Direct and Indirect Effects of Cancer Screening Invitations on Participation in Europe: A Quasi-Experimental Analysis

Direct and Indirect Effects of Cancer Screening Invitations on Participation in Europe: A Quasi-Experimental Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Direct and Indirect Effects of Cancer Screening Invitations on Participation in Europe: A Quasi-Experimental Analysis Sophie Guthmuller, Vincenzo Carrieri, Ansgar Wuebker This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7891986/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Organized cancer screening programs (OSPs) in Europe for breast, cervical, and colorectal cancers aim to improve early detection and reduce mortality. This study measures the effects of OSPs on participation and examines cross-program spillovers, as women are often invited to multiple screenings. We construct a regional-level dataset on OSP availability, merged with individual-level data from the European Health Interview Survey (EHIS) covering 122,000 women in 27 countries. We exploit cross-region and age-based variations in eligibility and employ a quasi-experimental difference-in-differences model to measure the causal effect of OSPs. OSPs substantially increase screening participation: mammography rises by 33.20 percentage points (pp) (95% CI: 24.56–41.85), fecal occult blood test (FOBT) by 19.41 pp (95% CI: 13.95–24.88), and pap test by 9.33 pp (95% CI: 5.26–13.41). Positive spillover effects occur when women are invited to two screenings (10 pp (95% CI: 4.81–15.20) for mammography, 3.35 pp (95% CI: 0.56–6.15) for pap test, 7.44 pp (95% CI:2.34–12.55) for FOBT) but targeting three cancers does not yield additional statistically significant gains. These findings highlight the strong impact of OSPs on participation and the value of coordinated screening and communication strategies as Europe expands organized screening to new cancer sites. Health sciences/Health care/Disease prevention/Preventive medicine Scientific community and society/Social sciences/Decision making Health sciences/Health care/Health policy Health sciences/Diseases/Cancer/Cancer prevention Organized Screening Programs screening participation cancer screening cross-program spillovers Europe Figures Figure 1 Figure 2 Figure 3 Introduction Cancer is the second leading cause of death worldwide, accounting for one in six deaths and a substantial share of disability-adjusted life years (DALYs) 1 . Despite advances in therapy and precision medicine, late-stage diagnosis continues to limit survival gains. For example, while the five-year survival rate for localized breast cancer exceeds 90%, it falls to 30% for metastatic cancer 2 . Early detection through screening is therefore a cornerstone of cancer control. Screening strategies differ across health systems. In the United States, screening is predominantly opportunistic and patient-initiated, whereas most European countries have adopted organized screening programs (OSPs). These OSPs systematically invite eligible individuals at regular intervals, free of charge, and provide balanced information on benefits and harms 3 – 6 . Strong evidence indicates that OSPs improve early diagnosis and reduce cancer mortality. Biennial invitations to breast cancer screening have been linked to a 10% reduction in mortality among women in their mid-fifties 7 . Likewise, OSP implementation has been shown to increase mammography uptake by 25 percentage points and reduce mortality by 10% within a decade 8 . Colorectal cancer screening programs demonstrate even larger effects, with mortality reductions of 30% 9,10 and up to 41.8% with long-term implementation 11 . Nevertheless, participation remains below recommended levels, with marked variation across regions and socioeconomic groups 12 , 13 . A recent scoping review of the German OSP found participation rates falling short of EU targets and are strongly shaped by income, migration background, rural versus urban residency, and type of health insurance 14 . While most studies address barriers such as financial costs, information gaps, or individual risk perceptions, little attention has been given to interactions across programs. In practice, women are often invited to multiple screenings (e.g. breast, colorectal, cervical), raising the possibility of spillover effects across cancer screening programs. Multiple invitations may reinforce preventive behaviors, but they could also overwhelm individuals and reduce participation. Evidence from other domains suggests both mechanisms are plausible. For instance, 15 showed that mandatory Tdap vaccination in the United States increased uptake of other adolescent vaccines through parental awareness and provider engagement. Similarly, workplace screening initiatives have documented peer spillovers in prevention uptake 16 . To date, however, no empirical study has evaluated spillover effects across cancer screening programs. Understanding whether overlapping invitations enhance participation or create unintended overload is important for optimizing coordination and improving the efficiency of OSPs at both regional and European levels. This paper makes two contributions. First, it provides causal evidence on the effect of invitations to organized screening programs on the uptake of the three cancer screening programs in Europe. Second, it examines whether such invitations generate cross-program spillover effects—either reinforcing or crowding out participation in other screenings. By distinguishing between the direct and indirect (spillover) effects of invitations, the study sheds new light on the design of coordinated prevention policies. To produce these findings, (i) we construct a regional dataset at the NUTS 2 level covering breast, colorectal, and cervical cancer screening programs and merge this dataset with individual-level records from the European Health Interview Survey (EHIS), which provide information on health status, healthcare use, and socioeconomic characteristics for over 122,000 women across 27 countries. (ii) We identify the causal direct and indirect effects of OSPs by using variation in regional availability of OSPs and variation in OSP target ages and follow a quasi-experimental Difference-in-Differences (DiD) approach. Quasi experimental designs, like DiD, yield credible evidence where there is lack of feasibility of randomized trials 17 . Recent studies, including evaluations of breast and colorectal cancer OSPs in Europe, demonstrate the strength of these designs to produce robust population-level evidence. 8 , 18 , 19 . Finally, we put our analysis in the context of the European Union Council Recommendations of 2003 3 , which fostered all Member States to implement OSPs for breast, cervical and breast cancer to reduce mortality by early detection. The European Cancer Screening Strategy 3 , 4 is described in detailed in the methods section. Results Descriptive statistics Screening uptake varies substantially between European regions and cancer sites. For mammography, uptake ranges from 8.6% to 65.6% (median 40%). For FOBT, lower uptakes are observed with a median of 19.9 % (range 0.1–50%), while for cervical cancer screening, there are higher uptakes (median 61.7 %, range 28.9–84.1%). These regional differences are illustrated in Figure 1. Panel a shows mammography use, panel b Pap test use, and panel c FOBT use. Figure 2 displays screening participation by age for each OSP defined by their target age. Panel a reports screening participation for breast cancer, panel b for cervical cancer, and panel c for colorectal cancer. Screening uptake sharply increases at the starting age of each OSP and sharply declines at the end of the target age range. For instance, participation in mammography screening among women living in a region with an OSP 50-69 years, is 33.7% (with 95% confidence interval: 28.8%–38.6%) at 49, 64.1% (60.2%–68.1%) at age 52, 55.7% (50.2%–51.2%) at 69 years old and 30.7% (27.5%–34.0%) at 72. At the same ages, in regions without OSPs, participation is 36.1% (31.4%–40.8%) at 49, 33.9% (29.3%–38.5%) at age 52, 18.7% (14.6%–22.8%) at 69, and 11.9% (7.3%–16.6%) at age 72 (Figure 2, panel a). We observe a similar pattern for colorectal cancer screening. In regions with organized screening programs (covering ages 50–74), FOBT uptake is 9.3% (95% Confidence Interval - CI: 6.7%–11.8%) at age 49, 39.0% (27.0%–50.0%) at age 52, 30.0% (19.3%–40.6%) at age 74, and 21.2% (15.6%–26.8%) at age 77. In regions without OSPs, uptake at the same ages is lower: 7.5% (4.1%–10.8%) at 49, 13.6% (7.0%–20.2%) at 52, 15.7% (9.9%–21.5%) at 74, and 12.6% (6.3%–18.9%) at 77 (Figure 2, panel c). Screening uptake for cervical cancer rises sharply between 20 and 30 years old, both in OSP and non-OSP regions. The sharp increase of the non-OSP-regions is explained by the number of EU regions that have screening programs for cervical cancer screening without an invitation system 6,20 . Women who go to the GP or to the gynecologist in a certain age range are offered a free Pap test every three or five years 20 . However, the visual inspection of Figure 2, panel b shows that the increase is generally higher for OSP–regions compared to non-OSP regions, and screening participation declines at the upper age limit of each OSP. The higher levels in OSP regions plausibly mirror systematic invitation and recall procedures. Average causal effects Table 1 presents the causal estimates of the effect of OSP on screening participation based on equation 1. OSP for breast cancer increases mammography participation by 33.21 percentage points (pp) (95% Confidence Interval:24.56-41.85) on average. The impact of OSP on colorectal cancer screening