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Background: With an increase in life expectancy over the last few decades, there has been a parallel increase in the prevalence of disabilities among the elderly population. To estimate the prevalence of dependency in activities of daily living (ADL) and its predictors among the rural elderly population. Material and Methods: This was a cross-sectional study carried out in the community among the rural geriatric population in the field practice area of PG Department of Community Medicine, Government Medical College Jammu. The Barthel Scale Index was used to measure ADL dependency. PSPP software was used to analyze the data. Results: The mean age of study participants was 68.31 ± 7.9 years. ADL dependency was observed in 46.3% of the subjects, with the majority demonstrating mild to moderate dependence. Only 2.5% of the respondents reported a severe degree of ADL dependence. The mean ADL score was 94.47 ± 8.98. On multivariate logistic regression analysis, age, educational status, the presence of stress in the family, personal history, and the presence of co-morbidities emerged to be independent predictors of ADL dependence. Conclusion: High prevalence of physical disability in the geriatric population is now an area of major concern. This emphasizes the significance of setting up geriatric care centers especially in rural areas preferably integrating with health and wellness centers.
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INTRODUCTION: Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (AB PM-JAY) has enabled the Government of India to become a strategic purchaser of health care services from private providers. To generate base cost evidence for evidence-based policymaking the Costing of Health Services in India (CHSI) study was commissioned in 2018 for the price setting of health benefit packages. This paper reports the findings of a process evaluation of the cost data collection in the private hospitals. METHODS: The process evaluation of health system costing in private hospitals was an exploratory survey with mixed methods (quantitative and qualitative). We used three approaches-an online survey using a semi-structured questionnaire, in-depth interviews, and a review of monitoring data. The process of data collection was assessed in terms of time taken for different aspects, resources used, level and nature of difficulty encountered, challenges and solutions. RESULTS: The mean time taken for data collection in a private hospital was 9.31 (± 1.0) person months including time for obtaining permissions, actual data collection and entry, and addressing queries for data completeness and quality. The longest time was taken to collect data on human resources (30%), while it took the least time for collecting information on building and space (5%). On a scale of 1 (lowest) to 10 (highest) difficulty levels, the data on human resources was the most difficult to collect. This included data on salaries (8), time allocation (5.5) and leaves (5). DISCUSSION: Cost data from private hospitals is crucial for mixed health systems. Developing formal mechanisms of cost accounting data and data sharing as pre-requisites for empanelment under a national insurance scheme can significantly ease the process of cost data collection.
Assuntos
Programas Governamentais , Serviços de Saúde , Humanos , Hospitais Privados , Formulação de Políticas , Inquéritos e Questionários , ÍndiaRESUMO
BACKGROUND: In low- and middle-income countries (LMICs), provisioning for surgical care is a public health priority. Ayushman Bharat Pradhan Mantri-Jan Aarogya Yojana (AB PM-JAY) is India's largest national insurance scheme providing free surgical and medical care. In this paper, we present the costs of surgical health benefit packages (HBPs) for secondary care in public district hospitals. METHODS: The costs were estimated using mixed (top-down and bottom-up) micro-costing methods. In phase II of the Costing of Health Services in India (CHSI) study, data were collected from a sample of 27 district hospitals from nine states of India. The district hospitals were selected using stratified random sampling based on the district's composite development score. We estimated unit costs for individual services-outpatient (OP) visit, per bed-day in inpatient (IP) and intensive care unit (ICU) stays, and surgical procedures. Together, this was used to estimate the cost of 250 AB PM-JAY HBPs. RESULTS: At the current level of utilization, the mean cost per OP consultation varied from US$4.10 to US$2.60 among different surgical specialities. The mean unit cost per IP bed-day ranged from US$13.40 to US$35.60. For the ICU, the mean unit cost per bed-day was US$74. Further, the unit cost of HBPs varied from US$564 for bone tumour excision to US$49 for lid tear repair. CONCLUSIONS: Data on the cost of delivering surgical care at the level of district hospitals is of critical value for evidence-based policymaking, price-setting for surgical care and planning to strengthen the availability of high quality and cost-effective surgical care in district hospitals.