Epidemiological and Clinical Characteristics Associated with COVID-19 Severity Among Hospitalized Patients in the United Arab Emirates: A Retrospective Multicentre Study.

OBJECTIVES: To investigate the clinical and epidemiological factors associated with severe COVID-19 cases in hospitalized patients across two emirates within the United Arab Emirates (UAE). METHODS: A retrospective observational analytical study analysed data from 738 medical records and conducted 573 in-depth interviews with patients hospitalized across multiple healthcare centers in the UAE, between 29 January 2020 and 14 October 2021. Regression analysis predicted risk factors for COVID-19 severity. RESULTS: Main risk factors identified were crowding (aOR 1.919; 95%CI 1.144, 3.221), obesity (aOR 2.383; 95%CI 1.332, 4.263), diabetes (aOR 11.14; 95%CI 2.653-46.797), severe dehydration (aOR 3.219; 95%CI 2.161, 4.795), cough or sore throat (aOR 1.607; 95%CI 1.032, 2.502), shortness of breath (aOR 1.921; 95%CI 1.294, 2.853), increased days from symptom onset to admission (aOR 1.055; 95%CI 1.006, 1.105), elevated ANC (aOR 1.263, 95%CI 1.121, 1.424), and AST/SGOT (aOR 1.055, 95% CI 1.016, 1.095). Protective factors included smoking (aOR 0.367; 95%CI 0.182, 0.740), first dose of COVID-19 vaccination (aOR 0.595; 95%CI 0.377, 0.93), higher oxygen saturation (aOR 0.853; 95%CI: 0.801, 0.907) and elevated ALC (aOR 0.540; 95%CI 0.323, 0.905). CONCLUSION: Identifying risk factors is crucial for high-risk individuals who may require closer monitoring to improve their outcomes. This can provide guidance for surveillance systems and early detection strategies to mitigate the impact of future outbreaks.

Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data.

Deep learning methods have achieved a lot of success in various applications involving converting wearable sensor data to actionable health insights. A common application areas is activity recognition, where deep-learning methods still suffer from limitations such as sensitivity to signal quality, sensor characteristic variations, and variability between subjects. To mitigate these issues, robust features obtained by topological data analysis (TDA) have been suggested as a potential solution. However, there are two significant obstacles to using topological features in deep learning: (1) large computational load to extract topological features using TDA, and (2) different signal representations obtained from deep learning and TDA which makes fusion difficult. In this paper, to enable integration of the strengths of topological methods in deep-learning for time-series data, we propose to use two teacher networks - one trained on the raw time-series data, and another trained on persistence images generated by TDA methods. These two teachers are jointly used to distill a single student model, which utilizes only the raw time-series data at test-time. This approach addresses both issues. The use of KD with multiple teachers utilizes complementary information, and results in a compact model with strong supervisory features and an integrated richer representation. To assimilate desirable information from different modalities, we design new constraints, including orthogonality imposed on feature correlation maps for improving feature expressiveness and allowing the student to easily learn from the teacher. Also, we apply an annealing strategy in KD for fast saturation and better accommodation from different features, while the knowledge gap between the teachers and student is reduced. Finally, a robust student model is distilled, which can at test-time uses only the time-series data as an input, while implicitly preserving topological features. The experimental results demonstrate the effectiveness of the proposed method on wearable sensor data. The proposed method shows 71.74% in classification accuracy on GENEActiv with WRN16-1 (1D CNNs) student, which outperforms baselines and takes much less processing time (less than 17 sec) than teachers on 6k testing samples.

Assessment of Knowledge, Perceptions, and Attitudes During the Global Mpox Outbreak in June 2022: A Cross-Sectional Study From the United Arab Emirates.

