API Exploration

Reproduction Rights an API analysis

1. Reproductive rights in Latin America

According to wikipedia

In Latin America abortion is:

• completely prohibited: El Salvador, Dominican Republic, Nicaragua, Honduras
• allowed only to save the mother’s life: Guatemala, Paraguay, Venezuela,
• allowed only to save the mother’s life and in case of rape: Brazil
• allowed only to save the mother’s life or health: Costa Rica, Peru, Ecuador (and rape of disabled women)
• allowed only to save the mother’s life and in case of rape or fetal malformation: Chile (since 2017), Panama
• allowed only to save the mother’s life or health, and in case of rape: Bolivia
• allowed only to save the mother’s life or health, and in case of rape, or fetal malformation: Colombia
• allowed on request: Argentina, Cuba, Guyana, Uruguay, and some states of Mexico (Mexico City and Oaxaca)

For this exercise let’s focus on Paraguay, Brazil, Colombia and Uruguay, given that they represent four different levels of reproductive rights. On the extremes we have Paraguay and Uruguay, the first one only allows abortion in the case where the health of the mother is in danger, while the later allows it by request. Brazil and Clombia allow it in specific scenarios.

countries <- c("ARG", "BOL", "BRA", "CHL", "COL", "ECU", "PRY", "PER", "URY", "VEN")

Data <- wb_data(country = c(countries, "World"), indicator = c(Adolescent_fertility_rate = 'SP.ADO.TFRT', Contraceptive = 'SP.DYN.CONU.ZS', Primary_educ_fem = 'SE.PRM.PRSL.FE.ZS', Maternal_mort = 'SH.STA.MMRT'), return_wide = TRUE, start_date = 1990, end_date = 2015)

First let’s try to have a general idea about how do some fertility related variables behave in this country. For this reason I am going to be looking at:

• Adolescent fertility rate (births per 1,000 women ages 15-19)
• Contraceptive prevalence, any methods (% of women ages 15-49)
• Persistence to last grade of primary, female (% of cohort)
• Maternal mortality ratio (modeled estimate, per 100,000 live births)

2. Fertility related variables (data from the World Bank)

Ado_fert <- ggplot() +
  geom_line(data = Data %>% filter(country == "Paraguay"), aes(x = date, y = Adolescent_fertility_rate, color = country)) +
  geom_line(data = Data %>% filter(country == "Brazil"), aes(x = date, y = Adolescent_fertility_rate, color = country))+
  geom_line(data = Data %>% filter(country == "Colombia"), aes(x = date, y = Adolescent_fertility_rate, color = country))+
  geom_line(data = Data %>% filter(country == "Uruguay"), aes(x = date, y = Adolescent_fertility_rate, color = country)) +
  ggtitle("Adolescent Fertility Rate") + xlab("Years") + ylab("births per 1,000 women ages 15-19") +
  scale_x_continuous(breaks = seq(1990, 2015, by = 5), expand = c(0,0)) +
  theme(panel.border = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        legend.title = element_blank(),
        legend.position = "bottom", legend.direction = "vertical", legend.justification = "center",
        legend.margin = margin(t = 0, r = 0, b = 2, l = 2, unit = "mm"),
        legend.key = element_rect(color = "transparent", fill = "white"), legend.key.size = unit(5,"mm"),
        axis.line = element_line(colour = "black"),
        axis.title = element_text(size = 8),
        plot.title = element_text(face = "bold", hjust = 0.5, size = 11))

Contra <- ggplot() +
  geom_line(data = Data %>% filter(country == "Paraguay"), aes(x = date, y = Contraceptive, color = country)) +
  geom_line(data = Data %>% filter(country == "Brazil"), aes(x = date, y = Contraceptive, color = country))+
  geom_line(data = Data %>% filter(country == "Colombia"), aes(x = date, y = Contraceptive, color = country))+
  geom_line(data = Data %>% filter(country == "Uruguay"), aes(x = date, y = Contraceptive, color = country)) +
  ggtitle("Contraceptive prevalence") + xlab("Years") + ylab("% of women ages 15-49") +
  scale_x_continuous(breaks = seq(1990, 2015, by = 5), expand = c(0,0)) +
  theme(panel.border = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        legend.title = element_blank(),
        legend.position = "bottom", legend.direction = "vertical", legend.justification = "center",
        legend.margin = margin(t = 0, r = 0, b = 2, l = 2, unit = "mm"),
        legend.key = element_rect(color = "transparent", fill = "white"), legend.key.size = unit(5,"mm"),
        axis.line = element_line(colour = "black"),
        axis.title = element_text(size = 8),
        plot.title = element_text(face = "bold", hjust = 0.5, size = 11))

Primary <- ggplot() +
  geom_line(data = Data %>% filter(country == "Paraguay"), aes(x = date, y = Primary_educ_fem, color = country)) +
  geom_line(data = Data %>% filter(country == "Brazil"), aes(x = date, y = Primary_educ_fem, color = country))+
  geom_line(data = Data %>% filter(country == "Colombia"), aes(x = date, y = Primary_educ_fem, color = country))+
  geom_line(data = Data %>% filter(country == "Uruguay"), aes(x = date, y = Primary_educ_fem, color = country)) +
  ggtitle("Persistence to last grade of primary, female") + xlab("Years") + ylab("% of cohort") +
  scale_x_continuous(breaks = seq(1990, 2015, by = 5), expand = c(0,0)) +
  theme(panel.border = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        legend.title = element_blank(),
        legend.position = "bottom", legend.direction = "vertical", legend.justification = "center",
        legend.margin = margin(t = 0, r = 0, b = 2, l = 2, unit = "mm"),
        legend.key = element_rect(color = "transparent", fill = "white"), legend.key.size = unit(5,"mm"),
        axis.line = element_line(colour = "black"),
        axis.title = element_text(size = 8),
        plot.title = element_text(face = "bold", hjust = 0.5, size = 11))

Mate_mort <- ggplot() +
  geom_line(data = Data %>% filter(country == "Paraguay"), aes(x = date, y = Maternal_mort, color = country)) +
  geom_line(data = Data %>% filter(country == "Brazil"), aes(x = date, y = Maternal_mort, color = country))+
  geom_line(data = Data %>% filter(country == "Colombia"), aes(x = date, y = Maternal_mort, color = country))+
  geom_line(data = Data %>% filter(country == "Uruguay"), aes(x = date, y = Maternal_mort, color = country)) +
  ggtitle("Maternal mortality ratio") + xlab("Years") + ylab("modeled estimate, per 100,000 live births") +
  scale_x_continuous(breaks = seq(1990, 2015, by = 5), expand = c(0,0)) +
  theme(panel.border = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        legend.title = element_blank(),
        legend.position = "bottom", legend.direction = "vertical", legend.justification = "center",
        legend.margin = margin(t = 0, r = 0, b = 2, l = 2, unit = "mm"),
        legend.key = element_rect(color = "transparent", fill = "white"), legend.key.size = unit(5,"mm"),
        axis.line = element_line(colour = "black"),
        axis.title = element_text(size = 8),
        plot.title = element_text(face = "bold", hjust = 0.5, size = 11))

ggarrange(Ado_fert, Contra, Primary, Mate_mort, ncol = 2, nrow = 2)

The graphs show us that the adolescent fertility rate varies depending on the country. In the four we observe how it begins to goes down for most of the years observed. When it comes to contraceptive measures there is not enough information. The information on primary education akso presents some measing information.

Finally the maternal mortality rate has information from the 2000 onwards. Here the levels are very different, Paraguay is the country with the higher levels of mortality rate and Uruguay the one with the lowest.

3. Abortion Legislation in Uruguay

library(gtrendsR)
library(reshape2)

googleData = gtrends(c("aborto", "misoprostol", "consecuencias aborto", "clinicas clandestinas aborto"), gprop = "web", geo ="UY", time = "2010-01-01 2021-03-21")[[1]]
googleData = dcast(googleData, date ~ keyword, value.var = "hits")

Some background, the most recent abortion law (there were other attempts in the XX’s century) legalized abortion by the will of the pregnant person in October of 2012.

4. What do we see in google trends?

ggplot() +
  geom_line(data = googleData, aes(x = date, y = aborto, color = "aborto")) +
  ggtitle("Google searches from Uruguay: abortion") + xlab("Date") + ylab("Hits") +
  theme(panel.border = element_blank(),
        panel.background = element_blank(),
        legend.title = element_blank(),
        legend.position = "bottom", legend.direction = "vertical", legend.justification = "center",
        legend.margin = margin(t = 0, r = 0, b = 2, l = 2, unit = "mm"),
        legend.key = element_rect(color = "transparent", fill = "white"), legend.key.size = unit(5,"mm"),
        axis.line = element_line(colour = "black"),
        axis.title = element_text(size = 8),
        plot.title = element_text(face = "bold", hjust = 0.5, size = 11))

The Google searches do not seem to have increased in 2012. It should be noted that there is, however, a considerable peak after 2020 which could be related to the pandemic.

ggplot() +
  geom_line(data = googleData, aes(x = date, y = misoprostol, color = "misoprostol")) +
  ggtitle("Google searches from Uruguay: misoprostol") + xlab("Date") + ylab("Hits") +
  theme(panel.border = element_blank(),
        panel.background = element_blank(),
        legend.title = element_blank(),
        legend.position = "bottom", legend.direction = "vertical", legend.justification = "center",
        legend.margin = margin(t = 0, r = 0, b = 2, l = 2, unit = "mm"),
        legend.key = element_rect(color = "transparent", fill = "white"), legend.key.size = unit(5,"mm"),
        axis.line = element_line(colour = "black"),
        axis.title = element_text(size = 8),
        plot.title = element_text(face = "bold", hjust = 0.5, size = 11))

When it comes to misoprostol, we can observe that there are not big differences on the level of this variable. However after 2012, the peaks are not as high as before.