Patient health outcomes associated with models of general practice in Aotearoa New Zealand: a national cross-sectional study

doi:10.21203/rs.3.rs-2310451/v1

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Research Article

Patient health outcomes associated with models of general practice in Aotearoa New Zealand: a national cross-sectional study

https://doi.org/10.21203/rs.3.rs-2310451/v1

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Journal Publication

published 04 May, 2023

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Background

Primary care in Aotearoa New Zealand is largely delivered by general practices which are heavily subsidised by government. At least seven models of primary care have evolved: Traditional, Corporate, Health Care Home, Māori practices, Pacific practices, and practices owned by Primary Health Organisations/District Health Boards and Trust/Non-Governmental Organisations. Te Tiriti o Waitangi (1840) guarantees equal outcomes for Māori and non-Māori, but stark differences are longstanding and ongoing. Pacific peoples and those living with material deprivation also have unequal health outcomes.

Methods

Cross-sectional study (30 September 2018), data from national datasets and practices at patient level. We sought associations between practice characteristics and patient health outcomes, adjusted for patient characteristics. Practice characteristics included: model of care, size, funding model, rurality; number of consultations and time spent with nurses and doctors; practice and doctor continuity. Six primary outcomes measures were chosen: polypharmacy (≥ 65 years), HbA1c testing in adults with diabetes, immunisations (6 months), ambulatory sensitive hospitalisations (0–14, 45–64 years) and emergency department attendances.

Results

The study included 924 general practices with 4,491,964 enrolled patients. Traditional practices enrolled 73% of the population, but, on average, the proportion of Māori, Pacific and people living with material deprivation was low in any one Traditional practice. Patients with high needs disproportionately clustered into Māori, Pacific and Trust/NGO practices. There were multiple associations between models of care and patient health outcomes in fully-adjusted regressions. Patient health outcomes were most strongly associated with: age, Māori or Pacific ethnicity, deprivation (IMD), multi-morbidity (M3), clinical input, number of first specialist assessments, changing practice, and prescribing (SSRIs, tramadol, antibiotics). Being Māori or Pacific remained associated with poorer outcomes after full adjustment including measures of deprivation. Patients with high health need received more clinical input but this was insufficient to achieve equity of outcomes. Practice-level variance was highest for emergency department attendances.

Conclusions

Resource models of care with disproportionately high and complex patient health need (Māori, Pacific and Trust/NGO practices). Associations between patient and practice characteristics, and patient health outcomes, should be central to investment decisions.

Models of care

primary care

patient health outcomes

ambulatory sensitive hospitalisations

polypharmacy

immunisations

emergency department attendance

Māori

Pacific

deprivation

Historical context

Fairness is a central value of Aotearoa New Zealand society [1, 2]; fairness is a matter of social justice [3]. However, health outcomes for Māori, the indigenous population, have been starkly worse than for non-Māori for more than a century [4]. Te Tiriti o Waitangi, an agreement between the British Crown and Māori signed in 1840, placed responsibility on the Crown “to protect actively Māori health and wellbeing through the provision of health services” [5]. Despite this, inequity remains embedded in health services, a legacy of the effects of colonisation and institutional racism [5]. Furthermore, persistent inequities for Pacific peoples and those living with material deprivation blight our health statistics [6].

Primary care in Aotearoa New Zealand operated on a fee-for-service basis from 1938 to 2001, part paid by government, and part paid by patients (a low proportion of whom were insured). Most care was provided by general practices, run as self-employed businesses with substantial government funding; this continues to be the basis of Traditional practices and the more recent development of Health Care Home (HCH) practices. From 1970 a practice nurse subsidy scheme supported general practitioners (GPs) to employ registered nurses to “provide a doctor’s assistant” [7]. By 1999 there was a nurse in 94% of practices [8] and a shift towards teamwork. Non-profit primary care organisations proliferated through the 1980s and 1990s – most notably Māori initiatives – in response to demands for affordable, culturally safe care [9]. General practice size increased through the 1990s, halving the number of solo-doctor practices from 32% in 1990 to 17% by 1999 [8], in response to government policy changes in contracting for health services [9].

A major change in health service delivery in 2001 came with a policy intent to shift towards primary health care [10], with its emphasis on addressing health inequalities [11] and attaining better health services for all [12]. The bulk of government funding shifted to a capitation formula, while retaining an option of patient part-payments. Capitation was adjusted primarily for the number, age and gender of patients enrolled in each practice, and whether practices contracted for additional payment together with a cap on patient co-payments in the Very Low Cost Access (VLCA) scheme [13]. Other funding adjustments were introduced over the years, for example, free consultations for children and limits to patient fees for prescription medicines. In this context several models of primary care delivery have evolved, mostly since 2001, providing the opportunity to compare models.

Along with these changes, other trends have shaped general practice. Private corporations have acquired existing practices as a business investment. Primary Health Organisations (PHOs), Government District Health Boards (DHBs) and community trusts have taken over some Traditional practices to ensure service to underserved geographic regions or communities. There has been an increase in the number of Māori health provider organisations and, more recently, Pacific health provider organisations, to address unmet health and social need in their communities. Our study was able to develop a typology of seven categories of primary care organisational models.

The New Zealand Government has developed policy and long-term plans to build a workforce that reflects diverse communities [14]. There has been an increase in the number of health care assistants (HCA) supporting clinicians under delegated authority. New roles include a registered profession of Fully Authorised Vaccinators [15] and a registered profession of Health Improvement Practitioner [16]. The role of nurses in general practice has expanded, supported by government scholarships [17]. The number of nurse prescribers and nurse practitioners (NP) was starting to increase at the time of this study. The patient-centred medical home has been adapted to Aotearoa New Zealand as the “Health Care Home” [18], and has been actively promoted by some DHBs and PHOs [19].

The health system in Aotearoa New Zealand is in the midst of reform under the Pae Ora (Healthy Futures) Act. A new Māori Health Authority (Te Aka Whai Ora) represents a new level of partnership that prioritises Māori health need, alongside that of the total population. The Act signals a focus on localities but is otherwise silent on primary health and community care. Meanwhile, the Budget for 2022-23 [20] ignores long-term under investment in this area. Neither Health New Zealand (a new entity to deliver hospital and specialist health services) nor the Ministry of Health has a directorate with a specific focus on primary health and community care. Overall, reform legislation, budgets and structures have downplayed the potential of the primary health and community care sector to improve health outcomes and equity [21]. Evidence is needed to support targeted investment in primary health and community care.

This study

The New Zealand Ministry of Health and Health Research Council of New Zealand sought research to guide investment in practice models that gave best patient health outcomes. The models they identified initially were Traditional, Corporate and Health Care Home. We defined these further and extended the classification to recognise a further four models; Māori practices, Pacific practices, and owned by a PHO/DHB or a Trust/Non-governmental organisation (NGO).

Studies of patient health outcomes in primary care can be grouped into those that evaluate a model of care, such as the patient centred medical home [22], those that consider specific features of primary care, such as continuity of care, access and the role of nurses [23–25], or features of individual practitioners [26]. Practice and practitioner processes may be considered together within a quality framework [27]. Many studies measure quality of care for a single condition, such as diabetes [28]. There is no single agreed set of outcomes [29]. Studies vary in the extent to which they adjust for patient need and few address issues of equity.

The aim of this study was to determine whether differences in patient health outcomes could be attributed to models of practice, after adjustment for other factors; whether these differences implied equity or inequity at the level of model of care; and whether we could explain the findings in terms that could indicate improvements to models of care. In regressions that adjust for ethnicity, material deprivation and other markers of patient need, an absence of significant association between a model of care and an outcome would imply equity at model-level. Significant associations with better outcomes would imply a model-level characteristic that might be applicable to other models.

Our priority was to consider the care of those less well-served by the current health system; this is disproportionately people who are Māori, Pacific, or living with material deprivation. The inquiry was at the level of general practice rather than individual practitioner. This paper reports on models of care and patient health outcome in the general population. Results from analyses on Māori-only and Pacific-only populations are reported elsewhere in the same collection of papers in this Journal.

A cross sectional, observational study was conducted of all Aotearoa New Zealand general practices and enrolled patients as of 30 September 2018. The date was chosen because funding changes in December 2018 were likely to confound data interpretation. Further detail on methods can be found in Supplementary file 1.

Data sources

Data came from national datasets, held by the Ministry of Health, and from practice information held by PHOs. All general practices belong to a PHO, which contracts to a DHB to provide primary care services. DHBs were responsible for publicly funded regional health services; they ceased to exist as independent entities from 1 July 2022. National datasets included PHO registers, inpatient, outpatient, laboratories, pharmaceutical dispensing, immunisations, the Virtual Diabetes Register (VDR), the Index of Multiple Deprivation (IMD) and the Measuring Multimorbidity Index (M3) at patient level.

The VDR lists all individuals considered to have diabetes based on administrative data [30]. The IMD assigns a deprivation score to each geographic data zone; this score is attributed to individuals resident in that zone, averaging about 700 people per zone. The index is constructed from seven domains, which can be used independently: employment, income, crime, housing, education, health, and access. We used all domains except health [31]. The M3 index is a score assigned to each individual based on number and type of conditions they have, derived from hospital discharge coding [32].

Data from these sources were available for all practices. The workforce numbers and Full Time Equivalents come from a practice survey sent to practices by all participating PHOs, so are self-reported.

The national PHO register lists patients enrolled in each practice. A patient unique identifier, their National Health Index (NHI), is used throughout the health system. Processes linking patient-level data using an encrypted NHI are well-established.

Every practice in Aotearoa New Zealand uses an electronic medical record. All PHOs extract data from practices, although details vary between PHOs. We requested data on number and length of consultations and the profession of the clinician seen. Data extracted from the appointment book represent face to face consultations, but not telephone, email or other contacts.

Defining practice models

Traditional practice: Typically centred upon the general practitioner, with mainly nursing support, operating as a small business, and owned by one or more doctors. These ranged from small to large organisations and served both high need and lower need populations. This is the longest-standing model and constitutes the majority of practices. Individual practices have a high degree of autonomy over service delivery.
Corporate practice: A group of practices owned and run as a for-profit business entity. Some delivered high volumes of care, with low costs for patients and often without the need for an appointment. Corporate practices had a relatively high degree of standardisation in business and clinical processes and information technology across different sites. Most corporate practices were Traditional practices before being bought by a corporate entity.
Health Care Home (HCH): An adaptation of the Patient Centred Medical Home; the New Zealand HCH Collaborative maturity matrix focuses on business efficiency and sustainability [18]. It is a relatively new concept in Aotearoa New Zealand, with the first practice formally enrolling in the programme in 2011. Only 14 had been fully certificated as mature HCHs by 30 September 2018 (A Maxwell, personal communication 2018). At the time of this study those not certificated were at different stages of meeting the maturity matrix criteria. Most had been Traditional practices prior to embarking on the HCH programme.
PHO/DHB practices: Practices owned by a PHO or a DHB. This was a small group that had mostly been taken over by a PHO or DHB to continue to provide primary care services in a specific location, often an underserved and/or rural area.
Trust/NGO practices: One or more practices owned by an entity that was a not-for-profit Trust or NGO. They had a stated purpose, identifying a health or social goal. Many were in small communities or served populations with high need. Some provided, for example, salary and premises to attract and retain staff.
Māori practices: Practices owned and governed by Māori organisations, serving Māori and non-Māori patients. They were identified through lists from the Ministry of Health and DHBs together with web searches, direct contact with practices or were known to investigators. There may be a small number of practices we did not identify as Māori practices.
Pacific practices: Practices owned and governed by Pacific organisations, serving mostly Pacific and some non-Pacific patients. They were identified through lists from the Ministry of Health and DHBs together with web searches, direct contact with practices or were known to investigators. There may be a small number of practices we did not identify as Pacific practices.

Traditional, Corporate, PHO/DHB or Trust/NGO were considered to be ownership types. We assigned every practice to one of these ownership types although some practices were difficult to categorise. HCH, Māori and Pacific practices could overlap with ownership types, and HCH could overlap with Māori and Pacific practices.

Patient health outcomes

Outcome measures were selected from existing performance indicators within collections of the New Zealand Health Quality and Safety Commission or the New Zealand Health Quality Measures collections, with already specified technical measurement standards [34]. Measures were known to show significant inequities between groups by health need, material deprivation or ethnicity but none had previously been examined for variation by primary care model of care. The six study outcomes were as follows.

Polypharmacy: Patients over 64 years old taking 5 or more long term medications over two consecutive quarters [35].
HbA1c Testing: Patients on the national VDR with one or more HbA1c test in the previous year.
6 Month Immunisation: Children who had received, by age 6 months, all the scheduled immunisations up to and including those due at 5 months. The calculation includes only children who were 6 months old at some point during the analysis period. The Ministry of Health definition of on-time immunisation allows for a window of 1 month after the due date [36].
Child ASH Admissions: The number of ambulatory sensitive hospital admissions for children who were under 15 years of age at the end of the analysis period [37, 38].
Adult ASH Admissions: The number of ambulatory sensitive hospital admissions for adults who were between 45 and 64 years of age at the end of the analysis period [38].
ED Attendances: The number of attendances at an ED for each patient over the analysis period.

The analysis period was the year 1 October 2017 to 30 September 2018. All measures used the national data sets. No adjustment was made for reduced data from those who died during the year of observation. Three outcomes were process measures: polypharmacy, HbA1c testing and immunisations. Three were measures of intermediate outcomes: child and adult ASH and ED attendances. Better outcomes were assumed to be lower polypharmacy, ASH and ED attendances, and higher HbA1c testing and immunisations.

Additional process measures

Data from a subset of practices (numbers are given with each result) allowed calculation of three items of preventive care: rates of cervical screening, cardiovascular risk assessment and HbA1c testing. Defining guideline-recommended eligible populations required data that were not available. For example, cardiovascular risk assessment is recommended for a population defined by age, gender, ethnicity, family history and additional risk factors. Therefore, while numerators are accurate, denominators included persons who not eligible for each process so the calculated rates may be lower than a “true” measure. However, since the same method is applied to all practice models, relative differences between models remain valid.

