Socio-economic inequality of utilization of cancer testing in Europe: A cross-sectional study.

There are currently screening programmes for breast, cervical and colorectal cancer in many European countries. However, the uptake of cancer screening in general may vary within and between countries. The aim of this study is to assess the inequalities in testing utilization by socio-economic status and whether the amount of inequality varies across European regions. We conducted an analysis based on cross-sectional data from the second wave of the European Health Interview Survey from 2013 to 2015. We analysed the use of breast, cervical, and colorectal cancer testing by socio-economic position (household income, educational level and employment status), socio-demographic factors, self-perceived health and smoking behaviour, by using multinomial logistic models, and inequality measurement based on the Slope index of inequality (SII) and Relative index of inequality (RII). The results show that the utilization of mammography (Odds Ratio (OR) = 0.55, 95% confidence interval (95%CI):0.50-0.61), cervical smear tests (OR = 0.60, 95%CI:0.56-0.65) and colorectal testing (OR = 0.82, 95%CI:0.78-0.86) was overall less likely among individuals within a low household income compared to a high household income. Also, individuals with a non-EU country of birth, low educational level and being unemployed (or retired) were overall less likely to be tested. The income-based inequality in breast (SII = 0.191;RII = 1.260) and colorectal testing utilization (SII = 0.161;RII = 1.487) was the greatest in Southern Europe. For cervical smears, this inequality was greatest in Eastern Europe (SII = 0.122;RII = 1.195). We concluded that there is considerable inequality in the use of cancer tests in Europe, with inequalities associated with household income, educational level, employment status, and country of birth.

Extrapolation of pre-screening trends: Impact of assumptions on overdiagnosis estimates by mammographic screening.

BACKGROUND: Overdiagnosis by mammographic screening is defined as the excess in breast cancer incidence in the presence of screening compared to the incidence in the absence of screening. The latter is often estimated by extrapolating the pre-screening incidence trend. The aim of this theoretical study is to investigate the impact of assumptions in extrapolating the pre-screening incidence trend of invasive breast cancer on the estimated percentage of overdiagnosis. METHODS: We extracted data on invasive breast cancer incidence and person-years by calendar year (1975-2009) and 5-year age groups (0-85 years) from Dutch databases. Different combinations of assumptions for extrapolating the pre-screening period were investigated, such as variations in the type of regression model, end of the pre-screening period, screened age range, post-screening age range and adjustment for a trend in women <45. This resulted in 69,120 estimates of the percentage of overdiagnosis, i.e. excess cancer incidence in the presence of screening as a proportion of the number of screen-detected and interval cancers. RESULTS: Most overdiagnosis percentages are overestimated because of inadequate adjustment for lead time. The overdiagnosis estimates range between -7.1% and 65.1%, with a median of 33.6%. The choice of pre-screening period has the largest influence on the estimated percentage of overdiagnosis: the median estimate is 17.1% for extrapolations using 1975-1986 as the pre-screening period and 44.7% for extrapolations using 1975-1988 as the pre-screening period. CONCLUSION: The results of this theoretical study most likely cover the true overdiagnosis estimate, which is unknown, and may not necessarily represent the median overdiagnosis estimate. This study shows that overdiagnosis estimates heavily depend on the assumptions made in extrapolating the incidence in the pre-screening period, especially on the choice of the pre-screening period. These limitations should be acknowledged when adopting this approach to estimate overdiagnosis.

Overdiagnosis by mammographic screening for breast cancer studied in birth cohorts in The Netherlands.

A drawback of early detection of breast cancer through mammographic screening is the diagnosis of breast cancers that would never have become clinically detected. This phenomenon, called overdiagnosis, is ideally quantified from the breast cancer incidence of screened and unscreened cohorts of women with follow-up until death. Such cohorts do not exist, requiring other methods to estimate overdiagnosis. We are the first to quantify overdiagnosis from invasive breast cancer and ductal carcinoma in situ (DCIS) in birth cohorts using an age-period-cohort -model (APC-model) including variables for the initial and subsequent screening rounds and a 5-year period after leaving screening. Data on the female population and breast cancer incidence were obtained from Statistics Netherlands, "Stichting Medische registratie" and the Dutch Cancer Registry for women aged 0-99 years. Data on screening participation was obtained from the five regional screening organizations. Overdiagnosis was calculated from the excess breast cancer incidence in the screened group divided by the breast cancer incidence in presence of screening for women aged 20-99 years (population perspective) and for women in the screened-age range (individual perspective). Overdiagnosis of invasive breast cancer was 11% from the population perspective and 17% from the invited women perspective in birth cohorts screened from age 49 to 74. For invasive breast cancer and DCIS together, overdiagnosis was 14% from population perspective and 22% from invited women perspective. A major strength of an APC-model including the different phases of screening is that it allows to estimate overdiagnosis in birth cohorts, thereby preventing overestimation.

Prediction of higher mortality reduction for the UK Breast Screening Frequency Trial: a model-based approach on screening intervals.

BACKGROUND: The optimal interval between two consecutive mammograms is uncertain. The UK Frequency Trial did not show a significant difference in breast cancer mortality between screening every year (study group) and screening every 3 years (control group). In this study, the trial is simulated in order to gain insight into the results of the trial and to predict the effect of different screening intervals on breast cancer mortality. METHODS: UK incidence, life tables and information from the trial were used in the microsimulation model MISCAN-Fadia to simulate the trial and predict the number of breast cancer deaths in each group. To be able to replicate the trial, a relatively low sensitivity had to be assumed. RESULTS: The model simulated a larger difference in tumour size distribution between the two groups than observed and a relative risk (RR) of 0.83 of dying from breast cancer in the study group compared with the control group. The predicted RR is lower than that reported from the trial (RR 0.93), but within its 95% confidence interval (0.63-1.37). CONCLUSION: The present study suggests that there is benefit of shortening the screening interval, although the benefit is probably not large enough to start annual screening.