participation is 19.41 pp (95% CI:13.95-24.88), and the effect of OSP on cervical cancer screening participation is 9.33 pp (95% CI:5.26-13.41) on average (Table 1, col 1, 3, and 5). The findings remain robust after adding control variables e.g. for age, education, marital status, employment, urbanicity, and self-reported health (Table 1, col 2, 4, and 6). Table S4 reports the full estimation outputs including the coefficients for the control variables. The causal effect of OSP for cervical cancer is substantially smaller in size compared to the effects of OSP for breast and colorectal cancer. This is explained by a large participation rate of 47.44% of women had a pap test in the last three years in the control group, which includes programs without an invitation system 6,20 . For breast and colorectal cancers, screening participation in the control group is lower and corresponds mainly to opportunistic screening (18.6% for breast cancer and 10.7% for colorectal cancer, Table 1). Table 1: Direct effects (1) (2) (3) (4) (5) (6) mammo mammo pap pap fobt fobt Treatment 0.332*** 0.333*** 0.0933*** 0.0903*** 0.194*** 0.197*** (0.0438) (0.0432) (0.0206) (0.0202) (0.0277) (0.0274) N 122,952 122,952 122,952 122,952 122,952 122,952 Mean control group (%) 18.60 18.60 47.44 47.44 10.67 10.67 St. dev. control group (%) 38.91 38.91 49.93 49.93 30.87 30.87 Age fixed effects X X X X X X Region fixed effects X X X X X X Confounding factors X X X Note: Region level clustered standard errors are in parentheses. The control group includes women who are not eligible for OSP because they live in a non-OSP region or because they are outside of the target age of the region specific OSP. Statistical significance: *** p<0.01, ** p<0.05, * p<0.1 Direct and indirect causal effects Table 2 reports the estimated effects of OSP, distinguishing between: OSP (direct effect only), OSP+ (direct effect plus eligibility for one additional screening), and OSP++ (direct effect plus eligibility for two additional screenings). These estimates include the set of control variables (see Table S6 for the detailed output including the coefficients of each control variable). The direct effect of OSP is 25.70 pp (95% CI:18.06-33.30) for breast cancer, 8.18 pp (95% CI:4.17-12.19) for cervical cancer, and 13.72 pp (95% CI:9.37-18.07) for colorectal cancer. The impact of OSP+ that is the direct effect of OSP for breast cancer and the indirect effect of another OSP on the probability of mammography uptake is 35.68 pp (95% CI:26.99-44.38). Thus, the cross-program spillover effect of receiving an invitation to screen for another cancer on mammography uptake is 10.00 pp (95% CI: 4.81-15.20): OSP+ is 10 pp larger than OSP only (0.357- 0.257). Table 2 column 1; the test of coefficient equality is rejected with a p-value of 0.000 (t-value:3.80). Positive spillover effects are also found for cervical and colorectal cancer screening; the cross-program spillover effect of receiving a screening invitation for another cancer is 3.35 pp 95% CI: 0.56-6.15 (11.5-8.18, t-value:2.37,p-value: 0.019) on pap test uptake, and 7.44 pp 95% CI:2.34-12.55 (21.2-13.7, t-value:2.88, p-value: 0.005) on FOBT uptake. However, receiving a third invitation to screen for another cancer (i.e. being eligible for the three OSPs) does not significantly change participation compared to being eligible for two OSPs. Table S5 reports causal estimates of the direct and indirect effects excluding the set of control variables. Table 2: Cross program spillover effects mammo t-test p-value pap t-test p-value fobt t-test p-value OSP 0.257*** OSP – OSP++: 0.0818*** OSP – OSP++: 0.137*** OSP – OSP++ (0.0386) 0.0006 (0.0203) 0.7355 (0.0220) 0.0004 OSP + 0.357*** OSP – OSP+ 0.115*** OSP – OSP+ 0.212*** OSP – OSP+ (0.0440) 0.0002 (0.0252) 0.0190 (0.0351) 0.0045 OSP ++ 0.369*** OSP+ OSP++ 0.0866*** OSP+ OSP++ 0.220*** OSP+ OSP++ (0.0509) 0.5651 (0.0215) 0.0768 (0.0314) 0.7925 N 122,952 122,952 122,952 Mean control group (%) 18.60 47.44 10.67 St. dev. control group (%) 38.91 49.93 30.87 Note: Region level clustered standard errors are in parentheses. Each column is a different regression. All regressions include age and region fixed effects, and the set of control variables. The control group includes women who are not eligible for OSP because they live in a non-OSP region or because they are outside of the target age of the region specific OSP. *** p<0.01, ** p<0.05, * p<0.1 Discussion We studied the causal effect of OSPs for breast, cervical, and colorectal cancer on screening participation in Europe using regional data on OSPs characteristics and individual level data on screening participation and information on socioeconomic, demographic, health and health care use from the European Health Interview Survey (EHIS). To identify the causal effects, we followed a Differences-in-Differences approach using variation in the availability of OSP across European regions and variation in the target age of the OSPs. This strategy allows us to separate the effect of the program from regional and age-related factors that could otherwise confound the results. We measured the average causal effect of OSP for each cancer site. We distinguished between the direct effect of OSP on screening participation from potential cross-program spillover effects of being eligible for more than one OSP. Our findings show that OSP significantly increases screening participation: the average causal effect of OSP for breast cancer is 33 pp in a sample of 27 EU countries in 2019, which is larger in size than previous studies in Europe found 8 , 19 ; 19 estimated an effect of 17 pp on a subset of 13 EU countries in 2006; 8 estimated an effect of 25 pp on a subset of 21 EU countries in 2014. There are several factors that may contribute to this trend. These include the increasing average invitation rate over time 21 , a higher intensity of invitations for programs that have been running longer, and advancements in screening techniques and breast cancer treatments that enhance the benefits of screening 22 . We found an average OSP effect for colorectal cancer with FOBT of around 20 pp. Although direct comparisons with the sparse previous evidence are limited, this EU-wide causal estimate is lower than those of other country-specific studies in Europe 23 , but higher than the estimates found in the United States, where screening is only covered by health insurance for certain target ages and no equivalent OSP is in place 24 , and is in line with the current trends estimated in CRC screening utilization in studies using the EHIS survey 25 . The impact of OSP for colorectal cancer is also lower than for breast cancer. Different reasons could explain this, among those, specific impeding factors related to the screening procedure or a perceived lower risk for colorectal cancer 26 among the target population. The more recent implementation of OSPs for colorectal cancer, where invitation rates have not yet reached the rates of OSPs for breast cancer is another reason 20 , 27 , 28 . For cervical cancer, the average causal effect (9 pp) is smaller than the other two cancer sites, as for cervical cancer, many European regions have cervical cancer screening programs with a lower level of organization in place that do not include an invitation system of the target population 20 among the criteria defining a screening program 6 . Participation in pap test is therefore much higher in non-OSP-regions (50%), as women have access to pap test screening within their regular reproductive health checkup at the GP or gynecologist. This is not the case in non-OSP-regions for breast and colorectal cancers, where screening outside of the OSP is merely opportunistic or symptomatic screening 27 . In addition to a positive direct effect of each of the three OSPs on screening participation, we found significant positive cross-program spillover effects when women are in the target population of two OSPs i.e. are receiving two invitations to screen for two different cancer sites. However, being in the target population of the three OSPs compared to two does not further increase participation nor decreases it. These findings are particularly relevant to help design coordinated cancer screening policies and communication strategies, and this even more in light of the implementation of OSP for other cancer sites, such as lung cancer 29 , 30 in the near future. A major strength of this study is the use of a large, representative dataset covering more than 122,000 women across 27 European countries, which provides strong external validity and allows for meaningful cross-country comparisons. The application of a Difference-in-Differences approach with regional and age fixed effects strengthens the causal interpretation of the results. Another important contribution is the novel examination of cross-program spillovers, which had not been systematically studied before. Nonetheless, some limitations should be acknowledged. First, despite the rich dataset, the analysis relies on self-reported screening outcomes from the EHIS, which may be subject to recall bias or misreporting. Second, although regional variation in OSP implementation provides a strong identification strategy, unobserved or time-varying contextual differences across regions—though uncommon—may still confound the results. Third, the study focuses only on FOBT-based colorectal screening programs, which may limit generalizability to regions where colonoscopy is the primary test. Finally, the cross-sectional nature of the EHIS data restricts the ability to assess longer-term behavioral dynamics or repeated participation across screening rounds. Methods European Cancer Screening Strategy In 