Patterns of non-participation in breast cancer screening

Transcription

1 Patterns of non-participation in breast cancer screening PhD dissertation Line Flytkjær Jensen Faculty of Health Aarhus University 2015

2 Patterns of non-participation in breast cancer screening PhD Student: Line Flytkjær Jensen, Cand. Scient. San. Publ., the Research Unit for General Practice, Danish Research Centre for Cancer Diagnosis in Primary Care, Department of Public Health, Aarhus University, Denmark and Department of Public Health Programmes, Randers Regional Hospital, Denmark. Supervisors: Peter Vedsted, Professor, MD, PhD. Research Unit for General Practice and Danish Research Centre for Cancer Diagnosis in Primary Care, Department of Public Health, Aarhus University, Denmark. Anette Fisher Pedersen, Associate Professor, PhD, Psychologist. Research Unit for General Practice and Danish Research Centre for Cancer Diagnosis in Primary Care, Department of Public Health, Aarhus University, Denmark. Berit Andersen, Associate Professor, PhD, MD. Department of Public Health Programmes, Randers Regional Hospital, Denmark. Assessment committee: Henrik Støvring, Associate Professor, Department of Biostatistics, Department of Public Health, Aarhus University, Aarhus, Denmark (Chairman). Christine Campbell, Senior Research Fellow, Centre for Population Health Sciences, the University of Edinburgh, Scotland. My Catarina von Euler-Chelpin, Associate Professor, Department of Public Health, Center of Epidemiology and Screening, University of Copenhagen, Denmark. Financial support: This project was founded by the Danish Cancer Society, the Novo Nordic Foundation, the Riisfort Foundation, the Health Research Fund of the Central Denmark Region and the Faculty of Health, Aarhus University. ISBN

3 Acknowledgements ACKNOWLEDGEMENTS I visited the Research Unit for General Practice for the first time in February Here, I was introduced to an inspiring research environment and a research field I felt proud of joining while writing my master thesis. For this introduction, I am very grateful to Mette Bach Larsen. So many people have played an important role during my PhD time. First of all, my supervisors. Peter Vedsted; your tremendous knowledge in this field has blown me away again and again. I m eternally grateful for your never-ending support at all hours of the day and for always aiming high on my behalf. Anette Fischer Pedersen; I want to thank you for always being available and for sharing your expertise in this field always with a smile. Berit Andersen; you are truly an inspiration when it comes to running a screening joint with passion and dedication. I want to thank you for trusting me with this assignment and for including me in the Department of Public Health Programmes. I would also like to thank my colleagues in Randers for always making me feel welcome. Besides my supervisors, I wish to thank the co-authors of the papers. Mogens Vestergaard and Bodil Hammer Bech; thank you for your invaluable comments and insights into the subjects of Papers II and III, respectively. Morgen Fenger- Grøn; you played a very important role in the analyses in Paper V, and I thank you for your patience and for helping me to accept the black box. I owe a special thanks to Kaare Rud Flarup; you have helped me over and over again with data, registries and Stata. Dorthe Toftdahl and Birthe Brauneiser; thank you for always being available with help solving practical issues; and my appreciation also goes to Lone Niedziella and Morten Pilegaard for language revision. To all the knights and the weapon keepers : Thank you so much for the last few years which have been full of inspirational, fun and supportive moments in your company. These last few years would not have been the same without all of our research-related discussions or, indeed, without the discussions and adventures that had nothing to do with research. In particular, Line Hvidberg, thank you for your fellowship, support and friendship during the last ten years. I would also like to thank Anne-Louise for great company during our 3

4 Patterns of non-participation in breast cancer screening commuting time. All my colleagues at the Research Unit for General Practice and the Section for General Medical Practice, Aarhus University; I warmly thank you for a great workplace with inspiring conversations. My work was made possible by financial support from the Danish Cancer Society, the Novo Nordic Foundation, the Riisfort Foundation, the Health Research Fund of Central Denmark Region and the Faculty of Health, Aarhus University. Thank you. I would also like to thank Statistics Denmark and the Department of Public Health and Quality Improvement for providing data for the thesis. Finally, friends and family have played a significant role in my life, contributing with advice on work and everything else when needed. Thank you Stine H, Tine, Mette U, Søren, Mona, my brother Christian and my great neighbours, Tina and Stig Bo. My in-laws and parents; thank you for all your practical help. I would like to thank my parents for never letting me forget that you are proud of me regardless of what I choose to do in my life. I owe my deepest gratitude to Daniel; thank you for always being there with your calm and peaceful attitude. Finally, Marcus, thank you for bringing life into the sweetest and most positive perspective. 4

