beta.tpp schema🔗

Available on backends: TPP

This defines all the data (both primary care and externally linked) available in the TPP backend.

To use this schema in an ehrQL file:

from ehrql.tables.beta.tpp import (
    addresses,
    appointments,
    clinical_events,
    emergency_care_attendances,
    hospital_admissions,
    household_memberships_2020,
    isaric_raw,
    medications,
    occupation_on_covid_vaccine_record,
    ons_cis,
    ons_deaths,
    open_prompt,
    patients,
    practice_registrations,
    sgss_covid_all_tests,
    vaccinations,
)

many rows per patient

addresses🔗

Columns

address_id 🔗 integer
start_date 🔗 date
end_date 🔗 date
address_type 🔗 integer
rural_urban_classification 🔗 integer
imd_rounded 🔗 integer
msoa_code 🔗 string: Matches regular expression: E020[0-9]{5}
has_postcode 🔗 boolean
care_home_is_potential_match 🔗 boolean
care_home_requires_nursing 🔗 boolean
care_home_does_not_require_nursing 🔗 boolean

Methods

for_patient_on(date) 🔗

Return each patient's registered address as it was on the supplied date.

Where there are multiple registered addresses we prefer any which have a known postcode (though we never have access to this postcode) as this is used by TPP to cross-reference other data associated with the address, such as the MSOA or index of multiple deprevation.

Where there are multiple of these we prefer the most recently registered address and then, if there are multiple of these, the one with the longest duration. If there's stil an exact tie we choose arbitrarily based on the address ID.

View method definition

spanning_addrs = addresses.where(addresses.start_date <= date).except_where(
    addresses.end_date < date
)
ordered_addrs = spanning_addrs.sort_by(
    case(when(addresses.has_postcode).then(1), default=0),
    addresses.start_date,
    addresses.end_date,
    addresses.address_id,
)
return ordered_addrs.last_for_patient()

many rows per patient

appointments🔗

Columns

booked_date 🔗 date
start_date 🔗 date

many rows per patient

clinical_events🔗

Columns

date 🔗 date
snomedct_code 🔗 SNOMED-CT code
ctv3_code 🔗 CTV3 (Read v3) code
numeric_value 🔗 float

many rows per patient

emergency_care_attendances🔗

Columns

id 🔗 integer
arrival_date 🔗 date
discharge_destination 🔗 SNOMED-CT code
diagnosis_01 🔗 SNOMED-CT code
diagnosis_02 🔗 SNOMED-CT code
diagnosis_03 🔗 SNOMED-CT code
diagnosis_04 🔗 SNOMED-CT code
diagnosis_05 🔗 SNOMED-CT code
diagnosis_06 🔗 SNOMED-CT code
diagnosis_07 🔗 SNOMED-CT code
diagnosis_08 🔗 SNOMED-CT code
diagnosis_09 🔗 SNOMED-CT code
diagnosis_10 🔗 SNOMED-CT code
diagnosis_11 🔗 SNOMED-CT code
diagnosis_12 🔗 SNOMED-CT code
diagnosis_13 🔗 SNOMED-CT code
diagnosis_14 🔗 SNOMED-CT code
diagnosis_15 🔗 SNOMED-CT code
diagnosis_16 🔗 SNOMED-CT code
diagnosis_17 🔗 SNOMED-CT code
diagnosis_18 🔗 SNOMED-CT code
diagnosis_19 🔗 SNOMED-CT code
diagnosis_20 🔗 SNOMED-CT code
diagnosis_21 🔗 SNOMED-CT code
diagnosis_22 🔗 SNOMED-CT code
diagnosis_23 🔗 SNOMED-CT code
diagnosis_24 🔗 SNOMED-CT code

many rows per patient

hospital_admissions🔗

Columns

id 🔗 integer
admission_date 🔗 date
discharge_date 🔗 date
admission_method 🔗 string
all_diagnoses 🔗 string
patient_classification 🔗 string
days_in_critical_care 🔗 integer
primary_diagnoses 🔗 string

one row per patient

household_memberships_2020🔗

Inferred household membership as of 2020-02-01, as determined by TPP using an as yet undocumented algorithm.

Columns

household_pseudo_id 🔗 integer
household_size 🔗 integer

many rows per patient

isaric_raw🔗

A subset of the ISARIC data.

