Nutrition is crucial for health and human development, especially in early childhood. The nutritional status of children is essential for their development and growth at an early stage of life, with implications extending into adult life. Undernutrition is recognized to be the biggest single threat to public health worldwide, particularly in developing countries. Nutritional status can be determined anthropometrically, which is the outcome of complex interactions between socio-economic and biological variables. This study aimed to measure undernutrition inequality among under-five children in India, and we used a regression-based decomposition analysis to see the impact of socioeconomic determinants on this inequality. The study utilizes data from the National Family Health Survey (NFHS-5) conducted between 2019 and 2021. The concentration indices for stunting, underweight, and wasting examine the magnitude of socio-economic inequality in child undernutrition. Furthermore, a decomposition analysis of the concentration indices is conducted to understand the role of socioeconomic factors in childhood undernutrition inequality. The prevalence of child undernutrition was more pronounced among children from lower socio-economic strata. The concentration index for stunting, underweight, and wasting were − 0.126, -0.138, and − 0.064, respectively, indicating a higher burden of undernutrition in lower socio-economic quintiles. Urban children in the lowest socio-economic quintiles experienced greater inequality in these conditions than their rural counterparts. Socio-economic status significantly impacted undernutrition inequality, accounting for approximately 31.26

Drawing on Sylvia Walby’s conception of patriarchy and R.W. Connell’s theory of masculinities, this paper shifts the analytical focus to men’s perspective within gender power relations. Conceptualising patriarchy as both structural and interactional processes, the study measures men’s patriarchal attitudes in two key domains: decision-making and sexual relations. Using data from a nationally representative sample of 101,839 men aged 15–54 from the National Family Health Survey 5 (2019–21), we employ bivariate analyses and multilevel regression models to determine the factors of men’s patriarchal attitudes. Additionally, spatial autocorrelation analysis using Moran’s I is conducted to assess the geographic clustering of patriarchal attitudes across the districts. The findings show that higher levels of education and wealth are associated with lower odds of endorsing patriarchal attitudes. Older men are also less likely to exhibit patriarchal attitudes. In contrast, men who own land, are employed, and report a family history of domestic violence are significantly more likely to endorse patriarchal attitudes. Significant variations are observed across caste and religions, suggesting the influence of broader socio-cultural factors rather than identity alone. Men in the Northeast are less likely to endorse patriarchal attitudes, whereas those in the South are more likely; this regional pattern should be interpreted cautiously, as the study focuses on attitudinal norms rather than outcome-based indicators of women’s empowerment. The significant positive Moran’s I values indicate that patriarchal attitudes are spatially clustered across districts rather than randomly distributed. Our results underscore the need for engaging men in attitudinal change programs alongside gender empowerment initiatives to collectively challenge patriarchal norms.

IntroductionOccupational health and safety (OHS) practices witnessed profound changes worldwide during the COVID-19 pandemic. The pandemic exposed critical vulnerabilities in workplace preparedness, particularly among workers in high-exposure and essential service sectors.MethodsA systematic review was conducted following PRISMA guidelines using PubMed, Google Scholar, DOAJ, ResearchGate, and gray literature sources. Studies published between January 2020 and December 2024 focusing on occupational exposure, workplace transmission, OHS guidelines, and workforce health during COVID-19 were included.ResultsHealthcare workers experienced the highest occupational risk due to sustained patient contact and aerosol-generating procedures, accompanied by significant psychological distress and burnout. Essential workers, manufacturing sectors, retail workers, educators, and informal sector workers also faced substantial occupational and socioeconomic challenges. Workplace interventions including engineering controls, administrative controls, PPE use, vaccination strategies, and digital surveillance tools reduced workplace transmission. Remote-enabled sectors demonstrated lower infection risk but increased mental health concerns.DiscussionThe COVID-19 pandemic transformed traditional OHS frameworks by integrating infectious disease preparedness, vaccination strategies, mental health support, and workplace surveillance into occupational safety systems. The findings emphasize the need for integrated, adaptive, and equitable OHS frameworks aligned with public health preparedness to strengthen workforce resilience against future pandemics.

