Related factors with NCD in developing countries: economic, diet and risk factors dimensions

Sergio Arturo Dominguez Miranda; Roman Rodríguez Aguilar

doi:10.4108/eetpht.10.3499

Authors

Sergio Arturo Dominguez Miranda Universidad Panamericana
Roman Rodríguez Aguilar Universidad Panamericana

DOI:

https://doi.org/10.4108/eetpht.10.3499

Keywords:

Noncommunicable diseases, principal components analysis, cluster analysis, random forest, pervasive healthcare

Abstract

INTRODUCTION: Noncommunicable diseases (NCD), such as cardiovascular, oncological, respiratory diseases and diabetes mellitus, remain the leading cause of mortality worldwide. These diseases are associated with factors such as lack of physical activity, poor diet, smoking and excessive alcohol consumption. The economic and social cost of NCD in developed countries is considerable. In addition to the effects on the quality of life and health of individuals, these diseases generate a significant financial burden on health systems and the economy in general. The main causes of mortality together with an analysis of mathematical models, can provide fundamental information to monitor trends in the health outcomes, recognize the pattern of diseases that affect mortality and disability, identify emerging health challenges, evaluate the effectiveness of interventions and aid in health decision-making.

OBJECTIVES: To evaluate the relationship of a selected set of economic, dietary health risk factors of the economically active population in 13 developing countries for the year 2019 in NCD. Apply a dimension reduction method to detect cross-sectional variability in the selected countries, carry out a behavioral analysis of the underlying variables, identify patterns and generate indices for monitoring related factors of NCD.

METHODS: A database was built for the 2019 period of 13 developing countries including 76 variables, considering economic, food and lifestyle indicators. The principal components method was used to create new dimensions to group relevant information from all the variables used and characterize the diseases in developing countries for 4 selected NCD: cardiovascular disease, chronic respiratory disease, neoplasia, and diabetes mellitus. NCD monitoring indices were created considering an index of diet, economic and factors that affect the mortality. Using the generated indices, a cluster model was applied to group countries with similar characteristics according to the information analysed for each index.

RESULTS: Some relevant characteristics were identified in the countries analyzed, as well as interesting patterns among the factors related to NCD. The countries could be grouped considering their economic and nutritional behavior. It was observed that Latin American countries and Poland behave similarly, just as Asian countries show a similarity in eating behavior. The economic indicators of investment in health, as well as hours worked, behave in a similar way. It was identified that there are certain foods that have a similar behavior both in their consumption and in how they affect NCD. Thanks to the elaboration of the indices, it was observed that the countries of the Middle East and North Africa have a better food balance, but not the countries of Latin America.

CONCLUSION: The application of a dimensionality reduction method and cluster analysis out of quantitative methods made it possible to characterize the behavior of a set of variables that impact NCD, as well as to synthesize this information into specific indices by category of analysis. Strategies focused on improving NCD indicators can have a greater impact by identifying similar behavior profiles among developing countries, in the same way, joint policies could be designed to address NCD through specific actions by dimension of analysis and extend these policies to countries with similar profiles.

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