2003, the European Union recommended EU countries to introduce cancer screening programs for breast, cervical and colorectal cancer to increase early detection and reduce cancer prevalence and mortality 3 . Since this recommendation, implementation of screening programs has increased and most countries in Europe have introduced organized screening programs (OSPs) or population-based screening programs for at least one of these three cancer sites 27 . An OSP defines the eligible population, the screening intervals, and the type of examinations. Within an OSP, the eligible population is identified and systematically invited to each round of screening with a personalized letter 5 . The EU recommends mammography screening for women aged 50 to 69 for breast cancer. For colorectal cancer, the recommended screening is a fecal occult blood test (FOBT) for men and women aged 50–74. At the EU level, screening for cervical cancer with a Pap test is recommended to start between the ages of 20 and 30 3 . At national or regional level, variations exist in the way OSPs have been implemented. In 2019, the most frequent target population of OSPs for breast cancer is 50–69. However, several European regions have enlarged the target population to younger women starting at 40 years, and or to older women up to 75 years (see Fig. 3 and Table S1 ). All OSPs for breast cancer invite eligible women for mammography every two years. For colorectal cancer, the EU's recommendation for cancer screening is usually to use a FOBT as the primary screening tool. The test kit is often sent to the patient's home. Invitations are sent every two years. Colonoscopy is most frequently used as second tier screening tool after an abnormal FOBT, but in some OSPs, colonoscopy is also offered as an alternative to FOBT. When this is the case, colonoscopy is offered every 10 years 27 . In this paper, we focus on OSPs for colorectal cancer that offer a FOBT as screening examination to be able to study the existence of potential spillover effects of regular sending of invitations. Invitation for FOBT screening is sent every two years. As of 2019, the wider target age range for FOBT is 50–75. Some regions have chosen a smaller target population (see Fig. 3 and Table S1 ). In 2019, as for the other two OSPs, OSPs for cervical cancer implemented in EU regions have chosen different age targets: the earliest start age is 20 years old, the older end age is 70 years old (see Table S1 and Fig. 3 ). Invitations are sent every three years for most of the OSPs. Some OSPs offer cervical cancer screening every five years, for example the 30–59/60 programs in the Netherlands and Estonia 27 . In 2022, EU recommendations on screening have been updated and generally extend the age targets of the OSPs defined in 2003 4 . Not all EU regions have implemented OSP for the three cancer sites. As of 2019, among the 195 EU regions included for analyses in this paper, few had not yet implemented any of three OSPs (EU regions of Romania, Greece, and Bulgaria), all other EU regions had at least an OSP for one of the three cancer sites in place (see Fig. 3 and Table S1 ). Organized screening programs for breast cancer Organized screening programs for cervical cancer Organized screening programs for colorectal cancer (FOBT) Note OSP = Organized Screening Program. No OSP = regions with no Organized Screening Program. Colored regions are those included in the analyses. Source : Authors‘ own database, 2019. Data We construct a regional dataset covering breast, colorectal, and cervical cancer screening programs, drawing on International Agency for Research on Cancer (IARC) factsheets 31 , reports 32 – 34 , and published studies 20 , 27 , 35 – 40 . Information on the three OSPs was collected at the NUTS 2 level: whether an OSP is in place, the year of introduction, the screening invitation intervals, the type of screening test and the target population. Figure 3 and Table S1 summarize the target populations of each OSP. For colorectal cancer, we consider only the programs that use an FOBT as screening tool. Programs with colonoscopy only and target age for colonoscopy are not listed. These regional data on OSPs are merged with individual level data from the most recent European Health Interview Survey wave 3 (EHIS 3) that took place between 2018 and 2020 in 29 countries 41 . EHIS 3 was conducted in 2019 in all EU member states, Iceland, Norway, and Serbia. Except for Belgium where the interviews took place in 2018, and in 2020 in Germany and Malta. EHIS includes four modules on health status, health care use, health determinants and socio-economic background variables. EHIS is run every five years and targets the population aged at least 15 years and living in private households 41 . Our three outcomes of interest, measuring self-reported individual level cancer screening participation, are defined as follows: (1) whether a woman had a mammography screening in the last two years (2); whether a woman had a fecal occult blood test (FOBT) screening in the last two years; (3) whether a woman had a pap test in the last three years. Age in years and an indicator of the region of residence (at the NUTS 2 level) are used to identify whether a woman is in the target group of each of the three OSPs. A set of control variables are also included. Educational attainment is measured by ISCED 2011, aggregated into three levels (low, medium, high). Income is measured with the net monthly equalized income of the household in quintiles. Labor status is defined as being employed, unemployed (including women fulfilling domestic tasks, students, pupils, in compulsory military or civilian service, women unable to work due to a longstanding illness) or retired. Type of household is a categorical variable differentiating between one person household, one parent with children, couple without children, couple with children under 25 years old, couple with children older than 25 years old, and other types of households. Degree of urbanization is defined as densely populated areas (cities), intermediate density areas (towns and suburbs), thinly populated areas (rural areas) 42 . The dataset also includes information on women’s health status based on their general self-perceived health (in 4 categories: very good, good, fair, bad or very bad) and whether they report long-standing health problem(s). Sample The study sample includes data from women aged 17 to 90 years old from 27 countries. We excluded data from Italy because age was not available in years. Women aged 15–16 and women above 90 years were also excluded, as these age groups were not consistently covered across countries. We kept all women with information on mammography, FOBT, and pap test. (We deleted proxy interviews.) Some of the confounding factors had missing values. We accounted for those by adding a category for missing values in the analyses, to keep all women with self-reported screening participation in the sample. The study sample includes 122,952 observations. Table S2 presents the steps of the sample construction. Estimation strategy As descriptive analysis, we first plot respectively, the proportion of women who had a mammography in the last two years, who had a pap test in the last three years, and who had an FOBT in the last two years at NUTS 2 level. We then plot screening participation for each of the three cancers by age, for each OSP defined by their target age. Based on these plots, we visually investigate whether screening participation increases when age is within each OSP target age. If this is the case, this would give use descriptive evidence that OSPs have a positive effect on screening participation. Next in multivariate quasi-experimental analysis 8 , we estimate the average program effect of OSPs on participation. To estimate the causal effect of the OSP, we rely on a Difference-in-Differences type strategy. This approach compares changes in screening participation between women who are eligible for the program and those who are not, across regions with and without OSPs. In this way, the estimated effect can be attributed to the program itself, rather than to pre-existing differences between regions or age groups that may influence also the screening participation. Formally, the model is specified as follows: \(\:{S}_{ir}=\:{\beta\:}_{0}+\:{\beta\:}_{1}{Region}_{r}+\:{\beta\:}_{2}{Age}_{i}+{{\beta\:}_{3}Treatment}_{ir}+{\beta\:}_{4}{Controls}_{ir}+{e}_{ir}\:\) Eq. 1 where \(\:{S}_{ir}\) indicates whether woman i living in region r screened, within the last 2 years for breast cancer and colorectal cancer, and within the last 3 years for cervical cancer. \(\:{Treatment}_{ir}\) is our variable of interest and is equal to one if woman i is eligible to the OSP i.e. has an age within the age range targetted by the screening programme and lives in a region r where the OSP is in place. \(\:{Region}_{r}\) and \(\:{Age}_{i}\) are respectively region fixed effects and age (in years) fixed effects. These fixed effects are key to the Differences-in-Differences estimation strategy. They allow us to isolate the OSP effect from confounders varying at regional level (i.e., general attitude towards prevention, average education and income) and confounders at age level (i.e. variation in the individual risk of being diagnosed with a cancer which varies across ages) that may influence screening decision irrespective of an OSP invitation. \(\:{Controls}_{ir}\) denotes the set of demographic, socioeconomic and health variables that are added to test the sensitivity of our estimates. \(\:{e}_{ir}\) is the error term. Identification of the program effect relies on regional variation in the availability of OSPs and variation