Introduction: Non-communicable diseases (NCDs) are a major cause of morbidity and mortality in both urban and rural populations, leading to substantial loss of potentially productive years of life, particularly among adults aged 35-64 years. Evidence indicates that without effective preventive strategies, NCD-related deaths may reach alarming levels, especially in low-resource countries, underscoring the need for timely public health interventions. Methodology: This study utilized secondary data on selected NCDs diabetes, thyroid disorders, asthma, and heart diseases from the 4th and 5th rounds of the National Family Health Survey (NFHS). The analysis included 98,702 women from NFHS-4 and 103,433 women from NFHS-5, aged 15-49 years, across eight northeastern states of India. Geospatial analysis was employed to identify district-level hotspot and cold-spot clustering based on disease density. Results: A comparison between NFHS-4 (2015-16) and NFHS-5 (2019-21) revealed heterogeneous trends across the four NCDs. In NFHS-4, diabetes prevalence was highest in Sikkim, heart disease in Meghalaya, and asthma in Tripura. In NFHS-5, Tripura reported the highest prevalence of diabetes and thyroid disorders, while asthma and heart disease were most prevalent in Mizoram. Conclusion: The observed rise in NCD prevalence and hotspot clustering among women over five years is concerning. Targeted, district-specific interventions are warranted to address the growing NCD burden in the region.

Background & objectives Caesarean Section (CS) is a crucial life-saving surgical procedure for maternal delivery when normal delivery is ruled out for the safety of mother and infant. This study investigated the spatio-temporal pattern of CS rates to assess the significant factors boosting this delivery in the northeastern States of India. Methods We analysed cross-sectional data from three rounds of the National Family Health Survey (NFHS 3, NFHS 4, and NFHS 5). We estimated the relative risk (RR) of CS delivery and assessed the global and local spatial autocorrelation for each year from 2011 to 2019. Furthermore, the posterior median RR with credible interval was estimated using Bayesian Spatio-temporal modelling with Markov Chain Monte Carlo simulation. Results The CS rates in the northeastern States have escalated by nearly 15 per cent over the last two decades, from 5.4 per cent in 2001 to 19.9 per cent in 2019. Furthermore, we observed a substantial increase in the high-high Local Indicator of Spatial Analysis clusters from 2011 to 2019. The estimated posterior median RR exceeded one for four significant predictors: maternal age, maternal education, obesity, and household wealth status. This analysis also revealed that the estimated spatio-temporal trend displayed a clear upward trend in CS risk during the nine-year study period. Interpretation & conclusions This study found a substantial increase in CS delivery rate over the nine-year period (2011 to 2019) in the northeastern States of India. The findings of the study provide important policy input for strengthening healthcare intervention and regulations by initiating targeted programmes to monitor and avert excessive use of CS facilities in districts with high CS delivery.

Univariate neuroimaging studies have shown brain differences in individuals with autism spectrum disorder (ASD) compared to healthy controls (CTL). In contrast, together with neuroimaging, machine learning (ML) provides a framework for building ASD diagnostic models with predictive accuracy assessed with cross-validation. Three types of ML methods in nine ML algorithms were investigated, i.e., boosting, bagging, and neural networks, to check the best algorithm for the classification of ASD from CTL using structural magnetic resonance imaging (MRI) data (N = 740, 344 ASD) from the Autism Brain Imaging Data Exchange (ABIDE) repository. The current study investigated model efficiencies in receiver operating characteristics (ROCs) during the training phase; and balanced accuracy used in the testing phase was captured to compare the algorithms. Findings showed Stochastic Gradient Boosting Machine (SGBM) with a balanced accuracy of 78.87