Explanatory variables

Patient characteristics. Age, gender, Māori or Pacific ethnicity, living in a deprivation area quintile 5 (most deprived quintile, Q5), Index of Multiple Deprivation (IMD) score of the area the patient lives in, distance to the nearest ED, being on the VDR and a multimorbidity score (M3); in the previous year having gout, being dispensed a selective serotonin reuptake inhibitor (SSRI, usually for depression), dispensed tramadol (for moderate to severe pain) or dispensed an antibiotic; during the previous year, whether patient changed their enrolled practice (a measure of practice continuity), and had a first medical specialist assessment (FSA), or did not attend a FSA.

Practice characteristics. VLCA practices agree to receive increased capitation funding while limiting their fees to patients. Practice uptake of this contract is voluntary subject to having an enrolled population of ≥ 50% Māori, Pacific or people living in Quintile 5 areas. Practices were designated as either urban or rural based on the rural status of a majority of their enrolled patients. The percentage of patient consultations, in the previous year, with the same GP was used as a measure of personal continuity.

Primary care clinical input. Activities recorded in the practice appointment record, taken to indicate face-to-face appointments, were attributed to a RN, NP, GP or Other. Other included health care assistants, dieticians, physiotherapists, Quit smoking providers and unidentified persons. Total Consultations refers to the number of consultations with a GP or NP in the previous year. Time spent with each patient was cumulated to a proportion of Full Time Equivalent (FTE) per 1000 enrolled patient, separately for GPs, NPs and RNs.

Uncertainty in classification. Some factors may represent both patient characteristics and clinician input; for example, attending a VLCA practice might be patient choice (due to low fees), but might be a practice business decision in response to local patient poverty. Having a FSA reflects patient need and a referral from primary care in response to that need; Did Not Attend a FSA might indicate a patient barrier to access. Total Consultations, GP and RN FTE can also be seen as markers of both patient need and system response.

Regression analyses

Multilevel mixed effects regression analyses used patient-level data adjusted for clustering at practice level. All analyses were conducted in R statistical software [39, 40]. Variables that do not appear in the final regressions were not statistically significant in development models. The comparators used in the regressions vary between practice models. Ownership categories Corporate, PHO/DHB and Trust/NGO were compared to Traditional. HCH, Māori practices and Pacific practices were compared with not-HCH, not-Māori practices and not-Pacific practices, respectively. Variance was partitioned at the level of patient and practice, but not at level of model of care because a given practice might be classified to more than one model. Statistical significance is cited at p ≤ 0.05, with no adjustment for repeated modelling and multiple outcomes.

At 30 September 2018 there were 988 practices in the national PHO dataset. A small number of practices were combined where direct enquiry confirmed a single practice had registered its patients under individual doctors, or where parent and satellite clinics operated a single practice. Others were excluded as not relevant to our research question (rest-home services, youth-only services, student services or practices that opened or merged, closed or changed PHO during the analysis period). This left 924 practices, with 4,491,964 enrolled patients, as the subject of this study. We classified each practice to one ownership category (top row in Table 1), and, where relevant, as also Māori practices or Pacific practices or Health Care Homes. Most Māori practices (59/65) and Pacific practices (11/15) were owned by a Trust or NGO. Not able to be shown in the table, is that 10 practices were HCHs and Māori and Trust/NGO; one practice was HCH, Māori and Corporate; and one practice was HCH Pacific and Trust/NGO.

Table 1

Practice models showing overlapping categories
	Traditional (n = 695)	Corporate (n = 103)	PHO/DHB (n = 27)	Trust/NGO (n = 99)
Māori (n = 65)	3	3	0	59
Pacific (n = 15)	4	0	0	11
HCH (n = 127)	90	14	7	16
No overlapping model	598	86	20	13

VLCA contracts (with lower patient co-payments, were held by 94% of Māori practices, 87% of Pacific practices, 78% of Trust/NGO, 56% of PHO/DHB, 35% of Corporate, 23% of HCH and 21% of Traditional practices.

Overall, 17% of practices were considered rural; the proportion was higher for Māori practices (34%), PHO/DHB practices (37%), Trust/Other practices (37%) and HCH (21%); models with lower percentages were Traditional (14%) and Corporate practice (11%); no Pacific practices were rural.

There were marked differences between the populations enrolled at the practice models, shown in Table 2. The majority of patients (73%) were enrolled in Traditional practices, however; this included smaller proportions of Māori (59%), Pacific (49%) and people living in quintile 5 deprivation areas (56%). Conversely, Corporate practices, with 17% of total patient numbers, served 33% of Pacific people and 23% of the quintile 5 population. Māori practices and Pacific practices enrolled 19% and 10%, respectively, of all Māori and Pacific patients. While Trust/NGO practices enrolled 8% of the population, the proportions of enrolled patients who were Māori, Pacific or people living in deprivation quintile 5 were 19%, 17% and 17%, respectively.

Table 2

Patients enrolled: practice model by ethnicity and deprivation
Practice model	Enrolled patient populations
	All patients (n = 4,491,965)	Māori (n = 660,752)	Pacific (n = 312,670)	Quintile 5 (n = 872,028)
Corporate	745,512 (17%)	119,585 (18%)	102,710 (33%)	202,297 (23%)
Traditional	3,261,719 (73%)	390,895 (59%)	152,756 (49%)	491,890 (56%)
PHO/DHB	142,507 (3%)	22,600 (3%)	4,548 (1%)	29,139 (3%)
Trust/NGO	342,226 (8%)	127,672 (19%)	52,656 (17%)	148,702 (17%)
Māori	241,503 (5%)	124,854 (19%)	21,544 (7%)	120,449 (14%)
Pacific	48,233 (1%)	4,816 (1%)	29,715 (10%)	26,026 (3%)
HCH	909,690 (20%)	132,448 (20%)	52,253 (17%)	165,623 (19%)

Note. Numbers do not add to 100% due to overlapping practice model categories

Three measures of preventive care were available, shown in Table 3. Data came from 292 practices in 10 PHOs over a three-year period. For cervical screening the denominator was all women age 18 and over; for cardiovascular risk assessment, all adults age 18 and over; and for HbA1c testing, all people known to have diabetes. The relevant clinical guidelines recommend these preventive care activities for more narrowly-defined target populations but the data were not available to specify the denominators more precisely. The rates given are therefore an underestimate.

Variation across the practice models was about two-fold for cervical screening and cardiovascular risk assessment, and 19% for HbA1c testing. Corporate practices had the highest rate for cervical screening but were near the low end for cardiovascular risk assessment. Traditional had the highest rate for HbA1c testing. HCH practices had the lowest rates of cervical screening and cardiovascular risk assessment. Pacific practices had the highest rate for cardiovascular risk assessment and were near the high end of HbA1c testing.

Table 3

Preventive care processes over 1 year, by practice model
Practice model	Cervical screening per 1000 women	Cardiovascular risk assessment per 1000 adults	HbA1c testing per 1000 adults with diabetes
Traditional	322	212	1934
Corporate	469	193	1855
PHO/DHB	330	302	1824
Trust/NGO	310	233	1693
Māori	394	238	1630
Pacific	332	319	1884
HCH	243	150	1784

Note. Cervical screening and cardiovascular risk assessment from PHO data, HbA1c from national laboratory dataset

Further differences between models, in terms of profile of enrolled patients, are shown in Table 4. Trust/NGO, Māori and Pacific practice models, compared with other models, show: smaller practice size; higher levels of deprivation quintile 5, IMD, M3, percentage Māori or Pacific; higher levels of nurse FTE and ratio of nurses to doctors (considered as a response to health service need; and lower levels of practice continuity.

