Insertion polymorphism of retrotransposable elements in populations of the insular, endemic species Drosophila madeirensis

The insertion site numbers of the retrotransposable elements (TE) 412, gypsy and bilbo were determined in individuals of five distinct natural populations of the endemic species Drosophila madeirensis from the island of Madeira. The TE distributions were compared to those of the paleartic, widespread and phylogenetically closely related species, D. subobscura. In situ hybridization and Southern blots showed that in D. madeirensis the number of insertion sites ranged between 10 and 15, three and six, and 35 and 42 for elements 412, gypsy and bilbo, respectively. The corresponding values for D. subobscura were similar. Two of these elements, 412 and gypsy, had very few insertions in the heterochromatin, unlike bilbo, which displayed a high heterochromatic insertion number. The Southern band polymorphism was very high, leading to within-population variation of 97.2%, whatever the population and the TE concerned. Using the polymorphic TE insertion sites as markers to analyse population structure by AMOVA, adapted for RAPD (Randomly Amplified Polymorphic DNA) data, we found small but significant genetic differences between the populations on Madeira. This slight differentiation, coupled with similar copy numbers for each TE between populations, suggests that the D. madeirensis species consists of a single, only slightly subdivided population. These data also show that insular populations and endemic species of Drosophila can have as many copies of TEs as more widespread species.info:eu-repo/semantics/publishedVersio

Letters

by S Gee, S Cotter, D O’Flanagan, on behalf of the national incident management tea

Geographical differences on the mortality impact of heat waves in Europe

Climate change is potentially the biggest global health threat in the 21st century. Deaths related with heat waves and spread of infectious diseases will be part of the menace though the major impact will be caused by malnutrition, diarrhea and extreme climate events. Consequently, loss of healthy life years as a result of global climate change is predicted to be 500 times greater in poor African populations than in European populations. However, the increase of more than 2°C of average temperature will result in a negative health impact in all regions, the potential benefits of a warmer temperature being negatively compensated, heat waves being one of the largest climate change threats in the developed world

Success Factors of European Syndromic Surveillance Systems: A Worked Example of Applying Qualitative Comparative Analysis

Introduction: Syndromic surveillance aims at augmenting traditional public health surveillance with timely information. To gain a head start, it mainly analyses existing data such as from web searches or patient records. Despite the setup of many syndromic surveillance systems, there is still much doubt about the benefit of the approach. There are diverse interactions between performance indicators such as timeliness and various system characteristics. This makes the performance assessment of syndromic surveillance systems a complex endeavour. We assessed if the comparison of several syndromic surveillance systems through Qualitative Comparative Analysis helps to evaluate performance and identify key success factors. Materials and Methods: We compiled case-based, mixed data on performance and characteristics of 19 syndromic surveillance systems in Europe from scientific and grey literature and from site visits. We identified success factors by applying crisp-set Qualitative Comparative Analysis. We focused on two main areas of syndromic surveillance application: seasonal influenza surveillance and situational awareness during different types of potentially health threatening events. Results: We found that syndromic surveillance systems might detect the onset or peak of seasonal influenza earlier if they analyse non-clinical data sources. Timely situational awareness during different types of events is supported by an automated syndromic surveillance system capable of analysing multiple syndromes. To our surprise, the analysis of multiple data sources was no key success factor for situational awareness. Conclusions: We suggest to consider these key success factors when designing or further developing syndromic surveillance systems. Qualitative Comparative Analysis helped interpreting complex, mixed data on small-N cases and resulted in concrete and practically relevant findings

Assessing the Short-Term Effects of Heatwaves on Mortality and Morbidity in Brisbane, Australia: Comparison of Case-Crossover and Time Series Analyses

BACKGROUND: Heat-related impacts may have greater public health implications as climate change continues. It is important to appropriately characterize the relationship between heatwave and health outcomes. However, it is unclear whether a case-crossover design can be effectively used to assess the event- or episode-related health effects. This study examined the association between exposure to heatwaves and mortality and emergency hospital admissions (EHAs) from non-external causes in Brisbane, Australia, using both case-crossover and time series analyses approaches. METHODS: Poisson generalised additive model (GAM) and time-stratified case-crossover analyses were used to assess the short-term impact of heatwaves on mortality and EHAs. Heatwaves exhibited a significant impact on mortality and EHAs after adjusting for air pollution, day of the week, and season. RESULTS: For time-stratified case-crossover analysis, odds ratios of mortality and EHAs during heatwaves were 1.62 (95% confidence interval (CI): 1.36-1.94) and 1.22 (95% CI: 1.14-1.30) at lag 1, respectively. Time series GAM models gave similar results. Relative risks of mortality and EHAs ranged from 1.72 (95% CI: 1.40-2.11) to 1.81 (95% CI: 1.56-2.10) and from 1.14 (95% CI: 1.06-1.23) to 1.28 (95% CI: 1.21-1.36) at lag 1, respectively. The risk estimates gradually attenuated after the lag of one day for both case-crossover and time series analyses. CONCLUSIONS: The risk estimates from both case-crossover and time series models were consistent and comparable. This finding may have implications for future research on the assessment of event- or episode-related (e.g., heatwave) health effects

