Studied population
Our study is an observational study based on routine data from four Swiss health insurers for the years 2005 and 2006. Data were collected with the support of the Swiss Federal Office of Public Health [
24]. The studied population included 2,022,019 individuals who were insured with one of those Swiss health insurers in 2005. They were followed from January 1 until December 31, 2005 (335,538), and until December 31, 2006 (1,686,481) for those who did not change insurer during the observation period (in Switzerland changes are only permitted at the end of the year). All their health service bills and drugs claims were systematically collected and matched against hospital medical records through an anonymous linkage code established by the Swiss Federal Statistical Office (SFSO) encryption process (only the sequential number of the patient was supplied) [
25]. Hospital data supplied by the Federal Statistical Office (inpatient diagnoses) are publicly available. Insurers’ data (dispensed drugs and ambulatory services) are not publicly available and were supplied only for the research project supported by the Federal Public Health Office, with the prerequisite of using the anonymous linkage code procedure of the Federal Statistical Office. All data were anonymous and did not include any information which might identify the individual (date of birth, ZIP code, etc.) [
26].
Outcome
The outcome was the number of PAH occurring between 1 July 2005 and 31 December 2005/2006, with 182 and 547 days of follow-up respectively. An ACSC was retained from the main inpatient diagnosis. Conditions were identified by ICD-10 codes listed by Purdy et al. and established from an exhaustive literature review updated in 2007 [
4]. A few additional codes were added for specific conditions (e.g. gastroenteritis due to food poisoning and otitis externa; see Additional file
1 for list). The following diagnoses were removed from Purdy’s list (see Additional file
1 for codes). It is probable that minor dental problems, which rarely require hospitalisation, are linked to admission practices rather than the efficiency of primary care. A few conditions did not fit into the clinical definition of ACSC: oesophagitis and oesophageal reflux (perforated or bleeding ulcer category): and aplastic or auto-immune anaemia (deficiency anaemia category). We also excluded eclampsia (convulsions category) and late complications of diabetes (which signal problems with the care provided at a much earlier stage) because these could not be managed by primary care during the observation period. Cases with a comorbid condition requiring hospitalization were also left out: newborns, deliveries, trauma and life-threatening diseases (see Additional file
2), as were cases involving therapeutic operations that required hospitalization, insofar as they were not the consequence of an ACSC. Consequently, the following operations associated with specific ACS conditions were deemed not to be exclusion criteria: operation on stomach, peritoneum and oesophagus for bleeding ulcer; lower limb amputation for gangrene; minor operation on uterus and operation on vagina for pelvic inflammatory diseases; minor operation on mouth and teeth for dental conditions. Diagnoses and surgical SQLape® categories were used to identify these exclusion conditions [
27].
Predictors of PAH
All independent variables were measured between January 1 and June 30 2005, which corresponds to the observation period prior to the cohort zero time (1 July 2005).
The characteristics of the insured were:
–their canton of residence (seven cantons that accounted for less than 0.5% of the insured were grouped with neighbouring cantons);
–gender and age by decade;
–morbidities deduced from inpatient diagnoses (up to 10 ICD-10 codes) and outpatient dispensed drugs (Anatomical Therapeutical Chemical, ATC codes) according to the SQLape® grouper, which is suited to the nomenclatures used in Switzerland (adaptation of ICD-10 diagnostic codes and ICD-9-CM procedures codes, as well as specific pharmaceutical codes). Clinically related SQLape® categories with a similar PAH risk were grouped (see Additional file
3 for a description of these morbidity groups). Although several chronic conditions have been consistently associated with PAH risk [
28,
29], it is the number of chronic conditions suffered by the patient that dramatically increases their PAH risk [
30]. A case mix measure, therefore, should capture the cumulative effect of multiple conditions. Because Charlson and Elixhauser indices consider only a limited number of chronic conditions [
31], we systematically extended the analysis to all groups of acute and chronic diseases (Additional file
3). A morbidity group identified both from inpatient and outpatient information was considered only once. When a condition could be classified into several categories related to the same pathology, only the most severe was retained (severe infection > complicated infection > other infection > urinary infection; complicated diabetes > diabetes without complications). Patients who could be classified into more than half of the morbidity categories related to an ACSC were allocated a specific category, referred to as “ACSC related multimorbidity” (see Table
1).
