Disability and Sensory Impairments

This calculator identifies learning disabilities (intellectual disabilities) and sensory impairments from ICD-10 diagnosis codes.

Overview

The disability identifier detects three categories of impairments:

1. Learning Disability / Autism Spectrum Disorder (ASD)
2. Visual Impairment
3. Hearing Impairment

Supported Codes

Learning Disability / ASD

ICD-10 codes starting with:

Prefix	Description
F70	Mild intellectual disabilities
F71	Moderate intellectual disabilities
F72	Severe intellectual disabilities
F73	Profound intellectual disabilities
F78	Other intellectual disabilities
F79	Unspecified intellectual disabilities
F84	Pervasive developmental disorders (including autism)

Visual Impairment

ICD-10 codes starting with:

Prefix	Description
H54	Blindness and low vision

Hearing Impairment

ICD-10 codes starting with:

Prefix	Description
H90	Conductive and sensorineural hearing loss
H91	Other and unspecified hearing loss

Usage

Python API

import polars as pl
from comorbidipy import disability

df = pl.DataFrame({
    "patient_id": ["P001", "P001", "P002", "P003"],
    "icd_code": ["F70", "H54", "H90", "J18"],
})

# Identify impairments
result = disability(
    df,
    id_col="patient_id",
    code_col="icd_code",
)

# Result includes binary columns for each impairment type

CLI

# Basic disability identification
comorbidipy disability input.csv output.csv

# With custom columns
comorbidipy disability input.parquet output.parquet --id-col pat_id --code-col diagnosis

# Output as Parquet
comorbidipy disability input.csv output.parquet

Output

The output DataFrame includes:

Column	Type	Description
ID column	varies	Patient identifier (original column name)
ld_asd	Int (0/1)	Learning disability or ASD present
impaired_vision	Int (0/1)	Visual impairment present
impaired_hearing	Int (0/1)	Hearing impairment present

Example

import polars as pl
from comorbidipy import disability

df = pl.DataFrame({
    "id": [
        "P001", "P001",  # LD + vision
        "P002",          # Hearing only
        "P003",          # No impairments
    ],
    "code": [
        "F70", "H541",   # Mild LD + low vision
        "H900",          # Conductive hearing loss
        "J18",           # Pneumonia (not an impairment)
    ],
})

result = disability(df, id_col="id", code_col="code")
print(result)

# Output:
# ┌──────┬────────┬─────────────────┬──────────────────┐
# │ id   ┆ ld_asd ┆ impaired_vision ┆ impaired_hearing │
# ╞══════╪════════╪═════════════════╪══════════════════╡
# │ P001 ┆ 1      ┆ 1               ┆ 0                │
# │ P002 ┆ 0      ┆ 0               ┆ 1                │
# │ P003 ┆ 0      ┆ 0               ┆ 0                │
# └──────┴────────┴─────────────────┴──────────────────┘

Use Cases

This calculator is useful for:

• Epidemiological studies: Identifying cohorts with specific impairments
• Healthcare planning: Understanding patient populations
• Reasonable adjustments: Flagging patients who may need communication support
• Quality metrics: Stratifying outcomes by disability status

Notes

1. ICD-10 only: This calculator only supports ICD-10 codes
2. Prefix matching: Codes are matched by their first 3 characters
3. Binary output: Each patient gets a 0 or 1 for each impairment type
4. Multiple impairments: A patient can have multiple impairments flagged

Clinical Considerations

When using these flags:

• Learning disability: May indicate need for easy-read materials, longer appointments, or carer involvement
• Visual impairment: Consider large print, audio information, or assistance with navigation
• Hearing impairment: Consider sign language interpreters, written communication, or hearing loops