increasing data

[rahul-n]

will changing to distribution =0.99; insist of { "distribution": 0.01, "transition": "Delay_Until_Arthritis" } of rheumatoid_arthritis.json will give 99% of RA records ?

[jawalonoski]

Yes.

[rahul-n]

But when I check encounter.csv file, and filter data according to reasondescription= rheumatoid arthritis , I am getting lesser number of records (~8% records).Am I interpretation the encounter.csv wrong just to get RA records ??

[jawalonoski]

The next state Delay_Until_Arthritis delays randomly between 25 and 80 years. Which means that 99% will get RA, but you won't necessarily see it until the condition onsets and they are diagnosed.

If you want to force the issue, lower or remove the delay state and/or filter on age.

[rahul-n]

ok , Thanks Jason.

[rahul-n]

Jason , just a small query , how is the logic for age progression is written in synthea code? For example :- Person is generated and he/she progress to complete age of 100 odd years or there is a bunch of population generated in starting and they progress to 100 odd year cycle.

and what is age progression interval ? I guess that is 1 week..

[jawalonoski]

Aging logic is here: https://github.com/synthetichealth/synthea/blob/57153110be898749ec8f55975abb688ffec4b8fa/src/main/java/org/mitre/synthea/modules/LifecycleModule.java#L262

The timestep of the simulation is configured with the generate.timestep property which defaults to 7 days. Here: https://github.com/synthetichealth/synthea/blob/57153110be898749ec8f55975abb688ffec4b8fa/src/main/resources/synthea.properties#L31

Patients are born on a date that is determined by the demographics of the location you are running. They then age until the present day, or die along the way.

[rahul-n]

Thanks mate :-)