DATA ANALAYSIS OF NON-COMMUNICABLE CHRONIC DISEASES IN BRAZIL
Non-communicable chronic diseases. Multimorbidity. Data analysis. Multi-period. Multicriteria. Factor analysis. Logistic regression.
This thesis is composed by statistical and decision-making applications in the health management context focused in non-communicable chronic diseases (NCD), that includes diseases that are not transmitted from person to person as high blood pressure, diabetes and cancer. Data analysis strategies with cleaning, organization, and data modelling were explored to extract useful patterns to support decision-making. It was used data from cross-sectional population-based researches in Brazil, considering the health supplements of the National Household Surveys (PNAD) and the National Health Survey (PNS). Initially, a systematic literature review was executed summarizing previous researches on NCDs in Brazil. Thereafter, it was investigated trends in the prevalence of NCDs related to age and sex from a temporal perspective showing that NCDs are highly associated with aging. Additionally, in a multiperiod analysis, it was observed that women have been more affected than men. Furthermore, evaluating NCDs in a geographic perspective, from the proposed Chronic Disease Index (gCDI) it was detected that the South of Brazil concentrated more chronic diseases compared with the North. It was executed an overtime analysis of multimorbidity, that is the co-occurrence of two or more chronic diseases in the same person. It was noticed that the risk of multimorbidity was higher for women and illiterate, increasing with age. People with multiple chronic diseases consider their health worse compared with people without chronic illnesses, demonstrating a greater need for health assistance and hospitalization. At the end, an integrated health assessment was proposed from an outranking decision model using multiple periods of time, considering objective and subjective perspectives of health, that can be used as an indicator for monitoring the population and to support prevention strategies overtime. Effectively, quantitative data analysis in different periods of time can facilitated trends observations to support health management. PNAD and PNS researches demonstrated to be valuable data sources about chronic diseases in Brazil. A better understating of health patterns might support policy-makers to improve preventative actions in epidemic situations that significantly affect groups of people with chronic conditions and multimorbidity.