Diabetes will be one of the defining health problems of the 21st century. The prevalence of diabetes has been steadily increasing in New Zealand over the last 30 years,1-4 with annual health system costs predicted to reach $1 billion in 2016.5 Diabetes prevention and treatment needs to be built not only on a foundation of robust epidemiology, but also evidenced-based interventions.Two recent sources of diabetes prevalence in New Zealand are the Virtual Diabetes Register and the 2013/14 New Zealand Health Survey. The Virtual Diabetes Register was compiled by the Ministry of Health, using nationwide information on hospital admissions, outpatient clinic attendance, medication prescribing, laboratory tests, and Primary Health Organisation (PHO) enrolments.3,6 In 2013, the Register estimated that there were 243,125 people with diagnosed diabetes. Data available from the 2013/14 New Zealand Health Survey showed a lower total diabetes prevalence of 5.5% (approximately 198,000 adults) of the adult population (515 years old).2 This lower rate may be due to the data being based on self-identification of diabetes. There was a clear increase in diabetes prevalence with increasing age.2 Men were substantially more likely than women to have diabetes, and there were also marked ethnic inequities, with Pacific, Mori and Asian people bearing a disproportionate burden.2 There was also evidence of a socio-economic gradient, with people living in the most deprived quintile of neighbourhoods (as defined by the New Zealand Deprivation Index 20137) having a prevalence of 7.9%, compared with those living in the least deprived quintile having a diabetes prevalence of 4.9%.2The Auckland Region is the largest metropolitan area in New Zealand, with a population of 1.5 million people, which constitutes approximately one-third of the national population.8 About 90% of its residents live in urban areas. This region is the most ethnically diverse in New Zealand, with the largest population of Mori and Pacific peoples.9 Diabetes prevalence in the Auckland region is higher than the national estimate (9%10 vs 5.5%2), and higher in the Counties Manukau District Health Board (DHB) catchment than the two other Auckland regional DHBs.10,11 In order to focus efforts towards reducing the burden of diabetes for those most in need, a better understanding of the geography of diabetes in Auckland is required. This study aimed to map the prevalence of diabetes in the Auckland region by General Electoral Districts (GEDs) stratified by age, gender and ethnicity. Using InstantAtlas\u2122 mapping software we present variations in the prevalence of diabetes among adults aged 530 years, which are freely available from www.fmhs.auckland.ac.nz/view-maps.MethodsEvery New Zealand resident has a unique health identifier, the National Health Index (NHI) number, which is used consistently across patient medical records within the health and disability support sectors.12We used encrypted NHIs (eNHIs) to anonymously link nationally held datasets that record a patient s interaction with New Zealand s universal health care system including PHOs, hospital discharges and mortality. We developed a regional cohort comprising the residing population aged 30 years and above enrolled in an Auckland regional PHO in the third quarter of 2011. We excluded the population aged below 30 years, consistent with our wider research programme, which focuses on the cardiovascular disease risk.13-15 People with diagnosed diabetes were identified from inpatient events in the national hospitalisation database (the National Minimum Data Set, NMDS), with a primary or secondary diagnosis coded under the ICD 10 AM classification system as: E10 to E14 Diabetes mellitus. They were also identified from the following classes of dispensed pharmaceuticals: oral hypoglycaemic agents (eg, Gliclazide, Metformin hydrochloride, Tolbutamide); hyperglycaemic agents (eg, Glucagon hydrochloride); and insulin preparations. Participants were eligible for inclusion in this study if they were: aged 530 years at 1 July 2011; were enrolled in any PHO within the Auckland Region between 1 July and 30 September 2011; and had complete information regarding age, gender, ethnicity, residential address, diabetes status (diagnosed/undiagnosed) as