The long-term impact of the MEMA kwa Vijana adolescent sexual and reproductive health intervention: effect of dose and time since intervention exposure.

BACKGROUND: Despite recent decreases in HIV incidence in many sub-Saharan African countries, there is little evidence that specific behavioural interventions have led to a reduction in HIV among young people. Further and wider-scale decreases in HIV require better understanding of when behaviour change occurs and why. The MEMA kwa Vijana adolescent sexual and reproductive health intervention has been implemented in rural Mwanza, Tanzania since 1999. A long-term evaluation in 2007/8 found that the intervention improved knowledge, attitudes to sex and some reported risk behaviours, but not HIV or HSV2 prevalence. The aim of this paper was to assess the differential impact of the intervention according to gender, age, marital status, number of years of exposure and time since last exposure to the intervention. METHODS: In 2007, a cross-sectional survey was conducted in the 20 trial communities among 13,814 young people (15-30 yrs) who had attended intervention or comparison schools between 1999 and 2002. Outcomes for which the intervention had an impact in 2001 or 2007 were included in this subgroup analysis. Data were analysed using cluster-level methods for stratified cluster-randomised trials, using interaction tests to determine if intervention impact differed by subgroup. RESULTS: Taking into account multiplicity of testing, concurrence with a priori hypotheses and consistency within the results no strong effect-modifiers emerged. Impact on pregnancy knowledge and reported attitudes to sex increased with years of exposure to high-quality intervention. CONCLUSIONS: The desirable long-term impact of the MEMA kwa Vijana intervention did not vary greatly according to the subgroups examined. This suggests that the intervention can have an impact on a broad cross-section of young people in rural Mwanza. TRIAL REGISTRATION: ClinicalTrials.gov NCT00248469

Characterization of Tunable Poly-ε-Lysine-Based Hydrogels for Corneal Tissue Engineering

A family of poly-ε-lysine hydrogels can be synthesized by crosslinking with bis-carboxylic acids using carbodiimide chemistry. In addition to creating hydrogels using a simple cast method, a fragmented method is used to introduce increased porosity within the hydrogel structure. Both methods have created tunable characteristics ranging in their mechanical properties, transparency, and water content, which is of interest to corneal tissue engineering and can be tailored to specific cellular needs and applications. With a worldwide shortage of cornea donor tissue available for transplant and limitations including rejection and potential infection, a synthetic material that can be used as a graft, or a partial thickness corneal replacement, would be an advantageous treatment method. These hydrogels can be tuned to have similar mechanical and transparency properties to the human cornea. They also support the attachment and growth of corneal epithelial cells and the integration of corneal stromal cells

Is chlamydia screening and testing in Britain reaching young adults at risk of infection? Findings from the third National Survey of Sexual Attitudes and Lifestyles (Natsal-3)

In the context of widespread opportunistic chlamydia screening among young adults, we aimed to quantify chlamydia testing and diagnosis among 16-24 year olds in Britain in relation to risk factors for prevalent chlamydia infection

Is chlamydia screening and testing in Britain reaching young adults at risk of infection? Findings from the third National Survey of Sexual Attitudes and Lifestyles (Natsal-3).

BACKGROUND: In the context of widespread opportunistic chlamydia screening among young adults, we aimed to quantify chlamydia testing and diagnosis among 16-24 year olds in Britain in relation to risk factors for prevalent chlamydia infection. METHODS: Using data from sexually experienced (≥1 lifetime sexual partner) 16-year-old to 24-year-old participants in Britain's third National Survey of Sexual Attitudes and Lifestyles (conducted 2010-2012), we explored socio-demographic and behavioural factors associated with prevalent chlamydia infection (detected in urine; n=1832), self-reported testing and self-reported diagnosis in the last year (both n=3115). RESULTS: Chlamydia prevalence was 3.1% (95% CI 2.2% to 4.3%) in women and 2.3% (1.5% to 3.4%) in men. A total of 12.3% of women and 5.3% men had a previous chlamydia diagnosis. Factors associated with prevalent infection were also associated with testing and diagnosis (eg, increasing numbers of sexual partners), with some exceptions. For example, chlamydia prevalence was higher in women living in more deprived areas, whereas testing was not. In men, prevalence was higher in 20-24 than 16-19 year olds but testing was lower. Thirty per cent of women and 53.7% of men with ≥2 new sexual partners in the last year had not recently tested. CONCLUSIONS: In 2010-2012 in Britain, the proportion of young adults reporting chlamydia testing was generally higher in those reporting factors associated with chlamydia. However, many of those with risk factors had not been recently tested, leaving potential for undiagnosed infections. Greater screening and prevention efforts among individuals in deprived areas and those reporting risk factors for chlamydia may reduce undiagnosed prevalence and transmission

Confirmatory assays are essential when using molecular testing for Neisseria gonorrhoeae in low-prevalence settings: insights from the third National Survey of Sexual Attitudes and Lifestyles (Natsal-3).

