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ABSTRACT: Obesity affects multiple points along the breast cancer control continuum from prevention to screening and treatment, often in opposing directions. Obesity is also more prevalent in Blacks than Whites at most ages so it might contribute to observed racial disparities in mortality. We use two established simulation models from the Cancer Intervention and Surveillance Modeling Network (CISNET) to evaluate the impact of obesity on race-specific breast cancer outcomes. The models use common national data to inform parameters for the multiple US birth cohorts of Black and White women, including age- and race-specific incidence, competing mortality, mammography characteristics, and treatment effectiveness. Parameters are modified by obesity (BMI of ≥30 kg/m(2)) in conjunction with its age-, race-, cohort- and time-period-specific prevalence. We measure age-standardized breast cancer incidence and mortality and cases and deaths attributable to obesity. Obesity is more prevalent among Blacks than Whites until age 74; after age 74 it is more prevalent in Whites. The models estimate that the fraction of the US breast cancer cases attributable to obesity is 3.9-4.5 % (range across models) for Whites and 2.5-3.6 % for Blacks. Given the protective effects of obesity on risk among women <50 years, elimination of obesity in this age group could increase cases for both the races, but decrease cases for women ≥50 years. Overall, obesity accounts for 4.4-9.2 % and 3.1-8.4 % of the total number of breast cancer deaths in Whites and Blacks, respectively, across models. However, variations in obesity prevalence have no net effect on race disparities in breast cancer mortality because of the opposing effects of age on risk and patterns of age- and race-specific prevalence. Despite its modest impact on breast cancer control and race disparities, obesity remains one of the few known modifiable risks for cancer and other diseases, underlining its relevance as a public health target.
Breast Cancer Research and Treatment 10/2012; · 4.43 Impact Factor
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Eveline A M Heijnsdijk,
Elisabeth M Wever,
Anssi Auvinen,
Jonas Hugosson,
Stefano Ciatto,
Vera Nelen,
Maciej Kwiatkowski,
Arnauld Villers,
Alvaro Páez,
Sue M Moss, [......],
Tuukka Mäkinen,
Sigrid Carlsson,
Ida J Korfage,
Marie-Louise Essink-Bot,
Suzie J Otto,
Gerrit Draisma,
Chris H Bangma,
Monique J Roobol,
Fritz H Schröder,
Harry J de Koning
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ABSTRACT: After 11 years of follow-up, the European Randomized Study of Screening for Prostate Cancer (ERSPC) reported a 29% reduction in prostate-cancer mortality among men who underwent screening for prostate-specific antigen (PSA) levels. However, the extent to which harms to quality of life resulting from overdiagnosis and treatment counterbalance this benefit is uncertain.
On the basis of ERSPC follow-up data, we used Microsimulation Screening Analysis (MISCAN) to predict the number of prostate cancers, treatments, deaths, and quality-adjusted life-years (QALYs) gained after the introduction of PSA screening. Various screening strategies, efficacies, and quality-of-life assumptions were modeled.
Per 1000 men of all ages who were followed for their entire life span, we predicted that annual screening of men between the ages of 55 and 69 years would result in nine fewer deaths from prostate cancer (28% reduction), 14 fewer men receiving palliative therapy (35% reduction), and a total of 73 life-years gained (average, 8.4 years per prostate-cancer death avoided). The number of QALYs that were gained was 56 (range, -21 to 97), a reduction of 23% from unadjusted life-years gained. To prevent one prostate-cancer death, 98 men would need to be screened and 5 cancers would need to be detected. Screening of all men between the ages of 55 and 74 would result in more life-years gained (82) but the same number of QALYs (56).
The benefit of PSA screening was diminished by loss of QALYs owing to postdiagnosis long-term effects. Longer follow-up data from both the ERSPC and quality-of-life analyses are essential before universal recommendations regarding screening can be made. (Funded by the Netherlands Organization for Health Research and Development and others.).
