Disease proportions attributable to environment

Population disease proportions attributable to various causal agents are popular as they present a simplified view of the contribution of each agent to the disease load. However they are only summary figures that may be easily misinterpreted or over-interpreted even when the causal link between an exposure and an effect is well established. This commentary discusses several issues surrounding the estimation of attributable proportions, particularly with reference to environmental causes of cancers, and critically examines two recently published papers. These issues encompass potential biases as well as the very definition of environment and of environmental agent. The latter aspect is not just a semantic question but carries implications for the focus of preventive actions, whether centred on the material and social environment or on single individuals

The impact of the environment on health by country: a meta-synthesis

<p>Abstract</p> <p>Background</p> <p>Health gains that environmental interventions could achieve are main questions when choosing environmental health action to prevent disease. The World Health Organization has recently released profiles of environmental burden of disease for 192 countries.</p> <p>Methods</p> <p>These country profiles provide an estimate of the health impacts from the three major risk factors 'unsafe water, sanitation & hygiene', 'indoor air pollution from solid fuel use' and 'outdoor air pollution'. The profiles also provide an estimate of preventable health impacts by the environment as a whole. While the estimates for the three risk factors are based on country exposures, the estimates of health gains for total environmental improvements are based on a review of the literature supplemented by expert opinion and combined with country health statistics.</p> <p>Results</p> <p>Between 13% and 37% of the countries' disease burden could be prevented by environmental improvements, resulting globally in about 13 million deaths per year. It is estimated that about four million of these could be prevented by improving water, sanitation and hygiene, and indoor and outdoor air alone. The number of environmental DALYs per 1000 capita per year ranges between 14 and 316 according to the country. An analysis by disease group points to main preventions opportunities for each country.</p> <p>Conclusion</p> <p>Notwithstanding the uncertainties in their calculation, these estimates provide an overview of opportunities for prevention through healthier environments. The estimates show that for similar national incomes, the environmental burden of disease can typically vary by a factor five. This analysis also shows that safer water, sanitation and hygiene, and safer fuels for cooking could significantly reduce child mortality, namely by more than 25% in 20 of the lowest income countries.</p

The application of wavelet technique to sensorless control of brushless DC (BLDC) motor

Bu &ccedil;alışmada fır&ccedil;asız doğru akım motorunun (FDAM) dalgacık teorisi yardımıyla algılayıcısız olarak kontrol&uuml; ve elektrikli otomobile uygulanması ama&ccedil;lanmıştır. Bu doğrultuda geliştirilen bir kalkış algoritması y&ouml;ntemiyle, motorun, end&uuml;klenen gerilim bilgisinin algılanabileceği belirli bir hıza kadar a&ccedil;ık &ccedil;evrim olarak hızlanması sağlanıp, bu hızdan sonra geribesleme olarak elde edilen beslenmeyen faz end&uuml;klenen geriliminden ve faz akımlarından yararlanılarak kapalı &ccedil;evrim olarak dalgacık d&ouml;n&uuml;ş&uuml;m&uuml; yardımıyla fır&ccedil;asız doğru akım motorunun algılayıcısız kontrol&uuml; ger&ccedil;ekleştirilmiştir. Komutasyon anlarının algılayıcısız olarak elde edilmesi sırasında, geliştirilen PID ve Bulanık kontrol&ouml;r algoritmaları yardımıyla motor hız ve faz akımları ger&ccedil;ek zamanlı olarak denetlenmiştir. Anahtar Kelimeler: Fır&ccedil;asız DA motoru, bulanık, PID, algılayıcısız, dalgacık, sonlu elemanlar y&ouml;ntemi (SEY).This paper deals with developing a novel sensorless drive technique for BLDC motors by using wavelet theory. Study adopts two methods of position prediction. The first method involves self phase inductance variation of which finite element analysis is employed. The second method is based upon the induced voltage and zero crossing point estimation. Starting problem is solved by using position inductance function for the first method and by providing a look-up table for each direction of rotation for the second method. The MATLAB/Simulink model of the motor is established and the simulation performances are obtained. A PID and a fuzzy control algorithms are developed, current and speed controlled performance predictions are obtained. Then as an experimental study, BLDC motor?s PWM pulses are produced by DS1005 processor and DS2201 board of dSPACE DSP kit. The time domain currents and induced-voltage waveforms are recorded. The Daubechies wavelet analyses of the experimental and simulation waveforms are obtained with an extra emphasis on commutation intervals. So an algorithm is developed to predict the commutation instants without any position sensor. This procedure is applied experimentally and it is successfully demonstrated that the proposed method described above could be useful for sensorless control of BLDC motors. It is also shown that, results of the simulation model and its wavelet analysis are in a very good agreement with those of experiments.   Keywords: Brushless DC motor, fuzzy, PID, sensorless, wavelet, finite element method (FEM)

