Epidemiology and economic burden of selected rare genetic diseases in Germany – a claims database study

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Despite their individual rarity, the cumulative burden of these diseases is substantial. Larger claims databases could enable a quantification and characterization of patients with rare diseases and contribute to determining their specific healthcare needs. This study examines the prevalence, comorbidities, and annual direct costs of Huntington's disease (HD), beta-thalassemia (BT), and spinal muscular atrophy type 1 (SMA) in Germany. Methods Utilizing anonymized claims data from the InGef research database, we conducted a cross-sectional analysis for each disease from 2017 to 2023. Patients and comorbidities were identified based on ICD-10-GM-codes. Prevalence was extrapolated to the German population. Costs were analyzed as direct costs per patient and by sector incurred. Results Prevalences for the selected diseases were 6.65 (HD), 12.81 (BT), and 1.24 (SMA) patients per 100,000 persons in Germany in 2023. The most frequent comorbidities identified are strongly related to the specific disease. For instance, in 2023, among the most common diagnoses were depression in HD patients (38.48%), dorsalgia in BT patients (37.03%), and acute upper respiratory infections in SMA patients (45.19%). Average total direct costs in 2023 amounted to 9,527.89 € for HD, 6,656.27 € for BT and 144,585.02 € for SMA, respectively. Conclusion Claims data enable robust identification and characterization of rare disease populations. The findings on prevalences and comorbidities align with existing literature and support the use of such data for treatment planning and post-marketing studies. (MeSH): rare diseases administrative claims healthcare epidemiology health care costs Figures Figure 1 Background Rare diseases, although individually uncommon, collectively affect a substantial number of individuals and are associated with considerable morbidity, reduced quality of life, and high socioeconomic burden for both families and healthcare systems. In many countries, including Germany, comprehensive and up-to-date epidemiological data on rare diseases are limited. Existing evidence is often fragmented, based on small cohorts, or derived from disease-specific registries with variable completeness and coverage.( 1 , 2 ) Routinely collected data from the statutory health insurance (SHI) system represent a largely untapped resource for rare disease research. These data include detailed information on diagnoses, comorbidities, pharmacotherapy, and healthcare utilization, enabling robust assessments of disease burden and care needs. Moreover, SHI data can support post-marketing surveillance and inform the development of evidence-based clinical guidelines.( 3 ) Recent regulatory developments have strengthened the potential of SHI data for rare disease research. Since 2023, the standardized coding of rare diseases in inpatient care using the identification number Alpha-ID-SE, which incorporates ORPHAcodes, has been mandated in Germany. Although the implementation was not yet extended to outpatient care and other sectors it might contribute to the diagnostic precision of SHI data and facilitates more consistent identification of rare disease cases in the future.( 4 ) This study explores the potential of SHI data for the epidemiological assessment and health services research of rare diseases in Germany, highlighting both opportunities and remaining limitations in current data infrastructures analyzing rare diseases. Three genetic conditions with specific ICD-10-GM codes serve as use cases to provide recent data on prevalence, comorbidities, and associated healthcare costs. Methods Data source: The InGef research database comprises anonymized claims data on the utilization of healthcare services of approximately 10 million insured individuals from 50 German statutory health insurance funds (SHIs) currently contributing data.( 5 ) The dataset includes insured persons from all federal states. All patient- and provider-level data are anonymized in accordance with German data protection regulations and federal legislation. Therefore, approval from an ethics committee was not required. Currently, the data are longitudinally linked over a period of ten years. In addition to sociodemographic information, the database contains data on outpatient services and diagnoses; hospital data including admission dates, main and secondary diagnoses, as well as performed surgeries and procedures;( 6 ) information on prescribed medications;( 7 ) and data on medical aids, remedies, and sick leave data. Clinical diagnoses are coded according to ICD-10-GM (German modification of the 10th revision of the International Classification of Diseases). Study design and study population: For this retrospective study, the insured population was analyzed cross-sectionally for each study year from 2017 to 2023. Individuals were included if they were fully observable either during the respective study year or from birth until December 31 or death of the same year. All analyses were conducted separately for the rare diseases under investigation: Huntington’s Disease (HD), Beta-Thalassemia (BT), and Spinal Muscular Atrophy type 1 (SMA). The prevalent rare diseases investigated were defined by the documentation of the corresponding ICD-10-GM diagnosis code (HD: G10; BT: D56.1; SMA: G12.0) recorded either as a main or secondary diagnosis in the inpatient setting or as a confirmed diagnosis in the outpatient setting within one calendar quarter. To meet the M2Q criterion, the initial outpatient diagnosis had to be confirmed by a subsequent confirmed outpatient or inpatient diagnosis in a different quarter of the calendar year, or by an outpatient diagnosis by a different physician in the same quarter. Prevalence was calculated for each study year by dividing the number of existing cases by the total study population and is presented per 100,000 persons along with 95% confidence intervals. Furthermore, prevalence estimates were extrapolated to the German population using population estimates provided by the Federal Statistical Office of Germany (DESTATIS)( 8 ). The most common comorbidities among included patients were identified based on at least one confirmed outpatient diagnosis or inpatient main or secondary diagnosis (ICD-10-GM codes at the three-digit level) within the respective calendar year. Costs incurred by the SHI were analyzed as direct costs per patient, both in total and stratified by healthcare sector, and were presented using descriptive statistics. For data protection reasons, no measures are reported for strata with less than five patients. Results Out of the 7,297,840 continuously insured persons in 2023, 564 patients with Huntington’s Disease (HD), 1,072 patients with beta-Thalassemia (BT), and 104 patients with Spinal Muscular Atrophy type 1 (SMA) were identified based on the respective ICD-10-GM diagnosis code in the InGef research database. Prevalences for the selected diseases were 7.73 (HD), 14.69 (BT), and 1.43 (SMA) patients per 100,000 persons in 2023. Prevalences directly standardized to the German population for the selected diseases over the analyzed data years are shown in Fig. 1 . Table 1 displays the demographic characteristics of the respective rare diseases for the most recent year 2023 based on the InGef research database. Median age was 57 years (Q1-Q3: 50–65) for HD, 43 years (Q1-Q3: 29–57) for BT and 19 years (Q1-Q3: 4–54) for SMA. The patient distribution by age group and the proportion of females remained constant over the observed data years for the reported diseases. Table 1 Demographic characteristics of patients with Huntington’s Disease (HD), b-Thalassemia (BT) and Spinal Muscular Atrophy type 1 (SMA) for the year 2023. HD BT SMA N % N % N % Total patients 564 100 1072 100 104 100 0–20 years 50 years 412 73.05 385 35.91 30 28.85 Male 271 48.05 458 42.72 55 52.88 Female 293 51.95 614 57.28 49 47.12 Comorbidities: As an excerpt for the entire analysis, results for the current year 2023 are presented. The most frequently documented comorbidities were largely specific for each disease (Table 2 ). Depressive episodes (38%) and abnormalities of gait and mobility (31%) were most frequently reported among patients with HD, while acute upper respiratory infections of multiple and unspecified sites were most prevalent in patients with BT (38%) and SMA (45%), followed by dorsalgia (BT, 37%) and dependence on enabling machines and devices (SMA, 38%). Table 2 Most frequent comorbidities based on ICD-10-GM (in- and outpatient setting) in 2023. Abbr. Huntington’s Disease (HD), b-Thalassemia (BT) and Spinal Muscular Atrophy type 1 (SMA). HD (n = 564) BT (n = 1.072) SMA (n = 73) Depressive episode n = 217 (38.48%) Acute upper respiratory infections of multiple and unspecified sites n = 408 (38.06%) Acute upper respiratory infections of multiple and unspecified sites n = 47 (45.19%) Abnormalities of gait and mobility n = 178 (31.56%) Dorsalgia n = 387 (37.03%) Dependence on enabling machines and devices n = 40 (38.46%) Essential (primary) hypertension n = 169 (29.96%) Essential (primary) hypertension n = 318 (29.66%) Scoliosis n = 39 (37.50%) Other mental disorders due to known physiological condition n = 166 (29.43%) Disorders of refraction and accommodation n = 270 (25.19%) Respiratory failure, not elsewhere classified n = 37 (35.58%) Aphagia and dysphagia n = 145 (25.71%) Abdominal and pelvic pain n = 247 (23.04%) Other disorders of muscle n = 26 (25.00%) Direct costs: The average total direct medical costs per patient in 2023, including inpatient and outpatient care, as well as costs for medications, aids, and remedies, amounted to 9,527.89 € for HD, 6,656.27 € for BT and 144,585.02 € for SMA, respectively. Costs incurred for patients with the three rare diseases analyzed increased substantially across all sectors over the years investigated. The largest increase in mean total costs is found for SMA (3.6-fold increase from 2017 to 2023), which is mostly due to a strong increase in inpatient and medication costs. Table 3 shows descriptive statistics of costs incurred by sector for the years 2017 and 2023. Costs for all observed data years from 2017–2023 are given in additional file 1. Table 3 Summary statistics of healthcare costs by sector for the selected rare diseases in 2017 and 2023. Abbr. Huntington’s Disease (HD), b-Thalassemia (BT) and Spinal Muscular Atrophy type 1 (SMA). HD BT SMA Cost sector Summary statistic* 2017 2023 2017 2023 2017 2023 Total costs Sum 4464207.86 5373729.03 4149219.86 7135526.05 4165802.84 15036842.52 Mean 8375.62 9527.89 4605.13 6656.27 46286.70 144585.02 SD 9567.05 13936.14 11320.79 18313.49 84342.37 256814.73 Min 177.37 101.10 58.96 35.73 245.62 219.21 Q1 1953.39 1850.79 537.89 664.72 3080.94 4786.37 Median 4982.33 5258.06 1205.89 1611.09 10121.01 39476.77 Q3 11195.82 11213.13 3567.74 5071.32 46597.51 259463.06 Max 70861.61 164874.48 146960.76 207468.16 432360.94 1682863.22 Outpatient costs Sum 567357.74 639213.00 843264.96 1382700.78 75739.74 101665.86 Mean 1064.46 1133.36 935.92 1289.83 841.55 977.56 SD 1621.65 1140.44 1146.24 2243.55 853.25 1025.77 Min 0.00 27.11 0.00 0.00 86.89 66.94 Q1 456.78 467.85 341.43 432.21 377.55 468.44 Median 767.57 834.81 608.82 753.83 655.55 693.32 Q3 1264.90 1406.06 1140.33 1445.97 938.47 1093.52 Max 31942.26 14327.57 16086.77 33696.53 6392.35 6583.75 Inpatient costs Sum 1808996.33 2176029.37 1714911.00 2887515.55 2159242.57 8059153.69 Mean 3393.99 3858.21 1903.34 2693.58 23991.58 77491.86 SD 7325.85 10779.89 8317.57 11877.25 69374.51 245642.92 Min 0.00 0.00 0.00 0.00 0.00 0.00 Q1 0.00 0.00 0.00 0.00 0.00 206.11 Median 135.00 204.03 0.00 0.00 2339.57 2878.57 Q3 2926.15 3174.54 624.15 517.58 8406.52 23054.50 Max 61158.41 126372.76 146960.76 182590.27 418986.38 1681079.42 Medication costs Sum 863283.04 936640.51 1288178.44 2291936.73 543777.30 4923752.92 Mean 1619.67 1660.71 1429.72 2138.00 6041.97 47343.78 SD 2146.53 3330.61 5601.61 10650.85 30306.25 100025.92 Min 0.00 0.00 0.00 0.00 0.00 0.00 Q1 234.19 203.91 32.92 36.68 87.94 93.17 Median 780.24 700.06 96.08 123.59 321.01 1247.29 Q3 2146.77 1756.33 406.84 550.34 1970.83 8126.30 Max 15245.06 36810.89 82334.41 202557.22 205589.93 364726.72 Aids and remedies costs Sum 1224570.75 1621846.15 302865.46 573372.99 1387043.23 1952270.05 Mean 2297.51 2875.61 336.14 534.86 15411.59 18771.83 SD 3529.51 4645.16 1264.68 2091.01 25630.55 36435.09 Min 0.00 0.00 0.00 0.00 0.00 0.00 Q1 0.00 0.00 0.00 0.00 624.49 779.85 Median 728.23 840.85 0.00 0.00 3429.14 6667.80 Q3 3242.87 4000.99 191.86 275.49 20094.89 20962.04 Max 21119.78 38382.19 25222.17 37374.10 155499.79 269614.10 Discussion This study provides a comprehensive overview of the prevalence, comorbidities, and healthcare costs associated with three rare diseases—Huntington’s disease (HD), Beta-Thalassemia (BT), and Spinal Muscular Atrophy type 1 (SMA)—using claims data from the InGef research database. The findings offer important insights into the epidemiology and economic burden of these conditions and underscore the need for improved surveillance and data availability in the field of rare diseases. Prevalence estimates observed in this study are largely consistent with previously published data where available. Minor deviations from existing literature may be explained by differences in study design, data sources, or temporal factors, as disease prevalence can shift over time. Moreover, in contrast to ORPHAcodes, the ICD-10-GM classification lacks the necessary granularity and does not provide the level of detail required to distinguish between specific rare diseases or even subtypes of disorders. For HD, a recent meta-analysis reported a pooled prevalence of 4.8 per 100,000, with higher rates in Europe and North America. ( 1 ) In Germany, a previous estimate indicated a prevalence of 9.3 per 100,000 during 2015–2016. ( 9 ) Our study reports a prevalence of 6.65 per 100,000 in 2023, aligning well with this range and supporting the validity of the findings. Despite advances in treatment and the implementation of newborn screening for SMA in Germany, data on the epidemiology and disease burden of SMA remain limited. A review by Verhaart et al. estimated a prevalence of 0.28 per 100,000 for SMA, noting substantial regional variation, the frequent reliance on outdated data sources and studies conducted before the approval of effective therapies.( 2 ) These early estimates are likely to reflect lower survival rates and reduced diagnostic awareness. In contrast, the prevalence of 1.24 per 100,000 observed in our study may thus, also reflect increased survival due to novel therapies and earlier diagnosis through screening programs.( 10 ) BT remains one of the most common monogenic disorders globally, with particularly high prevalence in the Mediterranean, Middle East, and parts of Asia. In Germany and other parts of Central Europe, the number of cases has risen over recent decades, likely driven by migration. ( 11 – 13 ) However, robust data on BT in Germany are scarce. Our study presents the first claims-based prevalence estimates for BT (all subtypes) in Germany, showing an increase from 10.86 per 100,000 in 2017 to 12.81 per 100,000 in 2023. This trend highlights the growing healthcare relevance of BT and the need for continued monitoring and tailored healthcare planning. Demographic characteristics observed for HD, BT, and SMA were consistent with existing literature. Median ages for HD (57 years)( 14 , 15 ), BT (43 years), and SMA (19 years) are consistent with the age profiles described in previous studies. ( 16 – 18 ) In contrast to BT and SMA, HD typically presents in adulthood, with nearly all prevalent cases observed in individuals aged 21 years or older. Across all three diseases, frequently observed comorbidities were closely related to the underlying condition, confirming the high disease burden experienced by affected individuals. Beyond the core symptoms of the index disease, patients commonly present with a wide range of additional health impairments that require independent clinical management or necessitate modifications to therapeutic strategies, especially in multimorbid patients. However, such comorbidities are often underrepresented in disease registries. Systematically capturing this information in administrative data can improve the understanding of multimorbidity patterns and inform the development of more comprehensive, patient-centered clinical guidelines that address the complex needs of individuals living with rare diseases. Rare diseases pose a significant financial burden on patients and society. In this study, average healthcare costs differed across diseases but increased for all three over time. The increase was most pronounced for SMA, largely driven by rising medication costs following the introduction of three novel therapeutic interventions in recent years.