Open Access

Variation in plasma calcium analysis in primary care in Sweden - a multilevel analysis

  • Sofia Dalemo1, 2Email author,
  • Per Hjerpe2, 3,
  • Henrik Ohlsson3,
  • Robert Eggertsen1,
  • Juan Merlo3 and
  • Kristina Bengtsson Boström2
Contributed equally
BMC Family PracticeBMC series ¿ open, inclusive and trusted201011:43

DOI: 10.1186/1471-2296-11-43

Received: 9 November 2009

Accepted: 30 May 2010

Published: 30 May 2010

Abstract

Background

Primary hyperparathyroidism (pHPT) is a common disease that often remains undetected and causes severe disturbance especially in postmenopausal women. Therefore, national recommendations promoting early pHPT detection by plasma calcium (P-Ca) have been issued in Sweden. In this study we aimed to investigate variation of P-Ca analysis between physicians and health care centres (HCCs) in primary care in county of Skaraborg, Sweden.

Methods

In this cross sectional study of patients' records during 2005 we analysed records from 154 629 patients attending 457 physicians at 24 HCCs. We used multilevel logistic regression analysis (MLRA) and adjusted for patient, physician and HCC characteristics. Differences were expressed as median odds ratio (MOR).

Results

There was a substantial variation in number of P-Ca analyses between both HCCs (MORHCC 1.65 [1.44-2.07]) and physicians (MORphysician 1.95 [1.85-2.08]). The odds for a P-Ca analysis were lower for male patients (OR 0.80 [0.77-0.83]) and increased with the number of diagnoses (OR 25.8 [23.5-28.5]). Sex of the physician had no influence on P-Ca test ordering (OR 0.93 [0.78-1.09]). Physicians under education ordered most P-Ca analyses (OR 1.69 [1.35-2.24]) and locum least (OR 0.73 [0.57-0.94]). More of the variance was attributed to the physician level than the HCC level. Different mix of patients did not explain this variance between physicians. Theoretically, if a patient were able to change both GP and HCC, the odds of a P-Ca analysis would in median increase by 2.45. Including characteristics of the patients, physicians and HCCs in the MLRA model did not explain the variance.

Conclusions

The physician level was more important than the HCC level for the variation in P-Ca analysis, but further exploration of unidentified contextual factors is crucial for future monitoring of practice variation.

Background

Primary hyperparathyroidism (pHPT) is a common disease that often remains undetected and causes severe disturbance especially in postmenopausal women. Therefore, national recommendations promoting early pHPT detection by plasma calcium (P-Ca) have been issued in Sweden [1, 2]. In this study we aimed to investigate variation of P-Ca analysis between physicians and health care centres (HCCs).

pHPT is a potentially serious condition leading to increased morbidity and mortality from cardiovascular disease [3] and cancer [4]. Although mild disease might not increase the risk [5]. PHPT gives raised plasma calcium (P-Ca) and because of the vague symptoms, pHPT is difficult to detect without an analysis of P-Ca.

Even though previous studies indicate that the frequency of P-Ca analyses differs between health care centres (HCC) [6] the understanding of the relative importance of the different levels (patients, physicians, HCCs) for these differences is limited. However, in a study from New Zealand where a defined clinical situation was presented to GPs, it was shown that inherent characteristics of the physicians more than the patients clinical situation determined which laboratory tests were ordered [7]. A study from the Netherlands, without patient characteristics, found a regional variation in laboratory testing and that factors at both the physician and HCC level influenced the inclination to order tests [8].

The aim of this study was to investigate the relative importance of the different levels in the health care organization for P-Ca analyses using the Skaraborg Primary Care Database (SPCD). Identification of factors contributing to the variation can be of relevance for planning interventions for an optimal frequency of P-Ca analyses and for evaluating the national recommendations.

