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PAF_070621.R
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PAF_070621.R
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### Script to process output on one-time screening optimal age ###
# This script takes output from the simulation model and processes it to produce plots and statistics for tables and text.
# The script expects four output files from each scenario:
# 1. Cervical cancer cases
# 2. Cervical cancer incidence rates (raw)
# 3. Cervical cancer incidence rates (age-standardized)
# 4. HIV prevalence in females
# For each, there should be separate tabs/sheets for each of the following groups: the Total population, HIV-negative women, HIV-positive untreated women, HIV-positive women on ART, all HIv-positive women __IN THAT ORDER__.
# Within each tab column 1 should show the year of simulation from 2020 to 2070. Column 2 should show the outcome for the relevant year, group, and scenario
## The filepaths and names correspond to these scenarios:
# S0b = Cytology at age 35 with 48% coverage throughout the timeframe. 9v HPV vaccination at 57%. No ART scale-up.
# S0 = Cytology at age 35 with 48% coverage throughout the timeframe. 9v HPV vaccination at 57%. ART scale-up to 90-90-90 by 2030.
# S1 = HPV screening at ages 35 and 45 scaled up to 90% by 2045 throughout the timeframe. 9v HPV vaccination at 90%. ART scale-up to 90-90-90 by 2030.
# S2 = S1 but with catch-up vaccination for WLHIV ages 15-24 at 50%. ART scale-up to 90-90-90 by 2030.
# S3 = S2 but with screening for WLHIV every 3 years ages 25-49. ART scale-up to 90-90-90 by 2030.
##########
# SETUP # ----
##########
## Load packages (if the packages are not yet installed, type: install.packages("packagename"))
library("ggplot2")
library("tidyverse")
library("knitr")
library("kableExtra")
library("readxl")
library("reshape2")
library("colorspace")
library("viridis")
##########
# SPECIFY FILEPATHS # ----
##########
## Set directory to folder where output is stored <<--
# setwd("C:/Users/dpwhite/Dropbox/HPV and cervical cancer modeling/Papers/PAF from HIV/Model output")
# For use on my Mac
setwd("H:/HHCoM/HHCoM_Results/22Apr20Ph2V11_2v57BaseVax_spCytoScreen_noVMMChpv_hivInt2017_pHIV-S0_6_1")
## Set filepaths <<--
# List file names for files containing incidence output for each scenario
# These files are expected to have one tab for each population subgroup (All, HIV-, untreated HIV+, treated HIV+, all HIV+)
# and for each tab to list the years in column 1 and the outcome in column 2
S0b.stART.inc.path <- "PAF_crudeAnnualCC_S0.xlsx"
S0b.stART.ir.path <- "PAF_crudeICC_S0.xlsx"
S0b.stART.sir.path <- "PAF_ASICC_S0.xlsx"
S0b.stART.hiv.path <- "PAF_crudeHivFaged15plus_S0.xlsx"
S0.inART.inc.path <- "PAF_crudeAnnualCC_S1.xlsx"
S0.inART.ir.path <- "PAF_crudeICC_S1.xlsx"
S0.inART.sir.path <- "PAF_ASICC_S1.xlsx"
S0.inART.hiv.path <- "PAF_crudeHivFaged15plus_S1.xlsx"
S1.inART.inc.path <- "PAF_crudeAnnualCC_S2.xlsx"
S1.inART.ir.path <- "PAF_crudeICC_S2.xlsx"
S1.inART.sir.path <- "PAF_ASICC_S2.xlsx"
S1.inART.hiv.path <- "PAF_crudeHivFaged15plus_S2.xlsx"
S2.inART.inc.path <- "PAF_crudeAnnualCC_S3.xlsx"
S2.inART.ir.path <- "PAF_crudeICC_S3.xlsx"
S2.inART.sir.path <- "PAF_ASICC_S3.xlsx"
S2.inART.hiv.path <- "PAF_crudeHivFaged15plus_S3.xlsx"
S3.inART.inc.path <- "PAF_crudeAnnualCC_S4.xlsx"
S3.inART.ir.path <- "PAF_crudeICC_S4.xlsx"
S3.inART.sir.path <- "PAF_ASICC_S4.xlsx"
S3.inART.hiv.path <- "PAF_crudeHivFaged15plus_S4.xlsx"
## Specify whether the excel sheets contain a header row with variable names (TRUE/FALSE) <<--
header <- FALSE
##########
# READ AND PROCESS MODEL OUTPUT FILES # ----
##########
## Load model output files
read_excel_allsheets <- function(filename, tibble = FALSE) {
sheets <- readxl::excel_sheets(filename)
x <- lapply(sheets, function(X) readxl::read_excel(filename, sheet = X, col_names = header))
if(!tibble) x <- lapply(x, as.data.frame)
names(x) <- sheets
x
}
S0b.stART.inc <- read_excel_allsheets(S0b.stART.inc.path)
S0b.stART.ir <- read_excel_allsheets(S0b.stART.ir.path)
S0b.stART.sir <- read_excel_allsheets(S0b.stART.sir.path)
S0b.stART.hiv <- read_excel_allsheets(S0b.stART.hiv.path)
S0.inART.inc <- read_excel_allsheets(S0.inART.inc.path)
S0.inART.ir <- read_excel_allsheets(S0.inART.ir.path)
S0.inART.sir <- read_excel_allsheets(S0.inART.sir.path)
S0.inART.hiv <- read_excel_allsheets(S0.inART.hiv.path)
S1.inART.inc <- read_excel_allsheets(S1.inART.inc.path)
S1.inART.ir <- read_excel_allsheets(S1.inART.ir.path)
S1.inART.sir <- read_excel_allsheets(S1.inART.sir.path)
S1.inART.hiv <- read_excel_allsheets(S1.inART.hiv.path)
S2.inART.inc <- read_excel_allsheets(S2.inART.inc.path)
S2.inART.ir <- read_excel_allsheets(S2.inART.ir.path)
S2.inART.sir <- read_excel_allsheets(S2.inART.sir.path)
S2.inART.hiv <- read_excel_allsheets(S2.inART.hiv.path)
S3.inART.inc <- read_excel_allsheets(S3.inART.inc.path)
S3.inART.ir <- read_excel_allsheets(S3.inART.ir.path)
S3.inART.sir <- read_excel_allsheets(S3.inART.sir.path)
S3.inART.hiv <- read_excel_allsheets(S3.inART.hiv.path)
colnames <- c("year", "median", "min", "max", "sim1", "sim2", "sim3", "sim4", "sim5", "sim6", "sim7", "sim8", "sim9",
"sim10", "sim11", "sim12", "sim13", "sim14", "sim15", "sim16", "sim17", "sim18", "sim19", "sim20",
