Introduction. Screening for prostate cancer (PCa) through ProstateSpecific Antigen (PSA) test widespread in highincome countries long before definitive results about its efficacy. Actually, even though PSA testing do contribute to reduce PCa mortality, the harm to benefit ratio remains controversial, given that PSA testing can detect cancers that may otherwise go undiagnosed during a man’s life (i.e., overdiagnosis and, consequently, overtreatment with severe side effects). To evaluate the impact of PSA testing diffusion in the Friuli Venezia Giulia (FVG) region, populationbased methods for estimating and projecting cancer morbidity and mortality indicators were applied to PCa and results were compared to observed data. Taking advantage of the complete coverage of the FVG population of both the regional cancer registry and the digital health archive, data on PCa cases and on PSA testing use were concurrently analyzed. Estimates of prostate cancer burden in Friuli Venezia Giulia. Methods. The Mortality and Incidence Analysis Model (MIAMOD) (Verdecchia, 1989) is a regression of mortality on observed mortality data (from official statistics) to backcalculate ageperiodcohort incidence. The MIAMOD method was applied for estimating and projecting incidence, mortality, and prevalence for selected cancers in FVG region in the period 19702015 (Zucchetto et al, 2013). For PCa, a specific procedure was used to better capture recent variations: preliminary mortality estimation up to 2010 was performed using regional mortality data for the period 19702007; relative survival estimates were calculated using data from EUROCARE4 in the period 19852002 and modeled by means of mixture cure models of the Weibull type with power function at macroarea level (NorthEast) for the period 20032005, and then assumed to be constant. Results. MIAMOD estimates showed a high goodness of fit with the observed incidence for all cancer sites, except for PCa. Although both increasing with time, PCa incidence rates estimates were much lower than observed incidence rates, especially in the period 19962007 and they seemed to converge in 20082009. Conversely, in the same period, mortality rates were almost stable. Analysis of PSA testing rates and PCa incidence rates trends in Friuli Venezia Giulia. Methods. Data on PSA tests performed in men aged 40+ years were retrieved from FVG digital health archive for the period 19982012. The overall PSA testing rates were calculated as the number of tested men each year (multiple prescriptions to the same man were counted once) over the male population. PCa incidence rates (from the FVG cancer registry) among men aged 40+ years or more were analyzed for the period 19952009. Joinpoint regression analysis was performed to identify knots where a statistically significant change over time in the logslope of the rates occurred. Ageperiodcohort analyses were also performed. In order to solve the unidentifiability problem, the following assumptions have been made (based on preliminary plots of rates by periods, birth cohorts, and age classes): a period was selected as reference; cohort effects were constrained to be 0 on average with 0 slope. Age effects represented agespecific rates in the reference period, after adjustment for the cohort effects; period effects were interpretable as rate ratios (RRs) relative to the reference period; cohort effects were interpretable as residual RRs relative to the ageperiod prediction. Results. PSA testing increased from 12,792 per 100,000 men in 1998, up to 30,407 in 2009, and then slightly decreased. Significant changes emerged in 2002 and 2008, with a high increase of rates in the period 19982002 (annual percent change, aPC=16.9; 95% confidence interval, CI: 12.9 to 21.2), a smaller increase in the period 20022008 (aPC=3.6; 95% CI: 1.7 to 5.5), and a subsequent stabilization (aPC= 0.7; 95% CI: 3.1 to 1.8). Similar patterns emerged by age strata. Compared to reference period 19982002, PSA testing rates were significantly higher in 20032007 and stabilized thereafter. Age effects indicated sharp increasing rates up to age 7074 years and then a reduction. No particular cohort effects emerged, except for a tendency of more recently born men to undergo PSA testing. Agedrift was equal to 4.7% (95% CI: 4.7% to 4.7%). The overall crude incidence rate of PCa increased from 219.8 per 100,000 men in 1995, up to 385.5 in 2007, and then decreased down to 328.3 in 2009. Joinpoint analysis estimated statistically significant changes in PCa incidence rates logslopes in 1998 and 2007: the aPCs in the periods 19951998, 19982007, and 20072009 were 