Objectives: To examine knowledge, worry, anxiety, and vaccine acceptance for mpox among UAE adults. Methods: An online survey, advertised on academic and social media platform in June 2022 collected data from 959 participants (aged 18 and above) on mpox beliefs, risks, knowledge, worry, anxiety, COVID-19 infection, vaccination, and willingness to receive the mpox vaccine. Bivariate and logistic regression analysis identified associations and predictors between variables. Results: 56% had optimal knowledge of mpox transmission and symptoms. 54% were worried, and 27% experienced anxiety related to the outbreak. Knowledge scores were higher among women, healthcare workers, and those with reliable information sources. High perceived infection risk, changes in precautionary measures, and belief in difficult treatment predicted more worry and anxiety. Higher worry and two or more doses of the COVID-19 vaccine predicted higher likelihood of taking the mpox vaccine. Conclusion: The UAE population showed low knowledge and high worry and anxiety during the global mpox outbreak. Increasing public awareness through targeted educational campaigns is vital. Promoting better understanding of infectious diseases, addressing concerns, and encouraging vaccine uptake can prepare for future outbreaks.

Gender roles perceptions and ideal number of children: case study of Emirati youth.

BACKGROUND: The United Arab Emirates (UAE) is a traditional society with patriarchal values. The country has been experiencing a decline in fertility rates, bringing the total fertility rate for the national population to 3.3 children per woman, the lowest since 1970s. Existing literature indicates that having gender-egalitarian attitudes is associated with lower fertility rates. Therefore, this study aims to investigate the perceptions of gender roles among the highly educated Emirati youth and examine whether these attitudes influence their desire to have children. By doing so, we aim to gain insights into the factors contributing to the country's declining fertility rates. METHODS: This study utilized data from a cross-sectional quantitative survey. The survey was developed and administered in February-March 2019 to a purposive sample of 300 young Emirati males and females aged 18-30 years, studying at the UAE University. Both bivariate and multivariate analysis were performed to examine the levels of youths' perception towards gender roles and desired fertility. RESULTS: The data collected from Emirati youths revealed that 50% of them supported the traditional perspective on marriage, 30% considered motherhood is the most important thing for women, and a small percentage supported economic independence of women and husband participation in household chores/child-rearing. On average Emirati youth ideally wished to have 5.4 children in their future families, with a preference for sons over daughters. Youths who favoured women's economic independence and equal participation in household work contribution by spouses desired a lower number of children which is in line with the modernization theory and cultural evolution. CONCLUSION: The UAE has been facing continuous decline in fertility rates. The study indicates that ideal number of children is much higher than the current fertility rates in the country. To bridge this gap, the government could implement family policies that create an environment conducive to fulfilling the ideal desires of young Emiratis regarding family size. Moreover, present findings indicate that perceptions of equal gender roles could be a contributing factor to the declining fertility rates among the young generation. Promoting gender equality attitudes and strengthening institutional support for childbearing could become key strategy to address these issues.

Cryo-EM structures of Uba7 reveal the molecular basis for ISG15 activation and E1-E2 thioester transfer.

ISG15 plays a crucial role in the innate immune response and has been well-studied due to its antiviral activity and regulation of signal transduction, apoptosis, and autophagy. ISG15 is a ubiquitin-like protein that is activated by an E1 enzyme (Uba7) and transferred to a cognate E2 enzyme (UBE2L6) to form a UBE2L6-ISG15 intermediate that functions with E3 ligases that catalyze conjugation of ISG15 to target proteins. Despite its biological importance, the molecular basis by which Uba7 catalyzes ISG15 activation and transfer to UBE2L6 is unknown as there is no available structure of Uba7. Here, we present cryo-EM structures of human Uba7 in complex with UBE2L6, ISG15 adenylate, and ISG15 thioester intermediate that are poised for catalysis of Uba7-UBE2L6-ISG15 thioester transfer. Our structures reveal a unique overall architecture of the complex compared to structures from the ubiquitin conjugation pathway, particularly with respect to the location of ISG15 thioester intermediate. Our structures also illuminate the molecular basis for Uba7 activities and for its exquisite specificity for ISG15 and UBE2L6. Altogether, our structural, biochemical, and human cell-based data provide significant insights into the functions of Uba7, UBE2L6, and ISG15 in cells.

Hysterectomy and women's health in India: evidence from a nationally representative, cross-sectional survey of older women.

