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. 2021 Feb 13;9(2):377.
doi: 10.3390/microorganisms9020377.

The Global Burden of Meningitis in Children: Challenges with Interpreting Global Health Estimates

Claire Wright  1 , Natacha Blake  1 , Linda Glennie  1 , Vinny Smith  1 , Rose Bender  2 , Hmwe Kyu  2 , Han Yong Wunrow  2 , Li Liu  3 , Diana Yeung  4 , Maria Deloria Knoll  5 , Brian Wahl  5 , James M Stuart  6   7 , Caroline Trotter  8
Affiliations

The Global Burden of Meningitis in Children: Challenges with Interpreting Global Health Estimates

Claire Wright et al. Microorganisms. .

Abstract

The World Health Organization (WHO) has developed a global roadmap to defeat meningitis by 2030. To advocate for and track progress of the roadmap, the burden of meningitis as a syndrome and by pathogen must be accurately defined. Three major global health models estimating meningitis mortality as a syndrome and/or by causative pathogen were identified and compared for the baseline year 2015. Two models, (1) the WHO and the Johns Hopkins Bloomberg School of Public Health's Maternal and Child Epidemiology Estimation (MCEE) group's Child Mortality Estimation (WHO-MCEE) and (2) the Institute for Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD 2017), identified meningitis, encephalitis and neonatal sepsis, collectively, to be the second and third largest infectious killers of children under five years, respectively. Global meningitis/encephalitis and neonatal sepsis mortality estimates differed more substantially between models than mortality estimates for selected infectious causes of death and all causes of death combined. Estimates at national level and by pathogen also differed markedly between models. Aligning modelled estimates with additional data sources, such as national or sentinel surveillance, could more accurately define the global burden of meningitis and help track progress against the WHO roadmap.

Keywords: Haemophilus influenzae; Neisseria meningitidis; Streptococcus pneumoniae; child mortality; global health; global health estimates; meningitis; modelling; neonatal sepsis.

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Conflict of interest statement

C.T. received a consulting payment from GSK in 2018 outside the submitted work. C.W., N.B., L.G., V.S. took part in this research as employees of Meningitis Research Foundation, which has received unrestricted educational grants from GSK, Pfizer and Sanofi. All other authors declare no conflict of interest. GSK, Pfizer and Sanofi had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Meningitis/encephalitis and neonatal sepsis mortality burden estimates by model in relation to other selected infectious causes of death in children under five for the year 2015.
Figure 2
Figure 2
Top twelve ranking countries by meningitis/encephalitis mortality (number and rate) according to model for the year 2015.
Figure 3
Figure 3
Estimated Hib/pneumococcal meningitis mortality and incidence amongst children aged 1–59 months according to model in relation to the proportion of children unimmunised with Hib vaccine and pneumococcal conjugate vaccine (PCV) over time.
Figure 4
Figure 4
Simplified schematic of the different mortality modelling approaches. VR Data—Data from 76 countries with high quality VR data covering >80% of the population was mapped directly to cause of death categories (see appendix for ICD10 codes mapped to meningitis and sepsis and other severe infections in the neonatal period). VRMCM—Data from the countries with high quality VR data was used to fit a multinomial logistic regression model which was used to predict cause of death proportions in 38 low mortality countries (<35 deaths/1000 live births 2000–2010) with low quality VR data. Covariates used in the model are provided in appendix. VAMCM—In 78 high mortality countries (>35 deaths/1000 live births 2000–2010) verbal autopsy data from 119 research studies in 39 high mortality countries was used to fit a multinomial model to predict causes of death. Cause of death proportions for India were estimated using a combination of VAMCM for the neonatal period and data from the million deaths study and INDEPTH sites in India for the post neonatal period. See appendix for model covariates Other – Cause of death proportions for China were estimated using data from the China Maternal and Child Health Surveillance system. A complete explanation of methods used to produce WHO/MCEE estimates is outlined elsewhere [27].
Figure 4
Figure 4
Simplified schematic of the different mortality modelling approaches. VR Data—Data from 76 countries with high quality VR data covering >80% of the population was mapped directly to cause of death categories (see appendix for ICD10 codes mapped to meningitis and sepsis and other severe infections in the neonatal period). VRMCM—Data from the countries with high quality VR data was used to fit a multinomial logistic regression model which was used to predict cause of death proportions in 38 low mortality countries (<35 deaths/1000 live births 2000–2010) with low quality VR data. Covariates used in the model are provided in appendix. VAMCM—In 78 high mortality countries (>35 deaths/1000 live births 2000–2010) verbal autopsy data from 119 research studies in 39 high mortality countries was used to fit a multinomial model to predict causes of death. Cause of death proportions for India were estimated using a combination of VAMCM for the neonatal period and data from the million deaths study and INDEPTH sites in India for the post neonatal period. See appendix for model covariates Other – Cause of death proportions for China were estimated using data from the China Maternal and Child Health Surveillance system. A complete explanation of methods used to produce WHO/MCEE estimates is outlined elsewhere [27].
Figure 5
Figure 5
Absolute difference between WHO-MCEE and GBD 2017 meningitis/encephalitis mortality estimates according to country.
See this image and copyright information in PMC

References

    1. United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) Levels & Trends in Child Mortality: Report 2020, Estimates Developed by the UN Inter-Agency Group for Child Mortality Estimation. [(accessed on 1 December 2020)];2020 Available online: https://www.un.org/development/desa/pd/news/levels-and-trends-child-mort....
    1. Zunt J.R., Kassebaum N.J., Blake N., Glennie L., Wright C., Nichols E., Abd-Allah F., Abdela J., Abdelalim A., A Adamu A., et al. Global, regional, and national burden of meningitis, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2018;17:1061–1082. doi: 10.1016/S1474-4422(18)30387-9. - DOI - PMC - PubMed
    1. GBD Causes of Death Collaborators Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1736–1788. doi: 10.1016/S0140-6736(18)32203-7. - DOI - PMC - PubMed
    1. World Health Organization Global Action Plan on Antimicrobial Resistance. [(accessed on 3 February 2020)]; Available online: https://www.who.int/antimicrobial-resistance/publications/global-action-...
    1. Global Strategy for Women’s, Children’s, and Adolescents’ Health 2016–2030. [(accessed on 3 February 2020)]; Available online: https://www.who.int/life-course/partners/global-strategy/globalstrategyr....

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