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

Background: Rangatahi Maori, the Indigenous adolescents of Aotearoa New Zealand (NZ), have poorer health outcomes than Pakeha (NZ European /other European/"White") adolescents. We explored the influence of policies for Indigenous youth by presenting health trends, inequities and contrasting policy case examples: tobacco control and healthcare access. Methods: Cross-sectional representative surveys of NZ secondary school students were undertaken in 2001, 2007, 2012 and 2019. Health indicators are presented for Maori and Pakeha adolescents (relative risks with 95% CI, calculated using modified Poisson regression) between 2001-2019 and 2012-2019. Policy examples were examined utilising Critical Te Tiriti Analysis (CTA). Findings: Rangatahi Maori reported significant health gains between 2001 and 2019, but an increase in depressive symptoms (13.8% in 2012 to 27.9% in 2019, RR 2.01 [1.65-2.46]). Compared to Pakeha youth there was a pattern of persistent Maori disadvantage, particularly for racism (RR 2.27 [2.08-2.47]), depressive symptoms (RR 1.42 [1.27-1.59]) and forgone healthcare (RR 1.63 [1.45-1.84]). Tobacco use inequities narrowed (RR 2.53 [2.12-3.02] in 2007 to RR 1.55 [1.25-1.93] in 2019). CTA reveals rangatahi Maori-specific policies, Maori leadership, and political support aligned with improved outcomes and narrowing inequities. Interpretation: Age-appropriate Indigenous strategies are required to improve health outcomes and reduce inequities for rangatahi Maori. Characteristics of effective strategies include: (1) evidence-based, sustained, and comprehensive approaches including both universal levers and Indigenous youth-specific policies; (2) Indigenous and rangatahi leadership; (3) the political will to address Indigenous youth rights, preferences, priorities; and (4) a commitment to an anti-racist praxis and healthcare Indigenisation. Funding: Two Health Research Council of New Zealand Project Grants: (a) Fleming T, Peiris-John R, Crengle S, Parry D. (2018). Integrating survey and intervention research for youth health gains. (HRC ref: 18/473); and (b) Clark TC, Le Grice J, Groot S, Shepherd M, Lewycka S. (2017) Harnessing the spark of life: Maximising whanau contributors to rangatahi wellbeing (HRC ref: 17/315).

New Zealand Youth19 survey: vaping has wider appeal than smoking in secondary school students, and most use nicotine-containing e-cigarettes.

OBJECTIVE: To investigate smoking and vaping in secondary school students (aged 13-18 years) in New Zealand (NZ) following the introduction of 'pod' e-cigarettes, which have been associated with the rapid escalation of youth vaping elsewhere. METHODS: Data on smoking and vaping were collected in 2019 as part of a comprehensive youth health survey (N=7,721). RESULTS: Vaping was 2-3 times more prevalent than smoking, with 10% of students vaping regularly (monthly or more often), and 6% weekly or more often, compared with 4% and 2%, respectively, for tobacco smoking. Nicotine-containing e-cigarettes were sometimes or always used by 80% of regular and 90% of weekly vapers. Regular and weekly smoking was rare in low deprivation (affluent) areas, whereas regular and weekly vaping prevalence was similar across the socioeconomic spectrum. More than 80% of ever-vapers (N=2732) reported they were non-smokers when they first vaped, and 49% of regular vapers (N=718) had never smoked. CONCLUSIONS: A significant proportion of New Zealand adolescents, many of whom have never smoked, use nicotine-containing e-cigarettes regularly. Implications for public health: Vaping is less harmful than smoking, but it is not harmless. Public health action is needed to support young non-smokers to remain smokefree and vape-free.

Is Cohort Representativeness Passé? Poststratified Associations of Lifestyle Risk Factors with Mortality in the UK Biobank.

