Ethnic Inequities in Achieving Glycaemic and Other Clinical Targets in Type 2 Diabetes

Abstract

Background/Objectives: Ethnic disparities in type 2 diabetes (T2D) outcomes remain a significant public health challenge in Aotearoa New Zealand (NZ), but are not accurately reported in large datasets. This cross-sectional study used linked regional health records to examine ethnic inequities in glycaemic control and achievement of clinical targets among adults with T2D in the Waikato and Auckland regions. Methods: A cross-sectional analysis was conducted on 57,734 adults aged 18–75 years with confirmed T2D enrolled in four Primary Healthcare Organisations. Clinical and sociodemographic data from February 2021 to December 2023 were linked via National Health Index numbers. Key outcomes included the percentage of patients at target for HbA1c, blood pressure, lipid profiles, renal and liver function tests. Logistic regression assessed associations between ethnicity, socioeconomic deprivation, and clinical target attainment. Results: The mean age was 56.5 ± 12.4 years, and 86.8% of the cohort were overweight or obese. Overall, only 46.3% achieved the HbA1c target (<53 mmol/mol) in their most recent test, with Māori (OR 1.35) and Pacific (OR 1.84) ethnicities, higher deprivation, obesity, and younger age independently associated with elevated HbA1c. Hypertension affected two-thirds of participants (71.9% above target), notably Asians and Pacific peoples. Māori and Pacific peoples had over twice the odds of renal impairment and were 2.5 times more likely to have elevated albumin-to-creatinine ratios. Abnormal liver function test decreased with age (OR ≤ 0.65), though Asians had over twice the odds of elevated ALT and AST compared to Europeans. Conclusions: Significant ethnic inequities exist in glycaemic and clinical target attainment among people with T2D in NZ. These findings highlight critical gaps in diabetes management and underscore the urgent need for targeted, equity-focused interventions addressing both socioeconomic and ethnic disparities to improve outcomes and reduce health inequities.

1. Introduction

Type 2 diabetes (T2D) is one of the most common chronic diseases managed in primary healthcare, affecting one in 18 people in Aotearoa New Zealand (NZ) [1]. T2D prevalence has been rising consistently and is estimated to climb significantly over the next several decades [2], as with other countries [3,4]. In NZ, there is strong evidence of racial and ethnic differences in T2D prevalence and outcomes [5]. Compared to European New Zealanders, Māori (Indigenous to NZ) and Pacific Islanders are diagnosed younger [1], have greater rates of complications [6], and their diseases often progress more quickly [5] due to both patient and system-level challenges, including institutional racism and unconscious bias within healthcare delivery, which contribute to ethnic inequities in outcomes [7,8]. Research also highlights that systemic and structural factors within the health system can reduce access to timely, culturally safe, and responsive care for Māori [9]. Limited access to healthcare services [10], long wait times for specialist care [11], and transportation barriers that make it difficult to attend medical appointments can make managing diabetes more challenging [12]. Socioeconomic factors such as income inequality, food insecurity, and unstable housing [13] further increase the risk of complications, making it harder for many individuals to maintain good health. However, recent data exploring inequities in T2D management are currently lacking, and updated reviews of large health datasets are required.

One of the main obstacles to evaluating the clinical management of T2D in NZ is the absence of thorough reporting [14]. The national Virtual Diabetes Register (VDR) is considered to be a pivotal source of information about diabetes in NZ. It contains basic demographic details and records of diabetes-related service use drawn from national datasets (e.g., whether a person had a diabetes-related laboratory test, retinal screening, or received diabetes medications). However, whilst it provides robust annual prevalence estimates, it does not include clinical information (such as HbA1c values) or details on diabetes type [1]. Clinical end-point measures such as HbA1c, blood pressure, lipids, and renal and liver function tests are essential to assess the quality of T2D management, identify areas of greatest need, and evaluate the risk of complications [15]. These indicators provide a robust framework to quantify ethnic inequities and inform the design of culturally safe and targeted interventions. To date, studies including clinical data have been limited in NZ, with data being either sourced from small primary care populations or from older studies [14]. Further, the diabetes prescribing landscape has changed dramatically in recent years with the funded availability of sodium–glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1RA) agents since February 2021 [16]. Recent data suggest that at least half of all eligible patients are now prescribed these agents [17], which in turn may have significantly improved markers of glycaemia, cardiovascular, and/or renal disease [18].

