Health-related quality of life (HRQOL) is increasingly measured as an outcome for clinical and health services research. However, relatively little is known about how non-health factors affect HRQOL. Personality is a potentially important factor, yet evidence regarding the effects of personality on HRQOL measures is unclear. This systematic review examined the relationships among aspects of personality and HRQOL. Eligible studies were identified from Medline and PsycINFO. The review included 76 English-language studies with HRQOL as a primary outcome and that assessed personality from the psychological perspective. Individuals with various health states, including ill (e.g., cancer, cardiovascular disorders), aging, and healthy, were included in this review study. Some personality characteristics were consistently related to psychosocial aspects more often than physical aspects of HRQOL. Personality characteristics, especially neuroticism, mastery, optimism, and sense of coherence were most likely to be associated with psychosocial HRQOL. Personality explained varying proportions of variance in different domains of HRQOL. The range of variance explained in psychosocial HRQOL was 0 to 45% and the range of explained variance in physical HRQOL was 0 to 39%. Personality characteristics are related to HRQOL. Systematic collection and analysis of personality data alongside HRQOL measures may be helpful in medical research, clinical practice, and health policy evaluation.

Financial hardship among survivors of pediatric cancer has been understudied. We investigated determinants and consequences of financial hardship among adult survivors of childhood cancer. Financial hardship, determinants, and consequences were examined in 2811 long-term survivors (mean age at evaluation = 31.8 years, years postdiagnosis = 23.6) through the baseline survey and clinical evaluation. Financial hardship was measured by material, psychological, and coping/behavioral domains. Outcomes included health and life insurance affordability, retirement planning, symptoms, and health-related quality of life (HRQOL). Odds ratios (ORs) estimated associations of determinants with financial hardship. Odds ratios and regression coefficients estimated associations of hardship with symptom prevalence and HRQOL, respectively. All statistical tests were two-sided. Among participants, 22.4% (95% confidence interval [CI] = 20.8% to 24.0%), 51.1% (95% CI = 49.2% to 52.9%), and 33.0% (95% CI = 31.1% to 34.6%) reported material, psychological, and coping/behavioral hardship, respectively. Risk factors across hardship domains included annual household income of $39 999 or less vs $80 000 or more (material OR = 3.04, 95% CI = 2.08 to 4.46, psychological OR = 3.64, 95% CI = 2.76 to 4.80, and coping/behavioral OR = 4.95, 95% CI = 3.57 to 6.86) and below high school attainment vs college graduate or above (material OR = 2.22, 95% CI = 1.45 to 3.42, psychological OR = 1.75, 95% CI = 1.18 to 2.62, and coping/behavioral OR = 2.05, 95% CI = 1.38 to 3.06). Myocardial infarction, peripheral neuropathy, subsequent neoplasm, seizure, stroke, reproductive disorders, amputation, and upper gastrointestinal disease were associated with higher material hardship (all P < .05). Hardship across three domains was associated with somatization, anxiety and depression (all P < .001), suicidal ideation (all P < .05), and difficulty in retirement planning (all P < .001). Survivors with hardship had statistically significantly lower HRQOL (all P < .001), sensation abnormality (all P < .001), and pulmonary (all P < .05) and cardiac (all P < .05) symptoms. A substantial proportion of adult survivors of childhood cancer experienced financial hardship. Vulnerable sociodemographic status and late effects were associated with hardship. Survivors with financial hardship had an increased risk of symptom prevalence and impaired HRQOL.