BackgroundThe COVID-19 pandemic posed significant challenges to healthcare systems worldwide. Maintaining essential health services, including maternal and child health (MCH), while addressing the pandemic is an enormous task. This study aimed to assess the impact of the COVID-19 pandemic on the utilization of MCH services in India's public primary care. It extends prior work by applying nationwide HMIS data within an interrupted time-series framework with seasonal and ARMA adjustments to estimate counterfactual trends, thereby providing national-level insights into both immediate and evolving disruptions.MethodsA retrospective analysis using Health Management Information System (HMIS) data examined 12 indicators of service utilization, covering maternal health, child health, deliveries, and newborn care. Interrupted time-series analysis compared pre-pandemic (April 2017–March 2020) and pandemic (March 2020–May 2021) was performed using Ordinary Least Squares (OLS) and Generalized Least Squares (GLS) regression models, adjusting for seasonality and autocorrelation with ARMA terms.ResultsAntenatal care (ANC) registrations decreased by 346,420 cases (−12.8%, p = 0.026) following the onset of the pandemic, with no significant recovery in the subsequent months. Tetanus toxoid vaccinations also declined markedly, with Td1 and Td2 falling by 276,152 (−13.9%, p = 0.029) and 306,607 (−16.9%, p = 0.010) cases, respectively, and remaining consistently below expected levels. Institutional deliveries dropped by 272,441 (−13.7%, p = 0.067), while home deliveries attended by skilled birth attendants decreased by 5,054 cases (−22.8%, p = 0.014). Child health services, including referrals to Special Newborn Care Units (SNCUs) and inborn admissions, were also lower than anticipated (−20.4% and −19.2%, respectively), though these changes were not statistically significant. Among all indicators, the largest and most persistent disruptions occurred in obstetric complications (maximum decline during Winter 2020–21) and SNCU inborn admissions (also at their lowest in Winter 2020–21). These two services showed minimal signs of recovery throughout the study period, underscoring the particular vulnerability of emergency obstetric and neonatal care during public health crises.ConclusionsThe COVID-19 pandemic caused declines in MCH service utilization, with varying recovery across indicators. While services like antenatal care and vaccinations showed some stabilization over time, child health admissions and obstetric complications remained below pre-pandemic trends. Strengthening healthcare systems to maintain essential services and support recovery during and after public health emergencies is critical.

It is difficult to achieve health related Sustainable Development Goals when a higher proportion of birth delivery occurs through cesarean section (CS) than vaginal delivery without considerable medical benefits. This study aims to identify the spatial hot spot clustering and determinants of cesarean section in northeastern states, India. The study utilized data from the fifth round of the National Family Health Survey (NFHS-5, 2019–2021), which included responses from 34,222 mothers who delivered live births in the five years preceding the survey. The study investigated spatial hot spot clustering of CS prevalence using Getis-Ord Gi* statistics and applied multiscale geographically weighted regression (MGWR) to identify spatial clusters in the relationships between predictor variables and CS delivery. The study identified spatial hot spot clustering of CS rates in districts of Sikkim, western and southern Tripura, eastern and western Assam, and central Manipur. MGWR results indicated that significant determinants of CS include maternal age (30–49 years), first birth order, highest educational level, high body mass index, and highest wealth quintile, with regression coefficients varying significantly by district in this region. The study found that CS rates vary by clusters in the districts of northeastern states of India. It suggests that piloting educational interventions for pregnant women and regularly monitoring CS facilities could be initial strategies to better understand and address the higher CS trends in these regions.

OBJECTIVES:Neonatal mortality remains a significant public health issue in India. This study investigates spatial patterns and contributing factors to neonatal mortality in the north-eastern states, identifying hotspot regions and spatial variations. METHODS:A sample of 34,222 mothers from India's National Family Health Survey (NFHS-5, 2019-21) in the north-eastern states was analysed. Descriptive and bivariate analyses were conducted alongside Bayesian multilevel logistic regression using integrated nested Laplace approximation to model neonatal mortality. Spatial hotspot analysis using Getis-Ord Gi* statistics identified clusters of high neonatal mortality, while geographically weighted regression (GWR) was used to examine spatial variations in the relationships between neonatal mortality and contributing factors. RESULTS:The neonatal mortality rate in the north-eastern states declined from 45 to 21 per 1,000 live births (NFHS-1 to NFHS-5) but remains higher than the national average. Assam reported the highest mortality (42.16%), whereas Sikkim had the lowest (0.87%). Higher mortality was observed among male infants, mothers with advanced age, low maternal education, and mothers who attended less than 5 antenatal care (ANC) visits. Spatial analysis identified hotspots in Assam, Meghalaya, and Tripura. GWR indicated that areas with less than 5 ANC visits had the strongest association with neonatal mortality. Bayesian multilevel analysis highlighted spatial variations of up to 51% across districts in northeast India. CONCLUSIONS:This study underscores spatial disparities in neonatal mortality across north-eastern India. Addressing childcare practices and healthcare access in hotspot regions is essential for improving new-born health outcomes. The findings provide critical insights for policymakers to develop targeted interventions aimed at reducing neonatal mortality in these underserved areas.