( 19 ) These treatments offer significant clinical benefits and improved survival, which may also contribute to increased resource use. It can be expected that further advances in the treatment of rare diseases, especially in the field of gene therapy ( 20 , 21 ), will drive up healthcare costs in the short term, but will offer substantial long-term benefits for patients. This study demonstrates the feasibility of using claims data to study rare diseases and provides important epidemiological and economic insights. With the recent implementation of mandatory inpatient Alpha-ID-SE coding and its linkage to ORPHAcodes, diagnostic specificity in German claims data is expected to improve, allowing for more accurate identification of rare diseases in future research. In the European context, studies on rare diseases could greatly benefit from the use of common data models (CDMs), which are designed to harmonize disparate data sources across various healthcare systems. Early results using the InGef database, mapped to the Observational Medical Outcomes Partnership (OMOP) CDM format, have successfully replicated the findings presented here ( 22 ) and shows promise for facilitating multi-center studies, enabling the testing of data-driven hypotheses on a larger, more diverse population scale. Broader adoption of such frameworks could enhance the comparability and generalizability of research on rare diseases and support international collaboration. Limitations: This study offers valuable insights but is subject to limitations inherent to claims data. First, the analysis was restricted to patients with rare diseases that were both diagnosed and documented within the administrative data, potentially excluding undiagnosed or miscoded cases. Second, the ICD-10-GM classification system lacks sufficient granularity to capture disease severity or specific subtypes, thereby limiting the precision of clinical differentiation and stratified analyses.( 23 ) Furthermore, the absence of clinical data such as laboratory parameters precludes assessments of disease severity or progression. Conclusion This study provides important data on the prevalence, comorbidities, and economic burden of HD, BT, and SMA, highlighting the challenges of rare disease management. Targeted SHI data analyses support both epidemiological insight and healthcare optimization in the German system. Abbreviations ATC - Anatomical-Therapeutic-Chemical Classification System BT – Beta-Thalassemia CDM – Common Data Model HD - Huntington’s Disease ICD-10-GM - International Classification of Disease and Health Related Problems, German Modification InGef – Institute for Applied Health Research Berlin GmbH OMOP - Observational Medical Outcomes Partnership SHI – Statutory Health Insurance SMA - Spinal Muscular Atrophy type 1 Declarations Ethics approval and consent to participate The anonymization of the InGef research database ensures that individual patients, health insurance companies and service providers can no longer be identified. In accordance with German law, separate approval of an institutional review board or Medical Ethics Committee was not required. Consent for publication Not applicable Availability of data and materials The data used in this study cannot be made publicly available in the manuscript, the additional files, or in a public repository due to German data protection laws (Bundesdatenschutzgesetz) and legal reasons. In Germany, the utilization of health insurance data for scientific research is regulated by the Code of Social Law. Researchers must obtain approval from the health insurance providers as well as their responsible authorities. As this approval is given only for a specific research question for a specific time and for a specific group of researchers, data cannot be made publicly available. To facilitate the replication of results, anonymized data used for this study are stored on a secure drive at the Institute for Applied Health Research Berlin (InGef). Access to the raw data used in this study can only be provided to external parties under the conditions a cooperation contract and can be accessed upon request, after written approval ( [email protected] ), if required. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions JJ, MA, DO, RN and ML designed and conceptualized the study, interpretation and revising of data and manuscript. VV, AC and RN worked on data analysis and revising of data. ML was responsible for drafting the manuscript. Acknowledgements Not applicable References Medina A, Mahjoub Y, Shaver L, Pringsheim T. Prevalence and Incidence of Huntington’s Disease: An Updated Systematic Review and Meta‐Analysis. Mov Disord. 2022 Dec;37(12):2327–35. Verhaart IEC, Robertson A, Wilson IJ, Aartsma-Rus A, Cameron S, Jones CC, et al. 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Available from: http://www.thieme-connect.com/products/ejournals/abstract/10.1055/a-2276-4871 Supplementary Files OrphanetRarediseasesAdditionalFile.docx Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Orphanet Journal of Rare Diseases → Version 1 posted Reviewers agreed at journal 14 Jul, 2025 Reviewers invited by journal 16 Jun, 2025 Editor invited by journal 13 Jun, 2025 Editor assigned by journal 01 May, 2025 First submitted to journal 30 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6564544","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472179989,"identity":"dc346198-ee28-4faf-8df0-9ad3fe59bc88","order_by":0,"name":"Marion Ludwig","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYJCCA0Asw8CQAGRUSADZjA8YeIjQwgPRcgakhdmAoBYGmBYGxjYGwlp0288+PPBxBwOPwfEcw4Nf51nkybs3MzC8qcCtxexMusHBmWeAWs68MTgsu02i2PDMYQbGOWfwaDmQxnCYtw2o5UbuhsOS2yQSN87IP8DM24ZHy/lnyFrmALXMf8zAzPsPj5YbSLYc/NggkThfghmopQGflmcMB2e2SfBInnn/4TDDMYnEDTzJDAfnHMPnsDTmDx/bbOT4jqclf/xRU5c4v/0w44M3Nbi1QIEEmGQGRYfBAUjkEgcYfwAJeTzeGAWjYBSMgpEJAApQWNy3SGFOAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-2446-0090","institution":"InGef - Institute for Applied Health Research Berlin GmbH","correspondingAuthor":true,"prefix":"","firstName":"Marion","middleName":"","lastName":"Ludwig","suffix":""},{"id":472179990,"identity":"edb94a05-08f3-4534-b48c-5b6e53f7c8b4","order_by":1,"name":"Marco Alibone","email":"","orcid":"","institution":"InGef - Institute for Applied Health Research Berlin GmbH","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"","lastName":"Alibone","suffix":""},{"id":472179991,"identity":"3546a51a-a485-4412-a5af-77f93f5446f4","order_by":2,"name":"Raeleesha Norris","email":"","orcid":"","institution":"InGef - Institute for Applied Health Research Berlin GmbH","correspondingAuthor":false,"prefix":"","firstName":"Raeleesha","middleName":"","lastName":"Norris","suffix":""},{"id":472179992,"identity":"8ad10485-160b-45e8-86f0-399e9f3d47c6","order_by":3,"name":"Josephine Jacob","email":"","orcid":"","institution":"InGef - Institute for Applied Health Research Berlin GmbH","correspondingAuthor":false,"prefix":"","firstName":"Josephine","middleName":"","lastName":"Jacob","suffix":""},{"id":472179993,"identity":"6fddc45e-cd3b-424e-97c3-4d6f71446fa2","order_by":4,"name":"Vukasin Viskovic","email":"","orcid":"","institution":"InGef - Institute for Applied Health Research Berlin GmbH","correspondingAuthor":false,"prefix":"","firstName":"Vukasin","middleName":"","lastName":"Viskovic","suffix":""},{"id":472179994,"identity":"e7fe0a2d-dc01-44e0-acdd-93989f3c4905","order_by":5,"name":"Anja Cengia","email":"","orcid":"","institution":"InGef - Institute for Applied Health Research Berlin GmbH","correspondingAuthor":false,"prefix":"","firstName":"Anja","middleName":"","lastName":"Cengia","suffix":""},{"id":472179995,"identity":"a74861a3-94b7-4fd9-bf7b-6f868beed62b","order_by":6,"name":"Dominik Obermüller","email":"","orcid":"","institution":"InGef - Institute for Applied Health Research Berlin GmbH","correspondingAuthor":false,"prefix":"","firstName":"Dominik","middleName":"","lastName":"Obermüller","suffix":""}],"badges":[],"createdAt":"2025-04-30 11:28:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6564544/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6564544/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13023-025-04147-8","type":"published","date":"2025-11-28T15:58:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85346811,"identity":"9d16843d-198d-41ec-9b8f-d82377bd5237","added_by":"auto","created_at":"2025-06-25 02:14:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25680,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of selected rare diseases in Germany from 2017 to 2023. The bar chart displays the annual prevalence (per 100,000 persons) of Huntington's disease (HD), β-thalassemia (BT), and Spinal Muscular Atrophy (SMA), including 95% confidence intervals for the German population.