Prioritization and Resource Allocation in the Context of the COVID-19 Pandemic: Recommendations for Colorectal and Pancreatic Cancer in Germany.

In the context of the COVID-19 pandemic, there has been a scarcity of resources with various effects on the care of cancer patients. This paper provides an English summary of a German guideline on prioritization and resource allocation for colorectal and pancreatic cancer in the context of the pandemic. Based on a selective literature review as well as empirical and ethical analyses, the research team of the CancerCOVID Consortium drafted recommendations for prioritizing diagnostic and treatment measures for both entities. The final version of the guideline received consent from the executive boards of nine societies of the Association of Scientific Medical Societies in Germany (AWMF), 20 further professional organizations and 22 other experts from various disciplines as well as patient representatives. The guiding principle for the prioritization of decisions is the minimization of harm. Prioritization decisions to fulfill this overall goal should be guided by (1) the urgency relevant to avoid or reduce harm, (2) the likelihood of success of the diagnostic or therapeutic measure advised, and (3) the availability of alternative treatment options. In the event of a relevant risk of harm as a result of prioritization, these decisions should be made by means of a team approach. Gender, age, disability, ethnicity, origin, and other social characteristics, such as social or insurance status, as well as the vehemence of a patient's treatment request and SARS-CoV-2 vaccination status should not be used as prioritization criteria. The guideline provides concrete recommendations for (1) diagnostic procedures, (2) surgical procedures for cancer, and (3) systemic treatment and radiotherapy in patients with colorectal or pancreatic cancer within the context of the German healthcare system.

[Does the Innovation Fund Improve Healthcare Provision? A Critical Assessment of the Status of Implementing Successful Innovation Fund Projects into Healthcare Practice]. / Verbessert der Innovationsfonds die Versorgung? Eine kritische Bestandsaufnahme zum Stand der Implementierung erfolgreicher Innovationsfondsprojekte in die Versorgungspraxis.

INTRODUCTION: Since 2015, the Federal Joint Committee (G-BA)'s Innovation Fund has been supporting projects in health services research and new health service models ("Neue Versorgungsformen", NVF). By the end of 2022, 211 projects in the NVF category had been funded. A key objective is the transfer of successful projects into standard care. This article analyzes previous projects regarding their incorporation into routine care based on transfer recommendations of the Innovation Fund Committee ("Innovationsausschuss" IA). METHOD: Descriptive analysis of all projects completed by August 1, 2023 with transfer recommendations in the "NVF" funding stream. Presentation by topic, project duration, time until IA transfer decision, categorization, and number of institutions and organizations (recipients) addressed per project, their feedback published on the G-BA website, response rates per recipient group, and a content classification and interpretation of exemplary feedback. Recommendations based on the results and their discussion in an expert workshop. RESULTS: Out of 57 NVF projects, 17 had a transfer recommendation. A total of 57 feedback responses were received from a total of 431 recipients addressed by the IA across these projects. Response rates varied significantly. One-third of inquiries to the G-BA and its member organizations received a response (31%), while only every fifth inquiry to federal states (18%) and professional societies (18%) got a response. Less than one in ten inquiries to the Federal Ministry of Health (8%), administrative bodies (6%), and the German Medical Association (0%) received a response. Project-specific feedback within a recipient group was often contradictory or limited to regional scope. DISCUSSION AND CONCLUSION: The transfer process reveals significant structural and procedural obstacles regarding the incorporation of projects evaluated as successful into routine health care. To ensure that funding from the innovation fund is most effectively used, there needs to be a realistic chance of successful transfer of positive project outcomes into routine care. The DNVF recommends stronger involvement of rule-competent institutions, mandatory publication of responses, structured moderation of the transfer process, expanding types of selective contracts, financing of implementation phases and of studies drawing on results across successful NVF projects.

Modelling the effect of demographic change and healthcare infrastructure on the patient structure in German hospitals - a longitudinal national study based on official hospital statistics.

