Modern mammography screening and breast cancer mortality: population study

Objective: To evaluate the effectiveness of contemporary mammography screening using individual information about screening history and breast cancer mortality from public screening programmes. Design: Prospective cohort study of Norwegian women who were followed between 1986 and 2009. Within that period (1995-2005), a national mammography screening programme was gradually implemented, with biennial invitations sent to women aged 50-69 years. Participants: All Norwegian women aged 50-79 between 1986 and 2009. Main outcome measures Multiple Poisson regression analysis was used to estimate breast cancer mortality rate ratios comparing women who were invited to screening (intention to screen) with women who were not invited, with a clear distinction between cases of breast cancer diagnosed before (without potential for screening effect) and after (with potential for screening effect) the first invitation for screening. We took competing causes of death into account by censoring women from further follow-up who died from other causes. Based on the observed mortality reduction combined with the all cause and breast cancer specific mortality in Norway in 2009, we used the CISNET (Cancer Intervention and Surveillance Modeling Network) Stanford simulation model to estimate how many women would need to be invited to biennial mammography screening in the age group 50-69 years to prevent one breast cancer death during their lifetime. Results: During 15 193 034 person years of observation (1986-2009), deaths from breast cancer occurred in 1175 women with a diagnosis after being invited to screening and 8996 women who had not been invited before diagnosis. After adjustment for age, birth cohort, county of residence, and national trends in deaths from breast cancer, the mortality rate ratio associated with being invited to mammography screening was 0.72 (95% confidence interval 0.64 to 0.79). To prevent one death from breast cancer, 368 (95% confidence interval 266 to 508) women would need to be invited to screening. Conclusion: Invitation to modern mammography screening may reduce deaths from breast cancer by about 28%

Understanding recent trends in incidence of invasive breast cancer in Norway: age-period-cohort analysis based on registry data on mammography screening and hormone treatment use

Objective To quantify the separate contributions of menopausal hormone treatment and mammography screening activities on trends in incidence of invasive breast cancer between 1987 and 2008

Breast cancer tumor growth estimated through mammography screening data

Introduction Knowledge of tumor growth is important in the planning and evaluation of screening programs, clinical trials, and epidemiological studies. Studies of tumor growth rates in humans are usually based on small and selected samples. In the present study based on the Norwegian Breast Cancer Screening Program, tumor growth was estimated from a large population using a new estimating procedure/model. Methods A likelihood-based estimating procedure was used, where both tumor growth and the screen test sensitivity were modeled as continuously increasing functions of tumor size. The method was applied to cancer incidence and tumor measurement data from 395,188 women aged 50 to 69 years. Results Tumor growth varied considerably between subjects, with 5% of tumors taking less than 1.2 months to grow from 10 mm to 20 mm in diameter, and another 5% taking more than 6.3 years. The mean time a tumor needed to grow from 10 mm to 20 mm in diameter was estimated as 1.7 years, increasing with age. The screen test sensitivity was estimated to increase sharply with tumor size, rising from 26% at 5 mm to 91% at 10 mm. Compared with previously used Markov models for tumor progression, the applied model gave considerably higher model fit (85% increased predictive power) and provided estimates directly linked to tumor size. Conclusion Screening data with tumor measurements can provide population-based estimates of tumor growth and screen test sensitivity directly linked to tumor size. There is a large variation in breast cancer tumor growth, with faster growth among younger women

Insomnia as a predictor of recurrent cardiovascular events in patients with coronary heart disease

Abstract Study Objectives Insomnia is highly prevalent in patients with coronary heart disease (CHD). However, the potential effect of insomnia on the risk of recurrent major adverse cardiovascular events (MACE) remains uncertain. Methods This prospective cohort study included 1082 consecutive patients 2–36 (mean 16) months after myocardial infarction and/or coronary revascularization. Data on clinical insomnia, coronary risk factors, and comorbidity were collected at baseline. Clinical insomnia was assessed using the Bergen Insomnia Scale (BIS). The primary composite endpoint of MACE (cardiovascular death, hospitalization due to myocardial infarction, revascularization, stroke, or heart failure) was assessed with an average follow-up of 4.2 (SD 0.3) years after baseline. Data were analyzed using Cox proportional hazard regression models stratified by prior coronary events before the index event. Results At baseline, mean age was 62 years, 21% were females, and 45% reported clinical insomnia. A total of 346 MACE occurred in 225 patients during the follow-up period. For clinical insomnia, the relative risk of recurrent MACE was 1.62 (95% confidence interval [CI]: 1.24–2.11, ppublishedVersio

Empirical evaluation of prediction intervals for cancer incidence

BACKGROUND: Prediction intervals can be calculated for predicting cancer incidence on the basis of a statistical model. These intervals include the uncertainty of the parameter estimates and variations in future rates but do not include the uncertainty of assumptions, such as continuation of current trends. In this study we evaluated whether prediction intervals are useful in practice. METHODS: Rates for the period 1993–97 were predicted from cancer incidence rates in the five Nordic countries for the period 1958–87. In a Poisson regression model, 95% prediction intervals were constructed for 200 combinations of 20 cancer types for males and females in the five countries. The coverage level was calculated as the proportion of the prediction intervals that covered the observed number of cases in 1993–97. RESULTS: Overall, 52% (104/200) of the prediction intervals covered the observed numbers. When the prediction intervals were divided into quartiles according to the number of cases in the last observed period, the coverage level was inversely proportional to the frequency (84%, 52%, 46% and 26%). The coverage level varied widely among the five countries, but the difference declined after adjustment for the number of cases in each country. CONCLUSION: The coverage level of prediction intervals strongly depended on the number of cases on which the predictions were based. As the sample size increased, uncertainty about the adequacy of the model dominated, and the coverage level fell far below 95%. Prediction intervals for cancer incidence must therefore be interpreted with caution

