prevalence: Ispra Jan 23, 2014

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1 From observed tocomplete prevalence: the Sandra Mallone Istituto Superiore di Sanità National Centre for Epidemiology Surveillance and Health Promotion Department of Cancer Epidemiology Ispra Jan 23, 2014

2 Outline Definition of cancer prevalence General definition Complete prevalence Limited-duration prevalence Definitions and notations Incidence and survival functions Interpreting and evaluating the results(examples and applications) Outline

3 Cancer prevalence General definition Person-based cancer prevalence alive people with cancer(first primary) in a population Tumour-based cancer prevalence tumours(first or subsequent primaries) of alive people in a population prevalence helps to quantify the demand for health care services depends on incidence and survival includes new and pre-existing cases Pag 1/20

4 Complete Prevalence Operational definition Complete prevalencen C (x) proportion of patients aged x previously diagnosed, who are still alive at a certain reference date Yearof diagnosis it can be estimated from self-reported population-based surveys by statistical modelling back calculation methods transition rates models incidence-mortality-prevalence approach completeness index method x(reference date) Pag 2/20

5 Limited-duration duration prevalencen O (x-l,x) Limited-duration duration prevalence-1 Operational definition proportion of cancer patients, who received a diagnosis in the L-years before a certainreferencedateandwhoarestillalive i.etheobserved observed prevalence directly observed only in populations covered by cancer registration for L-years maximum duration is given by the length of the registration period can be breaked down by different durations (3-year, 5-year, ) providing indications of health care needs for specific sections of the population Yearof diagnosis N o (2008,2013) N o (1998,2013) x-l L-years of observation x(reference date) Pag 3/20

6 Counting method The method counts from cancer registry(cr) data how many incident cases are alive at the reference date and incorporates an adjustment to take account of the effect of patient loss during follow-up. This adjustment assumes that patient loss during follow-up have the same probability of surviving as those not lost(i.e. patients diagnosed in the same year and having the same age). Method Input Output Software Countingmethod CRsincidenceand follow-up data Limited-duration prevalence SEER*Statsoftware (prevalence session) Pag 4/20

7 Limited-duration duration prevalence-2 Operational definition N O (x-l,x)itisabiasedestimateofn C (x)asthe proportionof cancerpatients, diagnosedbeforethe CR starteditsactivityand supposedtobealiveat the referencedate islosttothe estimate i.ethe unobserved prevalence theextentofthebiasdependson the lenght of the registration period the mean survival time of cancer patients varying by cancer site Yearof diagnosis unobserved period L-years of observation Reference date (x) Pag 5/20

8 * Definition and notations-1 Modelled-based estimates of complete prevalence N C (x) can be decomposed into the sum of the model-based estimates of unobserved and observed prevalence respectively, obtained combining incidence and survival 1 I(t) isthe incidencefunctionforcancerpatientat timeofdiagnosist S(t, x-t) is the relative survival function for a cancer patient from the time of diagnosis toagex (t<x) *CapocacciaR, De Angelis R. Estimating the completeness of prevalence based on cancer registry data. Stat Med 1997;16: I Corazziari, A Mariotto, and R Capocaccia. Correcting the completeness bias of observed prevalence. Tumori, 1999; 85: Pag 6/20

9 Definition and notations-2 Completeness index from equation 1 ratio between the model-based observed and complete prevalence It can be used to adjust the limited-duration prevalence as follows: Method Input Output Software Completeness Index method *ChildPrev method Limited-duration prevalence Incidence and survival models estimates (SEER cancer data) Completeness Index Complete Prevalence (estimatesand variance**) ComPrev Software **Gigli A., Mariotto A., Clegg LX, Tavilla A, Corazziari I, Capocaccia R, Hachey M, Steve S. Estimating the varianceofcancerprevalencefrompopulation-basedregistries. StatMethodsMedRes2006 Jun;15(3): *Simonetti A, Gigli A, Capocaccia R, Mariotto A. Estimating complete prevalence of cancers diagnosed in childhood. Stat Med 2008;27: Pag 7/20

10 Incidence function Incidence per Observed Estimated -polinomial model Estimated - exponential model age The incidence function used in ComPrev for describing the relationship between cancer incidence and age adjusting for birth cohort is the logistic model having as argument an exponential or a polinomial function of age Pag 8/20

11 Survival function As relative survival function, ComPrev uses the mixed cure models which assume a proportion of patients to die of cancer with a Weibull hazard function and the remaining proportion(cured patients) to have the same mortality of the general population MIAMOD/PIAMOD Prevalence of cured patients and survivorship see next presentations Pag 9/20

12 Intepreting the results Values ofr R close to 1 all prevalent cases are directly observed by CRs(long-established CRs) R closeto0 no prevalentcasesare directlyobservedbycrs a largepart of the complete prevalence has to be estimated General rule forr incidence function which rises very steeply with age(prostate cancer) low survival rates(pancreas, liver cancer) high survival rates(testicular, cervical cancer) low completeness index complete prevalence limited-duration prevalence and viceversa Pag 10/20