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6564544/v1/cd21b29e2a53c93c59a6784a.png"},{"id":97178616,"identity":"762b8122-e09d-46eb-8953-a7acfd817534","added_by":"auto","created_at":"2025-12-01 16:11:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":702086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6564544/v1/aaa2053e-9ab3-4063-ab7b-9de92e646552.pdf"},{"id":85346803,"identity":"63b5dbb1-a38e-4566-b8fa-f48f94d970e9","added_by":"auto","created_at":"2025-06-25 02:14:10","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":54563,"visible":true,"origin":"","legend":"","description":"","filename":"OrphanetRarediseasesAdditionalFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-6564544/v1/b0741a9c3b02268bfb0d8d59.docx"}],"financialInterests":"","formattedTitle":"Epidemiology and economic burden of selected rare genetic diseases in Germany – a claims database study","fulltext":[{"header":"Background","content":"\u003cp\u003eRare diseases, although individually uncommon, collectively affect a substantial number of individuals and are associated with considerable morbidity, reduced quality of life, and high socioeconomic burden for both families and healthcare systems. In many countries, including Germany, comprehensive and up-to-date epidemiological data on rare diseases are limited. Existing evidence is often fragmented, based on small cohorts, or derived from disease-specific registries with variable completeness and coverage.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eRoutinely collected data from the statutory health insurance (SHI) system represent a largely untapped resource for rare disease research. These data include detailed information on diagnoses, comorbidities, pharmacotherapy, and healthcare utilization, enabling robust assessments of disease burden and care needs. Moreover, SHI data can support post-marketing surveillance and inform the development of evidence-based clinical guidelines.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eRecent regulatory developments have strengthened the potential of SHI data for rare disease research. Since 2023, the standardized coding of rare diseases in inpatient care using the identification number Alpha-ID-SE, which incorporates ORPHAcodes, has been mandated in Germany. Although the implementation was not yet extended to outpatient care and other sectors it might contribute to the diagnostic precision of SHI data and facilitates more consistent identification of rare disease cases in the future.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis study explores the potential of SHI data for the epidemiological assessment and health services research of rare diseases in Germany, highlighting both opportunities and remaining limitations in current data infrastructures analyzing rare diseases. Three genetic conditions with specific ICD-10-GM codes serve as use cases to provide recent data on prevalence, comorbidities, and associated healthcare costs.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eData source: The InGef research database comprises anonymized claims data on the utilization of healthcare services of approximately 10\u0026nbsp;million insured individuals from 50 German statutory health insurance funds (SHIs) currently contributing data.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) The dataset includes insured persons from all federal states. All patient- and provider-level data are anonymized in accordance with German data protection regulations and federal legislation. Therefore, approval from an ethics committee was not required. Currently, the data are longitudinally linked over a period of ten years. In addition to sociodemographic information, the database contains data on outpatient services and diagnoses; hospital data including admission dates, main and secondary diagnoses, as well as performed surgeries and procedures;(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) information on prescribed medications;(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and data on medical aids, remedies, and sick leave data. Clinical diagnoses are coded according to ICD-10-GM (German modification of the 10th revision of the International Classification of Diseases).\u003c/p\u003e\u003cp\u003eStudy design and study population:\u003c/p\u003e\u003cp\u003eFor this retrospective study, the insured population was analyzed cross-sectionally for each study year from 2017 to 2023. Individuals were included if they were fully observable either during the respective study year or from birth until December 31 or death of the same year. All analyses were conducted separately for the rare diseases under investigation: Huntington’s Disease (HD), Beta-Thalassemia (BT), and Spinal Muscular Atrophy type 1 (SMA).\u003c/p\u003e\u003cp\u003eThe prevalent rare diseases investigated were defined by the documentation of the corresponding ICD-10-GM diagnosis code (HD: G10; BT: D56.1; SMA: G12.0) recorded either as a main or secondary diagnosis in the inpatient setting or as a confirmed diagnosis in the outpatient setting within one calendar quarter. To meet the M2Q criterion, the initial outpatient diagnosis had to be confirmed by a subsequent confirmed outpatient or inpatient diagnosis in a different quarter of the calendar year, or by an outpatient diagnosis by a different physician in the same quarter. Prevalence was calculated for each study year by dividing the number of existing cases by the total study population and is presented per 100,000 persons along with 95% confidence intervals. Furthermore, prevalence estimates were extrapolated to the German population using population estimates provided by the Federal Statistical Office of Germany (DESTATIS)(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe most common comorbidities among included patients were identified based on at least one confirmed outpatient diagnosis or inpatient main or secondary diagnosis (ICD-10-GM codes at the three-digit level) within the respective calendar year. Costs incurred by the SHI were analyzed as direct costs per patient, both in total and stratified by healthcare sector, and were presented using descriptive statistics. For data protection reasons, no measures are reported for strata with less than five patients.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOut of the 7,297,840 continuously insured persons in 2023, 564 patients with Huntington\u0026rsquo;s Disease (HD), 1,072 patients with beta-Thalassemia (BT), and 104 patients with Spinal Muscular Atrophy type 1 (SMA) were identified based on the respective ICD-10-GM diagnosis code in the InGef research database. Prevalences for the selected diseases were 7.73 (HD), 14.69 (BT), and 1.43 (SMA) patients per 100,000 persons in 2023. Prevalences directly standardized to the German population for the selected diseases over the analyzed data years are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the demographic characteristics of the respective rare diseases for the most recent year 2023 based on the InGef research database. Median age was 57 years (Q1-Q3: 50\u0026ndash;65) for HD, 43 years (Q1-Q3: 29\u0026ndash;57) for BT and 19 years (Q1-Q3: 4\u0026ndash;54) for SMA. The patient distribution by age group and the proportion of females remained constant over the observed data years for the reported diseases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of patients with Huntington\u0026rsquo;s Disease (HD), b-Thalassemia (BT) and Spinal Muscular Atrophy type 1 (SMA) for the year 2023.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eBT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSMA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal patients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eComorbidities:\u003c/p\u003e \u003cp\u003eAs an excerpt for the entire analysis, results for the current year 2023 are presented. The most frequently documented comorbidities were largely specific for each disease (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Depressive episodes (38%) and abnormalities of gait and mobility (31%) were most frequently reported among patients with HD, while acute upper respiratory infections of multiple and unspecified sites were most prevalent in patients with BT (38%) and SMA (45%), followed by dorsalgia (BT, 37%) and dependence on enabling machines and devices (SMA, 38%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMost frequent comorbidities based on ICD-10-GM (in- and outpatient setting) in 2023. Abbr. Huntington\u0026rsquo;s Disease (HD), b-Thalassemia (BT) and Spinal Muscular Atrophy type 1 (SMA).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD (n\u0026thinsp;=\u0026thinsp;564)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBT (n\u0026thinsp;=\u0026thinsp;1.072)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSMA (n\u0026thinsp;=\u0026thinsp;73)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepressive episode\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;217 (38.