Reports and Brochures

Peer-Reviewed Articles

2020 Reports and Brochures (Portuguese)

Findings for year 2020 are presented in a comprehensive report  brief report, and provinces-level fact sheets.

2020 Brochure (English)

Project design and results for the 2020 calendar year are summarized in this brief document.

2019 Reports and Brochures (Portuguese)

Findings for year 2019 are presented in a  comprehensive report, brief report and province-level fact sheets.

Formative Research Report (2018)

This report presents findings from formative research conducted prior to the surveillance system’s implementation, in order to inform the design of the surveillance system.  Download formative report.  

Building a Sample Vital Statistics System: Results From Countrywide Mortality Surveillance for Action (COMSA) in Mozambique

 The American Journal of Tropical Medicine and Hygiene (2023)

This journal supplement includes two editorials and 8 original research articles describing the results of the COMSA project during years 2019-2020.

Read the journal supplement.

Regularized Bayesian transfer learning for population-level etiological distributions

Datta et al. Biostatistics (2020)

Summary excerpt: Computer-coded verbal autopsy (CCVA) algorithms predict cause of death from high- dimensional family questionnaire data (verbal autopsy) of a deceased individual, which are then aggregated to generate national and regional estimates of cause-specific mortality fractions. These estimates may be inaccurate if CCVA is trained on non-local training data different from the local population of interest. 

Link to full article.

Generalized Bayesian Quantification Learning

Fiksel et al. Journal of the American Statistical Association (2022)

Abstract excerpt: Quantification learning is the task of prevalence estimation for a test population using predictions from a classifier trained on a differen population. Commonly used quantification methods either assume perfect sensitivity and specificity of the classifier, or use the training data to both train the classifier and also estimate its missclassification rates… We proposed a generalized Bayesian quantification learning (GBQL) approach that uses the entire compositional predictions from probabilistic classifiers and allows for uncertainty in true class labeled test data. Read article

Johns Hopkins Bloomberg School

of Public Health (JHSPH)

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