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Objective: Child mortality and stillbirth rates (SBR) remain high in low‐income countries but may be underestimated due to incomplete reporting of child deaths in retrospective pregnancy/birth histories. The aim of this study is to compare stillbirth and mortality estimates derived using two different methods: the method assuming full information and the prospective method. Methods: Bandim Health Project's Health and Demographic Surveillance Systems (HDSS) follows women of reproductive age and children under five through routine home visits every 1, 2 or 6 months. Between 2012 and 2020, we estimated and compared early neonatal (ENMR, <7 days), neonatal (NMR, <28 days), and infant mortality (IMR, <1 year) per 1000 live births and SBR per 1000 births. Risk time for children born to registered women was calculated from birth (the method assuming full information) versus date of first observation in the HDSS (the prospective method), either at birth (for pregnancy registration) or registration. Rates were calculated using the Kaplan–Meier estimator and compared in generalised linear models allowing for within‐child correlation obtaining relative risks (RR). Results: We registered and followed 29,413 infants (1380 deaths; 1459 stillbirths) prospectively. An additional 164 infant deaths and 129 stillbirths were registered retrospectively and included in the method assuming full information. The ENMR was 24.5 (95%CI: 22.6–26.4) for the method assuming full information and 25.8 (23.7–27.8) for the prospective method, RR = 0.96 (0.93–0.99). Differences were smaller for the NMRs and IMRs. For SBRs, the estimates were 53.5 (50.9–56.0) and 58.6 (55.7–61.5); RR = 0.91 (0.90–0.93). The difference between methods became more pronounced when the analysis was limited to areas visited every 6 months: RR for ENMR: 0.91 (0.86–0.96) and RR for SBR: 0.85 (0.83–0.87). Conclusions: Assuming full information underestimates SBR and ENMR. Accounting for omissions of stillbirths and early neonatal deaths may lead to more accurate estimates and improved ability to monitor mortality. [ABSTRACT FROM AUTHOR]

Objective: To describe the spatio-temporal distribution of infant mortality and its components in the city of Rio de Janeiro, Brazil, in 2010 and 2019. Methods: Infant mortality rate and the neonatal and postneonatal components were estimated. The standardized mortality rate was calculated to detect excess child mortality in the planning areas. Poisson regression was performed to estimate the effect of these planning areas on the years 2010 and 2019. Spatial analysis per neighborhoods was performed to identify the spatial autocorrelation rates, using the Moran's Index and local indicator of spatial association (LISA). Results: The planning areas are very heterogeneous, depending on the history and evolution of occupation. There is an excess of mortality in planning areas with greater social vulnerability. In the Poisson model, it was observed that in all components, the planning area (PA) of residence was statistically significant as well as the year. Moran's index did not show global spatial autocorrelation. However, when applying the LISA method, autocorrelation was observed at the local level and spatial clusters in the municipality of Rio de Janeiro. Conclusions: The spatial heterogeneity of the infant mortality rate in Rio de Janeiro suggests that local health policy strategies of each region consist in an efficient measure for reducing this rate. [ABSTRACT FROM AUTHOR]