Mapping inequalities in dementia diagnosis in England: a Bayesian spatial analysis of disease prevalence and case ascertainment

Background: By 2030, over one million people in the UK are projected to be living with dementia; however, the NHS estimates that only approximately 65\% of individuals with dementia currently receive a formal diagnosis. Understanding the geographic and socioeconomic factors contributing to variation in diagnosis is essential for reducing inequalities in dementia care. Methods: We developed a Bayesian spatial modelling framework to separate underlying dementia prevalence from case ascertainment using routinely collected public datasets at Local Authority District (LAD) level across England. Underlying prevalence was modelled using non-modifiable and modifiable risk factors identified by the Lancet Commission, while diagnosis rates were modelled as a function of healthcare provision and sociodemographic factors. Spatial random effects were incorporated to identify residual geographic variation after accounting for observed covariates. We additionally compared case ascertainment with regional variation in dementia subtype coding as a further measure of diagnostic quality. Findings: Substantial geographic variation in dementia diagnosis remained after accounting for differences in underlying disease prevalence. Urbanicity, deprivation, ethnicity, and healthcare provision were associated with variation in case ascertainment, with spatial effects explaining additional regional variation. Diagnostic specificity varied considerably between regions, with areas of lower case ascertainment tending to exhibit higher proportions of undifferentiated dementia diagnoses. Interpretation: By jointly modelling underlying dementia prevalence and case ascertainment, we can distinguish the factors associated with dementia burden from those associated with diagnosis. This enables the identification of areas with low case ascertainment, which when combined with poor diagnostic specificity, provides an evidence base for targeted regional interventions. Potential interventions include strengthening dementia care pathways through enhanced outreach, more equitable access to diagnostic assessment, and locally tailored models of care.

Authors

Institutions

Publication Details

Journal
medRxiv
Published
2026-11-10
DOI
https://doi.org/10.64898/2026.11.09.26367595
Primary Topic
Dementia and Cognitive Impairment Research
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
preprint

Mapping inequalities in dementia diagnosis in England: a Bayesian spatial analysis of disease prevalence and case ascertainment

Jessica Jiang, Sebastian James Crutch, Beatrice Taylor, Jason D. Warren
medRxiv
Dementia and Cognitive Impairment Research
preprint

Mapping inequalities in dementia diagnosis in England: a Bayesian spatial analysis of disease prevalence and case ascertainment

Jessica Jiang, Sebastian James Crutch, Beatrice Taylor, Jason D. Warren
preprint en

Abstract

Background: By 2030, over one million people in the UK are projected to be living with dementia; however, the NHS estimates that only approximately 65\% of individuals with dementia currently receive a formal diagnosis. Understanding the geographic and socioeconomic factors contributing to variation in diagnosis is essential for reducing inequalities in dementia care. Methods: We developed a Bayesian spatial modelling framework to separate underlying dementia prevalence from case ascertainment using routinely collected public datasets at Local Authority District (LAD) level across England. Underlying prevalence was modelled using non-modifiable and modifiable risk factors identified by the Lancet Commission, while diagnosis rates were modelled as a function of healthcare provision and sociodemographic factors. Spatial random effects were incorporated to identify residual geographic variation after accounting for observed covariates. We additionally compared case ascertainment with regional variation in dementia subtype coding as a further measure of diagnostic quality. Findings: Substantial geographic variation in dementia diagnosis remained after accounting for differences in underlying disease prevalence. Urbanicity, deprivation, ethnicity, and healthcare provision were associated with variation in case ascertainment, with spatial effects explaining additional regional variation. Diagnostic specificity varied considerably between regions, with areas of lower case ascertainment tending to exhibit higher proportions of undifferentiated dementia diagnoses. Interpretation: By jointly modelling underlying dementia prevalence and case ascertainment, we can distinguish the factors associated with dementia burden from those associated with diagnosis. This enables the identification of areas with low case ascertainment, which when combined with poor diagnostic specificity, provides an evidence base for targeted regional interventions. Potential interventions include strengthening dementia care pathways through enhanced outreach, more equitable access to diagnostic assessment, and locally tailored models of care.

medRxiv
UCL Queen Square Institute of Neurology (GB), University College London (GB)
Dementia and Cognitive Impairment Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon