---
title: "Maternal Mortality"
output: 
  flexdashboard::flex_dashboard:
    theme:
      version: 4
      bootswatch: zephyr
      primary: "#F285A6"
    orientation: columns
    vertical_layout: fill
    source_code: embed
---


<style>
.chart-title {    /* chart_title  */
  font-size: 25px;
  }
  body{ /* Normal */
         font: 64px;  
    }
    </style>

```{r setup, include=FALSE}
library(flexdashboard)
library(DT)
library(viridis)
library(plotly)
library(knitr)
```

Introduction
===

Column {.tabset data-width 500}
-----------------------------------------------------------------------
### Background Information 
As someone who is interested in the medical field I looked into things that I was curious about. When looking at maternal studies I found that black women were three times as likely to die from pregnancy-related causes as white women and that Hispanic women saw the largest maternal mortality increase of any racial or ethnic demographic group in studies. The US is known to have disparities among different races and medicine is the one that sees it the most. Due to systemic racism and discrimination POC have an unfair advantage to proper healthcare.

### Maternal and Infant Mortality Analysis

I am going to be analyzing mortality rates among pregnant individuals and infants and how race affects mortality rate in the United States from data sets collected. I will be first studying how race affects mortality rates on pregnant individuals among all states. These deaths are all related to pregnancy and no other factors. I will also be studying how race affects the survival rates of infants. There are various factors for deaths, but I will be analyzing if there is a trend of more mortality rates based on a certain race

Column {data-width=500}
---

### Goal
The goal is to determine if there are prejudices that are leading to negligence when it comes to pregnant individual and infant. I want to answer the following questions

- Is there a maternal race that has a higher mortality than the others?

- Is there a certain infant race that has a higher mortality than others?

- Is there a difference in infant, neonatal, and post-neonatal mortality?

- Are the mortality rates getting better or worse year by year?


Data Introduction
===

Column {.tabset data-width=550}
---

### Maternal Mortality

```{r}
pacman::p_load(tidyverse, knitr)

Maternal_deathcount <- read_csv("Maternal_deathcount.csv")
kable(Maternal_deathcount[224:234,])

Maternal_deathcount$Subgroup <- recode(Maternal_deathcount$Subgroup,
"Non-Hispanic American Indian or Alaska Native" = "Native_American",
  "Non-Hispanic Asian" = "Asian",
  "Non-Hispanic Black" = "Black",
  "Non-Hispanic Native Hawaiian or other Pacific Islander" = "Pacific_Isalnder",
  "Non-Hispanic White" = "White")

df_race <- Maternal_deathcount %>%
  filter(Group == "By Race and Hispanic origin")

df_age <- Maternal_deathcount %>%
  filter(Group == "By Age")

```

### Infant Mortality
```{r}
pacman::p_load(tidyverse, knitr)

Infant_deathcntt <- read_csv("Infant_Mortality.csv")
kable(Infant_deathcntt[1:10,])
```

Column {.tabset data-width=50}
---


```{r drop_race}
Hispanic <- Maternal_deathcount[Maternal_deathcount$Subgroup == "Hispanic" , ]

Native <- Maternal_deathcount[Maternal_deathcount$Subgroup == "Native_American" , ]

Asian <- Maternal_deathcount[Maternal_deathcount$Subgroup == "Asian" , ]

Black <- Maternal_deathcount[Maternal_deathcount$Subgroup == "Black" , ]

White <- Maternal_deathcount[Maternal_deathcount$Subgroup == "White" , ]

Race <- Maternal_deathcount[Maternal_deathcount$Group == "By Race and Hispanic origin" , ]

```

Maternal Mortality
===

column {.tabset data-width=450}
---

### By Race

```{r maternal_data}
Death_Count <- Maternal_deathcount %>%
  group_by(Subgroup) %>%
  summarize(Death = sum(`Maternal Deaths`, na.rm=T))
```

```{r race_v_deathcount}
Death_Count <- Death_Count[-c(1,2,9),]
datatable(Death_Count, colnames = c("Race", "Total Death"))
```


column {data-width=550}
---
### Race Analysis

There is a total of 47,514 maternal deaths counted in this study. 42% of maternal deaths come from white individuals; 32% of maternal deaths come from black individuals; 18% of maternal deaths come from Hispanic individuals; 4% of maternal deaths come from Asian individuals; 2% of maternal deaths come from Native American individuals. When analyzing this data it is also important to take into account population sizes. White Individuals make up the most of the population in the U.S. and Native American individuals make up the least of the population. 