This review focuses on important health programs introduced by the government in recent years that aim for better maternal health. The mother and child health outcomes have been markedly improved by the Government of India's programs for maternal health, which have yielded many benefits. Reducing problems during childbirth and maternal fatalities has been made possible by programs like the Janani Suraksha Yojana (JSY), which encourage institutional deliveries and provide experienced birth attendants. Improving nutrition has received significant attention as well. New initiatives /Programs like Poshan Abhiyaan and the Pradhan Mantri Matru Vandana Yojana (PMMVY) give pregnant and nursing women nutritional supplements and support, improving the health of both moms and their unborn children. Comprehensive care guarantees that women and newborns receive all-encompassing medical attention and includes vaccines, supplements, and routine check-ups.Pregnant women who participate in programs like PMMVY receive financial incentives that help them better manage their health, lessen the financial strain of being pregnant and postpartum, and spend less money out of their own pockets. High standards of care in labor rooms and maternity surgery theatres are ensured by the modernization of healthcare facilities and the training of healthcare professionals through programs like LaQshya. A number of programs involve health education and counselling, which equips women with information on labor, pregnancy, and child care. This review focuses on important health programs introduced by the government in recent years that aim for better maternal health.

Extra-pulmonary TB (EPTB) is difficult to diagnose due to paucibacillary nature of disease. Current study evaluated accuracy of Truenat MTB and MTB-Rif Dx (TN), for detection of Mycobacterium tuberculosis and resistance to rifampicin. Samples were collected from 2103 treatment naive adults with presumptive EPTB, and tested by smear microscopy, liquid culture (LC) (MGIT-960) and GeneXpert MTB/RIF (GX) (Microbiological Reference Standards, MRS). TN results were compared to MRS and Composite Reference Standards (CRS, Microbiology, histopathology, radiology, clinical features prompting decision to treat, response to treatment). CRS grouped patients into 551 confirmed, 1096 unconfirmed, and 409 as unlikely TB. TN sensitivity and specificity was 73.7% and 90.4% against GX. Against LC, Overall sensitivity of GX was 67.6%, while that of TN was 62.3%. Highest sensitivity by TN was observed in pus samples (89%) and highest specificity (92%) in CSF samples, similar to GX. TN sensitivity was better in fluid and biopsy samples and slightly inferior for lymph node aspirates compared to GX. TN sensitivity for RIF resistance detection was slightly superior to GX. TN and GX results were further compared to Clinical Reference Standards. TN detected 170 TB patients initiated on treatment missed by GX, while GX detected 113 such patients missed by TN. Of 124 samples with RIF resistance discordance between GX and TN, GX reported 103/124 as sensitive, 3/124 as indeterminate and 18 as resistant (13/18 samples had low/very low DNA load) while TN reported RIF resistance indeterminate in 103/111 low/very low DNA load samples. Due to paucibacillary nature of EPTB samples, culture yield was poor and phenotypic drug susceptibility testing failed to resolve the discordance. The study establishes TN at par with GX and can be utilized for quick and accurate diagnosis of EPTB.

A well-structured digital database is essential for any national priority project as it can provide real-time data analysis and facilitate quick decision making. In recent times, particularly after the COVID-19 pandemic, invasive fungal infections (IFIs) have emerged as a significant public health challenge in India, affecting vulnerable population, including immunocompromised individuals. The lack of comprehensive and well-structured data on IFIs has hindered efforts to understand their true burden and optimize patient care. To address this critical knowledge gap, the ICMR has undertaken a Pan-India pioneer initiative to develop a network of Advanced Mycology Diagnostic research centres in different geographical zones of the country (ICMR-MycoNet). Under the aegis of this project, a clinical registry on IFIs in the ICUs is initiated. This process paper presents a detailed account of the steps involved in the establishment of a web-based data entering and monitoring platform to capture data electronically, ensuring robust and secure data collection and management. This system not only allows participating ICMR-MycoNet centres to enter patient information directly into the database using standardized Case Report Form (CRF) but also includes data validation checks to ensure the accuracy and completeness of entered data. It is complemented by a real-time, web-based, and adaptable data visualization platform. This registry aims to provide crucial epidemiological insights, promote evidence-based hospital infection control programs, and ultimately improve patient outcomes in the face of this formidable healthcare challenge.

