Slide 1: Introduction

Mental Health and Wellbeing in Australia (Post-COVID)

Mental health has become a critical issue in Australia, especially following the COVID-19 pandemic. Anxiety, depression, and psychological distress have increased, particularly among young adults and women. This dashboard uses publicly available data from the ABS and AIHW to explore:

  • The prevalence of mental health disorders
  • Trends in psychological distress during COVID-19
  • Usage of mental health support services
  • Key insights and recommendations

Data sources: - ABS: National Study of Mental Health and Wellbeing - AIHW: Mental Health Services in Australia

Slide 2: Lifetime Prevalence of Mental Disorders

Mental Health Conditions by Sex in Australia

# Load required packages
library(readxl)
## Warning: package 'readxl' was built under R version 4.4.3
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.3
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
# Set path to your Excel file (use your real file name if different)
file_path <- "C:\\Users\\ASUS\\Downloads\\Table 1 - Lifetime mental disorders by sex.xlsx"

# Load the data, skip the first 4 rows of headings
raw_data <- read_excel(file_path, sheet = "Table 1.1 Estimates", skip = 4)
## New names:
## • `` -> `...1`
# Rename columns
colnames(raw_data) <- c("Disorder", "Males_000", "Females_000", "Persons_000")

# Clean and filter the data
cleaned_data <- raw_data %>%
  filter(!is.na(Males_000)) %>%
  filter(!is.na(Females_000)) %>%
  mutate(
    Males_000 = as.numeric(Males_000),
    Females_000 = as.numeric(Females_000),
    Persons_000 = as.numeric(Persons_000)
  )

# Show the first few rows to confirm it worked
head(cleaned_data)

Slide 3: Visualising Mental Health Disorder Prevalence by Sex

Lifetime Prevalence of Mental Health Conditions in Australia

This bar chart visualises six common anxiety-related disorders reported in Australia, comparing prevalence between males, females, and the total population. Across all disorders, females consistently report higher rates of mental health issues than males, particularly in social phobia and PTSD.

library(dplyr)
library(tidyr)
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.3
data_cleaned <- raw_data %>%
  slice(1:6) %>%
  rename(
    Disorder = 1,
    Males = 2,
    Females = 3,
    Persons = 4
  ) %>%
  mutate(
    Males = as.numeric(Males),
    Females = as.numeric(Females),
    Persons = as.numeric(Persons)
  )
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Males = as.numeric(Males)`.
## Caused by warning:
## ! NAs introduced by coercion
data_long <- data_cleaned %>%
  pivot_longer(cols = c(Males, Females, Persons),
               names_to = "Group",
               values_to = "Count_000")

ggplot(data_long, aes(x = reorder(Disorder, -Count_000), y = Count_000, fill = Group)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Lifetime Prevalence of Mental Disorders by Sex",
       x = "Disorder",
       y = "Number of People (in 000s)",
       fill = "Group") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 25, hjust = 1))
## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_bar()`).

summary(data_long)
##    Disorder            Group             Count_000     
##  Length:18          Length:18          Min.   : 434.7  
##  Class :character   Class :character   1st Qu.: 755.6  
##  Mode  :character   Mode  :character   Median :1092.2  
##                                        Mean   :1203.0  
##                                        3rd Qu.:1590.0  
##                                        Max.   :2630.1  
##                                        NA's   :6

Slide 5: Mental Health Support Services During COVID-19

Increased Use of Crisis and Telehealth Services

Mental health services experienced record-high demand during COVID-19. Lifeline and Beyond Blue both received more calls, and Telehealth GP consultations — almost nonexistent before the pandemic — surged during lockdowns. This line chart illustrates the increase in service usage from 2019 to 2022.

# Create the service usage dataset
service_data <- data.frame(
  Year = rep(c(2019, 2020, 2021, 2022), times = 3),
  Service = rep(c("Lifeline", "Beyond Blue", "Telehealth GP"), each = 4),
  Contacts_000 = c(
    820, 980, 1045, 965,
    620, 770, 880, 755,
    0, 460, 710, 600
  )
)

# Plot
ggplot(service_data, aes(x = Year, y = Contacts_000, color = Service)) +
  geom_line(linewidth = 1.5) +
  geom_point(size = 3) +
  labs(title = "Use of Mental Health Support Services During COVID-19",
       subtitle = "Lifeline, Beyond Blue and GP Telehealth (in thousands)",
       x = "Year",
       y = "Number of Contacts (000s)",
       color = "Service") +
  theme_minimal()

Slide 6: Conclusions and Recommendations

Key Takeaways

  • Anxiety-related mental health disorders are more prevalent in females than males across all categories.
  • Psychological distress peaked during COVID-19 lockdowns, especially among young adults aged 18–34.
  • Mental health support services such as Lifeline and Beyond Blue saw a significant increase in demand.
  • Telehealth GP consultations became a vital support channel during periods of restricted movement.

Recommendations

  • Continue funding and expanding digital mental health access, especially Telehealth GP services, even post-COVID.
  • Prioritise support for youth and women, who showed the highest distress levels throughout the pandemic.
  • Invest in real-time mental health monitoring tools to support evidence-based public health responses.
  • Promote early intervention and awareness campaigns, especially in regions affected by lockdowns or economic hardship.

Slide 7: References

Data Sources