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BMI_2019.R
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BMI_2019.R
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##### YEARLY COHORTS FOR BMI
##### Author: M Samuel
##### Updated: 7th March 2022, 15th April
## Specify libraries
library(pacman)
library(tidyverse)
library(Hmisc)
library(here)
library(arrow)
library(purrr)
library(data.table)
library(forcats)
library(rstatix)
library(janitor)
library(skimr)
#BMI_2019 <- read_feather (here::here ("Documents/Academic GP/Open Safely/Dummy Data", "complete_meds_2019.feather"))
####################################
###################################
##### read in files
BMI_2019 <- read_feather (here::here ("output/data", "complete_meds_2019.feather"))
###################
## 2019 analysis
###################
###################
## 2019 analysis
###################
BMI_2019 <- as_tibble (BMI_2019)
# label exposure variables
## recode and label some demographic components
## recode ethnicity so NA is 0
BMI_2019 <- BMI_2019 %>%
mutate(ethnic_no_miss = ifelse(is.na(ethnicity), 0, ethnicity ))
BMI_2019 <- BMI_2019 %>%
mutate(ethnicity_16_no_miss = ifelse(is.na(ethnicity_16), 0, ethnicity_16 ))
### label
BMI_2019$ethnic_no_miss[BMI_2019$ethnic_no_miss=="1"]<-"White"
BMI_2019$ethnic_no_miss[BMI_2019$ethnic_no_miss=="2"]<-"Mixed"
BMI_2019$ethnic_no_miss[BMI_2019$ethnic_no_miss=="3"]<-"Asian"
BMI_2019$ethnic_no_miss[BMI_2019$ethnic_no_miss=="4"]<-"Black"
BMI_2019$ethnic_no_miss[BMI_2019$ethnic_no_miss=="5"]<-"Other"
BMI_2019$ethnic_no_miss[BMI_2019$ethnic_no_miss=="0"]<-"Not_recorded"
BMI_2019 <- BMI_2019 %>%
mutate (ethnic_no_miss = as.factor(ethnic_no_miss)) %>%
mutate (ethnic_no_miss = fct_relevel(ethnic_no_miss, "White", "Asian", "Black", "Mixed","Other", "Not_recorded"))
BMI_2019 <- BMI_2019 %>%
dplyr::mutate(imd = na_if(imd, '0'))
BMI_2019 <- BMI_2019 %>%
mutate (imd = as.factor(imd)) %>%
mutate (imd = fct_relevel(imd, "1", "2", "3", "4", "5"))
BMI_2019 %>%
tabyl(imd)
# had_bmi_imd_m <- glm(had_bmi ~ imd, data=BMI_2019, family=binomial) %>%
# broom::tidy(exponentiate = TRUE, conf.int = TRUE) %>% # exponentiate and produce CIs
# dplyr::mutate(across(where(is.numeric), round, digits = 2))
BMI_2019 <- BMI_2019 %>%
mutate (eth_group_16=case_when(
ethnicity_16_no_miss == "1" ~ "White_British",
ethnicity_16_no_miss == "2" ~ "White_Irish",
ethnicity_16_no_miss == "3" ~ "Other_White",
ethnicity_16_no_miss == "4" ~ "White_Black_Carib",
ethnicity_16_no_miss == "5" ~ "White_Black_African",
ethnicity_16_no_miss == "6" ~ "White_Asian",
ethnicity_16_no_miss == "7" ~ "Other_Mixed",
ethnicity_16_no_miss == "8" ~ "Indian",
ethnicity_16_no_miss == "9" ~ "Pakistani",
ethnicity_16_no_miss == "10" ~ "Bangladeshi",
ethnicity_16_no_miss == "11" ~ "Other_Asian",
ethnicity_16_no_miss == "12" ~ "Caribbean",
ethnicity_16_no_miss == "13" ~ "African",
ethnicity_16_no_miss == "14" ~ "Other_Black",
ethnicity_16_no_miss == "15" ~ "Chinese",
ethnicity_16_no_miss == "16" ~ "Other",
ethnicity_16_no_miss == "0" ~ "Missing"))
BMI_2019 <- BMI_2019 %>%
mutate (eth_group_16 = as.factor(eth_group_16)) %>%
mutate ( eth_group_16= fct_relevel(eth_group_16,
"White_British",
"White_Irish",
"Other_White",
"Indian",
"Pakistani",
"Bangladeshi",
"Other_Asian",
"Caribbean",
"African",
"Other_Black",
"Chinese",
"White_Asian",
"White_Black_Carib",
"White_Black_African",
"Other_Mixed",
"Other",
"Missing"))
## Generate a data set of demographic and exposure variables: To link to BMI data after data manipulation
demog_2019 <- BMI_2019 %>%
dplyr::select(-starts_with("bmi")) %>%
dplyr::select(-starts_with("hba1c")) %>%