at 30 September 2011, and area deprivation. Residential address can be assigned to a meshblock, the smallest level of aggregation available for the analysis of census and other social data, designed to nest neatly within GEDs. The residential address was also used to assign New Zealand Index of Deprivation (NZDep2006) scores to each participant. NZDep2006 is a small-area measure of social conditions derived from nine variables from the 2006 Census.16 Typically, the deprivation scores assigned to a meshblock are categorised into deciles, with Decile 1 representing the 10% of least deprived meshblocks and Decile 10 depicting the most deprived 10% of meshblocks nationally. In this study, we collapsed the deciles into quintiles, each representing 20% of meshblocks, with quintile 5 representing the most deprived 20% of meshblocks in New Zealand.We excluded potentially eligible participants who were aged below 30 years, had died during the study period, missing geo-locators, and those who lived in the Northland and Waikato GEDs, whose boundaries overlap with the geographic limits of the Auckland Region.We used the Ministry of Health s protocol for prioritising ethnicity codes into Mori , Pacific, Indian, and New Zealand European and Other (NZEO) ethnic groups. According to the MOH protocols, the Indian ethnic group is a subset of the broader Asian ethnic category. However, given the CVD risk among South Asians is substantially higher than for other Asian sub-populations such as Chinese, Korean or Japanese, we separated Indian patients from Other Asians. Moreover, the Other Asian ethnic group was combined with the NZEO ethnic group. Statistical analysis was performed using SAS version 9.4.17 We report prevalence estimates and corresponding 95% confidence intervals. Population age- and gender-specific denominators were available from Statistics New Zealand s mid-year population estimates for 2011 by GED, and we used the World Health Organization s Standard Population to calculate age-sex standardised diabetes prevalence rates for the cohort overall and for gender by GED. Ethnic-specific population estimates were only available for the Mori, Pacific, or Other ethnic groupings, and the Other Ethnic group was used as the standard population. Unadjusted age-specific estimates were also calculated by GED. The GEDs were defined in 2007 and were used for the 2007 and 2011 general elections, and are designed by legislation to be areas of roughly equal population size, with an average of 60,000 residents.We use the extremal quotients (EQ) to measure variation within each ethnic or age group. The EQ is calculated as the ratio of the highest value to lowest values and its interpretation is similar to the relative risk; the larger the EQ, the larger the inequality.18To further investigate trends by GED, we modelled the likelihood of people having diabetes, controlling for their age, gender, ethnicity and neighbourhood deprivation to determine whether geographical variation remained. Given Indian people have a higher risk of CVD and diabetes-related events, the logistic regression models considered four ethnic groups: NZEO, Mori, Pacific and Indian.This research is part of the Auckland Region Vascular Atlas study and ethical approval was granted by the Northern X Regional Ethics Committee in 2010 (NXT/10/EXP/224).ResultsWe obtained data on 798,238 people enrolled in PHOs within the Auckland Region in Quarter 3, 2011 for this study. Figure 1 shows that there were 738,687 people aged 30 years and over living in the Auckland region in 2011, of whom 63,014 (8.5%) had diabetes, giving an age-standardised prevalence of 7.5%.Figure 1: Eligibility flowchart.Table 1 shows the prevalence of people diagnosed with diabetes, stratified by age, gender and ethnicity. The number of cases peaked among people aged 60 to 64 years, with 8,548 people with diagnosed diabetes, however the highest age-specific proportions were seen in those aged 70 to 74 and 75 to 79 years (both 15.6%). There was marked variation in diabetes by ethnicity with age-standardised prevalence rates of 10.3% among Mori, 15.8% among Pacific, and 6.3% among NZEO.