"sim21", "sim22", "sim23", "sim24", "sim25")
##########
# DEFINE DATA PROCESSING FUNCTIONS AND BASIC PLOT SETTINGS # ----
##########
#combinesheets <- function(scenario){
# # Set column names
# scenario <- lapply(scenario, setNames, colnames)
# # Add outcome variables
# scenario[[1]]$outcome <- "CCC"
# scenario[[2]]$outcome <- "CCC"
# scenario[[3]]$outcome <- "CCC"
# scenario[[4]]$outcome <- "CCC"
# scenario[[5]]$outcome <- "CCC"
# scenario[[6]]$outcome <- "ICC"
# scenario[[7]]$outcome <- "ICC"
# scenario[[8]]$outcome <- "ICC"
# scenario[[9]]$outcome <- "ICC"
# scenario[[10]]$outcome <- "ICC"
# scenario[[11]]$outcome <- "OS"
# scenario[[12]]$outcome <- "OS"
# scenario[[13]]$outcome <- "OS"
# scenario[[14]]$outcome <- "OS"
# scenario[[15]]$outcome <- "OS"
# scenario[[16]]$outcome <- "SCRN"
# scenario[[17]]$outcome <- "SCRN"
# scenario[[18]]$outcome <- "SCRN"
# scenario[[19]]$outcome <- "SCRN"
# scenario[[20]]$outcome <- "SCRN"
#
#
# # Combine into one sheet
# out <- rbind.data.frame(scenario[[1]], scenario[[2]], scenario[[3]], scenario[[4]], scenario[[5]],
# scenario[[6]], scenario[[7]], scenario[[8]], scenario[[9]], scenario[[10]],
# scenario[[11]], scenario[[12]], scenario[[13]], scenario[[14]], scenario[[15]],
# scenario[[16]], scenario[[17]], scenario[[18]], scenario[[19]], scenario[[20]])
# out[,c(2:6)] <- sapply(out[,c(2:6)], as.numeric)
# return(out)
#}
# Function to round years, set column names, restrict to years 2000 to 2121, and combine estimates from all groups onto one sheet
dataprep.absnum <- function(scenario){
# Set column names
scenario <- lapply(scenario, setNames, colnames)
# Restrict to years 2020 to 2121
scenario <- lapply(scenario, function(x) filter(x, year >= 2000 & year <=2121))
# Add group variables
scenario[[1]]$group <- "Total"
scenario[[2]]$group <- "HIVneg"
scenario[[3]]$group <- "HIVpos_noART"
scenario[[4]]$group <- "HIVpos_VS"
scenario[[5]]$group <- "HIVpos_all"
# Combine into one sheet
out <- rbind.data.frame(scenario[[1]], scenario[[2]], scenario[[3]], scenario[[4]], scenario[[5]])
out[,c(1:29)] <- sapply(out[,c(1:29)], as.numeric)
return(out)
}
dataprep.hivprev <- function(scenario){
# Set column names
scenario <- lapply(scenario, setNames, colnames)
# Restrict to years 2020 to 2121
scenario <- lapply(scenario, function(x) filter(x, year >= 2000 & year <=2121))
scenario[[1]]$group <- "Total"
out <- scenario[[1]]
return(out)
}
# Function to calculate the percent reduction by group from 2020 over time
# pctreduc <- function(scenario){
# pctreduc <- cbind.data.frame(year = scenario[,1], Total = scenario[,2]/scenario[1,2], HIVneg = scenario[,3]/scenario[1,3],
# HIVpos_noART = scenario[,4]/scenario[1,4], HIVpos_ART = scenario[,5]/scenario[1,5], HIVpos = scenario[,6]/scenario[1,6])
# return(pctreduc)
# }
# Function to calculate the proportion of cases among WLHIV
calc_prophiv <- function(scenario){
prophiv <- as.data.frame(matrix(ncol = 29, nrow = 122))
colnames(prophiv) <- colnames(scenario)[1:29]
prophiv$year <- seq(2000, 2121, 1)
for(i in c(2000:2121)){
prophiv[prophiv$year == i , c(5:29)] <- scenario[scenario$group == "HIVpos_all" & scenario$year == i, c(5:29)] / scenario[scenario$group == "Total" & scenario$year == i, c(5:29)]
prophiv[prophiv$year == i , c(2:4)] <- c(apply(prophiv[prophiv$year == i , c(5:29)] , 1 , median) ,
apply(prophiv[prophiv$year == i , c(5:29)] , 1 , min) ,
apply(prophiv[prophiv$year == i , c(5:29)] , 1 , max))
}
return(prophiv)
}
# Function to calculate the proportion of cases among WLHIV divided by HIV prevalence
calc_ccHivRatio <- function(propCasesHiv , hivPrev){
ccHivRatio <- as.data.frame(matrix(ncol = 29, nrow = 122))
colnames(ccHivRatio) <- colnames(propCasesHiv)[1:29]
ccHivRatio$year <- seq(2000, 2121, 1)
for(i in c(2000:2121)){
ccHivRatio[ccHivRatio$year == i , c(5:29)] <- propCasesHiv[propCasesHiv$year == i, c(5:29)] / hivPrev[hivPrev$year == i, c(5:29)]
ccHivRatio[ccHivRatio$year == i , c(2:4)] <- c(apply(ccHivRatio[ccHivRatio$year == i , c(5:29)] , 1 , median) ,
apply(ccHivRatio[ccHivRatio$year == i , c(5:29)] , 1 , min) ,
apply(ccHivRatio[ccHivRatio$year == i , c(5:29)] , 1 , max))
}
return(ccHivRatio)
}
# Function to calculate the percent of cases among WLHIV with VS
calc_prophivVS <- function(scenario){
prophivVS <- as.data.frame(matrix(ncol = 29, nrow = 122))
colnames(prophivVS) <- colnames(scenario)[1:29]
prophivVS$year <- seq(2000, 2121, 1)
for(i in c(2000:2121)){
prophivVS[prophivVS$year == i , c(5:29)] <- scenario[scenario$group == "HIVpos_VS" & scenario$year == i, c(5:29)] / scenario[scenario$group == "Total" & scenario$year == i, c(5:29)]
prophivVS[prophivVS$year == i , c(2:4)] <- c(apply(prophivVS[prophivVS$year == i , c(5:29)] , 1 , median) ,
apply(prophivVS[prophivVS$year == i , c(5:29)] , 1 , min) ,
apply(prophivVS[prophivVS$year == i , c(5:29)] , 1 , max))
}
return(prophivVS)
}
# Function to calculate the cumulative proportion of cases among WLHIV. prophiv.cuml = proportion of cases in WLHIV, propart.cuml = proportion of cases in virally suppressed WLHIV
calc_prophiv_cuml <- function(scenario, yearstart, yearend){
scenario.trunc <- filter(scenario, year >=yearstart, year <= yearend)
cuml <- scenario.trunc %>% group_by(group) %>% summarise(across(c(median:sim25), sum))
prophiv.cuml <- cuml[cuml$group == "HIVpos_all", c(2:29)] / cuml[cuml$group == "Total", c(2:29)]
