Public health utility of cause of death data : applying empirical algorithms to improve data quality

Background: Accurate, comprehensive, cause-specific mortality estimates are crucial for informing public health decision making worldwide. Incorrectly or vaguely assigned deaths, defined as garbage-coded deaths, mask the true cause distribution. The Global Burden of Disease (GBD) study has developed methods to create comparable, timely, cause-specific mortality estimates; an impactful data processing method is the reallocation of garbage-coded deaths to a plausible underlying cause of death. We identify the pattern of garbage-coded deaths in the world and present the methods used to determine their redistribution to generate more plausible cause of death assignments. Methods: We describe the methods developed for the GBD 2019 study and subsequent iterations to redistribute garbage-coded deaths in vital registration data to plausible underlying causes. These methods include analysis of multiple cause data, negative correlation, impairment, and proportional redistribution. We classify garbage codes into classes according to the level of specificity of the reported cause of death (CoD) and capture trends in the global pattern of proportion of garbage-coded deaths, disaggregated by these classes, and the relationship between this proportion and the Socio-Demographic Index. We examine the relative importance of the top four garbage codes by age and sex and demonstrate the impact of redistribution on the annual GBD CoD rankings. Results: The proportion of least-specific (class 1 and 2) garbage-coded deaths ranged from 3.7% of all vital registration deaths to 67.3% in 2015, and the age-standardized proportion had an overall negative association with the Socio Demographic Index. When broken down by age and sex, the category for unspecified lower respiratory infections was responsible for nearly 30% of garbage-coded deaths in those under 1 year of age for both sexes, representing the largest proportion of garbage codes for that age group. We show how the cause distribution by number of deaths changes before and after redistribution for four countries: Brazil, the United States, Japan, and France, highlighting the necessity of accounting for garbage-coded deaths in the GBD

COVID-19, seasonal influenza and measles: potential triple burden and the role of flu and MMR vaccines

Policy interventions aimed at reducing person-to-person transmission of SARS-CoV-2 (such as hand hygiene, physical distancing and wearing face coverings) were implemented globally to minimise healthcare burden, and to allow more time for an effective treatment and successful vaccine. After months of ‘lockdown’, many countries started to ease these measures recently only to see a surge in COVID-19 cases and deaths. During the winter of 2020–2021, we face the prospect of a dual burden of a COVID-19 pandemic and a seasonal influenza epidemic.3 However, what’s not being currently discussed is that the burden on healthcare could be further compounded by a potential surge of measles and rubella cases. This is due to: (1) a declining trend in Measles-Mumps-Rubella vaccine coverage accompanied by an increasing trend in Measles-Mumps-Rubella cases since 2016;4 and (2) disruption and suspension of Measles-Mumps-Rubella vaccination campaigns in 23 countries to cope with the COVID-19 pandemic

Summary of included studies.

<p>NR: not reported.</p><p>(*): the publication year was used instead when the study date was not reported.</p

Flow chart of study selection process.

<p>Flow chart of study selection process.</p

Directly Age-Standardised Mortality Rates per Million, England and Wales, 1993–2004

<p>Directly Age-Standardised Mortality Rates per Million, England and Wales, 1993–2004</p

Life expectancy by ethnic group in England

The disproportionate effect of covid-19 on ethnic minority populations led to a welcome and overdue focus on ethnic disparities in health.1 Their higher covid-19 mortality was widely viewed as having exacerbated pre-existing health inequalities, particularly for Black and South Asian people.12 Although previous evidence had shown a more mixed pattern of ethnic differences in health outcomes,34 our knowledge and understanding have been limited by a lack of nationally representative data on mortality by ethnic group. The first Office for National Statistics (ONS) estimates of life expectancy and cause-specific mortality by ethnicity based on census data are therefore timely. [Opening parapgraph

Healthcare workers potentially exposed to HIV: an update

No description supplied</p

Studies related to FCTC articles 6, 8, 11, 13 and 16.

<p>Reviewed studies relating to FCTC articles 6, 8, 11, 13 and 16 included in all aspects of synthesis. The numbers following the different quality categories (SA, US, NA) indicate the aspect of quality assessment (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122610#pone.0122610.t002" target="_blank">Table 2</a>) rated as satisfactory (SA), unsatisfactory (US) or not-assessable (NA). All studies were of cross sectional design, or secondary analyses of cross-sectional surveys. U = urban; R = rural; NR = not reported; GHPSS: Global Health Professions Student Survey; S = significant; NS = non-significant; SHS = second-hand smoke; GYTS = Global Youth Tobacco Survey</p><p>Studies related to FCTC articles 6, 8, 11, 13 and 16.</p

Life expectancy by ethnic group in England

The disproportionate effect of covid-19 on ethnic minority populations led to a welcome and overdue focus on ethnic disparities in health.1 Their higher covid-19 mortality was widely viewed as having exacerbated pre-existing health inequalities, particularly for Black and South Asian people.12 Although previous evidence had shown a more mixed pattern of ethnic differences in health outcomes,34 our knowledge and understanding have been limited by a lack of nationally representative data on mortality by ethnic group. The first Office for National Statistics (ONS) estimates of life expectancy and cause-specific mortality by ethnicity based on census data are therefore timely. [Opening parapgraph

Studies related to trialled interventions.

<p>Reviewed studies of trialled interventions, by FCTC Article. The numbers following the different quality categories (SA, US, NA) indicate the aspect of quality assessment (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122610#pone.0122610.t002" target="_blank">Table 2</a>), rated as satisfactory (SA), unsatisfactory (US) or not-assessable (NA). NR = not reported; RCT = randomised controlled trial; U = urban; R = rural; NS = non-significant; S = significant; QALY = quality-added life year; QE = quasi-experimental study; OR = odds ratio; CI = confidence interval</p><p>Studies related to trialled interventions.</p