New England Journal of Medicine 08/2012; 367(7):595-605. · 53.30 Impact Factor
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Ruth Etzioni,
Roman Gulati,
Alex Tsodikov,
Elisabeth M Wever,
David F Penson, Eveline A M Heijnsdijk,
Jeffrey Katcher,
Gerrit Draisma,
Eric J Feuer,
Harry J de Koning,
Angela B Mariotto
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ABSTRACT: BACKGROUND: Prostate cancer mortality rates in the United States declined by >40% between 1991 and 2005. The impact of changes in primary treatment and adjuvant and neoadjuvant hormone therapy on this decline is unknown. METHODS: The authors applied 3 independently developed models of prostate cancer natural history and disease detection under common assumptions about treatment patterns, treatment efficacy, and survival in the population. Primary treatment patterns were derived from the Surveillance, Epidemiology, and End Results registry; data on the frequency of hormone therapy were obtained from the CaPSURE (Cancer of the Prostate Strategic Urologic Research Endeavor) database; and treatment efficacy was based on estimates from randomized trials and comparative effectiveness studies of treatment alternatives. The models projected prostate cancer mortality without prostate-specific antigen screening and in the presence and absence of treatment benefit. The impact of primary treatment was expressed as a fraction of the difference between observed mortality and projected mortality in the absence of treatment benefit. RESULTS: The 3 models projected that changes in treatment explained 22% to 33% of the mortality decline by 2005. These contributions were accounted for mostly by surgery and radiation therapy, which increased in frequency until the 1990s, whereas hormone therapies contributed little to the mortality decline by 2005. Assuming that treatment benefit was less for older men, changes in treatment explained only 16% to 23% of the mortality decline by 2005. CONCLUSIONS: Changes in primary treatment explained a minority of the observed decline in prostate cancer mortality. The remainder of the decline probably was because of other interventions, such as prostate-specific antigen screening and advances in the treatment of recurrent and progressive disease. Cancer 2012. © 2012 American Cancer Society.
Cancer 05/2012; · 4.77 Impact Factor
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Nicolien T van Ravesteyn,
Diana L Miglioretti,
Natasha K Stout,
Sandra J Lee,
Clyde B Schechter,
Diana S M Buist,
Hui Huang, Eveline A M Heijnsdijk,
Amy Trentham-Dietz,
Oguzhan Alagoz,
Aimee M Near,
Karla Kerlikowske,
Heidi D Nelson,
Jeanne S Mandelblatt,
Harry J de Koning
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ABSTRACT: Timing of initiation of screening for breast cancer is controversial in the United States.
To determine the threshold relative risk (RR) at which the harm-benefit ratio of screening women aged 40 to 49 years equals that of biennial screening for women aged 50 to 74 years.
Comparative modeling study.
Surveillance, Epidemiology, and End Results program, Breast Cancer Surveillance Consortium, and medical literature.
A contemporary cohort of women eligible for routine screening.
Lifetime.
Societal.
Mammography screening starting at age 40 versus 50 years with different screening methods (film, digital) and screening intervals (annual, biennial).
Benefits: life-years gained, breast cancer deaths averted; harms: false-positive mammography findings; harm-benefit ratios: false-positive findings/life-years gained, false-positive findings/deaths averted.
Screening average-risk women aged 50 to 74 years biennially yields the same false-positive findings/life-years gained as biennial screening with digital mammography starting at age 40 years for women with a 2-fold increased risk above average (median threshold RR, 1.9 [range across models, 1.5 to 4.4]). The threshold RRs are higher for annual screening with digital mammography (median, 4.3 [range, 3.3 to 10]) and when false-positive findings/deaths averted is used as an outcome measure instead of false-positive findings/life-years gained. The harm-benefit ratio for film mammography is more favorable than for digital mammography because film has a lower false-positive rate.
The threshold RRs changed slightly when a more comprehensive measure of harm was used and were relatively insensitive to lower adherence assumptions.
Risk was assumed to influence onset of disease without influencing screening performance.
Women aged 40 to 49 years with a 2-fold increased risk have similar harm-benefit ratios for biennial screening mammography as average-risk women aged 50 to 74 years. Threshold RRs required for favorable harm-benefit ratios vary by screening method, interval, and outcome measure.
National Cancer Institute.
Annals of internal medicine 05/2012; 156(9):609-17. · 16.73 Impact Factor
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ABSTRACT: To quantify the extent to which a clinically significant prostate cancer mortality reduction due to screening could have been masked by control arm screening (contamination) in the Prostate, Lung, Colorectal, and Ovarian (PLCO) trial.
We used three independently developed models of prostate cancer natural history to conduct a virtual PLCO trial. Simulated participants underwent pre-trial screening based on population patterns. The intervention arm followed observed compliance during the trial then resumed population screening. A contaminated control arm followed observed contamination during the trial then resumed population screening, while an uncontaminated control arm discontinued screening upon entry. We assumed a clinically significant screening benefit, applied population treatments and survival patterns, and calculated mortality rate ratios relative to the contaminated and uncontaminated control arms.
The virtual trial reproduced observed incidence, including stage and grade distributions, and control arm mortality after 10 years of complete follow-up. Under the assumed screening benefit, the three models found that contamination increased the mortality rate ratio from 0.68-0.77 to 0.86-0.91, increased the chance of excess mortality in the intervention arm from 0-4 % to 15-28 %, and decreased the power of the trial to detect a mortality difference from 40-70 % to 9-25 %.
