Clustering and meso-level variables in cross-sectional surveys: an example of food aid during the Bosnian crisis

<p>Abstract</p> <p>Background</p> <p>Focus groups, rapid assessment procedures, key informant interviews and institutional reviews of local health services provide valuable insights on health service resources and performance. A long-standing challenge of health planning is to combine this sort of qualitative evidence in a unified analysis with quantitative evidence from household surveys. A particular challenge in this regard is to take account of the neighbourhood or clustering effects, recognising that these can be informative or incidental.</p> <p>Methods</p> <p>An example of food aid and food sufficiency from the Bosnian emergency (1995-96) illustrates two Lamothe cluster-adjustments of the Mantel Haenszel (MH) procedure, one assuming a fixed odds ratio and the other allowing for informative clustering by not assuming a fixed odds ratio. We compared these with conventional generalised estimating equations and a generalised linear mixed (GLMM) model, using a Laplace adjustment.</p> <p>Results</p> <p>The MH adjustment assuming incidental clustering generated a final model very similar to GEE. The adjustment that does not assume a fixed odds ratio produced a final multivariate model and effect sizes very similar to GLMM.</p> <p>Discussion</p> <p>In medium or large data sets with stratified last stage random sampling, the cluster adjusted MH is substantially more conservative than the naïve MH computation. In the example of food aid in the Bosnian crisis, the cluster adjusted MH that does not assume a fixed odds ratio produced similar results to the GLMM, which identified informative clustering.</p

Comparison of risk-scoring systems in the prediction of outcome after liver resection

Background: Risk prediction techniques commonly used in liver surgery include the American Society of Anesthesiologists (ASA) grading, Charlson Comorbidity Index (CCI) and cardiopulmonary exercise tests (CPET). This study compares the utility of these techniques along with the number of segments resected as predictive tools in liver surgery. Methods: A review of a unit database of patients undergoing liver resection between February 2008 and January 2015 was undertaken. Patient demographics, ASA, CCI and CPET variables were recorded along with resection size. Clavien-Dindo grade III–V complications were used as a composite outcome in analyses. Association between predictive variables and outcome was assessed by univariate and multivariate techniques. Results: One hundred and seventy-two resections in 168 patients were identified. Grade III–V complications occurred after 42 (24.4%) liver resections. In univariate analysis of CPET variables, ventilatory equivalents for CO2 (VEqCO2) was associated with outcome. CCI score, but not ASA grade, was also associated with outcome. In multivariate analysis, the odds ratio of developing grade III–V complications for incremental increases in VEqCO2, CCI and number of liver segments resected were 1.09, 1.49 and 2.94, respectively. Conclusions: Of the techniques evaluated, resection size provides the simplest and most discriminating predictor of significant complications following liver surgery

Prediction of Preterm Deliveries from EHG Signals Using Machine Learning

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier

The Active for Life Year 5 (AFLY5) school based cluster randomised controlled trial: study protocol for a randomized controlled trial

Background: Low levels of physical activity, high levels of sedentary behaviour and low levels of fruit and vegetable consumption are common in children and are associated with adverse health outcomes. The aim of this paper is to describe the protocol for a cluster randomised controlled trial (RCT) designed to evaluate a school-based intervention that aims to increase levels of physical activity, decrease sedentary behaviour and increase consumption of fruit and vegetables in school children. Methods/design: The Active for Life Year 5 (AFLY5) study is a school-based, cluster RCT that targets school children in Year 5 (age 9-10 years). All state junior/primary schools in the area covered by Bristol City and North Somerset Council are invited to participate; special schools are excluded. Eligible schools are randomised to one of two arms: intervention arm (receive the intervention 2011-2012) and control arm (receive the intervention after the final follow-up assessment, 2013-2014). The primary outcomes of the trial are levels of accelerometer assessed physical activity and sedentary behaviour and questionnaire assessed fruit and vegetable consumption. A number of secondary outcomes will also be measured, including body mass index, waist circumference and overweight/obesity. Outcomes will be assessed at baseline (prior to intervention when the children are in Year 4), at the end of intervention ‘immediate follow-up’ and ‘12 months long-term’ follow-up. We will use random effects linear and logistic regression models to compare outcomes by randomised arm. The economic evaluation from a societal perspective will take the form of a cost consequence analysis. Data from focus groups and interviews with pupils, parents and teachers will be used to increase understanding of how the intervention has any effect and is integrated into normal school activity. Discussion: The results of the trial will provide information about the public health effectiveness of a school-based intervention aimed at improving levels of physical activity, sedentary behaviour and diet in children.Debbie A Lawlor, Russell Jago, Sian M Noble, Catherine R Chittleborough, Rona Campbell, Julie Mytton, Laura D Howe, Tim J Peters and Ruth R Kippin