Morbidity And Prediction Of Multimorbidity

Multimorbidity is characterized by the cooccurrence of 2 or more chronic diseases,and has been a focus of the health care sector and health policymakers due to its severe adverse effects.This paper aims to uses the latest 2 decades of national health data in Brazil to analyze the effects of demographic factors and predict the impact of various risk factors on multimorbidity.Data analysis methods include descriptive analysis,logistic regression,and nomogram prediction.The research makes use of a set of national cross-sectional data with a sample size of 877 032.The study used data from 1998,2003,and 2008 from the Brazilian National Household Sample Survey(PNAD),and from 2013 and 2019 from the Brazilian National Health Survey(PNS).We developed a logistic regression model to assess the influence of risk factors on multimorbidity and predict the influence of the key risk factors in the future,based on prevalence of multimorbidity in Brazil.Overall,females were 1.7 times more likely to suffer from multimorbidity than males(OR1.72,95% CI 1.69～1.74).The prevalence of multimorbidity was 1.5 times higher among nonworking individuals than working individuals(OR1.51,95% CI1.49～1.53).Multimorbidity prevalence increased significantly with age.People over 60 years old were about 20 times more likely to have multiple chronic diseases than those between 18 and 29 years of age(OR19.6,95%CI 19.15～20.07).The prevalence of multimorbidity in illiterate individuals was 1.2 times higher than in literate ones(OR1.26,95%CI 1.24～1.28).The subjective well-being of seniors without multimorbidity was 15 times higher than that among people with multimorbidity(OR15.29,95%CI 14.97～15.63).Adults with multimorbidity were more than 1.5 times more likely to be hospitalized than those without(OR1.53,95%CI 1.50～1.56)and 1.9 times more likely to need medical care(OR1.94,95%CI 1.91～1.97).These patterns were similar and remained stable for over 5 years.A nomogram model was used to predict multimorbidity prevalence under the influence of various risk factors.The prediction results demographic older age and poorer subject well-being had the strongest correlation with multimorbidity.Our study shows that multimorbidity prevalence varied little in the past 2 decades,but varies widely across social groups.Identifying populations with higher rate of multimorbidity prevalence may improve policy-making around multimorbidity prevention and management.Government can create public health policies targeting these groups,and provide more medical treatment and health service to support and protect the multimorbidity population.