Update example data with properties useful for tutorial

[inglesp]

For the tutorial, we plan to help users work through writing ehrQL queries related to the following QOF rules for diabetes:

• DM_REG Patients aged at least 17 years old with an unresolved diabetes diagnosis.
• DM_006 The percentage of patients with diabetes, on the register, with a diagnosis of nephropathy (clinical proteinuria) or micro-albuminuria who are currently treated with an ACE-I (or ARBs).
• DM014 The percentage of patients newly diagnosed with diabetes, on the register, in the preceding 1 April to 31 March who have a record of being referred to a structured education programme within 9 months after entry on to the diabetes register.
• DM020 The percentage of patients with diabetes, on the register, without moderate or severe frailty in whom the last IFCC-HbA1c is 58 mmol/mol or less in the preceding 12 months.

For this, we'll need example data with the following properties:

• 100 patients
• 90 patients alive on 31st March 2024
• 90 patients registered on 31st March 2024
• Clinical event codes
  • all events should be between 1st April 2022 and 31st March 2024
  • diabetes status:
    • 75 patients with a diabetes diagnosis code and no diabetes resolved code
    • 5 patients with a diabetes diagnosis code followed by a diabetes resolved code
    • 5 patients with a diabetes diagnosis code followed by a diabetes resolved code, followed by another diagnosis code
  • frailty status:
    • 5 patients with a mild frailty code followed by a moderate frailty code
    • 5 patients with a moderate frailty code followed by a mild frailty code
    • 10 patients with a moderate frailty code
    • 10 patients with a severe frailty code
  • HbA1c status
    • 20 patients with HbA1c code with "low" value (between 38 and 58)
    • 20 patients with HbA1c code with "high" value (between 58 and 78)
    • 10 patients with HbA1c code with low value followed by HbA1c code with high value
    • 10 patients with HbA1c code with high value followed by HbA1c code with low value
  • other
    • 10 patients with nephropathy code
    • 10 patients with micro-albuminuria code
    • 20 patients with structured education programme code
• Prescriptions
  • all events should be between 1st April 2022 and 31st March 2024
  • 10 patients with prescription of ARB
  • 10 patients with prescription of ACE-I

For now, we should use arbitrary numbers to be the code for each kind of event/prescription:

• diabetes diagnosis 111
• diabetes resolved 222
• mild frailty 333
• moderate frailty 444
• severe frailty 555
• hba1c 666
• nephropath 777
• micro-albuminuria 888
• structured education programme 999

Here's some example code that could be adapted to create diabetes events.

if random.random() < 0.75:
    # patient has a diabetes diagnosis code and no diabetes resolved code
    yield "clinical_events", {"date": date, "dmd_code": 111}
elif random.random() < 0.05:
    # patient has a diabetes diagnosis code followed by a diabetes resolved code
    yield "clinical_events", {"date": date1, "dmd_code": 111}
    yield "clinical_events", {"date": date2, "dmd_code": 222}
elif random.random() < 0.05:
    # patient has a diabetes diagnosis code followed by a diabetes resolved code, followed by another diagnosis code
    yield "clinical_events", {"date": date1, "dmd_code": 111}
    yield "clinical_events", {"date": date2, "dmd_code": 222}
    yield "clinical_events", {"date": date3, "dmd_code": 111}