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute upper respiratory infections of multiple and unspecified sites\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;408 (38.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcute upper respiratory infections of multiple and unspecified sites\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;47 (45.19%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormalities of gait and mobility\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;178 (31.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDorsalgia\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;387 (37.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDependence on enabling machines and devices\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;40 (38.46%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEssential (primary) hypertension\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;169 (29.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEssential (primary) hypertension\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;318 (29.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScoliosis\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;39 (37.50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther mental disorders due to known physiological condition n\u0026thinsp;=\u0026thinsp;166 (29.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisorders of refraction and accommodation\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;270 (25.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRespiratory failure, not elsewhere classified\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;37 (35.58%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAphagia and dysphagia\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;145 (25.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbdominal and pelvic pain\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;247 (23.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOther disorders of muscle n\u0026thinsp;=\u0026thinsp;26 (25.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003c/h2\u003e \u003cp\u003eDirect costs:\u003c/p\u003e \u003cp\u003eThe average total direct medical costs per patient in 2023, including inpatient and outpatient care, as well as costs for medications, aids, and remedies, amounted to 9,527.89 \u0026euro; for HD, 6,656.27 \u0026euro; for BT and 144,585.02 \u0026euro; for SMA, respectively. Costs incurred for patients with the three rare diseases analyzed increased substantially across all sectors over the years investigated. The largest increase in mean total costs is found for SMA (3.6-fold increase from 2017 to 2023), which is mostly due to a strong increase in inpatient and medication costs. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows descriptive statistics of costs incurred by sector for the years 2017 and 2023. Costs for all observed data years from 2017\u0026ndash;2023 are given in additional file 1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics of healthcare costs by sector for the selected rare diseases in 2017 and 2023. Abbr. Huntington\u0026rsquo;s Disease (HD), b-Thalassemia (BT) and Spinal Muscular Atrophy type 1 (SMA).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eBT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eSMA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCost sector\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSummary statistic*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eTotal costs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4464207.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5373729.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4149219.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7135526.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4165802.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15036842.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8375.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9527.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4605.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6656.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46286.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e144585.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9567.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13936.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11320.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18313.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84342.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e256814.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e245.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e219.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1953.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1850.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e537.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e664.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3080.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4786.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4982.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5258.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1205.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1611.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10121.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39476.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11195.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11213.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3567.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5071.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46597.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e259463.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70861.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164874.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146960.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e207468.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e432360.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1682863.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eOutpatient costs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e567357.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e639213.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e843264.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1382700.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75739.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e101665.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1064.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1133.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e935.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1289.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e841.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e977.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1621.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1140.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1146.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2243.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e853.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1025.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e456.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e467.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e341.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e432.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e377.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e468.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e767.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e834.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e608.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e753.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e655.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e693.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1264.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1406.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1140.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1445.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e938.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1093.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31942.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14327.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16086.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33696.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6392.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6583.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eInpatient costs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1808996.