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The authors have declared that no competing interests exist.

In Switzerland, a nationwide Swiss Diagnosis related Groups (Swiss DRG) system for hospital reimbursement was introduced in 2012. However, the impact of DRG systems on primary care is still unclear with respect to number of consultations and costs. The aim of this study was to investigate the effect of the implementation of DRG on costs and volumes in the primary care sector, on a nationwide basis in Switzerland.

The study retrospectively analysed yearly data, from 2008 to 2014, of almost 60 Swiss health insurers that covered almost all Swiss general practitioners, with a total number of patients which represented approximately 76% of the Swiss population. GP consultations, total numbers and rates, and the relative costs reimbursed (TARMED tariff values) in the Swiss federal states, cantons, which already introduced a DRG-like system before 2012 (AP-DRG), were compared to the GP consultations and costs reimbursed in the other cantons (DRG-naive). Regression discontinuity design analysis and mixed regression models, at cantonal level, were performed to evaluate the effect of the nationwide implementation of the Swiss DRG on health care demand and costs in the primary care setting. Change in outcome level and yearly trend pattern difference between groups (AP-DRG vs. DRG-naive) were examined.

Overall, the total number of GP consultations and the relative TARMED values increased from 2008 to 2014. In the DRG naive, 15 cantons: in 2008, the number of GP consultations were 13,114,126, with a TARMED value of 1,194,957,157 CHF, and in 2014, the GP consultation were 13,752,511, with a TARMED value of 1,513,861,260 CHF. In the AP-DRG group, 11 cantons, the total number of GP consultations increased from 8,787,646, in 2008, to 9,347,168 in 2014 and the TARMED value increased from 896,673,657 CHF in 2008, to 1,100,203,508 CHF in 2014. The yearly trend pattern of GP consultations and TARMED values, in the AP-DRG group, were not significantly different from the respective trends in the DRG- naive and, overall, no significant change was detected in consultations and costs trends before and after 2012.

This study found no evidence of any effect of the introduction of the SwissDRG on the yearly trend of primary care consultations and costs. Nevertheless, potential negative impacts on vulnerable patients, as chronically ill patients, could not be excluded and further investigation is required.

Since 1970, when the Organization for Economic Cooperation and Development (OECD) began recording worldwide health spending, total and by type of financing, the Swiss health sector has been characterized by a steady increase in total health expenditure, from a yearly rate of 4.9% of GDP, in 1970, to a yearly rate of 12.14% of GDP in 2019 [

Other countries in Europe have already introduced the DRG system with the aim of improving transparency, increasing efficiency and reducing costs [

The DRG system might create false incentives because, in order to be economically efficient, hospitals could shorten the length of stay and the number of services provided, and at the same time, maximize the number of cases [

In addition, an increase in the number of cases may have an impact on admission regulations. It may lead to more non-medically indicated treatments, to inpatient rather than outpatient treatment, or to a division of care episodes into multiple admissions. These different effects can be relevant for primary care, as shifting these patients can lead to more morbidity and thus to more cost-intensive treatments.

In USA, and also in most European countries, the quality of care had not been significantly affected by the DRG introduction [

Previous research [

Therefore, the aim of the current study is to better investigate the impact of SwissDRG on costs and volumes in the primary care sector, analyzing data, from 2008 to 2014, of all Swiss cantons.

The data used for this study were provided by SASIS AG Switzerland, a data warehouse company of Santésuisse, an umbrella organization of Swiss statutory health insurers. The dataset, from almost 60 health insurers, included on average 76% of the Swiss population. The SASIS AG collects aggregated administrative claims data for drug prescriptions (SASIS Tarifpool) and health care services data of licensed physicians (SASIS Datenpool) from patients insured in the statutory health system.

