Search

Context: Despite the Weekly Iron folic acid supplementation (WIFS) program, the prevalence of anaemia among adolescent girls remains high. Phase 1 Indian Council of Medical Research (ICMR) task force study conducted in 2016 in Kallur showed that the IFA provision rate for adolescent girls was 72% but the consumption rate was only 15% in the Kallur area. The present study was done to identify the gaps for the difference between provision and consumption rate of weekly IFA tablets among adolescent girls using the WHO conceptual framework in home-based settings. Materials and Methods: This crosssectional study with a mixedmethod design was conducted from October 2020 to December 2021. Quantitative data were collected from 972 adolescent girls and their parents using a structured pretested questionnaire, whereas qualitative exploration was done by focus group discussions. Descriptive analysis and bivariate analysis were used to analyse the quantitative data. Qualitative data were analysed and integrated with quantitative results. Results: The overall number of girls who were aware of Iron Folic acid therapy (IFAT) was 704 (72%). However, only 132 (13%) adolescent girls were found to be adherent to IFA therapy. Multivariable regression analysis revealed that side effects encountered on intake of IFAT (Odds ratio (OR) =0.5, P = 0.009) were associated with higher rates of nonadherence, whereas regular supply (OR = 13.6, P = 0.000), health education to parents (OR = 2.76, P = 0.002), and experiencing benefits (OR = 1.72, P = 0.006) were associated with higher rates of adherence. These were substantiated by qualitative findings. Conclusions: Ignorance on the impact of anaemia on adolescent health, fear of side effects, unpleasant effects experienced on intake of Iron folic acid (IFA), and inadequate counselling determines the adherence to weekly IFA supplements among adolescent girls.

Aim: The aim of the study was to analyse the pattern of mobile usage and its impact on behaviour among college students. Methods: A cross-sectional, semi-structured questionnaire based study was conducted among 275 students of various colleges of Tamil Nadu after obtaining IEC clearance. Quantitative data is expressed as mean ± standard deviation and compared by independent sample t test or ANOVA. Qualitative data is expressed in percentages. Results: Out of the 275 study participants, 140 were from medical college and 135 were from non- medical colleges. Around 42.8% participants were using mobile phones for 3-6 hours per day and 58% were using social media more frequently in a day. The estimated mean of the behavioural analysis score with the social media usage and mobile phone usage revealed that the mean score increased if the usage of social media or mobile increased (p < 0.001). Conclusion: The pattern of mobile phone use among the college students reported in this study appears to have some behavioural influence on them. This has to be recognized at an earlier stage and measures must be taken to reduce it.

Abstract Background Autoinflammatory disorders are the group of inherited inflammatory disorders caused due to the genetic defect in the genes that regulates innate immune systems. These have been clinically characterized based on the duration and occurrence of unprovoked fever, skin rash, and patient’s ancestry. There are several autoinflammatory disorders that are found to be prevalent in a specific population and whose disease genetic epidemiology within the population has been well understood. However, India has a limited number of genetic studies reported for autoinflammatory disorders till date. The whole genome sequencing and analysis of 1029 Indian individuals performed under the IndiGen project persuaded us to perform the genetic epidemiology of the autoinflammatory disorders in India. Results We have systematically annotated the genetic variants of 56 genes implicated in autoinflammatory disorder. These genetic variants were reclassified into five categories (i.e., pathogenic, likely pathogenic, benign, likely benign, and variant of uncertain significance (VUS)) according to the American College of Medical Genetics and Association of Molecular pathology (ACMG-AMP) guidelines. Our analysis revealed 20 pathogenic and likely pathogenic variants with significant differences in the allele frequency compared with the global population. We also found six causal founder variants in the IndiGen dataset belonging to different ancestry. We have performed haplotype prediction analysis for founder mutations haplotype that reveals the admixture of the South Asian population with other populations. The cumulative carrier frequency of the autoinflammatory disorder in India was found to be 3.5% which is much higher than reported. Conclusion With such frequency in the Indian population, there is a great need for awareness among clinicians as well as the general public regarding the autoinflammatory disorder. To the best of our knowledge, this is the first and most comprehensive population scale genetic epidemiological study being reported from India.

Abstract Background Implicit bias instruction is becoming more prevalent in health professions education, with calls for skills-based curricula moving from awareness and recognition to management of implicit bias. Evidence suggests that health professionals and students learning about implicit bias (“learners”) have varying attitudes about instruction in implicit bias, including the concept of implicit bias itself. Assessing learner attitudes could inform curriculum development and enable instructional designs that optimize learner engagement. To date, there are no instruments with evidence for construct validity that assess learner attitudes about implicit bias instruction and its relevance to clinical care. Methods The authors developed a novel instrument, the Attitude Towards Implicit Bias Instrument (ATIBI) and gathered evidence for three types of construct validity- content, internal consistency, and relationship to other variables. Results Authors utilized a modified Delphi technique with an interprofessional team of experts, as well as cognitive interviews with medical students leading to item refinement to improve content validity. Seven cohorts of medical students, N = 1072 completed the ATIBI. Psychometric analysis demonstrated high internal consistency (α = 0.90). Exploratory factor analysis resulted in five factors. Analysis of a subset of 100 medical students demonstrated a moderate correlation with similar instruments, the Integrative Medicine Attitude Questionnaire (r = 0.63, 95% CI: [0.59, 0.66]) and the Internal Motivation to Respond Without Prejudice Scale (r = 0.36, 95% CI: [0.32, 0.40]), providing evidence for convergent validity. Scores on our instrument had low correlation to the External Motivation to Respond Without Prejudice Scale (r = 0.15, 95% CI: [0.09, 0.19]) and the Groningen Reflection Ability Scale (r = 0.12, 95% CI: [0.06, 0.17]) providing evidence for discriminant validity. Analysis resulted in eighteen items in the final instrument; it is easy to administer, both on paper form and online. Conclusion The Attitudes Toward Implicit Bias Instrument is a novel instrument that produces reliable and valid scores and may be used to measure medical student attitudes related to implicit bias recognition and management, including attitudes toward acceptance of bias in oneself, implicit bias instruction, and its relevance to clinical care.

Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of transforming predictive analytics, revolutionizing business strategies through the utilization of big data insights. The exponential growth of data in the digital era has made it imperative for businesses to leverage advanced analytics tools to remain competitive. AI and ML, with their ability to process vast amounts of data, identify patterns, and generate predictive models, offer unparalleled opportunities for decision-making and strategic planning. By automating data analysis and forecasting future trends, these technologies enable businesses to anticipate customer behavior, optimize operations, and improve overall efficiency. In predictive analytics, AI and ML algorithms can identify subtle correlations within complex datasets that traditional statistical methods might overlook. This leads to more accurate predictions in areas such as customer preferences, market demand, risk management, and supply chain optimization. Additionally, AI-driven predictive analytics helps businesses tailor marketing campaigns, reduce costs, and enhance customer experiences by delivering personalized services. Furthermore, real-time analytics supported by AI enhances agility, allowing businesses to swiftly respond to market changes and emerging trends. The integration of AI and ML into predictive analytics is not only reshaping industries but also paving the way for new business models, products, and services. The ability to harness big data for strategic advantage has become a critical factor for sustained growth in an increasingly data-driven world. As businesses continue to adopt these technologies, the gap between data-rich and data-poor organizations widens, further emphasizing the importance of embracing AI and ML in predictive analytics to stay ahead of the competition. [ABSTRACT FROM AUTHOR]