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  • Research article
  • Open Access
  • Open Peer Review

Active listing and more consultations in primary care are associated with reduced hospitalisation in a Swedish population

BMC Health Services ResearchBMC series – open, inclusive and trusted201818:101

https://doi.org/10.1186/s12913-018-2908-1

  • Received: 5 September 2016
  • Accepted: 31 January 2018
  • Published:
Open Peer Review reports

Abstract

Background

Healthcare systems are complex networks where relationships affect outcomes. The importance of primary care increases while health care acknowledges multimorbidity, the impact of combinations of different diseases in one person. Active listing and consultations in primary care could be used as proxies of the relationships between patients and primary care. Our objective was to study hospitalisation as an outcome of primary care, exploring the associations with active listing, number of consultations in primary care and two groups of practices, while taking socioeconomic status and morbidity burden into account.

Methods

A cross-sectional study using zero-inflated negative binomial regression to estimate odds of any hospital admission and mean number of days hospitalised for the population over 15 years (N = 123,168) in the Swedish county of Blekinge during 2007. Explanatory factors were listed as active or passive in primary care, number of consultations in primary care and primary care practices grouped according to ownership. The models were adjusted for sex, age, disposable income, education level and multimorbidity level.

Results

Mean days hospitalised was 0.94 (95%CI 0.90–0.99) for actively listed and 1.32 (95%CI 1.24–1.40) for passively listed. For patients with 0–1 consultation in primary care mean days hospitalised was 1.21 (95%CI 1.13–1.29) compared to 0.77 (95%CI 0.66–0.87) days for patients with 6–7 consultations. Mean days hospitalised was 1.22 (95%CI 1.16–1.28) for listed in private primary care and 0.98 (95%CI 0.94–1.01) for listed in public primary care, with odds for hospital admission 0.51 (95%CI 0.39–0.63) for public primary care compared to private primary care.

Conclusions

Active listing and more consultations in primary care are both associated with reduced mean days hospitalised, when adjusting for socioeconomic status and multimorbidity level.

Different odds of any hospitalisation give a difference in mean days hospitalised associated with type of primary care practice.

To promote well performing primary care to maintain good relationships with patients could reduce mean days hospitalised.

Keywords

  • Primary health care
  • Hospitalisation
  • Multimorbidity
  • Socioeconomic status

Key points

Relationships with primary care could be associated with hospitalisation within the complex networks constituting healthcare systems.

Active listing and more consultations are associated with reduced hospitalisation, and difference within primary care shown.

Promoting well performing primary care practices and their relationship with patients could be an option to reduce hospitalisation.

Background

The need for hospitalisation is expected to increase with severe morbidity, but is also related to individuals, local healthcare and society. Relationships are the central unit of analysis within the complex networks comprising healthcare systems [1]. Good relations between individuals in a population and well performing primary care has been shown to contribute to more adequate care, trust and better health [2, 3]. Patients’ attachment to and satisfaction with primary care are linked to their choice of primary care [4, 5]. To patients this is a complex choice related to trust [6, 7]. Socioeconomic status affect both individual morbidity and trust in health care [810]. Continuity of care from a primary care provider has impact on hospitalisation [11, 12].

Recognising primary care as a part of complex healthcare systems, hospitalisation could be analysed as an outcome of relationships in primary care [1]. Primary care could be described as a multidimensional care system, comprising structure and processes generating outcomes like quality, efficiency and equity [13]. Well performing primary care is characterised by a combination of person-focused care over time, use as first contact in health care, completeness of services and coordination of care [13]. Efficiency of care, such as reducing hospitalisation, is a primary care outcome [13, 14].

Several definitions of morbidity are associated with clinical management and health outcomes. Multiple diseases within the same person are acknowledged using comorbidity or multimorbidity. Sets of disorders are often used to study potentially avoidable hospital admissions [15, 16]. Morbidity burden is the overall impact of the different diseases in one person taking into account their severity [9, 10]. To analyse hospitalisation as an outcome of relationships with primary care a multimorbidity measure that allows for comparison between groups of patients with the same need for care, despite different patterns of disorders was considered most relevant [17].

