Modelling to estimate future trends in cancer prevalence

Health Care Management Science

Volumn 14, Issue 3, 2011, Pages 262-266

  Fiorentino, Francesca ^a   Maddams, Jacob ^b   Møller, Henrik ^b   Utley, Martin ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

Cancer prevalence; Forecasting; Incidence; Population health; Survival

Indexed keywords

ARTICLE; CLASSIFICATION; FORECASTING; HUMAN; INCIDENCE; MORTALITY; NEOPLASM; PREVALENCE; SEX DIFFERENCE; SOCIOECONOMICS; THEORETICAL MODEL; TIME;

FORECASTING; HUMANS; INCIDENCE; MODELS, THEORETICAL; NEOPLASMS; PREVALENCE; SEX FACTORS; SOCIOECONOMIC FACTORS; TIME FACTORS;

EID: 80051899402     PISSN: 13869620     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10729-011-9149-8     Document Type: Article

References (6)

1. Maddams J, Brewster D, Gavin A, Steward J, Elliott J, Utley M, Moller H (2009) Cancer prevalence in the United Kingdom: estimates for 2008. Br J Cancer 101(3): 541-547.
2. Utley M, Treasure T, Linklater K, Moller H (2008) Better out than in? The resection of pulmonary metastases from colorectal tumours. Operations Research for Health Care Delivery Engineering (ORAHS 2007): 493-500.
3. www. apho. org. uk/resource/view. aspx?RID=48308 accessed 11/1/11.
4. De Angelis G et al (1994) MIAMOD: A computer package to estimate chronic disease morbidity using mortality and survival data. Computer Methods and Programs in Biomedicine 44(2): 99-107.
5. Verdecchia A et al (2002) Estimation and projections of cancer prevalence from cancer registry data. Statistics in Medicine 21(22): 3511-3526.
6. National Cancer Information Service. www. ncin. org. uk/analysis/ncis. shtml.