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Health Impact of Realistic Consumable Availability Scenarios #1367
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b632850
add script to prepare HHFA data for regression analysis
a1646a5
add scripts to run regression analysis
eedb2e8
move file paths to main.R
1f2c5b7
Add script to generation regression-based predicted changes to availa…
64b2160
add predict.R to workflow
33c9039
draft script to generate consumable availability scenarios
3f1fdff
update consumable RF to include TLO-HHFA consumable mapping
1e46315
updates to imputation
6175495
check that the dataframe contains all districts
e9a2f51
temporary changes to revert
56dc371
update 'regression_application' column
e3fc740
update interpolation methodology
3e287a8
add scenarios 6-8
ed78828
update test for consumables availability ResourceFile
31f6ead
update test for consumables availability ResourceFile
0089527
add script to generate barplots to visualise change in available_prop…
e3211fd
update the method of generating scenarios 6-8
19aa36e
minor figure edit
a85db77
Merge remote-tracking branch 'origin/master' into sakshi/impact_of_co…
0135a23
allow simulation to import availability estimates based on new scenarios
f9c7df1
update health system parameter
2e5ed3f
revert to consumable RFs before master was merged in
84fdb73
update RF to include availability iunder the 8 realistic improved ava…
3539397
add the full list of scenario availability columns to consumable and …
7bf2b07
update assertion to ensure that only consumable availabilility at lev…
6cbe0ee
update helper function to load consuamable data
553d0cf
Revert "update helper function to load consuamable data"
d4fd581
update helper function to load consuamable data
242503a
update _process_consumables_data
82cb1b0
update _process_consumables_data
df28a99
correct _process_consumables_data
ee9616e
update duration of simulation for local run
8d2a79f
update _process_consumables_data
b78d5a1
update scenario for large run
be63ffc
add scenario analysis script
37d9b5f
add figures showing mechanisms of impact
30548ab
update figure legend to show cleaner scneario names
4d7ae42
minor script cleaning
db710c7
add heatmap aummarising consumable availability in the RF
1c68e6b
remove line plots for mechanisms of impact
998c5ae
update total DALYs averted and total DALYs accrued figures
c47d771
plot DALYs accrued by cause
847d4ae
plot DALYs averted by cause
fc41b9a
plot DALYs averted per person year
230f1a3
plot DALYs averted per person year by cause
52d54ca
update scenario names to be more clear
bd75d63
fix error in calculation of DALYs per person-year
30682a2
change plot labels from person-year to person
a96d78e
add bar plots of capacity utilised by cadre and level
1009734
update names of scenarios
030083a
update heatmap to only include two levels of care
0b671c9
fix average to only include 1a and 1b in the heatmap
2558e5f
update 'status quo' to 'actual'
18046f4
get heatmaps for all scenarios
0a3ac87
Merge remote-tracking branch 'origin/master' into sakshi/impact_of_co…
8dad43a
update paths from dropbox to sharepoint
d62e086
Clean code for assigning consumable category
fd106ae
Rename item category column and extract into consumable availability RF
4c2644c
Add 'item_category' to the function to check format of consumable ava…
b01904f
remove reindex() line because this is performed earlier in the script
4ab53e3
removed these scripts which are not being used in the TLO model
09d8bb2
Drop columns not needed from RF_items_and_packages
35d6ebe
Merge branch 'sakshi/sharepoint_update_for_consumables_RF' into saksh…
15aeb98
load 'item_category' directly from the small consumables RF
e51384a
update df name for readability
21fd8a8
add HHFA proxies for two more TLO model consumables
1b63b30
add scenario 9 (level 2 increased to 99th percentile)
6488940
add parallel supply chain scenarios
b4dedea
add heatmaps to represent scenarios
a733a89
add scenarios to simulation file
2eda2d4
add new scenarios to consumables.py
5f9b9c4
add new scenarios to healthsystem.py
106e322
[TEMPORARY] remove item_category
c8b1431
Revert "Drop columns not needed from RF_items_and_packages"
