Background: In a rapidly warming climate, heatwaves pose an increasing threat to human health. However, limited knowledge exists regarding the impact on health outcomes in the Northern Territory (NT) or the role humidity may play. Our study aimed to evaluate the association between heatwaves, humidity and emergency department (ED) presentations in the NT.
Methods: Using a space-time-stratified case-crossover design and conditional Poisson regression models, we analysed the association between heatwaves, humidity and ED presentations in the NT from 2001 to 2023. Heatwaves were identified using the Excess Heat Factor (EHF) method, with both temperature-only (heatwavestemp) and heat-index (heatwavestemp+humidity) metrics. Relative risks (95% confidence intervals) were estimated for all-cause ED presentations, and stratified by demographic and principal diagnostic categories.
Results All-cause ED presentations increased by 4.4% (RR=1.044, 95%CI 1.018-1.071) for severe/extreme and 1.6% (RR=1.016, 95%CI 1.002-1.030) for low-intensity heatwavestemp.. For heatwavestemp+humidity, presentations increased by 6.1% (RR=1.061, 95%CI 1.025-1.098, severe/extreme) and 0.9% (RR=1.009, 95%CI 0.995-1.024, low-intensity). Unique subpopulation increases for severe/extreme heatwavestemp occurred for ages 19-49 years (RR=1.052; 95%CI 1.018-1.087), visitors (RR=1.162, 95%CI 1.038-1.301) and presentations with skin conditions (RR=1.116, 95%CI 1.048-1.189). Specific to severe/extreme heatwavestemp+humidity, presentations increased for Aboriginal peoples (RR=1.059; 95%CI 1.006-1.114), ages 50-64 years (RR=1.141, 95%CI 1.059-1.230) and cardiovascular conditions (RR=1.111, 95%CI 1.015-1.216). Comparing heatwave indexes, 57.1% (17,580/30,767) of heatwavetemp days were not captured by heatwavetemp+humidity, and conversely 49.6% (12,996/26,183) of heatwavestemp+humidity days were not captured by heatwavetemp.
Conclusion: Health impacts increased with heatwave severity, varying by sub-groups and humidity inclusion. These differences call for a dual heatwave warning system in the NT ‘s tropical and arid regions, noting Australia’s heatwave warning system is based solely on temperature, and does not directly account for humidity. Further humidity-inclusive studies are required in varied climates. Preventative interventions should target high-risk populations, prioritising resources for severe and extreme heatwaves.

Background: Breast cancer, the most common cancer among women worldwide, shows significant disparities across populations. However, understanding the burden of breast cancer among Indigenous women is challenging, because specific data on Indigenous peoples is unavailable in global cancer statistics. This study was aimed at systematically reviewing existing global evidence of breast cancer incidence, mortality, and survival among Indigenous women.
Methods: PubMed, Web of Science, CINAHL, and Embase were searched for studies published in the past 10 years (2013–2023). The Newcastle-Ottawa quality assessment scale was used to assess the quality of the included studies. A random-effects meta-analysis was performed to determine pooled 5-year survival rate estimates and adjusted hazard ratios. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were used to report results.
Results: Overall, 54 studies were included in the systematic review, and 32 studies were included in the meta-analysis. The number of breast cancer cases ranged from 3 to 20,325 among Indigenous women, and 67 to 3,341,855 among non-Indigenous women. Age-adjusted breast cancer incidence rates ranged from 19 to 165.2 per 100,000 among Indigenous women, and from 21.5 to 190.4 per 100,000 among non-Indigenous women. Indigenous women had a 34% higher mortality risk (95% CI: 1.22–1.48) than non-Indigenous women. The 5-year survival rate was 77% among Indigenous women (95% CI: 70.81%–83.43%; p < 0.001) and 81% among non-Indigenous women (95% CI: 71.64%–90.21%; p < 0.001).
Conclusion: Despite having a lower incidence of breast cancer, Indigenous women experience higher mortality risk and lower survival rates than non-Indigenous women. Although survival has improved over time, disparities in breast cancer outcomes persist. Continued efforts to enhance screening participation and healthcare access are essential to address the disparities in breast cancer outcomes.
Keywords: Breast cancer; Indigenous women; Incidence; Mortality; Survival; Epidemiology

Background
Can linked administrative data be used to transform New Zealand's only sample survey on indigenous wellbeing into a longitudinal study?
NZ's Integrated Data Infrastructure (IDI) is a research database of administrative and survey datasets containing a range of variables linkable at the individual level. Te Kupenga is a large nationally representative post-censal survey of NZ's indigenous population (Māori) and is accessible in the IDI. Te Kupenga is the only official survey with Māori culturally-informed variables, but it is often under-utilised in research.

