Modeling gradually changing seasonal variation in count data using state space models: a cohort study of hospitalization rates of stroke in atrial fibrillation patients in Denmark from 1977 to 2011

Forecasting emergency department waiting time using a state space representation

The provision of waiting time information in emergency departments (ED) has become an increasingly popular practice due to its positive impact on patient experience and ED demand management. However, little scientific attention has been given to the quality and quantity of waiting time information presented to patients. To improve both aspects, we propose a set of state space models with flexible error structures to forecast ED waiting time for low acuity patients. Our approach utilizes a Bayesian framework to generate uncertainties associated with the forecasts. We find that the state-space models with flexible error structures significantly improve forecast accuracy of ED waiting time compared to the benchmark, which is the rolling average model. Specifically, incorporating time-varying and correlated error terms reduces the root mean squared errors of the benchmark by 10%. Furthermore, treating zero-recorded waiting times as unobserved values improves forecast performance. Our proposed model has the ability to provide patient-centric waiting time information. By offering more accurate and informative waiting time information, our model can help patients make better informed decisions and ultimately enhance their ED experience.

Seasonality of tuberculosis in intermediate endemicity setting dominated by reactivation diseases in Hong Kong

Summer-spring predominance of tuberculosis (TB) has been widely reported. The relative contributions of exogenous recent infection versus endogenous reactivation to such seasonality remains poorly understood. Monthly TB notifications data between 2005 and 2017 in Hong Kong involving 64,386 cases (41% aged ≥ 65; male-to-female ratio 1.74:1) were examined for the timing, amplitude, and predictability of variation of seasonality. The observed seasonal variabilities were correlated with demographics and clinical presentations, using wavelet analysis coupled with dynamic generalised linear regression models. Overall, TB notifications peaked annually in June and July. No significant annual seasonality was demonstrated for children aged ≤ 14 irrespective of gender. The strongest seasonality was detected in the elderly (≥ 65) among males, while seasonal pattern was more prominent in the middle-aged (45–64) and adults (30–44) among females. The stronger TB seasonality among older adults in Hong Kong suggested that the pattern has been contributed largely by reactivation diseases precipitated by defective immunity whereas seasonal variation of recent infection was uncommon.

Temperature and Precipitation Associate With Ischemic Stroke Outcomes in the United States

Background

There is disagreement in the literature about the relationship between strokes and seasonal conditions. We sought to (1) describe seasonal patterns of stroke in the United States, and (2) determine the relationship between weather variables and stroke outcomes.

Methods and Results

We performed a cross‐sectional study using Get With The Guidelines‐Stroke data from 896 hospitals across the continental United States. We examined effects of season, climate region, and climate variables on stroke outcomes. We identified 457 638 patients admitted from 2011 to 2015 with ischemic stroke. There was a higher frequency of admissions in winter (116 862 in winter versus 113 689 in spring, 113 569 in summer, and 113 518 in fall; P<0.0001). Winter was associated with higher odds of in‐hospital mortality (odds ratio [OR] 1.08 relative to spring, confidence interval [CI] 1.04–1.13, P=0.0004) and lower odds of discharge home (OR 0.92, CI 0.91–0.94, P<0.0001) or independent ambulation at discharge (OR 0.96, CI 0.94–0.98, P=0.0006). These differences were attenuated after adjusting for climate region and case mix and became inconsistent after controlling for weather variables. Temperature and precipitation were independently associated with outcome after multivariable analysis, with increases in temperature and precipitation associated with lower odds of mortality (OR 0.95, CI 0.93–0.97, P<0.0001 and OR 0.95, CI 0.90–1.00, P=0.035, respectively).

Conclusions

Admissions for ischemic stroke were more frequent in the winter. Warmer and wetter weather conditions were independently associated with better outcomes. Further studies should aim to identify sensitive populations and inform public health measures aimed at resource allocation, readiness, and adaptive strategies.

Decoding and modelling of time series count data using Poisson hidden Markov model and Markov ordinal logistic regression models

Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month for 11-year span at Christian Medical College, Vellore, India. Using Viterbi algorithm, the best estimate of the state sequence was obtained and hence the transition probability matrix. The mean passage time between the states were estimated. The 95% confidence interval for the mean passage time was estimated via Monte Carlo simulation. The three hidden states of the estimated Markov chain are labelled as 'Low', 'Moderate' and 'High' with the mean counts of 1.4, 6.6 and 20.2 and the estimated average duration of stay of 3, 3 and 4 months, respectively. Environmental risk factors were studied using Markov ordinal logistic regression analysis. No significant association was found between disease severity levels and climate components.

Seasonal Variation in Paroxysmal Atrial Fibrillation: A Systematic Review

INTRODUCTION

A variety of cardiovascular diseases have been demonstrated to have seasonal variations with peaks in the winter and troughs in the summer. Studies regarding atrial fibrillation (AF) have had varying results and this review describes the current data regarding the seasonal variation of AF and mechanisms mediating this seasonal fluctuation.

METHODS

A systematic review was conducted of PubMed, EBSCO and OVID for manuscripts describing the association between seasonal variation and the occurrence of AF. Studies meeting eligibility criteria were assessed for quality and reporting bias. Data was extracted in regards to the following associations: seasonal variation and AF paroxysms, temperature and AFparoxysms, duration of daylight and AF paroxysms, barometric pressure and AF paroxysms, alcohol and AF paroxysms, as well as seasonal variation and AF related stroke.

RESULTS

A total of 15 studies were identified for inclusion. Of these, 11 studies assessed seasonal variation and the remaining 4 studies assessed seasonal variation in AF related stroke. AF paroxysms peaked in winter with a trough in summer. There was an inverse correlation between temperatures as well barometric pressure and the occurrence of AF paroxysms and a positive correlation with duration of daylight.

CONCLUSIONS

The rate of occurrence of paroxysmal AF varies by seasons and is greatest during winter and least in summer.