User Guide to Unit Cost Estimator  

Background Information
The unit cost estimator uses statistical methods to generate a cost per outpatient visit or inpatient admission for a healthcare facility using a set of key variables. To develop the tool, a cost function model was constructed using data from the National Health System Cost Database- Round 1 data. The unit cost estimator provides estimates of the cost per inpatient admission and cost per outpatient visit for public sector healthcare facilities using data on geographical location, level of health facility, size of the facility and patient load etc.
Background Information

Type of Estimator

Number of States included

Health System Levels included
1.District Hospital
2.Community Health Centre/ Primary health care Centre

Step Guide for Users
When we click on the Access the Unit Cost Estimator button, a default window will open as shown in picture 1.

Picture 1: Default Opening Window
Step 1: Select the Estimator {Inpatient Cost Estimator} {Outpatient Cost Estimator} for which the user is interested to generate the estimate by clicking the buttons provided in the left panel as shown in picture 1.

Step 2: Select the state and level of health system for which the user is interested to generate the estimate as shown in picture 2 (Outpatient estimator taken as example).

Picture 2: Selecting State and Health System Level

Step 3: This step involves providing the data for input variables to generate the cost estimate. There are two options available in this step i.e. if the user has the data available on input variables that data can be inserted in the blank cells. If the data is not available on these variables, state-specific averages are used by the estimator as default values to generate the cost estimate.
1) Number of inpatient beds: Please fill in the actual value for number of beds at the health facility or average number of beds for the level of health facility for your sample as shown in picture 3.1. If you do not enter a value the default value - the state average - will be used.

Picture 3.1: Entering input data for Number of inpatient beds

2) Number of annual outpatient visits: Please fill in the actual value for number of annual outpatient visits for the health facility or average annual outpatient visits for level of health facility as shown in picture 3.2. If you do not enter a value the default value - the state average - will be used.

Picture 3.2: Entering input data for Number of outpatient visits per year

3) Number of doctors at the facility: Please fill in the actual value for number of doctors at health facility or average doctors for level of health facility (as shown in picture 3.3. If you do not enter a value the default value will be used.

Picture 3.3: Entering input data for Number of doctors at the facility

4) Doctor’s monthly salary (INR): Please fill in the actual value for average salary of doctors for level of health facility as shown in picture 3.4. If you do not enter a value the default value will be used.

Picture 3.4: Entering input data for Doctors’ monthly salary (INR)

Step 4: When the step 2 is complete, click on the Submit button. The cost estimator will produce the estimates for selected State selected Health system level. Results can be generated in 3 ways depending on information entered by user for input variables as shown in picture 4.1, 4.2 and 4.3.
1) In case the user enters the values for all the input variables i.e. Number of inpatient beds, Number of Outpatient visits per year, Number of doctors at the facility and Doctors’ monthly salary (INR).

Picture 4.1: Generating results

2) In case the user enters the values for all the input variables i.e. Number of inpatient beds and Number of Outpatient visits per year.

Picture 4.2: Generating results

3) In case the user does not enter the value for any of the input variables, the state averages will be used as default information (Please see Methods for more detail). The Default values are in the form of *Read only format.

Picture 4.3: Generating results

Step 5: The output generated will have a mean estimate of unit cost per outpatient visit and 2.5th and 97.5th confidence limits for the mean estimate in Indian National Rupee (INR)
Note: All the steps for Inpatient Cost Estimator are same as for the Outpatient Cost Estimator.

Unit cost estimator – Background and Methodology   

The unit cost estimator uses statistical methods to generate a cost per outpatient visit or inpatient admission for a healthcare facility using a set of key variables. To develop the tool, a cost function model was constructed using data from the National Health System Cost Database – Round 1 data.

