PR to record the prevalence of disease/condition, still births, neonatal death, maternal mortality

src/scripts/longterm_projections/longterm_projections_draws.json

@@ -0,0 +1,43 @@
+{
+  "scenario_script_path": "/Users/rem76/PycharmProjects/TLOmodel/src/scripts/longterm_projections/longterm_projections.py",
+  "scenario_seed": 0,
+  "arguments": [],
+  "runs_per_draw": 1,
+  "draws": [
+    {
+      "draw_number": 0,
+      "parameters": {
+        "SymptomManager": {
+          "spurious_symptoms": true
+        },
+        "HealthSystem": {
+          "Service_Availability": [
+            "*"
+          ],
+          "use_funded_or_actual_staffing": "funded_plus",
+          "mode_appt_constraints": 1,
+          "cons_availability": "default",
+          "beds_availability": "all",
+          "equip_availability": "default",
+          "cons_availability_postSwitch": "all",
+          "year_cons_availability_switch": 2020,
+          "equip_availability_postSwitch": "all",
+          "year_equip_availability_switch": 2020
+        },
+        "ImprovedHealthSystemAndCareSeekingScenarioSwitcher": {
+          "max_healthsystem_function": [
+            false,
+            true
+          ],
+          "max_healthcare_seeking": [
+            false,
+            true
+          ],
+          "year_of_switch": 2020
+        }
+      }
+    }
+  ],
+  "commit": "7ea3f5ae200444bf4a4688dbc2e40961fef84fd8",
+  "github": "https://github.com/UCL/TLOmodel/tree/7ea3f5ae200444bf4a4688dbc2e40961fef84fd8"
+}

src/tlo/methods/alri.py

@@ -50,6 +50,7 @@
 to_odds = lambda pr: pr / (1.0 - pr)  # noqa: E731
 to_prob = lambda odds: odds / (1.0 + odds)  # noqa: E731
 
+
 # ---------------------------------------------------------------------------------------------------------
 #   MODULE DEFINITION
 # ---------------------------------------------------------------------------------------------------------
@@ -995,6 +996,15 @@ def report_daly_values(self):
         daly_values_by_pathogen = daly_values_by_pathogen.add_prefix('ALRI_')
         return daly_values_by_pathogen
 
+    def report_prevalence(self):
+        # This returns dataframe that reports on the prevalence of ALRI for all individuals
+        df = self.sim.population.props
+        total_prev = len(
+            df[(df['is_alive']) & (df['ri_current_infection_status'])]
+        ) / len(df[df['is_alive']])
+
+        return {'ALRI': total_prev}
+
     def over_ride_availability_of_certain_consumables(self):
         """Over-ride the availability of certain consumables, according the parameter values provided."""
         p = self.parameters
@@ -1121,7 +1131,6 @@ def look_up_consumables(self):
             get_item_code(item='Salbutamol sulphate 1mg/ml, 5ml_each_CMST'): 2
         }
 
-
     def end_episode(self, person_id):
         """End the episode infection for a person (i.e. reset all properties to show no current infection or
         complications).
@@ -1336,7 +1345,7 @@ def _ultimate_treatment_indicated_for_patient(classification_for_treatment_decis
         elif classification_for_treatment_decision == 'chest_indrawing_pneumonia':
             return {
                 'antibiotic_indicated': (
-                    'Amoxicillin_tablet_or_suspension_5days',   # <-- # <-- First choice antibiotic
+                    'Amoxicillin_tablet_or_suspension_5days',  # <-- # <-- First choice antibiotic
                 ),
                 'oxygen_indicated': False
             }
@@ -1441,11 +1450,11 @@ def make_linear_model(age_effects=None):
                         'age_years',
                         conditions_are_mutually_exclusive=True,
                         conditions_are_exhaustive=True).when(0, age_effects[0])
-                                                       .when(1, age_effects[1])
-                                                       .when(2, age_effects[2])
-                                                       .when(3, age_effects[3])
-                                                       .when(4, age_effects[4])
-                                                       .when('>= 5', 0.0),
+                    .when(1, age_effects[1])
+                    .when(2, age_effects[2])
+                    .when(3, age_effects[3])
+                    .when(4, age_effects[4])
+                    .when('>= 5', 0.0),
                     Predictor('li_wood_burn_stove').when(False, p['rr_ALRI_indoor_air_pollution']),
                     Predictor().when('(va_measles_all_doses == False) & (age_years >= 1)',
                                      p['rr_ALRI_incomplete_measles_immunisation']),
@@ -1675,13 +1684,14 @@ def prob_die_of_alri(self,
 
         def get_odds_of_death(age_in_whole_months):
             """Returns odds of death given age in whole months."""
+
             def get_odds_of_death_for_under_two_month_old(age_in_whole_months):
                 return p[f'base_odds_death_ALRI_{_age_}'] * \
-                       (p[f'or_death_ALRI_{_age_}_by_month_increase_in_age'] ** age_in_whole_months)
+                    (p[f'or_death_ALRI_{_age_}_by_month_increase_in_age'] ** age_in_whole_months)
 
             def get_odds_of_death_for_over_two_month_old(age_in_whole_months):
                 return p[f'base_odds_death_ALRI_{_age_}'] * \
-                       (p[f'or_death_ALRI_{_age_}_by_month_increase_in_age'] ** (age_in_whole_months - 2))
+                    (p[f'or_death_ALRI_{_age_}_by_month_increase_in_age'] ** (age_in_whole_months - 2))
 
             return get_odds_of_death_for_under_two_month_old(age_in_whole_months=age_in_whole_months) \
                 if age_in_whole_months < 2 \
@@ -1887,6 +1897,7 @@ def _prob_treatment_fails_when_cough_or_cold():
         else:
             raise ValueError('Unrecognised imci_symptom_based_classification.')
 
