Retrospective, Observational Studies for Estimating Vaccine Effects on the Secondary Attack Rate of SARS-CoV-2.

Coronavirus disease 2019 (COVID-19) vaccines are highly efficacious at preventing symptomatic infection, severe disease, and death. Most of the evidence that COVID-19 vaccines also reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is based on retrospective, observational studies. Specifically, an increasing number of studies are evaluating vaccine effectiveness against the secondary attack rate of SARS-CoV-2 using data available in existing health-care databases or contact-tracing databases. Since these types of databases were designed for clinical diagnosis or management of COVID-19, they are limited in their ability to provide accurate information on infection, infection timing, and transmission events. We highlight challenges with using existing databases to identify transmission units and confirm potential SARS-CoV-2 transmission events. We discuss the impact of common diagnostic testing strategies, including event-prompted and infrequent testing, and illustrate their potential biases in estimating vaccine effectiveness against the secondary attack rate of SARS-CoV-2. We articulate the need for prospective observational studies of vaccine effectiveness against the SARS-CoV-2 secondary attack rate, and we provide design and reporting considerations for studies using retrospective databases.

Ensemble modelling in descriptive epidemiology: burden of disease estimation.

Ensemble modelling is a quantitative method that combines information from multiple individual models and has shown great promise in statistical machine learning. Ensemble models have a theoretical claim to being models that make the 'best' predictions possible. Applications of ensemble models to health research have included applying ensemble models like the super learner and random forests to epidemiological prediction tasks. Recently, ensemble methods have been applied successfully in burden of disease estimation. This article aims to provide epidemiologists with a practical understanding of the mechanisms of an ensemble model and insight into constructing ensemble models that are grounded in the epidemiological dynamics of the prediction problem of interest. We summarize the history of ensemble models, present a user-friendly framework for conceptualizing and constructing ensemble models, walk the reader through a tutorial of applying the framework to an application in burden of disease estimation, and discuss further applications.

The burden of injury in China, 1990-2017: findings from the Global Burden of Disease Study 2017.

BACKGROUND: A comprehensive evaluation of the burden of injury is an important foundation for selecting and formulating strategies of injury prevention. We present results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 of non-fatal and fatal outcomes of injury at the national and subnational level, and the changes in burden for key causes of injury over time in China. METHODS: Using the methods and results from GBD 2017, we describe the burden of total injury and the key causes of injury based on the rates of incidence, cause-specific mortality, and disability-adjusted life years (DALYs) in China estimated using DisMod-MR 2.1. We additionally evaluated these results at the provincial level for the 34 subnational locations of China in 2017, measured the change of injury burden from 1990 to 2017, and compared age-standardised DALYs due to injuries at the provincial level against the expected rates based on the Socio-demographic Index (SDI), a composite measure of development of income per capita, years of education, and total fertility rate. FINDINGS: In 2017, in China, there were 77·1 million (95% uncertainty interval [UI] 72·5-81·6) new cases of injury severe enough to warrant health care and 733 517 deaths (681 254-767 006) due to injuries. Injuries accounted for 7·0% (95% UI 6·6-7·2) of total deaths and 10·0% (9·5-10·5) of all-cause DALYs in China. In 2017, there was a three-times variation in age-standardised injury DALY rates between provinces of China, with the lowest value in Macao and the highest in Yunnan. Between 1990 and 2017, the age-standardised incidence rate of all injuries increased by 50·6% (95% UI 46·6-54·6) in China, whereas the age-standardised mortality and DALY rates decreased by 44·3% (41·1-48·9) and 48·1% (44·6-51·8), respectively. Between 1990 and 2017, all provinces of China experienced a substantial decline in DALY rates from all injuries ranging from 16·3% (3·1-28·6) in Shanghai and 60·4% (53·7-66·1) in Jiangxi. Age-standardised DALY rates for drowning; injuries from fire, heat and hot substances; adverse effects of medical treatments; animal contact; environmental heat and cold exposure; self-harm; and executions and police conflict each declined by more than 60% between 1990 and 2017. INTERPRETATION: Between 1990 and 2017, China experienced a decrease in the age-standardised DALY and mortality rates due to injury, despite an increase in the age-standardised incidence rate. These trends occurred in all provinces. The divergent trends in terms of incidence and mortality indicate that with rapid sociodemographic improvements, the case fatality of injuries has declined, which could be attributed to an improving health-care system but also to a decreasing severity of injuries over this time period. FUNDING: Bill & Melinda Gates Foundation.