Rare Diseases – Avoiding Misperceptions and Establishing Realities: The Need for Reliable Epidemiological Data

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

European Journal of Human Genetics ◽

10.1038/s41431-019-0508-0 ◽

2019 ◽

Vol 28 (2) ◽

pp. 165-173 ◽

Cited By ~ 63

Author(s):

Stéphanie Nguengang Wakap ◽

Deborah M. Lambert ◽

Annie Olry ◽

Charlotte Rodwell ◽

Charlotte Gueydan ◽

...

Keyword(s):

Rare Disease ◽

Rare Diseases ◽

Epidemiological Data ◽

Variable Number ◽

Point Prevalence ◽

Evidence Based ◽

Global Public Health ◽

Prevalence Data ◽

Population Prevalence ◽

Definition Of

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.

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A Retrospective Review of the Contribution of Rare Diseases to Paediatric Mortality in Ireland

10.21203/rs.3.rs-20723/v1 ◽

2020 ◽

Author(s):

Emer Anne Gunne ◽

Cliona McGarvey ◽

Karina Hamilton ◽

Eileen Treacy ◽

Deborah Lambert ◽

...

Keyword(s):

Rare Diseases ◽

Epidemiological Data ◽

Healthcare Services ◽

Incidence Rates ◽

Hospital Death ◽

Field Methods ◽

Healthcare Planning ◽

National Quality ◽

Icd 10 ◽

Irish Republic

Abstract Aims: To ascertain the number of paediatric deaths (0-14 years) with an underlying rare disease (RD) in the Irish Republic between the years 2006-2016, and to analyse bed usage by a paediatric cohort of RD inpatients prior to in-hospital death. Background: Rare Diseases are life-threatening or chronically debilitating diseases that affect fewer than 5 per 10,000 people in the EU. Although individually rare, collectively RDs are common, with a prevalence of 3.5-5.9% of the population. Under representation of RDs in hospital healthcare coding systems leads to a paucity of RD epidemiological data required for healthcare planning. Studies have cited variable incidence rates for RD, however the burden of RDs to healthcare services still remains unclear. This study represents a thorough effort to identify the percentage of child mortality and paediatric bed usage attributable to rare diseases in Ireland addressing a major gap in the RD field. Methods: Retrospective analysis of paediatric death registration details for the Irish Republic in the 11-year period 2006-2016 from the National Paediatric Mortality Register. Data was subcategorized as Neonatal (0-28 days), Post Neonatal (29 days < 1 year) and older (1-14 years). Bed usage data (ICD-10 code, narrative and usage) of paediatric inpatients who died during hospitalisation from January 2015 to December 2016 was extracted from the National Quality Assurance Intelligence System of in-patient data. Orphacodes were assigned to RD cases from narrative records of both datasets. Results: There were 4044 deaths registered from 2006-2016, aged <15yrs. 2368 (58.6%) had an underlying RD. Stratifying by age group; 55.6% (1140/2050) of neonatal deaths had an RD, 57.8% (450/778) post-neonatal, and 64% (778/1216) of children >1yr. Mortality coding using ICD-10 codes identified only 42% of RD cases with the remainder identified using death certificate narrative records. RD patients occupied 84% of bed days used by children <15 years discharged deceased in the analysis period January 2015 to December 2016. Conclusion: Additional routine RD coding is necessary to identify RDs within Irish healthcare systems to enable better healthcare planning. RD patients are overrepresented in paediatric mortality statistics and inpatient length of stay during hospital admission prior to death.

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Rare Diseases: Joining Mainstream Research and Treatment Based on Reliable Epidemiological Data

Advances in Experimental Medicine and Biology - Rare Diseases Epidemiology: Update and Overview ◽

10.1007/978-3-319-67144-4_1 ◽

2017 ◽

pp. 3-21 ◽

Cited By ~ 6

Author(s):

Stephen C. Groft ◽

Manuel Posada de la Paz

Keyword(s):

Rare Diseases ◽

Epidemiological Data

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A Retrospective Review of the Contribution of Rare Diseases to Paediatric Mortality in Ireland

10.21203/rs.3.rs-20723/v2 ◽

2020 ◽

Author(s):

Emer Anne Gunne ◽

Cliona McGarvey ◽

Karina Hamilton ◽

Eileen Treacy ◽

Deborah Lambert ◽

...

Keyword(s):

