Quality-based funding or pay-for-performance of the Norwegian hospital sector was introduced on 1 January 2014, meaning that a proportion of the budgets of the regional health authorities is made dependent on the quality of the treatment they provide to patients, as measured by selected quality indicators. In 2014, NOK 497 million was earmarked for such funding. The purpose of the scheme is to ensure high quality and patient safety (1 , 2 ).
The set of indicators used in quality-based funding in Norway includes structure, process and outcome indicators, altogether 29 different measures (3 ). In principle, this provides a broad description of the hospitals’ quality, and the diversity of indicators may in itself help prevent random variations in the budget allocations. The outcome indicators include a number of measures of survival, such as 30-day survival after a myocardial infarction. Survival and mortality are frequently used quality indicators internationally (4 , 5 ). The Norwegian data material does not specify, however, how the survival figures have been adjusted for risk.
If the information on variations in quality is to be used as a criterion in a funding system, the variations in quality observed between different hospitals must be attributable to conditions that are under their control, and not any underlying variables. The objective of this study was to investigate how various forms of risk adjustment had an impact on predicted mortality after myocardial infarctions in the different hospital trusts.
Material and method
Sample, definitions and data
The data set encompassed patients ≥ 18 years, who had been discharged from Norwegian hospitals in 2009 with myocardial infarction (ICD-10 codes I21 and I22) as their main diagnosis. We used 30-day mortality as a quality indicator and included all causes of death.
The methodological approach was developed as part of the Finnish PERFECT project (6 , 7 ) and further refined by EuroHOPE (8 – 10 ), a comparative project funded by the EU’s 7th framework programme. The unit of analysis was hospital episodes that started at admission and ended 30 days later or with the death of the patient (11 ).
A single hospital episode would include several hospitalisations if these were sequential and separated by one day or less. Two or more hospitalisations resulting from transfers between hospitals, for example as a result of percutaneous coronary intervention (PCI), were then counted as a single hospital episode. Only the first hospital episode in a year was included in the analysis, meaning that readmission did not generate a new episode. If the hospitals used different diagnoses over a single episode, the diagnostic codes from the highest-ranking hospital were chosen. If the hospital episode encompassed two or more hospitals at the same level of the hierarchy, the diagnostic codes from the first hospital in the episode were used.
To improve the comparability of the patient population across hospitals, we excluded patients who had been admitted to hospital for acute myocardial infarction during the 365 days prior to the index day. Thereafter, the data that described the patients’ risk profiles were linked at the individual level. In addition to gender and age, the following variables were included in the risk adjustment: comorbidity, socioeconomic status, procedures, travel time to the nearest PCI centre and type of myocardial infarction.
Data on hospitalisations, as well as dates of admission and discharge, main and secondary diagnoses, comorbidity and procedures were retrieved from the Norwegian Patient Register. Data on mortality were established on the basis of information from the Causes of Death Registry. Information on medication use was retrieved from the Prescription Registry. «FD-trygd» (Statistics Norway’s database on social assistance payments – «Forløpsdatabasen trygd») provided us with access to data on income, assets, education and disability benefits. The population data are as of 31 December 2009.
The EuroHOPE project has been approved by the Regional Committee on Medical and Health Research Ethics for South-Eastern Norway. Since the project also uses data from the Prescription Registry, a licence was applied for and granted by the Norwegian Data Protection Authority.
Variables
We established variables to describe comorbidity on the basis of hospital data on admissions over the last 365 days prior to the index day and data on medication use from the Norwegian Prescription Registry for the same period (8 ). For example, patients whose main or secondary diagnosis was hypertension, as well as patients who had been prescribed with anti-hypertension drugs, were registered with hypertension as a comorbidity.
The inclusion of medication use in addition to the registered main and secondary diagnoses caused the proportion of patients with hypertension as a comorbidity in the material to rise by 50 %. The proportion of patients with diabetes, asthma and depression rose by approximately 10 %. With regard to the remaining comorbidities, the inclusion of data from the Prescription Registry caused no more than minor changes.
Socioeconomic conditions were described in terms of variables that have been shown to correlate with mortality from myocardial infarction (10 , 12 , 13 ): income, assets, receipt of disability benefit and level of education. Income was defined as the average total personal gross income in the period 2000 – 2008, and assets as average gross personal assets in the same years. We registered whether the patient had received disability benefit in 2008 or earlier. Education was classified as primary/lower secondary, upper secondary and university/university college.
