Dementia in health claims data: The influence of different case definitions on incidence and prevalence estimates

Objectives

The epidemiology of dementia subtypes including Alzheimer's disease (AD) and vascular dementia (VD) and their reliance on different case definitions (“algorithms”) in health claims data are still understudied.

Methods

Based on health claims data, prevalence estimates (per 100 persons), incidence rates (IRs, per 100 person-years), and proportions of AD, VD, and other dementias (oD) were calculated. Five algorithms of increasing strictness considered inpatient/outpatient diagnoses (#1, #2), antidementia drugs (#3) or supportive diagnostics (#4, #5).

Results

Algorithm 1 detected 213,409 cases (#2: 197,400; #3: 48,688; #4: 3033; #5: 3105), a prevalence for any dementia of 3.44 and an IR of 1.39 (AD: 0.80/0.21, VD: 0.79/0.31). The prevalence decreased by algorithms for any dementia (#2: 3.19; #3: 0.75; #4: 0.04; #5: 0.05) as did IRs (#2: 1.13; #3: 0.18; #4: 0.05, #5: 0.05). Algorithms 1–2, and 4–5 revealed similar proportions of AD (23.3%–26.6%), VD (19.9%–23.2%), and oD (53.1%–53.8%), algorithm 3 estimated 45% (AD), 12.1% (VD), and 43.0% (oD).

Conclusions

Health claims data show lower estimates of AD than previously reported, due to markedly lower prevalent/incident proportions of patients with corresponding codes. Using medication in defining dementia potentially improves estimating the proportion of AD while supportive diagnostics were of limited use.