Predicting CBT modality, treatment participation, and reliable improvements for individuals with anxiety and depression in a specialized mental health centre: a retrospective population-based cohort study.

BACKGROUND: Cognitive Behaviour Therapy (CBT) is one of the most successful therapeutic approaches for treating anxiety and depression. Clinical trials show that for some clients, internet-based CBT (eCBT) is as effective as other CBT delivery modes. However, the fidelity of these effects may be weakened in real-world settings where clients and providers have the freedom to choose a CBT delivery mode and switch treatments at any time. The purpose of this study is to measure the CBT attendance rate and identify client-level characteristics associated with delivery mode selection and having reliable and clinically significant improvement (RCSI) of treatment in each delivery mode in a real-world CBT outpatient program. METHODS: This is a retrospective cohort analysis of electronic medical records collected between May 1, 2019, and March 31, 2022, at Ontario Shores Centre for Mental Health Sciences. Regression models were used to investigate the impact of individual client characteristics on participation and achieving RCSI of different CBT delivery modes. RESULTS: Our data show a high attendance rate for two and more CBT sessions across all modalities (98% of electronic, 94% of group, 100% of individual, and 99% of mixed CBT). Individuals were more likely to enter mixed and group CBT modality if they were younger, reported being employed, and reported higher depression severity at the baseline. Among the four modalities of CBT delivery, group CBT clients were least likely to have RCSI. Of those who started sessions, clients were significantly more likely to experience RCSI on the Patient Health Questionnaire (PHQ)-9 and the Generalized Anxiety Disorder (GAD)-7 if they were employed, reported more severe symptoms at baseline, and were living in the most deprived neighborhoods. CONCLUSIONS: This study will contribute to the body of knowledge about the implementation and treatment planning of different CBT delivery modes in real-world settings. With the changing clinical environment, it is possible to advocate for the adoption of the eCBT intervention to improve therapy practices and achieve better treatment success. The findings can help guide future CBT program planning based on client socio-demographic characteristics, allowing the optimal therapy type to be targeted to the right client at the right time.

Stability over time of the "hospital effect" on 30-day unplanned readmissions: Evidence from administrative data.

Past studies showed that hospital characteristics affect hospital performance in terms of 30-day unplanned readmissions, proving the existence of a "hospital effect". However, the stability over time of this effect has been under-investigated. This study offers new evidence about the stability over time of the hospital effect on 30-day unplanned readmissions. Using 78,907 heart failure (HF) records collected from 116 hospitals in the Lombardy Region (Northern Italy) over three years (2010-2012), this study analysed hospital performance in terms of 30-day unplanned readmissions. Hospitals with unusually high and low readmission rates were identified through multi-level regression that combined both patient and hospital covariates in each year. Our results confirm that although hospital covariates - and the connected managerial choices - affect the 30-day unplanned readmissions of a specific year, their effect is not stable in the short-term (3 years). This has important implications for pay-for-performance schemes and quality improvement initiatives.

Composite Outcomes of Mortality and Readmission in Patients with Heart Failure: Retrospective Review of Administrative Datasets.

BACKGROUND: Controlling the quality of care through readmissions and mortality for patients with heart failure (HF) is a national priority for healthcare regulators in developed countries. In this longitudinal cohort study, using administrative data such as hospital discharge forms (HDFs), emergency departments (EDs) accesses, and vital statistics, we test new covariates for predicting mortality and readmissions of patients hospitalized for HF and discuss the use of combined outcome as an alternative. METHODS: Logistic models, with a stepwise selection method, were estimated on 70% of the sample and validated on the remaining 30% to evaluate 30-day mortality, 30-day readmissions, and the combined outcome. We followed an extraction method for any-cause mortality and unplanned readmission within 30 days after incident HF hospitalization. Data on patient admission and previous history were extracted by HDFs and ED dataset. RESULTS: Our principal findings demonstrate that the model's discriminant ability is consistent with literature both for mortality (AUC=0.738, CI (0.729-0.748)) and readmissions (AUC=0.578, CI (0.562-0.594)). Additionally, the discriminant ability of the composite outcome model is satisfactory (AUC=0.675, CI (0.666-0.684)). CONCLUSION: Hospitalization characteristics and patient history introduced in the logistic models do not improve their discriminant ability. The composite outcome prediction is led more by mortality than readmission, without improvements for the comprehension of the readmission phenomenon.

Multi-level models for heart failure patients' 30-day mortality and readmission rates: the relation between patient and hospital factors in administrative data.

BACKGROUND: This study aims at gathering evidence about the relation between 30-day mortality and 30-day unplanned readmission and patient and hospital factors. By definition, we refer to 30-day mortality and 30-day unplanned readmission as the number of deaths and non-programmed hospitalizations for any cause within 30 days after the incident heart failure (HF). In particular, the focus is on the role played by hospital-level factors. METHODS: A multi-level logistic model that combines patient- and hospital-level covariates has been developed to better disentangle the role played by the two groups of covariates. Later on, hospital outliers in term of better-than-expected/worst-than-expected performers have been identified by comparing expected cases vs. observed cases. Hospitals performance in terms of 30-day mortality and 30-day unplanned readmission rates have been visualized through the creation of funnel plots. Covariates have been selected coherently to past literature. Data comes from the hospital discharge forms for Heart Failure patients in the Lombardy Region (Northern Italy). Considering incident cases for HF in the timespan 2010-2012, 78,907 records for adult patients from 117 hospitals have been collected after quality checks. RESULTS: Our results show that 30-day mortality and 30-day unplanned readmissions are explained by hospital-level covariates, paving the way for the design and implementation of evidence-based improvement strategies. While the percentage of surgical DRG (OR = 1.001; CI (1.000-1.002)) and the hospital type of structure (Research hospitals vs. non-research public hospitals (OR = 0.62; CI (0.48-0.80)) and Non-research private hospitals vs. non-research hospitals OR = 0.75; CI (0.63-0.90)) are significant for mortality, the mean length of stay (OR = 0.96; CI (0.95-0.98)) is significant for unplanned readmission, showing that mortality and readmission rates might be improved through different strategies. CONCLUSION: Our results confirm that hospital-level covariates do affect quality of care, and that 30-day mortality and 30-day unplanned readmission are affected by different managerial choices. This confirms that hospitals should be accountable for their "added value" to quality of care.