罕见病流行率估算方法回顾

N. Venugopal, G. Naik, Krishnamurthy Jayanna, Archisman Mohapatra, F. J. Sasinowski, Reena V. Kartha, Harsha K. Rajasimha
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引用次数: 0

摘要

罕见病面临的主要挑战之一是无法获得流行率和发病率的可靠估计值。流行病学数据的缺乏使治疗和管理方案的规划工作面临挑战。估算罕见病和遗传病流行率和发病率的方法主要依赖于准确的国家患者登记或出生缺陷数据库。在印度等中低收入国家(LMIC),这一差距更大,目前,这些国家的患病率和发病率估计值要么未知,要么必须使用发达国家的数据作为替代。在此,我们分析了目前用于估算罕见遗传病患病率和发病率的方法,以决策树的形式提供建议,以便选择最可行的方法,尤其是在印度这样资源有限的环境中。我们选择了十种对印美罕见病组织(IndoUSrare)及其患者联盟成员具有共同重要性的罕见病进行分析。我们的分析表明,回顾性研究设计是估算罕见病流行率和发病率最常用的方法。我们提出了一个通用决策树或流程图,以帮助流行病学研究人员选择估算罕见病或遗传病患病率和发病率的方法。
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Review of methods for estimating the prevalence of rare diseases
One of the main challenges in rare diseases is the unavailability of reliable estimates of prevalence and incidence. The lack of epidemiological data makes planning for therapeutic and management options challenging. Methods for estimating the prevalence and incidence of rare and genetic diseases primarily rely on the availability of accurate national patient registries or databases of birth defects. This gap is wider in Low- and Middle-Income countries (LMICs) such as India, where currently, the estimates of prevalence and incidence are either unknown or data from developed countries have to be used as a proxy. Here, we analyzed the current methods used to estimate the prevalence and incidence of rare genetic diseases to provide recommendations in the form of a decision tree to select the most feasible method, particularly in resource-constrained environments such as India. We selected ten rare diseases of shared importance to the Indo US Organization for Rare Diseases (IndoUSrare) and its Patients Alliance members for analysis. Our analysis suggests that retrospective study designs are the most commonly used method to estimate the prevalence and incidence of rare diseases. We propose a generalized decision tree or flowchart to aid epidemiology researchers during the selection of methods for estimating the prevalence and incidence of a rare or genetic disease.
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