Our computer simulation models indicate that contamination substantially limited the ability of the PLCO to identify a clinically significant screening benefit. While the trial shows annual screening does not reduce mortality relative to population screening, contamination prevents concluding whether screening reduces mortality relative to no screening.
Cancer Causes and Control 04/2012; 23(6):827-35. · 2.88 Impact Factor
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European urology 07/2011; 60(5):931-3; discussion 933-4. · 7.67 Impact Factor
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ABSTRACT: Estimates of overdiagnosis in mammography screening range from 1% to 54%. This review explains such variations using gradual implementation of mammography screening in the Netherlands as an example. Breast cancer incidence without screening was predicted with a micro-simulation model. Observed breast cancer incidence (including ductal carcinoma in situ and invasive breast cancer) was modeled and compared with predicted incidence without screening during various phases of screening program implementation. Overdiagnosis was calculated as the difference between the modeled number of breast cancers with and the predicted number of breast cancers without screening. Estimating overdiagnosis annually between 1990 and 2006 illustrated the importance of the time at which overdiagnosis is measured. Overdiagnosis was also calculated using several estimators identified from the literature. The estimated overdiagnosis rate peaked during the implementation phase of screening, at 11.4% of all predicted cancers in women aged 0-100 years in the absence of screening. At steady-state screening, in 2006, this estimate had decreased to 2.8%. When different estimators were used, the overdiagnosis rate in 2006 ranged from 3.6% (screening age or older) to 9.7% (screening age only). The authors concluded that the estimated overdiagnosis rate in 2006 could vary by a factor of 3.5 when different denominators were used. Calculations based on earlier screening program phases may overestimate overdiagnosis by a factor 4. Sufficient follow-up and agreement regarding the chosen estimator are needed to obtain reliable estimates.
Epidemiologic Reviews 06/2011; 33(1):111-21. · 7.58 Impact Factor
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ABSTRACT: Digital mammography has been shown to increase the detection of ductal carcinoma in situ (DCIS) compared to screen-film mammography. The benefits and risks of such an increase were assessed.
Breast cancer detection rates were compared between 502,574 screen-film and 83,976 digital mammograms performed between 2004 and 2006 among Dutch screening participants. The detection rates were then modeled using a baseline model and two extreme models that respectively assumed a high rate of progression and no progression of preclinical DCIS to invasive cancer. With these models, breast cancer mortality and overdiagnosis were predicted.
The DCIS detection rate was significantly higher at digital mammography (1.2 per 1000 mammograms (95% C.I. 1.0-1.5)) than at screen-film mammography (0.7 per 1000 mammograms (95% C.I. 0.6-0.7)). Consequently, 287 (range progressive- non progressive model: 1-598) extra breast cancer deaths per 1,000,000 women (a 4.4% increase) were predicted to be prevented. An extra 401 (range: 165-2271) cancers would be overdiagnosed (a 21% increase).
Modeling predicted that digital mammography screening would further reduce breast cancer mortality by 4.4%, at a 21% increased overdiagnosis rate. The consequences of digital screening, however, are sensitive to underlying assumptions on the natural history of DCIS.
Preventive Medicine 06/2011; 53(3):134-40. · 3.22 Impact Factor
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Roman Gulati,
Elisabeth M Wever,
Alex Tsodikov,
David F Penson,
Lurdes Y T Inoue,
Jeffrey Katcher,
Shih-Yuan Lee, Eveline A M Heijnsdijk,
Gerrit Draisma,
Harry J de Koning,
Ruth Etzioni
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ABSTRACT: Making an informed decision about treating a prostate cancer detected after a routine prostate-specific antigen (PSA) test requires knowledge about disease natural history, such as the chances that it would have been clinically diagnosed in the absence of screening and that it would metastasize or lead to death in the absence of treatment.
We use three independently developed models of prostate cancer natural history to project risks of clinical progression events and disease-specific deaths for PSA-detected cases assuming they receive no primary treatment.
The three models project that 20%-33% of men have preclinical onset; of these 38%-50% would be clinically diagnosed and 12%-25% would die of the disease in the absence of screening and primary treatment. The risk that men age less than 60 at PSA detection with Gleason score 2-7 would be clinically diagnosed in the absence of screening is 67%-93% and would die of the disease in the absence of primary treatment is 23%-34%. For Gleason score 8 to 10 these risks are 90%-96% and 63%-83%.
Risks of disease progression among untreated PSA-detected cases can be nontrivial, particularly for younger men and men with high Gleason scores. Model projections can be useful for informing decisions about treatment.
