Préférence pour les garçons et intervalles de durée de naissance

Son preference prevails widely in South and East Asia and is demonstrated by sex-selection methods such as differential stopping and sex-selective abortion. Differential birth-spacing is another possible way by which this disproportionate desire for sons could manifest itself. The time span before moving on to the next pregnancy may be short as long as sons have not been born. Shorter birth spacing leads to higher demand on the mother's body, leading to higher health risk to both mother and child. In addition there is greater competition among siblings for parental care and resources. In this study, we examine this phenomenon by using three demographic and health surveys of Pakistani households covering the period from 1990-91 to 2012-13 and carrying out a set of duration model estimations. We investigate if and how preference for sons affects birth-spacing, if this relationship has evolved over time, if it depends on the order, number or overall proportion of sons born,and whether it increases the probability of risky births (those less than 24 or 18 months from the previous birth). We gauge the type of households in which this phenomenon appears to be more prevalent. We find strong evidence for differential behaviour at early parities throughout the period. Women whose first or second children are sons have significantly longer subsequent birth intervals compared with women with no sons. Birth-spacing differs substantially by parity and number of children. Sex of the firstborn is another significant factor. The association seems to have undergone little significant change over the past two decades. Besides, the likelihood of risky births is higher among women without one or more sons. This phenomenon of gender-specific lengthy and risky birth intervals is prevalent more among households that are wealthier or nuclear and among women with greater say in intra-household decisions.L’espacement différencié entre les naissances est un exemple démontrant comment le phénomène de préférence pour les garçons peut se manifester. La période précédant la prochaine grossesse peut être courte tant que le nombre désiré des garçons n’est pas né. Une période limitée entre les naissances entraine plus de pression sur le corps des femmes, plus de risques en matière de santé pour la mère et son enfant. De plus, il existe une plus grande rivalité entre les enfants concernant les soins et les ressources des parents.Nous étudions ce phénomène à partir de trois enquêtes démographiques et de santé réalisées auprès de différents ménages pakistanais de 1990-91 à 2012-2013. Nous voulons savoir si et comment la préférence pour les garçons affecte l’écart entre deux naissances, si cette relation évolue sur la période, si elle dépend de l’ordre de naissance, du nombre ou de la part de garçons nés, et si cela accroit la probabilité de naissances risquées. Nous étudions également le profil de ménage où ce phénomène est plus récurrent.En utilisant des méthodes d’estimation paramétriques semi et non paramétriques, nous trouvons des indices forts en faveure d’espacement différencié pour les premières naissances tout au long de la période. Les femmes qui ont d’abord eu deux garçons attendent entre 13 et 17% plus de temps avant une troisième naissance que celles qui n’ont pas eu de garçons. L’espacement varie de façon significative par ordre des naissances et le nombre d’enfants. Le sexe du premier enfant également joue un rôle important. En outre il existe une probabilité plus forte de naissances risquées.Ce comportement est plus répandu dans les familles plus riches ou nucléaires, avec des femmes plus âgées, plus éduquées ou qui ont un poids plus important dans les prises de décision au sein du ménage. Ces résultats ont des répercussions importantes pour la santé maternelle et infantile au Pakistan

What are the Career Plans of GP Trainees and Newly Qualified General Practitioners in the UK? A National Online Survey

Aim: To conduct a national survey for the First5 GPs and GP trainees to see what their future career plans are, and their views on the current issues in general practice, on a background of significant workforce problems in general practice. Method: Survey monkey was used to create an online survey that was distributed with the help of RCGP in their AiT bulletin and through their social media accounts. The links were also posted on several GP Facebook groups, Twitter and LinkedIn accounts. The survey ran between 5th of October and the 31st of October 2015. Results: 322 and 249 responses were received from First5 GPs and GP trainees respectively, just over 60% of First5 GPs and 68% of GP trainees are either definitely or possibly considering a move abroad where they feel there is a better outlook for general practice. The most popular destinations were Australia, Canada and New Zealand, a significant proportion are not considering a return to the UK. The main reasons given for early retirement were workload, job related stress, current government health care policies, working long hours, administrative work, high patient demand and risk of litigation. A majority of First5 GPs and GP trainees do not feel 7 day access to general practice is a good idea, they think the future of general practice in the UK is looking bleak and that the NHS is heading towards privatization, which the majority are against. Only about 18% of trainees would still apply for GPVTS even if 7 day GP access working was in place, 34% would consider other careers and 47% would not recommend general practice to juniors. Conclusion: Government policies, patient demands, unsustainable workload and low morale are contributing to GP trainees and newly qualified GPs in significant numbers thinking about leaving the UK general practice permanently, and changing career or retiring early. They are also less likely to recommend a career in general practice. There may need to be an urgent review of the strategies currently being considered to mitigate the workforce difficulties in general practice

