added breakdown graphs and facet function #11
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viv3ckj
changed the title
milanwiedemann
approved these changes
Oct 1, 2024
Comment on lines
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to
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| age = patients.age_on(INTERVAL.start_date) | ||
| age_band = case( | ||
| when((age >= 0) & (age < 20)).then("0-19"), | ||
| when((age >= 20) & (age < 40)).then("20-39"), | ||
| when((age >= 40) & (age < 60)).then("40-59"), | ||
| when((age >= 60) & (age < 80)).then("60-79"), | ||
| when(age >= 80).then("80+"), | ||
| when(age.is_null()).then("Missing"), | ||
| ) |
| when(imd < int(32844 * 3 / 5)).then("3"), | ||
| when(imd < int(32844 * 4 / 5)).then("4"), | ||
| when(imd < int(32844 * 5 / 5)).then("5 (least deprived)"), | ||
| otherwise="unknown" |
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although the code does exactly the same as this suggestions in the tutorial, I'd use something like this because it's a bit easier to review and harder to make a mistake:
imd_rounded = addresses.for_patient_on(
INTERVAL.start_date
).imd_rounded
max_imd = 32844
dataset.imd_quintile = case(
when(imd_rounded < int(max_imd * 1 / 5)).then(1),
when(imd_rounded < int(max_imd * 2 / 5)).then(2),
when(imd_rounded < int(max_imd * 3 / 5)).then(3),
when(imd_rounded < int(max_imd * 4 / 5)).then(4),
when(imd_rounded <= max_imd).then(5),
)| when(imd < int(32844 * 3 / 5)).then("3"), | ||
| when(imd < int(32844 * 4 / 5)).then("4"), | ||
| when(imd < int(32844 * 5 / 5)).then("5 (least deprived)"), | ||
| otherwise="unknown" |
There was a problem hiding this comment.
to be consistent with the value for "Missing" data, I'd use the same as for age bands here as well
Suggested change
| otherwise="unknown" | |
| otherwise="Missing" |
Comment on lines
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| # Measures for age breakdown of clinical services | ||
| measures.define_measure( | ||
| name=f"count_{pharmacy_first_event}_by_age", | ||
| numerator=numerator, | ||
| denominator=denominator, | ||
| group_by={ | ||
| "age_band": age_band, | ||
| }, | ||
| intervals=months(monthly_intervals).starting_on(start_date), | ||
| ) | ||
|
|
||
| # Measures for sex breakdown of clinical services | ||
| measures.define_measure( | ||
| name=f"count_{pharmacy_first_event}_by_sex", | ||
| numerator=numerator, | ||
| denominator=denominator, | ||
| group_by={ | ||
| "sex": patients.sex, | ||
| }, | ||
| intervals=months(monthly_intervals).starting_on(start_date), | ||
| ) | ||
|
|
||
| # Measures for IMD breakdown of clinical services | ||
| measures.define_measure( | ||
| name=f"count_{pharmacy_first_event}_by_imd", | ||
| numerator=numerator, | ||
| denominator=denominator, | ||
| group_by={ | ||
| "imd": imd_quintile, | ||
| }, | ||
| intervals=months(monthly_intervals).starting_on(start_date), | ||
| ) | ||
|
|
||
| # Measures for region breakdown of clinical services | ||
| measures.define_measure( | ||
| name=f"count_{pharmacy_first_event}_by_region", | ||
| numerator=numerator, | ||
| denominator=denominator, | ||
| group_by={ | ||
| "region": registration.practice_nuts1_region_name, | ||
| }, | ||
| intervals=months(monthly_intervals).starting_on(start_date), | ||
| ) |
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this looks correct, but I think we can make this much shorter if we group by multiple features, see https://docs.opensafely.org/ehrql/explanation/measures/#grouping-by-multiple-features
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| # Measures for age breakdown of clinical conditions | ||
| measures.define_measure( | ||
| name=f"count_{condition_name}_by_age", | ||
| numerator=numerator, | ||
| denominator=denominator, | ||
| group_by={ | ||
| "age_band": age_band, | ||
| }, | ||
| intervals=months(monthly_intervals).starting_on(start_date), | ||
| ) | ||
|
|
||
| # Measures for age breakdown of clinical conditions | ||
| measures.define_measure( | ||
| name=f"count_{condition_name}_by_sex", | ||
| numerator=numerator, | ||
| denominator=denominator, | ||
| group_by={ | ||
| "sex": patients.sex, | ||
| }, | ||
| intervals=months(monthly_intervals).starting_on(start_date), | ||
| ) | ||
|
|
||
| # Measures for imd breakdown of clinical conditions | ||
| measures.define_measure( | ||
| name=f"count_{condition_name}_by_imd", | ||
| numerator=numerator, | ||
| denominator=denominator, | ||
| group_by={ | ||
| "imd": imd_quintile, | ||
| }, | ||
| intervals=months(monthly_intervals).starting_on(start_date), | ||
| ) | ||
|
|
||
| # Measures for region breakdown of clinical conditions | ||
| measures.define_measure( | ||
| name=f"count_{condition_name}_by_region", | ||
| numerator=numerator, | ||
| denominator=denominator, | ||
| group_by={ | ||
| "region": registration.practice_nuts1_region_name, | ||
| }, | ||
| intervals=months(monthly_intervals).starting_on(start_date), | ||
| ) |
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see comment above, I think we can make this consierably shorter
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