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Background: Life expectancy, especially life expectancy at birth is a good indicator of current health condition of the population and is estimated through life tables. The present study aims to examine the effect of partial and complete elimination of a specific disease on the gain in life expectancy at birth by sex in India and selected states using Medical Certification of Cause of Death (MCCD) secondary data for 2017. Methods: Life tables were constructed for all cause-specific deaths using Greville method for both males and females separately for India and selected states. Cause-deleted life tables were also constructed for partial (25%, 50%, 75%) and complete (100%) elimination of ten leading cause of death groups. Gain in life expectancy was estimated as the difference between values of cause-deleted life tables and cause-specific life tables. Results: Complete elimination of diseases of the circulatory system yielded maximum gain in life expectancy at birth in males (14.64 years) and in females (15.49 years) both in Haryana. It was followed by diseases of the respiratory system in males (6.45 years) in Jharkhand and in females (6.17 years) in Uttar Pradesh; certain infectious and parasitic diseases in males (3.82 years) in Uttar Pradesh and in females (3.58 years) in Delhi; neoplasms in males (2.71 years) and in females (2.11 years) both in Kerala. Similar results were obtained in case of remaining diseases. Conclusions: Maximum gained years would be obtained after reducing mortality from diseases of the circulatory system. These may have implications for practical decision making in setting up health goals, allocating resources, and launching tailor-made health care programs.

Standard inspections of buildings are not always possible because of human flaws in prediction. Hence, we need more stable, scalable, and efficient automated processes. Structure Health Monitoring (SHM) is one of the automation systems for forecasting potential losses in building structures. This article suggested how to monitor the strength status of buildings by using Hybrid Machine Learning Technique (HMLT). HMLT contains two-hybrid procedures. One for identifying the most significant features in Dataset using Hybrid Feature Selection Method (HFSM). HFSM uses the combined features of Mutual information (MI) and Rough Set Theory (RST) for feature selection. Another method is optimized classifiers such as Support Vector Machine (SVM) and Artificial Neural Networks (ANN) are used for the classification and predicting the accuracy i.e. predicting the strength status of buildings. Now the proposed method is applied on Earthquake Damage Dataset (Gorkha Earthquake in April 2015). Training and 10- fold cross-validation procedure pragmatic to features. Then the performance of proposed method has been evaluated using the F1-score and accuracy metrics and get 91% and 92% respectively. Finally, the result analysis demonstrates the importance of the proposed approach in predicting the status of the building strength.

Background The Joint United Nations Programme on AIDS (UNAIDS) has emphasized on the incidence-prevalence ratio (IPR) and incidence-mortality ratio (IMR) to measure the progress in HIV epidemic control. In this paper, we describe the status of epidemic control in India and in various states in terms of UNAIDS’s recommended metrices. Method The National AIDS Control Programme (NACP) of India spearheads work on mathematical modelling to estimate HIV burden based on periodically conducted sentinel surveillance for providing guidance to program implementation and policymaking. Using the results of the latest round of HIV Estimations in 2019, IPR and IMR were calculated. Results National level IPR was 0.029 [0.022–0.037] in 2019 and ranged from 0.01 to 0.15 in various States and Union Territories (UTs). Corresponding Incidence-Mortality Ratio was at 0.881 [0.754–1.014] nationally and ranged between 0.20 and 12.90 across the States/UTs. Conclusions Based on UNAIDS recommended indicators for HIV epidemic control, namely IPR and IMR; national AIDS response in India appears on track. However, the program success is not uniform and significant heterogeneity as well as expanding epidemic was observed at the level of States or UTs. Reinforcing States/UTs specific and focused HIV prevention, testing and treatment initiatives may help in the attainment of 2030 Sustainable Development Goals of ending AIDS as a public health threat by 2030.