BACKGROUND: Hysterectomy, particularly when conducted in women younger than 45 years, has been associated with increased risk of non-communicable diseases. In India, research indicates that hysterectomy is a common procedure for women, but there have been no studies on its long-term effects. We examined patterns of hysterectomy amongst women in India and associations with their health and well-being in later life. METHODS: This analysis utilised the first wave of the Longitudinal Study on Aging in India, a nationally representative study of adults that included a module on health and well-being. We analysed data on 35,083 women ≥45 years in India. We estimated prevalence of hysterectomy and performed multivariable logistic regression to identify associated risk factors and to examine the association between hysterectomy status and eight self-reported chronic conditions, hospitalisation and mobility. RESULTS: The prevalence of hysterectomy among women >=45 years was 11.4 (95% CI: 10.3, 12.6), with higher odds among urban women (aOR: 1.39; 1.17,1.64) and higher economic status (highest compared to lowest quintile: aOR: 1.95; 1.44, 2.63). Hysterectomy history was associated with four chronic conditions: hypertension (aOR: 1.51; 95% CI: 1.28, 1.79), high cholesterol (aOR: 1.43; 1.04, 1.97), diabetes (aOR: 1.69; 1.28, 2.24), and bone/joint disease (aOR: 1.54; 1.20, 1.97) and higher odds of any hospitalisation in the past year (aOR: 1.69; 1.36, 2.09). CONCLUSIONS: In India, evidence suggests that hysterectomy is associated with major chronic conditions. The assessment for hysterectomy as a treatment option for gynaecological morbidity should consider potential health consequences in later life.

Regulation of futile ligation during early steps of BER in M. tuberculosis is carried out by a ß-clamp-XthA-LigA tri-component complex.

The Class-II AP-endonuclease (XthA) is a mycobacterial DNA base excision repair (BER) pathway enzyme that functions in the initial steps. It acts on DNA substrates that contain abasic sites to create nicks with 3'-hydroxyl (OH) and 5'-deoxyribose phosphate (5'-dRP) moieties. The NAD+-dependent DNA ligase (LigA) is the terminal player in mycobacterial BER and seals such nicks efficiently. Here, we demonstrate that the Mtbß-clamp-MtbXthA complex that exists in the initial steps of BER engages with MtbLigA to form a novel tri-component BER complex. Size exclusion chromatography (SEC) experiments analysis show that the three proteins interact with equimolar stoichiometry. Small angle X-ray scattering (SAXS) analysis and associated studies reveal that the apo tri-component BER-complex adopts an extended conformation where MtbXthA is sandwiched between the Mtbß-clamp and MtbLigA. The studies support that in the apo-complex MtbXthA binds subsite-I of Mtbß-clamp through 239QLRFPKK245 motif and to MtbLigA by 104DGQPSWSGKP113 motif simultaneously. However, the complex adopts a less-extended conformation in the presence of substrate DNA, where MtbXthA interactions switch from predominantly subsite-I to subsite-II of the Mtbß-clamp. Overall, the novel tri-component complex prevents futile ligation activity of MtbLigA on the product of MtbXthA and ensures forward progression of the pathway and productive mycobacterial BER interactions.

Constrained Adaptive Distillation Based on Topological Persistence for Wearable Sensor Data.

Wearable sensor data analysis with persistence features generated by topological data analysis (TDA) has achieved great successes in various applications, however, it suffers from large computational and time resources for extracting topological features. In this paper, our approach utilizes knowledge distillation (KD) that involves the use of multiple teacher networks trained with the raw time-series and persistence images generated by TDA, respectively. However, direct transfer of knowledge from the teacher models utilizing different characteristics as inputs to the student model results in a knowledge gap and limited performance. To address this problem, we introduce a robust framework that integrates multimodal features from two different teachers and enables a student to learn desirable knowledge effectively. To account for statistical differences in multimodalities, entropy based constrained adaptive weighting mechanism is leveraged to automatically balance the effects of teachers and encourage the student model to adequately adopt the knowledge from two teachers. To assimilate dissimilar structural information generated by different style models for distillation, batch and channel similarities within a mini-batch are used. We demonstrate the effectiveness of the proposed method on wearable sensor data.

