Exploring the Global Cancer Burden: Trends in Cancer Mortality Across Countries

## Data Sources and Preparation

The project uses publicly available cancer mortality data from the World Health Organization accessed through Our World in Data. The datasets contain estimated cancer deaths and cancer death rates for countries and regions.


``` r
cancer_deaths_url <- "https://ourworldindata.org/grapher/deaths-from-cancer-who.csv?v=1&csvType=full&useColumnShortNames=false"

cancer_rate_url <- "https://ourworldindata.org/grapher/cancer-death-rate-who-mdb.csv?v=1&csvType=full&useColumnShortNames=false"

cancer_deaths <- read_csv(
  cancer_deaths_url,
  show_col_types = FALSE
)

cancer_rate <- read_csv(
  cancer_rate_url,
  show_col_types = FALSE
)

cancer_deaths_clean <- cancer_deaths %>%
  select(
    Entity,
    Code,
    Year,
    cancer_deaths = last_col()
  )

cancer_rate_clean <- cancer_rate %>%
  select(
    Entity,
    Code,
    Year,
    cancer_death_rate = last_col()
  )

fig1_data <- cancer_deaths_clean %>%
  filter(
    Entity %in% c(
      "India",
      "China",
      "United States",
      "Japan",
      "Germany"
    ),
    Year >= 2000,
    Year <= 2021
  )
fig2_data <- cancer_deaths_clean %>%
  filter(
    Year == 2021,
    !is.na(Code),
    !str_starts(Code, "OWID_")
  ) %>%
  arrange(desc(cancer_deaths)) %>%
  slice_head(n = 10)
  fig3_data <- cancer_rate_clean %>%
  filter(
    Year == 2021,
    !is.na(Code),
    Code != "",
    !str_starts(Code, "OWID_")
  )
  fig4_data <- cancer_deaths_clean %>%
  filter(
    Year == 2021,
    !is.na(Code),
    !str_starts(Code, "OWID_")
  ) %>%
  inner_join(
    cancer_rate_clean %>%
      filter(
        Year == 2021,
        !is.na(Code),
        !str_starts(Code, "OWID_")
      ),
    by = c("Entity", "Code", "Year")
  ) %>%
  filter(
    !is.na(cancer_deaths),
    !is.na(cancer_death_rate)
  )

Introduction

This project explores global cancer mortality patterns, trends, and differences across countries using publicly available World Health Organization data accessed through Our World in Data.

Project Goal

The goal of this project is to examine patterns in cancer mortality across countries and over time using a series of data-driven visualizations.

Figure 1: Cancer Deaths Over Time

The first visualization examines changes in the estimated number of cancer deaths from 2000 to 2021 in five selected countries: China, India, the United States, Japan, and Germany.

Interpretation

The line chart shows how estimated cancer deaths changed over time in the five selected countries. China has the largest number of estimated cancer deaths throughout the period, while the other countries show lower absolute numbers. India shows a substantial increase toward 2021. These differences represent the number of deaths rather than a population-adjusted mortality rate, so the figure primarily describes the overall mortality burden in terms of absolute deaths.

Figure 2: Top 10 Countries by Cancer Deaths

The second visualization compares the ten countries with the highest estimated number of cancer deaths in 2021. A horizontal bar chart is used because it makes the ranking and differences between countries easier to compare.

Interpretation

The bar chart shows the ten countries with the largest estimated numbers of cancer deaths in 2021. The ranking reflects the absolute number of deaths, so countries with larger populations may have larger totals even when their population-adjusted cancer mortality rates differ.

Figure 3: Global Cancer Death Rate Map

The third visualization maps age-standardized cancer death rates across countries in 2021. Unlike the first two figures, which show absolute numbers of deaths, this map uses a population age-standardized rate per 100,000 people to facilitate comparisons across countries with different population age structures.

Interpretation

The map shows geographic variation in age-standardized cancer death rates across countries in 2021. Because the measure is age-standardized, it is more suitable for comparing mortality rates across populations with different age structures than a simple count of cancer deaths. Countries with higher rates appear differently from countries with lower rates, allowing broad geographic patterns to be identified.

Figure 4: Cancer Deaths and Age-Standardized Death Rates

The fourth visualization compares the absolute number of estimated cancer deaths with the age-standardized cancer death rate across countries in 2021. Each point represents a country.

Interpretation

The scatter plot illustrates how absolute cancer deaths and age-standardized cancer death rates vary across countries. Countries with larger numbers of cancer deaths do not necessarily have the highest age-standardized mortality rates. This distinction demonstrates why both absolute burden and population-adjusted measures are useful when examining global cancer mortality.

Figure 5: Distribution of Cancer Death Rates

The fifth visualization examines the distribution of age-standardized cancer death rates across countries in 2021. A histogram is used to show how frequently different ranges of cancer death rates occur across countries.

Interpretation

The histogram shows the distribution of age-standardized cancer death rates across countries in 2021. The distribution demonstrates that cancer mortality rates vary substantially between countries, with countries occurring across a broad range of age-standardized death rates. This provides a broader view of global variation than examining individual countries alone.

Figure 6: Change in Cancer Death Rates

The sixth visualization compares age-standardized cancer death rates between 2000 and 2021 in five selected countries: Australia, Austria, Belgium, Brazil, and Canada. A dumbbell chart is used to highlight the change between the two years for each country.

Interpretation

The dumbbell chart compares age-standardized cancer death rates in 2000 and 2021 for five selected countries. The distance between the two points represents the change in the rate over the period. The visualization makes it easier to compare changes across countries while accounting for differences in population age structure through age-standardization.

Figure 7: Cancer Death Rate Trends by Country

The seventh visualization examines changes in age-standardized cancer death rates from 2000 to 2021 in five selected countries: Australia, Austria, Belgium, Brazil, and Canada. A faceted line chart is used to show the individual trend for each country and make changes over time easier to compare.

Interpretation

The faceted line chart shows how age-standardized cancer death rates changed over time in the five selected countries. Each panel represents one country, allowing the trend within each country to be examined separately. The visualization highlights differences in the level and direction of cancer death rates over the period while reducing the overlap that can occur when multiple countries are shown on a single line chart.

Figure 8: Cancer Death Rates Across Selected Countries

The eighth visualization compares age-standardized cancer death rates across selected countries in 2021. A ranked dot plot is used to make differences in cancer death rates easy to compare while keeping the country names clearly visible.

Interpretation

The ranked dot plot compares age-standardized cancer death rates across ten selected countries in 2021. Ranking the countries by their death rate makes differences between countries easier to identify than when the countries are presented in an unordered list. Because the measure is age-standardized, the comparison focuses on differences in cancer mortality rates while accounting for differences in population age structure.