Caesarean sections and the prevalence of preterm and early-term births in Brazil: secondary analyses of national birth registration

Objectives To investigate whether the high rates of caesarean sections (CSs) in Brazil have impacted on the prevalence of preterm and early-term births. Design Individual-level, cross-sectional analyses of a national database. Setting All hospital births occurring in the country in 2015. Participants 2 903 716 hospital-delivered singletons in 3157 municipalities, representing >96% of the country’s births. Primary and secondary outcome measures CS rates and gestational age distribution ( Results Prevalence of CS was 55.5%, preterm prevalence (12 years of education. The adjusted prevalence ratios of preterm and early-term birth were, respectively, 1.215 (1.174–1.257) and 1.643 (1.616–1.671) higher in municipalities with≥80% CS compared with those Conclusions Brazil faces three inter-related epidemics: a CS epidemic; an epidemic of early-term births, associated with the high CS rates; and an epidemic of preterm birth, also associated with CS but mostly linked to povertyrelated risk factors. The high rates of preterm and earlyterm births produce an excess of newborns at higher risk of short-term morbidity and mortality, as well as long-term developmental problems. Compared with high-income countries, there is an annual excess of 354 000 preterm and early-term births in Brazil.</p

Estimating completeness of national and subnational death reporting in Brazil: application of record linkage methods

BACKGROUND: In Brazil, both the Civil Registry (CR) and Ministry of Health (MoH) Mortality Information System (SIM) are sources of routine mortality data, but neither is 100% complete. Deaths from these two sources can be linked to facilitate estimation of completeness of mortality reporting and measurement of adjusted mortality indicators using generalized linear modeling (GLM). METHODS: The 2015 and 2016 CR and SIM data were linked using deterministic methods. GLM with covariates of the deceased's sex, age, state of residence, cause of death and place of death, and municipality-level education decile and population density decile, was used to estimate total deaths and completeness nationally, subnationally and by population sub-group, and to identify the characteristics of unreported deaths. The empirical completeness method and Global Burden of Disease (GBD) 2017 estimates were comparators at the national and state level. RESULTS: Completeness was 98% for SIM and 95% for CR. The vast majority of deaths in Brazil were captured by either system and 94% were reported by both sources. For each source, completeness was lowest in the north. SIM completeness was consistently high across all sub-groups while CR completeness was lowest for deaths at younger ages, outside facilities, and in the lowest deciles of municipality education and population density. There was no clear municipality-level relationship in SIM and CR completeness, suggesting minimal dependence between sources. The empirical completeness method model 1 and GBD completeness estimates were each, on average, less than three percentage points different from GLM estimates at the state level. Life expectancy was lowest in the northeast and 7.5 years higher in females than males. CONCLUSIONS: GLM using socio-economic and demographic covariates is a valuable tool to accurately estimate completeness from linked data sources. Close scrutiny of the quality of variables used to link deaths, targeted identification of unreported deaths in poorer, northern states, and closer coordination of the two systems will help Brazil achieve 100% death reporting completeness. The results also confirm the validity of the empirical completeness method