Thus, an updated picture of the state of diabetes care in NZ is needed, particularly regarding whether the proportion of patients meeting recommended clinical targets has improved over time. Accordingly, this study uses a large clinical primary care dataset to examine clinical outcomes among people with T2D, with a focus on differences across ethnic groups.

2. Materials and Methods

This cross-sectional, primary care-based study examined the clinical characteristics among adults with T2D in the Waikato and Auckland regions of NZ. Adults aged 18 to 75 years with a confirmed diagnosis of T2D in February 2021 were included in the study. The most recent patient data, including socioeconomic and clinical measures, were sourced from four Primary Healthcare Organisations (PHOs; Pinnacle, ProCare, National Hauora Coalition (NHC), and Hauraki). Data collected from the PHOs covered the period from February 2021 to December 2023.

Sociodemographic variables included age, gender, self-reported ethnicity (as recorded in the primary care record), and socioeconomic deprivation, which was assessed using NZDep18, an area-level index of disadvantage from 1 (least deprived) to 10 (most deprived) based on income, employment, education, housing, and access to services [19]. All values were collated during the study period for HbA1c, body mass index (BMI; categorised based on the CDC classification [20], with ethnicity-specific cut-offs applied for Asian, Māori, and Pacific patients [21,22]; estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (ACR; further categorised into moderately increased albuminuria (microalbuminuria) and severely increased albuminuria (macroalbuminuria)); blood pressure (systolic and diastolic); lipid profile measures (total cholesterol, triglycerides, low-density lipoprotein (LDL), and high-density lipoprotein (HDL); and liver function tests (alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and γ-Glutamyl-transferase (GGT) and were categorised into elevated or normal values [23]. Most recent values of each were used for analysis. For variables with missing or incomplete data, a complete-case approach was used, including only participants with available data for each analysis.

Continuous variables, such as clinical measures, are summarised using means and standard deviations, while categorical variables, such as sociodemographic data, are presented as frequencies and percentages. Chi-square tests were used to assess associations between sociodemographic variables and the proportion of individuals achieving target clinical measures. These measures included glycaemic control (HbA1c < 53 mmol/mol), blood pressure control (<130/80 mmHg), lipid targets (total cholesterol < 4 mmol/mol, LDL < 2.0 mmol/L, HDL > 1.0 mmol/L, triglycerides < 1.7 mmol/L), renal function (eGFR ≥ 60 mL/min/1.73 m², ACR < 3 mg/mmol, moderately increased albuminuria 3–30 mg/mmol and severely increased albuminuria >30 mg/mmol) and liver function (ALT < 45 U/L, AST < 45 U/L, ALP 40–130 U/L, GGT < 50 for females, <60 for males) [23]. Odds ratios were calculated using logistic regression to assess associations between sociodemographic factors and clinical measures. Covariates included in the multivariate models were age, sex, ethnicity, and socioeconomic deprivation. All analyses were conducted using SPSS Version 30.0; statistical significance is cited at p < 0.05.

3. Results

This cohort included 57,734 patients with T2D, with a mean age of 56.5 ± 12.4 years. Over half (54.7%) resided in the most deprived areas of New Zealand (NZDep 6–10), as shown in

Table 1. Demographics and proportion meeting clinical targets in adults with T2D (n = 57.734).

