Abstract
Background
In Aotearoa New Zealand, co-payments to see a general practitioner (GP, family doctor) or collect a prescription are payable by virtually all adults.
Objective
To examine the extent to which these user co-payments are a barrier to accessing health care, focussing on inequities for indigenous Māori.
Methods
Pooled data from sequential waves (years) of the New Zealand Health Survey, 2011/12 to 2018/19 were analysed. Outcomes were self-reported cost barriers to seeing a GP or collecting a prescription in the previous year. Logistic regression was used to estimate odds ratios (ORs) of barriers to care for Māori compared with non-Māori, sequentially adjusting for additional explanatory variables.
Results
Pooled data included 107,231 people, 22,292 (21%) were Māori. Across all years, 22% of Māori (13% non-Māori) experienced a cost barrier to seeing a GP, and 14% of Māori (5% non-Māori) reported a cost barrier to collecting a prescription. The age- and wave-adjusted OR comparing Māori/non-Māori was 1.71 (95% confidence interval [CI]: 1.61, 1.81) for the cost barrier to primary care and 2.97 (95% CI: 2.75, 3.20) for the cost barrier to collecting prescriptions. Sociodemographics accounted for about half the inequity for both outcomes; in a fully adjusted model, age, sex, low income, and poorer underlying health were determinants of both outcomes, and deprivation was additionally associated with the cost barrier to collecting a prescription but not to seeing a GP.
Conclusions
Māori experience considerable inequity in access to primary health care; evidence supports an urgent need for change to system funding to eliminate financial barriers to care.
Keywords: Aotearoa New Zealand, co-payments, fees, health equity, health expenditures, primary health care
Key messages.
Māori experience very high levels of cost barriers to seeing their GP.
Māori also experience high levels of cost barriers to collecting prescriptions.
Māori experience considerable inequity in access to primary health care.
Urgent long-term change to system funding is needed to eliminate cost barriers.
Background
Inequities in health between Māori, the indigenous people of Aotearoa New Zealand, and non-Māori have been evident throughout the colonial history of the country. As part of the international community, the New Zealand government is bound by human and social justice alongside legislative obligations, to ensure the health rights of all its citizens, including those of Indigenous Peoples.1Te Tiriti o Waitangi (the Treaty of Waitangi), signed between Māori leaders and the British Crown in 1840, reaffirmed Māori sovereignty and rights to collective self-determination, including the right to equitable health outcomes.2 The Waitangi Tribunal, in its recent report on the primary health care system; The Wai2575 Health Services and Outcomes Kaupapa Inquiry,3 concluded that “The Crown has breached the Treaty of Waitangi by failing to design and administer the current primary health care system to actively address persistent Māori health inequities.”3
The continuing effects of colonialism are evidenced in the poorer health outcomes experienced by Māori compared with non-Māori. Despite improvements in some areas of health,4 there remains continuing inequity for Māori across almost all health measures compared with non-Māori.5 Critical to this are the associated inequities in social, cultural and economic factors, with Māori disproportionately affected by the social determinants of health, including education, employment, and household overcrowding.6
Māori currently comprise approximately 16% of the total population of 5.2 million while Pacific and Asian peoples make up 8% and 15%, respectively; the remaining 70% (total over 100% as people identify with multiple ethnicities) are primarily those of European descent, predominantly from the United Kingdom and Europe. These latter 3 groups make up the “non-Māori” grouping used in the analyses presented in this paper.
