A team of young researchers from FEFU is developing a service for the analysis of mortality from CVD in the regions of the Russian Federation
Researchers of the Laboratory of Data Analysis and Applied Econometric Research of the School of Economics and Management of the Far Eastern Federal University (SEM FEFU) are developing a service based on data that can explain the dynamics of the mortality rate from cardiovascular diseases (CVD) in the regions of the Russian Federation.
The service should allow modeling of mortality rates from CVD in the regions of the Russian Federation to answer the question to what extent they are determined by the observed characteristics of the region (socio-economic and demographic factors, ecological and hygienic, etc.), the influence of neighboring regions, the effectiveness of the management system that has developed in the region, ceteris paribus (based on fixed effects).
The first prototype can be tested at the link. The service is being created with the support of the FEFU Target Capital Fund as part of the implementation of the project “Spatial-autoregression analysis of morbidity indicators by disease areas in the regions of the Russian Federation”.
At the moment, after selecting the region of interest and the time period, the user can see to what extent such indicators as the characteristics of the territory, for example, the share of the urban population, the level of education, tobacco consumption affect the mortality rate from CVD, as well as to what extent the contribution of these of factors in a specific region differs from their contribution to the dynamics of the mortality rate from CVD at the average Russian level or in other regions.
Despite the first obtained results, additional studies will have to be conducted in order to obtain true cause-and-effect relationships between the analyzed factors and the investigated mortality rate. The fact is that despite the use of spatial autoregression modeling, not all econometric problems have yet been solved. In particular, the problem of reverse causality and omitted variables is planned to be solved using quasi-experimental methods.
The service can be useful for managers in the field of health care, in particular, when designing measures within the framework of state and regional programs aimed at reducing mortality rates from specific areas of diseases.