Param Revamp - Prostate Cancer (#1666)

* fix analysis file

* param revamp

* update param labeling

* tox fix

* fix linting errors

* update param labeling

* change init prob to local

* add comment documenting discrepancy in values and parameters

* Change param name to 'months' to reflect true value, update comment

* remove junk files saved by accident

* updates from TH

* fix typo and improve error handling

* fix linting error

---------

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

Author: 3 people

resources/ResourceFile_Prostate_Cancer/parameter_values.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9a991f1aad62b3fef036accd8d95edeff0d22e4e3260ee799c838a05a05dcb20
-size 1199
+oid sha256:561d9d990599546f87c7ccbb72e1a6753e5570e260ea260ac14e8e87e9d5279b
+size 2953

src/scripts/prostate_cancer_analyses/prostate_cancer_analyses.py

@@ -22,16 +22,19 @@
     contraception,
     demography,
     enhanced_lifestyle,
+    epi,
     healthburden,
     healthseekingbehaviour,
     healthsystem,
+    hiv,
     labour,
     newborn_outcomes,
     oesophagealcancer,
     postnatal_supervisor,
     pregnancy_supervisor,
     prostate_cancer,
     symptommanager,
+    tb,
 )
 
 # Where will outputs go
@@ -71,7 +74,10 @@ def run_sim(service_availability):
                  pregnancy_supervisor.PregnancySupervisor(),
                  oesophagealcancer.OesophagealCancer(),
                  prostate_cancer.ProstateCancer(),
-                 postnatal_supervisor.PostnatalSupervisor()
+                 postnatal_supervisor.PostnatalSupervisor(),
+                 epi.Epi(),
+                 tb.Tb(),
+                 hiv.Hiv(),
                  )
 
     # Run the simulation

src/tlo/core.py

@@ -347,7 +347,9 @@ def load_parameters_from_dataframe(self, resource: pd.DataFrame) -> None:
             # For each parameter, raise error if the value can't be coerced
             parameter_value, prior_min, prior_max = resource.loc[parameter_name, ['value', 'prior_min', 'prior_max']]
             parameter_label = resource.at[parameter_name, 'param_label']
-            assert parameter_label in acceptable_labels, f'unrecognised parameter label {parameter_label}'
+            assert parameter_label in acceptable_labels, (
+                f'unrecognised parameter label for {parameter_name}: {parameter_label}'
+            )
 
             error_message = (
                 f"some values are not of type {parameter_definition.type_.name} and "

src/tlo/methods/prostate_cancer.py

@@ -153,6 +153,51 @@ def __init__(self, name=None):
         "sensitivity_of_biopsy_for_prostate_ca": Parameter(
             Types.REAL, "sensitivity of biopsy for prostate cancer"
         ),
+        "odds_ratio_health_seeking_urinary": Parameter(
+            Types.REAL, "odds ratio for health seeking in adults with urinary symptoms"
+        ),
+        "odds_ratio_health_seeking_pelvic_pain": Parameter(
+            Types.REAL, "odds ratio for health seeking in adults with pelvic pain symptoms"
+        ),
+        "no_health_seeking_urinary_children": Parameter(
+            Types.BOOL, "whether children seek healthcare for urinary symptoms"
+        ),
+        "no_health_seeking_pelvic_pain_children": Parameter(
+            Types.BOOL, "whether children seek healthcare for pelvic pain symptoms"
+        ),
+        "min_age_prostate_cancer": Parameter(
+            Types.INT, "minimum age for prostate cancer risk"
+        ),
+        "mid_age_threshold_prostate_cancer": Parameter(
+            Types.INT, "middle age threshold for prostate cancer (lower bound of middle age group)"
+        ),
+        "high_age_threshold_prostate_cancer": Parameter(
+            Types.INT, "high age threshold for prostate cancer (lower bound of high age group)"
+        ),
+        "max_age_prostate_cancer": Parameter(
+            Types.INT, "maximum age for prostate cancer modeling"
+        ),
+        "months_between_polling_events": Parameter(
+            Types.INT, "months between main polling events"
+        ),
+        "months_to_post_treatment_check": Parameter(
+            Types.INT, "months from treatment to first post-treatment check"
+        ),
+        "months_between_followup_appointments": Parameter(
+            Types.INT, "months between follow-up appointments after treatment"
+        ),
+        "months_between_palliative_care": Parameter(
+            Types.INT, "months between palliative care appointments"
+        ),
+        "weeks_palliative_care_schedule_window": Parameter(
+            Types.INT, "weeks for palliative care appointment window"
+        ),
+        "treatment_beddays": Parameter(
+            Types.INT, "number of general bed days required for treatment"
+        ),
+        "palliative_care_beddays": Parameter(
+            Types.INT, "number of general bed days required for palliative care"
+        ),
     }
 
