การวิเคราะห์ผลกระทบด้านงบประมาณในทางปฏิบัติ: นับคนให้ชัดก่อนคำนวณเงิน

# Budget Impact Analysis in Practice: Count People Before Counting Money
# Follow one district's stroke survivors from eligible people to the extra cash the fund pays.
# ติดตามผู้ป่วยหลังโรคหลอดเลือดสมองของอำเภอหนึ่ง จากผู้มีสิทธิจนถึงเงินสดส่วนเพิ่มที่กองทุนจ่าย
# Requires: R >= 4.5.2
# Tested: works in R 4.6.1
# tibble 3.3.1 (stable)
# dplyr 1.2.1 (stable)
# ggplot2 4.0.3 (stable)
# install.packages(c("tibble", "dplyr", "ggplot2"))
# update.packages(oldPkgs = c("tibble", "dplyr", "ggplot2"))
# remotes::install_version("tibble", "3.3.1")
# remotes::install_version("dplyr", "1.2.1")
# remotes::install_version("ggplot2", "4.0.3")
library(tibble)  # Write the years as an explicit table.
library(dplyr)  # Calculate people and money for each year.
library(ggplot2)  # Display the yearly cash without saving files.
stopifnot(getRversion() >= "4.5.2")

#%%@note 1. Define the illustrative inputs
# Every input is synthetic and written out once, in the order the article uses it.
# ค่าตั้งต้นทั้งหมดเป็นข้อมูลสมมติ เขียนไว้ครั้งเดียวตามลำดับที่บทความใช้
prevalent <- 500
annual_incident <- 100
exit_fraction <- 0.10
uptake <- c(0.20,0.40,0.60)
annual_incremental_cost <- 2000
current_care_cost <- 4500
nurse_places <- 300
discount_rate <- 0.03
big <- function(value) formatC(value, format = "f", digits = 0, big.mark = ",")

#%%@note 2. Follow the eligible people year by year
# New survivors join at the year start, the year's users are counted, then exits leave at year end.
# ผู้มีสิทธิใหม่เข้าต้นปี นับผู้ใช้บริการของปีนั้น แล้วผู้ที่ออกจึงออกเมื่อสิ้นปี
stock <- rep(prevalent + annual_incident, length(uptake))
for (year_index in 2:length(uptake)) {
  stock[year_index] <- stock[year_index - 1] * (1 - exit_fraction) + annual_incident
}
eligible <- c(600,640,676)
stopifnot(all(abs(stock - eligible) < 1e-9))
years <- tibble::tibble(year = seq_along(uptake), eligible = stock, uptake = uptake) |>
  dplyr::mutate(treated = eligible * uptake, exits = eligible * exit_fraction,
    remaining = eligible * (1 - exit_fraction),
    annual_budget = treated * annual_incremental_cost,
    cumulative_budget = cumsum(annual_budget))
stopifnot(all(abs(years$exits + years$remaining - years$eligible) < 1e-9))
treated <- years$treated
budget <- years$annual_budget

#%%@note 3. Price the two scenarios
# Everyone costs current care in both; only the service users add the extra cost.
# ทุกคนมีต้นทุนการดูแลเดิมในทั้งสองสถานการณ์ เฉพาะผู้ใช้บริการที่มีต้นทุนเพิ่ม
current <- eligible * current_care_cost
new <- (eligible - treated) * current_care_cost +
  treated * (current_care_cost + annual_incremental_cost)
stopifnot(all(abs(new - current - budget) < 1e-6))

#%%@note 4. Check timing, rounding and the nurse places
# Wrong timing and early rounding change the cash; the nurse places cap year 3; uptake moves 0.10.
# ลำดับเวลาที่ผิดและการปัดเศษก่อนเวลาเปลี่ยนเงินสด จำนวนผู้ใช้ที่พยาบาลรับได้จำกัดปีที่ 3
exits_first <- treated[1] * c(1, 1 - exit_fraction) * annual_incremental_cost
rounded <- c(treated[3], round(treated[3]))
today_value <- 1834 / (1 + discount_rate)^3
capped <- pmin(treated, nurse_places)
shift <- 0.10 * sum(eligible)
users <- sum(eligible * uptake) + c(0, -1, 1) * shift

#%%@note 5. Display the model
# Plot the yearly extra cash; the script does not save a graphics file.
# แสดงเงินสดส่วนเพิ่มรายปี โดยไม่บันทึกไฟล์ภาพ
print(ggplot2::ggplot(years, ggplot2::aes(x = year, y = annual_budget)) +
  ggplot2::geom_col() + ggplot2::labs(x = "Year", y = "Extra cash in the year (THB)"))

#%%@note 6. Numbers the article quotes
# Print exactly the short boxes in their reading order.
# พิมพ์ผลลัพธ์ตรงกับกล่องตัวอย่างตามลำดับในบทความ
cat("Year order:",seq_along(uptake),"\n")
cat("Eligible at year start (people):",eligible,"\n")
cat("Treated prevalence (people):",treated,"\n")
cat("Year-end exits (people):",eligible*exit_fraction,"\n")
cat("Remaining stock (people):",eligible*(1-exit_fraction),"\n")
cat("Annual incremental budget (THB):",big(budget),"\n")
cat("Cumulative incremental budget (THB):",big(cumsum(budget)),"\n")
cat("Shortcut, 500 x 2,000 x 3 years (THB):",big(prevalent*annual_incremental_cost*3),"\n")
cat("Cost per person (THB):",big(current_care_cost+c(0,annual_incremental_cost)),"\n")
cat("Current-care scenario (THB):",big(current),"\n")
cat("New scenario (THB):",big(new),"\n")
cat("New minus current (THB):",big(new-current),"\n")
cat("Year 1 users, exits counted after and before:",treated[1]*c(1,1-exit_fraction),"\n")
cat("Year 1, exits counted after and before (THB):",big(exits_first),"\n")
cat("Year 3 people, expected and rounded:",rounded,"\n")
cat("Year 3 budget, expected and rounded (THB):",big(rounded*annual_incremental_cost),"\n")
cat("Yearly rate back to today (%):",100*discount_rate,"\n")
cat("1,834 THB due in year 3, worth today (THB):",big(today_value),"\n")
cat("Year 3 wanted, offered, waiting:",treated[3],nurse_places,treated[3]-nurse_places,"\n")
cat("Year 3 and three years within the places (THB):",
  big(c(capped[3],sum(capped))*annual_incremental_cost),"\n")
cat("Users over three years, shift of 0.10:",users[1],shift,"\n")
cat("Users, every share 0.10 lower and higher:",users[2:3],"\n")
cat("Three years, every share 0.10 lower and higher (THB):",
  big(users[2:3]*annual_incremental_cost),"\n")

