Aircraft noise, air pollution, and mortality from myocardial infarction

Objective: Myocardial infarction has been associated with both transportation noise and air pollution. We examined residential exposure to aircraft noise and mortality from myocardial infarction, taking air pollution into account. Methods: We analyzed the Swiss National Cohort, which includes geocoded information on residence. Exposure to aircraft noise and air pollution was determined based on geospatial noise and air-pollution (PM10) models and distance to major roads. We used Cox proportional hazard models, with age as the timescale. We compared the risk of death across categories of A-weighted sound pressure levels (dB(A)) and by duration of living in exposed corridors, adjusting for PM10 levels, distance to major roads, sex, education, and socioeconomic position of the municipality. Results: We analyzed 4.6 million persons older than 30 years who were followed from near the end of 2000 through December 2005, including 15,532 deaths from myocardial infarction (ICD-10 codes I 21, I 22). Mortality increased with increasing level and duration of aircraft noise. The adjusted hazard ratio comparing ≥60 dB(A) with <45 dB(A) was 1.3 (95% confidence interval = 0.96-1.7) overall, and 1.5 (1.0-2.2) in persons who had lived at the same place for at least 15 years. None of the other endpoints (mortality from all causes, all circulatory disease, cerebrovascular disease, stroke, and lung cancer) was associated with aircraft noise. Conclusion: Aircraft noise was associated with mortality from myocardial infarction, with a dose-response relationship for level and duration of exposure. The association does not appear to be explained by exposure to particulate matter air pollution, education, or socioeconomic status of the municipality.

Quantifying transmission of norovirus during an outbreak

Healthcare workers are thought to play a role in nosocomial transmission of norovirus, but the level and direction of norovirus transmission between patients and healthcare workers in sustaining transmission during an outbreak have not been quantified.

Globally dispersed Y chromosomal haplotypes in wild and domestic sheep

To date, investigations of genetic diversity and the origins of domestication in sheep have utilised autosomal microsatellites and variation in the mitochondrial genome. We present the first analysis of both domestic and wild sheep using genetic markers residing on the ovine Y chromosome. Analysis of a single nucleotide polymorphism (oY1) in the SRY promoter region revealed that allele A-oY1 was present in all wild bighorn sheep (Ovis canadensis), two subspecies of thinhorn sheep (Ovis dalli), European Mouflon (Ovis musimon) and the Barbary (Ammontragis lervia). A-oY1 also had the highest frequency (71.4%) within 458 domestic sheep drawn from 65 breeds sampled from Africa, Asia, Australia, the Caribbean, Europe, the Middle East and Central Asia. Sequence analysis of a second locus, microsatellite SRYM18, revealed a compound repeat array displaying fixed differences, which identified bighorn and thinhorn sheep as distinct from the European Mouflon and domestic animals. Combined genotypic data identified 11 male-specific haplotypes that represented at least two separate lineages. Investigation of the geographical distribution of each haplotype revealed that one (H6) was both very common and widespread in the global sample of domestic breeds. The remaining haplotypes each displayed more restricted and informative distributions. For example, H5 was likely founded following the domestication of European breeds and was used to trace the recent transportation of animals to both the Caribbean and Australia. A high rate of Y chromosomal dispersal appears to have taken place during the development of domestic sheep as only 12.9% of the total observed variation was partitioned between major geographical regions.

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration

Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalizability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies.The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers.This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated web site (http://www.strobe-statement.org) should be helpful resources to improve reporting of observational research.

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies

Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalizability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. 18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed "Explanation and Elaboration" document is published separately and is freely available on the web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.

When one depends on the other: reporting of interaction in case-control and cohort studies

BACKGROUND: Interaction refers to the situation in which the effect of 1 exposure on an outcome differs across strata of another exposure. We did a survey of epidemiologic studies published in leading journals to examine how interaction is assessed and reported. METHODS: We selected 150 case-control and 75 cohort studies published between May 2001 and May 2007 in leading general medicine, epidemiology, and clinical specialist journals. Two reviewers independently extracted data on study characteristics. RESULTS: Of the 225 studies, 138 (61%) addressed interaction. Among these, 25 (18%) presented no data or only a P value or a statement of statistical significance; 40 (29%) presented stratum-specific effect estimates but no meaningful comparison of these estimates; and 58 (42%) presented stratum-specific estimates and appropriate tests for interaction. Fifteen articles (11%) presented the individual effects of both exposures and also their joint effect or a product term, providing sufficient information to interpret interaction on an additive and multiplicative scale. Reporting was poorest in articles published in clinical specialist articles and most adequate in articles published in general medicine journals, with epidemiology journals in an intermediate position. CONCLUSIONS: A majority of articles reporting cohort and case-control studies address possible interactions between exposures. However, in about half of these, the information provided was unsatisfactory, and only 1 in 10 studies reported data that allowed readers to interpret interaction effects on an additive and multiplicative scale.

