Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

Joint Confidence Region Estimation on Predictive Values

For evaluating diagnostic accuracy of inherently continuous diagnostic tests/biomarkers, sensitivity and specificity are well-known measures both of which depend on a diagnostic cut-off, which is usually estimated. Sensitivity (specificity) is the conditional probability of testing positive (negative) given the true disease status. However, a more relevant question is “what is the probability of having (not having) a disease if a test is positive (negative)?”. Such post-test probabilities are denoted as positive predictive value (PPV) and negative predictive value (NPV). The PPV and NPV at the same estimated cut-off are correlated, hence it is desirable to make the joint inference on PPV and NPV to account for such correlation. Existing inference methods for PPV and NPV focus on the individual confidence intervals and they were developed under binomial distribution assuming binary instead of continuous test results. Several approaches are proposed to estimate the joint confidence region as well as the individual confidence intervals of PPV and NPV. Simulation results indicate the proposed approaches perform well with satisfactory coverage probabilities for normal and non-normal data and, additionally, outperform existing methods with improved coverage as well as narrower confidence intervals for PPV and NPV. The Alzheimer\u27s Disease Neuroimaging Initiative (ADNI) data set is used to illustrate the proposed approaches and compare them with the existing methods

Urban vs. rural differences in insurance coverage and impact on employment among families caring for a child with cerebral palsy

Background: The purpose of this study was to examine urban vs. rural differences on the relationship between family contextual variables and adequacy of insurance coverage and impact on employment for among families with a child with Cerebral Palsy from a nationally representative sample. Methods: A retrospective, observational study was carried out using data from the National Survey of Children with Special Healthcare Needs. Results: A total of 744 participants reported as having a child with a diagnosis of Cerebral Palsy and were included in the sample. Logistic regression analyses, adjusting for urban and rural setting revealed different predictors of adequacy of insurance coverage and impact on employment. Among urban respondents, three variables with odds ratios ranging from 1.33 to 1.58 served as protective factors, increasing the likelihood of adequate insurance coverage. Four variables with odds ratios ranging from 1.41 to 1.79 decreased the likelihood of negatively impacting employment. Among rural families, there was only one significant protective factor for adequacy of insurance coverage (odds ratio 1.80) and one for decreasing the chances of impact on employment (odds ratio 2.53). Conclusion: Families in rural areas caring for a child with CP have few protective factors for adequate insurance coverage and impact on familial employment

Twitter Conversations and English News Media Reports on Poliomyelitis in Five Different Countries, January 2014 to April 2015

Introduction: Twitter and media coverage on poliomyelitis help maintain global support for its eradication.Objective: To test our hypothesis that themes of polio-related tweets and media articles would differ by location of interest (hashtag of country name mentioned in the tweet; country name mentioned in media articles) but would be similar to each other (tweets and media articles) for each location of interest.Methods: We retrospectively examined a 40% random sample of Twitter data containing the hashtag #polio from January 1, 2014, to April 30, 2015 (N = 79,333), from which we extracted 5 subcorpora each with a co-occurring hashtag #India (n = 5027), #Iraq (n = 1238), #Nigeria (n = 1364), #Pakistan (n = 11,427), and #Syria (n = 2952). We also retrieved and categorized 73 polio-related English-language news stories from within the same timeframe. We assessed the association between polio-related English news themes and the Twitter content. Descriptive analyses and unsupervised machine learning (latent Dirichlet allocation modeling) were conducted on the 5 Twitter subcorpora.Results: The results of the latent Dirichlet allocation modeling on the specific subcorpora with country co-occurring hashtags showed significant differences between the 5 countries in terms of content. English mass media content focused largely on violence/conflicts and cases of polio, whereas social media focused on eradication and vaccination efforts along with celebrations.Discussion: Contrary to our hypothesis, our evidence suggests Twitter content differs significantly from English mass media content. Evidence from our study helps inform media monitoring and communications surveillance during global public health crises, such as infectious disease outbreaks, as well as reactions to health promotion campaigns

Twitter Reactions to Global Health News Related to Five Different Countries: A Case Study of #Polio

Social media has become a vital tool for global health communication, given the penetration of the internet and mobile phones across the world. In addition to disseminating health information, public health professionals also monitor traditional media and social media to assess the communication environment. Prior research showed that outbreak-related social media contents were largely driven by traditional media reports. However, we hypothesized that different types of news contents could trigger different levels of reaction on social media. In this study, we retrospectively examined a 40% random sample of Twitter data containing the hashtag #polio from January 2014 to April 2015 (N=79333), from which we extracted five sub-corpora each with a co-occurring hashtag #India, #Iraq, #Nigeria, #Pakistan, and #Syria respectively. We also retrieved 104 polio-related traditional news stories from 2 newspapers, 2 television news stations, and 2 radio news stations within the same time frame. We assessed the relationship between polio-related news from traditional news sources and the Twitter content. We hypothesized that polio-specific Twitter conversations differed by the location of interest and they were reactions to traditional media news articles. Descriptive analyses and unsupervised machine learning were conducted on the 5 Twitter sub-corpora to elucidate their underlying topics. Traditional media articles were grouped according to the country of interest and were categorized into the following topics: celebrations or achievements; violence or crises; political actions; vaccinations or other programs/aid; new cases or spreading of polio; and miscellaneous. Strong Twitter reactions were observed following a few news stories published by traditional media but not the others. Our evidences suggest a nuanced relationship between outbreak-related traditional media stories and Twitter contents. Evidence from our study helps inform media monitoring and communication surveillance during global public health crises, such as infectious disease outbreaks, as well as reactions to health promotion campaigns