Chapter 4 Disease Definitions

UKBAnalytica identifies disease cases from up to four data sources:

Source	Code field	Date field
ICD-10 (hospital)	p41270	p41280_a*
ICD-9 (hospital)	p41271	p41281_a*
Self-report	p20002_i*_a*	p20008_i*_a*
Death registry	p40001, p40002	p40000

4.1 Predefined diseases

The package ships a curated library of definitions for common cardiovascular and metabolic conditions. Retrieve them with get_predefined_diseases():

library(UKBAnalytica)

diseases <- get_predefined_diseases()
names(diseases)

Select a subset by name:

my_diseases <- get_predefined_diseases()[
  c("AA", "Hypertension", "Diabetes")
]

Each definition is a list specifying ICD-10 codes, ICD-9 codes, self-report codes, and death-cause codes.

4.2 Inspecting a definition

diseases <- get_predefined_diseases()
str(diseases[["Hypertension"]])

A definition typically contains:

• icd10 – character vector of ICD-10 codes (prefix-matched).
• icd9 – character vector of ICD-9 codes.
• self_report – integer vector of self-report coding IDs.
• death_icd10 – character vector of death-cause ICD-10 codes.

4.3 Creating custom definitions

Use create_disease_definition() to define a new disease:

my_disease <- create_disease_definition(
  icd10       = c("K70", "K71", "K72", "K73", "K74"),
  icd9        = c("571"),
  self_report = c(1604),
  death_icd10 = c("K70", "K74")
)

You can combine it with predefined diseases:

all_diseases <- c(
  get_predefined_diseases()[c("Hypertension", "Diabetes")],
  list(LiverDisease = my_disease)
)

4.4 Composite endpoints

Create composite endpoints by merging multiple definitions:

mace <- combine_disease_definitions(
  get_predefined_diseases()[c("MI", "Stroke", "HF")]
)

This merges ICD-10, ICD-9, self-report, and death codes from all input definitions into a single combined definition.

4.5 Comparing data sources

To check how many cases each source contributes, use compare_data_sources():

library(data.table)
ukb_data <- fread("population.csv")
diseases <- get_predefined_diseases()[c("AA", "Hypertension")]

comparison <- compare_data_sources(ukb_data, diseases)
comparison

This returns a summary table showing case counts per source per disease, which is useful for justifying source selection in your manuscript.