# Budget Impact Analysis in Practice: Count People Before Counting Money
# Follow one district's stroke survivors from eligible people to the extra cash the fund pays.
# ติดตามผู้ป่วยหลังโรคหลอดเลือดสมองของอำเภอหนึ่ง จากผู้มีสิทธิจนถึงเงินสดส่วนเพิ่มที่กองทุนจ่าย
# Requires: R >= 4.5.2
# Tested: works in R 4.6.1
# tibble 3.3.1 (stable)
# dplyr 1.2.1 (stable)
# ggplot2 4.0.3 (stable)
# install.packages(c("tibble", "dplyr", "ggplot2"))
# update.packages(oldPkgs = c("tibble", "dplyr", "ggplot2"))
# remotes::install_version("tibble", "3.3.1")
# remotes::install_version("dplyr", "1.2.1")
# remotes::install_version("ggplot2", "4.0.3")
library(tibble)  # Write the years as an explicit table.
library(dplyr)  # Calculate people and money for each year.
library(ggplot2)  # Display the yearly cash without saving files.
stopifnot(getRversion() >= "4.5.2")

#%%@note 1. Define the illustrative inputs
# Every input is synthetic and written out once, in the order the article uses it.
# ค่าตั้งต้นทั้งหมดเป็นข้อมูลสมมติ เขียนไว้ครั้งเดียวตามลำดับที่บทความใช้
prevalent <- 500
annual_incident <- 100
exit_fraction <- 0.10
uptake <- c(0.20,0.40,0.60)
annual_incremental_cost <- 2000
current_care_cost <- 4500
nurse_places <- 300
discount_rate <- 0.03
big <- function(value) formatC(value, format = "f", digits = 0, big.mark = ",")

#%%@note 2. Follow the eligible people year by year
# New survivors join at the year start, the year's users are counted, then exits leave at year end.
# ผู้มีสิทธิใหม่เข้าต้นปี นับผู้ใช้บริการของปีนั้น แล้วผู้ที่ออกจึงออกเมื่อสิ้นปี
stock <- rep(prevalent + annual_incident, length(uptake))
for (year_index in 2:length(uptake)) {
  stock[year_index] <- stock[year_index - 1] * (1 - exit_fraction) + annual_incident
}
eligible <- c(600,640,676)
stopifnot(all(abs(stock - eligible) < 1e-9))
years <- tibble::tibble(year = seq_along(uptake), eligible = stock, uptake = uptake) |>
  dplyr::mutate(treated = eligible * uptake, exits = eligible * exit_fraction,
    remaining = eligible * (1 - exit_fraction),
    annual_budget = treated * annual_incremental_cost,
    cumulative_budget = cumsum(annual_budget))
stopifnot(all(abs(years$exits + years$remaining - years$eligible) < 1e-9))
treated <- years$treated
budget <- years$annual_budget

#%%@note 3. Price the two scenarios
# Everyone costs current care in both; only the service users add the extra cost.
# ทุกคนมีต้นทุนการดูแลเดิมในทั้งสองสถานการณ์ เฉพาะผู้ใช้บริการที่มีต้นทุนเพิ่ม
current <- eligible * current_care_cost
new <- (eligible - treated) * current_care_cost +
  treated * (current_care_cost + annual_incremental_cost)
stopifnot(all(abs(new - current - budget) < 1e-6))

#%%@note 4. Check timing, rounding and the nurse places
# Wrong timing and early rounding change the cash; the nurse places cap year 3; uptake moves 0.10.
# ลำดับเวลาที่ผิดและการปัดเศษก่อนเวลาเปลี่ยนเงินสด จำนวนผู้ใช้ที่พยาบาลรับได้จำกัดปีที่ 3
exits_first <- treated[1] * c(1, 1 - exit_fraction) * annual_incremental_cost
rounded <- c(treated[3], round(treated[3]))
today_value <- 1834 / (1 + discount_rate)^3
capped <- pmin(treated, nurse_places)
shift <- 0.10 * sum(eligible)
users <- sum(eligible * uptake) + c(0, -1, 1) * shift

#%%@note 5. Display the model
# Plot the yearly extra cash; the script does not save a graphics file.
# แสดงเงินสดส่วนเพิ่มรายปี โดยไม่บันทึกไฟล์ภาพ
print(ggplot2::ggplot(years, ggplot2::aes(x = year, y = annual_budget)) +
  ggplot2::geom_col() + ggplot2::labs(x = "Year", y = "Extra cash in the year (THB)"))