In Sweden, primary care practices comprise general practitioners (GPs) organised within multidisciplinary teams. Listing in primary care was introduced to empower patients and to introduce market models. County councils regulate local health care, organising primary care in several quasi-market models, mandatory since 2010 [18]. Blekinge County Council introduced listing in primary care in 2004. Listing was mandatory passive or active and individual. Active listing gave no favours compared to passive listing to patients or practices, but might be considered an act of the patient to protect their relationship with a primary care practice. Number of consultations in primary care quantifies other aspects of the relationship between population and primary care.

How relationships with primary care, i.e. active listing and number of consultations, could affect hospitalisation while accounting for socioeconomic status and morbidity burden, have not previously been studied. Prior studies have used reported data from small samples analysing whether or not patients were hospitalised [2, 5, 12]. We combined individual data on socioeconomic status with hospitalisation and morbidity burden from patient records for a population within the same healthcare system. The aim was to study hospitalisation, i.e. odds of any hospital admission and mean days hospitalised, as an outcome of relationships with primary care when adjusting for the overall impact of sex, age, socioeconomic status and morbidity burden. The additional aim was to analyse whether there was any difference within primary care.

Methods

Study population and settings

The year 2007 represents a period with stability in regulations, funding and workforce settings in primary care in Blekinge. On 31 December 2007 the county of Blekinge had 151,731 inhabitants. Of these, 50.5% were men, and the average age was 42.7 years [19]. Health care was provided by two hospitals, five psychiatric clinics and 25 primary care practices. Half of the primary care practices were privately owned, and were established in all municipalities. A total of 65% were actively listed, ranging between 50 and 85% according to municipality. A majority (84%) were listed in practices owned by the county council. The percentage of any hospitalisaton within this population was 8.7% and mean days hospitalised was 0.89.

Information on education level or residence was missing for 3471 and socioeconomic data for individuals < 16 years of age for 24,741. We therefore restricted this study to the remaining 123,168 individuals. This population had an average age of 50.1 years and 50.2% were men. A total of 68% were actively listed and 83% listed with practices owned by the county council. The share of actively listed ranged from 56 to 86% according to municipality. The study population had a hospitalisation rate of 9.6% and mean days hospitalised was 1.0.

Listing status and mean consultations were used as proxies of aspects of relationships with primary care. Listing was mandatory passive at the nearest primary care practice. Patients initiated change to active listing at will, at the same or another practice within the county. Family members over 15 years of age made their choice separately, and active listing could be changed at will. The practice of choice administrated active listing. Patients or practices gained no obvious favours from primary care by active instead of passive listing. Primary care practices were obliged to accept any patient and to distribute care according to medical need. Under these circumstances, active listing could be seen as patients acting to protect their relationship with primary care.

Number of consultations in primary care balances demand for health care against availability and need for care. Mean number of consultations in primary care was 0.9 and in all healthcare 2.0. The share of more than 5 consultations in primary care was 1.8% and in all healthcare 10%. Adjusting for morbidity burden, more than mean number of consultations could be used as a proxy of having, or searching for, a relationship with primary care.

Practices in primary care could be public, owned by the county council, or private. Public practices were typically older, with more listed patients and GPs, than private practices. The county council contracted all primary care practices. This gave equal funding and regulations, but different settings and processes amongst primary care practices. Of patients listed in private primary care 60% had little or no need for health care, compared to 35% in public primary care; income and education were equally distributed.

Local government area, municipality, was the available factor relating to local society and geographic location. Municipality is also correlated with active listing [20, 21].

Design

We performed a cross-sectional population-based study on hospitalisation as an outcome of primary care. Listing status and number of consultations were used as proxies of relationships with primary care, and the difference between two types of practices was investigated. We adjusted for sex, age, socioeconomic status and multimorbidity burden. Data on an individual level collected from electronic patient records and Statistics Sweden during year 2007 was used. This study was approved by the Regional Ethical Review Board at Lund University (application no. 2016/71). According to this approval, the study population was given the possibility to opt out from the study.

Outcome

Odds of any hospital admission for the population of Blekinge County during year 2007 were estimated.

Mean days hospitalised for the population were estimated from odds of ever being admitted to hospital and mean number of days hospitalised if at risk of hospitalisation, in all health care (somatic and psychiatric hospitalisation) in Blekinge County during year 2007.

Explanatory factors

Actively or passively listed at primary care practice, not listed was not an option. Active listing was considered a measure of good relationship in primary care.

Number of consultations with a doctor in primary care (GP) was categorised into six groups (0–1, 2–3, 4–5, 6–7, 8–9, 10-), and used to quantify other aspects of the patient-professional relationship in primary care. More than one consultation considered to be associated with having a relationship with primary care.

Primary care practices grouped in private (type A) and public (type B) according to ownership. The two categories also included other differences as size and time since establishment.

Sex and age, where age was grouped into 16–19, 20–39, 40–59, 60–79, 80- years.

Individual disposable income was categorised into four equally sized levels (quartiles).

Individual education was divided into four levels: 1) less than 9 years of education, 2) completed 9 years of compulsory education, 3) college degree, or 4) university degree.

Multimorbidity level, as a summary measure of morbidity burden, was calculated from patient records from all health care for 2007 using the Johns Hopkins Adjusted Clinical Groups Case Mix System (ACG). This is one of the summary measures aiming to link all diagnoses with need of healthcare, focussing on stratification of patients into groups according to diseases and conditions, age and sex. ACG weights patients’ diagnoses according to five clinical dimensions: duration, severity, diagnostic certainty, aetiology and need for specialist care into almost 100 mutually exclusive ACGs. Then ACGs are categorised into six multimorbidity levels with similar impact on need for healthcare despite different patterns of diagnoses. These multimorbidity levels are called Resource Utilization Bands (RUBs) and range from 0 (no need for health care) to 5 (very strong need for health care) [17, 22].

Multivariate statistical models were clustered on municipality (local government area, in order to account for covariation associated with local society). The five municipalities range from 10,849–49,931 inhabitants.

Statistical analysis

Statistical analyses were performed with STATA version 14.1 (Stata Corporation, Texas, USA). Associations between variables were studied using the Pearson correlation.

Coefficient, a linear correlation of 0.2 was considered meaningful. Number of days hospitalised was found to be skewed, non-normally distributed with over-representation of persons without need for hospitalisation. Count data models were tested, and a clustered zero-inflated negative binomial model considered most valid. This is a combined model using a binary method to assess odds ratios (OR) of being at risk, here a logit model. Incidence rate ratio (IRR) was used to show the influence of increasing the explanatory factors in the negative binomial part of the model by one unit if at risk. Mean days hospitalised for the population were calculated as average marginal effects combining the logit and negative binomial parts of the model. The multivariate statistical model was adjusted for sex, age, socioeconomic status and multimorbidity level, the same set of variables were used for both parts of the model, and the model was clustered on municipality [23].

Model performance was assessed using several different methods. Akaike’s Information Criterion (AIC) has a penalty term for additional parameters, lower values preferred. Likelihood ratio statistics (LR test) test differences between nested models. Higher values indicate greater difference from the simpler model than lower values. Coefficient of variance (CV) standardises standard deviation, using absolute mean to allow for comparison of variance across models. C-statistics (AUC), equivalent to the area under the receiver operating characteristics (ROC) curve, 1 indicating perfect discrimination and 0.5 equal to chance.

Results

Descriptive statistics

During 2007 the percentage hospitalised during year 2007 was 9.6% and mean hospitalisation was 1.0 day. Total hospitalisation was 123,690 days. The 68% actively listed accounted for 77% of those admitted to hospital and 79% of days hospitalised. The 22% with more than two consultations in primary care accounted for 37% of those admitted to hospital and 39% of days hospitalised. The 83% listed in public primary care accounted for 86% of those admitted to hospital and 87% of days hospitalised (Table 1).
Table 1

Descriptive statistics for the population > 15 years of age in Blekinge in 2007 (N = 123,168)