Table 4

Practice models showing differences between populations enrolled. Variables are considered to reflect aspects of patient need.
	All practices	Traditional	Corporate	PHO/DBH	Trust/NGO	Māori	Pacific	HCH
N enrolled ^a	3622 2074–6189	3597 2040–5971	5527 3133–9858	3642 1990–6865	2712 1528–4985	2954 1528–4975	2356 1722–5141	5750 3822–9502
Age ≤ 14 ^b	19% 17–22	19% 16–22	21% 17–25	21% 17–25	23% 20–26	25% 22–27	25% 23–28	20% 18–22
Age ≥ 65 ^b	16% 12–21	19% 16–22	21% 17–25	21% 17–25	23% 20–26	11% 8–15	9% 6–12	16% 12–20
Māori ^b	10% 6–17	9% 5–14	11% 7–18	11% 8–23	34% 10–71	62% 35–77	8% 5–13	10% 6–18
Pacific ^b	2% 1–5	2% 1–4	3% 1–10	2% 1–3	3% 1–11	4% 2–9	63% 30–85	2% 1–5
Quintile 5 ^b	12% 5–29	11% 4–21	17% 7–32	13% 3–35	47% 23–65	58% 38–72	46% 36–66	11% 5–23
IMD ^a	2882 2191–3775	2707 2095–3473	3205 2406–3945	2902 2340–3972	4331 3445–4901	4568 3987–5030	4623 3735–4906	2798 1917–3569
With diabetes ^b	5% 4–7	5% 4–6	5% 4–7	5% 4–6	7% 6–8	8% 7–9	11% 7–13	5% 4–6
Dispensed SSRI in last year ^b	7% 5–8	7% 6–9	7% 4–8	7% 5–8	5% 3–7	4% 3–7	3% 1–4	8% 7–9
Dispensed antibiotic in last year ^b	40% 35–44	40% 35–44	40% 37–45	38% 33–41	39% 32–44	42% 37–45	47% 44–50	38% 34–40
M3 ^a	0.14 0.11–0.16	0.13 0.11–0.16	0.13 0.11–0.15	0.13 0.10–0.16	0.18 0.14–0.19	0.18 0.16–0.21	0.16 0.13–0.19	0.14 0.12–0.17
Non-continuity of practice ^b	21% 17–27	20% 16–26	23% 18–31	22% 18–26	25% 19–32	27% 21–33	33% 24–42	21% 17–27
Distance to nearest ED ^c	7.4	7.3	6.7	12.7	9.4	8.7	5.4	8.1
FTE nurse: 1000 enrolled patients ^a	0.54 0.38–0.72	0.51 0.37–0.68	0.58 0.38–0.71	0.64 0.47–0.77	0.90 0.67–1.01	0.79 0.68–0.97	0.87 0.67–0.96	0.59 0.50–0.80
FTE GP: 1000 enrolled patients ^a	0.63 0.51–0.79	0.63 0.51–0.80	0.60 0.51–0.72	0.63 0.51–0.65	0.69 0.51-1.00	0.68 0.55–0.90	0.67 0.57–1.01	0.60 0.52–0.77
^a number median / 25th – 75th centile, ^b percentage median / 25th – 75th centile, ^c kilometres, median / 25th – 75th centile

The distance to nearest ED was largest in PHO/DHB practices, which fits with these typically being in small and/or rural areas. Pacific practices showed the lowest distance to ED, which fits with Pacific peoples being largely urban.

Statistical models

The final statistical models for each of the six primary outcomes are summarised and compared in Table 5. Age, numerical p-values and interactions are omitted from the summary, but the full models are available in Supplementary file 2, Table 1.

Table 5

Summary of final models for patient health outcomes across 924 practices. Age effects and interactions omitted – full regression outputs in Supplementary file 2.
Variable (reference values)	Polypharmacy Age 65+ N = 399,227 R² = 0.364	HbA1c for those with diabetes N = 133,985 R² = 0.1366	6 month immunisations N = 26,859 R² = 0.0795	Child ASH admissions N = 511,845 R² not applicable	Adult ASH admissions N = 655,088 R² not applicable	ED attendances N = 2,500,000 R² not applicable
Overall average	38.2%	86.9%	75.6%	31 per 1000 children	38 per 1000 adults	254 per 1000 patients
Practice models
Corporate (Traditional)	37.5% (38.3%)	86.3% (87.0%)	74.3% (75.7%)	-9.3%	20.9% ***	1.4%
PHO/DHB (Traditional)	35.5% (38.3%)	86.5% (86.9%)	74.9% (75.6%)	-14.5%	10.0%	10.3%
Trust/NGO (Traditional)	38.1% (38.2%)	88.4% (86.7%)	79.3% (75.2%)	38.3% **	31.5% ***	15.4% **
HCH Practice (All others)	38.7% (38.1%)	86.2% (87.1%)	78.5% (74.8%) ***	5.6%	-5.4%	-11.2% ***
Māori Practice (All others)	34.7% (38.0%) *	82.9% (87.0%) **	61.8% (76.4%) ***	-0.4%	5.9%	9.6%
Pacific Practice (All others)	36.9% (38.2%)	83.6% (87.0%)	66.5% (75.7%) *	-8.5%	-12.1%	-15.1% *
Patient characteristics
Male (Female)		87.5% (86.2%) ***			30.2% ***
Māori (Not Māori)	37.8% (38.2%)	85.5% (87.1%) ***	68.4% (77.0%) ***	28.1% ***	27.4% ***	20.8% ***
Pacific (Not Pacific)	34.2% (38.3%) ***	85.5% (87.1%) ***	76.1% (75.6) *	40.2% ***	28.0% ***	19.5% ***
Quintile 5 (Not Q5)		86.3% (87.1%) ***
IMD (25th, 50th, 75th centiles) (ASH&ED ref: average IMD)	36.0% *** 38.5% 41.0%		78.1% *** 76.1% 73.9%	-11.2% *** 0.5% 13.1%	-10.5% *** 0.5% 11.4%	-8.2% *** 0.0% 8.9%
Diabetes (No diabetes)	67.9% (32.9) ***				20.4% ***
Gout (No gout)	65.4% (35.7%) ***	89.0% (86.5%) ***			9.6% ***
HbA1c (No HbA1c)	40.8% (34.3%) ***
SSRI (No SSRI)	59.1% (36.3%) ***					36.5% ***
Antibiotic (No antibiotic)	41.9% (35.2%) ***	88.0% (85.6%) ***		145.9% ***	130.2% ***	86.2% ***
Tramadol (No tramadol)					58.2% ***	173.9% ***
M3 (25th, 50th, 75th centiles) (ASH&ED ref: M3 = 0)	34.9% *** 38.0% 44.1%	88.3% *** 87.6% 86.3%		19.3% *** 64.8% 256.6%	20.4% *** 49.2% 118.6%	13.8% *** 32.8% 77.5%
Continuity of practice (No continuity)				-24.3% ***	-18.9% ***	-20.2% ***
Distance to Nearest ED (1, 20, 100km) (ASH&ED ref: average distance)			76.8% *** 74.9% 66.0%		3.2% -1.3% -18.1%	6.1% *** -3.0% -33.4%
First Specialist Assessment (FSA 1, 2, 3) (ASH&ED ref: FSA = 0)	39.8% *** 42.1% 44.3%	87.8% *** 88.9% 90.0%		45.9% *** 112.9% 210.7%	45.1% *** 110.5% 205.5%	46.1% *** 113.6% 212.2%
First Specialist Assessment Did Not Attend (FSA DNA 1, 2, 3) (ASH&ED ref: FSA DNA = 0)			67.9% *** 58.9% 49.2%	15.5% ** 33.4% 54.2%	50.9% *** 127.6% 243.3%	50.7% *** 127.2% 242.5%
Practice characteristics
VLCA (not VLCA)		86.2% (87.3%) *	74.1% (76.3%) *
Urban (Rural)	38.1% (38.9%)	86.7% (87.8%) *	75.9% (73.6%)	-3.4%	-0.3%
Continuity of GP (25th, 50th, 75th centiles) (ASH&ED ref: Continuity = 0)				-6.6% *** -8.6% -12.7%
Primary care clinician input
GP + NP consultations (Consultations 1, 2, 3) (ASH&ED ref: Consultations = 0)	25.4% *** 28.3% 34.6%	81.8% *** 83.1% 85.3%	73.8% *** 74.3% 75.2%	8.7% *** 18.2% 39.8%	8.1% *** 16.9% 36.7%	7.5% *** 15.5% 33.3%
RN hours (Hours 1, 2, 4) (ASH&ED ref: average hours)		86.0% *** 86.2% 86.6%	74.1% *** 74.3% 75.1%
GP hours (Hours 1, 2, 4 ) (ASH&ED ref: average hours)			74.8% *** 75.3% 75.4%	0.4% 0.0% 0.0%
p < 0.05, p < 0.01, **p < 0.001.
• Polypharmacy, HbA1c testing and immunisation results are logistic regressions
o For binary variables, results are % of patients with that outcome, i.e. if variable = 1 (the result if variable = 0 is given in brackets) .
o For continuous variables, results are % of patients with that outcome, at specified value of variable, e.g. 25th, 50th, 75th centile, or 1, 2, 4 hours
• ASH and ED results are negative binomial regressions
o For binary variables, results are % change from the reference value i.e. result if variable = 1 compared to value if variable = 0.
o For continuous variables, results are % change, at stated values, relative to the stated reference value; e.g. at GP hours 1, 2 or 4 compared to average hours)
• Traditional practice is used as a reference for Corporate, PHO/Trust and Trust/NGO practice models.
o The value for Traditional practice is given in brackets. This value varies slightly as the R margin command does not implement MEM (Marginal Effect at the Mean). We coded our own version of MEM but it is an approximation and there is some variability.