Myocardial-infarction patients primarily receive thrombolytic treatment or percutaneous coronary intervention, while a minority undergo coronary artery bypass graft surgery (CABG). The patients were classified by their reception of thrombolytic or other drug-based treatment, percutaneous coronary intervention or coronary artery bypass graft. Travel time in minutes by car from the centre of the patient’s home municipality to the nearest PCI centre was estimated on the basis of data from Google Maps (14 ). We made an assumption of non-linearity, since car and boat ambulances are replaced by helicopters when travel distances are extensive and estimated to take more than one hour (15 ).
To improve the comparability of the patient population across hospitals, the type of myocardial infarction was registered. The hospitals’ classification of patients in the sub-groups of the ICD system is based on discretionary judgement. We therefore estimated the models with as well as without the variables that described the type of myocardial infarction.
Statistical analyses
Descriptive statistics are presented for each hospital trust as averages for continuous variables and as proportions for categorical variables. For reasons of space, we present only the average number of comorbidities for each patient (Table 1). Information on individual comorbidities included in the regression models are grouped by hospital trust and presented in e-Table 2.
Table 1 Description of the patient population discharged with the diagnosis myocardial infarction in Norwegian hospitals in 2009, by the hospital trusts’ catchment areas
Hospital trust
Number of hospital episodes
Incidence per 10 000 inhabitants
Proportion of deaths, 30 days (%)
Number of comorbidities (average)
Proportion men (%)
Age (years) (average)
Akershus University Hospital
600
33.5
9.0
1.6
61.5
69.7
Asker og Bærum
210
16.9
5.2
1.5
56.7
75.3
Bergen
864
27.6
9.3
1.5
64.7
71.2
Buskerud
616
30.8
8.6
1.5
65.7
70.2
Finnmark
222
39.8
8.1
1.4
66.7
69.7
Fonna
474
36.9
6.9
1.5
61.6
72.0
Førde
254
31.2
12.6
1.6
60.6
75.4
Helgeland
175
29.3
9.1
1.4
59.4
72.6
Innlandet
1 034
33.2
9.2
1.6
62.9
72.3
Nord-Trøndelag
330
32.3
7.9
1.4
66.1
71.4
Nordland
346
33.6
8.1
1.6
58.4
73.4
Nordmøre og Romsdal
290
32.2
7.6
1.4
55.9
74.0
Oslo
1 149
20.7
8.1
1.7
56.9
73.6
Stavanger
552
22.7
8.5
1.3
65.4
69.3
Sunnmøre
339
33.3
8.3
1.7
61.1
74.7
Sør-Trøndelag
631
28.0
8.6
1.5
60.4
71.6
Sørlandet
429
20.2
9.3
1.4
62.0
70.5
University Hospital of North Norway
466
33.0
8.6
1.4
66.5
70.7
Vestfold og Telemark
999
32.1
8.5
1.5
61.2
71.1
Østfold
737
35.0
9.1
1.7
61.2
71.0
Norway as a whole
10 717
28.6
8.6
1.5
61.8
71.8
Table 2 Prevalence of comorbidities in per cent, by hospital trust
Hospital trust
Hypertension
Coronary heart disease
Heart fibrillation
Heart failure
Diabetes mellitus
Atherosclerosis
Cancer
COPD/asthma
Dementia
Depression
Parkinson’s disease
Mental disorders
Renal failure
Alcoholism
Stroke
Akershus University Hospital
64
13
7
4
17
2
2
22
4
13
1
4
4
1
4
Asker og Bærum
56
12
7
7
19
1
4
15
3
12
0
3
4
0
3
Bergen
59
14
6
6
16
2
2
14
4
13
2
4
2
0
3
Buskerud
62
15
7
6
17
2
2
17
4
10
1
3
3
0
2
Finnmark
60
18
5
5
11
3
1
23
3
5
0
4
1
0
5
Fonna
67
13
6
7
12
1
2
17
4
14
0
3
3
0
3
Førde
66
12
9
8
15
1
2
14
8
11
0
4
5
1
5
Helgeland
63
13
5
3
13
0
3
14
3
15
1
3
2
0
3
Innlandet
68
16
6
7
17
2
2
18
3
14
1
3
3
1
4
Nord-Trøndelag
62
13
6
5
13
2
2
11
5
14
2
2
2
1
3
Nordland
65
14
7
7
14
3
1
19
4
9
2
3
5
1
3
Nordmøre og Romsdal
62
11
7
7
13
2
3
15
4
8
1
2
3
0
3
Oslo
62
17
8
8
18
2
3
16
7
12
1
4
4
2
5
Stavanger
57
12
5
4
12
1
0
14
2
14
1
4
3
1
2
Sunnmøre
64
18
9
6
15
3
2
17
6
14
0
2
4
1
5
Sør-Trøndelag
59
13
5
6
15
1
2
16
4
15
2
3
3
0
5
Sørlandet
59
7
5
5
14
2
2
18
3
12
2
2
3
1
2
University Hospital of North Norway
64
14
6
3
13
2
2
16
4
12
1
2
2
0
3
Vestfold
65
14
6
6
14
2
3
16
3
12
1
3
3
0
3
Østfold
70
13
7
8
20
2
3
18
2
15
1
5
3
1
4
Norway
63
14
7
6
16
2
2
17
4
13
1
3
3
1
4
We used logistic regression analysis with 30-day mortality as the outcome measure. To avoid non-linear effects on the log scale, all continuous covariates were categorised. The age categories were 18 – 49 years, followed by five-year categories up to 89 years, plus one category that encompassed patients who were 90 years or older. Income and assets were divided into the categories NOK 0 – 150 000, NOK 150 000 – 300 000, NOK 300 000 – 500 000 and NOK ≥ 500 000. Travel time was divided into the categories 0 – 60 minutes, 60 – 120 minutes, 120 – 180 minutes and ≥ 180 minutes.