in age targets. These variations are essentially related to the high degree of autonomy of regional health authorities in Europe for what concerns the definition of the target population. As regional heterogeneity largely depends on region-specific orientations toward the target population and not on other factors potentially related to screening participation and OSP implementation (i.e., evidence on region-specific age-cancer risks profiles) it allows us to treat it as good as random in our setting. The direct causal effect is estimated for breast, colorectal, and cervical cancers separately. We use robust standard errors clustered at NUTS-2 level. To distinguish the direct from potential spillovers effects of being eligible to more than one OSP, we re-estimate Eq. 1 where \(\:{Treatment}_{ir}\) is replaced by a categorical variables taking the following values: None when woman i living in region r is eligible to none of the three OSPs (either because she resides in a region with no OSP in place or because she is not in the target age of any of the three programs); one OSP when woman i is eligible to one OSP (either because she lives in a region where only one of three OSPs is introduced or because she is in a the target age of only one program), namely the one corresponding to the outcome of interest; OSP+ , in addition to the OSP targeted by the outcome of interest, woman i is eligible to a second OSP because she lives in a region where at least two OSPs are implemented and she is in the target age of two programs; or OSP++ , woman i is eligible to the breast, cervical and colorectal cancer screening programs because she lives in a region where the three OSPs are implemented and because she is in the target age of the three programs. Statements Declarations Competing Interests The authors have no other relevant financial or non-financial interests to disclose. Data availability This paper is based on data from Eurostat, EHIS, 2019, version released on: 12 May 2025. The responsibility for all conclusions drawn from the data lies entirely with the authors. Access to the EHIS microdata was granted by Eurostat with proposal number RPP 141/2021-EHIS, after ethical approval of each National Authority of the countries participating in EHIS. EHIS is authorized by EC Regulation 1338/2008 43 . Data collection for EHIS wave 3 at the country level was conducted in accordance with the EU Regulation 2018/255 44 . Further information on EHIS and data access are available: https://ec.europa.eu/eurostat/web/microdata/european-health-interview-survey. Code availability Data analyses have been performed with Stata/SE 19.5. Do files are available upon request per email to the corresponding author. 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1","display":"","copyAsset":false,"role":"figure","size":351270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening participation in European regions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: The screening rate is defined as having had a (a) mammography in the last two years; (b) a Pap test in the last three years; (c) an FOBT in the last two years; among women aged between 17 and 90 years in each region divided by the respective population of women aged 17-90 years in each region. Authors‘own calculations on EHIS – Eurostat data, 2019\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7891986/v1/7cae34b496c016ac814ab8d5.png"},{"id":95103728,"identity":"92d6ab3c-a18c-41a7-9375-6e89a5973697","added_by":"auto","created_at":"2025-11-04 10:24:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":539432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening participation by age, and OSPs and non-organized screening programs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: (a) Rate of mammography uptake in the last two years; (b) rate of Pap test uptake in the last three years; (c ) Rate of FOBT uptake in the last two years, by OSP target ages and regions with non-organized screening, raw data. Authors‘ own calculation based on EHIS data, 2019.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7891986/v1/d4b3eb3064c43ddd5a1b563e.png"},{"id":95103722,"identity":"4f9c3c7a-9ab9-4e07-b0d8-2509a14090a8","added_by":"auto","created_at":"2025-11-04 10:24:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":376366,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOrganized cancer screening programs in Europe\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eOSP = Organized Screening Program. No OSP = regions with no Organized Screening Program. Colored regions are those included in the analyses. \u003cem\u003eSource:\u003c/em\u003eAuthors‘ own database, 2019.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7891986/v1/a3d6f4c9b98498668aff3aff.png"},{"id":104781014,"identity":"e22b2d09-d065-4a86-8b39-c8e5b3ba0c79","added_by":"auto","created_at":"2026-03-17 07:54:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1871656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7891986/v1/72536e03-263a-43a5-a355-01b3c916faf6.pdf"},{"id":95103718,"identity":"9e54f760-b2c0-4298-ad27-99bbc8219b2b","added_by":"auto","created_at":"2025-11-04 10:24:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45934,"visible":true,"origin":"","legend":"Supplementary Files","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-7891986/v1/d9bc9c885c304c569157d66b.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nThis work was supported by the Leibniz Science Campus Ruhr (http:// www. lscr.de/) to which the authors are affiliated. The authors have no other relevant financial or non-financial interests to disclose.","formattedTitle":"Direct and Indirect Effects of Cancer Screening Invitations on Participation in Europe: A Quasi-Experimental Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer is the second leading cause of death worldwide, accounting for one in six deaths and a substantial share of disability-adjusted life years (DALYs) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Despite advances in therapy and precision medicine, late-stage diagnosis continues to limit survival gains. For example, while the five-year survival rate for localized breast cancer exceeds 90%, it falls to 30% for metastatic cancer \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Early detection through screening is therefore a cornerstone of cancer control.\u003c/p\u003e\u003cp\u003eScreening strategies differ across health systems. In the United States, screening is predominantly opportunistic and patient-initiated, whereas most European countries have adopted organized screening programs (OSPs). These OSPs systematically invite eligible individuals at regular intervals, free of charge, and provide balanced information on benefits and harms \u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Strong evidence indicates that OSPs improve early diagnosis and reduce cancer mortality. Biennial invitations to breast cancer screening have been linked to a 10% reduction in mortality among women in their mid-fifties \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Likewise, OSP implementation has been shown to increase mammography uptake by 25 percentage points and reduce mortality by 10% within a decade \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Colorectal cancer screening programs demonstrate even larger effects, with mortality reductions of 30% \u003csup\u003e9,10\u003c/sup\u003e and up to 41.8% with long-term implementation \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNevertheless, participation remains below recommended levels, with marked variation across regions and socioeconomic groups \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. A recent scoping review of the German OSP found participation rates falling short of EU targets and are strongly shaped by income, migration background, rural versus urban residency, and type of health insurance \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile most studies address barriers such as financial costs, information gaps, or individual risk perceptions, little attention has been given to interactions across programs. In practice, women are often invited to multiple screenings (e.g. breast, colorectal, cervical), raising the possibility of spillover effects across cancer screening programs. Multiple invitations may reinforce preventive behaviors, but they could also overwhelm individuals and reduce participation. Evidence from other domains suggests both mechanisms are plausible. For instance, \u003csup\u003e15\u003c/sup\u003e showed that mandatory Tdap vaccination in the United States increased uptake of other adolescent vaccines through parental awareness and provider engagement. Similarly, workplace screening initiatives have documented peer spillovers in prevention uptake \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo date, however, no empirical study has evaluated spillover effects across cancer screening programs. Understanding whether overlapping invitations enhance participation or create unintended overload is important for optimizing coordination and improving the efficiency of OSPs at both regional and European levels. This paper makes two contributions. First, it provides causal evidence on the effect of invitations to organized screening programs on the uptake of the three cancer screening programs in Europe. Second, it examines whether such invitations generate cross-program spillover effects\u0026mdash;either reinforcing or crowding out participation in other screenings. By distinguishing between the direct and indirect (spillover) effects of invitations, the study sheds new light on the design of coordinated prevention policies.