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Keywords:

  • asthma;
  • disease severity;
  • resource use;
  • cost of illness;
  • economics;
  • Switzerland

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

Objective: This article will address the effect of exacerbation status, disease severity (defined by medication required), and other variables on resource use and costs for asthma in Switzerland in 1996 to 1997.

Methods: A retrospective chart-based study was performed. A sample of 422 adults was analyzed. Target variables were stratified by disease severity and exacerbation status. Bivariate associations were assessed. Multiple linear regression was performed on the logarithm of direct medical costs.

Results: The probability of exacerbations was positively associated with disease severity. Resource use and costs were associated with both of these variables. Multiple linear regression identified age, presence of asthma-related comorbidities, degree of severity, exacerbation status, quick reliever versus controller therapy, and diagnosis or treatment by a pulmonologist as independent influences on direct costs. An interaction between severity and exacerbation status was also noted. Regression identified direct costs in the highest severity group to be 2.5 times higher than those in the lowest group, if there were no exacerbations. If exacerbations were present, costs were 5.7 times higher.

Conclusions: Because of its high prevalence, asthma has a high impact on public health. This impact depends on disease severity and, according to these findings, may also depend on the extent to which exacerbations are avoided or at least controlled.


Introduction

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

Asthma seriously affects both children and adults, and the prevalence of asthma has increased considerably during the past three decades [1,2]. The disease affects between 4% and 8% of the population in industrialized countries [3–6]. In Switzerland, Leuenberger [7] has reliably estimated prevalence at 6.7% in adults.

Diseases with such high prevalence require detailed knowledge not only of their clinical and medical aspects, but also of their economic implications. Information on the costs and cost structure of asthma is essential for sound health policy decisions in the field of respiratory diseases. Studies on the costs of asthma have therefore been performed in the United States as well as, more rarely, in European and other countries, as seen in an overview given in a recent article by Weiss and Sullivan [8].

Data from Switzerland on this topic, however, are sparse. In 1996 to 1997, Szucs and colleagues [9,10] published an analysis of the resource use and cost structure of asthma in Switzerland. They demonstrated that asthma has a large economic impact, with total costs of Swiss Francs (CHF) 1250 million per year and a structure of direct medical costs dominated by medication costs in children and by hospitalization costs in adults. No further Swiss data have been published.

The current analysis was based on the data set collected by Szucs and colleagues [10]. The goal was to identify independent determinants of asthma-related resource use and of direct medical asthma costs in adult Swiss patients. Particular importance was given to the impact of asthma severity and exacerbation status on direct medical costs.

Materials and Methods

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

Patient Sample and Study Design

The original data set by Szucs and colleagues [10] comprised 589 patients who were treated by 120 primary care physicians. There were 472 adults and 117 children aged 14 years or younger. The latter were excluded from this analysis because their clinical and economic characteristics differ considerably from those of the adult population.

Szucs et al. [10] collected data by retrospective chart review. There was a 1-year reference period in 1996 to 1997. Assessment of asthma-specific resource use comprised physician visits, hospital care, and medication. The decision as to whether a particular resource use was asthma-specific or not was left to the physician's judgment. The chart extractors were advanced medical students. Students performed extractions in the participating physicians’ offices and in close contact with the physicians. Any uncertainties had to be clarified with and decided by the physicians. Data on community nursing were not collected, because this kind of service provision does not play a major role in Swiss asthma management. The possibility cannot be ruled out that some patients received unrecorded asthma-specific services from other physicians. These facts may have led to a modest underestimation of absolute direct medical costs.

Loss of work because of personal illness or caring for relatives was recorded as a basis for calculating indirect costs that are not considered in this analysis. Additional information was collected on disease duration, demographics, and physiological variables, comprising height, weight, body mass index (BMI), 1-s forced expiratory volume (FEV1), and forced vital capacity (FVC). Asthma-related comorbidities were assessed by explicitly asking for the presence of chronic bronchitis, emphysema, cor pulmonale, reflux disease, and others.