13 Interpreting the results-ex.1 Calculation of prevalence for all cancers combined in European cancer registry areas EUROPREVAL project Capocaccia R, Colonna M, Corazziari I, R.De Angelis, Francisci S, Micheli A, et al. Measuring cancer prevalence in Europe: the EUROPREVAL project. Ann Oncol 2002;13(6): Pag 11/20

14 Interpreting the results- Ex.2 Completeness index (%) at for colorectal cancer in men by age at prevalence and length of registration period(5,10,15,25 years). The completeness index increases with increasing length of follow-up, for younger ages, and lower survival rates, since a worse prognosis leads to a restricted number of longterm survivors. AIRTUM WorkingGroup, Italiancancerfigures, report 2010: CancerPrevalencein Italy. EpidemiolPrev34(5-6) suppl.2 Pag 12/20

15 Applications-1 Prevalence proportion (ranked) per 100,000 at 31 st Dec 1992 by Country and time since diagnosis. Breast, women. EUROPREVAL project patients lived 10 years after diagnosis patients are still alive at the reference date, regardless of the date of diagnosis MicheliA,MugnoE,KroghV,QuinnMJ,ColemanM,HakulinenT,GattaG,BerrinoF,CapocacciaR; EUROPREVAL Working Group. Cancer prevalence in European registry areas. Ann Oncol Jun;13(6): Pag 11/20

16 Applications-2 Limited-duration prevalence by duration(2,5,15 years), complete prevalence(per 100,000)at1 st jan2003,prevalentscasesineuropeat2003,effectof demographic changes Female breast EUROCARE-4 GGatta,SMallone,JMvanderZwan,ATrama,SSiesling,RCapocaccia&theEUROCAREWorking Group. Cancer prevalence estimates in Europe at the beginning of Ann Oncol 2013; 00:1-7 Pag 14/20

17 Crucial Points CRs data Incidence incompleteness Poor quality of follow-up data Modeltofit exponentialvspolinomialincidencefunction?; more complex incidence function(calendar period?) cure survival models(simple or more complex?) Model validation goodness of fit confidence intervals visually inspection of observed vs estimated values Pag 15/20

18 Evaluating the results As the methodisbasedon statisticalmodellingof incidenceand survivalcrsdata, an evaluation of the results is necessary Validation analysis limited-duration prevalence vs complete prevalence estimates for longestablished CRs whose registration period is long enough to have registered virtually all the prevalent cases Sensitivity analysis changes of the completeness index corresponding to changes in the incidence and survival parameter estimates Pag 16/20

19 Validation analysis- EX.1 Empirical and modelled completeness indices with 21-years of follow-up for whitesinconnecticutandwhitesandblacksinseerbyageandgender. All cancers combined Validationshowed that the adjusted SEER cancer prevalence proportions provided reasonably unbiased prevalence proportion estimates in general Merrill RM, Feuer EJ, Capocaccia R, Mariotto A. Cancer Prevalence Estimates Based on Tumor Registry Data in the SEER Program. Int J Epidemiol 2000;29: Pag 17/20

20 N Discrepancies between 25-year observed and estimated prevalence - rare cancers Norway-10yrs Norway-15yrs Norway-20yrs Validation analysis- EX.2 Differences between the 25-year observed and estimated prevalence by rarity and length of registration period(10,15,20 years) for Norway and Scotland. 8 N Discrepancies between 25-year observed and estimated prevalence - rare cancers Scotland-10yrs Scotland-15yrs Scotland-20yrs abs. differences (%) abs. differences (%) 8 RARECARE As expected the differences dicrease dicrease with increasing registration length. For a Discrepancies between 25-year observed and Discrepancies between 25-year observed and restricted N subgroup estimated prevalence of rare - common cancers with low N incidence estimated prevalence and - survival, common cancers the method was Norway-10yrs Norway-15yrs Norway-20yrs Scotland-10yrs Scotland-15yrs Scotland-20yrs not able to adequately correct the observed prevalence even considering a registration period of20 years abs. differences (%) S Mallone, R De Angelis, JM van der Zwan, A Trama, S Siesling, G Gatta, R Capocaccia, The RARECARE WG. Methodological aspects of estimating rare cancer prevalence in Europe: The experience of the RARECARE project. Cancer Epid 2013; 37: Pag 18/ abs. differences (%)