     PROPERTIES = {
@@ -208,15 +253,15 @@ def read_parameters(self, resourcefilepath: Optional[Path] = None):
         # Register Symptom that this module will use
         self.sim.modules['SymptomManager'].register_symptom(
             Symptom(name='urinary',
-                    odds_ratio_health_seeking_in_adults=4.00,
-                    no_healthcareseeking_in_children=True)
+                    odds_ratio_health_seeking_in_adults=self.parameters['odds_ratio_health_seeking_urinary'],
+                    no_healthcareseeking_in_children=self.parameters['no_health_seeking_urinary_children'])
         )
 
         # Register Symptom that this module will use
         self.sim.modules['SymptomManager'].register_symptom(
             Symptom(name='pelvic_pain',
-                    odds_ratio_health_seeking_in_adults=4.00,
-                    no_healthcareseeking_in_children=True)
+                    odds_ratio_health_seeking_in_adults=self.parameters['odds_ratio_health_seeking_pelvic_pain'],
+                    no_healthcareseeking_in_children=self.parameters['no_health_seeking_pelvic_pain_children'])
         )
 
     def initialise_population(self, population):
@@ -244,9 +289,11 @@ def initialise_population(self, population):
             sum(p['init_prop_prostate_ca_stage']),
             Predictor('sex').when('M', 1.0).otherwise(0.0),
             Predictor('age_years', conditions_are_mutually_exclusive=True)
-            .when('.between(50,69)', p['rp_prostate_cancer_age5069'])
-            .when('.between(70,120)', p['rp_prostate_cancer_agege70'])
-            .when('.between(0,34)', 0.0)
+            .when(f'.between({p["mid_age_threshold_prostate_cancer"]},{p["high_age_threshold_prostate_cancer"]-1})',
+                  p['rp_prostate_cancer_age5069'])
+            .when(f'.between({p["high_age_threshold_prostate_cancer"]},{p["max_age_prostate_cancer"]})',
+                  p['rp_prostate_cancer_agege70'])
+            .when(f'.between(0,{p["min_age_prostate_cancer"]-1})', 0.0)
         )
 
         pc_status_ = \
@@ -387,7 +434,8 @@ def initialise_simulation(self, sim):
 
         # ----- SCHEDULE MAIN POLLING EVENTS -----
         # Schedule main polling event to happen immediately
-        sim.schedule_event(ProstateCancerMainPollingEvent(self), sim.date + DateOffset(months=1))
+        sim.schedule_event(ProstateCancerMainPollingEvent(self), sim.date +
+                           DateOffset(months=self.parameters['months_between_polling_events']))
 
         # ----- LINEAR MODELS -----
         # Define LinearModels for the progression of cancer, in each 3 month period
@@ -406,9 +454,11 @@ def initialise_simulation(self, sim):
             Predictor('pc_status').when('local_ln', 0),
             Predictor('pc_status').when('metastatic', 0),
             Predictor('age_years', conditions_are_mutually_exclusive=True)
-            .when('.between(50,69)', p['rr_prostate_confined_prostate_ca_age5069'])
-            .when('.between(70,120)', p['rr_prostate_confined_prostate_ca_agege70'])
-            .when('.between(0,34)', 0.0)
+            .when(f'.between({p["mid_age_threshold_prostate_cancer"]},{p["high_age_threshold_prostate_cancer"]-1})',
+                  p['rr_prostate_confined_prostate_ca_age5069'])
+            .when(f'.between({p["high_age_threshold_prostate_cancer"]},{p["max_age_prostate_cancer"]})',
+                  p['rr_prostate_confined_prostate_ca_agege70'])
+            .when(f'.between(0,{p["min_age_prostate_cancer"]-1})', 0.0)
         )
 