Three models for risk adjustment were used (9 , 10 ):
Model 1: adjustment for age and gender
Model 2: adjustment for age, gender, comorbidities, socioeconomic variables, travel time and type of myocardial infarction
Model 3: all variables in Model 2, plus adjustment for whether or not percutaneous coronary intervention had been undertaken in the course of 14 days after admission.
The variables were used to estimate risk-adjusted proportions of deaths within a 95 % confidence interval (95 % CI) for each hospital trust. To depict how well the models were able to classify the patients in the data material correctly, i.e. to identify the dead patients as dead and the living as still alive, we provide some AUC values («area under the curve»). An AUC value of 0.5 means that the model classifies no better than the toss of a coin.
Data management was undertaken in SAS version 9.2 (16 ) and the further analyses were made in Stata version 12.1 (17 ).
Other methodological issues
The analyses were undertaken at the hospital-trust level on the basis of the enterprise structure such as it was in the first half of 2009. The patients were grouped by hospital trust on the basis of the trusts’ catchment areas. This means that a patient with a residential address in a municipality that fell within the catchment area of a specific hospital trust was classified under this hospital trust, irrespective of where he or she was actually treated.
Results
Description of the patient population
After the exclusion of patients who had been treated for acute myocardial infarction during the last 365 days prior to the index date (n = 1 718), the data material encompassed 10 717 patients. Table 1 presents data on these patients. Supplementary information on comorbidities and sociodemographic factors is presented in e-Tables 2 and 3.
Table 3 Description of the patient population discharged with a diagnosis of myocardial infarction in Norwegian hospitals in 2009, by the hospital trusts’ catchment areas. N = 10 717
Hospital trust
Income in NOK 100 000 (average)
Assets in NOK 100 000 (average)
Disabled (%)
Upper secondary school (%)
University/ university college (%)
Trans infarction¹ (%)
Sub infarction² (%)
Spec. infarction³ (%)
Unspec. infarction⁴ (%)
Distance to PCI centre in minutes (average)
Akershus University Hospital
283
516
16.0
41.9
12.9
27.7
53.7
4.5
14.1
24
Asker og Bærum
342
1 004
10.0
44.8
37.4
19.5
58.6
1.4
20.5
16
Bergen
279
574
12.7
45.2
13.8
27.5
45.6
8.3
18.6
24
Buskerud
275
590
13.3
45.8
10.0
18.6
59.6
4.2
17.6
63
Finnmark
245
403
21.2
36.8
9.1
17.6
54.5
3.6
24.3
476
Fonna
253
576
16.0
48.3
10.0
19.8
54.6
3.2
22.4
136
Førde
225
562
13.4
48.8
9.6
22.4
48.8
7.1
21.7
229
Helgeland
217
390
22.2
34.5
8.0
26.3
57.7
7.4
8.6
370
Innlandet
238
494
16.0
39.9
8.7
23.0
53.7
4.8
18.5
87
Nord-Trøndelag
240
443
19.1
47.9
7.9
26.4
51.5
6.7
15.4
120
Nordland
227
433
19.1
39.4
7.3
14.7
59.5
9.2
16.6
412
Nordmøre og Romsdal
243
640
15.5
44.7
8.2
22.4
51.4
4.8
21.4
201
Oslo
297
697
11.8
45.9
21.1
27.2
53.4
5.0
14.4
14
Stavanger
289
548
13.2
48.2
11.5
36.3
47.2
7.4
9.1
25
Sunnmøre
235
518
12.7
46.6
7.4
17.7
54.3
6.8
21.2
337
Sør-Trøndelag
246
489
17.3
42.5
12.9
30.0
45.8
5.7
18.5
38
Sørlandet
251
498
22.1
50.7
14.5
26.8
57.6
6.3
9.3
54
University Hospital of North Norway
249
396
19.3
43.2
11.3
29.4
47.4
6.2
17.0
141
Vestfold og Telemark
260
532
18.1
47.7
11.6
19.7
59.2
5.1
16.0
90
Østfold
240
476
19.9
45.7
7.8
23.2
54.5
3.9
18.4
65
Norway
260
542
16.0
44.8
12.2
24.5
53.2
5.5
16.8
102
[i]
The incidence of myocardial infarction (the number of cases per 10 000 inhabitants) was 28.6 for the country as a whole, and varied from 16.9 in the catchment area of Asker og Bærum Hospital Trust to 39.8 in the catchment area of Finnmark Hospital Trust. The national average of unadjusted 30-day mortality amounted to 8.6 % – highest in Førde Hospital Trust (12.6 %) and lowest in Asker og Bærum (5.2 %). The prevalence of individual comorbidities varied considerably between the hospital trusts (e-Table 2).