Reliable health-care related cost estimates are difficult to obtain in Switzerland, because there are no large administrative databases allowing for access to claims data. Most health insurance companies are reluctant to provide case-specific cost information. In this situation, Szucs and colleagues [10] proceeded as follows to calculate direct medical costs from a health system perspective: Asthma-specific resource units were derived from the physician's medical charts as stated above. In the case of medication, prescriptions were used as a proxy of use. Unit costs were estimated as follows: First, the average charge per inpatient day on the general ward of a public acute care hospital was calculated from a tariff list covering all Swiss hospitals [11]. Cantonal subsidies were added to this average charge, to obtain a proxy of hospitalization costs. Second, costs of physician visits were calculated individually, based on the services performed. Mean charges per service were calculated from seven regional tariff lists (representing German and French speaking Switzerland) and used as proxy measures for costs. Third, medication unit costs were assumed to be represented by the Swiss public prescription prices as stated in the 1997 Swiss Drug Compendium [12]. All costs indicated in this article are given in 1997 CHF. In mid-1997, at the end of the reference period of the data collection, CHF 1=US$ 0.68.

Assessment of disease severity was not an explicit goal of the original study. Therefore, degree-of-severity classification was based on the 1995 Global Initiative for Asthma recommendations on medication use, which were in effect at the time of data collection [13]. This kind of procedure has been recommended if clinical information is insufficient [14]. Physicians’ prescriptions and dose instructions were used as a proxy of real medication use. Patients who, according to this information, regularly used short-acting β2-agonists only were classified as “mild intermittent.” Patients regularly using inhaled corticosteroids, alone or in combination, were classified as “mild persistent.” Patients regularly using long-acting β2-agonists or systemic corticosteroids, alone or in combination, were classified as “moderate persistent” or “severe persistent,” respectively. Complementary information was available on treatment type, that is, quick reliever versus controller therapy as defined by the treating physician, without any pregiven reference to guidelines, and on the presence or absence of asthma attacks/exacerbations, as defined by the treating physician during the reference period. The remaining sample comprised 422 patients; 50 patients were excluded due to lack of data for one or more of these variables. Exacerbations were not necessarily associated with a resource use episode. They could be self-reported to the treating physician at a later point in time.

Statistical Methods

Demographic and disease characteristics are presented in comparison with the complete adult study sample. Exacerbation frequencies were stratified by degree of severity. A chi-squared trend test was used to evaluate the differences observed. Main resource use and cost variables were stratified by degree of severity and exacerbation status.

Bivariate associations were assessed among direct medical costs, degree of severity, exacerbation status, and other possible influences. Costs were heavily right-skewed, but logarithmic transformation achieved an approximately normal distribution. Nonparametric tests of the untransformed and Student's t tests of the transformed cost variable were used. In general, chi-squared tests were used to compare two categorical variables. If one variable was continuous, Mann–Whitney U tests/ Kruskal–Wallis tests, or Student's t tests/ANOVA were used as appropriate [15]. If both variables were continuous, Spearman's or Pearson's correlation coefficients were calculated. Least-squares regression was used to evaluate interaction between degree of severity and exacerbation status in their effect on the number of physician visits, specialist referrals, and hospitalizations.

Finally, multiple least squares regression was performed on the logarithm of direct medical costs. Independent variables qualified as possible influence factors if an association with direct medical costs could reasonably be assumed (P=.2 in bivariate analysis). Resource use variables that were direct contributors to costs (e.g., emergency room visits, days spent in hospital) were excluded. Nevertheless there may be a problem of circularity, particularly because disease severity, in the absence of other options, was defined by medication use and thus directly linked to (medication) costs. Similar correlations may affect the treatment type variable and other parameters, albeit to a much lesser extent. To assess the scope of this problem, an additional regression was performed on the logarithm of direct medical costs excluding medication costs, and the proportion of the variance of medication costs explained by the degree-of-severity and treatment type variables was calculated. In all cases P=.05 was used as the level of statistical significance, and P values were two-tailed.

All calculations were performed using STATA (1999, Version 6.0) and SPSS (1999, version 10.0).

Results

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

Patient Characteristics

The demographic and disease-specific characteristics of the patient sample studied are shown in Table 1. In comparison with the complete adult study population, there were no unexpected differences. The percentage of patients with exacerbations was slightly higher, but the mean number of exacerbations was lower in the analyzed sample. Fewer French-speaking patients were included due to a higher number of missing degree-of- severity or exacerbation status data. Over 50% of the FEV1, FVC, and absence-from-work data were missing.

