Exposure misclassification bias in the estimation of vaccine effectiveness

In epidemiology, a typical measure of interest is the risk of disease conditional upon exposure. A common source of bias in the estimation of risks and risk ratios is misclassification. Exposure misclassification affects the measurement of exposure, i.e. the variable one conditions on. This article explains how to assess biases under non-differential exposure misclassification when estimating vaccine effectiveness, i.e. the vaccine-induced relative reduction in the risk of disease. The problem can be described in terms of three binary variables: the unobserved true exposure status, the observed but potentially misclassified exposure status, and the observed true disease status. The bias due to exposure misclassification is quantified by the difference between the naive estimand defined as one minus the risk ratio comparing individuals observed as vaccinated with individuals observed as unvaccinated, and the vaccine effectiveness defined as one minus the risk ratio comparing truly vaccinated with truly unvaccinated. The magnitude of the bias depends on five factors: the risks of disease in the truly vaccinated and the truly unvaccinated, the sensitivity and specificity of exposure measurement, and vaccination coverage. Non-differential exposure misclassification bias is always negative. In practice, if the sensitivity and specificity are known or estimable from external sources, the true risks and the vaccination coverage can be estimated from the observed data and, thus, the estimation of vaccine effectiveness based on the observed risks can be corrected for exposure misclassification. When analysing risks under misclassification, careful consideration of conditional probabilities is crucial.Peer reviewe

Birth cohort differences in height, weight and BMI among Indian women aged 15-30 years : analyses based on three cross-sectional surveys

Objective: To explore long-term trends in height, weight and BMI across birth cohorts among Indian women aged 15-30 years. Design: Nationally representative cross-sectional surveys. Setting: Data from three National Family Health Surveys were conducted in 1998-1999, 2005-2006 and 2015-2016. Height and weight were modelled jointly, employing a multivariate regression model with age and birth cohorts as explanatory variables. The largest birth cohort (born 1988-1992) was the reference cohort. Stratified analyses by place of residence and by marital status and dichotomised parity were also performed. Participants: 437 753 non-pregnant women aged 15-30 years. Results: The rate of increase in height, weight and BMI differed across birth cohorts. The rate of increase was much lower for height than weight, which was reflected in an increasing trend in BMI across all birth cohorts. In the stratified analyses, increase in height was found to be similar across urban and rural areas. Rural women born in the latest birth cohort (1998-2001) were lighter, whereas urban women were heavier compared to the reference cohort. A relatively larger increase in regression coefficients was observed among women born between 1978 and 1982 compared to women born between 1973 and 1977 when considering unmarried and nulliparous ever-married women and, one cohort later (1983-1987 v. 1978-1982), among parous ever-married women. Conclusion: As the rate of increase was much larger for weight than for height, increasing trends in BMI were observed across the birth cohorts. Thus, cohort effects show an important contributory role in explaining increasing trends in BMI among young Indian women.Peer reviewe

The latest buzz in comparative genomics

A second species of fruit fly has just been added to the growing list of organisms with complete and annotated genome sequences. The publication of the Drosophila pseudoobscura sequence provides a snapshot of how genomes have changed over tens of millions of years and sets the stage for the analysis of more fly genomes

Mitigation of biases in estimating hazard ratios under non-sensitive and non-specific observation of outcomes – applications to influenza vaccine effectiveness

Background: Non-sensitive and non-specific observation of outcomes in time-to-event data affects event counts as well as the risk sets, thus, biasing the estimation of hazard ratios. We investigate how imperfect observation of incident events affects the estimation of vaccine effectiveness based on hazard ratios. Methods: Imperfect time-to-event data contain two classes of events: a portion of the true events of interest; and false-positive events mistakenly recorded as events of interest. We develop an estimation method utilising a weighted partial likelihood and probabilistic deletion of false-positive events and assuming the sensitivity and the false-positive rate are known. The performance of the method is evaluated using simulated and Finnish register data. Results: The novel method enables unbiased semiparametric estimation of hazard ratios from imperfect time-to-event data. False-positive rates that are small can be approximated to be zero without inducing bias. The method is robust to misspecification of the sensitivity as long as the ratio of the sensitivity in the vaccinated and the unvaccinated is specified correctly and the cumulative risk of the true event is small. Conclusions: The weighted partial likelihood can be used to adjust for outcome measurement errors in the estimation of hazard ratios and effectiveness but requires specifying the sensitivity and the false-positive rate. In absence of exact information about these parameters, the method works as a tool for assessing the potential magnitude of bias given a range of likely parameter values.Peer reviewe