An Evaluation of Non-Communicable Diseases and Risk Factors Associated with COVID-19 Disease Severity in Dubai, United Arab Emirates: An Observational Retrospective Study.

The aim of this study was to determine the prevalence of non-communicable diseases and their correlation with COVID-19 disease severity among patients infected in Dubai. Clinical and demographic data were extracted from hospital records of 34,687 COVID-19 patients who visited or were admitted into Dubai hospitals between 28 January 2020 and 30 September 2020. Prevalence of co-morbidities in COVID-19 patients were assessed. The main risk factors associated with COVID-19 disease severity were also identified using three regression models. All co-morbidities were significantly associated with COVID-19 severity in the bivariate analysis (p-value ≤ 0.05) except for vitamin-D deficiency and chronic lower respiratory diseases. Patients with ischemic heart diseases (AOR: 2.08; 95% CI: 1.37, 3.15), pulmonary and other heart diseases (AOR: 2.13; 95% CI: 1.36, 3.32) and chronic kidney diseases (AOR: 1.81; 95% CI: 1.01, 3.25) had higher odds of severe COVID-19 symptoms. Suffering from multiple co-morbidities increased the odds of developing severe COVID-19 symptoms substantially in comparison to having only one co-morbidity i.e., (AOR: 1.52; 95% CI 1.76-2.60) to (AOR: 2.33; 95% CI: 1.37, 3.97). Identifying these risk factors could assist in the early recognition of high-risk populations and ensure the most appropriate preventive measures and required medical management during the pandemic.

Quality of care in abortion in the era of technological and medical advancements and self-care.

BACKGROUND: Discussions around quality of abortion care have been focused mainly on service-delivery aspects inside healthcare facilities. More recently, with availability of medical abortion (MA), increase in its self-use, and emergence of other delivery platforms such as telemedicine, the responsibility of quality care has broadened to actors outside of facilities. BODY OF TEXT: This commentary discusses the meaning of quality of abortion care with the paradigm shift brought by medical and technological advancement in abortions, and raises questions on the role of the state in ensuring quality in abortion management-especially in settings where abortion is decriminalized, but also in countries where abortion is permitted under certain circumstances. It consolidates the experience gained thus far in the provision of safe abortion services and also serves as a forward-thinking tool to keep pace with the uptake of newer health technologies (e.g., availability of medical abortion drugs), service delivery platforms (e.g., telemedicine, online pharmacies), and abortion care providers (e.g., community based pharmacists). CONCLUSIONS: This commentary provides context and rationale, and identifies areas for action that different stakeholders, including health advocates, policymakers, program managers, and women themselves, can adopt to fit into an alternative regime of abortion care.

Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data.

Deep neural networks are parametrized by several thousands or millions of parameters, and have shown tremendous success in many classification problems. However, the large number of parameters makes it difficult to integrate these models into edge devices such as smartphones and wearable devices. To address this problem, knowledge distillation (KD) has been widely employed, that uses a pre-trained high capacity network to train a much smaller network, suitable for edge devices. In this paper, for the first time, we study the applicability and challenges of using KD for time-series data for wearable devices. Successful application of KD requires specific choices of data augmentation methods during training. However, it is not yet known if there exists a coherent strategy for choosing an augmentation approach during KD. In this paper, we report the results of a detailed study that compares and contrasts various common choices and some hybrid data augmentation strategies in KD based human activity analysis. Research in this area is often limited as there are not many comprehensive databases available in the public domain from wearable devices. Our study considers databases from small scale publicly available to one derived from a large scale interventional study into human activity and sedentary behavior. We find that the choice of data augmentation techniques during KD have a variable level of impact on end performance, and find that the optimal network choice as well as data augmentation strategies are specific to a dataset at hand. However, we also conclude with a general set of recommendations that can provide a strong baseline performance across databases.