Although Māori are more likely than non-Māori to live in high deprivation areas,6 most analyses find that poorer Māori health is only partially explained by socioeconomic disadvantage.7–9 Inequity for Māori in aspects of the health service at the levels of quality, safety, and improvement, and across life-course stages, have recently been documented.5 With particular relevance to primary health care, Māori have rates 1.6 times higher than non-Māori for ambulatory sensitive hospitalizations, namely hospitalizations resulting from diseases which are responsive to prophylactic or therapeutic interventions deliverable in a primary health care setting.6 These observations culminate in a lower life expectancy for Māori males (73 vs 80.3 years) and females (77.1 vs 83.9 years) compared with their non-Māori counterparts.10
General practitioners (GPs), or family doctors, work in a private capacity, many in for-profit practices, others in not-for-profit practices. All are funded through central capitation funding. The level of capitation funding depends on various characteristics of the patients enrolled in the practice. Some practices (about 30% of the total), for whom more than 50% of their enrolled population are identified as “high needs” (based on Māori or Pacific ethnic group and/or being in New Zealand’s most deprived areas), have signed up to the Very Low Cost Access scheme.11 In return for higher capitation funding levels, Very Low Cost Access practices agree to charge no more than a fixed co-payment for adults (currently NZ$19.50). Practices not in the Very Low Cost Access scheme are able to set their own level of user co-payments. These are currently maximum $84 per GP consultation (Ministry of Health, personal communication).
The Pharmaceutical Management Agency (PHARMAC) has the responsibility for deciding which pharmaceuticals are subsidized. These are supplied through pharmacies to users for a fixed co-payment per item; prior to 2013 this was $3 and currently it is $5. There is a Prescription Subsidy Scheme in place, which is intended to ensure that the maximum number of items that are paid for by adult members of a household is 20 per year; the remaining items for the rest of the year are not subject to user co-payments. For this system to work, pharmacists and patients need to ensure that appropriate records of the number of prescriptions are kept. Prescription items are free for those under age 14, so their prescriptions do not count towards the 20-item limit.
Economic hardship and cost barriers are complicated concepts and not just a direct linear association between household income and price of GP appointments or prescriptions; therefore, the aim of the current study was to investigate the extent to which user co-payments are a barrier to accessing health care, with a specific focus on inequities faced by Māori.
Methods
The data presented in this paper derive from pooled sequential waves of the New Zealand Health Survey from 2011/12 to 2018/19. Details are described on the Ministry of Health website.12 Briefly, the survey is of complex design, using multistage, stratified, probability-proportional-to-size sampling design, with over-sampling of some groups, including Māori, to ensure sufficient statistical power. The survey questionnaire is administered face-to-face by trained interviewers, using computer-assisted personal interviewing. Only data from people aged 15 years and over are included in this analysis.
Variable definitions
It is acknowledged, that non-Māori is not an ethnic group per se, but rather a reference group or comparison group that includes a range of different ethnic groups. We have chosen to use a Māori/non-Māori analysis which recognizes Te Tiriti o Waitangi as fundamental to addressing Māori health inequities.13
Participants were asked about cost barriers to accessing primary health care using the following questions: “In the past 12 months, was there a time when you had a medical problem but did not visit a GP because of cost?” and “In the past 12 months, was there a time when you got a prescription for yourself but did not collect one or more prescription items from the pharmacy or chemist because of cost?”. We refer to this inability to visit a GP or pay for prescriptions as a “cost barrier”.
Area-based deprivation was measured using the New Zealand Deprivation Index which uses variables derived from the New Zealand census (2006 and 2013) and provides a deprivation score for each small area unit. These are ranked across the country, then divided into quintiles, where 1 represents the 20% of areas with the least deprived scores and 5 represents the 20% of areas with the most deprived scores.14 New Zealand Deprivation Index 2006 was used for waves up to 2011/12 and New Zealand Deprivation Index 2013 for waves 2013/14 onward.15
Income was self-reported in bands. Since the value of the dollar changed over the study period, as did the categories used to collect income data, a dollar value of “low income” was defined for each study year, based on the category closest to under 60% of the median household income for that year.16 The absolute cut-offs for low income were thus under $40,000 for each year from 2011/12 to 2014/15 and under $50,000 in 2015/16 to 2018/19. Ethnicity was self-identified and analysed to compare Māori with non-Māori.
