A latent process model for forecasting multiple time series in environmental public health surveillance: A latent process model for forecasting multiple time series in environmental public health surveillance

Spatio-temporal characterization of earthquake sequence parameters and forecasting of strong aftershocks in Xinjiang based on the ETAS model

In this paper, the Integrated Nested Laplace Algorithm (INLA) is applied to the Epidemic Type Aftershock Sequence (ETAS) model, and the parameters of the ETAS model are obtained for the earthquake sequences active in different regions of Xinjiang. By analyzing the characteristics of the model parameters over time, the changes in each earthquake sequence are studied in more detail. The estimated values of the ETAS model parameters are used as inputs to forecast strong aftershocks in the next period. We find that there are significant differences in the aftershock triggering capacity and aftershock attenuation capacity of earthquake sequences in different seismic regions of Xinjiang. With different cutoff dates set, we observe the characteristics of the earthquake sequence parameters changing with time after the mainshock occurs, and the model parameters of the Ms7.3 earthquake sequence in Hotan region change significantly with time within 15 days after the earthquake. Compared with the MCMC algorithm, the ETAS model fitted with the INLA algorithm can forecast the number of earthquakes in the early period after the occurrence of strong aftershocks more effectively and can forecast the sudden occurrence time of earthquakes more accurately.

Associations Between Wildfire-Related PM2.5 and Intensive Care Unit Admissions in the United States, 2006-2015

Wildfire smoke is a growing public health concern in the United States. Numerous studies have documented associations between ambient smoke exposure and severe patient outcomes for single-fire seasons or limited geographic regions. However, there are few national-scale health studies of wildfire smoke in the United States, few studies investigating Intensive Care Unit (ICU) admissions as an outcome, and few specifically framed around hospital operations. This study retrospectively examined the associations between ambient wildfire-related PM2.5 at a hospital ZIP code with total hospital ICU admissions using a national-scale hospitalization data set. Wildfire smoke was characterized using a combination of kriged PM2.5 monitor observations and satellite-derived plume polygons from National Oceanic and Atmospheric Administration's Hazard Mapping System. ICU admissions data were acquired from Premier, Inc. and encompass 15%-20% of all U.S. ICU admissions during the study period. Associations were estimated using a distributed-lag conditional Poisson model under a time-stratified case-crossover design. We found that a 10 μg/m3 increase in daily wildfire PM2.5 was associated with a 2.7% (95% CI: 1.3, 4.1; p = 0.00018) increase in ICU admissions 5 days later. Under stratification, positive associations were found among patients aged 0-20 and 60+, patients living in the Midwest Census Region, patients admitted in the years 2013-2015, and non-Black patients, though other results were mixed. Following a simulated severe 7-day 120 μg/m3 smoke event, our results predict ICU bed utilization peaking at 131% (95% CI: 43, 239; p < 10-5) over baseline. Our work suggests that hospitals may need to preposition vital critical care resources when severe smoke events are forecast.

Advances in spatiotemporal models for non-communicable disease surveillance

Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public.

Dynamic Bayesian network in infectious diseases surveillance: a simulation study

The surveillance of infectious diseases relies on the identification of dynamic relations between the infectious diseases and corresponding influencing factors. However, the identification task confronts with two practical challenges: small sample size and delayed effect. To overcome both challenges to imporve the identification results, this study evaluated the performance of dynamic Bayesian network(DBN) in infectious diseases surveillance. Specifically, the evaluation was conducted by two simulations. The first simulation was to evaluate the performance of DBN by comparing it with the Granger causality test and the least absolute shrinkage and selection operator (LASSO) method; and the second simulation was to assess how the DBN could improve the forecasting ability of infectious diseases. In order to make both simulations close to the real-world situation as much as possible, their simulation scenarios were adapted from real-world studies, and practical issues such as nonlinearity and nuisance variables were also considered. The main simulation results were: ① When the sample size was large (n = 340), the true positive rates (TPRs) of DBN (≥98%) were slightly higher than those of the Granger causality method and approximately the same as those of the LASSO method; the false positive rates (FPRs) of DBN were averagely 46% less than those of the Granger causality test, and 22% less than those of the LASSO method. ② When the sample size was small, the main problem was low TPR, which would be further aggravated by the issues of nonlinearity and nuisance variables. In the worst situation (i.e., small sample size, nonlinearity and existence of nuisance variables), the TPR of DBN declined to 43.30%. However, it was worth noting that such decline could also be found in the corresponding results of Granger causality test and LASSO method. ③ Sample size was important for identifying the dynamic relations among multiple variables, in this case, at least three years of weekly historical data were needed to guarantee the quality of infectious diseases surveillance. ④ DBN could improve the foresting results through reducing forecasting errors by 7%. According to the above results, DBN is recommended to improve the quality of infectious diseases surveillance.

