CSO data on suicide incidence in Ireland

Suicide deaths 2021-2024

CSO data can be downloaded from the CSO webpage or accessed directly in R Studio by installing the csodata package and then using the cso_get_date()function. Table 1 provides a breakdown of suicides in Ireland between 2021 and 2024 by age category and gender. Figure 1 shows the change in incidence by quarter for males and females.

Figure 2 provides a heat-map of incidence by quarter for each age category and Figure 3 provides a bar-chart, stacked by gender over each quarter and faceted by age category.

Table

tbl1 <- cso_get_data("VSD45") %>% 
  filter(Cause.of.Death == "Suicide and intentional self harm (X60-X84)")

prepared_data <- tbl1 %>%
  filter(Age.Group != "All ages") %>% # Exclude "All ages"
  select(Age.Group, Sex, `2022Q1`:`2024Q2`) %>%
  pivot_longer(cols = `2022Q1`:`2024Q2`, names_to = "Quarter", values_to = "Deaths") %>%
  group_by(Age.Group, Sex) %>%
  summarise(Total_Deaths = sum(Deaths, na.rm = TRUE), .groups = "drop") %>% # Aggregate deaths
  pivot_wider(names_from = Sex, values_from = Total_Deaths, values_fill = 0) %>% # Reshape data
  arrange(Age.Group) %>% 
  select(Age.Group, Male, Female, "Both sexes")


prepared_data %>%
  mutate(`Both sexes (Value)` = `Both sexes`) %>% # Create a separate column for numeric values
  gt(rowname_col = "Age.Group") %>% 
  fmt_number(
    columns = c(Male, Female, `Both sexes (Value)`), 
    decimals = 0
  ) %>%
  gt_theme_pff() %>% 
  gt_plt_bar(
    column = `Both sexes`, # Add the bar only to the "Plot" column
    color = "#70d2cd"
  ) %>%
  cols_move(
    columns = `Both sexes (Value)`, # Move the numeric value column before the bar column
    after = Female
  ) %>%
  cols_label(
    Male = "Males", 
    Female = "Females",
    `Both sexes (Value)` = "Both Sexes", 
    `Both sexes` = "Plot"
  ) %>%
  tab_header(
    title = "Suicide Deaths by Age Group and Sex", # Add a centered title
    subtitle = "Data from 2022Q1 to 2024Q2"
  ) %>%
  tab_style(
    style = cell_text(align = "center"), # Center the column values
    locations = cells_body(columns = everything())
  ) %>%
  tab_style(
    style = cell_text(align = "center"), # Center the title
    locations = cells_title(groups = "title")
  ) %>%
  tab_style(
    style = cell_text(align = "center"), # Center the subtitle
    locations = cells_title(groups = "subtitle")
  )

Suicide Deaths by Age Group and Sex
Data from 2022Q1 to 2024Q2
	Males	Females	Both Sexes	Plot
Under 1 year	0	0	0
1 - 4 years	0	0	0
5 - 14 years	3	2	5
15 - 24 years	87	18	105
25 - 34 years	121	32	153
35 - 44 years	133	36	169
45 - 54 years	127	25	152
55 - 64 years	105	32	137
65 - 74 years	66	15	81
75 - 84 years	23	7	30
85 years and over	4	1	5

Table 1: Suicide by age and gender

Time series

tbl1_long <- tbl1 %>%
  pivot_longer(
    cols = starts_with("202"), # Select columns with quarterly data
    names_to = "Quarter",
    values_to = "Deaths"
  ) %>%
  filter(!is.na(Deaths) & 
           Sex != "Both sexes") %>% # Remove rows with missing values and exclude 'Both sexes'
  group_by(Sex, Quarter) %>%
  summarise(Cumulative_Deaths = sum(Deaths), .groups = "drop") # Calculate cumulative deaths for each quarter by sex

# Plot the data
line_graph <- tbl1_long %>%
  ggplot(aes(x = Quarter, y = Cumulative_Deaths, color = Sex, group = Sex)) +
  geom_line(size = 1.2) +
  geom_point(size = 2) +
  scale_x_discrete(guide = guide_axis(angle = 45)) + # Tilt x-axis labels for readability
  scale_color_manual(values = c("Male" = "lightblue", "Female" = "lightpink")) + # Set line colors
  labs(
    title = "Suicide Deaths in Ireland",
    subtitle = "2021 to 2024 by quauter",
    x = "",
    y = "",
    color = ""
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16,
                              color = "#1a5276",
                              hjust = 0.5),
    plot.subtitle = element_text(
      color = "#1a5276",
      hjust = 0.5
    ),
    axis.text.x = element_text(size = 10),
    axis.title = element_text(size = 12,
                              color = "#1a5276"),
    legend.position = c(0.85, 0.95),
    legend.background = element_rect(fill= "white", 
                                     color = "white")
  )

