Nurse Shortages

resources/healthsystem/ResourceFile_HealthSystem_parameters.csv

@@ -26,3 +26,5 @@ year_use_funded_or_actual_staffing_switch,2100
 cons_override_treatment_ids,[]
 cons_override_treatment_ids_prob_avail,1.0
 clinic_configuration_name,Default
+year_HR_scaling_by_district_and_officer_type,2100
+HR_scaling_by_district_and_officer_type_mode,default

resources/healthsystem/human_resources/scaling_capabilities/ResourceFile_HR_scaling_by_district_and_officer_type/default.csv

@@ -0,0 +1,38 @@
+District,Clinical,Nursing_and_Midwifery,Pharmacy,DCSA,Dental,Laboratory,Mental,Nutrition,Radiography
+Balaka,1,1,1,1,1,1,1,1,1
+Blantyre,1,1,1,1,1,1,1,1,1
+Blantyre City,1,1,1,1,1,1,1,1,1
+Referral Hospital_Central,1,1,1,1,1,1,1,1,1
+Referral Hospital_Northern,1,1,1,1,1,1,1,1,1
+Referral Hospital_Southern,1,1,1,1,1,1,1,1,1
+Chikwawa,1,1,1,1,1,1,1,1,1
+Chiradzulu,1,1,1,1,1,1,1,1,1
+Chitipa,1,1,1,1,1,1,1,1,1
+Dedza,1,1,1,1,1,1,1,1,1
+Dowa,1,1,1,1,1,1,1,1,1
+Headquarter,1,1,1,1,1,1,1,1,1
+Karonga,1,1,1,1,1,1,1,1,1
+Kasungu,1,1,1,1,1,1,1,1,1
+Likoma,1,1,1,1,1,1,1,1,1
+Lilongwe,1,1,1,1,1,1,1,1,1
+Lilongwe City,1,1,1,1,1,1,1,1,1
+Machinga,1,1,1,1,1,1,1,1,1
+Mangochi,1,1,1,1,1,1,1,1,1
+Mchinji,1,1,1,1,1,1,1,1,1
+Mulanje,1,1,1,1,1,1,1,1,1
+Mwanza,1,1,1,1,1,1,1,1,1
+Mzimba,1,1,1,1,1,1,1,1,1
+Mzuzu City,1,1,1,1,1,1,1,1,1
+Neno,1,1,1,1,1,1,1,1,1
+Nkhata Bay,1,1,1,1,1,1,1,1,1
+Nkhotakota,1,1,1,1,1,1,1,1,1
+Nsanje,1,1,1,1,1,1,1,1,1
+Ntcheu,1,1,1,1,1,1,1,1,1
+Ntchisi,1,1,1,1,1,1,1,1,1
+Phalombe,1,1,1,1,1,1,1,1,1
+Rumphi,1,1,1,1,1,1,1,1,1
+Salima,1,1,1,1,1,1,1,1,1
+Thyolo,1,1,1,1,1,1,1,1,1
+Zomba,1,1,1,1,1,1,1,1,1
+Zomba City,1,1,1,1,1,1,1,1,1
+Zomba Mental Hospital,1,1,1,1,1,1,1,1,1

resources/healthsystem/human_resources/scaling_capabilities/ResourceFile_HR_scaling_by_district_and_officer_type/establishment_by_district_and_CNP.csv