Logistic regression analyses
Figure 1 shows the unadjusted proportions of deaths after 30 days, as well as the corresponding adjusted proportions for the three different approaches to risk adjustment. Precise figures and supplementary information on the regression coefficients are found in e-Table 4 – 7. On the whole, there were few confidence intervals for the individual hospital trusts that did not overlap with the national average of 8.6 % for 30-day mortality. Patients in the catchment area for Asker og Bærum Hospital Trust in the unadjusted analysis and in the analysis that was adjusted for gender and age, as well as patients in the catchment areas for Nordmøre og Romsdal and Sunnmøre hospital trusts are the exceptions. The two latter groups had a mortality of 5.2 % after risk adjustment for all covariates.
Figure 1 Proportion of deaths after 30 days by hospital trust with a 95 % confidence interval in patients discharged with a diagnosis of myocardial infarction in Norwegian hospitals in 2009. The vertical line denotes the national average. Red and blue lines: N = 10 717. Green and black lines: N = 10 379
Table 4 Odds ratios with a 95 % confidence interval (95 % CI) from logistic regression analyses with 30-day mortality as outcome: unadjusted and adjusted for age and gender. Ref. = reference category. N = 10 717
Unadjusted
95 % CI
Adjusted for age/gender
95 % CI
Man
1.02
0.89 – 1.18
8 – 49 years
0.05
0.03 – 0.09
50 – 54 years
0.02
0.01 – 0.06
55 – 59 years
0.02
0.01 – 0.04
60 – 64 years
0.06
0.04 – 0.09
65 – 69 years
0.12
0.08 – 0.17
70 – 74 years
0.19
0.14 – 0.26
75 – 79 years
0.26
0.20 – 0.34
80 – 84 years
0.48
0.38 – 0.60
85 – 89 years
0.70
0.57 – 0.86
> 90 years
Ref.
Ref.
Akershus University Hospital
Ref.
Ref.
Asker og Bærum
0.56
0.29 – 1.09
0.39
0.20 – 0.78
Bergen
1.03
0.72 – 1.48
0.92
0.63 – 1.33
Buskerud
0.95
0.64 – 1.41
0.93
0.62 – 1.41
Finnmark
0.89
0.51 – 1.56
0.98
0.55 – 1.74
Fonna
0.76
0.48 – 1.19
0.65
0.41 – 1.03
Førde
1.46
0.92 – 2.32
1.04
0.64 – 1.69
Helgeland
1.02
0.57 – 1.83
0.92
0.50 – 1.69
Innlandet
0.86
0.72 – 1.45
0.90
0.62 – 1.29
Nord-Trøndelag
0.83
0.53 – 1.41
0.81
0.49 – 1.34
Nordland
0.89
0.55 – 1.43
0.75
0.46 – 1.23
Nordmøre og Romsdal
0.83
0.50 – 1.39
0.64
0.38 – 1.09
Oslo
0.89
0.63 – 1.26
0.69
0.48 – 0.99
Stavanger
0.84
0.62 – 1.41
0.98
0.64 – 1.49
Sunnmøre
0.91
0.56 – 1.47
0.67
0.41 – 1.09
Sør-Trøndelag
0.95
0.64 – 1.40
0.87
0.58 – 1.31
Sørlandet
1.04
0.68 – 1.60
1.01
0.65 – 1.58
University Hospital of North Norway
0.95
0.62 – 1.45
0.90
0.58 – 1.41
Vestfold
0.94
0.66 – 1.34
0.90
0.62 – 1.30
Østfold
1.01
0.69 – 1.47
0.96
0.65 – 1.43
Table 5 Odds ratios with a 95 % confidence interval (95 % CI) from logistic regression analyses with 30-day mortality as outcome: adjusted for all background variables (age, gender, type of myocardial infarction, individual comorbidities for which not having the comorbidity is the reference category, distance and socioeconomic factors), as well as all and percutaneous coronary intervention (PCI) after 14 days. Ref. = reference category. N = 10 379
Adjusted for all
95 % CI
Adjusted for all + PCI
95 % CI
Man
1.06
0.90 – 1.24
1.12
0.95 – 1.31
18 – 49 years
0.05
0.02 – 0.09
0.08
0.04 – 0.16
50 – 54 years
0.02
0.01 – 0.06
0.04
0.02 – 0.11
55 – 59 years
0.02
0.01 – 0.05
0.03
0.01 – 0.08
60 – 64 years
0.05
0.03 – 0.08
0.08
0.05 – 0.14
65 – 69 years
0.09
0.06 – 0.15
0.15
0.10 – 0.25
70 – 74 years
0.17
0.12 – 0.24
0.25
0.18 – 0.37
75 – 79 years
0.27
0.21 – 0.36
0.38
0.28 – 0.50
80 – 84 years
0.49
0.39 – 0.62
0.60
0.47 – 0.75
85 – 89 years
0.69
0.56 – 0.86
0.75
0.61 – 0.93
> 90 years
Ref.