prophiv.cuml[1,c(1:3)] <- c(apply(prophiv.cuml[1,c(4:28)] , 1 , median) ,
apply(prophiv.cuml[1,c(4:28)] , 1 , min) ,
apply(prophiv.cuml[1,c(4:28)] , 1 , max))
prophiv.cuml$outcome = "Cuml proportion of cases in WLHIV"
propart.cuml <- cuml[cuml$group == "HIVpos_VS", c(2:29)] / cuml[cuml$group == "Total", c(2:29)]
propart.cuml[1,c(1:3)] <- c(apply(propart.cuml[1,c(4:28)] , 1 , median) ,
apply(propart.cuml[1,c(4:28)] , 1 , min) ,
apply(propart.cuml[1,c(4:28)] , 1 , max))
propart.cuml$outcome = "Cuml proportion of cases in VS WLHIV"
out <- rbind.data.frame(prophiv.cuml, propart.cuml)
return(out)
}
## Plot settings
plot_background <- theme(panel.background = element_rect(fill="white", colour = "black")) + theme(panel.grid.major = element_line(colour = "grey90"))
plot_titles <- theme(plot.title = element_text(hjust = 0.5, size=14, colour = "black", face = "bold")) +
theme(legend.title = element_text(colour = "black", size = 13), legend.text = element_text(colour = "black", size = 13)) +
theme(axis.title = element_text(colour = "black", size = 13), axis.text = element_text(colour = "black", size = 12))
colors <- RColorBrewer::brewer.pal(5, "Set2")
##########
# COMBINE ESTIMATES FOR EACH SCENARIO ONTO ONE SHEET # ----
##########
S0b.stART.inc.comb <- dataprep.absnum(S0b.stART.inc)
S0b.stART.ir.comb <- dataprep.absnum(S0b.stART.ir)
S0b.stART.sir.comb <- dataprep.absnum(S0b.stART.sir)
S0b.stART.hiv.comb <- dataprep.hivprev(S0b.stART.hiv)
S0.inART.inc.comb <- dataprep.absnum(S0.inART.inc)
S0.inART.ir.comb <- dataprep.absnum(S0.inART.ir)
S0.inART.sir.comb <- dataprep.absnum(S0.inART.sir)
S0.inART.hiv.comb <- dataprep.hivprev(S0.inART.hiv)
S1.inART.inc.comb <- dataprep.absnum(S1.inART.inc)
S1.inART.ir.comb <- dataprep.absnum(S1.inART.ir)
S1.inART.sir.comb <- dataprep.absnum(S1.inART.sir)
S1.inART.hiv.comb <- dataprep.hivprev(S1.inART.hiv)
S2.inART.inc.comb <- dataprep.absnum(S2.inART.inc)
S2.inART.ir.comb <- dataprep.absnum(S2.inART.ir)
S2.inART.sir.comb <- dataprep.absnum(S2.inART.sir)
S2.inART.hiv.comb <- dataprep.hivprev(S2.inART.hiv)
S3.inART.inc.comb <- dataprep.absnum(S3.inART.inc)
S3.inART.ir.comb <- dataprep.absnum(S3.inART.ir)
S3.inART.sir.comb <- dataprep.absnum(S3.inART.sir)
S3.inART.hiv.comb <- dataprep.hivprev(S3.inART.hiv)
##########
# CALCULATE IR in each year for each scenario # ----
##########
# S0.stART.ir.comb
# S0b.stART.ir.comb
# S1.stART.ir.comb
# S2.stART.ir.comb
# S3.stART.ir.comb
# S0.inART.ir.comb
# S0b.inART.ir.comb
# S1.inART.ir.comb
# S2.inART.ir.comb
# S3.inART.ir.comb
#
# S0.stART.sir.comb
# S0b.stART.sir.comb
# S1.stART.sir.comb
# S2.stART.sir.comb
# S3.stART.sir.comb
# S0.inART.sir.comb
# S0b.inART.sir.comb
# S1.inART.sir.comb
# S2.inART.sir.comb
# S3.inART.sir.comb
# Combine into a DF for raw IR
S0b.stART.ir.comb$scenario <- "Baseline cytology and vaccination, no ART scale-up"
S0.inART.ir.comb$scenario <- "Baseline cytology and vaccination, with ART scale-up"
S1.inART.ir.comb$scenario <- "Scaled up HPV testing and 90% vaccination"
S2.inART.ir.comb$scenario <- "S1 + 50% catch-up vaccination for WLHIV"
S3.inART.ir.comb$scenario <- "S2 + more frequent screening for WLHIV"
ir.comb.inART <- rbind.data.frame(S0b.stART.ir.comb, S0.inART.ir.comb, S1.inART.ir.comb, S2.inART.ir.comb, S3.inART.ir.comb)
ir.comb.inART$scenario <- factor(ir.comb.inART$scenario, levels = c("Baseline cytology and vaccination, no ART scale-up",
"Baseline cytology and vaccination, with ART scale-up",
"Scaled up HPV testing and 90% vaccination",
"S1 + 50% catch-up vaccination for WLHIV",
"S2 + more frequent screening for WLHIV"))
ir.comb.inART$outcome <- "Crude Incidence Rates"
# Combine into a DF for standardized IR
S0b.stART.sir.comb$scenario <- "Baseline cytology and vaccination, no ART scale-up"
S0.inART.sir.comb$scenario <- "Baseline cytology and vaccination, with ART scale-up"
S1.inART.sir.comb$scenario <- "Scaled up HPV testing and 90% vaccination"
S2.inART.sir.comb$scenario <- "S1 + 50% catch-up vaccination for WLHIV"
S3.inART.sir.comb$scenario <- "S2 + more frequent screening for WLHIV"
sir.comb.inART <- rbind.data.frame(S0b.stART.sir.comb, S0.inART.sir.comb, S1.inART.sir.comb, S2.inART.sir.comb, S3.inART.sir.comb)
sir.comb.inART$scenario <- factor(sir.comb.inART$scenario, levels = c("Baseline cytology and vaccination, no ART scale-up",
"Baseline cytology and vaccination, with ART scale-up",
"Scaled up HPV testing and 90% vaccination",
"S1 + 50% catch-up vaccination for WLHIV",
"S2 + more frequent screening for WLHIV"))
sir.comb.inART$outcome <- "AS Incidence Rates"
##########
# Combine incident case counts for plotting # ----
##########
# Combine into a DF
S0b.stART.inc.comb$scenario <- "Baseline cytology and vaccination, no ART scale-up"
S0.inART.inc.comb$scenario <- "Baseline cytology and vaccination, with ART scale-up"
S1.inART.inc.comb$scenario <- "Scaled up HPV testing and 90% vaccination"
S2.inART.inc.comb$scenario <- "S1 + 50% catch-up vaccination for WLHIV"
S3.inART.inc.comb$scenario <- "S2 + more frequent screening for WLHIV"
inc.comb.inART <- rbind.data.frame(S0b.stART.inc.comb, S0.inART.inc.comb, S1.inART.inc.comb, S2.inART.inc.comb, S3.inART.inc.comb)
inc.comb.inART$scenario <- factor(inc.comb.inART$scenario, levels = c("Baseline cytology and vaccination, no ART scale-up",
