Dataset Information

A statistical tool for comparing seasonal ILI surveillance data.

ABSTRACT: In this paper, we consider the yearly influenza epidemic, as reflected in the seasonal surveillance data compiled by the CDC (Center for Disease Control and Prevention, USA) and we explore a new methodology for comparing specific features of these data. In particular, we focus on the ten HHS (Health and Human Services) regions, and how the incidence data evolves in these regions. In order to perform the comparisons, we consider the relative distribution of weekly new cases over one season and replace the crude data with predicted values. These predictions are obtained after fitting a negative binomial regression model that controls for important covariates. The prediction is computed on a 'generic' set of covariate values that takes into account the relative size (population wise) of the regions to be compared. The main results are presented in graphical form, that quickly emphasizes relevant features of the seasonal data and facilitates the comparisons.

SUBMITTER: Ferland R

PROVIDER: S-EPMC6363783 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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A statistical tool for comparing seasonal ILI surveillance data.

Ferland René R Froda Sorana S

Scientific reports 20190205 1

In this paper, we consider the yearly influenza epidemic, as reflected in the seasonal surveillance data compiled by the CDC (Center for Disease Control and Prevention, USA) and we explore a new methodology for comparing specific features of these data. In particular, we focus on the ten HHS (Health and Human Services) regions, and how the incidence data evolves in these regions. In order to perform the comparisons, we consider the relative distribution of weekly new cases over one season and re ...[more]

PMID: 30723245

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Project description:BackgroundInfectious disease surveillance is a process the product of which reflects both actual disease trends and public awareness of the disease. Decisions made by patients, health care providers, and public health professionals about seeking and providing health care and about reporting cases to health authorities are all influenced by the information environment, which changes constantly. Biases are therefore imbedded in surveillance systems; these biases need to be characterized to provide better situational awareness for decision-making purposes. Our goal is to develop a statistical framework to characterize influenza surveillance systems, particularly their correlation with the information environment.MethodsWe identified Hong Kong influenza surveillance data systems covering healthcare providers, laboratories, daycare centers and residential care homes for the elderly. A Bayesian hierarchical statistical model was developed to examine the statistical relationships between the influenza surveillance data and the information environment represented by alerts from HealthMap and web queries from Google. Different models were fitted for non-pandemic and pandemic periods and model goodness-of-fit was assessed using common model selection procedures.ResultsSome surveillance systems - especially ad hoc systems developed in response to the pandemic flu outbreak - are more correlated with the information environment than others. General practitioner (percentage of influenza-like-illness related patient visits among all patient visits) and laboratory (percentage of specimen tested positive) seem to proportionally reflect the actual disease trends and are less representative of the information environment. Surveillance systems using influenza-specific code for reporting tend to reflect biases of both healthcare seekers and providers.ConclusionsThis study shows certain influenza surveillance systems are less correlated with the information environment than others, and therefore, might represent more reliable indicators of disease activity in future outbreaks. Although the patterns identified in this study might change in future outbreaks, the potential susceptibility of surveillance data is likely to persist in the future, and should be considered when interpreting surveillance data.

Project description:BACKGROUND:Uptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topics about these drugs from consumers for successful implementation of control measures. Traditional survey methods would have accomplished this study, but they are too costly in terms of resources needed, and they are subject to social desirability bias for topics discovery. Hence, there is a need to use alternative efficient means such as Twitter data and machine learning (ML) techniques. OBJECTIVE:Using Twitter data, the aim of the study was to (1) provide a methodological extension for efficiently extracting widely consumed drugs during seasonal influenza and (2) extract topics from the tweets of these drugs and to infer how the insights provided by these topics can enhance seasonal influenza surveillance. METHODS:From tweets collected during the 2012-13 flu season, we first identified tweets with mentions of drugs and then constructed an ML classifier using dependency words as features. The classifier was used to extract tweets that evidenced consumption of drugs, out of which we identified the mostly consumed drugs. Finally, we extracted trending topics from each of these widely used drugs' tweets using latent Dirichlet allocation (LDA). RESULTS:Our proposed classifier obtained an F1 score of 0.82, which significantly outperformed the two benchmark classifiers (ie, P<.001 with the lexicon-based and P=.048 with the 1-gram term frequency [TF]). The classifier extracted 40,428 tweets that evidenced consumption of drugs out of 50,828 tweets with mentions of drugs. The most widely consumed drugs were influenza virus vaccines that had around 76.95% (31,111/40,428) share of the total; other notable drugs were Theraflu, DayQuil, NyQuil, vitamins, acetaminophen, and oseltamivir. The topics of each of these drugs exhibited common themes or experiences from people who have consumed these drugs. Among these were the enabling and deterrent factors to influenza drugs uptake, which are keys to mitigating the severity of seasonal influenza outbreaks. CONCLUSIONS:The study results showed the feasibility of using tweets of widely consumed drugs to enhance seasonal influenza surveillance in lieu of the traditional or conventional surveillance approaches. Public health officials and other stakeholders can benefit from the findings of this study, especially in enhancing strategies for mitigating the severity of seasonal influenza outbreaks. The proposed methods can be extended to the outbreaks of other diseases.

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A statistical tool for comparing seasonal ILI surveillance data.

Publications

A statistical tool for comparing seasonal ILI surveillance data.

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