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Record W4321351446 · doi:10.1101/2023.02.18.23286127

Antipsychotic prescribing and mortality in people with dementia before and during the COVID-19 pandemic: retrospective cohort study

2023· preprint· en· W4321351446 on OpenAlexaff
Christian Schnier, Aoife McCarthy, Daniel R. Morales, Ashley Akbari, Reecha Sofat, Caroline Dale, Rohan Takhar, Mamas Mamas, Kamlesh Khunti, Francesco Zaccardi, Cathie Sudlow, Tim Wilkinson

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersBritish Heart FoundationSwansea UniversityLlywodraeth CymruEconomic and Social Research CouncilWelsh Ambulance Services NHS TrustPublic Health Wales
KeywordsDementiaMedicineAntipsychoticPandemicStroke (engine)CohortPopulationCohort studyPediatricsPsychiatryDiseaseEmergency medicineCoronavirus disease 2019 (COVID-19)Internal medicineSchizophrenia (object-oriented programming)Infectious disease (medical specialty)Environmental health

Abstract

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ABSTRACT Background Antipsychotic drugs have been associated with increased mortality, stroke and myocardial infarction in people with dementia. Concerns have been raised that antipsychotic prescribing may have increased during the COVID-19 pandemic due to social restrictions imposed to limit the spread of the virus. We used multisource, routinely-collected healthcare data from Wales, UK, to investigate prescribing and mortality trends in people with dementia before and during the COVID-19 pandemic. Methods We used individual-level, anonymised, population-scale linked health data to identify adults aged ≥60 years with a diagnosis of dementia in Wales, UK. We explored antipsychotic prescribing trends over 67 months between 1 st January 2016 and 1 st August 2021, overall and stratified by age and dementia subtype. We used time series analyses to examine all-cause, myocardial infarction (MI) and stroke mortality over the study period and identified the leading causes of death in people with dementia. Findings Of 57,396 people with dementia, 11,929 (21%) were prescribed an antipsychotic at any point during follow-up. Accounting for seasonality, antipsychotic prescribing increased during the second half of 2019 and throughout 2020. However, the absolute difference in prescribing rates was small, ranging from 1253 to 1305 per 10,000 person-months. Prescribing in the 60-64 age group and those with Alzheimer’s disease increased throughout the 5-year period. All-cause and stroke mortality increased in the second half of 2019 and throughout 2020 but MI mortality declined. From January 2020, COVID-19 was the second commonest underlying cause of death in people with dementia. Interpretation During the COVID-19 pandemic there was a small increase in antipsychotic prescribing in people with dementia. The long-term increase in antipsychotic prescribing in younger people and in those with Alzheimer’s disease warrants further investigation. Funding British Heart Foundation (BHF) (SP/19/3/34678) via the BHF Data Science Centre led by HDR UK, and the Scottish Neurological Research Fund. Research in Context Evidence before this study We searched Ovid MEDLINE for studies describing antipsychotic prescribing trends in people with dementia during the COVID-19 pandemic, published between 1st January 2020 and 22nd March 2022. The following search terms were used: (exp Antipsychotic Agents/ OR antipsychotic.mp OR neuroleptic.mp OR risperidone.mp OR exp Risperidone/ OR quetiapine.mp OR exp Quetiapine Fumarate/ OR olanzapine.mp OR exp Olanzapine/ OR exp Psychotropic Drugs/ or psychotropic.mp) AND (exp Dementia/ OR exp Alzheimer Disease/ or alzheimer.mp) AND (prescri*.mp OR exp Prescriptions/ OR exp Electronic Prescribing/ OR trend*.mp OR time series.mp). The search identified 128 published studies, of which three were eligible for inclusion. Two studies, based on data from England and the USA, compared antipsychotic prescribing in people with dementia before and during the COVID-19 pandemic. Both reported an increase in the proportion of patients prescribed an antipsychotic after the onset of the pandemic. A third study, based in the Netherlands, reported antipsychotic prescription trends in nursing home residents with dementia during the first four months of the pandemic, comparing prescribing rates to the timings of lifting of social restrictions, showing that antipsychotic prescribing rates remained constant throughout this period. Added value of this study We conducted age-standardised time series analyses using comprehensive, linked, anonymised, individual-level routinely-collected, population-scale health data for the population of Wales, UK. By accounting for seasonal variations in prescribing and mortality, we demonstrated that the absolute increase in antipsychotic prescribing in people with dementia of any cause during the COVID-19 pandemic was small. In contrast, antipsychotic prescribing in the youngest age group (60-64 years) and in people with a subtype diagnosis of Alzheimer’s disease increased throughout the five-year study period. Accounting for seasonal variation, all-cause mortality rates in people with dementia began to increase in late 2019 and increased sharply during the first few months of the pandemic. COVID-19 became the leading non-dementia cause of death in people with dementia from 2020 to 2021. Stroke mortality increased during the pandemic, following a similar pattern to that of all-cause mortality, whereas myocardial infarction rates decreased. Implications of all the available evidence During COVID-19 we observed a large increase in all-cause and stroke mortality in people with dementia. When seasonal variations are accounted for, antipsychotic prescribing rates in all-cause dementia increased by a small amount before and during the pandemic in the UK. The increased prescribing rates in younger age groups and in people with Alzheimer’s disease warrants further investigation.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.329
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
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