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Applied logistic regression, third edition
This new edition provides a focused introduction to the LR model and its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariables.
eBook
Monitoring the health of populations by tracking disease outbreaks : saving humanity from the next plague
\"Today the citizens of developed counties have never experienced a large-scale disease outbreak. One reason is the success of the public health community, including epidemiologists and biostatisticians, in tracking and identifying disease outbreaks. Monitoring the Health of Populations by Tracking Disease Outbreaks: Saving Humanity from the Next Plague is the story of the application of statistics for disease detection and tracking. The work of public health officials often critically depends on the use of statistical methods to help discern whether an outbreak may be occurring and, if there is sufficient evidence of an outbreak, then to locate and track it\"-- Provided by publisher.
Book
Statistical significance or clinical significance? A researcher's dilemma for appropriate interpretation of research results
It is incredibly essential that the current clinicians and researchers remain updated with findings of current biomedical literature for evidence-based medicine. However, they come across many types of research that are nonreproducible and are even difficult to interpret clinically. Statistical and clinical significance is one such difficulty that clinicians and researchers face across many instances. In simpler terms, the P value tests all hypothesis about how the data were produced (model as whole), and not just the targeted hypothesis that it is intended to test (such as a null hypothesis) keeping in mind how reliable are the of the research results. Most of the times it is misinterpreted and misunderstood as a measure to judge the results as clinically significant. Hence this review aims to impart knowledge about \"P\" value and its importance in biostatistics, also highlights the importance of difference between statistical and clinical significance for appropriate interpretation of research results.
Journal Article
Can mathematical modelling solve the current Covid-19 crisis?
Since COVID-19 transmission started in late January, mathematical modelling has been at the forefront of shaping the decisions around different non-pharmaceutical interventions to confine its’ spread in the UK and worldwide. This Editorial discusses the importance of modelling in understanding Covid-19 spread, highlights different modelling approaches and suggests that while modelling is important, no one model can give all the answers.
Journal Article
The impact of non-response bias due to sampling in public health studies: A comparison of voluntary versus mandatory recruitment in a Dutch national survey on adolescent health
Background
In public health monitoring of young people it is critical to understand the effects of selective non-response, in particular when a controversial topic is involved like substance abuse or sexual behaviour. Research that is dependent upon voluntary subject participation is particularly vulnerable to sampling bias. As respondents whose participation is hardest to elicit on a voluntary basis are also more likely to report risk behaviour, this potentially leads to underestimation of risk factor prevalence. Inviting adolescents to participate in a home-sent postal survey is a typical voluntary recruitment strategy with high non-response, as opposed to mandatory participation during school time. This study examines the extent to which prevalence estimates of adolescent health-related characteristics are biased due to different sampling methods, and whether this also biases within-subject analyses.
Methods
Cross-sectional datasets collected in 2011 in Twente and IJsselland, two similar and adjacent regions in the Netherlands, were used. In total, 9360 youngsters in a mandatory sample (Twente) and 1952 youngsters in a voluntary sample (IJsselland) participated in the study. To test whether the samples differed on health-related variables, we conducted both univariate and multivariable logistic regression analyses controlling for any demographic difference between the samples. Additional multivariable logistic regressions were conducted to examine moderating effects of sampling method on associations between health-related variables.
Results
As expected, females, older individuals, as well as individuals with higher education levels, were over-represented in the voluntary sample, compared to the mandatory sample. Respondents in the voluntary sample tended to smoke less, consume less alcohol (ever, lifetime, and past four weeks), have better mental health, have better subjective health status, have more positive school experiences and have less sexual intercourse than respondents in the mandatory sample. No moderating effects were found for sampling method on associations between variables.
Conclusions
This is one of first studies to provide strong evidence that voluntary recruitment may lead to a strong non-response bias in health-related prevalence estimates in adolescents, as compared to mandatory recruitment. The resulting underestimation in prevalence of health behaviours and well-being measures appeared large, up to a four-fold lower proportion for self-reported alcohol consumption. Correlations between variables, though, appeared to be insensitive to sampling bias.
Journal Article
Common pitfalls in statistical analysis: Logistic regression
Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.
Journal Article
Common pitfalls in statistical analysis: Measures of agreement
Agreement between measurements refers to the degree of concordance between two (or more) sets of measurements. Statistical methods to test agreement are used to assess inter-rater variability or to decide whether one technique for measuring a variable can substitute another. In this article, we look at statistical measures of agreement for different types of data and discuss the differences between these and those for assessing correlation.
Journal Article
The evolution of health literacy assessment tools: a systematic review
Background
Health literacy (HL) is seen as an increasingly relevant issue for global public health and requires a reliable and comprehensive operationalization. By now, there is limited evidence on how the development of tools measuring HL proceeded in recent years and if scholars considered existing methodological guidance when developing an instrument.
Methods
We performed a systematic review of generic measurement tools developed to assess HL by searching PubMed, ERIC, CINAHL and Web of Knowledge (2009 forward). Two reviewers independently reviewed abstracts/ full text articles for inclusion according to predefined criteria. Additionally we conducted a reporting quality appraisal according to the survey reporting guideline SURGE.
Results
We identified 17 articles reporting on the development and validation of 17 instruments measuring health literacy. More than two thirds of all instruments are based on a multidimensional construct of health literacy. Moreover, there is a trend towards a mixed measurement (self-report and direct test) of health literacy with 41% of instruments applying it, though results strongly indicate a weakness of coherence between the underlying constructs measured. Overall, almost every third instrument is based on assessment formats modeled on already existing functional literacy screeners such as the REALM or the TOFHLA and 30% of the included articles do not report on significant reporting features specified in the SURGE guideline.
Conclusions
Scholars recently developing instruments that measure health literacy mainly comply with recommendations of the academic circle by applying multidimensional constructs and mixing up measurement approaches to capture health literacy comprehensively. Nonetheless, there is still a dependence on assessment formats, rooted in functional literacy measurement contradicting the widespread call for new instruments. All things considered, there is no clear “consensus” on HL measurement but a convergence to more comprehensive tools. Giving attention to this finding can help to offer direction towards the development of comparable and reliable health literacy assessment tools that effectively respond to the informational needs of populations.
Journal Article