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Handling HSI_CardioMetabolicDisorders_Refill_Medication with climate disruptions. #1750

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Handling HSI_CardioMetabolicDisorders_Refill_Medication with climate disruptions. #1750

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fa303ad
Responding to aspects of Issue https://github.com/UCL/TLOmodel/issues…
RachelMurray-Watson Nov 13, 2025
1c3235f
isort
RachelMurray-Watson Nov 13, 2025
a9d5276
Re-did the delay, so now the topen is delayed as well as the tclose. …
RachelMurray-Watson Nov 13, 2025
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52 changes: 43 additions & 9 deletions src/tlo/methods/cardio_metabolic_disorders.py
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Original file line number Diff line number Diff line change
Expand Up @@ -149,7 +149,12 @@ class CardioMetabolicDisorders(Module, GenericFirstAppointmentsMixin):
**hsi_events_param_dicts, **initial_prev_param_dicts, **other_params_dict,
'prob_care_provided_given_seek_emergency_care': Parameter(
Types.REAL, "The probability that correct care is fully provided to persons that have sought emergency care"
" for a Cardio-metabolic disorder.")
" for a Cardio-metabolic disorder."),
"probability_of_seeking_further_med_appointment_if_unavailable": Parameter(
Types.REAL,
"Probability that a person who 'should' be on medication will seek another appointment (the following "
"day and try for each of the next 7 days) if refill appointment was not available.",
),
}

# Convert conditions and events to dicts and merge together into PROPERTIES
Expand Down Expand Up @@ -1600,13 +1605,14 @@ def apply(self, person_id, squeeze_factor):

# Don't advise those with CKD to lose weight, but do so for all other conditions if BMI is higher than normal
if self.condition != 'chronic_kidney_disease' and (df.at[person_id, 'li_bmi'] > 2):
self.add_equipment({'Weighing scale'})
if not df.at[person_id, 'nc_ever_weight_loss_treatment']:
self.add_equipment({'Weighing scale'})

self.sim.population.props.at[person_id, 'nc_ever_weight_loss_treatment'] = True
# Schedule a post-weight loss event for individual to potentially lose weight in next 6-12 months:
self.sim.schedule_event(CardioMetabolicDisordersWeightLossEvent(m, person_id),
random_date(self.sim.date + pd.DateOffset(months=6),
self.sim.date + pd.DateOffset(months=12), m.rng))
self.sim.population.props.at[person_id, 'nc_ever_weight_loss_treatment'] = True
# Schedule a post-weight loss event for individual to potentially lose weight in next 6-12 months:
self.sim.schedule_event(CardioMetabolicDisordersWeightLossEvent(m, person_id),
random_date(self.sim.date + pd.DateOffset(months=6),
self.sim.date + pd.DateOffset(months=12), m.rng))

# If person is already on medication, do not do anything
if person[f'nc_{self.condition}_on_medication']:
Expand Down Expand Up @@ -1732,10 +1738,38 @@ def apply(self, person_id, squeeze_factor):
priority=1
)

def did_not_run(self):
# If this HSI event did not run, then the persons ceases to be taking medication
def did_not_run_weather_event(self):
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@marghe-molaro marghe-molaro Nov 13, 2025

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I'm not sure it's worth renaming - this would either require, in the HealthSystem, you checking whether it is the appropriate CMD before calling did_not_run_weather_event, or ensuring that the did_not_run fnc in every HSI in the model is renamed did_not_run_weather_event

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@marghe-molaro marghe-molaro Nov 13, 2025

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this also applies to never ran

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@RachelMurray-Watson RachelMurray-Watson Nov 13, 2025

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As discussed: there is logic in the call_and_record_weather events that checks for CMD-related HSIs. Clunky, but functional for the moment!

# If this HSI event never ran, then the persons ceases to be taking medication. However, they have
# a probability of restarting treatment again.
person_id = self.target
self.sim.population.props.at[person_id, f'nc_{self.condition}_on_medication'] = False
# The individual will try again to restart treatment
if (self.module.rng.random_sample() <
self.module.parameters[f'{self.condition}_hsi'].get('pr_seeking_further_appt_if_drug_not_available')):
self.sim.modules['HealthSystem'].schedule_hsi_event(
hsi_event=HSI_CardioMetabolicDisorders_StartWeightLossAndMedication,
topen=self.sim.date + pd.DateOffset(days=1),
tclose=self.sim.date + pd.DateOffset(
days=self.sim.modules["HealthSystem"].parameters["scale_factor_delay_in_seeking_care_weather"] * 1),
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@marghe-molaro marghe-molaro Nov 13, 2025

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This delay should inform topen, not tclose; it would also be best if it could directly reference a function computing the climate delay, rather than being hardcoded

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You can keep the tclose - topen time gap as is usually coded when sheduling HSI_CardioMetabolicDisorders_StartWeightLossAndMedication

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@marghe-molaro marghe-molaro Nov 13, 2025

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These comments also apply to never_ran below

# number of days of climate disruption
priority=1
)
def never_ran_weather_event(self):
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@marghe-molaro marghe-molaro Nov 13, 2025

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it seems very odd that did_not_run and never_ran are identical. But I guess this makes sense since in the CMD any delay in care is equivalent to a complete cancellation...

# If this HSI event never ran, then the persons ceases to be taking medication. However, they have
# a probability of restarting treatment again.
person_id = self.target
self.sim.population.props.at[person_id, f'nc_{self.condition}_on_medication'] = False
# The individual will try again to restart treatment
if (self.module.rng.random_sample() <
self.module.parameters[f'{self.condition}_hsi'].get('pr_seeking_further_appt_if_drug_not_available')):
self.sim.modules['HealthSystem'].schedule_hsi_event(
hsi_event=HSI_CardioMetabolicDisorders_StartWeightLossAndMedication,
topen=self.sim.date + pd.DateOffset(days=1),
tclose=self.sim.date + pd.DateOffset(
days=self.sim.modules["HealthSystem"].parameters["scale_factor_delay_in_seeking_care_weather"] * 1),
# number of days of climate disruption
priority=1
)


class HSI_CardioMetabolicDisorders_SeeksEmergencyCareAndGetsTreatment(HSI_Event, IndividualScopeEventMixin):
Expand Down
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