	Total		At Target (%)												Present (%)
	N	%	HbA1c	eGFR	ACR	Blood Pressure	Cholesterol	Triglyceride	LDL	HDL	ALT	AST	ALP	GGT	Moderately Increased Albuminuria	Severely Increased Albuminuria
Total	57,734	100	46.3	88.1	64.4	32.0	40.8	43.3	40.1	78.0	83.9	83.7	83.5	72.9	26.9	9.5
Ethnicity
Asian	10,973	19.0	50.8	85.5	69.2	28.1	45.5	46.6	43.8	80.2	78.8	72.5	86.4	76.5	24.8	6.7
European	23,255	40.3	50.5	84.2	72.9	36.6	39.3	44.4	40.3	77.5	83.3	83.4	83.8	75.6	22.3	5.5
Māori	11,554	20.0	43.1	84.2	51.8	29.3	35.3	39.3	34.4	77.3	83.2	80.4	81.2	79.2	33.7	15.3
MELAA	844	1.5	50.7	85.8	76.1	31.1	38.5	47.3	34.8	77.0	86.9	86.8	83.0	65.1	18.9	5.7
Others	475	0.8	48.1	86.6	69.1	34.7	36.5	44.7	35.2	78.4	82.0	74.1	76.3	81.3	23.3	8.3
Pacific	10,633	18.4	35.7	84.4	53.3	28.1	44.2	47.3	42.4	77.5	84.3	83.1	81.5	69.4	32.5	14.9
p-value			<0.001	0.043	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	0.538	<0.001	<0.001	<0.001
Age
18–44	10,040	17.4	40.5	84.9	63.2	15.6	25.4	41.0	23.0	74.4	75.5	77.3	80.9	72.4	27.3	10.2
45–59	20,849	36.1	42.2	84.7	66.0	28.4	35.9	39.8	35.5	78.5	81.4	82.3	80.6	69.8	25.5	9.1
60–75	26,845	46.5	51.7	84.3	63.5	38.8	49.5	46.4	49.8	78.9	89.4	87.4	86.7	75.5	27.9	9.4
p-value			<0.001	0.316	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	0.018
Gender
Female	26,317	45.6	47.3	84.6	66.5	32.2	32.8	42.8	33.7	86.2	88.1	84.9	82.1	72.1	26.2	8.1
Male	31,406	54.4	45.6	84.5	62.5	31.8	47.3	43.7	45.4	71.1	80.4	82.6	84.7	73.5	27.5	10.6
p-value			<0.001	0.894	<0.001	0.399	<0.001	0.158	<0.001	<0.001	<0.001	0.001	0.012	0.097	<0.001	<0.001
NZDep18
1–2	6228	12.1	51.7	84.5	72.7	33.9	41.2	46.5	42.5	79.7	82.0	79.4	87.8	77.3	22.7	5.4
3–4	8123	15.8	48.4	84.9	70.4	33.5	41.6	45.9	42.2	78.5	82.1	81.9	84.1	75.5	23.7	6.6
5–6	8955	17.4	47.1	84.9	67.4	33.6	40.4	44.3	41.0	78.9	82.4	81.9	84.5	75.3	25.4	8.0
7–8	11,968	23.2	44.7	84.2	63.1	32.9	40.8	43.8	41.0	78.0	85.4	85	84.1	72.8	27.4	10.1
9–10	16,208	31.5	40.2	84.3	55.4	30.1	41.9	40.6	40.5	76.8	85.3	85.4	80.8	67.9	31.7	13.7
p-value			<0.001	0.584	<0.001	<0.001	0.238	<0.001	0.032	<0.001	<0.001	<0.001	0.009	<0.001	<0.001	<0.001

Clinical targets: HbA1c < 53 mmol/mol; blood pressure < 130/80 mmHg; cholesterol < 4 mmol/mol; LDL < 2.0 mmol/L; HDL > 1.0 mmol/L; triglycerides < 1.7 mmol/L; eGFR ≥ 60 mL/min/1.73m²; ACR < 3 mg/mmol; moderately increased albuminuria 3–30 mg/mmol; severely increased albuminuria > 30 mg/mmol; ALT < 45 U/L; AST < 45 U/L; ALP 40–130 U/L; GGT < 50 for females, <60 for males [23].

. Rates of overweight and obesity were high overall (86.8%; n = 50,113). Asians had the lowest rate of overweight/obesity (69.6%, n = 7637) while Māori (n = 10,860) and Pasifika (n = 9995) had the highest rates (both 93.9%; p < 0.001).