\r\nTable 1: Number of people aged 30 years and over with diagnosed diabetes in the Auckland region in 2011 by age, gender and ethnicity (unadjusted). \r\n \r\n \r\n \r\n \r\n Diagnosed Diabetes\r\n \r\n Enrolled PHO Study Population (N)\r\n \r\n \r\n \r\n Population (N)\r\n \r\n %\r\n \r\n \r\n \r\n Total\r\n \r\n 63,014\r\n \r\n 8.5\r\n \r\n 738,687\r\n \r\n \r\n \r\n 30 to 74 years\r\n \r\n 54,345\r\n \r\n 7.9\r\n \r\n 684,438\r\n \r\n \r\n \r\n Gender\r\n \r\n \r\n \r\n Male\r\n \r\n 31,194\r\n \r\n 9.0\r\n \r\n 345,346\r\n \r\n \r\n \r\n Female\r\n \r\n 31,820\r\n \r\n 8.1\r\n \r\n 393,341\r\n \r\n \r\n \r\n Age group\r\n \r\n \r\n \r\n 30 to 34\r\n \r\n 2,546\r\n \r\n 2.8\r\n \r\n 89,793\r\n \r\n \r\n \r\n 35 to 39\r\n \r\n 3,502\r\n \r\n 3.6\r\n \r\n 97,305\r\n \r\n \r\n \r\n 40 to 44\r\n \r\n 4,733\r\n \r\n 4.7\r\n \r\n 100,405\r\n \r\n \r\n \r\n 45 to 49\r\n \r\n 6,287\r\n \r\n 6.4\r\n \r\n 98,409\r\n \r\n \r\n \r\n 50 to 54\r\n \r\n 7,423\r\n \r\n 8.7\r\n \r\n 84,987\r\n \r\n \r\n \r\n 55 to 59\r\n \r\n 8,014\r\n \r\n 11.3\r\n \r\n 70,887\r\n \r\n \r\n \r\n 60 to 64\r\n \r\n 8,548\r\n \r\n 13.6\r\n \r\n 62,690\r\n \r\n \r\n \r\n 65 to 69\r\n \r\n 7,204\r\n \r\n 15.9\r\n \r\n 45,421\r\n \r\n \r\n \r\n 70 to 74\r\n \r\n 6,088\r\n \r\n 17.6\r\n \r\n 34,541\r\n \r\n \r\n \r\n 75 to 79\r\n \r\n 4,164\r\n \r\n 17.6\r\n \r\n 23,669\r\n \r\n \r\n \r\n 80 to 84\r\n \r\n 2,753\r\n \r\n 16.0\r\n \r\n 17,174\r\n \r\n \r\n \r\n 585\r\n \r\n 1,752\r\n \r\n 13.1\r\n \r\n 13,406\r\n \r\n \r\n \r\n Ethnicity\r\n \r\n \r\n \r\n Mori\r\n \r\n 6,048\r\n \r\n 12.3\r\n \r\n 49,052\r\n \r\n \r\n \r\n Pacific\r\n \r\n 16,171\r\n \r\n 19.5\r\n \r\n 82,758\r\n \r\n \r\n \r\n Indian\r\n \r\n 7,399\r\n \r\n 17.4\r\n \r\n 42,521\r\n \r\n \r\n \r\n NZEO\r\n \r\n 33,396\r\n \r\n 5.9\r\n \r\n 564,300\r\n \r\n \r\n \r\nTable 2 presents the age-standardised rates of diabetes overall and by gender with age-specific rates and EQ, for each GED in the Auckland region. A customisable online version of this data with the accompanying maps is available to access from http://view.ac.nz/AKL_Diabetes_Prevalence_SingleMap/.\r\nTable 2: Prevalence rates (%) of diagnosed diabetes by General Electoral District (GED) in Auckland in 2011.** Note that the World Health Organization s Standard Population was used to calculate age-sex standardised diabetes prevalence rates for the cohort overall and for gender by GED. Therefore the rates for these groups may differ slightly from the (unadjusted) rates reported in Table 1.We found marked geographical variation in the prevalence of diabetes with the highest rates in GEDs in the south (Mangere, Manakau East and Manurewa, Mt Roskill and to a lesser extent Maungakiekie, Botany and Papakura) and lowest rates in GEDs in central and north Auckland (Auckland Central, Epsom, North Shore, and East Coast Bays). The highest diabetes prevalence was 17.3% in Mangere and the lowest was 3.2% on the North Shore, resulting in an EQ of 4.5. The GEDs on the urban/rural fringe also had lower rates, including Rodney, Helensville and Hunua. The west Auckland areas of Waitakere, Te Atatu and New Lynn had intermediate levels of diabetes.Overall, males had a higher rate of diabetes than females. There was considerable variation in the prevalence of diabetes both within and between ethnic groups. Overall, Pacific participants had the highest rate of diabetes (15.8%), more than two-and-a-half times greater than for the NZEO population (6.3%). Controlling for other socio-demographic variables, we found that the Indian people were nearly four times as likely as NZEO people to have diabetes (OR 3.85 [3.73 to 3.97])\u2014significantly higher than both Pacific and Mori .Table 2 suggests that the geography of diabetes by GED shows a remarkably consistent trend, in which residents in the Mangere GED have significantly more diabetes cases than other GEDs in Auckland. To move beyond univariate analyses however, this trend was further confirmed in our adjusted model in Table 3. We also modelled the geographic variation in the prevalence of diabetes using the North Shore GED as the reference, since this GED had the lowest diabetes prevalence for the total population. Figure 2 shows that after controlling for age, gender, ethnicity and deprivation, substantial geographical variations in diabetes remain. While the odds of people in the Rodney (OR: 0.98 [0.91 to 1.05]) and Auckland Central (OR: 1.01 [0.94 to1.09]) GEDs having diabetes was not significantly different to residents in the North Shore GED, the odds of people living in South Auckland GEDs having diabetes are at least 65% higher than North Shore GED residents. In the Papakura GED, for example, the adjusted odds ratio was 1.65 (1.55 to 1.75), increasing to 1.79 (1.69 to 1.91) in Manurewa, 1.84 (1.72 to 1.96) in Botany, and being highest in Mangere at 1.87 (1.76 to 2.00).