Modeling the natural history of ductal carcinoma in situ based on population data

Background: The incidence of ductal carcinoma in situ (DCIS) has increased substantially since the introduction of mammography screening. Nevertheless, little is known about the natural history of preclinical DCIS in the absence of biopsy or complete excision. Methods: Two well-established population models evaluated six possible DCIS natural history submodels. The submodels assumed 30%, 50%, or 80% of breast lesions progress from undetectable DCIS to preclinical screen-detectable DCIS; each model additionally allowed or prohibited DCIS regression. Preclinical screen-detectable DCIS could also progress to clinical DCIS or invasive breast cancer (IBC). Applying US population screening dissemination patterns, the models projected age-specific DCIS and IBC incidence that were compared to Surveillance, Epidemiology, and End Results data. Models estimated mean sojourn time (MST) in the preclinical screen-detectable DCIS state, overdiagnosis, and the risk of progression from preclinical screen-detectable DCIS. Results: Without biopsy and surgical excision, the majority of DCIS (64-100%) in the preclinical screen-detectable state progressed to IBC in submodels assuming no DCIS regression (36-100% in submodels allowing for DCIS regression). DCIS overdiagnosis differed substantially between models and submodels, 3.1-65.8%. IBC overdiagnosis ranged 1.3-2.4%. Submodels assuming DCIS regression resulted in a higher DCIS overdiagnosis than submodels without DCIS regression. MST for progressive DCIS varied between 0.2 and 2.5 years. Conclusions: Our findings suggest that the majority of screen-detectable but unbiopsied preclinical DCIS lesions progress to IBC and that the MST is relatively short. Nevertheless, due to the heterogeneity of DCIS, more research is needed to understand the progression of DCIS by grades and molecular subtypes

Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches

Background. Ductal carcinoma in situ (DCIS) can be a precursor to invasive breast cancer. Since the advent of screening mammography in the 1980’s, the incidence of DCIS has increased dramatically. The value of screen detection and treatment of DCIS, however, is a matter of controversy, as it is unclear the extent to which detection and treatment of DCIS prevents invasive disease and reduces breast cancer mortality. The aim of this paper is to provide an overview of existing Cancer Intervention and Surveillance Modelling Network (CISNET) modeling approaches for the natural history of DCIS, and to compare these to other modeling approaches reported in the literature. Design. Five of the 6 CISNET models currently include DCIS. Most models assume that some, but not all, lesions progress to invasive cancer. The natural history of DCIS cannot be directly observed and the CISNET models differ in their assumptions and in the data sources used to estimate the DCIS model parameters. Results. These model differences translate into variation in outcomes, such as the amount of overdiagnosis of DCIS, with estimates ranging from 34% to 72% for biennial screening from ages 50 to 74 y. The other models described in the literature also report a large range in outcomes, with progression rates varying from 20% to 91%. Limitations. DCIS grade was not yet included in the CISNET models. Conclusion. In the future, DCIS data by grade from active surveillance trials, the development of predictive markers of progression probability, and evidence from other screening modalities, such as tomosynthesis, may be used to inform and improve the models’ representation of DCIS, and might lead to convergence of the model estimates. Until then, the CISNET model results consistently show a considerable amount of overdiagnosis of DCIS, supporting the safety and value of observational trials for low-risk DCIS

Modelling breast cancer incidence, progression and screening test sensitivity using screening data

I fire vitenskaplige arbeider, har vi studert data fra det Norske mammografi programmet (brystkreft screening programmet), med moderne statistiske metoder og kraftige datamaskiner. I det første arbeidet ser vi at risikoen for brystkreft har økt betydelig i Norge, selv korrigert for økt screening aktivitet. I arbeide nummer to og tre ser vi at det norske screeningprogrammet trolig flytter diagnosen lengre enn hva som er anslått ut i fra mange tidligere programmer, men også med betydelig høyere usikkerhet (lavere sensitivt). I det siste arbeidet går vi videre og bygger en helt ny modell for å estimere veksthastigheten til brystkreftsvulster. Resultatene tyder på svært store individuelle variasjoner i veksthastighet, hvor noen svulster bruker under en måned på å vokse fra 10 til 20 mm i diameter, mens andre bruker over 6 år. Brystkreft er den vanligste årsaken til tapte leveår hos Norske kvinner under 65 år, og har dessverre vist seg å være relativt vanskelig å forebygge. For å redusere antall dødsfall av brystkreft via tidligere diagnose/behandling har Norge et landsdekkende offentlig mammografi screening program. Selv om studier tyder på en gevinst ved mammografiscreening, er det fortsatt mye uklart når det gjelder hvor ofte og til hvilke aldersgrupper tilbudet bør gis. Sentrale spørsmål i denne sammenhengen er hvor fort brystkreftsvulster vokser, og hvor tidlig i utviklingen mammografiscreeningen klarer å avdekke brystkreften. Dessverre har informasjon om veksthastigheten til brystkreft svulster stort sett vært basert på små og selekterte kliniske studier, da så godt som alle diagnostisert brystkrefttilfeller i land med god registrering behandles. Som et alternativ til kliniske studier kan vekstraten estimeres ved å utnytte variasjoner i brystkrefthyppighet under screening programmer. I dette arbeidet har vi studert disse variasjonene, og bygd nye matematiske modeller for å kunne anslå både den underliggende veksthastigheten til brystkreftsvulstene og sensitiviteten til screeningundersøkelsene