German Insurance Research Articles

Employees Perceive Planned Organizational Change: Insights from a Workspace Change Project in the German Insurance Industry

Based on a symbolic–interpretive and social–constructivist research approach, this longitudinal study explores how employees perceive workspace changes. Rich empirical processual data that were continuously collected during the change process were used to analyze workspace changes as a new type of organizational change, thereby contributing to sensemaking research. Three types of change recipients from employees’ responses to a workspace change based on differences in their underlying cognitive, behavioral, and affective patterns throughout the sensemaking process were identified: those with positive attitudes and perceptions, those with negative attitudes and perceptions, and those exhibiting a change toward positive attitudes and perceptions. As a practical contribution, change agents and leaders can gain insights and guidance for managing different employees by considering the individual patterns that trigger changes in employees’ perceptions throughout the change process. These insights about sensemaking also contribute to organizational development and change management research.

Uncovering the Impact of Digitalization on the Performance of Insurance Distribution

This study explores the impact of digitalization on the performance of insurance intermediaries, who still play a key role in the revenue generation of insurance companies. By using an interdisciplinary approach, this study is the first to examine the extent and type of digital technologies used by intermediaries, their impact on performance with respect to revenue, productivity, and interaction with clients, and the role of digital stress in this context. The research is exploratory, which is why a research model with many variables and relationships between them was built. The quantitative multivariate method of Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied as it allows the simultaneous estimation of models with multiple dependent variables and their interconnections. In this context, data collected in 2022 from 671 insurance intermediaries from Germany, whose demographic distribution in the sample is representative of the German insurance market, were analyzed. The findings show that insurance intermediaries use many digital technologies compared to other industries, particularly those that create added value in their daily work. Empirical evidence also showed that using digital technologies positively affects performance but induces perceived digital stress. As this study reveals, the latter diminishes the positive effects on performance. Technology optimism, technological skills, and organizational support reduce the severity of stress. This means that insurers can start here to support intermediaries to mitigate the performance-limiting effects. This study adds to the insurance literature by providing a broader understanding of how insurance intermediaries deal with digitalization and what it means for their performance.

Evaluating the Population-Based Usage and Benefit of Digitally Collected Patient-Reported Outcomes and Experiences in Patients With Chronic Diseases: The PROMchronic Study Protocol.

Chronic diseases are associated with a high disease burden. Under- and overprovision of care as well as quality variation between health care providers persists, while current quality indicators rarely capture the patients' perspective. Capturing patient-reported outcome measures (PROMs) as well as patient-reported experience measures (PREMs) is becoming more and more important to identify gaps in care provision, prioritize services most valuable to patients, and aid patients' self-management. This study aims to measure the potential benefits and effectiveness of using electronic patient-reported outcome measures (ePROMs) and electronic patient-reported experience measures in a structured and population-based manner to enhance health care for chronic disease patients in Germany. This prospective cohort study aims to evaluate the potential benefits of PROM usage in patients with chronic diseases. We evaluate whether (1) digitally collected PROMs and PREMs can be used for health system performance assessment by generating a representative response of chronically diseased individuals with asthma, chronic obstructive pulmonary disease, diabetes, and coronary artery disease across Germany, and (2) based on the PROMs and PREMs, low-value care can be identified. As patient-reported outcomes (PROs) are rarely presented back to patients, (3) this study also examines patients' reactions to their PROM scores in the form of digital PRO feedback. For these purposes, randomly selected patients from a nationwide German insurer are digitally surveyed with generic and disease-specific PROMs and PREMs, as well as additional questions on their health-related behavior, 4 times over 1 year. Individual PRO feedback is presented back to patients longitudinally and compared to a peer group after each survey period. Patient-reported data is linked with health insurance data. Response rates, changes in health and experience outcomes over time, self-reported changes in health behavior, and health care system usage will be analyzed. The PROMchronic study explores the usage of PROMs in patients with chronic diseases. Data collection began in October 2023, after the initial invitation letter. All the 200,000 potential patients have been invited to participate in the study. Data have not yet been analyzed. Publication of the interim results is planned for the autumn of 2024, and the results are planned to be published in 2025. We aim to fill the research gap on the population-based usage of PROMs and PREMs in patients with chronic diseases and add to the current understanding of PROM data-sharing with patients. The study's results can thereby inform whether a health care system-wide approach to collecting PROMs and PREMs can be used to identify low-value care, assess quality variation within and across chronic conditions, and determine whether PRO feedback is helpful and associated with any changes in patients' health behaviors. German Clinical Trials Register DRKS00031656; https://drks.de/search/en/trial/DRKS00031656. DERR1-10.2196/56487.

