Prevalence of self-reported diabetes mellitus in women and associated factors: a population-based study in São Leopoldo, Rio Grande do Sul, Brazil, 2015.

Objective to analyze the prevalence of Diabetes Mellitus (DM) and associated factors in women aged 20 to 69 years in São Leopoldo, RS, Brazil. Method this was a population-based cross-sectional study, the outcome of which was self-reported medical diagnosis of DM. Poisson regression was used.

Characteristics of women who do not consult a doctor: a population-based study.

OBJECTIVE To analyze the prevalence of not consulting a doctor within a year. METHODS Cross-sectional population-based study, including women aged 20-60 years, living in the urban area of São Leopoldo, state of Rio Grande do Sul, in 2015. The association between variables and outcome was assessed using prevalence ratios and 95% confidence intervals (95%CI).

Hospitalization for primary care susceptible conditions, health spending and Family Health Strategy: an analysis of trends.

The goal of this study was to analyze the trend over time of hospitalizations due to conditions susceptible to primary healthcare (HCSPC), and how it relates to healthcare spending and Family Health Strategy (FHS) coverage in the city of São Leopoldo, Rio Grande do Sul State, Brazil, between 2003 and 2012. This is an ecological, time-trend study. We used secondary data available in the Unified Healthcare System Hospital Data System, the Primary Care Department and Public Health Budget Data System.

[Trends in hospitalization for primary care-sensitive conditions and associated factors in Porto Alegre, Rio Grande do Sul, Brazil].

An ecological study was conducted to analyze trends in hospitalization for primary care-sensitive conditions linking the results to the investments in health and coverage of the Family Health Strategy in Porto Alegre, between 1998 and 2012. The causes of hospitalization for primary care-sensitive conditions were based on the national list provided by the Ministry of Health. The data were obtained from the Hospital Information System of the Unified Health System (SUS).