BACKGROUND: Effects of demographic change, such as declining birth rates and increasing individual life expectancy, require health system adjustments offering age- and needs-based care. In addition, healthcare factors can also influence health services demand. METHODS: The official German hospital statistics database with odd-numbered years between 1995 and 2011 was analysed. This is a national comprehensive database of all general hospital inpatient services delivered. Official data from hospital statistics were linked at the district level with demographic and socio-economic data as well as population figures from the official regional statistics. Panel data regression, modelling case numbers per hospital, was performed for 13 diagnosis groups that characterised the patient structure. Socio-demographic variables included age, sex, household income, and healthcare factors included bed capacity, personnel and hospital characteristics. RESULTS: The median number of annual treatments per hospital increased from 6 015 (5th and 95th percentile [670; 24 812]) in 1995 to 7 817 in 2011 (5th and 95th percentile [301; 33 651]). We developed models characterising the patient structure of health care in Germany, considering both socio-demographic and hospital factors. Demographic factors influenced case numbers across all major diagnosis groups. For example, the age groups 65-74 and 75 + influenced cerebrovascular disease case numbers (p < 0.001). Other important factors included human and material resources of hospitals or the household income of patients. Distinct differences between the models for the individual diagnosis groups were observed. CONCLUSIONS: Hospital planning should not only consider demographic change but also hospital infrastructure and socio-economic factors.

Assessment of the Potential of Concentrating Cancer Care in Hospitals With Certification Through Survival Analysis. / Potenzialabschätzung für die Konzentration der Versorgung von Krebspatient:innen in Kliniken mit DKG-Zertifizierung mittels Überlebenszeitanalyse.

BACKGROUND: Certification programs seek to improve the quality of complex interdisciplinary models of care such as cancer treatment through structuring the process of care in accordance with evidence-based guidelines. In Germany, the German Cancer Society (Deutsche Krebsgesellschaft, DKG) provides a certification programme for cancer care that covers more than one thousand centers. In a recent retrospective cohort study, it has been shown on a large, nationwide data set based on data from a statutory health insurance and selected clinical cancer registries, that there is a benefit in survival for cancer patients who have received initial treatment in hospitals certified by the DKG. Here, we deduce two absolute measures from the relative benefit in survival with the aim to quantify this benefit if all patients had been treated in a certified center. METHODS: The WiZen study analysed survival of adult patients insured by the AOK with a cancer diagnosis between 2009 and 2017 in certified hospitals vs. non-certified hospitals. Besides Kaplan-Meier-estimators, Cox regression with shared frailty was used for 11 types of cancer in total, adjusting for patient-specific information such as demographic characteristics and comorbidities as well as hospital characteristics and temporal trend. Based on this regression, we predict adjusted survival curves that directly address the certification effect. From the adjusted survivals, we calculated years of life lost (YLL) and number needed to treat (NNT), along with a difference in deaths 5 years after diagnosis. RESULTS: Based on our estimate for the 537,396 patients that were treated in a non-certified hospital included in the WiZen study, corresponding to 68,7% of the study population, we find a potential of 33,243 YLL per year in Germany based on the size of the German population as of 2017. The potential to avoid death cases 5 years from diagnosis totals 4,729 per year in Germany. CONCLUSION: While Cox regression is an important tool to evaluate the benefit that arises from variables with a potential impact on survival such as certification, its direct results are not well suited to quantify this benefit for decision makers in health care. The estimated years of life lost and the number of deaths that could have been avoided 5 years from diagnosis avoid mis-interpretation of the hazard ratios commonly used in survival analysis and should help to inform key stakeholders in health care without specialist background knowledge in statistics. Our measures, directly adressing the effect of certification, can furthermore be used as a starting point for health-economic calculations. Steering the care of cancer patients primarily to certified hospitals would have a high potential to improve outcomes.

[Methodology and Attribution Success of a Data Linkage of Clinical Registry Data with Health Insurance Data]. / Methodik und Zuordnungserfolg eines Linkage von Daten klinischer Krebsregister mit Abrechnungsdaten gesetzlicher Krankenkassen.