Evidence‐based support provided to struggling readers in later primary years in the UK: a scoping review

Background In the last two decades, a number of empirical studies investigated the impact of UK-based interventions for struggling readers in later primary years (called Key Stage 2 or KS2 in the UK). However, to date, there are no reviews that look at the extent and nature of the existing UK-based literature. This scoping review explores the extent of the available literature focusing on struggling readers in KS2 and aims to summarise the findings of available research. Methods A scoping review methodology was used, and six databases were searched from 2000 to 2022. The initial search yielded 1236 studies, of which 24 met the eligibility criteria and were included in this review. Results Most of the included studies (21 out of 24) demonstrated positive outcomes, and the support provided led to improvement in the reading skills of struggling readers in KS2. The available intervention programmes included a wide range of intensity, varied group sizes and targeted different reading skills. There is currently insufficient evidence to suggest the relative efficacy of one intervention over another. Conclusions The review confirmed the need for more robust research in this area and highlighted the importance of learning lessons from the international evidence base

Open defecation and childhood stunting in India: an ecological analysis of new data from 112 districts.

Poor sanitation remains a major public health concern linked to several important health outcomes; emerging evidence indicates a link to childhood stunting. In India over half of the population defecates in the open; the prevalence of stunting remains very high. Recently published data on levels of stunting in 112 districts of India provide an opportunity to explore the relationship between levels of open defecation and stunting within this population. We conducted an ecological regression analysis to assess the association between the prevalence of open defecation and stunting after adjustment for potential confounding factors. Data from the 2011 HUNGaMA survey was used for the outcome of interest, stunting; data from the 2011 Indian Census for the same districts was used for the exposure of interest, open defecation. After adjustment for various potential confounding factors--including socio-economic status, maternal education and calorie availability--a 10 percent increase in open defecation was associated with a 0.7 percentage point increase in both stunting and severe stunting. Differences in open defecation can statistically account for 35 to 55 percent of the average difference in stunting between districts identified as low-performing and high-performing in the HUNGaMA data. In addition, using a Monte Carlo simulation, we explored the effect on statistical power of the common practice of dichotomizing continuous height data into binary stunting indicators. Our simulation showed that dichotomization of height sacrifices statistical power, suggesting that our estimate of the association between open defecation and stunting may be a lower bound. Whilst our analysis is ecological and therefore vulnerable to residual confounding, these findings use the most recently collected large-scale data from India to add to a growing body of suggestive evidence for an effect of poor sanitation on human growth. New intervention studies, currently underway, may shed more light on this important issue

The Feature Importance Ranking Measure

Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly accessible to humans and cannot easily be used to gain insights about the application domain. Therefore, one often resorts to linear models in combination with variable selection, thereby sacrificing some predictive power for presumptive interpretability. Here, we introduce the Feature Importance Ranking Measure (FIRM), which by retrospective analysis of arbitrary learning machines allows to achieve both excellent predictive performance and superior interpretation. In contrast to standard raw feature weighting, FIRM takes the underlying correlation structure of the features into account. Thereby, it is able to discover the most relevant features, even if their appearance in the training data is entirely prevented by noise. The desirable properties of FIRM are investigated analytically and illustrated in simulations.Comment: 15 pages, 3 figures. to appear in the Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 200

