The name is absent

Nutrition and Coronary Heart Disease

For example, a relatively small proportion of the doctors recruited for the intervention
could be responsible for the majority of the success.

It may be reasonable to assume that doctors with a predisposition towards advocating
preventive measures via nutrition are more likely to join the program and be more
effective in promoting the virtues of nutritional interventions. Thus adverse selection,
resulting in a relatively low take-up rate could lead to a high degree of success. For
example, 70% of the GPs could achieve 90% of the effectiveness. This outcome is
considered in table 12 and leads to a lowering of the cost-effectiveness ratio.

There is a counter argument that somewhat negates the above. The highly motivated
GPs mentioned above might already be involved in the types of programs that make up
the GP intervention. Thus if a program is going to be successful, above and beyond the
status quo, it must draw upon those who currently are not involved in Opportiuiistic
interventions. However, these GPs may be harder to motivate, and hence the adverse
selection would operate in the opposite direction. If 30% of the GPs who are least
likely to be involved in the program are the most effective then a low take-up rate would
lead to an even lower success rate. Such an example is also considered in Table 12.

TABLE 12: SENSITIVITY ANALYSIS: ADVERSE SELECTION (WITH 70% TAKE-UP RATE
AMONGST GPs)

The adverse selection sensitivity analysis leads to a confidence interval of ($61,422:
$34,123), whilst the length of effectiveness led to a remarkably similar ($60,448:
$34,541). Even though these results seem to concur, it is necessary to consider the case
where the best case scenario in terms of length of effectiveness is compounded by the
best case scenario for adverse selection. The same can be said for the double worse
case scenario. Table 13 highlights the best and worse cases when considering both
types of risk.

CHERE Project Report 11 — November 1999

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