#%%@note 6. Numbers the article quotes
# Print exactly the short boxes in their reading order.
# พิมพ์ผลลัพธ์ตรงกับกล่องตัวอย่างตามลำดับในบทความ
cat("Year order:",seq_along(uptake),"\n")
cat("Eligible at year start (people):",eligible,"\n")
cat("Treated prevalence (people):",treated,"\n")
cat("Year-end exits (people):",eligible*exit_fraction,"\n")
cat("Remaining stock (people):",eligible*(1-exit_fraction),"\n")
cat("Annual incremental budget (THB):",big(budget),"\n")
cat("Cumulative incremental budget (THB):",big(cumsum(budget)),"\n")
cat("Shortcut, 500 x 2,000 x 3 years (THB):",big(prevalent*annual_incremental_cost*3),"\n")
cat("Cost per person (THB):",big(current_care_cost+c(0,annual_incremental_cost)),"\n")
cat("Current-care scenario (THB):",big(current),"\n")
cat("New scenario (THB):",big(new),"\n")
cat("New minus current (THB):",big(new-current),"\n")
cat("Year 1 users, exits counted after and before:",treated[1]*c(1,1-exit_fraction),"\n")
cat("Year 1, exits counted after and before (THB):",big(exits_first),"\n")
cat("Year 3 people, expected and rounded:",rounded,"\n")
cat("Year 3 budget, expected and rounded (THB):",big(rounded*annual_incremental_cost),"\n")
cat("Yearly rate back to today (%):",100*discount_rate,"\n")
cat("1,834 THB due in year 3, worth today (THB):",big(today_value),"\n")
cat("Year 3 wanted, offered, waiting:",treated[3],nurse_places,treated[3]-nurse_places,"\n")
cat("Year 3 and three years within the places (THB):",
  big(c(capped[3],sum(capped))*annual_incremental_cost),"\n")
cat("Users over three years, shift of 0.10:",users[1],shift,"\n")
cat("Users, every share 0.10 lower and higher:",users[2:3],"\n")
cat("Three years, every share 0.10 lower and higher (THB):",
  big(users[2:3]*annual_incremental_cost),"\n")

# Budget Impact Analysis in Practice: Count People Before Counting Money
# Follow one district's stroke survivors from eligible people to the extra cash the fund pays.
# ติดตามผู้ป่วยหลังโรคหลอดเลือดสมองของอำเภอหนึ่ง จากผู้มีสิทธิจนถึงเงินสดส่วนเพิ่มที่กองทุนจ่าย
# Requires: R >= 4.5.2
# Tested: works in R 4.6.1
# tibble 3.3.1 (stable)
# dplyr 1.2.1 (stable)
# ggplot2 4.0.3 (stable)
# install.packages(c("tibble", "dplyr", "ggplot2"))
# update.packages(oldPkgs = c("tibble", "dplyr", "ggplot2"))
# remotes::install_version("tibble", "3.3.1")
# remotes::install_version("dplyr", "1.2.1")
# remotes::install_version("ggplot2", "4.0.3")
library(tibble)  # Write the years as an explicit table.
library(dplyr)  # Calculate people and money for each year.
library(ggplot2)  # Display the yearly cash without saving files.
stopifnot(getRversion() >= "4.5.2")

#%%@note 1. Define the illustrative inputs
# Every input is synthetic and written out once, in the order the article uses it.
# ค่าตั้งต้นทั้งหมดเป็นข้อมูลสมมติ เขียนไว้ครั้งเดียวตามลำดับที่บทความใช้
prevalent <- 500
annual_incident <- 100
exit_fraction <- 0.10
uptake <- c(0.20,0.40,0.60)
annual_incremental_cost <- 2000
current_care_cost <- 4500
nurse_places <- 300
discount_rate <- 0.03
big <- function(value) formatC(value, format = "f", digits = 0, big.mark = ",")

#%%@note 2. Follow the eligible people year by year
# New survivors join at the year start, the year's users are counted, then exits leave at year end.
# ผู้มีสิทธิใหม่เข้าต้นปี นับผู้ใช้บริการของปีนั้น แล้วผู้ที่ออกจึงออกเมื่อสิ้นปี
stock <- rep(prevalent + annual_incident, length(uptake))
for (year_index in 2:length(uptake)) {
  stock[year_index] <- stock[year_index - 1] * (1 - exit_fraction) + annual_incident
}
eligible <- c(600,640,676)
stopifnot(all(abs(stock - eligible) < 1e-9))
years <- tibble::tibble(year = seq_along(uptake), eligible = stock, uptake = uptake) |>
  dplyr::mutate(treated = eligible * uptake, exits = eligible * exit_fraction,
    remaining = eligible * (1 - exit_fraction),
    annual_budget = treated * annual_incremental_cost,
    cumulative_budget = cumsum(annual_budget))
stopifnot(all(abs(years$exits + years$remaining - years$eligible) < 1e-9))
treated <- years$treated
budget <- years$annual_budget

#%%@note 3. Price the two scenarios
# Everyone costs current care in both; only the service users add the extra cost.
# ทุกคนมีต้นทุนการดูแลเดิมในทั้งสองสถานการณ์ เฉพาะผู้ใช้บริการที่มีต้นทุนเพิ่ม
current <- eligible * current_care_cost
new <- (eligible - treated) * current_care_cost +
  treated * (current_care_cost + annual_incremental_cost)
stopifnot(all(abs(new - current - budget) < 1e-6))

#%%@note 4. Check timing, rounding and the nurse places
# Wrong timing and early rounding change the cash; the nurse places cap year 3; uptake moves 0.10.
# ลำดับเวลาที่ผิดและการปัดเศษก่อนเวลาเปลี่ยนเงินสด จำนวนผู้ใช้ที่พยาบาลรับได้จำกัดปีที่ 3
exits_first <- treated[1] * c(1, 1 - exit_fraction) * annual_incremental_cost
rounded <- c(treated[3], round(treated[3]))
today_value <- 1834 / (1 + discount_rate)^3
capped <- pmin(treated, nurse_places)
shift <- 0.10 * sum(eligible)
users <- sum(eligible * uptake) + c(0, -1, 1) * shift