Statistical methods
Data were analysed using an approach which accounts for the complex design of the survey, using the weights supplied by Statistics New Zealand, and accounting for clustering by primary sampling unit and strata, as appropriate for each wave. Estimated proportions were used to describe the relationship between barriers to care for Māori and non-Māori in each socioeconomic category. To investigate explanations of inequity, we ran logistic regression models to estimate odds ratios (ORs) of barriers to care for Māori compared with non-Māori, sequentially adjusting for additional explanatory variables. Base models were adjusted for year of survey; additional variables were: age group, sex, low income, household size, deprivation quintile, and number of GP visits in the previous 12 months. Uncertainty was described using 95% confidence intervals (CIs). For the descriptive analysis, there were negligible levels of missing data, other than for the income variable. For the multivariable analysis, a complete-case analysis was performed. Analyses were conducted in Stata v16, using the survey (“svy”) suite of commands.
Results
The pooled data included 107,231 people, 22,292 (21%) of whom were Māori. A total of 196 people (44 Māori) had missing data on either the cost barrier to GP care or to filling a prescription and were excluded from all analyses. There were appreciable levels of missing data for income (22% overall, 29% in Māori, 20% in non-Māori).
Across all years, 14% of people reported not having seen a GP when in need, during the past 12 months, due to cost. This proportion remained static over the 8 years of analysis (±1%). The proportion of Māori experiencing this cost barrier was consistently higher than non-Māori in each year, averaging 22%, compared with 13% for non-Māori. The age- and wave-adjusted OR comparing Māori to non-Māori was 1.71 (95% CI: 1.61–1.81). At each level of deprivation, Māori were more likely than non-Māori to have reported a cost barrier to care (see Table 1). At higher income levels, Māori were more likely than non-Māori to have reported a cost barrier to care by 6 percentage points, whereas at lower income levels, the difference was 15 percentage points.
Table 1.
Proportion of people experiencing a cost barrier to primary health care, 2011/12 to 2016/17.
| Māori | Non-Māori | |
|---|---|---|
| Deprivation quintile | ||
| 1 (least deprived) | 15.7% (13.3%–18.4%) | 9.4% (8.7%–10.1%) |
| 2 | 17.7% (15.5%–20.2%) | 11.0% (10.3%–11.7%) |
| 3 | 20.4% (19.6%–23.4%) | 13.3% (12.6%–14.1%) |
| 4 | 22.0% (20.5%–23.6%) | 14.7% (14.0%–15.4%) |
| 5 (most deprived) | 25.3% (24.1%–26.4%) | 17.6% (16.8%–18.4%) |
| Income levela | ||
| Higher income | 16.5% (15.5%–17.5%) | 10.7% (10.3%–11.1%) |
| Lower income | 31.9% (30.4%–33.4%) | 17.2% (16.5%–17.9%) |
Note: Percentages are weighted, to reflect the New Zealand population.
aSee methods for definition of calculation of income categories.
Similar inequities were seen when the cost barrier of not being able to fill a prescription due to cost was analysed, shown in Table 2. Overall, 6% of people reported this cost barrier, with small and inconsistent changes over time. There was a marked inequity between Māori and non-Māori, with a much greater burden of this barrier experienced by Māori (14% compared with 5%). The age- and wave-adjusted OR comparing Māori to non-Māori was 2.97 (95% CI: 2.75–3.20). The inequity was present at each level of deprivation and was most marked in low-income households (Table 2).
Table 2.
Proportion of people reporting not being able to fill a prescription due to cost, 2011/12 to 2016/17.
| Māori | Non-Māori | |
|---|---|---|
| Deprivation quintile | ||
| 1 (least deprived) | 7.1% (5.5%–9.2%) | 2.7% (2.3%–3.2%) |
| 2 | 8.9% (7.3%–10.8%) | 3.3% (2.9%–3.7%) |
| 3 | 11.7% (10.2%–13.4%) | 5.0% (4.5%–5.5%) |
| 4 | 12.9% (11.7%–14.1%) | 6.4% (5.9%–6.8%) |
| 5 (most deprived) | 18.6% (17.7%–19.7%) | 10.4% (9.8%–11.0%) |
| Income levela | ||
| Higher income | 8.0% (7.3%–8.8%) | 3.4% (3.2%–3.7%) |
| Lower income | 22.9% (21.6%–24.2%) | 8.7% (8.3%–9.3%) |
Note: Percentages are weighted, to reflect the New Zealand population.
aSee methods for definition of calculation of income categories.