BACKGROUND: The aim of the project "Effectiveness of care in oncological centres" (WiZen), funded by the innovation fund of the federal joint committee, is to investigate the effectiveness of certification in oncology. The project uses nationwide data from the statuory health insurance AOK and data from clinical cancer registries from three different federal states from 2006-2017. To combine the strengths of both data sources, these will be linked for eight different cancer entities in compliance with data protection regulations. METHODS: Data linkage was performed using indirect identifiers and validated using the health insurance's patient ID ("Krankenversichertennummer") as a direct identifier and gold standard. This enables quantification of the quality of different linkage variants. Sensitivity and specificity as well as hit accuracy and a score addressing the quality of the linkage were used as evaluation criteria. The distributions of relevant variables resulting from the linkage were validated against the original distributions in the individual datasets. RESULTS: Depending on the combination of indirect identifiers, we found a range of 22,125 to 3,092,401 linkage hits. An almost perfect linkage could be achieved by combining information on cancer type, date of birth, gender and postal code. A total of 74,586 one-to-one linkages were achieved with these characteristics. The median hit quality for the different entities was more than 98%. In addition, both the age and sex distributions and the dates of death, if any, showed a high degree of agreement. DISCUSSION AND CONCLUSION: SHI and cancer registry data can be linked with high internal and external validity at the individual level. This robust linkage enables completely new possibilities for analysis through simultaneous access to variables from both data sets ("the best of both worlds"): Information on the UICC stage that stems from the registries can now be combined, for instance, with comorbidities from the SHI data at the individual level. Due to the use of readily available variables and the high success of the linkage, our procedure constitutes a promising method for future linkage processes in health care research.

Can we trust the standardized mortality ratio? A formal analysis and evaluation based on axiomatic requirements.

BACKGROUND: The standardized mortality ratio (SMR) is often used to assess and compare hospital performance. While it has been recognized that hospitals may differ in their SMRs due to differences in patient composition, there is a lack of rigorous analysis of this and other-largely unrecognized-properties of the SMR. METHODS: This paper proposes five axiomatic requirements for adequate standardized mortality measures: strict monotonicity (monotone relation to actual mortality rates), case-mix insensitivity (independence of patient composition), scale insensitivity (independence of hospital size), equivalence principle (equal rating of hospitals with equal actual mortality rates in all patient groups), and dominance principle (better rating of unambiguously better performing hospitals). Given these axiomatic requirements, effects of variations in patient composition, hospital size, and actual and expected mortality rates on the SMR were examined using basic algebra and calculus. In this regard, we distinguished between standardization using expected mortality rates derived from a different dataset (external standardization) and standardization based on a dataset including the considered hospitals (internal standardization). The results were illustrated by hypothetical examples. RESULTS: Under external standardization, the SMR fulfills the axiomatic requirements of strict monotonicity and scale insensitivity but violates the requirement of case-mix insensitivity, the equivalence principle, and the dominance principle. All axiomatic requirements not fulfilled under external standardization are also not fulfilled under internal standardization. In addition, the SMR under internal standardization is scale sensitive and violates the axiomatic requirement of strict monotonicity. CONCLUSIONS: The SMR fulfills only two (none) out of the five proposed axiomatic requirements under external (internal) standardization. Generally, the SMRs of hospitals are differently affected by variations in case mix and actual and expected mortality rates unless the hospitals are identical in these characteristics. These properties hamper valid assessment and comparison of hospital performance based on the SMR.

[Reduction of Days in Inpatient Care in Psychiatric Hospitals with Flexible and Integrated Treatment for Patient-Centered Care with a Global Budget - Results with Three-Year Follow-up from the Evaluation Study EVA64]. / Verringerung vollstationärer Behandlungstage in psychiatrischen Kliniken mit Modellvorhaben zur patientenzentrierten Versorgung mit globalem Budget (§â€Š64b SGB V).

