adding attributable deaths and non-attributable deaths

dag/health.yml

@@ -969,9 +969,18 @@ steps:
     - data://meadow/antibiotics/2024-12-03/glass_enrolment
   data://grapher/antibiotics/2024-12-03/glass_enrolment:
     - data://garden/antibiotics/2024-12-03/glass_enrolment
+  # MICROBE - total deaths by pathogen
   data-private://meadow/antibiotics/2024-12-04/microbe_total_pathogens:
     - snapshot-private://antibiotics/2024-12-04/microbe_total_pathogens.csv
   data-private://garden/antibiotics/2024-12-04/microbe_total_pathogens:
     - data-private://meadow/antibiotics/2024-12-04/microbe_total_pathogens
   data-private://grapher/antibiotics/2024-12-04/microbe_total_pathogens:
     - data-private://garden/antibiotics/2024-12-04/microbe_total_pathogens
+  # MICROBE - total deaths by pathogen and amr resistance
+  data-private://meadow/antibiotics/2024-12-04/microbe_total_pathogens_amr:
+    - snapshot-private://antibiotics/2024-12-04/microbe_total_pathogens_amr.csv
+  data-private://garden/antibiotics/2024-12-04/microbe_total_pathogens_amr:
+    - data-private://meadow/antibiotics/2024-12-04/microbe_total_pathogens_amr
+    - data-private://garden/antibiotics/2024-12-04/microbe_total_pathogens
+  data-private://grapher/antibiotics/2024-12-04/microbe_total_pathogens_amr:
+    - data-private://garden/antibiotics/2024-12-04/microbe_total_pathogens_amr

etl/steps/data/garden/antibiotics/2024-12-04/microbe_total_pathogens_amr.countries.json

@@ -0,0 +1,3 @@
+{
+  "Global": "World"
+}

etl/steps/data/garden/antibiotics/2024-12-04/microbe_total_pathogens_amr.meta.yml

@@ -0,0 +1,42 @@
+# NOTE: To learn more about the fields, hover over their names.
+definitions:
+  common:
+    presentation:
+      topic_tags:
+        - Antibiotics
+
+# Learn more about the available fields:
+# http://docs.owid.io/projects/etl/architecture/metadata/reference/
+dataset:
+  update_period_days: 365
+
+tables:
+  microbe_total_pathogens_amr:
+    variables:
+      amr_attributable_deaths:
+        title: Total deaths from infections attributed to AMR, by pathogen
+        unit: deaths
+        description_short: Estimated number of deaths from infections that are attributed to antimicrobial resistance.
+        presentation:
+          title_public: Total deaths from infections attributed to AMR, by pathogen
+        display:
+          roundingMode: significantFigures
+          numSignificantFigures: 3
+      non_amr_attributable_deaths:
+        title: Total global deaths from infections not attributed to AMR, by pathogen
+        unit: deaths
+        description_short: Estimated number of deaths from infections that are not attributed to antimicrobial resistance.
+        presentation:
+          title_public: Total global deaths from infections not attributed to AMR, by pathogen
+        display:
+          roundingMode: significantFigures
+          numSignificantFigures: 3
+      total_deaths:
+        title: Total global deaths from infections
+        unit: deaths
+        description_short: Estimated number of deaths from infections.
+        presentation:
+          title_public: Total global deaths from infections
+        display:
+          roundingMode: significantFigures
+          numSignificantFigures: 3

etl/steps/data/garden/antibiotics/2024-12-04/microbe_total_pathogens_amr.py

@@ -0,0 +1,55 @@
+"""Load a meadow dataset and create a garden dataset."""
+
+from etl.data_helpers import geo
+from etl.helpers import PathFinder, create_dataset
+
+# Get paths and naming conventions for current step.
+paths = PathFinder(__file__)
+
+
+def run(dest_dir: str) -> None:
+    #
+    # Load inputs.
+    #
+    # Load meadow dataset.
+    ds_meadow = paths.load_dataset("microbe_total_pathogens_amr")
+    ds_total = paths.load_dataset("microbe_total_pathogens")
+
+    # Read table from meadow dataset.
+    tb = (
+        ds_meadow.read("microbe_total_pathogens_amr")
+        .drop(columns=["upper", "lower"])
+        .rename(columns={"value": "amr_attributable_deaths"})
+    )
+    tb_total = (
+        ds_total.read("microbe_total_pathogens")
+        .drop(columns=["upper", "lower"])
+        .rename(columns={"value": "total_deaths"})
+    )
+    #
+    # Process data.
+    #
+    tb = geo.harmonize_countries(
+        df=tb,
+        countries_file=paths.country_mapping_path,
+    )
+
+    tb = tb.merge(tb_total, on=["country", "year", "pathogen", "pathogen_type"], how="right")
+
+    tb["amr_attributable_deaths"] = tb["amr_attributable_deaths"].fillna(0)
+    tb["non_amr_attributable_deaths"] = tb["total_deaths"] - tb["amr_attributable_deaths"]
+    # Process data.
+    tb = tb.drop(columns=["country", "pathogen_type"]).rename(columns={"pathogen": "country"})
+
+    tb = tb.format(["country", "year"])
+
+    #
+    # Save outputs.
+    #
+    # Create a new garden dataset with the same metadata as the meadow dataset.
+    ds_garden = create_dataset(
+        dest_dir, tables=[tb], check_variables_metadata=True, default_metadata=ds_meadow.metadata
+    )
+
+    # Save changes in the new garden dataset.
+    ds_garden.save()

