Evidence map›Paper›PMID 38875036›Full record

Trial reportJournal of Alzheimer's disease : JAD2024

Healthcare Needs Patterns and Pattern-Predicting Factors in Dementia: Results of the Comprehensive, Computerized Unmet Needs Assessment from the Randomized, Controlled Interventional Trial InDePendent.

Bernhard Michalowsky, Anika Rädke, Annelie Scharf, Franka Mühlichen, Maresa Buchholz, Moritz Platen, Fabian Kleinke, Peter Penndorf, Stefanie Pfitzner, Neeltje van den Berg and 1 more

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of Alzheimer's disease : JAD, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Trial
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Bernhard MichalowskyGerman Center for Neurodegenerative Diseases - DZNE, Rostock/Greifswald, Greifswald, Germany.
Anika RädkeGerman Center for Neurodegenerative Diseases - DZNE, Rostock/Greifswald, Greifswald, Germany.
Annelie ScharfGerman Center for Neurodegenerative Diseases - DZNE, Rostock/Greifswald, Greifswald, Germany.
Franka MühlichenGerman Center for Neurodegenerative Diseases - DZNE, Rostock/Greifswald, Greifswald, Germany.
Maresa BuchholzGerman Center for Neurodegenerative Diseases - DZNE, Rostock/Greifswald, Greifswald, Germany.
Moritz PlatenGerman Center for Neurodegenerative Diseases - DZNE, Rostock/Greifswald, Greifswald, Germany.
Fabian KleinkeSection of Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
Peter PenndorfSection of Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
Stefanie PfitznerSection of Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
Neeltje van den BergSection of Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
Wolfgang HoffmannGerman Center for Neurodegenerative Diseases - DZNE, Rostock/Greifswald, Greifswald, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Determining unmet need patterns and associated factors in primary care can potentially specify assessment batteries and tailor interventions in dementia more efficiently. Objective: To identify latent unmet healthcare need patterns and associated sociodemographic and clinical factors. Methods: This Latent Class Analysis (LCA) includes n = 417 community-dwelling people living with dementia. Subjects completed a comprehensive, computer-assisted face-to-face interview to identify unmet needs. One-hundred-fifteen predefined unmet medical, medication, nursing, psychosocial, and social care needs were available. LCA and multivariate logistic regressions were performed to identify unmet needs patterns and patient characteristics belonging to a specific pattern, respectively. Results: Four profiles were identified: [1] "few needs without any psychosocial need" (n = 44 (11%); mean: 7.4 needs), [2] "some medical and nursing care needs only" (n = 135 (32%); 9.7 needs), [3] "some needs in all areas" (n = 139 (33%); 14.3 needs), and [4] "many medical and nursing needs" (n = 99 (24%); 19.1 needs). Whereas the first class with the lowest number of needs comprised younger, less cognitively impaired patients without depressive symptoms, the fourth class had the highest number of unmet needs, containing patients with lower health status, less social support and higher comorbidity and depressive symptoms. Better access to social care services and higher social support reduced unmet needs, distinguishing the second from the third class (9.7 versus 14.3 needs). Conclusions: Access to the social care system, social support and depressive symptoms should be assessed, and the patient's health status and comorbidities monitored to more comprehensively identify unmet needs patterns and more efficiently guide tailored interventions.

Indexed as

DementiaHealth Services Needs and DemandNeeds AssessmentAgedAged, 80 and overFemaleHumansIndependent LivingLatent Class AnalysisMaleMiddle AgedAlzheimer’s diseasedementialatent class analysisprimary careprofilesunmet needs

Identifiers

PMID38875036
PMCPMC11307004

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.