Updates to Lifestyle Module (#545)

Co-authored-by: Asif Tamuri <[email protected]>
Co-authored-by: Tim Hallett <[email protected]>
Co-authored-by: Matt Graham <[email protected]>
Co-authored-by: joehcollins <[email protected]>

Author: 5 people

resources/ResourceFile_Lifestyle_Enhanced.xlsx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:945e620bb0ce47d61623791bafd884282b1c8b3643b0b38652c8210277c7ae69
-size 4149998
+oid sha256:fb953d8b31023d90a258701bf943bc13fed91f2e2741a233d973cc25b1e92cc0
+size 4153552

src/scripts/enhanced_lifestyle_analyses/enhanced_lifestyle_analyses.py

@@ -1151,7 +1151,11 @@ def apply(self, person_id):
             return
         else:
             if self.module.rng.random_sample() < self.module.parameters['pr_bmi_reduction']:
-                df.at[person_id, 'li_bmi'] -= 1
+                # for bmi equal to 1, we decrease it to np.nan to avoid a new category error
+                if not df.at[person_id, 'li_bmi'] > 1:
+                    df.at[person_id, 'li_bmi'] = np.nan
+                else:
+                    df.at[person_id, 'li_bmi'] -= 1
                 df.at[person_id, 'nc_weight_loss_worked'] = True
 
 

src/scripts/enhanced_lifestyle_analyses/enhanced_lifestyle_calibrations.py

@@ -4,6 +4,7 @@
  is that every pregnancy results in a birth."""
 
 import json
+from pathlib import Path
 
 import pandas as pd
 
@@ -105,12 +106,12 @@ def read_parameters(self, data_folder):
         """Load parameters for probability of pregnancy/birth and breastfeeding status for newborns"""
 
         self.parameters['age_specific_fertility_rates'] = \
-            pd.read_csv(self.resourcefilepath / 'demography' / 'ResourceFile_ASFR_WPP.csv')
+            pd.read_csv(Path(self.resourcefilepath) / 'demography' / 'ResourceFile_ASFR_WPP.csv')
 
         self.parameters['months_between_pregnancy_and_delivery'] = 9
 
         # Breastfeeding status for newborns (importing from the Newborn resourcefile)
-        rf = pd.read_excel(self.resourcefilepath / 'ResourceFile_NewbornOutcomes.xlsx')
+        rf = pd.read_excel(Path(self.resourcefilepath) / 'ResourceFile_NewbornOutcomes.xlsx')
         param_as_string = rf.loc[rf.parameter_name == 'prob_breastfeeding_type']['value'].iloc[0]
         parameter = json.loads(param_as_string)[0]
         self.parameters['prob_breastfeeding_type'] = parameter

src/tlo/methods/cardio_metabolic_disorders.py

@@ -1,12 +1,18 @@
-
+""""
+This is a test file for Lifestyle Module. It contains a number of checks to ensure everything is running as expected
+"""
 import os
 from pathlib import Path
 
+import numpy as np
+import pandas as pd
 import pytest
 
-from tlo import Date, Simulation
+from tlo import Date, DateOffset, Module, Simulation
+from tlo.events import PopulationScopeEventMixin, RegularEvent
 from tlo.methods import demography, enhanced_lifestyle
 
+resourcefilepath = Path(os.path.dirname(__file__)) / '../resources'
 start_date = Date(2010, 1, 1)
 end_date = Date(2012, 4, 1)
 popsize = 10000
@@ -22,14 +28,15 @@ def simulation(seed):
     return sim
 
 
-def __check_properties(df):
+def check_properties(df):
     # no one under 15 can be overweight, low exercise, tobacco, excessive alcohol, married
     under15 = df.age_years < 15
-    assert not (under15 & df.li_bmi > 0).any()
+    assert not (under15 & pd.notna(df.li_bmi)).any()
     assert not (under15 & df.li_low_ex).any()
     assert not (under15 & df.li_tob).any()
     assert not (under15 & df.li_ex_alc).any()
     assert not (under15 & (df.li_mar_stat != 1)).any()
+
     # education: no one 0-5 should be in education
     assert not ((df.age_years < 5) & (df.li_in_ed | (df.li_ed_lev != 1))).any()
 