5 Contents CONTENTS 5

6 Patterns of non-participation in breast cancer screening 6

7 ACKNOWLEDGEMENTS... 3 CONTENTS... 5 PREFACE... 9 THE FIVE PAPERS OF THE THESIS ABBREVIATIONS CHAPTER 1: INTRODUCTION BREAST CANCER EPIDEMIOLOGY CANCER SURVIVAL IN DENMARK BREAST CANCER DETECTION IN DENMARK BENEFITS AND DISADVANTAGES OF BREAST CANCER SCREENING REDUCTION IN MORTALITY AS A RESULT OF BREAST CANCER SCREENING BREAST CANCER SCREENING IN DENMARK BREAST CANCER SCREENING IN THE CENTRAL DENMARK REGION SCREENING BEHAVIOUR AND THE SOCIAL-ECOLOGICAL MODEL INTRODUCTION AT A GLANCE AIMS OF THEIS THESIS CHAPTER 2: MATERIAL AND METHODS STUDY DESIGN SETTING THE CIVIL REGISTRATION SYSTEM STUDY POPULATION DATA SOURCES DEFINITION OF KEY VARIABLES STATISTICAL ANALYSES APPROVALS CHAPTER 3: RESULTS SOCIO-DEMOGRAPHY (PAPER I) CHRONIC DISEASES AND MULTIMORBIDITY (PAPER II) PSYCHIATRIC DISEASES (PAPER III) HRQOL AND PERCEIVED STRESS (PAPER IV) DISTANCE TO THE SCREENING SITE (PAPER V) CHAPTER 4: DISCUSSION OF METHODS SELECTION BIAS INFORMATION BIAS CONFOUNDING STATISTICAL ANALYSES AND PRECISION GENERALISABILITY

8 Patterns of non-participation in breast cancer screening CHAPTER 5: DISCUSSION OF RESULTS RESULTS IN GENERAL THE SOCIAL-ECOLOGICAL MODEL AND SCREENING BEHAVIOUR INEQUALITY IN BREAST CANCER SCREENING PARTICIPATION CHAPTER 6: MAIN CONCLUSIONS CHAPTER 7: PERSPECTIVES AND FUTURE RESEARCH ENGLISH SUMMARY DANSK RESUME REFERENCES PAPER I PAPER II PAPER III PAPER IV PAPER V

9 Preface PREFACE A Danish law was passed in 1999, stating that organised breast cancer screening should be offered free-of-charge to all women between 50 and 69 years of age (1). The law did not specify when the programme should be implemented and the importance of such a programme was thus emphasised in Cancer Plan I in 2000 and in Cancer Plan II in 2005, and finally introduced nationally as an organised programme in The programme aims to reduce breast cancer mortality, and a high attendance among the invited population is therefore crucial. In the Central Denmark Region, almost 80% of the invited women participated in the first screening round which exceeded the coverage of 75% recommended by the European Commission (2). Nonetheless, one in five women did not participate, and non-participation may not be equally distributed among groups in society. Non-participation may be attributed to various factors, including women s individual characteristics, social relations or the way the screening programme is organised. In order to be able to design measures promoting equal access and programme effectiveness, it is important to study patterns of non-participation in breast screening programmes which is the particular focus of the present thesis. 9

10 Patterns of non-participation in breast cancer screening THE FIVE PAPERS OF THE THESIS This PhD thesis is based on the following papers I. Jensen LF, Pedersen AF, Andersen B, Vedsted P. Identifying specific non-attending groups in breast cancer screening-population-based registry study of participation and socio-demography. BMC Cancer. 2012;12: II. Jensen LF, Pedersen AF, Andersen B, Vestergaard M, Vedsted P. Nonparticipation in breast cancer screening for persons with chronic diseases and multimorbidity: a population-based cohort study (Accepted for publication in the BMC Cancer, October 2015). III. Jensen, LF, Pedersen AF, Bech BH, Andersen B, Vedsted P. Psychiatric morbidity and non-participation in breast cancer screening: A Danish cohort study (Accepted for publication in the Breast, October 2015). IV. Jensen LF, Pedersen AF, Andersen B, Vedsted P. Health-related quality of life, perceived stress and non-participation in breast cancer screening: A Danish cohort study (Accepted for publication in the Preventive Medicine, October 2015). V. Jensen LF, Pedersen AF, Andersen B, Fenger-Grøn M, Vedsted P. Distance to screening site and non-participation in screening for breast cancer: A population-based study. J Public Health (Oxf). 2013;36(2):

11 Preface ABBREVIATIONS ATC Anatomical Therapeutic Chemical classification CDG Chronic disease group CI Confidence interval CRN Civil registration number CRS Civil Registration System DAGs Directed acyclic graphs DCR The Danish Cancer Registry EUROCARE European Cancer Registry based study on survival and care of cancer patients GLM Generalised linear models GP General practitioner HRQoL Health-related quality of life HSR National Health Service Registry ICBP International Cancer Benchmarking Partnership ICD-10 International Classification of Diseases 10 th revision Km Kilometre RMPS Register of Medicinal Product Statistics MCS Mental Component Summary NPR National Patient Registry NVR National Vehicle Registry PCRR Psychiatric Central Research Registry PCS Physical Component Summary PPV Positive predictive value PR Prevalence ratio PSS Perceived stress scale RAR Regional administrative register RCT Randomised controlled trials RMPS Register of Medicinal Product Statistics RR Relative risk RS Relative survival SEP Socio-economic position 11

12 Patterns of non-participation in breast cancer screening 12

13 Introduction CHAPTER 1: INTRODUCTION 13

14 Patterns of non-participation in breast cancer screening BREAST CANCER EPIDEMIOLOGY Breast cancer is the most common cancer among Danish women. Approximately 4,100 new cases were diagnosed in 2007, which was the year before the national organised screening round was introduced. In 2013, a few years after the first organised screening round was introduced, about 4,700 new cases were diagnosed (3, 4). About half of all breast cancer cases in Denmark are diagnosed among women between years of age (5). The incidence of breast cancer has risen markedly since the 1950s and peaked around (Figure 1). This peak reflects the prevalent cases diagnosed during the first organised screening round and therefore only applies to women between 50 to 69 years of age. Figure 1: Age-standardised incidence rate per 100,000 women over time (red line indicates incidence, green line mortality) (6) Risk factors for breast cancer include older age, smoking, unhealthy diet, postmenopausal obesity, use of hormone replacement therapy and heredity. Opposite this, reproduction factors such as giving birth at a young age, breast feeding and more than one full-term pregnancy have been associated with a lower risk of breast cancer (7). The prognosis of breast cancer depends on the disease stage at the time of diagnosis (8). A study investigating breast cancer diagnosed between 2000 and 2006 found a 3-year net survival of 99.4% (95% confidence interval (CI) ) 14