Ref.
Transmural first-time infarctions
Ref.
Ref.
Subendocardial first-time infarctions
0.52
0.43 – 0.63
0.40
0.33 – 0.49
Specified localised subsequent infarctions
1.09
0.80 – 1.47
0.88
0.64 – 1.20
Unspecified localised subsequent infarctions
0.68
0.54 – 0.86
0.52
0.41 – 0.66
Hypertension
0.82
0.69 – 0.98
0.82
0.68 – 0.98
Coronary heart disease
0.74
0.59 – 0.92
0.74
0.59 – 0.93
Heart fibrillation
1.19
0.92 – 1.54
1.15
0.89 – 1.48
Heart failure
1.54
1.18 – 2.00
1.48
1.13 – 1.92
Diabetes mellitus
1.22
1.01 – 1.48
1.19
0.98 – 1.45
Atherosclerosis
1.53
0.99 – 2.36
1.49
0.97 – 2.31
Cancer
2.08
1.41 – 3.06
1.88
1.28 – 2.77
COPD/asthma
0.92
0.76 – 1.12
0.88
0.72 – 1.07
Dementia
1.90
1.47 – 2.45
1.71
1.33 – 2.21
Depression
1.15
0.94 – 1.41
1.10
0.90 – 1.35
Parkinson’s disease
1.13
0.63 – 2.00
1.03
0.58 – 1.83
Mental disorders
1.93
1.34 – 2.77
1.90
1.31 – 2.74
Renal failure
1.75
1.28 – 2.39
1.65
1.20 – 2.26
Alcoholism
1.10
0.38 – 3.17
0.92
0.32 – 2.66
Stroke
1.42
1.04 – 1.93
1.32
0.97 – 1.79
Income NOK 0 – 150 000
Ref.
Ref.
NOK 150 000 – 300 000
0.81
0.68 – 0.97
0.82
0.68 – 0.98
NOK 300 000 – 500 000
0.69
0.49 – 0.98
0.72
0.51 – 1.03
More than NOK 500 000
0.90
0.51 – 1.59
0.91
0.52 – 1.62
Assets NOK 0 – 150 000
Ref.
Ref.
NOK 150 000 – 300 000
0.89
0.71 – 1.11
0.90
0.71 – 1.13
NOK 300 000 – 500 000
0.91
0.73 – 1.15
0.92
0.73 – 1.16
More than NOK 500 000
0.93
0.74 – 1.18
0.93
0.74 – 1.18
Education primary/lower secondary
Ref.
Ref.
Upper secondary
0.91
0.77 – 1.07
0.90
0.77 – 1.06
University/university college
0.86
0.63 – 1.17
0.90
0.66 – 1.23
Disability benefit before 2008
1.73
1.19 – 2.49
1.64
1.13 – 2.37
Travel distance 0 – 60 minutes
Ref.
Ref.
60 – 120 minutes
1.07
0.85 – 1.35
1.05
0.83 – 1.33
120 – 180 minutes
1.16
0.83 – 1.62
1.12
0.80 – 1.57
More than 180 minutes
1.50
0.97 – 2.31
1.40
0.90 – 2.17
PCI after 14 days
0.24
0.19 – 0.31
Akershus University Hospital
Ref.
Ref.