@@ -50,10 +57,80 @@ def __check_properties(df):
 
 def test_properties_and_dtypes(simulation):
     simulation.make_initial_population(n=popsize)
-    __check_properties(simulation.population.props)
+    check_properties(simulation.population.props)
     simulation.simulate(end_date=end_date)
-    __check_properties(simulation.population.props)
+    check_properties(simulation.population.props)
     # check types of columns
     df = simulation.population.props
     orig = simulation.population.new_row
     assert (df.dtypes == orig.dtypes).all()
+
+
+def test_assign_rural_urban_by_district(simulation):
+    """ test linear model integrity in assigning individual rural urban status based on their districts """
+    # make an initial population
+    simulation.make_initial_population(n=1)
+
+    # Make this individual rural
+    df = simulation.population.props
+    df.loc[0, 'is_alive'] = True
+    df.loc[0, 'is_urban'] = False
+    df.loc[0, 'district_of_residence'] = 'Lilongwe'
+
+    # confirm an individual is rural
+    assert not df.loc[0, 'li_urban']
+
+    # reset district of residence to an urban district(Here we choose a district of residence with 1.0 urban
+    # probability i.e Lilongwe City), run the rural urban linear model and check the individual is now urban.
+    df.loc[0, 'district_of_residence'] = 'Lilongwe City'
+    rural_urban_lm = enhanced_lifestyle.LifestyleModels(simulation.modules['Lifestyle']).rural_urban_linear_model()
+    df.loc[df.is_alive, 'li_urban'] = rural_urban_lm.predict(df.loc[df.is_alive], rng=np.random)
+
+    # check an individual is now urban
+    assert df.loc[0, 'li_urban']
+
+
+def test_check_properties_daily_event():
+    """ A test that seeks to test the integrity of lifestyle properties. It contains a dummy module with an event that
+    runs daily to ensure properties what they are expected """
+    class DummyModule(Module):
+        """ a dummy module for testing lifestyle properties """
+        def read_parameters(self, data_folder):
+            pass
+
+        def initialise_population(self, population):
+            pass
+
+        def initialise_simulation(self, sim):
+            event = DummyLifestyleEvent(self)
+            sim.schedule_event(event, sim.date)
+
+        def on_birth(self, mother, child):
+            pass
+
+    class DummyLifestyleEvent(RegularEvent, PopulationScopeEventMixin):
+        """ An event that runs daily to check the integrity of lifestyle properties """
+
+        def __init__(self, module):
+            """schedule to run everyday
+            """
+            self.repeat_months = 1
+            self.module = module
+            super().__init__(module, frequency=DateOffset(days=1))
+
+        def apply(self, population):
+            """ Apply this event to the population.
+            :param population: the current population
+            """
+            # check lifestyle properties
+            check_properties(population.props)
+
+    # Create simulation:
+    sim = Simulation(start_date=start_date)
+    sim.register(
+        demography.Demography(resourcefilepath=resourcefilepath),
+        enhanced_lifestyle.Lifestyle(resourcefilepath=resourcefilepath),
+        DummyModule()
+    )
+    sim.make_initial_population(n=2000)
+    sim.simulate(end_date=end_date)

src/tlo/methods/enhanced_lifestyle.py

@@ -230,8 +230,9 @@ def test_event_scheduling_for_care_seeking_during_home_birth(seed):
     mni[mother_id]['sought_care_labour_phase'] = 'none'
     sim.date = sim.date + pd.DateOffset(days=5)
 
-    # force a complication
+    # force a complication and care seeking
     params['prob_pph_uterine_atony'] = 1.0
+    params['prob_careseeking_for_complication_pn'] = 1.0
 
     # run postpartum home event
     home_birth_pp = labour.BirthAndPostnatalOutcomesEvent(mother_id=mother_id, module=sim.modules['Labour'])

src/tlo/methods/simplified_births.py

@@ -25,7 +25,7 @@
     resourcefilepath = "resources"
 
 simulation_start_date = Date(2010, 1, 1)
-simulation_end_date = Date(2010, 4, 1)
+simulation_end_date = Date(2010, 9, 1)
 simulation_initial_population = 1000