"Baseline cytology and vaccination, with ART scale-up",
"Scaled up HPV testing and 90% vaccination",
"S1 + 50% catch-up vaccination for WLHIV",
"S2 + more frequent screening for WLHIV"))
inc.comb.inART$outcome <- "Incident cases"
ratesandcases.inART <- rbind.data.frame(inc.comb.inART, ir.comb.inART, sir.comb.inART)
##########
# Combine HIV prevalence across scenarios # ----
##########
# Combine into a DF
S0b.stART.hiv.comb$scenario <- "Baseline cytology and vaccination, no ART scale-up"
S0.inART.hiv.comb$scenario <- "Baseline cytology and vaccination, with ART scale-up"
S1.inART.hiv.comb$scenario <- "Scaled up HPV testing and 90% vaccination"
S2.inART.hiv.comb$scenario <- "S1 + 50% catch-up vaccination for WLHIV"
S3.inART.hiv.comb$scenario <- "S2 + more frequent screening for WLHIV"
hiv.comb.inART <- rbind.data.frame(S0b.stART.hiv.comb, S0.inART.hiv.comb, S1.inART.hiv.comb, S2.inART.hiv.comb, S3.inART.hiv.comb)
hiv.comb.inART$scenario <- factor(hiv.comb.inART$scenario, levels = c("Baseline cytology and vaccination, no ART scale-up",
"Baseline cytology and vaccination, with ART scale-up",
"Scaled up HPV testing and 90% vaccination",
"S1 + 50% catch-up vaccination for WLHIV",
"S2 + more frequent screening for WLHIV"))
hiv.comb.inART$outcome <- "HIV prevalence"
##########
# CALCULATE PAF in each year and cumulative PAF for each scenario # ----
##########
(prophiv.S0b.stART <- calc_prophiv(S0b.stART.inc.comb))
(ccHivRatio.S0b.stART <- calc_ccHivRatio(prophiv.S0b.stART , S0b.stART.hiv.comb))
(prophivVS.S0b.stART <- calc_prophivVS(S0b.stART.inc.comb))
calc_prophiv_cuml(S0b.stART.inc.comb, 2021, 2121)
(prophiv.S0.inART <- calc_prophiv(S0.inART.inc.comb))
(ccHivRatio.S0.inART <- calc_ccHivRatio(prophiv.S0.inART , S0.inART.hiv.comb))
(prophivVS.S0.inART <- calc_prophivVS(S0.inART.inc.comb))
calc_prophiv_cuml(S0.inART.inc.comb, 2021, 2121)
(prophiv.S1.inART <- calc_prophiv(S1.inART.inc.comb))
(ccHivRatio.S1.inART <- calc_ccHivRatio(prophiv.S1.inART , S1.inART.hiv.comb))
(prophivVS.S1.inART <- calc_prophivVS(S1.inART.inc.comb))
calc_prophiv_cuml(S1.inART.inc.comb, 2021, 2121)
(prophiv.S2.inART <- calc_prophiv(S2.inART.inc.comb))
(ccHivRatio.S2.inART <- calc_ccHivRatio(prophiv.S2.inART , S2.inART.hiv.comb))
(prophivVS.S2.inART <- calc_prophivVS(S2.inART.inc.comb))
calc_prophiv_cuml(S2.inART.inc.comb, 2021, 2121)
(prophiv.S3.inART <- calc_prophiv(S3.inART.inc.comb))
(ccHivRatio.S3.inART <- calc_ccHivRatio(prophiv.S3.inART , S3.inART.hiv.comb))
(prophivVS.S3.inART <- calc_prophivVS(S3.inART.inc.comb))
calc_prophiv_cuml(S3.inART.inc.comb, 2021, 2121)
# Combine into a data frame
prophiv.S0b.stART$scenario <- "Baseline cytology and vaccination, no ART scale-up"
prophiv.S0.inART$scenario <- "Baseline cytology and vaccination, with ART scale-up"
prophiv.S1.inART$scenario <- "Scaled up HPV testing and 90% vaccination"
prophiv.S2.inART$scenario <- "S1 + 50% catch-up vaccination for WLHIV"
prophiv.S3.inART$scenario <- "S2 + more frequent screening for WLHIV"
prophiv.inART.comb <- rbind.data.frame(prophiv.S0b.stART, prophiv.S0.inART, prophiv.S1.inART, prophiv.S2.inART, prophiv.S3.inART)
prophiv.inART.comb$scenario <- factor(prophiv.inART.comb$scenario, levels = c("Baseline cytology and vaccination, no ART scale-up",
"Baseline cytology and vaccination, with ART scale-up",
"Scaled up HPV testing and 90% vaccination",
"S1 + 50% catch-up vaccination for WLHIV",
"S2 + more frequent screening for WLHIV"))
ccHivRatio.S0b.stART$scenario <- "Baseline cytology and vaccination, no ART scale-up"
ccHivRatio.S0.inART$scenario <- "Baseline cytology and vaccination, with ART scale-up"
ccHivRatio.S1.inART$scenario <- "Scaled up HPV testing and 90% vaccination"
ccHivRatio.S2.inART$scenario <- "S1 + 50% catch-up vaccination for WLHIV"
ccHivRatio.S3.inART$scenario <- "S2 + more frequent screening for WLHIV"
ccHivRatio.inART.comb <- rbind.data.frame(ccHivRatio.S0b.stART, ccHivRatio.S0.inART, ccHivRatio.S1.inART, ccHivRatio.S2.inART, ccHivRatio.S3.inART)
ccHivRatio.inART.comb$scenario <- factor(ccHivRatio.inART.comb$scenario, levels = c("Baseline cytology and vaccination, no ART scale-up",
"Baseline cytology and vaccination, with ART scale-up",
"Scaled up HPV testing and 90% vaccination",
"S1 + 50% catch-up vaccination for WLHIV",
"S2 + more frequent screening for WLHIV"))
prophivVS.S0b.stART$scenario <- "Baseline cytology and vaccination, no ART scale-up"
prophivVS.S0.inART$scenario <- "Baseline cytology and vaccination, with ART scale-up"
prophivVS.S1.inART$scenario <- "Scaled up HPV testing and 90% vaccination"
prophivVS.S2.inART$scenario <- "S1 + 50% catch-up vaccination for WLHIV"
prophivVS.S3.inART$scenario <- "S2 + more frequent screening for WLHIV"
prophivVS.inART.comb <- rbind.data.frame(prophivVS.S0b.stART, prophivVS.S0.inART, prophivVS.S1.inART, prophivVS.S2.inART, prophivVS.S3.inART)
prophivVS.inART.comb$scenario <- factor(prophivVS.inART.comb$scenario, levels = c("Baseline cytology and vaccination, no ART scale-up",
"Baseline cytology and vaccination, with ART scale-up",
"Scaled up HPV testing and 90% vaccination",
"S1 + 50% catch-up vaccination for WLHIV",
"S2 + more frequent screening for WLHIV"))
############################
## PLOTS
############################
colors <- RColorBrewer::brewer.pal(5, "Set2")
