Ten-Year Evaluation of the Population-Based Integrated Health Care System “Gesundes Kinzigtal”

Background: The population-based integrated health care system called “Gesundes Kinzigtal” (Integrierte Versorgung Gesundes Kinzigtal, IVGK) was initiated more than 10 years ago in the Kinzig River Valley region, which is located in the Black Forest in the German state of Baden-Württemberg. IVGK is intended to optimize health care while maximizing cost-effectiveness. It consists of programs for promoting health and for enabling cooperation among service providers, as well as of a shared-savings contract that has enabled resources to be saved every year. The goal of the present study was to investigate trends in the quality of care provided by IVGK over the past ten years in comparison to conventional care.

Methods: This is a non-randomized observational study with a control-group design (Kinzig River Valley versus 13 structurally comparable control regions), employing data collected by AOK, a large statutory health-insurance provider in Germany, over the period 2006–2015. Quality assessment was conducted with the aid of a set of indicators, developed by the authors, that was based exclusively on claims data. The statistical analysis of the trends in these indicators over time was conducted with preset criteria for the relevance of any observed changes, as well as preset mechanisms of controlling for confounding factors.

Results: For 88 of the 101 evaluable indicators, no relevant difference was seen between the trend over time in the region of the intervention and the average trend in the control regions. Relevant differences in favor of the IVGK were observed for six indicators, and negatively divergent trends compared to the controls were observed for seven indicators. In the main summarizing statistical analysis, no positive or negative difference was found between the Kinzig River Valley and the other regions with respect to trends in the health-care indicators over time.

Conclusion: An evaluation based on 101 indicators derived from health-insurance data did not reveal any improvement of the quality of care by IVGK and the totality of the programs that were implemented under it. However, under the conditions of the shared-savings contract, no relevant diminution in the quality of care was observed over a period of 10 years either, compared with structurally similar control regions without an integrated care model.

Health care in Germany is provided—historically grown—in sectors that are largely distinct from one another, at the interfaces of which recurrent losses of information and “breaks in care” occur, leading to negative effects on the quality and cost of care (1, 2). One approach to improving the effectiveness and efficiency of health care is seen in “integrated care” with horizontal and vertical networks of actors. A concept of this kind was developed for a rural area by Optimedis AG and the “Kinzigtal Medical Quality Network Medical Initiative” (MQNK), which undertook a joint venture to form “Gesundes Kinzigtal GmbH.” The selective contract “Gesundes [healthy] Kinzigtal Integrated Care” (Integrierte Versorgung Gesundes Kinzigtal, IVGK) was entered into with two health insurance companies (Box).

Box

“Healthy Kinzigtal integrated care”

The population-based IVGK is considered to be a best-practice example of integrated care in Germany as well as internationally (3, 4, 5, 6). The early years saw extensive accompanying research and process evaluation, for example, by means of surveys among members (e1), as well as surveys among service partners regarding satisfaction, cooperation, and their commitment to integrated care (e2), health promotion among the elderly (e3), patient satisfaction, and preference of insured persons in terms of their participation in decision-making (e4). Based on routine data, a survey on underuse, overuse, and misuse of care was conducted, involving four authors of the study reported here. The survey investigated the quality of care only for the period 2005–2011 compared to care in Baden-Württemberg (random sample)—using 18 indicators for selected diseases that formed the focus of the IVGK (7).

No external outcome evaluation based on routine statutory health insurance data for the period after 2011 has been carried out as yet. This represents a serious gap in evidence—especially if one assumes that undesirable trends such as underuse, for example, as a result of managed care elements like the shared savings contract (e5, 8), would only become demonstrably evident after a number of years. It also appears conceivable that unintended side effects as a result of focusing integrated care management on specific programs only emerge after years. Although evaluation studies, albeit with inconsistent results, from other countries are available (4, 9, 10, 11, 12, 13, 14, 15, 16, 17), the differences in health care as well as in the understanding of integrated care make it impossible to conclude from this that a care model such as the IVGK is effective in Germany. Therefore, the present study evaluates the quality of care provided by the IVGK over a 10-year period using a methodological approach developed specifically for this study. The study investigates whether the quality of care in the Kinzig River Valley has decreased, remained constant, or increased compared to structurally similar control regions.