Asker og Bærum
0.47
0.23 – 0.94
0.49
0.24 – 0.99
Bergen
0.86
0.58 – 1.28
0.98
0.66 – 1.46
Buskerud
0.93
0.60 – 1.44
0.93
0.60 – 1.45
Finnmark
0.69
0.33 – 1.44
0.73
0.35 – 1.54
Fonna
0.58
0.33 – 1.02
0.67
0.38 – 1.16
Førde
0.66
0.35 – 1.23
0.68
0.36 – 1.28
Helgeland
0.59
0.28 – 1.27
0.64
0.30 – 1.38
Innlandet
0.84
0.56 – 1.27
0.81
0.54 – 1.22
Nord-Trøndelag
0.66
0.38 – 1.17
0.65
0.37 – 1.15
Nordland
0.48
0.25 – 0.94
0.49
0.25 – 0.97
Nordmøre og Romsdal
0.41
0.21 – 0.81
0.43
0.22 – 0.85
Oslo
0.64
0.43 – 0.94
0.65
0.44 – 0.95
Stavanger
0.94
0.60 – 1.47
1.19
0.75 – 1.87
Sunnmøre
0.42
0.22 – 0.81
0.41
0.21 – 0.81
Sør-Trøndelag
0.81
0.52 – 1.24
0.82
0.53 – 1.27
Sørlandet
0.98
0.61 – 1.58
1.06
0.65 – 1.71
University Hospital of North Norway
0.67
0.39 – 1.15
0.77
0.44 – 1.35
Vestfold
0.91
0.59 – 1.42
0.96
0.62 – 1.50
Østfold
0.87
0.56 – 1.36
0.89
0.57 – 1.39
Table 6 Proportion of deaths after 30 days with a 95 % confidence interval (95 % CI), unadjusted (N = 10 717), adjusted for age and gender (N = 10 717) and adjusted for all covariates (age, gender, type of myocardial infarction, comorbidity, distance and socioeconomic factors, N = 10 379). All figures are percentages
Hospital trust
Unadjusted
95 % CI
Adjusted for age/gender
95 % CI
Adjusted for all
95 % CI
Akershus University Hospital
9.0
6.7 – 11.3
9.9
7.5 – 12.3
10.9
8.0 – 13.8
Asker og Bærum
5.2
2.2 – 8.3
4.3
1.9 – 6.8
5.8
2.5 – 9.1
Bergen
9.3
7.3 – 11.2
9.2
7.3 – 11.0
9.7
7.5 – 11.8
Buskerud
8.6
6.4 – 10.8
9.3
7.0 – 11.6
10.3
7.7 – 12.9
Finnmark
8.1
4.5 – 11.7
9.7
5.7 – 13.7
8.1
4.1 – 12.1
Fonna
6.9
4.6 – 9.3
6.8
4.6 – 9.0
7.0
4.5 – 9.5
Førde
12.5
8.5 – 16.7
10.2
7.0 – 13.5
7.8
4.7 – 10.8
Helgeland
9.1
4.9 – 13.4
9.2
5.1 – 13.4
7.1
3.3 – 10.9
Innlandet
9.2
7.4 – 10.9
9.0
7.4 – 10.7
9.5
7.7 – 11.3
Nord-Trøndelag
7.9
5.0 – 10.8
8.3
5.3 – 11.1
7.8
5.0 – 10.7
Nordland
8.1
5.2 – 11.0
7.8
5.1 – 10.5
6.0
3.3 – 8.6
Nordmøre og Romsdal
7.6
4.5 – 10.6
6.8
4.1 – 9.4
5.2
2.8 – 7.6
Oslo
8.1
6.5 – 9.7
7.2
5.8 – 8.6
7.6
5.8 – 9.3
Stavanger
8.5
6.2 – 10.8
9.7
7.2 – 12.2
10.3
7.5 – 13.2
Sunnmøre
8.3
5.3 – 11.1
7.0
4.6 – 9.4
5.2
2.9 – 7.6
Sør-Trøndelag
8.6
6.3 – 10.7
8.8
6.6 – 10.9
9.2
6.8 – 11.5
Sørlandet
9.3
6.6 – 12.1
10.0
7.2 – 12.8
10.7
7.6 – 13.8
University Hospital of North Norway
8.6
6.0 – 11.1
9.1
6.5 – 11.6
7.8
5.3 – 10.4
Vestfold og Telemark
8.5
6.8 – 10.2
9.0
7.3 – 10.8
10.1
8.0 – 12.3
Østfold
9.1
7.0 – 11.1
9.6
7.5 – 11.7
9.8
7.4 – 12.1
Norway
8.6
8.1 – 9.1
Table 7 Proportion of deaths after 30 days with a 95 % confidence interval (95 % CI), adjusted for all covariates (age, gender, type of myocardial infarction, comorbidity, distance and socioeconomic factors) and percutaneous coronary intervention (PCI) after 14 days. N = 10 379. All figures are percentages