#%%@note 5. Display the model
# Plot the yearly extra cash; the script does not save a graphics file.
# แสดงเงินสดส่วนเพิ่มรายปี โดยไม่บันทึกไฟล์ภาพ
print(ggplot2::ggplot(years, ggplot2::aes(x = year, y = annual_budget)) +
  ggplot2::geom_col() + ggplot2::labs(x = "Year", y = "Extra cash in the year (THB)"))

#%%@note 6. Numbers the article quotes
# Print exactly the short boxes in their reading order.
# พิมพ์ผลลัพธ์ตรงกับกล่องตัวอย่างตามลำดับในบทความ
cat("Year order:",seq_along(uptake),"\n")
cat("Eligible at year start (people):",eligible,"\n")
cat("Treated prevalence (people):",treated,"\n")
cat("Year-end exits (people):",eligible*exit_fraction,"\n")
cat("Remaining stock (people):",eligible*(1-exit_fraction),"\n")
cat("Annual incremental budget (THB):",big(budget),"\n")
cat("Cumulative incremental budget (THB):",big(cumsum(budget)),"\n")
cat("Shortcut, 500 x 2,000 x 3 years (THB):",big(prevalent*annual_incremental_cost*3),"\n")
cat("Cost per person (THB):",big(current_care_cost+c(0,annual_incremental_cost)),"\n")
cat("Current-care scenario (THB):",big(current),"\n")
cat("New scenario (THB):",big(new),"\n")
cat("New minus current (THB):",big(new-current),"\n")
cat("Year 1 users, exits counted after and before:",treated[1]*c(1,1-exit_fraction),"\n")
cat("Year 1, exits counted after and before (THB):",big(exits_first),"\n")
cat("Year 3 people, expected and rounded:",rounded,"\n")
cat("Year 3 budget, expected and rounded (THB):",big(rounded*annual_incremental_cost),"\n")
cat("Yearly rate back to today (%):",100*discount_rate,"\n")
cat("1,834 THB due in year 3, worth today (THB):",big(today_value),"\n")
cat("Year 3 wanted, offered, waiting:",treated[3],nurse_places,treated[3]-nurse_places,"\n")
cat("Year 3 and three years within the places (THB):",
  big(c(capped[3],sum(capped))*annual_incremental_cost),"\n")
cat("Users over three years, shift of 0.10:",users[1],shift,"\n")
cat("Users, every share 0.10 lower and higher:",users[2:3],"\n")
cat("Three years, every share 0.10 lower and higher (THB):",
  big(users[2:3]*annual_incremental_cost),"\n")

# Budget Impact Analysis in Practice: Count People Before Counting Money
# Follow one district's stroke survivors from eligible people to the extra cash the fund pays.
# ติดตามผู้ป่วยหลังโรคหลอดเลือดสมองของอำเภอหนึ่ง จากผู้มีสิทธิจนถึงเงินสดส่วนเพิ่มที่กองทุนจ่าย
# Requires: R >= 4.5.2
# Tested: works in R 4.6.1
# tibble 3.3.1 (stable)
# dplyr 1.2.1 (stable)
# ggplot2 4.0.3 (stable)
# install.packages(c("tibble", "dplyr", "ggplot2"))
# update.packages(oldPkgs = c("tibble", "dplyr", "ggplot2"))
# remotes::install_version("tibble", "3.3.1")
# remotes::install_version("dplyr", "1.2.1")
# remotes::install_version("ggplot2", "4.0.3")
library(tibble)  # Write the years as an explicit table.
library(dplyr)  # Calculate people and money for each year.
library(ggplot2)  # Display the yearly cash without saving files.
stopifnot(getRversion() >= "4.5.2")

#%%@note 1. Define the illustrative inputs
# Every input is synthetic and written out once, in the order the article uses it.
# ค่าตั้งต้นทั้งหมดเป็นข้อมูลสมมติ เขียนไว้ครั้งเดียวตามลำดับที่บทความใช้
prevalent <- 500
annual_incident <- 100
exit_fraction <- 0.10
uptake <- c(0.20,0.40,0.60)
annual_incremental_cost <- 2000
current_care_cost <- 4500
nurse_places <- 300
discount_rate <- 0.03
big <- function(value) formatC(value, format = "f", digits = 0, big.mark = ",")

#%%@note 2. Follow the eligible people year by year
# New survivors join at the year start, the year's users are counted, then exits leave at year end.
# ผู้มีสิทธิใหม่เข้าต้นปี นับผู้ใช้บริการของปีนั้น แล้วผู้ที่ออกจึงออกเมื่อสิ้นปี
stock <- rep(prevalent + annual_incident, length(uptake))
for (year_index in 2:length(uptake)) {
  stock[year_index] <- stock[year_index - 1] * (1 - exit_fraction) + annual_incident
}
eligible <- c(600,640,676)
stopifnot(all(abs(stock - eligible) < 1e-9))
years <- tibble::tibble(year = seq_along(uptake), eligible = stock, uptake = uptake) |>
  dplyr::mutate(treated = eligible * uptake, exits = eligible * exit_fraction,
    remaining = eligible * (1 - exit_fraction),
    annual_budget = treated * annual_incremental_cost,
    cumulative_budget = cumsum(annual_budget))
stopifnot(all(abs(years$exits + years$remaining - years$eligible) < 1e-9))
treated <- years$treated
budget <- years$annual_budget

#%%@note 3. Price the two scenarios
# Everyone costs current care in both; only the service users add the extra cost.
# ทุกคนมีต้นทุนการดูแลเดิมในทั้งสองสถานการณ์ เฉพาะผู้ใช้บริการที่มีต้นทุนเพิ่ม
current <- eligible * current_care_cost
new <- (eligible - treated) * current_care_cost +
  treated * (current_care_cost + annual_incremental_cost)
stopifnot(all(abs(new - current - budget) < 1e-6))