Table 1.  Demographic and disease-specific characteristics of study population
 Value (mean ± SD or %)
Adults: information on severity and exacerbation status available (n=422)*Complete adult study sample (n=472)*
  • *

    Differing sample sizes owing to missing values are indicated separately.

  • Quick reliever therapy, treatment only when symptoms occur; controller therapy, prophylactic treatment.

  • Base, patients with exacerbations.

  • Abbreviation: GINA, Global Initiative for Asthma.

Age (years)53.4 ± 20.652.5 ± 20.8
German/French speaking (%)78.0/22.075.4/24.6
Height (cm)167.0 ± 8.9 (n=219)167.3 ± 9.3 (n=246)
Weight (kg)72.0 ± 15.0 (n=240)72.0 ± 15.7 (n=268)
BMI (m/kg2)25.8 ± 5.4 (n=204)25.6 ± 5.3 (n=231)
FEV1 (L/sec)2.4 ± 1.0 (n=193)2.5 ± 1.1 (n=216)
FVC (L)3.3 ± 1.3 (n=173)3.3 ± 1.3 (n=192)
Duration of asthma (years)12.4 ± 12.8 (n=268)11.3 ± 12.5 (n=301)
Employed (%)44.2 (n=410)45.6 (n=458)
Absences from work (% of employed)25.8 (n=168)23.9 (n=188 of 209)
Type of treatment (n=461)
 Quick reliever therapy (%)23.724.9
 Controller therapy (%)76.375.1
Degree of severity (GINA) (n=432)
 Mild intermittent (%)10.410.7
 Mild persistent (%)26.026.2
 Moderate persistent (%)32.031.9
 Severe persistent (%)31.531.3
Presence of exacerbation(s) during
 observation period (%) (n=422)37.736.4
 Number of exacerbations (n=159)1.6 ± 1.31.8 ± 2.5

Degree of Severity and Exacerbation Status

The distributions of the medication-derived degree of severity groups and the occurrence and number of exacerbations are shown in Table 1. Spearman's correlation between FEV1 and medication-based degree of severity was −0.22 (P=.002).

The proportion of patients who experienced at least one exacerbation during the reference period was almost constant in the mild intermittent (31.8%) to moderate persistent (31.1%) groups, with a minimum in the patients classified as mild persistent (28.2%). This percentage was distinctly higher in the severe persistent group (54.1%), leading to a highly significant chi-squared trend test (p < .0005).

Resource Use

The presence of exacerbations during the reference period was significantly associated with more physician visits (7.2 vs. 4.9, P < .005), specialist referrals (0.33 vs. 0.17, P=.048), and hospitalizations (0.19 vs. 0.015, P < .005) per year. Similar associations with degree of severity were observed; there were 7.9 versus 3.8 physician visits (P < .005), 0.3 versus 0.02 specialist referrals (P=.13), and 0.16 versus 0.02 hospitalizations (P =.05) per year in the highest versus lowest severity groups. Stratification by both exacerbation status and severity is shown in Table 2. Again, the number of resource units consumed increased with severity, and higher levels were reached in the presence of exacerbations. These tendencies were evident in all subgroups, albeit somewhat less unambiguously in the specialist referrals. Regression analysis demonstrated interaction between degree of severity and exacerbation status in their effect on the number of physician visits (P=.03 for a set of three dummy variables representing interaction) and hospitalizations (P =.04), but not the number of specialist referrals (P=.53).

Table 2.  Resource use by degree of severity and exacerbation status in units per patient-year
GroupNPhysician visits*Specialist referrals*No. of hospitalizations*
  • *

    Mean ± SD.

