Dataset Information

Model selection in time series studies of influenza-associated mortality.

ABSTRACT:

Background

Poisson regression modeling has been widely used to estimate influenza-associated disease burden, as it has the advantage of adjusting for multiple seasonal confounders. However, few studies have discussed how to judge the adequacy of confounding adjustment. This study aims to compare the performance of commonly adopted model selection criteria in terms of providing a reliable and valid estimate for the health impact of influenza.

Methods

We assessed four model selection criteria: quasi Akaike information criterion (QAIC), quasi bayesian information criterion (QBIC), partial autocorrelation functions of residuals (PACF), and generalized cross-validation (GCV), by separately applying them to select the Poisson model best fitted to the mortality datasets that were simulated under the different assumptions of seasonal confounding. The performance of these criteria was evaluated by the bias and root-mean-square error (RMSE) of estimates from the pre-determined coefficients of influenza proxy variable. These four criteria were subsequently applied to an empirical hospitalization dataset to confirm the findings of simulation study.

Results

GCV consistently provided smaller biases and RMSEs for the influenza coefficient estimates than QAIC, QBIC and PACF, under the different simulation scenarios. Sensitivity analysis of different pre-determined influenza coefficients, study periods and lag weeks showed that GCV consistently outperformed the other criteria. Similar results were found in applying these selection criteria to estimate influenza-associated hospitalization.

Conclusions

GCV criterion is recommended for selection of Poisson models to estimate influenza-associated mortality and morbidity burden with proper adjustment for confounding. These findings shall help standardize the Poisson modeling approach for influenza disease burden studies.

SUBMITTER: Wang XL

PROVIDER: S-EPMC3380027 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Model selection in time series studies of influenza-associated mortality.

Wang Xi-Ling XL Yang Lin L Chan King-Pan KP Chiu Susan S SS Chan Kwok-Hung KH Peiris J S Malik JS Wong Chit-Ming CM

PloS one 20120620 6

<h4>Background</h4>Poisson regression modeling has been widely used to estimate influenza-associated disease burden, as it has the advantage of adjusting for multiple seasonal confounders. However, few studies have discussed how to judge the adequacy of confounding adjustment. This study aims to compare the performance of commonly adopted model selection criteria in terms of providing a reliable and valid estimate for the health impact of influenza.<h4>Methods</h4>We assessed four model selectio ...[more]

PMID: 22745751

Similar Datasets

Project description:In this paper, we introduce a novel model selection approach to time series forecasting. For linear stationary processes, such as AR processes, the direction of time is independent of the model parameters. By combining theoretical principles of time-reversibility in time series with conventional modeling approaches such as information criteria, we construct a criterion that employs the backwards prediction (backcast) as a proxy for the forecast. Hereby, we aim to adopt a theoretically grounded, data-driven approach to model selection. The novel criterion is named the backwards validated information criterion (BVIC). The BVIC identifies suitable models by trading off a measure of goodness-of-fit and a models ability to predict backwards. We test the performance of the BVIC by conducting experiments on synthetic and real data. In each experiment, the BVIC is examined in contrast to conventionally employed criteria. Our experimental results suggest that the BVIC has comparable performance as conventional information criteria. Specifically, in most of the experiments performed, we did not find statistically significant differences between the forecast error of the BVIC under certain parameterizations and that of the different information criteria. Nonetheless, it is worth emphasizing that the BVIC guarantees are established by design where the model order penalization term depends on strong mathematical properties of time-reversible time series forecasting properties and a finite data assessment. In particular, the penalization term is replaced by a weighted trade-off between functional dimensions pertaining to forecasting.That said, we observed that the BVIC recovered more accurately the real order of the underlying process than the other criteria, which rely on a static penalization of the model order. Lastly, leveraging the latter property we perform the assessment of the order model (or, memory) of time series pertaining to epileptic seizures recorded using electrocorticographic data. Our results provide converging evidence that the order of the model increases during the epileptic events.