BackgroundFungal infections are now a great public health threat, especially in those with underlying risk factors such as neutropenia, diabetes, high-dose steroid treatment, cancer chemotherapy, prolonged intensive care unit stay, and so on, which can lead to mycoses with higher mortality rates. The rates of these infections have been steadily increasing over the past 2 decades due to the increasing population of patients who are immunocompromised. However, the data regarding the exact burden of such infection are still not available from India. Therefore, this registry was initiated to collate systematic data on invasive fungal infections (IFIs) across the country. ObjectiveThe primary aim of this study is to create a multicenter digital clinical registry and monitor trends of IFIs and emerging fungal diseases, as well as early signals of any potential fungal outbreak in any region. The registry will also capture information on the antifungal resistance patterns and the contribution of fungal infections on overall morbidity and inpatient mortality across various conditions. MethodsThis multicenter, prospective, noninterventional observational study will be conducted by the Indian Council of Medical Research through a web-based data collection method from 8 Advanced Mycology Diagnostic and Research Centers across the country. Data on age, gender, clinical signs and symptoms, date of admission, date of discharge or death, diagnostic tests performed, identified pathogen details, antifungal susceptibility testing, outcome, and so on will be obtained from hospital records. Descriptive and multivariate statistical methods will be applied to investigate clinical manifestations, risk variables, and treatment outcomes. ResultsThese Advanced Mycology Diagnostic and Research Centers are expected to find the hidden cases of fungal infections in the intensive care unit setting. The study will facilitate the enhancement of the precision of fungal infection diagnosis and prompt treatment modalities in response to antifungal drug sensitivity tests. This registry will improve our understanding of IFIs, support evidence-based clinical decision-making ability, and encourage public health policies and actions. ConclusionsFungal diseases are a neglected public health problem. Fewer diagnostic facilities, scanty published data, and increased vulnerable patient groups make the situation worse. This is the first systematic clinical registry of IFIs in India. Data generated from this registry will increase our understanding related to the diagnosis, treatment, and prevention of fungal diseases in India by addressing pertinent gaps in mycology. This initiative will ensure a visible impact on public health in the country. International Registered Report Identifier (IRRID)DERR1-10.2196/54672

Background:Physician-coded verbal autopsy (PCVA) is the most widely used method to determine causes of death (COD) in countries where medical certification of death is low. Computer-coded verbal autopsy (CCVA), an alternative method to PCVA for assigning the COD is considered to be efficient and cost-effective. However, the performance of CCVA as compared to PCVA is yet to be established in the Indian context. Methods:We evaluated the performance of PCVA and three CCVA methods i.e., InterVA 5, InSilico, and Tariff 2.0 on verbal autopsies done using the WHO 2016 VA tool on 2,120 reference standard cases developed from five tertiary care hospitals of Delhi. PCVA methodology involved dual independent review with adjudication, where required. Metrics to assess performance were Cause Specific Mortality Fraction (CSMF), sensitivity, positive predictive value (PPV), CSMF Accuracy, and Kappa statistic. Results:In terms of the measures of the overall performance of COD assignment methods, for CSMF Accuracy, the PCVA method achieved the highest score of 0.79, followed by 0.67 for Tariff_2.0, 0.66 for Inter-VA and 0.62 for InSilicoVA. The PCVA method also achieved the highest agreement (57%) and Kappa scores (0.54). The PCVA method showed the highest sensitivity for 15 out of 20 causes of death. Conclusion:Our study found that the PCVA method had the best performance out of all the four COD assignment methods that were tested in our study sample. In order to improve the performance of CCVA methods, multicentric studies with larger sample sizes need to be conducted using the WHO VA tool.