15 Introduction for Danish breast cancer patients diagnosed in stage I (localised cancer), whereas it was 35.6% (95% CI: %) (9) for patients diagnosed in stage IV (metastatic cancer). Between approximately 1,100 and 1,350 women died of breast cancer each year between year 2000 and 2013 (10); and in 2007 it was estimated that breast cancer mortality accounted for 17% of all cancer-related death among women in Denmark (11). The breast cancer-specific mortality rate has in general decreased during recent years (Figure 1) owing, among others, to improved treatment procedures (12). Breast cancer incidence and mortality vary according to socioeconomic position (SEP). A Danish study reported a higher breast cancer incidence among highly educated women (relative risk (RR) 1.25, 95% CI: ) compared to women with basic or high-school education (13). Inversely, the same study found that the overall 5-year survival was 77% (95% CI: 76-68%) among Danish breast cancer patients with basic or high-school education, but 84% (95% CI: 82-85%) among women with higher education (13). The same trends are reported in other studies (14-16). CANCER SURVIVAL IN DENMARK Breast cancer survival has improved significantly in Denmark in recent years (12). Still, over the past few decades, studies have consistently shown that Danish cancer patients have lower cancer survival rates than cancer patients from other Nordic countries (17, 18), European countries (the EUROCARE studies (19, 20)) and worldwide (International Cancer Benchmarking Partnership (ICBP) collaborations (9, 21)). For example, the 3-year agestandardised relative survival was 89.0% (95% CI: %) for Danish breast cancer patients diagnosed in ; in the same period, it was 94.3% (95% CI: %) for Swedish patients and 90.6% (95% CI: %) for Norwegian patients (9). These differences in cancer outcomes have been ascribed to variations in registration methods across cancer registries (17), differences in medical technology, discrepancies in the availability of treatments between countries, individual lifestyle factors (17, 22) and country-specific comorbidity patterns (17). Finally, it has been suggested that Danish cancer patients may be 15

16 Patterns of non-participation in breast cancer screening diagnosed at more advanced disease stages than cancer patients from other countries (9). These studies have informed several political initiatives in Denmark, e.g. Cancer Plan I in 2000 and Cancer Plan II in 2005 (22, 23). These plans laid out guidelines and recommendations to improve survival of Danish cancer patients, and priority was given to improvements in surgery techniques, guarantees of reduced waiting time and the introduction of cancer-specific fast-track diagnostics. Of special interest for this thesis is that both cancer plans emphasised the importance of organised screening for breast cancer, and Cancer Plan II recommended that such screening be implemented as quickly as possible. BREAST CANCER DETECTION IN DENMARK Symptomatic diagnostics of breast cancer is often initiated after a woman s presentation of symptoms to her general practitioner (GP) who initiates further diagnostic work-up which is performed by the hospital sector. In England, where breast cancer screening is also implemented, a study found that the GP was involved in the initial diagnostics of 72% of all breast cancer cases (24). A study from Denmark, before the organised screening programme was implemented, it was found that the GP was involved in the initial diagnostics of 93% of breast cancer cases (25). Breast cancer diagnosis as a result of emergency presentation is relatively rare (26). Opportunistic screening is also sometimes used as a diagnostic tool by the GP for asymptomatic women (27); and it is often initiated in relation to general health checks. Historically, the use of opportunistic screening as a diagnostic tool is low in Denmark, and a study found that opportunistic screening was rare among both attenders and nonattenders of the Danish screening programme (27). Detection of breast cancer through screening may also be initiated as a secondary prevention measure where screening is offered at a population level to a predefined target group with the purpose of detecting early-stage breast cancer among asymptomatic women (8). 16

17 Introduction BENEFITS AND DISADVANTAGES OF BREAST CANCER SCREENING Population-based breast cancer screening has advantages as well as disadvantages (12). The main benefit lies in the possibility of reducing breast cancer mortality and morbidity (8) owing to the fact that screening may help detect tumours before they become symptomatic and clinically detectable. Less aggressive treatment is often possible when cancer is diagnosed at a less advanced stage (8). No diagnostic tests are 100% accurate. The risk of a false-positive result is a serious side-effect of a screening programme. In the worst case, false-positive results lead to over-diagnosing and over-treatment, i.e. that benign lesions are identified and referred to surgery even if these lesions may not have developed into detectable breast cancer during the woman s lifetime (8, 12). A recent report from the UK estimated that over-diagnosis in breast cancer screening reached 11% (28); otherwise, the literature reports over-diagnosis in the range 1% to 54% (29). There are currently no tools for identifying women at risk of over-diagnosis (28). The risk of a false-negative result should also be considered. The failure to detect a cancer can be due to several factors, such as poor radiation technique or interpretation errors (30). Participation in a screening programme may thus give the woman false reassurance, and even regular participation in breast cancer screening may not guarantee that an interval cancer cannot develop (31). Another negative side-effect of breast cancer screening is that it may give rise to worry and psychological distress (32). Some studies indicate psychological distress after a false-positive result (32-34); although this is not confirmed in all studies (35, 36). Two reviews suggest that false-positive results negatively affect future screening participation, but they also argue that this conclusion should be interpreted with caution and raise methodological issues concerning the reviewed studies (37, 38). For participants with true-negative screening results, studies seem to agree that participation has no long-term psychological effect (36, 39). Finally, lead time bias (40) may be a problem if survival from breast cancer is not improved as a result of screening, but the time lived with the disease is longer because of earlier diagnosis (31). 17