Total sample4225.8 ± 5.0 0.23 ± 0.800.083 ± 0.34
Exacerbations absent2634.9 ± 4.5 0.17 ± 0.680.015 ± 0.12
 By degree of severity
  Mild intermittent 303.7 ± 4.50.033 ± 0.180.033 ± 0.18
  Mild persistent 794.1 ± 4.7 0.18 ± 0.530.013 ± 0.11
  Moderate persistent 395.2 ± 4.0 0.16 ± 0.520.011 ± 0.10
  Severe persistent 616.3 ± 4.70.25 ± 1.10.016 ± 0.13
Exacerbations present1597.2 ± 5.4 0.33 ± 0.96 0.19 ± 0.51
 By degree of severity
  Mild intermittent 144 ± 2.7 0 ± 0 0 ± 0
  Mild persistent 314 ± 3.4 0.29 ± 0.780.097 ± 0.30
  Moderate persistent 427.1 ± 5.30.45 ± 1.3 0.19 ± 0.40
  Severe persistent 729.3 ± 5.7 0.35 ± 0.86 0.27 ± 0.65

Costs

The presence of exacerbations during the reference period was associated with higher direct medical costs (CHF 3202 vs. CHF 1029, P=.0001), physician costs (CHF 269 vs. CHF 207, P < .00005), medication costs (CHF 724 vs. CHF 901, P =.056), and hospitalization costs (CHF 2031 vs. CHF 99, P < .00005) per year. There also was a steady increase with degree of severity. In the highest versus lowest severity groups, direct medical costs were CHF 3075 versus CHF 627, physician costs were CHF 284 versus CHF 109, medication costs were CHF 1122 versus CHF 336, and hospital costs were CHF 1669 versus CHF 182 (p < .005 in all cases). Stratification by exacerbation status as well as severity revealed further details (Table 3). In the patients with no exacerbations, there was a clear positive association of severity with direct medical costs and medication costs, but less of an association with physician costs and no association with hospitalization costs. In absolute terms, hospitalization costs were minimal here. In the patients who experienced exacerbations, a positive association with severity was seen in all cost categories. Absolute hospitalization costs were important here. In the moderate and severe patients with hospitalizations, mean hospitalization costs were CHF 10333 (SD 7018) and CHF 13875 (SD 11039), respectively.

Table 3.  Costs by degree of severity and exacerbation status in CHF per patient-year
GroupnDirect medical costs*Physician costs*Medication costs*Hospitalization costs*
  • *

    Mean ± SD.

  • Abbreviation: CHF, Swiss Francs.

Total sample4221848 ± 4134230 ± 257 791 ± 746 827 ± 3887
Exacerbations absent2631029 ± 1274207 ± 266 724 ± 654 99 ± 1010
 By degree of severity
  Mild intermittent 30 708 ± 1509 99 ± 101 342 ± 481 267 ± 1461
  Mild persistent 79 804 ± 676225 ± 362 541 ± 380 38 ± 338
  Moderate persistent 931187 ± 1617211 ± 217 826 ± 516 151 ± 1452
  Severe persistent 611238 ± 1089229 ± 230 992 ± 978 16 ± 128
Exacerbations present1593202 ± 6315269 ± 237 901 ± 8682031 ± 6019
 By degree of severity
  Mild intermittent 14 452 ± 476130 ± 75 322 ± 4330 ± 0
  Mild persistent 311275 ± 3249153 ± 134 380 ± 329 742 ± 3151
  Moderate persistent 423089 ± 4947297 ± 224 912 ± 7891881 ± 4964
  Severe persistent 724631 ± 8058331 ± 2721231 ± 9693069 ± 7717

Consequently, the relative proportions of cost categories differed greatly between those with and without exacerbations: In the latter, physician costs accounted for 20.1% of total direct medical costs, medication costs accounted for 70.4%, and hospitalization costs for 9.6%. In those with exacerbations, physician costs contributed 8.4% and medication costs contributed 28.1%, but hospitalization costs contributed 63.4%. The absolute levels of physician and medication costs in those with exacerbations were only slightly higher than in those without exacerbations. To a very large extent, hospitalization costs accounted for the differences observed.

Spearman's correlation coefficients of annual direct medical costs with the number of physician visits (.69), the number of specialist referrals (.14), and the number of hospitalizations (.44) were significant at the 5% level (P < .005 in all three cases). Spearman's correlation coefficients, however, with possible nonresource use influence factors were weak, except in the case of age (.25, P < .005), FEV1 (–.19, P=.008), and disease duration (.13, P=.033). Pearson's coefficients of the same variables with the logarithm of direct costs were almost identical. There appeared to be no relevant correlations between costs and BMI or FVC.

