Early Warning Signals of Vaccine Scares.

There exists strong evidence that vaccines are extremely effective in the prevention of pediatric infectious disease, yet despite this evidence, vaccine refusal is still popular amongst some parents. Existing mathematical models of disease and vaccinating dynamics are parsimonious with post scare empirical data, yet lack the ability to predict when a scare will occur. This thesis frames the problem of predicting when a vaccine scare is imminent as a problem in bifurcation theory and critical transitions theory. As a system of differential equations nears a bifurcation point, the system experiences critical slowing down, a loss in resilience to small perturbations from equilibrium. This loss in resilience manifests itself as an increase in the variance and lag-1 autocorrelation of the time series. Using an existing model for vaccinating dynamics, I demonstrate this critical slowing as the system bifurcates from a state of high vaccine coverage to a state of suboptimal vaccine coverage. I also demonstrate that critical slowing can be detected in the population by using the social media site Twitter and Google Trends data. It will be shown that leading to the 2014 measles outbreak in Disneyland, a statistically significant increase in the lag-1 autocorrelation in the time series of tweets with anti-vaccine sentiment is detected. Post outbreak, a statistically significant decrease in the lag-1 autocorrelation is detected, suggesting that population comes sufficiently close to a vaccine scare to elicit outbreaks in those individuals who ceased vaccinating. The results of this thesis provide new tools to monitor vaccine sentiment and maintain vaccine coverage

Drug interactions and pharmacogenetic factors contribute to variation in apixaban concentration in atrial fibrillation patients in routine care

Factor Xa-inhibitor apixaban is an oral anticoagulant prescribed in atrial fibrillation (AF) for stroke prevention. Its pharmacokinetic profile is known to be affected by cytochrome P450 (CYP)3A metabolism, while it is also a substrate of the efflux transporters ATP-binding cassette (ABC)B1 (P-glycoprotein) and ABCG2 (breast cancer resistance protein, BCRP). In this study, we assessed the impact of interacting medication and pharmacogenetic variation to better explain apixaban concentration differences among 358 Caucasian AF patients. Genotyping (ABCG2, ABCB1, CYP3A4*22, CYP3A5*3) was performed by TaqMan assays, and apixaban quantified by mass spectrometry. The typical patient was on average 77.2 years old, 85.5 kg, and had a serum creatinine of 103.1 µmol/L. Concomitant amiodarone, an antiarrhythmic agent and moderate CYP3A/ABCB1 inhibitor, the impaired-function variant ABCG2 c.421C \u3e A, and sex predicted higher apixaban concentrations when controlling for age, weight and serum creatinine (multivariate regression; R2 = 0.34). Our findings suggest that amiodarone and ABCG2 genotype contribute to interpatient apixaban variability beyond known clinical factors

Healthcare-Associated Adverse Events in Alternate Level of Care Patients Awaiting Long-Term Care in Hospital

Introduction: A growing number of Canadian older adults are designated alternate level of care (ALC) and await placement into long-term care (LTC) while admitted to hospital. This creates infrastructural challenges by using resources allocated for acute care during disproportionately long hospital stays. For ALC patients, hospital environments maladapted to their needs impart risk of healthcare-associated adverse events. Methods: In this retrospective descriptive study, we examined healthcare-associated adverse events in 156 ALC patients, 65 years old and older, awaiting long-term care while admitted to two hospitals in London, Ontario in 2015–2018. We recorded incidence of infections and antimicrobial days prescribed. We recorded incidence of non-infectious adverse events including delirium, falls, venothrombotic events, and pressure ulcers. We used a restricted cubic spline model to characterize adverse events as a function of length of stay. Results: Patients waited an average of 56 ALC days (ranging from 6 to 333 days) before LTC placement, with seven deaths occurring prior to placement. We recorded 362 total adverse events accrued over 8668 ALC days: 94 infections and 268 non-infectious adverse events. The most common hospital-acquired infections were urinary-tract infections and respiratory infections. The most common non-infectious adverse events were delirium and falls. A total of 620 antimicrobial days were prescribed for infections. Conclusions: ALC patients incur a meaningful and predictable number of adverse events during their stay in acute care. The incidence of these adverse events should be used to educate stakeholders on risks of ALC stay and to advocate for strategies to minimize ALC days

A Bayesian approach to estimating the population prevalence of mood and anxiety disorders using multiple measures

Aims: There is currently no universally accepted measure for population-based surveillance of mood and anxiety disorders. As such, the use of multiple linked measures could provide a more accurate estimate of population prevalence. Our primary objective was to apply Bayesian methods to two commonly employed population measures of mood and anxiety disorders to make inferences regarding the population prevalence and measurement properties of a combined measure. Methods: We used data from the 2012 Canadian Community Health Survey - Mental Health linked to health administrative databases in Ontario, Canada. Structured interview diagnoses were obtained from the survey, and health administrative diagnoses were identified using a standardised algorithm. These two prevalence estimates, in addition to data on the concordance between these measures and prior estimates of their psychometric properties, were used to inform our combined estimate. The marginal posterior densities of all parameters were estimated using Hamiltonian Monte Carlo (HMC), a Markov Chain Monte Carlo technique. Summaries of posterior distributions, including the means and 95% equally tailed posterior credible intervals, were used for interpretation of the results. Results: The combined prevalence mean was 8.6%, with a credible interval of 6.8-10.6%. This combined estimate sits between Bayesian-derived prevalence estimates from administrative data-derived diagnoses (mean = 7.4%) and the survey-derived diagnoses (mean = 13.9%). The results of our sensitivity analysis suggest that varying the specificity of the survey-derived measure has an appreciable impact on the combined posterior prevalence estimate. Our combined posterior prevalence estimate remained stable when varying other prior information. We detected no problematic HMC behaviour, and our posterior predictive checks suggest that our model can reliably recreate our data. Conclusions: Accurate population-based estimates of disease are the cornerstone of health service planning and resource allocation. As a greater number of linked population data sources become available, so too does the opportunity for researchers to fully capitalise on the data. The true population prevalence of mood and anxiety disorders may reside between estimates obtained from survey data and health administrative data. We have demonstrated how the use of Bayesian approaches may provide a more informed and accurate estimate of mood and anxiety disorders in the population. This work provides a blueprint for future population-based estimates of disease using linked health data