18 Patterns of non-participation in breast cancer screening REDUCTION IN MORTALITY AS A RESULT OF BREAST CANCER SCREENING Since the 1980s, a number of randomised controlled trials (RCTs) have been conducted to study mortality reduction as a result of organised breast cancer screening. A meta-analysis of the RCTs estimated that the mortality from breast cancer was statistically significantly reduced (RR 0.78 (95% CI: 0.70 to 0.87)) among women 50 years or older who had been screened (41). However, the methodological quality of the trials varied. This led Gøtzsche and Jørgensen to conduct new analyses among eight RCTs (42) which were stratifying according to the quality of their randomisation processes. They (42) found that randomisation was sufficient in only three of the RCTs; and that mortality was non-significantly reduced in these three studies (RR 0.90, 95% CI: ). Pooling the data of RCTs with sufficient and suboptimal randomisation procedures, the authors found that mortality was statistically significantly reduced (RR 0.81, 95% CI 0.74 to 0.87) (42). The authors argue that women s gains from participation are small and may not outweigh the risk of harm (42). The use of screening programmes has been much contested over the years, both nationally and internationally. This led the British government to set up a task force in 2012 to assess current knowledge in the field. A total of 11 RCTs were reviewed, and a 20% mortality reduction (RR 0.80, 95% CI: ) among women invited to screening was found (28). The overall conclusion was that the benefits of the British screening programme outweighed the harms. BREAST CANCER SCREENING IN DENMARK At the turn of the millennium, organised screening was provided only in few parts of Denmark. The municipality of Copenhagen was the first part of the country to offer organised screening in 1991 followed by the county of Funen and the municipality of Frederiksberg in 1993 and 1994, respectively (8). A law was passed in 1999 stating that screening for breast cancer should be offered free-of-charge to all Danish women between years of age (1). With the Danish municipality reform in 2007, the implementation of breast cancer screening became the responsibility of the five Danish regions. It was decided that the first screening round should have taken place no later than by the end of

19 Introduction The five regions are responsible for inviting and offering biennial screening within their regions to female residents aged years. In the first screening round in , approximately 670,000 women were invited nationally and 77.6% participated (43). BREAST CANCER SCREENING IN THE CENTRAL DENMARK REGION The first screening round in the Central Denmark Region was introduced during the period from 28 February 2008 until 31 December The programme is organised and run by the Department of Public Health Programmes, Randers Regional Hospital which is responsible for inviting, booking, rebooking and informing the women of the result by postal mail. Every other year, every woman in the region is sent a postal letter with information about the screening offer together with an assigned screening date and time. Women can reschedule or decline participation. In the first screening round, no reminders were sent out to women who did not show up for their screening appointment. Screening for breast cancer was offered at six geographical locations in the first screening round. A screening unit was placed in the following cities scattered across the region: Aarhus, Viborg, Randers, Horsens, Holstebro and Kjellerup. To accommodate the legal demand that all women should have been invited by 31 December 2009, the Kjellerup screening unit was used as an extra facility during the first screening round, and it is no longer in use. Almost 150,000 women were invited to the first screening round in the Central Denmark Region. The participation rate reached 78.9% (44), with the highest participation in the municipalities of Holstebro and Lemvig and the lowest participation in the municipality of Samsoe (Figure 2). 19

20 Patterns of non-participation in breast cancer screening Figure 2. Participation in percent stratified on municipality in the Central Denmark Region. The screening units are marked with a black star (Kjellerup screening unit is located in the municipality of Silkeborg). 20

21 Introduction SCREENING BEHAVIOUR AND THE SOCIAL-ECOLOGICAL MODEL As the screening programme is free of charge, all invited women should, in principle, have equal access to participation. Yet, screening behaviour, like any human behaviour, is not straightforward (45) but rather complex as illustrated in social-ecological behaviour models (46), which essentially argue that health behaviour is influenced by many factors, individual as well as contextual (45, 46). Figure 3 illustrates the social-ecological model, modified to illustrate factors important for screening behaviour. These factors, often operationalised into levels, are termed differently in the literature; yet, for the present purpose, four levels of factors influencing screening behaviour are applied: 1) The intrapersonal level, which includes factors like personal socio-demographic and economic position; personal traits such as self-efficacy, attitudes and knowledge; and, finally, health status including perception of own health. 2) The interpersonal level, which includes social support and work relations and social relations in general, among others. 3) The community/organisational level, which refers to the five regions and the authorities organising the screening programme following a set of national regulations. This level also includes, e.g., the location of the screening units and hence the geographical distance to the screening facilities. Finally, 4) the public policy level, which includes the laws and national guidelines of the programme which are determined nationally. Figure 3. The social-ecological model modified to illustrate factors of importance for screening behaviour. The variables in black are studied in this thesis, and variables in grey are examples of additional variables which could potentially influence screening behaviour. 21

