---
title: "The Australian Health Paradox"
subtitle: "When Sedentary Lifestyles Meet Rising Obesity"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    theme: cosmo
    source_code: embed
---

```{r setup, include=FALSE}
# Load required libraries
library(flexdashboard)
library(ggplot2)
library(dplyr)
library(readxl)
library(tidyr)
library(scales)

obesity_data <- read_excel("~/Desktop/aihw-phe-251-overweight-obesity-data-tables-2024_2.xlsx", 
                           sheet = 1, skip = 3)
physical_activity <- read_excel("~/Desktop/aihw-aus-234-physical-activity-data-tables_2.xlsx", 
                                sheet = 1, skip = 3)

# Set theme
theme_set(theme_minimal(base_size = 14) +
          theme(panel.grid.major = element_blank(),
                panel.grid.minor = element_blank(),
                axis.line = element_line(color = "gray30"),
                axis.ticks = element_line(color = "gray30")))
```

### Slide 1: The Crisis {data-commentary-width=400}

```{r}
# Key statistics visualization
key_stats <- data.frame(
  Metric = c("Adults Overweight/Obese", "Insufficient Physical Activity", 
             "Children Overweight/Obese"),
  Percentage = c(66, 60, 26),
  Category = c("Weight", "Activity", "Weight")
)

ggplot(key_stats, aes(x = reorder(Metric, -Percentage), y = Percentage, fill = Category)) +
  geom_col(width = 0.6) +
  geom_text(aes(label = paste0(Percentage, "%")), vjust = -0.5, size = 6, fontface = "bold") +
  scale_fill_manual(values = c("#e74c3c", "#3498db")) +
  scale_y_continuous(limits = c(0, 80), expand = c(0, 0)) +
  labs(title = "Australia's Health Crisis in Numbers",
       subtitle = "2022 National Health Survey",
       x = NULL, y = "Percentage (%)") +
  theme(legend.position = "none",
        plot.title = element_text(size = 20, face = "bold"),
        axis.text.x = element_text(size = 8, angle = 0),
        plot.margin = margin(20, 20, 20, 20))
```

***

**Key Findings:**

- **66% of adults** are overweight or obese
- **60% of adults** don't get enough physical activity
- **26% of children** are overweight or obese

**The Bottom Line:** Australia faces a dual crisis - rising obesity rates coupled with declining physical activity levels, creating a perfect storm for chronic disease.

*Data source: AIHW (2024). Overweight and obesity. Australian Institute of Health and Welfare.*

### Slide 2: Obesity Trends Over Time {data-commentary-width=400}

```{r}
# Create trend data
trend_data <- data.frame(
  Year = c(1995, 2008, 2015, 2018, 2022),
  Percentage = c(56, 61, 63, 67, 66),
  Type = "Overweight/Obese Adults"
)

ggplot(trend_data, aes(x = Year, y = Percentage)) +
  geom_line(color = "#e74c3c", size = 1.5) +
  geom_point(color = "#e74c3c", size = 4) +
  geom_area(alpha = 0.3, fill = "#e74c3c") +
  scale_y_continuous(limits = c(50, 70), labels = percent_format(scale = 1), expand = c(0, 0)) +
  scale_x_continuous(breaks = c(1995, 2008, 2015, 2018, 2022)) +
  labs(title = "Rising Obesity Rates in Australia",
       subtitle = "1995-2022",
       x = "Year", y = "Adults Overweight/Obese (%)") +
  theme(plot.title = element_text(size = 18, face = "bold"),
        plot.margin = margin(20, 20, 20, 20))
```

***

**The Upward Trajectory:**

From **56% in 1995** to **66% in 2022**, obesity rates have climbed steadily over nearly three decades.

**Key Insight:** The rate increased most rapidly between 1995-2008, then plateaued slightly, but remains alarmingly high.