Project description:ObjectiveThis study comprehensively estimated the excess cardiovascular disease (CVD) mortality attributable to influenza in an older (age ≥65 years) population.DesignEcological study.SettingAggregated data from administrative systems on CVD mortality, influenza surveillance and meteorological data in Beijing, China.Main outcome measureExcess overall CVD, and separately for ischaemic heart disease (IHD), ischaemic stroke, haemorrhagic stroke mortality attributable to influenza, adjusting for influenza activity, time trend, seasonality and ambient temperature.ResultsCVD (risk ratio (RR) 1.02, 95% CI 1.01, 1.02), IHD (RR 1.01, 95% CI 1.01, 1.02), ischaemic stroke (RR=1.03, 95% CI 1.02, 1.04), but not haemorrhagic stroke (RR=1.00, 95% CI 0.99, 1.01) mortality, were significantly associated with every 10% increase in influenza activity. An increase in circulating A(H1N1)09pdm, A(H3N2) and B type virus were all significantly associated with CVD and ischaemic stroke mortality, but only A(H3N2) and B type virus with IHD mortality. The strongest increase in disease mortality was in the same week as the increase in influenza activity. Annual excess CVD mortality rate attributable to influenza ranged from 54 to 96 per 100 000 population. The 3%-6% CVD mortality attributable to influenza activity was related to an annual excess of 916-1640 CVD deaths in Beijing, China.ConclusionsInfluenza activity has moderate to strong associations with CVD, IHD and ischaemic stroke mortality in older adults in China. Promoting influenza vaccination could have major health benefit in this population.BackgroundInfluenza may trigger serious CVD events. An estimation of excess CVD mortality attributable to influenza has particular relevance in China where vaccination is low and CVD burden is high.MethodsThis study analysed data at the population level (age ≥65 years) using linked aggregated data from administrative systems on CVD mortality, influenza surveillance and meteorological data during 2011 to 2018. Quasi-Poisson regression models were used to estimate the excess overall CVD, and separately for IHD, ischaemic stroke, haemorrhagic stroke mortality attributable to influenza, adjusting for influenza activity, time trend, seasonality and ambient temperature. Analyses were also undertaken for influenza subtypes (A(H1N1)09pdm, A(H3N2) and B viruses), and mortality risk with time lags of 1-5 weeks following influenza activity in the current week.

Project description:BACKGROUND:Measures of the contribution of influenza to Streptococcus pneumoniae infections, both in the seasonal and pandemic setting, are needed to predict the burden of secondary bacterial infections in future pandemics to inform stockpiling. The magnitude of the interaction between these two pathogens has been difficult to quantify because both infections are mainly clinically diagnosed based on signs and symptoms; a combined viral-bacterial testing is rarely performed in routine clinical practice; and surveillance data suffer from confounding problems common to all ecological studies. We proposed a novel multivariate model for age-stratified disease incidence, incorporating contact patterns and estimating disease transmission within and across groups. METHODS AND FINDINGS:We used surveillance data from England over the years 2009 to 2017. Influenza infections were identified through the virological testing of samples taken from patients diagnosed with influenza-like illness (ILI) within the sentinel scheme run by the Royal College of General Practitioners (RCGP). Invasive pneumococcal disease (IPD) cases were routinely reported to Public Health England (PHE) by all the microbiology laboratories included in the national surveillance system. IPD counts at week t, conditional on the previous time point t-1, were assumed to be negative binomially distributed. Influenza counts were linearly included in the model for the mean IPD counts along with an endemic component describing some seasonal background and an autoregressive component mimicking pneumococcal transmission. Using age-specific counts, Akaike information criterion (AIC)-based model selection suggested that the best fit was obtained when the endemic component was expressed as a function of observed temperature and rainfall. Pneumococcal transmission within the same age group was estimated to explain 33.0% (confidence interval [CI] 24.9%-39.9%) of new cases in the elderly, whereas 50.7% (CI 38.8%-63.2%) of incidence in adults aged 15-44 years was attributed to transmission from another age group. The contribution of influenza on IPD during the 2009 pandemic also appeared to vary greatly across subgroups, being highest in school-age children and adults (18.3%, CI 9.4%-28.2%, and 6.07%, CI 2.83%-9.76%, respectively). Other viral infections, such as respiratory syncytial virus (RSV) and rhinovirus, also seemed to have an impact on IPD: RSV contributed 1.87% (CI 0.89%-3.08%) to pneumococcal infections in the 65+ group, whereas 2.14% (CI 0.87%-3.57%) of cases in the group of 45- to 64-year-olds were attributed to rhinovirus. The validity of this modelling strategy relies on the assumption that viral surveillance adequately represents the true incidence of influenza in the population, whereas the small numbers of IPD cases observed in the younger age groups led to significant uncertainty around some parameter estimates. CONCLUSIONS:Our estimates suggested that a pandemic wave of influenza A/H1N1 with comparable severity to the 2009 pandemic could have a modest impact on school-age children and adults in terms of IPD and a small to negligible impact on infants and the elderly. The seasonal impact of other viruses such as RSV and rhinovirus was instead more important in the older population groups.

Dataset Information

Model selection in time series studies of influenza-associated mortality.

Background

Methods

Results

Conclusions

Publications

Model selection in time series studies of influenza-associated mortality.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets