GROWTH STATUS OF CHILDREN IN NORTH WEST SYRIA: A COMPARISON OF THREE RURAL LIVELIHOOD GROUPS

Undernutrition, a multifactorial phenomenon, has severe consequences. A study was conducted in Syria to compare nutritional differences in children from three rural livelihood groups: a “barley-livestock” group in the southeast (Khanasser), and an “olive/fruit tree” group and an “irrigation” group in the northwest of Aleppo province (Afrin). An anthropometric assessment was conducted on 541 rural children from 207 rural households and 199 urban children (2-10 years) from a middle income urban group. Comparisons were made with the 1978 NCHS/WHO (National Center for Health Statistics/ World Health Organization) international growth reference (WHO, 1995) and a -2 SD Z-score was used as a cut-off. Prevalence rates and mean Z-scores were calculated and independent sample t-tests used to compare totals and age-group disaggregated children (both boys and girls). Total stunting prevalence was highest in the barley-livestock group (23%) and lowest in the irrigation group (12.5%). Girls in the barley-livestock group displayed the highest rates of stunting (28.3%), followed by the boys (22%) and the girls (21.08%) in the olive/fruit tree group. The prevalence of underweight children was highest in the barley-livestock and olive/fruit tree livelihood groups (14.29% and 13.25%, respectively). Wasting rates were very low. The rates of stunting and underweight were higher in the barley-livestock and olive/fruit tree groups, as compared with the country-level estimates of 20.8 percent and 12.9 percent respectively, in children under five. A comparison of rates and mean scores indicates that, amongst rural groups, there was considerable variation: the barley-livestock and olive/fruit tree group, belonging to drier and poorer areas, exhibited higher rates and lower mean scores.     

Public health challenges in the political economy of conflict: the case of Syria

Recent uprisings in the Arab world and a full‐scale war in Syria are widely viewed as popular demand for political voice against repressive regimes. However, growing economic inequalities and serious economic dysfunction played a role as trigger for conflict than is commonly accepted. Tunisia, Egypt and Syria all implemented policies of liberalization over the past two decades, leading to the worsening of living standards for the majority. The various forms of liberalization played a significant role in embedding social division and discontent whose outcomes affected other countries of the region with the onset of market reforms in nascent welfare states. Egypt, for example, was viewed by the World Bank as an economic ‘best performer’, despite regular riots over food prices, job losses and land expropriation for tourism. Tunisia was praised by donors just prior to the uprising (in 2010), for ‘weathering well’ the global economic downturn through ‘sound macroeconomic management’. In Syria, the market economy made its mark over the 90s, but macroeconomic adjustment policies were implemented in a bilateral agreement with the European Union and approved by the International Monetary Fund in 2003. The economic stabilization programme that followed had limited concern for social impacts such as jobs losses, price rises and national debt, which ultimately caused immense hardship for the population at large, acting as a trigger for the initial uprising in 2011, prior to its transformation into a fully blown conflict. This article focuses on reforms implemented in the health sector and sets these in the context of the current political economy of Syria. It suggests that a protective approach to public health services during and in the aftermath of conflict may increase the possibilities of reconstruction and reconciliation between warring sides. Copyright © 2015 John Wiley & Sons, Ltd.     

Description of a rare β-globin gene mutation,IVS-II-848 (C>A) (HBB: c.316-3C>A) in association with IVS-I-1 (G>A) (HBB: c.92 + 1G>A), observed in a Syrian family: a case report

Abstract

β-Thalassemia (β-thal) is a hereditary and heterogeneous group of disorders caused by mutations on the β-globin gene that result in the reduced or non production of β-globin chains. We report a rare β-globin mutation, IVS-II-848 (C>A) (HBB: c.316-3C>A), which was found in a female Syrian patient. This mutation was associated with the IVS-I-1 (G>A) (HBB: c.92+1G>A) mutation, and the genotype is a compound heterozygote for IVS-I-1(G>A)/IVS-II-848(C>A). This combination was found for the first time in Syria.     

UK parliamentary debate analysis: bombing ISIL in Syria

This paper examines the arguments presented for and against the UK government’s motion for the UK to intervene militarily in Syria in the House of Commons debate on ISIL in Syria that took place on 2 December 2015. It considers what the most common arguments were in favour of and in opposition to the motion as well as which arguments were given the most emphasis, in order to understand the prime justifications given that led to the decision to approve the motion. It suggests that due to the shadow of the 2003 Iraq war, politicians in the debate placed a considerable emphasis on the legal justification for military intervention. It argues that the focus on the national security of the UK and its allies in this particular debate seems to contrast with previous military interventions where humanitarian motives were more widely stated. This paper calls for further comparative research of parliamentary debates in order to track such changes in the rhetoric used by UK politicians to defend their support for military intervention.     