dplyr::select(-("eth")) %>%
dplyr::select(-starts_with("ethnicity"))
colnames(demog_2019)
######################################### CODE to: 1) link BMI values and actual measured data; 2) de-duplicate any duplicate values due to monthly_bmi formula
######################################### Will need to also create a cohort with all values for median_bmi analysis
bmi_2019_long <- BMI_2019 %>%
dplyr::select(patient_id, starts_with("bmi")) %>%
dplyr::select(-("bmi"), -("bmi_date_measured")) %>%
pivot_longer( ## 1. pivot_longer date measured columns
cols = c('bmi_march_date_measured', 'bmi_apr_date_measured', 'bmi_may_date_measured', 'bmi_june_date_measured', 'bmi_july_date_measured', 'bmi_aug_date_measured', 'bmi_sep_date_measured', 'bmi_oct_date_measured', 'bmi_nov_date_measured', 'bmi_dec_date_measured', 'bmi_jan_date_measured', 'bmi_feb_date_measured', 'bmi_jan_date_measured'),
values_to = "bmi_measured_date" ) %>%
dplyr::arrange(patient_id, bmi_measured_date) %>%
tidyr::drop_na(bmi_measured_date) %>%
group_by(patient_id, bmi_measured_date) %>% ## filter out duplicate values!!
slice_head %>% # step 2. Pivot longer the values.
pivot_longer(
cols = c('bmi_march', 'bmi_apr', 'bmi_may', 'bmi_june', 'bmi_july', 'bmi_aug', 'bmi_sep', 'bmi_oct', 'bmi_nov', 'bmi_dec', 'bmi_jan', 'bmi_feb', 'bmi_jan'),
names_to = "date",
values_to = "monthly_bmi") %>% # step 3. filter out duplicate row created by the pivot step
mutate(measured_month = str_sub(name, 1, -15)) %>% #3a. create a column to identify matching events
dplyr::filter(measured_month == date) %>%
select(-'name', -'measured_month')
## Join with demographic data set to create a data set of all BMI values
BMI_complete_long <- demog_2019 %>%
dplyr::left_join(bmi_2019_long) %>%
dplyr::mutate(year=2019)
## DATA QUALITY CHECKS AND REPORTING: HOW MANY VALUES FILTERED OUT ETC
### Check how many patients had a BMI check before filtering out very high and very low values
## TOTAL BMI VALUES BEFORE FILTERING HIGH AND LOW VALUES
BMI_data_checks <- BMI_complete_long %>%
ungroup()%>%
skim_without_charts%>%
dplyr::select('skim_variable', 'n_missing', 'complete_rate') %>%
dplyr::mutate(N_total = n_missing/(1-complete_rate)) %>%
dplyr::mutate(N_values = N_total - n_missing) %>%
dplyr::filter(skim_variable == 'monthly_bmi')
## NUMBER OF PATIENTS WITH A BMI BEFORE FILTERING OUT HIGH AND LOW VALUES
patients_with_bmi <- BMI_complete_long %>%
dplyr::group_by(patient_id) %>%
dplyr::summarise(patients_with_bmi_all = median(monthly_bmi, na.rm = TRUE))
patients_with_bmi_check <- skim_without_charts(patients_with_bmi)%>%
dplyr::select('skim_variable', 'n_missing', 'complete_rate') %>%
dplyr::mutate(N_total = n_missing/(1-complete_rate)) %>%
dplyr::mutate(N_values = N_total - n_missing) %>%
dplyr::filter(skim_variable == 'patients_with_bmi_all')
### REPLACE HIGH and LOW values with Missing
BMI_complete_long$monthly_bmi[BMI_complete_long$monthly_bmi<15|BMI_complete_long$monthly_bmi>65] <- NA
BMI_filtered_checks <- BMI_complete_long %>%
dplyr::mutate(monthly_bmi_filtered = monthly_bmi) %>%
ungroup()%>%
skim_without_charts%>%
dplyr::select('skim_variable', 'n_missing', 'complete_rate') %>%
dplyr::mutate(N_total = n_missing/(1-complete_rate)) %>%
dplyr::mutate(N_values = N_total - n_missing) %>%
dplyr::filter(skim_variable == 'monthly_bmi_filtered')
#>>>>>> FINAL LONG DATA SET:: BMI_complete_long
########################################################################################################################################################
#######################################################################################################################################################
## MEDIAN BMI ANALYSIS
## create a data set with patient's median BMI
### Very high and low values already filtered out
median_bmi <- BMI_complete_long %>%
dplyr::group_by(patient_id) %>%
dplyr::summarise(median_bmi = median(monthly_bmi, na.rm = TRUE))
## First complete the data checks to identify how many patients lost at each step
patients_with_bmi_filtered <- median_bmi %>%
dplyr::mutate(patients_with_bmi_filtered = median_bmi) %>%
skim_without_charts()%>%
dplyr::select('skim_variable', 'n_missing', 'complete_rate') %>%
dplyr::mutate(N_total = n_missing/(1-complete_rate)) %>%
dplyr::mutate(N_values = N_total - n_missing) %>%
dplyr::filter(skim_variable == 'patients_with_bmi_filtered')
## COMPLETE DATA CHECK TABLE
BMI_data_checks <- BMI_data_checks %>%
bind_rows(BMI_filtered_checks) %>%
bind_rows(patients_with_bmi_check) %>%
bind_rows(patients_with_bmi_filtered)
#################################################################################
## JOIN MEDIAN BMI RESULTS TO DEMOGRAPHIC AND COVARIATES TABLES
BMI_2019_median <- median_bmi %>%
left_join(demog_2019) %>%
dplyr::mutate("year" = "2019")
### classify as underweight, healthyweight, overweight, obese
BMI_complete_categories <- ungroup(BMI_2019_median)
BMI_complete_categories$BMI_categories <- cut(BMI_complete_categories$median_bmi,
breaks=c(0, 20,25,30,1000),
labels= c("underweight", "healthy", "overweight", "obese"))
## classify as above 27.5
BMI_complete_categories$BMI_over27.5 <- cut(BMI_complete_categories$median_bmi,
breaks=c(0,27.5,1000),
labels=c("<27.5", "27.5+"))
##########
########## .. Generate variable indicating DWMP eligibility
## confirm ethnicity categories
# dict_eth = {1: ‘White’, 2: ‘Mixed’, 3: ‘Asian’, 4: ‘Black’, 5: ‘Other’, np.nan: ‘Unknown’, 0: ‘Unknown’}
BMI_complete_categories_DWMP <- BMI_complete_categories %>%
dplyr::mutate(
DWMP = if_else(
condition = ((((ethnic_no_miss=="White"| ethnic_no_miss=="Not_recorded") & median_bmi >=30) | ((ethnic_no_miss=="Asian"| ethnic_no_miss=="Black"| ethnic_no_miss=="Mixed"| ethnic_no_miss=="Other") & median_bmi >=27.5))
& ((hypertension==1| diabetes_t1==1| diabetes_t2==1))),
true = "eligible",
false = "not_eligible"
)
)
# "White", "Asian", "Black", "Mixed","Other", "Not_recorded"
BMI_complete_categories_DWMP <-BMI_complete_categories_DWMP %>%
dplyr::mutate(
across(
.cols = c(learning_disability,depression, dementia,psychosis_schiz_bipolar, diabetes_type, diabetes_t1, diabetes_t2, asthma, COPD, stroke_and_TIA, chronic_cardiac, hypertension, all_cancer),
.names = "comorbid_{col}"
)
)
BMI_complete_categories_DWMP <- BMI_complete_categories_DWMP %>%
dplyr::select(-c(learning_disability,depression, dementia,psychosis_schiz_bipolar, diabetes_type, diabetes_t1, diabetes_t2, asthma, COPD, stroke_and_TIA, chronic_cardiac, hypertension, all_cancer, type1_diabetes, type2_diabetes, unknown_diabetes))
BMI_complete_categories_DWMP <- ungroup (BMI_complete_categories_DWMP)
### add binary obese variable
BMI_complete_categories_DWMP <- BMI_complete_categories_DWMP %>%
dplyr::mutate(
obese = if_else(
condition = (median_bmi >= 30),
true = 1,
false = 0
),
.after = "median_bmi"
)
skim_without_charts(BMI_complete_categories_DWMP)
## SAVE BMI_data_checks as csv
###########################################################################################################
write.csv (BMI_data_checks, here::here ("output/data","BMI_data_checks_2019.csv"))
write_feather (BMI_complete_categories_DWMP, here::here ("output/data","BMI_complete_median_2019.feather"))
write_feather (BMI_complete_long, here::here ("output/data","BMI_complete_long_2019.feather"))