Background The rural–urban gap in socioeconomic and morbidity status among older adults is prevalent in India. These disparities may impact the levels and factors of self-rated health (SRH). The objective of the study is to compare the levels and determinants of SRH between rural and urban areas by considering the moderating effects of marital status and living arrangements. Subjects and methods The present study used data from the Longitudinal Ageing Study in India (LASI) wave 1 (2017–18). A total sample of 30,633 older adults aged 60 years and above were selected for the study. Descriptive statistics, bivariate chi-square test, the interaction effect of living arrangements and marital status, and logistic estimation were applied to accomplish the study objectives. Results The prevalence of poor SRH was found 7% higher in rural areas compared to urban counterparts. A substantial rural–urban disparity in the patterns of poor SRH was also observed. The interaction effect of marital status and living arrangement on self-rated health suggested that older adults who were currently unmarried and living alone were 38% more likely to report poor SRH than those who were currently married and co-residing in rural India. In addition to marital status and living situation, other factors that significantly influenced SRH include age, socio-cultural background (educational attainment and religion), economic background (employment status), health status (ADLs, IADLs, multi-morbidities), and geographic background (region). Conclusion The present study's findings demonstrated that, notwithstanding local variations, marital status and living circumstances significantly influenced SRH in India. In the present study, unmarried older people living alone were more susceptible to poor SRH in rural areas. The present study supports the importance of reinforcing the concepts of care and support for older individuals. There is a need for special policy attention to older individuals, particularly those unmarried and living alone. Although older individuals had difficulty performing ADLs and IADLs and had multi-morbidities, they reported poorer health. Therefore, offering them social support and top-notch medical assistance is crucial.

Background Postnatal care is crucial to prevent the child mortality. Despite the improvement in the PNC coverage for the neonates, it is still far away from the universal health coverage. Along with, some specific regions mostly are natural hazard prone areas of India show very under coverage of PNC for the neonates. Considering the substantial spatial variation of PNC coverage and natural hazard prevalence, present study aimed to examine spatial variation of PNC coverage and its association with natural hazard at the district level. Methods The cross-sectional exploratory study utilized National Family Health Survey, 2019-21, which included 1,76,843 children using multistage stratified sampling method to examine postnatal care within 42 days for neonates born within five years prior to the survey. Additionally, the study utilized Vulnerability Atlas of India,2019 maps to categorize regions into hazardous (flood, earthquake, and landslide) and non-hazardous areas. Spatial univariate and bivariate analyses, logistic and geographically weighted regressions were conducted using ArcGIS Pro, GeoDa, and Stata 16.0 software to identify associations between PNC coverage, hazard exposure, and spatial variation. Results The univariate spatial analysis showed some specific regions such as north, east, and north-east region of India had a high concentration of natural hazard and low access of PNC coverage. Bivariate analysis also showed that PNC coverage was low in flood (75.9%), earthquake (68.3%), and landslide (80.6%) effected areas. Compared to the national PNC coverage (81.1%), all these natural hazards effected areas showed low coverage. Further, logic regression showed that these hazard prone areas were less (OR:0.85 for flood, 0.77 for earthquake, and 0.77 for landslide) likely to get PNC coverage than their counterparts. LISA cluster maps significantly showed low PNC and high disaster concentration in these disaster-prone areas. Geographic weighted regression results also showed similar result. Conclusions The present study elucidates notable heterogeneity in the coverage of postnatal care (PNC) services, with lower concentrations observed in disaster-prone areas. In order to enhance the accessibility and quality of PNC services in these areas, targeted interventions such as the deployment of mobile health services and fortification of health systems are recommended.

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In 2018, according to the National Sample Survey Report, the number of cases of hospitalization per 1000 persons in 365 days was 29 in India (26 per 1000 in rural and 34 per 1000 in urban areas). Between 2004 and 2014, for example, the average medical expenditure per hospitalization for urban patients increased by about 176%, and for rural patients, it jumped by a little over 160%.  Most of these hospitalizations are for infections, but a significant number also for treatment for cancer and blood-related diseases.  The increase in access to healthcare has also brought with it a massive spike in costs. India is rapidly undergoing an epidemiological transition with a sudden change in the disease profile of its population. This study aimed to analyze hospitalization due to different factors like age and morbidity and its effect on health care utilization from nationally representative data from 2018 among the total population of India.  75th round of National Sample Survey Organisation (NSSO) conducted in July 2017- June 2018 has been used to examine what are the determinant factors that affect the hospitalization and mean monthly disease-specific expenditure in the different age group populations in India. We have used cross-tabulation to understand the association between morbidity patterns and healthcare utilization with other socio-demographic variables.  A set of logistic regression analyses was carried out to understand the role of age patterns on hospitalization. A log-linear regression model was used to understand the significant predictors of out-of-pocket expenditure (OOPE).