These columns are deliberately all taken as strings while in a preliminary phase. They will later change to more appropriate data types.

Descriptions taken from: CCP_REDCap_ISARIC_data_dictionary_codebook.pdf

Columns

age 🔗 string: Age
age_factor 🔗 string: TODO
calc_age 🔗 string: Calculated age (comparing date of birth with date of enrolment). May be inaccurate if a date of February 29 is used.
sex 🔗 string: Sex at birth.
ethnic___1 🔗 string: Ethnic group: Arab.
ethnic___2 🔗 string: Ethnic group: Black.
ethnic___3 🔗 string: Ethnic group: East Asian.
ethnic___4 🔗 string: Ethnic group: South Asian.
ethnic___5 🔗 string: Ethnic group: West Asian.
ethnic___6 🔗 string: Ethnic group: Latin American.
ethnic___7 🔗 string: Ethnic group: White.
ethnic___8 🔗 string: Ethnic group: Aboriginal/First Nations.
ethnic___9 🔗 string: Ethnic group: Other.
ethnic___10 🔗 string: Ethnic group: N/A.
covid19_vaccine 🔗 string: Has the patient received a Covid-19 vaccine (open label licenced product)?
covid19_vaccined 🔗 date: Date first vaccine given (Covid-19) if known.
covid19_vaccine2d 🔗 date: Date second vaccine given (Covid-19) if known.
covid19_vaccined_nk 🔗 string: First vaccine given (Covid-19) but date not known.
corona_ieorres 🔗 string: Suspected or proven infection with pathogen of public health interest.
coriona_ieorres2 🔗 string: Proven or high likelihood of infection with pathogen of public health interest.
coriona_ieorres3 🔗 string: Proven infection with pathogen of public health interest.
inflammatory_mss 🔗 string: Adult or child who meets case definition for inflammatory multi-system syndrome (MIS-C/MIS-A).
cestdat 🔗 date: Onset date of first/earliest symptom.
chrincard 🔗 string: Chronic cardiac disease, including congenital heart disease (not hypertension).

Possible values: YES, NO, Unknown
hypertension_mhyn 🔗 string: Hypertension (physician diagnosed).

Possible values: YES, NO, Unknown
chronicpul_mhyn 🔗 string: Chronic pulmonary disease (not asthma).

Possible values: YES, NO, Unknown
asthma_mhyn 🔗 string: Asthma (physician diagnosed).

Possible values: YES, NO, Unknown
renal_mhyn 🔗 string: Chronic kidney disease.

Possible values: YES, NO, Unknown
mildliver 🔗 string: Mild liver disease.

Possible values: YES, NO, Unknown
modliv 🔗 string: Moderate or severe liver disease

Possible values: YES, NO, Unknown
chronicneu_mhyn 🔗 string: Chronic neurological disorder.

Possible values: YES, NO, Unknown
malignantneo_mhyn 🔗 string: Malignant neoplasm.

Possible values: YES, NO, Unknown
chronichaemo_mhyn 🔗 string: Chronic haematologic disease.

Possible values: YES, NO, Unknown
aidshiv_mhyn 🔗 string: AIDS/HIV.

Possible values: YES, NO, Unknown
obesity_mhyn 🔗 string: Obesity (as defined by clinical staff).

Possible values: YES, NO, Unknown
diabetes_type_mhyn 🔗 string: Diabetes and type.

Possible values: NO, 1, 2, N/K
diabetescom_mhyn 🔗 string: Diabetes with complications.

Possible values: YES, NO, Unknown
diabetes_mhyn 🔗 string: Diabetes without complications.

Possible values: YES, NO, Unknown
rheumatologic_mhyn 🔗 string: Rheumatologic disorder.

Possible values: YES, NO, Unknown
dementia_mhyn 🔗 string: Dementia.

Possible values: YES, NO, Unknown
malnutrition_mhyn 🔗 string: Malnutrition.

Possible values: YES, NO, Unknown
smoking_mhyn 🔗 string: Smoking.