Spotlight influenza : Estimation of influenza vaccine effectiveness in elderly people with assessment of residual confounding by negative control outcomes, Finland, 2012/13 to 2019/20

Publisher Copyright: © This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence and indicate if changes were made.Background: Cohort studies on vaccine effectiveness are prone to confounding bias if the distribution of risk factors is unbalanced between vaccinated and unvaccinated study subjects. Aim: We aimed to estimate influenza vaccine effectiveness in the elderly population in Finland by controlling for a sufficient set of confounders based on routinely available register data. Methods: For each of the eight consecutive influenza seasons from 2012/13 through 2019/20, we conducted a cohort study comparing the hazards of laboratory-confirmed influenza in vaccinated and unvaccinated people aged 65-100 years using individual-level medical and demographic data. Vaccine effectiveness was estimated as 1 minus the hazard ratio adjusted for the confounders age, sex, vaccination history, nights hospitalised in the past and presence of underlying chronic conditions. To assess the adequacy of the selected set of confounders, we estimated hazard ratios of off-season hospitalisation for acute respiratory infection as a negative control outcome. Results: Each analysed cohort comprised around 1 million subjects, of whom 37% to 49% were vaccinated. Vaccine effectiveness against laboratory-confirmed influenza ranged from 16% (95% confidence interval (CI): 12-19) to 48% (95% CI: 41-54). More than 80% of the laboratory-confirmed cases were hospitalised. The adjusted off-season hazard ratio estimates varied between 1.00 (95% CI: 0.94-1.05) and 1.08 (95% CI: 1.01-1.15), indicating that residual confounding was absent or negligible. Conclusion: Seasonal influenza vaccination reduces the hazard of severe influenza disease in vaccinated elderly people. Data about age, sex, vaccination history and utilisation of hospital care proved sufficient to control confounding.Peer reviewe

Estimation of marriage incidence rates by combining two cross-sectional retrospective designs : Event history analysis of two dependent processes

Peer reviewe

Anthropometric Characteristics of Underprivileged Adolescents : A Study from Urban Slums of India

Corrigendum: https://doi.org/10.1155/2017/8595129Peer reviewe

Bayesian Hierarchical Model for Estimating Gene Expression Intensity Using Multiple Scanned Microarrays

We propose a method for improving the quality of signal from DNA microarrays by using several scans at varying scanner sen-sitivities. A Bayesian latent intensity model is introduced for the analysis of such data. The method improves the accuracy at which expressions can be measured in all ranges and extends the dynamic range of measured gene expression at the high end. Our method is generic and can be applied to data from any organism, for imaging with any scanner that allows varying the laser power, and for extraction with any image analysis software. Results from a self-self hybridization data set illustrate an improved precision in the estimation of the expression of genes compared to what can be achieved by applying standard methods and using only a single scan

The Architecture of Generativity in a Digital Ecosystem: A Network Biology Perspective

Firms are increasingly relying on third-party developers to innovate by building open digital ecosystems. We draw on a network biology approach to explore the structural pattern of how individual modules in a digital ecosystem interact with one another to produce seemingly ever-changing landscape of the ecosystem. We conduct an empirical study using the data from WordPress.org, which is the world’s largest blog service platform. Using text mining and network analysis, we extract the API used in plug-ins developed by third party developers. We characterize each plug-ins as a combination of API modules. This allows us to examine the underlying structure of generativity in the WordPress ecosystem through a co-expression network of APIs. Even though there is no central designer coordinating the design of the entire WordPress ecosystem, the way APIs are used to form this complex and dynamic ecosystem follow a discernible pattern that is distinctive from known distributions

Mental Wellbeing and Self-reported Symptoms of Reproductive Tract Infections among Girls: Findings from a Cross-sectional Study in an Indian Slum

This study examined the self-reported mental wellbeing among slum-dwelling adolescents in Western India and asked whether adolescent postmenarcheal girls’ mental wellbeing and self-reported symptoms suggestive of reproductive tract infections (RTIs) were associated. A sub-section of a cross-sectional personal interview survey among unmarried 10–18-year-old adolescents (n= 85) in a slum in the city of Nashik was analyzed. Logistic regression models were used to assess the associations between sociodemographic variables, physical health indicators, and adolescent postmenarcheal girls’ mental wellbeing. Nearly every other postmenarcheal girl reported having experienced symptoms suggestive of RTIs during the last twelve months. Adolescent postmenarcheal girls’ mental health and some aspects of somatic health appear to be closely interrelated. Understanding the relationship between adolescent mental wellbeing and reproductive health in low-income countries requires further investigation. Health service development in growing informal urban agglomerations in India and beyond should provide combined mental and reproductive health services for adolescents. &nbsp