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Background: The New Zealand (NZ) Ministry of Health ethnicity data protocols recommend that people of South Asian (SAsian) ethnicity, other than Indian, are combined with people of Japanese and Korean ethnicity at the most commonly used level of aggregation in health research (level two). This may not work well for perinatal studies, as it has long been observed that women of Indian ethnicity have higher rates of adverse pregnancy outcomes, such as perinatal death. It is possible that women of other SAsian ethnicities share this risk. Aims: This study was performed to identify appropriate groupings of women of SAsian ethnicity for perinatal research. Materials and Methods: National maternity and neonatal data, and singleton birth records between 2008 and 2017 were linked using the Statistics NZ Integrated Data Infrastructure. Socio‐demographic risk profiles and pregnancy outcomes were compared between 15 ethnic groups. Recommendations were made based on statistical analyses and cultural evaluation with members of the SAsian research community. Results: Similarities were observed between women of Indian, Fijian Indian, South African Indian, Sri Lankan, Bangladeshi and Pakistani ethnicities. A lower‐risk profile was seen among Japanese and Korean mothers. Risk profiles of women of combined Indian‐Māori, Indian‐Pacific and Indian‐New Zealand European ethnicity more closely represented their corresponding non‐Indian ethnicities. Conclusions: Based on these findings, we suggest a review of current NZ Ministry of Health ethnicity data protocols. We recommend that researchers understand the risk profiles of participants prior to aggregation of groups in research, to mitigate risks associated with masking differences. [ABSTRACT FROM AUTHOR]

Purpose: To measure the iodine status and iodine intake of New Zealand adults 18-64 years of age following mandatory fortification of bread with iodine. Methods: A cross-sectional survey of NZ adults living in Dunedin and Wellington during February-November 2012. Three hundred and one men and women aged 18-64 years randomly selected from the New Zealand Electoral Roll completed a 24-h urine collection, a demographic and iodine-specific food frequency questionnaire (FFQ), and had height and weight measured. Urine collections were analysed for iodine and reported as median urinary iodine concentration (UIC) µg/L and median urinary iodine excretion (UIE) µg/day. The FFQ was used to estimate iodine intake with and without discretionary iodised salt use. Results: The median UIC for all adults was 73 µg/L, indicative of mild iodine deficiency. The mean urinary volume was 2.0 L. As an estimate of iodine intake, the median UIE was 127 µg/day. Estimated iodine intake, using the FFQ which included discretionary iodised salt use, was 132 µg/day. Iodine intakes were associated with UIC ( P = 0.040) and UIE ( P = 0.003), but not with bread iodine intake and iodised salt use. Conclusion: Using the WHO/UNICEF/ICCIDD target for iodine sufficiency (a UIC of >100 µg/L) based on school-aged children with a mean urinary volume of 1.0 L, the iodine status of NZ adults does not reach adequate levels (73 µg/L). A more realistic parameter in a population with a higher urinary volume excretion (2.0 L) is the UIE. A median UIE of 127 µg/day suggests that the iodine status of NZ adults is now likely to be adequate. [ABSTRACT FROM AUTHOR]

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]