The aim of the cost function is to predict unit costs of healthcare services in different settings, at different service volumes by nature of service delivery (inpatient and outpatient care) and identify the degree to which other factors influence average costs. Unit costs are dependent on the total costs of input resources consumed (numerator) and output in terms of services provided (as denominator). Total costs can broadly be divided into the hotel components i.e. human workforce, capital resources (like building and equipment) and overheads; and those associated with specific treatments i.e. the costs of medicine, consumables and diagnostics, and are dependent on the beneficiaries. Hotel costs generally comprise 70-80% of hospital costs [1,2]. Other costs i.e. of medicines, consumables and diagnostics are subject to different types of market influences than hotel type services with patients typically contributing to a large share of these inputs in India [3]. These costs tend to be variable so that the average cost remains constant as volume of services changes. ‘Hotel costs’ are more fixed in nature, with average costs varying with scale as well as other characteristics that might shape the production of a particular service. The cost function is therefore designed to predict the ‘hotel costs’ component of total cost.

The starting point for the cost function is the WHO CHOICE refined model for predicting national level unit costs (version 2) 2017/18 [4]. This model regresses unit costs against a set of explanatory variables said to explain unit cost such that:



Where UC is unit cost, X_i are the explanatory variables and α_0 and α_(1……n) are the estimated parameters and e is the error term. The independent variables can include prices of labour and capital as well as other explanatory factors such as volume of services, geographical location, capacity utilisation, quality and the nature of the health care market [5–8]. To estimate unit costs for a particular service within a large production unit, a regression model can be run separately for each different service based on the assumption that the production relationship between different services at any single facility is constant. In this case, separate models were run for inpatient care and outpatient care.

A range of explanatory variables were considered for inclusion in the model based on a combination of theoretical considerations and previous cost models (for example: [4,5,9–11]. To identify the best specification, guided stepwise linear regression was used. The variables considered included the outputs of each service category, prices and quantities of labour, proxy of capital and any other significant inputs into production as well as supply side factors that might lead to cost variation [12]. Importantly, the models needed readily accessible data so that a user could predict costs for their settings.

Following Serje et al, 4 categories of labour should be considered [13]: professional, technical/auxiliary, support, clerks/secretaries, physical labourers. However, our data only allowed distinction between 3 categories: doctors, medical support staff (nurses/technicians/pharmacists) and other support staff. The capital stock can be represented by the number of beds at the facility for both inpatient and outpatient services. Number of beds gives an indication of the relative capital stock even where inpatient beds are equal to zero. Outputs used are hospitalisations for the inpatient model and outpatient visits for the outpatient model.

To account for the influence of supply side factors we considered the use of the level of health facility, capacity utilisation and state level per capita health expenditure as well as 2 proxy variables to represent quality and the nature of the health care market. These proxies are the state health index which reflects a combination of health outcomes, governance and inputs to the health sector [14]; and we considered the use of the government “Aspirational District Programme” index – a composite index of socio-economic progress, health and education sector performance and basic infrastructure indicators[15].

The best fit models were then selected based on based on both statistical, theoretical and pragmatic considerations for each of the outpatient and inpatient cost functions. Base models for prediction were selected based on the trade-off between the data requirements and high predictive power of the model in terms of adjusted r-square. First, models with multi-collinearity (VIF scores >10) were excluded [16]. Subsequently models were listed in order of the adjusted r-squared. Starting with the model with the highest score, the models were assessed for their suitability to be used in the state level predictions, according to data availability and ability to interpret the coefficient on the independent variable. The first model to fit these criteria was selected for the state level cost predictions.

Table 1 : Model Coefficients

Once selected the coefficients obtained from the best fit model were used in the construction of the unit cost estimator (Table 1). Unit costs can then be generated with uncertainty limits for facilities with different characteristics. The predicted unit cost generated from the models is a median value due to the log transformations. This is adjusted to the arithmetic mean by applying a smearing factor [4,17] . Uncertainty intervals around the predicted values are constructed by first generating a random sample of 1000 based on the mean and standard error of each of the coefficients in the model and extracting the values at the 5th and 95th percentiles.

The cost prediction models were validated by using data and results from preliminary findings of the ongoing national costing study funded by the Government of India [18]. Actual costs and the explanatory variables were extracted from the primary data collected from the sampled health facilities for this study. The explanatory variables were used to predict the facility unit cost estimates based on the state prediction models. The predicted cost estimates were then compared with the actual unit cost estimates generated as part of the 2020 national costing study.

Variable Definition