\u003c/p\u003e\u003cp\u003eTo produce these findings, (i) we construct a regional dataset at the NUTS 2 level covering breast, colorectal, and cervical cancer screening programs and merge this dataset with individual-level records from the European Health Interview Survey (EHIS), which provide information on health status, healthcare use, and socioeconomic characteristics for over 122,000 women across 27 countries. (ii) We identify the causal direct and indirect effects of OSPs by using variation in regional availability of OSPs and variation in OSP target ages and follow a quasi-experimental Difference-in-Differences (DiD) approach. Quasi experimental designs, like DiD, yield credible evidence where there is lack of feasibility of randomized trials \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRecent studies, including evaluations of breast and colorectal cancer OSPs in Europe, demonstrate the strength of these designs to produce robust population-level evidence.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFinally, we put our analysis in the context of the European Union Council Recommendations of 2003 \u003csup\u003e3\u003c/sup\u003e, which fostered all Member States to implement OSPs for breast, cervical and breast cancer to reduce mortality by early detection. The European Cancer Screening Strategy \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e is described in detailed in the methods section.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eDescriptive statistics\u003c/h2\u003e\n\u003cp\u003eScreening uptake varies substantially between European regions and cancer sites. For mammography, uptake ranges from 8.6% to 65.6% (median 40%). For FOBT, lower uptakes are observed with a median of 19.9 % (range 0.1\u0026ndash;50%), while for cervical cancer screening, there are higher uptakes (median 61.7 %, range 28.9\u0026ndash;84.1%).\u003c/p\u003e\n\u003cp\u003eThese regional differences are illustrated in Figure 1. Panel a shows mammography use, panel b Pap test use, and panel c FOBT use.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2 displays screening participation by age for each OSP defined by their target age. Panel a reports screening participation for breast cancer, panel b for cervical cancer, and panel c for colorectal cancer. Screening uptake sharply increases at the starting age of each OSP and sharply declines at the end of the target age range. For instance, participation in mammography screening among women living in a region with an OSP 50-69 years, is 33.7% (with 95% confidence interval: 28.8%\u0026ndash;38.6%) at 49, 64.1% (60.2%\u0026ndash;68.1%) at age 52, 55.7% (50.2%\u0026ndash;51.2%) at 69 years old and 30.7% (27.5%\u0026ndash;34.0%) at 72. At the same ages, in regions without OSPs, participation is 36.1% (31.4%\u0026ndash;40.8%) at 49, 33.9% (29.3%\u0026ndash;38.5%) at age 52, 18.7% (14.6%\u0026ndash;22.8%) at 69, and 11.9% (7.3%\u0026ndash;16.6%) at age 72 (Figure 2, panel a).\u003c/p\u003e\n\u003cp\u003eWe observe a similar pattern for colorectal cancer screening. In regions with organized screening programs (covering ages 50\u0026ndash;74), FOBT uptake is 9.3% (95% Confidence Interval - CI: 6.7%\u0026ndash;11.8%) at age 49, 39.0% (27.0%\u0026ndash;50.0%) at age 52, 30.0% (19.3%\u0026ndash;40.6%) at age 74, and 21.2% (15.6%\u0026ndash;26.8%) at age 77. In regions without OSPs, uptake at the same ages is lower: 7.5% (4.1%\u0026ndash;10.8%) at 49, 13.6% (7.0%\u0026ndash;20.2%) at 52, 15.7% (9.9%\u0026ndash;21.5%) at 74, and 12.6% (6.3%\u0026ndash;18.9%) at 77 (Figure 2, panel c). Screening uptake for cervical cancer rises sharply between 20 and 30 years old, both in OSP and non-OSP regions. The sharp increase of the non-OSP-regions is explained by the number of EU regions that have screening programs for cervical cancer screening without an invitation system \u003csup\u003e6,20\u003c/sup\u003e. Women who go to the GP or to the gynecologist in a certain age range are offered a free Pap test every three or five years \u003csup\u003e20\u003c/sup\u003e. However, the visual inspection of Figure 2, panel b shows that the increase is generally higher for OSP\u0026ndash;regions compared to non-OSP regions, and screening participation declines at the upper age limit of each OSP. The higher levels in OSP regions plausibly mirror systematic invitation and recall procedures.\u003c/p\u003e\n\u003ch2\u003eAverage causal effects\u003c/h2\u003e\n\u003cp\u003eTable 1 presents the causal estimates of the effect of OSP on screening participation based on equation 1. OSP for breast cancer increases mammography participation by 33.21 percentage points (pp) (95% Confidence Interval:24.56-41.85) on average. The impact of OSP on colorectal cancer screening participation is 19.41 pp (95% CI:13.95-24.88), and the effect of OSP on cervical cancer screening participation is 9.33 pp (95% CI:5.26-13.41) on average (Table 1, col 1, 3, and 5). The findings remain robust after adding control variables e.g. for age, education, marital status, employment, urbanicity, and self-reported health (Table 1, col 2, 4, and 6). Table S4 reports the full estimation outputs including the coefficients for the control variables. The causal effect of OSP for cervical cancer is substantially smaller in size compared to the effects of OSP for breast and colorectal cancer. This is explained by a large participation rate of 47.44% of women had a pap test in the last three years in the control group, which includes programs without an invitation system \u003csup\u003e6,20\u003c/sup\u003e. For breast and colorectal cancers, screening participation in the control group is lower and corresponds mainly to opportunistic screening (18.6% for breast cancer and 10.7% for colorectal cancer, Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Direct effects\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"702\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003emammo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003emammo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003epap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003epap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003efobt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003efobt\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.332***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.333***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.0933***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.0903***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.194***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.197***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.0438)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e(0.0432)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e(0.0206)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e(0.0202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e(0.0277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e(0.0274)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e122,952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e122,952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e122,952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e122,952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e122,952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e122,952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMean control group (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e18.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e18.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e47.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e47.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e10.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e10.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eSt. dev. control group (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e38.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e38.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e49.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e49.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e30.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e30.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAge fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eRegion fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eConfounding factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Region level clustered standard errors are in parentheses. The control group includes women who are not eligible for OSP because they live in a non-OSP region or because they are outside of the target age of the region specific OSP. Statistical significance: *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDirect and indirect causal effects\u003c/h2\u003e\n\u003cp\u003eTable 2 reports the estimated effects of OSP, distinguishing between: OSP (direct effect only), OSP+ (direct effect plus eligibility for one additional screening), and OSP++ (direct effect plus eligibility for two additional screenings). These estimates include the set of control variables (see Table S6 for the detailed output including the coefficients of each control variable). The direct effect of OSP is 25.70 pp (95% CI:18.06-33.30) for breast cancer, 8.18 pp (95% CI:4.17-12.19) for cervical cancer, and 13.72 pp (95% CI:9.37-18.07) for colorectal cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe impact of OSP+ that is the direct effect of OSP for breast cancer and the indirect effect of another OSP on the probability of mammography uptake is 35.68 pp (95% CI:26.99-44.38). Thus, the cross-program spillover effect of receiving an invitation to screen for another cancer on mammography uptake is 10.00 pp (95% CI: 