Results of the inequity analysis are provided in Table 3, which shows the odds of experiencing each cost barrier, comparing Māori to non-Māori, with sequential addition of adjustment variables. Household income, size, and deprivation explained about half of the inequity in barriers to primary health care between Māori and non-Māori, reducing the age-/wave-adjusted excess in inability to see a GP due to cost from 71% to 37%. Having additionally accounted for previous GP visits, Māori remained 35% more likely than non-Māori to report not having seen a GP due to cost. Considering inability to pay for a prescription, Māori were nearly 3 times as likely to report this, having accounted for survey year and age group. Over half the inequity was explained by income, household size, and deprivation quintile, but even after adjustment for all variables in the model, Māori remained 82% more likely than non-Māori to report not being able to fill a prescription due to cost. We further investigated whether using multiple imputation for the income data affected the results, but it did not appreciably change the determinants of access to low-cost care for Māori and non-Māori.
Table 3.
Explanations of Māori: non-Māori inequity in cost barriers to seeing a GP or filling a prescription, 2011/12 to 2016/17.
| Base model + additionally adjusted for… |
Unable to afford seeing a GP | Unable to afford filling a prescription |
|---|---|---|
| Base model, adj for wave | 1.94 (1.83–2.05) | 2.97 (2.75–3.20) |
| + Age group | 1.71 (1.61–1.81) | 2.73 (2.53–2.94) |
| + Low income | 1.46 (1.37–1.55) | 2.24 (2.07–2.42) |
| + Household size | 1.47 (1.36–1.58) | 2.16 (2.00–2.34) |
| + Deprivation quintile | 1.37 (1.29–1.47) | 1.84 (1.70–2.00) |
| + Number of GP visits in past 12 months | 1.35 (1.27–1.45) | 1.82 (1.67–1.97) |
Note: The results in this table are a complete case analysis, so the data are marginally different to the age-/wave-adjusted data reported in the text.
The final analysis investigated determinants of cost barriers to care among Māori, shown in Table 4. Working age adults (25–34 year olds) were at the highest risk of not being able to afford to see a GP or collect a prescription, with lower rates in young adults, and the lowest in those past usual retirement age. Māori women were much more likely to report facing each of the 2 costs barrier than men. Families in large households did not report facing a cost barrier. Unsurprisingly, income remained a strong predictor of experiencing cost barriers. Living in a deprived area, and living in a larger household, did not affect inability to pay for a GP, but did affect the inability to pay for prescriptions. Focussing on area-level deprivation, it was household income rather than any of the other variables in the model which explained the previously observed association between New Zealand Deprivation Index quintile and cost barrier to seeing a GP. Among people who had been to the GP at least once, the more times someone went to the GP (a marker of ill health), the more likely they were to have reported not being able to afford to see a GP or pay for a prescription.
Table 4.
Determinants of cost barriers to seeing a GP or filling a prescription among Māori, 2011/12 to 2016/17.