Descriptive 2007 Population > 15 years

Group size

Actively listed

Admitted to hospital

Hospitalisation

N =

%

N =

%

N =

%

Total days

Mean days

Listing status in primary care

 Passively listed

39,430

32.0

2704

6.9

25,722

0.65

 Actively listed

83,738

68.0

9099

10.9

97,968

1.17

Consultations, primary care

 0 or 1 consultation

95,810

77.8

59,077

61.7

7453

7.8

72,982

0.76

 2 or 3 consultations

19,673

16.0

17,332

88.1

2661

13.5

29,699

1.51

 4 or 5 consultations

5383

4.4

5108

94.9

1064

19.8

12,430

2.31

 6 or 7 consultations

1522

1.2

1457

95.7

372

24.4

4622

3.04

 8- consultations

781

0.6

765

97.9

253

32.4

3956

5.07

Type of practice

 A, private practice

20,428

16.6

15,798

77.3

1654

8.1

15,982

0.78

 B, public practice

102,740

83.4

67,940

66.1

10,149

9.9

107,708

1.05

Sex

 Women

61,386

49.8

45,004

73.3

6447

10.5

67,729

1.10

 Men

61,782

50.2

38,734

62.7

5356

8.7

55,961

0.91

Age groups

 16–19 years

6846

5.6

3777

55.2

182

2.7

2077

0.30

 20–39 years

34,067

27.7

17,904

52.6

3571

10.5

25,571

0.75

 40–59 years

39,374

32.0

26,176

66.5

2261

5.7

21,197

0.54

 60–79 years

33,420

27.1

27,386

81.9

3646

10.9

45,225

1.35

 80+ years

9461

7.7

8495

89.8

2143

22.7

29,620

3.13

Individual income

 First income quartile

29,588

24.0

18,843

63.7

2948

10.0

36,702

1.19

 Second income quartile

30,933

25.1

23,764

76.8

4279

13.8

51,886

1.68

 Third income quartile

31,339

25.4

21,415

68.3

2553

8.1

20,703

0.67

 Fourth income quartile

31,308

25.4

19,716

63.0

2023

6.5

14,399

0.47

Educational level

 Less than 9 years

21,602

17.5

18,034

83.5

3173

14.7

41,848

1.94

 Compulsory 9 years

15,956

13.0

10,128

63.5

1135

7.1

12,663

0.79

vCollege degree

54,693

44.4

37,319

68.2

4931

9.0

47,750

0.87

vUniversity degree

30,917

25.1

18,257

59.0

2564

8.3

21,429

0.69

Multimorbidity, all healthcare

 RUB 0

48,211

39.1

25,030

51.9

21

4.4

280

0.01

 RUB 1

15,315

12.4

10,391

67.8

1224

8.0

5405

0.35

 RUB 2

25,242

20.5

18,861

74.7

1603

 

11,149

0.44

 RUB 3

30,566

24.8

25,983

85.0

6629

21.7

62,098

2.03

 RUB 4

3233

2.6

2909

90.0

1849

57.2

29,134

9.01

 RUB 5

601

0.5

564

93.8

477

79.4

15,622

26.03

Municipality

 A

49,931

40.5

27,700

55.5

5164

10.3

53,785

1.08

 B

23,286

18.9

15,297

65.7

2269

9.7

24,044

1.03

 C

25,405

20.6

21,723

85.5

2338

9.2

25,337

1.00

 D

13,697

11.1

10,924

79.5

1107

8.1

11,819

0.86

 E

10,849

8.8

8094

74.6

925

8.5

8705

0.80

Population

123,168

100.0

83,738

67.9

11,803

9.6

123,690

1.00

Unadjusted active listing on 31 December 2007 and hospitalisation during 2007 for the population of Blekinge > 15 years of age. RUB Resource Utilization Band; Quartile Four equally sized groups; Municipality Local government area

Clustered zero-inflated negative binomial model

The zero-negative binomial model shows odds of any hospitalisation. Actively listed had OR 0.69 (95%CI 0.61–0.77) for any hospitalisation compared to passively listed. Having 6–7 consultations in primary care gave OR for any hospitalisation 0.60 (95%CI 0.48–0.72) compared to less than two consultations. Those listed in public primary care had OR 0.51 (95%CI 0.39–0.63) for any hospitalisation compared to those listed in private primary care (Table 2).
Table 2

Associations between active listing, consultations in primary care and hospitalisation for the population > 15 years of age in Blekinge in 2007 (N = 123,168), adjusting for sex, age, income, education and multimorbidity level

Multivariate binomial model

Odds for any hospitalisation

Mean days hospitalised

OR

(95%CI)

Days

(95%CI)

Listing status

 Passively listed

1.00

 

1.32**

(1.24–1.40)

 Actively listed

0.69**

(0.61–0.77)

0.94**

(0.90–0.99)

Consultations, primary care

 0 or 1 consultation

1.00

 

1.21**

(1.13–1.29)

 2 or 3 consultations

0.44**

(0.34–0.54)

0.80**

(0.75–0.84)