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2176029.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1714911.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2887515.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2159242.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8059153.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3393.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3858.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1903.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2693.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23991.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e77491.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7325.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10779.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8317.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11877.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69374.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e245642.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e206.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e204.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2339.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2878.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2926.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3174.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e624.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e517.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8406.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23054.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61158.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126372.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146960.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e182590.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e418986.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1681079.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eMedication costs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e863283.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e936640.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1288178.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2291936.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e543777.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4923752.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1619.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1660.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1429.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2138.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6041.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47343.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2146.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3330.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5601.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10650.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30306.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100025.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e203.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e780.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e700.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e123.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e321.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1247.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2146.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1756.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e406.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e550.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1970.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8126.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15245.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36810.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82334.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e202557.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e205589.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e364726.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eAids and remedies costs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1224570.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1621846.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e302865.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e573372.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1387043.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1952270.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2297.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2875.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e336.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e534.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15411.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18771.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3529.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4645.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1264.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2091.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25630.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36435.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e624.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e779.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e728.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e840.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3429.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6667.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3242.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4000.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e191.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e275.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20094.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20962.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21119.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38382.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25222.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37374.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e155499.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e269614.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a comprehensive overview of the prevalence, comorbidities, and healthcare costs associated with three rare diseases\u0026mdash;Huntington\u0026rsquo;s disease (HD), Beta-Thalassemia (BT), and Spinal Muscular Atrophy type 1 (SMA)\u0026mdash;using claims data from the InGef research database. The findings offer important insights into the epidemiology and economic burden of these conditions and underscore the need for improved surveillance and data availability in the field of rare diseases.\u003c/p\u003e \u003cp\u003ePrevalence estimates observed in this study are largely consistent with previously published data where available. Minor deviations from existing literature may be explained by differences in study design, data sources, or temporal factors, as disease prevalence can shift over time. Moreover, in contrast to ORPHAcodes, the ICD-10-GM classification lacks the necessary granularity and does not provide the level of detail required to distinguish between specific rare diseases or even subtypes of disorders. For HD, a recent meta-analysis reported a pooled prevalence of 4.8 per 100,000, with higher rates in Europe and North America. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) In Germany, a previous estimate indicated a prevalence of 9.3 per 100,000 during 2015\u0026ndash;2016. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) Our study reports a prevalence of 6.65 per 100,000 in 2023, aligning well with this range and supporting the validity of the findings.\u003c/p\u003e \u003cp\u003eDespite advances in treatment and the implementation of newborn screening for SMA in Germany, data on the epidemiology and disease burden of SMA remain limited. A review by Verhaart et al. estimated a prevalence of 0.28 per 100,000 for SMA, noting substantial regional variation, the frequent reliance on outdated data sources and studies conducted before the approval of effective therapies.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) These early estimates are likely to reflect lower survival rates and reduced diagnostic awareness. In contrast, the prevalence of 1.24 per 100,000 observed in our study may thus, also reflect increased survival due to novel therapies and earlier diagnosis through screening programs.