Methods

The methodology of the study—development of indicators (18), selection of comparison regions, data base, and statistical approach—is presented in the eMethods Section. In accordance with the population-based contract, the evaluation relates to all persons insured with the AOK (a large statutory health-insurance provider in Germany) living in the Kinzig River Valley and not only to AOK-insured persons enrolled in the integrated care system. For the period 2006–2011, the evaluation uses raw data that are comparable to the previous study (7), but chooses a fundamentally different methodological and statistical approach, as defined in advance in a study protocol (19). The following elements are central to this approach:

• A comprehensive set of indicators—based on the literature, developed in a structured manner, and agreed upon using a Delphi consensus process—that focuses both on the health care of insured individuals who participated in one of the 14 IVGK programs offered during the observation period as well as on the prevention and treatment of common chronic conditions with high potential for prevention or coordination (“tracers”) (18)
• A comparison with 13 structurally similar control regions (eTable 1)
• An evaluation of the difference between the trend in the intervention region and the mean trend in the control regions for the individual indicator prevalences by means of a grading procedure, i.e., weakly/moderately/strongly positive and negative hints (eMethods Section)
• The main summarizing statistical analysis using a permutation test (e6) to determine the overall evidence across all indicators.

Results

Depending on the year, 28 208–29 206 AOK-insured persons in the intervention region and 381 251–400 033 AOK-insured persons in the control regions were included (eTable 1). The study was initially based on the analysis of 119 quality indicators measurable via routine data. For 13 indicators, the number of cases was too small for the statistical analysis (1 x no patient numbers meeting the denominator criterion, 12 x no meaningful estimation of prevalence possible due to all or none of the patients being considered as fulfilling the numerator criterion due to small group sizes). A further five indicators are descriptive in nature, meaning that the development of the respective indicator prevalence cannot be definitively classified as an improvement or a deterioration. Thus, the evaluation results are based on 101 indicators (eTable 2).

Table 1

Indicators showing a relevant difference in the trend difference of indicators in favor of the Kinzig River Valley region compared to 13 control regions

Table 2

Indicators showing a relevant difference in the trend difference of indicators to the disadvantage ofthe Kinzig River Valley region compared to 13 control regions

eTable 1

Characteristics of the populations in the Kinzig River Valley region, the control regions, and the comparison region Baden-Württemberg

eTable 2

Indicators that could be operationalized and evaluated

Overview of indicators

For 88 of these indicators, a comparison between the trend in indicator prevalence in the Kinzig River Valley region and the mean trend in the control regions did not indicate any relevant difference (eFigure 1). A total of 13 indicators fulfilled the evaluation criteria defined prior to the data analysis with regard to significance and clinical relevance (Tables 1, 2; for temporal course, see eFigures 2, 3), while six showed a relevant difference in trend in favor of and seven to the disadvantage of the Kinzig River Valley region (Tables 1, 2).

Figure 1

Temporal trends of two indicators showing a relevant difference in a comparison of the Kinzig River Valley (KT) region with the control regions

eFigure 1

Estimation of the trend difference between intervention region and control regions

For example, with indicator 1.3, the proportion of heart failure patients with prescribed anti-inflammatory drugs is expected to decrease. The decline in the intervention region was far more pronounced compared to the average decline in the control regions, leading to a “moderately positive hint” in the evaluation in terms of the magnitude of change (difference in trend) and conspicuousness in the range of variation (z-score) compared to the control regions (Figure 1a). Table 2 summarizes the indicators with a negative trend. With regard to the prevalence of treatment with ACE inhibitors or sartans in patients following myocardial infarction and left ventricular systolic dysfunction, the initially high level in the Kinzig River Valley region could not be maintained over time compared to the other regions (Figure 1b). In this regard, the Kinzig River Valley region fulfills the criteria for a “moderately strong negative hint” compared to the average for the control regions (Figure 1a, b).

