Inequalities in cardiovascular disease among elderly Indians: A gender perspective analysis using LASI wave-I (2017-18).

BACKGROUND: While Cardiovascular disease (CVD) affects both men and women, emerging evidence suggests notable gender differentials in disease prevalence. This study aims to explore and analyse the gender differentials in CVD disease prevalence in India. METHODS: The present study utilizes data from first wave of the nationally representative survey "Longitudinal Ageing Study in India" (LASI, WAVE-I, 2017-18) with the eligible sample size of 31,464 individuals aged 60 years and above. Logistic regression analysis was used to understand risk of CVD by demographic characteristics. Factors contribution to gender differences in CVD prevalence was examined using a non-linear Fairlie decomposition. RESULTS: The prevalence of CVD was lower in men (31.06%) compared to women (38.85%). Women have a 33% higher likelihood of CVD compared to men (OR: 1.33; 95% CI: 1.25-1.42). Lack of education also confers a lower risk, more pronounced in women with no schooling (OR: 0.81; 95% CI: 0.7-0.94) compared to men (OR: 0.52; 95% CI: 0.47-0.58). Morbidity influences CVD presence more among women than men, with individuals suffering from three or more diseases having markedly increased odds (Men: OR: 3.89; 95% CI: 3.54-4.3, Women: OR: 6.97; 95% CI: 6.48-10.11). Smoking accounted increase in (20.52%) the gender gap while years of schooling dramatically lessened the gender gap (-46.30%). CONCLUSION: Result show gender differential in CVD prevalence and underlying risk factors, underscoring the need for gender-specific preventive strategies and interventions. Our findings highlight the importance of refined approach to cardiovascular health that considers the complex interplay of biological, social, and environmental determinants.

Association of Child Growth Failure Indicators With Household Sanitation Practices in India (1998-2021): Spatiotemporal Observational Study.

BACKGROUND: Undernutrition among children younger than 5 years is a subtle indicator of a country's health and economic status. Despite substantial macroeconomic progress in India, undernutrition remains a significant burden with geographical variations, compounded by poor access to water, sanitation, and hygiene services. OBJECTIVE: This study aimed to explore the spatial trends of child growth failure (CGF) indicators and their association with household sanitation practices in India. METHODS: We used data from the Indian Demographic and Health Surveys spanning 1998-2021. District-level CGF indicators (stunting, wasting, and underweight) were cross-referenced with sanitation and sociodemographic characteristics. Global Moran I and Local Indicator of Spatial Association were used to detect spatial clustering of the indicators. Spatial regression models were used to evaluate the significant determinants of CGF indicators. RESULTS: Our study showed a decreasing trend in stunting (44.9%-38.4%) and underweight (46.7%-35.7%) but an increasing prevalence of wasting (15.7%-21.0%) over 15 years. The positive values of Moran I between 1998 and 2021 indicate the presence of spatial autocorrelation. Geographic clustering was consistently observed in the states of Madhya Pradesh, Jharkhand, Odisha, Uttar Pradesh, Chhattisgarh, West Bengal, Rajasthan, Bihar, and Gujarat. Improved sanitation facilities, a higher wealth index, and advanced maternal education status showed a significant association in reducing stunting. Relative risk maps identified hotspots of CGF health outcomes, which could be targeted for future interventions. CONCLUSIONS: Despite numerous policies and programs, malnutrition remains a concern. Its multifaceted causes demand coordinated and sustained interventions that go above and beyond the usual. Identifying hotspot locations will aid in developing control methods for achieving objectives in target areas.

Cardiovascular consequences of financial stress: A systematic review and meta-analysis.

BACKGROUND: Cardiovascular diseases (CVD), notably coronary artery disease (CAD) and coronary heart disease (CHD), are predominant contributors to global morbidity and mortality. Financial stress is recognized as a non-traditional risk factor for CVD. The objective of this study is to conduct a systematic review and meta-analysis on the association between financial stress and the incidence of major cardiac outcomes. METHODS: A literature search was conducted across multiple databases up until September 20, 2023. Primary studies reporting the association between financial stress and the incidence of CAD, CHD, or major cardiovascular outcomes were included. The quality of the incorporated studies was evaluated using the Newcastle-Ottawa Scale. Statistical analysis was performed using R version 4.3, employing a random-effects model. RESULTS: Out of 2,740 identified studies, seven satisfied the inclusion criteria, displaying a diverse range in design, settings, and participant demographics. A significant association was found between financial stress and major cardiac outcomes, with a combined hazard ratio (HR) of 1.191 (95% CI: 1.00 to 1.47), p<0.001 from five studies. Possible publication bias and variations in definitions and measurements of financial stress were noted among the studies. CONCLUSION: The available literature substantiates an association between financial stress and the incidence of CAD/CHD or major cardiac outcomes, underscoring an urgent need for standardized definitions and measurements of financial stress. Our findings support the integration of financial stress assessments in patient care and the development of health policies emphasizing economic strains to enhance cardiovascular health outcomes and overall well-being.

Are paid tools worth the cost? A prospective cross-over study to find the right tool for plagiarism detection.

Background: The increasing pressure to publish research has led to a rise in plagiarism incidents, creating a need for effective plagiarism detection software. The importance of this study lies in the high cost variation amongst the available options for plagiarism detection. By uncovering the advantages of these low-cost or free alternatives, researchers could access the appropriate tools for plagiarism detection. This is the first study to compare four plagiarism detection tools and assess factors impacting their effectiveness in identifying plagiarism in AI-generated articles. Methodology: A prospective cross-over study was conducted with the primary objective to compare Overall Similarity Index(OSI) of four plagiarism detection software(iThenticate, Grammarly, Small SEO Tools, and DupliChecker) on AI-generated articles. ChatGPT was used to generate 100 articles, ten from each of ten general domains affecting various aspects of life. These were run through four software, recording the OSI. Flesch Reading Ease Score(FRES), Gunning Fog Index(GFI), and Flesch-Kincaid Grade Level(FKGL) were used to assess how factors, such as article length and language complexity, impact plagiarism detection. Results: The study found significant variation in OSI(p < 0.001) among the four software, with Grammarly having the highest mean rank(3.56) and Small SEO Tools having the lowest(1.67). Pairwise analyses revealed significant differences(p < 0.001) between all pairs except for Small SEO Tools-DupliChecker. Number of words showed a significant correlation with OSI for iThenticate(p < 0.05) but not for the other three. FRES had a positive correlation, and GFI had a negative correlation with OSI by DupliChecker. FKGL negatively correlated with OSI by Small SEO Tools and DupliChecker. Conclusion: Grammarly is unexpectedly most effective in detecting plagiarism in AI-generated articles compared to the other tools. This could be due to different softwares using diverse data sources. This highlights the potential for lower-cost plagiarism detection tools to be utilized by researchers.