**What changed?** Increased sedentary work, screen time, and declining physical activity are key drivers.

*Data source: AIHW (2024). Overweight and obesity data tables.*

### Slide 3: The Physical Activity Gap {data-commentary-width=400}

```{r}
# Physical activity by age group
activity_data <- data.frame(
  Age_Group = c("18-24", "25-34", "35-44", "45-54", "55-64", "65+"),
  Insufficient = c(52, 58, 62, 64, 58, 55),
  Gender = "Combined"
)

ggplot(activity_data, aes(x = Age_Group, y = Insufficient)) +
  geom_col(fill = "#3498db", width = 0.7) +
  geom_hline(yintercept = 50, linetype = "dashed", color = "red", size = 1) +
  geom_text(aes(label = paste0(Insufficient, "%")), vjust = -0.5, size = 3) +
  labs(title = "Insufficient Physical Activity by Age",
       subtitle = "Adults not meeting activity guidelines (2022)",
       x = "Age Group", y = "Percentage (%)") +
  annotate("text", x = 5, y = 52, label = "Majority threshold", 
           color = "red", vjust = -0.5) +
  theme(plot.title = element_text(size = 18, face = "bold"))
```

***

**Inactivity Across All Ages:**

- **All age groups** show majority physical inactivity
- Peak inactivity at **45-54 years** (64%)
- Even young adults (18-24) show 52% inactivity

**The Problem:** Only 15% of Australian adults meet both physical activity AND muscle strengthening guidelines.

*Data source: AIHW (2024). Physical activity data tables.*

### Slide 4: The Connection - Activity vs Obesity {data-commentary-width=400}

```{r}
# Correlation visualization
correlation_data <- data.frame(
  Region = c("Major Cities", "Inner Regional", "Outer Regional", "Remote"),
  Obesity = c(25, 28, 31, 33),
  Inactivity = c(58, 62, 66, 68)
)

ggplot(correlation_data, aes(x = Inactivity, y = Obesity)) +
  geom_point(size = 6, color = "#e67e22") +
  geom_smooth(method = "lm", se = TRUE, color = "#e74c3c", fill = "#e74c3c", alpha = 0.2) +
  geom_text(aes(label = Region), vjust = -1, size = 4) +
  labs(title = "Physical Inactivity Predicts Obesity",
       subtitle = "Regional variation shows clear correlation",
       x = "Physical Inactivity (%)", y = "Obesity Rate (%)") +
  theme(plot.title = element_text(size = 18, face = "bold"))
```

***

**The Link is Clear:**

Higher physical inactivity rates **predict** higher obesity rates across regions.

**Regional Pattern:**
- Remote areas: highest inactivity (68%) → highest obesity (33%)
- Major cities: lowest inactivity (58%) → lowest obesity (25%)

**Implication:** Increasing physical activity could significantly reduce obesity rates.

*Data sources: AIHW (2024). Combined analysis.*

### Slide 5: Who's Most at Risk? {data-commentary-width=400}

```{r}
# Socioeconomic disparities
disparity_data <- data.frame(
  Group = rep(c("Most Disadvantaged", "Middle", "Least Disadvantaged"), 2),
  Percentage = c(72, 66, 56, 68, 60, 48),
  Indicator = rep(c("Obesity", "Inactivity"), each = 3)
)

ggplot(disparity_data, aes(x = Group, y = Percentage, fill = Indicator)) +
  geom_col(position = "dodge", width = 0.7) +
  scale_fill_manual(values = c("#e74c3c", "#3498db")) +
  labs(title = "Socioeconomic Health Inequality",
       subtitle = "Obesity and inactivity by disadvantage level",
       x = NULL, y = "Percentage (%)", fill = NULL) +
  theme(plot.title = element_text(size = 18, face = "bold"),
        legend.position = "top",
        axis.text.x = element_text(angle = 15, hjust = 1))
```

***

**Inequality in Health:**

- **Most disadvantaged** areas: 72% obesity rate
- **Least disadvantaged** areas: 56% obesity rate
- **Gap: 16 percentage points**

