Cargando…
Estimating the prevalence of hematological malignancies and precursor conditions using data from Haematological Malignancy Research Network (HMRN)
OBJECTIVE: Well-established cancer registries that routinely link to death registrations can estimate prevalence directly by counting patients alive at a particular point in time (observed prevalence). Such direct methods can only provide prevalence for the years over which the registry has been ope...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4958130/ https://www.ncbi.nlm.nih.gov/pubmed/27351920 http://dx.doi.org/10.1007/s10552-016-0780-z |
_version_ | 1782444273893900288 |
---|---|
author | Li, Jinlei Smith, Alex Crouch, Simon Oliver, Steven Roman, Eve |
author_facet | Li, Jinlei Smith, Alex Crouch, Simon Oliver, Steven Roman, Eve |
author_sort | Li, Jinlei |
collection | PubMed |
description | OBJECTIVE: Well-established cancer registries that routinely link to death registrations can estimate prevalence directly by counting patients alive at a particular point in time (observed prevalence). Such direct methods can only provide prevalence for the years over which the registry has been operational. Time-defined estimates, including 5- and 10-year prevalence, may however underestimate the total cancer burden, and compared with other cancers, there is a lack of accurate information on the total prevalence of hematological malignancy subtypes. Accordingly, we aimed to estimate prevalence (observed and total prevalence) of hematological malignancies and precursor conditions by clinically meaningful subtypes using data from the UK’s specialist population-based register, the Haematological Malignancy Research Network (www.hmrn.org). METHODS: Observed and total prevalences were estimated from 15,810 new diagnoses of hematological malignancies from 2004 to 2011 and followed up to the 31 August 2011 (index data). Observed prevalence was calculated by the counting method, and a method based on modelling incidence and survival was used to estimate total prevalence. Estimates were made according to current disease classification for the HMRN region and for the UK. RESULTS: The overall observed and total prevalence rates were 281.9 and 548.8 per 100,000, respectively; the total number of observed and total prevalent cases in the UK was estimated as 165,841 and 327,818 cases, as expected variation existed by disease subtype reflecting the heterogeneity in underlying disease incidence, survival and age distribution of hematological cancers. CONCLUSIONS: This study demonstrates the importance of estimating ‘total’ prevalence rather than ‘observed’ prevalence by current disease classification (ICD-O-3), particularly for subtypes that have a more indolent nature and for those that are curable. Importantly, these analyses demonstrate that relying on observed prevalence alone would result in a significant underestimation of the relative burden of some subtypes. While many of these cases may be considered cured and no longer being actively treated, people in this survivorship phase may have long-term medical needs and accordingly, it is important to provide accurate counts to allow for healthcare planning. |
format | Online Article Text |
id | pubmed-4958130 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-49581302016-08-01 Estimating the prevalence of hematological malignancies and precursor conditions using data from Haematological Malignancy Research Network (HMRN) Li, Jinlei Smith, Alex Crouch, Simon Oliver, Steven Roman, Eve Cancer Causes Control Original Paper OBJECTIVE: Well-established cancer registries that routinely link to death registrations can estimate prevalence directly by counting patients alive at a particular point in time (observed prevalence). Such direct methods can only provide prevalence for the years over which the registry has been operational. Time-defined estimates, including 5- and 10-year prevalence, may however underestimate the total cancer burden, and compared with other cancers, there is a lack of accurate information on the total prevalence of hematological malignancy subtypes. Accordingly, we aimed to estimate prevalence (observed and total prevalence) of hematological malignancies and precursor conditions by clinically meaningful subtypes using data from the UK’s specialist population-based register, the Haematological Malignancy Research Network (www.hmrn.org). METHODS: Observed and total prevalences were estimated from 15,810 new diagnoses of hematological malignancies from 2004 to 2011 and followed up to the 31 August 2011 (index data). Observed prevalence was calculated by the counting method, and a method based on modelling incidence and survival was used to estimate total prevalence. Estimates were made according to current disease classification for the HMRN region and for the UK. RESULTS: The overall observed and total prevalence rates were 281.9 and 548.8 per 100,000, respectively; the total number of observed and total prevalent cases in the UK was estimated as 165,841 and 327,818 cases, as expected variation existed by disease subtype reflecting the heterogeneity in underlying disease incidence, survival and age distribution of hematological cancers. CONCLUSIONS: This study demonstrates the importance of estimating ‘total’ prevalence rather than ‘observed’ prevalence by current disease classification (ICD-O-3), particularly for subtypes that have a more indolent nature and for those that are curable. Importantly, these analyses demonstrate that relying on observed prevalence alone would result in a significant underestimation of the relative burden of some subtypes. While many of these cases may be considered cured and no longer being actively treated, people in this survivorship phase may have long-term medical needs and accordingly, it is important to provide accurate counts to allow for healthcare planning. Springer International Publishing 2016-06-28 2016 /pmc/articles/PMC4958130/ /pubmed/27351920 http://dx.doi.org/10.1007/s10552-016-0780-z Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Paper Li, Jinlei Smith, Alex Crouch, Simon Oliver, Steven Roman, Eve Estimating the prevalence of hematological malignancies and precursor conditions using data from Haematological Malignancy Research Network (HMRN) |
title | Estimating the prevalence of hematological malignancies and precursor conditions using data from Haematological Malignancy Research Network (HMRN) |
title_full | Estimating the prevalence of hematological malignancies and precursor conditions using data from Haematological Malignancy Research Network (HMRN) |
title_fullStr | Estimating the prevalence of hematological malignancies and precursor conditions using data from Haematological Malignancy Research Network (HMRN) |
title_full_unstemmed | Estimating the prevalence of hematological malignancies and precursor conditions using data from Haematological Malignancy Research Network (HMRN) |
title_short | Estimating the prevalence of hematological malignancies and precursor conditions using data from Haematological Malignancy Research Network (HMRN) |
title_sort | estimating the prevalence of hematological malignancies and precursor conditions using data from haematological malignancy research network (hmrn) |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4958130/ https://www.ncbi.nlm.nih.gov/pubmed/27351920 http://dx.doi.org/10.1007/s10552-016-0780-z |
work_keys_str_mv | AT lijinlei estimatingtheprevalenceofhematologicalmalignanciesandprecursorconditionsusingdatafromhaematologicalmalignancyresearchnetworkhmrn AT smithalex estimatingtheprevalenceofhematologicalmalignanciesandprecursorconditionsusingdatafromhaematologicalmalignancyresearchnetworkhmrn AT crouchsimon estimatingtheprevalenceofhematologicalmalignanciesandprecursorconditionsusingdatafromhaematologicalmalignancyresearchnetworkhmrn AT oliversteven estimatingtheprevalenceofhematologicalmalignanciesandprecursorconditionsusingdatafromhaematologicalmalignancyresearchnetworkhmrn AT romaneve estimatingtheprevalenceofhematologicalmalignanciesandprecursorconditionsusingdatafromhaematologicalmalignancyresearchnetworkhmrn |