To investigate the occurrence of unconfirmed positive gonorrhoea results when using molecular testing within a large population-based survey

Latent class analysis of sexual health markers among men and women participating in a British probability sample survey.

BACKGROUND: Despite known associations between different aspects of sexual health, it is not clear how patterning of adverse sexual health varies across the general population. A better understanding should contribute towards more effective problem identification, prevention and treatment. We sought to identify different clusters of sexual health markers in a general population, along with their socio-demographic, health and lifestyle correlates. METHODS: Data came from men (N = 5113) and women (N = 7019) aged 16-74 who reported partnered sexual activity in the past year in Britain's third National Survey of Sexual Attitudes and Lifestyles, undertaken in 2010-2012. Latent class analysis used 18 self-reported variables relating to adverse sexual health outcomes (STI and unplanned pregnancy, non-volitional sex, and sexual function problems). Correlates included socio-demographics, early debut, alcohol/drug use, depression, and satisfaction/distress with sex life. RESULTS: Four classes were found for men (labelled Good Sexual Health 83%, Wary Risk-takers 4%, Unwary Risk-takers 4%, Sexual Function Problems 9%); six for women (Good Sexual Health 52%, Wary Risk-takers 2%, Unwary Risk-takers 7%, Low Interest 29%, Sexual Function Problems 7%, Highly Vulnerable 2%). Regardless of gender, Unwary Risk-takers reported lower STI/HIV risk perception and more condomless sex than Wary Risk-takers, but both were more likely to report STI diagnosis than Good Sexual Health classes. Highly Vulnerable women reported abortion, STIs and functional problems, and more sexual coercion than other women. Distinct socio-demographic profiles differentiated higher-risk classes from Good Sexual Health classes, with depression, alcohol/drug use, and early sexual debut widely-shared correlates of higher-risk classes. Females in higher-risk classes, and men with functional problems, evaluated their sex lives more negatively than those with Good Sexual Health. CONCLUSIONS: A greater prevalence and diversity of poor sexual health appears to exist among women than men in Britain, with more consistent effects on women's subjective sexual well-being. Shared health and lifestyle characteristics of higher-risk groups suggest widespread benefits of upstream interventions. Several groups could benefit from tailored interventions: men and women who underestimate their STI/HIV risk exposure, women distressed by low interest in sex, and women experiencing multiple adverse outcomes. Distinctive socio-demographic profiles should assist with identification and targeting

Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality

The final publication is available at Springer via http://dx.doi.org/DOI 10.1007/s10618-014-0378-6. Published online.Knowledge discovery on biomedical data can be based on on-line, data-stream analyses, or using retrospective, timestamped, off-line datasets. In both cases, changes in the processes that generate data or in their quality features through time may hinder either the knowledge discovery process or the generalization of past knowledge. These problems can be seen as a lack of data temporal stability. This work establishes the temporal stability as a data quality dimension and proposes new methods for its assessment based on a probabilistic framework. Concretely, methods are proposed for (1) monitoring changes, and (2) characterizing changes, trends and detecting temporal subgroups. First, a probabilistic change detection algorithm is proposed based on the Statistical Process Control of the posterior Beta distribution of the Jensen–Shannon distance, with a memoryless forgetting mechanism. This algorithm (PDF-SPC) classifies the degree of current change in three states: In-Control, Warning, and Out-of-Control. Second, a novel method is proposed to visualize and characterize the temporal changes of data based on the projection of a non-parametric information-geometric statistical manifold of time windows. This projection facilitates the exploration of temporal trends using the proposed IGT-plot and, by means of unsupervised learning methods, discovering conceptually-related temporal subgroups. Methods are evaluated using real and simulated data based on the National Hospital Discharge Survey (NHDS) dataset.The work by C Saez has been supported by an Erasmus Lifelong Learning Programme 2013 Grant. This work has been supported by own IBIME funds. The authors thank Dr. Gregor Stiglic, from the Univeristy of Maribor, Slovenia, for his support on the NHDS data.Sáez Silvestre, C.; Pereira Rodrigues, P.; Gama, J.; Robles Viejo, M.; García Gómez, JM. (2014). Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Mining and Knowledge Discovery. 28:1-1. doi:10.1007/s10618-014-0378-6S1128Aggarwal C (2003) A framework for diagnosing changes in evolving data streams. In Proceedings of the International Conference on Management of Data ACM SIGMOD, pp 575–586Amari SI, Nagaoka H (2007) Methods of information geometry. American Mathematical Society, Providence, RIArias E (2014) United states life tables, 2009. Natl Vital Statist Rep 62(7): 1–63Aspden P, Corrigan JM, Wolcott J, Erickson SM (2004) Patient safety: achieving a new standard for care. Committee on data standards for patient safety. The National Academies Press, Washington, DCBasseville M, Nikiforov IV (1993) Detection of abrupt changes: theory and application. Prentice-Hall Inc, Upper Saddle River, NJBorg I, Groenen PJF (2010) Modern multidimensional scaling: theory and applications. Springer, BerlinBowman AW, Azzalini A (1997) Applied smoothing techniques for data analysis: the Kernel approach with S-plus illustrations (Oxford statistical science series). Oxford University Press, OxfordBrandes U, Pich C (2007) Eigensolver methods for progressive multidimensional scaling of large data. In: Kaufmann M, Wagner D (eds) Graph drawing. Lecture notes in computer science, vol 4372. Springer, Berlin, pp 42–53Brockwell P, Davis R (2009) Time series: theory and methods., Springer series in statisticsSpringer, BerlinCesario SK (2002) The “Christmas Effect” and other biometeorologic influences on childbearing and the health of women. J Obstet Gynecol Neonatal Nurs 31(5):526–535Chakrabarti K, Garofalakis M, Rastogi R, Shim K (2001) Approximate query processing using wavelets. VLDB J 10(2–3):199–223Cruz-Correia RJ, Pereira Rodrigues P, Freitas A, Canario Almeida F, Chen R, Costa-Pereira A (2010) Data quality and integration issues in electronic health records. Information discovery on electronic health records, pp 55–96Csiszár I (1967) Information-type measures of difference of probability distributions and indirect observations. Studia Sci Math Hungar 2:299–318Dasu T, Krishnan S, Lin D, Venkatasubramanian S, Yi K (2009) Change (detection) you can believe. In: Finding distributional shifts in data streams. In: Proceedings of the 8th international symposium on intelligent data analysis: advances in intelligent data analysis VIII, IDA ’09. Springer, Berlin, pp 21–34Endres D, Schindelin J (2003) A new metric for probability distributions. IEEE Trans Inform Theory 49(7):1858–1860Gama J, Gaber MM (2007) Learning from data streams: processing techniques in sensor networks. Springer, BerlinGama J, Medas P, Castillo G, Rodrigues P (2004) Learning with drift detection. In: Bazzan A, Labidi S (eds) Advances in artificial intelligence—SBIA 2004., Lecture notes in computer scienceSpringer, Berlin, pp 286–295Gama J (2010) Knowledge discovery from data streams, 1st edn. Chapman & Hall, LondonGehrke J, Korn F, Srivastava D (2001) On computing correlated aggregates over continual data streams. SIGMOD Rec 30(2):13–24Guha S, Shim K, Woo J (2004) Rehist: relative error histogram construction algorithms. In: Proceedings of the thirtieth international conference on very large data bases VLDB, pp 300–311Han J, Kamber M, Pei J (2012) Data mining: concepts and techniques. Morgan Kaufmann, Elsevier, Burlington, MAHowden LM, Meyer JA, (2011) Age and sex composition. 