Across the six outcome regressions, the bulk of variance was at patient level. The proportion of variance at practice level was 4% for polypharmacy, 7% for HbA1c testing, 10% for immunisations, 6% for child ASH, 4% for adult ASH, and 17% for ED attendance. The highest rate for ED attendance was in Trust/NGO practices, and the lowest in HCH and Pacific practices.

Practice models

The analysis found the following statistically significant associations between practice models and the six patient health outcomes, after adjusting for patient characteristics and primary care clinical input. Percentages are relative differences from the comparator for each outcome.

Traditional: Reference category.
Corporate: Adult ASH rate 21% higher than Traditional.
PHO/DHB: No statistically significant associations.
Trust/NGO: Child ASH rate 38% higher, adult ASH rate 31% higher, ED attendances 15% higher than Traditional.
Māori practices: Polypharmacy 9% lower, HbA1c testing 5% lower, immunisations 18% lower than other practices.
Pacific practices: Immunisation rate 12% lower, ED attendances 15% lower than other practices.
HCH: Immunisation rate 4% higher, ED attendances 11% lower than other practices.

These comparisons do not indicate absolute quality of care, which can be assessed from the activity summaries in Table 3 and the population median outcomes given in Table 5.

Māori and Pacific patients

Overall, Māori patients showed poorer outcomes on 5 of 6 measures, with no difference for polypharmacy. Pacific patients showed poorer outcomes on 5 of 6 measures, with an immunisation rate above the population average. The effect of ethnicity remained after adjustment for deprivation and vice-versa, and remained after adjustment for multiple patient and health service characteristics including practice model of care.

Further patient and practice characteristics

Age was strongly associated with all outcomes (except immunisations, which were assessed at a specific age), see full regression outputs in Supplementary file 2, Table 1.
Male gender was associated with an increase in HbA1c testing and adult ASH.
Deprivation (Quintile 5 rather than quintiles 1 to 4, or an increasing IMD score) was associated with increased polypharmacy, ASH, ED attendances, and a decrease in immunisations. IMD at the 75th centile was associated with 13.1% more child ASH than at the average IMD.
Rural practice was associated with a small increase in HbA1c testing.
VLCA practice was associated with a small decrease in HbA1c testing and immunisations.
Having diabetes was associated with polypharmacy, and 20.4% more adult ASH than patients without diabetes.
Having gout was associated with an increase in polypharmacy, HbA1c testing and adult ASH.
SSRI dispensing was associated with an increase in polypharmacy and a 36.5% increase in ED attendances.
Antibiotic dispensing was associated with an increase in polypharmacy and HbA1c testing; and increases in child ASH, adult ASH and ED attendances of 145.9%, 130.2% and 86.2%, respectively.
Tramadol dispensing was associated with an increase of 58.2% in adult ASH and 173.9% in ED attendances.
As M3 increased, polypharmacy increased and Hba1c testing decreased. M3 at the 75th centile (for patients with a non-zero score) was associated with 256.6% more child ASH, 118.6% more adult ASH and 77.5% more ED attendances (compared to average IMD for patients with a non-zero score).
Continuity of practice was associated with a decrease in child ASH of 24.3%, adult ASH of 18.9% and ED attendances of 20.2%.
As the distance increased from a patient’s home address to the nearest ED, there was a decrease in immunisations, adult ASH and ED attendances.
Continuity of GP, at the 75th centile, was associated with a decrease in child ASH of 12.% compared to a child with no continuity.
More FSA were associated with an increase in polypharmacy and HbA1c testing. Compared with no FSA, patients 3 FSA were associated with increases in child ASH, adult ASH and ED attendances of 210.7%, 205.5% and 212.2%, respectively.
More FSA Did Not Attend: a count of 3 FSA DNA was associated with a 49.2% immunisation (noting the overall average was 75.6%). A count of 3 FSA was also associated with increases in child ASH, adult ASH and ED attendances of 54.2%, 243.3% and 242.5%, respectively.