| Unable to afford seeing a GP | Unable to afford a prescription | |
|---|---|---|
| Age group | ||
| 15–24 years | 0.67 (0.56–0.81) | 0.72 (0.58–0.90) |
| 25–34 years | 1.18 (1.02–1.37) | 1.12 (0.93–1.34) |
| 35–44 years | 1 | 1 |
| 45–54 years | 0.71 (0.61–0.84) | 0.94 (0.78–1.14) |
| 55–64 years | 0.45 (0.37–0.54) | 0.69 (0.56–0.86) |
| 65–74 years | 0.20 (0.16–0.26) | 0.26 (0.20–0.35) |
| 75–90 years | 0.11 (0.07–0.18) | 0.16 (0.10–0.24) |
| Sex | ||
| Female | 1 | 1 |
| Male | 0.64 (0.57–0.71) | 0.66 (0.57–0.76) |
| Household income | ||
| Low | 2.67 (2.37–3.01) | 3.31 (2.87–3.82) |
| High | 1 | 1 |
| Household size | ||
| Lives alone | 1 | 1 |
| 2–4 | 1.01 (0.89–1.15) | 0.96 (0.82–1.11) |
| 5–7 | 1.03 (0.86–1.23) | 1.41 (1.13–1.75) |
| 8–11 | 0.96 (0.67–1.37) | 1.22 (0.79–1.89) |
| Area-level deprivation | ||
| Most affluent quintile | 1 | 1 |
| Quintile 2 | 0.96 (0.71–1.29) | 1.12 (0.75–1.68) |
| Quintile 3 | 1.24 (0.95–1.62) | 1.21 (0.85–1.73) |
| Quintile 4 | 1.11 (0.86–1.44) | 1.32 (0.94–1.85) |
| Most deprived quintile | 1.12 (0.87–1.43) | 1.76 (1.28–2.44) |
| Number of GP visits in previous 12 months | ||
| 0 | 0.74 (0.62–0.89) | 0.46 (0.35–0.61) |
| 1 | 1 | 1 |
| 2 | 1.15 (0.96–1.37) | 1.44 (1.14–1.82) |
| 3–5 | 1.36 (1.16–1.60) | 1.93 (1.57–2.37) |
| 6–11 | 1.76 (1.44–2.15) | 2.89 (2.29–3.65) |
| 12+ | 2.22 (1.77–2.80) | 5.48 (4.11–7.31) |
Note: The results in this table are a complete case analysis, so the data are marginally different to the age-/wave-adjusted data reported in the text.
Discussion
In summary, we have described the magnitude of the cost barriers to primary health care in Aotearoa New Zealand, both for seeing a GP and paying for prescriptions, faced by Māori. These barriers exist at all levels of area-level deprivation and household income, and are not fully explained by adjusting for income, deprivation, household size, or previous GP visits. In the fully adjusted model, important predictors of experiencing a cost barrier were being of working age, being female, having low household income and having high health needs. Living in a deprived area was additionally associated with not being able to afford a prescription. Although the high barriers to primary health care faced by Māori are well described,5,17,18 and reiterated with each passing year of the New Zealand Health Survey,12 this is, to the best of our knowledge, the first analysis which attempts to understand these barriers in more detail.
Results from the Commonwealth Fund Survey showed that New Zealanders were twice as likely as Australians to not consult a doctor due to cost and 40% more likely to skip a consultation, test, or medication due to cost.19 Further results showed that consultations, tests, or medications were skipped due to cost by 27% of low income New Zealanders; the second greatest proportion of the 11 participating countries.20
An important contribution to the understanding of what a cost barrier to seeing a GP actually means to individuals in Aotearoa New Zealand comes from findings from the Primary Care Patient Experience Survey.21 In total, 90% (85% for Māori) of patients stated that it was the cost of the appointment that was the barrier. Māori were more likely than other ethnicities to report that travel costs were a barrier (18%, compared with 8% for non-Māori, non-Pacific, non-Asian people). Interestingly, patients in areas served by the providers “Hauora Tairawhiti” (40%) and “Counties Manukau” (34%) were most likely to report not being able to afford taking time off work as an explanation of the cost barrier, compared with the 25% national average21; both are areas comprising high proportions of Māori populations. This has implications for large providers of primary health care in these areas, where many of their practices only offer walk-in services. Although this means that potentially people can get seen on the day they need care, it also highlights a barrier for patients who are paid by the hour, and for whom spending time in a waiting room means hours unpaid.
The data derive from the New Zealand Health Survey, and the analyses account for the integral survey weights. The results are therefore reasonably representative of the national population, rather than being biased by nonresponse (New Zealand Health Survey consistently reports around 80% response).15 The equity analyses, which attempted to “explain” the inequity through statistical adjustment, could have suffered from residual confounding, and must therefore be interpreted with caution. For example, area-based deprivation, measured in quintiles, cannot precisely document the deprivation that individuals face, and it is likely that within any one quintile, Māori face greater material hardship than non-Māori.
