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

  • Sofia Dalemo1, 2Email author,

    Affiliated with

    • Per Hjerpe2, 3Email author,

      Affiliated with

      • Henrik Ohlsson3,

        Affiliated with

        • Robert Eggertsen1,

          Affiliated with

          • Juan Merlo3 and

            Affiliated with

            • Kristina Bengtsson Boström2

              Affiliated with

              BMC Family Practice201011:43

              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.
              http://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

              http://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.

              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

              References

              1. Tryding N: Laborera rätt och lagom i primärvården- klinisk kemi i primärvården (Correct and Optimal use of Clinical Chemistry in Primary Care, English summary). Stockholm: SPRI; 1999.
              2. Laborera rätt och lagom - klinisk kemi i primärvården, analysdatabasen, (Correct and Optimal use of Clinical Chemistry in Primary Care, English version [http://​www3.​svls.​se/​sektioner/​sfkk/​nyckel/​]
              3. Ogard CG, Engholm G, Almdal TP, Vestergaard H: Increased mortality in patients hospitalized with primary hyperparathyroidism during the period 1977–1993 in Denmark. World J Surg 2004, 28:108–111.View ArticlePubMed
              4. Nilsson IL, Zedenius J, Yin L, Ekbom A: The association between primary hyperparathyroidism and malignancy: nationwide cohort analysis on cancer incidence after parathyroidectomy. Endocr Relat Cancer 2007, 14:135–140.View ArticlePubMed
              5. Wermers RA, Khosla S, Atkinson EJ, Grant CS, Hodgson SF, O'Fallon WM, Melton LJ: Survival after the diagnosis of hyperparathyroidism: a population-based study. Am J Med 1998, 104:115–122.View ArticlePubMed
              6. Dalemo S, Hjerpe P, Bostrom Bengtsson K: Diagnosis of patients with raised serum calcium level in primary care, Sweden. Scand J Prim Health Care 2006, 24:160–165.View ArticlePubMed
              7. Malcolm L, Wright L, Seers M, Davies L, Guthrie J: Laboratory expenditure in Pegasus Medical Group: a comparison of high and low users of laboratory tests with academics. N Z Med J 2000, 113:79–81.PubMed
              8. Verstappen WH, ter Riet G, Dubois WI, Winkens R, Grol RP, Weijden T: Variation in test ordering behaviour of GPs: professional or context-related factors? Fam Pract 2004, 21:387–395.View ArticlePubMed
              9. Hjerpe P, Merlo J, Ohlsson H, Bengtsson Boström K, Lindblad U: Validity of registration of ICD codes and prescriptions in a research database in Swedish primary care - A cross-sectional study in Skaraborg primary care database. BMC Med Inform Decis Mak 10:23.
              10. Bilezikian JP, Brandi ML, Rubin M, Silverberg SJ: Primary hyperparathyroidism: new concepts in clinical, densitometric and biochemical features. J Intern Med 2005, 257:6–17.View ArticlePubMed
              11. Merlo J, Gerdtham UG, Eckerlund I, Hakansson S, Otterblad-Olausson P, Pakkanen M, Lindqvist PG: Hospital level of care and neonatal mortality in low- and high-risk deliveries: reassessing the question in Sweden by multilevel analysis. Med Care 2005, 43:1092–1100.View ArticlePubMed
              12. Goldstein H: Multilevel Statistical Models. London: Hodder Arnold; 2003.
              13. Snijders T, Bosker R: Multilevel Analysis An Intro- duction to Basic and Advanced Multilevel Modeling. London: Sage; 1999.
              14. Fielding A, Goldstein H: Cross-classified and Multiple Membership Structures in Multilevel Models: An Introduction and Review. Birmingham: University of Birmingham; 2006.
              15. Larsen K, Merlo J: Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression. Am J Epidemiol 2005, 161:81–88.View ArticlePubMed
              16. Larsen K, Petersen JH, Budtz-Jorgensen E, Endahl L: Interpreting parameters in the logistic regression model with random effects. Biometrics 2000, 56:909–914.View ArticlePubMed
              17. Gilks WR, Richardson S, Spiegelhalter DJ: Markov Chain Monte Carlo in Pratice. London, Chapman and Hall; 1996.
              18. Rasbash JSF, Browne W: A User's Guide to MLwiN, Version 2.0. Documentation Version 2.1e. London: Centre for Multilevel Modelling, Institute of Education, University of London; 2003.
              19. Borgquist L, Gustafsson S, Hultén G, Jansson U, Paulsson E, Tryding N: Klinisk kemi i primärvården (Clinical chemistry in primary care, English summary). Stockholm: SPRI; 1996:422.
              20. Vinker S, Kvint I, Erez R, Elhayany A, Kahan E: Effect of the characteristics of family physicians on their utilisation of laboratory tests. Br J Gen Pract 2007, 57:377–382.PubMed
              21. Salloum S, Franssen E: Laboratory investigations in general practice. Can Fam Physician 1993, 39:1055–1061.PubMed
              22. Larsson A, Biom S, Wernroth ML, Hulten G, Tryding N: Effects of an education programme to change clinical laboratory testing habits in primary care. Scand J Prim Health Care 1999, 17:238–243.View ArticlePubMed
              23. Merlo J, Ohlsson H, Lynch KF, Chaix B, Subramanian S: Individual and collective bodies: using measures of variance and association in contextual epidemiology. Journal of epidemiology and community health 2009, in press.
              24. Larsson A, Palmer M, Hulten G, Tryding N: Large differences in laboratory utilisation between hospitals in Sweden. Clin Chem Lab Med 2000, 38:383–389.View ArticlePubMed
              25. Pre-publication history

                1. The pre-publication history for this paper can be accessed here:http://​www.​biomedcentral.​com/​1471-2296/​11/​43/​prepub

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              © Dalemo et al. 2010

              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.