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eBT remains one of the most common monogenic disorders globally, with particularly high prevalence in the Mediterranean, Middle East, and parts of Asia. In Germany and other parts of Central Europe, the number of cases has risen over recent decades, likely driven by migration. (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) However, robust data on BT in Germany are scarce. Our study presents the first claims-based prevalence estimates for BT (all subtypes) in Germany, showing an increase from 10.86 per 100,000 in 2017 to 12.81 per 100,000 in 2023. This trend highlights the growing healthcare relevance of BT and the need for continued monitoring and tailored healthcare planning.\u003c/p\u003e \u003cp\u003eDemographic characteristics observed for HD, BT, and SMA were consistent with existing literature. Median ages for HD (57 years)(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), BT (43 years), and SMA (19 years) are consistent with the age profiles described in previous studies. (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) In contrast to BT and SMA, HD typically presents in adulthood, with nearly all prevalent cases observed in individuals aged 21 years or older.\u003c/p\u003e \u003cp\u003eAcross all three diseases, frequently observed comorbidities were closely related to the underlying condition, confirming the high disease burden experienced by affected individuals. Beyond the core symptoms of the index disease, patients commonly present with a wide range of additional health impairments that require independent clinical management or necessitate modifications to therapeutic strategies, especially in multimorbid patients. However, such comorbidities are often underrepresented in disease registries. Systematically capturing this information in administrative data can improve the understanding of multimorbidity patterns and inform the development of more comprehensive, patient-centered clinical guidelines that address the complex needs of individuals living with rare diseases.\u003c/p\u003e \u003cp\u003eRare diseases pose a significant financial burden on patients and society. In this study, average healthcare costs differed across diseases but increased for all three over time. The increase was most pronounced for SMA, largely driven by rising medication costs following the introduction of three novel therapeutic interventions in recent years.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) These treatments offer significant clinical benefits and improved survival, which may also contribute to increased resource use. It can be expected that further advances in the treatment of rare diseases, especially in the field of gene therapy (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), will drive up healthcare costs in the short term, but will offer substantial long-term benefits for patients.\u003c/p\u003e \u003cp\u003eThis study demonstrates the feasibility of using claims data to study rare diseases and provides important epidemiological and economic insights. With the recent implementation of mandatory inpatient Alpha-ID-SE coding and its linkage to ORPHAcodes, diagnostic specificity in German claims data is expected to improve, allowing for more accurate identification of rare diseases in future research. In the European context, studies on rare diseases could greatly benefit from the use of common data models (CDMs), which are designed to harmonize disparate data sources across various healthcare systems. Early results using the InGef database, mapped to the Observational Medical Outcomes Partnership (OMOP) CDM format, have successfully replicated the findings presented here (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and shows promise for facilitating multi-center studies, enabling the testing of data-driven hypotheses on a larger, more diverse population scale. Broader adoption of such frameworks could enhance the comparability and generalizability of research on rare diseases and support international collaboration.\u003c/p\u003e \u003cp\u003eLimitations:\u003c/p\u003e \u003cp\u003eThis study offers valuable insights but is subject to limitations inherent to claims data. First, the analysis was restricted to patients with rare diseases that were both diagnosed and documented within the administrative data, potentially excluding undiagnosed or miscoded cases. Second, the ICD-10-GM classification system lacks sufficient granularity to capture disease severity or specific subtypes, thereby limiting the precision of clinical differentiation and stratified analyses.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) Furthermore, the absence of clinical data such as laboratory parameters precludes assessments of disease severity or progression.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides important data on the prevalence, comorbidities, and economic burden of HD, BT, and SMA, highlighting the challenges of rare disease management. Targeted SHI data analyses support both epidemiological insight and healthcare optimization in the German system.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eATC - Anatomical-Therapeutic-Chemical Classification System\u003c/p\u003e\n\u003cp\u003eBT \u0026ndash; Beta-Thalassemia\u003c/p\u003e\n\u003cp\u003eCDM \u0026ndash; Common Data Model\u003c/p\u003e\n\u003cp\u003eHD - Huntington\u0026rsquo;s Disease\u003c/p\u003e\n\u003cp\u003eICD-10-GM - International Classification of Disease and Health Related Problems, German Modification\u003c/p\u003e\n\u003cp\u003eInGef \u0026ndash; Institute for Applied Health Research Berlin GmbH\u003c/p\u003e\n\u003cp\u003eOMOP -\u0026nbsp;Observational Medical Outcomes Partnership\u003c/p\u003e\n\u003cp\u003eSHI \u0026ndash; Statutory Health Insurance\u003c/p\u003e\n\u003cp\u003eSMA - Spinal Muscular Atrophy type 1\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003eEthics approval and consent to participate\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe anonymization of the InGef research database ensures that individual patients, health insurance companies and service providers can no longer be identified. In accordance with German law, separate approval of an institutional review board or Medical Ethics Committee was not required.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eConsent for publication\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eAvailability of data and materials\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe data used in this study cannot be made publicly available in the manuscript, the additional files, or in a public repository due to German data protection laws (Bundesdatenschutzgesetz) and legal reasons. In Germany, the utilization of health insurance data for scientific research is regulated by the Code of Social Law. Researchers must obtain approval from the health insurance providers as well as their responsible authorities. As this approval is given only for a specific research question for a specific time and for a specific group of researchers, data cannot be made publicly available. To facilitate the replication of results, anonymized data used for this study are stored on a secure drive at the Institute for Applied Health Research Berlin (InGef). Access to the raw data used in this study can only be provided to external parties under the conditions a cooperation contract and can be accessed upon request, after written approval ([email protected]), if required.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eCompeting interests\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eFunding\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eAuthors\u0026apos; contributions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eJJ, MA, DO, RN and ML designed and conceptualized the study, interpretation and revising of data and manuscript. VV, AC and RN worked on data analysis and revising of data. ML was responsible for drafting the manuscript.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eAcknowledgements\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMedina A, Mahjoub Y, Shaver L, Pringsheim T. Prevalence and Incidence of Huntington\u0026rsquo;s Disease: An Updated Systematic Review and Meta‐Analysis. Mov Disord. 2022 Dec;37(12):2327\u0026ndash;35. \u003c/li\u003e\n\u003cli\u003eVerhaart IEC, Robertson A, Wilson IJ, Aartsma-Rus A, Cameron S, Jones CC, et al. Prevalence, incidence and carrier frequency of 5q\u0026ndash;linked spinal muscular atrophy \u0026ndash; a literature review. Orphanet J Rare Dis. 2017 Jul 4;12(1):124. \u003c/li\u003e\n\u003cli\u003ePai M, Yeung CHT, Akl EA, Darzi A, Hillis C, Legault K, et al. Strategies for eliciting and synthesizing evidence for guidelines in rare diseases. BMC Med Res Methodol. 