Mann–Whitney U or Kruskal–Wallis tests revealed significantly higher costs for the following influences: controller therapy compared to quick reliever therapy (P < .005); involvement of a pulmonologist in diagnosis or regular treatment (P < .005); nonemployment at the beginning of the reference period in patients aged 65 or younger (P=.005); and presence of asthma-related comorbidities (P=.029). The use of Student's t tests or ANOVA with the logarithm of direct costs led to the same results. Absences from work in the employed, German language region, and rural versus urban dwelling were associated with higher costs and had nonsignificant P values below .2, whereby these factors qualified as candidate predictors in multivariate analysis. Evaluation by insurance coverage did not reveal any existing associations.

There were no unexpected associations between possible influence factors on direct costs. Degree of severity and treatment type correlated significantly (P < .00005), but Spearman's correlation coefficient was only .33.

Multivariate Analysis of Direct Medical Costs

Multiple regression analysis on direct medical costs was based on the following possible influence factors derived from bivariate analysis: degree of severity, presence of exacerbations, quick reliever versus controller therapy, involvement of a pulmonologist (in diagnosis or regular treatment), age, duration of disease, FEV1, presence of asthma-related comorbidities, employment status, absences from work, language region, and urban or rural dwelling. Other potential influences such as height, weight, BMI, and FVC were also explored. Resource use variables (e.g., number of physician visits, number of hospital days) were not taken into account, because they were direct contributors to costs.

Models using the logarithm of direct medical costs identified degree of severity, exacerbation status, quick reliever versus controller therapy, age and age squared, presence of asthma-related comorbidities, and involvement of a pulmonologist (in diagnosis or regular treatment) as relevant and significant influence factors, allowing for an adjusted R2 value of .34. Influences of language region and urban versus rural dwelling were not confirmed.

A four-level ordinal variable, represented by three dummy variables, was introduced in the model to allow for interaction between medication-based degree of severity and exacerbation status. Thus, the effect of exacerbations could be described separately for each degree of severity. This resulted in a partial F test with P value of .0008 for the set of dummy variables and increased the adjusted R2 value to .36, showing a greater effect of exacerbations on costs in the more severe asthma patients.

Terms representing employment status in the patients aged 65 or younger, and absences from work in the employed, were not included in the final analysis albeit significant or near significant, because they altered the model only slightly (adjusted R2 =.38).

Age and age squared were centered to avoid a colinearity problem with these variables. After this procedure, variance inflation factors showed a mean of 3.91. The highest value was seen in the exacerbation status variable (VIF 10.60), with the dummy variables representing interaction between degree of severity and exacerbation status showing VIFs of 8.28, 5.26, and 4.40. Other criteria were clearly noncritical: there were no standardized regression coefficients larger than 1. After inclusion of the interaction terms, the parameter estimates and standard errors for the other variables changed very little, except of course for those terms which were bound to change because their meaning is different in the model with the interaction terms.

Details of the main model (n=420) are shown in Table 4. In larger models including other potential confounders, the degree of severity and exacerbation variables, and the respective interaction terms, had very similar coefficients. Relevant coefficient changes only occurred when FEV1 and body height or BMI were included. These models, though, had to rely on less than 100 observations.

Table 4.  Multiple linear regression on the logarithm of direct medical costs (n=420)
F(12, 407)=20.63; P > F=0.0000; R2=.3782; adjusted R2=.3599
CovariatesCoefficientSEtP > |t|95% CI
  • *

    Compared to mild intermittent.

  • Dichotomous variables, values coded “0” or “1.” Presence of exacerbations, presence of comorbidities, involvement of a pulmonologist in diagnosis or treatment, and controller therapy are coded “1.”

  • This coefficient indicates the effect of exacerbations being present in a patient with the lowest degree of severity. The effect of exacerbations at higher degrees of severity is the sum of this coefficient and that for the relevant interaction term.

