Summary Background Causes of mortality are a crucial input for health systems for identifying appropriate interventions for child survival. We present an updated series of cause-specific mortality for neonates and children younger than 5 years from 2000 to 2019. Methods We updated cause-specific mortality estimates for neonates and children aged 1–59 months, stratified by level (low, moderate, or high) of mortality. We made a substantial change in the statistical methods used for previous estimates, transitioning to a Bayesian framework that includes a structure to account for unreported causes in verbal autopsy studies. We also used systematic covariate selection in the multinomial framework, gave more weight to nationally representative verbal autopsy studies using a random effects model, and included mortality due to tuberculosis. Findings In 2019, there were 5·30 million deaths (95% uncertainty range 4·92–5·68) among children younger than 5 years, primarily due to preterm birth complications (17·7%, 16·1–19·5), lower respiratory infections (13·9%, 12·0–15·1), intrapartum-related events (11·6%, 10·6–12·5), and diarrhoea (9·1%, 7·9–9·9), with 49·2% (47·3–51·9) due to infectious causes. Vaccine-preventable deaths, such as for lower respiratory infections, meningitis, and measles, constituted 21·7% (20·4–25·6) of under-5 deaths, and many other causes, such as diarrhoea, were preventable with low-cost interventions. Under-5 mortality has declined substantially since 2000, primarily because of a decrease in mortality due to lower respiratory infections, diarrhoea, preterm birth complications, intrapartum-related events, malaria, and measles. There is considerable variation in the extent and trends in cause-specific mortality across regions and for different strata of all-cause under-5 mortality. Interpretation Progress is needed to improve child health and end preventable deaths among children younger than 5 years. Countries should strategize how to reduce mortality among this age group using interventions that are relevant to their specific causes of death. Funding Bill & Melinda Gates Foundation; WHO.

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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.

Importance: COVID-19 racial disparities have gained significant attention yet little is known about how age distributions obscure racial-ethnic disparities in COVID-19 case fatality ratios (CFR). Objective: We filled this gap by assessing relevant data availability and quality across states, and in states with available data, investigating how racial-ethnic disparities in CFR changed after age adjustment. Design/Setting/Participants/Exposure: We conducted a landscape analysis as of July 1st, 2020 and developed a grading system to assess COVID-19 case and death data by age and race in 50 states and DC. In states where age- and race-specific data were available, we applied direct age standardization to compare CFR across race-ethnicities. We developed an online dashboard to automatically and continuously update our results. Main Outcome and Measure: Our main outcome was CFR (deaths per 100 confirmed cases). We examined CFR by age and race-ethnicities. Results: We found substantial variations in disaggregating and reporting case and death data across states. Only three states, California, Illinois and Ohio, had sufficient age- and race-ethnicity-disaggregation to allow the investigation of racial-ethnic disparities in CFR while controlling for age. In total, we analyzed 391,991confirmed cases and 17,612 confirmed deaths. The crude CFRs varied from, e.g. 7.35% among Non-Hispanic (NH) White population to 1.39% among Hispanic population in Ohio. After age standardization, racial-ethnic differences in CFR narrowed, e.g. from 5.28% among NH White population to 3.79% among NH Asian population in Ohio, or an over one-fold difference. In addition, the ranking of race-ethnic-specific CFRs changed after age standardization. NH White population had the leading crude CFRs whereas NH Black and NH Asian population had the leading and second leading age-adjusted CFRs respectively in two of the three states. Hispanic populations age-adjusted CFR were substantially higher than the crude. Sensitivity analysis did not change these results qualitatively. Conclusions and Relevance: The availability and quality of age- and race-ethnic-specific COVID-19 case and death data varied greatly across states. Age distributions in confirmed cases obscured racial-ethnic disparities in COVID-19 CFR. Age standardization narrows racial-ethnic disparities and changes ranking. Public COVID-19 data availability, quality, and harmonization need improvement to address racial disparities in this pandemic.

Background China accounts for 13% of the world’s 5-19-year-olds population. We estimated levels and trends of mortality by sex-age-cause among 5-19-year-olds at national and subnational levels in China annually from 2004 to 2019, to inform strategies for reducing child and adolescent mortality in China and other countries. Methods We used adjusted empirical data on levels and causes of deaths from the China Center for Disease Control and Prevention’s Disease Surveillance Point (DSP) system. We considered underreporting and surveillance sampling design, applied smoothing techniques to produce reliable time trends, and fitted age-specific deaths and population to national estimates produced by international agencies to allow for cross-national comparisons. Results The top leading causes for 54 594 deaths among 5-19-year-olds were neoplasms, road traffic injuries, and drowning. All-cause mortality in 5-19-year-olds has been declining steadily between 2004-2019, with evident yet narrowing geographical and gender disparities. Injury mortalities were one of the fastest declining causes, but widespread disparities were observed across subpopulations. Falling injuries and rising non-communicable diseases had the most pronounced epidemiological transition in the eastern region. Decrease in drowning fractions stalled for 15-19-year-olds in central/western rural areas. Suicide shares sustained or increased for 15-19-year-olds except among females in eastern rural areas. Conclusions China made significant improvements in child and adolescent survival since 2004. However, constant targeted investments are needed to maintain and accelerate progress. A sustainable sample registration system like the DSP is likely essential for supporting such a process.

In this paper, a comparison is provided for volatility estimation in Bayesian and frequentist settings. We compare the predictive performance of these two approaches under the generalized autoregressive conditional heteroscedasticity (GARCH) model. Our results indicate that the frequentist estimation provides better predictive potential than the Bayesian approach. The finding is contrary to some of the work in this line of research. To illustrate our finding, we used the six major foreign exchange rate datasets.