The dataset contained, in one file, the list of all Swiss ambulatory care providers and the area of their practice locations. Each provider was anonymously identified through a number (ZSR) and classified into the medical specialties, according to the Swiss Medical Association (FMH). In another file, the dataset contains, for each provider, the aggregated number of consultations and the total cost of reimbursement (including medical treatments) per year. Consultations and reimbursements were classified by patient’s gender, by patient’s age groups (5 years classes, from age 0 to 95) and by community of patient residence. For this study, only consultations provided by primary care physicians, in the period from 2008 to 2014, were considered. Moreover, accident-related consultations were excluded due to different health insurance coverage. The study frame was chosen according to the data availability at the time when our research started and in order to have at least two years of observations before and after the DRG introduction. In fact the first year after DRG introduction might be not representative due to the system change (learning curve). Therefore having a second year of follow up after introducing the novel system provided more robust data.

SASIS data were grouped at cantonal level, 26 cantons, and then at DRG groups: DRG-naive (No AP-DRG) and AP-DRG (

In the primary analysis, we included only the consultations of patients living in the same canton of the GP. As a sensitivity analysis, we considered all the consultations at GP cantonal level. Overall 11 cantons already used AP-DRG before 2012: Bern (BE), Geneva (GE), Neuchâtel (NE), Nidwalden (NW), Obwalden (OW), Schwyz (SZ), Ticino (TI), Uri (UR), Vaud (VD), Valais (VS), Zug (ZG). The other 15 cantons introduced DRG after 2012: AG (Aargau), Appenzell I. Rh. (AI), Appenzell A. Rh. (AR), Basel-Landschaft (BL), Basel-Stadt (BS), Fribourg (FR), Glarus (GL), Graubünden (GR), Jura (JU), Lucerne (LU), St. Gallen (SG), Schaffhausen (SH), Solothurn (SO), Thurgau (TG), Zurich (ZH). Cantons in the AP-DRG group did not introduce AP-DRG simultaneously, but all cantons introduced it before 2008 [

GP consultations were adjusted for the permanent resident population by canton, age-group, and sex. Data were collected from the Federal Statistics Office data (

According to the national ethical and legal regulation, an ethical approval was not needed. License to access the study data was provided by SASIS AG. Since data were anonymised, no consent of patients was required. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

The study was retrospective with a quasi-experimental fuzzy design [

Yearly data were aggregated by DRG groups as described above (AP-DRG = Yes, 11 cantons; AP-DRG = No, DRG-naive, 15 cantons). Descriptive tables of GP consultations reported, for each year and group: the total number of GP consultations (observed and expected), the total population, the average number of GP consultations per canton (or the GP consultations at canton level), the rate of expected GP consultations, the number of GPs, the number of patients. The rate of expected GP consultations was defined as the total number of expected GP consultations divided by the total number of inhabitants in 2010, chosen as a reference population. We also reported the overall average percentage of the number of consultations where the patient’s canton of residence did not differ from the provider’s canton. Descriptive tables of the costs for GP consultations reported, for each year and group: the total gross costs, the TARMED tariff value, observed and expected, total and per capita (per person, patients and consultation), the total expected patient cost-sharing and the ratio cost-sharing versus Total expected TARMED. The TARMED tariff per person was computed, dividing the total expected TARMED value by the total number of inhabitants in 2010, chosen as the reference population. The TARMED per patient was computed, dividing the total expected TARMED value by the number of patients in each group/year. The TARMED per consultation was computed, dividing the total expected TARMED value by the total number of expected GP consultations. The total expected patient cost-sharing was computed, adjusting the observed cost-sharing for the permanent resident population by canton, age-group, and sex. An additional descriptive table for GP consultations and costs, stratified by the patient’s sex and age group, was reported as

Moreover, to evaluate the cantonal disparities, in terms of GP consultations and costs, we calculate the Gini coefficient, overall, within DRG groups and by years. The Gini coefficient is a measure of inequality in a distribution. It ranges from 0, perfect equality, and 1, perfect inequality [

We performed mixed models with random effects at canton level to identify the trend pattern, of the GP consultations and the relative TARMED tariff in the two DRG groups, corrected for canton’s correlation from 2008 to 2014. DRG area was a fixed effect, alone and with interaction with time. A quadratic growth over time (Time^{2}+Time) was detected. An autocorrelation of order 1, AR(1), described the within-group correlation structure. Time was scaled defining calendar year 2012 as time 0. In details, the model was specified as follows:
^{2}+Time): AP-DRG group denoted the interaction between time and AP-DRG group; x_{2} was another predictor, in dependence of the specific outcome.