etl/steps/data/grapher/antibiotics/2024-12-04/microbe_total_pathogens_amr.py

@@ -0,0 +1,28 @@
+"""Load a garden dataset and create a grapher dataset."""
+
+from etl.helpers import PathFinder, create_dataset
+
+# Get paths and naming conventions for current step.
+paths = PathFinder(__file__)
+
+
+def run(dest_dir: str) -> None:
+    #
+    # Load inputs.
+    #
+    # Load garden dataset.
+    ds_garden = paths.load_dataset("microbe_total_pathogens_amr")
+
+    # Read table from garden dataset.
+    tb = ds_garden.read("microbe_total_pathogens_amr", reset_index=False)
+
+    #
+    # Save outputs.
+    #
+    # Create a new grapher dataset with the same metadata as the garden dataset.
+    ds_grapher = create_dataset(
+        dest_dir, tables=[tb], check_variables_metadata=True, default_metadata=ds_garden.metadata
+    )
+
+    # Save changes in the new grapher dataset.
+    ds_grapher.save()

etl/steps/data/meadow/antibiotics/2024-12-04/microbe_total_pathogens_amr.py

@@ -0,0 +1,39 @@
+"""Load a snapshot and create a meadow dataset."""
+
+from etl.helpers import PathFinder, create_dataset
+
+# Get paths and naming conventions for current step.
+paths = PathFinder(__file__)
+
+
+def run(dest_dir: str) -> None:
+    #
+    # Load inputs.
+    #
+    # Retrieve snapshot.
+    snap = paths.load_snapshot("microbe_total_pathogens_amr.csv")
+
+    # Load data from snapshot.
+    tb = snap.read()
+    assert all(tb["Age"] == "All Ages")
+    assert all(tb["Sex"] == "Both sexes")
+    assert all(tb["Measure"] == "Deaths")
+    assert all(tb["Metric"] == "Number")
+    assert all(tb["Counterfactual"] == "Attributable")
+    assert all(tb["Infectious syndrome"] == "All infectious syndromes")
+
+    #
+    # Process data.
+    tb = tb.drop(columns=["Age", "Sex", "Measure", "Metric", "Infectious syndrome", "Counterfactual"])
+    tb = tb.rename(columns={"Location": "country", "Year": "year", "Pathogen": "pathogen"})
+    # Ensure all columns are snake-case, set an appropriate index, and sort conveniently.
+    tb = tb.format(["country", "year", "pathogen"])
+
+    #
+    # Save outputs.
+    #
+    # Create a new meadow dataset with the same metadata as the snapshot.
+    ds_meadow = create_dataset(dest_dir, tables=[tb], check_variables_metadata=True, default_metadata=snap.metadata)
+
+    # Save changes in the new meadow dataset.
+    ds_meadow.save()

snapshots/antibiotics/2024-12-04/microbe_total_pathogens_amr.csv.dvc

@@ -0,0 +1,31 @@
+# Learn more at:
+# http://docs.owid.io/projects/etl/architecture/metadata/reference/
+meta:
+  origin:
+    # Data product / Snapshot
+    title: Total deaths by pathogen attributable to antimicrobial resistance
+    description: |-
+      The MICROBE (Measuring Infectious Causes and Resistance Outcomes for Burden Estimation) tool visualizes the fatal and nonfatal health outcomes of infections, pathogens, and antimicrobial resistance across different countries and regions. The tool shows a novel estimation method, [published in The Lancet](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)01867-1/fulltext), including the burden of infections and their underlying pathogens, as well as, the burden of pathogens that are both susceptible and resistant to antibiotics. This tool is useful for understanding the burden of these outcomes, as well as illustrating how they nest together. The tabs explore different health outcomes by geography, age and sex. All tabs include a bar visualization for comparison, as well as a map view for a global perspective.
+    date_published: "2024-09-28"
+
+    # Citation
+    producer: Institute for Health Metrics and Evaluation (IHME); University of Oxford
+    citation_full: |-
+      Institute for Health Metrics and Evaluation (IHME), University of Oxford. MICROBE. Seattle, WA: IHME, University of Washington, 2024. Available from [https://vizhub.healthdata.org/microbe](https://vizhub.healthdata.org/microbe)
+    attribution_short: MICROBE
+
+    # Files
+    url_main: https://vizhub.healthdata.org/microbe/
+    date_accessed: 2024-12-04
+
+    # License
+    license:
+      name: IHME's Free-of-Charge Non-commercial User Agreement
+      url: https://www.healthdata.org/Data-tools-practices/data-practices/ihme-free-charge-non-commercial-user-agreement
+
+
+  is_public: false
+outs:
+  - md5: 95bd7ca4c721a4e5113fd54ee598dad3
+    size: 3989
+    path: microbe_total_pathogens_amr.csv

snapshots/antibiotics/2024-12-04/microbe_total_pathogens_amr.py

@@ -0,0 +1,25 @@
+"""Script to create a snapshot of dataset."""
+
+from pathlib import Path
+
+import click
+
+from etl.snapshot import Snapshot
+
+# Version for current snapshot dataset.
+SNAPSHOT_VERSION = Path(__file__).parent.name
+
+
+@click.command()
+@click.option("--upload/--skip-upload", default=True, type=bool, help="Upload dataset to Snapshot")
+@click.option("--path-to-file", "-f", prompt=True, type=str, help="Path to local data file.")
+def main(path_to_file: str, upload: bool) -> None:
+    # Create a new snapshot.
+    snap = Snapshot(f"antibiotics/{SNAPSHOT_VERSION}/microbe_total_pathogens_amr.csv")
+
+    # Copy local data file to snapshots data folder, add file to DVC and upload to S3.
+    snap.create_snapshot(filename=path_to_file, upload=upload)
+
+
+if __name__ == "__main__":
+    main()