22 Patterns of non-participation in breast cancer screening The social-ecological model was used as a theoretical framework in this thesis to identify possible factors that may be associated with non-participation in screening and as a framework to illustrate the complex nature of screening behaviour. To describe patterns of non-participation, this thesis focuses on three groups of factors: 1) socio-demographic and socio-economic factors, 2) women s competing health interests, including diagnosed diseases and self-assessed health and perceived stress, and 3) geographical distance to the screening site. These groups of factors are represented at the intrapersonal, interpersonal and organisational level of the social-ecological model as shown in Figure 3. In previous studies, we examined the interpersonal level by assessing the association between social support and non-participation (47), and we furthermore assessed the organisational level by studying the association between the GPs attitudes towards screening and the screening behaviour of the women listed with their practices (48). In the following, the background and the literature of the included variables will be presented. Socio-economic position Socio-economic position (SEP) can be operationalised as the social and economic factors that influence what positions individuals or groups hold within the structure of a society (49). Hence, variables such as age, marital status, ethnicity, income, education and occupation can describe a person s SEP. These factors describe mainly the intrapersonal level in the social-ecological model, but variables like occupation and marital status can also be defined within the interpersonal level. Some of these variables can also be argued to belong to more than one level; e.g., education is related to the intrapersonal level, but could also be argued to be related to both the interpersonal and the organisational level. Epidemiological studies have consistently confirmed differences in health outcomes among socio-economic groups. People with low SEP are more likely to get diseased and to have a shorter life-expectancy than people with high SEP (50-52). This has been related to a wide range of explanations, including the ability to speak, understand and communicate with health professionals (i.e. health literacy) (53-55). 22

23 Introduction SEP has the possibility to affect health across a person s life span, but SEP is not always stable over time. Income, for example, often increases after an educational degree has been obtained and decreases again when a person leaves the workforce. Marital status is also dynamic and may change as a result of marriage, divorce or the death of a partner. Contrarily, ethnicity (i.e. country of origin) remains the same during a lifespan. Women s SEP has often been studied as a factor that has a strong bearing on their screening participation. The majority of these studies identify social inequalities in screening participation; e.g. being unemployed and having low income are related to lower participation in most studies (56-60), although not in all (61). Low participation is often associated with ethnic minority status (58, 62-66). Older age is associated with higher non-participation likelihood in some studies (60, 61, 65), but not in all (56, 63, 67). Being married or cohabitating are also associated with a higher participation propensity in most studies (56, 59, 60, 65), but not in all (61). The association between education and screening participation is less firmly established. Some studies found higher participation among higher educated women (68, 69), others found higher non-participation among higher educated (56, 57); some studies reported a U-shaped association (65, 70) and, finally, one study reported no association between education and participation (67). Home-ownership, used as a proxy for high SEP, has been studied a few times, with inconclusive results (62, 71, 72). In sum, SEP is a key variable explaining health behaviour, and further studies are needed to determine the specific patterns of social inequalities in breast cancer screening participation. Women s competing health interests Women s competing health interests can be operationalised at the intrapersonal level of the social-ecological model; and they may influence a woman s ability to participate in screening. In the present thesis, women s competing health interests are assessed in terms of chronic diseases, psychiatric morbidity and self-assessed health and perceived stress. 23

24 Patterns of non-participation in breast cancer screening Chronic disease and multimorbidity The association between health and non-participation in breast cancer screening remains poorly investigated (73, 74). Different chronic diseases have been studied in relation to breast cancer screening participation (57, 73-76), and the results remain inconclusive. Few studies have investigated multimorbidity (74, 75, 77-79), and most found that higher morbidity is associated with a higher non-participation propensity (75, 77-79), but one study found the opposite (74). Psychiatric morbidity The association between psychiatric disease and non-participation in breast cancer screening remains inconclusive. Some studies found no association between psychiatric diseases and non-participation (74, 80, 81), whereas others concluded that women with psychiatric diseases were more likely not to participate (82-84). Different psychiatric diseases are rarely compared in the same study, and the association between the chronicity of psychiatric morbidity and non-participation has never been investigated. It can, for instance, be hypothesised that women who experience psychiatric morbidity at the time of screening and women with chronic psychiatric morbidity will be less likely to participate than women with past psychiatric problems. Self-assessed health and perceived stress Women s own assessment of their physical, mental and social health, conceptualised as health-related quality of life (HRQoL) (85), is another important measure of health (86). Studies have investigated the association between HRQoL and screening participation. Most applied a single question to assess self-assessed health (68, 70, 87-90) and only a few studies applied a scale (91, 92), and no studies adjusted for the specific influence of having chronic diseases. The results remain inconclusive; and a review calls for further studies applying consistent definitions of self-assessed health (93). In line with selfassessment of health, perceived stress is a possible factor of importance for screening participation. Only few studies have investigated the association between perceived stress and non-participation, and the results are inconclusive (57, 94, 95). 24