The impact of environmental and climate parameters on the incidence and mortality of COVID-19 in the six Gulf Cooperation Council countries: A cross-country comparison study.

BACKGROUND: Environmental factors can influence the epidemiological dynamics of COVID-19. To estimate the true impact of these factors on COVID-19, climate and disease data should be monitored and analyzed over an extended period of time. The Gulf Cooperation Council (GCC) countries are particularly lacking in such studies. This ecological study investigates the association between climate parameters and COVID-19 cases and deaths in the GCC. METHODS: Data on temperature, wind-speed and humidity and COVID-19 cases and deaths from the six countries of the GCC were collected between 29/1/2020 and 30/3/2021. Using Spearman's correlation coefficient, we examined associations between climate parameters and COVID-19 cases and deaths by month, over four different time periods. A two-step cluster analysis was conducted to identify distinct clusters of data using climate parameters and linear regression analysis to determine which climate parameters predicted COVID-19 new cases and deaths. RESULTS: The United Arab Emirates (UAE) had the highest cumulative number of COVID-19 cases while Bahrain had the highest prevalence rate per 100,000. The Kingdom of Saudi Arabia (KSA) reported the highest cumulative number of deaths while Oman recorded the highest death rate per 100,000. All GCC countries, except the UAE, reported a positive correlation between temperature and cases and deaths. Wind speed was positively correlated with cases in Qatar, but negatively correlated with cases in the UAE and deaths in KSA. Humidity was positively correlated with cases and deaths in Oman, negatively correlated in Bahrain, Kuwait, Qatar and KSA but there was no correlation in the UAE. The most significant predictors in cluster analysis were temperature and humidity, while in the regression analysis, temperature, humidity and wind speed predicted new COVID-19 cases and deaths. CONCLUSION: This study provides comprehensive epidemiological information on COVID-19 and climate parameters and preliminary evidence that climate may play a key role in the transmission of the COVID-19 virus. This study will assist decision makers in translating findings into specific guidelines and policies for the prevention and elimination of COVID-19 transmission and infection.

Machine-Learning Approach for Automatic Detection of Wild Beluga Whales from Hand-Held Camera Pictures.

A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. However, marine mammal observation through vessel surveys requires significant workforce resources, including for the post-processing of pictures, and is further challenged due to animal bodies being partially hidden underwater, small-scale object size, occlusion among objects, and distracter objects (e.g., waves, sun glare, etc.). To relieve the human expert's workload while improving the observation accuracy, we propose a novel system for automating the detection of beluga whales (Delphinapterus leucas) in the wild from pictures. Our system relies on a dataset named Beluga-5k, containing more than 5.5 thousand pictures of belugas. First, to improve the dataset's annotation, we have designed a semi-manual strategy for annotating candidates in images with single (i.e., one beluga) and multiple (i.e., two or more belugas) candidate subjects efficiently. Second, we have studied the performance of three off-the-shelf object-detection algorithms, namely, Mask-RCNN, SSD, and YOLO v3-Tiny, on the Beluga-5k dataset. Afterward, we have set YOLO v3-Tiny as the detector, integrating single- and multiple-individual images into the model training. Our fine-tuned CNN-backbone detector trained with semi-manual annotations is able to detect belugas despite the presence of distracter objects with high accuracy (i.e., 97.05 mAP@0.5). Finally, our proposed method is able to detect overlapped/occluded multiple individuals in images (beluga whales that swim in groups). For instance, it is able to detect 688 out of 706 belugas encountered in 200 multiple images, achieving 98.29% precision and 99.14% recall.

Membrane insertion mechanism of the caveola coat protein Cavin1.