Methods

Study population

Skaraborg is a rural area in Sweden and comprised 255 758 inhabitants in 2005. The public primary care is a part of the Västra Götaland region and serves 97% of the population (n = 247 985). All the HCCs (n = 24) use the same computerised medical record, ProfDoc Journal III (PDIII ProfDoc AB: Uppsala) facilitating data extraction. SPCD has been created containing encrypted data from patients and caregivers from all HCCs. The database contains patients' age, sex, diagnoses, laboratory analyses, and drug prescriptions. The HCCs' laboratory facilities are accredited by SWEDAC (the Swedish Board for Accreditation and Conformity Assessment). The validity of the information in the database has recently been audited and judged to be mostly appropriate but varying with type of diagnosis [9]. All 154 629 individuals that attended any of the 24 HCCs during 2005 are included in the analysis. The local ethics committee at Gothenburg University approved the study (255-09).

Study procedure and assessment of variables

The outcome variable was P-Ca analyses during 2005 (yes/no). Sex of the patient and P-Ca analyses during 2004, were included as explanatory variables. We also selected ICD-10 coded diagnoses and symptoms associated with pHPT [10] . A risk score for a P-Ca analysis was created with stepwise logistic regression [11] based on age, concomitant diagnosis and drug treatment, in order to control for confounding factors. The risk score was divided in quintiles, patients with the lowest risk of P-Ca analyses (group 1) were used as reference. The main characteristics included in the risk score are listed in Table 1 A more detailed description can be found in additional file 1.
Table 1

Examples of diagnoses with strong influence of the chance of having a plasma calcium analyses the risk score equation. Total number of patients 154 629.

Title

All the positive diagnoses in the stepwise regression

ICD-10 Codes

Odds ratio

95%

CI

Number of P-Ca analysis

Neoplasms

Sarcoidosis

D86

8.4

3.3

21.4

21

Endocrine disorders

Nontoxic goitre

E04

3.1

2.2

4.5

177

 

Other disorders of thyroid

E07

3.6

2.0

6.4

63

Mental disorders

Unspecified dementia

F03

2.5

2.0

3.0

523

 

Depressive episode

F32

2.3

2.0

2.5

3196

 

Anxiety disorder

F41.9

1.7

1.5

2.0

1438

 

Nonorganic sleeping disorders

F51

1.5

1.3

1.7

1636

Diseases of the circulatory system

Essential hypertension

I10

1.8

1.7

1.9

12867

 

Atrial fibrillation and flutter

I48

1.5

1.3

1.6

1792

 

Heart failure

I50

1.7

1.5

1.9

1937

Diseases of the digestive system

Constipation

K59.0

1.7

1.4

2.0

730

Diseases of the musculoskeletal system

Other artritis and rheumatism unspecified

M13

2.4

1.9

2.9

577

 

Myalgia

M79.1

1.8

1.6

2.0

3749

Symptoms

Abnormal blood-pressure reading, without diagnosis

R03.0

2.7

2.3

3.3

580

 

Polyuria

R35

2.0

1.5

2.7

355

 

Headache

R51

2.5

2.2

2.9

1392

 

Malaise and fatigue

R53

6.5

5.9

7.2

2261

Contact with health services

General medical examination

Z00.0

3.3

3.0

3.7

1911

 

Worried well

Z71.1

2.5

2.2

2.8

1732

Drug

Calcium and vitamin D supplements

 

3.3

2.1

5.1

2938

 

Thiazide diuretics

 

1.3

1.2

1.4

8305

P-Ca = plasma calcium

95% CI = 95% credible interval

The physicians were categorised according to sex and title. GP and locum were also dichotomised at 46 year. GPs, 46 years or older, were used as reference in the analysis. As only six doctors among house officers and preregistration house officers were above 45 years, they were not dichotomised.

The HCCs had different standardised group analyses, for instance analyses of electrolytes, hypertension check ups and diagnosing dementia, in which P-Ca was included. We categorised HCCs as having none, 1-2, and ≥ 3 standardised groups including P-Ca. The HCCs having no group analyses were used as reference.

Statistical analysis

We used multilevel logistic regression analysis (MLRA) to estimate the odds of patients being ordered a P-Ca analysis, as the data had a hierarchical structure (i.e., patients nested within physicians nested within HCCs) [12, 13]. As one patient could attend several physicians and several HCCs, we used a multiple membership model (Figure 1) [14] . The weights were constructed according to number of visits to a certain physician/HCC during our study period.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2296-11-43/MediaObjects/12875_2009_Article_465_Fig1_HTML.jpg
Figure 1

Multilevel model employed in plasma calcium analyses in primary care County of Skaraborg, Sweden. A three-level logistic regression model including health care centre (HCC), physician and patient levels. At each level the analysed effect and the number of elements are described. The arrows depict the nesting of patients within physicians and physicians within HCCs and crossing arrows the multiple memberships.