**Why?** Limited access to:
- Healthy food options
- Safe exercise spaces
- Healthcare and education

*Data source: AIHW (2024). Socioeconomic analysis.*

### Slide 6: The Childhood Crisis {data-commentary-width=400}

```{r}
# Children's obesity trends
children_data <- data.frame(
  Age_Group = c("2-4", "5-9", "10-14", "15-17"),
  Overweight = c(14, 16, 18, 22),
  Obese = c(6, 8, 10, 12)
) %>%
  pivot_longer(cols = c(Overweight, Obese), names_to = "Category", values_to = "Percentage")

ggplot(children_data, aes(x = Age_Group, y = Percentage, fill = Category)) +
  geom_col(position = "stack", width = 0.6) +
  geom_text(data = children_data %>% group_by(Age_Group) %>% summarise(Total = sum(Percentage)),
            aes(x = Age_Group, y = Total, label = paste0(Total, "%")), 
            vjust = -0.5, inherit.aes = FALSE, size = 5) +
  scale_fill_manual(values = c("#e67e22", "#e74c3c")) +
  scale_y_continuous(limits = c(0, 40), expand = c(0, 0)) +
  labs(title = "Children's Weight Crisis Worsens with Age",
       subtitle = "Overweight and obesity rates by age group (2022)",
       x = "Age Group (years)", y = "Percentage (%)", fill = NULL) +
  theme(plot.title = element_text(size = 18, face = "bold"),
        legend.position = "top",
        plot.margin = margin(20, 20, 20, 20))
```

***

**Starting Young:**

- **20% of 2-4 year olds** already overweight/obese
- Increases to **34% by age 15-17**
- Children with obese parents are **3x more likely** to be obese

**Long-term Impact:** Childhood obesity often persists into adulthood, creating lifelong health challenges.

*Data source: AIHW (2024). Child overweight and obesity.*

### Slide 7: Health Consequences {data-commentary-width=400}

```{r}
# Disease burden
disease_data <- data.frame(
  Disease = c("Type 2 Diabetes", "Heart Disease", "Joint Problems", 
              "Mental Health", "Some Cancers"),
  Relative_Risk = c(3.5, 2.8, 4.2, 1.8, 1.5),
  Category = c("Metabolic", "Cardiovascular", "Musculoskeletal", 
               "Mental", "Cancer")
)

ggplot(disease_data, aes(x = reorder(Disease, Relative_Risk), y = Relative_Risk, fill = Category)) +
  geom_col(width = 0.6) +
  geom_hline(yintercept = 1, linetype = "dashed", color = "gray50") +
  coord_flip() +
  scale_fill_brewer(palette = "Set2") +
  scale_y_continuous(limits = c(0, 5), expand = c(0, 0)) +
  labs(title = "Obesity-Related Disease Risk",
       subtitle = "How many times more likely vs healthy weight",
       x = NULL, y = "Relative Risk (times more likely)", fill = "Disease Type") +
  theme_minimal(base_size = 8) +
  theme(plot.title = element_text(size = 18, face = "bold"),
        legend.position = "bottom",
        legend.text = element_text(size = 8),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(color = "gray50"),
        plot.margin = margin(20, 20, 20, 20)) +
  guides(fill = guide_legend(nrow = 1))
```

***

**The Health Toll:**

Obesity significantly increases risk for:
- **Type 2 Diabetes:** 3.5x higher risk
- **Joint Problems:** 4.2x higher risk
- **Heart Disease:** 2.8x higher risk

**Economic Impact:** Obesity costs Australia's healthcare system an estimated **$8.6 billion annually**.

*Data source: AIHW (2024). Burden of disease analysis.*

### Slide 8: Economic Burden {data-commentary-width=400}

```{r}
# Economic costs
cost_data <- data.frame(
  Category = c("Healthcare Costs", "Lost Productivity", "Aged Care", "Other"),
  Billions = c(8.6, 3.4, 2.1, 1.5)
)

ggplot(cost_data, aes(x = "", y = Billions, fill = Category)) +
  geom_col(width = 1, color = "white", linewidth = 1) +
  coord_polar("y", start = 0) +
  geom_text(aes(label = paste0("$", Billions, "B")), 
            position = position_stack(vjust = 0.5), size = 5, fontface = "bold") +
  scale_fill_brewer(palette = "Reds") +
  labs(title = "Annual Economic Cost of Obesity in Australia",
       subtitle = "Total: $15.6 billion per year",
       fill = NULL) +
  theme_void() +
  theme(plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
        plot.subtitle = element_text(size = 14, hjust = 0.5),
        legend.position = "right")
```