A predictive model relating daily fluctuations in summer temperatures and mortality rates

<p>Abstract</p> <p>Background</p> <p>In the context of climate change, an efficient alert system to prevent the risk associated with summer heat is necessary. The authors' objective was to describe the temperature-mortality relationship in France over a 29-year period and to define and validate a combination of temperature factors enabling optimum prediction of the daily fluctuations in summer mortality.</p> <p>Methods</p> <p>The study addressed the daily mortality rates of subjects aged over 55 years, in France as a whole, from 1975 to 2003. The daily minimum and maximum temperatures consisted in the average values recorded by 97 meteorological stations. For each day, a cumulative variable for the maximum temperature over the preceding 10 days was defined.</p> <p>The mortality rate was modelled using a Poisson regression with over-dispersion and a first-order autoregressive structure and with control for long-term and within-summer seasonal trends. The lag effects of temperature were accounted for by including the preceding 5 days. A "backward" method was used to select the most significant climatic variables. The predictive performance of the model was assessed by comparing the observed and predicted daily mortality rates on a validation period (summer 2003), which was distinct from the calibration period (1975–2002) used to estimate the model.</p> <p>Results</p> <p>The temperature indicators explained 76% of the total over-dispersion. The greater part of the daily fluctuations in mortality was explained by the interaction between minimum and maximum temperatures, for a day <it>t </it>and the day preceding it. The prediction of mortality during extreme events was greatly improved by including the cumulative variables for maximum temperature, in interaction with the maximum temperatures. The correlation between the observed and estimated mortality ratios was 0.88 in the final model.</p> <p>Conclusion</p> <p>Although France is a large country with geographic heterogeneity in both mortality and temperatures, a strong correlation between the daily fluctuations in mortality and the temperatures in summer on a national scale was observed. The model provided a satisfactory quantitative prediction of the daily mortality both for the days with usual temperatures and for the days during intense heat episodes. The results may contribute to enhancing the alert system for intense heat waves.</p

Reconciling research and implementation in micro health insurance experiments in India: study protocol for a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Microinsurance or Community-Based Health Insurance is a promising healthcare financing mechanism, which is increasingly applied to aid rural poor persons in low-income countries. Robust empirical evidence on the causal relations between Community-Based Health Insurance and healthcare utilisation, financial protection and other areas is scarce and necessary. This paper contains a discussion of the research design of three Cluster Randomised Controlled Trials in India to measure the impact of Community-Based Health Insurance on several outcomes.</p> <p>Methods/Design</p> <p>Each trial sets up a Community-Based Health Insurance scheme among a group of micro-finance affiliate families. Villages are grouped into clusters which are congruous with pre-existing social groupings. These clusters are randomly assigned to one of three waves of implementation, ensuring the entire population is offered Community-Based Health Insurance by the end of the experiment. Each wave of treatment is preceded by a round of mixed methods evaluation, with quantitative, qualitative and spatial evidence on impact collected. Improving upon practices in published Cluster Randomised Controlled Trial literature, we detail how research design decisions have ensured that both the households offered insurance and the implementers of the Community-Based Health Insurance scheme operate in an environment replicating a non-experimental implementation.</p> <p>Discussion</p> <p>When a Cluster Randomised Controlled Trial involves randomizing within a community, generating adequate and valid conclusions requires that the research design must be made congruous with social structures within the target population, to ensure that such trials are conducted in an implementing environment which is a suitable analogue to that of a non-experimental implementing environment.</p

European all-cause excess and influenza-attributable mortality in the 2017/18 season: should the burden of influenza B be reconsidered?

Objectives Weekly monitoring of European all-cause excess mortality, the EuroMOMO network, observed high excess mortality during the influenza B/Yamagata dominated 2017/18 winter season, especially among elderly. We describe all-cause excess and influenza-attributable mortality during the season 2017/18 in Europe. Methods Based on weekly reporting of mortality from 24 European countries or sub-national regions, representing 60% of the European population excluding the Russian and Turkish parts of Europe, we estimated age stratified all-cause excess morality using the EuroMOMO model. In addition, age stratified all-cause influenza-attributable mortality was estimated using the FluMOMO algorithm, incorporating influenza activity based on clinical and virological surveillance data, and adjusting for extreme temperatures. Results Excess mortality was mainly attributable to influenza activity from December 2017 to April 2018, but also due to exceptionally low temperatures in February-March 2018. The pattern and extent of mortality excess was similar to the previous A(H3N2) dominated seasons, 2014/15 and 2016/17. The 2017/18 overall all-cause influenza-attributable mortality was estimated to be 25.4 (95%CI 25.0-25.8) per 100,000 population; 118.2 (116.4-119.9) for persons aged 65. Extending to the European population this translates into over-all 152,000 deaths. Conclusions The high mortality among elderly was unexpected in an influenza B dominated season, which commonly are considered to cause mild illness, mainly among children. Even though A(H3N2) also circulated in the 2017/18 season and may have contributed to the excess mortality among the elderly, the common perception of influenza B only having a modest impact on excess mortality in the older population may need to be reconsidered.Peer Reviewe