Dear readers,

Thanks for your attention. I am wondering about the health economic problem of quantifying the value of interventions which a) prevent, b) improve symptom profile and c) ultimately reverse (i.e. cure) diseases.

As a rough number if a preventative measure reduces incidence of a disease by 10%, I can calculate savings by multiplying this by the total direct and indirect costs of the disease.

However, for conditions where the intervention exerts a partial improvement in symptom profile (not total reversal), the calculation of savings can be more difficult. What I was thinking is using absolute changes in an accepted scale. Such as the HAMD scale for depression, which defines cutoffs as:

no depression (0-7); mild depression (8-16); moderate depression (17-23); and severe depression (≥24).

Max value: 52

From other information, I have found that depression for a single patient brings costs of $11446 per patient (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5798200/). Assuming spending per patient scales linearly with severity, and goes to zero at clinical remission.

Of course the figure is an average, so one would in principle find the distribution of HAMD scores for diagnosed patients to find where the 'average' patient lies in terms of severity. Even more preferable, you would have expenditure-severity data for multiple individuals, but we have to extrapolate otherwise. Roughly, I'll put the $11446 as the cost per patient with a severity of someone in the middle of the scale, which is roughly 14 on the scale Article Omega-3 Fatty Acids Supplementation in the Treatment of Depr...

If that is the case, one point is roughly (and this is very rough) would be $11446/14 or $820. It's probably less as the value 14 is probably too low, but somewhere in this range.

I am wondering if there is a better methodology to estimate the value of interventions which improve symptoms? Or if this approach is acceptable.