• We’ve developed a model which simulated how the health state of our population will evolve over the next two decades (https://realworlddatascience.net/case-studies/posts/2024/05/08/dpm.html).
• This is crucially underpinned by what has happened to the health state of our population in the recent data.
• Essentially, the model ‘baseline scenario’ extrapolates these recent health state changes.
• However, we only have three years of data to determine these health state changes (the key restriction here is the all-important primary care data – other data sources do go back further).
• We are interested to understand how the health state of our population (or ALSPAC cohort thereof) has changed over a longer time period.
• This could help us better shape the ‘baseline scenario’ by extrapolating behaviour from a longer time period (not just the last three years).
• Crucial to this is data on individual attributes over time, which we source from our primary care data (2020+).
• Specifically, we use the individual’s Cambridge Multimorbidity Score (CMS), published here: https://doi.org/10.1503/cmaj.190757.
• This is calculated from 37 chronic conditions, see here: https://www.cmaj.ca/content/cmaj/suppl/2020/01/28/192.5.E107.DC1/190757-....
• Would it be possible to calculate these over time for the ALSPAC cohort, and thus calculate the CMS score for each individual, going back as long as possible?
• Additionally, we’d like to understand (1) inward/outward migration to the cohort, re patient characteristics, and (2) any detectable/inferable delays in diagnoses, re patient characteristics.
• The outputs of this will help us better understand health state changes in our population, and so better inform the ‘baseline scenario’ in our abovementioned simulation model.