page 25 one with only the VH Ws, and measuring the impact of each intervention on health status relative to a control group where neither intervention is introduced.[17] Alternatively and less satisfactorily, the parameters a and b could be estimated at relatively little cost by multiple regression on nonexperimental data from the region of interest. Either of these estimation techniques has the additional advantage over "guess-timation" of correcting for the problems of disease interdependence and competing risk, and thus allowing relaxation of the unpalatable assumption that health effects are additive.

The cost function parameters could be "gvess-timated" in a specific country by working with experienced health ministry managers and depending on their judgement as to the costs of various combinations of activities.[l8] Here too it would be feadLble and preferable to estimate these parameters statistcally using a sample of mobile teams and fixed centers that are performing some or all of the vaccination and VHW supervision functions. With enough observations, a moie flexible functional form could be chosen in lieu of the constant elasticity form used here. With increased flexibility, changes in average unit cost could be attributed to changes in coverage and intensity as well as to changes in output mix as modeled here. [19] It is useful to contraet the present study with two other cost-effectiveness studies of primary health care in developing countries, both of which were led by economists from the University of Michigan. A team based at the School of Public Health constructed the linear-programming model referenced in Tables 2 and 4 above, which depends on 3,696 different parameters in place of the two parameters in the objective function used here (Grosse et al, 1979, Appendices A, B and C). Although the SPH model deals with separ4te packages of interventions as discrete administrative entities just as the present paper treats Strategy F and Strategy X as distinct - the SPH model is completely linear and thus would require modification to address the problem of strategy choice with joint costs.

An independent team based at Michigan's Center for Research on Economic Development constructed a programming model which has a non-linear objective function, but linear cost constraints (Barnum et al, 1980). Although more parsimonious than the SPH model, the CRED model neveztheless includes 221 parameters. With modification to incorporate nonlinear cost constraints, this model could also be used to address the strategy choice problem. Given the available computer time and resources, models patterned after the SPH and CRED models would be useful tools for health planners in developing countries to address almost any health planning problem. However, the suze and complexity of these models makes thess costly and unwieldy and may reduce the degree to which they are understood, believed and used by decisionmakers. Until these models are generally available, understood and believed, smaller, special purpose models such as the present one may play an important role in guiding policy decisions and generating demand among decision-makers for modelling exercises.

A consideration which is difficult to introduce explicitly into the model, but must be addressed in the choice of primary health care integration strategy is the degree of uncertainty in the present about various aspects of the future. Two variables are particularly importanc in this regard and act in opposite directions on the preferrred strategy choice.