Table 1
Part A. Multivariate analysis of PAH incidence rates using different risk adjustment models (N = 2,022,019)
Demographic | | | | | | | | | | | |
Men 0-10 | 96,229 | (4.8) | 2.53 | 2.54 | 2.80 | 2.82 | 2.52 | 3.16 | 5.28 | 4.70 | 5.93 |
Men 11-20 | 110,428 | (5.5) | 0.98ns
| 1.02ns
| 1.18 | 1.21 | 1.06 | 1.39 | 2.71 | 2.36 | 3.11 |
Men 21-30 | 125,720 | (6.2) | 0.98ns
| 1.00ns
| 1.18 | 1.19 | 1.04 | 1.36 | 2.48 | 2.17 | 2.84 |
Men 31-40 | 156,812 | (7.8) | 1.01ns
| 1.03ns
| 1.16 | 1.16 | 1.03 | 1.32 | 2.14 | 1.89 | 2.43 |
Men 41-50 | 164,037 | (8.1) | 1.30 | 1.29 | 1.39 | 1.37 | 1.22 | 1.53 | 2.08 | 1.85 | 2.33 |
Men 51-60 | 137,066 | (6.8) | 2.31 | 2.15 | 1.97 | 1.85 | 1.68 | 2.09 | 2.33 | 2.09 | 2.60 |
Men 61-70 | 101,636 | (5.0) | 4.08 | 3.48 | 2.66 | 2.42 | 2.17 | 2.69 | 2.60 | 2.34 | 2.89 |
Men 71-80 | 65,069 | (3.2) | 7.78 | 6.12 | 3.67 | 3.25 | 2.91 | 3.61 | 3.28 | 2.96 | 3.65 |
Men 81-90 | 32,187 | (1.6) | 12.95 | 9.80 | 5.26 | 4.67 | 4.16 | 5.24 | 4.55 | 4.08 | 5.08 |
Men 91-100 | 6,655 | (0.3) | 15.52 | 12.86 | 7.79 | 6.87 | 5.72 | 8.27 | 6.65 | 5.62 | 7.88 |
Women 0-10 | 92,082 | (4.6) | 2.03 | 2.00 | 2.34 | 2.32 | 2.06 | 2.61 | 5.05 | 4.47 | 5.70 |
Women 11-20 | 104,850 | (5.2) | 1.10ns
| 1.13ns
| 1.30 | 1.32 | 1.16 | 1.45 | 2.65 | 2.11 | 3.03 |
Women 21-30 | 123,566 | (6.1) | 1.26 | 1.27 | 1.32 | 1.34 | 1.18 | 1.52 | 1.44 | 1.27 | 1.63 |
Women 41-50 | 157,341 | (7.8) | 1.16 | 1.13 | 1.09ns
| 1.08 | 0.96 | 1.22 | 1.55 | 1.37 | 1.74 |
Women 51-60 | 134,850 | (6.7) | 1.68 | 1.59 | 1.35 | 1.19 | 1.16 | 1.45 | 1.87 | 1.67 | 2.09 |
Women 61-70 | 105,225 | (5.2) | 2.87 | 2.61 | 1.81 | 1.73 | 1.55 | 1.93 | 2.29 | 2.06 | 2.55 |
Women 71-80 | 81,157 | (4.0) | 5.01 | 4.28 | 2.36 | 2.24 | 2.00 | 2.50 | 2.74 | 2.47 | 3.05 |
Women 81-90 | 53,433 | (2.6) | 9.36 | 7.57 | 3.91 | 3.66 | 3.28 | 4.08 | 4.17 | 3.75 | 4.63 |
Women 91-100 | 15,375 | (0.8) | 9.39 | 8.08 | 4.86 | 4.54 | 3.93 | 5.26 | 5.12 | 4.45 | 5.88 |
ACSC related conditions
| | | | | | | | | | | |
Bronchitis and asthma | 17,302 | (0.86) | | 3.05 | 3.65 | 3.07 | 2.88 | 3.27 | 2.12 | 2.01 | 2.23 |
Diabetes, complicated | 2,627 | (0.13) | | 1.61 | - | 1.19 | 0.99 | 1.42 | 1.03 | .89 | 1.20 |
Diabetes, no complicated | 14,714 | (0.73) | | 1.14ns
| 1.89 | 1.75 | 1.62 | 1.90 | 1.33 | 1.24 | 1.42 |
Epilepsy | 6,058 | (0.30) | | 7.57 | 2.48 | 2.85 | 2.54 | 3.20 | 1.62 | 1.47 | 1.78 |
Female genital tract | 20,589 | (1.02) | | 1.50 | 1.41 | 1.24 | 1.13 | 1.36 | 0.70 | 0.65 | 0.77 |
Gastro-intestinal tract | 34,765 | (1.72) | | 2.33 | 1.41 | 1.37 | 1.30 | 1.46 | 1.06 | 1.01 | 1.11 |
Heart diseases | 22,805 | (1.13) | | 1.40 | 1.37 | .99 | .99 | 1.07 | 1.03 | .97 | 1.09 |
HTA and other circulatory disorder | 75,163 | (3.72) | | 0.94ns
| 2.73 | 3.00 | 2.75 | 3.08 | 1.13 | 1.08 | 1.18 |
Infection, complicated | 2,526 | (0.12) | | 2.85 | - | 2.88 | 2.35 | 3.53 | 1.32 | 1.12 | 1.55 |
