Tkt Lo

Background: Knowledge pertaining to the health and health care utilization of patients after recovery from acute COVID-19 is limited. We sought to assess the frequency of new diagnoses of disease and health care use after hospitalization with COVID-19. Methods: We included all patients hospitalized with COVID-19 in Alberta between Mar. 5 and Dec. 31, 2020. Additionally, 2 matched controls (SARS-CoV-2 negative) per case were included and followed up until Apr. 30, 2021. New diagnoses and health care use were identified from linked administrative health data. Repeated measures were made for the periods 1–30 days, 31–60 days, 61–90 days, 91–180 days, and 180 and more days from the index date. We used multivariable regression analysis to evaluate the association of COVID-19-related hospitalization with the number of physician visits during follow-up. Results: The study sample included 3397 cases and 6658 controls. Within the first 30 days of follow-up, the case group had 37.12% (95% confidence interval [CI] 35.44% to 38.80%) more patients with physician visits, 11.12% (95% CI 9.77% to 12.46%) more patients with emergency department visits and 2.92% (95% CI 2.08% to 3.76%) more patients with hospital admissions than the control group. New diagnoses involving multiple organ systems were more common in the case group. Regression results indicated that recovering from COVID-19-related hospitalization, admission to an intensive care unit, older age, greater number of comorbidities and more prior health care use were associated with increased physician visits. Interpretation: Patients recovered from the acute phase of COVID-19 continued to have greater health care use up to 6 months after hospital discharge. Research is required to further explore the effect of post-COVID-19 conditions, pre-existing health conditions and health-seeking behaviours on health care use.

Background Description of risk factors of severe acute COVID-19 outcomes with the consideration of vaccination status in the era of the Omicron variant of concern are limited. Objectives To examine the association of age, sex, underlying medical conditions, and COVID-19 vaccination with hospitalization, intensive-care unit (ICU) admission, or death due to the disease, using data from a period when Omicron was the dominant strain. Methods A population-based case-control study based on administrative health data, that included confirmed COVID-19 patients during January (2022) in Alberta, Canada. Patients who were non-residents, without the provincial healthcare insurance coverage, or ≤18 years of age were excluded. Patients with any severe outcome were the cases; and those without any hospitalization, ICU admission, or death were controls. Adjusted odds ratios, of the explanatory factors of a severe outcome, were estimated using a logistic regression model. Results There were 90,989 COVID-19 patients included in the analysis; 2% had severe outcomes and 98% were included in the control group. Overall, more COVID patients were found in the younger age-groups (72.0% ≤49 years old), females (56.5%), with no underlying conditions (59.5%), and fully vaccinated patients (90.4%). However, the adjusted odds ratios were highest in the 70–79 age group (28.32; 95% CI 20.6–38.9) or among ≥80 years old (29.8; 21.6–41.0), males (1.4; 1.3–1.6); unvaccinated (16.1; 13.8–18.8), or patients with ≥3 underlying conditions (13.1; 10.9–15.8). Conclusion Higher risk of severe acute COVID-19 outcomes were associated with older age, the male sex, and increased number of underlying medical conditions. Unvaccination or undervaccination remained as the greatest modifiable risk factor in prevention of severe COVID outcomes. These findings help inform medical decisions and allocation of scarce healthcare resources.

Purpose Organizational context influences the effect of facilitation efforts on research use in care settings. The interactions of these factors are complex. Therefore, the use of traditional statistical methods to examine their interrelationships is often impractical. Big Data analytics can automatically detect patterns within the data. We applied the chi-squared automatic interaction detection (CHAID) algorithm and classification tree technique to explore the dynamic and interdependent relationships between the implementation science concepts—context, facilitation, and research use. Design Observational, cross-sectional study based on survey data collected from a representative sample of nursing homes in western Canada. Methods We assessed three major constructs: (a) Conceptual research utilization (CRU) using the CRU scale; (b) facilitation of research use measured by the frequency of contacts between the frontline staff and a clinical educator, or person who brings new ideas to the care unit; and (c) organizational context at the unit level using the Alberta Context Tool (ACT). CHAID analysis was performed to detect the interactions between facilitation and context variables. Results were illustrated in a classification tree to provide a straightforward visualization. Findings Data from 312 care units in three provinces were included in the final analysis. Results indicate significant multiway interactions between facilitation and various aspects of the organizational context, including leadership, culture, evaluation, structural resources, and organizational slack (staffing). Findings suggested the preconditions of the care settings where research use can be maximized. Conclusions CHAID analysis helped transform data into usable knowledge. Our findings provide insight into the dynamic relationships of facilitators’ efforts and organizational context, and how these factors’ interplay and their interdependence together may influence research use. Clinical relevance Knowledge of the combined effects of facilitators’ efforts and various aspects of organizational context on research use can contribute to effective strategies to narrow the evidence-practice gap in care settings.

Objective This study explored the effect of clinical educators as facilitators of research use and how it may be modified by organisational context in the settings. Design Cross-sectional observational study. Setting A representative sample of 91 residential long-term care (LTC) facilities across Western Canada. Participants We used surveys to collect data from the frontline care aides and information about the organisational context of the care units. Outcome measure and explanatory variables We assessed research use (the outcome) with the Conceptual Research Utilization (CRU) scale. Explanatory variables in the multiple regression analysis were facilitation, organisational context and the interaction terms. Facilitation was measured by the frequency of contacts between care aides and clinical educator or person who brings new ideas about resident care. Three core organisational context variables were measured using the Alberta Context Tool. Results We included data of 3873 care aides from 294 care units in the LTC facilities. We found significant associations between CRU and facilitation, leadership, culture and evaluation. Interactions of facilitation x leadership and facilitation x culture were negative. The coefficient of the facilitation x evaluation term in the regression model was positive (0.019, 95% CI 0.012 to 0.026), suggesting synergistic effects between facilitation and a well-developed process to evaluate care quality using relevant data. Conclusions Findings indicate clinical educators are effective facilitators of research use among the care aides, but the effect is modified by organisational context. For greatest impact, managers can direct efforts of the clinical educators to care units where leadership and culture ratings are lowest, but a proficient feedback and evaluation process is in place. This understanding enables managers to deploy clinical educators (a scarce resource in LTC settings) most efficiently.

To examine the factors associated with higher healthcare cost in women with arthritis, using generalized linear models (GLMs) and quantile regression (QR). This is a cross-sectional healthcare cost study of individuals with arthritis that focused on older Australian women. Cost data were drawn from the Medicare Australia datasets. GLM results show that healthcare cost was significantly associated with various socio-demographic and health factors. Although QR analysis results show the same direction of association between these factors and healthcare cost as in the GLMs, they indicate progressively increased effect sizes at the 50th, 75th, 90th and 95th percentiles. Findings suggest traditional regression models such as GLMs that assume a single rate of change to accurately describe the relationships between explanatory variables and healthcare costs across the entire distribution of cost can produce biased results. QR should be considered in future healthcare cost research.