Implications

5.4.1 Implications for Maori

Maori who participated in the Northland Diabetes Screening and Cardiovascular risk Assessment pilot in this practice, who had a normal baseline test, were less likely to progress to diabetes and die from any cause compared to those in the non-pilot group. Whether this was because pilot participants had inherently lower risk, were screened earlier in the disease progression, or because of early exposure to their blood glucose results being interpreted in the context of their CVD risk, was not able to be determined in this study.

Diabetes prevalence has been estimated to be around three times higher in Maori and Pacific people and diagnosis occurs at younger ages compared with the rest of New Zealand population (MoH, 2007; Joshy & Simmons, 2006). A family history diabetes, suggestive of a genetic susceptibility does not make diabetes inevitable (Mayer-Davis et al.,2011; Uusitupa et al., 2011). Pilot participants themselves could potentially have the greatest impact on reducing the incidence of diabetes in their community. How this is most likely to occur is through their influence within their families, friends and colleagues. They can influence others by strengthening social norms that have a protective effect against developing diabetes and premature death. Sharing the results of this study to this Maori community is therefore vital.

Beyond this population, the distribution of demographics in this study population was different to many other parts of Northland and most other parts of New Zealand. How then could these results apply to Maori living in other areas of New Zealand? When Joshy et al., (2009) considered this for the results of their study, they put forward the view that because rates of obesity, lifestyle and other risk factors are likely to be similar for Maori in other parts of New Zealand, it is reasonable to generalise the results for Maori living in areas with the most deprivation.

There are a couple of considerations that need to be made before applying the same view to these results. Firstly, there may be differences in underlying risk

factors between those living in urban areas compared to the people in this study which were predominantly residents of an independent settlement or the surrounding rural areas. Secondly, in places where the proportion of Maori in the population is much less than 70% and the burden of diabetes is less evident, general practices may not have the same awareness of screening for diabetes as demonstrated in this practice.

Even with a total of 71% people screened for diabetes, Maori aged 35-49 were less likely to be screened at all and were estimated to be most likely to have undiagnosed diabetes. The low uptake to screening coupled with the lower likelihood of being detected with diabetes either through a formal or opportunistic approach suggested that this age group may have barriers beyond general practice service delivery such as work and other time commitments and stresses competing for priority. Further investigation is needed to look at the issues occurring for the 35-49 year old age group as this study shows that this is potentially an important age to direct interventions to reduce future diabetes and death from any cause.

5.4.2 Measuring diabetes incidence

Researchers and government authorities have shown a commitment to

improve the quality of diabetes prevalence methodology. However, much less is reported on the rates of diabetes incidence, even with all the resources that has gone into risk reduction.

This study showed that most of the relevant information was available to calculate diabetes incidence rates following a population that had a fasting glucose test less than 6.1mmol/L at baseline. General practice data could also be used to determine the number of screenings and the length of time between diabetes screenings. Compared with other methods to calculate diabetes prevalence, diabetes diagnoses based on primary care Read Codes in a high- risk population predominantly from Auckland and Northland regions were considered a reliable source of diagnosed diabetes (Thornley et al., 2011). Therefore Read Codes could be used to identify incidence cases of diabetes. As yet CVDRA has not included the increased risk of CVD in those with non- diabetic hyperglycaemia. This has the potential to underestimate an

individual’s risk, as even high levels of blood glucose are associated with increased risk for CVD if other CVD risk factors are present (Chamnan et al., 2011). Chamnan et al., (2011) supported the view that cardiovascular risk calculations should include non-diabetic hyperglycaemia as a continuous risk factor. If this were to occur, blood glucose results stored in either general practice records or in a system such as PREDICT TM ,could be used to monitor

the effect of prevention efforts to reduce the risk of progression to diabetes. However, given the difficulties experienced by the researcher when extracting the data from general practice records, this may be an information system challenge.

In New Zealand, a lot of resource has gone into cardiovascular risk

assessments. Measuring the effect of diabetes screening on reducing diabetes incidence in the context of cardiovascular risk assessment programmes may become more important as promising intensive lifestyle interventions become more integrated into best practice. This study gives some indication of

incidence rates in the context of early and later exposure to CVDRAs and may be able to be used to compare the effect of future interventions in high risk populations.