We developed four consecutive models. Model A included the random parameters (physicians and HCCs), in order to partition the variance at different levels. Model B included the patient characteristics, model C the patient and physician characteristics and model D the patient, physician and HCC characteristics. In this way we could investigate whether the contextual characteristics explained the residual variation at the physician and HCC levels.

In the fixed-effects part of the MLRA, we calculated odds ratios (OR) and their 95% credible intervals (95% CI). In the random-effects part of the MLRA, we obtained the variance at the physician and HCC levels. In order to quantify the importance of the different levels in the analysis we calculated the median odds ratio (MOR) [15, 16]. The MOR translates the variance into the widely used OR scale, and can thereby be directly compared with the ORs of individual or area variables. In very simple terms, the MOR could be interpreted as how much a patient's odds of being ordered a P-Ca analysis would (in median) increase if this patient moved to a physician/HCC with higher odds of ordering a P-Ca analysis. A MOR of one indicates that there are no differences between physicians/HCCs in their odds of ordering P-Ca analysis. The larger the differences between physicians/HCCs are, the larger the MOR will be. The MORphysician+HCC is applicable to patients that visited only one physician during the study period. Parameters were estimated by MCMC methods [17] in the MLwiN 2.02 software [18].

Results

Overall 5.8% of the inhabitants in Skaraborg and 9% of the patients (11% of the women and 8% of the men) attending the HCCs had a P-Ca analysis. The mean age of the patients with P-Ca analysis was 62 years compared to 45 years for patients with no P-Ca analyses. At the different HCCs the number of standardised group analyses including P-Ca analyses varied from zero to seven. The locums were most numerous shown in Table 2, but had short periods of attendance.
Table 2

Staffing of physicians at health care centres, characteristics of physicians and number of patients visits and frequency of plasma calcium analyses per title in the county of Skaraborg during 2005.

 

Physicians

Physicians/HCC

Patients visits

 

Total number

% women

Mean age (year)

SD

Median age (year)

Range age (year)

Mean

SD

Median

Range

Total number

With P-Ca test %

Preregistration house officer

51

39

31

5.6

29

26-48

2.2

2.7

1

0-7

21 424

11

House officer

68

69

35

6.0

34

26-50

3.0

1.9

3

0-9

35 712

11

GP < 46 year

39

41

38

3.6

35

32-45

1.7

1.4

2

0-4

45 491

10

GP ≥ 46 year

85

32

55

8.6

52

46-67

3.7

1.6

4

1-6

92 109

8

Locum < 46 year

112

17

36

5.4

36

27-45

6.2

8.9

3

0-36

23 153

7

Locum ≥ 46 year

102

17

55

11.5

46

46-76

6.5

9.2

2

0-32

23 573

8

Total

457

32

43

11.6

41

26-76

23.3

18.1

18

2-85

241 529

9

GP = general practioner

HCC = health care centre

P-Ca = plasma calcium

The multilevel model

There was a substantial variation in number of P-Ca analyses between HCCs and physicians. The four models used in the analyses are shown in Table 3. In model A the MORphysician+HCC indicated that for a patient changing both GP and HCC, to a GP and HCC with higher odds for a P-Ca analysis, the odds would in median increase by 2.31. The physician level, MORphysician = 1. 95 (95% CI: 1. 85-2.08) contributed more than the HCC level, MORHCC = 1.65 (95% CI: 1.44-2.07). Figure 2 shows the residuals for physicians (Panel A) and for HCCs (Panel B) from the multilevel analysis.
Table 3

Multi-level logistic regression analysis of plasma calcium analyses in primary care in the county of Skaraborg, Sweden

 

Model A

Model B

Model C

Model D

Fixed effects

OR (95%CI)

OR (95%CI)

OR (95%CI)

OR (95%CI)

Patient

    