The outcomes, Y, were: a) GP consultations expected at canton level; b) rate of expected GP consultations at canton level; c) the expected TARMED tariff values of GP consultations per capita (per person). The interaction term was only considered for outcome a), since the fit for the other outcomes, was better without it. The other predictor x_{2} was the number of GP for outcome a), the rate of GP (GP/Population at time 2010)×10,000 inhabitants, for outcome b), and the rate of expected GP consultation for outcome c). The results of these statistical models were reported as estimates (standard errors). The autocorrelation structure coefficient ɸ was also reported. For outcome a), random effects at both coefficient and slope were used since the fit was better. For the other outcomes, random intercept models were sufficient. For outcome b) and c), an additional analysis, including as covariate patient’s sex and age group, was performed, and results were shown graphically as

Regression discontinuity design analysis allowed a visual comparison of the time series pattern, before the DRG introduction, with the pattern after the DRG introduction and the assessment of the change in outcome level, after the DRG intervention, in relation to the pre-introduction pattern. Local linear regressions were performed for outcome b) and c) in the neighborhood of the threshold, 2012. Predictors were time, DRG-naive group indicator, the interaction term (time: DRG), and the additional covariate x_{2} for outcome b) and c) respectively. Models were also corrected for correlated observations at the canton level. The “neighborhood” of the threshold was defined using weights based on a quartic kernel and an “optimal” bandwidth as defined in [

All tests with P < 0.05 were considered statistically significant. All analyses were carried out using statistical software R (

In

Year | AP-DRG | Total GP Consultations (Observed) |
Total GP Consultations (Expected) | Total Population | GP Consultations at canton level (mean per canton) | Rate of expected GP consultations | Number of GP | Number of Patients |
---|---|---|---|---|---|---|---|---|

2008 | No | 13,114,126 | 13,514,496 | 4,423,223 | 874,275 | 2.98 | 3704 | 3,245,308 |

2008 | Yes | 8,787,646 | 9,069,437 | 3,262,019 | 798,877 | 2.71 | 3144 | 2,191,284 |

2009 | No | 13,003,076 | 13,202,226 | 4,474,721 | 866,872 | 2.91 | 3745 | 3,291,528 |

2009 | Yes | 8,752,835 | 8,898,288 | 3,299,053 | 795,712 | 2.66 | 3175 | 2,222,252 |

2010 | No | 13,034,052 | 13,034,052 | 4,529,598 | 868,937 | 2.88 | 3831 | 3,408,959 |

2010 | Yes | 8,898,772 | 8,898,772 | 3,341,983 | 808,979 | 2.66 | 3231 | 2,277,371 |

2011 | No | 13,072,219 | 12,882,920 | 4,581,050 | 871,481 | 2.84 | 3921 | 3,498,973 |

2011 | Yes | 8,795,917 | 8,674,814 | 3,374,988 | 799,629 | 2.60 | 3302 | 2,340,248 |

2012 | No | 13,161,802 | 12,786,713 | 4,631,856 | 877,453 | 2.82 | 4023 | 3,577,940 |

2012 | Yes | 8,858,915 | 8,612,361 | 3,408,555 | 805,356 | 2.58 | 3406 | 2,374,829 |

2013 | No | 13,882,635 | 13,295,635 | 4,686,368 | 925,509 | 2.94 | 4108 | 3,876,695 |

2013 | Yes | 9,301,225 | 8,899,227 | 3,454,488 | 845,566 | 2.66 | 3521 | 2,497,368 |

2014 | No | 13,752,511 | 12,978,062 | 4,742,717 | 916,834 | 2.87 | 4181 | 3,882,785 |

2014 | Yes | 9,347,168 | 8,803,229 | 3,496,121 | 849,743 | 2.63 | 3639 | 2,515,008 |

Two DRG groups were identified: 11 cantons which used AP-DRG (BE, GE, NE, NW, OW, SZ, TI, UR, VD, VS, ZG) and 15 cantons which introduced DRG after 2012 (in details, we have AG, AI, AR, BL, BS, FR, GL, GR, JU, LU, SG, SH, SO, TG, ZH). The rate of expected GP consultation was defined as the total GP Consultations (Expected) / Total Population in 2010.

