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Emerging Themes in Epidemiology

Stefano Nembrini
The journal published a review of the literature on recursive partition in epidemiological research comparing two decision tree methods: classification and regression trees (CARTs) and conditional inference trees (CITs). There are two sources of potential confusion in the paper for readers: one lies in the definition and the comparison of CITs and CARTs, while the other is more general and it refers to the use of hyper-parameters and their tuning through resampling techniques.
2019: Emerging Themes in Epidemiology
Louise Biddle, Natalja Menold, Martina Bentner, Stefan Nöst, Rosa Jahn, Sandra Ziegler, Kayvan Bozorgmehr
Background: Health monitoring in Germany falls short on generating timely, reliable and representative data among migrants, especially transient and marginalized groups such as asylum seekers and refugees (ASR). We aim to advance current health monitoring approaches and obtain reliable estimates on health status and access to essential healthcare services among ASR in Germany's third largest federal state, Baden-Württemberg. Methods: We conducted a state-wide, cross-sectional, population-based health monitoring survey in nine languages among ASR and their children in collective accommodation centres in 44 districts...
2019: Emerging Themes in Epidemiology
Charlotte Christiane Hammer, Julii Brainard, Alexandria Innes, Paul R Hunter
Vulnerability has become a key concept in emergency response research and is being critically discussed across several disciplines. While the concept has been adopted into global health, its conceptualisation and especially its role in the conceptualisation of risk and therefore in risk assessments is still lacking. This paper uses the risk concept pioneered in hazard research that assumes that risk is a function of the interaction between hazard and vulnerability rather than the neo-liberal conceptualisation of vulnerability and vulnerable groups and communities...
2019: Emerging Themes in Epidemiology
Anders Huitfeldt, Mats J Stensrud, Etsuji Suzuki
The relationship between collapsibility and confounding has been subject to an extensive and ongoing discussion in the methodological literature. We discuss two subtly different definitions of collapsibility, and show that by considering causal effect measures based on counterfactual variables (rather than measures of association based on observed variables) it is possible to separate out the component of non-collapsibility which is due to the mathematical properties of the effect measure, from the components that are due to structural bias such as confounding...
2019: Emerging Themes in Epidemiology
Laura A Schieve, Shericka Harris, Matthew J Maenner, Aimee Alexander, Nicole F Dowling
Background: Participation in epidemiologic studies has declined, raising concerns about selection bias. While estimates derived from epidemiologic studies have been shown to be robust under a wide range of scenarios, additional empiric study is needed. The Georgia Study to Explore Early Development (GA SEED), a population-based case-control study of risk factors for autism spectrum disorder (ASD), provided an opportunity to explore factors associated with non-participation and potential impacts of non-participation on association studies...
2018: Emerging Themes in Epidemiology
Stephen Nash, Victoria Tittle, Andrew Abaasa, Richard E Sanya, Gershim Asiki, Christian Holm Hansen, Heiner Grosskurth, Saidi Kapiga, Chris Grundy
Background: Information on the size of populations is crucial for planning of service and resource allocation to communities in need of health interventions. In resource limited settings, reliable census data are often not available. Using publicly available Google Earth Pro and available local household survey data from fishing communities (FC) on Lake Victoria in Uganda, we compared two simple methods (using average population density) and one simple linear regression model to estimate populations of small rural FC in Uganda...
2018: Emerging Themes in Epidemiology
C Mary Schooling, Heidi E Jones
Background: In biomedical research much effort is thought to be wasted. Recommendations for improvement have largely focused on processes and procedures. Here, we additionally suggest less ambiguity concerning the questions addressed. Methods: We clarify the distinction between two conflated concepts, prediction and explanation, both encompassed by the term "risk factor", and give methods and presentation appropriate for each. Results: Risk prediction studies use statistical techniques to generate contextually specific data-driven models requiring a representative sample that identify people at risk of health conditions efficiently (target populations for interventions)...