Caveolae are small plasma membrane invaginations, important for control of membrane tension, signaling cascades, and lipid sorting. The caveola coat protein Cavin1 is essential for shaping such high curvature membrane structures. Yet, a mechanistic understanding of how Cavin1 assembles at the membrane interface is lacking. Here, we used model membranes combined with biophysical dissection and computational modeling to show that Cavin1 inserts into membranes. We establish that initial phosphatidylinositol (4, 5) bisphosphate [PI(4,5)P2]-dependent membrane adsorption of the trimeric helical region 1 (HR1) of Cavin1 mediates the subsequent partial separation and membrane insertion of the individual helices. Insertion kinetics of HR1 is further enhanced by the presence of flanking negatively charged disordered regions, which was found important for the coassembly of Cavin1 with Caveolin1 in living cells. We propose that this intricate mechanism potentiates membrane curvature generation and facilitates dynamic rounds of assembly and disassembly of Cavin1 at the membrane.

In the grey zone-survival and morbidities of periviable births.

OBJECTIVE: To compare the survival and morbidities of infants born between 22 0/7-25 6/7 weeks of gestation. STUDY DESIGN: This observational cohort study consisted of 187 eligible infants liveborn at a single, Level III Neonatal Intensive Care Unit (NICU) between June 1, 2009, and December 31, 2016, in Cleveland, Ohio. Infants with recognized syndromes or major congenital malformations were excluded from the review. RESULT: The rate of survival to discharge for NICU-admitted infants born at 22- and 23- week was 56% and 54% respectively at our institution. There was no trend observed between gestational ages and incidence of necrotizing enterocolitis (NEC), patent ductus arteriousus (PDA), sepsis, or severe intraventricular hemorrhage (IVH- Grade 3 or 4). The infants born at 22 weeks had a higher incidence of retinopathy of prematurity (ROP) as compared to 25 weeks gestation (p < 0.001). The need for home oxygen was significantly higher in the smallest infants 70% at 22 weeks, 62% and 60% at 23 and 24 weeks versus 33% at 25 weeks gestation (p < 0.007). Those born at 22 weeks had the same rate of survival to discharge with severe IVH as those born at 23 weeks but required fewer VP shunts (p > 0.52). CONCLUSIONS: The course of extremely preterm infants shows no difference between those born at 22 and 23 weeks of gestation in our NICU with regards to both mortality and short-term morbidities, although they differed marginally from 24 week gestation infants and significantly from those born at 25 weeks gestation.

Topological Knowledge Distillation for Wearable Sensor Data.

Converting wearable sensor data to actionable health insights has witnessed large interest in recent years. Deep learning methods have been utilized in and have achieved a lot of successes in various applications involving wearables fields. However, wearable sensor data has unique issues related to sensitivity and variability between subjects, and dependency on sampling-rate for analysis. To mitigate these issues, a different type of analysis using topological data analysis has shown promise as well. Topological data analysis (TDA) captures robust features, such as persistence images (PI), in complex data through the persistent homology algorithm, which holds the promise of boosting machine learning performance. However, because of the computational load required by TDA methods for large-scale data, integration and implementation has lagged behind. Further, many applications involving wearables require models to be compact enough to allow deployment on edge-devices. In this context, knowledge distillation (KD) has been widely applied to generate a small model (student model), using a pre-trained high-capacity network (teacher model). In this paper, we propose a new KD strategy using two teacher models - one that uses the raw time-series and another that uses persistence images from the time-series. These two teachers then train a student using KD. In essence, the student learns from heterogeneous teachers providing different knowledge. To consider different properties in features from teachers, we apply an annealing strategy and adaptive temperature in KD. Finally, a robust student model is distilled, which utilizes the time series data only. We find that incorporation of persistence features via second teacher leads to significantly improved performance. This approach provides a unique way of fusing deep-learning with topological features to develop effective models.

Determinants of healthcare workers perceptions, acceptance and choice of COVID-19 vaccines: a cross-sectional study from the United Arab Emirates.

