In ocelhay/AMULaos: App on Antimicrobial Use (AMU) in Laos.

knitr::opts_chunk$set(echo = FALSE)
library(ggplot2)
library(tidyverse)

include_graphics("LOMWRU.jpg")

Antimicrobial resistance is a great public health concern in Laos now. Misuse of antimicrobials is likely to be a key driver of the worsening AMR situation in Laos. The use of antimicrobials in hospital has not been well documented in Laos. Conducting point prevalence surveys (PPS) on hospital AMU in Laos is one method for monitoring the rational use of antimicrobials in Laos.

Patients Receiving an Antimicrobial Prescription per Quarter

dta_patient <- patients_filter() %>%
  mutate(spec_quarter = floor_date(surdate, "3 months")) %>% 
  mutate(spec_quarter = as.character(quarter(spec_quarter, with_year = TRUE))) %>% 
  group_by(spec_quarter, ipdopd) %>%
  summarise(n = n_distinct(patient_id), .groups = "drop")

# Complete dataset
missing <- seq(min(patients_filter()$surdate), max(patients_filter()$surdate), by = "month") %>%
  floor_date("3 months") %>%
  quarter(with_year = TRUE) %>% 
  as.character() %>% 
  unique() %>% 
  setdiff(unique(dta_patient$spec_quarter))

dta_patient <- bind_rows(dta_patient, tibble(spec_quarter = missing, n = 0)) %>% 
  arrange(spec_quarter) %>%
  mutate(spec_quarter = str_replace(spec_quarter, "[.]", " Q")) %>%
  complete(spec_quarter, ipdopd, fill = list(n = 0)) %>%
  filter(!is.na(ipdopd))

dta_ward <- wards_filter() %>%
  mutate(spec_quarter = floor_date(surdate, "3 months")) %>% 
  mutate(spec_quarter = as.character(quarter(spec_quarter, with_year = TRUE))) %>% 
  mutate(screened_patient = case_when(
    ipdopd == "Inpatient" ~ numadm,
    ipdopd == "Outpatient" ~ numconsu
  )) %>%
  group_by(spec_quarter, ipdopd) %>%
  summarise(nb_screened_patient = sum(screened_patient), .groups = "drop")

# Complete dataset
missing <- seq(min(wards_filter()$surdate), max(wards_filter()$surdate), by = "month") %>%
  floor_date("3 months") %>%
  quarter(with_year = TRUE) %>% 
  as.character() %>% 
  unique() %>% 
  setdiff(unique(dta_ward$spec_quarter))

dta_ward <- bind_rows(dta_ward, tibble(spec_quarter = missing, nb_screened_patient = 0)) %>% 
  arrange(spec_quarter) %>%
  mutate(spec_quarter = str_replace(spec_quarter, "[.]", " Q")) %>%
  complete(spec_quarter, ipdopd, fill = list(nb_screened_patient = 0)) %>%
  filter(!is.na(ipdopd))

dta <- left_join(dta_ward, dta_patient, by = c("spec_quarter", "ipdopd")) %>%
  mutate(prop_receiving_am = round(100*n / nb_screened_patient, 1))

dta %>%
  ggplot(aes(x = spec_quarter, y = prop_receiving_am, fill = ipdopd)) +
  scale_fill_manual(values = c("Inpatient" = "#af8dc3", "Outpatient" = "#f1a340")) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Patients Receiving an Antimicrobial Prescription", x = "", y = "%") +
  theme_light(base_size = 17) +
  theme(axis.text.x = element_text(angle = 90), legend.title = element_blank())