Summary health benefit

This document contains the dataset needed and calculation for the summary benefit(deaths and Disability adjusted life years (DALYs)).

Library and settings

library(tidyverse)
library(readxl)
library(nutriR)
library(ggplot2)
#library(tabulizer)
library(rnaturalearth)
library(sf)
library(DT)
library(lhs)
library(stringr)
library(plotly)
library(countrycode)

Summary health benefits

Read in dataset and calculate the parameter

Preterm birth

Import the data for all the preterm birth related parameter

preterm1= read.csv("data/IHME-GBD_2019_DATA-26ced726-1/IHME-GBD_2019_DATA-26ced726-1.csv") %>% 
  janitor::clean_names() 

preterm2= read.csv("data/IHME-GBD_2019_DATA-fe699dfb-1/IHME-GBD_2019_DATA-fe699dfb-1.csv") %>% 
  janitor::clean_names() %>% 
  dplyr::select(location_name, val, upper, lower) %>% 
  rename(val_ma_yld = val,
         upper_ma_yld=upper,
         lower_ma_yld=lower) 

preterm = preterm1 %>% 
 dplyr:: select(measure_name, location_name, val, upper, lower) %>% 
  group_by(location_name) %>% 
  pivot_wider(
    names_from = measure_name,
    values_from = c("val", "upper", "lower"),
    names_sep = "_"
  ) %>% 
  janitor::clean_names() %>% 
  left_join(preterm2)

Calculation the Infant deaths per preterm birth (\(m_i^{inf\_ptb}\)) , Infant YLLs per preterm bith case (\(n_i^{inf\_ptb}\)), Infant YLDs per preterm birth (\(o_i^{inf\_ptb}\)), maternal YLDs per preterm birth (\(o_i^{adu\_ptb}\)). Take the mean of the preterm birth incidence as denominator and the mean of the needed parameter as numerator.

The discounted yll calculation First calculated the yll per person with yll divided by death. Take the d= 0.03, yll as n. The formula is \(pv = (1-(1+d)^{-n})/d\). Then the scale is calculated and apply to the accumulated ylls to calculated the discount ylls.

calculate discoutn daly

pretermcal=
  preterm %>% 
  mutate(death= val_deaths/val_incidence,
         yll=val_yl_ls_years_of_life_lost/val_incidence,
         yll_person=yll/death,
         discount_yll_person = (1-(1+0.03)^(-yll_person))/0.03,
         discount_yll= discount_yll_person/yll_person*yll,
         yld=val_yl_ds_years_lived_with_disability/val_incidence,
         ma_yld_pre=val_ma_yld/val_incidence,
         iso3=countrycode(location_name, "country.name", "iso3c")) %>% 
  rename(country =location_name) %>% 
  dplyr::select(iso3, country, death, yll, yll_person, discount_yll_person, discount_yll, yld, ma_yld_pre )

# calculate the discounted yll
 

datatable(
data = pretermcal,
caption = "Table 1: The preterm birth dataset",
filter = "top",
style = "bootstrap4"
)  

Preeclampsia

Import the data for all the preeclampsia related parameter

preecla=read_csv("data/IHME-GBD_2019_DATA-f5ed2da1-1/IHME-GBD_2019_DATA-f5ed2da1-1.csv") %>% 
  janitor::clean_names() %>% 
  dplyr::select(measure_name, location_name, val, upper, lower) %>% 
  pivot_wider(
    names_from = measure_name,
    values_from = c("val", "upper", "lower"),
    names_sep = "_"
  ) %>% 
  janitor::clean_names() 

Calculation the Maternal deaths per preeclampsia case (\(m_i^{adu\_pre}\)) , Maternal YLLs per preeclampsia case (\(n_i^{adu\_pre}\)), Maternal YLDs per preeclampsia case (\(o_i^{adu\_pre}\)). Take the mean of the preeclampsia incidence as denominator and the mean of the needed parameter as numerator.

preeccalcul=
  preecla %>% 
  mutate(
    ma_death=val_deaths/val_incidence,
    ma_yll=val_yl_ls_years_of_life_lost/val_incidence,
    ma_yll_person=ma_yll/ma_death,
    ma_discountyll_person = (1-(1+0.03)^(-ma_yll_person))/0.03,
    discount_ma_yll= ma_discountyll_person/ma_yll_person*ma_yll,
    ma_yld=val_yl_ds_years_lived_with_disability/val_incidence,
    iso3=countrycode(location_name, "country.name", "iso3c")) %>% 
  rename(country =location_name) %>% 
  dplyr::select(iso3, country, ma_death, ma_yll,ma_yll_person, ma_discountyll_person, discount_ma_yll, ma_yld)

datatable(
data = preeccalcul,
caption = "Table 2: The preelampsia birth dataset",
filter = "top",
style = "bootstrap4"
)  

Summary health benefit calculation

• Infant and maternal deaths averted (\(deaths_i^{infant}, deaths_i^{adult}\)):

\[ deaths_i^{infant}=k_i*m_i^{inf_ptb}\]

\[deaths_i^{adult}=l_i*m_i^{adu_pre}\]

• Infant and maternal YLLs (\(YLL_i^{infant}, YLL_i^{adult}\)): \[YLL_i^{infant}=k_i*n_i^{inf\_ptb}\]

\[YLL_i^{adult}=l_i*n_i^{adu\_pre}\]

