels = "")+
  theme(axis.text.x = element_text(face = c('plain', 'bold', 'plain', 'plain', 'plain', "plain","plain",'plain', "plain",'plain', "plain",'plain', "plain",'plain', "plain")))

        



# Primary School Ages Children


primary_school_aged <- 
  respiratory2 %>%
  filter(IndicatorID == 93691)%>%
  filter(decile == 4)%>%
  filter(TimeperiodSortable == max(TimeperiodSortable))%>%
  arrange(.,desc(Value))

primary_school_aged$AreaName <- 
  factor(primary_school_aged$AreaName,
         levels=primary_school_aged$AreaName[order(primary_school_aged$Value,
                                       decreasing=TRUE)])


plot_primary <- 
  ggplot(primary_school_aged,
       aes(x=AreaName,
           y=Value))+
  geom_bar(stat = "identity")+ 
  scale_x_discrete(limits = rev(levels(primary_school_aged$AreaName))) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x = "County and UA's (Fourth more deprived decile)", 
       y = "Proportion (%)", 
       title = "Flu Vaccination Coverage, Ages 4 - 11 years, 2021")+
  geom_hline(data = yintercept65, aes(yintercept = yi, linetype = factor(yi)), show_guide = TRUE, size = 1.5) +
  scale_linetype_manual(name = "Goal >= 65%", values = "solid", labels = "")+
  theme(axis.text.x = element_text(face = c('bold', 'plain', 'plain', 'plain', 'plain', "plain","plain",'plain', "plain",'plain', "plain",'plain', "plain",'plain', "plain")))