A proportional incidence rate model for aggregated data to study the vaccine effectiveness against COVID-19 hospital and ICU admissions.

We develop a proportional incidence model that estimates vaccine effectiveness (VE) at the population level using conditional likelihood for aggregated data. Our model assumes that the population counts of clinical outcomes for an infectious disease arise from a superposition of Poisson processes with different vaccination statuses. The intensity function in the model is calculated as the product of per capita incidence rate and the at-risk population size, both of which are time-dependent. We formulate a log-linear regression model with respect to the relative risk, defined as the ratio between the per capita incidence rates of vaccinated and unvaccinated individuals. In the regression analysis, we treat the baseline incidence rate as a nuisance parameter, similar to the Cox proportional hazard model in survival analysis. We then apply the proposed models and methods to age-stratified weekly counts of COVID-19-related hospital and ICU admissions among adults in Ontario, Canada. The data spanned from 2021 to February 2022, encompassing the Omicron era and the rollout of booster vaccine doses. We also discuss the limitations and confounding effects while advocating for the necessity of more comprehensive and up-to-date individual-level data that document the clinical outcomes and measure potential confounders.

Characteristics and clinical outcomes of nirmatrelvir/ritonavir (PaxlovidTM) recipients in Canada, 2022: a descriptive cohort study.

Background: Nirmatrelvir/ritonavir (N/R) (PaxlovidTM) was introduced in Canada in January 2022. This was the first oral coronavirus disease 2019 (COVID-19) antiviral therapy that was deployed on a large scale in Canada. Since N/R was a new therapeutic option to reduce severe outcomes in high-risk populations, clinical and implementation questions were raised about its real-world utilization and impact. The objective of this retrospective observational study was to describe the characteristics and clinical outcomes of recipients of N/R in the first several months of its availability in Canada, during the Omicron wave. Methods: Provincial summary data were pooled together for the analysis. Descriptive statistics were used to explore the characteristics and clinical outcomes of the recipients. Pearson's Chi-square test and unadjusted odds ratio along with 95% confidence intervals were used to identify the potential risk factors for severe outcomes. Data were generally collected between January and September 2022. Results: Seventy-six percent of N/R recipients were 60 years of age and older and 56% were female. Eighty-four percent of recipients had received three or more COVID-19 vaccinations and 67% had comorbidities. All-cause severe 30-day outcomes were uncommon, with 0.4% reported as deceased, 0.1% admitted to the intensive care unit and 2.0% hospitalized after N/R administration. Risk factors statistically associated with severe outcomes were immunosuppression, comorbidities, age of 60 years and older, and being unvaccinated. Conclusion: In the first months of its availability in Canada, N/R was mostly used in vaccinated patients 60 years and older with one or more comorbidities. Severe outcomes in N/R recipients were uncommon and mostly reported in patients with risk factors.

A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in Canada.

During a pandemic, data are very "noisy" with enormous amounts of local variation in daily counts, compared with any rapid changes in trend. Accurately characterizing the trends and reliable predictions on future trajectories are important for planning and public situation awareness. We describe a semi-parametric statistical model that is used for short-term predictions of daily counts of cases and deaths due to COVID-19 in Canada, which are routinely disseminated to the public by Public Health Agency of Canada. The main focus of the paper is the presentation of the model. Performance indicators of our model are defined and then evaluated through extensive sensitivity analyses. We also compare our model with other commonly used models such as generalizations of logistic models for similar purposes. The proposed model is shown to describe the historical trend very well with excellent ability to predict the short-term trajectory.

The performance of phenomenological models in providing near-term Canadian case projections in the midst of the COVID-19 pandemic: March - April, 2020.

BACKGROUND: The COVID-19 pandemic has had an unprecedented impact on citizens and health care systems globally. Valid near-term projections of cases are required to inform the escalation, maintenance and de-escalation of public health measures, and for short-term health care resource planning. METHODS: Near-term case and epidemic growth rate projections for Canada were estimated using three phenomenological models: the logistic model, Generalized Richard's model (GRM) and a modified Incidence Decay and Exponential Adjustment (m-IDEA) model. Throughout the COVID-19 epidemic in Canada, these models have been validated against official national epidemiological data on an ongoing basis. RESULTS: The best-fit models estimated that the number of COVID-19 cases predicted to be reported in Canada as of April 1, 2020 and May 1, 2020 would be 11,156 (90 % prediction interval: 9,156-13,905) and 54,745 (90 % prediction interval: 54,252-55,239). The three models varied in their projections and their performance over the first seven weeks of their implementation. Both the logistic model and GRM under-predicted cases reported a week following the projection date in nearly all instances. The logistic model performed best at the early stages, the m-IDEA model performed best at the later stages, and the GRM performed most consistently during the full period assessed. CONCLUSIONS: All three models have yielded qualitatively comparable near-term forecasts of cases and epidemic growth for Canada. Under or over-estimation of projected cases and epidemic growth by these models could be associated with changes in testing policies and/or public health measures. Simple forecasting models can be invaluable in projecting the changes in trajectory of subsequent waves of cases to provide timely information to support the pandemic response.

