Pankaj Bahuguna1, Lorna Guinness2, Sameer Sharma1, Akashdeep Singh Chauhan1, Laura Downey3,4, Shankar Prinja5. 1. Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India. 2. International Decision Support Initiative, London, UK. lornaguinness@gmail.com. 3. International Decision Support Initiative, London, UK. 4. School of Public Health, Imperial College London, London, W2 1NY, UK. 5. Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India. shankarprinja@gmail.com.
Abstract
BACKGROUND: India's flagship National Health insurance programme (AB-PMJAY) requires accurate cost information for evidence-based decision-making, strategic purchasing of health services and setting reimbursement rates. To address the challenge of limited health service cost data, this study used econometric methods to identify determinants of cost and estimate unit costs for each Indian state. METHODS: Using data from 81 facilities in six states, models were developed for inpatient and outpatient services at primary and secondary level public health facilities. A best-fit unit cost function was identified using guided stepwise regression and combined with data on health service infrastructure and utilisation to predict state-level unit costs. RESULTS: Health service utilisation had the greatest influence on unit cost, while number of beds, facility level and the state were also good predictors. For district hospitals, predicted cost per inpatient admission ranged from 1028 (313-3429) Indian Rupees (INR) to 4499 (1451-14,159) INR and cost per outpatient visit ranged from 91 (44-196) INR to 657 (339-1337) INR, across the states. For community healthcare centres and primary healthcare centres, cost per admission ranged from 412 (148-1151) INR to 3677 (1359-10,055) INR and cost per outpatient visit ranged from 96 (50-187) INR to 429 (217-844) INR. CONCLUSION: This is the first time cost estimates for inpatient admissions and outpatient visits for all states have been estimated using standardised data. The model demonstrates the usefulness of such an approach in the Indian context to help inform health technology assessment, budgeting and forecasting, as well as differential pricing, and could be applied to similar country contexts where cost data are limited.
BACKGROUND: India's flagship National Health insurance programme (AB-PMJAY) requires accurate cost information for evidence-based decision-making, strategic purchasing of health services and setting reimbursement rates. To address the challenge of limited health service cost data, this study used econometric methods to identify determinants of cost and estimate unit costs for each Indian state. METHODS: Using data from 81 facilities in six states, models were developed for inpatient and outpatient services at primary and secondary level public health facilities. A best-fit unit cost function was identified using guided stepwise regression and combined with data on health service infrastructure and utilisation to predict state-level unit costs. RESULTS: Health service utilisation had the greatest influence on unit cost, while number of beds, facility level and the state were also good predictors. For district hospitals, predicted cost per inpatient admission ranged from 1028 (313-3429) Indian Rupees (INR) to 4499 (1451-14,159) INR and cost per outpatient visit ranged from 91 (44-196) INR to 657 (339-1337) INR, across the states. For community healthcare centres and primary healthcare centres, cost per admission ranged from 412 (148-1151) INR to 3677 (1359-10,055) INR and cost per outpatient visit ranged from 96 (50-187) INR to 429 (217-844) INR. CONCLUSION: This is the first time cost estimates for inpatient admissions and outpatient visits for all states have been estimated using standardised data. The model demonstrates the usefulness of such an approach in the Indian context to help inform health technology assessment, budgeting and forecasting, as well as differential pricing, and could be applied to similar country contexts where cost data are limited.