#%%@note 4. Check timing, rounding and the nurse places
# Wrong timing and early rounding change the cash; the nurse places cap year 3; uptake moves 0.10.
# ลำดับเวลาที่ผิดและการปัดเศษก่อนเวลาเปลี่ยนเงินสด จำนวนผู้ใช้ที่พยาบาลรับได้จำกัดปีที่ 3
exits_first <- treated[1] * c(1, 1 - exit_fraction) * annual_incremental_cost
rounded <- c(treated[3], round(treated[3]))
today_value <- 1834 / (1 + discount_rate)^3
capped <- pmin(treated, nurse_places)
shift <- 0.10 * sum(eligible)
users <- sum(eligible * uptake) + c(0, -1, 1) * shift

#%%@note 5. Display the model
# Plot the yearly extra cash; the script does not save a graphics file.
# แสดงเงินสดส่วนเพิ่มรายปี โดยไม่บันทึกไฟล์ภาพ
print(ggplot2::ggplot(years, ggplot2::aes(x = year, y = annual_budget)) +
  ggplot2::geom_col() + ggplot2::labs(x = "Year", y = "Extra cash in the year (THB)"))

#%%@note 6. Numbers the article quotes
# Print exactly the short boxes in their reading order.
# พิมพ์ผลลัพธ์ตรงกับกล่องตัวอย่างตามลำดับในบทความ
cat("Year order:",seq_along(uptake),"\n")
cat("Eligible at year start (people):",eligible,"\n")
cat("Treated prevalence (people):",treated,"\n")
cat("Year-end exits (people):",eligible*exit_fraction,"\n")
cat("Remaining stock (people):",eligible*(1-exit_fraction),"\n")
cat("Annual incremental budget (THB):",big(budget),"\n")
cat("Cumulative incremental budget (THB):",big(cumsum(budget)),"\n")
cat("Shortcut, 500 x 2,000 x 3 years (THB):",big(prevalent*annual_incremental_cost*3),"\n")
cat("Cost per person (THB):",big(current_care_cost+c(0,annual_incremental_cost)),"\n")
cat("Current-care scenario (THB):",big(current),"\n")
cat("New scenario (THB):",big(new),"\n")
cat("New minus current (THB):",big(new-current),"\n")
cat("Year 1 users, exits counted after and before:",treated[1]*c(1,1-exit_fraction),"\n")
cat("Year 1, exits counted after and before (THB):",big(exits_first),"\n")
cat("Year 3 people, expected and rounded:",rounded,"\n")
cat("Year 3 budget, expected and rounded (THB):",big(rounded*annual_incremental_cost),"\n")
cat("Yearly rate back to today (%):",100*discount_rate,"\n")
cat("1,834 THB due in year 3, worth today (THB):",big(today_value),"\n")
cat("Year 3 wanted, offered, waiting:",treated[3],nurse_places,treated[3]-nurse_places,"\n")
cat("Year 3 and three years within the places (THB):",
  big(c(capped[3],sum(capped))*annual_incremental_cost),"\n")
cat("Users over three years, shift of 0.10:",users[1],shift,"\n")
cat("Users, every share 0.10 lower and higher:",users[2:3],"\n")
cat("Three years, every share 0.10 lower and higher (THB):",
  big(users[2:3]*annual_incremental_cost),"\n")

# Budget Impact Analysis in Practice: Count People Before Counting Money
# Follow one district's stroke survivors from eligible people to the extra cash the fund pays.
# ติดตามผู้ป่วยหลังโรคหลอดเลือดสมองของอำเภอหนึ่ง จากผู้มีสิทธิจนถึงเงินสดส่วนเพิ่มที่กองทุนจ่าย
# Requires: R >= 4.5.2
# Tested: works in R 4.6.1
# tibble 3.3.1 (stable)
# dplyr 1.2.1 (stable)
# ggplot2 4.0.3 (stable)
# install.packages(c("tibble", "dplyr", "ggplot2"))
# update.packages(oldPkgs = c("tibble", "dplyr", "ggplot2"))
# remotes::install_version("tibble", "3.3.1")
# remotes::install_version("dplyr", "1.2.1")
# remotes::install_version("ggplot2", "4.0.3")
library(tibble)  # Write the years as an explicit table.
library(dplyr)  # Calculate people and money for each year.
library(ggplot2)  # Display the yearly cash without saving files.
stopifnot(getRversion() >= "4.5.2")

#%%@note 1. Define the illustrative inputs
# Every input is synthetic and written out once, in the order the article uses it.
# ค่าตั้งต้นทั้งหมดเป็นข้อมูลสมมติ เขียนไว้ครั้งเดียวตามลำดับที่บทความใช้
prevalent <- 500
annual_incident <- 100
exit_fraction <- 0.10
uptake <- c(0.20,0.40,0.60)
annual_incremental_cost <- 2000
current_care_cost <- 4500
nurse_places <- 300
discount_rate <- 0.03
big <- function(value) formatC(value, format = "f", digits = 0, big.mark = ",")

#%%@note 2. Follow the eligible people year by year
# New survivors join at the year start, the year's users are counted, then exits leave at year end.
# ผู้มีสิทธิใหม่เข้าต้นปี นับผู้ใช้บริการของปีนั้น แล้วผู้ที่ออกจึงออกเมื่อสิ้นปี
stock <- rep(prevalent + annual_incident, length(uptake))
for (year_index in 2:length(uptake)) {
  stock[year_index] <- stock[year_index - 1] * (1 - exit_fraction) + annual_incident
}
eligible <- c(600,640,676)
stopifnot(all(abs(stock - eligible) < 1e-9))
years <- tibble::tibble(year = seq_along(uptake), eligible = stock, uptake = uptake) |>
  dplyr::mutate(treated = eligible * uptake, exits = eligible * exit_fraction,
    remaining = eligible * (1 - exit_fraction),
    annual_budget = treated * annual_incremental_cost,
    cumulative_budget = cumsum(annual_budget))
stopifnot(all(abs(years$exits + years$remaining - years$eligible) < 1e-9))
treated <- years$treated
budget <- years$annual_budget

