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Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database.

ABSTRACT: Rare diseases, an emerging global public health priority, require an evidence-based estimate of the global point prevalence to inform public policy. We used the publicly available epidemiological data in the Orphanet database to calculate such a prevalence estimate. Overall, Orphanet contains information on 6172 unique rare diseases; 71.9% of which are genetic and 69.9% which are exclusively pediatric onset. Global point prevalence was calculated using rare disease prevalence data for predefined geographic regions from the 'Orphanet Epidemiological file' (http://www.orphadata.org/cgi-bin/epidemio.html). Of the 5304 diseases defined by point prevalence, 84.5% of those analysed have a point prevalence of <1/1 000 000. However 77.3-80.7% of the population burden of rare diseases is attributable to the 4.2% (n?=?149) diseases in the most common prevalence range (1-5 per 10 000). Consequently national definitions of 'Rare Diseases' (ranging from prevalence of 5 to 80 per 100 000) represent a variable number of rare disease patients despite sharing the majority of rare disease in their scope. Our analysis yields a conservative, evidence-based estimate for the population prevalence of rare diseases of 3.5-5.9%, which equates to 263-446 million persons affected globally at any point in time. This figure is derived from data from 67.6% of the prevalent rare diseases; using the European definition of 5 per 10 000; and excluding rare cancers, infectious diseases, and poisonings. Future registry research and the implementation of rare disease codification in healthcare systems will further refine the estimates.

SUBMITTER: Nguengang Wakap S

PROVIDER: S-EPMC6974615 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database.

Nguengang Wakap Stéphanie S Lambert Deborah M DM Olry Annie A Rodwell Charlotte C Gueydan Charlotte C Lanneau Valérie V Murphy Daniel D Le Cam Yann Y Rath Ana A

European journal of human genetics : EJHG 20190916 2

Rare diseases, an emerging global public health priority, require an evidence-based estimate of the global point prevalence to inform public policy. We used the publicly available epidemiological data in the Orphanet database to calculate such a prevalence estimate. Overall, Orphanet contains information on 6172 unique rare diseases; 71.9% of which are genetic and 69.9% which are exclusively pediatric onset. Global point prevalence was calculated using rare disease prevalence data for predefined ...[more]

PMID: 31527858

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Project description:IntroductionDetermining the epidemiology of disease is critical for multiple reasons, including to perform risk assessment, compare disease rates in varying populations, support diagnostic decisions, evaluate health care needs and disease burden, and determine the economic benefit of treatment. However, establishing epidemiological measures for rare diseases can be difficult owing to small patient populations, variable diagnostic techniques, and potential disease and diagnostic heterogeneity. To determine the epidemiology of rare diseases, investigators often develop estimation models to account for missing or unobtainable data, and to ensure that their findings are representative of their desired patient population.MethodsA modeling methodology to estimate the prevalence of rare diseases in one such population-patients with long-chain fatty acid oxidation disorders (LC-FAOD)-as an illustrative example of its applicability.ResultsThe proposed model begins with reliable source data from newborn screening reports and applies to them key modifiers. These modifiers include changes in population growth over time and variable standardization rates of LC-FAOD screening that lead to (1) a confirmed diagnosis and (2) improvements in standards of care and survival estimates relative to the general population. The model also makes necessary assumptions to allow the broad applicability of the estimation of LC-FAOD prevalence, including rates of diagnosed versus undiagnosed patients in the USA over time.ConclusionsAlthough each rare disease is unique, the approach described here and demonstrated in the estimation of LC-FAOD prevalence provides the necessary tools to calculate key epidemiological estimates useful in performing risk assessment analyses; comparing disease rates between different subgroups of people; supporting diagnostic decisions; planning health care needs; comparing disease burden, including cost; and determining the economic benefit of treatment.

Project description:The ability to discover genetic variants in a patient runs far ahead of the ability to interpret them. Databases with accurate descriptions of the causal relationship between the variants and the phenotype are valuable since these are critical tools in clinical genetic diagnostics. Here, we introduce a comprehensive and global genotype-phenotype database focusing on rare diseases.This database (CentoMD ®) is a browser-based tool that enables access to a comprehensive, independently curated system utilizing stringent high-quality criteria and a quickly growing repository of genetic and human phenotype ontology (HPO)-based clinical information. Its main goals are to aid the evaluation of genetic variants, to enhance the validity of the genetic analytical workflow, to increase the quality of genetic diagnoses, and to improve evaluation of treatment options for patients with hereditary diseases. The database software correlates clinical information from consented patients and probands of different geographical backgrounds with a large dataset of genetic variants and, when available, biomarker information. An automated follow-up tool is incorporated that informs all users whenever a variant classification has changed. These unique features fully embedded in a CLIA/CAP-accredited quality management system allow appropriate data quality and enhanced patient safety.More than 100,000 genetically screened individuals are documented in the database, resulting in more than 470 million variant detections. Approximately, 57% of the clinically relevant and uncertain variants in the database are novel. Notably, 3% of the genetic variants identified and previously reported in the literature as being associated with a particular rare disease were reclassified, based on internal evidence, as clinically irrelevant.The database offers a comprehensive summary of the clinical validity and causality of detected gene variants with their associated phenotypes, and is a valuable tool for identifying new disease genes through the correlation of novel genetic variants with specific, well-defined phenotypes.

