The Global Impact of COVID‐19 Control Measures on People With Dementia Living at Home and Their Carers: A Systematic Review of Quantitative and Qualitative Research Across 27 Countries
Bibliographic record
Abstract
BACKGROUND: COVID-19 control measures have had a unique impact on people with dementia (PWD) and their carers living at home. Yet, uncertainty exists regarding the global impact of such measures and whether differences exist between countries and global regions. We aimed to synthesize evidence on this topic. METHODS: We searched Medline, PsycINFO, EMBASE, Web of Science, CINAHL, Latin American and Caribbean Health Literature (LILACS), Scientific Electronic Library Online (SciELO), and EM Premium from the start of the pandemic to July 2022. At least two researchers independently screened citations and performed quality assessment following recommended criteria for critical appraisal according to study methodology. We analyzed data by country and region and synthesized results descriptively. RESULTS: Sixty-nine studies met inclusion criteria (74% quantitative and 26% qualitative; 22% included PWD, 44% carers of PWD, and 4% dyads), with a total of 209,738 participants. Most studies were conducted in Europe (59%), followed by Asia and North America (15% each), South America (7%), and Oceania (1%). Two studies presented data from multiple regions (3%). The quality of the studies varied, with the majority (62%) being of moderate quality. Across the study populations and global regions, COVID-19 control measures had implications for PWD and carers' access to health services, physical and mental health and daily routine, cognition, behavior, with accompanying social and economic costs. The impact on mental health for PWD and on loneliness and well-being for carers were the two most frequently studied outcomes. CONCLUSION: People with dementia and their carers represent a heterogeneous group of people across countries and communities; despite that, the impacts of COVID-19 control measures on PWD and their carers were broadly consistent across regions. Our evidence synthesis highlights the critical need for decision-makers to account for the needs of PWD and their carers when designing and implementing public health measures. OTHER: This work was funded by the JPND Call for Expert Working Groups: The Impact of COVID-19 on Neurodegenerative Diseases in partnership with the CIHR-Institute of Aging and the Public Health Agency (CIHR #02342-000). PROSPERO CRD42024554701.
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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 | 0.090 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".