The mean HbA1c for the whole cohort was 59.9 ± 17.6 mmol/mol, with 46.3% of participants achieving the recommended target of <53 mmol/mol (Table 1). Increasing socioeconomic deprivation was negatively associated with the proportion of patients attaining the target HbA1c (p < 0.001). Further regression analysis showed a trend of increasing odds of HbA1c above target with greater sociodemographic deprivation, with individuals in the most deprived areas having 1.5 times higher odds above target compared to those in the least deprived areas (p < 0.001;

Table 2. Odds ratio for being above the target of clinical measurements.

	HbA1c	Blood Pressure	ACR	Moderately Increased Albuminuria	Severely Increased Albuminuria	eGFR	Triglyceride	LDL	Low HDL	Cholesterol	ALT	AST	ALP	GGT
Ethnicity
European	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref
Asian	0.99 (0.95–1.04)	1.48 (1.39–1.57) *	1.20 (1.14–1.26) *	1.15 (1.08–1.21) *	1.24 (1.13–1.37) *	0.91 (0.84–0.97) **	0.91 (0.84–0.99) **	0.87 (0.83–0.91) *	0.85 (0.81–0.90) *	0.78 (0.74–0.82) *	2.24 (1.41–3.57) *	4.67 (1.21–18.04) *	0.53 (0.06–5.06)	0.88 (0.43–1.78)
Māori	1.35 (1.29–1.41) *	1.39 (1.31–1.48) *	2.51 (2.39–2.64) *	1.77 (1.68–1.87) *	3.12 (2.88–3.39) *	1.00 (0.94–1.07)	1.23 (1.17–1.31) *	1.29 (1.23–1.35) *	1.02 (0.96–1.07)	1.19 (1.13–1.25) *	0.39 (0.30–0.52) *	0.62 (0.33–1.17)	0.63 (0.22–1.77)	2.60 (1.56–4.33) *
MELAA	0.99 (0.87–1.14)	1.28 (1.07–1.53) **	0.85 (0.72–1.00)	0.81 (0.67–0.97) **	1.05 (0.77–1.43)	0.88 (0.71–1.09)	0.89 (0.68–1.16)	1.27 (1.09–1.27) **	1.03 (0.87–1.22)	1.04 (0.89–1.21)	1.04 (0.32–3.40)	0.48 (0.01–17.05)	0.11 (0.00–130.64)	0.48 (0.08–2.92)
Others	1.10 (0.92–1.33)	1.09 (0.86–1.37)	1.20 (0.98–1.48)	1.06 (0.85–1.33)	1.55 (1.09–2.21) **	0.83 (0.62–1.11)	0.99 (0.80–1.23)	1.24 (1.02–1.51) **	0.95 (0.76–1.19)	1.13 (0.92–1.38)	1.36 (0.56–3.31)	0.78 (0.05–13.32)	0.22 (0.00–141.82)	0.32 (0.08–1.26)
Pacific	1.84 (1.75–1.93) *	1.48 (1.39–1.58) *	2.36 (2.24–2.48) *	1.68 (1.59–1.77) *	3.03 (2.79–3.29) *	0.99 (0.92–1.06)	0.89 (0.80–1.00) **	0.92 (0.87–0.96) *	1.00 (0.94–1.06)	0.82 (0.78–0.86) *	0.50 (0.29–0.87) *	0.90 (0.26–3.15)	0.71 (0.09–5.66)	0.91 (0.37–2.27)
