Practice models
The study included 924 practices with 4,491,964 enrolled patients. Each practice was classified to one ownership category from Traditional, Corporate, PHO/DHB or Trust/NGO. Where relevant, we also classified practices as Māori or Pacific practices or Health Care Homes, which overlapped with the ownership categories. Most Māori and Pacific practices were owned by a Trust or NGO. Ten practices were HCH, Māori and Trust/NGO; one was HCH, Māori and Corporate; and one was HCH, Pacific and Trust/NGO.
Table 1. Practice models showing overlapping categories across 924 practices
|
Traditional
(n=695)
|
Corporate
(n=103)
|
PHO/DHB
(n=27)
|
Trust/NGO
(n=99)
|
Māori practice (n=65)
|
3
|
3
|
0
|
59
|
Pacific practice (n=15)
|
4
|
0
|
0
|
11
|
Health Care Home (n=127)
|
90
|
14
|
7
|
16
|
PHOs and practices contributing data
Data on activities, consultations and FTE came from practice data provided by PHOs. At the time of the study there were 35 PHOs in Aotearoa New Zealand, of which 13 provided at least some data. Overall, enrolled patient population demographics covered by these PHOs was considered representative of the country. Full data were not available from every practice in every PHO. Data were missing for the following reasons: data were not recorded at practice level, not collected from practices by the PHO, or was not in a form that could not validly be compared across practices. The variance in available data reflected the widely varying data management capabilities of PHOs, and also the widely varying data sharing arrangements between PHOs and practices. The number of practices contributing data to each analysis is reported. Analyses that combine data from different practice numbers should be seen as indicative only, as not all practices contributed full data to each calculation.
Full or partial data were contributed by 415 (60%) of 695 Traditional practices, 66 (64%) of 103 Corporate practices, 18 (67%) of 27 PHO/DHB practices, 43 (43%) of 99 Trust/NGO practices, 25 (38%) of 65 Māori practices, 12 (80%) of 15 Pacific practices, and 94 (74%) of 127 HCH practices.
Practitioner consultations, defining GPs
Data in Table 2 came from 292 practices in 10 PHOs over a three-year period. These data on consultations were taken from the appointment book and represent face to face consultations, but not telephone, email or other contacts. Of consultations with a doctor, 82% were vocationally registered in general practice, 17% had no vocational registration (primarily doctors in a postgraduate training pathway) and 1% with other specialist vocational registration. For this study, all doctors are referred to as GPs. Compared with GP consultations, RN consultations were shorter by 14% and NP consultations were longer by 16%.
Table 2. Face-to-face consultation number and length, by practitioner
|
Number consultations
|
FTE consultations
|
FTE per 10,000 consultations
|
Minutes per consultation, average
|
GP
|
4,218,012 (63.1%)
|
417.1 (66.1%)
|
0.99
|
11.4
|
NP
|
38,113 (0.6%)
|
4.4 (0.7%)
|
1.15
|
13.2
|
RN
|
1,551,883 (23.2%)
|
132.4 (21%)
|
0.85
|
9.9
|
Other
|
873,014 (13.1%)
|
77.4 (12.3%)
|
0.89
|
10.3
|
Note: Other includes health care assistants, dieticians, physiotherapists, Quit smoking providers and unidentified persons.
Registered Nurse and Nurse Practitioner activities
Data in Table 3 came from 212 to 364 practices and show the proportions of activities undertaken by RN and NP combined, or GP.
Cervical screening. A high percentage of screening was attributed to RNs and NPs in Pacific (78%) and Trust/NGO (71%) practices, followed by Māori (61%) and Corporate (58%) practices.
Cardiovascular risk assessment. A high percentage of screening was attributed to RNs and NPs in Pacific (73%), Māori (70%) and Trust/NGO (68%) practices, followed by Corporate (61%) practices. Traditional, Health Care Home and PHO/DHB practices were considerably lower.
PHQ9 (depression questionnaire). Not all practices used the PHQ9, preferring one of several alternatives. Where the PHQ9 was used, RNs and NPs were more likely to conduct the screening in Māori (33%), Trust/NGO (21%) and Corporate (19%) practices.
HbA1c testing. A low percentage of tests across all practice models was attributed to RNs and NPs, except in the small number of PHO/DHB (27%) practices.
Consultations after hours. RN and NP after-hours consultations were a lower percentage compared to GP consultations in all practice models, except in PHO/DHB (45%) and Pacific (41%) practices. Corporate and Traditional practices were most likely to have GPs undertaking after-hours consultations.
Consultations with unenrolled patients. This analysis comes from comparing national data on people who were not enrolled in any PHO but attended a general practice, most likely as a “casual” or “walk in” patient. A higher percentage of unenrolled patients were seen by RNs and NPs in PHO/DHB (41%), Pacific (40%), Trust/NGO (37%) and Māori (36%) practices compared to Traditional (23%), Corporate (23%) and HCH (18%) practices.