12.1 (95% CI: 6.6 to 17.9), 1.9 (95% CI: 0.9 to 2.8), and 7.0 (95% CI: 14.3 to 0.9), respectively. The results of ageperiodcohort analysis with natural splines showed, as compared to the reference period 19951999, higher incidence rates in the subsequent 5year period and then a plateau (the analysis performed using 1year time spans, highlighted an increase up to 2007 followed by a reduction). Agespecific PCa incidence rates were sharply increasing up to 7075 years and then reduced. Residual cohort effects highlighted high increasing risks for the most recently born men (i.e., after 1950). The agedrift was equal to 2.3% (95% CI: 1.9%2.7%). Discussion. The diffusion of PSA testing in FVG has inflated the incidence of PCa without affecting the overall mortality. Given that MIAMOD estimates are based on mortality data, which have not been so heavily modified by the introduction of PSA test as PCa incidence rates, this could explain the difference between MIAMOD estimates and observed incidence rates, especially in the period 19962007. Though the period was not totally coincident, trend over time of PCa incidence rates resembled to some extent those of PSAtesting rates and the age, period, and cohort effects were somewhat similar (though PCa incidence was more affected by cohort effects). The PCa incidence estimates produced using MIAMOD could be considered as the rates that would be observed in FVG in the absence of such a great increase of PSA testing use (observed since 1998, but reasonably started before). Therefore, the difference between observed and estimated incidence rates (ranging between 7% and 18% in the period 19962007, median 11%), could be attributable to screening with PSA and, reasonably, to overdiagnosis. These results are in line with estimates of overdiagnosis derived from microsimulation models based on randomized trials results. Conclusion. Estimates of PCa incidence and prevalence based on mortality data should be carefully evaluated taking into account of the age, period, and cohort trends of PSA testing data, which are available in several areas, including those not yet covered by cancer registration.
THE IMPACT OF THE WIDESPREAD SCREENING THROUGH PROSTATESPECIFIC ANTIGEN (PSA) TEST ON POPULATIONBASED MODELS FOR ESTIMATING PROSTATE CANCER BURDEN
ZUCCHETTO, ANTONELLA
2015
Abstract
Introduction. Screening for prostate cancer (PCa) through ProstateSpecific Antigen (PSA) test widespread in highincome countries long before definitive results about its efficacy. Actually, even though PSA testing do contribute to reduce PCa mortality, the harm to benefit ratio remains controversial, given that PSA testing can detect cancers that may otherwise go undiagnosed during a man’s life (i.e., overdiagnosis and, consequently, overtreatment with severe side effects). To evaluate the impact of PSA testing diffusion in the Friuli Venezia Giulia (FVG) region, populationbased methods for estimating and projecting cancer morbidity and mortality indicators were applied to PCa and results were compared to observed data. Taking advantage of the complete coverage of the FVG population of both the regional cancer registry and the digital health archive, data on PCa cases and on PSA testing use were concurrently analyzed. Estimates of prostate cancer burden in Friuli Venezia Giulia. Methods. The Mortality and Incidence Analysis Model (MIAMOD) (Verdecchia, 1989) is a regression of mortality on observed mortality data (from official statistics) to backcalculate ageperiodcohort incidence. The MIAMOD method was applied for estimating and projecting incidence, mortality, and prevalence for selected cancers in FVG region in the period 19702015 (Zucchetto et al, 2013). For PCa, a specific procedure was used to better capture recent variations: preliminary mortality estimation up to 2010 was performed using regional mortality data for the period 19702007; relative survival estimates were calculated using data from EUROCARE4 in the period 19852002 and modeled by means of mixture cure models of the Weibull type with power function at macroarea level (NorthEast) for the period 20032005, and then assumed to be constant. Results. MIAMOD estimates showed a high goodness of fit with the observed incidence for all cancer sites, except for PCa. Although both increasing with time, PCa incidence rates estimates were much lower than observed incidence rates, especially in the period 19962007 and they seemed to converge in 20082009. Conversely, in the same period, mortality rates were almost stable. Analysis of PSA testing rates and PCa incidence rates trends in Friuli Venezia Giulia. Methods. Data