Query Technologies and Applications for Program Comprehension

Industrial software systems are large and complex, both in terms of the software entities and their relationships. Consequently, understanding how a software system works requires the ability to pose queries over the design-level entities of the system. Traditionally, this task has been supported by simple tools (e.g., grep) combined with the programmer's intuition and experience. Recently, however, specialized code query technologies have matured to the point where they can be used in industrial situations, providing more intelligent, timely, and efficient responses to developer queries. This working session aims to explore the state of the art in code query technologies, and discover new ways in which these technologies may be useful in program comprehension. The session brings together researchers and practitioners. We survey existing techniques and applications, trying to understand the strengths and weaknesses of the various approaches, and sketch out new frontiers that hold promise.

Direct and indirect effects of screening for Chlamydia trachomatis on the prevention of pelvic inflammatory disease: a mathematical modeling study

BACKGROUND Pelvic inflammatory disease (PID) results from the ascending spread of microorganisms, including Chlamydia trachomatis, to the upper genital tract. Screening could improve outcomes by identifying and treating chlamydial infections before they progress to PID (direct effect) or by reducing chlamydia transmission (indirect effect). METHODS We developed a compartmental model that represents a hypothetical heterosexual population and explicitly incorporates progression from chlamydia to clinical PID. Chlamydia screening was introduced, with coverage increasing each year for 10 years. We estimated the separate contributions of the direct and indirect effects of screening on PID cases prevented per 100,000 women. We explored the influence of varying the time point at which clinical PID could occur and of increasing the risk of PID after repeated chlamydial infections. RESULTS The probability of PID at baseline was 3.1% by age 25 years. After 5 years, the intervention scenario had prevented 187 PID cases per 100,000 women and after 10 years 956 PID cases per 100,000 women. At the start of screening, most PID cases were prevented by the direct effect. The indirect effect produced a small net increase in PID cases, which was outweighed by the effect of reduced chlamydia transmission after 2.2 years. The later that progression to PID occurs, the greater the contribution of the direct effect. Increasing the risk of PID with repeated chlamydial infection increases the number of PID cases prevented by screening. CONCLUSIONS This study shows the separate roles of direct and indirect PID prevention and potential harms, which cannot be demonstrated in observational studies.

Estimating HIV Incidence, Time to Diagnosis, and the Undiagnosed HIV Epidemic Using Routine Surveillance Data.

BACKGROUND Estimates of the size of the undiagnosed HIV-infected population are important to understand the HIV epidemic and to plan interventions, including "test-and-treat" strategies. METHODS We developed a multi-state back-calculation model to estimate HIV incidence, time between infection and diagnosis, and the undiagnosed population by CD4 count strata, using surveillance data on new HIV and AIDS diagnoses. The HIV incidence curve was modelled using cubic splines. The model was tested on simulated data and applied to surveillance data on men who have sex with men in The Netherlands. RESULTS The number of HIV infections could be estimated accurately using simulated data, with most values within the 95% confidence intervals of model predictions. When applying the model to Dutch surveillance data, 15,400 (95% confidence interval [CI] = 15,000, 16,000) men who have sex with men were estimated to have been infected between 1980 and 2011. HIV incidence showed a bimodal distribution, with peaks around 1985 and 2005 and a decline in recent years. Mean time to diagnosis was 6.1 (95% CI = 5.8, 6.4) years between 1984 and 1995 and decreased to 2.6 (2.3, 3.0) years in 2011. By the end of 2011, 11,500 (11,000, 12,000) men who have sex with men in The Netherlands were estimated to be living with HIV, of whom 1,750 (1,450, 2,200) were still undiagnosed. Of the undiagnosed men who have sex with men, 29% (22, 37) were infected for less than 1 year, and 16% (13, 20) for more than 5 years. CONCLUSIONS This multi-state back-calculation model will be useful to estimate HIV incidence, time to diagnosis, and the undiagnosed HIV epidemic based on routine surveillance data.