Acceptance of COVID-19 vaccine among health-care workers (HCWs) is crucial for controlling the pandemic and ensuring HCW and patient safety. Information on the acceptance of different COVID-19 vaccines is lacking. Despite the United Arab Emirates (UAE) having vaccinated most of its population, vaccine acceptance still raises concerns. This study explores COVID-19 vaccine acceptance, vaccine choice, and associated factors among HCWs in the UAE. An online national cross-sectional study was conducted among 517 HCWs. Acceptance and choice of COVID-19 vaccines were assessed, and logistic regression analysis identified predictors for vaccine acceptance. More than half (58%) of HCWs were willing to take the vaccine and give it to their family. Reasons for taking the vaccine were concerns for families contracting COVID-19 (67%) and social responsibility (64%). Reasons for refusals included concerns with side-effects (61%). Most HCWs knew of the Pfizer (79%) and Sinopharm (57%) vaccines; however, acceptance was higher for Pfizer (35%) and AstraZeneca (21%) vaccines. Being male and being influenza vaccinated predicted willingness to take the vaccine (aOR: 2.34; 95% CI:1.34-4.08; p ≤ 0.001) and (aOR: 2.13; 95% CI: 1.29-3.51; p ≤ 0.001), respectively. HCWs who expressed concerns with inadequate safety data were less likely to take the vaccine (aOR: 0.17; 95% CI: 0.10-0.30; p ≤ 0.001). Additionally, side effects, perception of risk, and level of trust of company and country of manufacture predicted acceptance and choice of vaccines. Effective vaccine policy campaigns to improve acceptance should target HCW's knowledge and awareness of perceived risks of COVID-19, safety data, social responsibility, and individual preferences for vaccine choice.

Contraception for married adolescents (15-19 years) in India: insights from the National Family Health Survey-4 (NFHS-4).

PURPOSE: Despite the fact that marriage below the age of 18 years is illegal in India, a considerable number of females get married and start childbearing during their adolescent years. There is low prevalence of contraceptive methods and high unmet need for family planning (FP). Realizing this, new government programs have been launched to increase the uptake of sexual and reproductive health services among adolescents. However, evidence specific to this age group remains scarce. AIM AND OBJECTIVES: The present study was conducted to assess the prevalence of modern contraceptives among married adolescents, and to determine its association with sociodemographic variables, health worker outreach, and media exposure to FP messages in India. METHODS: Data for this analysis was drawn from the fourth round of the National Family Health Survey (NFHS-4) conducted in India during 2015-16. The sample size is restricted to 13,232 currently married adolescent girls aged 15-19 years, who were not pregnant at the time of the survey. Bivariate and multivariate analysis were conducted to assess the levels of contraceptive use and its predictors among married adolescents. RESULTS: The use of modern contraceptives among married adolescents increased from 4 to 10% between 1992-93 and 2015-16. The uptake of modern contraceptives was found to be low among the uneducated, those residing in rural areas, among backward classes, those practising Hindu religion, women in the poorest wealth quintile, women without children, and those with no exposure to FP messages via media or health care workers. Among those who met health care workers and discussed FP issues with them, 34.11% were using modern contraceptives as compared to 11.53% of those who did not have discussions with health care workers. CONCLUSIONS: The evidence suggests that contact with health care workers significantly influences the use of modern contraceptives. Further focus on increasing contact between married adolescents' and health care workers, and improving the quality of counselling will protect adolescents from early marriage and pregnancy.

Complications related to early pregnancies and childbirth are a leading cause of death among adolescent girls. Still, a considerable number of young girls in India get married and start childbearing in adolescence. Data shows that 9% of the girls aged 15­19 years in rural areas and 5% in urban areas have already begun childbearing. The World Health Organisation (WHO) recommends access to high-quality sexual and reproductive health information and services as a fundamental right of adolescents. In recent years, the Indian government has also taken several initiatives to cater to the needs of adolescents.The present study focuses on the levels of contraceptive use and the influence of health care workers and media outreach on the uptake of modern contraceptives among married adolescents in India.Findings from the present study suggest that though there has been an increase in the use of modern contraceptives among married adolescents over time, overall the level of contraceptive use is very low. There is visible variation in the use of modern contraceptives depending on the educational status, residence, religion, caste, and household wealth of adolescent girls. Contact of married adolescents with health care workers positively influences the uptake of modern contraceptives. However, the proportion of married adolescents who had contact with health care workers is very low.