• Infant and maternal YLDs (\(YLD_i^{infant}, YLD_i^{adult}\)): \[YLD_i^{infant}=k_i*o_i^{inf\_ptb}\]

\[YLD_i^{adult}=k_i*o_i^{adu\_ptb}+l_i*n_i^{adu\_pre}\]

• Infant and maternal DALYs (\(DALY_i^{infant}, DALY_i^{adult}\)):

\[DALY_i^{infant}=YLL_i^{infant}+YLD_i^{infant}\]

\[DALY_i^{adult}=YLL_i^{adult}+YLD_i^{adult}\]

load(file="dashboard/immediate.RData")

preecsum = imm_prec %>% 
  dplyr::select(iso3, country, region, health_prec) %>% 
  left_join(preeccalcul) %>% 
  mutate(adu_death= health_prec*ma_death,
         adu_yll=health_prec*ma_yll,
         adu_dis_yll = health_prec*discount_ma_yll,
         adu_yld=health_prec*ma_yld) 

pretsum=imm_pret %>% 
  dplyr::select(iso3, country, region, health_pret) %>% 
  left_join(pretermcal) %>% 
  mutate(inf_death=death*health_pret,
         inf_yll=yll*health_pret,
         inf_dis_yll = discount_yll*health_pret,
         inf_yld=yld*health_pret)

sumhealth= merge(preecsum, pretsum) %>% 
  mutate(adu_yld=adu_yld+ health_pret*ma_yld_pre,
         inf_daly = inf_yll+ inf_yld,
         adu_daly =adu_yll+adu_yld,
         dis_inf_daly= inf_dis_yll + inf_yld,
         dis_adu_daly = adu_dis_yll + adu_yld,
         total_daly = inf_daly+ adu_daly,
         total_dis_daly = dis_inf_daly + dis_adu_daly
         ) %>% 
  dplyr::select(iso3, country, region, adu_death,inf_death,  adu_yll, inf_yll, adu_yld,inf_yld, adu_daly, inf_daly, dis_adu_daly,dis_inf_daly, total_daly, total_dis_daly )

datatable(
data = sumhealth,
caption = "Table 3: The Summary benefit table",
filter = "top",
style = "bootstrap4"
) 

EDA

WORLD map view for DALY in infants

world= read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
summaryplot=world %>% 
  left_join(sumhealth, by = c("CODE" = "iso3"))

plot_ly(summaryplot, type="choropleth", locations = summaryplot$CODE, z=summaryplot$inf_daly, text=summaryplot$COUNTRY, colorscale="Red") %>% 
  colorbar(title=list(text="DALYs",font = list(size = 12)),
           tickfont = list(size = 8),
          len=0.5, x=1, y=0.85, thickness=10) %>% 
  layout(title =list(text= "Worldwide Infants DALYs", y=0.9),
          annotations = list(
      list(
        text = "IHME",
        x = 1.1,
        y= 0.1,
        xref = "paper",
        yref = "paper",
        showarrow = FALSE,
        font = list(size = 10)
      )))

world = ne_countries(scale = “medium”, returnclass = “sf”)

summaryplot = world %>% left_join( sumhealth %>% mutate(iso3 = case_when( iso3 == “SSD” ~ “SDS”, iso3 == “PSE” ~ “PSX”, TRUE ~ iso3 )), by = c(“adm0_a3” = “iso3”) )

ggplot(summaryplot) + geom_sf(aes(fill = inf_daly)) + scale_fill_gradient(low = “white”, high = “red”, na.value = “grey50”) + theme_minimal() + labs(fill = “DALY”, title = “Worldwide infants DALYs”, caption = “IHME”)

WORLD map view for DALY in maternal

plot_ly(summaryplot, type="choropleth", locations = summaryplot$CODE, z=summaryplot$adu_daly, text=summaryplot$COUNTRY, colorscale="Red") %>% 
  colorbar(title=list(text="DALYs",font = list(size = 12)),
           tickfont = list(size = 8),
          len=0.5, x=1, y=0.85, thickness=10) %>% 
  layout(title =list(text= "Worldwide Maternal DALYs", y=0.9),
          annotations = list(
      list(
        text = "IHME",
        x = 1.1,
        y= 0.1,
        xref = "paper",
        yref = "paper",
        showarrow = FALSE,
        font = list(size = 10)
      )))

ggplot(summaryplot) + geom_sf(aes(fill = adu_daly)) + scale_fill_gradient(low = “white”, high = “red”, na.value = “grey50”) + theme_minimal() + labs(fill = “DALY”, title = “Worldwide maternal DALYs”, caption = “IHME”)

maternal_sum=
     sumhealth %>% 
      dplyr::select(country, adu_yld, adu_yll, adu_death, adu_daly)

colnames(maternal_sum) <- gsub("^adu_", "", colnames(maternal_sum))

infant_sum=
     sumhealth %>% 
     dplyr:: select(country, inf_yld, inf_yll, inf_death, inf_daly)

colnames(infant_sum) <- gsub("^inf_", "", colnames(infant_sum))

save the data for dashboard: save(maternal_sum, infant_sum, pretermcal, sumhealth, summaryplot, file=“/Users/hec442/Desktop/harvard/calcium/dashboard/summaryhealth.RData”)