BackgroundWeight at age 5 is a predictor for future health of the individual. This study examines risk factors for childhood obesity with a focus on ethnicity.MethodsData from the Millennium Cohort study were used. 17,561 singleton children of White/European (n = 15,062), Asian (n = 1,845) or African (n = 654) background were selected. Logistic regression and likelihood ratio tests were used to examine factors associated with obesity at age 5. All participants were interviewed in their own homes. The main exposures examined included; Birth weight, sedentary lifestyle, family health behaviours, ethnicity, education and income.ResultsChildren with a sedentary lifestyle, large at birth, with high risk family health behaviours (overweight mothers, smoking near the child, missing breakfast) and from a family with low income or low educational attainment, were more likely to be obese regardless of ethnicity. Feeding solid food before 3 months was associated with obesity in higher income White/European families. Even when controlling for socioeconomic status, ethnic background is an important independent risk factor for childhood obesity [Odds ratio of obesity; was 1.7 (95%CI: 1.2-2.3) for Asian and 2.7 (95%CI: 1.9-3.9) for African children, compared to White/European]. The final adjusted model suggests that increasing income does not have a great impact on lowering obesity levels, but that higher academic qualifications are associated with lower obesity levels [Odds of obesity: 0.63 (95%CI: 0.52-0.77) if primary carer leaves school after age 16 compared at age 16].ConclusionsEducation of the primary carer is an important modifiable factor which can be targeted to address rising obesity levels in children. Interventions should be family centred supporting and showing people how they can implement lifestyle changes in their family.

The relationship between child health, wellbeing and education demonstrates that healthier and happier children achieve higher educational attainment. An engaging curriculum that facilitates children in achieving their academic potential has strong implications for educational outcomes, future employment prospects, and health and wellbeing during adulthood. Outdoor learning is a pedagogical approach used to enrich learning, enhance school engagement and improve pupil health and wellbeing. However, its non-traditional means of achieving curricular aims are not yet recognised beyond the early years by education inspectorates. This requires evidence into its acceptability from those at the forefront of delivery. This study aimed to explore headteachers’, teachers’ and pupils’ views and experiences of an outdoor learning programme within the key stage two curriculum (ages 9–11) in South Wales, United Kingdom. We examine the process of implementation to offer case study evidence through 1:1 interviews with headteachers (n = 3) and teachers (n = 10) and focus groups with pupils aged 9–11 (n = 10) from three primary schools. Interviews and focus groups were conducted at baseline and six months into implementation. Schools introduced regular outdoor learning within the curriculum. This study found a variety of perceived benefits for pupils and schools. Pupils and teachers noticed improvements in pupils’ engagement with learning, concentration and behaviour, as well as positive impacts on health and wellbeing and teachers’ job satisfaction. Curriculum demands including testing and evidencing work were barriers to implementation, in addition to safety concerns, resources and teacher confidence. Participants supported outdoor learning as a curriculum-based programme for older primary school pupils. However, embedding outdoor learning within the curriculum requires education inspectorates to place higher value on this approach in achieving curricular aims, alongside greater acknowledgment of the wider benefits to children which current measurements do not capture.

BackgroundToday, health care is patient-centred with patients more involved in medical decision making and taking an active role in managing their disease. It is important that patients are appropriately informed about their condition and that their health care needs are met. We examine the information utilisation, sources and needs of people with Ankylosing Spondylitis (AS).MethodsParticipants in an existing AS cohort study were asked to complete a postal or online questionnaire containing closed and open-ended questions, regarding their information access and needs. Participants were stratified by age and descriptive statistics were performed using STATA 11, while thematic analysis was performed on open-ended question narratives. Qualitative data was handled in Microsoft Access and explored for emerging themes and patterns of experiences.ResultsDespite 73% of respondents having internet access, only 49% used the internet to access information regarding AS. Even then, this was only infrequently. Only 50% of respondents reported accessing written information about AS, which was obtained mainly in specialist clinics. Women were more likely than men to access information (63% (women) 46% (men)) regardless of the source, while younger patients were more likely to use online sources. The main source of non-written information was the rheumatologist. Overall, the respondents felt there was sufficient information available, but there was a perception that the tone was often too negative. The majority (95%) of people would like to receive a regular newsletter about AS, containing positive practical and local information. Suggestions were also made for more information about AS to be made available to non-specialist medical professionals and the general public.ConclusionsThere appears to be sufficient information available for people with AS in the UK and this is mostly accessed by younger AS patients. Many patients, particularly men, choose not to access AS information and concerns were raised about its negative tone. Patients still rely on written and verbal information from their specialists. Future initiatives should focus on the delivery of more positive information, targeting younger participants in particular and increasing the awareness in the general population and wider non-specialist medical community.

Objectives1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs.MethodsThis study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge.ResultsPrimary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods.ConclusionData-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs.

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