#%%@note 3. Price the two scenarios
# Everyone costs current care in both; only the service users add the extra cost.
# ทุกคนมีต้นทุนการดูแลเดิมในทั้งสองสถานการณ์ เฉพาะผู้ใช้บริการที่มีต้นทุนเพิ่ม
current <- eligible * current_care_cost
new <- (eligible - treated) * current_care_cost +
  treated * (current_care_cost + annual_incremental_cost)
stopifnot(all(abs(new - current - budget) < 1e-6))

#%%@note 4. Check timing, rounding and the nurse places
# Wrong timing and early rounding change the cash; the nurse places cap year 3; uptake moves 0.10.
# ลำดับเวลาที่ผิดและการปัดเศษก่อนเวลาเปลี่ยนเงินสด จำนวนผู้ใช้ที่พยาบาลรับได้จำกัดปีที่ 3
exits_first <- treated[1] * c(1, 1 - exit_fraction) * annual_incremental_cost
rounded <- c(treated[3], round(treated[3]))
today_value <- 1834 / (1 + discount_rate)^3
capped <- pmin(treated, nurse_places)
shift <- 0.10 * sum(eligible)
users <- sum(eligible * uptake) + c(0, -1, 1) * shift

#%%@note 5. Display the model
# Plot the yearly extra cash; the script does not save a graphics file.
# แสดงเงินสดส่วนเพิ่มรายปี โดยไม่บันทึกไฟล์ภาพ
print(ggplot2::ggplot(years, ggplot2::aes(x = year, y = annual_budget)) +
  ggplot2::geom_col() + ggplot2::labs(x = "Year", y = "Extra cash in the year (THB)"))

#%%@note 6. Numbers the article quotes
# Print exactly the short boxes in their reading order.
# พิมพ์ผลลัพธ์ตรงกับกล่องตัวอย่างตามลำดับในบทความ
cat("Year order:",seq_along(uptake),"\n")
cat("Eligible at year start (people):",eligible,"\n")
cat("Treated prevalence (people):",treated,"\n")
cat("Year-end exits (people):",eligible*exit_fraction,"\n")
cat("Remaining stock (people):",eligible*(1-exit_fraction),"\n")
cat("Annual incremental budget (THB):",big(budget),"\n")
cat("Cumulative incremental budget (THB):",big(cumsum(budget)),"\n")
cat("Shortcut, 500 x 2,000 x 3 years (THB):",big(prevalent*annual_incremental_cost*3),"\n")
cat("Cost per person (THB):",big(current_care_cost+c(0,annual_incremental_cost)),"\n")
cat("Current-care scenario (THB):",big(current),"\n")
cat("New scenario (THB):",big(new),"\n")
cat("New minus current (THB):",big(new-current),"\n")
cat("Year 1 users, exits counted after and before:",treated[1]*c(1,1-exit_fraction),"\n")
cat("Year 1, exits counted after and before (THB):",big(exits_first),"\n")
cat("Year 3 people, expected and rounded:",rounded,"\n")
cat("Year 3 budget, expected and rounded (THB):",big(rounded*annual_incremental_cost),"\n")
cat("Yearly rate back to today (%):",100*discount_rate,"\n")
cat("1,834 THB due in year 3, worth today (THB):",big(today_value),"\n")
cat("Year 3 wanted, offered, waiting:",treated[3],nurse_places,treated[3]-nurse_places,"\n")
cat("Year 3 and three years within the places (THB):",
  big(c(capped[3],sum(capped))*annual_incremental_cost),"\n")
cat("Users over three years, shift of 0.10:",users[1],shift,"\n")
cat("Users, every share 0.10 lower and higher:",users[2:3],"\n")
cat("Three years, every share 0.10 lower and higher (THB):",
  big(users[2:3]*annual_incremental_cost),"\n")

# Budget Impact Analysis in Practice: Count People Before Counting Money
# Follow one district's stroke survivors from eligible people to the extra cash the fund pays.
# ติดตามผู้ป่วยหลังโรคหลอดเลือดสมองของอำเภอหนึ่ง จากผู้มีสิทธิจนถึงเงินสดส่วนเพิ่มที่กองทุนจ่าย
# Requires: R >= 4.5.2
# Tested: works in R 4.6.1
# tibble 3.3.1 (stable)
# dplyr 1.2.1 (stable)
# ggplot2 4.0.3 (stable)
# install.packages(c("tibble", "dplyr", "ggplot2"))
# update.packages(oldPkgs = c("tibble", "dplyr", "ggplot2"))
# remotes::install_version("tibble", "3.3.1")
# remotes::install_version("dplyr", "1.2.1")
# remotes::install_version("ggplot2", "4.0.3")
library(tibble)  # Write the years as an explicit table.
library(dplyr)  # Calculate people and money for each year.
library(ggplot2)  # Display the yearly cash without saving files.
stopifnot(getRversion() >= "4.5.2")

#%%@note 1. Define the illustrative inputs
# Every input is synthetic and written out once, in the order the article uses it.
# ค่าตั้งต้นทั้งหมดเป็นข้อมูลสมมติ เขียนไว้ครั้งเดียวตามลำดับที่บทความใช้
prevalent <- 500
annual_incident <- 100
exit_fraction <- 0.10
uptake <- c(0.20,0.40,0.60)
annual_incremental_cost <- 2000
current_care_cost <- 4500
nurse_places <- 300
discount_rate <- 0.03
big <- function(value) formatC(value, format = "f", digits = 0, big.mark = ",")