Unveiling the untreated: development of a database algorithm to identify potential Fabry disease patients in Germany

BackgroundFabry disease (FD), an X-linked lysosomal storage disorder, is caused by mutations in the gene encoding α-galactosidase A, resulting in lysosomal accumulation of globotriaosylceramide and other glycosphingolipids. Early detection of FD is challenging, accounting for delayed diagnosis and treatment initiation. This study aimed to develop an algorithm using a logistic regression model to facilitate early identification of patients based on ICD-10-GM coding using a German Sickness Fund Database.MethodsThe logistic regression model was fitted on a binary outcome variable based on either a treated FD cohort or a control cohort (without FD). Comorbidities specific to the involved organs were used as covariates to identify potential FD patients with ICD-10-GM E75.2 diagnosis but without any FD-specific medication. Specificity and sensitivity of the model were optimized to determine a likely threshold. The cut-point with the largest values for the Youden index and concordance probability method and the lowest value for closest to (0,1) was identified as 0.08 for each respective value. The sensitivity and specificity for this cut-point were 80.4% and 79.8%, respectively. Additionally, a sensitivity analysis of the potential FD patients with at least two codes of E75.2 diagnoses was performed.ResultsA total of 284 patients were identified in the potential FD cohort using the logistic regression model. Most potential FD patients were < 30 years old and female. The identification and incidence rates of FD in the potential FD cohort were markedly higher than those of the treated FD cohort.ConclusionsThis model serves as a tool to identify potential FD patients using German insurance claims data.

BMI-Stratified Exploration of the 'Obesity Paradox': Heart Failure Perspectives from a Large German Insurance Database.

Background: The global rise of obesity and its association with cardiovascular risk factors (CVRF) have highlighted its connection to chronic heart failure (CHF). Paradoxically, obese CHF patients often experience better outcomes, a phenomenon known as the 'obesity paradox'. This study evaluated the 'obesity paradox' within a large cohort in Germany and explored how varying degrees of obesity affect HF outcome. Methods: Anonymized health claims data from the largest German insurer (AOK) for the years 2014-2015 were utilized to analyze 88,247 patients hospitalized for myocardial infarction. This analysis encompassed baseline characteristics, comorbidities, interventions, complications, and long-term outcomes, including overall survival, freedom from CHF, and CHF-related rehospitalization. Patients were categorized based on body mass index. Results: Obese patients encompassed 21.3% of our cohort (median age 68.69 years); they exhibited a higher prevalence of CVRF (p < 0.001) and comorbidities than non-obese patients (median age 70.69 years). Short-term outcomes revealed lower complication rates and mortality (p < 0.001) in obese compared to non-obese patients. Kaplan-Meier estimations for long-term analysis illustrated increased incidences of CHF and rehospitalization rates among the obese, yet with lower overall mortality. Multivariable Cox regression analysis indicated that obese individuals faced a higher risk of developing CHF and being rehospitalized due to CHF but demonstrated better overall survival for those classified as having low-level obesity (p < 0.001). Conclusions: This study underscores favorable short-term outcomes among obese individuals. The 'obesity paradox' was confirmed, with more frequent CHF cases and rehospitalizations in the long term, alongside better overall survival for certain degrees of obesity.

