Cost of hospital services in India: a multi-site study to inform provider payment rates and Health Technology Assessment

Abstract

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.

Refining the provider payment system of India’s government-funded health insurance programme: an econometric analysis

“…8 An Indian study identified that hospital bed size, annual number of hospital admissions and level of care predominantly predicted the unit cost of hospitalisation at the district hospital level. 42 Consistent with these findings, our study found that the number of beds, ALOS, annual number of hospital admissions and teaching status of hospitals were significantly associated with heterogeneity in unit costs per bed-day hospitalisation while controlling for state-level variations Open access financing and system characteristics. In theory, the cost gradients generated from the regression analysis can be used for weighting where there is sufficient justification for differential reimbursement.…”

“…Further, a global multicountry study found that ownership of hospitals, teaching status of hospitals, GDP of country, ALOS, bed occupancy rate and number of admissions were significant predictors of cost per bed-day hospitalisation 8. An Indian study identified that hospital bed size, annual number of hospital admissions and level of care predominantly predicted the unit cost of hospitalisation at the district hospital level 42…”

Budget impact and cost-utility analysis of prophylactic emicizumab versus on-demand bypassing agents for adolescent severe haemophilia A patients with inhibitors in India

“…Unit costs of health system visit was also added for each bleeding event. For severe bleeding events, additional costs (as mentioned earlier in prophylactic emicizumab costs) were added [ 19 ].…”

National hospital costing systems matter for universal healthcare: the India PM-JAY experience

“…Similarly, supply-side factors associated with unit costs include provider type (private vs public, district vs tertiary), the scale of activity (size of the facility or number of patients), geography (rural vs urban or metro city vs non-metro), input prices (salaries and prices of consumables and equipment) and the input mix (skill level of human resources, the ratio of staff to beds) 16–18. The CHSI study provided an assessment of heterogeneity in healthcare costs attributable to supply-side factors 19. One of the findings of the assessment revealed significant differences in adjusted bed day costs when comparing hospitals located in tier 3 cities with those in tier 1 and tier 2 cities (tier 1 cities are those with the highest cost of living, tier 3 are those with the lowest) and adjusted procedure costs when comparing tier 2 and tier 3 cities.…”