The Effects of <scp>COVID</scp>‐19 lockdown on neuropsychiatric symptoms in patients with dementia or mild cognitive impairment: A systematic review and meta‐analysis
Bibliographic record
Abstract
The COVID‐19 pandemic may have a disproportionate impact on people with dementia/mild cognitive impairment (MCI) due to isolation and loss of services. The aim of this systematic review was to investigate the effects of the COVID‐19 lockdown on neuropsychiatric symptoms (NPS) in people living with dementia/MCI. Two authors searched major electronic databases from inception to June 2021 for observational studies investigating COVID‐19 and NPS in people with dementia/MCI. Summary estimates of mean differences in NPS scores pre‐ versus post‐COVID‐19 were calculated using a random‐effects model, weighting cases using inverse variance. Study quality and risk of bias were assessed by the Newcastle–Ottawa Scale. From 2730 citations, 21 studies including 7139 patients (60.0% female, mean age 75.6 ± 7.9 years, 4.0% MCI) with dementia were evaluated in the review. Five studies found no changes in NPS, but in all other studies, an increase in at least one NPS or the pre‐pandemic Neuropsychiatric Inventory (NPI) score was found. The most common aggravated NPS were depression, anxiety, agitation, irritability, and apathy during lockdown, but 66.7% of the studies had a high bias. Seven studies including 420 patients (22.1% MCI) yielded enough data to be included in the meta‐analysis. The mean follow‐up time was 5.9 ± 1.5 weeks. The pooled increase in NPI score before compared to during COVID‐19 was 3.85 (95% CI:0.43 to 7.27; P = 0.03; I2 = 82.4%). All studies had high risk of bias. These results were characterized by high heterogeneity, but there was no presence of publication bias. There is an increase in the worsening of NPS in people living with dementia/MCI during lockdown in the COVID pandemic. Future comparative studies are needed to elucidate whether a similar deterioration might occur in people without dementia/MCI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".