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Objectives: Reimbursement rates in national health insurance schemes are frequently weighted to account for differences in the costs of service provision. To determine weights for a differential case-based payment system under India's publicly financed national health insurance scheme, the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (PM-JAY), by exploring and quantifying the influence of supply-side factors on the costs of inpatient admissions and surgical procedures., Design: Exploratory analysis using regression-based cost function on data from a multisite health facility costing study-the Cost of Health Services in India (CHSI) Study., Setting: The CHSI Study sample included 11 public sector tertiary care hospitals, 27 public sector district hospitals providing secondary care and 16 private hospitals, from 11 Indian states., Participants: 521 sites from 57 healthcare facilities in 11 states of India., Interventions: Medical and surgical packages of PM-JAY., Primary and Secondary Outcome Measures: The cost per bed-day and cost per surgical procedure were regressed against a range of factors to be considered as weights including hospital location, presence of a teaching function and ownership. In addition, capacity utilisation, number of beds, specialist mix, state gross domestic product, State Health Index ranking and volume of patients across the sample were included as variables in the models. Given the skewed data, cost variables were log-transformed for some models., Results: The estimated mean costs per inpatient bed-day and per procedure were 2307 and 10 686 Indian rupees, respectively. Teaching status, annual hospitalisation, bed size, location of hospital and average length of hospitalisation significantly determine the inpatient bed-day cost, while location of hospital and teaching status determine the procedure costs. Cost per bed-day of teaching hospitals was 38-143.4% higher than in non-teaching hospitals. Similarly, cost per bed-day was 1.3-89.7% higher in tier 1 cities, and 19.5-77.3% higher in tier 2 cities relative to tier 3 cities, respectively. Finally, cost per surgical procedure was higher by 10.6-144.6% in teaching hospitals than non-teaching hospitals; 12.9-171.7% higher in tier 1 cities; and 33.4-140.9% higher in tier 2 cities compared with tier 3 cities, respectively., Conclusion: Our study findings support and validate the recently introduced differential provider payment system under the PM-JAY. While our results are indicative of heterogeneity in hospital costs, other considerations of how these weights will affect coverage, quality, cost containment, as well as create incentives and disincentives for provider and consumer behaviour, and integrate with existing price mark-ups for other factors, should be considered to determine the future revisions in the differential pricing scheme., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The 'Cost of Health Services in India (CHSI)' is the first large scale multi-site facility costing study to incorporate evidence from a national sample of both private and public sectors at different levels of the health system in India. This paper provides an overview of the extent of heterogeneity in costs caused by various supply-side factors.A total of 38 public (11 tertiary care and 27 secondary care) and 16 private hospitals were sampled from 11 states of India. From the sampled facilities, a total of 327 specialties were included, with 48, 79 and 200 specialties covered in tertiary, private and district hospitals respectively. A mixed methodology consisting of both bottom-up and top-down costing was used for data collection. Unit costs per service output were calculated at the cost centre level (outpatient, inpatient, operating theatre, and ICU) and compared across provider type and geographical location.The unadjusted cost per admission was highest for tertiary facilities (₹ 5690, 75 USD) followed by private facilities (₹ 4839, 64 USD) and district hospitals (₹ 3447, 45 USD). Differences in unit costs were found across types of providers, resulting from both variations in capacity utilisation, length of stay and the scale of activity. In addition, significant differences in costs were found associated with geographical location (city classification).The reliance on cost information from single sites or small samples ignores the issue of heterogeneity driven by both demand and supply-side factors. The CHSI cost data set provides a unique insight into cost variability across different types of providers in India. The present analysis shows that both geographical location and the scale of activity are important determinants for deriving the cost of a health service and should be accounted for in healthcare decision making from budgeting to economic evaluation and price-setting., (© 2022. The Author(s).)

Introduction: Cervical cancer is a major public health problem in India leading to high economic burden, which is disproportionately borne by the patients as out-of-pocket expenditure (OOPE). Several publicly financed health insurance schemes (PFHIs) in India cover the treatment for cervical cancer. However, the provider payment rates for health benefit packages (HBP) under these PFHIs are not based on scientific evidence. We undertook this study to estimate the cost of services provided for treatment of cervical cancer and  cost of the package of care for cervical cancer in India., Methods: The study was undertaken at a large public tertiary hospital in North India. The health system cost was assessed using a mixed micro-costing approach. The data were collected for all the resources utilized during service delivery for cervical cancer patients. To evaluate the OOPE, randomly selected 248 patients were interviewed following the cost of illness approach. Logistic regression was used to assess the factors associated with catastrophic health expenditure (CHE)., Results: Health system cost for different cervical cancer treatment modalities i.e. radiotherapy, brachytherapy, chemotherapy and surgery, ranges from INR 19,494 to 41,388 (USD 291 - 617). Furthermore, patients spent INR 4,042 to 23,453 ( USD 60 - 350) as OOPE. Nearly 62% patients incurred CHE, and 30% reported distress financing. The odds of CHE (OR: 25.39, p-value: <0.001) and distress financing (OR: 15.37, p-value: 0.001) were significantly higher in poorest-income quintile. The HBP cost varies from INR 45,364 to 64,422 (USD 676 - 960) for brachytherapy and radiotherapy respectively., Conclusion: Cervical cancer treatment leads to high OOPE in India, which imposes financial hardship, especially for the poorest. The coverage of risk pooling mechanisms like PHFIs should be enhanced. The findings of our study should be used to set the reimbursement rates of providing cervical cancer treatment under PFHI schemes.

Background: A national study, 'Costing of healthcare services in India' (CHSI) aimed at generating reliable healthcare cost estimates for health technology assessment and price-setting is being undertaken in India. CHSI sampled 52 public and 40 private hospitals in 13 states and used a mixed micro-costing approach. This paper aims to outline the process, challenges and critical lessons of cost data collection to feed methodological and quality improvement of data collection., Methods: An exploratory survey with 3 components-an online semi-structured questionnaire, group discussion and review of monitoring data, was conducted amongst CHSI data collection teams. There were qualitative and quantitative components. Difficulty in obtaining individual data was rated on a Likert scale., Results: Mean time taken to complete cost data collection in one department/speciality was 7.86(±0.51) months, majority of which was spent on data entry and data issues resolution. Data collection was most difficult for determination of equipment usage (mean difficulty score 6.59±0.52), consumables prices (6.09±0.58), equipment price(6.05±0.72), and furniture price(5.64±0.68). Human resources, drugs & consumables contributed to 78% of total cost and 31% of data collection time. However, furniture, overheads and equipment consumed 51% of time contributing only 9% of total cost. Seeking multiple permissions, absence of electronic records, multiple sources of data were key challenges causing delays., Conclusions: Micro-costing is time and resource intensive. Addressing key issues prior to data collection would ease the process of data collection, improve quality of estimates and aid priority setting. Electronic health records and availability of national cost data base would facilitate conducting costing studies., Competing Interests: The authors have declared that no competing interests exist.