Infant Mortality
===


column {.tabset data-width=550}
---

### Distribution of Race and Infant Death

```{r race_v_infantdeathcount}

pacman::p_load(tidyverse, knitr)
Infant_Mortality <- read_csv("Infant_Mortality.csv")
```

```{r race_infant}

Infant_Mortality$`Materal Race or Ethnicity` <- recode(Infant_Mortality$`Materal Race or Ethnicity`, 
      "Black Non-Hispanic" = "Black",
      "Non-Hispanic Black" = "Black",
      "Black NH" = "Black",
      "White Non-Hispanic" = "White",
      "Non-Hispanic White" = "White", 
      "White NH" = "White",
      "Other Hispanic" = "Hispanic",
      "Puerto Rican" = "Hispanic",
      "Asian and Pacific Islander" = "Asian")
```

```{r infantdata}
Death_Counts <- Infant_Mortality %>%
  group_by(`Materal Race or Ethnicity`) %>%
  summarize(Death = sum(c(`Infant Deaths`,`Neonatal Infant Deaths`, `Postneonatal Infant Deaths`) , na.rm=T))
```

```{r Infantdeath}
Death_Counts <- Death_Counts[-c(1,9,10),]
datatable(Death_Counts, colnames = c("Race", "Total Death"))
```


### Infant 

```{r}

ggplot(Infant_Mortality, aes(y = `Materal Race or Ethnicity`, x = `Infant Deaths`)) + geom_boxplot(fill = "pink")
```

### Neonatal  
```{r}

ggplot(Infant_Mortality, aes(y = `Materal Race or Ethnicity`, x = `Neonatal Infant Deaths`)) + geom_boxplot(fill = "pink")
```

### Postneonatal 
```{r}

ggplot(Infant_Mortality, aes(y = `Materal Race or Ethnicity`, x = `Postneonatal Infant Deaths`)) + geom_boxplot(fill = "pink")
```

### Live Births

```{r liveinfant_data}
Live_Count <- Infant_Mortality %>%
  group_by(`Materal Race or Ethnicity`) %>%
  summarize(Death = sum(`Number of Live Births`, na.rm=T))
```

```{r Infantlive}
Live_Count <- Live_Count[-c(1,9,10),]
datatable(Live_Count, colnames = c("Race", "Total Births"))
```

column {.tabset data-width=450}
---

### Infant Mortality Table Analysis
 
After analyzing the table and graph there can be a few conclusions made. Looking at the table you can see that there are a total of 14,152 mortalities taken into account which include all infant, neonatal, and post-neonatal deaths. of all the deaths black infants made up the most moralities with being 38% of the mortalities; Hispanic infants make up 31% of the infant mortalities; White infants make up 21% of the mortalities; Asian Infants make up 10%, and then less than one percent are two or more races/ unknown. 

### Graph Analysis - Infant

Starting with the infant graph it is shown that black infants had the highest median and max range of deaths compared to the other races. Hispanic infants had a wider range but overall making up the second most infant deaths. White infants have a short range and also make up the least of the infant deaths. This is also the only one with outliers. Asian infants make up the least mortalities of known races; they have the lowest median and has a short range.

### Graph Analysis - Neonatal

After analyzing the neonatal graph it is shown again that the majority of mortalities come from black neonatal infants  with a higher median and max range. White neonatal infants are in the middle for median point and have a shorter range than the Hispanic and black neonatal infants. Asian neonatal infants have the lowest median


### Graph Analysis - post-neonatal

After analyzing the post-neonatal data it is similar to the other graphs with black neonatal infants making up the majority of deaths with a few outliers. They have the highest median and have a wide range. White neonatal infants had a short range but had the second highest median still. Hispanic post-neonatal infants had a lower median than white and black post-neonatal infants but have a wide range and quartile's. Asian post-neonatal infants had the lowest median and the shortest range of the other races.

### Live Birth Table Analysis
On the other side of things there were 1,549,414 live births and you can see that white infants make up the most infants that survive with 33%; Hispanic infants at 30%; black infants at  23%; Asian infants at 15%; Other/ Two or More at 2%; and unknown at less than 1%. The US is bound to have more white infant births because they make up the majority of the races in the US. 


Yearly Change  
===

column {.tabset data-width=450}
---

### Infant Mortality

```{r}
Infant_Mortality<- Infant_Mortality %>%
  mutate(Grouped_Ethnicity = case_when(
    `Materal Race or Ethnicity` %in% c("Other Hispanic", "Puerto Rican") ~ "Hispanic",
    `Materal Race or Ethnicity` %in% c("Black Non-Hispanic", "Non-Hispanic Black", "Black NH") ~ "Black NH",
    `Materal Race or Ethnicity` %in% c("White Non-Hispanic", "Non-Hispanic White", "White NH") ~ "White NH",
    `Materal Race or Ethnicity` %in% c("Asian and Pacific Islander", "API") ~ "API",
    TRUE ~ `Materal Race or Ethnicity`  # Default case, keeps the original value for other categories
  ))
```

```{r}
Infant_Mortality$Grouped_Ethnicity <-
  factor(Infant_Mortality$Grouped_Ethnicity,
         levels = c("White",
                    "Black",
                    "Asian",
                    "Hispanic"))
```

```{r}
font <- list(
  family = "Arial",
  size = 14,
  color = "white"
)

label <- list(
  bgcolor = "#232F34",
  bordercolor = "transparent",
  font = font
)
```

```{r}
p1 <- ggplot(Infant_Mortality, aes(x = Year, y = `Infant Mortality Rate`, color = Grouped_Ethnicity)) +
  geom_line() +
  geom_point() +
  theme_minimal() +
  scale_color_brewer(palette = "Set2") +
  labs(title = "Infant Mortality Rate by Maternal Race or Ethnicity Over the Years",
       x = "Year",
       y = "Infant Mortality Rate",
       fill = "Race or Ethnicity") +
  theme(text = element_text(size = 18))

ggplotly(p1) %>%
style(hoverlabel = label) %>%
  layout(font = font)