We limited our multivariable analysis to a complete-case approach, thus assuming that the missing data are missing completely at random. The problems with doing this have been well described,22–25 and in particular, income data are unlikely to be missing with this pattern. However, sensitivity analysis showed that using multiple imputation for the income data did not appreciably change our results. We are also limited in our analysis by the question asked in the survey, which only relates to a single episode of not having seen a GP, and/or not having filled a prescription due to cost. Knowing whether this was happening on a regular basis, or how many items could not be paid for, would have added a further dimension to our analysis. Additionally, this would also allow us to understand whether an appropriate policy might be to reduce the threshold of the prescription subsidy policy, or whether fully subsidized prescriptions should be offered to Māori. A recent initiative by some multichain pharmacies is the offering of free or discounted prescriptions26; however, the location of these is generally in urban areas, and may not be easily accessible to those unable to afford prescription charges. Monitoring of the potential inequities induced by this initiative is therefore needed.
Unfortunately rurality was not available in this study. Access to health services in urban and rural areas differs, and a higher proportion of Māori live in less urban and more rural areas; therefore, this could have confounded our results.
We chose to analyse Māori and compare to non-Māori in this study. This approach aligns with Te Tiriti o Waitangi, in which all people who are not indigenous are included in the non-Māori group.13 However, due to high barriers to primary health care faced by Pacific peoples, we acknowledge that this approach underestimates the inequity that Māori face, compared with New Zealand European people.
Despite these limitations, the results presented in this paper raise a number of important policy issues. Very Low Cost Access practices serve high-needs population (defined as over 50% Māori, Pacific or living in the most deprived quintile). It is apparent that the current model of targeting of low-cost access primary health care is failing Māori, based on our finding that 1 in 4 Māori living in the most deprived areas have faced a cost barrier to care in the previous 12 months. Interviews with Māori patients admitted with an ASH condition identified free or low-cost GP care and after-hours care as key enablers for accessing a GP.27
It is also likely that the barriers to care that we report have a wider impact than only on the individual who is unable to pay. Recent data from the New Zealand Health Survey show that young Māori (<15 years) are 3 times more likely to have not seen a GP due to cost in the past 12 months.28 GP care is free for under 14s, and when analysed by age, the same inequity is seen for those aged 13 years and under. Barker et al. identified that owing money to a practice is a barrier for both adults and children seeking primary health care.27 We suggest that further work, including routine monitoring of specific reasons for cost barriers is important, as the policy implications for different reasons will vary.
The current government in Aotearoa New Zealand has recently announced a policy change which will remove co-payments for prescriptions, however, the opposition party has also announced that they will reverse this if elected, thus, this issue is far from settled. The policy of prescription co-payments affects not only those on low incomes, but also those with high health needs, most. Although the Prescription Subsidy Scheme29 is aimed at ensuring that patients with high health needs do not have to pay more than $100 per year, this bar is set too high; furthermore, there is no corresponding bar for GP care. People who see their GP once a month or more are 6 times more likely to not be able to afford a prescription than those who see their GP once a year. As a result of the COVID-19 pandemic, and subsequent social and financial impacts on low-income families in particular, it is likely that a higher proportion of people will be unable to afford prescriptions, which will have knock-on effects on the demands placed on the health system. A small trial in 1 area of Aotearoa New Zealand (Hutt Valley) found that for each $5 prescription co-payment that a patient did not have to pay $1,200 was saved in terms of hospital bed-days.30 The consequences of the inability to pay to see a GP or for a prescription could not be addressed in this study, due to the cross-sectional nature of the survey design. We plan further work, using linked data within the Integrated Data Infrastructure,31 to determine whether facing this cost barrier results in higher hospital usage on a national level.