## Look at *median* outcomes across scenarios.
sir.comb.inART %>% # Change the name of the dataframe to different outcomes (ir.comb.inART, sir.comb.inART, inc.comb.inART, )
filter(group %in% c("Total", "HIVneg", "HIVpos_all" , "HIVpos_noART", "HIVpos_VS")) %>%
ggplot() +
geom_line(aes(x=year, y = median, colour = scenario)) +
facet_wrap(vars(group), scales = "free")
ggplot(prophiv.inART.comb) +
geom_line(aes(x=year, y = median, colour = scenario)) +
#geom_line(data = hiv.comb.inART, aes(x=year, y = median, colour = scenario)) +
geom_line(data = hiv.comb.inART, aes(x=year, y = median, colour = scenario), linetype = "dashed")
### Incidence rates
## Building up the scenarios for presentation
# Scenario 0b - Total pop only
png("sirS0b_tot.png", width = 800, height = 400)
#pdf("sirS0b_tot.pdf", width = 10, height = 5)
S0b.stART.sir.comb %>%
filter(group == "Total") %>%
ggplot() +
geom_line(aes(x = year, y = median), colour = "black", size = 1.25) +
scale_y_continuous(breaks = seq(0, 105, 25), limits = c(0, 115)) +
scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
labs(x = "Year", y = "Age-standardized incidence rate per 100,000 women") +
theme(axis.title = element_text(size = 14),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black")) +
plot_background
dev.off()
# Scenario 0b - Adding in HIV states
png("sirS0b_byhiv.png", width = 800, height = 400)
#pdf("sirS0b_byhiv.pdf", width = 10, height = 5) # legend position for this size should be c(0.85, 0.85)
#pdf("sirS0b_byhiv.small.pdf", width = 6, height = 5) # save it again at half size, and change legend position to c(0.8, 0.85)
S0b.stART.sir.comb %>%
filter(group %in% c("Total", "HIVneg", "HIVpos_all")) %>%
ggplot() +
geom_line(aes(x = year, y = median, colour = group), size = 1.25) +
scale_colour_manual(values = c("Total" = "black", "HIVneg" = "#38b1b1", "HIVpos_all" = colors[3]),
breaks = c("Total", "HIVneg", "HIVpos_all"),
labels = c("All females", "HIV-negative", "HIV-positive"),
name = NULL) +
scale_y_continuous(breaks = seq(0, 200, 25), limits = c(0, 200)) +
scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
labs(x = "Year", y = "Age-standardized incidence rate per 100,000 women") +
theme(axis.title = element_text(size = 12),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 14),
legend.position = c(0.8, 0.85)) +
plot_background
dev.off()
# png("irS0_byhivandart.png", width = 800, height = 400)
# ggplot(S0b.stART.ir.comb) +
# geom_line(aes(x = year, y = Total, colour = "Total"), size = 1.25) +
# geom_line(aes(x = year, y = HIVneg, colour = "HIVneg"), size = 1) +
# geom_line(aes(x = year, y = HIVpos, colour = "HIVpos"), size = 1) +
# geom_line(aes(x = year, y = HIVpos_noART, colour = "HIVpos_noART"), size = 1) +
# geom_line(aes(x = year, y = HIVpos_ART, colour = "HIVpos_ART"), size = 1) +
# scale_colour_manual(values = c("Total" = "black", "HIVneg" = colors[5], "HIVpos" = colors[3], "HIVpos_noART" = colors[2], "HIVpos_ART" = colors[4]),
# breaks = c("Total", "HIVneg", "HIVpos", "HIVpos_noART", "HIVpos_ART"),
# labels = c("All females", "HIV-negative", "HIV-positive (all)", "HIV-positive, untreated", "HIV-positive, virally suppressed"),
# name = NULL) +
# labs(x = "year", y = "Incidence rate per 100,000 women") +
# theme(axis.title = element_text(size = 18),
# axis.text = element_text(size = 16),
# axis.line = element_line(color = "black"),
# legend.text = element_text(size = 18),
# legend.position = c(0.85, 0.85)) +
# plot_background
# dev.off()
# Add in S0 with increasing ART
png("sirS0bS0_byhiv_inART.png", width = 800, height = 400)
#pdf("sirS0bS0_byhiv_inART.pdf", width = 6, height = 5)
ggplot() +
geom_line(data = filter(S0b.stART.sir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.75, alpha = 0.45) +
geom_line(data = filter(S0b.stART.sir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1.5, alpha = 0.45) +
geom_line(data = filter(S0b.stART.sir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1.5, alpha = 0.45) +
geom_line(data = filter(S0.inART.sir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25) +
geom_line(data = filter(S0.inART.sir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1) +
geom_line(data = filter(S0.inART.sir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1) +
scale_colour_manual(values = c("Total" = "black", "HIVneg" = "#38b1b1", "HIVpos_all" = colors[3]),
breaks = c("Total", "HIVneg", "HIVpos_all"),
labels = c("All females", "HIV-negative", "HIV-positive"),
name = NULL) +
scale_y_continuous(breaks = seq(0, 200, 25), limits = c(0, 200)) +
#scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
scale_x_continuous(breaks = seq(2000 , 2070 , 10), limits = c(2000, 2071)) +
labs(x = "Year", y = "Age-standardized incidence rate per 100,000 women") +
theme(axis.title = element_text(size = 12),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 14),
legend.position = c(0.8, 0.85)) +
plot_background
dev.off()
png("irS0bS0_byhiv_inART.png", width = 800, height = 400)
#pdf("irS0bS0_byhiv_inART.pdf", width = 6, height = 5)
ggplot() +
geom_line(data = filter(S0b.stART.ir.comb, group == "Total"), aes(x = year, y = median, colour = "Total", alpha = "artLevel"), size = 1.75) +
geom_line(data = filter(S0b.stART.ir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg", alpha = "artLevel"), size = 1.5) +
geom_line(data = filter(S0b.stART.ir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all", alpha = "artLevel"), size = 1.5) +
geom_line(data = filter(S0.inART.ir.comb, group == "Total"), aes(x = year, y = median, colour = "Total" , alpha = "artScale"), size = 1.25) +
geom_line(data = filter(S0.inART.ir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg" , alpha = "artScale"), size = 1) +
geom_line(data = filter(S0.inART.ir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all" , alpha = "artScale"), size = 1) +
scale_colour_manual(values = c("Total" = "black", "HIVneg" = "#38b1b1", "HIVpos_all" = colors[3]),
breaks = c("Total", "HIVneg", "HIVpos_all"),
labels = c("All women", "HIV-negative", "Women with HIV"),
name = "HIV status") +
scale_alpha_manual(values = c("artLevel" = 0.45, "artScale" = 1),
breaks = c("artLevel", "artScale"),
labels = c("Baseline", "Baseline, with ART scale-up"),
name = "Scenario") +
scale_y_continuous(breaks = seq(0, 250, 25), limits = c(0, 250)) +
#scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
scale_x_continuous(breaks = seq(2000 , 2070 , 10), limits = c(2000, 2071)) +
labs(x = "Year", y = "Crude cervical cancer incidence rate per 100,000 women") +
theme(axis.title = element_text(size = 12),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 11),
legend.position = c(0.35, 0.85),
legend.box = "horizontal") +
plot_background
dev.off()
# Add in S1 with increasing ART
#png("sirS0S1_byhiv.png", width = 800, height = 400)
pdf("sirS0S1_byhiv.pdf", width = 6, height = 5)
ggplot() +
geom_line(data = filter(S0.inART.sir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25) +
geom_line(data = filter(S0.inART.sir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1) +
geom_line(data = filter(S0.inART.sir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1) +
geom_line(data = filter(S1.inART.sir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25, linetype = "dashed") +
geom_line(data = filter(S1.inART.sir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1, linetype = "dashed") +
geom_line(data = filter(S1.inART.sir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1, linetype = "dashed") +
scale_colour_manual(values = c("Total" = "black", "HIVneg" = "#38b1b1", "HIVpos_all" = colors[3]),