Standardizing questionnaire items for the assessment of waterpipe tobacco use in epidemiological studies

Lessons from surveillance of the smoking epidemic show that the lack of adequate attention to standardizing measures and instruments for epidemiological studies has negatively influenced our ability to assess spatial and secular trends in smoking worldwide. Waterpipe smoking, another hazardous form of tobacco use, is gaining popularity worldwide, with societies in the Eastern Mediterranean region (EMR) being most affected. Several research groups are currently investigating the epidemiology of waterpipe smoking among various populations in the EMR. Initial evidence shows that in contrast to usage patterns observed in adult cigarette smoking, waterpipe smoking is characterized mainly by intermittent and social use. As such, many measures that have been traditionally used for the study of usage patterns and dependence among adult cigarette smokers are uninformative for waterpipe smoking. Thus, the need to develop standardized measures and terminology for assessment of the epidemiology of waterpipe smoking in different populations is of paramount importance. As the monitoring of waterpipe smoking is in its infancy, the development of consensus measures should facilitate the initiation of effective surveillance that can guide public health response to this emerging epidemic.     

From the Cover: Monitoring war destruction from space using machine learning

Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available.

Building destruction during war is a specific form of violence that is particularly harmful to civilians, commonly used to displace populations, and therefore warrants special attention. Yet, data from war-ridden areas are typically scarce, often incomplete, and highly contested, when available. The lack of such data from conflict zones severely limits media reporting, humanitarian relief efforts, human-rights monitoring, and reconstruction initiatives, as well as the study of violent conflict in academic research. A novel solution to this problem is to use remote sensing to identify destruction in satellite images (1–3). This approach is gaining momentum as high-resolution imagery is becoming readily available at ever-higher frequency, yielding weekly, or even daily, images. At the same time, recent methodological advances related to deep learning have provided sophisticated tools to extract data from these images (4–7).While seminal research has demonstrated the use of automated classifiers for destruction detection, practical applications have so far been hampered by severe problems with labeling, domain transfer, and class imbalance in real-world imagery from urban war zones. As a consequence, international organizations such as the United Nations, the World Bank, and Amnesty International use remote sensing with manual human classification to produce damage-assessment case studies (8–10). On the other hand, providers of conflict data for research purposes still rely heavily on news and eyewitness reports, which leads to large data-publishing lags and potential biases (11–17). An automated building-damage classifier for use with satellite imagery, which has a low rate of false positives in unbalanced samples and allows tracking on-the-ground destruction in close to real-time, would therefore be extremely valuable for the international community and academic researchers alike.In this article, we present a way of combining computer-vision techniques and publicly available high-resolution satellite images to produce building-destruction estimates that are of practical use to both practitioners and researchers. The standard architectures for this task are convolutional neural networks (CNNs),^* as they have achieved unprecedented success in large-scale visual image classification with error rates beating humans (18, 19). We train a CNN to spot destruction features from heavy weaponry attacks (i.e., artillery and bombing) in satellite images, such as the rubble from collapsed buildings or the presence of bomb craters.We make three relevant methodological contributions. First, we introduce a label-augmentation method for expanding destruction class labels by making reasonable assumptions about the data-generating process using contextual information. Second, we introduce a two-stage classification process to control for spatial and temporal noise where the results from the CNN are processed through a random-forest model that relies on spatial and temporal leads and lags to improve classification performance. Third, we apply our trained computer-vision model to repeated satellite images of the entire populated areas of major Syrian cities, including parks and highways, and produce longitudinal estimates of building destruction over the course of the recent civil war.We demonstrate that our method yields high performance in out-of-sample tests and validate its ability for destruction monitoring using a separate database of heavy weaponry attacks. Our results highlight the importance of repeated satellite imagery in combination with temporal filtering to improve monitoring performance. As a result, our approach can be applied to any populated area, provided that repeated, high-resolution (i.e., submeter) satellite imagery is available.     

Measuring exposure to environmental tobacco smoke (ETS): a developing country's perspective

AIMS: To assess exposure to ETS among nonsmokers in the community and examine the relation between various subjective and objective measures of exposure to ETS in a developing country's setting. METHODS: An interviewer-administered population-based survey of adults 18-65 years residing in Aleppo, Syria. From a total number of 2038 participants, a sub-sample of 419 nonsmokers (27.2% men, 72.8% women, mean age 34 years) underwent subjective and objective assessment of exposure to ETS (saliva cotinine, breath CO, self-reported measures of exposure combined into ETS exposure scale). RESULTS: Overall, 97.6% of adults nonsmokers assessed in this study, 72.9% of whom were women, have detectable saliva cotinine levels (mean +/- SD 1.7 +/- 1.5 ng/ml). Correlation between self-reported exposure measures and saliva cotinine was moderate with the strongest observed for number of cigarette smokers in the house, average number of cigarettes smoked daily in the house, house policy regarding smoking, and total ETS score (r 0.3-0.4). These same variables were among the best predictors of saliva cotinine according to stepwise linear regression analysis, but their individual relevance differed between men and women reflecting underlying differences in gender-based behavior-mobility patterns. Generally, subjective measures could explain 22% of the variability in cotinine levels in men and 19% in women. CONCLUSIONS: Exposure to ETS is universal among adult nonsmokers in Syria. Saliva cotinine correlated moderately with self-reported measures, whereby selected subjective measures can be as informative as composite scores incorporating multiple measures. Even in this environment of omnipresence of smoking, household restrictions seem to offer protection against ETS exposure.