How Mental Health Models Work: Main Steps

1. Initialization

1. Define the Model Population

   • Specify total population by age, sex, etc.
   • Specify baseline prevalence of mental health conditions if applicable.
   • Set "base year" indexes to align the simulation with real calendar years.

2. Set Up Health States

   • Each mental health condition typically has multiple health states (e.g., healthy, subclinical, clinical, remission, etc.).
   • The model maps each "health state" to an internal index to track transitions among these states.

3. Load Parameters

   • Incidence rates, remission rates, relapse rates, mortality rates, plus other key parameters are loaded from data files.
   • Intervention coverage or other system settings (like "scale up" toggles) are set here.

2. Year-by-Year (or Timestep-by-Timestep) Progression

Once initialized, the core model runs in a loop over each year (or each time step). For each year:

1. Set or Reset Baseline Transitions

   • The model has baseline transition rates (from healthy to subclinical, from subclinical to clinical, from clinical to remission, etc.).
   • It may reset these to default values before applying the new year's adjustments.

2. Apply Intervention Impacts

   • For any prevention or treatment programs (e.g., screening, psychotherapy, medication), the model adjusts the transition rates accordingly.
   • This might be multiplicative or additive (e.g., remission rate is multiplied by 1.5 if we have a 50% increase).

3. Iterate Through Time Steps

   • Within each time step, the model moves people from one health state to another according to those transition rates.
   • This captures the natural progression of mental health conditions including onset, remission, and relapse.

3. Population Dynamics and Aging

1. Move Individuals Across Age Groups

   • At the end of each simulation year, the model "ages" everyone by one year.
   • The oldest age category typically has a special rule so that it never empties entirely.

2. Births and Deaths

   • The model adds new births (who enter the youngest age group as healthy).
   • It removes from the population those who die (due to condition-related suicide or background mortality).

3. Keep Track of Incidence, Remission, Relapse, and Mortality

   • Each cycle, the model calculates how many new cases occur, how many existing cases enter remission, how many relapse, and how many die.
   • These are recorded as output results for that time period.

4. Calculating Health Metrics

Common health metrics in mental health models include:

• Prevalence: How many people have the condition in a given year.
• Incidence: How many new cases occur during the year.
• Remission: How many cases enter remission during the year.
• Relapse: How many cases in remission relapse during the year.
• Mortality: How many die (condition-specific or all-cause).
• YLD (Years Lived with Disability): Prevalence × disability weight.
• YLL (Years of Life Lost): Deaths × remaining life expectancy.
• DALYs (Disability-Adjusted Life Years): YLD + YLL.

At each time step, the model aggregates these based on the updated condition counts.

5. Equilibrium Runs (Optional)

Some models do an "equilibrium run" before the main simulation:

• This means they run the model for a certain number of years to stabilize prevalence.
• Then they begin their official "projection" from a stable baseline.
• This is particularly useful for mental health conditions with complex progression patterns.

6. Final Outputs and Totals

After looping through all years:

1. Aggregate Final Results

   • The model sums or stores each year's incidence, prevalence, remission, relapse, mortality, or other measures.
   • It might also produce outputs by age group, sex, and year.

2. Reporting

   • Finally, results are displayed or saved (for instance, to show how the mental health burden changes over time).

Summary

Mental health models generally follow this pattern:

1. Initialize (set up population, health states, parameters).
2. Update (apply interventions, parameter changes each year).
3. Advance (apply transitions for incidence, remission, relapse, mortality, stepping through sub-timesteps).
4. Aging & Turnover (age cohorts, add births, remove deaths).
5. Measure (record incidence, prevalence, remission, deaths, DALYs, etc.).
6. Loop over each simulation year until the end.
7. Compile Results (totals or time-series data).