# Display the plot
print(line_graph)

Figure 1: Suicide incidence rates

Heat map

# Assuming your filtered data is in a data frame called `tbl1`
# Reshape the data for plotting
heatmap_data <- tbl1 %>%
  pivot_longer(
    cols = starts_with("202"), # Select columns with quarterly data
    names_to = "Quarter",
    values_to = "Deaths"
  ) %>%
  filter(!is.na(Deaths) & Sex != "Both sexes" & Age.Group != "All ages") %>% # Remove rows with missing values, exclude 'Both sexes' and 'All ages'
  group_by(Age.Group, Quarter) %>%
  summarise(Total_Deaths = sum(Deaths), .groups = "drop") # Calculate total deaths for each age group by quarter

# Plot the heat map
heat_map <- ggplot(heatmap_data, aes(x = Quarter, y = Age.Group, fill = Total_Deaths)) +
  geom_tile() +
  scale_fill_gradient(low = "white", high = "#d63f38") + # Set color gradient from white to red
  labs(
    title = "Suicide Deaths by Age Group",
    subtitle = "2022 to 2024 by quauter",
    x = "",
    y = "",
    fill = "Deaths"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, color = "#1a5276", hjust = 0.5),
    axis.text.x = element_text(size = 10, angle = 45), # Tilt x-axis labels for readability
    axis.title = element_text(size = 12),
    panel.grid = element_blank(),
    plot.subtitle = element_text(
      color = "#1a5276",
      hjust = 0.5)
  )

# Display the plot
print(heat_map)

Figure 2: Heatmap of deaths by age and date

Facet

# Assuming your filtered data is in a data frame called `tbl1`
# Reshape the data for plotting
bar_data <- tbl1 %>%
  pivot_longer(
    cols = starts_with("202"), # Select columns with quarterly data
    names_to = "Quarter",
    values_to = "Deaths"
  ) %>%
  filter(!is.na(Deaths) & Sex != "Both sexes" & Age.Group != "All ages") %>% # Remove rows with missing values, exclude 'Both sexes' and 'All ages'
  group_by(Sex, Quarter, Age.Group) %>%
  summarise(Total_Deaths = sum(Deaths), .groups = "drop") %>% # Calculate total deaths for each sex, quarter, and age group
  mutate(Sex = factor(Sex, levels = c("Female", "Male"))) # Ensure females are on top

# Create a stacked bar chart for each quarter with segments colored by sex and facets by age group
stacked_bar_chart <- ggplot(bar_data, aes(x = Quarter, y = Total_Deaths, fill = Sex)) +
  geom_bar(stat = "identity", position = "stack", color = "white") +
  scale_fill_manual(values = c("Male" = "lightblue", "Female" = "lightpink")) + # Set colors for male and female
  facet_wrap(~ Age.Group, scales = "fixed") + # Create facets for each age group with the same y-axis scale
  labs(
    title = "Suicide Deaths by Sex, Quarter, and Age Group",
    x = "",
    y = "",
    fill = ""
  ) +
  theme_bw() +
  theme(
    plot.title = element_text(size = 16, color = "#1a5276", hjust = 0.5),
    axis.text.x = element_text(size = 6, angle = 45, hjust = 1), # Tilt x-axis labels for readability
    axis.title = element_text(size = 12), 
    legend.position = "top",
  )

# Display the plot
print(stacked_bar_chart)

Figure 3: Age and sex distribution by year

Comparisons

Figure 4 compares suicide incidence with death from transport accidents and drowning and highlights the fact that there are more than twice the number of suicides than transport accidents and drowning combine. This data includes figures from 2015 to 2021.

Figure 5 shows suicide incidence over time by country and allows for selected comparisons between counties. Note that these are absolute numbers and not per capita rates.

Comparison with other causes of death

# Time Series Analysis for Intentional Suicide and Transport Accidents

# Ensure all years are included during pivoting
tbl2 <- cso_get_data("VSA112")

long_data <- tbl2 %>% 
  pivot_longer(
    cols = starts_with("20"),  # Matches all column names starting with '20'
    names_to = "Year", 
    values_to = "Deaths"
  ) %>% 
  mutate(Year = as.integer(Year))

# Replace NA values in Deaths with 0
long_data <- long_data %>% 
  mutate(Deaths = ifelse(is.na(Deaths), 0, Deaths))

# Filter the dataset for intentional self-harm, transport accidents, and accidental drowning, excluding 'Ireland' from County and 'Both sexes' from Sex
selected_causes <- long_data %>% 
  filter(Cause.of.Death %in% c("Intentional self-harm (X60-X84)", "Transport accident (V01-V09)", "Accidental drowning and submersion (W65-W74)"),
         County != "Ireland",
         Sex != "Both sexes") %>%
  mutate(Cause.of.Death = recode(Cause.of.Death,
                                 "Intentional self-harm (X60-X84)" = "Intentional self-harm",
                                 "Transport accident (V01-V09)" = "Transport accident",
                                 "Accidental drowning and submersion (W65-W74)" = "Accidental drowning"))