25 Introduction In conclusion, there is a need for further studies of women s physical and mental health status as well as of their own perception of health and its association with screening participation. Distance to screening site The organisation of the screening programme most likely has an impact on the public s response to the screening offer. In this context, it is relevant to discuss practical barriers such as opening hours, the possibility to rebook appointments and the distance each woman has to travel to reach the screening location. These factors belong to the organisational level in the social-ecological model. The geographical accessibility to screening locations has been studied to some extent. Most studies found that longer distance was associated with higher likelihood of non-participation (58, 66, 72, 96-98); yet, others have not been able to find any association (99, 100). The studies in this field are often limited methodologically, and different methods have been used to measure the distance; some have relied on women s self-reported distance (72, 96), others have made estimations based on different geographical software (58, 97, 98). Only few studies exist on the association between access to vehicle (e.g. car, motorbike) and screening participation (72, 99), and this association has only been studied descriptively (72) or without individual-level information on access to vehicle (99). Therefore, there is a need for further study of the association between distance, access to vehicle and participation in breast cancer screening. 25

26 Patterns of non-participation in breast cancer screening INTRODUCTION AT A GLANCE Organised breast cancer screening of women aged 50 to 69 years of age was introduced in the Central Denmark Region in with the purpose of reducing breast cancer mortality and morbidity. Both harms and benefits are associated with the programme and the issue of mortality reduction is continuously being debated. Still, when a society has decided to offer screening to a large proportion of its population, it remains crucial to ensure that the intended populations have equal access to this programme. Screening efficiency is contingent on high participation. Using the socialecological model to identify factors which possible affect screening behaviour, this background section illustrates that multiple, complex dimensions affect screening behaviour. Many studies in this field draw on data from countries that are non-comparable to Denmark, for example in terms of the organisation of the screening programmes. Moreover, some of the existing studies are characterised by little consistency in the choice of methods and the definitions used to define exposure variables. Thus, many factors that may influence breast cancer screening behaviour are not fully established and more studies are needed to elucidate this important issue in the Danish context. 26

27 Introduction AIMS OF THEIS THESIS This thesis aims to investigate patterns of non-participation in the first organised breast cancer screening programme in the Central Denmark Region. In this connection, this thesis will: 1) study if there is an association between socio-demography, socioeconomy and non-participation in breast cancer screening (Paper I) 2) study if there is an association between chronic diseases, multimorbidity and non-participation in breast cancer screening (Paper II ) 3) study if psychiatric morbidity is associated with non-participation in breast cancer screening (Paper III) 4) study if there is an association between health-related quality of life and perceived stress assessed in 2006 and non-participation in breast cancer screening (Paper IV) 5) study if distance to screening site is associated with non-participation in breast cancer screening (Paper V) 27

28 Patterns of non-participation in breast cancer screening 28

29 Material and methods CHAPTER 2: MATERIAL AND METHODS The core methodological aspects of the included papers are presented in this section. For a detailed description of the included methods, data and variable operationalisations, please see Papers I-V from page

30 Patterns of non-participation in breast cancer screening Table 1 presents an overview of Papers I-V, including the design of the studies, the data sources, exposure and outcome variables. Table 1: The data sources and key variables for Papers I-V Paper: Study design Data source* Independent variables* (I) Sociodemography Cross- Statistics Denmark Marital status and sectional Ethnicity non-participation study Education in breast cancer Income screening Occupation Home- ownership Administrative Age registry (II) Chronic diseases, multimorbidity and nonparticipation (III) Psychiatric diseases and nonparticipation (V) Distance to screening site and non-participation Historical cohort study Historical cohort study Historical cohort study (IV) HRQoL, perceived stress and nonparticipation Crosssectional study Danish Health and Medicines Authority & Krak.dk Software: ArcGIS The Danish Cancer Registry The Danish National Vehicle Registry The Danish Patient Registry & the Psychiatric Central Research Registry The Danish Patient Registry & the Psychiatric Central Research Registry The Register of Medicinal Product Statistics The Danish National Health Service Registry Regional Health Survey Danish Health and Medicines Authority & Krak.dk Software: ArcGIS Distance to screening site Cancer diagnosis prior to screening Access to vehicle Chronic diseases and multimorbidity Psychiatric illness Prescription of antidepressives, antianxiety and antipsychotic medicine First consultation with a private psychiatrist Self-assessed mental and physical health (SF-12) Perceived stress (PSS) Distance to screening site *Data sources and variables used as possible covariates are not listed here **Treated as the reference group in all analyses Outcome variable Participation in breast cancer screening: yes**/no Non-participation: Active nonparticipation**/passive non-participation Participation: yes**/no Participation: yes**/no Participation: yes**/no Participation: yes**/no 30

31 Material and methods STUDY DESIGN Papers I and V were carried out as population-based, cross-sectional studies. Papers, II, III and IV were carried out as observational, population-based, historical cohort studies as data were collected longitudinally but assessed retrospectively because the exposure (e.g. chronic diseases) and the outcome (non-participation) had already occurred (101). SETTING The study population of this thesis originates from the Central Denmark Region which is located in central Jutland. The population of the region was n=1,247,732 on 1 January 2009 (female citizens years per 1 January 2009, n= 151,415). The geographic composition consists of mixed urban and rural areas with few larger cities, the largest city being Aarhus with approximately 300,000 inhabitants. The educational level in the Central Denmark Region is similar to the national level with about 70% having an education beyond elementary school. However, variabilities exists within the region as about 37% of the population is not educated beyond elementary school in the municipality of Samsø; in the municipality of Holstebro, the corresponding percentage is 26% (102). Between 2008 and 2012, it was estimated that breast cancer accounted for 28.6% of all diagnosed cancers in the Central Denmark Region with more than 1,000 new cases each year. Approximately 50% of the diagnosed breast cancer cases in the region occurred among women aged 50 to 69 years (6). THE CIVIL REGISTRATION SYSTEM As a cornerstone of the present thesis, the Danish Civil Registration System (CRS) was used to identify the study population and to link data in all studies. All Danish citizens have a unique ID called the civil registration number (CRN) which is a 10-digit code containing birthday and gender information together with a unique four-digit code (103). The CRN was used to identify women in the target group for the first screening round and, hence, to identify the study 31