@@ -0,0 +1,38 @@
+District,Clinical,Nursing_and_Midwifery,Pharmacy,DCSA,Dental,Laboratory,Mental,Nutrition,Radiography
+Balaka,1.174526999,1.169316612,2.052879982,1,1,1,1,1,1
+Blantyre,0.759811383,0.89730864,1.578474944,1,1,1,1,1,1
+Blantyre City,0.759811383,0.89730864,1.578474944,1,1,1,1,1,1
+Referral Hospital_Central,2.486954763,1.355424224,1.412864458,1,1,1,1,1,1
+Referral Hospital_Northern,2.197010075,1.070124208,0.897056799,1,1,1,1,1,1
+Referral Hospital_Southern,2.177651732,1.402012229,0.710875199,1,1,1,1,1,1
+Chikwawa,1.571206225,1.98442501,1.947217042,1,1,1,1,1,1
+Chiradzulu,1.508760236,1.900692263,2.173637628,1,1,1,1,1,1
+Chitipa,1.145249933,1.936513522,1.690607044,1,1,1,1,1,1
+Dedza,1.23965271,1.543188619,2.264205862,1,1,1,1,1,1
+Dowa,1.435421075,1.737431437,1.792297693,1,1,1,1,1,1
+Headquarter,1.183424931,3.62272938,1.449091752,1,1,1,1,1,1
+Karonga,1.335881459,1.872527654,3.043092679,1,1,1,1,1,1
+Kasungu,1.482635542,1.937984358,2.557220739,1,1,1,1,1,1
+Likoma,2.233502608,3.155265589,3.042577533,1,1,1,1,1,1
+Lilongwe,1.380087383,1.167518839,1.709475426,1,1,1,1,1,1
+Lilongwe City,1.380087383,1.167518839,1.709475426,1,1,1,1,1,1
+Machinga,1.584340315,1.853985036,1.96662452,1,1,1,1,1,1
+Mangochi,1.212312054,1.260791694,2.940803849,1,1,1,1,1,1
+Mchinji,1.319220321,1.722723439,1.716029706,1,1,1,1,1,1
+Mulanje,1.442124965,1.488502197,3.550274792,1,1,1,1,1,1
+Mwanza,2.032262823,1.646695173,1.368586655,1,1,1,1,1,1
+Mzimba,1.664191309,1.462212301,2.258878319,1,1,1,1,1,1
+Mzuzu City,1.664191309,1.462212302,2.258878319,1,1,1,1,1,1
+Neno,1.90022409,2.133827415,2.264205862,1,1,1,1,1,1
+Nkhata Bay,1.64711602,2.260579269,2.781346584,1,1,1,1,1,1
+Nkhotakota,1.798270487,1.964020582,2.225390905,1,1,1,1,1,1
+Nsanje,1.560560348,2.028728453,2.475531743,1,1,1,1,1,1
+Ntcheu,1.436599582,1.544519745,2.753274328,1,1,1,1,1,1
+Ntchisi,1.511195684,1.885047321,2.041902014,1,1,1,1,1,1
+Phalombe,1.81136469,1.561895301,1.783497541,1,1,1,1,1,1
+Rumphi,1.680946432,1.643251104,1.494375869,1,1,1,1,1,1
+Salima,1.156345943,1.449091752,2.371241048,1,1,1,1,1,1
+Thyolo,1.283305831,1.360371849,2.083069393,1,1,1,1,1,1
+Zomba,1.12943916,1.10442287,1.332229514,1,1,1,1,1,1
+Zomba City,1.12943916,1.10442287,1.332229514,1,1,1,1,1,1
+Zomba Mental Hospital,1.521546339,0.85520169,2.173637628,1,1,1,1,1,1

resources/healthsystem/human_resources/scaling_capabilities/ResourceFile_HR_scaling_by_district_and_officer_type/establishment_by_district_and_N.csv

