Intensive diabetes management has become the standard of care in type 1 diabetes (T1D) since landmark trials such as the Diabetic Control and Complications Trial (DCCT) and the Epidemiology of Diabetes Intervention and Complication Trial (EDIC) confirmed that tighter glycaemic control significantly reduces rates of long-term complications.1,2 With this has come an increase in uptake of insulin pump therapy (CSII) and other forms of associated diabetes technology, particularly among children.3,4 CSII offers a range of potential advantages over traditional injection regimens, including improvements in glycaemic control, reductions in severe hypoglycaemia and improvements in patient-reported quality of life.5–10

Despite ongoing technological advances, many are still not achieving target blood glucose control.3,11 In particular, those of lower socioeconomic position and non-European ethnicity appear particularly disadvantaged.12,13 There are many factors that are likely to influence these observations, including inequalities in healthcare due to deprivation as well other factors. Inequalities in access to diabetes technology have now been confirmed in single-centre cohorts,14,15 Canadian,16 German17 and New Zealand national data.4

The New Zealand Pharmaceutical Management Agency (PHARMAC) introduced publicly funded access to CSII in September 2012. Access to funding can be complex, and has undergone some changes since 2012. Currently there are two main categories: 1) those experiencing severe unexplained and recurrent hypoglycaemia, and 2) those with suboptimal glycaemic control (defined as typical HbA1c results of between 65 and 90mmol/mol. Up to 2014, uptake was continuing to rise for all groups, but was considerably slower in those of non-European ethnicity, and in those who were more socioeconomically deprived.4 Both these factors, while related, were independent predictors of CSII4 uptake. What remains unclear is why this should be the case given the overall improved public access provided by PHARMAC. In addition, the role that New Zealand’s district health board (DHB) structure may play in access to CSII has not been examined. DHBs in New Zealand are diverse in not only geography, but also in population sociodemographic make up. Funding priorities and challenges also differ, as well as access to sub-speciality services. Diabetes specialists may also differ in their comfort or support for new technologies, as seen in recent paediatric data from Australasia, where access to intensive therapy from new diagnosis of T1D differed based on geographical location.18

The aim of the current study was therefore to examine the New Zealand-wide national uptake of publicly funded CSII from 2012 to 2016 (being the most recent complete year data at the time of data request), with a focus on the proportion of patients using pumps with analysis according to DHB and demographic characteristics.

Methods

Data sources

The New Zealand Ministry of Health holds several national data collections covering the estimated 4.87 million people living in New Zealand.19 These data sets provided all data for this study,20 including the National Health Index Collection (demographic information attached to a unique alphanumeric patient identifier, the National Health Index [NHI]), the National Minimum Dataset (hospital discharges from 1988), the Mortality Collection (all hospital and community deaths), and the Pharmaceutical Collection (PHARMS, all claims by community-based pharmacists for the dispensing of publicly funded prescription pharmaceuticals and associated equipment, including all dispensing of diabetes-related drugs/devices for New Zealand). The data in each of these collections are indexed to individualised patient National Health Index numbers or NHIs (from 2005 onwards in PHARMS), allowing linkage of patient-level demographic, hospital discharge, mortality and pharmaceutical dispensing data.

Diagnoses in the National Minimum Dataset and the Mortality Collection are coded to successive revisions of the International Statistical Classification of Diseases and Related Health Problems, Australian Modification (ICDAM). The Ministry of Health also holds the Virtual Diabetes Register (VDR), an annually updated national register of all patients with any form of diabetes mellitus (except gestational diabetes) identified using records of hospital admissions with diabetes, diabetes outpatient clinic visits, retinal screening, repeated HbA1c laboratory tests, and insulin or oral hypoglycemic use.21,22 At the time the study was initiated (November 2017), linked data from the latest VDR iteration22 and the above collections were available up to 31 December 2016.

Study population

The Ministry of Health identified all patients who appeared on the VDR at any time between 1 September 2012, and 31 December 2016 (the study period). This was then linked to demographic information, diabetes-related dispensing records, hospital discharge, and mortality data from the relevant National Collections. All data were indexed to an encrypted NHI to preserve anonymity, prior to providing to the study team.

