Chronic Chagas cardiomyopathy: characterization of cases and possibilities of action in primary healthcare

Cad Saude Publica. 2022 Jun 8;38(6):e00290321. doi: 10.1590/0102-311XEN290321. eCollection 2022.

Abstract

Despite the drastic decrease in the incidence of Chagas disease in Brazil, past cases still greatly impact health services in the country. Thus, this study aimed to characterize Chagas disease cases regarding their cardiac staging and death prognosis and, based on that, to propose primary healthcare (PHC) case follow-ups. This is a cross-sectional study based on secondary data from the medical records of patients with chronic Chagas cardiomyopathy (CCC). A logistic regression was applied to estimate crude and adjusted odds ratios (OR). A total of 433 medical records were evaluated. More severe CCC cases were associated with a greater number of hospitalizations (OR = 3.41; 95%CI: 1.59-7.30) and longer hospitalization (OR = 3.15; 95%CI: 1.79-5.53). Cases with a higher risk of death were associated with a higher number of hospitalizations (OR = 1.92; 95%CI: 1.09-3.37), longer hospital stays (OR = 2.04; 95%CI: 1.30-3.18), and visits to the outpatient clinic (OR = 2.18; 95%CI: 1.39-3.41) and the emergency department of the assessed hospital (OR = 3.12; 95%CI: 1.27-7.66). Analyzing the medical records at two moments, 72.9% of the cases remained in the stages in which they were initially evaluated. Overall, 44.4% of cases were classified as mild to moderate risk of death and 68.3% as low ones. The cases classified in the most severe stages of CCC and with high or intermediate risk of death were associated with greater hospital dependence. However, most cases were classified as milder forms of the disease, with a low risk of death and clinical stability. These findings aim to promote the role of PHC as a protagonist in the longitudinal follow-up of CCC cases in Brazil.

MeSH terms

  • Brazil / epidemiology
  • Chagas Cardiomyopathy* / epidemiology
  • Chagas Disease* / epidemiology
  • Cross-Sectional Studies
  • Humans
  • Logistic Models
  • Primary Health Care