Setting up Affordability Scenarios

Having determined the size of the relevant population, the incidence rate of the condition being considered, and the number of cases each year (by age and gender) the next step is determine what proportion and number of persons that can afford the treatment at a specific price and under a specific funding scenario.

This involves selecting the sheet ‘Affordability’ or clicking on the button labeled ‘Go to Affordability Analysis’ in the Table Sheet. You should not go to ‘Affordability’ analysis until you have set up an analysis and run the tables for that. Otherwise the correct data will not be loaded.

Afford setup.JPG

The Affordability analysis involves two stages - setting parameters shown here, and the running the Scenarios (typically 25 different solutions) and reviewing the results.

First you should set two different years for testing the scenarios. In this case 2017 and 2027 have been selected, but you can choose any two years,

Second - enter the price range to be tested, IN this example the minimum is US$4,000 and the maximum is US$5,000. Please note it is US$ and per annum/course of treatment before taking into account compliance rates as set in the Scenario sheet.

Note that the model specifies 5 steps between the lowest price and the highest price.

Then there are two possible funding scenarios -

The Chronic low cost condition.

This is where the condition is ongoing and requires on going purchase of the treatments and is at a relatively low price point.

In that case most households will fund it from the annual expenditure on health and the issue is what proportion of that expenditure will be devoted to this one treatment for one individual. In short, how much will the other members of the family give up.

The model will use 5 steps starting from 0 rising to the percent value selected - in this case 20%

The Expensive and perhaps one-off Treatment (e.g. most forms of oncology)

This is the second box in the form shown opposite - ‘Serious conditions’.

Here the funding comes from a combination of savings and cutting discretionary expenditure and the user defines a range in terms of the proportion of savings and discretionary expenditure is devoted to this treatment

The two sources (savings and discretionary expenditure) are lumped together as there is not definitive rule in terms which is used by a family. It may be both (especially if very expensive relative to income) or from savings (no change in life style) or from discretionary (change in lifestyle) to preserve savings. Basically it is an issue of bulk funding beyond the level that can be provided by the normal annual household per cap expenditure on health.

In this example the range has been set from 20% to 50% - but it can be any two values between 0% and 100%.

Again the model uses five steps between these two points.

Finally note that some values are shaded in red.

These are where the income break would cover the cost given price and proportion that is begin allocated to the treatment. This is to help the user determine what values to enter in the minimum /maximum boxes. If none are shaded red then only the very highest income households are likely to be able to afford it under the defined scenario. In such instance you might consider either increasing the proportion allocated to the treatment or lowering the price range.

Running the Analysis

To do this move to the options at the top right of the screen - select which funding scenario to ise and click the button ‘Run Scenarios’.

The computer will iterate through the different scenarios (and income ranges) to determine what proportion of persons with the condition can afford to pay for the treatment at the cost and funding levels of each scenario. It also writes the results of the best solution to the ‘Report Page’ (see later).

The output is divided into two parts

The four colored boxes at the top are volume/revenue sensitivity to price and funding changes. The two on the left are for the first of the two test years, and the two on the right are for the second year. Basically they show by what percentage the number of patients and total revenue change from the base (lowest price) scenario as the price increases.

The second set of tables give the details for each of the two years for each of the scenarios.

Afford result.JPG

Next Section of the Manual - Affordability Analysis