@@ -0,0 +1,38 @@
+District,Clinical,Nursing_and_Midwifery,Pharmacy,DCSA,Dental,Laboratory,Mental,Nutrition,Radiography
+Balaka,1,1.169316612,1,1,1,1,1,1,1
+Blantyre,1,0.89730864,1,1,1,1,1,1,1
+Blantyre City,1,0.89730864,1,1,1,1,1,1,1
+Referral Hospital_Central,1,1.355424224,1,1,1,1,1,1,1
+Referral Hospital_Northern,1,1.070124208,1,1,1,1,1,1,1
+Referral Hospital_Southern,1,1.402012229,1,1,1,1,1,1,1
+Chikwawa,1,1.98442501,1,1,1,1,1,1,1
+Chiradzulu,1,1.900692263,1,1,1,1,1,1,1
+Chitipa,1,1.936513522,1,1,1,1,1,1,1
+Dedza,1,1.543188619,1,1,1,1,1,1,1
+Dowa,1,1.737431437,1,1,1,1,1,1,1
+Headquarter,1,3.62272938,1,1,1,1,1,1,1
+Karonga,1,1.872527654,1,1,1,1,1,1,1
+Kasungu,1,1.937984358,1,1,1,1,1,1,1
+Likoma,1,3.155265589,1,1,1,1,1,1,1
+Lilongwe,1,1.167518839,1,1,1,1,1,1,1
+Lilongwe City,1,1.167518839,1,1,1,1,1,1,1
+Machinga,1,1.853985036,1,1,1,1,1,1,1
+Mangochi,1,1.260791694,1,1,1,1,1,1,1
+Mchinji,1,1.722723439,1,1,1,1,1,1,1
+Mulanje,1,1.488502197,1,1,1,1,1,1,1
+Mwanza,1,1.646695173,1,1,1,1,1,1,1
+Mzimba,1,1.462212301,1,1,1,1,1,1,1
+Mzuzu City,1,1.462212302,1,1,1,1,1,1,1
+Neno,1,2.133827415,1,1,1,1,1,1,1
+Nkhata Bay,1,2.260579269,1,1,1,1,1,1,1
+Nkhotakota,1,1.964020582,1,1,1,1,1,1,1
+Nsanje,1,2.028728453,1,1,1,1,1,1,1
+Ntcheu,1,1.544519745,1,1,1,1,1,1,1
+Ntchisi,1,1.885047321,1,1,1,1,1,1,1
+Phalombe,1,1.561895301,1,1,1,1,1,1,1
+Rumphi,1,1.643251104,1,1,1,1,1,1,1
+Salima,1,1.449091752,1,1,1,1,1,1,1
+Thyolo,1,1.360371849,1,1,1,1,1,1,1
+Zomba,1,1.10442287,1,1,1,1,1,1,1
+Zomba City,1,1.10442287,1,1,1,1,1,1,1
+Zomba Mental Hospital,1,0.85520169,1,1,1,1,1,1,1

resources/healthsystem/human_resources/scaling_capabilities/ResourceFile_HR_scaling_by_level_and_officer_type/establishment_staffing_CNP.csv

@@ -0,0 +1,10 @@
+Officer_Category,L0_factor,L1a_factor,L1b_factor,L2_factor,L3_factor,L4_factor,L5_factor
+Clinical,1.536932742,1.536932742,1.536932742,1.536932742,1.536932742,1.536932742,1.536932742
+DCSA,1,1,1,1,1,1,1
+Dental,1,1,1,1,1,1,1
+Laboratory,1,1,1,1,1,1,1
+Mental,1,1,1,1,1,1,1
+Nursing_and_Midwifery,1.455369535,1.455369535,1.455369535,1.455369535,1.455369535,1.455369535,1.455369535
+Nutrition,1,1,1,1,1,1,1
+Pharmacy,1.855698791,1.855698791,1.855698791,1.855698791,1.855698791,1.855698791,1.855698791
+Radiography,1,1,1,1,1,1,1

resources/healthsystem/human_resources/scaling_capabilities/ResourceFile_HR_scaling_by_level_and_officer_type/establishment_staffing_N.csv

@@ -0,0 +1,10 @@
+Officer_Category,L0_factor,L1a_factor,L1b_factor,L2_factor,L3_factor,L4_factor,L5_factor
+Clinical,1,1,1,1,1,1,1
+DCSA,1,1,1,1,1,1,1
+Dental,1,1,1,1,1,1,1
+Laboratory,1,1,1,1,1,1,1
+Mental,1,1,1,1,1,1,1
+Nursing_and_Midwifery,1.455369535,1.455369535,1.455369535,1.455369535,1.455369535,1.455369535,1.455369535
+Nutrition,1,1,1,1,1,1,1
+Pharmacy,1,1,1,1,1,1,1
+Radiography,1,1,1,1,1,1,1

resources/healthsystem/human_resources/scaling_capabilities/ResourceFile_HR_scaling_by_level_and_officer_type/worse_staffing_N.csv

