Retrieve annual capacity by cadre

Author: sangeetabhatia03

src/scripts/lcoa_inputs_from_tlo_analyses/analysis_effect_of_treatment_ids.py

@@ -172,6 +172,7 @@ def apply(
     results['input_costs'] = input_costs
 
     # Computing incremental costs
+    # TODO Check with Sakshi if these are annual costs; as everything else is annual.
     if do_comparison:
         print("Computing incremental_scenario_cost...")
         start = perf_counter()
@@ -191,9 +192,6 @@ def apply(
 
         incremental_scenario_cost_summarized = compute_summary_statistics(incremental_scenario_cost, 'median').iloc[0].unstack()
 
-
-
-
     # Get total population by year
     print("Extracting population data...")
     total_population_by_year = (
@@ -307,7 +305,7 @@ def apply(
             central_measure='median'
         ).iloc[0].unstack()
         # Run-by-run incremental cost-effectiveness ratio calculation
-        icers = incremental_scenario_cost.T /dalys_averted
+        icers = incremental_scenario_cost.T / dalys_averted
         icers_summarized = compute_summary_statistics(icers.T, central_measure='median').iloc[0].unstack()
         dalys_averted = compute_summary_statistics(dalys_averted.T, central_measure='median').iloc[0].unstack()
 
@@ -318,6 +316,7 @@ def apply(
     # From this we will extract the run-wise delta in capacity used relative to the Nothing scenario, for each cadre
     # and summarise. However since no HSIs are delivered in the Nothing scenario, the capacity used in that scenario is zero,
     # so the delta relative to Nothing is just the capacity used in each scenario.
+    # TODO: Check if this should be scaled with population or used as is.
     annual_capacity_used_by_cadre_and_level = extract_results(
         results_folder,
         module='tlo.methods.healthsystem.summary',
@@ -326,25 +325,44 @@ def apply(
         do_scaling=True,
         autodiscover=True,
     )
-    # Sum across all years and facility levels; so we get the *total* capacity used over the whole period
+    # Sum across all facility levels and average across years; so we get the *average* annual capacity used over the whole period
     # TODO: Check with Sakshi if this is what we want.
     mask = annual_capacity_used_by_cadre_and_level.index.get_level_values(0).isin(range(2026, 2040))
     capacity_used_by_cadre = (
-        annual_capacity_used_by_cadre_and_level[mask].groupby(['OfficerType']).
+        annual_capacity_used_by_cadre_and_level[mask].groupby(['OfficerType', 'year']).
         sum().
+        groupby(['OfficerType']).
+        mean().
         pipe(set_param_names_as_column_index_level_0, param_names=param_names)
     )
 
     capacity_used_by_cadre = (
         compute_summary_statistics(capacity_used_by_cadre, central_measure='median')
     )
 
+    # Get the total available caapacity by cadre needed for LCOA
+    # resources/healthsystem/human_resources/actual/ResourceFile_Daily_Capabilities.csv
+    daily_capacity_by_cadre_and_level = (
+        pd.read_csv(resourcefilepath / "healthsystem" / "human_resources" / "actual" / "ResourceFile_Daily_Capabilities.csv")
+    )
+    # This gives the total minutes available per day by cadre and facility level.
+    # Sum across levels to get cadre specific constraints, and multiply by 365 to get annual capacity
+    annual_capacity_by_cadre = (
+        daily_capacity_by_cadre_and_level.groupby('Officer_Category')['Total_Mins_Per_Day'].sum() * 365
+    )
+
+    # Add consumables budget to this dictionary so that we have everything in one place
+    # USD 225,602,946 (203136642 from donors + 22466304 from the government)
+    # Ref Revision of Malawi’s Health Bene ts Package: A Critical Analysis of Policy Formulation and Implementation
+    results['annual_consumables_budget'] = 225602946
+
     results['dalys'] = dalys
     results['dalys_averted'] = dalys_averted if do_comparison else None
     results['pc_dalys_averted'] = pc_dalys_averted if do_comparison else None
     results['icers_summarized'] = icers_summarized if do_comparison else None
     results['incremental_scenario_cost'] = incremental_scenario_cost_summarized if do_comparison else None
     results['capacity_used_by_cadre'] = capacity_used_by_cadre
+    results['annual_capacity_by_cadre'] = annual_capacity_by_cadre
 
     return results
 

src/scripts/lcoa_inputs_from_tlo_analyses/figures_effect_of_treatment_ids.py

@@ -6,28 +6,25 @@
 import pickle
 import pandas as pd
 import matplotlib.pyplot as plt
-from scripts.calibration_analyses.analysis_scripts import plot_legends
+
 from scripts.lcoa_inputs_from_tlo_analyses.results_processing_utils import (
     get_parameter_names_from_scenario_file,
-    get_periods_within_target_period,
     format_scenario_name,
-    target_period,
 )
 from scripts.lcoa_inputs_from_tlo_analyses.fig_utils import (
     make_graph_file_name,
     do_barh_plot_with_ci,
-    do_bar_plot_with_ci,
     plot_cadre_time_by_draw_stacked,
     plot_deaths_by_period_for_cause,
     plot_deaths_by_period_for_draw,
     plot_hsi_counts_by_period_for_draw,
     plot_population_by_year,
 )
-from tlo import Date
+
 
 # python src/scripts/lcoa_inputs_from_tlo_analyses/figures_effect_of_treatment_ids.py outputs/generated_outputs/2041-01-01_fullresults.pkl --output_folder=figs2
 
-TARGET_PERIOD = (Date(2025, 1, 1), Date(2041, 1, 1))
+
 PERIOD_LENGTH_YEARS_FOR_BAR_PLOTS = 1
 
 
@@ -73,7 +70,6 @@ def apply(results_files: list[Path], output_folder: Path, resourcefilepath: Path
     counts_of_hsi_in_implementation_period = counts_of_hsi_in_implementation_period.drop(['2010-2041'], level=1)
     capacity_used_by_cadre = primary_results.get("capacity_used_by_cadre")
 
-
     result_df_by_period = pd.DataFrame([
         {'treatment_id_included': draw, 'nonzero_hsis': treatment_id, 'period': period}
         for draw in counts_of_hsi_in_implementation_period.columns.get_level_values(0).unique()

src/scripts/lcoa_inputs_from_tlo_analyses/run_preaggregated_optimizer.py

@@ -86,10 +86,7 @@ def _aggregate_hr_by_intervention(
     if "central" not in capacity_used_by_cadre.columns:
         raise ValueError("results['capacity_used_by_cadre'] must contain a 'central' column.")
 
-    if capacity_used_by_cadre.index.nlevels == 1:
-        officer_types = capacity_used_by_cadre.index.astype(str).tolist()
-    else:
-        officer_types = capacity_used_by_cadre.index.get_level_values(0).astype(str).tolist()
+    officer_types = capacity_used_by_cadre.index.get_level_values(0).astype(str).tolist()
 
     mapping = _build_hr_mapping(officer_types)
     if not mapping: