Systematization of effective population-based preventive measures under uncertainty: an ontological approach

Effective management decisions in the field of health care and preventive medicine requires a systematic, holistic and scientifically based approach. However, there is a problem of fragmentation and insufficient data.

Aim. To develop approaches to modeling population-based preventive measures in Russia, applicable under uncertainty.

Material and methods. At the first stage, we selected the central chronic noncommunicable diseases (NCDs) associated with high morbidity and mortality in Russia, for which there are effective preventive measures. At the next stage, based on the literature analysis, we selected risk factors of these NCDs. Further, population-based preventive measures were selected. The following population-based preventive measures were considered: economic measures, taxes, subsidies; information campaigns in the media and public education; changing the environment, infrastructure; labeling, information for the consumer, prohibition, and other legislative measures.

Results. An ontological model in the form of a graph was created. Modeling the socio-economic effect of population-based strategies begins with the choice of a preventive measure with a proven effect, which can indirectly, through a decrease in the risk factors’ prevalence, preclude new cases of chronic diseases among the population of Russia and reduce the related costs in the future.

Conclusion. Ontological analysis made it possible to identify the functional structure of population-based prevention and its action under uncertainty. The development of ontology provides an additional means of access to the available research data, which is necessary for evidence-based management decision-making.

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