on PSA tests performed in men aged 40+ years were retrieved from FVG digital health archive for the period 19982012. The overall PSA testing rates were calculated as the number of tested men each year (multiple prescriptions to the same man were counted once) over the male population. PCa incidence rates (from the FVG cancer registry) among men aged 40+ years or more were analyzed for the period 19952009. Joinpoint regression analysis was performed to identify knots where a statistically significant change over time in the logslope of the rates occurred. Ageperiodcohort analyses were also performed. In order to solve the unidentifiability problem, the following assumptions have been made (based on preliminary plots of rates by periods, birth cohorts, and age classes): a period was selected as reference; cohort effects were constrained to be 0 on average with 0 slope. Age effects represented agespecific rates in the reference period, after adjustment for the cohort effects; period effects were interpretable as rate ratios (RRs) relative to the reference period; cohort effects were interpretable as residual RRs relative to the ageperiod prediction. Results. PSA testing increased from 12,792 per 100,000 men in 1998, up to 30,407 in 2009, and then slightly decreased. Significant changes emerged in 2002 and 2008, with a high increase of rates in the period 19982002 (annual percent change, aPC=16.9; 95% confidence interval, CI: 12.9 to 21.2), a smaller increase in the period 20022008 (aPC=3.6; 95% CI: 1.7 to 5.5), and a subsequent stabilization (aPC= 0.7; 95% CI: 3.1 to 1.8). Similar patterns emerged by age strata. Compared to reference period 19982002, PSA testing rates were significantly higher in 20032007 and stabilized thereafter. Age effects indicated sharp increasing rates up to age 7074 years and then a reduction. No particular cohort effects emerged, except for a tendency of more recently born men to undergo PSA testing. Agedrift was equal to 4.7% (95% CI: 4.7% to 4.7%). The overall crude incidence rate of PCa increased from 219.8 per 100,000 men in 1995, up to 385.5 in 2007, and then decreased down to 328.3 in 2009. Joinpoint analysis estimated statistically significant changes in PCa incidence rates logslopes in 1998 and 2007: the aPCs in the periods 19951998, 19982007, and 20072009 were 12.1 (95% CI: 6.6 to 17.9), 1.9 (95% CI: 0.9 to 2.8), and 7.0 (95% CI: 14.3 to 0.9), respectively. The results of ageperiodcohort analysis with natural splines showed, as compared to the reference period 19951999, higher incidence rates in the subsequent 5year period and then a plateau (the analysis performed using 1year time spans, highlighted an increase up to 2007 followed by a reduction). Agespecific PCa incidence rates were sharply increasing up to 7075 years and then reduced. Residual cohort effects highlighted high increasing risks for the most recently born men (i.e., after 1950). The agedrift was equal to 2.3% (95% CI: 1.9%2.7%). Discussion. The diffusion of PSA testing in FVG has inflated the incidence of PCa without affecting the overall mortality. Given that MIAMOD estimates are based on mortality data, which have not been so heavily modified by the introduction of PSA test as PCa incidence rates, this could explain the difference between MIAMOD estimates and observed incidence rates, especially in the period 19962007. Though the period was not totally coincident, trend over time of PCa incidence rates resembled to some extent those of PSAtesting rates and the age, period, and cohort effects were somewhat similar (though PCa incidence was more affected by cohort effects). The PCa incidence estimates produced using MIAMOD could be considered as the rates that would be observed in FVG in the absence of such a great increase of PSA testing use (observed since 1998, but reasonably started before). Therefore, the difference between observed and estimated incidence rates (ranging between 7% and 18% in the period 19962007, median 11%), could be attributable to screening with PSA and, reasonably, to overdiagnosis. These results are in line with estimates of overdiagnosis derived from microsimulation models based on randomized trials results. Conclusion. Estimates of PCa incidence and prevalence based on mortality data should be carefully evaluated taking into account of the age, period, and cohort trends of PSA testing data, which are available in several areas, including those not yet covered by cancer registration.File  Dimensione  Formato  

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https://hdl.handle.net/20.500.14242/81854
URN:NBN:IT:UNIMI81854