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Childhood growth is of interest in medical research concerned with determinants and consequences of variation from healthy growth and development. Linear spline multilevel modelling is a useful approach for deriving individual summary measures of growth, which overcomes several data issues (co-linearity of repeat measures, the requirement for all individuals to be measured at the same ages and bias due to missing data). Here, we outline the application of this methodology to model individual trajectories of length/height and weight, drawing on examples from five cohorts from different generations and different geographical regions with varying levels of economic development. We describe the unique features of the data within each cohort that have implications for the application of linear spline multilevel models, for example, differences in the density and inter-individual variation in measurement occasions, and multiple sources of measurement with varying measurement error. After providing example Stata syntax and a suggested workflow for the implementation of linear spline multilevel models, we conclude with a discussion of the advantages and disadvantages of the linear spline approach compared with other growth modelling methods such as fractional polynomials, more complex spline functions and other non-linear models.

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These findings are relevant to the design and conduct of research studies of nursing care and practice and present ways for investigators to optimize the skills and knowledge of nurses working as CRNs.

Background: Wound infections are a common complication of surgery that add significantly to the morbidity of patients and costs of treatment. The global trend towards reducing length of hospital stay post-surgery and the increase in day case surgery means that surgical site infections (SSI) will increasingly occur after hospital discharge. Surveillance of SSIs is important because rates of SSI are viewed as a measure of hospital performance, however accurate detection of SSIs posthospital discharge is not straightforward.

BackgroundAdvancements in knowledge of obesity aetiology and mobile phone technology have created the opportunity to develop an electronic tool to predict an infant’s risk of childhood obesity. The study aims were to develop and validate equations for the prediction of childhood obesity and integrate them into a mobile phone application (App).Methods and FindingsAnthropometry and childhood obesity risk data were obtained for 1868 UK-born White or South Asian infants in the Born in Bradford cohort. Logistic regression was used to develop prediction equations (at 6±1.5, 9±1.5 and 12±1.5 months) for risk of childhood obesity (BMI at 2 years >91st centile and weight gain from 0–2 years >1 centile band) incorporating sex, birth weight, and weight gain as predictors. The discrimination accuracy of the equations was assessed by the area under the curve (AUC); internal validity by comparing area under the curve to those obtained in bootstrapped samples; and external validity by applying the equations to an external sample. An App was built to incorporate six final equations (two at each age, one of which included maternal BMI). The equations had good discrimination (AUCs 86–91%), with the addition of maternal BMI marginally improving prediction. The AUCs in the bootstrapped and external validation samples were similar to those obtained in the development sample. The App is user-friendly, requires a minimum amount of information, and provides a risk assessment of low, medium, or high accompanied by advice and website links to government recommendations.ConclusionsPrediction equations for risk of childhood obesity have been developed and incorporated into a novel App, thereby providing proof of concept that childhood obesity prediction research can be integrated with advancements in technology.

BackgroundThere are limited data on detection disparities of common mental disorders in minority ethnic women.AimsDescribe the natural history of common mental disorders in primary care in the maternal period, characterise women with, and explore ethnic disparities in, detected and potentially missed common mental disorders.MethodSecondary analyses of linked birth cohort and primary care data involving 8991 (39.4% White British) women in Bradford. Common mental disorders were characterised through indications in the electronic medical record. Potentially missed common mental disorders were defined as an elevated General Health Questionnaire (GHQ-28) score during pregnancy with no corresponding common mental disorder markers in the medical record.ResultsEstimated prevalence of pre-birth common mental disorders was 9.5%, rising to 14.0% 3 years postnatally. Up to half of cases were potentially missed. Compared with White British women, minority ethnic women were twice as likely to have potentially missed common mental disorders and half as likely to have a marker of screening for common mental disorders.ConclusionsCommon mental disorder detection disparities exist for minority ethnic women in the maternal period.

BackgroundObserving the fast during the holy month of Ramadan is one of the five pillars of Islam. Although pregnant women and those with pre-existing illness are exempted from fasting many still choose to fast during this time. The fasting behaviours of pregnant Muslim women resident in Western countries remain largely unexplored and relationships between fasting behaviour and offspring health outcomes remain contentious. This study was undertaken to assess the prevalence, characteristics of fasting behaviours and offspring health outcomes in Asian and Asian British Muslim women within a UK birth cohort.MethodsProspective cohort study conducted at the Bradford Royal Infirmary UK from October to December 2010 comprising 310 pregnant Muslim women of Asian or Asian British ethnicity that had a live singleton birth at the Bradford Royal Infirmary. The main outcome of the study was the decision to fast or not during Ramadan. Secondary outcomes were preterm births and mean birthweight. Logistic regression analyses were used to investigate the relationship between covariables of interest and women’s decision to fast or not fast. Logistic regression was also used to investigate the relationship between covariables and preterm birth as well as low birth weight.ResultsMutually adjusted analysis showed that the odds of any fasting were higher for women with an obese BMI at booking compared to women with a normal BMI, (OR 2.78 (95% C.I. 1.29-5.97)), for multiparous compared to nulliparous women(OR 3.69 (95% C.I. 1.38-9.86)), and for Bangladeshi origin women compared to Pakistani origin women (OR 3.77 (95% C.I. 1.04-13.65)). Odds of fasting were lower in women with higher levels of education (OR 0.40 (95% C.I. 0.18-0.91)) and with increasing maternal age (OR 0.87 (95% C.I. 0.80-0.94). No associations were observed between fasting and health outcomes in the offspring.ConclusionsPregnant Muslim women residing in the UK who fasted during Ramadan differed by social, demographic and lifestyle characteristics compared to their non-fasting peers. Fasting was not found to be associated with adverse birth outcomes in this sample although these results require confirmation using reported fasting data in a larger sample before the safety of fasting during pregnancy can be established.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2393-14-335) contains supplementary material, which is available to authorized users.