Project description:BackgroundThe burden of rare diseases on society and patients' families has increased in Korea. However, because of the infrequency of rare diseases, there is a lack of resources and information to address these cases and inadequate funding for the management of these patients. We investigated the average annual cumulative incidence of rare diseases and the trends in annual cumulative incidence from 2011 to 2015 in Korea by using nationwide administrative data from the Korean National Health Insurance Service (NHIS) database for patients registered with the co-payment assistance policy for rare and incurable diseases. Annual cumulative incidence per 10,000,000 was calculated as the total number of newly enrolled patients with the Korean Standard Classification of Diseases (KCD)-7 code in the register, divided by the number of residents with health insurance coverage during each year. We employed simple linear regression analysis to evaluate the trends in annual cumulative incidence/10,000,000 population per year for each rare disease.ResultsOverall, national support was provided for patients with 415 KCD codes listed among the targeted rare diseases. The total number of newly enrolled patients with rare diseases was 53,831 in 2011, 52,658 in 2012, 52,955 in 2013, 71,530 in 2014, and 70,559 in 2015. The number of rare diseases with an average annual cumulative incidence of 100/10,000,000 and above was 22 (5.30%), while there were 227 (54.70%) and 148 (35.66%) with an average cumulative incidence between 1/10,000,000 and 100/10,000,000 and less than 1/10,000,000, respectively. The trends in the annual cumulative incidence for 43 rare diseases were statistically significant (p-value < 0.05). The rare diseases for which the incremental trend per year was statistically significant were sarcoidosis (D86, D86.0, D86.1, D86.2, D86.3, D86.8, D86.9), Parkinson's disease (G20), Guillain-Barré syndrome (G61.0), primary biliary cirrhosis (K74.3) and Sjogren's syndrome (M35.0).ConclusionsThe number of rare diseases showing an increasing trend in annual cumulative incidence was higher than the number of diseases showing a decreasing trend in annual cumulative incidence. Given that the definition and diagnosis vary based on country and that there is difficulty in identifying valid cases, further detection strategies are needed to establish the incidence of each rare disease considering the importance of establishing a health policy based on the actual incidence of the targeted diseases.

Project description:IntroductionTransthyretin amyloid cardiomyopathy (ATTR-CM)-a debilitating, fatal disease resulting from the deposition of transthyretin (TTR) amyloid fibrils-can be hereditary due to mutations in the TTR gene (ATTRm) or wild type (ATTRwt). The global prevalence of ATTR-CM is largely unknown, although likely underestimated, with no formal epidemiological prevalence studies in Japan. This study aimed to estimate the prevalence of ATTR-CM in a large in-hospital database in Japan.MethodsThis was a retrospective, observational, cross-sectional study which utilized data from all adult patients (aged ≥ 20 years) in the hospital-based Japan Medical Data Vision (MDV) database from January 2010 to September 2018 to estimate the number of currently diagnosed ATTR-CM patients and describe their demographic and clinical characteristics and diagnostic modalities. ATTR-CM patients (ATTRwt and ATTRm) were identified using a range of diagnosis codes that were applied to create broad and narrow definitions of the disease.ResultsOver the 9 years of this study, there were 3255 (155.8 per million adult patients in the MDV database) to 3992 (191.1 per million) diagnoses of ATTRwt and 67 (3.2 per million) to 106 (5.1 per million) diagnoses of ATTRm in the MDV database (based on the narrow and broad definitions, respectively). There were 444 (21.2 per million) diagnoses of amyloid light-chain (AL) amyloidosis. Considering only those patients who were also diagnosed with heart failure, there were 1468 (70.3 per million) to 1798 (86.1 per million) diagnoses of ATTRwt and 50 (2.4 per million) to 61 (2.9 per million) diagnoses of ATTRm. Most ATTRwt patients (~ 90%) did not have a record of endomyocardial or abdominal wall biopsy, or of scintigram.ConclusionThis retrospective study provides an estimate of the number of patients diagnosed with ATTR-CM in a large in-hospital database in Japan over a period of 9 years. Improving awareness of disease prevalence may improve diagnosis and treatment.FundingPfizer.

Dataset Information

Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database.

Publications

Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database.

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