Developing core sets for persons following amputation based on the International Classification of Functioning, Disability and Health as a way to specify functioning

Amputation is a common late stage sequel of peripheral vascular disease and diabetes or a sequel of accidental trauma, civil unrest and landmines. The functional impairments affect many facets of life including but not limited to: Mobility; activities of daily living; body image and sexuality. Classification, measurement and comparison of the consequences of amputations has been impeded by the limited availability of internationally, multiculturally standardized instruments in the amputee setting. The introduction of the International Classification of Functioning, Disability and Health (ICF) by the World Health Assembly in May 2001 provides a globally accepted framework and classification system to describe, assess and compare function and disability. In order to facilitate the use of the ICF in everyday clinical practice and research, ICF core sets have been developed that focus on specific aspects of function typically associated with a particular disability. The objective of this paper is to outline the development process for the ICF core sets for persons following amputation. The ICF core sets are designed to translate the benefits of the ICF into clinical routine. The ICF core sets will be defined at a Consensus conference which will integrate evidence from preparatory studies, namely: (a) a systematic literature review regarding the outcome measures of clinical trails and observational studies, (b) semi-structured patient interviews, (c) international experts participating in an internet-based survey, and (d) cross-sectional, multi-center studies for clinical applicability. To validate the ICF core sets field-testing will follow. Invitation for participation: The development of ICF Core Sets is an inclusive and open process. Anyone who wishes to actively participate in this process is invited to do so

A bacterial effector counteracts host autophagy by promoting degradation of an autophagy component

Beyond its role in cellular homeostasis, autophagy plays anti- and promicrobial roles in host-microbe interactions, both in animals and plants. One prominent role of antimicrobial autophagy is to degrade intracellular pathogens or microbial molecules, in a process termed xenophagy. Consequently, microbes evolved mechanisms to hijack or modulate autophagy to escape elimination. Although well-described in animals, the extent to which xenophagy contributes to plant-bacteria interactions remains unknown. Here, we provide evidence that Xanthomonas campestris pv. vesicatoria (Xcv) suppresses host autophagy by utilizing type-III effector XopL. XopL interacts with and degrades the autophagy component SH3P2 via its E3 ligase activity to promote infection. Intriguingly, XopL is targeted for degradation by defense-related selective autophagy mediated by NBR1/Joka2, revealing a complex antagonistic interplay between XopL and the host autophagy machinery. Our results implicate plant antimicrobial autophagy in the depletion of a bacterial virulence factor and unravel an unprecedented pathogen strategy to counteract defense-related autophagy in plant-bacteria interactions

Including information about co-morbidity in estimates of disease burden: results from the World Health Organization World Mental Health Surveys

Background The methodology commonly used to estimate disease burden, featuring ratings of severity of individual conditions, has been criticized for ignoring co-morbidity. A methodology that addresses this problem is proposed and illustrated here with data from the World Health Organization World Mental Health Surveys. Although the analysis is based on self-reports about one's own conditions in a community survey, the logic applies equally well to analysis of hypothetical vignettes describing co-morbid condition profiles. Method Face-to-face interviews in 13 countries (six developing, nine developed; n=31 067; response rate=69.6%) assessed 10 classes of chronic physical and nine of mental conditions. A visual analog scale (VAS) was used to assess overall perceived health. Multiple regression analysis with interactions for co-morbidity was used to estimate associations of conditions with VAS. Simulation was used to estimate condition-specific effects. Results The best-fitting model included condition main effects and interactions of types by numbers of conditions. Neurological conditions, insomnia and major depression were rated most severe. Adjustment for co-morbidity reduced condition-specific estimates with substantial between-condition variation (0.24-0.70 ratios of condition-specific estimates with and without adjustment for co-morbidity). The societal-level burden rankings were quite different from the individual-level rankings, with the highest societal-level rankings associated with conditions having high prevalence rather than high individual-level severity. Conclusions Plausible estimates of disorder-specific effects on VAS can be obtained using methods that adjust for co-morbidity. These adjustments substantially influence condition-specific rating