Hospital trust
30-day survival
95 % CI
Akershus University Hospital
10.5
7.7 – 13.3
Asker og Bærum
5.8
2.6 – 9.1
Bergen
10.4
8.1 – 12.6
Buskerud
9.9
7.5 – 12.4
Finnmark
8.2
4.2 – 12.2
Fonna
7.6
4.9 – 10.2
Førde
7.7
4.7 – 10.7
Helgeland
7.3
3.5 – 11.2
Innlandet
8.9
7.2 – 10.6
Nord-Trøndelag
7.4
4.7 – 10.1
Nordland
5.9
3.3 – 8.5
Nordmøre og Romsdal
5.2
2.8 – 7.6
Oslo
7.4
5.7 – 9.1
Stavanger
11.9
8.8 – 15.1
Sunnmøre
5.0
2.8 – 7.3
Sør-Trøndelag
9.0
6.7 – 11.2
Sørlandet
11.0
7.8 – 14.1
University Hospital of North Norway
8.5
5.8 – 11.3
Vestfold og Telemark
10.2
8.0 – 12.3
Østfold
9.6
7.3 – 11.9
Norway
8.6
8.1 – 9.1
The prevalence of percutaneous coronary intervention and/or coronary artery bypass surgery after two and fourteen days by age group and hospital trust showed that the former treatment is more common in hospital trusts that have their own PCI centre when compared to the other hospital trusts (e‑Table 8).
Table 8 Description of treatment. Percutaneous coronary intervention (PCI) after 2 and 14 days, PCA = PCI and/or coronary artery bypass surgery after 2 and 14 days by age group (18 – 79 years/> 80 years) and hospital trust. All figures are percentages
Hospital trust
PCI 2 d
18 – 79
PCI 2 d
> 80
PCI 14 d
18 – 79
PCI 14 d
> 80
PCA 2 d
18 – 79
PCA 2 d
> 80
PCA14 d
18 – 79
PCA 14 d
> 80
Akershus University Hospital
46.1
8.7
54.3
13.8
46.1
9.2
60.7
14.9
Asker og Bærum
45.2
11.6
48.7
20.0
46.1
11.6
59.1
20.0
Bergen
50.4
13.1
68.5
24.5
51.6
13.1
73.3
24.8
Buskerud
43.7
8.1
52.5
10.2
45.3
8.1
59.9
12.6
Finnmark
27.2
11.7
45.1
18.3
30.2
11.7
56.8
23.3
Fonna
37.0
12.9
54.5
19.9
38.3
12.9
60.4
20.5
Førde
33.6
8.1
55.0
10.6
34.4
8.1
60.3
10.6
Helgeland
23.5
0.0
54.6
10.7
26.9
0.0
75.6
16.1
Innlandet
32.6
4.4
47.8
9.7
34.3
4.6
54.0
10.8
Nord-Trøndelag
30.9
5.3
48.4
8.0
32.7
5.3
58.1
8.8
Nordland
16.3
2.3
44.7
9.1
18.1
2.3
57.7
11.5
Nordmøre og Romsdal
30.1
3.4
53.5
7.6
32.0
3.4
55.8
9.3
Oslo
44.4
10.7
51.8
13.2
45.3
10.7
55.7
13.4
Stavanger
71.7
32.3
73.5
32.9
72.5
32.3
75.8
32.9
Sunnmøre
18.9
1.9
40.0
4.5
22.7
1.9
48.6
5.8
Sør-Trøndelag
59.4
7.4
66.1
8.8
61.0
7.4
69.7
9.3
Sørlandet
61.0
12.2
67.2
17.3
64.1
12.2
74.1
19.4
University Hospital of North Norway
49.8
15.9
64.4
23.6
55.0
16.6
76.7
28.0
Vestfold og Telemark
43.3
10.8
51.6
17.6
44.4
10.8
61.2
18.5
Østfold
43.6
4.1
57.2
11.8
46.8
4.1
64.4
14.6
Norway
43.4
9.5
56.0
14.7
45.2
9.6
63.0
16.0
The unadjusted model had an AUC value of 0.52, i.e. about the same as the toss of a coin. When we added age and gender, the value rose to 0.76. It rose further to 0.80 when the type of myocardial infarction, comorbidities, socioeconomic variables and travel distance were added, climbing to 0.81 when we also included percutaneous coronary intervention after 14 days.
