Co-operation rate of parents to the interview in the Pediatric hospital

<p><b>Copyright information:</b></p><p>Taken from "A comparison of populations vaccinated in a public service and in a private hospital setting in the same area"</p><p>http://www.biomedcentral.com/1471-2458/8/278</p><p>BMC Public Health 2008;8():278-278.</p><p>Published online 6 Aug 2008</p><p>PMCID:PMC2531109.</p><p></p

Co-operation rate of parents to the interview in the public immunization service

<p><b>Copyright information:</b></p><p>Taken from "A comparison of populations vaccinated in a public service and in a private hospital setting in the same area"</p><p>http://www.biomedcentral.com/1471-2458/8/278</p><p>BMC Public Health 2008;8():278-278.</p><p>Published online 6 Aug 2008</p><p>PMCID:PMC2531109.</p><p></p

Influenza-Like Illness Surveillance on Twitter through Automated Learning of Naïve Language

<div><p>Twitter has the potential to be a timely and cost-effective source of data for syndromic surveillance. When speaking of an illness, Twitter users often report a combination of symptoms, rather than a suspected or final diagnosis, using naïve, everyday language. We developed a minimally trained algorithm that exploits the abundance of health-related web pages to identify all jargon expressions related to a specific technical term. We then translated an influenza case definition into a Boolean query, each symptom being described by a technical term and all related jargon expressions, as identified by the algorithm. Subsequently, we monitored all tweets that reported a combination of symptoms satisfying the case definition query. In order to geolocalize messages, we defined 3 localization strategies based on codes associated with each tweet. We found a high correlation coefficient between the trend of our influenza-positive tweets and ILI trends identified by US traditional surveillance systems.</p> </div

Weekly reported ILI (CDC) and tweets including the words “flu” or “influenza”.

<p>The blue line represents in all graphs the z-scores of CDC’s reported ILI for the 14-week period starting in week 5 (January 2013) through week 18 (May 2013). The red line represents the z-scores of tweets including the words “flu” or “influenza”, geolocalized with the extended narrow localization pattern.</p

Weekly reported ILI (CDC) and tweets satisfying ILI query.

<p>The blue line represents in all graphs the z-scores of CDC’s reported ILI for the 14-week period starting in week 5 (January 2013) through week 18 (May 2013). The red line represents the z-scores of tweets satisfying the ECDC ILI query, selected with a different geolocalization strategy in each of the four graphs: a) all tweets (independently from geolocalization); b) US GEO(GPS localized tweets); c) Extended wide localization pattern; d) Extended narrow localization pattern.</p

Distribution of cosine similarity measured for each node of the full contact network (overall) and for each single household.

<p>Distributions are obtained by measuring the cosine similarity of each node's neighbourhood, for each pair of days of data collection. The box and whisker plots show the interquartile range, and the red line indicates the median value. The error bars extend from the box to the highest and lowest values. The households are ordered from left to right by increasing number of edges in the contact network.</p

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

Can Twitter Be a Source of Information on Allergy? Correlation of Pollen Counts with Tweets Reporting Symptoms of Allergic Rhinoconjunctivitis and Names of Antihistamine Drugs

<div><p>Pollen forecasts are in use everywhere to inform therapeutic decisions for patients with allergic rhinoconjunctivitis (ARC). We exploited data derived from Twitter in order to identify tweets reporting a combination of symptoms consistent with a case definition of ARC and those reporting the name of an antihistamine drug. In order to increase the sensitivity of the system, we applied an algorithm aimed at automatically identifying jargon expressions related to medical terms. We compared weekly Twitter trends with National Allergy Bureau weekly pollen counts derived from US stations, and found a high correlation of the sum of the total pollen counts from each stations with tweets reporting ARC symptoms (Pearson’s correlation coefficient: 0.95) and with tweets reporting antihistamine drug names (Pearson’s correlation coefficient: 0.93). Longitude and latitude of the pollen stations affected the strength of the correlation. Twitter and other social networks may play a role in allergic disease surveillance and in signaling drug consumptions trends.</p></div

Results of the OLS regression predicting the effects of infant’s feeding (exclusive breastfeeding vs partial breastfeeding/artificial feeding), parents’ sex, presence of siblings (yes/no), parents’ age (≤ 35 or > 35 years), and infant’s age (0–3 or 4–6 months) on parents’ Infant proximity score (IPS).

<p>The value of the estimate refers to the variable in brackets.</p

Additional file 1: of Investigating paternal preconception risk factors for adverse pregnancy outcomes in a population of internet users

Survey items on paternal preconception risk factor for APOs. (DOCX 16 kb