```

### Maternal Age 

```{r}
Maternal_Age <- Maternal_deathcount %>%
  filter(Subgroup %in%
           c("25-39 years", "40 years and over", "Under 25 years")
  )
```

```{r}
Maternal_Age$Subgroup <- recode(Maternal_Age$Subgroup,
                                "25-39 years" = "25-39",
                                "40 years and over" = "40+",
                                "Under 25 years" = "<25"
                                )
```

```{r}
Maternal_Age_summary <- Maternal_Age %>%
  group_by(`Year of Death`, Subgroup) %>%
  summarise(Death = sum(`Maternal Deaths`))
```

```{r}
datatable(Maternal_Age_summary,
              colnames = c("Year of Death", "Age", "Maternal Deaths"))
```


### Maternal 

```{r}
p4 <- ggplot(Maternal_Age_summary, aes(x = `Year of Death`, y = Death, group = Subgroup, color = Subgroup)) +
  geom_line() +
  geom_point() +
  theme_minimal() +
  labs(title = "Trend of Maternal Deaths Over Time by Age Group",
       x = "Year of Death",
       y = "Maternal Deaths",
       color = "Age Group") +
  scale_color_brewer(palette = "Set2") +
  theme(text = element_text(size = 18))

ggplotly(p4) %>%
  style(hoverlabel = label) %>%
  layout(font = font) 

```

column {.tabset data-width=450}
---

### Infant Analysis

Based on the graph I analyzed that as the years are increasing, there are less mortalities among all the races. Black infants have the highest mortality rates than any of the other races. 

### Maternal Age analysis

Analyzing the graph and table it is shown that there has been an increase as years go by adn then a decrease in 2023, the current year, possibly due to lack of complete data collection. Ages 25-39 have the highest mortality rates which is possibly due to the fact that this is when most women are fertile and having children.


Conclusion 
===

column {data-width=450}
---

### Findings 

From my analysis, I found that there is a correlation of mortality rates due to race. There were high percentages of infant deaths for POC while White infants had a higher survival rate. For Maternal Deaths there were more white maternal deaths than POC and Black Individuals came in with the second highest.

Looking at the graph we can see that among all races, the infant mortality rate is going down as the years go by. There are more babies. being born, but there could be more of an advancement in medicine to help ensure the survival of the infants. Asian and White infants have the lowest rates. Hispanic and Black infants have the highest rates with Black infants being the most.

Looking at the age table it is seen that as the years go by the amount of maternal deaths from all ages is increasing; this could be due to the fact that there are more people being born as the years go by. Mothers <25 years of age have the lowest death count whereas 25-39 have the highest and 40 being in the middle. This is also due to the fact that most people are having babies ages 25-39 as that is the most fertile time for most women. In the graph it lowers at the end but this is due to insufficient data collected for 2023 as this is still currently being analyzed.

column {data-width=550}
---

### Limits

There were many limitations in my data and the analysis I was trying to make. For starters, I chose to only study the US and the races are not equal in the US. It is very diverse here but the majority of the population is white; so there is going to be more white deaths total. I had to conclude if the data was significant for the race based on population sizes.

Another limitation is that the factors of these mortalities could be due to other factors like age, economic status, healthcare availability, etc.

### References

[Infant Mortality]- https://catalog.data.gov/dataset/infant-mortality

[Maternal Mortality]-
https://catalog.data.gov/dataset/vsrr-provisional-maternal-death-counts

https://www.cdc.gov/nchs/data/hestat/maternal-mortality/2020/maternal-mortality-rates-2020.htm

https://tcf.org/content/commentary/worsening-u-s-maternal-health-crisis-three-graphs/?gclid=Cj0KCQiA4NWrBhD-ARIsAFCKwWsloKo9iNUAekmgkfcIfSoNeO2Vu_afWcf4LQ4n9Tqmw5HzS0HJ5Z0aArr2EALw_wcB



About Me
===
Taonanyasha Banda is a third year student at the University of Dayton. She is pursuing a bachelor's degree in Pre-Medicine with a minor in Data Analytic. She hopes to attend Medical School fore post-grad with a specialization in surgery. 

Taonanyasha enjoys reading and writing, working out, running, and volunteering. She is a part of organization at the university such as BATU (Black Action Through Unity), ASA (African Student Association), and Club Boxing.

You can connect with her at Bandat1@udayton.edu