 4 or 5 consultations

0.55**

(0.37–0.74)

0.77**

(0.72–0.83)

 6 or 7 consultations

0.60**

(0.48–0.72)

0.77**

(0.66–0.87)

 8 or 9 consultations

0.73*

(0.48–0.99)

0.80**

(0.51–1.10)

 10- consultations

0.91

(0.63–1.19)

1.06**

(0.67–1.46)

Type of practice

 A, private practice

1.00

 

1.22**

(1.16–1.28)

 B, public practice

0.51**

(0.39–0.63)

0.98**

(0.94–1.01)

Sex

 Women

1.00

 

0.94**

(0.89–0.98)

 Men

1.13**

(1.09–1.17)

1.09**

(1.03–1.15)

Age

 16–19 years

1.00

 

0.63**

(0.37–0.90)

 20–39 years

2.45**

(2.26–2.63)

1.12**

(1.01–1.22)

 40–59 years

1.45**

(1.27–1.64)

0.77**

(0.69–0.86)

 60–79 years

1.52**

(1.33–1.71)

0.91**

(0.85–0.98)

 80+ years

2.05**

(1.88–2.21)

1.39**

(1.30–1.47)

Individual income

 First income quartile

1.00

 

1.10**

(1.08–1.13)

 Second income quartile

1.10**

(1.06–1.14)

1.15**

(1.07–1.22)

 Third income quartile

1.01

(0.84–1.04)

0.84**

(0.80–0.87)

 Fourth income quartile

1.02

(0.97–1.08)

0.72**

(0.66–0.78)

Education level

 Less than 9 years

1.00

 

1.03**

(1.01–1.07)

 Compulsory 9 years

0.89*

(0.79–1.00)

1.12**

(1.06–1.18)

 College degree

0.87**

(0.61–0.93)

0.97**

(0.89–1.06)

 University degree

0.88

(0.72–1.04)

0.97**

(0.93–1.01)

Multimorbidity level

 RUB 0

1.00

 

0.00*

(0.00–0.01)

 RUB 1

6.66**

(5.95–7.37)

0.40**

(0.34–0.45)

 RUB 2

6.50**

(5.78–7.23)

0.47**

(0.44–0.51)

 RUB 3

8.13**

(7.49–8.77)

2.34**

(2.12–2.57)

 RUB 4

9.85**

(9.21–10.49)

9.21**

(8.70–9.84)

 RUB 5

11.13**

(10.66–11.60)

24.96**

(23.87–26.05)

OR Odds Ratio, CI Confidence Interval; * = p < 0.05, ** = p < 0.01; RUB Resource Utilization Band; Mean days hospitalised = average marginal effects combining both parts of the zero-inflated binomial model

Mean number of days hospitalised for the entire population was calculated combining both parts of the multivariate model. Actively listed were in mean hospitalised for 0.94 (95%CI 0.90–0.99) days and passively listed 1.32 (95%CI 1.24–1.40) days. Patients with 0–1 consultations in primary care were in mean hospitalised 1.21 (95%CI 1.13–1.29) days, and with 6–7 consultations in primary care 0.77 (95%CI 0.66–0.87) days. Mean number of days hospitalised for those listed in private primary care was 1.22 days (95%CI 1.16–1.28) and for those listed in public primary care 0.98 days (95%CI 0.94–1.01) (Fig. 1, Table 2).
Fig. 1
Fig. 1

Predicted mean days hospitalised for the population > 15 years of age for listed in type A (privately owned practices with N = 20,428) and type B (publicly owned practices with N = 102,740) practices according to active listing and number of consultations, adjusting for sex, age, income, education and multimorbidity level

Setting active listing at 68% and number of consultations in primary care at 0.9 predicted totally 130,559 (95%CI 123589–146,273) days hospitalised. Comparing multivariate models showed that AIC for the logistic model including age, sex and multimorbidity level was 56,466. Including socioeconomic factors to this model gave LR-test 46 or relationship with primary care LR-test 850. AIC for a model including sex, age, multimorbidity level, socioeconomic factors and relationships with primary care was 55,587 (Table 3).
Table 3

Multivariate models on hospitalisation for the population > 15 years of age in Blekinge in 2007 (N = 123,168). Tests comparing multivariate models adjusting for relationships with primary care, sex, age, socioeconomic status and multimorbidity level