Possible values: Yes, Never Smoked, Former Smoker, N/K
hostdat 🔗 date: Admission date at this facility.
hooccur 🔗 string: Transfer from other facility?
hostdat_transfer 🔗 date: Admission date at previous facility.
hostdat_transfernk 🔗 string: Admission date at previous facility not known.
readm_cov19 🔗 string: Is the patient being readmitted with Covid-19?
dsstdat 🔗 date: Date of enrolment.
dsstdtc 🔗 date: Outcome date.

many rows per patient

medications🔗

Columns

date 🔗 date
dmd_code 🔗 dm+d code

many rows per patient

occupation_on_covid_vaccine_record🔗

Columns

is_healthcare_worker 🔗 boolean

many rows per patient

ons_cis🔗

Data from the ONS Covid Infection Survey.

Columns

visit_date 🔗 date
visit_num 🔗 integer
is_opted_out_of_nhs_data_share 🔗 boolean
last_linkage_dt 🔗 date
imd_decile_e 🔗 integer
imd_quartile_e 🔗 integer
rural_urban 🔗 integer

many rows per patient

ons_deaths🔗

Columns

date 🔗 date
place 🔗 string: Possible values: Care Home, Elsewhere, Home, Hospice, Hospital, Other communal establishment
cause_of_death_01 🔗 ICD-10 code
cause_of_death_02 🔗 ICD-10 code
cause_of_death_03 🔗 ICD-10 code
cause_of_death_04 🔗 ICD-10 code
cause_of_death_05 🔗 ICD-10 code
cause_of_death_06 🔗 ICD-10 code
cause_of_death_07 🔗 ICD-10 code
cause_of_death_08 🔗 ICD-10 code
cause_of_death_09 🔗 ICD-10 code
cause_of_death_10 🔗 ICD-10 code
cause_of_death_11 🔗 ICD-10 code
cause_of_death_12 🔗 ICD-10 code
cause_of_death_13 🔗 ICD-10 code
cause_of_death_14 🔗 ICD-10 code
cause_of_death_15 🔗 ICD-10 code

many rows per patient

open_prompt🔗

Columns

ctv3_code 🔗 CTV3 (Read v3) code: The question, as a CTV3 code

Never NULL
snomedct_code 🔗 SNOMED-CT code: The question, as a SNOMED CT code or None
consultation_date 🔗 date: The date the survey was administered

Never NULL
consultation_id 🔗 integer: The ID of the survey

Never NULL
numeric_value 🔗 float: The response to the question

Never NULL

one row per patient

patients🔗

Columns

date_of_birth 🔗 date: Patient's date of birth, rounded to first of month.

Always the first day of a month

Never NULL
sex 🔗 string: Patient's sex.

Possible values: female, male, intersex, unknown

Never NULL
date_of_death 🔗 date: Patient's date of death.

Methods

age_on(date) 🔗

Patient's age as an integer, in whole elapsed calendar years, as it would be on the supplied date.

Note that this takes no account of whether the patient is alive at the given date. In particular, it may return negative values if the date is before the patient's date of birth.

View method definition

return (date - patients.date_of_birth).years

many rows per patient

practice_registrations🔗

Columns

start_date 🔗 date
end_date 🔗 date
practice_pseudo_id 🔗 integer
practice_stp 🔗 string: Matches regular expression: E540000[0-9]{2}
practice_nuts1_region_name 🔗 string: Name of the NUTS level 1 region of England to which the practice belongs. For more information see: https://www.ons.gov.uk/methodology/geography/ukgeographies/eurostat

Possible values: North East, North West, Yorkshire and The Humber, East Midlands, West Midlands, East, London, South East, South West

Methods

for_patient_on(date) 🔗

Return each patient's practice registration as it was on the supplied date.

Where a patient is registered with multiple practices we prefer the most recent registration and then, if there are multiple of these, the one with the longest duration. If there's stil an exact tie we choose arbitrarily based on the practice ID.

View method definition

spanning_regs = practice_registrations.where(practice_registrations.start_date <= date).except_where(
    practice_registrations.end_date < date
)
ordered_regs = spanning_regs.sort_by(
    practice_registrations.start_date,
    practice_registrations.end_date,
    practice_registrations.practice_pseudo_id,
)
return ordered_regs.last_for_patient()

many rows per patient

sgss_covid_all_tests🔗

Columns

specimen_taken_date 🔗 date
is_positive 🔗 boolean

many rows per patient

vaccinations🔗

Columns

vaccination_id 🔗 integer
date 🔗 date
target_disease 🔗 string
product_name 🔗 string