BACKGROUND: The UK Biobank (UKB) has been used widely to examine associations between lifestyle risk factors and mortality outcomes. It is unknown whether the extremely low UKB response rate (5.5%) and lack of representativeness materially affects the magnitude and direction of effect estimates. METHODS: We used poststratification to match the UKB sample to the target population in terms of sociodemographic characteristics and prevalence of lifestyle risk factors (physical inactivity, alcohol intake, smoking, and poor diet). We compared unweighted and poststratified associations between each lifestyle risk factor and a lifestyle index score with all-cause, cardiovascular disease (CVD), and cancer mortality. We also calculated the unweighted to poststratified ratio of HR (RHR) and 95% confidence interval as a marker of effect-size difference. RESULTS: Of 371,974 UKB participants with no missing data, 302,009 had no history of CVD or cancer, corresponding to 3,298,958 person years of follow-up. Protective associations between alcohol use and CVD mortality observed in the unweighted UKB were substantially altered after poststratification, for example, from a hazard ratio (HR) of 0.63 (0.45-0.87) unweighted to 0.99 (0.65-1.50) poststratified for drinking ≥5 times/week versus never drinking. The magnitude of the poststratified all-cause mortality hazard ratio comparing least healthy with healthiest tertile of lifestyle risk factor index was 9% higher (95% confidence interval: 4%, 14%) than the unweighted estimates. CONCLUSIONS: Lack of representativeness may distort the associations of alcohol with CVD mortality, and may underestimate health hazards among those with cumulatively the least healthy lifestyles.

Ecological study of playground space and physical activity among primary school children.

OBJECTIVES: To examine the relationship between school playground size and total physical activity (PA), fitness and fundamental movement skills (FMS) of primary school students. DESIGN: Cross-sectional ecological analysis. SETTING: 43 primary schools in New South Wales, Australia. PARTICIPANTS: Data were from 5238 students, aged 5 to 12 years, participating in the Schools Physical Activity and Nutrition Survey. OUTCOME MEASURES: Self (for age ≥11 years) and parent (for age <11 years) report of PA (meeting PA recommendations and number of days meeting recommendations), objectively measured FMS and cardiorespiratory and muscular fitness. RESULTS: Associations between playground space and measures of PA and fitness were mostly non-linear and moderated by loose equipment. Students in schools with no loose equipment showed a weak association between space and meeting PA recommendations (self-report). In schools with equipment, students' predicted probability of meeting PA recommendations increased sharply between 15 m2 and 25 m2 per student from 0.04 (95% CI: 0.01 to 0.08) to 0.30 (95% CI: 0.14 to 0.46), but at 30 m2 returned to levels comparable to students in schools with no equipment (0.18, 95% CI: 0.07 to 0.28). For cardiorespiratory fitness, in schools with no loose equipment, probabilities for being in the healthy cardiovascular fitness zone varied between 0.66 and 0.77, showing no consistent trend. Students in schools with loose equipment had a predicted probability of being in the healthy fitness zone of 0.56 (95% CI: 0.41 to 0.71) at 15 m2 per student, which rose to 0.75 (95% CI: 0.63 to 0.86) at 20 m2 per student. There was no relationship between space and FMS. CONCLUSIONS: School space guidelines need to incorporate sufficient playground space for students. Our study provides evidence supporting better PA outcomes with increasing space up to 25 m2 per student, and access to loose equipment, however further research is required to determine precise thresholds for minimum space. Intersectoral planning and cooperation is required to meet the needs of growing school populations.

Sociodemographic factors associated with food insecurity: data from the 2003-2014 New South Wales Population Health Survey.

OBJECTIVE: To determine the prevalence and sociodemographic factors associated with food insecurity in the state of New South Wales (NSW), Australia. DESIGN: Cross-sectional analysis of food insecurity data collected by the NSW Population Health Survey between 2003 and 2014. Multiple logistic regression was used to examine associations with key sociodemographic variables. SETTING: NSW, Australia. PARTICIPANTS: 212 608 survey participants responded to the food insecurity survey question between 2003 and 2014. 150 767 of them were aged ≥16 years. The survey sample was randomly selected and weighted to be representative of the NSW population. RESULTS: On average 6 % of adults aged ≥16 years experienced food insecurity in NSW. The odds of food insecurity appeared to increase from one survey year to the next by a factor of 1·05. Food insecurity was found to be independently associated with age, sex, marital status, household size, education, employment status, household income, smoking status, alcohol intake and self-rated health. The association with income, smoking status and self-rated health appeared to be the strongest among all covariates and showed a gradient effect. Food insecurity appeared to increase significantly between the age of 16 and 19 years. CONCLUSIONS: The prevalence of food insecurity appears to be rising over time. Given the negative health consequences of food insecurity, more rigorous measurement and monitoring of food insecurity in NSW and nationally is strongly recommended. The findings provide support for interventions targeting low-income and younger population groups.