*On average, in 94% of all patient consultations there was concordance between patient residency (e.g. canton level) and GP location.

The analysis showed that in 94% of all patient consultations there was concordance between patient residency (e.g. canton level) and GP location. Overall, in the DRG-naive group, the sum of GP consultations (observed) increased from 13,114,126 in 2008 to 13,752,511 in 2014. Instead, the sum of GP consultations (expected) decreased from 13,514,496 in 2008 to 12,978,062 in 2014. Analogously, the rate of expected GP consultation, per inhabitant, decreased at the same rate, from 2.98 in 2008 to 2.87 in 2014. The number of patients increased more compared to the number of GP (yearly average of growth of 3.03% vs 2.04%). In the AP-DRG area, the sum of GP consultations (observed) increased more compared to DRG-naive (yearly average of growth 1.03% vs 0.79%). The total of GP consultations (expected), instead, decreased less, compared to DRG-naive (yearly rate of growth of -0.49% vs. -0.67%) with 2.71 consultations per inhabitant. Differently from the DRG-naive, the number of GP increased more than the number of patients (yearly rate of growth of 2.47% for GP compared to a rate of growth of 2.32% for the number of patients). We also observed that the ratio number of patients / number of GP was higher in the DRG-naive group compared to the AP-DRG group.

As regarding the inequality, between cantons, in the distribution of the total GP consultations, weighted for the total population, we found an overall Gini coefficient of 0.40, which remained approximately unchanged during years 2008–2014. Within the groups, we found that the Gini coefficient in AP-DRG decreased from 0.35, in 2008, to 0.32 in 2014. In DRG-naive, instead increased from 0.40, in 2008, to 0.41 in 2014.

In

Year | AP-DRG | Total gross costs (CHF) | TARMED tariff values (CHF) of GP consultations | Patient cost-sharing | Ratio (b)/(a) | ||||
---|---|---|---|---|---|---|---|---|---|

observed | Total observed | Total expected (a) | Per person(exp) | Per patient (exp) | Per cons. (exp) | Total expected (b) | |||

2008 | No | 1,033,802,791 | 1,194,957,157 | 1,232,028,426 | 278.54 | 379.63 | 91.16 | 295,031,186 | 0.24 |

2008 | Yes | 808,987,625 | 896,673,657 | 926,230,867 | 283.94 | 422.69 | 102.13 | 197,763,616 | 0.21 |

2009 | No | 1,052,663,923 | 1,215,981,007 | 1,234,928,262 | 275.98 | 375.18 | 93.54 | 275,137,331 | 0.22 |

2009 | Yes | 818,001,421 | 908,110,753 | 923,683,617 | 279.98 | 415.65 | 103.80 | 190,368,471 | 0.21 |

2010 | No | 1,069,789,748 | 1,234,935,134 | 1,234,935,134 | 272.64 | 362.26 | 94.75 | 272,464,090 | 0.22 |

2010 | Yes | 834,166,336 | 925,536,753 | 925,536,753 | 276.94 | 406.41 | 104.01 | 187,771,964 | 0.20 |

2011 | No | 1,113,170,879 | 1,282,725,304 | 1,263,857,915 | 275.89 | 361.21 | 98.10 | 282,703,775 | 0.22 |

2011 | Yes | 858,712,824 | 952,714,607 | 939,343,182 | 278.32 | 401.39 | 108.28 | 193,065,309 | 0.21 |

2012 | No | 1,152,184,860 | 1,326,852,153 | 1,288,529,212 | 278.19 | 360.13 | 100.77 | 299,799,366 | 0.23 |

2012 | Yes | 888,128,440 | 987,304,470 | 959,473,683 | 281.49 | 404.02 | 111.41 | 205,777,687 | 0.21 |

2013 | No | 1,252,157,168 | 1,442,374,897 | 1,380,623,625 | 294.60 | 356.13 | 103.84 | 323,745,885 | 0.23 |