32 Patterns of non-participation in breast cancer screening population and to exclude women who had migrated or died between the invitations were sent out and the screening date. STUDY POPULATION The source population was all women eligible for the first breast cancer screening round in the Central Denmark Region which included women between years of age who were living in the Central Denmark Region during the first screening round in A flow chart for each of the five studies is presented in Figure 4. For the purpose of all studies, some general exclusion criteria were used, which established a study population of 144,264 persons in Papers I, II and III. In Paper IV, the study population was women who responded to a Health Survey in 2006 and who were also members of the target group for first screening round in (n=4,512). In Paper V, only women with a valid distance calculation were included in the analyses (n=127,628) (Figure 4). In the first screening round in the Central Denmark Region, the invitations were sent out following the GPs patient lists. Thus for each GP, all of their listed women in the target group were invited to participate within the same time frame. The order in which the GPs were scheduled to have their listed women invited to screening was randomly determined. This meant, for example, that some women who turned 50 years during the first screening round were not invited to the first screening round if her registered GP already had been scheduled for the round. Thus, the population was not fixed when the screening round started (February 2008). A total of 98% of the Danish population is listed with a GP whom they contact for medical advice (104). The 2% who are not listed with a general practice were invited at the end of the first screening round. 32

33 Material and methods Women invited to the first screening round: n=149,234 Women included in 2006 Health Survey n=15,779 Excluded (n=4,970) Dead between invitation sent out and screening date: 110 Migrated between invitation sent out and screening date: 123 Breast cancer prior to the screening date: 4,646 Outside catchment area: 91 Excluded (n=9,824) Not in age group for screening: 8,601 Migrated or dead between survey and first screening round: 1,111 Immigrants : 112 Excluded (n=16,636) No geocode: 6,847 Excluded because of reschedule of screening site: 7,769 Incomplete data on covariates (n=2,020) Excluded Non-responder in the Health Survey: 1,443 Women included in study: n=144,264 Women included in study: n=127,628 Women included in study: n=4,512 Paper I, II & III Paper V Paper IV Figure 4. Flow chart for the study population of the five papers 33

34 Patterns of non-participation in breast cancer screening DATA SOURCES Besides the Danish CRS (described above), the following data sources were applied: The Regional Administrative Registry All women invited to the first screening round were registered in a regional administrative registry (RAR) run by the Department of Public Health Programmes, the Central Denmark Region. This register holds, among others, the following information for each invited woman: the CRN, affiliated screening site, participation status and the scheduled screening date. The scheduled screening date corresponds to the date the woman was invited to participate in the screening, and the date was also registered for women who eventually did not participate. This scheduled screening date was applied as an index date to draw data for all studies. Statistics Denmark Statistics Denmark plays a key role in Danish registry management. Besides being responsible for publishing statistical data on the Danish population, Statistics Denmark administers data accessible for research purposes, and within an authorized system, researchers can work on anonymised and encrypted data on Statistics Denmark s server. For this thesis, Statistics Denmark (105) provided data on women s sociodemographic and socio-economic variables. The included variables were: marital status, ethnicity, education, OECD-modified household income, occupation and home ownership. Socio-demographic variables were used as the primary independent variables (Paper I) or as possible confounders (Papers II- V). The Danish Cancer Registry The Danish Cancer Registry (DCR) was founded in 1942 and contains data on Danish cancer cases including date of diagnosis, International Classification of Diseases 10 th revision (ICD-10), tumour stage, etc. The validity of the DCR is ensured by daily quality control routines which demonstrate nearly complete registration of cancer cases (106). This registry was used in this thesis for two 34

35 Material and methods purposes. The first was to identify women registered with a breast cancer diagnosis before the scheduled screening date. These women were excluded from further analyses (Figure 4) because an unknown proportion of them underwent a separate post-cancer follow-up programme and were therefore encouraged not to participate in the organised programme. Secondly, the registry was used in Paper I to identify women registered with a cancer diagnosis other than breast cancer. The Danish National Patient Registry Established in 1977, the Danish National Patient Registry (NPR) holds data on hospital-related contacts. Since 1995, this registry has included information on all emergency and psychiatric contracts together with all inpatient and outpatient contacts to all private and public hospitals. All contacts are classified according to the ICD-10 system. The NPR is run by the Danish Health and Medicines Authority who performs ongoing validation (107). Contacts to general practice do not appear in this register. The NPR was used in Papers II, III and IV to identify chronic and psychiatric diseases that were registered in the hospital setting up to 10 years before the scheduled screening date. The Danish Psychiatric Central Research Registry The Danish Psychiatric Central Research Registry (PCRR) became an electronic database in 1969; and since 1970, all psychiatric hospitals and psychiatric departments have been obligated to report data to the register (108). As from 1995, all outpatient treatments and emergency room contacts were included in addition to admissions. All contacts are classified according to the ICD-10 system. The validity of the database is ensured by the PCRR and the Centre for Psychiatric Research where the electronic data are compared with data from the individual departments. The register does not obtain data from the general practice setting (108). The PCRR was used in Papers II, III and IV to identify women with selected psychiatric diagnoses in the hospital setting up to 10 years before the scheduled screening date. Register of Medicinal Product Statistics The Register of Medicinal Product Statistics (RMPS) was established in 1994 and holds information on the sale of medical products in Denmark (109). All 35