   Female

-

REF

REF

REF

   Male

-

0,80 (0,77-0,83)

0,80 (0,77-0,83)

0,80 (0,77-0,83)

   P-Ca test 2004

-

1,44 (1,37-1,51)

1,44 (1,36-1,51)

1,44 (1,37-1,51)

   Risc score

    

   Group 1

-

REF

REF

REF

   Group 2

-

2,40 (2,15-2,70)

2,43 (2,18-2,71)

2,40 (2,14-2,68)

   Group 3

-

4,51 (4,08-5,04)

4,56 (4,13-5,06)

4,51 (4,08-4,97)

   Group 4

-

8,92 (8,11-9,87)

9,01 (8,14-9,96)

8,91 (8,12-9,78)

   Group 5

-

25,8 (23,5-28,5)

26,1 (23,7-28,8)

25,8 (23,5-28,4)

Doctor

    

   Female

-

-

REF

REF

   Male

-

-

0,93 (0,78-1,09)

0,95 (0,78-1,24)

   Preregistration house officer

-

-

1,48 (1,00-2,00)

1,51 (1,07-2,05)

   House officer

-

-

1,69 (1,35-2,24)

1,57 (1,26-2,09)

   GP < 46 year

-

-

1,30 (1,02-1,76)

1,16 (0,93-1,60)

   GP ≥ 46 year

-

-

REF

REF

   Locum < 46 year

-

-

0,84 (0,61-1,08)

0,78 (0,58-1,03)

   Locum ≥ 46 year

  

0,73(0,57-0,94)

0,69(0,51-0,89)

HCC

    

   Number groups include P-Ca

    

   Group 1

-

-

-

REF

   Group 2

-

-

-

2,59 (1,56-3,53)

   Group 3

-

-

-

2,79 (1,25-5,09)

Random effects

Variance (95%CI)

Variance (95%CI)

Variance (95%CI)

Variance (95%CI)

HCC (Intercept)

0,28 (0,15-0,58)

0,32 (0,16-0,67)

0,32 (0,18-0,66)

0,36 (0,16-0,80)

   MOR HCC

1,65 (1,44-2,07)

1,71 (1,47-2,18)

1,72 (1,49-2,17)

1,77 (1,48-2,34)

Physician (Intercept)

0,49 (0,41-0,59)

0,59 (0,50-0,71)

0,52 (0,43-0,62)

0,52 (0,43-0,63)

   MOR Physician

1,95 (1,85-2,08)

2,09 (1,96-2,24)

1,98 (1,87-2,12)

1,99 (1,88-2,13)

HCC and Physician (Intercept)

0,77

0,91

0,84

0,88

   MORHCC+Physician

2,31

2,48

2,4

2,45

DIC

89 550

76 438

76 427

76 427

Figure in italics are significant at 0.05 level

P-Ca = plasma calcium

95% CI = 95% credible interval

MOR = median odds ratio. OR = odds ratio

https://static-content.springer.com/image/art%3A10.1186%2F1471-2296-11-43/MediaObjects/12875_2009_Article_465_Fig2_HTML.jpg
Figure 2

Plasma calcium analyses per physician and HCC in primary care in county of Skaraborg, Sweden. A. percent plasma calcium analyses per physician. B. percent plasma calcium analyses per health care centre (HCC)

Model B, C and D

Model B illustrates that female sex and increased number of diagnoses in the risk score was associated with higher propensity of a P-Ca analysis However, the inclusion of other patient characteristics did not explain the variance at the physician or HCC level. Model C illustrates that house officers, preregistration house officer and younger GPs ordered more P-Ca analysis compared to older GPs. Locums, regardless of age, ordered fewer P-Ca analyses. There were no differences between male and female physicians. Inclusion of the physician characteristics did not explain the variance in model A. Model D illustrates that a high number of standardised group analyses were associated with a high number of P-Ca analyses. The inclusion of HCC characteristics did not explain the variance at the higher levels.

Including all explanatory variables and controlling for confounders, a patient changing both GP and HCC, from low to high odds for P-Ca analysis, the odds for a P-Ca analysis would in median increase by 2.5 times, MORphysician+HCC 2.45.

