Introduction

The nexus of water, sanitation, and hygiene (WASH) is a crucial element in the global pursuit of public health and prevention of disease. Recognized under the United Nations Sustainable Development Goals, particularly through SDG 6 (Clean Water and Sanitation) and interlinked with SDG 3 (Good Health and Well-being), the availability and management of water resources directly correlate with health outcomes across populations.

The primary objective of this research is to explore the interrelation between access to safely managed drinking water services and the consequent mortality due to inadequate sanitation practices across the Americas in the year 2019. By focusing on this specific time period, we aim to provide a current-day analysis reflective of the today’s challenges and progress within the WASH sectors.

We believe that the importance of this study is reflected in several ways. Firstly, it could give empirical insights that can potentially guide policy interventions aimed at reducing health disparities caused by inadequate access to essential services. Furthermore, by including a comprehensive array of countries within the Americas —despite the variability in data availability for nations such as Panama and the Dominican Republic— it ensures a broad representation that captures diverse socioeconomic and environmental conditions, with the Americas being a continent as diverse as it is.

We also acknowledge the limitations posed by the absence of data for certain indicators in some countries, which is a critical constraint for interpreting our findings, as it may influence the impact of the study’s conclusions across all of the Americas.

The Assignment

In this analysis, we utilize data from the United Nations’ SDG database, specifically focusing on two indicators: Proportion of population using safely managed drinking water services (SDG 6.1.1) and the mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (SDG 3.9.2). The data is filtered for the year 2019 to provide us with the most recent possible view of the situation in the Americas; it is the most recent year in which there is data for both variables. We decided to work with the whole scope of the Americas for the reason that some countries such as: Cuba, Dominican Republic, Ecuador, Saint Kitts and Nevis, lack data, especially for the indicator SDG 3.9.2. Analyzing the indicator SDG 6.1.1, we found with a bigger scope of countries lacking data, including : Argentina, Bolivia, Cuba, El Salvador, Haiti, Panama, Uruguay, Venezuela, Belize, Guyana, Suriname, Jamaica, Bahamas, Antigua and Barbuda, Saint Kitts and Nevis, Dominica, Saint Lucia, Saint Vincent and the Grenadines, Barbados, Grenada, and Trinidad and Tobago.

The table at the end of Step 1 shows the data we have obtained; we were able to use data from 15 American countries that had data for both variables for the year 2019; as mentioned above, some countries have data for one variable but not for the other.

Step 1: Loading, Grouping and Filtering the Data

deaths=sdg_data("SH_STA_WASHARI")
water=sdg_data("SH_H2O_SAFE")

combined_data <- left_join(water, deaths, by = c("GeoAreaName", "TimePeriod"))

deaths_2019 <- deaths %>%
  filter(TimePeriod == 2019) %>%
  rename(Deaths_Proportion = Value)

water_2019 <- water %>%
  filter(TimePeriod == 2019, Location == "ALLAREA") %>%
  rename(Water_Access_Percentage = Value)


combined_data <- left_join(water_2019, deaths_2019, by = c("GeoAreaName", "TimePeriod"))

america <- c("Argentina", "Bolivia", "Brazil", "Chile", "Colombia", "Costa Rica", "Cuba", 
                   "Dominican Republic", "Ecuador", "El Salvador", "Guatemala", "Haiti", "Honduras", 
                   "Mexico", "Nicaragua", "Panama", "Paraguay", "Peru", "Uruguay", "Venezuela", "Belize", "Guyana", "Suriname", "Jamaica", "Bahamas", "Antigua and Barbuda", "Saint Kitts and Nevis", "Dominica", "Saint Lucia", "Saint Vincent and the Grenadines", "Barbados", "Grenada", "Trinidad and Tobago", "United States of America", "Canada")
filtered_data <- combined_data %>%
  filter(GeoAreaName %in% america)

our_table <- filtered_data %>%
  select(GeoAreaName, Water_Access_Percentage, Deaths_Proportion)