Discussion
Use of quality-based funding, in which data on survival are included in the quality indicators, may encourage the hospitals to treat patients wherever good results may be expected. If no control for underlying variables is made, this may give rise to misallocation of resources. Absence of risk adjustment may thus be a disadvantage, for patients with serious diseases that are hard to treat, as well as for the hospitals that provide care to these patients. For example, it has been shown that the introduction of quality indicators for heart surgery in New York and Pennsylvania gave rise to patient selection among the hospitals (18 ).
The model used for quality-based funding in Norway does not clarify how the risk adjustment has been undertaken (3 ). The objective of this study was thus to investigate how various forms of risk adjustment affected the predicted mortality, using patients discharged with a diagnosis of acute myocardial infarction as an example.
The main conclusions from the analysis are that gender, age, comorbidity, disability and travel time are candidate variables that should be considered for inclusion in the adjustment. Other variables to which we have had no access, for example information on genetic factors (19 ), may also be relevant. The socioeconomic variables made a smaller contribution to the risk adjustment once the former variables had been included.
Even though this analysis has a limited statistical strength and there were few confidence intervals that did not overlap with the national average for 30-day mortality, the changes that occurred as a result of the risk adjustments are important in terms of how resources are allocated in a quality-based funding system. Estimated mortality changed significantly for patients in the catchment areas for the Asker og Bærum, Stavanger, Sunnmøre, and Nordmøre og Romsdal hospital trusts, and also for Førde when further variables were included. Therefore, the main conclusion from the analysis is that controls for a broad range of underlying variables should be made, both when quality information is published and when quality indicators are used as a basis for the funding system.
In the normative debate on how quality-based funding systems should be designed, questions are raised regarding whether the use of procedures should be included as a quality indicator (20 ). The only variable included in our analysis that can be linked to the treatment provided by the hospital trust was percutaneous coronary intervention.
Two issues render it obvious that rewards for this type of treatment may not be a good solution. It may give rise to overtreatment and result in a geographically skewed distribution of resources due to the current structure of PCI centres, since this method is more common in hospital trusts that have a PCI centre when compared to those that have none. This applies also to percutaneous coronary interventions after 14 days and reflects the opportunities for emergency treatment with the aid of this method as well as the expected effectiveness of such treatment after the acute phase. The list of procedures that could be included in the analysis may be expanded, however. With regard to myocardial-infarction patients, rehabilitation, check-ups and medication use may provide relevant procedure variables.
While the decision to perform percutaneous coronary intervention is made at the clinical level in the hospital, a number of other variables may be influenced at the local or regional level within the health-enterprise system. Travel time to the PCI centre is one example. Seen in isolation, if long distances give rise to higher mortality and a decision is made to adjust for distances in the allocation model, this may entail a premium on centralisation of the activities. The size of the hospital trusts is another example of issues that can be influenced and may have an effect on the results of the type of analyses that we have undertaken. If the analyses are undertaken on the basis of the hospital trusts’ catchment areas, large hospital trusts will, seen in isolation, demonstrate less inter-trust variation when compared to a greater number of small hospital trusts.
Our analysis represents an example of how risk adjustment may be undertaken to provide a basis for quality-based funding. The analysis has its limitations, however. To increase the strength of the analysis we should ideally have used data for more than one year. For some hospital trusts, however, the results reveal confidence intervals that do not overlap with the national average if their mortality rates lie 3 – 4 % above or below this level. One key question that remains unclarified concerns how much the mortality rate for each hospital trust should be permitted to deviate from the national average before this will have an effect on the budget allocations to the regional hospital trusts.
The data need to be as updated as possible, to permit the hospital staff to see the financial effects of quality changes in the course of one or two years. For practical reasons, especially because of the time it takes to process applications for data access and retrieve the data, we have used data for 2009. Improvements to contingency planning have been made in recent years, and the analysis may therefore have yielded different results today. The travel-time variable that we have used could also be improved by including patient-level data from the database that registers information on ambulance call-outs (Akuttmedisinsk informasjonssystem, AMIS).
We have based this study on the responsibility of the hospital trusts to provide care, meaning that the patient’s residential address decides his or her affiliation to a particular hospital trust. The Norwegian Knowledge Centre for the Health Services base their analyses of survival and readmissions to Norwegian hospitals on the actual place of treatment, weighted by hospitalisation time if multiple locations are involved.
These two different perspectives entail consequences in terms of who will be held responsible for the patient’s treatment outcome. While we hold responsible the hospital trust that in practice is charged with providing care to the patient, the Knowledge Centre places this responsibility with the hospital trust that actually provides treatment. In a system of quality-based funding it is essential to have a well-considered opinion regarding the choice of one perspective or the other. We see no reason why the funding in this respect should deviate from the principles that have been chosen for funding of the hospital trusts in general.
