Statistical modelling of global communicable diseases in the aspect of demographic, economic, and environmental indicators using generalized linear mixed models with multi-random effects
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Keywords:
Generalized Linear Model, Generalized Linear Mixed Model, Random Effect, Logit, Probit, Cloglog, Cauchit Link Function, Communicable Disease, Disability-Adjusted Life YearsAbstract
Communicable diseases are infectious diseases that can spread from person to person every year,
causing the death of hundreds of thousands of people and significantly threatening public health. Disability
adjusted life years (DALYs) is an important criterion measuring the years of life lost by a person due to a
negative situation such as illness, injury or infectious diseases, and the quality of life. In this study, the
relationships between DALYs from communicable diseases and countries’ income levels, urbanization, net
immigration rate, median age, forest area, and human development index (HDI) for 187 countries from six
continents in 2019. Four generalized linear models and twelve generalized linear mixed models (both
GLMs and GLMMs) having binomial distribution with different random effects such as countries,
continents, and both of them under “logit”, “probit”, “cloglog”, and “cauchit” link functions are used for
modelling DALYs data in the global aspect of population and demographic change, and also economic,
development, and environmental indicators. As a result of sixteen modelling, GLMM having binomial
distribution with country and continent-random effects under “logit” link function is detected as the best
fitted model according to information criteria as AIC with 100.766, AICc with 102.870, BIC with 142.770,
and CAIC with 155.770. According to these statistical findings, it has been detected that increases in
urbanization and net immigration rates have a positive effect on DALYs from communicable diseases,
while increases in countries' income levels, median age, forest area, and HDI have a negative effect.
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