@@ -0,0 +1,10 @@
+Officer_Category,L0_factor,L1a_factor,L1b_factor,L2_factor,L3_factor,L4_factor,L5_factor
+Clinical,1,1,1,1,1,1,1
+DCSA,1,1,1,1,1,1,1
+Dental,1,1,1,1,1,1,1
+Laboratory,1,1,1,1,1,1,1
+Mental,1,1,1,1,1,1,1
+Nursing_and_Midwifery,0.85,0.85,0.85,0.85,0.85,0.85,0.85
+Nutrition,1,1,1,1,1,1,1
+Pharmacy,1,1,1,1,1,1,1
+Radiography,1,1,1,1,1,1,1

src/scripts/nurses_analyses/analysis_nurses_scenario.py

@@ -0,0 +1,215 @@
+"""This file uses the results of the results of running `nurse_analyses/nurses_scenario_analyses.py` to make some summary
+ graphs."""
+
+import argparse
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+from scripts.nurses_analyses.nurses_scenario_analyses import StaffingScenario
+from tlo.analysis.utils import (
+    extract_results,
+    get_scenario_info,
+    load_pickled_dataframes,
+    make_age_grp_lookup,
+    make_age_grp_types,
+    summarize,
+)
+
+
+# Rename draw numbers to scenario names
+def set_param_names_as_column_index_level_0(_df, param_names):
+    """Set column index level 0 (draw numbers) to scenario names."""
+    ordered_param_names = {i: x for i, x in enumerate(param_names)}
+    names_of_cols_level0 = [
+        ordered_param_names.get(col)
+        for col in _df.columns.levels[0]
+    ]
+    _df.columns = _df.columns.set_levels(names_of_cols_level0, level=0)
+    return _df
+
+
+def extract_total_deaths(results_folder):
+    def extract_deaths_total(df: pd.DataFrame) -> pd.Series:
+        return pd.Series({"Total": len(df)})
+
+    return extract_results(
+        results_folder,
+        module="tlo.methods.demography",
+        key="death",
+        custom_generate_series=extract_deaths_total,
+        do_scaling=True
+    )
+
+
+def plot_summarized_total_deaths(summarized_total_deaths):
+    fig, ax = plt.subplots()
+
+    scenario_names = summarized_total_deaths.columns.get_level_values(0).unique()
+
+    means = np.array([
+        summarized_total_deaths[(s, "mean")].values[0]
+        for s in scenario_names
+    ])
+    lowers = np.array([
+        summarized_total_deaths[(s, "lower")].values[0]
+        for s in scenario_names
+    ])
+    uppers = np.array([
+        summarized_total_deaths[(s, "upper")].values[0]
+        for s in scenario_names
+    ])
+
+    ax.bar(
+        scenario_names,
+        means,
+        yerr=[means - lowers, uppers - means],
+        capsize=5
+    )
+
+    ax.set_ylabel("Total number of deaths")
+    ax.set_xticklabels(scenario_names, rotation=45, ha="right")
+    fig.tight_layout()
+
+    return fig, ax
+
+
+def compute_difference_in_deaths_across_runs(total_deaths, scenario_info):
+    deaths_difference_by_run = [
+        total_deaths[0][run_number]["Total"] - total_deaths[1][run_number]["Total"]
+        for run_number in range(scenario_info["runs_per_draw"])
+    ]
+    return np.mean(deaths_difference_by_run)
+
+
+def extract_deaths_by_age(results_folder):
+    def extract_deaths_by_age_group(df: pd.DataFrame) -> pd.Series:
+        _, age_group_lookup = make_age_grp_lookup()
+        df["Age_Grp"] = df["age"].map(age_group_lookup).astype(make_age_grp_types())
+        df = df.rename(columns={"sex": "Sex"})
+        return df.groupby(["Age_Grp"])["person_id"].count()
+
+    return extract_results(
+        results_folder,
+        module="tlo.methods.demography",
+        key="death",
+        custom_generate_series=extract_deaths_by_age_group,
+        do_scaling=True
+    )
+
+
+def plot_summarized_deaths_by_age(deaths_summarized_by_age):
+    fig, ax = plt.subplots()
+
+    scenario_names = deaths_summarized_by_age.columns.get_level_values(0).unique()