Indicators with a trend classified as “inconclusive” could well have developed in the desired direction during the observation period, but then the trend in the Kinzig River Valley showed no conspicuousness compared to the mean and variation of trends in the control regions (20).

Table 3 shows the results broken down according to disease groups and topics, with different numbers of indicators in each category (21). The points with which the respective indicator groups contribute to S_hint (total points average across all indicators) with weak or moderately strong hints is shown (Figure 2). It is apparent that there is no concentration of indicators with a positive or negative hint for specific disease groups or issues. Areas that appear to be particularly amenable to integrated care (high level of coordination required between primary care physicians and specialists/hospital, for example, coronary heart disease, myocardial infarction, and stroke) do not contrast positively to control regions (Table 3).

Figure 2

Summarizing result of the indicator analysis

Table 3

Overview of the indicator groups and overall result

Mortality

Improved health status of the population, as formulated in the IVGK as a goal, can be represented, among other things, by means of the indicator „deaths per 100 insured persons.“ The analysis conducted to this end revealed no difference between the Kinzig River Valley region and the control regions (eFigure 4).

eFigure 4

Mortality: change in deaths per 100 insured persons over 5 years

Preventable hospital cases

The indicator set includes typical integrated care indicators, such as potentially preventable hospital (re-)admissions for heart failure, chronic obstructive pulmonary disease (COPD), depression, diabetes, and adverse drug reactions. The results are inconsistent, indicating an advantage for IVGK only in relation to inpatient admissions for moderate depression (eFigure 2). Inpatient admissions due to COPD for patients with COPD, as well as hospital admissions for adverse drug reactions in old age, revealed negative trends compared to control regions (eTable 3, eFigure 3).

eFigure 2

Time course of indicators with favorable development in the Kinzigtal region compared to the control regions

eFigure 3

Time course of indicators with unfavorable development in the Kinzigtal region compared to the control regions

eTable 3

Hospital (re-)admissions and ambulatory-care--sensitive conditions

Selection of antibiotics

Two indicators relate to the prescribing of antibiotics (fluoroquinolones/cephalosporins for uncomplicated cystitis, and reserve antibiotics for urinary tract infection). When considering the time course, all regions showed a clear increase and no advantage for the Kinzig River Valley population (eFigures 5a–c, 6a–c).

Overall evidence

The central research question was answered with a summarizing statistical analysis (permutation analysis) that looked at trends across all 101 indicators: none of the three pre-defined statistical key performance indicators (hints, difference in trend, and z-score) revealed any statistical abnormality with regard to the role of the Kinzig River Valley region as an intervention region. The graphical representation of results (Figure 2, eFigure 7a, b) clearly shows that the key performance indicator calculated for the Kinzig River Valley (red) is in each case within the range that results from the corresponding calculation for the control regions (light green). The values should be assessed in each case in comparison to the mean value of the control regions, the value at which the bell curve reaches its highest point. The S_hint measure, which evaluates the number and extent of indicators rated as a “positive hint” or a “negative hint” according to a point scheme (Table 3), was slightly less favorable in Kinzig River Valley (−0.05) than the mean of the control regions (eTable 4), without being statistically significant. The results on the other measurements also provide no evidence of an extreme position for the values in the intervention region and thus no evidence of a difference in the development of quality of care as a result of the IVGK compared to the control regions (eFigure 7a, b). There are also no noteworthy differences between the 53 program-specific indicators and the 48 non-program-specific indicators.

eFigure 7a

Overall evidence for summary statistic Sdiff and summary statistic SIzI

eFigure 7b

Overall evidence for summary statistic Sdiff and summary statistic SIzI

eTable 4

Representation of the overall evidence for summary statistics

Discussion and conclusion

Overall, the evaluation of the IVGK over a 10-year period shows no evidence of a relevant (positive or negative) difference in terms of the trend in the quality of care for the period 2006–2015 compared to the 13 structurally similar control regions. The improvement in quality of care aimed for by the IVGK could not be demonstrated. Thus, the slight trend toward an improvement in the quality of care observed for the first few years—based on different methodology and only a handful of indicators (7)—cannot be confirmed. One might suspect that the IVGK achieved an improvement ahead of the other regions due to its spirit of optimism in the early years. However, the trajectories of the indicators provided no evidence for this.

