This is illustrated in Fig. 1, where concentration indices to the left are interpreted as costs accumulating among lower SES groups, while the right-side contributions are interpreted reversely.
Thus, a cost level higher than expected is interpreted as higher costs, while fewer complications than expected are interpreted as higher quality.
These costs can be interpreted as approximate values for disease specific treatment costs in similar facility types in Kenya, and perhaps similarly placed low-income countries.
Future health care costs may be interpreted as a measure of the intensity of contacts with the health care sector and may hence be seen as a proxy for morbidity.
This would mean that the specific direct marketing costs could also be interpreted by installation operators as a corresponding reduction of the remuneration rates.
Clearly the implications of any given reduction in the absolute number of new cases, deaths or health care costs have to be interpreted locally with regard to the size of the population.
In this way, the annualised costs and benefits can be interpreted as the costs and benefits associated with a single year of intervention.
Then, the bi-objective optimization model can be used in a formally identical way, with the exception that now, the opening costs c i are to be interpreted as opening costs per delivery period, i.e., they are computed by dividing the total opening costs for the supply phase by the number of delivery periods.
Given these logistical problems the unit costs of hospitalization should be interpreted with care and not considered as an indicator of efficient resource usage.
The variance and mean repeat number in a population will then reflect susceptibilities for each disease and their fitness costs, which must therefore be interpreted in the physiological, geographical, and ecological context in which they have, and presumably are, evolving.