Dispensed medicines. A low percentage of dispensed medicines was attributed to RNs and NPs, across all practice models, at the time of this study.
Rural and urban practices. In rural practices: 13% of PHQ9s were undertaken by a NP and 11% by a nurse, compared to 2% and 5%, respectively, in urban practices; 5% of HbA1c tests on people with diabetes were undertaken by a NP and 16% by a nurse, compared to 0.4% and 8%, respectively, in urban practices; 4% of cardiovascular risk assessments were undertaken by a NP and 58% by a nurse, compared to 0.3% and 52%, respectively, in urban practices.
Preventative care. RNs and NPs undertook more cervical screening than GPs in Pacific, Trust/NGO, Māori and Corporate practices. They undertook more cardiovascular risk assessment than doctors in all models except PHO/DHB. The percentage of RNs and NPs undertaking each activity varies dramatically between models of care. There was an 8-fold difference in percentage of PHQ9 undertaken by RNs and NPs, a 5-fold difference in cervical screening and HbA1c testing and a 2-fold difference in cardiovascular risk assessment. Summing RN and NP contributions across preventative care (cervical screening, cardiovascular risk assessment, PHO9 and HbA1c testing) showed the strongest RN and NP contribution was in Māori, Trust/NGO, Pacific and Corporate practices.
Work to support access. RNs and NPs undertook more consultations than GPs afterhours in PHO/DHB practices. Summing RN and NP contributions across care to support access (consultations after hours, consultations with unenrolled patients) showed the strongest nurse contribution was in PHO/DHB, Pacific, Trust/NGO and Māori practices.
Table 3. Percentage of activities by RN and NP, or GP, by practice model, rural or urban
|
Cervical screening
|
CV risk assessment
|
PHQ9
|
HbA1c testing
|
Consultationafter hours
|
Consultationunenrolled patients
|
Dispensed medicines
|
Practice N
|
276
|
220
|
212
|
384
|
275
|
277
|
291
|
Traditional
|
39 (57)
|
52 (45)
|
7 (91)
|
9 (89)
|
19 (72)
|
23 (66)
|
<1.2 (99)
|
Corporate
|
58 (31)
|
61 (26)
|
19 (76)
|
11 (82)
|
21 (67)
|
23 (64)
|
<1.2 (99)
|
PHO/DHB
|
17 (81)
|
43 (58)
|
1 (99)
|
27 (72)
|
45 (40)
|
41 (50)
|
<1.2 (100)
|
Trust/NGO
|
71 (23)
|
68 (26)
|
21 (75)
|
11 (81)
|
27 (46)
|
37 (55)
|
5 (95)
|
HCH
|
42 (49)
|
47 (40)
|
4 (88)
|
8 (89)
|
17 (61)
|
18 (55)
|
<1.2 (99)
|
Māori
|
61 (32)
|
70 (14)
|
33 (66)
|
11 (87)
|
26 (57)
|
36 (58)
|
3 (97)
|
Pacific
|
78 (21)
|
73 (20)
|
5 (95)
|
5 (93)
|
41 (46)
|
40 (59)
|
<1.2 (100)
|
Rural
|
59 (36)
|
62 (37)
|
24 (76)
|
20 (75)
|
26 (71)
|
35 (60)
|
4 (96)
|
Urban
|
41 (54)
|
52 (42)
|
7 (90)
|
8 (89)
|
20 (70)
|
24 (65)
|
<1.2 (99)
|
Note 1. Results are percentage by RN and NP (%GP).
Note 2. Percentages by RN, NPs and GPs may not sum to 100 due to work by Others.
RN, NP, and HCA FTE associations with patient need for health services
Data in the scatterplots came from 373 practices for RN FTE, 224 practices for NP FTE, and 201 practices for HCA FTE. Scatterplots suggest an increase in NP, RN, and HCA FTE, respectively, with an increasing: average practice M3 score, practice percentage Māori and average practice IMD (Figure 1). Practices with no RN, NP or HCA were not included in these plots.
RN, NP and GP FTE associations between practice models and characteristics
Data in Table 4 come from 67 practices for NP data, 364 for RN data and 375 for GP data. Calculation of RN numbers per 1000 patients include only those practices where there is a RN present, and similarly a NP or GP. Calculations combine FTE across RN, NP and GP. In Table 4, a ratio of total nursing to medical FTE is indicated by RN+NP:GP.
Traditional, Corporate and HCH have the lowest FTE for nursing workforce (RN+NP) and total workforce (RN+NP+GP) and the lowest ratio (RN+NP:GP). Trust/NGO, Māori, Pacific and PHO/DHB practices have the highest nursing workforce, total workforce, and ratio (RN+NP:GP).