Compliance with Taxonomy Regulation Disclosures in the German Insurance Market During the Eligibility Transition Period

Compliance with Taxonomy Regulation Disclosures in the German Insurance Market During the Eligibility Transition Period

Digital Transformation in Back-Offices of German Insurance Companies

Digital transformation affects the back offices of incumbent insurance companies. Digital innovations are implemented to be able to offer customers new products and services. We conduct multiple case studies with four German insurance companies. We analyze the impact of digital transformations on company-internal stakeholders with the Technology-Organization-Environment (TOE) framework. We derive 12 influencing factors and corresponding practical implications. Back-office systems are updated using standard software, if available. The goal is to stay flexible for future developments, too. State-of-the-art technologies are tested and only carefully implemented. Necessary reorganizations have significant impacts and are accomplished best with a trusting company culture, good internal communication, and well-trained employees. Efficiency gains from automating routine functions can and are intended to counter labor shortages. After the implementations, skilled employees must handle more specialized and demanding tasks, which cannot be automated easily. Impulses for digital transformation mainly result from continuously changing customer requirements and respective competitive pressure, while regulatory requirements often complicate implementations.

Dental care utilization among home care recipients, nursing home residents, and older adults not in need of long-term care: An observational study based on German insurance claims data

ObjectivesTo describe and compare dental care utilization (DCU) among home care recipients, nursing home residents, and older adults not in need of long-term care (LTC). MethodsUsing nationwide claims data of 8 German statutory health and LTC insurance funds, proportions of home care recipients (n = 68,137), nursing home residents (n = 21,167), and non-LTC dependents (n = 632,205) aged 65+ years with DCU in 2017 were determined and compared. Associations between DCU and individual characteristics and setting were investigated via multivariable logistic regression. The proportions of individuals with DCU one year before and after transition to (a) home care (n = 23,590) and (b) nursing home care (n = 6,583) were compared. ResultsProportions of home care recipients and nursing home residents with DCU were lower compared to non-LTC dependents (51.9, 53.1, and 73.2%, respectively). Adjusted odds ratios for DCU for home care recipients vs. non-LTC dependents ranged from 0.55 (LTC grades 1/2; 95% confidence interval 0.54–0.56) to 0.38 (LTC grades 4/5; 0.36–0.40). For nursing home residents vs. non-LTC dependents they ranged from 0.69 (3; 0.65–0.72) to 0.67 (4/5; 0.63–0.71). Women, older individuals, those with 0–1 diseases of the Elixhauser comorbidity index, dementia, and those from West Germany were also less likely to utilize dental care than their counterparts. Utilization decreased after transition to home care (60.0 vs. 55.6%) and increased after transition to nursing homes (46.1 vs. 53.5%). ConclusionsNursing home residents and especially home care recipients utilized dental care less frequently than older non-LTC dependents. Organizational barriers for dental care utilization and ways to remove them should be investigated. Clinical significanceDental care utilization among LTC dependents is low and should be improved in both the home care and nursing home setting.

On the network suppression of the pathogen spread within the healthcare system

In the paper, we describe the transfer of patients in the healthcare system by SIS-type equations coupled by impulses at fixed times. The first aim for these considerations is to provide a rigorous mathematical analysis of a general theoretical model, which incorporates the structure of patients’ transfers. Based on theoretical results, we disprove the intuitions taken from decoupled systems, showing a damping of infection in the case when the transmission rate is locally higher than the recovery rate. Observations on network suppression of pathogen spread allow us to indicate units prone to the high prevalence and propose, possibly low-cost, interventions reducing the infection spread in a whole system. Core results combine the dynamical and structural properties of a system. Finally, we consider a model of the transmission of hospital-acquired multidrug-resistant bacteriae infections, based on real patient hospital records provided by the German insurance company – AOK Lower Saxony, to ilustrate the theoretical considerations.