breaks = c("Total", "HIVneg", "HIVpos_all"),
labels = c("All females", "HIV-negative", "HIV-positive"),
name = NULL) +
scale_y_continuous(breaks = seq(0, 200, 25), limits = c(0, 200)) +
#scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
scale_x_continuous(breaks = seq(2000 , 2070 , 10), limits = c(2000, 2071)) +
labs(x = "Year", y = "Age-standardized incidence rate per 100,000 women") +
theme(axis.title = element_text(size = 12),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 14),
legend.position = c(0.8, 0.85)) +
plot_background
dev.off()
#png("irS0S1_byhiv.png", width = 800, height = 400)
pdf("irS0S1_byhiv.pdf", width = 6, height = 5)
ggplot() +
geom_line(data = filter(S0.inART.ir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25) +
geom_line(data = filter(S0.inART.ir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1) +
geom_line(data = filter(S0.inART.ir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1) +
geom_line(data = filter(S1.inART.ir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25, linetype = "dashed") +
geom_line(data = filter(S1.inART.ir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1, linetype = "dashed") +
geom_line(data = filter(S1.inART.ir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1, linetype = "dashed") +
scale_colour_manual(values = c("Total" = "black", "HIVneg" = "#38b1b1", "HIVpos_all" = colors[3]),
breaks = c("Total", "HIVneg", "HIVpos_all"),
labels = c("All females", "HIV-negative", "HIV-positive"),
name = NULL) +
scale_y_continuous(breaks = seq(0, 200, 25), limits = c(0, 200)) +
#scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
scale_x_continuous(breaks = seq(2000 , 2070 , 10), limits = c(2000, 2071)) +
labs(x = "Year", y = "Age-standardized incidence rate per 100,000 women") +
theme(axis.title = element_text(size = 12),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 14),
legend.position = c(0.8, 0.85)) +
plot_background
dev.off()
# Add in S2,S3 with increasing ART
#png("sirS0S1S2S3_byhiv.png", width = 800, height = 400)
#pdf("sirS0S1S2S3_byhiv.pdf", width = 10, height = 5)
pdf("sirS0S1S2S3_byhiv.small.pdf", width = 6, height = 5) # half size
ggplot() +
geom_line(data = filter(S0.inART.sir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25) +
geom_line(data = filter(S0.inART.sir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1) +
geom_line(data = filter(S0.inART.sir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1) +
geom_line(data = filter(S1.inART.sir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25, linetype = "dashed") +
geom_line(data = filter(S1.inART.sir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1, linetype = "dashed") +
geom_line(data = filter(S1.inART.sir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1, linetype = "dashed") +
geom_line(data = filter(S2.inART.sir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25, linetype = "dotdash") +
geom_line(data = filter(S2.inART.sir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1, linetype = "dotdash") +
geom_line(data = filter(S2.inART.sir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1, linetype = "dotdash") +
geom_line(data = filter(S3.inART.sir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25, linetype = "dotted") +
geom_line(data = filter(S3.inART.sir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1, linetype = "dotted") +
geom_line(data = filter(S3.inART.sir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1, linetype = "dotted") +
scale_colour_manual(values = c("Total" = "black", "HIVneg" = "#38b1b1", "HIVpos_all" = colors[3]),
breaks = c("Total", "HIVneg", "HIVpos_all"),
labels = c("All females", "HIV-negative", "HIV-positive"),
name = NULL) +
scale_y_continuous(breaks = seq(0, 200, 25), limits = c(0, 200)) +
scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
labs(x = "year", y = "Age-standardized incidence rate per 100,000 women") +
theme(axis.title = element_text(size =12),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 14),
legend.position = c(0.8, 0.85)) +
plot_background
dev.off()
#png("irS0S1S2S3_byhiv.png", width = 800, height = 400)
#pdf("irS0S1S2S3_byhiv.pdf", width = 10, height = 5)
pdf("irS0S1S2S3_byhiv.small.pdf", width = 6, height = 5) # half size
ggplot() +
geom_line(data = filter(S0.inART.ir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25) +
geom_line(data = filter(S0.inART.ir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1) +
geom_line(data = filter(S0.inART.ir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1) +
geom_line(data = filter(S1.inART.ir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25, linetype = "dashed") +
geom_line(data = filter(S1.inART.ir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1, linetype = "dashed") +
geom_line(data = filter(S1.inART.ir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1, linetype = "dashed") +
geom_line(data = filter(S2.inART.ir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25, linetype = "dotdash") +
geom_line(data = filter(S2.inART.ir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1, linetype = "dotdash") +
geom_line(data = filter(S2.inART.ir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1, linetype = "dotdash") +
geom_line(data = filter(S3.inART.ir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25, linetype = "dotted") +
geom_line(data = filter(S3.inART.ir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1, linetype = "dotted") +
geom_line(data = filter(S3.inART.ir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1, linetype = "dotted") +
scale_colour_manual(values = c("Total" = "black", "HIVneg" = "#38b1b1", "HIVpos_all" = colors[3]),
breaks = c("Total", "HIVneg", "HIVpos_all"),
labels = c("All females", "HIV-negative", "HIV-positive"),
name = NULL) +
scale_y_continuous(breaks = seq(0, 200, 25), limits = c(0, 200)) +
scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
labs(x = "year", y = "Crude incidence rate per 100,000 women") +
theme(axis.title = element_text(size =12),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 14),
legend.position = c(0.8, 0.85)) +
plot_background
dev.off()
# Add in S3 with increasing ART
#png("sirS0S1S3_byhiv.png", width = 800, height = 400)
#pdf("sirS0S1S3_byhiv.pdf", width = 10, height = 5)
pdf("sirS0S1S3_byhiv.small.pdf", width = 6, height = 5) # half size
ggplot() +
geom_line(data = filter(S0.inART.sir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25) +
geom_line(data = filter(S0.inART.sir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1) +
geom_line(data = filter(S0.inART.sir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1) +
geom_line(data = filter(S1.inART.sir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25, linetype = "dashed") +
geom_line(data = filter(S1.inART.sir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1, linetype = "dashed") +
geom_line(data = filter(S1.inART.sir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1, linetype = "dashed") +
geom_line(data = filter(S3.inART.sir.comb, group == "Total"), aes(x = year, y = median, colour = "Total"), size = 1.25, linetype = "dotted") +
geom_line(data = filter(S3.inART.sir.comb, group == "HIVneg"), aes(x = year, y = median, colour = "HIVneg"), size = 1, linetype = "dotted") +
geom_line(data = filter(S3.inART.sir.comb, group == "HIVpos_all"), aes(x = year, y = median, colour = "HIVpos_all"), size = 1, linetype = "dotted") +
scale_colour_manual(values = c("Total" = "black", "HIVneg" = "#38b1b1", "HIVpos_all" = colors[3]),
breaks = c("Total", "HIVneg", "HIVpos_all"),
labels = c("All females", "HIV-negative", "HIV-positive"),