Infection, other | 65,106 | (3.22) | | 3.53 | 2.81 | 3.01 | 2.86 | 3.17 | 1.19 | 1.14 | 1.24 |
Infection, severe | 1,699 | (0.08) | | 3.47 | - | 3.85 | 3.14 | 4.71 | 1.91 | 1.63 | 2.24 |
Intestinal or urinary obstruction | 3,640 | (0.18) | | 0.97ns
| - | .74 | .60 | .91 | .52 | .44 | .63 |
Nutritional anemia | 4,036 | (0.20) | | 1.67 | - | 1.57 | 1.35 | 1.84 | 1.24 | 1.09 | 1.41 |
Severe lung disease | 7,603 | (0.38) | | 2.94 | - | 2.20 | 1.98 | 2.44 | 1.54 | 1.42 | 1.67 |
Urinary infection | 4,347 | (0.21) | | 1.57 | - | 1.87 | 1.48 | 2.38 | 1.07 | .87 | 1.31 |
ACSC related multi-morbidity | 2,281 | (0.11) | | 17.07 | 15.13 | 29.39 | 25.33 | 34.10 | 5.12 | 4.55 | 5.76 |
Comorbidity categories
| | | | | | | | | | | |
Cancer | 7,944 | (0.39) | | 0.83 | 0.94ns
| .70 | .62 | .78 | .61 | .55 | .68 |
CNS diseases | 3,014 | (0.15) | | 1.22 | - | 1.03 | .88 | 1.21 | 1.00 | .88 | 1.14 |
Endocrine diseases | 8,408 | (0.42) | | 1.13ns
| 0.97ns
| 1.02 | .92 | 1.13 | .98 | .90 | 1.06 |
Liver & biliary tract | 6,272 | (0.31) | | 1.12ns
| 1.04ns
| .94 | .83 | 1.06 | .87 | .79 | .96 |
Mental disorders | 51,605 | (2.55) | | 1.51 | 1.52 | 1.48 | 1.40 | 1.57 | 1.05 | 1.01 | 1.10 |
Metabolic disorders | 19,825 | (0.98) | | 1.33 | 1.26 | 1.08 | 1.00 | 1.16 | 1.04 | .98 | 1.10 |
Nephritis | 6,841 | (0.34) | | 1.22 | - | 1.05 | 0.94 | 1.16 | 1.16 | 1.06 | 1.26 |
Other lung disease | 5,478 | (0.27) | | 1.28 | .76ns
| 1.06 | 0.95 | 1.19 | 1.12 | 1.03 | 1.23 |
Pain & chronic restriction of mobility | 27,280 | (1.35) | | 0.91ns
| 1.22 | .93 | .87 | .99 | .80 | .76 | .85 |
Systemic rheumatic diseases and transplantation | 1,388 | (0.07) | | 1.16ns
| 1.90 | 1.29 | 1.05 | 1.58 | 1.23 | 1.04 | 1.45 |
Skin diseases | 35,802 | (1.77) | | 1.26 | 1.45 | 1.32 | 1.24 | 1.40 | .99 | .95 | 1.04 |
Thrombosis | 39,704 | (1.96) | | 0.81 | 1.36 | 1.32 | 1.24 | 1.39 | 1.08 | 1.04 | 1.45 |
Trauma | 9,074 | (0.45) | | 0.62 | - | .53 | .47 | .60 | .49 | .45 | .54 |
Physician self-dispensation
| | | - | - | 1.16 | 1.15 | 1.11 | 1.20 | .92 | .89 | .95 |
2006 follow-up
| 1,686,481 | (83.41) | 2.02 | 2.16 | 1.74 | 1.80 | 1.64 | 1.97 | .99 | .90 | 1.08 |
Number of medical contacts
| | | | | | | | |
See Figure 1 |
| | | | | |
Measures of model fit
|
AIC | | | 179,024 | 172,473 | 162,835 | 159,196 | 130,962 |
BIC | | | 179,299 | 173,112 | 163,361 | 159,847 | 131,776 |
Pseudo R2 | | | 5.2 | 8.7 | 14.1 | 15.8 | 30.8 |
The intensity of care was measured by the number of physician visits during the six-month observation period (before July 2005), including ambulatory visits made by hospital physicians. However, we excluded ambulatory consultations with a radiologist and hospital visits in the 24 hours prior to hospitalization. The number of physician visits may depend on several factors, including disease severity, the propensity of the subject to seek care, and the physicians’ behaviour.