NP numbers per 1000 patients were higher in Trust/NGO, Māori and PHO/DHB practices and lower in Corporate and HCH practices. There were no NPs in Pacific practices.
Nursing workforce and total workforce is higher in small practices, rural practices, practices with more multimorbidity (M3), more Māori patients and more patients living in deprivation (Q5). The highest ratio of RNs and NPs to GPs was in small practices. NPs had a greater presence in small practices, rural practices, VLCA practices, and practices with more multimorbidity.
Table 4. RN, NP, GP FTE per 1000 patients by practice model or characteristic
|
RN
|
NP
|
GP
|
RN+NP
|
RN+NP+GP
|
RN+NP:GP
|
Overall
|
0.55 (364)
|
0.14 (67)
|
0.63 (375)
|
0.69
|
1.32
|
1.10
|
Practice model
|
|
|
|
|
|
|
Traditional
|
0.52 (270)
|
0.14 (29)
|
0.63 (273)
|
0.66
|
1.29
|
1.05
|
Corporate
|
0.58 (61)
|
0.08 (20)
|
0.60 (63)
|
0.66
|
1.26
|
1.10
|
PHO/DHB
|
0.64 (7)
|
0.19 (3)
|
0.63 (9)
|
0.83
|
1.46
|
1.32
|
Trust/NGO
|
0.90 (26)
|
0.19 (15)
|
0.69 (30)
|
1.09
|
1.78
|
1.58
|
Māori provider
|
0.79 (16)
|
0.19 (11)
|
0.68 (19)
|
0.98
|
1.66
|
1.44
|
Pacific provider
|
0.87 (7)
|
0 (0)
|
0.67 (8)
|
0.87
|
1.54
|
1.30
|
Health Care Home
|
0.59 (33)
|
0.08 (15)
|
0.60 (37)
|
0.67
|
1.27
|
1.12
|
Practice characteristic
|
|
|
|
|
|
|
Rural
|
0.65 (28)
|
0.20 (8)
|
0.71 (28)
|
0.85
|
1.56
|
1.20
|
Urban
|
0.54 (336)
|
0.13 (347)
|
0.63 (59)
|
0.67
|
1.3
|
1.06
|
VLCA
|
0.58 (130)
|
0.18 (27)
|
0.59 (347)
|
0.76
|
1.35
|
1.29
|
Not VLCA
|
0.54 (234)
|
0.12 (40)
|
0.64 (237)
|
0.66
|
1.3
|
1.03
|
Patients enrolled
|
|
|
|
|
|
|
>20,000 (8)
|
0.53 (5)
|
0.04 (3)
|
0.60 (5)
|
0.57
|
1.17
|
0.95
|
>10,000-20,000 (86)
|
0.55 (41)
|
0.07 (15)
|
0.57 (42)
|
0.62
|
1.19
|
1.09
|
>2,000-10,000 (615)
|
0.53 (247)
|
0.17 (42)
|
0.62 (249)
|
0.70
|
1.32
|
1.13
|
<2,000 (215)
|
0.65 (71)
|
0.85 (7)
|
0.77 (79)
|
1.50
|
2.27
|
1.95
|
Patient characteristics
|
|
|
|
|
|
|
Māori >30%
|
0.67 (37)
|
0.19 (20)
|
0.68 (44)
|
0.86
|
1.54
|
1.26
|
Pacific >30%
|
0.51 (40)
|
0.19 (3)
|
0.55 (43)
|
0.70
|
1.25
|
1.27
|
Quintile 5 >30%
|
0.65 (94)
|
0.19 (27)
|
0.65 (105)
|
0.84
|
1.49
|
1.29
|
IMD >median
|
0.57 (273)
|
0.13 (53)
|
0.63 (295)
|
0.70
|
1.33
|
1.11
|
M3 >median
|
0.68 (28)
|
0.47 (11)
|
0.75 (32)
|
1.15
|
1.9
|
1.53
|
Note. Results are median FTE per 1000 enrolled patients (number of practices contributing data)
Primary care clinical input
The regressions report the associations of primary care clinical input in two different ways – as direct effects and as interactions. Direct effects were estimated for a total count of consultations with a NP or GP, and as RN FTE and GP FTE. Interactions were estimated if the direct effect was statistically significant, and test for different effects between the exclusive models of care – Corporate, PHO/DHB and Trust/NGO, with Traditional as a reference category. Table 5 shows prediction outputs. Table 6 and associated text provides the full statistical outputs.
For discussion of the regression outputs in relation to model of care and patient and practice characteristics, see the primary outcomes from this study in this issue of the Journal.
Polypharmacy. The probability of polypharmacy, for the average person aged 65 or over, was 38.2%. If the average patient received one NP or GP consultation, their probability of polypharmacy was 25.4%. If they received three consultations their probability was 34.6%.
