05_causes-and-risks.Rmd

# Causes and Risks

**Learning objectives:**

- Overview of the causes and risks related to health metrics calculation
- Identify the objective of the research question
- Learn how causal inference can be used to estimate the effect of an exposure on an outcome

## Introduction {-}

> 👉 “**Fear is the most pervasive emotion of modern society**—shaping our decisions, driving public policy, and influencing how we perceive risk. But how much of it is justified?”

![This book [Joanna Bourke, Fear: A Cultural History (Catapult, 2007)](https://www.amazon.com/Fear-Cultural-History-Joanna-Bourke/dp/1593761437) explores the history of fear and how it has shaped our society. Fear is a powerful emotion that can influence our decisions and perceptions of risk. It can be a useful tool for survival, but it can also lead to irrational behavior and poor decision-making.](images/ch5-fear.png){width=100% align=center}


```{r echo=FALSE, fig.align='center', fig.cap="Risk Perception", out.width = "100%"}
library(DiagrammeR)

grViz("
digraph risk_perception {
  
  # Define graph attributes
  graph [layout = dot, rankdir = TB]
  
  # Define nodes
  node [shape = box, style = filled, fillcolor = lightblue] 
  A [label = 'Historical Risks']
  B1 [label = 'Starvation']
  B2 [label = 'Infectious Diseases']
  B3 [label = 'Violent Conflicts']
  
  node [shape = box, style = filled, fillcolor = green]
  C [label = 'Modern Risks']
  D1 [label = 'Chronic Diseases (Obesity, CVD, Cancer)']
  D2 [label = 'Perceived Threats (Terrorism, Pandemics, Environment)']
  
  node [shape = ellipse, style = filled, fillcolor = lightcoral]
  E [label = 'GBD Risk Factors']
  F1 [label = 'Behavioral']
  F2 [label = 'Environmental']
  F3 [label = 'Occupational']
  F4 [label = 'Metabolic']

  # Define edges
  A -> B1
  A -> B2
  A -> B3
  A -> C
  C -> D1
  C -> D2
  C -> E
  E -> F1
  E -> F2
  E -> F3
  E -> F4
}
")
```


## 🛑 Conditions and Injuries {-}


```{r echo=FALSE, fig.align='center', fig.cap="Risk Factors", out.width = "100%"}
library(DiagrammeR)

grViz("
digraph risk_factors {
  
  # Define graph attributes
  graph [layout = dot, rankdir = LR]

  # Define nodes
  node [shape = box, style = filled, fillcolor = lightblue] 
  A [label = 'Causes and Risk Factors']
  
  node [shape = box, style = filled, fillcolor = green]
  B1 [label = 'Lifestyle Choices']
  B2 [label = 'Environmental Factors']
  B3 [label = 'Infections']
  B4 [label = 'Poverty']
  B5 [label = 'Ageing']
  B6 [label = 'Genetics']
  B7 [label = 'Injuries']

  node [shape = ellipse, style = filled, fillcolor = lightcoral]
  C1 [label = 'Heart Disease, Stroke, Cancer, Liver Disease']
  C2 [label = 'Respiratory Diseases, Organ Damage']
  C3 [label = 'Tuberculosis, HIV/AIDS, Malaria']
  C4 [label = 'Limited Healthcare, Poor Nutrition']
  C5 [label = 'Chronic Diseases, Disabilities']
  C6 [label = 'Hereditary Diseases, Certain Cancers']
  C7 [label = 'Falls, Accidents, Violence']

  # Define edges
  A -> B1
  A -> B2
  A -> B3
  A -> B4
  A -> B5
  A -> B6
  A -> B7
  
  B1 -> C1
  B2 -> C2
  B3 -> C3
  B4 -> C4
  B5 -> C5
  B6 -> C6
  B7 -> C7
}
")
```

## 💥 Risk Factors and Health Metrics {-}

> the likelihood that an individual will experience a specific health outcome, such as illness or injury, due to certain behaviours, exposures, or conditions.

### Risk-Specific Exposures

- smoking, physical inactivity, high blood pressure (hypertension), and others.

### Risk-Specific Outcomes

- **Injuries**: falls, accidents, violence

- **Diseases**: heart disease, stroke, cancer, liver disease, respiratory diseases, organ damage, tuberculosis, HIV/AIDS, malaria, chronic diseases, disabilities, hereditary diseases, certain cancers

### Risk-Specific Populations

- **Ageing**: older adults
- **Genetics**: individuals with hereditary diseases or certain cancers
- **Poverty**: individuals with limited access to healthcare and poor nutrition


### Risk Measures

- Relative Risks $RR = \frac{P(E|D)}{P(E|D^c)}$ which is the ratio of the probability of exposure among cases to the probability of exposure among non-cases.
- Theoretical Minimum-Risk Exposure Levels $TMREL = \frac{P(E|D)}{P(E|D^c)}$ which is the lowest level of exposure that would result in the lowest risk of disease.
- Population Attributable Fractions $PAF = \frac{P(D) - P(D|E)}{P(D)}$ which is the proportion of disease that would be eliminated if the exposure were removed.

## Causal Inference {-}

> 👉 “the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.”

## Summarising the Relationship Between Risk and Outcome {-}

![Summarizing the Relationship Between Risk and Outcome](images/ch5-risk-outcome.png){width=100% align=center}

## Meeting Videos {-}

### Cohort 1 {-}

`r knitr::include_url("https://www.youtube.com/embed/URL")`

<details>
<summary> Meeting chat log </summary>

```
LOG
```
</details>