Biological Monitoring of Blood Naphthalene Levels as a Marker of Occupational Exposure to PAHs among Auto-Mechanics and Spray Painters in Rawalpindi

<p>Abstract</p> <p>Background</p> <p>Routine exposure to chemical contaminants in workplace is a cause for concern over potential health risks to workers. In Pakistan, reports on occupational exposure and related health risks are almost non-existent, which reflects the scarce availability of survey data and criteria for determining whether an unsafe exposure has occurred. The current study was designed to evaluate blood naphthalene (NAPH) levels as an indicator of exposure to polycyclic aromatic hydrocarbons (PAHs) among automobile workshop mechanics (MCs) and car-spray painters (PNs). We further determined the relationship between blood NAPH levels and personal behavioural, job related parameters and various environmental factors that may further be associated with elevated risks of occupational exposures to PAHs.</p> <p>Methods</p> <p>Sixty blood samples (n = 20 for each group i.e. MC, PN and control group) were collected to compare their blood NAPH levels among exposed (MCs and PNs) and un-exposed (control) groups. Samples were analyzed using high pressure liquid chromatography (HPLC). Data regarding demographic aspects of the subjects and their socioeconomic features were collected using a questionnaire. Subjects were also asked to report environmental hygiene conditions of their occupational environment.</p> <p>Results</p> <p>We identified automobile work areas as potential sites for PAHs exposure, which was reflected by higher blood NAPH levels among MCs. Blood NAPH levels ranged from 53.7 to 1980.6 μgL^-1and 54.1 to 892.9 μgL^-1among MCs and PNs respectively. Comparison within each group showed that smoking enhanced exposure risks several fold and both active and passive smoking were among personal parameters that were significantly correlated with log-transformed blood NAPH levels. For exposed groups, work hours and work experience were job related parameters that showed strong associations with the increase in blood NAPH levels. Poor workplace hygiene and ventilation were recognized as most significant predictors related to differences among workplaces that may enhance the extent of exposure to chemical contaminants.</p> <p>Conclusions</p> <p>It appeared that chemical exposure at the workplace may be influenced by multiple environmental factors, but poor workplace hygiene and duration of exposure (long work hours) were the most important factors. Smoking and negligence of workers regarding self protection were among some of the important personal behaviours than can be addressed with better training. There is also a need to improve workplaces hygiene and to rationalize work hours to minimize health risks. Since smoking was an important confounding factor that supplemented most of the actual occupational exposure, a study based on non-smoker subjects is needed to separate out the effects of smoking and other confounding factors that may obscure measurements of actual extent of occupational exposure.</p

R2C-GAN: Restore-to-Classify GANs for Blind X-Ray Restoration and COVID-19 Classification

Restoration of poor quality images with a blended set of artifacts plays a vital role for a reliable diagnosis. Existing studies have focused on specific restoration problems such as image deblurring, denoising, and exposure correction where there is usually a strong assumption on the artifact type and severity. As a pioneer study in blind X-ray restoration, we propose a joint model for generic image restoration and classification: Restore-to-Classify Generative Adversarial Networks (R2C-GANs). Such a jointly optimized model keeps any disease intact after the restoration. Therefore, this will naturally lead to a higher diagnosis performance thanks to the improved X-ray image quality. To accomplish this crucial objective, we define the restoration task as an Image-to-Image translation problem from poor quality having noisy, blurry, or over/under-exposed images to high quality image domain. The proposed R2C-GAN model is able to learn forward and inverse transforms between the two domains using unpaired training samples. Simultaneously, the joint classification preserves the disease label during restoration. Moreover, the R2C-GANs are equipped with operational layers/neurons reducing the network depth and further boosting both restoration and classification performances. The proposed joint model is extensively evaluated over the QaTa-COV19 dataset for Coronavirus Disease 2019 (COVID-19) classification. The proposed restoration approach achieves over 90% F1-Score which is significantly higher than the performance of any deep model. Moreover, in the qualitative analysis, the restoration performance of R2C-GANs is approved by a group of medical doctors. We share the software implementation at https://github.com/meteahishali/R2C-GAN