4.81-15.20): OSP+ is 10 pp larger than OSP only (0.357- 0.257). Table 2 column 1; the test of coefficient equality is rejected with a p-value of 0.000 (t-value:3.80). Positive spillover effects are also found for cervical and colorectal cancer screening; the cross-program spillover effect of receiving a screening invitation for another cancer is 3.35 pp 95% CI: 0.56-6.15 (11.5-8.18, t-value:2.37,p-value: 0.019) on pap test uptake, and 7.44 pp 95% CI:2.34-12.55 (21.2-13.7, t-value:2.88, p-value: 0.005) on FOBT uptake. However, receiving a third invitation to screen for another cancer (i.e. being eligible for the three OSPs) does not significantly change participation compared to being eligible for two OSPs. Table S5 reports causal estimates of the direct and indirect effects excluding the set of control variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Cross program spillover effects\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"696\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003emammo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003et-test p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003epap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003et-test p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003efobt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003et-test p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eOSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.257***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eOSP \u0026ndash; OSP++:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.0818***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eOSP \u0026ndash; OSP++:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.137***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eOSP \u0026ndash; OSP++\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e(0.0386)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e(0.0203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.7355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.0220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eOSP +\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.357***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eOSP \u0026ndash; OSP+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.115***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eOSP \u0026ndash; OSP+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.212***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eOSP \u0026ndash; OSP+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e(0.0440)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e(0.0252)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.0190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.0351)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.0045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eOSP ++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.369***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eOSP+ OSP++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.0866***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eOSP+ OSP++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.220***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eOSP+ OSP++\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e(0.0509)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.5651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e(0.0215)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.0768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.0314)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.7925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e122,952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e122,952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e122,952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMean control group (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e18.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e47.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eSt. dev. control group (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e38.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e49.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e30.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Region level clustered standard errors are in parentheses. Each column is a different regression. All regressions include age and region fixed effects, and the set of control variables. The control group includes women who are not eligible for OSP because they live in a non-OSP region or because they are outside of the target age of the region specific OSP. *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e We studied the causal effect of OSPs for breast, cervical, and colorectal cancer on screening participation in Europe using regional data on OSPs characteristics and individual level data on screening participation and information on socioeconomic, demographic, health and health care use from the European Health Interview Survey (EHIS). To identify the causal effects, we followed a Differences-in-Differences approach using variation in the availability of OSP across European regions and variation in the target age of the OSPs. This strategy allows us to separate the effect of the program from regional and age-related factors that could otherwise confound the results. We measured the average causal effect of OSP for each cancer site. We distinguished between the direct effect of OSP on screening participation from potential cross-program spillover effects of being eligible for more than one OSP.\u003c/p\u003e\u003cp\u003eOur findings show that OSP significantly increases screening participation: the average causal effect of OSP for breast cancer is 33 pp in a sample of 27 EU countries in 2019, which is larger in size than previous studies in Europe found \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e; \u003csup\u003e19\u003c/sup\u003e estimated an effect of 17 pp on a subset of 13 EU countries in 2006; \u003csup\u003e8\u003c/sup\u003e estimated an effect of 25 pp on a subset of 21 EU countries in 2014. There are several factors that may contribute to this trend. These include the increasing average invitation rate over time \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, a higher intensity of invitations for programs that have been running longer, and advancements in screening techniques and breast cancer treatments that enhance the benefits of screening \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe found an average OSP effect for colorectal cancer with FOBT of around 20 pp. Although direct comparisons with the sparse previous evidence are limited, this EU-wide causal estimate is lower than those of other country-specific studies in Europe \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, but higher than the estimates found in the United States, where screening is only covered by health insurance for certain target ages and no equivalent OSP is in place \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and is in line with the current trends estimated in CRC screening utilization in studies using the EHIS survey \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The impact of OSP for colorectal cancer is also lower than for breast cancer. Different reasons could explain this, among those, specific impeding factors related to the screening procedure or a perceived lower risk for colorectal cancer \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e among the target population. The more recent implementation of OSPs for colorectal cancer, where invitation rates have not yet reached the rates of OSPs for breast cancer is another reason \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor cervical cancer, the average causal effect (9 pp) is smaller than the other two cancer sites, as for cervical cancer, many European regions have cervical cancer screening programs with a lower level of organization in place that do not include an invitation system of the target population \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e among the criteria defining a screening program \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Participation in pap test is therefore much higher in non-OSP-regions (50%), as women have access to pap test screening within their regular reproductive health checkup at the GP or gynecologist. This is not the case in non-OSP-regions for breast and colorectal cancers, where screening outside of the OSP is merely opportunistic or symptomatic screening \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn addition to a positive direct effect of each of the three OSPs on screening participation, we found significant positive cross-program spillover effects when women are in the target population of two OSPs i.e. are receiving two invitations to screen for two different cancer sites. However, being in the target population of the three OSPs compared to two does not further increase participation nor decreases it. These findings are particularly relevant to help design coordinated cancer screening policies and communication strategies, and this even more in light of the implementation of OSP for other cancer sites, such as lung cancer \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e in the near future.\u003c/p\u003e\u003cp\u003eA major strength of this study is the use of a large, representative dataset covering more than 122,000 women across 27 European countries, which provides strong external validity and allows for meaningful cross-country comparisons. The application of a Difference-in-Differences approach with regional and age fixed effects strengthens the causal interpretation of the results. Another important contribution is the novel examination of cross-program spillovers, which had not been systematically studied before.