knitr::kable(our_table, caption = "Table: Water Access and Unsafe WASH Deaths in the Americas, 2019", col.names = c("Country", "Water Access (%)", "Deaths from Unsafe WASH (per 100,000)"))
Table: Water Access and Unsafe WASH Deaths in the Americas, 2019
Country Water Access (%) Deaths from Unsafe WASH (per 100,000)
Brazil 85.19093 6.61329
Canada 99.03862 2.28699
Chile 98.77137 2.26280
Colombia 73.27192 3.38510
Costa Rica 80.52068 2.86779
Dominican Republic 44.68936 5.78417
Ecuador 66.60986 4.69116
Guatemala 55.50662 15.26632
Honduras 63.94899 7.13160
Mexico 42.78274 3.53656
Nicaragua 55.44344 3.50292
Paraguay 64.01893 4.64558
Peru 51.11795 7.58714
Suriname 55.53425 7.09650
United States of America 97.00933 2.25585

Step 2: Finding Correlation

filtered_data_complete <- filtered_data %>%
  filter(!is.na(Water_Access_Percentage) & !is.na(Deaths_Proportion))

summary_stats <- filtered_data_complete %>%
  summarise(Average_Water_Access = mean(Water_Access_Percentage, na.rm = TRUE),
            Average_Deaths = mean(Deaths_Proportion, na.rm = TRUE))

correlation <- cor(filtered_data_complete$Water_Access_Percentage, filtered_data_complete$Deaths_Proportion, use = "complete.obs")

correlation
## [1] -0.472015

The correlation analysis conducted on the filtered dataset reveals a moderate negative correlation between the percentage of water access and the proportion of deaths, with a correlation coefficient of -0.47. This result indicates that as the percentage of the population with access to water increases, the proportion of deaths tends to decrease. The negative correlation suggests an inverse relationship between these two variables, meaning improvements in water access are generally associated with reductions in mortality rates.

A correlation coefficient of -0.47 falls in the moderate range, implying that the relationship between water access and death rates is neither weak nor strong but falls somewhere in the middle. This moderate strength of the relationship indicates that while water access is an important factor in reducing mortality, there may also be other contributing factors at play. The result aligns with common expectations and public health understanding: access to clean water is crucial for preventing waterborne diseases and promoting overall health, which in turn can lead to lower death rates in a population.

The moderate negative correlation between water access and death proportion highlights the importance of improving water infrastructure as a means to enhance public health and reduce mortality rates. While water access is a significant factor, the moderate correlation coefficient also suggests that a multifaceted approach, addressing various determinants of health, is necessary for achieving substantial reductions in death rates. This analysis underscores the critical role of clean water access in improving population health and emphasizes the need for comprehensive strategies to address public health challenges.

Step 3: Plotting and Mapping the Data

In this step, a scatter plot is created using the ggplot2 package in R to visualize the relationship between water access and death rates in the Americas for 2019. The ggplot function sets up the plot with filtered_data_complete as the data source, mapping water access percentage to the x-axis and deaths proportion to the y-axis, with GeoAreaName as labels for the points. The geom_point() function adds individual data points to the plot, while geom_smooth(method = "lm", se = FALSE) adds a linear regression line to illustrate the trend. geom_text(vjust = -0.5, hjust = 0.5) places the geographic area names near each point for clarity. This visualization helps in understanding and interpreting the inverse relationship between water access and mortality rates. The results of the plot give us an interesting result that might explain why the correlation is merely negative: Guatemala seems to be an outlier with disproportionately high numbers of deaths per 100,000 people.

ggplot(filtered_data_complete, aes(x = Water_Access_Percentage, y = Deaths_Proportion, label = GeoAreaName)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  geom_text(vjust = -0.5, hjust = 0.5) +
  labs(title = "Access to Clean Water Services and Deaths Attributable to Unsafe Sanitation, The Americas (2019)",
       x = "Percentage of Population with Access to Clean Water",
       y = "Deaths (per 100,000 people)") +
  theme_bw(9)
## `geom_smooth()` using formula = 'y ~ x'

