intecomm-edc
Controlling chronic diseases in Africa: Development and evaluation of an integrated community-based management model for HIV, Diabetes and Hypertension in Tanzania and Uganda (INTE-COMM study)
Liverpool School of Tropical Medicine
University College London (UCL)
http://www.isrctn.com/ISRCTN15319595
See also https://github.com/clinicedc/edc
Django 5.1 / python 3.12
ClinicEDC (see setup.cfg for version)
We run LIVE and UAT on Ubuntu with nginx/gunicorn/mysql 8.1
Basic install
conda create -n edc python=3.12
conda activate edc
git clone https://github.com/intecomm-trial/intecomm-edc.git ~/apps
cd ~/apps
pip install -U .
python manage.py migrate --settings=intecomm_edc.settings.live # or uatRandomization
Groups of patients are randomized to community integrated care (intervention) or facility integrated care (control).
A patient group is represented by the PatientGroup model. A PatientGroup model instance contains patients who are represented by PatientLog model instances.
Before a group is “ready” to randomize:
the group membership must meet the ratio of HIV, HTN, DM or multi-morbidity patients.
the group must meet the minimum group size.
all patients must be screened as eligible and consented
If “ready”, the patient group is randomized when the PatientGroup model instance saves succcessfully with field randomize_now set to YES.
Randomization occurs in the signal randomize_patient_group_on_post_save. The signal leaves most of the work to the class RandomizeGroup. RandomizeGroup calls it’s randomize method does the following:
The group is randomized to intervention or control;
PatientGroup model instance is allocated a group_identifier;
Each PatientGroup model instance in the group is updated with the group_identifier;
Each SubjectConsent model instance in the group is updated with the group_identifier (which triggers another signal associated with the subject consent. This signal puts the subject on schedule);
Each RegisteredSubject model instance in the group is updated with the group_identifier;
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