2018: Emerging Themes in Epidemiology
Lawrence M Paul
Background: The use of meta-analysis to aggregate multiple studies has increased dramatically over the last 30 years. For meta-analysis of homogeneous data where the effect sizes for the studies contributing to the meta-analysis differ only by statistical error, the Mantel-Haenszel technique has typically been utilized. If homogeneity cannot be assumed or established, the most popular technique is the inverse-variance DerSimonian-Laird technique. However, both of these techniques are based on large sample, asymptotic assumptions and are, at best, an approximation especially when the number of cases observed in any cell of the corresponding contingency tables is small...
2018: Emerging Themes in Epidemiology
Emmanuel Grellety, Michael H Golden
Background: Representative surveys collecting weight, height and MUAC are used to estimate the prevalence of acute malnutrition. The results are then used to assess the scale of malnutrition in a population and type of nutritional intervention required. There have been changes in methodology over recent decades; the objective of this study was to determine if these have resulted in higher quality surveys. Methods: In order to examine the change in reliability of such surveys we have analysed the statistical distributions of the derived anthropometric parameters from 1843 surveys conducted by 19 agencies between 1986 and 2015...
2018: Emerging Themes in Epidemiology
Marissa Becker, Sharmistha Mishra, Sevgi Aral, Parinita Bhattacharjee, Rob Lorway, Kalada Green, John Anthony, Shajy Isac, Faran Emmanuel, Helgar Musyoki, Lisa Lazarus, Laura H Thompson, Eve Cheuk, James F Blanchard
Background: Program Science is an iterative, multi-phase research and program framework where programs drive the scientific inquiry, and both program and science are aligned towards a collective goal of improving population health. Discussion: To achieve this, Program Science involves the systematic application of theoretical and empirical knowledge to optimize the scale, quality and impact of public health programs. Program Science tools and approaches developed for strategic planning, program implementation, and program management and evaluation have been incorporated into HIV and sexually transmitted infection prevention programs in Kenya, Nigeria, India, and the United States...
2018: Emerging Themes in Epidemiology
Miguel Marino, Marcello Pagano
Background: Nationally-representative surveys suggest that females have a higher prevalence of HIV than males in most African countries. Unfortunately, these results are made on the basis of surveys with non-ignorable missing data. This study evaluates the impact that differential survey nonresponse rates between males and females can have on the point estimate of the HIV prevalence ratio of these two classifiers. Methods: We study 29 Demographic and Health Surveys (DHS) from 2001 to 2010...
2018: Emerging Themes in Epidemiology
Robert W Eyre, Thomas House, F Xavier Gómez-Olivé, Frances E Griffiths
Background: Central to the study of populations, and therefore to the analysis of the development of countries undergoing major transitions, is the calculation of fertility patterns and their dependence on different variables such as age, education, and socio-economic status. Most epidemiological research on these matters rely on the often unjustified assumption of (generalised) linearity, or alternatively makes a parametric assumption (e.g. for age-patterns). Methods: We consider nonlinearity of fertility in the covariates by combining an established nonlinear parametric model for fertility over age with nonlinear modelling of fertility over other covariates...
2018: Emerging Themes in Epidemiology
Matthew R Grigsby, Junrui Di, Andrew Leroux, Vadim Zipunnikov, Luo Xiao, Ciprian Crainiceanu, William Checkley
Background: Literature surrounding the statistical modeling of childhood growth data involves a diverse set of potential models from which investigators can choose. However, the lack of a comprehensive framework for comparing non-nested models leads to difficulty in assessing model performance. This paper proposes a framework for comparing non-nested growth models using novel metrics of predictive accuracy based on modifications of the mean squared error criteria. Methods: Three metrics were created: normalized, age-adjusted, and weighted mean squared error (MSE)...