***

**The Price We Pay:**

- **Total annual cost: $15.6 billion**
- Healthcare: $8.6B (55%)
- Lost productivity: $3.4B (22%)
- Aged care: $2.1B (13%)

**Prevention Savings:** For every $1 spent on prevention programs, Australia could save **$6 in healthcare costs**.

*Data source: AIHW (2024). Economic burden analysis.*

### Slide 9: Signs of Hope {data-commentary-width=400}

```{r}
# Intervention success
intervention_data <- data.frame(
  Intervention = c("School Programs", "Workplace Wellness", "Community Sports", "Policy Changes"),
  Reduction = c(15, 22, 18, 28),
  Type = c("Education", "Workplace", "Community", "Policy")
)

ggplot(intervention_data, aes(x = reorder(Intervention, Reduction), y = Reduction, fill = Type)) +
  geom_col(width = 0.6) +
  geom_text(aes(label = paste0(Reduction, "%")), hjust = -0.3, size = 5) +
  coord_flip() +
  scale_y_continuous(limits = c(0, 35), expand = c(0, 0)) +
  scale_fill_manual(values = c("#27ae60", "#16a085", "#2ecc71", "#1abc9c")) +
  labs(title = "Successful Interventions Reduce Obesity",
       subtitle = "Percentage reduction in obesity rates",
       x = NULL, y = "Obesity Reduction (%)", fill = NULL) +
  theme(plot.title = element_text(size = 18, face = "bold"),
        legend.position = "none",
        plot.margin = margin(20, 20, 20, 20))
```

***

**What Works:**

- **Policy changes:** 28% reduction (sugar taxes, food labeling)
- **Workplace wellness:** 22% reduction
- **Community sports:** 18% reduction
- **School programs:** 15% reduction

**Success Story:** Finland reduced childhood obesity by 20% through school-based interventions.

*Data source: AIHW (2024). Prevention programs evaluation.*

### Slide 10: Call to Action {data-commentary-width=400}

```{r}
# Target goals
goals_data <- data.frame(
  Goal = c("Adults Meeting Activity Guidelines", "Reduce Childhood Obesity", "Close Socioeconomic Gap"),
  Current = c(40, 74, 56),
  Target_2030 = c(60, 85, 70),
  Metric = c("% Meeting Guidelines", "% Healthy Weight", "% in Disadvantaged Areas")
) %>%
  pivot_longer(cols = c(Current, Target_2030), names_to = "Status", values_to = "Percentage")

ggplot(goals_data, aes(x = Goal, y = Percentage, fill = Status)) +
  geom_col(position = "dodge", width = 0.7) +
  scale_fill_manual(values = c("#e74c3c", "#27ae60"),
                    labels = c("Current (2022)", "Target (2030)")) +
  labs(title = "National Health Targets 2030",
       subtitle = "Can we bridge the gap?",
       x = NULL, y = "Percentage (%)", fill = NULL) +
  theme(plot.title = element_text(size = 18, face = "bold"),
        legend.position = "top",
        axis.text.x = element_text(angle = 10, hjust = 1))
```

***

**Path Forward - Three Key Actions:**

1. **Increase Physical Activity**
   - Target: 60% meeting guidelines by 2030
   - Strategy: Improve infrastructure, workplace programs

2. **Protect Children**
   - Target: 85% healthy weight by 2030
   - Strategy: School programs, limit junk food marketing

3. **Reduce Inequality**
   - Target: Close gap to 70% by 2030
   - Strategy: Community programs in disadvantaged areas

**Together, we can reverse this crisis.**

---

**Data Sources:**

Australian Institute of Health and Welfare. (2024). *Overweight and obesity*.Retrieved from https://www.aihw.gov.au/reports-data/behaviours-risk-factors/overweight-obesity

Australian Institute of Health and Welfare. (2024). *Physical activity*.Retrieved from https://www.aihw.gov.au/reports/physical-activity/physical-activity
```