Prevalence of anemia and its social determinants among the male residents of an urban area in Tamil Nadu, India.

Introduction: India has a high burden of anemia among the South Asian countries. Anemia has been extensively studied in the female and child population, with less attention given to males. The present study aims to assess the prevalence of anemia among males residing in an urban area and associated social determinants. Methods: A community-based cross-sectional study was conducted among 300 males (during September and October 2021) in an urban area of Coimbatore, Tamil Nadu. A portable hemoglobin photometer was used to detect the prevalence of anemia. A structured questionnaire was used to collect details on social determinants of anemia such as dietary practice, education, income, and sanitary practices. Results: The mean age of study participants was 34 ± 5.23 years. The prevalence of overall anemia among men was 20.3% (95% confidence interval [CI]: 15.9-25.3%), with moderate and severe anemia being 5.7% (95% CI: 3.3-8.9%) and 1% (95% CI: 0.2-2.9%), respectively. Belonging to a lower socioeconomic class (odds ratio [OR] = 6.50, P < 0.05) and consuming more than two cups of tea (OR = 7.28, P < 0.05) were significantly associated with anemia. Conclusion: Our study depicts a high burden of anemia among males. Health education on the role of dietary factors contributing to anemia needs to be emphasized with special focus on the frequency of tea intake. Primary care physicians can include screening for anemia in their routine practice for men belonging to lower socioeconomic groups. Consideration should be made to include male populations also under the National Anemia Control program.

Factors associated with unexplained sudden deaths among adults aged 18-45 years in India - A multicentric matched case-control study.

BACKGROUND OBJECTIVES: In view of anecdotal reports of sudden unexplained deaths in India's apparently healthy young adults, linking to coronavirus disease 2019 (COVID-19) infection or vaccination, we determined the factors associated with such deaths in individuals aged 18-45 years through a multicentric matched case-control study. METHODS: This study was conducted through participation of 47 tertiary care hospitals across India. Cases were apparently healthy individuals aged 18-45 years without any known co-morbidity, who suddenly (<24 h of hospitalization or seen apparently healthy 24 h before death) died of unexplained causes during 1 st October 2021-31 st March 2023. Four controls were included per case matched for age, gender and neighborhood. We interviewed/perused records to collect data on COVID-19 vaccination/infection and post-COVID-19 conditions, family history of sudden death, smoking, recreational drug use, alcohol frequency and binge drinking and vigorous-intensity physical activity two days before death/interviews. We developed regression models considering COVID-19 vaccination ≤42 days before outcome, any vaccine received anytime and vaccine doses to compute an adjusted matched odds ratio (aOR) with 95 per cent confidence interval (CI). RESULTS: Seven hundred twenty nine cases and 2916 controls were included in the analysis. Receipt of at least one dose of COVID-19 vaccine lowered the odds [aOR (95% CI)] for unexplained sudden death [0.58 (0.37, 0.92)], whereas past COVID-19 hospitalization [3.8 (1.36, 10.61)], family history of sudden death [2.53 (1.52, 4.21)], binge drinking 48 h before death/interview [5.29 (2.57, 10.89)], use of recreational drug/substance [2.92 (1.1, 7.71)] and performing vigorous-intensity physical activity 48 h before death/interview [3.7 (1.36, 10.05)] were positively associated. Two doses lowered the odds of unexplained sudden death [0.51 (0.28, 0.91)], whereas single dose did not. INTERPRETATION CONCLUSIONS: COVID-19 vaccination did not increase the risk of unexplained sudden death among young adults in India. Past COVID-19 hospitalization, family history of sudden death and certain lifestyle behaviors increased the likelihood of unexplained sudden death.

Experiences of hospitalized patients with COVID-19 - A single centre qualitative study.

Introduction: COVID-19 pandemic caused a huge impact on the healthcare system worldwide. With a spectrum of outcome ranging from mild symptoms to mortality, the experiences of patients admitted in isolation wards have not been documented. Objective: To explore the experience of COVID-19 patients during hospitalization during the early phase of pandemic. Materials and Methods: A qualitative study using in-depth interviews was conducted among 30 COVID positive patients admitted in COVID wards of a private medical college in Coimbatore, South India between May and June 2020. Qualitative data analysis was done using the seven steps of Colaizzi's descriptive phenomenological method. Results: The experiences of COVID-19 patients during their hospitalization were categorized into three themes. Firstly, their initial reaction to being diagnosed positive for COVID-19. Secondly, experience during hospitalization with regards to their mental health, treatment providers, caregivers (family and relatives) and other COVID patients admitted in the same ward. Lastly, perceived change in their quality of life after discharge. Conclusion: The study highlights that the psychological experiences of COVID-19 patients admitted to the hospital were heterogeneous. Therefore, primary care physicians should provide individualized psychological interventions based on the needs. Since many expressed fear on diagnosis and stigma of the disease, hence policy makers and health care providers should plan intervention for control and treatment without causing stigma to patients and their families.

Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images.

Lung cancer is a prevalent malignancy that impacts individuals of all genders and is often diagnosed late due to delayed symptoms. To catch it early, researchers are developing algorithms to study lung cancer images. The primary objective of this work is to propose a novel approach for the detection of lung cancer using histopathological images. In this work, the histopathological images underwent preprocessing, followed by segmentation using a modified approach of KFCM-based segmentation and the segmented image intensity values were dimensionally reduced using Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Algorithms such as KL Divergence and Invasive Weed Optimization (IWO) are used for feature selection. Seven different classifiers such as SVM, KNN, Random Forest, Decision Tree, Softmax Discriminant, Multilayer Perceptron, and BLDC were used to analyze and classify the images as benign or malignant. Results were compared using standard metrics, and kappa analysis assessed classifier agreement. The Decision Tree Classifier with GWO feature extraction achieved good accuracy of 85.01% without feature selection and hyperparameter tuning approaches. Furthermore, we present a methodology to enhance the accuracy of the classifiers by employing hyperparameter tuning algorithms based on Adam and RAdam. By combining features from GWO and IWO, and using the RAdam algorithm, the Decision Tree classifier achieves the commendable accuracy of 91.57%.

Surveying the Indian research ethics committee response to the COVID-19 pandemic.

Research ethics committees (RECs) have played a crucial role in expediting the review of research protocols amidst the COVID-19 pandemic. To improve their performance and identify areas of enhancement, a multicentric study was conducted in India by the Forum for Ethical Review Committees in the Asian and Western Pacific Region (FERCAP). The study aimed to evaluate the preparedness of Indian RECs during the COVID-19 outbreak while conducting protocol reviews and comprehend the challenges they encountered. After obtaining ethics committee approval, a cross-sectional observational study was conducted using two validated questionnaires, one for REC member secretaries/chairpersons and another for REC members. The questionnaires consisted of 13 multiple-choice questions, 10 yes or no questions, and 2 open-ended questions each. The study was distributed to multiple RECs. A total of 109/200 participants, including 13 REC member secretaries, 12 chairpersons and 84 REC members from a total of 34 REC's, consented to participate in the study. During the COVID-19 pandemic, 23/25 (92%) of the RECs conducted online meetings. The most common challenges faced by RECs included risk-benefit analysis (12/25 RECs), review of informed consent (12/25 RECs), and protocols involving vulnerable populations (10/25 RECs). 65% of the REC members reported the need for ethics review training, and 66/84 REC members agreed or strongly agreed that RECs require training in COVID-19 protocol review. Additionally, 62/84 REC members agreed or strongly agreed that central/joint RECs should review multicenter COVID-19 protocols. RECs in India encountered difficulties while reviewing risk-benefit analyses, informed consent documents (ICDs), and COVID-19 protocols and they suggested providing training on these topics.

Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes.

The deployment of machine learning (ML) systems in applications with societal impact has motivated the study of fairness for marginalized groups. Often, the protected attribute is absent from the training dataset for legal reasons. However, datasets still contain proxy attributes that capture protected information and can inject unfairness in the ML model. Some deployed systems allow auditors, decision makers, or affected users to report issues or seek recourse by flagging individual samples. In this work, we examine such systems and consider a feedback-based framework where the protected attribute is unavailable and the flagged samples are indirect knowledge. The reported samples are used as guidance to identify the proxy attributes that are causally dependent on the (unknown) protected attribute. We work under the causal interventional fairness paradigm. Without requiring the underlying structural causal model a priori, we propose an approach that performs conditional independence tests on observed data to identify such proxy attributes. We theoretically prove the optimality of our algorithm, bound its complexity, and complement it with an empirical evaluation demonstrating its efficacy on various real-world and synthetic datasets.

Peer Educators as Change Leaders - Effectiveness of Peer Education Process in Creating Awareness on Reproductive Health among Women Workers in Textile Industry.

CONTEXT: India is one of the leading manufacturers of garments with roughly 80% of the workforce dominated by female migrant workers. Lack of education and access to resources in these women contribute to unsafe sexual behaviors, sometimes leading to unwanted pregnancies or sexually transmitted infections (STIs). Awareness raising, and peer education in particular, is extremely effective in addressing reproductive health issues. AIMS: This study aims to study the effectiveness of peer health education in improving the awareness on reproductive health among women workers in textile industries. SETTINGS AND DESIGN: A health educational interventional study for women in garment and spinning mills using peer education process carried out over a period of 18 months. SUBJECTS AND METHODS: The process includes orientation to factory management, health needs' assessment, identifying health topics, peer educators' selection, peer educator trainings followed by outreach sessions, and impact measurement. The impact of peer education on reproductive health has been assessed among 597 participants from 9 factories. STATISTICAL ANALYSIS USED: Data were entered in SPSS version 24 and analyzed. Results were expressed as percentages. Chi square test was used where appropriate to find association between categorical variables. RESULTS: The mean age of the workers in both the groups is 25.4 (±9.6) years and 25.1 (±8.3) years. In both the groups, most of the workers were single and more than half of them reside within the factory premises. Significant change in menstrual hygiene practices, knowledge in family planning methods, STIs, and cervical cancer has been observed following peer health education. CONCLUSIONS: A well-designed health educational interventional program using peer educators could be a successful approach to create awareness on sensitive topics.

Efficient Algorithms for Coded Multicasting in Heterogeneous Caching Networks.

Coded multicasting has been shown to be a promising approach to significantly improve the performance of content delivery networks with multiple caches downstream of a common multicast link. However, the schemes that have been shown to achieve order-optimal performance require content items to be partitioned into several packets that grows exponentially with the number of caches, leading to codes of exponential complexity that jeopardize their promising performance benefits. In this paper, we address this crucial performance-complexity tradeoff in a heterogeneous caching network setting, where edge caches with possibly different storage capacity collect multiple content requests that may follow distinct demand distributions. We extend the asymptotic (in the number of packets per file) analysis of shared link caching networks to heterogeneous network settings, and present novel coded multicast schemes, based on local graph coloring, that exhibit polynomial-time complexity in all the system parameters, while preserving the asymptotically proven multiplicative caching gain even for finite file packetization. We further demonstrate that the packetization order (the number of packets each file is split into) can be traded-off with the number of requests collected by each cache, while preserving the same multiplicative caching gain. Simulation results confirm the superiority of the proposed schemes and illustrate the interesting request aggregation vs. packetization order tradeoff within several practical settings. Our results provide a compelling step towards the practical achievability of the promising multiplicative caching gain in next generation access networks.