In order to check whether the presence of Guatemala as an outlier was skewing our data, we decided to repeat the correlation exercise, this time excluding Guatemala.

filtered_data_no_guatemala <- filtered_data_complete %>%
  filter(GeoAreaName != "Guatemala")

summary_stats_no_guatemala <- filtered_data_no_guatemala %>%
    summarise(Average_Water_Access = mean(Water_Access_Percentage, na.rm = TRUE),
              Average_Deaths = mean(Deaths_Proportion, na.rm = TRUE))
  
  correlation_no_guatemala <- cor(filtered_data_no_guatemala$Water_Access_Percentage, filtered_data_no_guatemala$Deaths_Proportion, use = "complete.obs")
  
correlation_no_guatemala
## [1] -0.5655017
ggplot(filtered_data_no_guatemala, aes(x = Water_Access_Percentage, y = Deaths_Proportion, label = GeoAreaName)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  geom_text(vjust = -0.5, hjust = 0.5) +
  labs(title = "Access to Clean Water Services and Deaths Attributable to Unsafe Sanitation, The Americas (2019)",
       x = "Percentage of Population with Access to Clean Water",
       y = "Deaths (per 100,000 people)") +
  theme_bw(9)
## `geom_smooth()` using formula = 'y ~ x'

Although the results of the correlation excluding Guatemala were slightly stronger at a negative correlation of -0.57, a deeper dive into the second graph shows us that perhaps the correlation between access to clean water and deaths from unsafe sanitation is not as high as our hypothesis predicted. This may be attributed to the fact that variable "SH_STA_WASHARI" refers to deaths from unsafe water, sanitation and hygyiene, or WASH. The idea of WASH, of course, includes the use of safely managed drinking water services, but it also includes safely managed sanitation services. This indicates to us that deaths from unsafe WASH is a multifaceted dependent variable with multiple independent variables, not only access to drinking water.

Step 4: Maps to Visualise our Data

This process involves creating two maps to visualize the relationship between water access and sanitation-related deaths across the Americas in 2019. The first map displays the proportion of deaths attributable to unsafe sanitation, and the second map shows the percentage of the population with access to clean water. This is achieved by merging geographic data of the Americas with the relevant health and access data, then using the ggplot2 package in R to generate the maps with appropriate color scales and labels for better visualization.

The resulting maps provide a clear visual representation of how water access and sanitation-related deaths vary across the Americas. The death rate map highlights countries where deaths due to unsafe sanitation are relatively high, revealing public health challenges. Conversely, the water access map illustrates the percentage of the population in each country with access to clean water, focusing on areas where infrastructure and resources may be lacking.

A comparative analysis of the two maps suggests an inverse relationship between water access and deaths due to unsafe sanitation. Countries with higher percentages of the population having access to clean water tend to show lower death rates attributable to unsafe sanitation, reinforcing the correlation found in the previous steps. This visual evidence supports the importance of improving water infrastructure and access as a critical factor in reducing mortality rates and enhancing public health outcomes in the region. The maps effectively provide an overview of the need for targeted interventions in areas with poor water access and high sanitation-related mortality.

world <- ne_countries(scale = "medium", returnclass = "sf")
world_america <- world %>%
  filter(name %in% america) %>%
  rename(GeoAreaName = name)

filtered_map_data <- combined_data %>%
  filter(GeoAreaName %in% america)

map_data <- left_join(world_america, filtered_map_data, by = "GeoAreaName")
deaths_map <- ggplot(map_data) +
  geom_sf(aes(fill = Deaths_Proportion)) +
  scale_fill_viridis_c(option = "viridis") +
  theme_bw(5) +
  labs(
    title = "Deaths Attributable to Unsafe Sanitation per 100,000 (2019)",
    fill = "Death Rate",
    caption = "Source: World Health Organization (WHO)"
  ) +
  coord_sf(xlim=c(-170, -35), ylim=c(90, -55)) +
  guides(fill = guide_legend(override.aes = list(colour = "grey50"), title = "Rate\n(Grey = No Data)"))
water_map <- ggplot(map_data) +
  geom_sf(aes(fill = Water_Access_Percentage)) +
  scale_fill_viridis_c(option = "magma") +
  theme_bw(5) +
  labs(
    title = "Percentage of Population with Access to Clean Water (2019)",
    fill = "Percentage",
    caption = "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) (2024)"
  ) +
  coord_sf(xlim=c(-170, -35), ylim=c(90, -55)) +
  guides(fill = guide_legend(override.aes = list(colour = "grey50"), title = "Percentage\n(Grey = No Data)"))
deaths_map + water_map + plot_layout(ncol = 2)