Utilizing the omnipresent: Incorporating digital documents into predictive process monitoring using deep neural networks

Predictive process monitoring (PPM) allows companies to improve the efficiency of their business processes by predicting aspects such as the process outcome, the next event, or the time until the next event. So far, existing studies have mainly focused on developing novel predictive models while using features solely from event logs. In this study, we aim to go beyond log data and increase the focus of PPM research towards external context information. To this end, we consider digital documents as they are omnipresent in many business processes and their inclusion can often be justified by a business rationale. However, incorporating digital documents into PPM models poses considerable challenges as they present unstructured data that can contain visual and textual cues of future process behavior, while manual feature extraction is generally not feasible. Therefore, we propose an approach that processes digital documents based on automated visual and textual feature extraction methods. Furthermore, we design a tailored integration module which transforms the extracted features from multiple document pages into a fixed-size representation that subsequently serves as input for the predictive models. Our evaluation, based on a real-world dataset of insurance claims from a mid-sized German insurance company, featuring 5131 process instances with 32,058 events and 39,242 document pages, shows that incorporating digital documents improves the performance by significant margins in predicting the damage type, the next event, and the time until the next event. Finally, we analyze how digital documents contribute to the model’s predictions in terms of Shapley additive explanations.

Chances and Challenges of Business Intelligence: Insights from the German Insurance Market

Chances and Challenges of Business Intelligence: Insights from the German Insurance Market

Limitations of german insurance disclosures to improve consumer understanding, with lessons for U.S. insurance practices

Germany changed its insurance regulations to require intermediaries to provide disclosures at the time of application to improve consumer knowledge and protection. The German experience is that the disclosures were incorporated well into the business process but were largely ineffective to improve consumer knowledge. This outcome is consistent with numerous studies on the limitations of disclosures, due to cognitive limitations (bounded rationality) and financial literacy. We review the German practices for effectiveness, compare German and U.S. experiences with insurance disclosures, and conclude that disclosures show little impact on consumer decisions. We recommend that disclosures could provide benefit if conveyed in better formats in line with existing research on financial services disclosures. Regulators and consumer advocates should, therefore, be restrained about general proscriptions for disclosures. Greater benefit to consumers may come from improving default coverages and raising the advisory standard for intermediaries.

Cost–benefit analysis of the CoCare intervention to improve medical care in long-term care nursing homes: an analysis based on claims data

BackgroundProviding adequate medical care to nursing home residents is challenging. Transfers to emergency departments are frequent, although often avoidable. We conducted the complex CoCare intervention with the aim to optimize nursing staff–physician collaboration to reduce avoidable hospital admissions and ambulance transportations, thereby reducing costs.MethodsThis prospective, non-randomized study, based on German insurance data, includes residents in nursing homes. Health care cost from a payer perspective and cost–savings of such a complex intervention were investigated. The utilisation of services after implementation of the intervention was compared with services in previous quarters as well as services in the control group. To compensate for remaining differences in resident characteristics between intervention and control group, a propensity score was determined and adjusted for in the regression analyses.ResultsThe study population included 1240 residents in the intervention and 7552 in the control group. Total costs of medical services utilisation were reduced by €468.56 (p < 0.001) per resident and quarter in the intervention group. Hospital stays were reduced by 0.08 (p = 0.001) and patient transports by 0.19 (p = 0.049). This led to 1.66 (p < 0.001) avoided hospital days or €621.37 (p < 0.001) in costs–savings of inpatient services. More services were billed by general practitioners in the intervention group, which led to additional costs of €97.89 (p < 0.001).ConclusionThe benefits of our intervention clearly exceed its costs. In the intervention group, avoided hospital admissions led to additional outpatient billing. This indicates that such a multifactorial intervention program can be cost-saving and improve medical care in long-term care homes.

