Background: Maternal influenza vaccination provides effective protection against influenza infections in pregnant women and their newborns. In India, the influenza vaccine has not yet been offered through immunization programs, owing to the lack of sufficient safety data for pregnant Indian women. Methods: This cross-sectional observational study enrolled 558 women admitted to the obstetrics ward of a civic hospital in Pune. Study-related information was obtained from the participants through hospital records and interviews using structured questionnaires. Univariate and multivariable analysis was used, and the chi-square test with adjusted odds ratio was estimated to account for vaccine exposure and the temporal nature of each outcome, respectively. Results: Women not vaccinated against influenza during pregnancy had a higher risk of delivering very LBW infants, and possible protective effects were suggested (AOR 2.29, 95% CI 1.03 to 5.58, p = 0.03). No association was observed between maternal influenza vaccination for Caesarean section (LSCS) (AOR 0.97, 95% CI, 0.78, 1.85), stillbirth (AOR 1.8, 95% CI 0.18, 24.64) and NICU admission (AOR, 0.87, 0.29 to 2.85), and congenital anomaly (AOR, 0.81, 0.10 to 3.87). Interpretation: These results show that the influenza vaccine administered during pregnancy is safe and might lower the risk of negative birth outcomes.

Background: Tobacco use is a leading cause of preventable morbidity and mortality globally, with early initiation strongly linked to lifelong dependence and adverse health outcomes. In India, socio-cultural and economic heterogeneity complicates tobacco control, particularly among youth. Understanding early initiation patterns is essential for designing age-targeted interventions. Aim: The present study aims to use GATS-2 data to estimate the age of initiation of tobacco use in India, assess the prevalence of early initiation across population subgroups, and identify its key socio-demographic and economic correlates. Methods: We analyzed data from GATS-2 (2016-17), a nationally representative cross-sectional survey in India. The sample included 7647 ever-smokers and 12 772 ever-users of smokeless tobacco aged ≥15 years. Early initiation was defined as starting tobacco use at age ≤14. Weighted bivariate and multivariate logistic regression analyses were performed using STATA v17.0. Results: Early initiation occurred in 8.0% of smokers and 10.4% of smokeless users, with over half initiating between ages 15-24. Scheduled Tribes and Muslims had higher early use, while education showed a strong protective effect; postgraduates were 87% less likely to initiate early. Students (AOR = 5.31) and the unemployed (AOR = 2.14) faced greater risk. Rural residence lowered odds for early smoking (AOR = 0.22) but increased it for smokeless use (AOR = 1.16). Females had 36% higher odds for early smokeless tobacco use. High-burden states included Sikkim, Bihar, Odisha, and Arunachal Pradesh. Conclusions: Early tobacco initiation in India is prevalent among youth, marginalized groups, and certain regions. Strengthened education, policy enforcement, and culturally tailored interventions are vital to curb long-term dependence.

Dengue is the most important vector-borne viral disease of humans and likely more important than malaria globally in terms of morbidity and economic impact [8]. Transmission of dengue is now present in every region of the world and more than 125 countries are known to be dengue endemic. Maharashtra has the highest dengue mortality rate (3.4%) in India, as per data obtained from National Vector Borne Diseases Control Program (NVBDCP-2013). The case fatality rate is significantly high as compared that with other infectious diseases. This observational study was done to obtain the information on treatment given & its outcome in clinical profile & also to assess treatment gap & outcome of Dengue patients admitted in KEM Hospital. The data was collected from the case records of dengue diagnosed patients from Medical record department and it was filled in case record form. The statistical-analysis & the conclusion were drawn with the help of observations. The study revealed that maximum number (121) of dengue cases were in the age group of 21-30 yrs. There was an association between platelet count and treatment outcome. Severity in sign & symptoms lead to complication (n=23) and death (n= 21) in dengue cases. There were 22 deaths occurred in year 2013 because of dengue. Out of which maximum 12 cases reported in months of September (n=6) & October (n=6). There was no treatment gap found in cases of Deaths in study but 36% of died patients received antimalarial treatment even after diagnosed with Dengue. Remaining 64% of died patients received appropriate, rational and life saving treatment for dengue.

Asthma is a growing public health concern in India, but research has largely focused on children or general adult populations, often overlooking women of reproductive age. Prior studies typically use linear models that fail to capture the complex interactions among environmental, socio-demographic, and behavioural risk factors. This study addresses this gap by estimating asthma prevalence in Indian women (15–49 years) and applying machine learning techniques to identify non-linear, high-dimensional predictors using NFHS-5 data. This study analysed NFHS-5 data (2019–2021) using a nationally representative stratified two-stage sampling design. A total of 550,746 women aged 15–49 was included after excluding non-responses to asthma-related questions. Asthma status was self-reported. Bivariate Chi-square tests examined associations with environmental, socio-economic, behavioral, nutritional, and geographic variables. A one-sample t-test assessed dietary score differences. Three machine learning models like Logistic Regression, Random Forest, and XGBoost were developed on a balanced dataset using up-sampling in R (caret package). Model performance was evaluated using AUC and accuracy; key predictors were identified via feature importance and predicted probabilities. Asthma prevalence was 15.4 per 1,000 women (95% CI: 14.9–16.0). Significant associations were observed with environmental (housing, fuel type, sanitation), socio-economic (age, education, caste, religion), behavioral (tobacco, alcohol), and nutritional factors (BMI, dietary score). Random Forest outperformed other models (AUC: 0.912; accuracy: 84.3%; p < 0.001), with dietary score, age, and wealth index as top predictors. Predicted risk was notably higher among older, overweight, less-educated, and urban women (p < 0.05 for all comparisons). Asthma among Indian women is shaped by diverse social, environmental, and behavioral factors. Machine learning, particularly Random Forest, offers valuable predictive insights, highlighting high-risk groups and supporting targeted public health interventions. Not applicable.