Tobacco Use

Tobacco use is a major risk factor for a wide range of non-communicable diseases (NCDs) and a leading cause of preventable death globally. This document describes how tobacco use and related interventions are modeled, focusing on the mechanisms that link interventions to changes in disease incidence and mortality.

Used in

Tobacco use is incorporated as a risk factor in the following disease models:

• Asthma
• COPD
• Diabetes
• CVD

Mechanism

The core mechanism is that the interventions primarily reduce the prevalence of tobacco use. This reduction in prevalence, in turn, reduces the PAFs for tobacco-related diseases, leading to lower incidence and mortality. Here's a step-by-step breakdown:

1. Baseline Prevalence: The model begins with a baseline prevalence of tobacco use for each age and sex group.

2. Intervention Impact on Prevalence: Each intervention is assigned an "impact" value. This impact represents the percentage reduction in tobacco use prevalence among those reached by the intervention. For example:

   • NC_RFTobaccoWarnLabel: Reduces prevalence by 5%.
   • NC_RFTobaccoBan: Reduces prevalence by 3%.
   • NC_RFTobaccoQuitBrief: Reduces prevalence by 3.67%.
   • NC_RFTobaccoWarnMassMedia: Reduces prevalence by 6%.
   • (And so on for the other interventions)
   • Note that some interventions, such as taxes have impacts defined elsewhere in the model, because they rely on different data (Demand Elasticities)

3. Coverage: The model considers the coverage of each intervention. Coverage represents the percentage of the target population that is actually reached by the intervention. An intervention might have a high potential impact, but if coverage is low, its overall effect will be limited.

4. Calculating the Overall Prevalence Reduction: The model combines the impact of each intervention with its coverage to calculate the overall reduction in tobacco use prevalence. This is not a simple sum, as interventions can overlap, and individuals can be affected by multiple interventions. The precise calculation is complex, but the general idea is to avoid double-counting the effects.

5. Calculating the PAF Adjustment: After calculating the reduction in prevalence, the model recalculates the Population Attributable Fractions (PAFs) using the new, lower prevalence values. The PAF represents how much of the disease burden can be attributed to tobacco use. When prevalence decreases due to interventions, the PAF also decreases proportionally, which means less disease can be attributed to tobacco exposure in the population.

6. New Incidence and Mortality: The reduced PAFs are then used (in other parts of the model, not shown in the code snippet) to calculate the new, lower incidence and mortality rates for tobacco-related diseases.

Example: Tobacco Warning Labels

Imagine a population where 20% of adults are smokers (baseline prevalence). Suppose the government implements strong warning labels on cigarette packs (NC_RFTobaccoWarnLabel), which have an impact of 5%. Let us assume the coverage for this policy is 80%

1. Impact: The warning labels are expected to reduce smoking prevalence by 5% among those who see them.

2. Coverage: 80% of the population is exposed to the warning labels.

3. Prevalence Reduction: The overall reduction in prevalence would be approximately 5% * 80% = 4%. So, the prevalence of smoking might drop from 20% to around 19.2% (this is a simplification; the model's actual calculation is more precise).

4. PAF Adjustment: The PAFs are adjusted to take into account this reduction.

5. Lower Incidence and Mortality: This lower prevalence, through the reduced PAFs, leads to lower incidence and mortality rates for tobacco-related diseases in subsequent years of the simulation.

In Summary

The model simulates the impact of tobacco control interventions by:

• Reducing the prevalence of tobacco use.
• This reduction in prevalence leads to a reduction in the Population Attributable Fractions (PAFs) for tobacco-related diseases.
• The reduced PAFs, in turn, lead to lower incidence and mortality rates for those diseases.

The model accounts for the impact of each intervention and its coverage to estimate the overall effect on tobacco use and, consequently, on public health.

Key assumptions about the data sources, relative risks, intervention impacts, and coverage levels are detailed in the [/data-sources] section. This includes information on the specific studies used to derive these parameters.

Interventions

Intervention Table

Coverage Map

Assumptions

Relative Risks

Impact on Prevalence