As the VDR does not distinguish between types of diabetes, we utilised a previously developed and published algorithm4 to identify and exclude diabetes patients affected by non-T1D subtypes (eg, type 2 diabetes, cystic fibrosis, monogenic and neonatal forms of diabetes). The algorithm was further adapted to capture T1D patients who may have taken metformin (traditionally used for type 2 diabetes) at some stage currently or in the past (Figure 1). Due to coding issues, some patients determined by our algorithm to have T1D had had at least one hospital discharge with a coded diagnosis of type 2 diabetes. These patients were retained in the study population, but they were flagged so that a sensitivity analysis could be undertaken.

Figure 1: Study flow diagram.

Population characteristics

For each member of the study population, basic demographic and socioeconomic data was available, including prioritised self-identified ethnicity, the funding district health board (DHB), as recorded at the time of each dispensing claim, and the New Zealand Index of Deprivation 2006 (NZDep2006),23 a validated measure of socioeconomic position (using an area-based measure of social deprivation). In brief, the NZDep2006 Index enables assignment of levels of social and economic deprivation to a given residential address. From this, a numerical indication of deprivation for small geographical units can be calculated, using a 1–10 scale (1 representing the least deprived and 10 representing the most deprived). These were converted to quintiles for all data presented in this study.

Patient characteristics at study entry were taken from the first insulin dispensing record during the study period. For the analysis of insulin pump use by demographic characteristics and region over time, we used the values of the prioritised ethnicity, NZDep2006 score, and funding DHB associated with the first insulin dispensing in the relevant calendar year.

Statistical analysis

The methods shown in Figure 1 were used to ascertain the number of patients with T1D during the study period, and in each year (annual denominators). Similarly, we ascertained the number of patients who received at least one dispensing of an insulin pump and/or pump consumables per calendar year (annual numerators). We then determined the annual numbers of patients with T1D according to their DHB of residence and sociodemographic characteristics, identified pump users within these subgroups and calculated the corresponding proportions.

Pearson’s Chi-square tests were used to compare differences between proportions. We repeated these analyses in a sensitivity analysis confined to the subgroup of patients who had at least one hospital discharge with a diagnosis of type 1 diabetes (T1D) and no discharge diagnoses of type 2 diabetes, as well as to those included who had been prescribed metformin. Finally, to explore the independent contributions of DHB region and sociodemographic characteristics to variations in pump use, we undertook a logistic regression analysis to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs), adjusted for the demographic characteristics and region. All statistical analyses were performed using R version 3.4.24

Ethical approval

The study received ethical approval from the University of Otago Human Ethics Committee (Health), reference number HD17/022.

Results

Patients with T1D were identified from national data collections (see Figure 1). The basic demographics of these 17,338 patients identified with T1D are seen in Table 1. CSII uptake, between September 2012 (when CSII was publicly funded) and the end of 2016, as a proportion of total the T1D population and by individual patient characteristics are shown in Table 2, as well as graphically in Figure 2.

Figure 2: Insulin pump uptake over time as a proportion of the total T1D population by individual patient characteristics.

(A) Gender; (B) ethnicity, NZ/EUR–NZ European/European; (C) deprivation quintile (Q1 least deprived, Q5 most deprived—derived from NZdeprivation2006 score); (D) age in years.  

Table 1: Characteristics of the study population at entry.

‍

These highlight that during the five years of publicly funded CSII, from 2012 to end of 2016 the proportion using CSII has increased from 1.6% in 2012 to 11.3% in 2016. Over this period increases are seen in the proportions using CSII among all available individual patient characteristics (eg, age, gender, ethnicity, level of deprivation and DHB of residence), however the rate of increase was not equally distributed (Table 2, Figure 2, Table 4).

Table 2: Annual proportions (%) of patients with type 1 diabetes using an insulin pump, overall and by demographic characteristics.

1NZDep2006—New Zealand Deprivation index 2006.