+
+    for i, scenario in enumerate(scenario_names):
+        central_values = deaths_summarized_by_age[(scenario, "mean")].values
+        lower_values = deaths_summarized_by_age[(scenario, "lower")].values
+        upper_values = deaths_summarized_by_age[(scenario, "upper")].values
+
+        ax.plot(
+            deaths_summarized_by_age.index,
+            central_values,
+            label=scenario
+        )
+
+        ax.fill_between(
+            deaths_summarized_by_age.index,
+            lower_values,
+            upper_values,
+            alpha=0.3
+        )
+
+    ax.set(xlabel="Age-Group", ylabel="Total deaths")
+    ax.set_xticks(deaths_summarized_by_age.index)
+    ax.set_xticklabels(deaths_summarized_by_age.index, rotation=90)
+    ax.legend()
+    fig.tight_layout()
+    return fig, ax
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(
+        "Analyse scenario results for nurses scenario"
+    )
+    parser.add_argument(
+        "--scenario-outputs-folder",
+        type=Path,
+        required=True,
+        help="Path to folder containing scenario outputs",
+    )
+    parser.add_argument(
+        "--show-figures",
+        action="store_true",
+        help="Whether to interactively show figures",
+    )
+    parser.add_argument(
+        "--save-figures",
+        action="store_true",
+        help="Whether to save figures to results folder",
+    )
+    args = parser.parse_args()
+
+    # results_folder = args.scenario_outputs_folder
+
+    results_folder = Path(
+        './outputs/wamulwafu@kuhes.ac.mw/nurses_scenario_outputs-2026-04-20T111238Z'
+    )
+
+    # Load log (optional, but useful)
+    log = load_pickled_dataframes(results_folder)
+
+    scenario_info = get_scenario_info(results_folder)
+
+    # Get scenario names directly from Scenario class
+    param_names = tuple(StaffingScenario()._scenarios.keys())
+
+    # Keep only scenarios with Default Healthsystem Function
+    default_hs_scenarios = [
+        "Baseline Nurses / Default Healthsystem Function",
+        "Fewer Nurses / Default Healthsystem Function",
+        "More Nurses / Default Healthsystem Function",
+    ]
+
+    # Total deaths
+    total_deaths = extract_total_deaths(results_folder).pipe(
+        set_param_names_as_column_index_level_0,
+        param_names=param_names
+    )
+
+    summarized_total_deaths = summarize(total_deaths)
+
+    # Filter to Default Healthsystem Function scenarios only
+    summarized_total_deaths = summarized_total_deaths.loc[
+                              :,
+                              summarized_total_deaths.columns.get_level_values(0).isin(default_hs_scenarios)
+                              ]
+
+    fig_1, ax_1 = plot_summarized_total_deaths(summarized_total_deaths)
+
+    # Deaths by age
+    deaths_by_age = extract_deaths_by_age(results_folder).pipe(
+        set_param_names_as_column_index_level_0,
+        param_names=param_names
+    )
+
+    summarized_deaths_by_age = summarize(deaths_by_age)
+
+    # Filter to Default Healthsystem Function scenarios only
+    summarized_deaths_by_age = summarized_deaths_by_age.loc[
+                               :,
+                               summarized_deaths_by_age.columns.get_level_values(0).isin(default_hs_scenarios)
+                               ]
+
+    fig_2, ax_2 = plot_summarized_deaths_by_age(summarized_deaths_by_age)
+
+    if args.show_figures:
+        plt.show()
+
+    if args.save_figures:
+        fig_1.savefig(results_folder / "total_deaths_across_scenarios.pdf")
+        fig_2.savefig(results_folder / "deaths_by_age_across_scenarios.pdf")