Oral Presentation ESA-SRB-APEG-NZSE 2022

National health care that does not fund continuous glucose monitoring drives inequity in Paediatric Diabetes: The New Zealand example. (#255)

Mercedes J Burnside 1 , Jonathan Williman 2 , Hannah Davis 1 , Craig Jefferies 3 , Ryan Paul 4 , Ben Wheeler 5 , Esko Wiltshire 6 , Yvonne Anderson 7 , Martin de Bock 1
  1. University of Otago, Christchurch, Christchurch, CANTERBURY, New Zealand
  2. Biostatistics and Computation Biology Unit, University of Otago, Christchurch., University of Otago, Christchurch, Christchurch, New Zealand
  3. Department of Pediatric Endocrinology, Starship Children’s Health, Auckland District Health Board, Auckland, New Zealand
  4. Waikato Regional Diabetes Service, Waikato District Health Board, Hamilton, New Zealand
  5. Department of Women’s and Children’s Health, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
  6. Department of Paediatrics and Child Health, University of Otago, Wellington, New Zealand
  7. Department of Paediatrics: Child and Youth Health, Faculty of Medicine and Health Sciences, University of Auckland, Auckland, New Zealand

Aims: Continuous glucose monitoring (CGM) improves glycaemia for people affected by type 1 diabetes (T1D), but is not funded in Aotearoa/New Zealand. This study explores the impact of non-funded CGM on equity of access and associated glycaemic outcomes.


Methods: Cross-sectional population-based study collected socio-demographic (age, gender, prioritised ethnicity, socioeconomic status) and clinical data from all regional diabetes centres in New Zealand with children <15 years with T1D as of 1st October 2021. De-identified data were obtained from existing databases or chart review. Outcomes compared socio-demographic characteristics between those using all forms of CGM and self-monitoring of blood glucose (SMBG), and association with HbA1c.


Results: 1209 eligible children were evaluated: 70·2% European, 18·1% Māori, 7·1% Pacific, 4·6% Asian, with even distribution across socioeconomic quintiles. Median HbA1c was 64mmol/mol (8·0%), 40·0% utilised intermittently scanned (is)CGM, and 27% real-time (rt)CGM. CGM utilisation was lowest with Pacific ethnicity (37% lower than Māori) and the most deprived socioeconomic quintiles (quintile 5 vs. 1 adjusted RR 0·68; 95% CI, 0·56 to 0·83). CGM use was associated with regional diabetes centre (P < 0·001). The impact of CGM use on HbA1c differed by ethnicity: Māori children had the greatest difference in HbA1c between SMBG and rtCGM (adjusted difference -15.7mmol/mol; 95% CI, -21.9 to -9.5), with less pronounced differences seen with other ethnicities.


Conclusion: Inequities in CGM use exist based on prioritised ethnicity and socioeconomic status. Importantly, CGM was independently associated with lower HbA1c, suggesting that lack of CGM funding contributes to health disparity in children affected by T1D.