LASSO type penalized spline regression for binary data.

BACKGROUND: Generalized linear mixed models (GLMMs), typically used for analyzing correlated data, can also be used for smoothing by considering the knot coefficients from a regression spline as random effects. The resulting models are called semiparametric mixed models (SPMMs). Allowing the random knot coefficients to follow a normal distribution with mean zero and a constant variance is equivalent to using a penalized spline with a ridge regression type penalty. We introduce the least absolute shrinkage and selection operator (LASSO) type penalty in the SPMM setting by considering the coefficients at the knots to follow a Laplace double exponential distribution with mean zero. METHODS: We adopt a Bayesian approach and use the Markov Chain Monte Carlo (MCMC) algorithm for model fitting. Through simulations, we compare the performance of curve fitting in a SPMM using a LASSO type penalty to that of using ridge penalty for binary data. We apply the proposed method to obtain smooth curves from data on the relationship between the amount of pack years of smoking and the risk of developing chronic obstructive pulmonary disease (COPD). RESULTS: The LASSO penalty performs as well as ridge penalty for simple shapes of association and outperforms the ridge penalty when the shape of association is complex or linear. CONCLUSION: We demonstrated that LASSO penalty captured complex dose-response association better than the Ridge penalty in a SPMM.

Modeling perinatal mortality in twins via generalized additive mixed models: a comparison of estimation approaches.

BACKGROUND: The analysis of twin data presents a unique challenge. Second-born twins on average weigh less than first-born twins and have an elevated risk of perinatal mortality. It is not clear whether the risk difference depends on birth order or their relative birth weight. This study evaluates the association between birth order and perinatal mortality by birth order-specific weight difference in twin pregnancies. METHODS: We adopt generalized additive mixed models (GAMMs) which are a flexible version of generalized linear mixed models (GLMMs), to model the association. Estimation of such models for correlated binary data is challenging. We compare both Bayesian and likelihood-based approaches for estimating GAMMs via simulation. We apply the methods to the US matched multiple birth data to evaluate the association between twins' birth order and perinatal mortality. RESULTS: Perinatal mortality depends on both birth order and relative birthweight. Simulation results suggest that the Bayesian method with half-Cauchy priors for variance components performs well in estimating all components of the GAMM. The Bayesian results were sensitive to prior specifications. CONCLUSION: We adopted a flexible statistical model, GAMM, to precisely estimate the perinatal mortality risk differences between first- and second-born twins whereby birthweight and gestational age are nonparametrically modelled to explicitly adjust for their effects. The risk of perinatal mortality in twins was found to depend on both birth order and relative birthweight. We demonstrated that the Bayesian method estimated the GAMM model components more reliably than the frequentist approaches.

A Comparison of Visual Rating Scales and Simulated Virtual Reality Metrics in Neurosurgical Training: A Generalizability Theory Study.

BACKGROUND: Adequate assessment and feedback remains a cornerstone of psychomotor skills acquisition, particularly within neurosurgery where the consequence of adverse operative events is significant. However, a critical appraisal of the reliability of visual rating scales in neurosurgery is lacking. Therefore, we sought to design a study to compare visual rating scales with simulated metrics in a neurosurgical virtual reality task. METHODS: Neurosurgical faculty rated anonymized participant video recordings of the removal of simulated brain tumors using a visual rating scale made up of seven composite elements. Scale reliability was evaluated using generalizability theory, and scale subcomponents were compared with simulated metrics using Pearson correlation analysis. RESULTS: Four staff neurosurgeons evaluated 16 medical student neurosurgery applicants. Overall scale reliability and internal consistency were 0.73 and 0.90, respectively. Reliability of 0.71 was achieved with two raters. Individual participants, raters, and scale items accounted for 27%, 11%, and 0.6% of the data variability. The hemostasis scale component related to the greatest number of simulated metrics, whereas respect for no-go zones and tissue was correlated with none. Metrics relating to instrument force and patient safety (brain volume removed and blood loss) were captured by the fewest number of rating scale components. CONCLUSIONS: To our knowledge, this is the first study comparing participant's ratings with simulated performance. Given rating scales capture less well instrument force, quantity of brain volume removed, and blood loss, we suggest adopting a hybrid educational approach using visual rating scales in an operative environment, supplemented by simulated sessions to uncover potentially problematic surgical technique.

Determinants of tuberculosis trends in six Indigenous populations of the USA, Canada, and Greenland from 1960 to 2014: a population-based study.

