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INTRODUCTION: Central obesity has been shown to better indicate health risks compared to general obesity. Measures of central obesity include waist-to-height ratio (WHtR), waist-to-hip ratio (WHR) and waist circumference (WC). The National Institute of Health and Care Excellence (NICE) recently recommended the use of WHtR alongside body mass index (BMI) to identify risks in adults and children, whilst recognising the need for more evidence relating to WHtR in children. This study explores risk thresholds for central obesity measures throughout adolescence. It compares these with those currently recommended in England and discusses whether these thresholds are age- and sex-specific. METHODS: Data on adolescents aged 11 to 18 years from the Health Survey for England (HSE) during 2005 to 2014 was used to calculate WHtR, WHR and WC percentiles. Next, smoothed lambda-mu-sigma (LMS) curves were created and the percentiles which align with the adult thresholds at age 18 years identified. This allows the most appropriate risk related thresholds for each measure during adolescence to be determined. RESULTS: WHtR LMS curves are stable and flat throughout adolescence. WHR decreases in girls and WC increases in both boys and girls, during adolescence. Across all measures, there is slightly more fluctuation in higher percentiles, and in girls' WHR. DISCUSSION: In practice, WHtR thresholds are simple to use to identify central obesity related risks. In particular, they are recommended because the same thresholds can be used for males and females and for adolescents and adults. The results support NICE guidance to use WHtR thresholds alongside BMI thresholds to identify individual risk. IMPLICATIONS AND CONTRIBUTION: This study uses central obesity measures, including waist-to-height and waist-to-hip ratios, to investigate risk-related thresholds for adolescents. It is the first to do so using English data. It provides support for current NICE recommendations to use adult waist-to-height thresholds in adults and children, alongside BMI measures in clinical and non-clinical settings.
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Body Mass Index (BMI) trajectories are important for understanding how BMI develops over time. Missing data is often stated as a limitation in studies that analyse BMI over time and there is limited research exploring how missing data influences BMI trajectories. This study explores the influence missing data has in estimating BMI trajectories and the impact on subsequent analysis. This study uses data from the English Longitudinal Study of Ageing. Distinct BMI trajectories are estimated for adults aged 50 years and over. Next, multiple methods accounting for missing data are implemented and compared. Estimated trajectories are then used to predict the risk of developing type 2 diabetes mellitus (T2DM). Four distinct trajectories are identified using each of the missing data methods: stable overweight, elevated BMI, increasing BMI, and decreasing BMI. However, the likelihoods of individuals following the different trajectories differ between the different methods. The influence of BMI trajectory on T2DM is reduced after accounting for missing data. More work is needed to understand which methods for missing data are most reliable. When estimating BMI trajectories, missing data should be considered. The extent to which accounting for missing data influences cost-effectiveness analyses should be investigated.
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Índice de Massa Corporal , Diabetes Mellitus Tipo 2 , Humanos , Pessoa de Meia-Idade , Diabetes Mellitus Tipo 2/epidemiologia , Feminino , Masculino , Estudos Longitudinais , Idoso , Sobrepeso/epidemiologia , Obesidade/epidemiologiaRESUMO
BACKGROUND: Since the early 2000s, there have been marked trends in adolescent health and wellbeing indicators across Europe, North America and Australia. In particular, there have been substantial declines in youth drinking. We know little about how these trends are underpinned by co-occurring indicators within individuals. This paper aims to analyse change over time in how indicators cluster within individuals and differences in these patterns between five countries with different trends in youth drinking. METHODS: We analysed four waves of repeat cross-sectional survey data from 15-year-olds in England (n = 5942), Italy (n = 5234), the Netherlands (n = 5408), Hungary (n = 5274), and Finland (n = 7446), which were included in the Health Behaviours in School-aged Children (HBSC) study between 2001/02 and 2013/14. We defined clusters of individuals using multigroup latent class analyses which accounts for change over time. The class indicators included health behaviours, attitudes, wellbeing and relationships. We modelled associations between class membership, sex, and family affluence over time. RESULTS: We identified four classes in all countries: Overall unhealthy, Overall healthy, Moderately healthy and Substance abstainers with behaviour risk indicators. The proportion of adolescents in the Overall unhealthy class declined between 2001/02 and 2013/14 by between 22.8 percentage points (pp) in England and 3.2pp in Italy. The extent to which indicators of health and wellbeing changed as linked clusters differed across countries, but changes in alcohol consumption, smoking, drug use and sexual activity were typically concurrent. Adolescents with low family affluence were more likely to be in the Overall unhealthy class in all years. CONCLUSIONS: Improvements in indicators of adolescent health and well-being are due mainly to concurrent declines in drinking, smoking, sexual activity, and cannabis use, but these declines are not consistently associated with improvements in other domains. They have also not led to reductions in inequalities in indicators of health and well-being.
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Saúde do Adolescente , Análise de Classes Latentes , Humanos , Adolescente , Estudos Transversais , Masculino , Feminino , Inglaterra , Itália/epidemiologia , Comportamentos Relacionados com a Saúde , Países Baixos/epidemiologia , Comportamento do Adolescente/psicologia , Hungria , Finlândia , Consumo de Bebidas Alcoólicas/epidemiologiaRESUMO
BACKGROUND: The Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) is a widely used non-preference-based measure of mental health in the UK. The primary aim of this paper is to construct an algorithm to translate the SWEMWBS scores to utilities using the Recovering Quality of Life Utility Index (ReQoL-UI) measure. METHODS: Service users experiencing mental health difficulties were recruited in two separate cross-sectional studies in the UK. The following direct mapping functions were used: Ordinary Least Square, Tobit, Generalised Linear Models. Indirect (response) mapping was performed using seemingly unrelated ordered probit to predict responses to each of the ReQoL-UI items and subsequently to predict using UK tariffs of the ReQoL-UI from SWEMWBS. The performance of all models was assessed by the mean absolute errors, root mean square errors between the predicted and observed utilities and graphical representations across the SWEMWBS score range. RESULTS: Analyses were based on 2573 respondents who had complete data on the ReQoL-UI items, SWEMWBS items, age and sex. The direct mapping methods predicted ReQoL-UI scores across the range of SWEMWBS scores reasonably well. Very little differences were found among the three regression specifications in terms of model fit and visual inspection when comparing modelled and actual utility values across the score range of the SWEMWBS. However, when running simulations to consider uncertainty, it is clear that response mapping is superior. CONCLUSIONS: This study presents mapping algorithms from SWEMWBS to ReQoL as an alternative way to generate utilities from SWEMWBS. The algorithm from the indirect mapping is recommended to predict utilities from the SWEMWBS.