Revisiting patient satisfaction following total knee arthroplasty: a longitudinal observational study.

BACKGROUND: Total knee arthroplasty (TKA) is the most common joint replacement surgery in Canada. Earlier Canadian work reported 1 in 5 TKA patients expressing dissatisfaction following surgery. A better understanding of satisfaction could guide program improvement. We investigated patient satisfaction post-TKA in British Columbia (BC). METHODS: A cohort of 515 adult TKA patients was recruited from across BC. Survey data were collected preoperatively and at 6 and 12 months, supplemented by administrative health data. The primary outcome measure was patient satisfaction with outcomes. Potential satisfaction drivers included demographics, patient-reported health, quality of life, social support, comorbidities, and insurance status. Multivariable growth modeling was used to predict satisfaction at 6 months and change in satisfaction (6 to 12 months). RESULTS: We found dissatisfaction rates ("very dissatisfied", "dissatisfied" or "neutral") of 15% (6 months) and 16% (12 months). Across all health measures, improvements were seen post-surgery. The multivariable model suggests satisfaction at 6 months is predicted by: pre-operative pain, mental health and physical health (odds ratios (ORs) 2.65, 3.25 and 3.16), and change in pain level, baseline to 6 months (OR 2.31). Also, improvements in pain, mental health and physical health from 6 to 12 months predicted improvements in satisfaction (ORs 1.24, 1.30 and 1.55). CONCLUSIONS: TKA is an effective intervention for many patients and most report high levels of satisfaction. However, if the TKA does not deliver improvements in pain and physical health, we see a less satisfied patient. In addition, dissatisfied TKA patients typically see limited improvements in mental health.

Attachment avoidance, alexithymia, and gender: Examining their associations with distress disclosure tendencies and event-specific disclosure.

Distress disclosure has been linked with reduced psychological distress, increased wellbeing, and successful psychotherapeutic outcome. Because of the importance of distress disclosure, researchers have worked to develop and improve theoretical models of disclosure to facilitate counseling practices that reduce impediments to disclosure. Presently, we conducted a 2-part study to investigate distress disclosure's associations with attachment avoidance, gender, and alexithymia-3 constructs frequently linked with disclosure. In Part 1, we examined the extent to which attachment avoidance, alexithymia, and gender predicted general disclosure tendencies. In Part 2, we examined the extent to which attachment avoidance, alexithymia, and gender predicted event-specific disclosure. Participants were recruited from a crowdsourcing website (N = 178 in Part 1; N = 108 in Part 2). In Part 1, alexithymia partially mediated the association between attachment avoidance and disclosure tendencies, and the link between attachment avoidance and alexithymia was stronger for men than women. In Part 2, the association between distress intensity and event-specific disclosure was weaker for people with high levels of alexithymia. Implications for counseling theory and practice are discussed. (PsycINFO Database Record

Developing and evaluating a relevant and feasible instrument for measuring health literacy of Canadian high school students.

Health literacy has come to play a critical role in health education and promotion, yet it is poorly understood in adolescents and few measurement tools exist. Standardized instruments to measure health literacy in adults assume it to be a derivative of general literacy. This paper reports on the development and the early-stage validation of a health literacy tool for high school students that measured skills to understand and evaluate health information. A systematic process was used to develop, score and validate items. Questionnaire data were collected from 275, primarily 10th grade students in three secondary schools in Vancouver, Canada that reflected variation in demographic profile. Forty-eight percent were male, and 69.1% spoke a language other than English. Bivariate correlations between background variables and the domain and overall health literacy scores were calculated. A regression model was developed using 15 explanatory variables. The R(2) value was 0.567. Key findings were that lower scores were achieved by males, students speaking a second language other than English, those who immigrated to Canada at a later age and those who skipped school more often. Unlike in general literacy where the family factors of mother's education and family affluence both played significant roles, these two factors failed to predict the health literacy of our school-aged sample. The most significant contributions of this work include the creation of an instrument for measuring adolescent health literacy and further emphasizing the distinction between health literacy and general literacy.

Item Response Tree Models to Investigate Acquiescence and Extreme Response Styles in Likert-Type Rating Scales.