\u003c/p\u003e\u003cp\u003eNonetheless, some limitations should be acknowledged. First, despite the rich dataset, the analysis relies on self-reported screening outcomes from the EHIS, which may be subject to recall bias or misreporting. Second, although regional variation in OSP implementation provides a strong identification strategy, unobserved or time-varying contextual differences across regions\u0026mdash;though uncommon\u0026mdash;may still confound the results. Third, the study focuses only on FOBT-based colorectal screening programs, which may limit generalizability to regions where colonoscopy is the primary test. Finally, the cross-sectional nature of the EHIS data restricts the ability to assess longer-term behavioral dynamics or repeated participation across screening rounds.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEuropean Cancer Screening Strategy\u003c/h2\u003e\u003cp\u003eIn 2003, the European Union recommended EU countries to introduce cancer screening programs for breast, cervical and colorectal cancer to increase early detection and reduce cancer prevalence and mortality \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSince this recommendation, implementation of screening programs has increased and most countries in Europe have introduced organized screening programs (OSPs) or population-based screening programs for at least one of these three cancer sites \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. An OSP defines the eligible population, the screening intervals, and the type of examinations. Within an OSP, the eligible population is identified and systematically invited to each round of screening with a personalized letter \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The EU recommends mammography screening for women aged 50 to 69 for breast cancer. For colorectal cancer, the recommended screening is a fecal occult blood test (FOBT) for men and women aged 50\u0026ndash;74. At the EU level, screening for cervical cancer with a Pap test is recommended to start between the ages of 20 and 30 \u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAt national or regional level, variations exist in the way OSPs have been implemented. In 2019, the most frequent target population of OSPs for breast cancer is 50\u0026ndash;69. However, several European regions have enlarged the target population to younger women starting at 40 years, and or to older women up to 75 years (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). All OSPs for breast cancer invite eligible women for mammography every two years. For colorectal cancer, the EU's recommendation for cancer screening is usually to use a FOBT as the primary screening tool. The test kit is often sent to the patient's home. Invitations are sent every two years. Colonoscopy is most frequently used as second tier screening tool after an abnormal FOBT, but in some OSPs, colonoscopy is also offered as an alternative to FOBT. When this is the case, colonoscopy is offered every 10 years \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In this paper, we focus on OSPs for colorectal cancer that offer a FOBT as screening examination to be able to study the existence of potential spillover effects of regular sending of invitations. Invitation for FOBT screening is sent every two years. As of 2019, the wider target age range for FOBT is 50\u0026ndash;75. Some regions have chosen a smaller target population (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn 2019, as for the other two OSPs, OSPs for cervical cancer implemented in EU regions have chosen different age targets: the earliest start age is 20 years old, the older end age is 70 years old (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Invitations are sent every three years for most of the OSPs. Some OSPs offer cervical cancer screening every five years, for example the 30\u0026ndash;59/60 programs in the Netherlands and Estonia \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In 2022, EU recommendations on screening have been updated and generally extend the age targets of the OSPs defined in 2003 \u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNot all EU regions have implemented OSP for the three cancer sites. As of 2019, among the 195 EU regions included for analyses in this paper, few had not yet implemented any of three OSPs (EU regions of Romania, Greece, and Bulgaria), all other EU regions had at least an OSP for one of the three cancer sites in place (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eOrganized screening programs for breast cancer\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eOrganized screening programs for cervical cancer\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eOrganized screening programs for colorectal cancer (FOBT)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eOSP\u0026thinsp;=\u0026thinsp;Organized Screening Program. No OSP\u0026thinsp;=\u0026thinsp;regions with no Organized Screening Program. Colored regions are those included in the analyses. \u003cem\u003eSource\u003c/em\u003e: Authors\u0026lsquo; own database, 2019.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData\u003c/h3\u003e\n\u003cp\u003eWe construct a regional dataset covering breast, colorectal, and cervical cancer screening programs, drawing on International Agency for Research on Cancer (IARC) factsheets \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, reports \u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and published studies \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Information on the three OSPs was collected at the NUTS 2 level: whether an OSP is in place, the year of introduction, the screening invitation intervals, the type of screening test and the target population. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e summarize the target populations of each OSP. For colorectal cancer, we consider only the programs that use an FOBT as screening tool. Programs with colonoscopy only and target age for colonoscopy are not listed.\u003c/p\u003e\u003cp\u003eThese regional data on OSPs are merged with individual level data from the most recent European Health Interview Survey wave 3 (EHIS 3) that took place between 2018 and 2020 in 29 countries \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. EHIS 3 was conducted in 2019 in all EU member states, Iceland, Norway, and Serbia. Except for Belgium where the interviews took place in 2018, and in 2020 in Germany and Malta. EHIS includes four modules on health status, health care use, health determinants and socio-economic background variables. EHIS is run every five years and targets the population aged at least 15 years and living in private households \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Our three outcomes of interest, measuring self-reported individual level cancer screening participation, are defined as follows: (1) whether a woman had a mammography screening in the last two years (2); whether a woman had a fecal occult blood test (FOBT) screening in the last two years; (3) whether a woman had a pap test in the last three years. Age in years and an indicator of the region of residence (at the NUTS 2 level) are used to identify whether a woman is in the target group of each of the three OSPs.\u003c/p\u003e\u003cp\u003eA set of control variables are also included. Educational attainment is measured by ISCED 2011, aggregated into three levels (low, medium, high). Income is measured with the net monthly equalized income of the household in quintiles. Labor status is defined as being employed, unemployed (including women fulfilling domestic tasks, students, pupils, in compulsory military or civilian service, women unable to work due to a longstanding illness) or retired. Type of household is a categorical variable differentiating between one person household, one parent with children, couple without children, couple with children under 25 years old, couple with children older than 25 years old, and other types of households. Degree of urbanization is defined as densely populated areas (cities), intermediate density areas (towns and suburbs), thinly populated areas (rural areas) \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The dataset also includes information on women\u0026rsquo;s health status based on their general self-perceived health (in 4 categories: very good, good, fair, bad or very bad) and whether they report long-standing health problem(s).\u003c/p\u003e\n\u003ch3\u003eSample\u003c/h3\u003e\n\u003cp\u003eThe study sample includes data from women aged 17 to 90 years old from 27 countries. We excluded data from Italy because age was not available in years. Women aged 15\u0026ndash;16 and women above 90 years were also excluded, as these age groups were not consistently covered across countries. We kept all women with information on mammography, FOBT, and pap test. (We deleted proxy interviews.) Some of the confounding factors had missing values. We accounted for those by adding a category for missing values in the analyses, to keep all women with self-reported screening participation in the sample. The study sample includes 122,952 observations. Table S2 presents the steps of the sample construction.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eEstimation strategy\u003c/h2\u003e\u003cp\u003eAs descriptive analysis, we first plot respectively, the proportion of women who had a mammography in the last two years, who had a pap test in the last three years, and who had an FOBT in the last two years at NUTS 2 level. We then plot screening participation for each of the three cancers by age, for each OSP defined by their target age. Based on these plots, we visually investigate whether screening participation increases when age is within each OSP target age. If this is the case, this would give use descriptive evidence that OSPs have a positive effect on screening participation.