Enhancing employer attractiveness: the impact of COVID-19 on Generation Z

This paper investigates how employer attractiveness has been affected by developments in the attitudes, values, and goals of Generation Z (born between 1995-2010) due to the COVID-19 pandemic. Research shows the challenging environment for employers who are increasingly facing a ‘war for talent’ and the need to focus on generational needs and expectations. Work concepts in many cases have been adapted to Generation Y, but a revision of strategies is needed for the newest generation on the labour market. The insurance industry has long struggled to attract young talent and along with retail, logistics, tourism, and banking has been ranked the lowest in employer attractiveness by Generation Z. This is corroborated by the fact that larger corporations are also often perceived as unattractive by young people. While many industries have struggled with the consequences and challenges of the pandemic, the insurance sector can be seen as having mastered the crisis comparatively well. This paper questions whether the relative job security offered by the insurance sector, can be increasingly influential in post pandemic job choices. Since Generation Z was already described as security-oriented before the pandemic, this is expected to have increased as a result of COVID-19 and be reflected in their career and employer choices. The perspectives of Generation Z and employers from the German insurance industry are compared through survey and interview data. The young cohort suspects a worsening of their situation particularly in job offerings and security. At the same time, the insurance industry positions itself well in exactly these areas. Furthermore, the aspirations and expectations of Generation Z towards the professional world coincide strongly with the offerings of insurers as employers. However, the respondents see the sector’s image as a deterrent. Although this paper focuses on the insurance industry, strategic recommendations given on how the sector can position itself, are relevant for other sectors facing the challenge of attracting Generation Z employees.

Predicting mortality in the very old: a machine learning analysis on claims data

Machine learning (ML) may be used to predict mortality. We used claims data from one large German insurer to develop and test differently complex ML prediction models, comparing them for their (balanced) accuracy, but also the importance of different predictors, the relevance of the follow-up period before death (i.e. the amount of accumulated data) and the time distance of the data used for prediction and death. A sample of 373,077 insured very old, aged 75 years or above, living in the Northeast of Germany in 2012 was drawn and followed over 6 years. Our outcome was whether an individual died in one of the years of interest (2013–2017) or not; the primary metric was (balanced) accuracy in a hold-out test dataset. From the 86,326 potential variables, we used the 30 most important ones for modeling. We trained a total of 45 model combinations: (1) Three different ML models were used; logistic regression (LR), random forest (RF), extreme gradient boosting (XGB); (2) Different periods of follow-up were employed for training; 1–5 years; (3) Different time distances between data used for prediction and the time of the event (death/survival) were set; 0–4 years. The mortality rate was 9.15% in mean per year. The models showed (balanced) accuracy between 65 and 93%. A longer follow-up period showed limited to no advantage, but models with short time distance from the event were more accurate than models trained on more distant data. RF and XGB were more accurate than LR. For RF and XGB sensitivity and specificity were similar, while for LR sensitivity was significantly lower than specificity. For all three models, the positive-predictive-value was below 62% (and even dropped to below 20% for longer time distances from death), while the negative-predictive-value significantly exceeded 90% for all analyses. The utilization of and costs for emergency transport as well as emergency and any hospital visits as well as the utilization of conventional outpatient care and laboratory services were consistently found most relevant for predicting mortality. All models showed useful accuracies, and more complex models showed advantages. The variables employed for prediction were consistent across models and with medical reasoning. Identifying individuals at risk could assist tailored decision-making and interventions.

Disease progression of heart failure in type 2 diabetes patients in Germany; a real world data analysis using health insurance claims data