Age
18–44	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref
45–59	0.93 (0.89–0.98) **	0.47 (0.43–0.51) *	0.88 (0.84–0.93) *	0.91 (0.86–0.97) **	0.88 (0.81–0.96) **	1.02 (0.95–1.09)	1.06 (0.98–1.14)	0.54 (0.51–0.58) *	0.80 (0.75–0.84) *	0.61 (0.57–0.65) *	0.37 (0.27–0.51) *	0.39 (0.19–0.79)	1.07 (0.36–3.16)	1.11 (0.73–1.71)
60–75	0.64 (0.61–0.67) *	0.29 (0.27–0.32) *	0.99 (0.94–1.04)	1.03 (0.98–1.09)	0.92 (0.84–0.99) **	1.05 (0.98–1.12)	0.81 (0.75–0.86) *	0.30 (0.29–0.32) *	0.78 (0.73–0.82) *	0.35 (0.33–0.37) *	0.15 (0.11–0.20) *	0.28 (0.14–0.58)	0.65 (0.20–2.09)	0.61 (0.38–0.96) *
Gender
Female	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref
Male	1.07 (1.04–1.11) *	1.02 (0.98–1.06)	1.19 (1.14–1.23) *	1.07 (1.03–1.11) *	1.35 (1.27–1.44) *	1.00 (0.96–1.05)	0.97 (0.92–1.01)	0.61 (0.59–0.63) *	2.55 (2.44–2.66) *	0.54 (0.52–0.56) *	2.82 (2.23–3.57) *	1.38 (0.80–2.39)	0.65 (0.27–1.55)	0.79 (0.54–1.17)
NZDep18
1–2	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref
3–4	1.14 (1.07–1.22) *	1.02 (0.94–1.11)	1.12 (1.03–1.21) **	1.05 (0.97–1.14)	1.25 (1.08–1.45) **	0.97 (0.88–1.07)	1.03 (0.93–1.14)	1.01 (0.95–1.08)	1.08 (0.99–1.17)	0.99 (0.92–1.06)	1.18 (0.74–1.87)	0.80 (0.25–2.58)	2.86 (0.30–27.53)	1.05 (0.51–2.15)
5–6	1.20 (1.13–1.28) *	1.01 (0.93–1.10)	1.28 (1.19–1.38) *	1.15 (1.06–1.25) *	1.53 (1.33–1.76) *	0.97 (0.88–1.07)	1.09 (0.99–1.20)	1.07 (0.99–1.14)	1.05 (0.97–1.14)	1.03 (0.96–1.11)	1.14 (0.73–1.78)	0.68 (0.22–2.07)	2.55 (0.29–22.21)	1.16 (0.58–2.30)
7–8	1.32 (1.24–1.41) *	1.05 (0.97–1.13)	1.55 (1.44–1.67) *	1.29 (1.19–1.39) *	1.99 (1.74–2.27) *	1.02 (0.93–1.12)	1.12 (1.02–1.22) **	1.06 (0.99–1.13)	1.11 (1.02–1.18) **	1.02 (0.95–1.09)	0.98 (0.64–1.49)	0.48 (0.17–1.40)	1.67 (0.20–14.02)	1.46 (0.75–2.84)
9–10	1.59 (1.5.0–1.69) *	1.19 (1.11–1.29) *	2.14 (2.00–2.29) *	1.58 (1.47–1.70) *	2.80 (2.47–3.18) *	1.01 (0.93–1.11)	1.28 (1.17–1.39) *	1.09 (1.02–1.15) **	1.19 (1.10–1.28) *	0.97 (0.92–1.04)	0.84 (0.55–1.27)	0.48 (0.17–1.35)	2.84 (0.34–23.75)	2.26 (1.14–4.45) *