\u003c/p\u003e\u003cp\u003eNext in multivariate quasi-experimental analysis \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, we estimate the average program effect of OSPs on participation. To estimate the causal effect of the OSP, we rely on a Difference-in-Differences type strategy. This approach compares changes in screening participation between women who are eligible for the program and those who are not, across regions with and without OSPs. In this way, the estimated effect can be attributed to the program itself, rather than to pre-existing differences between regions or age groups that may influence also the screening participation. Formally, the model is specified as follows:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{ir}=\\:{\\beta\\:}_{0}+\\:{\\beta\\:}_{1}{Region}_{r}+\\:{\\beta\\:}_{2}{Age}_{i}+{{\\beta\\:}_{3}Treatment}_{ir}+{\\beta\\:}_{4}{Controls}_{ir}+{e}_{ir}\\:\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;1\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{ir}\\)\u003c/span\u003e\u003c/span\u003e indicates whether woman \u003cem\u003ei\u003c/em\u003e living in region \u003cem\u003er\u003c/em\u003e screened, within the last 2 years for breast cancer and colorectal cancer, and within the last 3 years for cervical cancer. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Treatment}_{ir}\\)\u003c/span\u003e\u003c/span\u003e is our variable of interest and is equal to one if woman \u003cem\u003ei\u003c/em\u003e is eligible to the OSP i.e. has an age within the age range targetted by the screening programme and lives in a region r where the OSP is in place. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Region}_{r}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Age}_{i}\\)\u003c/span\u003e\u003c/span\u003e are respectively region fixed effects and age (in years) fixed effects. These fixed effects are key to the Differences-in-Differences estimation strategy. They allow us to isolate the OSP effect from confounders varying at regional level (i.e., general attitude towards prevention, average education and income) and confounders at age level (i.e. variation in the individual risk of being diagnosed with a cancer which varies across ages) that may influence screening decision irrespective of an OSP invitation. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Controls}_{ir}\\)\u003c/span\u003e\u003c/span\u003e denotes the set of demographic, socioeconomic and health variables that are added to test the sensitivity of our estimates. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{e}_{ir}\\)\u003c/span\u003e\u003c/span\u003e is the error term. Identification of the program effect relies on regional variation in the availability of OSPs and variation in age targets. These variations are essentially related to the high degree of autonomy of regional health authorities in Europe for what concerns the definition of the target population. As regional heterogeneity largely depends on region-specific orientations toward the target population and not on other factors potentially related to screening participation and OSP implementation (i.e., evidence on region-specific age-cancer risks profiles) it allows us to treat it as good as random in our setting. The direct causal effect is estimated for breast, colorectal, and cervical cancers separately. We use robust standard errors clustered at NUTS-2 level.\u003c/p\u003e\u003cp\u003eTo distinguish the direct from potential spillovers effects of being eligible to more than one OSP, we re-estimate Eq.\u0026nbsp;1 where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Treatment}_{ir}\\)\u003c/span\u003e\u003c/span\u003e is replaced by a categorical variables taking the following values: \u003cem\u003eNone\u003c/em\u003e when woman \u003cem\u003ei\u003c/em\u003e living in region \u003cem\u003er\u003c/em\u003e is eligible to none of the three OSPs (either because she resides in a region with no OSP in place or because she is not in the target age of any of the three programs); one \u003cem\u003eOSP\u003c/em\u003e when woman \u003cem\u003ei\u003c/em\u003e is eligible to one OSP (either because she lives in a region where only one of three OSPs is introduced or because she is in a the target age of only one program), namely the one corresponding to the outcome of interest; \u003cem\u003eOSP+\u003c/em\u003e, in addition to the OSP targeted by the outcome of interest, woman \u003cem\u003ei\u003c/em\u003e is eligible to a second OSP because she lives in a region where at least two OSPs are implemented and she is in the target age of two programs; or \u003cem\u003eOSP++\u003c/em\u003e, woman \u003cem\u003ei\u003c/em\u003e is eligible to the breast, cervical and colorectal cancer screening programs because she lives in a region where the three OSPs are implemented and because she is in the target age of the three programs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStatements\u003c/h2\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no other relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThis paper is based on data from Eurostat, EHIS, 2019, version released on: 12 May 2025. The responsibility for all conclusions drawn from the data lies entirely with the authors. Access to the EHIS microdata was granted by Eurostat with proposal number RPP 141/2021-EHIS, after ethical approval of each National Authority of the countries participating in EHIS. EHIS is authorized by EC Regulation 1338/2008 \u003csup\u003e43\u003c/sup\u003e. Data collection for EHIS wave 3 at the country level was conducted in accordance with the EU Regulation 2018/255 \u003csup\u003e44\u003c/sup\u003e. Further information on EHIS and data access are available: https://ec.europa.eu/eurostat/web/microdata/european-health-interview-survey.\u003c/p\u003e\n\u003ch2\u003eCode availability\u003c/h2\u003e\n\u003cp\u003eData analyses have been performed with Stata/SE 19.5. Do files are available upon request per email to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal Burden of Disease Cancer Collaboration. 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Territorial typologies manual - degree of urbanisation. \u003cem\u003eAccessed: 18 September 2025\u003c/em\u003e (2025). https://doi.org/https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Territorial_typologies_manual_-_degree_of_urbanisation#SE_MAIN_TT\u003c/li\u003e\n\u003cli\u003eEuropean Parliament and Council of the EU. (ed Official Journal of the European Union) (2021).\u003c/li\u003e\n\u003cli\u003eEuropean Commission. in \u003cem\u003eOJ L 48\u003c/em\u003e (ed Official Journal of the European Union) (2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Organized Screening Programs, screening participation, cancer screening, cross-program spillovers, Europe","lastPublishedDoi":"10.21203/rs.3.rs-7891986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7891986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOrganized cancer screening programs (OSPs) in Europe for breast, cervical, and colorectal cancers aim to improve early detection and reduce mortality. This study measures the effects of OSPs on participation and examines cross-program spillovers, as women are often invited to multiple screenings. We construct a regional-level dataset on OSP availability, merged with individual-level data from the European Health Interview Survey (EHIS) covering 122,000 women in 27 countries. We exploit cross-region and age-based variations in eligibility and employ a quasi-experimental difference-in-differences model to measure the causal effect of OSPs. OSPs substantially increase screening participation: mammography rises by 33.20 percentage points (pp) (95% CI: 24.56\u0026ndash;41.85), fecal occult blood test (FOBT) by 19.41 pp (95% CI: 13.95\u0026ndash;24.88), and pap test by 9.33 pp (95% CI: 5.26\u0026ndash;13.41). Positive spillover effects occur when women are invited to two screenings (10 pp (95% CI: 4.81\u0026ndash;15.20) for mammography, 3.35 pp (95% CI: 0.56\u0026ndash;6.15) for pap test, 7.44 pp (95% CI:2.34\u0026ndash;12.55) for FOBT) but targeting three cancers does not yield additional statistically significant gains. These findings highlight the strong impact of OSPs on participation and the value of coordinated screening and communication strategies as Europe expands organized screening to new cancer sites.\u003c/p\u003e","manuscriptTitle":"Direct and Indirect Effects of Cancer Screening Invitations on Participation in Europe: A Quasi-Experimental Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 10:24:21","doi":"10.21203/rs.3.rs-7891986/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d0b21a73-9909-4f4c-84b1-c608729e3de3","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57251620,"name":"Health sciences/Health care/Disease prevention/Preventive medicine"},{"id":57251621,"name":"Scientific community and society/Social sciences/Decision making"},{"id":57251622,"name":"Health sciences/Health care/Health policy"},{"id":57251623,"name":"Health sciences/Diseases/Cancer/Cancer prevention"}],"tags":[],"updatedAt":"2026-03-12T14:32:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 10:24:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7891986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7891986","identity":"rs-7891986","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}