Background: The sexual behaviour of adolescents is of importance due to the engagement in risky sexual activity at a too early age, which may be associated with the adverse outcomes. The study aims to understand the transitions in adolescent boys and young men’s high-risk sexual behaviour in India using two rounds of Indian demographic health survey, NFHS-3 (2005–06) and NFHS-4 (2015–16). Methods: A total of 25,538 in NFHS-3 (2005–06) and 35,112 in NFHS-4 (2015–16) men were considered for the analysis. Men have been divided into two age groups as 15–19 years (adolescent) and 20–24 (young men) for comparison purposes. Descriptive and multivariate statistics have been used. Results: Overall, high-risk sexual behaviour has increased among adolescent boys (64 to 70%) and young men (18 to 27%) from 2005–06 to 2015–16. The trend of live-in relationship has increased among adolescent boys of rural areas (0.6 to 6.0%) as well as in urban areas (3.1 to 10.9%) over the last 10 years. Adolescent boys having 10th and above years of schooling (AOR = 1.98; p < 0.01), residing in urban areas (AOR = 2.23; p < 0.01), and belonging to the affluent class of households (AOR = 1.41; p < 0.05) were more likely to engage in high-risk sexual activity than the young men in India. The odds of high-risk sexual behaviour was higher among alcohol-using adolescent boys (AOR = 1.82; p < 0.01) and young men (AOR = 2.38; p < 0.01) in 2015–16. Conclusions: The study concludes that early sexual debut, lower prevalence of condom use at first sexual experience, tendency of live-in-relationship, and alcohol consumption indicate the hazardous interconnection between such behaviours among adolescent boys over the last decade which placed them at higher-risky sexual behaviour as compared to young men. Adolescent’ sexual behaviours have both short-term and long-term consequences, and interventions that focus on multiple domains of risk may be the most effective in helping to promote broad reproductive health among young adults.

Objective Older male and female adults differ in key characteristics such as disease-specific life expectancy, health behaviours and clinical presentations and non-communicable disease multimorbidity (NCD-MM). Therefore, examining the sex differences in NCD-MM among older adults is vital, as this issue is understudied in low-income and middle-income country (LMIC) contexts such as India, and has been growing in the past few decades. Design Large scale nationally representative cross-sectional study. Settings and participants Longitudinal Ageing Study in India (LASI 2017–2018) had data on 27 343 men and 31 730 women aged 45+, drawn from a sample of 59 073 individuals across India. Primary and secondary outcomes measures We operationalised NCD-MM based on prevalence of the presence of two or more long-term chronic NCD morbidities. Descriptive statistics and bivariate analysis along with multivariate statistics were used. Results Women aged 75+ had a higher prevalence of multimorbidity as compared with men (52.1% vs 45.17%). NCD-MM was more common among widows (48.5%) than widowers (44.8%). The female-to-male ratios of ORs (RORs) for NCD-MM associated with overweight/obesity and prior history of chewing tobacco were 1.10 (95% CI: 1.01 to 1.20) and 1.42 (95% CI: 1.12 to 1.80), respectively. The female-to-male RORs show that the odds of NCD-MM were greater in formerly working women (1.24 (95% CI: 1.06 to 1.44)) relative to formerly working men. The effect of increasing NCD-MM on limitations in activities of daily living and instrumental ADL was greater in men than women but reversed for the hospitalisation. Conclusions We found significant sex differences in NCD-MM prevalence among older Indian adults, with various associated risk factors. The patterns underlying these differences warrant greater study, given existing evidence on differential longevity, health burdens and health-seeking patterns all of which operate in a larger structural context of patriarchy. Health systems in turn must respond to NCD-MM mindful of these patterns and aim to redress some of the larger inequities they reflect.

