Estimating prevalence of haematological malignancies using data from the Haematological Malignancy Research Network (HMRN)
Year: 2014
Type: dissertation
Abstract: The prevalence of the haematological malignancies enumerates those currently living with past diagnosis of this class of diseases, and provides insights regarding survivor populations and their burden. However, there is a lack of accurate information regarding the prevalence of the haematological malignancies. This is partly because of changing disease classifications and the fact that the current methods available to estimate total prevalence have not always been appropriate due to the characteristics of the disease including age at diagnosis and the introduction of novel treatments that have altered outcomes. \n \nUsing data from the Haematological Malignancy Research Network (HMRN) a method was developed to estimate the prevalence of the haematological malignancies, according to current disease classification, in HMRN region and for the UK as a whole. The method used a mathematical model and flexible statistical methods to estimate the total prevalence on 31st, August, 2011. \n \nTotal prevalence estimates that about 19,700 cases in HMRN area are living with a prior diagnosis of haematological malignancy on the index date. Among them, about 9,600 living cases were diagnosed before the establishment of HMRN registry. Using observed prevalence, it was estimated that in the UK there are 165,841 cases of haematological malignancies; however, total prevalence estimates 327,818 cases. Subtypes showed different disease burdens due to their own characteristics. \n \nThis thesis is the first study to calculate the prevalence of haematological malignancies using current disease classification (ICD-O-3). It provides indicators of real burden of haematological malignancies for each of the subtypes in HMRN area; these can then be extrapolated to the UK as a whole.
Author: Jinlei Li
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W1482384375