2013 | Yes | 949,029,295 | 1,060,182,149 | 1,013,514,187 | 293.39 | 405.83 | 113.89 | 217,475,689 | 0.21 |

2014 | No | 1,318,154,823 | 1,513,861,260 | 1,427,652,061 | 301.02 | 367.69 | 110.01 | 327,354,480 | 0.23 |

2014 | Yes | 985,663,061 | 1,100,203,508 | 1,034,662,063 | 278.54 | 379.63 | 91.16 | 220,266,528 | 0.21 |

Values reported in CHF: total gross costs, TARMED tariff values (total, per person, per patient, per consultation = per cons.) and patient cost-sharing. In the last column, the ratio patient cost-sharing / TARMED expected total value was reported.

Overall, the total gross costs increased from 2008 to 2014 in both groups, with a yearly growth rate of 4.13%, in the DRG-naive group, compared to a yearly rate growth rate of 3.35% in the AP-DRG area. The total TARMED tariff values, observed and expected, also increased in both groups and the yearly growth was higher in the DRG-naive group compared to the AP-DRG group. The per-capita TARMED expected values were higher in the AP-DRG group. The tariff values per person were increasing in both groups at, approximately, the same rate. The tariff values per patient, instead, were decreasing in both groups. The tariff values per consultation, instead, increased from 2008 to 2014 in both groups with a greater yearly rate in the DRG-naive group compared to the AP-DRG group (3.18% vs. 2.37%).

Additional details on the number of GP consultations and TARMED positions, stratified by sex and age group of the patient, were provided in the

As regarding the inequality, between cantons, in the distribution of the TARMED positions, weighted for the total population, we found an overall Gini coefficient of 0.39, in 2008 and of 0.41 in 2014. Within the groups, the Gini coefficient in AP-DRG was of 0.32, unchanged from 2008 to 2014. In DRG-naive, instead increased from 0.41, in 2008, to 0.43 in 2014.

In

GP Consultations (expected) | TARMED tariff |
||
---|---|---|---|

Total | Consultation rate | Per person | |

AP-DRG = 1 | -161,612.70 (122,695.10) | -0.164 (0.151) | 21.919 (11.651) |

Time^{2} |
2,000.17 |
0.007 |
0.860 |

Time | -12,910.77 |
-0.020 (0.012) | 8.262 |

Number of GP | 2,224.40 |
||

AP-DRG = 1:Time^{2} |
-1,840.84 (1,176.99) | ||

AP-DRG = 1:Time | -7,352.81 (7,425.69) | ||

Rate of GP's × 10'000 inhabitants | 0.022 (0.029) | ||

Intercept | 267,219.40 |
2.600 |
53.713 |

Consultation rate | 77.122 |
||

N | 182 | 182 | 182 |

Autocorrelation ɸ | 0.37 | 0.73 | 0.98 |

*p<0.05

**p<0.01

***p<0.001

Mixed models with fixed effects (interaction term AP-DRG and time, with quadratic trend), random effects (canton) and autocorrelation. For each effect, estimates and standard errors (within parentheses) were reported. Moreover, ɸ, autocorrelation structure coefficient, for each model was shown.

After correcting for measurement correlation at canton level, the difference between the DRG groups was not due to the introduction of the nationwide DRG financing system. There was no significant effect of AP-DRG group, alone, or in interaction with time. The expected GP consultations significantly decreased over time in both groups, according to a quadratic trend. Any additional GP, had an effect of 2,224 (se = 126.38, p<0.001) GP yearly consultations, at canton level. The autocorrelation within groups was ɸ = 0.37.

Analogously, for the rate of expected GP consultations the trend was decreasing in both groups, according to a quadratic trend statistically significant. There was no significant effect of AP-DRG group and of the rate of the number of GP over the population. The autocorrelation within groups was ɸ = 0.73.

Instead, the TARMED tariff per patient, in both groups, was increasing, according to a quadratic trend, statistically significant. The costs per capita were higher in the AP-DRG area but we did not find a statistically significant effect due to the introduction of the DRG. Each additional consultation, per person, or consultation rate, had an effect of near 77 CHF in the TARMED tariff pro capita. The autocorrelation within groups was ɸ = 0.98.