Primary care clinician input

More total consultations (GP and NP) were associated with increased polypharmacy, HbA1c testing and immunisations. A count of 3 consultations, compared with none, was associated with increases child ASH, adult ASH and ED attendances of 39.8%, 36.7%, and 33.3%, respectively. These associations suggest more clinical input in response to need, but with insufficient resources available to achieve equitable outcomes
More RN hours were associated with increased HbA1c testing and immunisations. More GP hours were associated with increased immunisations. These associations suggest effective clinician input.

Patient characteristics explained the bulk of the variance in associations between practices and outcomes. Nevertheless, there remained sufficient variance at the level of practice to be considered a target of policy, interventions and ongoing data monitoring. The largest practice-level variance was 17% for ED attendance. The highest rate for ED attendance was in Trust/NGO practices, and the lowest in HCH and Pacific practices. The regressions further allowed us to assess the associations of seven practice models of care, after adjusting for patient and practice characteristics. There were 36 associations between model of care and patient outcomes (six outcome regressions for seven models less a reference model). Twenty-four associations were statistically non-significant, indicating no difference in outcome attributable to model of care after adjustment for patient and practice characteristics. At the level of model of care there were seven associations with worse patient outcomes and four associations with better patient outcomes. While some findings are small in absolute terms, it should be noted that they are cumulative over time and over the six outcomes measured. Furthermore, there are likely to be similar small but cumulative differences accruing across multiple health outcomes we did not measure.

Broadly, models fall into two groups, based on patient profiles and outcomes in Tables 2, 3, 4 and 5; Traditional, Corporate, HCH and PHO/DHB; and Trust/Other, Māori and Pacific. This is consistent with the fact that most Corporate, HCH and PHO/DHB practices started life as Traditional practices. It is also consistent with the overlap of classifications Trust/Other, Māori and Pacific practices shown in Table 1.

Traditional practices as a reference

In the regressions, Traditional practice was used as a reference group for the other ownership categories of Corporate, PHO/DHB and Trust/NGO. In 14 of 18 estimates, Traditional was not statistically different from these models. Because 73% of all patients were enrolled in Traditional practices, these had a dominant effect on the overall averages for each outcome. These averages can be directly compared with the estimates for other models of care and for explanatory variables. Table 3 shows Traditional practices to have the highest rate of HbA1c testing, while being mid-range for rates of cervical smears and cardiovascular risk assessment.

Trust/NGO, Māori and Pacific practices

One or more of these practice models show worse patient outcomes for HbA1c testing, immunisations, child ASH, adult ASH and ED attendances (Table 5). In each case worsening outcome is also associated with increased GP and NP consultations combined, often also with increased GP and RN time. Together, this seems to show that practices increase clinical input in response to need, but the increase was not sufficient to meet overwhelming patient need. That need, and related complexity, is shown in high levels of poverty (quintile 5, IMD) and comorbidity (M3, diabetes, and need for specific medications) (Table 4).

Most of these practices had low patient fees. Very Low Cost Access contracts were held by 94% of Māori practices, 87% of Pacific practices and 78% of Trust/NGO practices, compared with the national average of 30%.

Trust/NGO, Māori and Pacific models, compared to other models, had higher ratios of nurses to patients, GPs to patients, and nurses to GPs (Table 4). This suggests differences in function within the models, which are discussed further in the accompanying Nursing paper in this Journal.

Māori practices

Many Māori Provider Organisations were established in the 1980s as predominantly nursing services that employed GPs. These services placed an emphasis on meeting the clinical and cultural needs of local communities. In this study 34% of Māori practices were considered rural which is twice that of the overall practice average (17%).

Patients enrolled in Māori practices showed a lower rate of polypharmacy in people over 64 than patients enrolled in other practice models. A beneficial effect of lower polypharmacy might be mediated by engagement in a culturally safe environment with gains, for example, in communication, health literary, and adherence to therapies and prescribed medications [41–43]. However, given the suggestion above that Māori practices are overwhelmed (together with Trust/NGO and Pacific practices), lower polypharmacy might indicate under-prescribing. Overwhelmed practices seem the likely explanation for lower rates of HbA1c testing and immunisations.

Pacific practices

ED admissions were lower in Pacific practices. These practices were all in urban areas. The enrolled population in a given practice often comprise (mostly) a single Pacific ethnic population using a shared first language. There is some evidence that language is a primary barrier to Pacific peoples engaging with primary care that, elsewhere, is largely conducted in English [44]. Pacific practices also have a high ratio of nurses to patients; all nurses speak English but many also speak one or more Pacific languages. Cultural concordance goes beyond language as noted above for Māori practices.

Corporate practices

Adult ASH rates were higher in Corporate than Traditional practices. Corporate practices that serve large numbers of patients in urban, high need areas are typically accessible via longer opening hours and lower fees than are Traditional practices, suggesting they could be more readily available to provide care that reduces need for ED attendances or hospital admissions. It is possible that referral patterns from primary care to hospital varies between practice models, but we have no data to explore this. Furthermore, patients can attend ED and be admitted to hospital without going through primary care, but it is not known whether the rate of self-referral varies between models of care.

In terms of preventative care, Corporate practices achieved the highest rate for cervical screening, while being at the low end for cardiovascular risk assessment and mid-range for Hba1c testing.

There appear to be differences in prioritisation of preventive care between models of care, for reasons that are not clear. Cardiovascular risk assessment attracted fee-for-service funding, as a national target, at the time of this study. HbA1c testing, as a component of a free diabetes annual review, had long been subject to a fee for service although this had ceased well before this study. Cervical screening has never been subject to national targets or a fee-for-service subsidy except in local initiatives for women considered high risk.

Health Care Homes

The enrolled population profile for HCH practices showed lower patient need than for all practice models except Traditional. HCH practices were associated with a higher immunisation rate, fewer ED attendances and the lowest rates of cervical screening and cardiovascular risk assessment. The HCH Collaborative emphasises systematic care processes and data recording [18]. It is uncertain how well developed the defined HCH features are in non-certificated practices that have started HCH implementation. At the time of this study only 14 of 127 HCH practices were certificated as mature examples of the HCH, with others newer to this style of practice. Early adopters of the HCH model were self-selected and were acknowledged to be high-functioning Traditional practices. As the model is spread further, the same level of performance may not be achieved in all practices. Nevertheless, all HCH practices have committed to uniform standards which may result in less variation between HCH practices than between practices in other models.

Immunisations

Immunisation at 6 months were lower in Māori practices and Pacific practices than for others. With the immunisation schedule requiring vaccines at 6 weeks, 3 months and 5 months, measuring immunisation rates at 6 months leaves a window of only one month in which to administer vaccines due at 5 months [36]. Residential mobility is one barrier to timely immunisation. The proportion of patients who changed practice in the previous year, in our data, was 21% overall but 27% in Māori practices and 33% in Pacific practices. While dissatisfied patients do change practice, it is perhaps more likely that these data reflect residential mobility, some of which will be associated with poverty [45]. Another barrier to immunisation is respiratory infection, a common reason to delay immunisation, and there is evidence of high rates of respiratory illness in Māori and Pacific children [46].

Primary care clinical input

We considered the level of primary care clinical input (GP, NP, RN consultations and time) into the management of individual patients to be a marker of patient need. Secondary care clinical input via First Specialist Assessment is considered to be a measure of both patient need and primary care response by referral.

