AATD prevalence — population planning demo

Live prototype for alpha-1 antitrypsin deficiency (AATD): translate population size and cited prevalence scenarios into expected case counts — with optional confidence ranges, visible methodology, and assumptions you can defend in workshops.

Published: 27 April 2026

Updated: 27 April 2026

Planning & modelling demo

Population modelling
Scale cited prevalence rates to the population you are planning for.
Explainable outputs
Expected counts, scenario rate, and methodology visible together.
Responsive by design
Desktop, tablet, and phone layouts for workshops and field use.
Source-backed assumptions
Each scenario carries description and citation — ready for scrutiny.

Layout preview

Optional tablet and phone widths for stakeholder reviews. Full width remains the default.

Population planning

Set a population and choose a cited prevalence scenario to explore expected case scale. Sources stay visible with each option for workshop-ready scrutiny.

Population size

Whole numbers only (e.g. national or regional planning horizon).

Prevalence scenario

Base-case (UK Biobank)Based on UK Biobank data showing PI*ZZ prevalence of 30.6 per 100,000 population (95% CI 25.9–36.1). This represents the most robust UK population estimate.Source: UK Biobank, 2020England 1:4,440Conservative estimate based on 1:4,440 prevalence in England. This represents a lower bound estimate for planning purposes.Source: NHS England, 2019EU sensitivity (1:2,000)Higher prevalence estimate based on European data suggesting 1:2,000 prevalence. This represents an upper bound for sensitivity analysis.Source: European AATD Registry, 2018EU sensitivity (1:5,000)Lower prevalence estimate based on European data suggesting 1:5,000 prevalence. This represents a lower bound for sensitivity analysis.Source: European AATD Registry, 2018Diagnosed vs genetic (10% diagnosed)Estimated diagnosed prevalence assuming only 10% of genetic cases are diagnosed. This represents the current diagnosed burden.Source: Clinical estimates, 2023Diagnosed vs genetic (20% diagnosed)Estimated diagnosed prevalence assuming 20% of genetic cases are diagnosed. This represents improved diagnosis rates.Source: Clinical estimates, 2023

Estimated cases

20,502

Expected cases under the selected scenario (whole people, rounded down).

Scenario rate: 0.03% (30.6 per 100,000)
Population: 67,000,000
Approximate range (95% confidence interval): 17,353 to 24,187 people

Methodology (visible by design)

Expected cases equal population × (rate per 100,000 ÷ 100,000), then rounded down to whole people — consistent with the Eye4Health reference toolkit. Where a scenario includes published confidence bounds, those rates are scaled to your population in the same way to show an approximate range.

Assumptions and limitations

Prototype for professional planning conversations — not a clinical calculator, screening tool, or substitute for specialist advice.
Outputs follow the epidemiological assumptions in each scenario label and citation; methods and populations differ between sources.
Expected counts use integer truncation after scaling (whole people), matching the reference implementation behaviour.
This release uses curated scenarios only; bespoke rate entry is a common next step for client builds.

The problem we are addressing

Stakeholders often need a credible sense of scale before commissioning deeper modelling — without losing the line of sight back to published assumptions and sources.

What Eye4Health demonstrates here

Combines your population with curated AATD prevalence scenarios, each labelled with source context.
Surfaces expected case counts and, where published bounds exist, an approximate scaled range for planning discussions.
Keeps calculation logic auditable and portable (plain TypeScript, automated checks) so teams can review outputs with confidence.

Governance, inputs, and related work

How scenarios are sourced, what you can enter here, and where we take planning tools next — kept below so the interactive surface stays the focus.

Scenarios and inputs

Reference prevalence scenarios (rate per 100,000, descriptions, and source labels) aligned with the Eye4Health toolkit catalogue.
Population size as a whole number — adjust for country, region, or planning horizon.

Governance and appropriate use

Showroom prototype: confirm fit for purpose before regulated or patient-facing deployment.
Scenario labels and citations should be refreshed periodically against current literature and local policy.

Related Eye4Health capabilities

AATD Case-Finder — governed decision-support demo
Companion showroom: clinical prompts and explainable rule outputs from the same programme.
Epidemiology and market evaluation
Healthcare data visualisation