Abstract Individuals with Type 2 Diabetes (T2D) show two- to four-fold increased risk of Heart failure (HF). Given the increasing T2D prevalence in Germany, researching the interaction of T2DM and HF is of high importance. HF still progresses rapidly. Left ventricular ejection fraction (LVEF) plays an important role in understanding disease progression. Commonly, LVEF is being distinguished into three categories: HF with preserved LVEF (HFpEF), HF with moderate reduced LVEF (HFmEF) and HF with reduced LVEF (HFrEF). In Germany there are no recent data on disease progression in HF especially according to LVEF categories. Purpose of this study is to a) measure how many T2DM patients acquire HF over two years; b) understand the progression of HF in these patients in a five-year follow-up; and c) visualize disease progression with Sankey plots. We used insurance claims data from German Statutory Health Insurances (SHI). As LVEF category is not included in these data, a model was used to classify patients into HFrEF or HFpEF (with omitting the HFmEF category due to better statistical performance of a binary model). The model was derived from a set of 34 proxy variables (disease coding, interventions, drug prescriptions). Selection period for T2D patients is 2013. Inclusion period was 2014–2015, follow-up 2016–2020. Baseline characteristics include demographic data, disease stage, comorbidities, and risk factors. Follow-up criteria were MACE (including hospital admission) changes in LVEF category and mortality. 173,195 individuals with T2D were identified in 2013, median age 66 yrs. 6,725 (3.88% of the overall sample) developed HF in 2014 or 2015, median age 74 yrs. As Sankey plot visualizations show, 34.4% of the patients had MACE over the course of five years; 24.5% were still alive and 9.9% died from CV-death. Further 33% died of other causes. Myocardial infarct was the most common MACE, followed by stroke (32%), hospital admission for HF (28%) and CV death (7%). 40% of patients were never admitted to a hospital over the study period. Exploratory analyses identified 5,282 HFpEF patients (78.54%) and 1,443 HFrEF patients (21.46%). Survival after 5 years in HFpEF patients was 71%, in HFrEF patients 29%. After five years 3,430 (90%) surviving patients were still in HFpEF and 399 (10%) in HFrEF. This analysis provides disease progression insights in T2D patients who developed HF in Germany. The sample is representative for the country and numbers can be extrapolated to the overall German SHI population. A significant number of patients die within 5 years of initial diagnosis. As echo diagrams are not available in German insurance claims, validity of the predicted LVEF cannot be assessed. Further research featuring real world LVEF score validation would be highly desirable. Beyond therapeutic care, digital solutions for closer monitoring of these patients may improve the outcome of these patients. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Sanofi

A practitioners approach to individual claims models for bodily injury claims in german non-life insurance

The aim of our paper is to discuss the difficulties non-life actuaries are currently facing from a practical point of view. Based on this, we show that individual claims models are the key to address these difficulties and discuss how such models give actuaries a new and very powerful tool to explore further fields of application. Moreover, we address a very essential question: What data is needed for developing individual claims models? For bodily injury claims in German motor liability insurance, we shall derive specific attributes based on a detailed discussion of the legal background. All our ideas are based on practical experience for a large German motor insurance portfolio.

The status quo of digital transformation in insurance sales: an empirical analysis of the german insurance industry

In this paper, we focus on the impact of digital transformation on traditional ways to sell insurance products. Our goal is to investigate how widespread the use of digital technologies in insurance sales is and which values of these digital technologies are perceived by insurance intermediaries. Moreover, we aim to analyze underlying influencing factors in this regard. We conducted a survey in July 2020, i.e., after the first wave of COVID-19, in which 671 exclusive agents from various insurance companies, independent agents and independent brokers from Germany participated. Our results show that even at this point of time, in a high proportion of sales workforces, digital transformation is not yet very advanced. Further analyses show that exclusive agents and younger people are further ahead in digital transformation even though COVID-19 pushes digital life across all ages and social classes. Based on our results, we derive initial recommendations for action for the insurer.

How regulatory changes are driven by a need for control in reputational scandals: a case study in the German insurance industry

PurposeThis paper aims to contribute to the understanding of the mechanisms that evolve during reputational scandals and lead to changes in industry regulation. It explores the processes by which a demand for external industry regulation evolves, also addressing the consequences of firms’ competitive behaviors which lead to substantial misbehavior and the destruction of reputational capital. The authors are interested in whether and how regulatory activities – in the case analyzed here, changes in insurance regulation regarding sales commissions for insurance brokers – are used as a costly, external behavioral control mechanism (third-loop learning) to terminate a reputational scandal that cannot be stopped by internal controls at a firm level (first-loop and second-loop learning) anymore.Design/methodology/approachThe paper explores a real-life case in the German insurance industry that peaked in 2012 and has been well documented by broad media coverage, complemented by interviews with leading industry representatives. Using causal process tracing as a methodology, the authors study the factors in the case that led to an industry scandal. The authors further analyze why the insurance firms involved were not able to limit the scandal’s impact by internally controlling their behaviors, but had to call for external regulation, thus imposing costly restrictions on sales and contract processes. To identify the mechanisms underlying this result, theories from the fields of economics (game theory) and sociology (vicious cycle of bureaucracies), as well as organizational learning theory, are used.FindingsThe authors find that individual rationality does not suffice to prevent insurance firms from scandalous business practices, e.g. via implementing appropriate internal behavioral control measures within their organizations. If, as a result, misbehavior leads to reputational scandals, and the destruction of reputational capital spills over to the whole industry, a vicious cycle is set in motion which can be terminated by regulation as an externally enforced control mechanism.Research limitations/implicationsThis study is limited to the analysis of a single case study, combining published materials, e.g. broad media coverage, with interviews from representatives of the insurance industry. Nevertheless, the underlying mechanisms that have been identified can be used in other case studies as well.Practical implicationsThe paper shows that if firms want to avoid increasing regulation, they must implement strong reputational risk management (RRM) to counteract short-term profit pressure and to avoid restrictive regulation imposed on the industry as a whole. Furthermore, it sheds light on the relevance of spillover effects for RRM, as not only employee behavior within an organization might lead to the destruction of reputational capital but also that from other firms, e.g. from elsewhere within an industry.Originality/valueThe paper contributes by emphasizing a direct causal link between corporate scandals, loss of reputation and regulatory change within the insurance industry. Furthermore, the paper contributes by combining economic theories with organizational theories to understand real-life phenomena.