Several cantons allow physicians to dispense drugs to patients directly (self-dispensation). In such cases, information on dispensed drugs was missing. To adjust for a possible bias, we introduced an additional variable, which took the value of 1 if self-dispensation represented more than 5% of drug costs.
To examine the influence of cantonal differences in health care supply and demand, cantonal level variables were obtained from the SFSO [
32]. A higher number of ambulatory care facilities, reflected in the annual costs for ambulatory care services per inhabitant, were expected to be associated with lower PAH rates, whereas greater hospital capacities were expected to be linked with higher rates. Health care supply was measured by the number of primary care independent office-based general practitioners (generalists, internists, and paediatricians), the number of independent office-based clinical specialists, as well as the number of hospital beds and pharmacists. All were expressed per 1,000 inhabitants.
The aim of using demand variables was to identify potential variations in the propensity to use ambulatory care services. A higher level of education (average number of years), higher annual income per capita, a higher proportion of urban residency, and a higher proportion of insured persons who use primary care providers as gatekeepers might be variables associated with a greater propensity to seek medical care earlier, and thus with lower PAH rates.
Other variables had a possible link to poor health (unemployment rate and low deductible with a ceiling effect) or to reduced access to physicians (higher deductible with a floor effect).
Statistical analysis
The number of PAH observed per patient (count data) was modeled by a binomial negative regression with the number of follow-up days as exposure [
33]. To remove bias due to unmeasured characteristics linked to the fact of changing insurer, a dummy variable was added in the model (2006 follow-up: 1 = yes, 0 = no). The demographic model included only gender and age. The clinical model accounted for morbidities in three ways: outpatient conditions inferred from data on dispensed drugs; inpatient conditions only; both conditions. All morbidity categories were included as dummy variables, and were retained if IRR differed significantly from 1.0 in at least one model. The full model included all available variables, including the number of physicians’ visits with three dummy variables: “no visit” (1 if zero visit), “20-29 visits” (1 if the number of visits is between 20 and 29) and “30 and more visits” (1 if there are at least 30 visits).
The Akaike information criterion (AIC) and the Bayesian information criterion (BIC) were used to assess the predictive performance of the models and a
χ
2 was used to test the goodness of fit [
34]. The relative contribution of the independent variables to predicting PAH was gauged by comparing pseudo-R squared across the alternative models.
While information on inpatient diagnoses are routinely available from state agencies, data on drugs and ambulatory care use are generally available from payers’ organizations alone. To assess how the omission of additional information on health status affected the cantons’ profiling based on adjusted PAH rates, we plotted the difference (y-axis) in the expected number of PAH by canton under nested models, i.e. one based on complete information and one with fewer predictors, against the average expected values (x-axis) [
35]. This method is similar to Bland and Altman plots for assessing agreement between two measurement methods, which have been shown to be more appropriate than the often misleading correlation coefficient [
36]. The selection of risk adjustors was determined by the availability of data reflecting patient morbidity: inpatient diagnoses collected by the SFSO; morbidity based on information about dispensed drugs collected by insurers and billing organizations; and complete data [
37]. The expected number of PAH per canton was computed by summing individual predicted counts. We determined the lower and upper limit of y-random variation according to the method adopted by Campbell et al. [
38]. We also added two guidelines to the graph, each of which indicated a 10% relative increase or decrease in the standardized ratio (see Additional file
4 for computation details). Cantons lying above or below these lines were those whose standardized ratio increased (poorer performance) or decreased (better performance) by more than 10%.
The Spearman rank correlation coefficient (rs) was used to test if there was an association between the cantonal standardized ratio of PAH and each thematic group of cantonal variables (for α = 0.05, significant if rs ≥0.472, 18 degrees of freedom). Estimates of the standardized ratio for each canton were obtained by incorporating cantonal effects as fixed in the final model.
All computations were performed using Stata version 11.