Authors: Carla AbouZahr, Martin W Bratschi, Daniel Cobos Muñoz...

Topics: Cause of Death; Data Accuracy; Humans; Population Surveillance; Registries; Reproducibility of Results; Vital Statistics

PubMed: 29695876
DOI: 10.2471/BLT.18.211086

Authors: Tim Adair, Azza Badr, Lene Mikkelsen...

OBJECTIVE

To assess civil registration and vital statistics completeness for births in World Health Organization's Member States and identify data completeness gaps.

METHODS

For the 194 Member States, we sourced birth registration data from the United Nations Children's Fund database of national surveys, and, where available, vital registration reports. We acquired publicly available vital statistics compiled by national authorities. We determined civil registration completeness as the percentage of living children younger than five years whose births have been reported as registered. We evaluated vital statistics completeness against the United Nations World Population Prospects' live birth estimates, and grouped countries into seven categories based on their civil registration and vital statistics completeness.

FINDINGS

Globally, civil registration completeness for births was 77%, exceeding vital statistics completeness for births at 63%. Twenty countries had limited civil registration (25% to 74% completeness) and had nascent or no vital statistics data (completeness < 25%) for births. Five countries had nascent or no civil registration and vital statistics for births. Twenty countries had functional civil registration (75% to 94% completeness) but nascent or no available vital statistics. Approximately half (96) of the countries had complete civil registration and vital statistics for births, but contributed to only 22% of global births.

CONCLUSION

The gap in completeness between civil registration data and vital statistics for births is most pronounced in countries with lower civil registration completeness. Enhancing data transfer processes for birth registration, along with targeted investments to elevate registration rates, is crucial for yielding comprehensive fertility statistics for governmental planning.

Topics: Child; Humans; Registries; Vital Statistics; Global Health; United Nations; Fertility

PubMed: 38024250
DOI: 10.2471/BLT.22.289035

Authors: Dede Anwar Musadad, Tri Juni Angkasawati, Yuslely Usman...

Civil Registration and Vital Statistics (CRVS) systems are the optimal source for data on births, deaths and causes of death for health policy, programme evaluation and research. In Indonesia, indicators such as life expectancy at birth, childhood and maternal mortality rates and cause-specific death rates need to be routinely monitored for national health policy. However, the CRVS system is not yet producing reliable vital statistics, which creates a challenge for evidence-based health action. In 2019, the Indonesian government released a national strategy for the CRVS system, with targets for improved coverage and data quality by 2024. This article describes findings from a programme of formative and implementation research to guide the application of the national strategy. At first, a detailed CRVS assessment and gap analysis were undertaken using an international framework. The assessment findings were used to develop a revised business process model for reporting deaths and their causes at village, subdistrict and district level. In addition, a field instruction manual was also developed to guide personnel in implementation. Two field sites in Java-Malang District and Kudus Regency were selected for pilot testing the reporting procedures, and relevant site preparation and training were carried out. Data compilations for Malang in 2019 and Kudus in 2020 were analysed to derive mortality indicators. High levels of death reporting completeness (83% to 89%) were reported from both districts, along with plausible cause-specific mortality profiles, although the latter need further validation. The study findings establish the feasibility of implementing revised death reporting procedures at the local level, as well as demonstrate sustainability through institutionalisation and capacity building, and can be used to accelerate further development of the CRVS system in Indonesia.

Topics: Infant, Newborn; Humans; Child; Indonesia; Vital Statistics; Data Accuracy; Life Expectancy; Capacity Building

PubMed: 37474276
DOI: 10.1136/bmjgh-2023-012358

Authors:

Topics: Biometry; Communicable Diseases; Vital Statistics

PubMed: 13009203
DOI: No ID Found

Authors:

Topics: Biometry; Communicable Diseases; Vital Statistics

PubMed: 13009178
DOI: No ID Found

Authors:

Topics: Biometry; Communicable Diseases; Vital Statistics

PubMed: 13009250
DOI: No ID Found

Authors:

Topics: Cholera; Disease; Incidence; Measles; Plague; Scarlet Fever; Smallpox; Typhus, Epidemic Louse-Borne; Variola virus; Vital Statistics

PubMed: 15412994
DOI: No ID Found

Authors:

Topics: Biometry; Communicable Diseases; Humans; Vital Statistics

PubMed: 13059428
DOI: No ID Found

Authors: Hedley Knewjen Quintana, Fernando Gutierrez, Fulvia Ibarra...

INTRODUCTION

The National Mortality Register (NMR) of Panama is a key element in demographic analysis and in acquiring an updated picture of population health in Panama. The main objectives of this study are to characterize the NMR and to enumerate its strengths and weaknesses.

METHODS

We describe the history, processes, and structure of the Vital Statistics Section of the National Institute of Statistics and Census (the curator of the NMR database). In addition, we discuss publication punctuality, underregistration of the data, the proportion of registered deaths certified by medical doctors, and the top 5 causes of death according to the 80 groups of the . We also examine works derived from the register's data, from the first publication on its website (2002) until 2019.

RESULTS

The NMR procedures were described. The web reports of the NMR were performed with a delay of between 1 to 2 years. The underregistration of deaths in 2002-2019 was 14.7%, and the national yearly proportion of deaths certified by medical doctors was always above 90%. Hard-to-reach areas had higher underregistration proportions and fewer deaths certified by medical doctors. Information extracted from the NMR supports several national and international reports, geographic information systems, and studies. The most common causes of death between 2002 and 2019 were noncommunicable diseases.

CONCLUSIONS

The NMR is a robust official information system. However, hard-to-reach areas require improvement in terms of the NMR. The NMR is used for publishing official reports, writing studies, and updating reports on the current health status of Panama in a timely fashion following international guidelines.

Topics: Humans; Vital Statistics; Panama; Cause of Death

PubMed: 38504706
DOI: No ID Found

Estimating misclassification errors in the reporting of maternal mortality in national civil registration vital statistics systems: A Bayesian hierarchical bivariate random walk model to estimate sensitivity and specificity for multiple countries and years with missing data.

Authors: Emily Peterson, Doris Chou, Ann-Beth Moller...

Civil registration vital statistics (CRVS) systems provide data on maternal mortality that can be used for monitoring trends and to inform policies and programs. However, CRVS maternal mortality data may be subject to substantial reporting errors due to misclassification of maternal deaths. Information on misclassification is available for selected countries and periods only. We developed a Bayesian hierarchical bivariate random walk model to estimate sensitivity and specificity for multiple populations and years and used the model to estimate misclassification errors in the reporting of maternal mortality in CRVS systems. The proposed Bayesian misclassification (BMis) model captures differences in sensitivity and specificity across populations and over time, allows for extrapolations to periods with missing data, and includes an exact likelihood function for data provided in aggregated form. Validation exercises using maternal mortality data suggest that BMis is reasonably well calibrated and improves upon the CRVS-adjustment approach used until 2018 by the UN Maternal Mortality Inter-Agency Group (UN-MMEIG) to account for bias in CRVS data resulting from misclassification error. Since 2019, BMis is used by the UN-MMEIG to account for misclassification errors when estimating maternal mortality using CRVS data.

Topics: Bayes Theorem; Bias; Humans; Maternal Mortality; Sensitivity and Specificity; Vital Statistics; Female

PubMed: 35165916
DOI: 10.1002/sim.9335