BACKGROUND: Tuberculosis continues to disproportionately affect many Indigenous populations in the USA, Canada, and Greenland. We aimed to investigate whether population-based tuberculosis-specific interventions or changes in general health and socioeconomic indicators, or a combination of these factors, were associated with changes in tuberculosis incidence in these Indigenous populations. METHODS: For this population-based study we examined annual tuberculosis notification rates between 1960 and 2014 in six Indigenous populations of the USA, Canada, and Greenland (Inuit [Greenland], American Indian and Alaska Native [Alaska, USA], First Nations [Alberta, Canada], Cree of Eeyou Istchee [Quebec, Canada], Inuit of Nunavik [Quebec, Canada], and Inuit of Nunavut [Canada]), as well as the general population of Canada. We used mixed-model linear regression to estimate the association of these rates with population-wide interventions of bacillus Calmette-Guérin (BCG) vaccination of infants, radiographic screening, or testing and treatment for latent tuberculosis infection (LTBI), and with other health and socioeconomic indicators including life expectancy, infant mortality, diabetes, obesity, smoking, alcohol use, crowded housing, employment, education, and health expenditures. FINDINGS: Tuberculosis notification rates declined rapidly in all six Indigenous populations between 1960 and 1980, with continued decline in Indigenous populations in Alberta, Alaska, and Eeyou Istchee thereafter but recrudescence in Inuit populations of Nunavut, Nunavik, and Greenland. Annual percentage reductions in tuberculosis incidence were significantly associated with two tuberculosis control interventions, relative to no intervention, and after adjustment for infant mortality and smoking: BCG vaccination (-11%, 95% CI -6 to -17) and LTBI screening and treatment (-10%, -3 to -18). Adjusted associations were not significant for chest radiographic screening (-1%, 95% CI -7 to 5). Declining tuberculosis notification rates were significantly associated with increased life expectancy (-37·8 [95% CI -41·7 to -33·9] fewer cases per 100 000 for each 1-year increase) and decreased infant mortality (-9·0 [-9·5 to -8·6] fewer cases per 100 000 for each death averted per 1000 livebirths) in all six Indigenous populations, but no significant associations were observed for other health and socioeconomic indicators examined. INTERPRETATION: Population-based BCG vaccination of infants and LTBI screening and treatment were associated with significant decreases in tuberculosis notification rates in these Indigenous populations. These interventions should be reinforced in populations still affected by tuberculosis, while also addressing the persistent health and socioeconomic disparities. FUNDING: Public Health Department of the Cree Board of Health and Social Services of James Bay.

Impact of acute stress on psychomotor bimanual performance during a simulated tumor resection task.

OBJECTIVE Severe bleeding during neurosurgical operations can result in acute stress affecting the bimanual psychomotor performance of the operator, leading to surgical error and an adverse patient outcome. Objective methods to assess the influence of acute stress on neurosurgical bimanual psychomotor performance have not been developed. Virtual reality simulators, such as NeuroTouch, allow the testing of acute stress on psychomotor performance in risk-free environments. Thus, the purpose of this study was to explore the impact of a simulated stressful virtual reality tumor resection scenario by utilizing NeuroTouch to answer 2 questions: 1) What is the impact of acute stress on bimanual psychomotor performance during the resection of simulated tumors? 2) Does acute stress influence bimanual psychomotor performance immediately following the stressful episode? METHODS Study participants included 6 neurosurgeons, 6 senior and 6 junior neurosurgical residents, and 6 medical students. Participants resected a total of 6 simulated tumors, 1 of which (Tumor 4) involved uncontrollable "intraoperative" bleeding resulting in simulated cardiac arrest and thus providing the acute stress scenario. Tier 1 metrics included extent of blood loss, percentage of tumor resected, and "normal" brain tissue volume removed. Tier 2 metrics included simulated suction device (sucker) and ultrasonic aspirator total tip path length, as well as the sum and maximum forces applied in using these instruments. Advanced Tier 2 metrics included efficiency index, coordination index, ultrasonic aspirator path length index, and ultrasonic aspirator bimanual forces ratio. All metrics were assessed before, during, and after the stressful scenario. RESULTS The stress scenario caused expected significant increases in blood loss in all participant groups. Extent of tumor resected and brain volume removed decreased in the junior resident and medical student groups. Sucker total tip path length increased in the neurosurgeon group, whereas sucker forces increased in the senior resident group. Psychomotor performance on advanced Tier 2 metrics was altered during the stress scenario in all participant groups. Performance on all advanced Tier 2 metrics returned to pre-stress levels in the post-stress scenario tumor resections. CONCLUSIONS Results demonstrated that acute stress initiated by simulated severe intraoperative bleeding significantly decreases bimanual psychomotor performance during the acute stressful episode. The simulated intraoperative bleeding event had no significant influence on the advanced Tier 2 metrics monitored during the immediate post-stress operative performance.

