Param Revamp - Depression (#1701)

* param revamp depression

* fix linting errors

* update param labeling

* update refill window to be local param

---------

Co-authored-by: Tim Hallett <[email protected]>

Author: mmsuarezcosta

resources/ResourceFile_Depression/parameter_values.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d742f62f0915073bb672c43d9d558599ef38f456f2134d2729d246a6d6d130de
-size 3438
+oid sha256:f9bb95649b9b8fbf76571b56617c1a4e590ccea40b7f9154565bafe28f4ba3e1
+size 5440

src/tlo/methods/depression.py

@@ -198,6 +198,42 @@ def __init__(self, name=None):
         'pr_assessed_for_depression_for_perinatal_female': Parameter(
             Types.REAL,
             'Probability that a perinatal female is assessed for depression during antenatal or postnatal services'),
+
+        'recently_pregnant_threshold_years': Parameter(
+            Types.INT, 'Time period to consider a woman as recently pregnant'
+        ),
+
+        'max_talking_therapy_sessions': Parameter(
+            Types.INT, 'Maximum number of talking therapy sessions per course'
+        ),
+
+        'talking_therapy_interval_months': Parameter(
+            Types.INT, 'Interval between talking therapy sessions in months'
+        ),
+
+        'antidepr_refill_interval_months': Parameter(
+            Types.INT, 'Interval for antidepressant refill appointments in months'
+        ),
+
+        'antidepr_refill_window_days': Parameter(
+            Types.INT, 'Window period for antidepressant refill appointments in days'
+        ),
+
+        'selfharm_suicide_event_max_days': Parameter(
+            Types.INT, 'Maximum days for scheduling self-harm/suicide events'
+        ),
+
+        'depression_polling_frequency_months': Parameter(
+            Types.INT, 'Frequency of depression polling events in months'
+        ),
+
+        'age_threshold_adult_care': Parameter(
+            Types.INT, 'Minimum age for care in non-peds in years'
+        ),
+
+        'age_threshold_depression': Parameter(
+            Types.INT, 'Minimum age for depression'
+        ),
     }
 
     # Properties of individuals 'owned' by this module
@@ -267,13 +303,15 @@ def read_parameters(self, resourcefilepath: Optional[Path]=None):
         # risk of ever having depression in initial population
         self.linearModels['Depression_Ever_At_Population_Initialisation_Males'] = LinearModel.multiplicative(
             Predictor('age_years').apply(
-                lambda x: (x if x > 15 else 0) * self.parameters['init_rp_ever_depr_per_year_older_m']
+                lambda x: (x if x > p['age_threshold_depression'] else 0) *
+                          self.parameters['init_rp_ever_depr_per_year_older_m']
             )
         )
 
         # risk of ever having depression in initial population (female)
         self.linearModels['Depression_Ever_At_Population_Initialisation_Females'] = LinearModel.multiplicative(
-            Predictor('age_years').apply(lambda x: (x if x > 15 else 0) * p['init_rp_ever_depr_per_year_older_f'])
+            Predictor('age_years').apply(lambda x: (x if x > p['age_threshold_depression'] else 0) *
+                                                   p['init_rp_ever_depr_per_year_older_f'])
         )
 
         # risk of ever having diagnosed depression in initial population
@@ -391,6 +429,7 @@ def apply_linear_model(self, lm, df):
 
     def initialise_population(self, population):
         df = population.props
+        p = self.parameters
         df['de_depr'] = False
         df['de_ever_depr'] = False
         df['de_date_init_most_rec_depr'] = pd.NaT
@@ -401,7 +440,7 @@ def initialise_population(self, population):
         df['de_ever_talk_ther'] = False
         df['de_ever_non_fatal_self_harm_event'] = False
         df['de_recently_pregnant'] = df['is_pregnant'] | (
-            df['date_of_last_pregnancy'] > (self.sim.date - DateOffset(years=1))
+            df['date_of_last_pregnancy'] > (self.sim.date - DateOffset(years=p['recently_pregnant_threshold_years']))
         )
         df['de_cc'] = False
 
@@ -487,12 +526,14 @@ def initialise_simulation(self, sim):
         df = sim.population.props
         if df['de_on_antidepr'].sum():
             for person_id in df.loc[df['de_on_antidepr']].index:
-                date_of_next_appt_scheduled = self.sim.date + DateOffset(days=self.rng.randint(0, 30))
+                date_of_next_appt_scheduled = self.sim.date + DateOffset(
+                    days=self.rng.randint(0, self.parameters['antidepr_refill_interval_months']*30)
+                )
                 self.sim.modules['HealthSystem'].schedule_hsi_event(
                     hsi_event=HSI_Depression_Refill_Antidepressant(person_id=person_id, module=self),
                     priority=1,
                     topen=date_of_next_appt_scheduled,
-                    tclose=date_of_next_appt_scheduled + DateOffset(days=7)
+                    tclose=date_of_next_appt_scheduled + DateOffset(days=self.parameters['antidepr_refill_window_days'])
                 )
 
     def on_birth(self, mother_id, child_id):
@@ -668,7 +709,7 @@ def diagnosis_function(tests, use_dict: bool = False, report_tried: bool = False
     def do_at_generic_first_appt(
         self, individual_properties: IndividualProperties, **kwargs
     ) -> None:
-        if individual_properties["age_years"] > 5:
+        if individual_properties["age_years"] > self.parameters["age_threshold_adult_care"]:
             self.do_at_generic_first_appt_emergency(
                 individual_properties=individual_properties,
                 **kwargs,
@@ -710,7 +751,7 @@ class DepressionPollingEvent(RegularEvent, PopulationScopeEventMixin):
     """
 
