Recording life table outputs

The multi-state life table contains a vast amount of information for each population cohort at each time-step of a model simulation. Since the primary objective of MSLT models is to predict the impact of preventative interventions on population morbidity and mortality, only some of these data are relevant and worth recording.

The core concepts are:

1. MSLT components, such as diseases, risk factors, and interventions, will record quantities of interest as columns in the population table;

2. Observers will record the values of these columns (and also those of columns that identify each cohort, such as their age and sex) at each time-step; and

3. At the end of the simulation, observers will concatenate the values observed at each time-step into a single table, calculate summary statistics (if required), and save the resulting table to disk.

The MSLT framework provides a number of “observers” that record tailored summary statistics during a model simulation. We now introduce each of the provided observers in turn.

Note

Typically, each observer will record summary statistics for the “business-as-usual” (BAU) scenario and for the intervention scenario.

Population morbidity and mortality

The MorbidityMortality observer records the core life table quantities (as shown in the example table) at each year of the simulation. This includes calculating quantities such as the life expectancy and health-adjusted life expectancy (HALE) for each cohort at each time-step.

Chronic disease incidence, prevalence, and mortality

The Disease observer records the chronic disease incidence and prevalence, and the number of deaths caused by this disease, at each year of the simulation. For example, with an intervention that reduces the incidence of chronic heart disease (CHD) by 5% for all cohorts at all time-steps, it will produce the following output:

disease	year	age	sex	BAU incidence	Incidence	BAU prevalence	Prevalence	BAU deaths	Deaths	Change in incidence	Change in prevalence
…	…	…	…	…	…	…	…	…	…	…	…
CHD	2011	53	male	0.005339172657680636	0.005072214024796604	0.040773746292951045	0.04054116957472282	0.58533431153149	0.583569340293451	-0.00026695863288403194	-0.00023257671822822512
CHD	2012	54	male	0.005698168146464383	0.005413259739141163	0.04517666366247726	0.044700445256575384	1.2175752762775431	1.2105903765985175	-0.0002849084073232198	-0.0004762184059018751
…	…	…	…	…	…	…	…	…	…	…	…
CHD	2066	108	male	0.039465892849735555	0.037492598207248776	0.18918308469826292	0.18921952179569373	687.8956907787912	670.391700818296	-0.0019732946424867795	3.643709743081369e-05
CHD	2067	109	male	0.039465892849735555	0.037492598207248776	0.1848858097143421	0.1854028718371867	701.5550104751002	684.0712684559271	-0.0019732946424867795	0.0005170621228446082
…	…	…	…	…	…	…	…	…	…	…	…

Risk factor prevalence

The TobaccoPrevalence observer records the smoking status of each cohort at each time-step. Note that all of the post-cessation exposure categories are summed together.

year	age	sex	BAU never smoked	BAU currently smoking	BAU previously smoked	BAU population	Never smoked	Currently smoking	Previously smoked	Population
…	…	…	…	…	…	…	…	…	…	…
2011	53	male	0.5613260600324522	0.15550808606224473	0.283165853905303	129435.28592207265	0.5613260600324521	0.0	0.4386739399675479	129435.55873403646
2012	54	male	0.5614856404922235	0.1493582211992655	0.289156138308511	128994.49027457157	0.5614797822892069	0.0	0.4385202177107931	128995.65724459664
…	…	…	…	…	…	…	…	…	…	…
2066	108	male	0.5890673671092423	5.160204075837396e-05	0.4108810308499993	136.85271732801016	0.5650606908555851	0.0	0.4349393091444149	150.1088283279755
2067	109	male	0.5890897533263759	3.947771215412827e-05	0.41087076896146996	84.60051616850757	0.565060690855585	0.0	0.43493930914441503	92.95319866896016
…	…	…	…	…	…	…	…	…	…	…