Model tests, multivariate models on hospitalisation

Area under ROC Curve

CV

AIC

LR test

AUC

(95% CI)

%

Sex and age

0.637

(0.632–0.642)

4.9

74,959

0

Adjusted for sex and age

    

0

Individual income and education

0.652

(0.647–0.658)

5.2

74,599

372

Relationships with primary care

0.668

(0.663–0.674)

6.2

73,638

1331

Multimorbidity level

0.858

(0.855–0.861)

13.7

56,466

18,503

Adjusted for sex, age and multimorbidity

    

0

Individual income and education

0.859

(0.856–0.862)

13.7

56,432

46

Relationships with primary care

0.864

(0.861–0.866)

14.1

55,626

850

Socioeconomy and relationship

0.864

(0.861–0.867)

14.2

55,587

901

N = 123,168; AUC Area Under the Curve; CI Confidence Interval; CV Coefficient of Variance; AIC Akaike’s Information Criterion; LR test Likelihood Ratio test

Discussion

Summary of main findings

Active listing is associated with reduced mean days hospitalised compared to passive listing, adjusting for socioeconomic status, multimorbidity level, sex and age.

Two or more consultations with a GP are associated with reduced mean days hospitalised compared to less than two, taking active listing, socioeconomic status and multimorbidity level into account.

Different odds of any hospitalisation give a difference in mean days hospitalised comparing private and public practices in primary care.

Strengths and limitations of the study

We had the opportunity to combine healthcare data at individual level with socioeconomic data. Proxies of the relationship between patients and primary care, i.e. listing status and number of consultations, could be identified using patient records. We could analyse differences within primary care and adjust for sex, age, socioeconomic status and multimorbidity level. To our knowledge, this has not been done before. Our hypothesis was to find associations between hospitalisation and relationships with primary care, as well as differences within primary care.

Active listing and consultations in primary care could be regarded as measures of different aspects of the relationship between the population and primary care. Within this healthcare system, active listing could be regarded as patients acting to protect their relationship with a primary care practice. We expect active listing to underestimate relationships between patients and primary care, since they could be maintained by passive listing as well. Listing status was unlikely to be changed from active to passive during 2007 according to listing regulations. Data on listing by physician and consultations with staff members other than physicians were unreliable, hence not used. Number of consultations in primary care measures relationship as contacts between patients and primary care, but also includes morbidity burden, demand for healthcare services and availability of care. To account for these factors, we adjusted for sex, age socioeconomic status and multimorbidity level and clustered on municipality.

Primary care practices were grouped by ownership to estimate differences in efficiency within a primary care system. These groups had different characteristics regarding size and competence of the staff, number of listed patients and time since establishment. Further exploration of the impact of these characteristics was not possible. The established difference within primary care implicates that hospitalisation could be affected by differences in settings and processes between primary care practices although the regulations and funding are the same [13].

In Sweden, listing in primary care was introduced to empower patients and to introduce market models [18]. County councils regulate and organise local health care. Descriptions of health care in several European countries are available; and the listing system in Blekinge in 2007 is comparable to the Swedish system legislated in 2010 [18]. The European primary care monitor, describing primary care across Europe [2426], facilitates comparisons within a European context.

We adjusted for morbidity using a summary measure of morbidity burden aiming to adjust for all cause multimorbidity. The aim was to study the association between relationships with primary care and all cause hospitalisation. Estimating morbidity burden from all health care adjusted for as much need for care, including need for secondary care, as possible. Other factors related to secondary care were not possible to adjust for, but the actual total hospitalisation was within lower 95%CI of the model prediction. Our use of diagnoses during year 2007 could underestimate multimorbidity for those hospitalised on December 31, if the diagnose was not registered before this admission.

Including socioeconomic status to adjust for demand for healthcare services and availability of care gave little improvement to the multivariate model. The cross-sectional design and choice of explanatory factors does not fulfil requirements of enough cause or causality, only of associations.

Comparison with existing literature

Studies on larger populations, as countries, tend to show significant benefits of primary care, and there is evidence of regional and local variation in quality of care [27]. At county level, we confirmed significant benefits of active listing and 2–7 consultations in primary care on odds of any hospital admission and mean days hospitalised. Studies on predictors of high quality primary care have stated that longer consultations and good teamwork are important for quality of care, and also that no single type of practice has a monopoly on high quality care [28, 29]. Our study confirmed that more consultations in primary care reduced mean days hospitalised. We also found a difference between groups of primary care practices in mean days hospitalised, indicating a potential to improve the capability of primary care to reduce hospitalisation by modified settings and processes.

