Evidence map›Paper›PMID 41345596›Full record

ArticleBMC public health2025

Cut-off score for the short form informal caregiver burden assessment questionnaire for predicting depression.

Teresa Martins, Daniela França, Maria José Peixoto, Elisangela Zanatta, Fátima Araújo

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Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Teresa MartinsEscola Superior de Enfermagem Do Porto; CINTESIS RISE-Health, Porto, Portugal. teresam@esenf.pt.
Daniela FrançaEscola Superior de Saúde de Santa Maria, Porto, Portugal.
Maria José PeixotoEscola Superior de Enfermagem Do Porto; CINTESIS RISE-Health, Porto, Portugal.
Elisangela ZanattaUniversidade do Estado de Santa Catarina, Cachepó, Brasil.
Fátima AraújoEscola Superior de Enfermagem Do Porto; CINTESIS RISE-Health, Porto, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCaregiver burden is associated with long-term caregiving, which can manifest through physical and emotional issues, including stress, anxiety, depression, and exhaustion. Assessing caregiver burden allows for determining its impact and monitoring its evolution, aiding in professional intervention and the implementation of health-promoting measures. This study aimed to determine the cut-off score of the Informal Caregiver Burden Assessment Questionnaire in its short form version (QASCI-VR) to identify caregivers at risk of anxiety and depression.

methodsA secondary study that used data from two primary studies conducted with informal caregivers in northern Portugal. The sample consisted of 201 family caregivers. The SF36 Quality of Life Questionnaire and Hospital Anxiety and Depression Scale (HADS) were utilised.

resultsThe prevalence of depression among caregivers was high (40.8%). Receiver Operating Characteristic (ROC) curve analysis showed an area under the curve (AUC) of 0.82 for predicting anxiety and depression, with a cut-off point of 40 providing 82% accuracy, 78% sensitivity, and 75% specificity. Cluster analysis confirmed that the QASCI-VR is a measure that discriminates between participants with and without depression.

conclusionThe QASCI-VR cut-off score of 40 provides healthcare professionals with a validated threshold for identifying caregivers at high risk of anxiety and depression, with good discriminatory accuracy. This cut-off enables early detection and timely intervention, facilitating more accurate screening and monitoring of caregiver burden in clinical practice. The study addresses a critical gap by establishing an evidence-based reference point for clinical decision-making in caregiver mental health assessment.

Indexed as

CaregiversCost of IllnessDepressionAdultAgedAnxietyFemaleHumansMaleMiddle AgedPortugalROC CurveSurveys and QuestionnairesDiagnostic AccuracyInformal caregivingPsychological DistressROC curve analysis

Identifiers

PMID41345596
PMCPMC12797362

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