To further explore this, logistic regression analyses were conducted. Table 3 reveals that certain patient characteristic were significantly less likely to access CSII, when all other variables are controlled for. Specifically, females were more likely than males to be using a pump (adjusted OR 2 [(1.8–2.2)] (this was particularly so from age 20 years to age 60 years—data not shown); Maori, Pacific and Asian patients were significantly less likely to be using a pump than New Zealand Europeans (adjusted ORs 0.4 [95% CI 0.3–0.4], 0.2 [95% CI 0.1–0.3] and 0.2 [95% CI 0.1–0.3], respectively); and those in more deprived deciles were significantly less likely to be using a pump than those in decile 1 (decile 5 OR 0.6 [95% CI 0.5–0.7]). To test assumptions made in our T1D study algorithm (Figure 1), further sensitivity analyses were done. The results and odds ratios were very similar in sensitivity analyses confined to the subgroup of the study population with at least one discharge diagnosis of type 1 diabetes and no type 2 diagnoses (data not shown), and with additional analyses subtracting those who had been previously been exposed to metformin. Table 4 highlights regional disparities based on DHB of residence adjusting for age, gender, ethnicity and deprivation. These reveal a greater than four-fold difference in CSII between the DHBs with the highest and lowest uptake, despite identical PHARMAC access. In addition, for three DHBs (not for the other 17), there is a significant interaction between childhood age and DHB. As seen in Figure 2 children are more likely to get pumps than adults overall, and this relationship is stronger for Counties Manakau, Midcentral and Nelson Marlborough. In Counties Manakau, the OR for child is between 4.3 and 10.5, and for an adult it is between 0.7 and 1.4; In Midcentral, these ORs are [4.4–11.2] for a child and [0.7–1.6] for an adult, and in Nelson they are [5.4–13.1] and [0.7–1.6].

Table 3: Proportions of patients with type 1 diabetes using an insulin pump in 2016 according to demographic characteristics, with crude and adjusted odds ratios.

‍

aOdds ratios are adjusted for DHB and all other variables in the table.
bNZDep2006—New Zealand Deprivation Index 2006.

Table 4: Proportion of patients in 2016 using pump therapy by district health board funding region.1

District health board

Proportion using pump (%)

Crude odds ratio

‍

1In descending order of type 1 diabetes population size; 2adjusting for all variables in Table 2.

Discussion

This study examines national patterns of insulin pump uptake, in New Zealanders living with T1D, from the introduction of PHARMAC publicly funded access criteria. The main findings are that since funding was introduced, access has considerably expanded. Overall New Zealand uptake of CSII was at 11.3% by end of 2016, compared to at least 40% in the US,25 38% in Canada16 and 16% in Italy.26 Despite public access criteria, several sociodemographic disparities in access appear to exist. When controlled for all available variables, these include differences in utilisation by DHB, age, gender, ethnicity and socioeconomic position.

Insulin pump uptake has not previously been examined by DHB including controlling for other potential confounders. The key finding of this study is that DHB of residence is independently associated with insulin pump uptake despite theoretically equal access to PHARMAC access criteria (up to four-fold difference between highest and lowest using DHBs). Differing access to diabetes regimens has previously been described in children at new diagnosis in Australasia, with quite different approaches taken by diabetes teams depending on centre of treatment.27 This suggests that availability of diabetes team members with experience or enthusiasm for new diabetes technologies may in part be an influencing factor in CSII uptake. Staff may also be using personal judgements28 or centre-specific access criteria,16 which may be impacting access to CSII for individuals living in some DHBs. These factors appear more likely than inadequate size of DHB or inadequate access to specialised services in some DHBs, as differences were also observed between DHBs with tertiary specialist services and large urban populations, eg, Canterbury DHB had one of the lower proportions of CSII use. Other factors that need further exploration are differences in funding and staffing levels between various diabetes DHB diabetes teams available to support technological advances. These have been previously shown to be quite disparate between different Australasian centres, including those within New Zealand.29