Our work also has methodological implications for future analyses of health equity in Aotearoa New Zealand. As shown in the equity analysis, even while employing frequently used variables such as deprivation quintiles, and using an extreme definition of low income, we are unable to “explain away” the inequity. This is a result of determinants of cost barriers to care not being equal between Māori and non-Māori, even at the same level of socioeconomic classification. Future analyses need to consider this unequal distribution, whether the socioeconomic variable is a key variable of interest, or a confounding variable.
Levesque et al.32 identified 5 dimensions of accessibility of services, of which affordability is only 1 barrier, the others being approachability; acceptability; availability and accommodation; and appropriateness. With this in mind, we acknowledge that removing cost barriers will not remedy access for everyone; nevertheless, it will help a large proportion of people access the care that they need within our primary health care system as cost has been shown to be a major obstacle to care.21
Conclusions
In summary, we have described very high levels of cost barriers to primary care for Māori, particularly those living on low incomes. It is inevitable that this is going to have knock-on effects to hospital care, and national health expenditure, as well as the obvious detrimental effects on people’s health. Not only does our work highlight the gap in Aotearoa New Zealand of the primary health care illusion of essential services being affordable to all, we also demonstrate key predictors of those experiencing cost barriers to primary health.
Urgent changes to funding of primary health care and prescriptions in Aotearoa New Zealand are needed, to remove the cost barriers for those in need of health care and to meaningfully address health inequity.
Supplementary Material
Acknowledgements
The authors are grateful to the participants in the New Zealand Health Survey. Statistics New Zealand provided access to the data used in this study under conditions designed to keep individuals’ data secure in line with requirements of the Statistics Act 1975. The opinions presented are those of the authors and do not necessarily represent an official view of Statistics New Zealand. The authors thank the Health Research Council of New Zealand for the funding of the work.
Contributor Information
Mona Jeffreys, Te Hikuwai Rangahau Hauora | Health Services Research Centre, Te Herenga Waka-Victoria University of Wellington, Wellington, New Zealand; Flax Analytics Ltd, Wellington, New Zealand.
Lis Ellison-Loschmann, Flax Analytics Ltd, Wellington, New Zealand.
Maite Irurzun-Lopez, Te Hikuwai Rangahau Hauora | Health Services Research Centre, Te Herenga Waka-Victoria University of Wellington, Wellington, New Zealand.
Jacqueline Cumming, Te Hikuwai Rangahau Hauora | Health Services Research Centre, Te Herenga Waka-Victoria University of Wellington, Wellington, New Zealand.
Fiona McKenzie, Te Hikuwai Rangahau Hauora | Health Services Research Centre, Te Herenga Waka-Victoria University of Wellington, Wellington, New Zealand; Flax Analytics Ltd, Wellington, New Zealand.
Funding
This work was funded by a Programme Grant from the Health Research Council of New Zealand (HRC 18/667), awarded to Dr Jacqueline Cumming, entitled “Enhancing primary health care services to improve health in Aotearoa/New Zealand.”
Conflict of interest
None declared.
Ethical approval and consent to participate
This research was conducted under the Helsinki Declaration Ethical Principles for Medical Research Involving Human Subjects protocol. The New Zealand Health and Disability Multi-Region Ethics Committee granted approval for the New Zealand Health Survey (MEC/10/10/103) in 2011. The need for informed consent was waived by the Health and Disability Ethics Committee (Ministry of Health, dated 17/5/19).
Consent for publication
Not applicable.
Authors’ contributions
MJ and FMcK performed the analysis. MJ, LE-L, and FMcK drafted the manuscript. MJ, LE-L, MIL, and FMcK interpreted the data. JC conceptualized the idea and obtained funding. All authors critically reviewed the manuscript.
Data availability
The data that support the findings of this study are available from Statistics New Zealand but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from Statistics New Zealand on application via access2microdata@stats.govt.nz and the statistical code used is available from the authors upon reasonable request via Dr Jeffreys Mona.Jeffreys@vuw.ac.nz.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from Statistics New Zealand but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from Statistics New Zealand on application via access2microdata@stats.govt.nz and the statistical code used is available from the authors upon reasonable request via Dr Jeffreys Mona.Jeffreys@vuw.ac.nz.