21 Sensitivity analysis- EX.1 R(15)-values for changes of ±20% in survival and incidence parameter estimates R(15) 95.0 increasing 85.0 increasing survival (+20 20%)with 75.0 period of diagnosis (negative values of β 2 ) is reflected 65.0 by a general rise of the 55.0 completeness index because survival of recently observed cases increases while 45.0 the less recent unobserved cases decreases RARECARE completeness 45.0 that ofthe of diagnosis(positive values R(15) for +20% in survival parameters R(15) ageof values of β 1 ) indicates generally lead to a reduction ofthe completeness index in general 55.0 assumptions 45.0 R(15) R(15) general the completeness index method indicates that survival worsen R(15) for -20% in survival parameters method is not 55.0 are 45.0 available worsen with age and not sensitive to modelling assumptions when observed prevalence data are available for15 years or more R(15) for +20% in incidence parameters R(15) for -20% in incidence parameters Pag 19/20

22 Further methodological developments Prevalence projections tothe the future MIAMOD PIAMOD incidence-mortality-prevalence approach Prevalence estimationsin in populations not covered by registration ecological regression approach Curedand survivorship prevalence estimation PIAMOD cure mixed models see next presentations Pag 20/20

23 Thank you Sandra Mallone Istituto Superiore di Sanità National Centre for Epidemiology Surveillance and Health Promotion Department of Cancer Epidemiology

24 EUROPREVAL Publications Merrill RM, Feuer EJ, Capocaccia R, Mariotto A. Cancer Prevalence Estimates Based on Tumor Registry Data in the SEER Program. Int J Epidemiol 2000;29: Simonetti A, Gigli A, Capocaccia R, Mariotto A. Estimating complete prevalence of cancers diagnosed in childhood. Stat Med 2008;27: Gigli A., Mariotto A., Clegg LX, Tavilla A, Corazziari I, Capocaccia R, Hachey M, Steve S. Estimating the variance of cancer prevalence from population-based registr ies. Stat Methods Med Res 2006 Jun;15(3): Verdecchia A, Micheli A, Colonna M, Moreno V, Izarzugaza MI, Paci E; EUROPREVAL Working Group. A comparative analysis of cancer prevalence in cancer registry areas of France, Italy and Spain. Ann Oncol Jul;13(7): Capocaccia R, Colonna M, Corazziari I, R.De Angelis, Francisci S, Micheli A, et al. Measuring cancer prevalence in Europe: the EUROPREVAL project. Ann Oncol 2002;13(6): MicheliA,Mugno E,KroghV,QuinnMJ,ColemanM,HakulinenT,GattaG,BerrinoF, Capocaccia R; EUROPREVAL Working Group. Cancer prevalence in European registry areas. Ann Oncol Jun;13(6):

25 Publications Lutz JM, Francisci S, Mugno E, Usel M, Pompe-Kirn V, Coebergh JW, Bieslka-Lasota M; EUROPREVAL Working Group. Cancer prevalence in Central Europe: the EUROPREVAL Study. Ann Oncol Feb;14(2): Möller T, Anderson H, Aareleid T, Hakulinen T, Storm H, Tryggvadottir L, et al. Cancer prevalence in Northern Europe: results from the EUROPREVAL study. Ann Oncol 2003;14(4): Forman D, Stockton D, Møller H, Quinn M, Babb P, De Angelis R, et al. Cancer prevalence in the UK: results from the EUROPREVAL study. Ann Oncol 2003;14(4): G. Gatta, R. Capocaccia, F. Berrino, M. R. Ruzza, P. Contiero & the EUROPREVAL Working Group. Colon cancer prevalence and estimation of differing care needs of colon cancer patients. Ann Oncol 2004;15: ITAPREVAL -AIRTUM I Corazziari, A Mariotto, and R Capocaccia. Correcting the completeness bias of observed prevalence. Tumori, 1999; 85: MicheliA, Francisci S, Krogh V et al. Cancer prevalence in Italian cancer registry areas: the ITAPREVAL study. ITAPREVAL Working Group. Tumori 1999, 85: Vercelli M, QuagliaA, ParodiS, CrosignaniP. Cancer prevalence in the elderly. ITAPREVAL Working Group. Tumori Sep-Oct;85(5):391-9.

26 EUROCARE- RARECARE Publications AIRTUM Working Group, Italian cancer figures, report 2010: Cancer Prevalence in Italy. EpidemiolPrev34(5-6) suppl.2 Gemma G, JM van der Zwan, PG Casali, S Siesling, AP Dei Tos, I Kunkler, R Otter, L Licitra, S Mallone, A Tavilla, A Trama, R Capocaccia, The RARECARE working group. Rarecancersarenotsorare:TherarecancerburdeninEurope2011;EJC7: G Gatta, S Mallone, JM van der Zwan, A Trama, S Siesling, R Capocaccia & the EUROCARE Working Group. Cancer prevalence estimates in Europe at the beginning of Ann Oncol 2013; 00:1-7 SMallone,RDeAngelis,JMvanderZwan,ATrama,SSiesling,GGatta,RCapocaccia, The RARECARE WG. Methodological aspects of estimating rare cancer prevalence in Europe: The experience of the RARECARE project. Cancer Epid 2013; 37: