The Double Burden of Malnutrition and Pediatric Sepsis in Tanzania

1. Defining malnutrition

1.1. Prerequisites

library(tidyverse)
library(zscorer)
library(readr)
library(dplyr)
library (lubridate)
library (ggplot2)
library (rmarkdown)

PSFI_df_malnutrition <- read_csv("Ben_cut_4_27.csv")

  PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
  mutate(
    date_present = as.POSIXct(date_present, format = "%d%b%Y:%H:%M:%S", tz = "UTC"),
    dob = mdy(dob),
    age_days_exact = as.numeric(difftime(date_present, dob, units = "days"))
  )
  
  
summary(PSFI_df_malnutrition$age_days_exact)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  32.03  289.19  604.61 1124.44 1461.64 5452.40 

View(PSFI_df_malnutrition)

1.2. Anthroprometric analysis

# Layout to split the screen
layout(mat = matrix(c(1,2),2,1, byrow=TRUE),  height = c(1,8))
 
# Draw the boxplot and the histogram 
par(mar=c(0, 3.1, 1.1, 2.1))
boxplot(PSFI_df_malnutrition$ht , horizontal=TRUE , ylim=c(0,200), xaxt="n" , col=rgb(0.8,0.8,0,0.5) , frame=F)
par(mar=c(4, 3.1, 1.1, 2.1))

hist(PSFI_df_malnutrition$ht
     , breaks=40 , col=rgb(1,0.8,0.8,1) , border=F , main="" , xlab="Height (cm)", xlim=c(0,200))

layout(mat = matrix(c(1,2),2,1, byrow=TRUE),  height = c(1,8))
 par(mar=c(0, 3.1, 1.1, 2.1))
boxplot(PSFI_df_malnutrition$wt , horizontal=TRUE , ylim=c(0,200), xaxt="n" , col=rgb(0.8,0.8,0,0.5) , frame=F)
par(mar=c(4, 3.1, 1.1, 2.1))

hist(PSFI_df_malnutrition$wt
     , breaks=40 , col=rgb(1,0.8,0.8,1) , border=F , main="" , xlab="Weight (kg)", xlim=c(0,100))

layout(mat = matrix(c(1,2),2,1, byrow=TRUE),  height = c(1,8))
par(mar=c(0, 3.1, 1.1, 2.1))
boxplot(PSFI_df_malnutrition$muac , horizontal=TRUE , ylim=c(0,200), xaxt="n" , col=rgb(0.8,0.8,0,0.5) , frame=F)
par(mar=c(4, 3.1, 1.1, 2.1))

hist(PSFI_df_malnutrition$muac
     , breaks=20 , col=rgb(1,0.8,0.8,1) , border=F , main="" , xlab="Mid-upper arm circumference (cm)", xlim=c(0,30))
abline(v = 11.5, col = "red", lwd = 2, lty = 2)   # severe

abline(v = 12.5, col = "blue", lwd = 2, lty = 2)  # moderate

PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
  mutate(age_years = age_days_exact / 365.25)

summary(PSFI_df_malnutrition$age_years)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 0.08768  0.79176  1.65535  3.07854  4.00176 14.92786 

layout(mat = matrix(c(1,2),2,1, byrow=TRUE),  height = c(1,8))
 par(mar=c(0, 3.1, 1.1, 2.1))
boxplot(PSFI_df_malnutrition$age_years , horizontal=TRUE , ylim=c(0,300), xaxt="n" , col=rgb(0.8,0.8,0,0.5) , frame=F)
par(mar=c(4, 3.1, 1.1, 2.1))

hist(PSFI_df_malnutrition$age_years
     , breaks=40 , col=rgb(1,0.8,0.8,1) , border=F , main="" , xlab="Age (years)", xlim=c(0,15))

1.3. Z-scorer

PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
  mutate(sex_who = if_else(sex == 1, 1, 2))

summary(PSFI_df_malnutrition$sex_who)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   1.000   1.427   2.000   2.000 

PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
  mutate(
    wflz = addWGSR(
      data = .,
      sex = "sex_who",
      firstPart = "wt",
      secondPart = "ht",
      index = "wfl"
    )$wflz,
    
    wfhz = addWGSR(
      data = .,
      sex = "sex_who",
      firstPart = "wt",
      secondPart = "ht",
      index = "wfh"
    )$wfhz,
    
    baz = addWGSR(
      data = .,
      sex = "sex_who",
      firstPart = "wt",
      secondPart = "ht",
      thirdPart = "age_days_exact",
      index = "bfa"
    )$bfaz,
  )

========================================================================================
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PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
  mutate(
    wflz = addWGSR(
      data = .,
      sex = "sex_who",
      firstPart = "wt",
      secondPart = "ht",
      index = "wfl"
    )$wflz,
    
    wfhz = addWGSR(
      data = .,
      sex = "sex_who",
      firstPart = "wt",
      secondPart = "ht",
      index = "wfh"
    )$wfhz,
    
    baz = addWGSR(
      data = .,
      sex = "sex_who",
      firstPart = "wt",
      secondPart = "ht",
      thirdPart = "age_days_exact",
      index = "bfa"
    )$bfaz,
  )

========================================================================================
========================================================================================
========================================================================================

PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
  mutate(
    muacz = case_when(
      muac >= 12.5 ~ 0,
      muac >= 11.5 & muac < 12.5 ~ -2.5,
      muac < 11.5 ~ -4,
      TRUE ~ NA_real_
    )
  )

PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
  mutate(
    zscore_unified = case_when(
      age_group == 0 & ht >= 45 & ht < 65 ~ wflz,
      age_group == 0 & ht >= 65 & ht < 120 ~ wfhz,
      age_group == 0 & (ht < 45 | ht >= 120 | is.na(ht)) ~ muacz,
      age_group == 1 ~ baz,
      TRUE ~ NA_real_
    )
  )

PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
  mutate(
    malnutrition = case_when(
  is.na(zscore_unified) ~ NA_integer_,
  zscore_unified < -3 ~ 2L,
  zscore_unified >= -3 & zscore_unified < -2 ~ 1L,
  TRUE ~ 0L
    )
  )

PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
  mutate(
    malnutrition_source = case_when(
      age_group == 0 & ht >= 45 & ht < 65  ~ "WFL",
      age_group == 0 & ht >= 65 & ht < 120 ~ "WFH",
      age_group == 0 & (ht < 45 | ht >= 120 | is.na(ht)) ~ "MUAC",
      age_group == 1 ~ "BFA",
      TRUE ~ NA_character_
    )
  )

1.4. Data check

PSFI_df_malnutrition%>%
  group_by(malnutrition) %>%
  summarize(
    malnutrition_missing = sum(is.na(malnutrition)),
    mort_inhosp_missing = sum(is.na(mort_inhosp)), #103 mort_inhosp_miissing  = controls
  )

PSFI_df_malnutrition %>%
  drop_na(mort_inhosp) %>%
  group_by(malnutrition_source) %>%
  summarize(
    count_malnut = n()
  )

# NA due to child with height of 20 cm (under WHO curve for wfh/wfl) and 38 days old (too young for MUAC)

PSFI_df_malnutrition %>%
  drop_na(mort_inhosp) %>%
  group_by(malnutrition) %>%
  summarize(
    count_malnut = n()
  )

PSFI_df_malnutrition %>%
  drop_na(malnutrition, mort_inhosp) %>%
  ggplot(aes(x = factor(malnutrition))) +
  geom_bar() +
  labs(
    x = "Malnutrition",
    y = "N"
  )

PSFI_df_malnutrition%>%
  drop_na(mort_inhosp) %>% #drops controls
  count(malnutrition, mort_inhosp) %>%
  group_by(malnutrition) %>%
  mutate(prop = n / sum(n))

PSFI_df_malnutrition %>%
  drop_na(mort_inhosp, malnutrition) %>% #only cases & minus the one previously mentioned NA
  ggplot +
  (aes(x = factor(malnutrition), fill = factor(mort_inhosp))) +
  geom_bar(stat = "count", position = "fill") +
  labs(
    x = "Malnutrition",
    y = "Proportion",
    fill = "Mortality"
  )

PSFI_df_malnutrition %>%
  count(malnutrition, case_control) %>%
  group_by(malnutrition) %>%
  mutate(prop = n / sum(n))

PSFI_df_malnutrition %>%
  drop_na(malnutrition) %>%
  ggplot +
  (aes(x = factor(malnutrition), fill = factor(case_control))) +
  geom_bar(stat = "count", position = "fill") +
  labs(
    x = "Malnutrition",
    y = "Proportion",
    fill = "Case/Control"
  )