* p-value < 0.05; ** p-value < 0.001 Clinical targets: HbA1c < 53 mmol/mol; blood pressure < 130/80 mmHg; cholesterol < 4 mmol/mol; LDL < 2.0 mmol/L; HDL > 1.0 mmol/L; triglycerides < 1.7 mmol/L; eGFR ≥ 60 mL/min/1.73m²; ACR < 3 mg/mmol; moderately increased albuminuria 3–30 mg/mmol; severely increased albuminuria > 30 mg/mmol; ALT < 45 U/L; AST < 45 U/L; ALP 40–130 U/L; GGT < 50 for females, <60 for males [23].

). In contrast, older age was associated with a higher likelihood of attaining HbA1c target, with patients aged 60–75 years having 36% lower odds of elevated HbA1c compared with those younger than 45 years (p < 0.001).

Across ethnic groups, approximately 50% of Asian, European, and MELAA (Middle Eastern, Latin American, and African) patients met HbA1c targets, compared with 43.1% of Māori and 35.7% of Pacific peoples (p < 0.001). Logistic regression analysis supported these findings, identifying Māori (OR 1.35, 95% CI 1.29–1.41, p < 0.001) and Pacific peoples (OR 1.84, 95% CI 1.75–1.93, p < 0.001) as having higher odds of elevated HbA1c compared to European (reference group; Table 2).

Hypertension was present in two-thirds of participants, with Asian and Pacific peoples having the highest rates of all ethnic groups (both 71.9% above target, p < 0.001; Table 1). Across all ethnicities, the likelihood of Māori and Pacific people developing hypertension was significantly higher by at least 28% compared to European participants (p < 0.05; Table 2). Further, increasing age was negatively associated with hypertension, with individuals aged 60–75 years less likely to have hypertension compared to those under 45 years (OR 0.29, 95% CI 0.27–0.32, p < 0.001).

The mean eGFR was 77.8 ± 17.3 mL/min/1.73m², with most participants (88%) reaching the target of ≥60 mL/min/1.73m². Approximately 64% of the cohort achieved the target UACR (<3 mg/mmol), with the lowest proportions at target observed among Māori (52%) and Pacific peoples (53%) (Table 1). Logistic regression showed Māori and Pacific people with T2D had 2.5 times higher odds of having elevated ACR levels compared to Europeans (p < 0.001).

Moderately increased albuminuria was present in 26.9% of the cohort, while severely increased albuminuria affected 9.5% of the cohort. Severely increased albuminuria was disproportionately observed among Māori (15.3%) and Pacific peoples (14.9%) compared with other ethnicities (p < 0.001; Table 1), with these groups having approximately three times the odds of severely increased albuminuria compared with Europeans (p < 0.001; Table 2).

The proportions of participants at target for liver function tests varied, with the highest for ALT (83.9%), AST (83.7%), and ALP (83.5%), while GGT had the lowest proportion at target (72.9%). Increasing age was a factor influencing ALT, AST, and GGT levels, with older participants having up to 35% reduced odds of having elevated liver test function values than those younger than 45 years of age (p < 0.001; Table 2). Māori and Pacific peoples were more likely than Europeans to meet clinical targets for ALT, whereas Asians had over twice the odds of elevated ALT and AST levels compared to Europeans (p < 0.001; Table 2).

4. Discussion

This study examined a large cohort of 57,734 patients with T2D and highlighted ethnic disparities in clinical outcomes, with Māori and Pacific peoples less likely to meet recommended targets and having higher odds of elevated clinical measures.

Fewer than half of all participants achieved the HbA1c target (<53 mmol/mol), with Māori and Pacific peoples overrepresented among those with suboptimal control and elevated albuminuria. Importantly, this is still being seen despite the funding of empagliflozin, dulaglutide from 2021 (and more recently liraglutide) under Special Authority criteria with prioritised access for Māori and Pacific peoples. Recent studies suggest that this policy change has possibly led to a shift toward more equitable diabetes care/improved access, as approximately half of eligible patients initiated on these therapies within 18 months of funding, with Māori and Pacific patients more likely to receive them compared with other groups [17,24]. However, clearly, further work is required to ensure that this translates to equitable clinical outcomes. Regardless, the inclusion of ethnicity within Special Authority Criteria has been an important policy step that appears to have reduced some inequities in access [17].

Importantly, our study demonstrates that there are significant ethnic differences in the proportion of patients meeting clinical targets, and the reasons for this are likely multifactorial. Several studies report on the inequities of healthcare access for Māori and Pacific people in NZ [9,10,25,26,27] and a Westernised healthcare system that often does not meet the cultural needs of these groups [27,28]. Optimal medication use is also essential for patients to have the greatest success in meeting clinical targets, yet clinical inertia often leads to reduced prescribing, particularly for newer medications [26]. Recent analyses using the same dataset have shown that while the introduction of Special Authority funding criteria has improved access to medications such as empagliflozin and dulaglutide, overall prescribing remains suboptimal [17,24]. Although Māori and Pacific peoples were more likely to be initiated on these therapies compared to other groups, a substantial proportion of clinically eligible patients across all ethnicities were not prescribed these agents, highlighting persistent gaps in treatment uptake.