Item response tree (IRTree) models are recently introduced as an approach to modeling response data from Likert-type rating scales. IRTree models are particularly useful to capture a variety of individuals' behaviors involving in item responding. This study employed IRTree models to investigate response styles, which are individuals' tendencies to prefer or avoid certain response categories in a rating scale. Specifically, we introduced two types of IRTree models, descriptive and explanatory models, perceived under a larger modeling framework, called explanatory item response models, proposed by De Boeck and Wilson. This extends the typical application of IRTree models for studying response styles. As a demonstration, we applied the descriptive and explanatory IRTree models to examine acquiescence and extreme response styles in Rosenberg's Self-Esteem Scale. Our findings suggested the presence of two distinct extreme response styles and acquiescence response style in the scale.

Investigating site-level longitudinal effects of population health interventions: Gay-Straight Alliances and school safety.

There is limited research on evaluating nonrandomized population health interventions. We aimed to introduce a new approach for assessing site-level longitudinal effects of population health interventions (SLEPHI) by innovatively applying multiple group multilevel (MG-ML) modeling to repeated cycles of cross-sectional data collected from different individuals of the same sites at different times, a design commonly employed in public health research. For illustration, we used this SLEPHI method to examine the influence of Gay-Straight Alliances (GSAs) on school-level perceived safety among lesbian, gay, and bisexual (LGB) and heterosexual (HET) adolescents. Individual-level data of perceived school safety came from 1625 LGB students (67.4% female; mean age, 15.7 years) and 37,597 HET students (50.2% female; mean age, 15.4 years) attending Grades 7-12 in 135 schools, which participated in 3 British Columbia Adolescent Health Surveys (BCAHS: 2003, 2008, 2013) in Canada. School-level data of GSA length since established were collected by telephone in 2008 and 2014. Nested MG-ML models suggested that after accounting for secular trend, cohort effects, measurement error, measurement equivalence, and student age, GSA length linearly related to increased school-level perceived safety among LGB students (b = 1.57, SE = 0.21, p < .001, ß = 0.32) and also among HET students (ß = 0.34 in 2003 & 2013, ß = 0.32 in 2008) although statistical differences between years for HET youth were likely due to the large sample size. By conducting MG-ML analysis on repeated cross-sectional surveys, this SLEPHI method accounted for many confounding factors and followed schools for a longer period than most longitudinal designs can follow individuals. Therefore, we drew a stronger conclusion than previous observational research about GSAs and LGB students' well-being. The SLEPHI method can be widely applied to other repeated cycles of cross-sectional data in public health research.

The Accuracy of Computerized Adaptive Testing in Heterogeneous Populations: A Mixture Item-Response Theory Analysis.

BACKGROUND: Computerized adaptive testing (CAT) utilizes latent variable measurement model parameters that are typically assumed to be equivalently applicable to all people. Biased latent variable scores may be obtained in samples that are heterogeneous with respect to a specified measurement model. We examined the implications of sample heterogeneity with respect to CAT-predicted patient-reported outcomes (PRO) scores for the measurement of pain. METHODS: A latent variable mixture modeling (LVMM) analysis was conducted using data collected from a heterogeneous sample of people in British Columbia, Canada, who were administered the 36 pain domain items of the CAT-5D-QOL. The fitted LVMM was then used to produce data for a simulation analysis. We evaluated bias by comparing the referent PRO scores of the LVMM with PRO scores predicted by a "conventional" CAT (ignoring heterogeneity) and a LVMM-based "mixture" CAT (accommodating heterogeneity). RESULTS: The LVMM analysis indicated support for three latent classes with class proportions of 0.25, 0.30 and 0.45, which suggests that the sample was heterogeneous. The simulation analyses revealed differences between the referent PRO scores and the PRO scores produced by the "conventional" CAT. The "mixture" CAT produced PRO scores that were nearly equivalent to the referent scores. CONCLUSION: Bias in PRO scores based on latent variable models may result when population heterogeneity is ignored. Improved accuracy could be obtained by using CATs that are parameterized using LVMM.