BackgroundThe prevalence of stunting among under- three Indian children though decreased, still it is considered to be alarmingly high. In most of the previous studies, traditional (linear and logistic) regression analyses were applied. They were limited to encapsulated cross-distribution variations. The objective of the current study was to examine how the different determinants were heterogeneous in various percentiles of height for age (HAZ) distribution.Methods and findingsThis article examined the change in the HAZ distribution of children and examined the relationships between the key co-variate trends and patterns in HAZ among children aged ConclusionsThe outcome of various covariates working differently across the HAZ distribution was suggested by quantile regression. The major discrepancies in different aspects were underlined by socioeconomic and demographic aspects among the Indian population. The heterogeneity of this effect was shown using quantile regression. Policymakers may choose to concentrate on the most important factors when formulating policies to lessen the prevalence of stunting in India.

Background & objectives: Contrary to overall declining trend in smokeless tobacco (SLT) use in India, an increase is observed in north-east (NE) India. This study examined the predictors of daily SLT use by gender and assessed the demographic and socio-economic characteristics that contribute to gender differences in SLT use in NE India. Methods: Data collected from 15,259 and 13,574 adults in the two rounds of Global Tobacco Adult Survey 1 and 2 for NE India during 2009-2010 and 2016-2017 were analyzed. Relative change, multivariable binary logistic regression and Blinder-Oaxaca decomposition analysis were used for analysis. Results: The findings suggest that among women in NE India, the daily SLT use significantly increased by 58 per cent between 2009-2010 to 2016-2017. Women residing in Nagaland, Manipur, Mizoram and Tripura were 3.5 and 2.5 times, respectively more likely to be daily SLT users compared to those in Assam. While age, education and wealth were the significant predictors of SLT use in both women and men, increased odds of SLT use were observed with women’s type of occupation and the State of residence. The majority of the gender differences in daily SLT use was explained by differences in work status (44%), age (26%), education (14%) and wealth status (9%) between men and women. Interpretation & conclusions: Increasing prevalence of SLT use amongst women in the NE States necessitates integration of gender-specific messages on harmful effects of SLT in the ongoing tobacco control programmes and development of culturally appropriate community-based interventions for cessation of SLT use. [ABSTRACT FROM AUTHOR]

Introduction: The effects of COVID-19 infection persist beyond the active phase. Comprehensive description and analysis of the post COVID sequelae in various population groups are critical to minimise the long-term morbidity and mortality associated with COVID-19. This analysis was conducted with an objective to estimate the frequency of post COVID sequelae and subsequently, design a framework for holistic management of post COVID morbidities., Methods: Follow-up data collected as part of a registry-based observational study in 31 hospitals across India since September 2020-October 2022 were used for analysis. All consenting hospitalised patients with COVID-19 are telephonically followed up for up to 1 year post-discharge, using a prestructured form focused on symptom reporting., Results: Dyspnoea, fatigue and mental health issues were reported among 18.6%, 10.5% and 9.3% of the 8042 participants at first follow-up of 30-60 days post-discharge, respectively, which reduced to 11.9%, 6.6% and 9%, respectively, at 1-year follow-up in 2192 participants. Patients who died within 90 days post-discharge were significantly older (adjusted OR (aOR): 1.02, 95% CI: 1.01, 1.03), with at least one comorbidity (aOR: 1.76, 95% CI: 1.31, 2.35), and a higher proportion had required intensive care unit admission during the initial hospitalisation due to COVID-19 (aOR: 1.49, 95% CI: 1.08, 2.06) and were discharged at WHO ordinal scale 6-7 (aOR: 49.13 95% CI: 25.43, 94.92). Anti-SARS-CoV-2 vaccination (at least one dose) was protective against such post-discharge mortality (aOR: 0.19, 95% CI: 0.01, 0.03)., Conclusion: Hospitalised patients with COVID-19 experience a variety of long-term sequelae after discharge from hospitals which persists although in reduced proportions until 12 months post-discharge. Developing a holistic management framework with engagement of care outreach workers as well as teleconsultation is a way forward in effective management of post COVID morbidities as well as reducing mortality., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)