A nationwide study of 331 rare diseases among 58 million individuals: prevalence, demographics, and COVID-19 outcomes

Background The Global Burden of Disease study has provided key evidence to inform clinicians, researchers, and policy makers across common diseases, but no similar effort with single study design exists for hundreds of rare diseases. Consequently, many rare conditions lack population-level evidence including prevalence and clinical vulnerability. This has led to the absence of evidence-based care for rare diseases, prominently in the COVID-19 pandemic. Method This study used electronic health records (EHRs) of more than 58 million people in England, linking nine National Health Service datasets spanning healthcare settings for people alive on Jan 23, 2020. Starting with all rare diseases listed in Orphanet, we quality assured and filtered down to analyse 331 conditions with ICD-10 or SNOMED-CT mappings clinically validated in our dataset. We report 1) population prevalence, clinical and demographic details of rare diseases, and 2) investigate differences in mortality with SARs-CoV-2. Findings Among 58,162,316 individuals, we identified 894,396 with at least one rare disease. Prevalence data in Orphanet originates from various sources with varying degrees of precision. Here we present reproducible age and gender-adjusted estimates for all 331 rare diseases, including first estimates for 186 (56.2%) without any reported prevalence estimate in Orphanet. We identified 49 rare diseases significantly more frequent in females and 62 in males. Similarly we identified 47 rare diseases more frequent in Asian as compared to White ethnicity and 22 with higher Black to white ratios as compared to similar ratios in population controls. 37 rare diseases were overrepresented in the white population as compared to both Black and Asian ethnicities. In total, 7,965 of 894,396 (0.9%) of rare-disease patients died from COVID-19, as compared to 141,287 of 58,162,316 (0.2%) in the full study population. Eight rare diseases had significantly increased risks for COVID-19-related mortality in fully vaccinated individuals, with bullous pemphigoid (8.07[3.01-21.62]) being worst affected. Interpretation Our study highlights that National-scale EHRs provide a unique resource to estimate detailed prevalence, clinical and demographic data for rare diseases. Using COVID-19-related mortality analysis, we showed the power of large-scale EHRs in providing insights to inform public health decision-making for these often neglected patient populations. Funding British Heart Foundation Data Science Centre, led by Health Data Research UK. Evidence before the study We have previously published the largest study looking at COVID-19 across rare diseases, but with a sample size of 158 COVID-19 infected rare disease patients and 125 unaffected relatives, from Genomics England, the power of that study was limited. We searched PubMed from database inception to Apr 21, 2023, for publications using the search terms “COVID-19” or “SARS-CoV-2” and “rare disease” or “ORPHANET”, without language restrictions. There are many studies examining the severity of COVID-19 in rare disease patients. However, to date, most studies have focused on a single or a few rare diseases associated with severity of COVID-19, and not taken a comprehensive rare disease wide approach. So far no studies have examined the impact of vaccination on mortality in rare disease patients. Moreover, the sample size used to examine rare diseases is limited in most studies. The largest study we identified included 168,680 individuals but only focused on autoimmune rheumatic disease. Added value of this study In this study we use national scale EHR data from England to report age and gender adjusted point prevalence for 331 rare diseases, with clinically-validated ICD-10 and/or SNOMED-CT code lists. Among these, 186 (56.2%) diseases did not have existing point prevalence data available in Orphanet. To our knowledge, this is the first time that rare diseases have been examined on a national scale, encompassing a population of over 58 million people. The large sample size provides sufficient statistical power to detect and describe enough carriers of even very rare conditions <1 case per million. Our analysis of COVID-related mortality has demonstrated the clinical relevance of national data for rare diseases. Specifically, we identified eight rare conditions that are associated with a significantly increased risk of mortality from COVID-19, even among fully vaccinated individuals. Implication of all the available evidence These findings provide robust reproducible prevalence, gender, and ethnicity estimates for disease that may often have been under prioritised, and where such information in most cases was not previously available. Our COVID-19 mortality findings highlight the need for targeted policy and support addressing the high level of vulnerability of these patients to COVID-19. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The British Heart Foundation Data Science Centre (grant No SP/19/3/34678, awarded to Health Data Research (HDR) UK) funded co-development (with NHS Digital) of the trusted research environment, provision of linked datasets, data access, user software licences, computational usage, and data management and wrangling support, with additional contributions from the HDR UK data and connectivity component of the UK governments chief scientific advisers national core studies programme to coordinate national covid-19 priority research. Consortium partner organisations funded the time of contributing data analysts, biostatisticians, epidemiologists, and clinicians. This work was funded by the Longitudinal Health and Wellbeing COVID-19 National Core Study, which was established by the UK Chief Scientific Officer in October 2020 and funded by UK Research and Innovation (grant references MC\_PC\_20030 and MC\_PC\_20059). This work was supported by National Institute for Health and Care Research (NIHR202639), NIHR/HDR UK Winter Pressure Award (WP0006) and Medical Research Council (MR/S004149/2). The work is also founded by the HDR UK Discretionary fund - Rare Disease Phenomics (TF2022.42), which receives its funding from HDR UK Ltd (HDRUK 2022.0137) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust. This work was supported by National Institute of Health Research University College London CL Hospitals Biomedical Research Centre (UCLH BRC), London, UK, Health Data Research UK, which receives its funding from HDR UK Ltd (HDR-9006) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The North East-Newcastle and North Tyneside 2 research ethics committee provided ethical approval for the CVD-COVID-UK/COVID-IMPACT research programme (REC No 20/NE/0161). This has been described in detail previously11. Data access approval was granted to the CVD-COVID-UK consortium (under project proposal CCU013 High-throughput electronic health record phenotyping approaches) through the NHS Digital online Data Access Request Service34 (ref. DARS-NIC-381078-Y9C5K). NHS Digital data have been made available for research under the Control of Patient Information (COPI) notice which mandated the sharing of national electronic health records for COVID-19 research (more info: ). For further detail see supplementary methods. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The authors and colleagues across the CVD-COVID-UK consortium have invested considerable time and energy in developing the data resource described here and are keen to ensure that it is used widely to maximise its value. For inquiries about data access, please see [www.healthdatagateway.org/dataset/7e5f0247-f033-4f98-aed3-3d7422b9dc6d][1] or email bhfdsc{at}hdruk.ac.uk. [1]: http://www.healthdatagateway.org/dataset/7e5f0247-f033-4f98-aed3-3d7422b9dc6d