Despite the availability of newer glucose-lowering therapies, the proportion of patients achieving HbA1c targets in NZ has remained relatively unchanged. In the current cohort, only 46.3% of participants achieved the HbA1c target (<53 mmol/mol), which is comparable to earlier national estimates where approximately 49% of Europeans, 30% of Māori, and 27% of Pacific peoples met glycaemic targets [5]. This suggests that while access to medications has improved in NZ, clinical inertia and systemic barriers continue to limit the full impact of these medications on population-level outcomes. This highlights the need for health system actions that extend beyond pharmacotherapy and address structural barriers to timely, effective, and culturally safe diabetes care.

Importantly, the proportion of participants meeting the HbA1c clinical target in this cohort (46.3%) is also consistent with international findings, where rates typically range from 35% to 55% depending on region and healthcare setting [29]. Further, the disparities observed among Māori and Pacific peoples reflect global trends seen in Indigenous populations. In countries such as the United States, Canada, and Australia, Indigenous communities experience earlier onset of T2D, suboptimal glycaemic levels, and higher rates of complications, including renal and liver disease, compared to non-minority populations [30,31,32]. These patterns highlight the need for equity-focused, culturally grounded interventions that address both clinical outcomes and broader structural determinants of health.

In addition, our results showed that Māori and Pacific patients were less likely to meet multiple clinical targets simultaneously, including blood pressure and lipid control, compared with NZ Europeans. Having multiple suboptimal clinical outcomes greatly increases the likelihood of cardiovascular morbidity and mortality in people with T2D [33]. The pattern observed in our cohort suggests that inequities are not confined to single measures but rather extend across the spectrum of cardiometabolic risk, potentially amplifying long-term complications and widening health gaps if not addressed.

We also observed ethic differences in obesity rates, with higher rates among Māori and Pacific people compared to other ethnicities. Most of the cohort were classified as overweight or obese, a well-established risk factor for T2D that contributes to insulin resistance and metabolic dysfunction [34]. NZ has among the highest rates of overweight and obesity globally, affecting 67% of adults, with prevalence even higher among Māori (77%) and Pacific peoples (89%) [35]. Given the strong association between obesity and adverse metabolic outcomes [36], these findings highlight the need to prioritise culturally appropriate weight management interventions within T2D care [37].

Reducing these disparities requires sustained and targeted approaches. Contributing factors include multimorbidity, therapeutic inertia, structural barriers to care, and inconsistent engagement between patients and health services [26]. Potential strategies include Māori and Pacific health support workers within diabetes services, strengthening clinician capability in managing multimorbidity and equity-focused care, and supporting patient and whānau engagement [10,28,38]. Additionally, embedding co-designed care models and community-based intervention pathways within diabetes services could support earlier intervention, improve treatment adherence, and help ensure that clinical advances translate into equitable outcomes [28,39]. Improving prescribing equity for both established and newer medications must be combined with culturally safe models of care to ensure that pharmacological advances translate into improved outcomes for marginalised groups in NZ [27,40].

Limitations

This study has several limitations. First, the cross-sectional design prevents causal interpretation and only provides a snapshot of clinical outcomes at a single point in time. Second, while we used the most recent biomarker value to ensure consistency across the cohort, this approach may oversimplify individual disease trajectories and does not capture longitudinal variation in glycaemia, blood pressure, or lipid control. Third, the dataset did not include information on medication adherence, duration of diabetes, or comorbidity burden, which limits our ability to fully explore mechanisms underlying the observed ethnic disparities. Future longitudinal studies are needed to track changes in clinical outcomes over time, better understand causal relationships, and clarify the factors driving these disparities.

5. Conclusions

This study highlights the link between obesity, socioeconomic deprivation, and ethnic disparities in T2D, particularly among Māori and Pacific peoples. Addressing these inequities requires targeted interventions that improve healthcare access, support healthier lifestyles, and reduce socioeconomic barriers by supporting culturally tailored healthcare interventions. Future research should explore long-term trends and the impact of tailored strategies to improve outcomes.