UCL
diff --git a/‎resources/ResourceFile_RecordSummaryStats.csv‎
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diff --git a/‎src/tlo/analysis/utils.py‎
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diff --git a/‎src/tlo/methods/__init__.py‎
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diff --git a/‎src/tlo/methods/alri.py‎
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diff --git a/‎src/tlo/methods/bladder_cancer.py‎
Lines changed: 10 additions & 1 deletion b/‎src/tlo/methods/bladder_cancer.py‎
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diff --git a/‎src/tlo/methods/breast_cancer.py‎
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diff --git a/‎src/tlo/methods/cardio_metabolic_disorders.py‎
Lines changed: 13 additions & 1 deletion b/‎src/tlo/methods/cardio_metabolic_disorders.py‎
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diff --git a/‎src/tlo/methods/cervical_cancer.py‎
Lines changed: 14 additions & 1 deletion b/‎src/tlo/methods/cervical_cancer.py‎
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diff --git a/‎src/tlo/methods/copd.py‎
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diff --git a/‎src/tlo/methods/depression.py‎
Lines changed: 19 additions & 1 deletion b/‎src/tlo/methods/depression.py‎
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94601cde7187f0dc5a6c98c664ac0595651a3cc39b4118f9529031c27693b707
+size 63
@@ -11,7 +11,7 @@
 from collections.abc import Mapping
 from pathlib import Path
 from types import MappingProxyType
-from typing import Callable, Dict, Iterable, List, Literal, Optional, TextIO, Tuple, Union
+from typing import Any, Callable, Dict, Iterable, List, Literal, Optional, TextIO, Tuple, Union
 
 import git
 import matplotlib.colors as mcolors
@@ -1460,3 +1460,34 @@ def mix_scenarios(*dicts) -> Dict:
                 d[mod].update({param: value})
 
     return d
+
+
+def flatten_nested_dict(my_dict, sep='_'):
+    """Flatten a nested dictionary into a single level dictionary."""
+    return pd.pandas.io.json._normalize.nested_to_record(my_dict, sep=sep)
+
+
+def get_counts_by_sex_and_age_group(df: pd.DataFrame, property: str, targets: Optional[Tuple[Any]|str] = None) -> dict:
+    """Returns dict giving counts (by sex and age-group) of alive individuals with truthy
+    values for that property (if no `target` is provided) or with a value included in `targets` (if a `target` is
+    provided).
+
+    Returns: {sex: {age_group: count}}
+    """
+
+    if targets is None:
+        counts = df.loc[
+            df.is_alive & df[property]
+        ].groupby(['sex', 'age_range']).size().unstack(fill_value=0)
+
+    elif isinstance(targets, tuple):
+        counts = df.loc[
+            df.is_alive & df[property].isin(targets)
+        ].groupby(['sex', 'age_range']).size().unstack(fill_value=0)
+
+    elif isinstance(targets, str):
+        counts = df.loc[
+            df.is_alive & (df[property] == targets)
+        ].groupby(['sex', 'age_range']).size().unstack(fill_value=0)
+
+    return counts.to_dict(orient='index')
@@ -6,3 +6,4 @@ class Metadata(Enum):
     USES_SYMPTOMMANAGER = auto()  # The 'Symptom Manager' recognises modules with this label.
     USES_HEALTHSYSTEM = auto()    # The 'HealthSystem' recognises modules with this label.
     USES_HEALTHBURDEN = auto()    # The 'HealthBurden' module recognises modules with this label.
+    REPORTS_DISEASE_NUMBERS = auto() # The 'DiseaseNumbers' module recognises modules with this label.
@@ -30,6 +30,7 @@
 import pandas as pd
 
 from tlo import DAYS_IN_YEAR, DateOffset, Module, Parameter, Property, Types, logging
+from tlo.analysis.utils import get_counts_by_sex_and_age_group
 from tlo.events import Event, IndividualScopeEventMixin, PopulationScopeEventMixin, RegularEvent
 from tlo.lm import LinearModel, LinearModelType, Predictor
 from tlo.methods import Metadata
@@ -75,7 +76,8 @@ class Alri(Module, GenericFirstAppointmentsMixin):
         Metadata.DISEASE_MODULE,
         Metadata.USES_SYMPTOMMANAGER,
         Metadata.USES_HEALTHSYSTEM,
-        Metadata.USES_HEALTHBURDEN
+        Metadata.USES_HEALTHBURDEN,
+        Metadata.REPORTS_DISEASE_NUMBERS
     }
 
     pathogens = {
@@ -1017,6 +1019,12 @@ def report_daly_values(self):
         daly_values_by_pathogen = daly_values_by_pathogen.add_prefix('ALRI_')
         return daly_values_by_pathogen
 