Methods
The Te Kupenga survey was used as a foundational cohort and linked at an individual level to outcomes and determinants from various administrative datasets from different time periods. Outcomes included Ambulatory Sensitive Hospitalisations (ASH) and COVID-19 vaccinations, while determinants included individual, household, and geographic variables.

Results
Results outlined the impact of culturally relevant wellbeing indicators and their effect on health outcomes for Māori. However, linking a representative survey to admin data created issues of loss to follow-up and missing data, as the original sample was not maintained after linkages. Loss to follow-up and missingness differed depending on variables and time periods. Consequently, creating new universally applicable weights was not possible. However, we constructed a robust, generally applicable process for re-weighting survey data to account for missingness and loss to follow-up in admin data.

Conclusion
This project demonstrated an approach for turning a sample survey into a longitudinal cohort using admin data, creating a method that can be used for other official statistics surveys. Furthermore, this research signified the importance of cultural wellbeing and its effects on health disparities for the Māori population.

Background: In Aotearoa New Zealand there are large and enduring health inequities between Māori (Indigenous) and non-Māori tamariki (children). These inequities result from systems failures, differential access to the determinants of health and unresponsive, poor quality, and siloed health service delivery. The Harti Hauora Tamariki (Harti) programme was a whānau (family) Māori centric multilevel programme to support wellbeing via culturally safe engagement, electronic needs assessment and navigation to wellbeing services.

Methods: A randomised controlled trial (RCT) measured the impact of Harti at a hospital paediatric ward. Primary measures were hospital readmission risk at 30-days, 6 and 12 months post-hospital discharge. Analysis is also underway to examine 5-year outcomes.

Results: In total, 965 children were recruited; 485 (50.3%) in the intervention group; 480 (49.7%) in the control group, with over half of participants Māori. No significant differences in readmission risk were found for any timepoints in the first 12 months post discharge. Significant differences were found in the documentation of unmet need, referrals to and receipt of wellbeing services, and satisfaction with care. Further, qualitative research highlighted the critical role of Māori navigation staff. Following the RCT, and through the COVID-19 environment, Harti has been implemented in emergency and community services and has evolved into the WHIRI model of care with research and clinical arms.

Conclusions: Harti had positive individual, whānau and systems level impacts and we will present 5-year outcomes. Harti also enables health equity gain for Māori, maximising the value delivered from scarce resources for improving wellbeing. We will discuss the Harti model of care, WHIRI evolution into multiple settings including cancer and maternity care, and describe how lessons learnt from these processes can upscale and be adapted to support further capacity building to advance health improvement and health equity in the future.

Objective: Early life exposure to air pollution can trigger physiological reactions that potentially influence bone development. We aimed to identify periods of susceptibility to air pollution in early life in relation to bone health at age six years.
Methods: We used data from the COPSAC2010 study, an ongoing population-based mother-child cohort in Denmark (n=700). We modelled daily ambient air pollution concentrations of nitrogen dioxide and particulate matter with a diameter of ≤2.5 and ≤10 μm at the home addresses during pregnancy and childhood, using the Danish Eulerian Hemispheric Model (DEHM)–urban background model (UBM)–Danish Air Pollution and Human Exposure Modelling System (AirGIS) model system. Bone mineral density (BMD) and area-adjusted bone mineral content (aBMC) were measured by dual-energy x-ray absorptiometry at age six. We performed distributed lag non-linear modelling (DNLM) adjusted for several socioeconomic characteristics to assess the associations between air pollution and bone health and identify windows of susceptibility.
Results: 518 children were included. We identified a window of susceptibility for PM10 exposure between 2.8 to 3.2 years of age with aBMC (-9.0; CI -17.9 to -0.1 per 10 µg/m3 increase in PM10). In sex-stratified analyses, associations for both PM2.5 and PM10 with aBMC followed similar trends, but were only statistically significant in boys, and not in girls. Also, we identified a positive association between PM10 and PM2.5 from conception to ~ 7 months of gestation with aBMC (e.g., 14.4; CI 0.7 to 28.2 per 5 µg/m3 increase in PM2.5) (Figure). Nevertheless, all associations disappeared after correction for multiple testing.
Conclusion: We observed conflicting evidence regarding the impact of air pollution during pregnancy and early childhood on bone health outcomes at age six. We showed a protective effect during pregnancy, while, around 3 years of age may represent a critical period for negative effects on bone health, particular in boys.