National estimates from the Youth ’19 Rangatahi smart survey: A survey calibration approach

Abstract

Background Significant progress has been made addressing adolescent health needs in New Zealand, but some areas, such as mental health issues remain, particularly for rangatahi Māori (indigenous Māori young people). Little is known about how contemporary Māori whānau (families) and communities influence health outcomes, health literacy and access to services. Previous nationally representative secondary school surveys were conducted in New Zealand in 2001, 2007 and 2012, as part of the Youth2000 survey series. This paper focuses on a fourth survey conducted in 2019 (https://www.youth19.ac.nz/). In 2019, the survey also included kura kaupapa Māori schools (Māori language immersion schools), and questions exploring the role of family connections in health and wellbeing. This paper presents the overall study methodology, and a weighting and calibration framework in order to provide estimates that reflect the national student population, and enable comparisons with the previous surveys to monitor trends. Methods Youth19 was a cross sectional, self-administered health and wellbeing survey of New Zealand high school students. The target population was the adolescent population of New Zealand (school years 9–13). The study population was drawn from three education regions: Auckland, Tai Tokerau (Northland) and Waikato. These are the most ethnically diverse regions in New Zealand. The sampling design was two-stage clustered stratified, where schools were the clusters, and strata were defined by kura schools and educational regions. There were four strata, formed as follows: kura schools (Tai Tokerau, Auckland and Waikato regions combined), mainstream-Auckland, mainstream-Tai Tokerau and mainstream-Waikato. From each stratum, 50% of the schools were randomly sampled and then 30% of students from the selected schools were invited to participate. All students in the kura kaupapa schools were invited to participate. In order to make more precise estimates and adjust for differential non-response, as well as to make nationally relevant estimates and allow comparisons with the previous national surveys, we calibrated the sampling weights to reflect the national secondary school student population. Results There were 45 mainstream and 4 kura schools included in the final sample, and 7,374 mainstream and 347 kura students participated in the survey. There were differences between the sampled population and the national secondary school student population, particularly in terms of sex and ethnicity, with a higher proportion of females and Asian students in the study sample than in the national student population. We calculated estimates of the totals and proportions for key variables that describe risk and protective factors or health and wellbeing factors. Rates of risk-taking behaviours were lower in the sampled population than what would be expected nationally, based on the demographic profile of the national student population. For the regional estimates, calibrated weights yield standard errors lower than those obtained with the unadjusted sampling weights. This leads to significantly narrower confidence intervals for all the variables in the analysis. The calibrated estimates of national quantities provide similar results. Additionally, the national estimates for 2019 serve as a tool to compare to previous surveys, where the sampling population was national. Conclusions One of the main goals of this paper is to improve the estimates at the regional level using calibrated weights to adjust for oversampling of some groups, or non-response bias. Additionally, we also recommend the use of calibrated estimators as they provide nationally adjusted estimates, which allow inferences about the whole adolescent population of New Zealand. They also yield confidence intervals that are significantly narrower than those obtained using the original sampling weights.

Indigenous adolescent health in Aotearoa New Zealand: Trends, policy and advancing equity for rangatahi Maori, 2001–2019

“…13 Proportions of students by age, sex, school decile, and geographical location across the four waves are reported ( Table 2 ) and were generally representative of students in NZ. 14 (p. 14). Rangatahi Māori are over-represented in low decile (poorer) schools and have a younger age structure compared to Pākehā students.…”

“…The Youth2000 survey series are anonymous, cross-sectional, health and wellbeing surveys of New Zealand secondary school students, undertaken in 2001, 2007, 2012 and 2019. Previously reported methods 13 , 14 are summarised here.…”

“…Data were initially weighted using inverse probability of selection (IPS) weights [calculated for each student as: (total number of schools ÷ schools that participated) × (total number of eligible students in the student's school ÷ students from that school that participated)]. Generalised raking 16 was used to correct for non-response and to calibrate the results of each survey wave to the national secondary school population 14 based on school decile (a school-level measure of socioeconomic status), student age, sex, and ethnicity.…”

“…National estimates were calculated for all survey waves to allow comparison over time, but comparisons between 2019 and earlier waves could be biased by regional differences over and above demographic differences. 14 Although survey questions were available in Māori, key questions largely focused on personal health and wellbeing issues considered important in a Eurocentric context. Critiques of mainstream policies and strategies are not a reflection of ongoing efforts by youth organisations, and Māori providers and organisations, who continue to innovate and push the boundaries on culturally safe services for rangatahi Māori.…”

Rapid and unequal decline in adolescent mental health and well-being 2012–2019: Findings from New Zealand cross-sectional surveys

“…Comprehensive, cross-sectional youth health and wellbeing surveys were completed by representative samples of New Zealand secondary school students in 2001, 2007, 2012 and 2019. Detailed methods are available elsewhere (Adolescent Health Research Group, 2003Clark et al, 2013;Fleming et al, 2020b;Rivera-Rodriguez et al, 2021). In brief, the surveys used a two-stage cluster design.…”