# Create a time series for deaths by year and cause of death (both sexes combined)
time_series_plot <- selected_causes %>% 
  group_by(Year, Cause.of.Death) %>% 
  summarise(Total_Deaths = sum(Deaths, na.rm = TRUE),
            .groups = "drop") %>% 
  mutate(Cause.of.Death = factor(Cause.of.Death, levels = c(
    "Intentional self-harm",
    "Transport accident",
    "Accidental drowning"
  ))) %>% 
  ggplot(aes(x = Year, y = Total_Deaths, color = Cause.of.Death)) +
  geom_line(alpha = 0.4, size = 1.2) +
  scale_x_continuous(breaks = seq(min(selected_causes$Year), max(selected_causes$Year), by = 1)) +
  labs(
    title = "Deaths in Ireland by cause",
    subtitle = "2015-2021",
    color = ""
  ) +
  annotate("text", x = 2017, y = 350, label = "Intentional self-harm accounts for\nmore than twice the deaths of\ntransport accidents and drowning\ncombined", hjust = 0, color = "#1a5276") +
  annotate("segment", x = 2017.5, xend = 2017.5, y = 410, yend = 510, arrow = arrow(type = "open"), color = "#1a5276", size = 0.4) +
  annotate("segment", x = 2017.5, xend = 2017.5, y = 290, yend = 190, arrow = arrow(type = "open"), color = "#1a5276", size = 0.4) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, color = "#1a5276", size = 14),
    plot.subtitle = element_text(hjust = 0.5, color = "#1a5276", size = 12),
    axis.title = element_blank(), 
    legend.position.inside = c(2019.5, 300)
  )

# Note:
# Replace `tbl2` with your actual dataset name.
# Adjust column names if needed to match your data structure.

# Visualize
print(time_series_plot)

Figure 4: Comparison with traffic accidents and drownings

Comparison of suicide incidence between counties

library(plotly)



# Filter for rows where Cause.of.Death is 'Intentional self-harm (X60-X84)', excluding Ireland and Both sexes
filtered_df <- tbl2 %>%
  filter(Cause.of.Death == "Intentional self-harm (X60-X84)",
         County != "Ireland",
         Sex != "Both sexes")

# Aggregate the data by County and Year
aggregated_df <- filtered_df %>%
  pivot_longer(
    cols = `2015`:`2021`,
    names_to = "Year",
    values_to = "Incidents"
  ) %>%
  group_by(County, Year) %>%
  summarise(Incidents = sum(Incidents, na.rm = TRUE), .groups = "drop") %>%
  mutate(Year = as.integer(Year))

# Create the interactive time series plot with counties in the legend, defaulting to no counties visible
fig <- plot_ly(
  data = aggregated_df,
  x = ~Year,
  y = ~Incidents,
  color = ~County,
  type = 'scatter',
  mode = 'lines',
  colors = scales::hue_pal()(length(unique(aggregated_df$County))),  # Use a scalable color palette from scales
  visible = "legendonly"  # Default all lines to be hidden
) %>%
  layout(
    title = "Intentional Self-Harm Incidents by County (2015-2021)",
    xaxis = list(title = ""),
    yaxis = list(title = ""),
    legend = list(title = list(text = ""))  # Add legend for counties
  )

# Display the plot
fig

Figure 5: Suicide rates over time by county

Suicide prevention

Suicide prevention in Ireland has been a significant public health priority, driven by the recognition of the profound impact of suicide on individuals, families, and communities. The government’s national suicide prevention strategy, Connecting for Life, launched in 2015, provides a framework for reducing suicide and self-harm by promoting mental health and enhancing support systems. The strategy emphasizes collaboration across sectors, including healthcare, education, and community organizations. Key initiatives include improving access to mental health services, implementing early intervention programs, and reducing the stigma surrounding mental illness. A critical component has been the development of suicide surveillance systems to monitor trends and inform targeted interventions. Community-based programs, like those run by Pieta House and Samaritans, offer direct support to individuals in crisis, emphasizing the importance of accessible, non-judgmental care.

Despite these efforts, challenges remain. Suicide rates in Ireland, while declining in some demographics, continue to pose significant concerns, particularly among young men and rural populations. Factors such as economic pressures, social isolation, and limited access to mental health resources in certain areas exacerbate the issue. Additionally, barriers like stigma and underfunding of mental health services hinder progress. Addressing these challenges requires sustained commitment to enhancing mental health literacy, ensuring equitable access to care, and fostering environments where seeking help is normalized and encouraged. By building on current efforts and addressing gaps, Ireland can continue to make strides in reducing suicide and supporting those affected.