OBJECTIVE: This analysis describes the development of the duration of inpatient and daycare treatment as well as contacts in outpatient PIA in hospitals with flexible and integrated treatment (FIT) over a three-year follow-up period. METHODS: Patients with treatment in the first year after FIT-onset and a follow-up period of three years were examined for 12 FIT-hospitals and compared with matched patients from hospitals providing routine care. RESULTS: 11,536 patients were analyzed. In the year after first treatment, the number of inpatient days was lower in almost all FIT-hospitals compared to routine care. On the other hand, the number of daycare treatment days was mostly higher than in routine care. FIT hospitals with a pre-existing FIT-like structure showed stronger effects. PIA utilization differed between FIT-hospitals. CONCLUSION: This analysis supports evidence-based health policy decisions on the adoption of patient-centered global budget care concepts.

Effectiveness of Global Treatment Budgets for Patients With Mental Disorders-Claims Data Based Meta-Analysis of 13 Controlled Studies From Germany.

Background: Individuals with mental disorders need continuous and efficient collaboration between different sectors of care. In 2012, a new law in Germany enabled the implementation of novel budgets in psychiatry (flexible and integrated treatment = FIT). Hospitals implementing FIT programs have been evaluated in controlled cohort studies. We present first results based on a meta-analysis from 13 FIT hospitals. Methods/Design: We undertook a series of claims-data-based controlled cohort studies. Data from over 70 statutory health insurance (SHI) funds in Germany were analyzed. All patients insured by any of the participating SHI funds and treated in one of the FIT hospitals for any of 16 predefined mental disorders were compared with matched control patients from routine care. The patient collective was subdivided into hospital-new and hospital-known patients. Analyses included utilization of inpatient care, day care, outpatient PIA (psychiatrische Institutsambulanz) care, outpatient care with established practitioners, and durations of sick leave. Individual treatment effects of the 13 FIT hospitals were pooled in a random-effects meta-analysis. Meta-regression analysis was used to explore potential reasons for heterogeneity in model effectiveness. Results: The meta-analysis revealed a significant reduction by over 5 days of inpatient care in hospital-new patients in FIT hospitals compared to control hospitals. This effect was stronger among FIT hospitals with a preexisting FIT-like environment. There was no overall significant effect regarding sick leave between the two groups. Further meta-regression for hospital-new patients revealed a significantly reduced duration of sick leave by almost 13 days for patients in FIT hospitals with a preexisting FIT-like contract compared to FIT hospitals without such a contract. Conclusions: This study suggests positive effects of FIT programs for patients with mental disorders pointing toward a shorter duration of inpatient treatment. Furthermore, contracts already existent prior to initialization of FIT programs appear to have facilitated the transition into the new treatment environment. For FIT hospitals without such contracts, supposedly there is a certain implementation phase for effects to be apparent. The results should still be interpreted with caution as this manuscript only covers the first year of the 5 year evaluation period in 13 of 18 FIT hospitals. Clinical Trial Registration: This study was registered in the database "Health Services Research Germany" (trial number: VVfD_EVA64_15_003713).

[Methodological Challenges when Using Claims Data of more than 70 Statutory Health Insurances - A Progress Report from the EVA64 Study]. / Methodische Herausforderungen bei der Nutzung von Daten von mehr als 70 gesetzlichen Krankenkassen ­ Ein Werkstattbericht aus der EVA64-Studie.

AIM OF THE STUDY: The adequate and need-based medical care of mentally ill patients places special demands on psychiatric care. The §64b Social Code Book (SGB) V enables mentoring mentally ill people through multiprofessional, cross-sectoral model projects across the treatment phase and implementing new forms of financing. These model projects have been evaluated in a prospective and retrospective claims data-based controlled cohort study (EVA64) since 2015. METHODS: In September 2016 and since then annually, the data transfer of all statutory health insurance funds (SHI) involved in this evaluation took place for the first time on the basis of a consented data set description. For later analysis, the clear identification of the index hospital admission and the assignment to the model or control group are important. The methodological challenges of data provision by the data owner, the formal and content-related data preparation as well as the subsequent establishing of an evaluation data set are discussed in detail. RESULTS: So far, data from 71 SHI has been taken into account. In each case 20 tables with claims data from outpatient and inpatient care (including psychiatric institute outpatient departments [PIA]), drug and medical supplies as well as data from incapacity to work and personal data of the insurees. Not all tables could be filled completely by the SHIs. In addition, updates of the study designs require the adaptation of the data selection process. Even though data sets have been delievered regularly the data preparation process is still not routine. CONCLUSION: The scientific use of claims data of numerous SHIs in the context of an evaluation study represents a great challenge. In the absence of reference values for abnormalities and implausibilities, an a priori determination of test algorithms was limited; instead they had to be updated every year. The individual examination of the data of all health insurance companies remains very complex. The detailed documentation of these algorithms provides support for future comparable studies.