This is the first study to project population-based natural history summaries in the absence of screening or primary treatment and risks of clinical progression events following PSA detection in the absence of primary treatment.
Cancer Epidemiology Biomarkers & Prevention 05/2011; 20(5):740-50. · 4.12 Impact Factor
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ABSTRACT: Simulation models are essential tools for estimating benefits of cancer screening programs. Such models include a screening-effect model that represents how early detection by screening followed by treatment affects disease-specific survival. Two commonly used screening-effect models are the stage-shift model, where mortality benefits are explained by the shift to more favorable stages, and the cure model, where early detection enhances the chances of cure from disease.
This article describes commonly used screening-effect models and analyses their predicted mortality benefit in a model for prostate cancer screening.
The MISCAN simulation model was used to predict the reduction of prostate cancer mortality in the European Randomized Study of Screening for Prostate Cancer (ERSPC) Rotterdam. The screening-effect models were included in the model. For each model the predictions of prostate cancer mortality reduction were calculated. The study compared 4 screening-effect models, which are versions of the stage-shift model or the cure model.
The stage-shift models predicted, after a follow-up of 9 years, reductions in prostate cancer mortality varying from 38% to 63% for ERSPC-Rotterdam compared with a 27% reduction observed in the ERSPC. The cure models predicted reductions in prostate cancer mortality varying from 21% to 27%.
The differences in predicted mortality reductions show the importance of validating models to observed trial mortality data. The stage-shift models considerably overestimated the mortality reduction. Therefore, the stage-shift models should be used with care, especially when modeling the effect of screening for cancers with long lead times, such as prostate cancer.
Medical Decision Making 03/2011; 31(4):550-8. · 2.33 Impact Factor
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ABSTRACT: U.S. Black women have higher breast cancer mortality rates than White women despite lower incidence. The aim of this study is to investigate how much of the mortality disparity can be attributed to racial differences in natural history, uptake of mammography screening, and use of adjuvant therapy.
Two simulation models use common national race, and age-specific data for incidence, screening and treatment dissemination, stage distributions, survival, and competing mortality from 1975 to 2010. Treatment effectiveness and mammography sensitivity are assumed to be the same for both races. We sequentially substituted Black parameters into the White model to identify parameters that drive the higher mortality for Black women in the current time period.
Both models accurately reproduced observed breast cancer incidence, stage and tumor size distributions, and breast cancer mortality for White women. The higher mortality for Black women could be attributed to differences in natural history parameters (26-44%), use of adjuvant therapy (11-19%), and uptake of mammography screening (7-8%), leaving 38% to 46% unexplained.
Black women appear to have benefited less from cancer control advances than White women, with a greater race-related gap in the use of adjuvant therapy than screening. However, a greater portion of the disparity in mortality appears to be due to differences in natural history and undetermined factors.
Breast cancer mortality may be reduced substantially by ensuring that Black women receive equal adjuvant treatment and screening as White women. More research on racial variation in breast cancer biology and treatment utilization is needed.
Cancer Epidemiology Biomarkers & Prevention 01/2011; 20(1):112-22. · 4.12 Impact Factor
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New England Journal of Medicine 01/2011; 364(3):282-3; author reply 285-6. · 53.30 Impact Factor
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ABSTRACT: Dissemination of prostate-specific antigen (PSA) testing in the United States coincided with an increasing incidence of prostate cancer, a shift to earlier stage disease at diagnosis, and decreasing prostate cancer mortality. We compared PSA screening performance with respect to prostate cancer detection in the US population vs in the Rotterdam section of the European Randomized Study of Screening for Prostate Cancer (ERSPC-Rotterdam). We developed a simulation model for prostate cancer and PSA screening for ERSPC-Rotterdam. This model was then adapted to the US population by replacing demography parameters with US-specific ones and the screening protocol with the frequency of PSA tests in the US population. We assumed that the natural progression of prostate cancer and the sensitivity of a PSA test followed by a biopsy were the same in the United States as in ERSPC-Rotterdam. The predicted prostate cancer incidence peak in the United States was then substantially higher than the observed prostate cancer incidence peak (13.3 vs 8.1 cases per 1000 man-years). However, the actual observed incidence was reproduced by assuming a substantially lower PSA test sensitivity in the United States than in ERSPC-Rotterdam. For example, for nonpalpable local- or regional-stage cancers (ie, stage T1M0), the estimates of PSA test sensitivity were 0.26 in the United States vs 0.94 in ERSPC-Rotterdam. We conclude that the efficacy of PSA screening in detecting prostate cancer was lower in the United States than in ERSPC-Rotterdam.
CancerSpectrum Knowledge Environment 02/2010; 102(5):352-5. · 14.07 Impact Factor