Diagnostic Accuracy of Increased Total Leucocyte Count (TLC) And Decreased Absolute Neutrophil Count (ANC) For Diagnosis Of Neonatal Sepsis Taking Culture Sensitivity As Gold Standard

Objective: To find diagnostic accuracy of increased total leucocyte count (TLC) and decreased absolute neutrophil count (ANC) for diagnosis of neonatal sepsis taking culture and sensitivity as gold standard.Methodology: Samples was collected from all the neonates with suspicion of sepsis admitted to neonatal due to severe bad health condition in the Children Hospital, Lahore. Parents were informed for consent. Total 3.5 cc sample was drawn by venipuncture.2.0 cc in CBC voil containing trisodium citrate, this sample was sent to CBC lab for automated CBC on sysmex KX-21 analyzer and peripheral smear. 1.5 cc was taken in specialized containers for blood culture and was sent to microbiology lab. Neonatal sepsis on TLC, ANC and blood count were be recorded. Patients of sepsis was managed as per hospital protocol. Data was analyzed using SPSS.Results: Neonates in this study was at mean age of 3.86±1.81 days. There was almost equal number of males and females neonates. Male were 155(51.7%) and female babies 145(48.3%). It was noted that 182(60.7%) babies were on term while 118(39.3%) were preterm neonates in this study. Neonatal sepsis was positive on TLC in 101(33.7%) cases. in 115(38.3%) on ANC.It was noted that sensitivity of the TLC was 71% while specificity was 66% with a diagnostic accuracy of 68%. On the other and, ANC sowed a sensitivity as 65%, specificity as 56% and diagnostic accuracy of 65%.Conclusion: Conclusively, TLC and ANC is not a good marker for the taking a neonate for the consideration of neonatal sepsis

Early myocardial infarction detection over multi-view echocardiography

Myocardial infarction (MI) is the leading cause of mortality in the world. Its early diagnosis can mitigate the extent of myocardial damage by facilitating early therapeutic interventions. The regional wall motion abnormality (RWMA) of the ischemic myocardial segments is the earliest change to set in that can be captured by echocardiography. However, assessing the motion only from a single echocardiography view may lead to missing the diagnosis of MI as the RWMA may not be visible on that specific view. Therefore, in this study, we propose to fuse apical 4-chamber (A4C) and apical 2-chamber (A2C) views in which a total of 12 myocardial segments can be analyzed for MI detection. The proposed method first estimates the motion of the left ventricle wall by Active Polynomials (APs), which extract and track the endocardial boundary to compute myocardial segment displacements. The features are extracted from the displacements, which are concatenated and fed into the classifiers to detect MI. The main contributions of this study are (1) creation of a new benchmark dataset by including both A4C and A2C views in a total of 260 echocardiography recordings, which is publicly shared with the research community, (2) improving the performance of the prior work of threshold-based APs by a machine learning based approach, and (3) a pioneer MI detection approach via multi-view echocardiography by fusing the information of A4C and A2C views. The proposed method achieves 90.91% sensitivity and 86.36% precision for MI detection over multi-view echocardiography. The software implementation is shared at https://github.com/degerliaysen/MultiEchoAI.Peer reviewe

COVID-19 Infection Map Generation and Detection from Chest X-Ray Images

Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity

Advance Warning Methodologies for COVID-19 using Chest X-Ray Images

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent \textit{state-of-the-art} Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labelled by the medical doctors and 12 544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity.Comment: 12 page