In

In

In this study, we investigated the impact of the introduction of the SwissDRG on GP consultations and their relative costs. Based on data provided by GPs, we used a quasi-experimental approach with an interrupted time series, comparing cantons using AP-DRG before the implementation of SwissDRG with DRG-naive cantons after implementation.

The implementation of a DRG-based payment system is a well-established way to create transparency of costs and to help to reduce health-related expenditures [

When comparing the trends over 2008–2014, we found no difference regarding the sum and rate of expected GP consultations and expected reimbursement.

This finding supports the results of other Swiss studies, which showed no rise in GP-consultations [

Moreover, we observed a significant non-linear decrease in GP consultations and a significant non-linear increase in costs in both groups, independent of the DRG system. These trends had the following explanations: in DRG-naive, from 2008 to 2014, the number of patients, and so the demand for primary care, increased more compared to the supply of primary care; in AP-DRG group, cantons were smaller and less populated, with fewer patients and, therefore, fewer consultations per inhabitant, meaning higher and increasing per-capita costs.

In order to investigate a possible "shift" of older and possibly more vulnerable patients to primary care, we also conducted a stratified patient analysis according to age and gender. We found no effect of the introduction of SwissDRG on GP consultations, changing with patient age, but a slight effect on the relative per-capita costs. However, for both results, this analysis did not indicate an overall difference in outcome between the two DRG-groups, as the main term was not significant. In fact, the trend was similar in both DRG-groups for each age class, but the levels of costs and consultations depended on age. This is in line with other studies: [

Moreover, an increase in costs covered by other payment systems, such as rehabilitation, transitional care, or medical home care could not be excluded. Swiss Acute and Transitional Care Act (ATC) was introduced, one year before the SwissDRG, to reduce and prevent possible adverse effects of the DRG reimbursement system, primarily with regard to vulnerable patient groups. Impact of ATC and its effects on discharge of patients, with persisting care needs after hospitalisation, were investigated in a study by Kone et al. [

Some limitations of our study have to be acknowledged. First, we used claim data only measuring age, gender and residency as patient characteristics and we did not include other relevant characteristics affecting GP consultations and costs. Second, we did not consider in our model, for the total of expected GP consultations, any other confounding factors related to cantonal disparities between the DRG groups (i.e., socio-economic characteristics). However, to face this limitation, we measured the inequality of GP consultations and costs, through the Gini-coefficient. The overall coefficients of 0.4, for both GP consultations and costs, weighted for the population, showed an almost high inequality. This came directly from the fact that almost 70% of the 26 cantons, represented only 40% of the total GP consultations and costs. Moreover, the indexes of inequality resulted slightly lower in the AP-DRG group, compared to the DRG- naive. However, the overall cantonal disparities remained unchanged from 2008 to 2014, and therefore we could exclude a possible influence of the DRG introduction.

Third, we have no data on inpatient-outpatient transitions regarding patients, which are not directly discharged due to the requirement of transitional care.

However, our study has several strengths. It provides a large sample size, with almost 76% of patients-data in primary care. Another strength is the period of overall six years, two years before and four years after SwissDRG-implementation. Furthermore, we used a strong study-design, which allowed us to describe temporal changes by using AP-DRG cantons as a control group. Finally, the statistical models were accurate, controlling for both random effects and autocorrelation. In fact, autocorrelation in consultation rate and per-capita costs resulted high and therefore relevant to be accounted for.

The number of consultations of general practitioners (GP) at the canton level showed a decreasing trend. Instead, the relative costs per capita, showed an increasing trend. However, we could not give evidence of a 'cost shift' after the introduction of the SwissDRG in 2012. Detected structural and cantonal differences were independent of the fact that some cantons had already introduced the DRG in the form of AP-DRG before 2012. Future studies should evaluate the impact of DRG focusing on vulnerable patient groups and quality of care. Accurate information on inpatient-outpatient transitions is also required.

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Total GP Cons. Exp., GP consultation expected; Rate of exp cons, consultation rate. TARMED position expected, Total and per Person, were reported by DRG groups.

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