A complex of regional ecological and medico-social factors: evaluation of dysplastic dependent pathology of the bronchopulmonary system

Abstract

Respiratory diseases are becoming one of the most widespread threats to the world’s population. It is important to focus on new public health management mechanisms for the development and promotion of public health at the national and municipal levels. Dysplastic dependent pathology (DDP) of the bronchopulmonary system (BPS) is a group of diseases or conditions, which includes bronchopulmonary dysplasia (BPD), recurrent bronchitis, chronic bronchitis, obliterating bronchiolitis, interstitial lung disease. The formation of the risk of these diseases and pathological conditions is influenced by several (to date, almost unexplored) factors, among which are medico-social, regional ecological and other influential factors of periods of child development.

The aim of the study was to substantiate evidence-based (clinically and statistically) algorithm for risk assessment by a set of regional ecological factors relevant for the assessment of the health of children with dysplastic dependent pathology of the bronchopulmonary system. Material and methods. A personalized analysis of the existing factors in 252 children (with BPD and DDP BPS) of two administrative regions of Ukraine and 252 healthy children groups of comparison group was accomplished. While examining the regional-population characteristics of healthy and sick children, specially compiled expert-prognostic chart was used, that was completed for each child and included data on BPD or DDP BPS presence, and characteristics of the regional ecological clusters. Based on the study of the true prevalence of 30 possible risk factors, the most informative ones were identified, the prognostic value of which was used as criteria for the risk assessment of bronchopulmonary system disease in children. Based on the methodology of Wald’s sequential analysis in the modification of E.V. Gubler a standardized threatometric (pathometric) tabular algorithm was compiled and an example of its application at the individual level was provided. The application of this algorithm allows to document the available significant risk factors and to identify individuals (and homogeneous territorially ontogenetic groups) with high risk of bronchopulmonary system diseases. Results. The algorithm verification was carried out among children of two groups and was stated, that the error frequency of the first kind amounted to =3,0%, while errors of the second kind (low risk with pathology was identified) =7,2%. Thus, the specificity of the prognostic algorithm is 92,8%, and its efficiency is 97,0%, which enables to recommend it as a stage of population monitoring. Conclusions. Prospects for further research on this issue are determined by the need to develop the algorithms for population and individual prognosis of bronchopulmonary system disease in the antenatal period and at the stages of postnatal ontogeny, considering other informative (medical, organizational, genealogical and anamnestic) factors.