A model comparison algorithm for increased forecast accuracy of dengue fever incidence in Singapore and the auxiliary role of total precipitation information

Many time-series models for disease counts utilise information from environmental variables. We focus on weekly dengue fever (DF) incidence rates in Singapore and demonstrate the strong negative correlation between an appropriately time-lagged total weekly rainfall and DF incidence. A Bayesian neural network time-series model for predicting DF incidence which utilizes rainfall data is introduced. A comparison is made between this neural network model and a time-series model which does not use any covariate information. An easily implementable method for choosing between the models which optimizes future prediction accuracy is suggested as well. We note that our proposed comparison method is applicable to any competing time-series models. This algorithm is demonstrated through examples of comparisons between pairs of different time-series models.

Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation

Surveillance is critical for improving population health. Public health surveillance systems generate information that drives action, and the data must be of sufficient quality and with a resolution and timeliness that matches objectives. In the context of scientific advances in public health surveillance, changing health care and public health environments, and rapidly evolving technologies, the aim of this article is to review public health surveillance systems. We consider their current use to increase the efficiency and effectiveness of the public health system, the role of system stakeholders, the analysis and interpretation of surveillance data, approaches to system monitoring and evaluation, and opportunities for future advances in terms of increased scientific rigor, outcomes-focused research, and health informatics.

Ambient particulate matter, landscape fire smoke, and emergency ambulance dispatches in Sydney, Australia

BACKGROUND

Emergency ambulance dispatches (EAD) are a novel outcome for evaluating the public health impacts of air pollution. We assessed the relationships between ambient particulate matter (PM) from all sources, PM from landscape fire smoke (LFS), and EADs likely to be associated with cardiorespiratory problems in the Sydney greater metropolitan region for an 11-year period from 2004 to 2015.

METHODS

EAD codes are assigned at the time of the call to emergency services using standard computer assisted algorithms. We assessed EADs coded as: breathing problems, chest pain, stroke or cerebrovascular accident (stroke), cardiac or respiratory arrest and death (arrest), and heart or defibrillator problems (other heart problems). Using a daily times series study design with a generalized linear Poisson regression model we quantified the association between EAD and daily PM_2.5 from all sources (PM_2.5,all) and PM_2.5 primarily due to LFS (PM_2.5,LFS).

RESULTS

Increases of 10μg·m^-3 in PM_2.5,all were positively associated with same day EAD for breathing problems (RR=1.03, 95% CI 1.02 to 1.04), arrest (RR=1.03, 95% CI 1.00 to 1.06), and chest pain (RR=1.01 CI 1.00 to 1.02) but not with other outcomes. Increases of 10μg·m^-3 PM_2.5,LFS were also positively associated with breathing problems on the same day (RR=1.04, 95% CI 1.02 to 1.05) and other heart problems at lag of two days (RR=1.05, 95% CI 1.01 to 1.09).

CONCLUSIONS

Emergency dispatches for breathing problems are associated with PM_2.5,all and PM_2.5,LFS and provide a sensitive end point for continued research and surveillance activities investigating the impacts of daily fluctuations in ambient PM_2.5.

Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

Exposure to high levels of air pollutant concentration is known to be associated with respiratory problems which can translate into higher morbidity and mortality rates. The link between air pollution and population health has mainly been assessed considering air quality and hospitalisation or mortality data. However, this approach limits the analysis to individuals characterised by severe conditions. In this paper we evaluate the link between air pollution and respiratory diseases using general practice drug prescriptions for chronic respiratory diseases, which allow to draw conclusions based on the general population. We propose a two-stage statistical approach: in the first stage we specify a space-time model to estimate the monthly NO2 concentration integrating several data sources characterised by different spatio-temporal resolution; in the second stage we link the concentration to the β2-agonists prescribed monthly by general practices in England and we model the prescription rates through a small area approach.