         lm['local_ln'] = LinearModel(
@@ -522,8 +572,9 @@ def initialise_simulation(self, sim):
             self.sim.modules['HealthSystem'].schedule_hsi_event(
                 hsi_event=HSI_ProstateCancer_PalliativeCare(module=self, person_id=person_id),
                 priority=0,
-                topen=self.sim.date + DateOffset(months=1),
-                tclose=self.sim.date + DateOffset(months=1) + DateOffset(weeks=1)
+                topen=self.sim.date + DateOffset(months=self.parameters['months_between_palliative_care']),
+                tclose=self.sim.date + DateOffset(months=self.parameters['months_between_palliative_care']) +
+                       DateOffset(weeks=self.parameters['weeks_palliative_care_schedule_window'])
             )
 
     def on_birth(self, mother_id, child_id):
@@ -911,7 +962,8 @@ def __init__(self, module, person_id):
         self.TREATMENT_ID = "ProstateCancer_Treatment"
         self.EXPECTED_APPT_FOOTPRINT = self.make_appt_footprint({"MajorSurg": 1})
         self.ACCEPTED_FACILITY_LEVEL = '3'
-        self.BEDDAYS_FOOTPRINT = self.make_beddays_footprint({"general_bed": 5})
+        self.BEDDAYS_FOOTPRINT = (
+            self.make_beddays_footprint({"general_bed": self.module.parameters['treatment_beddays']}))
 
     def apply(self, person_id, squeeze_factor):
         df = self.sim.population.props
@@ -973,13 +1025,13 @@ def apply(self, person_id, squeeze_factor):
             df.at[person_id, "pc_date_treatment"] = self.sim.date
             df.at[person_id, "pc_stage_at_which_treatment_given"] = df.at[person_id, "pc_status"]
 
-            # Schedule a post-treatment check for 12 months:
+            # Schedule a post-treatment check:
             hs.schedule_hsi_event(
                 hsi_event=HSI_ProstateCancer_PostTreatmentCheck(
                     module=self.module,
                     person_id=person_id,
                 ),
-                topen=self.sim.date + DateOffset(months=12),
+                topen=self.sim.date + DateOffset(months=self.module.parameters['months_to_post_treatment_check']),
                 tclose=None,
                 priority=0
             )
@@ -1046,7 +1098,7 @@ def apply(self, person_id, squeeze_factor):
                     module=self.module,
                     person_id=person_id
                 ),
-                topen=self.sim.date + DateOffset(months=12),
+                topen=self.sim.date + DateOffset(years=self.module.parameters['months_between_followup_appointments']),
                 tclose=None,
                 priority=0
             )
@@ -1069,7 +1121,8 @@ def __init__(self, module, person_id):
         self.TREATMENT_ID = "ProstateCancer_PalliativeCare"
         self.EXPECTED_APPT_FOOTPRINT = self.make_appt_footprint({})
         self.ACCEPTED_FACILITY_LEVEL = '2'
-        self.BEDDAYS_FOOTPRINT = self.make_beddays_footprint({'general_bed': 15})
+        self.BEDDAYS_FOOTPRINT = (
+            self.make_beddays_footprint({'general_bed': self.module.parameters['palliative_care_beddays']}))
 
     def apply(self, person_id, squeeze_factor):
         df = self.sim.population.props
@@ -1105,13 +1158,13 @@ def apply(self, person_id, squeeze_factor):
             if pd.isnull(df.at[person_id, "pc_date_palliative_care"]):
                 df.at[person_id, "pc_date_palliative_care"] = self.sim.date
 
-            # Schedule another instance of the event for one month
+            # Schedule another instance of the event
             hs.schedule_hsi_event(
                 hsi_event=HSI_ProstateCancer_PalliativeCare(
                     module=self.module,
                     person_id=person_id
                 ),
-                topen=self.sim.date + DateOffset(months=1),
+                topen=self.sim.date + DateOffset(months=self.module.parameters['months_between_palliative_care']),
                 tclose=None,
                 priority=0
             )