The other variables that appear to influence CSII uptake were age, gender, ethnicity and socioeconomic position. These findings confirm the results of previous New Zealand and international research showing disparities in access to CSII due to sociodemographic factors.4,17,30 Importantly, in this 2016 data, disparities in access for those of Māori and Pacific ethnicity were present, and appear to be independent of socioeconomic deprivation, which was also an independent predictor of pump uptake. This independent relationship to inequality by ethnicity and deprivation has been seen in other New Zealand Health data.4,31 Combined with the DHB data, these findings have important implications for diabetes care and funding. Those of non-European ethnicity and with greater degrees of socioeconomic deprivation have previously been shown to have poorer diabetes outcomes.32,33 In Australia and the US the vast majority of CSII is funded via private health insurance and these access disparities therefore are likely to be even greater. CSII is an important tool, and New Zealand has world-leading public access, when specific criteria are met. Our criteria currently disadvantage those with less healthy control and this is likely contributing to disparity and unequal of access. In the UK for instance, having less healthy glycaemic control is actually a potential indication for a trial of CSII, and those with very poor glycaemic control have previously been shown to have potentially very good outcomes when using CSII.34 However, it also remains important to remember that other aspects of diabetes care have been shown to be equally important for successful outcomes, including education and close diabetes team support.2,35 Unfortunately, our funding model via PHARMAC is unable to allocate equal money between devices/pharmaceuticals and diabetes support and education. Funders should pay close attention to this issue when funding is allocated to new diabetes technologies, ideally if access criteria are used to limit access based on glycaemic control, equal funding would also be put in place for supporting those who may have barriers to meeting or understanding access criteria.

Like all studies, this one has strengths and weaknesses. Key strengths are the complete national data drawn from routinely collected linked data sources, as well as the uniformity of access to the national access criteria between DHBs. In addition this data spans the period of time between access criteria being monitored by an “insulin pump panel” (2012–2014) and transition to independent online specialist access criteria (2014–ongoing), and highlights that speed of uptake does not appear to have been specifically altered by this change in administration. Potential weaknesses are the methods used to determine T1D from these linked data sources. We have used an algorithm technique to deal with this. This algorithm ensures likely contamination of the sample with type 2 diabetes is negligible but may lead to small loss of T1D from the total sample. As an example, 100 individuals using CSII were excluded with our algorithm, a number considered to be of negligible impact to the conclusions drawn. In addition, given the use of national de-identified routinely collected data, we did not have access to the full electronic records of each participant. This means that we are left with an incomplete understanding of why sociodemographic variables such as DHB of residence and deprivation adversely impact access to CSII.

As this data comes from public pharmaceutical dispensing data, we also have no access to data on privately funded CSII, and those who moved between private- or hospital-funded access following availability of access criteria. However, as no insurance companies in New Zealand reimburse for CSII, and public access criteria are relatively broad (based on hypoglycaemia or elevated glycaemic control) it is expected that numbers accessing ongoing private CSII are negligible. In addition, by early 2013 it is anticipated that nearly all eligible pre-existing patients using private- or hospital-funded CSII would have crossed over to fully funded access and thus been included in our data. This is likely the explanation for the more rapid rise in access seen between 2012 and 2013. Finally, this study addresses uptake of CSII only. Due to weaknesses in current data linking it remains impossible to accurately describe glycaemic outcomes using national data collections. Efforts to address this deficit are required to allow for adequate clinical benchmarking.

In conclusion, since approval for publicly funded insulin pumps from September 2012 there has been a steadily increasing uptake. Overall use still appears lower than many similar high-income countries. Despite public access criteria, disparities in uptake appear to exist, and in addition to traditionally described socio-demographic barriers to healthcare, DHB of residence is also an independent predictor of uptake. Efforts to reduce these disparities are required. In particular, changes to aspects of the access criteria that appear to limit access based on age, socioeconomic position and ethnicity should be considered, and for those who do not meet access criteria particularly due to unhealthy glycaemic control, more funding for education and support to ensure more equal overall outcomes should also be incorporated.

‍