name = NULL) +
scale_y_continuous(breaks = seq(0, 200, 25), limits = c(0, 200)) +
#scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
scale_x_continuous(breaks = seq(2000 , 2070 , 10), limits = c(2000, 2071)) +
labs(x = "year", y = "Age-standardized incidence rate per 100,000 women") +
theme(axis.title = element_text(size =12),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 14),
legend.position = c(0.8, 0.85)) +
plot_background
dev.off()
#################################################### FIGURE 1A #############################################################
# Crude ICC among all women
#png("irS0S1S3_byhiv.png", width = 800, height = 400)
#pdf("irS0S1S3_byhiv.pdf", width = 10, height = 5)
pdf("irS0S1S3_byhiv.small.pdf", width = 6, height = 5) # half size
ggplot() +
geom_line(data = filter(S0b.stART.ir.comb, group == "Total"), aes(x = year, y = median, colour = "artLevel", linetype = "Total"), size = 1.25) +
geom_line(data = filter(S0.inART.ir.comb, group == "Total", year > "2019"), aes(x = year, y = median, colour = "artScale", linetype = "Total"), size = 1.25) +
geom_line(data = filter(S1.inART.ir.comb, group == "Total", year > "2019"), aes(x = year, y = median, colour = "hpvEnhanc", linetype = "Total"), size = 1.25) +
geom_line(data = filter(S3.inART.ir.comb, group == "Total", year > "2019"), aes(x = year, y = median, colour = "hivEnhanc", linetype = "Total"), size = 1.25) +
scale_colour_manual(values = c("artLevel" = "black", "artScale" = "grey45", "hpvEnhanc" = "dodgerblue2", "hivEnhanc" = "aquamarine3"),
breaks = c("artLevel" , "artScale" , "hpvEnhanc" , "hivEnhanc"),
labels = c("Baseline" , "Baseline, with ART scale-up" , "Enhanced HPV interventions" , "Enhanced HPV interventions for women with HIV"),
name = "Scenario") +
scale_linetype_manual(values = c("Total" = "solid", "HIVpos_all" = "dotted" , "HIVneg" = "dashed"),
breaks = c("Total", "HIVpos_all" , "HIVneg"),
labels = c("All women", "Women with HIV" , "Women without HIV"),
name = "HIV status") +
scale_y_continuous(breaks = seq(0, 200, 50), limits = c(0, 200)) +
#scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
scale_x_continuous(breaks = seq(2000 , 2070 , 10), limits = c(2000, 2071)) +
labs(x = "Year", y = "Crude cervical cancer incidence rate per 100,000 women") +
theme(axis.title = element_text(size =14),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 11),
legend.key.width = unit(1.5, 'cm'),
legend.position = "bottom",
legend.direction="vertical") +
plot_background
dev.off()
################################################## FIGURE 1b ##########################################################
# Crude ICC among HIV-negative and women with HIV
pdf("irS0S1S3_byhiv.small.pdf", width = 6, height = 5) # half size
ggplot() +
geom_line(data = filter(S0b.stART.ir.comb, group == "HIVpos_all"), aes(x = year, y = median, linetype = "HIVpos_all", colour = "artLevel"), size = 1.25) +
geom_line(data = filter(S0b.stART.ir.comb, group == "HIVneg"), aes(x = year, y = median, linetype = "HIVneg", colour = "artLevel"), size = 1.25) +
geom_line(data = filter(S0.inART.ir.comb, group == "HIVpos_all", year > "2019"), aes(x = year, y = median, linetype = "HIVpos_all", colour = "artScale"), size = 1.25) +
geom_line(data = filter(S0.inART.ir.comb, group == "HIVneg", year > "2019"), aes(x = year, y = median, linetype = "HIVneg", colour = "artScale"), size = 1.25) +
geom_line(data = filter(S1.inART.ir.comb, group == "HIVpos_all", year > "2019"), aes(x = year, y = median, linetype = "HIVpos_all", colour = "hpvEnhanc"), size = 1.25) +
geom_line(data = filter(S1.inART.ir.comb, group == "HIVneg", year > "2019"), aes(x = year, y = median, linetyper = "HIVneg", colour = "hpvEnhanc"), size = 1.25) +
geom_line(data = filter(S3.inART.ir.comb, group == "HIVpos_all", year > "2019"), aes(x = year, y = median, linetype = "HIVpos_all", colour = "hivEnhanc"), size = 1.25) +
geom_line(data = filter(S3.inART.ir.comb, group == "HIVneg", year > "2019"), aes(x = year, y = median, linetype = "HIVneg", colour = "hivEnhanc"), size = 1.25) +
scale_linetype_manual(values = c("HIVpos_all" = "dotted" , "HIVneg" = "dashed"),
breaks = c("HIVpos_all" , "HIVneg"),
labels = c("Women with HIV" , "HIV-negative"),
name = "HIV status") +
scale_colour_manual(values = c("artLevel" = "black", "artScale" = "grey45", "hpvEnhanc" = "dodgerblue2", "hivEnhanc" = "aquamarine3"),
breaks = c("artLevel" , "artScale" , "hpvEnhanc" , "hivEnhanc"),
labels = c("Baseline, no ART scale-up" , "Baseline" , "Enhanced HPV interventions" , "Enhanced HPV interventions for women with HIV"),
name = "Scenario") +
scale_y_continuous(breaks = seq(0, 200, 50), limits = c(0, 200)) +
#scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
scale_x_continuous(breaks = seq(2000 , 2070 , 10), limits = c(2000, 2071)) +
labs(x = "Year", y = "") +
theme(axis.title = element_text(size =12),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_blank(),
legend.key.width = unit(1.5, 'cm'),
legend.position = "none",
legend.direction="vertical") +
plot_background
dev.off()
### Proportion of cases in WLHIV and HIV prevalence
## Scenario S0b
pdf("prophiv.s0b.small.pdf", width = 6, height = 5)
ggplot(prophiv.S0b.stART) +
geom_area(aes(x = year, y = median), fill = colors[3]) +
scale_y_continuous(breaks = seq(0, 1, 0.1), limits = c(0, 1)) +
scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
labs(x = "Year", y = "Proportion of cases among WLHIV") +
theme(axis.title = element_text(size = 12),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 14),
legend.position = c(0.85, 0.85)) +
plot_background
dev.off()
# Add HIV prevalence
pdf("prophiv.prev.s0b.small.pdf", width = 6, height = 5) # save again at half size
ggplot() +
geom_area(data = prophiv.S0b.stART, aes(x = year, y = median), fill = colors[3]) +
geom_area(data = S0b.stART.hiv.comb, aes(x = year, y = median), fill = "#5F4BB6") +
scale_y_continuous(breaks = seq(0, 1, 0.1), limits = c(0, 1)) +
scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
labs(x = "Year", y = "Proportion of cases / HIV prevalence") +
theme(axis.title = element_text(size = 12),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 14),
legend.position = c(0.85, 0.85)) +
plot_background
dev.off()
## Scenario S0 with increasing ART
#pdf("prophiv.prev.s0bS0.inART.small.pdf", width = 6, height = 5) # save again at half size
#ggplot() +
# geom_area(data = prophiv.S0b.stART, aes(x = year, y = median), fill = colors[3], alpha = 0.6) +
# geom_area(data = prophiv.S0.inART, aes(x = year, y = median), fill = colors[3], alpha = 0.6) +
# geom_line(data = prophiv.S0.inART, aes(x = year, y = median), colour = "#486199") +
# geom_area(data = S0b.stART.hiv.comb, aes(x = year, y = median), fill = "#8B7DCA") +
# geom_area(data = S0.inART.hiv.comb, aes(x = year, y = median), fill = "#5F4BB6") +
# geom_line(data = S0.inART.hiv.comb, aes(x = year, y = median), colour = "#3C2F74") +
# scale_y_continuous(breaks = seq(0, 1, 0.1), limits = c(0, 1)) +
# #scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
# scale_x_continuous(breaks = seq(2000 , 2070 , 10), limits = c(2000, 2071)) +
# labs(x = "Year", y = "Proportion of cases / HIV prevalence") +
# theme(axis.title = element_text(size = 12),
# axis.text = element_text(size = 14),
# axis.line = element_line(color = "black"),
# legend.text = element_text(size = 14),
# legend.position = c(0.85, 0.85)) +
# plot_background
#dev.off()
## Scenario S0 with increasing ART
pdf("prophiv.prev.s0bS0.inART.small.pdf", width = 6, height = 5) # save again at half size
ggplot() +
