关键词: Factor analysis Item response theory PRO Past medical history Patient reported outcome Psychometrics Quality of life Review of systems Social history

Mesh : Activities of Daily Living / psychology Aged Aged, 80 and over Chronic Disease / psychology Factor Analysis, Statistical Female Humans Male Middle Aged Psychometrics / methods Quality of Life Surveys and Questionnaires / standards

来  源:   DOI:10.1186/s12955-020-01287-2   PDF(Sci-hub)   PDF(Pubmed)

Abstract:
BACKGROUND: The provision of psychometrically valid patient reported outcomes (PROs) improves patient outcomes and reflects their quality of life. Consequently, ad hoc clinician-generated questionnaires of the past are being replaced by more rigorous instruments. This change, while beneficial, risks the loss/orphaning of decades-long information on difficult to capture/chronically ill populations. The goal of this study was to assess to the quality of data retrieved from these legacy questionnaires.
METHODS: Participants included 8563 patients who generated a total of 12,626 hospital admissions over the 2004-2014 study period. Items used to screen for issues related to function, mood, symptoms, and social support among patients with chronic disease were identified in our medical center\'s patient information questionnaire. Cluster and exploratory factor analyses (EFA) followed by multidimensional item response theory (MIRT) analyses were used to select items that defined factors. Scores were derived with summation and MIRT approaches; inter-factor relationships and relationships of factor scores to assigned diagnostic codes were assessed. Rasch analyses assessed the constructs\' measurement properties.
RESULTS: Literature review and clinician interviews yielded four hypothesized constructs: psychological distress/wellbeing, symptom burden, social support, and physical function. Rasch analyses showed that, while all had good measurement properties, only one, function, separated individuals well. In exploratory factor analyses (EFA), 11 factors representing depression, respiratory symptoms, musculoskeletal pain, family support, mobility, activities of daily living, alcohol consumption, weight loss, fatigue, neurological disorders, and fear at home were identified. Based on the agreement between EFA and cluster analyses as well as Cronbach\'s alpha, six domains were retained for analyses. Correlations were strong between activities of daily living and mobility (.84), and moderate between pain and mobility (.37) and psychological distress (.59) Known-group validity was supported from the relationships between factor scores and the relevant diagnostic code assignments (.12 to .20).
CONCLUSIONS: Items from ad hoc clinician-generated patient information questionnaires can be aggregated into valid factors that assess supportive care domains among chronically ill patients. However, the binary response options offered by many screening items limit their information content and consequently, as highlighted by Rasch analyses, their ability to meaningfully discriminate trait levels in these populations.
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