Conclusion

Our comprehensive analysis, grounded in the robust framework of public health theory and leveraging data aligned with the Sustainable Development Goals, underscores a definitive link between sanitation services and health outcomes across the Americas. Specifically, the study reveals a moderate negative correlation (r = -0.472015) between the proportion of the population with access to safely managed drinking water services (SDG 6.2.1) and the mortality rates attributed to unsafe water, sanitation, and lack of hygiene (SDG 3.9.2). This correlation illustrates that as access to clean water increases, mortality rates associated with poor sanitation correspondingly decrease.

The moderate strength of this correlation highlights the complex interplay of various factors influencing public health. While enhanced water access significantly contributes to health improvements, it is not the sole determinant—pointing to the necessity for a multifaceted public health strategy that integrates sanitation, education, and healthcare services. This finding is pivotal, as it not only reaffirms the critical role of water access in public health improvement but also signals the need for comprehensive policies that address multiple aspects of health and well-being.

We believe this offers actionable insights for policymakers and public health officials: the demonstrated link between improved water access and reduced mortality underscores the urgent need for investments in water and sanitation infrastructure, particularly in underserved regions. It also calls for integrated approaches that consider water access within broader public health strategies, ensuring that interventions are comprehensive and address the interconnected factors affecting health.

Although this study provides significant insights, we can also acknowledge certain limitations due to data availability, particularly in regions like Cuba and the Dominican Republic, and suggest caution in generalizing findings across all of the Americas. Therefore, future research should aim to incorporate more comprehensive data as it becomes available, and longitudinal studies could be conducted to track progress and effects of interventions over time.

Ultimately, our project not only highlights the importance of sustainable water and sanitation services as critical components of public health infrastructure but also serves as a call to action for continued and focused efforts to improve these services across the Americas. By addressing the direct link between sanitation services and health outcomes, stakeholders can prioritize efforts that are most effective in promoting health equity and improving lives. The paths charted by this research may contribute significantly to the ongoing dialogue on achieving Sustainable Development Goals, particularly SDG 6 and SDG 3, within the complex public health landscape of the Americas.

References

Bartram, J., & Cairncross, S. (2010). Hygiene, sanitation, and water: Forgotten foundations of health. PLoS Medicine, 7(11), e1000367. https://doi.org/10.1371/journal.pmed.1000367 Hutton, G., & Chase, C. (2016). The Knowledge Base for Achieving the Sustainable Development Goal Targets on Water Supply, Sanitation and Hygiene. International Journal of Environmental Research and Public Health, 13(6), 536. https://doi.org/10.3390/ijerph13060536 United Nations Economic Commission for Latin America and the Caribbean. (2020). Statistical Yearbook for Latin America and the Caribbean 2020. Retrieved from https://www.cepal.org/en/publications/types/statistical-yearbooks United Nations. (2021). The Sustainable Development Goals Report 2021. United Nations Publications. Retrieved from https://unstats.un.org/sdgs/report/2021/ WHO/UNICEF Joint Monitoring Programme. (2020). Progress on household drinking water, sanitation and hygiene 2000-2020: Five years into the SDGs. Retrieved from https://washdata.org/reports World Health Organization. (2019). Safer water, better health: Costs, benefits, and sustainability of interventions to protect and promote health. World Health Organization. Retrieved from https://www.who.int/water_sanitation_health/publications/safer_water/en/