+    def report_summary_stats(self):
+        # This reports age- and sex-specific prevalence of ALRI for all individuals
+        df = self.sim.population.props
+        number_by_age_group_sex = get_counts_by_sex_and_age_group(df, 'ri_current_infection_status')
+        return {'number_current_ri': number_by_age_group_sex}
+
     def over_ride_availability_of_certain_consumables(self):
         """Over-ride the availability of certain consumables, according the parameter values provided."""
         p = self.parameters
 
@@ -13,6 +13,7 @@
 import pandas as pd
 
 from tlo import DateOffset, Module, Parameter, Property, Types, logging
+from tlo.analysis.utils import get_counts_by_sex_and_age_group
 from tlo.events import IndividualScopeEventMixin, PopulationScopeEventMixin, RegularEvent
 from tlo.lm import LinearModel, LinearModelType, Predictor
 from tlo.methods import Metadata
@@ -52,7 +53,8 @@ def __init__(self, name=None):
         Metadata.DISEASE_MODULE,
         Metadata.USES_SYMPTOMMANAGER,
         Metadata.USES_HEALTHSYSTEM,
-        Metadata.USES_HEALTHBURDEN
+        Metadata.USES_HEALTHBURDEN,
+        Metadata.REPORTS_DISEASE_NUMBERS
     }
 
     # Declare Causes of Death
@@ -635,6 +637,13 @@ def report_daly_values(self):
 
         return disability_series_for_alive_persons
 
+    def report_summary_stats(self):
+        # This reports age- and sex-specific prevalence of bladder cancer for all individuals
+        df = self.sim.population.props
+        number_by_age_group_sex = get_counts_by_sex_and_age_group(df, 'bc_status', ("tis_t1", "t2p", "metastatic"))
+        return {'number_at_any_stage': number_by_age_group_sex}
+
+
     def do_at_generic_first_appt(
         self,
         person_id: int,
 
@@ -12,6 +12,7 @@
 import pandas as pd
 
 from tlo import DateOffset, Module, Parameter, Property, Types, logging
+from tlo.analysis.utils import get_counts_by_sex_and_age_group
 from tlo.events import IndividualScopeEventMixin, PopulationScopeEventMixin, RegularEvent
 from tlo.lm import LinearModel, LinearModelType, Predictor
 from tlo.methods import Metadata
@@ -50,7 +51,8 @@ def __init__(self, name=None):
         Metadata.DISEASE_MODULE,
         Metadata.USES_SYMPTOMMANAGER,
         Metadata.USES_HEALTHSYSTEM,
-        Metadata.USES_HEALTHBURDEN
+        Metadata.USES_HEALTHBURDEN,
+        Metadata.REPORTS_DISEASE_NUMBERS
     }
 
     # Declare Causes of Death
@@ -579,6 +581,13 @@ def report_daly_values(self):
 
         return disability_series_for_alive_persons
 
+    def report_summary_stats(self):
+        # This reports age- and sex-specific prevalence of breast cancer for all individuals
+        df = self.sim.population.props
+        number_by_age_group_sex = (
+            get_counts_by_sex_and_age_group(df, 'brc_status', ("stage1", "stage2", "stage3", "stage4")))
+        return {'number_at_any_stage': number_by_age_group_sex}
+
     def do_at_generic_first_appt(
         self,
         person_id: int,
 
@@ -21,6 +21,7 @@
 import pandas as pd
 
 from tlo import DAYS_IN_YEAR, DateOffset, Module, Parameter, Property, Types, logging
+from tlo.analysis.utils import get_counts_by_sex_and_age_group
 from tlo.events import Event, IndividualScopeEventMixin, PopulationScopeEventMixin, RegularEvent
 from tlo.lm import LinearModel, LinearModelType, Predictor
 from tlo.methods import Metadata
@@ -71,7 +72,8 @@ class CardioMetabolicDisorders(Module, GenericFirstAppointmentsMixin):
         Metadata.DISEASE_MODULE,
         Metadata.USES_SYMPTOMMANAGER,
         Metadata.USES_HEALTHSYSTEM,
-        Metadata.USES_HEALTHBURDEN
+        Metadata.USES_HEALTHBURDEN,
+        Metadata.REPORTS_DISEASE_NUMBERS
     }
 
     # Declare Causes of Death
@@ -848,6 +850,16 @@ def left_censor(obs, window_open):
 
         return dw
 
+    def report_summary_stats(self):
+        """Report age- and sex-specific prevalence of diseases to the HealthBurden module"""
+        df = self.sim.population.props
+        number_by_age_group_sex_dict = {}
+
+        for condition in self.conditions:
+            number_by_age_group_sex_dict[f'number with {condition}'] = \
+                get_counts_by_sex_and_age_group(df, f'nc_{condition}')
+        return number_by_age_group_sex_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.
 