     def __init__(self, module):
-        super().__init__(module, frequency=DateOffset(months=3))
+        super().__init__(module, frequency=DateOffset(months=module.parameters['depression_polling_frequency_months']))
 
     def apply(self, population):
         # Create some shortcuts
@@ -721,12 +762,12 @@ def apply(self, population):
         # -----------------------------------------------------------------------------------------------------
         # Update properties that are used by the module
         df['de_recently_pregnant'] = df['is_pregnant'] | (
-            df['date_of_last_pregnancy'] > (self.sim.date - DateOffset(years=1)))
+            df['date_of_last_pregnancy'] > (self.sim.date - DateOffset(years=p['recently_pregnant_threshold_years'])))
 
         assert (df.loc[df['is_pregnant'], 'de_recently_pregnant']).all()
         assert not (df['de_recently_pregnant'] & pd.isnull(df['date_of_last_pregnancy'])).any()
         assert (df.loc[((~df['is_pregnant']) & df['de_recently_pregnant']), 'date_of_last_pregnancy'] > (
-            self.sim.date - DateOffset(years=1))).all()
+            self.sim.date - DateOffset(years=p['recently_pregnant_threshold_years']))).all()
 
         # -----------------------------------------------------------------------------------------------------
         # Determine who will be onset with depression among those who are not currently depressed
@@ -769,17 +810,25 @@ def apply(self, population):
             df.loc[df['is_alive'] & df['de_depr']]
         )
         for person_id in will_self_harm_in_next_3mo.loc[will_self_harm_in_next_3mo].index:
-            self.sim.schedule_event(DepressionSelfHarmEvent(self.module, person_id),
-                                    self.sim.date + DateOffset(days=self.module.rng.randint(0, 90)))
+            self.sim.schedule_event(
+                DepressionSelfHarmEvent(self.module, person_id),
+                self.sim.date + DateOffset(
+                    days=self.module.rng.randint(0, p['selfharm_suicide_event_max_days'])
+                )
+            )
 
         # Schedule Suicide events for those with current depression (individual level events)
         will_suicide_in_next_3mo = apply_linear_model(
             self.module.linearModels['Risk_of_Suicide_per3mo'],
             df.loc[df['is_alive'] & df['de_depr']]
         )
         for person_id in will_suicide_in_next_3mo.loc[will_suicide_in_next_3mo].index:
-            self.sim.schedule_event(DepressionSuicideEvent(self.module, person_id),
-                                    self.sim.date + DateOffset(days=self.module.rng.randint(0, 90)))
+            self.sim.schedule_event(
+                DepressionSuicideEvent(self.module, person_id),
+                self.sim.date + DateOffset(
+                    days=self.module.rng.randint(0, p['selfharm_suicide_event_max_days'])
+                )
+            )
 
 
 class DepressionSelfHarmEvent(Event, IndividualScopeEventMixin):
@@ -928,10 +977,10 @@ def apply(self, person_id, squeeze_factor):
         if not df.at[person_id, 'de_ever_talk_ther']:
             df.at[person_id, 'de_ever_talk_ther'] = True
 
-        if self.num_of_sessions_had < 5:
+        if self.num_of_sessions_had < self.module.parameters['max_talking_therapy_sessions']:
             self.sim.modules['HealthSystem'].schedule_hsi_event(
                 hsi_event=self,
-                topen=self.sim.date + pd.DateOffset(months=6),
+                topen=self.sim.date + pd.DateOffset(months=self.module.parameters['talking_therapy_interval_months']),
                 tclose=None,
                 priority=1
             )
@@ -952,6 +1001,7 @@ def __init__(self, module, person_id):
 
     def apply(self, person_id, squeeze_factor):
         df = self.sim.population.props
+        p = self.module.parameters
 
         # If person is already on anti-depressants, do not do anything
         if df.at[person_id, 'de_on_antidepr']:
@@ -972,8 +1022,12 @@ def apply(self, person_id, squeeze_factor):
             self.sim.modules['HealthSystem'].schedule_hsi_event(
                 hsi_event=HSI_Depression_Refill_Antidepressant(person_id=person_id, module=self.module),
                 priority=1,
-                topen=self.sim.date + DateOffset(months=1),
-                tclose=self.sim.date + DateOffset(months=1) + DateOffset(days=7)
+                topen=self.sim.date + DateOffset(months=p['antidepr_refill_interval_months']),
+                tclose=(
+                    self.sim.date +
+                    DateOffset(months=p['antidepr_refill_interval_months']) +
+                    DateOffset(days=p['antidepr_refill_window_days'])
+                )
             )
 
 
@@ -994,6 +1048,7 @@ def __init__(self, module, person_id):
 
     def apply(self, person_id, squeeze_factor):
         df = self.sim.population.props
+        p = self.module.parameters
 
         assert df.at[person_id, 'de_ever_diagnosed_depression'], "The person is not diagnosed and so should not be " \
                                                                  "receiving an HSI. "
@@ -1013,8 +1068,12 @@ def apply(self, person_id, squeeze_factor):
             self.sim.modules['HealthSystem'].schedule_hsi_event(
                 hsi_event=HSI_Depression_Refill_Antidepressant(person_id=person_id, module=self.module),
                 priority=1,
-                topen=self.sim.date + DateOffset(months=1),
-                tclose=self.sim.date + DateOffset(months=1) + DateOffset(days=7)
+                topen=self.sim.date + DateOffset(months=p['antidepr_refill_interval_months']),
+                tclose=(
+                    self.sim.date +
+                    DateOffset(months=p['antidepr_refill_interval_months']) +
+                    DateOffset(days=p['antidepr_refill_window_days'])
+                )
             )
         else:
             # If medication was not available, the persons ceases to be taking antidepressants