Primary care clinical input was associated, independent of practice models, with higher (worse) outcomes for polypharmacy, child ASH, adult ASH and ED attendances, and higher (better) outcomes for HbA1c testing and immunisations. This suggests that, independent of model, primary care clinical input was sufficient to meet need for HbA1c and immunisations but not for the other outcomes. This implies a need for more GPs, NPs, RNs and other health care workers, across models, to address hospital use in particular.

HbA1c testing and child ASH

HbA1c testing is a direct measure of quality of care because it is a necessary step on the pathway to good glucose control. Associations with decreased HbA1c testing (Māori practice, Māori patients, Pacific patients, Quintile 5 deprivation, urban practice, M3) suggest this may be due to diabetes being lower priority in the presence of complexity and multimorbidity.

Respiratory illnesses, including pre-school asthma, contribute to the national statistics for child ASH [47]. Young children are vulnerable to rapid deterioration with respiratory illnesses and primary care clinicians appropriately send infants and children to hospital; some children go directly to hospital without attending primary care. The optimum rate of child ASH remains unknown [47]. However, statistically significant differences still indicate real practice-level and model-level variation.

Differences in enrolled populations

Differences in patient health outcomes were associated more strongly with patient need than with practice model. Table 4 shows 15 measures, thought to indicate individual and population need; a markedly higher proportion of higher-risk patients were enrolled in Māori, Pacific and Trust/NGO practices than in other practice models. Taken together, these point to a raised workload necessary to respond toa concentration of complexity in Māori, Pacific and Trust/NGO practices, with direct implications for resourcing.

The majority (59%) of Māori patients and half (49%) of Pacific patients were enrolled in Traditional practices where they were typically a small percentage of enrolees in any one practice. Traditional practices face the challenge of how to specifically address cultural safety when caring for Māori, Pacific, and other ethnic groups in these practices.

Trust/NGO, Māori and Pacific practices employed a higher ratio of nurses to patients, and nurses to doctors, than other models of care. They explicitly address both the health and social needs of patients and often work in a complex organisation. Most Māori and Pacific practices were constrained within the same funding streams as other practices, although some have received additional support through the Māori Provider Development Fund or the equivalent Pacific fund.

Current resourcing options do not target the range of patient need

Equitable resource allocation is required to improve health outcomes for Māori, Pacific and people living with material deprivation. At the time of this study, funding and resources to support primary care were provided through a range of mechanisms that target financial support for health services to: individuals (Capitation and the High Use Card [48]; the individual and family (Community Services Card [49]); the whanau / household (Prescription subsidy scheme [50]); the practice at which the patient is enrolled (Very Low Cost Access (VLCA) [51]); specific services (DHB and PHO programmes e.g. palliative care); specific conditions (DHB and PHO disease management programmes) and on residential area deprivation (Services to Improve Access). All these subsidies are paid to health care providers and do little to address the multiple additional barriers to access [52], especially for patients with complex clinical or social circumstances. In addition, many PHOs support locality-based resources – such as employing kaiawhina / community health workers who work across multiple practices.

Practice size

Practice size was not retained in any of the final models. It is possible that any effect of practice size was captured in factors that correlated with size such as rurality, workforce consultations and FTE and continuity of GP. The literature is mixed on the relationship of practice size to measures of process or outcomes. A report from the UK analysed data on practice size in relation to measures from the Quality and Outcomes Framework and rates of ASH [53]. They found that larger practices performed better, but with wide variation, and without being able to adjust for patient characteristics. A systematic review in 2013 included 13 cross-sectional studies. Five of 10 studies reported better scores on some processes of care in larger practices, while three studies of patient-reported outcomes found better access in smaller practices [54].

Rurality

In many jurisdictions, rurality is a risk factor for poor health outcomes. This is likely to be due to associations of rurality and other known risk factors, including access to health services and socio-economic disadvantage compared to urban dwellers [55]. In the current study, rural patients are, by a small margin, more likely to have an HbA1c test, but otherwise rurality is not associated with the outcomes we measured, after adjustment for a wide range of patient characteristics. We note, however, that rurality is associated with several measures that may themselves influence health service access including greater distance to nearest ED, small practices, PHO/DHB practices and Trust/Other practices and Māori practices, and Māori but not Pacific ethnicity, .

Primary care data

Despite collecting the largest data set on primary care in Aotearoa New Zealand to date, we are aware of gaps in the data, such as for mental health care and nurse work.

National datasets were largely clean and consistent, reflecting the substantial infrastructure behind them. However, significant effort was needed to clean and interpret national data from practice registers. Historically, the registers were assembled for capitation purposes, and did not always match the definition of a practice from other perspectives. For example, satellite clinics might be listed as separate entities or might be merged with the parent clinic. It is unclear whether the National Enrolment System, implemented since we extracted our data, will make this task easier in the future.

Extracting data from practices via PHOs presented significant challenges. Each PHO had a different approach to permitting data to be used and the extent of data they collected routinely so were able to the study.

There is a need to standardise data collection and analysis from all practices, using, for example, existing measures in the Atlas of Variation. There are important unresolved questions about who owns patient data and the extent to which analysis and reporting anonymises practitioners and practices preventing scrutiny that may be in the patient or public interest. Without data, inequity remains hidden [56] and the health and social systems cannot allocate resources to address equity.

We have identified markers of individual patient risk that could be used to help target resources. Those currently in use are age, gender, ethnicity and deprivation as measured by Quintile 5. In addition, there are strong and consistent associations with patient health outcomes and M3 score, number of NP or GP consultations, FSA or not attending a FSA, change of practice enrolled in, and being prescribed an antibiotic or tramadol. Furthermore, deprivation as measured by IMD was more consistently associated with patient health outcomes than was Quintile 5. We have identified models of care where most practices had high measures of accumulated patient health need, compared to other models of care (Māori, Pacific and Trust/NGO practices).

2022 Aotearoa New Zealand health reforms

The health system in Aotearoa New Zealand is being restructured under the Pae Ora (Healthy Futures) Act, which took effect on 1 July 2022. A central driver of reform is the need to address decades of disparities in health outcomes, especially for Māori. The 20 DHBs present when this study was conducted have been dis-established and hospital and health services now operate under a single national entity, Te Whatu Ora – Health New Zealand. Also newly established, Te Aka Whai Ora – Māori Health Authority, is an independent statutory authority to lead improvement in Māori health [57]. Te Aka Whai Ora is not a separate health system for Māori, but an entity to co-design and co-commission for the new health system.

Up to 80 community localities are planned across Aotearoa New Zealand to provide health service advice. The relationships between localities, PHOs and general practices, are in development. PHOs might be dis-established; many of their functions such as clinical improvement and service coordination will continue, albeit under another entity [58]. The primary care sector is unsettled. There are longstanding staffing shortages, COVID has created additional demands for services and there is concern about a legacy of unmet preventive care.

Study Limitations

We acknowledge that measurement of associations cannot prove causality, that many factors affecting outcomes reside outside primary care and many remain unmeasured. The purpose of regressions was to “remove” the effects of patient characteristics but this can never be done perfectly due to imprecise measurement of each concept for which the variables are a proxy, exacerbated when the differences between practice populations are large.