@@ -12,6 +12,7 @@
 import pandas as pd
 
 from tlo import DAYS_IN_YEAR, DateOffset, Module, Parameter, Property, Types, logging
+from tlo.analysis.utils import get_counts_by_sex_and_age_group
 from tlo.events import Event, IndividualScopeEventMixin, PopulationScopeEventMixin, RegularEvent
 from tlo.lm import LinearModel, LinearModelType, Predictor
 from tlo.methods import Metadata
@@ -44,7 +45,8 @@ class CervicalCancer(Module, GenericFirstAppointmentsMixin):
         Metadata.DISEASE_MODULE,
         Metadata.USES_SYMPTOMMANAGER,
         Metadata.USES_HEALTHSYSTEM,
-        Metadata.USES_HEALTHBURDEN
+        Metadata.USES_HEALTHBURDEN,
+        Metadata.REPORTS_DISEASE_NUMBERS
     }
 
     # Declare Causes of Death
@@ -745,6 +747,17 @@ def report_daly_values(self):
 
         return disability_series_for_alive_persons
 
+    def report_summary_stats(self):
+        df = self.sim.population.props
+        number_by_age_group_sex = (
+            get_counts_by_sex_and_age_group(df,
+                                            'ce_hpv_cc_status',
+                                            ("cin1", "cin2", "cin3",
+                                             "stage1","stage2a", "stage2b", "stage3", "stage4")
+                                            )
+        )
+        return {'number_at_any_stage': number_by_age_group_sex}
+
     def do_at_generic_first_appt(
         self,
         person_id: int,
 
@@ -6,7 +6,10 @@
 import pandas as pd
 
 from tlo import Module, Parameter, Property, Types, logging
-from tlo.analysis.utils import flatten_multi_index_series_into_dict_for_logging
+from tlo.analysis.utils import (
+    flatten_multi_index_series_into_dict_for_logging,
+    get_counts_by_sex_and_age_group,
+)
 from tlo.events import Event, IndividualScopeEventMixin, PopulationScopeEventMixin, RegularEvent
 from tlo.lm import LinearModel, LinearModelType, Predictor
 from tlo.methods import Metadata
@@ -42,6 +45,7 @@ class Copd(Module, GenericFirstAppointmentsMixin):
         Metadata.USES_SYMPTOMMANAGER,
         Metadata.USES_HEALTHSYSTEM,
         Metadata.USES_HEALTHBURDEN,
+        Metadata.REPORTS_DISEASE_NUMBERS
     }
 
     CAUSES_OF_DEATH = {
@@ -218,6 +222,12 @@ 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_summary_stats(self):
+        # This reports age- and sex-specific prevalence of COPD for all individuals
+        df = self.sim.population.props
+        number_by_age_group_sex = get_counts_by_sex_and_age_group(df, 'ch_lungfunction', (4, 5, 6))
+        return {'number_with_poor_ch_lungfunction': number_by_age_group_sex}
+
     def define_symptoms(self):
         """Define and register Symptoms"""
         self.sim.modules['SymptomManager'].register_symptom(
 
@@ -47,7 +47,8 @@ def __init__(self, name=None):
         Metadata.DISEASE_MODULE,
         Metadata.USES_SYMPTOMMANAGER,
         Metadata.USES_HEALTHSYSTEM,
-        Metadata.USES_HEALTHBURDEN
+        Metadata.USES_HEALTHBURDEN,
+        Metadata.REPORTS_DISEASE_NUMBERS
     }
 
     # Declare Causes of Death
@@ -595,6 +596,23 @@ def right_censor(obs, window_close):
 
         return av_daly_wt_last_month
 
+    def report_summary_stats(self):
+        # This reports age- and sex-specific 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)))
+            )
+        ]
+        number_depressed = (
+            any_depr_in_the_last_month.groupby(['sex', 'age_range']).size().unstack(fill_value=0)
+        ).to_dict(orient='index')
+        return {'number_with_depressive_episode_in_past_month': number_depressed}
+
     def _check_for_suspected_depression(
         self, symptoms: List[str], treatment_id: str, has_even_been_diagnosed: bool
     ):
Original file line number	Diff line number	Diff line change
`@@ -0,0 +1,3 @@`
	`1`	`+version https://git-lfs.github.com/spec/v1`
	`2`	`+oid sha256:94601cde7187f0dc5a6c98c664ac0595651a3cc39b4118f9529031c27693b707`
	`3`	`+size 63`