geom_line(data = prophiv.S0b.stART, aes(x = year, y = median , colour = "propCases"), size = 1.75, alpha = 0.45) +
geom_line(data = prophiv.S0.inART, aes(x = year, y = median, colour = "propCases"), size = 1.25) +
geom_line(data = S0b.stART.hiv.comb, aes(x = year, y = median, colour = "hivPrev"), size = 1.75, alpha = 0.45) +
geom_line(data = S0.inART.hiv.comb, aes(x = year, y = median, colour = "hivPrev"), size = 1.25) +
scale_y_continuous(breaks = seq(0, 1, 0.1), limits = c(0, 1)) +
#scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
scale_x_continuous(breaks = seq(2000 , 2070 , 10), limits = c(2000, 2071)) +
labs(x = "Year", y = "Proportion") +
scale_colour_manual(values = c("propCases" = "#486199", "hivPrev" = "#3C2F74"),
breaks = c("propCases", "hivPrev"),
labels = c("Proportion of cases in WLHIV", "HIV prevalence"),
name = NULL) +
theme(axis.title = element_text(size = 12),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 14),
legend.position = c(0.75, 0.85)) +
plot_background
dev.off()
#Highlight the initial increase in PAF and then eventual decrease
S0b.min <- pmin(prophiv.S0b.stART$median, prophiv.S0.inART$median)
prophiv.S0b.min <- cbind.data.frame(year = prophiv.S0b.stART$year, min_prophiv = S0b.min)
pdf("prophiv.prev.s0bS0.inART.showincr.small.pdf", width = 6, height = 5)
ggplot() +
geom_area(data = prophiv.S0.inART, aes(x = year, y = median), fill = "#A4F283") +
geom_area(data = prophiv.S0b.stART, aes(x = year, y = median), fill = colors[3], alpha = 0.6) +
geom_area(data = prophiv.S0b.min, aes(x = year, y = min_prophiv), fill = colors[3]) +
geom_line(data = prophiv.S0.inART, aes(x = year, y = median), colour = "#486199") +
geom_area(data = S0b.stART.hiv.comb, aes(x = year, y = median), fill = "#8B7DCA") +
geom_area(data = S0.inART.hiv.comb, aes(x = year, y = median), fill = "#5F4BB6") +
geom_line(data = S0.inART.hiv.comb, aes(x = year, y = median), colour = "#3C2F74") +
scale_y_continuous(breaks = seq(0, 1, 0.1), limits = c(0, 1)) +
scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
labs(x = "Year", y = "Proportion of cases / HIV prevalence") +
theme(axis.title = element_text(size = 14),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 14),
legend.position = c(0.85, 0.85)) +
plot_background
dev.off()
pdf("prophiv.prev.s0bS0.inART.showdecr.small.pdf", width = 6, height = 5)
ggplot() +
geom_area(data = prophiv.S0.inART, aes(x = year, y = median), fill = "#A4F283") +
geom_area(data = prophiv.S0b.stART, aes(x = year, y = median), fill = "#C76F98", alpha = 0.8) +
geom_area(data = prophiv.S0b.min, aes(x = year, y = min_prophiv), fill = colors[3]) +
geom_line(data = prophiv.S0.inART, aes(x = year, y = median), colour = "#486199") +
geom_area(data = S0b.stART.hiv.comb, aes(x = year, y = median), fill = "#8B7DCA") +
geom_area(data = S0.inART.hiv.comb, aes(x = year, y = median), fill = "#5F4BB6") +
geom_line(data = S0.inART.hiv.comb, aes(x = year, y = median), colour = "#3C2F74") +
scale_y_continuous(breaks = seq(0, 1, 0.1), limits = c(0, 1)) +
scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
labs(x = "Year", y = "Proportion of cases / HIV prevalence") +
theme(axis.title = element_text(size = 14),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 14),
legend.position = c(0.85, 0.85)) +
plot_background
dev.off()
## Add in S1
pdf("prophiv.prev.s0s1.inART.small.pdf", width = 6, height = 5) # save again at half size
ggplot() +
geom_area(data = prophiv.S0.inART, aes(x = year, y = median, fill = "Baseline")) +
geom_area(data = prophiv.S1.inART, aes(x = year, y = median, fill = "S1"), alpha = 0.55) +
geom_line(data = prophiv.S0.inART, aes(x = year, y = median), colour = "#486199") +
geom_line(data = prophiv.S1.inART, aes(x = year, y = median), colour = "#486199", linetype = "dashed") +
# geom_line(data = prophiv.S0.inART, aes(x = year, y = median), colour = "#738ABF") +
# geom_line(data = prophiv.S1.inART, aes(x = year, y = median), colour = "#7AB4F5") +
geom_area(data = S0.inART.hiv.comb, aes(x = year, y = median), fill = "#5F4BB6") +
scale_y_continuous(breaks = seq(0, 1, 0.1), limits = c(0, 1)) +
#scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
scale_x_continuous(breaks = seq(2000 , 2070 , 10), limits = c(2000, 2071)) +
scale_fill_manual(values = c("Baseline" = colors[3], "S1" = "#97C4F7"),
breaks = c("Baseline cytology and vaccination, with ART scale-up", "Scaled up HPV testing and 90% vaccination"),
labels = c("Baseline cytology and vaccination, with ART scale-up", "Scaled up HPV testing and 90% vaccination"),
name = NULL) +
labs(x = "Year", y = "Proportion of cases / HIV prevalence") +
theme(axis.title = element_text(size = 14),
axis.text = element_text(size = 14),
axis.line = element_line(color = "black"),
legend.text = element_text(size = 14),
legend.position = "none") +
plot_background
dev.off()
## Add in S3
#pdf("prophiv.prev.s0s1S3.inART.small.pdf", width = 6, height = 5) # save again at half size
#ggplot() +
# geom_area(data = prophiv.S0.inART, aes(x = year, y = median, fill = "Baseline")) +
# geom_area(data = prophiv.S1.inART, aes(x = year, y = median, fill = "S1"), alpha = 0.55) +
# geom_area(data = prophiv.S3.inART, aes(x = year, y = median, fill = "S3")) +
# geom_line(data = prophiv.S0.inART, aes(x = year, y = median), colour = "#486199") +
# geom_line(data = prophiv.S1.inART, aes(x = year, y = median), colour = "#486199", linetype = "dashed") +
# geom_line(data = prophiv.S3.inART, aes(x = year, y = median), colour = "#486199", linetype = "dotdash") +
# geom_area(data = S1.inART.hiv.comb, aes(x = year, y = median), fill = "#A15AD1") +
# geom_area(data = S3.inART.hiv.comb, aes(x = year, y = median), fill = "#8771EB") +
# geom_area(data = S0.inART.hiv.comb, aes(x = year, y = median), fill = "#5F4BB6") +
# scale_y_continuous(breaks = seq(0, 1, 0.1), limits = c(0, 1)) +
# #scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
# scale_x_continuous(breaks = seq(2000 , 2070 , 10), limits = c(2000, 2071)) +
# scale_fill_manual(values = c("Baseline" = colors[3], "S1" = "#97C4F7", "S3" = "#A0C9F8"),
# breaks = c("Baseline", "S1", "S3"),
# labels = c("Baseline cytology and vaccination, with ART scale-up", "Scaled up HPV testing and 90% vaccination",
# "S2 + more frequent screening for WLHIV"),
# name = NULL) +
# labs(x = "year", y = "Proportion of cases / HIV prevalence") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 14),
# axis.line = element_line(color = "black"),
# legend.text = element_text(size = 14),
# legend.position = "none") +
# plot_background
#dev.off()
## Add in S3
pdf("prophiv.prev.s0s1S3.inART.small.pdf", width = 6, height = 5) # save again at half size
ggplot() +
geom_line(data = prophiv.S0.inART, aes(x = year, y = median, colour = "propCases"), size = 1.75) +
geom_line(data = prophiv.S1.inART, aes(x = year, y = median, colour = "propCases"), size = 1.25, linetype = "dashed") +
geom_line(data = prophiv.S3.inART, aes(x = year, y = median, colour = "propCases"), size = 1.25, linetype = "dotdash") +
geom_line(data = S0.inART.hiv.comb, aes(x = year, y = median, colour = "hivPrev"), size = 1.75) +
geom_line(data = S1.inART.hiv.comb, aes(x = year, y = median, colour = "hivPrev"), size = 1.25, linetype = "dashed") +
geom_line(data = S3.inART.hiv.comb, aes(x = year, y = median, colour = "hivPrev"), size = 1.25, linetype = "dotdash") +
scale_y_continuous(breaks = seq(0, 1, 0.1), limits = c(0, 1)) +
#scale_x_continuous(breaks = seq(2000 , 2120 , 20), limits = c(2000, 2120)) +
scale_x_continuous(breaks = seq(2000 , 2070 , 10), limits = c(2000, 2071)) +
scale_colour_manual(values = c("propCases" = "#486199", "hivPrev" = "#3C2F74"),