Background: Smokeless tobacco (SLT) use among women is widely prevalent in Manipur state accounting for 45% users as per Global Adult Tobacco Survey (GATS)-2 India. Studies from India and elsewhere indicate changes in the way people used SLT during COVID-19 lockdown. This study explores individual and economic influences on SLT consumption and cessation attempts by tribal women in Manipur during the first COVID-19 lockdown (March-June, 2020) in India. Methods: Twenty in-depth interviews, both in-person and telephonically, were conducted among tribal women from Imphal west, Manipur, India, who used any SLT, from April to September 2020. Objective of the study was to understand the use, factors associated with consumption, purchasing behaviors, and cessation attempts of SLT during the lockdown. Thematic content analysis was used to identify core themes and codes. Results: Study participants reported of changes in current SLT use during restrictions imposed to contain COVID-19 pandemic in India. Majority reported of reduction or quit attempts in SLT use. Reasons included inaccessibility due to travel restrictions, limited availability and price rise of SLT products, fear of COVID-19, and disposable income for purchase of SLT products. However, a few women reported of increased consumption due to bulk purchasing, or switching to other SLT products as a result of unavailability or price rise of preferred products or to cope up with social isolation caused by the lockdown. Conclusion: Study findings on factors influencing quit attempts and strategies used for reducing SLT use by tribal women in Imphal, Manipur provide valuable insights for development of appropriate intervention for prevention of SLT use among women.

BACKGROUND:People with disabilities are vulnerable because of the many challenges they face attitudinal, physical, and financial. The National Policy for Persons with Disabilities (2006) recognizes that Persons with Disabilities are valuable human resources for the country and seeks to create an environment that provides equal opportunities, and protection of their rights, and full. There are limited studies on health care burden due to disabilities of various types.AIM:The present study examines the socioeconomic and state-wise differences in the prevalence of disabilities and related household financial burden in India.METHODS:Data for this study was obtained from the National Sample Survey (NSS), 76th round Persons with Disabilities in India Survey 2018. The survey covered a sample of 1,18,152 households, 5,76,569 individuals, of which 1,06,894 of had any disability. This study performed descriptive statistics, and bivariate estimates.RESULTS:The finding of the analysis showed that prevalence of disability of any kind was 22 persons per 1000. Around, one-fifth (20.32%) of the household's monthly consumption expenditure was spent on out-of-pocket expenditure for disability. More than half (57.1%) of the households were pushed to catastrophic health expenditure due to one of the members being disabled. Almost one-fifth (19.1%) of the households who were above the poverty line before one of members was treated for disability were pushed below the poverty line after the expenditure of the treatment and average percentage shortfall in income from the poverty line was 11.0 percent due to disability treatment care expenditure.CONCLUSION:The study provides an insight on the socioeconomic differentials in out-of-pocket expenditure, catastrophic expenditure for treatment of any kind of disability. To attain SDG goal 3 that advocates healthy life and promote well-being for all at all ages, there is a need to recognize the disadvantaged and due to disability.

Rapid, cost-effective, and sensitive diagnostic assays are essential for global tuberculosis (TB) control, especially in high TB burden, resource-limited settings. The current study was designed to evaluate diagnostic accuracy of Truenat MTB-Rif Dx (MolBio) in children less than 18 years of age, with symptoms suggestive of TB. Gastric aspirate, induced sputum, and broncho-alveolar lavage samples were subjected simultaneously to AFB-smear, GeneXpert MTB/RIF, liquid culture (MGIT-960) and Truenat MTB-Rif Dx. The index-test results were evaluated against microbiological reference standards (MRS). Truenat MTB-Rif Dx had a sensitivity of 57.1%, specificity of 92% against MRS. The sensitivity and specificity of the Truenat MTB-RIF Dx compared with liquid culture was 58.7% and 87.5% while GeneXpert MTB/RIF was 56% and 91.4%. The performance of both GeneXpert MTB/RIF and Truenat MTB-Rif Dx are comparable. Result of our study demonstrates that Truenat MTB-Rif can aid in early and efficient diagnosis of TB in children.

Sudden upsurge in cases of COVID-19 Associated Mucormycosis (CAM) following the second wave of the COVID-19 pandemic was recorded in India. This study describes the clinical characteristics, management and outcomes of CAM cases, and factors associated with mortality. Microbiologically confirmed CAM cases were enrolled from April 2021 to September 2021 from ten diverse geographical locations in India. Data were collected using a structured questionnaire and entered into a web portal designed specifically for this investigation. Bivariate analyses and logistic regression were conducted using R version 4.0.2. A total of 336 CAM patients were enrolled; the majority were male (n = 232, 69.1