+
 # ---------------------------------------------------------------------------------------------------------
 #   DISEASE MODULE EVENTS
 # ---------------------------------------------------------------------------------------------------------
@@ -3040,7 +3051,8 @@ def apply(self, person_id):
 
         assert 'fast_breathing_pneumonia' == \
                self.module.get_imci_classification_based_on_symptoms(
-                   child_is_younger_than_2_months=False, symptoms=self.sim.modules['SymptomManager'].has_what(person_id=person_id)
+                   child_is_younger_than_2_months=False,
+                   symptoms=self.sim.modules['SymptomManager'].has_what(person_id=person_id)
                )
 
 

src/tlo/methods/bladder_cancer.py

@@ -594,6 +594,15 @@ def report_daly_values(self):
 
         return disability_series_for_alive_persons
 
+    def report_prevalence(self):
+        # This reports on the prevalence of bladder cancer for all individuals
+
+        df = self.sim.population.props
+        total_prev = len(
+            df[(df.bc_status != 'none') & (df.is_alive)]
+        ) / len(df[df.is_alive])
+        return {'Bladder Cancer': total_prev}
+
     def do_at_generic_first_appt(
         self,
         person_id: int,

src/tlo/methods/breast_cancer.py

@@ -571,6 +571,14 @@ def report_daly_values(self):
 
         return disability_series_for_alive_persons
 
+    def report_prevalence(self):
+        # This returns dataframe that reports on the prevalence of breast cancer for all individuals
+        df = self.sim.population.props
+        total_prev = len(
+            df[(df['is_alive']) & (df['brc_status'] != 'none')]
+        ) / len(df[df['is_alive']])
+
+        return {'Breast Cancer': total_prev}
     def do_at_generic_first_appt(
         self,
         person_id: int,

src/tlo/methods/cardio_metabolic_disorders.py

@@ -807,6 +807,18 @@ def left_censor(obs, window_open):
 
         return dw
 
+    def report_prevalence(self):
+        """Report prevalence of disease to the HealthBurden module"""
+        df = self.sim.population.props
+        prevalence_dict = {}
+
+        for condition in self.conditions:
+            prevalence = df[f'nc_{condition}'].sum() / len(df)
+            prevalence_dict[condition] = prevalence
+
+        # Create a DataFrame from the prevalence dictionary
+        return prevalence_dict
+
     def on_hsi_alert(self, person_id, treatment_id):
         """
         This is called whenever there is an HSI event commissioned by one of the other disease modules.

src/tlo/methods/chronicsyndrome.py

@@ -283,6 +283,8 @@ def report_daly_values(self):
 
         return health_values
 
+    def report_prevalence(self):
+        return {'ChronicSyndrome':None}
     def do_at_generic_first_appt_emergency(
         self,
         person_id: int,

src/tlo/methods/copd.py

@@ -182,6 +182,15 @@ def report_daly_values(self):
         df = self.sim.population.props
         return df.loc[df.is_alive, 'ch_lungfunction'].map(self.models.disability_weight_given_lungfunction)
 
+    def report_prevalence(self):
+        # This returns dataframe that reports on the prevalence of COPD for all individuals
+        df = self.sim.population.props
+        total_prev = len(
+            df[(df['is_alive']) & (df['ch_lungfunction'] > 3)] # 3 is mild COPD
+        ) / len(df[df['is_alive']])
+
+        return {'COPD': total_prev}
+
     def define_symptoms(self):
         """Define and register Symptoms"""
         self.sim.modules['SymptomManager'].register_symptom(

src/tlo/methods/demography.py

@@ -134,7 +134,7 @@ def __init__(self, name=None, resourcefilepath=None, equal_allocation_by_distric
         ),
 
         'district_num_of_residence': Property(
-            Types.CATEGORICAL, 
+            Types.CATEGORICAL,
             'The district number in which the person is resident',
             categories=['SET_AT_RUNTIME']
         ),
@@ -383,6 +383,54 @@ def on_birth(self, mother_id, child_id):
                   'mother_age_at_pregnancy': _mother_age_at_pregnancy}
         )
 