c76624a
update scenario for local run
208fc4e
Remove item_category from RF_small and move to RF_designations
d1b7d78
add item_category from RF_designations
7aae87e
update scenario file for local run
3989c44
add description of scenarios
fedc2fc
minor update to correct direction of mapping
82cc7f2
update scenario to submit to Azure
c9bffa5
minor changes to figures + allow new results to be loaded
ecaf310
minor changes to figures
c262452
Explicitly specify colours for each scenario
3cd31b6
drop the DHO scenario
f22d845
rename columns for df_for_plot and streamline the choice of scenarios…
ec338b8
update figures for think tank presentation
05156d2
Update scenarios 10-12 to apply max conditions as necessary
52f12bf
update bar plot
280c753
update bar plot
a018c3a
update scenarios 6-9 so that availability is max(original availabilit…
4edcdcf
Merge remote-tracking branch 'origin/master' into sakshi/impact_of_co…
0f55c9c
add scenarios 9-12 to test_consumables.py
693518d
correct num_dalys plots to account for median rather than mean values
9c4b4b4
update DALYs averted plot to add heatmap of average consumable availa…
96ae730
correct resourcefilepath
cdaa1b2
add items 1237, 1239, 2678, and 1124 to consumables RFs (include Fans…
8eae35a
added item code 75 (Gauze)
a281527
add detailed heatmaps for actual and 75th percentile scenarios for co…
b90dd13
Assume that 50% of the expenditure reported under 'Vehicles - Fuel an…
08acc9b
Revert "Assume that 50% of the expenditure reported under 'Vehicles -…
aeee9ee
edit axis titles
c1cebe8
update heatmap figures so that average availability across rows is ba…
7d15cb6
add heatmaps for ROI paper
9d8a3d0
minor changes to consumables heatmap for ROI manuscript
sakshimohan 5701ee0
Clean plot for costing manuscript
sakshimohan de8aba6
add a fix to the interpolation loop to overcome the error when some i…
sakshimohan ef069cf
Add 3 new scenarios for HIV consumable availability to represent aid …
sakshimohan 47a2d78
add plots for the new HIV strategies
sakshimohan a53ed90
Create README.md
sakshimohan 877b10c
update x-axis labels of plot for ROI paper
sakshimohan 7d5e161
Merge remote-tracking branch 'origin/sakshi/impact_of_consumable_scen…
sakshimohan 1bb9025
extract small file showing the ratio of consumables inflow to outflow
sakshimohan ac60444
update small file showing the ratio of consumables inflow to outflow …
sakshimohan 0a37b66
Update Resource Files after re-running consumable_availability_estima…
sakshimohan 3628915
Merge branch 'master' into sakshi/impact_of_consumable_scenarios
tbhallett d68f18b
pick up colummns organically
tbhallett 095b269
tidy checking
tbhallett 2e0bac5
strengthen test
tbhallett 05dbd31
fix updating of consumables
tbhallett 8042df9
refactor so that availability is set the same way at init as if chang…
tbhallett f767297
tidy
tbhallett c396ccc
tidy
tbhallett 26d04dd
linting
tbhallett 35e8d69
Fix linting errors & ruff always ignores `_version.py` generated file
tamuri 141a9f8
Prefer specific import over top-level
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3
resources/costing/ResourceFile_Consumables_Inflow_Outflow_Ratio.csv
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| ## Consumable Availability Scenarios | ||
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| This README describes the different **scenarios** used in consumable availability analysis. Each scenario corresponds to a specific level of availability of consumables across health facilities and months. The data for these scenarios are stored in columns such as `available_prop_scenario1`, `available_prop_scenario2`, etc., in the `ResourceFile_Consumables_availability_small.csv`. This is generated by the the following scripts - 1. `consumable_availability_estimation.py` generates the `available_prop` column representing the proportion of instances that a consumables is likely to be available given the levels of availability observed during 2018, 2. `generate_consumable_availability_scenarios_for_impact_analysis.py` generates additional scenarios of consumable availability ans appends them to the file generated by the first script. | ||
