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Measures

The Measures framework allows you to extract multiple datasets covering different time periods, and calculates a set of measures for each period.

Measures are expressed as a quotient (i.e., a numerator divided by a denominator) which in practice could be used to calculate proportions, ratios, means, counts, and so on.

For example, we may wish to calculate, for each month in 2020 and each STP (administrative health regions in England), the proportion of patients who were admitted to hospital at least once and the proportion of patients who died.

Without Measures, this would require creating a set of variables (or a study definition) for each month of interest containing: the region; whether or not they were admitted to hospital; whether or not they died, and we would then need to use these datasets to calculate the desired proportion and aggregate the results.

Measures makes this process simple.

Defining measures🔗

Generating measures is a three step process:

  1. Define a study definition that includes a measures variable, which should be a list of calls to the Measure() function.
  2. Extract the data by running generate_cohort using the --index-date-range option to cover the range of time periods we want to calculate the measure for.
  3. Calculate the measures by running generate_measures which takes the files extracted in step 2 and produces files like measure_<measure_id>.csv.gz.

Define a study definition🔗

The study_definition.py script for the example above is:

from cohortextractor import StudyDefinition, Measure, patients

study = StudyDefinition(
    # Configure the expectations framework
    default_expectations={
        "date": {"earliest": "2020-01-01", "latest": "today"},
        "rate": "exponential_increase",
        "incidence": 0.2,
    },

    index_date="2020-01-01",

    population=patients.registered_as_of("index_date"),

    stp=patients.registered_practice_as_of(
        "index_date",
        returning="stp_code",
        return_expectations={
            "category": {"ratios": {"stp1": 0.1, "stp2": 0.2, "stp3": 0.7}},
            "incidence": 1,
        },
    ),

    sex=patients.sex(
        return_expectations={
            "rate": "universal",
            "category": {"ratios": {"M": 0.49, "F": 0.51}},
        }
    ),

    admitted=patients.admitted_to_hospital(
        returning="binary_flag",
        between=["index_date", "last_day_of_month(index_date)"],
        return_expectations={"incidence": 0.1},
    ),

    died=patients.died_from_any_cause(
        between=["index_date", "last_day_of_month(index_date)"],
        returning="binary_flag",
        return_expectations={"incidence": 0.05},
    ),
)

measures = [
    Measure(
        id="hosp_admission_by_stp",
        numerator="admitted",
        denominator="population",
        group_by="stp",
    ),
    Measure(
        id="death_by_stp",
        numerator="died",
        denominator="population",
        group_by="stp",
        small_number_suppression=True,
    ),
]

This differs from a normal study definition due to the addition of the measures object, which is a list of calls to the Measure() function, for each measure.

The index_date🔗

Each month/week start date covered by your study period is passed to the study definition as the index_date in turn, thus allowing the cohort and variables to vary each month/week as required. Some variables can retain fixed or no date ranges, e.g. a person's ethnicity would not be expected to vary over time.

The Measure() function🔗

  • id is just a string used to identify the measure output file.
  • numerator and denominator are the columns in the dataset that define the measure. They must be numeric or boolean (encoded as 0 or 1).
  • group_by column can be of any type. To calculate the measure across the entire population, you can set group_by="population". If not specified, group_by will default to None and the measure will be calculated at the individual patient level.
  • small_number_suppression is an optional boolean that defaults to False. If set to True, small numbers (greater than zero, less than or equal to five) in numerators and denominators will be replaced with blanks. If a numerator or denominator has been suppressed then the corresponding value will also be blank. In cases where numerators or denominators are suppressed but the total suppressed in a column is not greater than five, additional values will be suppressed to bring the total above five.

You can calculate measures for more than one group in a single measure by specifying multiple variables as follows:

measures = [
    Measure(
        id="hosp_admission_by_stp_and_sex",
        numerator="admitted",
        denominator="population",
        group_by=["stp", "sex"],
    ),
    Measure(
        id="death_by_stp_and_sex",
        numerator="died",
        denominator=" population",
        group_by=["stp", "sex"],
    ),
]

Extract the data🔗

To run multiple study definitions over a series of dates, use the --index-date-range option of the generate_cohort command in cohortextractor. Rather than generating a single output CSV file this generates multiple output files across a range of dates, modifying the study's index date each time.

Note

Although the dates defined by the --index-date-range will replace the index_date that is defined in the study definition itself, index_date must still be defined.

Running over multiple index dates can be resource intensive and thus slow to run. It is therefore recommended to: use months rather than weeks if appropriate; use a shorter period for testing if applicable; minimise the population size (e.g. apply all necessary exclusions to your population, e.g. age limits, in the study definition rather than filtering in a later step) and use a compressed output format (e.g. csv.gz as below).

The range is specified as:

--index-date-range "YYYY-MM-DD to YYYY-MM-DD by (week|month)"

It also accepts today in place of a date.

Output files are named like: output/input_YYYY-MM-DD.csv.gz

There is also a --skip-existing option which will cause the cohortextractor to skip the extraction step for any dates where the corresponding file already exists. This makes it easier to incrementally extract data for new months/weeks without having to re-extract everything.

Example:

cohortextractor:latest generate_cohort --index-date-range "2020-01-01 to 2020-12-01 by month" --output-format=csv.gz

...which will output:

output/input_2020-01-01.csv.gz
output/input_2020-02-01.csv.gz
...
output/input_2020-12-01.csv.gz

Calculate the measures🔗

This is done using the generate_measures command:

cohortextractor:latest generate_measures

For each defined measure, and for each file extracted in step 2, this generates an output file with the measure calculated for that month or week.

output/measure_hosp_admission_by_stp_2020-01-01.csv.gz
output/measure_death_by_stp_2020-01-01.csv.gz
output/measure_hosp_admission_by_stp_2020-02-01.csv.gz
output/measure_death_by_stp_2020-02-01.csv.gz
...
output/measure_hosp_admission_by_stp_2020-12-01.csv.gz
output/measure_death_by_stp_2020-12-01.csv.gz

Finally, for each measure, it combines all the output into a single file with an additional date column indicating the date associated with each row.

output/measure_hosp_admission_by_stp.csv.gz
output/measure_death_by_stp.csv.gz

This command also respects the --skip-existing flag. This will prevent it from recalculating the measure for any months or weeks which have already been calculated. However the final step, which combines output across time periods, will always be run.

This command accepts an --output-dir argument, which defines the directory in which the output measure files will be created. If the --output-dir argument is configured, generate_measures expects the input cohort files to also be in the output directory. To ensure this, include the same --output-dir argument in the previous generate_cohort step.

Note

As we saw, you can extract the data in a compressed output format, such as csv.gz, feather, dta, or dta.gz. For example:

$ cohortextractor:latest generate_cohort --output-format=csv.gz

If you do, then the generate_measures command will automatically work with this compressed output format; you need not pass --output-format to generate_measures, in other words. However, the generate_measures command will always output CSV files.

Putting it all together in a pipeline🔗

To generate the final outputs measure_hosp_admission_by_stp.csv.gz and measure_death_by_stp.csv.gz in a project pipeline, you would use the following actions:

  generate_study_population:
    run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2020-01-01 to 2020-12-01 by month" --skip-existing --output-dir=output/measures --output-format=csv.gz
    outputs:
      highly_sensitive:
        cohort: output/measures/input_*.csv.gz

  generate_measures:
    run: cohortextractor:latest generate_measures --study-definition study_definition --skip-existing --output-dir=output/measures --output-format csv.gz
    needs: [generate_study_population]
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measure_*.csv.gz