Medical Tourism

Despite considerable progress in improving the health infrastructure, gender disparities persist in a number of important areas—particularly in access to educational opportunity and in important demographic indicators like the maternal mortality ratios (MMRs) and adolescent birth rates. Specifically, India, Iran, Jordan, and Brazil have a growing burden of maternal mortality ratios (MMRs), adolescent birth rates,

(The size of the bubble refers to the # of medical tourists and the color of the bubbles refers to the HDI of that country)

and gender inequality in educational attainment.

Syntax:

library(RColorBrewer)

library(classInt)

mt <- read.csv("C:/Users/User/Documents/DeltaState/Gender/MedicalTourism2.csv")

mt$diff_sch <- mt$Schooling_Female - mt$Schooling_Male

mt <- mt[which(mt$n_tourists > 0), ]

radius <- sqrt(mt$n_tourists/pi)

nclr <- 5

YlOrBr <- c("#FF9999", "#FF8080", "#FF4D4D", "#FF0000", "#B20000")

class <- classIntervals(mt$HDI2012, nclr, style="quantile")

colcode <- findColours(class, YlOrBr)

# Figure 1

symbols(mt$AdolBR10, mt$MMR10, circles=radius, inches=0.5, bg = colcode, fg="gray30",

xlab="2010 Adolescent Birth Rate", ylab="Maternal Mortality Ratio")

text(mt$AdolBR10, mt$MMR10, mt$Country, cex=1.0)

# Figure 2

symbols(mt$FemSecEd0512, mt$MMR10, circles=radius, inches=0.5,

fg="gray30", bg=colcode, xlab="% Females with at least a secondary education", ylab="Maternal Mortality Ratio")

text(mt$FemSecEd0512, mt$MMR10, mt$Country, cex=1.0)

# Figure 3

symbols(mt$diff_sch, mt$HDI2012, circles=radius, inches=0.35,

fg="white", bg="red", xlab="Gender differences in mean years of schooling", ylab="Human Development Index")

text(mt$diff_sch, mt$HDI2012, mt$Country, cex=1.0)