\r\nTable 3: Unadjusted and adjusted* odds ratios of the likelihood of people aged 30 years and over having diagnosed diabetes in the Auckland Region, in Quarter 3 2011.\r\n \r\n \r\n \r\n \r\n \r\n Unadjusted Model\r\n \r\n Adjusted Model*\r\n \r\n \r\n \r\n \r\n \r\n Odds Ratio\r\n \r\n 95% CI\r\n \r\n Odds Ratio\r\n \r\n 95% CI\r\n \r\n \r\n \r\n Age Group\r\n \r\n \r\n \r\n 30\u201334\r\n \r\n 0.21\r\n \r\n 0.20\u20130.22\r\n \r\n 0.14\r\n \r\n 0.14\u20130.15\r\n \r\n \r\n \r\n 35\u201344\r\n \r\n 0.31\r\n \r\n 0.30\u20130.32\r\n \r\n 0.24\r\n \r\n 0.23\u20130.25\r\n \r\n \r\n \r\n 45\u201354\r\n \r\n 0.57\r\n \r\n 0.56\u20130.59\r\n \r\n 0.50\r\n \r\n 0.49\u20130.52\r\n \r\n \r\n \r\n 55\u201364\r\n \r\n REF\r\n \r\n \r\n \r\n REF\r\n \r\n \r\n \r\n \r\n \r\n 65\u201374\r\n \r\n 1.41\r\n \r\n 1.37\u20131.44\r\n \r\n 1.53\r\n \r\n 1.49\u20131.57\r\n \r\n \r\n \r\n 75\u201384\r\n \r\n 1.44\r\n \r\n 1.40\u20131.49\r\n \r\n 1.71\r\n \r\n 1.66\u20131.77\r\n \r\n \r\n \r\n 85+\r\n \r\n 1.06\r\n \r\n 1.01\u20131.12\r\n \r\n 1.45\r\n \r\n 1.37\u20131.53\r\n \r\n \r\n \r\n Gender\r\n \r\n \r\n \r\n Female\r\n \r\n REF\r\n \r\n \r\n \r\n REF\r\n \r\n \r\n \r\n \r\n \r\n Male\r\n \r\n 1.13\r\n \r\n 1.11\u20131.15\r\n \r\n 1.17\r\n \r\n 1.15\u20131.19\r\n \r\n \r\n \r\n Ethnicity\r\n \r\n \r\n \r\n NZEO\r\n \r\n REF\r\n \r\n \r\n \r\n REF\r\n \r\n \r\n \r\n \r\n \r\n Mori\r\n \r\n 2.24\r\n \r\n 2.17\u20132.30\r\n \r\n 2.35\r\n \r\n 2.28\u20132.43\r\n \r\n \r\n \r\n Pacific\r\n \r\n 3.86\r\n \r\n 3.78\u20133.94\r\n \r\n 3.55\r\n \r\n 3.46\u20133.64\r\n \r\n \r\n \r\n Indian\r\n \r\n 3.35\r\n \r\n 3.26\u20133.44\r\n \r\n 3.85\r\n \r\n 3.73\u20133.97\r\n \r\n \r\n \r\n NZDep06 Quintiles\r\n \r\n \r\n \r\n Q1 (Least deprived)\r\n \r\n REF\r\n \r\n \r\n \r\n REF\r\n \r\n \r\n \r\n \r\n \r\n Q2\r\n \r\n 1.25\r\n \r\n 1.21\u20131.29\r\n \r\n 1.21\r\n \r\n 1.17\u20131.25\r\n \r\n \r\n \r\n Q3\r\n \r\n 1.61\r\n \r\n 1.56\u20131.66\r\n \r\n 1.45\r\n \r\n 1.41\u20131.50\r\n \r\n \r\n \r\n Q4\r\n \r\n 2.27\r\n \r\n 2.21\u20132.34\r\n \r\n 1.68\r\n \r\n 1.62\u20131.73\r\n \r\n \r\n \r\n Q5 (Most deprived)\r\n \r\n 3.46\r\n \r\n 3.37\u20133.55\r\n \r\n 1.93\r\n \r\n 1.87\u20131.99\r\n \r\n \r\n \r\n* controlling for age, gender, ethnicity, deprivation and GED.Figure 2: Geography matters. Geographical variation in the odds ratios of patients being diagnosed with diabetes in the Auckland region by GED in Quarter 3 2011, controlling for age, gender, ethnicity, and deprivation (Note: N.P.= No Population).DiscussionThis study investigated the prevalence of diabetes in the greater Auckland Region in 2011 when an estimated total of 63,014 people had (diagnosed) diabetes. Although previous studies have not used the GED as their geographical area, the GEDs experiencing the greatest burden of diabetes fall within the boundaries of the Counties Manukau DHB, as has been noted previously.10,11,19 This study reveals that residents of the Mangere GED are particularly affected, having the highest rates of diabetes across all strata of the variables we assessed. We also found significant variation by age, gender, and ethnicity. By age, the highest prevalence was amongst 75 to 79 year olds (15.6%) and there was substantial variation within age groups by GED. The largest differences were seen in the 45 to 49 year olds, with the highest rate in the Mangere GED at 16.3%, and the lowest rate of 2.2% on the North Shore (EQ 7.6). We found that geography matters: the odds of people in South Auckland with diabetes was 65\u201387% higher than the odds of people living in the North Shore GED.Our findings are broadly consistent with previous research. We estimated an overall prevalence of 8.5% which is very similar to the estimates of Balalla10 (9%, using the same definitions, with people aged 530, for territorial authorities in