#%%@note 2. Follow the eligible people year by year
# New survivors join at the year start, the year's users are counted, then exits leave at year end.
# ผู้มีสิทธิใหม่เข้าต้นปี นับผู้ใช้บริการของปีนั้น แล้วผู้ที่ออกจึงออกเมื่อสิ้นปี
stock <- rep(prevalent + annual_incident, length(uptake))
for (year_index in 2:length(uptake)) {
  stock[year_index] <- stock[year_index - 1] * (1 - exit_fraction) + annual_incident
}
eligible <- c(600,640,676)
stopifnot(all(abs(stock - eligible) < 1e-9))
years <- tibble::tibble(year = seq_along(uptake), eligible = stock, uptake = uptake) |>
  dplyr::mutate(treated = eligible * uptake, exits = eligible * exit_fraction,
    remaining = eligible * (1 - exit_fraction),
    annual_budget = treated * annual_incremental_cost,
    cumulative_budget = cumsum(annual_budget))
stopifnot(all(abs(years$exits + years$remaining - years$eligible) < 1e-9))
treated <- years$treated
budget <- years$annual_budget

#%%@note 3. Price the two scenarios
# Everyone costs current care in both; only the service users add the extra cost.
# ทุกคนมีต้นทุนการดูแลเดิมในทั้งสองสถานการณ์ เฉพาะผู้ใช้บริการที่มีต้นทุนเพิ่ม
current <- eligible * current_care_cost
new <- (eligible - treated) * current_care_cost +
  treated * (current_care_cost + annual_incremental_cost)
stopifnot(all(abs(new - current - budget) < 1e-6))

#%%@note 4. Check timing, rounding and the nurse places
# Wrong timing and early rounding change the cash; the nurse places cap year 3; uptake moves 0.10.
# ลำดับเวลาที่ผิดและการปัดเศษก่อนเวลาเปลี่ยนเงินสด จำนวนผู้ใช้ที่พยาบาลรับได้จำกัดปีที่ 3
exits_first <- treated[1] * c(1, 1 - exit_fraction) * annual_incremental_cost
rounded <- c(treated[3], round(treated[3]))
today_value <- 1834 / (1 + discount_rate)^3
capped <- pmin(treated, nurse_places)
shift <- 0.10 * sum(eligible)
users <- sum(eligible * uptake) + c(0, -1, 1) * shift

#%%@note 5. Display the model
# Plot the yearly extra cash; the script does not save a graphics file.
# แสดงเงินสดส่วนเพิ่มรายปี โดยไม่บันทึกไฟล์ภาพ
print(ggplot2::ggplot(years, ggplot2::aes(x = year, y = annual_budget)) +
  ggplot2::geom_col() + ggplot2::labs(x = "Year", y = "Extra cash in the year (THB)"))

#%%@note 6. Numbers the article quotes
# Print exactly the short boxes in their reading order.
# พิมพ์ผลลัพธ์ตรงกับกล่องตัวอย่างตามลำดับในบทความ
cat("Year order:",seq_along(uptake),"\n")
cat("Eligible at year start (people):",eligible,"\n")
cat("Treated prevalence (people):",treated,"\n")
cat("Year-end exits (people):",eligible*exit_fraction,"\n")
cat("Remaining stock (people):",eligible*(1-exit_fraction),"\n")
cat("Annual incremental budget (THB):",big(budget),"\n")
cat("Cumulative incremental budget (THB):",big(cumsum(budget)),"\n")
cat("Shortcut, 500 x 2,000 x 3 years (THB):",big(prevalent*annual_incremental_cost*3),"\n")
cat("Cost per person (THB):",big(current_care_cost+c(0,annual_incremental_cost)),"\n")
cat("Current-care scenario (THB):",big(current),"\n")
cat("New scenario (THB):",big(new),"\n")
cat("New minus current (THB):",big(new-current),"\n")
cat("Year 1 users, exits counted after and before:",treated[1]*c(1,1-exit_fraction),"\n")
cat("Year 1, exits counted after and before (THB):",big(exits_first),"\n")
cat("Year 3 people, expected and rounded:",rounded,"\n")
cat("Year 3 budget, expected and rounded (THB):",big(rounded*annual_incremental_cost),"\n")
cat("Yearly rate back to today (%):",100*discount_rate,"\n")
cat("1,834 THB due in year 3, worth today (THB):",big(today_value),"\n")
cat("Year 3 wanted, offered, waiting:",treated[3],nurse_places,treated[3]-nurse_places,"\n")
cat("Year 3 and three years within the places (THB):",
  big(c(capped[3],sum(capped))*annual_incremental_cost),"\n")
cat("Users over three years, shift of 0.10:",users[1],shift,"\n")
cat("Users, every share 0.10 lower and higher:",users[2:3],"\n")
cat("Three years, every share 0.10 lower and higher (THB):",
  big(users[2:3]*annual_incremental_cost),"\n")

# Budget Impact Analysis in Practice: Count People Before Counting Money
# Follow one district's stroke survivors from eligible people to the extra cash the fund pays.
# ติดตามผู้ป่วยหลังโรคหลอดเลือดสมองของอำเภอหนึ่ง จากผู้มีสิทธิจนถึงเงินสดส่วนเพิ่มที่กองทุนจ่าย
# Requires: R >= 4.5.2
# Tested: works in R 4.6.1
# tibble 3.3.1 (stable)
# dplyr 1.2.1 (stable)
# ggplot2 4.0.3 (stable)
# install.packages(c("tibble", "dplyr", "ggplot2"))
# update.packages(oldPkgs = c("tibble", "dplyr", "ggplot2"))
# remotes::install_version("tibble", "3.3.1")
# remotes::install_version("dplyr", "1.2.1")
# remotes::install_version("ggplot2", "4.0.3")
library(tibble)  # Write the years as an explicit table.
library(dplyr)  # Calculate people and money for each year.
library(ggplot2)  # Display the yearly cash without saving files.
stopifnot(getRversion() >= "4.5.2")