Background: To compare associations of symptom prevalence, chronic conditions, and health-related quality of life (HRQOL) between cancer survivors and non-cancer individuals using the U.S. National Health Interview Survey. Methods: Study samples comprised 604 survivors and 6,166 non-cancer individuals. Symptoms included sensation abnormality, pain, fatigue, cognitive disturbance, depression, and anxiety. Physical and mental HRQOL was measured by the Patient-Reported Outcomes Measurement Information System. Results: Compared with non-cancer individuals, survivors had higher prevalence in sensation abnormality (OR = 2.4; 95% CI = 1.9 to 3.0), pain (OR = 2.1; 95% CI = 1.7 to 2.6), fatigue (OR = 1.4; 95% CI = 1.1 to 1.8), and decremented physical HRQOL (difference = −3.7; 95% CI = −4.7 to −2.6). The prevalence of individual symptoms was significantly associated with decremented physical HRQOL [range = −5.9 (anxiety) to −8.9 (pain)] and mental HRQOL [range = −4.7 (sensation) to −8.4 (depression)]. The association between cancer experience and physical and mental HRQOL was chiefly explained by the prevalence of six symptoms and presence of chronic conditions. Pain (β = −4.0; 95% CI = −4.5 to −3.6) and ≥2 chronic conditions (β = −9.2; 95% CI = −10.2 to −8.2) significantly decremented physical HRQOL. Depression (β = −5.2; 95% CI = −5.8 to −4.6) and ≥2 chronic conditions (β = −3.3; 95% CI = −4.4 to −2.3) significantly decremented mental HRQOL. Conclusions: Cancer survivors experience more symptom burden than non-cancer individuals, which is associated with more chronic conditions and impaired HRQOL. Impacts: Interventions to manage symptom prevalence especially for older cancer survivors and survivors with more chronic conditions may improve their HRQOL outcomes. Cancer Epidemiol Biomarkers Prev; 26(7); 1124–32. ©2017 AACR.

Background: Assessing patient-reported outcomes (PROs) through interviews or conversations during clinical encounters provides insightful information about survivorship. Objective: This study aims to test the validity of natural language processing (NLP) and machine learning (ML) algorithms in identifying different attributes of pain interference and fatigue symptoms experienced by child and adolescent survivors of cancer versus the judgment by PRO content experts as the gold standard to validate NLP/ML algorithms. Methods: This cross-sectional study focused on child and adolescent survivors of cancer, aged 8 to 17 years, and caregivers, from whom 391 meaning units in the pain interference domain and 423 in the fatigue domain were generated for analyses. Data were collected from the After Completion of Therapy Clinic at St. Jude Children’s Research Hospital. Experienced pain interference and fatigue symptoms were reported through in-depth interviews. After verbatim transcription, analyzable sentences (ie, meaning units) were semantically labeled by 2 content experts for each attribute (physical, cognitive, social, or unclassified). Two NLP/ML methods were used to extract and validate the semantic features: bidirectional encoder representations from transformers (BERT) and Word2vec plus one of the ML methods, the support vector machine or extreme gradient boosting. Receiver operating characteristic and precision-recall curves were used to evaluate the accuracy and validity of the NLP/ML methods. Results: Compared with Word2vec/support vector machine and Word2vec/extreme gradient boosting, BERT demonstrated higher accuracy in both symptom domains, with 0.931 (95% CI 0.905-0.957) and 0.916 (95% CI 0.887-0.941) for problems with cognitive and social attributes on pain interference, respectively, and 0.929 (95% CI 0.903-0.953) and 0.917 (95% CI 0.891-0.943) for problems with cognitive and social attributes on fatigue, respectively. In addition, BERT yielded superior areas under the receiver operating characteristic curve for cognitive attributes on pain interference and fatigue domains (0.923, 95% CI 0.879-0.997; 0.948, 95% CI 0.922-0.979) and superior areas under the precision-recall curve for cognitive attributes on pain interference and fatigue domains (0.818, 95% CI 0.735-0.917; 0.855, 95% CI 0.791-0.930). Conclusions: The BERT method performed better than the other methods. As an alternative to using standard PRO surveys, collecting unstructured PROs via interviews or conversations during clinical encounters and applying NLP/ML methods can facilitate PRO assessment in child and adolescent cancer survivors.