2019 Mar 28;19(1):67. \u003c/li\u003e\n\u003cli\u003eBfArM - Alpha-ID-SE [Internet]. [cited 2024 Nov 11]. Available from: https://www.bfarm.de/DE/Kodiersysteme/Terminologien/Alpha-ID-SE/_node.html\u003c/li\u003e\n\u003cli\u003eLudwig M, Enders D, Basedow F, Walker J, Jacob J. Sampling strategy, characteristics and representativeness of the InGef research database. Public Health. 2022 May;206:57\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eDeutsches Institut f\u0026uuml;r Medizinische Dokumentation und Informatik (DIMDI), editor. OPS Version 2020 Systematisches Verzeichnis - Operationen- und Prozedurenschl\u0026uuml;sselInternationale Klassifikation der Prozeduren in der Medizin (OPS) Band 1: Systematisches Verzeichnis. 2019. \u003c/li\u003e\n\u003cli\u003eWIdO \u0026ndash; Wissenschaftliches Institut der AOK [Internet]. [cited 2021 Jul 7]. Amtliche ATC-Klassifikation. Available from: https://www.wido.de/publikationen-produkte/arzneimittel-klassifikation/amtliche-atc-klassifikation/\u003c/li\u003e\n\u003cli\u003eStatistisches Bundesamt [Internet]. [cited 2025 Apr 14]. Bev\u0026ouml;lkerung nach Altersgruppen. Available from: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Bevoelkerungsstand/Tabellen/bevoelkerung-altersgruppen-deutschland.html\u003c/li\u003e\n\u003cli\u003eOhlmeier C, Saum KU, Galetzka W, Beier D, Gothe H. Epidemiology and health care utilization of patients suffering from Huntington\u0026rsquo;s disease in Germany: real world evidence based on German claims data. BMC Neurol. 2019 Dec;19(1):318. \u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller-Felber W, Blaschek A, Schwartz O, Gl\u0026auml;ser D, Nennstiel U, Brockow I, et al. Newbornscreening SMA \u0026ndash; From Pilot Project to Nationwide Screening in Germany. J Neuromuscul Dis. 2023 Jan 3;10(1):55. \u003c/li\u003e\n\u003cli\u003eCario H, Lobitz S. H\u0026auml;moglobinopathien als Herausforderung der Migrantenmedizin. Monatsschr Kinderheilkd. 2018 Nov 1;166(11):968\u0026ndash;76. \u003c/li\u003e\n\u003cli\u003eKattamis A, Forni GL, Aydinok Y, Viprakasit V. Changing patterns in the epidemiology of \u0026beta;‐thalassemia. Eur J Haematol. 2020 Dec;105(6):692\u0026ndash;703. \u003c/li\u003e\n\u003cli\u003eGesellschaft f\u0026uuml;r P\u0026auml;diatrische Onkologie und H\u0026auml;matologie. S1-Leitlinie Thalass\u0026auml;mien [Internet]. 2023 [cited 2024 Mar 19]. Available from: https://www.awmf.org/leitlinien/detail/ll/025-017.html\u003c/li\u003e\n\u003cli\u003eAnil M, Mason SL, Barker RA. The Clinical Features and Progression of Late-Onset Versus Younger-Onset in an Adult Cohort of Huntington\u0026rsquo;s Disease Patients. J Huntingt Dis. 9(3):275\u0026ndash;82. \u003c/li\u003e\n\u003cli\u003eSolberg OK, Filkukov\u0026aacute; P, Frich JC, Feragen KJB. Age at Death and Causes of Death in Patients with Huntington Disease in Norway in 1986\u0026ndash;2015. J Huntingt Dis. 7(1):77\u0026ndash;86. \u003c/li\u003e\n\u003cli\u003eFinkel RS, McDermott MP, Kaufmann P, Darras BT, Chung WK, Sproule DM, et al. Observational study of spinal muscular atrophy type I and implications for clinical trials. Neurology. 2014 Aug 26;83(9):810\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eOskoui M, Levy G, Garland CJ, Gray JM, O\u0026rsquo;Hagen J, De Vivo DC, et al. The changing natural history of spinal muscular atrophy type 1. Neurology. 2007 Nov 13;69(20):1931\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eFarmakis D, Giakoumis A, Angastiniotis M, Eleftheriou A. The changing epidemiology of the ageing thalassaemia populations: A position statement of the Thalassaemia International Federation. Eur J Haematol. 2020;105(1):16\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eHjartarson HT, Nathorst-B\u0026ouml;\u0026ouml;s K, Sejersen T. Disease Modifying Therapies for the Management of Children with Spinal Muscular Atrophy (5q SMA): An Update on the Emerging Evidence. Drug Des Devel Ther. 2022;16:1865\u0026ndash;83. \u003c/li\u003e\n\u003cli\u003eKunz JB, Kulozik AE. Gene Therapy of the Hemoglobinopathies. HemaSphere. 2020 Oct;4(5):e479. \u003c/li\u003e\n\u003cli\u003eByun S, Lee M, Kim M. Gene Therapy for Huntington\u0026rsquo;s Disease: The Final Strategy for a Cure? J Mov Disord. 2022 Jan;15(1):15\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eJacob J, Norris R, Oberm\u0026uuml;ller D, Alibone M, Ludwig M. ISPOR | International Society For Pharmacoeconomics and Outcomes Research. [cited 2025 Apr 14]. Data Standardization of Claims Data in the InGef Research Database: A Comparison of Data Models for Epidemiological and Economic Studies of Rare Genetic Diseases in Germany. Available from: https://www.ispor.org/heor-resources/presentations-database/presentation/euro2024-4014/143357\u003c/li\u003e\n\u003cli\u003eKratzer VR Verena; Bidlingmaier, Christoph; Klamroth, Robert; Behringer, Jochen; Schramm, Anja; Mansmann, Ulrich; Berger, Karin. Can German Health Insurance Claims Data Fill Information Gaps in Rare Chronic Diseases: Use Case of Haemophilia A. H\u0026auml;mostaseologie [Internet]. 2024 Jul 1;(EFirst). Available from: http://www.thieme-connect.com/products/ejournals/abstract/10.1055/a-2276-4871\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"(MeSH): rare diseases, administrative claims, healthcare, epidemiology, health care costs","lastPublishedDoi":"10.21203/rs.3.rs-6564544/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6564544/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eResearch on rare genetic diseases is challenging due to their low prevalence and complex clinical manifestations. Despite their individual rarity, the cumulative burden of these diseases is substantial. Larger claims databases could enable a quantification and characterization of patients with rare diseases and contribute to determining their specific healthcare needs. This study examines the prevalence, comorbidities, and annual direct costs of Huntington's disease (HD), beta-thalassemia (BT), and spinal muscular atrophy type 1 (SMA) in Germany.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUtilizing anonymized claims data from the InGef research database, we conducted a cross-sectional analysis for each disease from 2017 to 2023. Patients and comorbidities were identified based on ICD-10-GM-codes. Prevalence was extrapolated to the German population. Costs were analyzed as direct costs per patient and by sector incurred.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePrevalences for the selected diseases were 6.65 (HD), 12.81 (BT), and 1.24 (SMA) patients per 100,000 persons in Germany in 2023. The most frequent comorbidities identified are strongly related to the specific disease. For instance, in 2023, among the most common diagnoses were depression in HD patients (38.48%), dorsalgia in BT patients (37.03%), and acute upper respiratory infections in SMA patients (45.19%). Average total direct costs in 2023 amounted to 9,527.89 \u0026euro; for HD, 6,656.27 \u0026euro; for BT and 144,585.02 \u0026euro; for SMA, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eClaims data enable robust identification and characterization of rare disease populations. The findings on prevalences and comorbidities align with existing literature and support the use of such data for treatment planning and post-marketing studies.\u003c/p\u003e","manuscriptTitle":"Epidemiology and economic burden of selected rare genetic diseases in Germany – a claims database study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 02:14:05","doi":"10.21203/rs.3.rs-6564544/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-07-14T14:30:22+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-16T20:20:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Orphanet Journal of Rare Diseases","date":"2025-06-13T14:31:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-01T19:10:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Orphanet Journal of Rare Diseases","date":"2025-04-30T07:28:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6f68e5a2-efeb-4560-85c0-35be3cdafe38","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:04:40+00:00","versionOfRecord":{"articleIdentity":"rs-6564544","link":"https://doi.org/10.1186/s13023-025-04147-8","journal":{"identity":"orphanet-journal-of-rare-diseases","isVorOnly":false,"title":"Orphanet Journal of Rare Diseases"},"publishedOn":"2025-11-28 15:58:28","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-06-25 02:14:05","video":"","vorDoi":"10.1186/s13023-025-04147-8","vorDoiUrl":"https://doi.org/10.1186/s13023-025-04147-8","workflowStages":[]},"version":"v1","identity":"rs-6564544","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6564544","identity":"rs-6564544","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}