Critical dynamics in population vaccinating behavior

Vaccine refusal can lead to renewed outbreaks of previously eliminated diseases and even delay global eradication. Vaccinating decisions exemplify a complex, coupled system where vaccinating behavior and disease dynamics influence one another. Such systems often exhibit critical phenomena-special dynamics close to a tipping point leading to a new dynamical regime. For instance, critical slowing down (declining rate of recovery from small perturbations) may emerge as a tipping point is approached. Here, we collected and geocoded tweets about measles-mumps-rubella vaccine and classified their sentiment using machine-learning algorithms. We also extracted data on measles-related Google searches. We find critical slowing down in the data at the level of California and the United States in the years before and after the 2014-2015 Disneyland, California measles outbreak. Critical slowing down starts growing appreciably several years before the Disneyland outbreak as vaccine uptake declines and the population approaches the tipping point. However, due to the adaptive nature of coupled behavior-disease systems, the population responds to the outbreak by moving away from the tipping point, causing "critical speeding up" whereby resilience to perturbations increases. A mathematical model of measles transmission and vaccine sentiment predicts the same qualitative patterns in the neighborhood of a tipping point to greatly reduced vaccine uptake and large epidemics. These results support the hypothesis that population vaccinating behavior near the disease elimination threshold is a critical phenomenon. Developing new analytical tools to detect these patterns in digital social data might help us identify populations at heightened risk of widespread vaccine refusal

pymc-devs/pymc: v5.9.1

<p><!-- Release notes generated using configuration in .github/release.yml at main --></p> <h2>What's Changed</h2> <h3>New Features </h3> <ul> <li>Allow batched parameters in MvNormal and MvStudentT distributions by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6897</li> <li>Logprob derivation of Max for Discrete IID distributions by @Dhruvanshu-Joshi in https://github.com/pymc-devs/pymc/pull/6790</li> <li>Support logp derivation of <code>power(base, rv)</code> by @LukeLB in https://github.com/pymc-devs/pymc/pull/6962</li> </ul> <h3>Bugfixes </h3> <ul> <li>Make <code>Model.str_repr</code> robust to variables without monkey-patch by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6942</li> <li>Fix bug in GP Periodic and WrappedPeriodic kernel full method by @lucianopaz in https://github.com/pymc-devs/pymc/pull/6952</li> <li>Fix rejection-based truncation of scalar variables by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6923</li> </ul> <h3>Documentation </h3> <ul> <li>Add expression for NegativeBinomial variance by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6957</li> </ul> <h3>Maintenance </h3> <ul> <li>Add constant and observed data to nutpie idata by @Y0dler in https://github.com/pymc-devs/pymc/pull/6943</li> <li>Improve multinomial moment by @aerubanov in https://github.com/pymc-devs/pymc/pull/6933</li> <li>Fix HurdleLogNormal Docstring by @amcadie in https://github.com/pymc-devs/pymc/pull/6958</li> <li>Use numpy testing utilities instead of custom close_to* by @erik-werner in https://github.com/pymc-devs/pymc/pull/6961</li> <li>Include more PyTensor functions in math module by @jaharvey8 in https://github.com/pymc-devs/pymc/pull/6956</li> <li>Improve blackjax sampling integration by @junpenglao in https://github.com/pymc-devs/pymc/pull/6963</li> </ul> <h2>New Contributors</h2> <ul> <li>@Y0dler made their first contribution in https://github.com/pymc-devs/pymc/pull/6943</li> <li>@amcadie made their first contribution in https://github.com/pymc-devs/pymc/pull/6958</li> <li>@erik-werner made their first contribution in https://github.com/pymc-devs/pymc/pull/6961</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/pymc-devs/pymc/compare/v5.9.0...v5.9.1</p&gt

pymc-devs/pymc: v5.9.2

<p><!-- Release notes generated using configuration in .github/release.yml at main --></p> <h2>What's Changed</h2> <h3>New Features </h3> <ul> <li>Recognize alternative form of sigmoid in logprob inference by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6978</li> <li>Allow IntervalTransform to handle dynamic infinite bounds by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/7001</li> </ul> <h3>Bugfixes </h3> <ul> <li>Fix compute_test_value error when creating observed variables by @vandalt in https://github.com/pymc-devs/pymc/pull/6982</li> <li>Fix memory leak in logp of transformed variables by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6991</li> </ul> <h3>Documentation </h3> <ul> <li>fix typo in notebook about Distribution Dimensionality by @nicrie in https://github.com/pymc-devs/pymc/pull/7005</li> </ul> <h3>Maintenance </h3> <ul> <li>Add more missing functions to math module by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6979</li> </ul> <h2>New Contributors</h2> <ul> <li>@vandalt made their first contribution in https://github.com/pymc-devs/pymc/pull/6982</li> <li>@nicrie made their first contribution in https://github.com/pymc-devs/pymc/pull/7005</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/pymc-devs/pymc/compare/v5.9.1...v5.9.2</p&gt