This study examines malnutrition’s triple burden, including anaemia, overweight, and stunting, among children aged 6–59 months. Using data from the National Family Health Survey-5 (2019–2021), the study identifies risk factors and assesses their contribution at different levels to existing malnutrition burden. A random intercept multilevel logistic regression model and spatial analysis are employed to identify child, maternal, and household level risk factors for stunting, overweight, and anaemia. The study finds that 34% of children were stunted, 4% were overweight, and 66% were anaemic. Stunting and anaemia prevalence were higher in central and eastern regions, while overweight was more prevalent in the north-eastern and northern regions. At the macro-level, the coexistence of stunting, overweight, and anaemia circumstantiates the triple burden of childhood malnutrition with substantial spatial variation (Moran’s I: stunting-0.53, overweight-0.41, and anaemia-0.53). Multilevel analysis reveals that child, maternal, and household variables play a substantial role in determining malnutrition burden in India. The nutritional health is significantly influenced by a wide range of determinants, necessitating multilevel treatments targeting households to address this diverse group of coexisting factors. Given the intra-country spatial heterogeneity, the treatment also needs to be tailor-made for various disaggregated levels.

Background: Publicly Funded Health Insurance Schemes (PFHIS) are intended to play a role in achieving Universal Health Coverage (UHC). In countries like India, PFHISs have low penetrance and provide limited coverage of services and of family members within households, which can mean that women lose out. Gender inequities in relation to financial risk protection are understudied. Given the emphasis being placed on achieving UHC for all in India, this paper examined intersecting gender inequalities and changes in PFHIS coverage in southern India, where its penetrance is greater and of longer duration. Data and methods: This study used the fourth (NFHS-4, 2015–16) and fifth (NFHS-5, 2019–21) rounds of India’s National Family Health Survey for five southern states: namely, Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, and Telangana. The World Health Organization’s Health Equity Assessment Toolkit (HEAT) Plus and Stata were used to analyse PFHIS coverage disaggregated by seven dimensions of inequality. Ratios and differences for binary dimensions; Between Group Variance and Theil Index for unordered dimensions; Absolute and Relative Concentration Index (RCI) for ordered dimensions were computed separately for women and men. Results: Overall, PFHIS coverage increased significantly (p < 0.001) among women and men in Andhra Pradesh, and Kerala from NFHS-4 to NFHS-5. Overall, men had higher PFHIS coverage than women, especially in Andhra Pradesh, Tamil Nadu, and Telangana in both surveys. In both absolute and relative terms, PFHIS coverage was concentrated among older women and men across all states; age-related inequalities were higher among women than men in both surveys in Andhra Pradesh, Kerala, and Telengana. The magnitude of education-related inequalities was twice as high as among women in Telangana (RCI_NFHS-4: -12.23; RCI_NFHS-5: -9.98) and Andhra Pradesh (RCI_NFHS-4: -8.05; RCI_NFHS-5: -7.84) as compared to men in Telangana (RCI_NFHS-4: -5.58; RCI_NFHS-5: -2.30) and Andhra Pradesh (RCI_NFHS-4: -4.40; RCI_NFHS-5: -3.12) and these inequalities remained in NFHS-5, suggesting that lower education level women had greater coverage. In the latter survey, a high magnitude of wealth-related inequality was observed in women (RCI_NFHS-4: -15.78; RCI_NFHS-5: -14.36) and men (RCI_NFHS-4: -20.42; RCI_NFHS-5: -13.84) belonging to Kerala, whereas this inequality has decreased from NFHS-4 to NFHS-5., again suggestive of greater coverage among poorer populations. Caste-related inequalities were higher in women than men in both surveys, the magnitude of inequalities decreased between 2015–16 and 2019–20. Conclusions: We found gender inequalities in self-reported enrolment in southern states with long-standing PFHIS. Inequalities favoured the poor, uneducated and elderly, which is to some extend desirable when rolling out a PFHIS intended for harder to reach populations. However, religion and caste-based inequalities, while reducing, were still prevalent among women. If PFHIS are to truly offer financial risk protection, they must address the intersecting marginalization faced by women and men, while meeting eventual goals of risk pooling, indicated by high coverage and low inequality across population sub-groups.