the Auckland Region rather than GEDs) and Thornley19 (9.6%) using capture-recapture methodology in south Auckland in 2007; aged 515 and unadjusted. Our estimates were higher than those of Smith et al,11 who reported age- and sex-standardised diabetes prevalence estimates of 7.1% (included all age groups) in Counties Manukau DHB (CMDHB) and 5.2% for the other three northern DHBs (Auckland DHB, Waitemata DHB, Northland DHB) in 2006\u20137. Similar to previous research, our results show that the prevalence of diabetes increased significantly with age,2,4,10,11 and that women have slightly lower rates than men.10,19Investigating the ethnic trends of diabetes has been a focus of numerous research projects. A cross-sectional study of people aged 35\u201374 carried out in 2002\u20133 by Sundborn20 found significant ethnic inequalities: Pacific people had a prevalence of diabetes more than four times higher than New Zealand Europeans (n=4049). The overall prevalence for Pacific people with new and previously diagnosed diabetes was 4.0% and 19.5% respectively. The highest rates were found among Samoan men (26.2%) and Tongan women (35.8%). Smith et al11 found high rates among Pacific people in Auckland too, with Pacific women having the highest diabetes prevalence of all the groups measured (15.0%). In agreement with that research, we found that Pacific people had the highest age-standardised prevalence of diabetes (15.8%).Mori also face a high burden of diabetes. Balalla10 found that Auckland Mori were three times as likely to have diabetes than NZEO people. In CMDHB, Smith et al11 estimated the age-standardised prevalence to be 12.2% for Mori men, compared to 5.0% of NZEO men, and 10.6% for Mori women, compared to 4.0% of NZEO women. Our findings were similar, finding an age standardised prevalence of 10.3% for Mori.The geographical and ethnic inequities in diabetes prevalence in the Auckland region shown in this study are stark reminders that even in one relatively small metropolitan area, there can be huge variation in rates of key health conditions. The reasons behind such variation are complex, and are most likely the result of a multitude of factors. While traditional explanations have focused on the development of unhealthy behaviours, such as excess calorie intake, physical inactivity and smoking in adulthood leading to obesity and diabetes, the social determinants of health that begin even before birth are increasingly being seen as important causal factors.21 These distal antecedents of diabetes include material deprivation (of the mother when pregnant, as well as material resources as people grow up and age), obesogenic environments (which promote calorie-dense nutrient-poor food, driven by urbanisation and globalisation), psychosocial stress (which relates to both the neuroendocrine mechanisms of stress and the more indirect path of increasing the likelihood of the development of unhealthy behaviours), and access to health care.21,22 Addressing these factors presents a number of challenges and requires concerted action across a range of government departments and services.Looking internationally, Fano et al23 investigated the link between type 2 diabetes prevalence and deprivation in Rome (n=27,642; aged 535), and found a social gradient, as has been reported in this study.23 Cox et al24 analysed type 2 diabetes by area in Tayside, Scotland (n=3,917; aged 45\u201375+). Interestingly, they found that neighbouring areas made a significant difference to an area s incidence of diabetes. That is, areas with less deprived areas around them had lower rates of diabetes, and areas with more deprived areas around them had higher rates. They propose this may be due to a variety of factors, in which less deprived areas have better access to healthy food options, outdoor areas (eg, parks), health care, as well as increased employment opportunities, all of which may also have influenced our results for Auckland.To our knowledge this is the first study to investigate diabetes prevalence by GED. The GEDs are of particular importance as these are the areas for which elected members of parliament stand. This has the advantage of allowing researchers, and the public, the opportunity to present politicians with data that directly relates to the