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Patterns, relevance, and predictors of dyadic mental health over time in lung cancer
Author(s) -
Lee Christopher S.,
Lyons Karen S.
Publication year - 2019
Publication title -
psycho‐oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.41
H-Index - 137
eISSN - 1099-1611
pISSN - 1057-9249
DOI - 10.1002/pon.5153
Subject(s) - dyad , mental health , psychology , similarity (geometry) , clinical psychology , health care , multilevel model , relevance (law) , sample (material) , developmental psychology , psychiatry , economics , image (mathematics) , economic growth , chemistry , chromatography , political science , law , artificial intelligence , machine learning , computer science
Objective To identify distinct patterns of dyadic mental health in a sample of lung cancer dyads over 12 months and associations with other health characteristics and individual, dyadic, and familial predictors. Methods A sample of 113 patient‐care partner dyads living with nonsmall cell lung cancer were examined five times over 12 months. An integrative multilevel and mixture modeling approach was used to generate dyadic mental health summaries and identify common dyadic patterns of mental health over time, respectively. Results Three distinct patterns of dyadic mental health were observed: a congruent pattern (32.7%) characterized by almost identical mental health between members of the dyad, a disparate pattern (29.2%) characterized by better mental health of the patient compared with the care partner, and a parallel pattern (38.1%) characterized by care partner patterns of improvement and greater similarity in mental health over time. Membership of patterns was associated with physical health characteristics of both patient and care partner, levels of patient concealment regarding worries and concerns, and relationship quality reported by the care partner. Patterns did not differ by patient gender, care partner strain, or levels of social support. Conclusions Findings emphasize the importance of examining patterns of dyadic mental health to identify dyads most at risk so we may optimize the health of the dyad in tailored ways.