Bimanual Psychomotor Performance in Neurosurgical Resident Applicants Assessed Using NeuroTouch, a Virtual Reality Simulator.

OBJECTIVE: Current selection methods for neurosurgical residents fail to include objective measurements of bimanual psychomotor performance. Advancements in computer-based simulation provide opportunities to assess cognitive and psychomotor skills in surgically naive populations during complex simulated neurosurgical tasks in risk-free environments. This pilot study was designed to answer 3 questions: (1) What are the differences in bimanual psychomotor performance among neurosurgical residency applicants using NeuroTouch? (2) Are there exceptionally skilled medical students in the applicant cohort? and (3) Is there an influence of previous surgical exposure on surgical performance? DESIGN: Participants were instructed to remove 3 simulated brain tumors with identical visual appearance, stiffness, and random bleeding points. Validated tier 1, tier 2, and advanced tier 2 metrics were used to assess bimanual psychomotor performance. Demographic data included weeks of neurosurgical elective and prior operative exposure. SETTING: This pilot study was carried out at the McGill Neurosurgical Simulation Research and Training Center immediately following neurosurgical residency interviews at McGill University, Montreal, Canada. PARTICIPANTS: All 17 medical students interviewed were asked to participate, of which 16 agreed. RESULTS: Performances were clustered in definable top, middle, and bottom groups with significant differences for all metrics. Increased time spent playing music, increased applicant self-evaluated technical skills, high self-ratings of confidence, and increased skin closures statistically influenced performance on univariate analysis. A trend for both self-rated increased operating room confidence and increased weeks of neurosurgical exposure to increased blood loss was seen in multivariate analysis. CONCLUSIONS: Simulation technology identifies neurosurgical residency applicants with differing levels of technical ability. These results provide information for studies being developed for longitudinal studies on the acquisition, development, and maintenance of psychomotor skills. Technical abilities customized training programs that maximize individual resident bimanual psychomotor training dependant on continuously updated and validated metrics from virtual reality simulation studies should be explored.

Effect of Smoothing in Generalized Linear Mixed Models on the Estimation of Covariance Parameters for Longitudinal Data.

Besides being mainly used for analyzing clustered or longitudinal data, generalized linear mixed models can also be used for smoothing via restricting changes in the fit at the knots in regression splines. The resulting models are usually called semiparametric mixed models (SPMMs). We investigate the effect of smoothing using SPMMs on the correlation and variance parameter estimates for serially correlated longitudinal normal, Poisson and binary data. Through simulations, we compare the performance of SPMMs to other simpler methods for estimating the nonlinear association such as fractional polynomials, and using a parametric nonlinear function. Simulation results suggest that, in general, the SPMMs recover the true curves very well and yield reasonable estimates of the correlation and variance parameters. However, for binary outcomes, SPMMs produce biased estimates of the variance parameters for high serially correlated data. We apply these methods to a dataset investigating the association between CD4 cell count and time since seroconversion for HIV infected men enrolled in the Multicenter AIDS Cohort Study.

Assessing bimanual performance in brain tumor resection with NeuroTouch, a virtual reality simulator.

BACKGROUND: Validated procedures to objectively measure neurosurgical bimanual psychomotor skills are unavailable. The NeuroTouch simulator provides metrics to determine bimanual performance, but validation is essential before implementation of this platform into neurosurgical training, assessment, and curriculum development. OBJECTIVE: To develop, evaluate, and validate neurosurgical bimanual performance metrics for resection of simulated brain tumors with NeuroTouch. METHODS: Bimanual resection of 8 simulated brain tumors with differing color, stiffness, and border complexity was evaluated. Metrics assessed included blood loss, tumor percentage resected, total simulated normal brain volume removed, total tip path lengths, maximum and sum of forces used by instruments, efficiency index, ultrasonic aspirator path length index, coordination index, and ultrasonic aspirator bimanual forces ratio. Six neurosurgeons and 12 residents (6 senior and 6 junior) were evaluated. RESULTS: Increasing tumor complexity impaired resident bimanual performance significantly more than neurosurgeons. Operating on black vs glioma-colored tumors resulted in significantly higher blood loss and lower tumor percentage, whereas altering tactile cues from hard to soft decreased resident tumor resection. Regardless of tumor complexity, significant differences were found between neurosurgeons, senior residents, and junior residents in efficiency index and ultrasonic aspirator path length index. Ultrasonic aspirator bimanual force ratio outlined significant differences between senior and junior residents, whereas coordination index demonstrated significant differences between junior residents and neurosurgeons. CONCLUSION: The NeuroTouch platform incorporating the simulated scenarios and metrics used differentiates novice from expert neurosurgical performance, demonstrating NeuroTouch face, content, and construct validity and the possibility of developing brain tumor resection proficiency performance benchmarks.