2010 Census Briefs US Department of Commerce. Economics and Statistics Administration, US Census BureauHrovat G, Stiglic G, Kokol P, Ojstersek M (2014) Contrasting temporal trend discovery for large healthcare databases. Comput Methods Program Biomed 113(1):251–257Keim DA (2000) Designing pixel-oriented visualization techniques: theory and applications. IEEE Trans Vis Comput Graph 6(1):59–78Kifer D, Ben-David S, Gehrke J (2004) Detecting change in data streams. In: Proceedings of the thirtieth international conference on Very large data bases, VLDB Endowment, VLDB ’04, vol 30, pp 180–191Klinkenberg R, Renz I (1998) Adaptive information filtering: Learning in the presence of concept drifts. In: Workshop notes of the ICML/AAAI-98 workshop learning for text categorization. AAAI Press, Menlo Park, pp 33–40Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biolog Cybern 43(1):59–69Lin J (1991) Divergence measures based on the Shannon entropy. IEEE Trans Inform Theory 37:145–151Mitchell TM, Caruana R, Freitag D, McDermott J, Zabowski D (1994) Experience with a learning personal assistant. Commun ACM 37(7):80–91Mouss H, Mouss D, Mouss N, Sefouhi L (2004) Test of page-hinckley, an approach for fault detection in an agro-alimentary production system. In: Proceedings of the 5th Asian Control Conference, vol 2, pp 815–818National Research Council (2011) Explaining different levels of longevity in high-income countries. The National Academies Press, Washington, DCNHDS (2010) United states department of health and human services. Centers for disease control and prevention. National center for health statistics. National hospital discharge survey codebookNHDS (2014) National Center for Health Statistics, National Hospital Discharge Survey (NHDS) data, US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, Hyattsville, Maryland. http://www.cdc.gov/nchs/nhds.htmPapadimitriou S, Sun J, Faloutsos C (2005) Streaming pattern discovery in multiple time-series. In: Proceedings of the 31st international conference on very large data bases, VLDB endowment, VLDB ’05, pp 697–708Parzen E (1962) On estimation of a probability density function and mode. Ann Math Statist 33(3):1065–1076Ramsay JO, Silverman BW (2005) Functional data analysis. Springer, New YorkRodrigues P, Correia R (2013) Streaming virtual patient records. In: Krempl G, Zliobaite I, Wang Y, Forman G (eds) Real-world challenges for data stream mining. University Magdeburg, Otto-von-Guericke, pp 34–37Rodrigues P, Gama J, Pedroso J (2008) Hierarchical clustering of time-series data streams. IEEE Trans Knowl Data Eng 20(5):615–627Rodrigues PP, Gama Ja (2010) A simple dense pixel visualization for mobile sensor data mining. In: Proceedings of the second international conference on knowledge discovery from sensor data, sensor-KDD’08. Springer, Berlin, pp 175–189Rodrigues PP, Gama J, Sebastiã o R (2010) Memoryless fading windows in ubiquitous settings. In Proceedings of ubiquitous data mining (UDM) workshop in conjunction with the 19th european conference on artificial intelligence—ECAI 2010, pp 27–32Rodrigues PP, Sebastiã o R, Santos CC (2011) Improving cardiotocography monitoring: a memory-less stream learning approach. In: Proceedings of the learning from medical data streams workshop. Bled, SloveniaRubner Y, Tomasi C, Guibas L (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput Vision 40(2):99–121Sebastião R, Gama J (2009) A study on change detection methods. In: 4th Portuguese conference on artificial intelligenceSebastião R, Gama J, Rodrigues P, Bernardes J (2010) Monitoring incremental histogram distribution for change detection in data streams. In: Gaber M, Vatsavai R, Omitaomu O, Gama J, Chawla N, Ganguly A (eds) Knowledge discovery from sensor data, vol 5840., Lecture notes in computer science. Springer, Berlin, pp 25–42Sebastião R, Silva M, Rabiço R, Gama J, Mendonça T (2013) Real-time algorithm for changes detection in depth of anesthesia signals. Evol Syst 4(1):3–12Sáez C, Martínez-Miranda J, Robles M, García-Gómez JM (2012) O rganizing data quality assessment of shifting biomedical data. Stud Health Technol Inform 180:721–725Sáez C, Robles M, García-Gómez JM (2013) Comparative study of probability distribution distances to define a metric for the stability of multi-source biomedical research data. In: Engineering in medicine and biology society (EMBC), 2013 35th annual international conference of the IEEE, pp 3226–3229Sáez C, Robles M, García-Gómez JM (2014) Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statist Method Med Res (forthcoming)Shewhart WA, Deming WE (1939) Statistical method from the viewpoint of quality control. Graduate School of the Department of Agriculture, Washington, DCShimazaki H, Shinomoto S (2010) Kernel bandwidth optimization in spike rate estimation. J Comput Neurosci 29(1–2):171–182Solberg LI, Engebretson KI, Sperl-Hillen JM, Hroscikoski MC, O’Connor PJ (2006) Are claims data accurate enough to identify patients for performance measures or quality improvement? the case of diabetes, heart disease, and depression. Am J Med Qual 21(4):238–245Spiliopoulou M, Ntoutsi I, Theodoridis Y, Schult R (2006) monic: modeling and monitoring cluster transitions. In: Proceedings of the 12th ACm SIGKDD international conference on knowledge discovery and data mining, KDD ’06. ACm, New York, NY, pp 706–711Stiglic G, Kokol P (2011) Interpretability of sudden concept drift in medical informatics domain. In Proceedings of the 2010 IEEE international conference on data mining workshops, pp 609–613Torgerson W (1952) Multidimensional scaling: I theory and method. Psychometrika 17(4):401–419Wang RY, Strong DM (1996) Beyond accuracy: what data quality means to data consumers. J Manage Inform Syst 12(4):5–33Weiskopf NG, Weng C (2013) M ethods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 20(1):144–151Wellings K, Macdowall W, Catchpole M, Goodrich J (1999) Seasonal variations in sexual activity and their implications for sexual health promotion. J R Soc Med 92(2):60–64Westgard JO, Barry PL (2010) Basic QC practices: training in statistical quality control for medical laboratories. Westgard Quality Corporation, Madison, WIWidmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23(1):69–10