The question was whether a deterioration in the quality of care could be observed under the conditions of a shared-savings contract. Our results suggest that this is not the case. This is consistent with investigations conducted on the Accountable Care Organizations (ACO) in the USA: for example, the systematic review by Kaufman et al. (2019) (17) on the impact of ACOs on utilization, outcomes, and cost also indicates that there was no evidence to suggest that cost-saving incentives lead to negative outcomes.

If one considers the results of our evaluation in comparison to the international literature, one must first of all point out that there is no standard concept for integrated care and, therefore, different implementation strategies exist (5, 22, 23, 24). Failure to take this into consideration could lead to incorrect conclusions and generalizations about the effectiveness of integrated care (24). The available international studies evaluating integrated care programs report inconsistent results (4, 9, 10, 11, 12, 13, 14, 15, 16, 17), for example, a reduction in costs but no improvement in quality (25). The fact that quality improvements often cannot be shown is attributed by the authors of the Nuffield Report (12) to the fact that integrated care was not implemented to levels of 90% or 100% as expected, but at most to a level of 30%. We have no data at our disposal on the basis of which we could assess this in relation to the IVGK. It was only possible to evaluate the integrated care contract independently of the extent of its implementation.

There is a consensus that groups at high risk for interventions need to be identified in order to achieve measurable effects of an intervention. According to the international literature, hospitalization and readmission are key indicators for assessing health care (14, 24). One starting point are diseases for which it is assumed that preventive measures and early and effective outpatient care can prevent hospitalization (26, 27). The indicators presented on this (eTable 3) show—compared with the control regions for the Kinzig River Valley—comparable trends for seven out of 10 indicators, slightly negative trends for two indicators, and a slightly positive trend for one indicator. Mortality is another key indicator for the assessment of the effectiveness of integrated care (24) and the health status of the population (OECD indicator) (28). According to a systematic review, there are mixed results in this regard for integrated care (24). We cannot confirm the survival benefits reported by Schulte et al. (29) in 2014 for the insured population enrolled on the IVGK for the overall AOK population in the Kinzig River Valley region.

Since the prevention of antibiotic resistance is of great public health relevance, the monitoring of antibiotic prescriptions provides hints on the implementation of recommendations on antibiotic selection (30). Recent nationwide analyses show that prescriptions have declined slightly (31). Following a significant increase, the IVGK has shown a slight decrease in the prescription of reserve antibiotics (not quinolones) since 2012, possibly due to expert pharmaceutical consultations.

The strengths of our evaluation lie in its long observation period of 10 years. International investigations often only consider extremely short time periods, and the results are described as preliminary (5). In contrast to many international evaluations, we used a comprehensive, systematically developed set of indicators that can be mapped with routine data (18) and which also take into account non-program-specific areas of care in order to identify possible neglect of other areas of care; however, this was not the case here. A further strength lies in the inclusion of multiple control regions that are structurally similar to the intervention region to minimize biases that cannot be eliminated from the analysis. The observed variation between regions highlights the fact that a simple comparison with a single control population or with nationwide averages (32, 33, 34)—as has been widely conducted—is insufficient (15, 35, 36). To the best of our knowledge, this is also the first time that a cross-indicator summarizing analysis that allows the evaluation result to be described using a small number of key indicators has been conducted.

