We aim to identify trajectories of probable maternal common mental disorders (CMD), as well as risk and protective factors associated with maternal mental health among postpartum women during the pandemic using life course theory approach.

Prospective individual level cohort study from the Iracema-COVID Study.

Mothers (n=335) at postpartum period who delivered during the COVID-19 pandemic in the fourth largest city in Brazil.

Probable CMDs were accessed using validated instruments in five cohort waves at postpartum period. Sequence analyses (SA) were employed to extract CMD trajectories, and a set of generalised binomial logistic and log-Poisson multivariable regression models with robust variance were employed to assess risk and protective factors for probable CMDs diagnosis.

Trajectories patterns of probable maternal common mental disorders.

Fit indices demonstrated a two-cluster-SA solution of probable CMD. The patterns of probable CMDs indicated that 335 mothers were clustered into occasional/transitory (n=240, 71.64%) and mostly/persistent (n=95, 28.36%) CMD trajectories. We found that mothers with low education (OR: 2.44; 95% CI 1.13 to 5.23), single (OR: 1.97; 95% CI 1.03 to 3.75) or in a stable union (OR: 2.00; 95% CI 1.02 to 3.90) and travel time spent to access the nearest primary healthcare unit (OR: 1.02; 95% CI 1.006 to 1.04) were associated with increased OR of belonging to the mostly/persistent CMD trajectory. Deprived green areas acted as a risk factor to maternal CMDs prevalence (OR: 1.37; 95% CI 1.002 to 1.87).

This study provides evidence that individual vulnerabilities and neighbourhood deprivation play an important role in understanding maternal mental health, beyond the patterns and trajectories of probable maternal CMD due to issues confronted during the COVID-19 outbreak in the northeastern region of Brazil. Policies to prevent and treat maternal mental health issues and improvement in neighbourhood deprivation need to be developed and addressed to avoid exacerbation of probable maternal CMDs.

Gestational weight gain (GWG) is an important indicator of maternal nutrition to be monitored during pregnancy. However, there is no evidence-based tool that can be used to monitor it across all geographic locations and pre-pregnancy body mass index (BMI) categories. The WHO is undertaking a project to develop GWG charts by pre-pregnancy BMI category, and to identify GWG ranges associated with the lowest risks of adverse maternal and infant outcomes. This protocol describes all the steps that will be used to accomplish the development of these GWG charts.

This project will involve the analysis of individual participant data (researcher-collected or administrative). To identify eligible datasets with GWG data, a literature review will be conducted and a global call for data will be launched by the WHO. Eligible individual datasets obtained from multiple sources will be harmonised into a pooled database. The database will undergo steps of cleaning, data quality assessment and application of individual-level inclusion criteria. Heterogeneity of maternal weight and GWG will be assessed to verify the possibility of combining datasets from multiple sources and regions into a single database. Generalized Additive Models for Location, Scale and Shape will be applied for the construction of the centile curves. Diagnostic measures, internal and external validation procedures will also be performed.

This project will include an analysis of existing study de-identified data. To be included in the pooled database, each included study should have received ethics approvals from relevant committees. Manuscripts will be submitted to open-access journals and a WHO document will be published, including the GWG charts and cut-offs for application in antenatal care.