  • §

    Levels 1 to 3 represent a four-level ordinal interaction variable. The reference is level 0, which summarizes all situations without exacerbations or a degree of severity higher than “mild intermittent” being present. Using more levels instead did not result in any further improvement of the model. The effect of exacerbations being present in mild intermittent patients is modeled by the “exacerbations present” term of the model. Levels 1 through 3 represent the additional effect of exacerbations being present in patients with mild persistent, moderate persistent, or severe persistent asthma.

  • ||

    Compared to level 0.

  • Partial F test for this set of variables: P=.0008.

  • **

    Compared to quick reliever therapy.

Degree of severity
 Mild persistent 0.8218005*0.19605164.19<.00050.4364003 to 1.207201
 Moderate persistent 1.012774*0.19299555.25<.00050.6333813 to 1.392166
 Severe persistent 0.8962967*0.20536434.36<.00050.4925895 to 1.300004
 Exacerbations present 0.3082570.29656761.04.299−0.2747384 to 0.8912525
Interaction variable, ordinal§
 Level 1−0.4850357||0.3542102−1.37.172−1.181346 to 0.2112742
 Level 2 0.2323904||0.3411030.68.496−0.4381532 to 0.9029339
 Level 3 0.5271099||0.33691621.56.118−0.1352033 to 1.189423
Age (centered) 0.00541290.00239792.26.0250.000699 to 0.0101268
Age squared (centered)−0.00025520.0001084−2.36.019−0.0004683 to −0.0000422
Asthma-related comorbiditiy present †¶ 0.373210.12676282.94.0030.1240184 to 0.6224016
Involvement of pulmonologist 0.27581210.091963.00.0030.0950363 to 0.456588
Controller therapy 0.6000914**0.11604865.17<.00050.3719619 to 0.8282209
Intercept 5.149590.184990727.84<.00054.785934 to 5.513247

Residual analysis (based on scatter plots of residuals vs. predicted values and age, box plots of residuals grouped by noncontinuous influence factors, and normality plots of residuals) gave satisfactory results. Exclusion of influential points identified by Cook's distance and the covariance ratio (resulting n=393) did not affect significance or greatly alter coefficients, but increased the R2 value to .43. Alternative versions of the model, for example, using the frequency of exacerbations rather than their presence or absence, gave very similar results.

Costs increased with age, but the effect was mild, nonlinear, and less pronounced in older age groups. Assuming constant other parameters, the presence of asthma-related comorbidities was associated with a 50% increase in direct medical asthma costs, controller therapy versus quick reliever therapy with an 80% increase, and involvement of a pulmonologist in diagnosis or treatment with a 30% increase. Greater degrees of severity and the presence of exacerbations during the reference period were also associated with higher costs (Table 5). Because the effect of these variables is greater than multiplicative, patients with a greater degree of asthma severity who experienced exacerbations were particularly expensive. Patients classed as severe persistent who experienced exacerbations cost more than five times as much as mild intermittent patients who did not.

Table 5.  Effect of degree of severity and exacerbation status on direct medical costs according to the estimated regression model
Degree of severityMultiplication factor (95% CI)*
Exacerbations absentExacerbations present
  • *

    Calculations performed using STATA's lincom command.

Mild intermittent1 (reference)1.4 (0.8–2.4)
Mild persistent2.3 (1.5–3.3)1.9 (1.2–3.0)
Moderate persistent2.8 (1.9–4.0)4.7 (3.1–7.3)
Severe persistent2.5 (1.6–3.7)5.7 (3.8–8.4)

Regression analysis on the logarithm of direct medical costs excluding medication costs resulted in a model comprising the same influence factors as described above, except for age and the interaction term between degree of severity and exacerbation status (n=420, R2=.22). These variables themselves remained highly significant. Degree of severity and treatment type accounted for 19% of the variance seen in the medication costs.

Discussion

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

This analysis is concerned with the determinants of the direct medical costs of adult asthma, which are most relevant from a third-party payer perspective. An inclusion of indirect costs, whose analysis may require a different set of predictors, was not undertaken.

Stratification by exacerbation status and degree of severity reveals these variables to be positively associated with all subcategories of direct medical costs. However, there is no association between severity and hospitalization costs in the patients without exacerbations. In those with exacerbations, such an association is clearly present. In absolute terms, the cost difference observed between those with and without exacerbations mainly stems from dramatically increased hospitalization costs, while the levels of physician and medication costs are only slightly higher.