area they represent. The GEDs allow the inequities in health outcomes to speak for themselves, so that politicians can represent their constituents when seeking to improve the health of their populations. We believe this innovative methodology could be a catalyst for change. Second, the data are robust and based on a large cohort. Using the encrypted NHIs and linking health databases is a reliable method for estimating the prevalence of diabetes, and avoids the drawbacks of relying on self-report.This study is not without its limitations. First, we did not stratify the data by deprivation level. While it has become customary to provide analysis of differences between socioeconomic groups, we felt it was inappropriate to do so in this study. The GEDs, although of equal size, are heterogeneous in their socioeconomic composition. That is, neighbourhoods within a GED can be substantially different making it difficult to have one value that represents the entire community. In the absence of individual-level indicators of socioeconomic position (SEP), NZDep06 was included as a proxy measure. Nevertheless, as NZDep06 measures the area-level social conditions rather than the circumstances of individuals, caution is required for its interpretation, given the implications of the ecological fallacy. Furthermore, as NZDep06 is a small-area measure of deprivation its aggregation to the GED would mask extreme levels of deprivation experienced within a GED s constituent neighbourhoods.Second, it should be noted that while our GED classification is based on the 2007 boundaries, the configuration of the GEDs changed for the 2014 election resulting in the splitting of some GEDs to create the new Upper Harbour and Kelston GEDs. This could potentially result in reduced political accountability, particularly where those boundary splits affect areas of higher diabetes prevalence. Our ongoing research program will explore geographic variations in diabetes and other factors associated with CVD nationally, mapped to the 2014 GEDs.Third, we were unable to account for cases of undiagnosed diabetes. It has been noted that rates of undiagnosed diabetes are in themselves inequitable, with research suggesting the highest prevalence of undiagnosed diabetes is found among Pacific people (6.4%), followed by Mori (2.2%), and NZEO (1.5%).4 Our results will have therefore not only underestimated the overall prevalence of diabetes, but also the extent of the ethnic and GED inequities.Fourth, our analysis only included people who were enrolled in a PHO. While the vast majority of people are indeed enrolled (an estimated 94% of the population in the Auckland region25), there is a chance that we are missing people with diabetes who do not have access to the primary health care system. While likely to be a small number of cases, this could further contribute to the underestimation of the prevalence of diabetes.Finally, there is some inherent difficulty in distinguishing between cases of pre-diabetes and diabetes. Increasingly, people with prediabetes are prescribed metformin, and by using medication to identify cases, this could have led to an over-estimation of the prevalence.This research highlights the extensive inequities of diabetes prevalence among the GEDs in the Auckland region. It is beyond the scope of this research to speculate on the local mechanisms that underlie this significant inequity, but further research into potential geographic factors such as food affordability and availability are required. For example, a recent systematic review found that taxing unhealthy foods in combination with subsidising healthy foods at sufficiently high rates has the potential to promote healthier eating and weight loss.26 Other interventions that have been shown to be cost-effective in reducing obesity include: reducing advertising of unhealthy food and beverages to children; and school-based programmes aiming to reduce viewing of television; improving knowledge about nutrition; increasing physical activity; and reducing consumption of sugar-sweetened beverages.27Researching these topics in the context of Aotearoa New Zealand, and subsequently implementing evidence-based practices could have a substantial impact on the increasing rates and associated inequities of d