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Objective To derive novel emergency weight estimation tables for New Zealand children aged 5-10 years using ethnicity and sex to increase accuracy and precision. Methods Using an existing dataset (collected in five New Zealand primary schools during July 2013; n = 376), body mass index and current emergency weight estimates were calculated. Stepwise regression with Akanke Information produced two best-fit models for predicting weight, one based on age and the other on height. Potential explanatory variables included ethnicity, sex and body habitus. Results The length-based model included height, ethnicity and body habitus. Accuracy (weight estimates within 10% of actual weight) for the length-based lookup table (74.8%) was similar to that for the Broselow-Luten tape (73.4%). The age-based model included age, sex, ethnicity and body habitus. Accuracy (51.3%) for the age-based lookup table was better than for existing formulae including Shann (45.7%), Advanced Paediatric Life Support (39.1%) and Theron (28.7%). Conclusion The most accurate method for weight estimation in Auckland children aged 5-10 years is either the novel length-based lookup table or the existing Broselow-Luten tape. When length-based methods are not possible, the age-based lookup tables incorporating age, sex, ethnicity and body habitus are more accurate than existing methods of weight estimation. [ABSTRACT FROM AUTHOR]

The aim of the study was to validate the self-report Multimedia Activity Recall for Children and Adolescents (MARCA) against accelerometry for the assessment of physical activity in New Zealand children. Participants (n = 716, 10–18 years) recalled 3–4 days of activity using the MARCA and underwent a partially overlapping 7-day accelerometry protocol during a national survey. Spearman correlation coefficients (ρ) assessed the association between accelerometer-derived counts per minute and MARCA-derived physical activity level and time in locomotion. Both data sources estimated time spent in light and moderate–vigorous physical activity. Association and agreement between methods for light physical activity and moderate–vigorous physical activity was assessed using correlations and Bland–Altman plots respectively, and pairedt-tests conducted. Accelerometer-derived activity counts were moderately correlated with both MARCA-derived physical activity level and locomotion (ρ = 0.38,P < 0.0001). The correlation between methods was –0.14 for light physical activity and 0.28 for moderate–vigorous physical activity (P < 0.0001). The MARCA overestimated moderate–vigorous physical activity compared with accelerometry (120 min,P < 0.0001), which increased as moderate–vigorous physical activity time increased. Some sex and ethnicity (Māori [indigenous] versus non-Māori) differences were observed. Overall, the MARCA indicated moderate validity for assessment of physical activity level, locomotion and moderate–vigorous physical activity and poor validity for assessment of light physical activity. This was comparable to other self-report tools. The MARCA has utility for future large-scale research. [ABSTRACT FROM PUBLISHER]

Summary: Background: There is a relationship between childhood obesity and area‐level deprivation. While the New Zealand Index of Deprivation (NZDep) has been used widely in research for the past 20 years, the Index of Multiple Deprivation (IMD) was released in 2017. This study aims to investigate the association between deprivation and childhood obesity in New Zealand and compare measures of deprivation. Methods: Data from 316 794 4‐year‐olds in New Zealand undertaking the B4 School Check in 2010 to 2016, a national health and development screen, were analysed. Multilevel logistic regression models assessed the relationship between area‐level deprivation and individual‐level child obesity. Models were adjusted for age, sex, immigration status, ethnicity, and year. Deprivation was measured using the census‐based NZDep2013 (deciles) and the administrative data‐based IMD (deciles). The seven domains of the IMD were also considered. Results: The relationship between deprivation and obesity was very similar for the IMD and NZDep2013, point estimates were near identical, and confidence intervals overlapped substantially. Higher levels of deprivation were associated with a higher prevalence of child obesity. The relationship between deprivation and child obesity varied considerably across IMD domains. The education domain had the strongest association with child obesity and had an association with child obesity independent of the other domains of deprivation. Conclusion: Overall, there was little difference between the NZDep and IMD. However, the IMD's domains and IMD‐1 approach reveal more nuanced understandings of the deprivation‐obesity gradient, including the importance of area‐level education deprivation for predicting child obesity rates. [ABSTRACT FROM AUTHOR]