Background: Despite a commendable rise in the number of women seeking delivery care at public health institutions in South India, it is unclear if the benefit accrues to wealthier or poorer socio-economic groups. The study’s aim was to investigate at how the public subsidy is distributed among Indian women who give birth in public hospitals in the southern regions. Methods: Data from the Indian Demographic Health Survey’s fifth wave (NFHS-5, 2019–21) was used in this study. A total of 22, 403 were institutional deliveries across all the southern states of India were included. Out-of-pocket expenditure (OOPE) on childbirth in health institutions was the outcome variable. We used summary statistics, Benefits Incidence Analysis (BIA), concentration index (CI), and concentration curve (CC) were used. Results: Most women in the lowest, poorest, and medium quintiles of wealth opted to give birth in public facilities. In contrast, about 69% of mothers belonging to highest quintile gave birth in private health institutions. The magnitude of CI and CC of institutional delivery indicates that public sector usage was concentrated among poorer quintiles [CIX: − 0.178; SE: 0.005; p < 0.001] and private sector usage was concentrated among wealthier quintiles [CIX: 0.239; SE: 0.006; p < 0.001]. Benefit incidence analyses suggest that middle quintile of women received the maximum public subsidy in primary health centres (33.23%), followed by richer quintile (25.62%), and poorer wealth quintiles (24.84%). These pattern in the secondary health centres was similar. Conclusion: Poorer groups utilize the public sector for institutional delivery in greater proportions than the private sector. Middle quintiles seem to benefit the most from public subsidy in terms of the median cost of service and non-payment. Greater efforts must be made to understand how and why these groups are being left behind and what policy measures can enhance their inclusion and financial risk protection.

Background: Increasing financial risk protection is a key feature of Universal Health Coverage and the path towards health for all. Publicly Funded Health Insurance Schemes (PFHIS) have been considered as one of the pathways to safeguard against financial shocks and potentially reduce Out-of-Pocket Expenditure (OOPE). The south Indian state of Kerala has roughly a decade-long experience in implementing PFHIS. To date, there have been very few assessments of the coverage of these schemes and their impact on expenditure. Aiming to fill this gap, we explored the extent of and inequalities in insurance coverage, as well as choice of providers, and median cost of hospitalization in Kerala among insured and uninsured individuals. Methods: A cross-sectional household survey was conducted in four districts of Kerala as part of a larger health systems research study from July–October 2019. We employed multistage random sampling to collect data from 13,064 individuals covering 3234 households in the catchment area of eight primary health care facilities. We used descriptive statistics, bivariate and multivariate analysis. We evaluated socioeconomic disparities using an absolute measure of inequality—the Slope Index of Inequality (SII) and a relative measure—the Relative Concentration Index (RCI). Results: A substantial proportion of our study respondents reported that they were covered by PFHIS (45.8%). Respondents belonging to lowest and middle wealth quintiles of household had significantly greater odds of being covered by insurance than respondents belonging to the richest wealth quintile. The negative magnitude of RCI [-16.8% (95%CI: -25.3, -8.4)] and SII [-21.5% (95%CI: -36.1, -7.0)] suggest a higher concentration of PFHIS coverage among the poor. Median OOPE for hospitalisation at private health facilities was INR 9000 (approx. USD 108.70) among those covered by PFHIS, whereas it was INR 10500 (approx. USD 126.82) at private health facilities among those not covered by insurance. Conclusion: While PFHIS seems to be appropriately targeting poorer populations, among the insured, OOPE for hospitalization persists. Among the uninsured, population subgroups with advantage are spending the greatest amount, raising questions about whether those facing relative disadvantage are forgoing care altogether or seeking care using cheaper, public avenues. Further policy action to more effectively reduce financial burden among left behind eligible populations under PFHIS will be essential to UHC progress in the state.