Objective-This study investigated the schizophrenia phenotype in 24 subjects with 22q11 deletion syndrome (22qDS) and schizophrenia (22qDS-schizophrenia), a rare but relatively homogenous genetic subtype of schizophrenia associated with a microdeletion on chromosome 22. Individuals with 22qDS are at genetically high risk for schizophrenia.Method-Standard measures of signs, symptoms, and course of schizophrenia were assessed in 16 adults with 22qDS-schizophrenia who did not meet criteria for mental retardation and in 46 adults with schizophrenia without evidence of 22qDS from a community familial sample.Results-There were no significant differences in age at onset, lifetime or cross-sectional core positive and negative schizophrenic symptoms, or global functioning between the two groups of patients with schizophrenia. Patients with 22qDS-schizophrenia had higher excitement subscale scores and less lifetime substance use than the comparison patients with schizophrenia, but no significant differences in anxiety-depression symptom severity were found between the groups.Conclusions-These findings indicate that the core clinical schizophrenia phenotype would not distinguish individuals with a 22qDS subtype from those with schizophrenia who did not have the 22qDS subtype. The results provide further support for the utility of 22qDS-schizophrenia as a neurodevelopmental model of schizophrenia as well as support for prospective studies of individuals with 22qDS to help identify precursors of schizophrenia.

Background: Globalization and the potential for rapid spread of emerging infectious diseases have heightened the need for ongoing surveillance and early detection. The Global Public Health Intelligence Network (GPHIN) was established to increase situational awareness and capacity for the early detection of emerging public health events.Objective: To describe how the GPHIN has used Big Data as an effective early detection technique for infectious disease outbreaks worldwide and to identify potential future directions for the GPHIN.Findings: Every day the GPHIN analyzes over more than 20,000 online news reports (over 30,000 sources) in nine languages worldwide. A web-based program aggregates data based on an algorithm that provides potential signals of emerging public health events which are then reviewed by a multilingual, multidisciplinary team. An alert is sent out if a potential risk is identified. This process proved useful during the Severe Acute Respiratory Syndrome (SARS) outbreak and was adopted shortly after by a number of countries to meet new International Health Regulations that require each country to have the capacity for early detection and reporting. The GPHIN identified the early SARS outbreak in China, was credited with the first alert on MERS-CoV and has played a significant role in the monitoring of the Ebola outbreak in West Africa. Future developments are being considered to advance the GPHIN's capacity in light of other Big Data sources such as social media and its analytical capacity in terms of algorithm development. Conclusion:The GPHIN's early adoption of Big Data has increased global capacity to detect international infectious disease outbreaks and other public health events. Integration of additional Big Data sources and advances in analytical capacity could further strengthen the GPHIN's capability for timely detection and early warning.

Mild childhood head injury may play a role in the expression of schizophrenia in families with a strong genetic predisposition. Prospective studies of mild head injury should consider genetic predisposition for possible long-term neurobehavioral sequelae.

This paper offers a state-of-the-art overview of the intertwined privacy, confidentiality, and security issues that are commonly encountered in health research involving disaggregate geographic data about individuals. Key definitions are provided, along with some examples of actual and potential security and confidentiality breaches and related incidents that captured mainstream media and public interest in recent months and years. The paper then goes on to present a brief survey of the research literature on location privacy/confidentiality concerns and on privacy-preserving solutions in conventional health research and beyond, touching on the emerging privacy issues associated with online consumer geoinformatics and location-based services. The 'missing ring' (in many treatments of the topic) of data security is also discussed. Personal information and privacy legislations in two countries, Canada and the UK, are covered, as well as some examples of recent research projects and events about the subject. Select highlights from a June 2009 URISA (Urban and Regional Information Systems Association) workshop entitled 'Protecting Privacy and Confidentiality of Geographic Data in Health Research' are then presented. The paper concludes by briefly charting the complexity of the domain and the many challenges associated with it, and proposing a novel, 'one stop shop' case-based reasoning framework to streamline the provision of clear and individualised guidance for the design and approval of new research projects (involving geographical identifiers about individuals), including crisp recommendations on which specific privacy-preserving solutions and approaches would be suitable in each case.