When evaluating the results, a number of limitations need to be considered. Results of the evaluation for the period 2005–2011 were known prior to conducting the study. Due to previous experience, we chose a different approach and statistical methodology in the present study. One must bear in mind that indicator prevalence depends not only on the numerator, but also on the denominator of the reference population for the indicator. Significant changes in the denominator over time, for example, due to modified diagnosis documentation, as observed for depression, affect indicator prevalence—and thus also the calculated trend difference—without necessarily changing the treatment provided by physicians or therapists (effect on the numerator of the indicator). Since these changes are not necessarily equally pronounced in all regions, artifacts cannot be ruled out in individual cases. One can also not exclude the possibility that, despite adjustment, bias exists due to unrecorded confounding variables. However, a critical review of the indicator-specific evaluations gave no indication that important differences between the Kinzig River Valley and the control regions would not have been identified using the methodology employed. Finally, limitations also lie in the routine data themselves: for example, some indicators rated as relevant and important could not be mapped (medication plan, palliative care, nursing care, emergency) (18). It was also not possible to consider important parameters such as patient-reported outcomes and experiences with health care or, for example, possible delayed onset of disease due to health-promoting programs offered by the IVGK. Other questions of interest include efficiency of care, which could not be examined as part of this study. Therefore, a wide field of research remains for future evaluations.

eFigure 5a

Proportion of patients with uncomplicated cystitis that were prescribed fluoroquinolones and/or cephalosporins as firstline antibiotics

eFigure 5b

Proportion of patients with uncomplicated cystitis that were prescribed fluoroquinolones and/or cephalosporins as firstline antibiotics

eFigure 5c

Proportion of patients with uncomplicated cystitis that were prescribed fluoroquinolones and/or cephalosporins as firstline antibiotics

eFigure 6a

Proportion of urinary tract infection patients that were prescribed reserve antibiotics* in the previous 12 months

eFigure 6b

Proportion of urinary tract infection patients that were prescribed reserve antibiotics* in the previous 12 months

eFigure 6c

Proportion of urinary tract infection patients that were prescribed reserve antibiotics* in the previous 12 months

eTable 5

Reserve antibiotics

eTable 6

Specifications for the classification of results

Acknowledgments
The “INTEGRAL – 10-year evaluation of population-based integrated care Gesundes Kinzigtal in the set-up and consolidation phase” project on which this publication is based was supported with funds from the Innovation Committee of the Federal Joint Committee under grant number 01VSF16002. The authors would like to thank Prof. Werner Vach (Universities of Freiburg [until 12/2017] and Basel [from 1/2017]) for the statistical analysis and for valuable suggestions on the selection of control regions.

Conflict of interest statement
Dr. Schubert, Peter Ihle, Ingrid Köster, and Dr. Siegel were involved in the evaluation of the IVGK set-up phase between 2008 and 2014, which was funded by third-party funds from AOK Baden-Württemberg and Gesundes Kinzigtal GmbH. Dr. Siegel was coordinator of the external evaluation of the IVGK at the University and at the Freiburg University Hospital from 2006 to 2016; this position was funded by third-party funds from Gesundes Kinzigtal GmbH. Dr. Siegel held a part-time position at Gesundes Kinzigtal GmbH from 1 June to  31 December 2015.

Dr. Graf received consulting fees from Roche Pharma AG.

The remaining authors declare that no conflict of interests exists.

Manuscript submitted on 15 October 2020, revised version accepted on 22 February 2021.

Translated from the original German by Christine Rye.

Corresponding author
Dr. rer. soc. Ingrid Schubert
PMV forschungsgruppe an der Klinik und Poliklinik für Psychiatrie, Psychosomatik
und Psychotherapie des Kindes- und Jugendalters
Medizinischen Fakultät und Uniklinik der Universität zu Köln
Herderstraße 52, 50931 Köln, Germany

Cite this as
Schubert I, Stelzer D, Siegel A, Köster I, Mehl C, Ihle P, Günster C, Dröge P, Klöss A, Farin-Glattacker E, Graf E, Geraedts M: Ten-year evaluation of the population-based integrated health care system „Gesundes Kinzigtal“.
Dtsch Arztebl Int 2021; 118: 465–72. DOI: 10.3238/arztebl.m2021.0163

►Supplementary material

eReferences, eMethods Section, eTables, eFigures:
www.aerzteblatt-international.de/m2021.0163

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