This observational cohort study aimed to characterise demographics, comorbidities, co-medications, and healthcare resource utilisation (HCRU) among patients newly diagnosed with Alzheimer's disease (AD) in England. The authors used published and validated AD case-identification algorithms applied to linked routine care databases to quantify the England-specific burden of multimorbidity, polypharmacy, and healthcare use at the time of cohort qualification.
The study used real-world data from the Clinical Practice Research Datalink (CPRD) Aurum primary care database linked to the Hospital Episode Statistics (HES) secondary care database for England. The index period for patient selection ran from 1 January to 31 December 2019. The analysis described patient characteristics, comorbidities, co-medications, and HCRU present up to and including the date of cohort qualification.
Three published algorithms from the literature were selected to identify patients with AD: two disease-based algorithms (Imfeld et al and Douros et al) and one medication-based algorithm (Schroeder et al). Patients meeting criteria according to each algorithm were assigned to three non-mutually exclusive cohorts (A–C). To focus on the population most relevant to typical AD onset, patients aged 60 years and older were analysed as three corresponding subcohorts (subcohorts 1–3).
Among patients aged ≥60 years, subcohort sizes were reported as follows: subcohort 1 included 9,826 patients; subcohort 2 included 10,265 patients; and subcohort 3 included 7,355 patients. Across these subcohorts, the mean age at index ranged from 81 to 83 years. The majority of patients were female, comprising 59% to 63% of the samples, and the cohort was predominantly White (93%).
Common comorbid conditions present up to and including the date of cohort qualification were:
These comorbidities highlight the high burden of chronic disease in patients newly identified with AD and underscore the need to consider multimorbidity when planning care.
Medication use at cohort qualification reflected substantial exposure to symptomatic and anti-inflammatory agents. The most frequently recorded co-medications were:
These figures indicate a high level of polypharmacy in the AD population studied, with potential implications for prescribing complexity, drug–disease interactions, and the need for medication review.
Up to 34% of patients had a specialist referral or visit recorded by cohort qualification. Among specialist contacts, up to 18% involved a neurologist. Evaluating healthcare use in the 12 months prior to and including the index date, as many as 46% of patients had an emergency department visit or an inpatient admission for any cause. These findings demonstrate substantial HCRU around the time of AD diagnosis.
The study reports that findings were broadly consistent across the three subcohorts identified using the disease-based and medication-based algorithms. This concordance across different published and validated AD case definitions strengthens the internal validity of the results and suggests that the described demographic, comorbidity, medication, and HCRU patterns are robust to the method of AD case ascertainment.
In England, patients newly diagnosed with AD in 2019 were predominantly White females in their early 80s and carried a high burden of chronic comorbidities such as hypertension, respiratory disease (asthma/COPD), and arthritis. High rates of analgesic and anti-inflammatory medication use point to widespread polypharmacy. Substantial specialist contact and acute care use were documented in the year leading up to and including diagnosis.
By quantifying comorbidity, polypharmacy, and HCRU in an England-specific AD population using multiple validated algorithms, the study fills a key evidence gap. The results may help clinicians and healthcare planners better identify patients living with AD, anticipate common comorbid needs, and tailor treatment and service strategies accordingly.