Rare Diseases ◽

Epidemiological Data ◽

Healthcare Services ◽

Incidence Rates ◽

Hospital Death ◽

Neonatal Deaths ◽

Healthcare Planning ◽

National Quality ◽

Icd 10 ◽

Irish Republic

Abstract Aims: To ascertain the number of paediatric deaths (0-14 years) with an underlying rare disease (RD) in the Irish Republic between the years 2006-2016, and to analyse bed usage by a paediatric cohort of RD inpatients prior to in-hospital death.Background: Rare diseases are often chronically debilitating and sometimes life-threatening diseases, affecting fewer than 5 per 10,000 people in the EU. Although individually rare, collectively RDs are common, with a prevalence of 3.5-5.9% of the population. Under-representation of RDs in hospital healthcare coding systems leads to a paucity of RD epidemiological data required for healthcare planning. Studies have cited variable incidence rates for RD, however the burden of RDs to healthcare services still remains unclear. This study represents a thorough effort to identify the percentage of child mortality and paediatric bed usage attributable to rare diseases in Ireland addressing a major gap in the RD field.Methods: Retrospective analysis of paediatric death registration details for the Irish Republic in the 11-year period 2006-2016 from the National Paediatric Mortality Register. Data was subcategorised as Neonatal (0-28 days), Post Neonatal (29 days < 1 year) and older (1-14 years). Bed usage data (ICD-10 code, narrative and usage) of paediatric inpatients who died during hospitalisation from January 2015 to December 2016 was extracted from the National Quality Assurance Intelligence System of in-patient data. Orphacodes were assigned to RD cases from narrative records of both datasets.Results: There were 4044 deaths registered from 2006-2016, aged <15yrs, of these 2368 (58.6%) had an underlying RD. Stratifying by age group; 55.6% (1140/2050) of neonatal deaths had an RD, 57.8% (450/778) post-neonatal, and 64% (778/1216) of children >1yr. Mortality coding using ICD-10 codes identified 42% of RD cases with the remainder identified using death certificate narrative records. RD patients occupied 87% of bed days used by children <15 years who died during hospitalisation from January 2015 to December 2016.Conclusion: Additional routine RD coding is necessary to identify RDs within Irish healthcare systems to enable better healthcare planning. RD patients are overrepresented in paediatric mortality statistics and inpatient length of stay during hospital admission prior to death.

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The Collaborative Network Approach: a model for advancing patient-centric research for Castleman disease and other rare diseases

Emerging Topics in Life Sciences ◽

10.1042/etls20180178 ◽

2019 ◽

Vol 3 (1) ◽

pp. 97-105

Author(s):

Mary Zuccato ◽

Dustin Shilling ◽

David C. Fajgenbaum

Keyword(s):

Rare Diseases ◽

Treatment Options ◽

Castleman Disease ◽

High Impact ◽

Collaborative Network ◽

Network Approach ◽

Grant Applications ◽

Research Questions ◽

Novel Strategy ◽

Patient Centric

Abstract There are ∼7000 rare diseases affecting 30 000 000 individuals in the U.S.A. 95% of these rare diseases do not have a single Food and Drug Administration-approved therapy. Relatively, limited progress has been made to develop new or repurpose existing therapies for these disorders, in part because traditional funding models are not as effective when applied to rare diseases. Due to the suboptimal research infrastructure and treatment options for Castleman disease, the Castleman Disease Collaborative Network (CDCN), founded in 2012, spearheaded a novel strategy for advancing biomedical research, the ‘Collaborative Network Approach’. At its heart, the Collaborative Network Approach leverages and integrates the entire community of stakeholders — patients, physicians and researchers — to identify and prioritize high-impact research questions. It then recruits the most qualified researchers to conduct these studies. In parallel, patients are empowered to fight back by supporting research through fundraising and providing their biospecimens and clinical data. This approach democratizes research, allowing the entire community to identify the most clinically relevant and pressing questions; any idea can be translated into a study rather than limiting research to the ideas proposed by researchers in grant applications. Preliminary results from the CDCN and other organizations that have followed its Collaborative Network Approach suggest that this model is generalizable across rare diseases.

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Shoulder Pain at Work: Causation Analysis

Guides Newsletter ◽

10.1001/amaguidesnewsletters.2017.janfeb02 ◽

2017 ◽

Vol 22 (1) ◽

pp. 11-16

Author(s):

Joel Weddington ◽

Charles N. Brooks ◽

Mark Melhorn ◽

Christopher R. Brigham

Keyword(s):

Epidemiological Data ◽

Case Law ◽

Individual Case ◽

Shoulder Injury ◽

Work Related ◽

Clear Cut ◽

The Individual ◽

Causation Analysis ◽

Injury Causation ◽

Relevant Factors

Abstract In most cases of shoulder injury at work, causation analysis is not clear-cut and requires detailed, thoughtful, and time-consuming causation analysis; traditionally, physicians have approached this in a cursory manner, often presenting their findings as an opinion. An established method of causation analysis using six steps is outlined in the American College of Occupational and Environmental Medicine Guidelines and in the AMA Guides to the Evaluation of Disease and Injury Causation, Second Edition, as follows: 1) collect evidence of disease; 2) collect epidemiological data; 3) collect evidence of exposure; 4) collect other relevant factors; 5) evaluate the validity of the evidence; and 6) write a report with evaluation and conclusions. Evaluators also should recognize that thresholds for causation vary by state and are based on specific statutes or case law. Three cases illustrate evidence-based causation analysis using the six steps and illustrate how examiners can form well-founded opinions about whether a given condition is work related, nonoccupational, or some combination of these. An evaluator's causal conclusions should be rational, should be consistent with the facts of the individual case and medical literature, and should cite pertinent references. The opinion should be stated “to a reasonable degree of medical probability,” on a “more-probable-than-not” basis, or using a suitable phrase that meets the legal threshold in the applicable jurisdiction.

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