Benefits of a Disease Management Program for SCD in Germany 2011 - 2019: The Increased Use of Hydroxyurea Correlates with a Reduced Frequency of Acute Chest Syndrome

BackgroundWorldwide, Sickle Cell Disease (SCD) is the most common single gene disorder affecting >250,000 newborns annually. In Germany, SCD qualifies as a rare disease and almost exclusively affects immigrants from endemic countries and their descendants. The recent surge of immigration from high-prevalence countries increased the numbers of patients with SCD in Germany and raised awareness for the need of specialized care.In 2012, the German Society for Pediatric Oncology and Hematology (GPOH) mandated a consortium of five university hospitals to develop a disease management program for patients with SCD. This consortium issued treatment guidelines for SCD that strongly favor the use of hydroxyurea, initiated patient and physician education events, prepared for a universal newborn screening program that will start 09/2021, moderated a consensus on the indication of allogeneic stem cell transplantation for patients with SCD, and established a national patient registry.MethodsIn order to quantify the effect of these activities, we made use of claims data that were collected by the research institute (WIdO) of the major German insurance company, the Allgemeine Ortskrankenkasse (AOK), and of publicly accessible data collected by the Federal Statistical Office (Statistisches Bundesamt, Destatis). ICD10 codes were used to identify patients with SCD and their comorbidities. Pharmacologic treatments were quantified using the German Anatomical Therapeutic Chemical (ATC)-Classification with defined daily doses.ResultsWe estimate that the number of patients with SCD in Germany increased from approximately 2,200 in 2011 to approximately 3,200 in 2019. Analyses of administered treatments illustrate that important components of recently issued national treatment guidelines have been largely implemented. Specifically, the use of hydroxyurea has more than doubled, resulting in a proportion of approximately 45% of all patients with SCD being treated with hydroxyurea in 2019 (Figure 1A). In strong negative correlation with the use of hydroxyurea, the frequency of acute chest syndromes decreased (Figure 1B). While before the widespread use of hydroxyurea (2011-2013) 8.1% of patients with SCD were admitted at least once per year for ACS, this proportion dropped to 6.6% in the period 2017 to 2019. Similarly, the proportion of patients who required analgesics, red blood cell transfusions and hospitals admissions declined from 2011 to 2019, indicating a reduced burden of SCD with the increased use of hydroxyurea.ConclusionIn sum, these data demonstrate an association between the dissemination of nationwide treatment guidelines and changes in clinical practice in particular relating to the use of hydroxyurea. These changes translate into a remarkable improvement of key measures of disease activity in a representative population based analysis. [Display omitted] DisclosuresLobitz: Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; AddMedica: Honoraria, Membership on an entity's Board of Directors or advisory committees; Vertex: Honoraria, Membership on an entity's Board of Directors or advisory committees. Kulozik: BioMedX: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Consultancy, Honoraria; bluebird bio, Inc.: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.