#%%@note 1. Define the illustrative inputs
# Every input is synthetic and written out once, in the order the article uses it.
# ค่าตั้งต้นทั้งหมดเป็นข้อมูลสมมติ เขียนไว้ครั้งเดียวตามลำดับที่บทความใช้
prevalent <- 500
annual_incident <- 100
exit_fraction <- 0.10
uptake <- c(0.20,0.40,0.60)
annual_incremental_cost <- 2000
current_care_cost <- 4500
nurse_places <- 300
discount_rate <- 0.03
big <- function(value) formatC(value, format = "f", digits = 0, big.mark = ",")

#%%@note 2. Follow the eligible people year by year
# New survivors join at the year start, the year's users are counted, then exits leave at year end.
# ผู้มีสิทธิใหม่เข้าต้นปี นับผู้ใช้บริการของปีนั้น แล้วผู้ที่ออกจึงออกเมื่อสิ้นปี
stock <- rep(prevalent + annual_incident, length(uptake))
for (year_index in 2:length(uptake)) {
  stock[year_index] <- stock[year_index - 1] * (1 - exit_fraction) + annual_incident
}
eligible <- c(600,640,676)
stopifnot(all(abs(stock - eligible) < 1e-9))
years <- tibble::tibble(year = seq_along(uptake), eligible = stock, uptake = uptake) |>
  dplyr::mutate(treated = eligible * uptake, exits = eligible * exit_fraction,
    remaining = eligible * (1 - exit_fraction),
    annual_budget = treated * annual_incremental_cost,
    cumulative_budget = cumsum(annual_budget))
stopifnot(all(abs(years$exits + years$remaining - years$eligible) < 1e-9))
treated <- years$treated
budget <- years$annual_budget

#%%@note 3. Price the two scenarios
# Everyone costs current care in both; only the service users add the extra cost.
# ทุกคนมีต้นทุนการดูแลเดิมในทั้งสองสถานการณ์ เฉพาะผู้ใช้บริการที่มีต้นทุนเพิ่ม
current <- eligible * current_care_cost
new <- (eligible - treated) * current_care_cost +
  treated * (current_care_cost + annual_incremental_cost)
stopifnot(all(abs(new - current - budget) < 1e-6))

#%%@note 4. Check timing, rounding and the nurse places
# Wrong timing and early rounding change the cash; the nurse places cap year 3; uptake moves 0.10.
# ลำดับเวลาที่ผิดและการปัดเศษก่อนเวลาเปลี่ยนเงินสด จำนวนผู้ใช้ที่พยาบาลรับได้จำกัดปีที่ 3
exits_first <- treated[1] * c(1, 1 - exit_fraction) * annual_incremental_cost
rounded <- c(treated[3], round(treated[3]))
today_value <- 1834 / (1 + discount_rate)^3
capped <- pmin(treated, nurse_places)
shift <- 0.10 * sum(eligible)
users <- sum(eligible * uptake) + c(0, -1, 1) * shift

#%%@note 5. Display the model
# Plot the yearly extra cash; the script does not save a graphics file.
# แสดงเงินสดส่วนเพิ่มรายปี โดยไม่บันทึกไฟล์ภาพ
print(ggplot2::ggplot(years, ggplot2::aes(x = year, y = annual_budget)) +
  ggplot2::geom_col() + ggplot2::labs(x = "Year", y = "Extra cash in the year (THB)"))

#%%@note 6. Numbers the article quotes
# Print exactly the short boxes in their reading order.
# พิมพ์ผลลัพธ์ตรงกับกล่องตัวอย่างตามลำดับในบทความ
cat("Year order:",seq_along(uptake),"\n")
cat("Eligible at year start (people):",eligible,"\n")
cat("Treated prevalence (people):",treated,"\n")
cat("Year-end exits (people):",eligible*exit_fraction,"\n")
cat("Remaining stock (people):",eligible*(1-exit_fraction),"\n")
cat("Annual incremental budget (THB):",big(budget),"\n")
cat("Cumulative incremental budget (THB):",big(cumsum(budget)),"\n")
cat("Shortcut, 500 x 2,000 x 3 years (THB):",big(prevalent*annual_incremental_cost*3),"\n")
cat("Cost per person (THB):",big(current_care_cost+c(0,annual_incremental_cost)),"\n")
cat("Current-care scenario (THB):",big(current),"\n")
cat("New scenario (THB):",big(new),"\n")
cat("New minus current (THB):",big(new-current),"\n")
cat("Year 1 users, exits counted after and before:",treated[1]*c(1,1-exit_fraction),"\n")
cat("Year 1, exits counted after and before (THB):",big(exits_first),"\n")
cat("Year 3 people, expected and rounded:",rounded,"\n")
cat("Year 3 budget, expected and rounded (THB):",big(rounded*annual_incremental_cost),"\n")
cat("Yearly rate back to today (%):",100*discount_rate,"\n")
cat("1,834 THB due in year 3, worth today (THB):",big(today_value),"\n")
cat("Year 3 wanted, offered, waiting:",treated[3],nurse_places,treated[3]-nurse_places,"\n")
cat("Year 3 and three years within the places (THB):",
  big(c(capped[3],sum(capped))*annual_incremental_cost),"\n")
cat("Users over three years, shift of 0.10:",users[1],shift,"\n")
cat("Users, every share 0.10 lower and higher:",users[2:3],"\n")
cat("Three years, every share 0.10 lower and higher (THB):",
  big(users[2:3]*annual_incremental_cost),"\n")