Ranking hospitals when performance and risk factors are correlated: A simulation-based comparison of risk adjustment approaches for binary outcomes.

BACKGROUND: The conceptualization of hospital quality indicators usually includes some form of risk adjustment to account for hospital differences in case mix. For binary outcome variables like in-hospital mortality, frequently utilized risk adjusted measures include the standardized mortality ratio (SMR), the risk standardized mortality rate (RSMR), and excess risk (ER). All of these measures require the estimation of expected hospital mortality, which is often based on logistic regression models. In this context, an issue that is often neglected is correlation between hospital performance (e.g. care quality) and patient-specific risk factors. The objective of this study was to investigate the impact of such correlation on the adequacy of hospital rankings based on different measures and methods. METHODS: Using Monte Carlo simulation, the impact of correlation between hospital care quality and patient-specific risk factors on the adequacy of hospital rankings was assessed for SMR/RSMR, and ER based on logistic regression and random effects logistic regression. As an alternative method, fixed effects logistic regression with Firth correction was considered. The adequacies of the resulting hospital rankings were assessed by the shares of hospitals correctly classified into quintiles according to their true (unobserved) care qualities. RESULTS: The performance of risk adjustment approaches based on logistic regression and random effects logistic regression declined when correlation between care quality and a risk factor was induced. In contrast, fixed-effects-based estimations proved to be more robust. This was particularly true for fixed-effects-logistic-regression-based ER. In the absence of correlation between risk factors and care quality, all approaches showed similar performance. CONCLUSIONS: Correlation between risk factors and hospital performance may severely bias hospital rankings based on logistic regression and random effects logistic regression. ER based on fixed effects logistic regression with Firth correction should be considered as an alternative approach to assess hospital performance.

The cost-effectiveness of interventions targeting lifestyle change for the prevention of diabetes in a Swedish primary care and community based prevention program.

BACKGROUND: Policymakers need to know the cost-effectiveness of interventions to prevent type 2 diabetes (T2D). The objective of this study was to estimate the cost-effectiveness of a T2D prevention initiative targeting weight reduction, increased physical activity and healthier diet in persons in pre-diabetic states by comparing a hypothetical intervention versus no intervention in a Swedish setting. METHODS: A Markov model was used to study the cost-effectiveness of a T2D prevention program based on lifestyle change versus a control group where no prevention was applied. Analyses were done deterministically and probabilistically based on Monte Carlo simulation for six different scenarios defined by sex and age groups (30, 50, 70 years). Cost and quality adjusted life year (QALY) differences between no intervention and intervention and incremental cost-effectiveness ratios (ICERs) were estimated and visualized in cost-effectiveness planes (CE planes) and cost-effectiveness acceptability curves (CEA curves). RESULTS: All ICERs were cost-effective and ranged from 3833 €/QALY gained (women, 30 years) to 9215 €/QALY gained (men, 70 years). The CEA curves showed that the probability of the intervention being cost-effective at the threshold value of 50,000 € per QALY gained was very high for all scenarios ranging from 85.0 to 91.1%. DISCUSSION/CONCLUSION: The prevention or the delay of the onset of T2D is feasible and cost-effective. A small investment in healthy lifestyle with change in physical activity and diet together with weight loss are very likely to be cost-effective.