The multiple regression model presented identifies a set of factors, which while easy to measure, explain a considerable percentage of the variance seen in the direct medical costs of asthma. Age, comorbidity status, and several factors linked to the concept of disease severity (primarily degree of severity defined by medication and exacerbation status) influence the direct medical costs of asthma. Treatment type (quick reliever vs. controller therapy, as judged by the treating physician) reflects treatment habits, but also comprises aspects of disease severity. The same is probably true for the involvement of a pulmonologist, which can be assumed to be more frequent in cases that are more difficult to treat. If so, the higher cost associated with such an involvement can be expected to be partly due to patient or disease characteristics not covered by our degree-of-severity variable. Different treatment patterns in similar patients would be an additional or alternative explanation. However, there is some evidence that these patterns, albeit more costly at the outpatient level, are cost saving overall by reducing complication costs [9].

It may seem improper to include several explanatory variables linked to the concept of disease severity that directly impact on cost, which is the outcome variable. In particular, the use of medication as a proxy measure of severity may overestimate the impact of this factor on costs and introduces circularity, which may exaggerate statistical significance. It also assumes that empirical treatment follows guidelines, at least to a certain extent. In contrast, the results obtained demonstrate that the economic impact of the degree-of-severity and treatment type variables was not restricted to medication costs (representing 43% of direct medical costs) and that the latter were not to a very large extent explained by these variables. In clinical terms, a higher degree of severity, defined by medication use, is probably associated with higher baseline medication costs (drug costs per usual daily dose), but also with considerably greater amounts of medication needed and, as confirmed by resource use analysis, other health-care resources consumed. Restriction of regression analysis to the nonmedication costs would exclude the first of these two aspects and therefore lead to an incomplete picture.

The finding that greater degrees of asthma severity lead to higher medical costs confirms the results of other studies [8,16–20]. The findings of our multivariate analysis are in accordance with data obtained by Hoskins and colleagues [21] on the influence of disease severity on the frequency of asthma exacerbations and the influence of exacerbations on costs. However, detailed comparisons are hindered by the different health systems and methodologies used.

The modest impact of age on direct medical costs in this analysis does not contradict other studies [19,22]. Plaza et al. [22] reported the costs of asthma patients aged 65 or older to be twice as high as those of adults under 65, but the investigators did not correct for disease severity or apply multivariate methods. The current analysis could not demonstrate a significant effect of insurance status. Such an effect was described in other studies, but insurance systems may induce greater cost differences in the United States and in Canada than in Switzerland [19,20]. In the United States, health plans differ greatly in terms of coverage, and situations of underinsurance have been reported [23,24]. In Canada, drug plan participation is an issue [19]. In Switzerland, participation in the statutory health insurance, which is obligatory, guarantees a high level of medical care to everybody.

Possible effects of BMI or FEV1 were difficult to assess because of missing values. Inclusion of these variables led to models based on less than 100 observations. Relevant coefficient changes of the medication-based degree of severity and exacerbation variables occurred, which supports the idea that these variables and FEV1 contain competing information on disease severity.

The current analysis was based on a data collection primarily targeted at resource use and costs, not at identifying influence factors on costs. For this reason it was not possible to take into account some potentially important variables such as patient compliance, inhaler technique, or smoking status [16,19,21,25]. Future studies addressing associations between costs and medical correlates of asthma should assess these. Severity classification should be based on clinical parameters. A clear definition of exacerbations would allow a distinction between different kinds of events with different economic impacts, thus leading to more precise results.

Despite the limitations discussed, the findings of this multivariate analysis show that severity and the presence of exacerbations have considerable and interacting effects on direct medical costs of asthma. Because of its high prevalence, asthma has a high impact on public health. This impact depends on disease severity and may also depend on the extent to which exacerbations are avoided or at least controlled. A prospective study would be needed to finally clarify this issue. If the findings presented here are confirmed, further efforts at preventing exacerbations may well be repaid in reduced treatment costs, as well as reduced patient suffering.

This study was supported by an unrestricted educational grant from Novartis Pharma AG, Basel, Switzerland.

References

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References
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