Our explanations for differences between the practice models of care, as shown in the regression outputs, are based on available data and research team expertise. While the best outcome for individual patients remains unknown, we have assumed that, at a system level, a lower rate is better for polypharmacy, ASH and ED attendances, and a higher rate is better for HbA1c testing and immunisations.

Trends over time for each outcome may have been more informative than a cross sectional analysis. However, patient turnover within practices (21% per year in our data) and major changes to practices with opening, closing or merging during the year studied (35 practices in our initial data) and periodic changes to practice funding policies, all make longitudinal data difficult to interpret.

Although explanatory variables were divided into three categories – patient characteristics, practice characteristics and clinical input, it is clear that some factors fall into more than one category. For example, attending a VLCA practice might be patient choice due to lower fees or a practice financial decision. Having a First Specialist Assessment reflects both patient need and a referral from primary care in response to that need. Patients changing practice within a year might reflect dissatisfaction with a practice but are more likely to reflect changing patient circumstances such as change of address, itself a correlate of poverty.

We recognise that each practice had its own history of adapting to their enrolled patient population and region, and changes in policy and funding context. Grouping them together into “models” as we have done was a necessary simplification to address our research question.

The highest proportions of enrolled patients with high health and social needs were consistently found in Māori, Pacific and Trust/NGO models of care. The Traditional model of care comprised the largest number of practices (695) and, on average, the smallest number of patients with high need per practice. HCH practices (127) had a similar patient profile. Resources need to align more strongly with models of care and practices relative to the number of patients with high health needs.

Recent health system reforms (July 2022), include establishment of Te Aka Whai Ora – the Māori Health Authority, which represents a new level of partnership that prioritises Māori needs, alongside that of the total population. Details on how the reforms will affect PHOs and primary care are still emerging. However, it is clear that investing in health care for Māori is a priority, and evidence from this study can guide investment.

We have identified markers of individual patient risk that could be used to help target resources, in addition to those currently used: deprivation as measured by IMD, M3 score, number of NP or GP consultations, number of FSA or not attending a FSA, change of practice enrolled in, and being prescribed an antibiotic or tramadol. We identified models of care where most practices had high measures of accumulated patient health need (Māori, Pacific and Trust/NGO practices).

Primary care workforce numbers need boosting. Ongoing, improved measurement of health disparities to evaluate health reforms and investment requires more standardised data collection, available in near real-time.

Ethics approval and consent to participate

Ethics approval was granted by the Massey University Human Ethics Committee: Human Ethics Southern A Committee, reference SOA 18/58. The quantitative data was all collected anonymously so that no individual consent was required.

Consent for publication

Not applicable.

Availability of data and materials

Data for this project were collected on condition of anonymity of patients, practices and PHOs, with an agreement to delete data once the purpose of the project was met. Data collected from practices and PHOs are not available. Data collated from national data sets is available in summary form at on appropriate request to author TK ([email protected]).

Competing interests

We maintained a register of interests throughout the project. Each author declares no competing interests.

Funding

The project was jointly funded by the New Zealand Ministry of Health and the Health Research Council of New Zealand reference via a competitive Request for Proposal managed by the Health Research Council of New Zealand, grant reference HRC 18/788. The funders provided a Steering Committee to guide us in terms of what were the most policy-relevant issues to pursue, and to facilitate access to people and data. They had no role in collection, analysis or interpretation of data, nor in any decision about publication.

Authors’ contributions

NS, TK and TL designed the study, collected, analysed and interpreted the data and wrote the initial draft of the manuscript. The co-investigators and others listed below contributed to the study design; collection, analysis and interpretation of data; and review of the manuscript.

Acknowledgements

The authors acknowledge Erin Meads for advice on primary care data collection; CEOs and data managers for access to PHO and practice data; and the Steering Committee appointed by the funders, for facilitating access to national data.

The following people meet authorship criteria for the Journal and PubMed citation.

Primary Care Models Research Group Co-investigators

Nelson Aguirre-Duarte, University of Auckland, Aotearoa New Zealand [email protected]

Bruce Arroll, University of Auckland, Aotearoa New Zealand [email protected]

Carol Atmore, University of Otago, Aotearoa New Zealand [email protected]

Jenny Carryer, Massey University, Aotearoa New Zealand [email protected]

Peter Crampton, University of Otago, Aotearoa New Zealand [email protected]

Anthony Dowell, University of Otago, Aotearoa New Zealand [email protected]

Tana Fishman, Alliance Health Plus, Aotearoa New Zealand [email protected]

Robin Gauld, University of Otago, Aotearoa New Zealand [email protected]

Matire Harwood, University of Auckland, Aotearoa New Zealand [email protected]

Karen Hoare, Massey University, Aotearoa New Zealand [email protected]

Gary Jackson, Te Whatu Ora Counties Manukau, Aotearoa New Zealand [email protected]

Rawiri McKree Jansen, National Hauora Coalition, Aotearoa New Zealand [email protected]

Ngaire Kerse, University of Auckland, Aotearoa New Zealand [email protected]

Debra Lampshire, University of Auckland, Aotearoa New Zealand [email protected]

Lynn McBain, University of Otago, Aotearoa New Zealand [email protected]

Jayden MacRae, Datacraft Analytics, Aotearoa New Zealand [email protected]

Jane Mills, Massey University, Aotearoa New Zealand [email protected]

John Øvretveit, Karolinska Institutet, Sweden [email protected]

Teuila Percival, Moana Research, Aotearoa New Zealand [email protected]

Roshan Perera, Massey University, Aotearoa New Zealand [email protected]

Martin Roland, University of Cambridge, United Kingdom [email protected]

Debbie Ryan, Pacific Perspectives, Aotearoa New Zealand [email protected]

Jacqueline Schmidt-Busby, Comprehensive Care PHO, Aotearoa New Zealand [email protected]

Tim Stokes, University of Otago, Aotearoa New Zealand [email protected]

Maria Stubbe, University of Otago, Aotearoa New Zealand [email protected]

PhD Candidate

Sarah Hewitt, Massey University, Aotearoa New Zealand [email protected]

Other contributing authors

Daniel Watt, Sapere Research Group, Aotearoa New Zealand [email protected]

Chris Peck, Te Whatu Ora, Aotearoa New Zealand [email protected]

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No competing interests reported.

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published 04 May, 2023

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You are reading this latest preprint version

Patient health outcomes associated with models of general practice in Aotearoa New Zealand: a national cross-sectional study

Status:

Journal Publication

Version 1

Abstract

Background

Historical context

This study

Methods

Data sources

Defining practice models

Patient health outcomes

Additional process measures

Explanatory variables

Regression analyses

Results

Statistical models

Practice models

Māori and Pacific patients

Further patient and practice characteristics

Primary care clinician input

Discussion

Traditional practices as a reference

Trust/NGO, Māori and Pacific practices

Māori practices

Pacific practices

Corporate practices

Health Care Homes

Immunisations

Primary care clinical input

HbA1c testing and child ASH

Differences in enrolled populations

Current resourcing options do not target the range of patient need

Practice size

Rurality

Primary care data

2022 Aotearoa New Zealand health reforms

Study Limitations

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1