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| The scenario names below can be specified as parameters for `cons_availability` under `HealthSystem` when running simulations. | ||
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| ### **Base Scenario** | ||
| - `default` | ||
| - **Description:** Actual 2018 availability estimates based on OpenLMIS data. | ||
| - **Purpose:** Serves as the baseline for comparisons. | ||
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| ### **Scenarios 1–5: System-Level Improvements at Level 1a + 1b ** | ||
| These are based on regression analysis performed in [Mohan et al (2024)](https://pubmed.ncbi.nlm.nih.gov/38762283/). | ||
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| | Scenario | Column Name | Description | | ||
| |----------|---------------------------|-------------| | ||
| | 1 | `scenario1` | All items perform like *non-drug/diagnostic* consumables. | | ||
| | 2 | `scenario2` | Scenario 1 + all items match performance of *vital* medicines. | | ||
| | 3 | `scenario3` | Scenario 2 + all facilities match those managed by *pharmacists*. | | ||
| | 4 | `scenario4` | Scenario 3 + Level 1a facilities perform like Level 1b. | | ||
| | 5 | `scenario5` | Scenario 4 + All facilities perform like *CHAM* facilities. | | ||
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| --- | ||
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| ### **Scenarios 6–9: Benchmarking Against Best Facilities** | ||
| | Scenario | Column Name | Description | | ||
| |----------|---------------------------|-------------| | ||
| | 6 | `scenario6` | All Level 1a/1b facilities match the *75th percentile* facility for each item. | | ||
| | 7 | `scenario7` | Same as above but using the *90th percentile*. | | ||
| | 8 | `scenario8` | Same as above but using the *99th percentile*. | | ||
| | 9 | `scenario9` | Level 1a, 1b, and 2 facilities match the *99th percentile*. | | ||
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| ### **Scenarios 10–11: Horizontal Supply Chain Alignment** | ||
| | Scenario | Column Name | Description | | ||
| |----------|---------------------------|-------------| | ||
| | 10 | `scenario10` | All programs perform as well as *HIV* programs (only levels 1a and 1b updated). | | ||
| | 11 | `scenario11` | All programs perform as well as *EPI* programs (only levels 1a and 1b updated). | | ||
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| Scenarios 6-8 and 10-11 only include levels 1a and 1b where most of the service is delivered and consumable availability is particularly a challenge to match up with the regression-analysis-based scenarios 1-5 | ||
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| ### **Scenarios 12–15: HIV Drug Supply Adjustments** | ||
| | Scenario | Column Name | Description | | ||
| |----------|---------------------------|-------------| | ||
| | 12 | `scenario12` | HIV performs as well (i.e. as badly) as general programs (excluding EPI and Cancer), benchmarked at *Facility_Level*. | | ||
| | 13 | `scenario13` | HIV performs as well as other programs, benchmarked at *Facility_ID*. | | ||
| | 14 | `scenario14` | Same as Scenario 13, but availability scaled *up* by 25%. | | ||
| | 15 | `scenario15` | Same as Scenario 13, but availability scaled *down* by 25%. | | ||
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| ### **Upper Bound Scenario** | ||
| - **`all`** | ||
| - **Description:** All consumables are always available (i.e., 100% availability). | ||
| - **Purpose:** Represents the theoretical upper bound for health gains due to supply improvements. | ||
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4 changes: 2 additions & 2 deletions
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resources/healthsystem/consumables/ResourceFile_Consumables_Item_Designations.csv
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resources/healthsystem/consumables/ResourceFile_Consumables_availability_small.csv
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resources/healthsystem/consumables/ResourceFile_consumables_matched.csv
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