2018: Emerging Themes in Epidemiology
Nandita Perumal, Daniel E Roth, Johnna Perdrizet, Aluísio J D Barros, Iná S Santos, Alicia Matijasevich, Diego G Bassani
Background: Postmenstrual and/or gestational age-corrected age (CA) is required to apply child growth standards to children born preterm (< 37 weeks gestational age). Yet, CA is rarely used in epidemiologic studies in low- and middle-income countries (LMICs), which may bias population estimates of childhood undernutrition. To evaluate the effect of accounting for GA in the application of growth standards, we used GA-specific standards at birth (INTERGROWTH-21st newborn size standards) in conjunction with CA for preterm-born children in the application of World Health Organization Child Growth Standards postnatally (referred to as 'CA' strategy) versus postnatal age for all children, to estimate mean length-for-age (LAZ) and weight-for-age (WAZ) z scores at 0, 3, 12, 24, and 48-months of age in the 2004 Pelotas (Brazil) Birth Cohort...
2018: Emerging Themes in Epidemiology
Kate Sabot, Tanya Marchant, Neil Spicer, Della Berhanu, Meenakshi Gautham, Nasir Umar, Joanna Schellenberg
Background: Understanding the context of a health programme is important in interpreting evaluation findings and in considering the external validity for other settings. Public health researchers can be imprecise and inconsistent in their usage of the word "context" and its application to their work. This paper presents an approach to defining context, to capturing relevant contextual information and to using such information to help interpret findings from the perspective of a research group evaluating the effect of diverse innovations on coverage of evidence-based, life-saving interventions for maternal and newborn health in Ethiopia, Nigeria, and India...
2018: Emerging Themes in Epidemiology
Mette Lise Lousdal
The instrumental variable method has been employed within economics to infer causality in the presence of unmeasured confounding. Emphasising the parallels to randomisation may increase understanding of the underlying assumptions within epidemiology. An instrument is a variable that predicts exposure, but conditional on exposure shows no independent association with the outcome. The random assignment in trials is an example of what would be expected to be an ideal instrument, but instruments can also be found in observational settings with a naturally varying phenomenon e...
2018: Emerging Themes in Epidemiology
R P Cornish, J Macleod, J R Carpenter, K Tilling
Background: When an outcome variable is missing not at random (MNAR: probability of missingness depends on outcome values), estimates of the effect of an exposure on this outcome are often biased. We investigated the extent of this bias and examined whether the bias can be reduced through incorporating proxy outcomes obtained through linkage to administrative data as auxiliary variables in multiple imputation (MI). Methods: Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) we estimated the association between breastfeeding and IQ (continuous outcome), incorporating linked attainment data (proxies for IQ) as auxiliary variables in MI models...
2017: Emerging Themes in Epidemiology
Dan Li, Ruth Keogh, John P Clancy, Rhonda D Szczesniak
Background: Epidemiologic surveillance of lung function is key to clinical care of individuals with cystic fibrosis, but lung function decline is nonlinear and often impacted by acute respiratory events known as pulmonary exacerbations. Statistical models are needed to simultaneously estimate lung function decline while providing risk estimates for the onset of pulmonary exacerbations, in order to identify relevant predictors of declining lung function and understand how these associations could be used to predict the onset of pulmonary exacerbations...
2017: Emerging Themes in Epidemiology
Christopher Jarvis, Gian Luca Di Tanna, Daniel Lewis, Neal Alexander, W John Edmunds
BACKGROUND: Cluster randomised trials (CRTs) often use geographical areas as the unit of randomisation, however explicit consideration of the location and spatial distribution of observations is rare. In many trials, the location of participants will have little importance, however in some, especially against infectious diseases, spillover effects due to participants being located close together may affect trial results. This review aims to identify spatial analysis methods used in CRTs and improve understanding of the impact of spatial effects on trial results...
2017: Emerging Themes in Epidemiology
Ashwini Venkatasubramaniam, Julian Wolfson, Nathan Mitchell, Timothy Barnes, Meghan JaKa, Simone French
BACKGROUND: In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods. MAIN TEXT: We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable...
2017: Emerging Themes in Epidemiology
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