+    def report_prevalence(self):
+        """
+        This function reports the prevalence of maternal death and neonatal deaths for this module generated in the previous month.
+        Returns a dataframe with these values.
+        """
+        if 'SimplifiedBirths' in self.sim.modules:
+            neonatal_mortality_rate = None
+            maternal_mortality_rate = None
+            live_births = None
+        else:
+            df = self.sim.population.props
+            if len(df[(df['age_days'] < 29)]) == 0:
+                neonatal_mortality_rate = 0
+                maternal_mortality_rate = 0
+                live_births = 0
+            else:
+                neonatal_deaths = len(df[(df['age_days'] < 29) & (df['age_years'] == 0) & ~(df['is_alive']) & (
+                        df['date_of_death'] >= (self.sim.date - DateOffset(months=1)))])
+                live_births = len(df[(df['age_days'] < 29)])
+                neonatal_mortality_rate = neonatal_deaths/live_births * 1000
+                maternal_direct_deaths = len(df.loc[
+                                                 (df['cause_of_death'] == 'Maternal Disorders') &
+                                                 (df['date_of_death'] >= (self.sim.date - DateOffset(months=1)))
+                                                 ])
+                indirect_deaths_non_hiv = len(df.loc[
+                                                  (df['is_pregnant'] | df['la_is_postpartum']) &
+                                                  (df['cause_of_death'].str.contains(
+                                                      'Malaria|Suicide|ever_stroke|diabetes|chronic_ischemic_hd|ever_heart_attack|chronic_kidney_disease') |
+                                                   (df['cause_of_death'] == 'TB')) &
+                                                  (df['date_of_death'] >= (self.sim.date - DateOffset(months=1)))
+                                                  ])
+                indirect_deaths_hiv = len(df.loc[
+                                                (df['is_pregnant'] | df['la_is_postpartum']) &
+                                                df['cause_of_death'].str.contains('AIDS_non_TB|AIDS_TB') &
+                                                (df['date_of_death'] >= (self.sim.date - DateOffset(months=1)))
+                                                ])
+                indirect_deaths_hiv = indirect_deaths_hiv * 0.3  # https://www.who.int/publications/i/item/9789240068759
+                maternal_deaths = maternal_direct_deaths + indirect_deaths_non_hiv + indirect_deaths_hiv
+                maternal_mortality_rate = maternal_deaths/live_births * 1000
+            print(live_births)
+
+        metrics_dict = {
+            'NMR': neonatal_mortality_rate,
+            'MMR': maternal_mortality_rate,
+            'live_births': live_births
+        }
+        return metrics_dict
+
     def _edit_init_pop_to_prevent_persons_greater_than_max_age(self, df, max_age: int):
         """Return an edited version of the `pd.DataFrame` describing the probability of persons in the population being
         created with certain characteristics to reflect the constraint the persons aged greater than `max_age_initial`

src/tlo/methods/depression.py

@@ -554,6 +554,20 @@ def right_censor(obs, window_close):
 
         return av_daly_wt_last_month
 
+    def report_prevalence(self):
+        # This reports on the prevalence of depression for all individuals
+
+        df = self.sim.population.props
+        any_depr_in_the_last_month = df[((df['is_alive']) & (
+            ~pd.isnull(df['de_date_init_most_rec_depr']) & (df['de_date_init_most_rec_depr'] <= self.sim.date)
+        ) & (
+                                         pd.isnull(df['de_date_depr_resolved']) |
+                                         (df['de_date_depr_resolved'] >= (self.sim.date - DateOffset(months=1)))
+                                     ))]
+        total_prev = len(
+            any_depr_in_the_last_month
+        ) / len(df[df.is_alive])
+        return {'Depression': total_prev}
     def _check_for_suspected_depression(
         self, symptoms: List[str], treatment_id: str, has_even_been_diagnosed: bool
     ):

src/tlo/methods/diarrhoea.py

@@ -649,6 +649,13 @@ def report_daly_values(self):
         average_daly_weight_in_last_month = pd.Series(values, idx) / days_last_month
         return average_daly_weight_in_last_month.reindex(index=df.loc[df.is_alive].index, fill_value=0.0)
 
+    def report_prevalence(self):
+        df = self.sim.population.props
+        total_prev = len(
+            df[df.gi_has_diarrhoea & df.is_alive]
+        ) / len(df[df.is_alive])
+        return {'Diarrhoea': total_prev}
+
     def look_up_consumables(self):
         """Look up and store the consumables item codes used in each of the HSI."""
         ic = self.sim.modules['HealthSystem'].get_item_code_from_item_name

src/tlo/methods/epilepsy.py

@@ -241,6 +241,16 @@ def report_daly_values(self):
         df = self.sim.population.props  # shortcut to population properties dataframe
         return df.loc[df.is_alive, 'ep_disability']
 
+    def report_prevalence(self):
+        # This returns dataframe that reports on the prevalence of epilepsy for all individuals
+        df = self.sim.population.props
+        total_prev = len(
+            df[(df['is_alive']) & (df['ep_seiz_stat'] != '0')]
+        ) / len(df[df['is_alive']])
+
+        return {'Epilepsy': total_prev}
+
+
     def transition_seizure_stat(self):
         """
         This function handles all transitions in epilepsy seizure status, for those on and off anti epileptics. The