HbA1c testing. The probability of HbA1c testing, for the average person with diabetes, was 86.9%. If the average patient received one NP or GP consultation, their probability of HbA1c testing was 81.8%. If they received three consultations their probability was 85.3%. If the average patient received one hour of RN time, their probability of testing was 86.0%. If they received four hours their probability was 86.6%.
Immunisations. The probability of complete immunisation at age 6 months, for the average child, was 75.6%. If the average child received the one NP or GP consultation, their probability of complete immunisations was 73.8%. If they received three consultations, their probability was 75.1%. If the average child received one hour of RN time, their probability of being fully immunised was 74.1%. If they received four hours their probability was 75.1%. If the average child received one hour of GP time, their probability of being fully immunised was 74.8%. If they received four hours their probability was 75.4%.
Child ASH. The average number of child ASH was 31 per 1000 enrolled children. If the average child received one NP or GP consultation, their probability of an ASH was increased by 8.7%; if they received three consultations their probability of an ASH was increased by 39.8%; both relative to having no NP or GP consultations.
Adult ASH. The average number of adult ASH was 38 per 1000 enrolled adults. If the average patient received one NP or GP consultation, their probability of an ASH was increased by 8.1%; if they received three consultations their probability of an ASH was increased by 36.7%; both relative to having no NP or GP consultations.
ED attendances. The average number of ED attendances was 254 per 1000 enrolled patients. If the average patient received one NP or GP consultation, their probability of an ED attendance was increased by 7.5%; if they received three consultations their probability of an ASH was increased by 33.3%; both relative to having no NP or GP consultations.
Interactions. Examples are given here to confirm interpretation of the full results in Tables 5 and 6.
- For the average child aged 6 months at a Traditional practice, an additional hour of RN time was associated with an absolute increase of 1.9% in the probability of being up to date with immunisations. The equivalent figure in Trust/NGO practice was a decrease of 0.5%.
- For the average child at a Traditional practice, an additional contact with a NP or GP was associated with an absolute increase of 8.7% in the probable number of ASH admissions. The equivalent figure in a Corporate practice was 10.8%.
- For the average adult at a Traditional practice, an additional contact with a NP or GP was associated with an absolute increase of 8.1% in the probable number of adult ASH. The equivalent figure in a Corporate practice was 6.2%; in a PHO/DHB practice, 11.0%; and in a Trust/NGO practice, 6.5%.
Table 5. Selected output from final models for patient health outcomes across 924 practices. Age and some other patient characteristics omitted – full regression outputs in primary outcomes paper, 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
|
|
|
|
|
|
|
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%
|
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%
|
|
|
Interactions
|
|
|
|
|
|
|
GP+NP Consultations X Corporate
|
4.1% ***
(3.5%)
|
1.0 %
(1.0%)
|
0.2%
(0.5%)
|
10.8% **
(8.7%)
|
6.2% ***
(8.1%)
|
7.6%
(7.5%)
|
GP+NP Consultations X PHO/DHB
|
4.7% ***
(3.5%)
|
1.1%
(1.0%)
|
0.4%
(0.5%)
|
8.7%
(8.7%)
|
11.0% *
(8.1%)
|
8.5% *
(7.5%)
|
GP+NP Consultations X Trust/NGO
|
4.1% ***
(3.5%)
|
0.9%
(1.0%)
|
0.1%
(0.5%)
|
9.5%
(8.7%)
|
6.5% **
(8.1%)
|
7.3%
(7.5%)
|
RN hours X Corporate
|
|
0.3% *
(1.1%)
|
0.2%
(1.9%)
|
|
|
|
RN hours X PHO/DHB
|
|
1.1%
(1.1%)
|
0.7%
(1.9%)
|
|
|
|
RN hours X Trust/NGO
|
|
1.1%
(1.1%)
|
- 0.5% *
(1.9%)
|
|
|
|
GP hours X Corporate
|
|
|
0.1%
(0.8%)
|
5.5% *
(0.6%)
|
|
|
GP hours X PHO/DHB
|
|
|
0.3%
(0.8%)
|
4.2%
(0.6%)
|
|
|
GP hours X Trust/NGO
|
|
|
0.8%
(0.8%)
|
4.4%
(0.6%)
|
|
|
*p<0.05, **p<0.01, ***p<0.001.
- Polypharmacy, HbA1c testing and immunisation results are logistic regressions
- For binary variables, results are % of patients with that outcome, i.e. if variable = 1 (the result if variable = 0 is given in brackets) .
- 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
- For binary variables, results are % change from the reference value i.e. result if variable = 1 compared to value if variable = 0.
- 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 including in Interactions.
- The value for Traditional practice is given in brackets. This value can vary 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.