Hospitalisation has been studied using different concepts. An avoidable hospitalisation is one that could have been prevented by effective and available outpatient care, including primary care. Sets of ambulatory care sensitive disorders including chronic disorders as diabetes and asthma, as well as acute disorders, such as pneumonia or acute appendicitis has been used since decades [15, 16]. Most studies show lower hospitalisation rates for ambulatory care sensitive disorders in areas with greater access to primary care [15, 16]. Avoidable hospitalisations are often used as an indicator of primary care quality. However, the concept of avoidable hospitalisation does not address neither the complexity of the healthcare system nor the contribution of multimorbidity in individual patients. We showed that a summation measure of morbidity burden also could be used to study hospitalisation as an outcome of primary care. The multivariate model showed that increasing multimorbidity level was positively associated with both odds of any hospitalisation and mean days hospitalised (Table 2). When studying the association between relationships with primary care and hospitalisation we needed to address consultations for coexisting disorders. We also needed to understand the role of multimorbidity in use of healthcare resources [9, 17]. Within this healthcare system offering the same accessibility of primary care and adjusting for both socioeconomic status and morbidity burden we showed consistent difference according to relationship with primary care and also difference within primary care. The differences in mean days hospitalised between primary care practices aggregated from differences in odds of ever being hospitalised.

In a German study, associations between costs for hospitalisation and socioeconomic status were not found for the elderly [30]. Others have studied the associations between social deprivation, multimorbidity and hospitalisation and found complex associations, depending on settings [31, 32]. We found that morbidity burden and relationships with primary care had a stronger relation to mean days hospitalised than socioeconomic status for this population. We also found a difference related to primary care practices implicating that differences in structure and processes within primary care are associated with mean days hospitalised.

Conclusions

Hospitalisation could be analysed as an outcome of primary care. Good relationships with primary care, i.e. active listing and more consultations in primary care, are associated with reduced use of hospital care, taking sex, age, socioeconomic status and morbidity burden into account. To promote well performing primary care practices and their relationship with patients is an option to reduce hospitalisation, and failing to do so a risk of increasing hospitalisation.

The relationship between patients and primary care and how it is related to active listing and number of consultations needs further research. The relation between relationships with primary care and the outcomes of primary care also needs to be studied further. To study how different settings and processes worked to produce the differences we found between primary care practices could answer how to promote primary care to perform well.

To confirm our findings, more studies on hospitalisation as an outcome of primary care in other and larger populations including proxies of the relationship with primary care are needed. Different measures of multimorbidity needs to be compared when studying hospitalisation as outcome of primary care to conceptualise the differences between use of avoidable hospitalisation for sets of disorders and all cause hospitalisation using morbidity burden or patient complexity. The contribution of individual disorders to hospitalisation could also be studied comparing groups with the same need for care despite different patterns of disorders.

Abbreviations

ACG: 

Adjusted Clinical Groups Case Mix System

GP: 

General practitioner

IRR: 

Incidence rate ratio

OR: 

Odds ratio

RUB: 

Resource utilization band

STATA: 

Stata Corporation, Texas, USA

Declarations

Acknowledgements

We are indebted to Lise Keller Stark for her expertise and invaluable advice in proofreading the manuscript.

Funding

This study was supported by a grant from Blekinge County Council to KR.

Availability of data and materials

The data that support the findings of this study are available from Statistics Sweden but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

Authors’ contributions

In accordance with the Vancouver Protocol, KR, PM and AH have contributed to the study and the writing process. KR performed the statistical analyses and interpreted the data together with AH. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study was approved by the Regional Ethical Review Board at Lund University (application no. 2016/71). According to this consent the population was given the opportunity not to participate.

Consent for publication

Not Applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Clinical Sciences in Malmö, Centre for Primary Health Care Research, Clinical Research Centre (CRC), Lund University, Skåne University Hospital, Jan Waldenströms gata 35, 205 02 Malmö, Sweden
(2)
Nättraby Primary Health Care Centre, Nättraby, Sweden

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Copyright

© The Author(s). 2018

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