Discussion

The main finding of this study was that the ordering of P-Ca analyses was influenced by factors both at the physician and at the HCC level, with the physician level being more important than the HCC level. Theoretically, if a patient were able to change both GP and HCC, the odds of undergoing a P-Ca analysis would in median increase by 2.45 times. Including compositional and contextual characteristics in the model did not explain the variance at the higher levels.

Overall 5,8% of the inhabitants underwent a P-Ca analysis, which is comparable with an earlier study from Skaraborg (6,1%) [6] and two-fold compared with a study from primary care in southern Stockholm [19] Female patients and patients with previous P-Ca analysis were more likely to have a P-Ca analysis, which could be explained by women's greater risk of pHPT and recurrent check-ups of patients with chronic diseases.

In order to control for compositional confounding at the patient level we included an individual risk score for P-Ca-analysis. The inclusion of this variable did not explain the variation between physicians and between HCCs. Further, our empirical analysis found that the sex of the physician had no influence on P-Ca test ordering, in contrast to a study from Israel where female physicians ordered more test [20]. Older and more experienced physicians were less likely to order a P-Ca-test, which is in line with previous studies indicating that test ordering behaviour of GPs was influenced by years of experience [21]. P-Ca analyses done as part of group analyses used in surveillance of different chronic conditions may inflate the number of P-Ca analyses [22]. However, even though the number of group analyses was associated with higher frequency of P-Ca analysis, it could not explain the variation at the HCC level.

As explained in previous studies [23]; the measures of variation (e.g. median odds ratio) should be interpreted only for the specific time and place of the study, as there may be pattern of variance produced by different conditions. The associations, however, between characteristics of, on the one hand, the patients, physicians, and HCCs and on the other the frequency of P-Ca analysis, intend to provide information that can be generalised and applied to contexts beyond the one where the study was performed.

The risk for selection bias is low since this study is based on a large sample from a primary care area serving 97% of the population. Moreover, as this study is a retrospective database study, the ordering of analyses is not influenced by the study. A limitation of the study is that the frequency of ICD coded patient visits varies both between HCCs and according to diagnosis [9]. This might affect the risk score calculation.

Different views of the reason for screening could also affect the result. However national recommendations are well known in Swedish primary care [1, 2] thus the risk for bias is minor. Due to regional variation in laboratory testing [8] the results from this study might not be applicable in all regions in Sweden.

In this study only the variables available in the SPCD database were included. In previous studies, other characteristics of the physician, such as attitude to risk taking and involvement in development of guidelines, explained parts of the higher level variance [8].

We found that there was variation between physicians and between HCC in ordering of P-Ca analysis, which is in line with previous studies [24]. However, in this study we also tried to quantify the contribution of each level by using the median odds ratio. Even though our multilevel approach identified factors, both at the physician and HCC level, which are important to consider for understanding the inclination to order a P-Ca test, none of the included variables could explain the variation at the higher level. The identification of yet unidentified factors that contribute to the variation is needed for monitoring of practice variation and quality assessment and for applying appropriate interventions to achieve optimal frequency of P-Ca analyses.

Conclusions

National recommendations in Sweden have been issued to increase the frequency of P-Ca analyses to detect more patients with pHPT. There is a substantial variation in number of P-Ca analyses primarily between physicians but also between Health Care Centres. Female sex of the patient and increasing number of diagnoses is associated with higher propensity of P-Ca analysis. Physicians under education order most P-Ca analyses and locum least, but sex of the physician has no influence.

Notes

Declarations

Acknowledgement

This research was funded by Health and Medical Care Executive Board of the Region Västra Götaland, the Swedish Research Council and ALF Government Research Grant. We are most grateful to Birgitta Lindberg for excellent technical assistance in preparation of the manuscript. This paper was presented in part at the Nordic Congresses of General Practice in June 2007.

Authors’ Affiliations

(1)
Dept of Public Health and Community Medicine/Primary Health Care, Sahlgrenska academy Gothenburg University
(2)
R&D Centre Skaraborg Primary Care
(3)
Unit of Social Epidemiology, CRC, Faculty of Medicine, Lund University, Skåne University Hospital

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  25. Pre-publication history

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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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