Cargando…
Predictors of Out-of-Pocket Expenditure on Health Incurred by Elderly Persons Residing in a Rural Area of Faridabad District
Background: A significant portion of India's 1.2 billion population consists of elderly individuals, accounting for approximately 8.6%, who incur substantial out-of-pocket (OOP) healthcare expenses. Any policy for the elderly should encompass financial protection from illness-related expenditur...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cureus
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191236/ https://www.ncbi.nlm.nih.gov/pubmed/37206499 http://dx.doi.org/10.7759/cureus.37626 |
Sumario: | Background: A significant portion of India's 1.2 billion population consists of elderly individuals, accounting for approximately 8.6%, who incur substantial out-of-pocket (OOP) healthcare expenses. Any policy for the elderly should encompass financial protection from illness-related expenditures. However, the lack of comprehensive information on OOP expenditure and its determinants precludes such action. Methods: We conducted a cross-sectional study of 400 elderly persons residing in the rural town of Ballabgarh. The participants were randomly selected using the health demographic surveillance system. We utilized questionnaires and tools to assess the costs associated with outpatient and inpatient services in the previous year, as well as gather information on socio-demographics (individual characteristics), morbidity (motivation for seeking care), and social engagement (health-seeking). Results: A total of 396 elderly persons participated, with a mean (SD) age of 69.4 (6.7), and 59.4% females. Nearly 96% and 50% of the elderly availed of outpatient and inpatient services, respectively, in the preceding year. The mean (IQR) annual OOP expenditure, as per the consumer price index 2021, was INR 12,543 (IQR, INR 8,288-16,787), with a median of INR 2,860 (IQR, INR 1,458-7,233), explained significantly by sex, morbidity status, social engagement, and mental health. Conclusion: In low-middle-income countries like India, policymakers may consider pre-payment mechanisms like health insurance for the elderly, using such prediction scores. |
---|