Changes in the socio-demographic patterning of late adolescent health risk behaviours during the 1990s: analysis of two West of Scotland cohort studies

Background: Substance use and sexual risk behaviour affect young people's current and future health and wellbeing in many high-income countries. Our understanding of time-trends in adolescent health-risk behaviour is largely based on routinely collected survey data in school-aged adolescents (aged 15 years or less). Less is known about changes in these behaviours among older adolescents. Methods: We compared two cohorts from the same geographical area (West of Scotland), surveyed in 1990 and 2003, to: describe time-trends in measures of smoking, drinking, illicit drug use, early sexual initiation, number of opposite sex sexual partners and experience of pregnancy at age 18-19 years, both overall and stratified by gender and socioeconomic status (SES); and examine the effect of time-trends on the patterning of behaviours by gender and SES. Our analyses adjust for slight between-cohort age differences since age was positively associated with illicit drug use and pregnancy. Results: Rates of drinking, illicit drug use, early sexual initiation and experience of greater numbers of sexual partners all increased significantly between 1990 and 2003, especially among females, leading to attenuation and, for early sexual initiation, elimination, of gender differences. Most rates increased to a similar extent regardless of SES. However, rates of current smoking decreased only among those from higher SES groups. In addition, increases in 'cannabis-only' were greater among higher SES groups while use of illicit drugs other than cannabis increased more in lower SES groups. Conclusion: Marked increases in female substance use and sexual risk behaviours have implications for the long-term health and wellbeing of young women. More effective preventive measures are needed to reduce risk behaviour uptake throughout adolescence and into early adulthood. Public health strategies should reflect both the widespread prevalence of risk behaviour in young people as well as the particular vulnerability to certain risk behaviours among those from lower SES groups

IRS-2 Deficiency Impairs NMDA Receptor-Dependent Long-term Potentiation

The beneficial effects of insulin and insulin-like growth factor I on cognition have been documented in humans and animal models. Conversely, obesity, hyperinsulinemia, and diabetes increase the risk for neurodegenerative disorders including Alzheimer's disease (AD). However, the mechanisms by which insulin regulates synaptic plasticity are not well understood. Here, we report that complete disruption of insulin receptor substrate 2 (Irs2) in mice impairs long-term potentiation (LTP) of synaptic transmission in the hippocampus. Basal synaptic transmission and paired-pulse facilitation were similar between the 2 groups of mice. Induction of LTP by high-frequency conditioning tetanus did not activate postsynaptic N-methyl-D-aspartate (NMDA) receptors in hippocampus slices from Irs2−/− mice, although the expression of NR2A, NR2B, and PSD95 was equivalent to wild-type controls. Activation of Fyn, AKT, and MAPK in response to tetanus stimulation was defective in Irs2−/− mice. Interestingly, IRS2 was phosphorylated during induction of LTP in control mice, revealing a potential new component of the signaling machinery which modulates synaptic plasticity. Given that IRS2 expression is diminished in Type 2 diabetics as well as in AD patients, these data may reveal an explanation for the prevalence of cognitive decline in humans with metabolic disorders by providing a mechanistic link between insulin resistance and impaired synaptic transmission