36 Patterns of non-participation in breast cancer screening registrations are classified according to global Anatomical Therapeutic Chemical classification (ATC) codes. This registry includes information on medical products sold on prescription and over-the-counter as well as drugs used for hospitalised patients. The RMPS was used in Paper III to obtain information on anti-depressant, anti-anxiety and anti-psychotics medicine. Danish National Health Service Registry The Danish National Health Service Registry (HSR) contains data about health activities conducted among professionals contracted with the tax-funded public healthcare system, e.g. GPs and medical specialists (110). This registry was used in Paper III to obtain information on consultation(s) with a private psychiatrist. Data sources for distance calculations The distance to each woman s affiliated screening site was calculated using the software programme ArcGIS Network Analyst. Geographical coordinates for each woman s residence were obtained from the Danish Health and Medicines Authority. Data on the Danish road network system were obtained from Krak.dk. This information was loaded to the software programme together with the six street addresses of the screening sites. Using ArcGIS, the shortest road distance from each woman s home to her affiliated screening site was calculated in kilometres (km). One-way streets and speed limits were accounted for (111). This variable was used exploratively in Paper I and studied in detail in Paper V. The Danish National Vehicle Registry The Danish National Vehicle Registry (NVR) contains data on ownership and co-ownership of vehicles in Denmark (cars, vans, motorbikes, etc.). Data are provided on a monthly basis by SKAT, i.e. the Danish tax authorities (105). The NVR was used in Papers I and V to assess if the woman was registered as an owner or co-owner of a vehicle. The 2006 Health Survey Data on HRQoL and perceived stress were collected as part of a Health Survey conducted in the Central Denmark Region in 2006 (survey title in Danish: Hvordan har du det?) (112). The questionnaire contained approximately 400 items on, e.g., health behaviour, perceived stress and HRQoL. Most of the items were 36

37 Material and methods based on a previous national survey (113). In total, 31,500 Danish citizens between years of age living in the Central Denmark Region with at least one parent born in Denmark received the questionnaire in Danish. This survey was used to obtain data on HRQoL and perceived stress and studied in Paper IV. DEFINITION OF KEY VARIABLES This section describes the central definitions of variables applied in the thesis. For further detail, please see the individual papers. Participation in breast cancer screening (all Papers) A woman was categorised as a participant if she was invited to the first screening round and was registered in the administrative registry as having participated, and as a non-participant if not. In Paper I, the group of non-participants was further divided into active non-participants which refers to women who actively called and declined participation, and passive non-participants which refers to women who stayed away without cancelling or rescheduling the appointment (referred to as no-shows ). Socio-economic position (SEP) (all Papers) Data on the woman s SEP was obtained from Statistics Denmark, and we used data the year before the scheduled screening date in all studies except Paper IV where the woman s SEP in 2006 was applied as this was the year the Health Survey was conducted. Sub-analysis indicated that women s education, ethnicity and marital status were stable over time. Between % were registered in the same category in 2006 as the year before their screening appointment (Table 2). These analyses also indicated that income was rather dynamic, with 34.4% of the population being registered with a different income category in 2006 than in (Table 2). The available data did not allow us to determine what had caused this change in income, and it could not be ruled out that income was a mediating variable in the historical cohort studies (Papers II-IV). Income was therefore excluded as a possible confounder in these papers. However, sensitivity 37

38 Patterns of non-participation in breast cancer screening analyses where income was included were conducted in Papers II-IV; these analyses gave rise to the same conclusions, with comparable but slightly smaller associations. Table 2. Stability of SEP variables from 2006 to first screening round SEP variable Categories Women with different status in 2006 than in N (%) Ethnicity 1) Danish and descendants 132 (0.09) 2) Immigrants Marital status 1) Married 7,505 (5.2) 2) Cohabiting 3) Single Education 1) Low ( 10 years) 508 (0.3) (UNESCOs classicication) 2) Middle (11-15 years) 3) High (>15 years) Income* 1) Low (lowest tertile) 2) Middle (middle tertile) 3) High (highest tertile) 49,680 (34.4) *OECD adjusted household income was used, which adjust for the number of person in the household Chronic diseases (Paper II) Chronic diseases were assessed based on the diagnosis of a group of diseases registered in the hospital system through the NPR and the PCRR. The included chronic diseases were selected based on recommendations from the leading literature in the field (114, 115). We included a large number of specific chronic diseases and grouped these diseases into 11 chronic disease groups (CDGs) (Table 3). The CDGs were studied separately; and hospital contacts as a consequence of one of the included chronic diseases were studied in two time periods: 0-2 years before the screening date and 2-10 years before the screening date. Only the results of the 0-2-year period are presented in the thesis (see Paper II for full analysis, p. 115). 38

39 Material and methods Table 3: Included chronic diseases and ICD-10 codes Included CDGs (grey) and included ICD-10 codes diseases (white) Cardiovascular diseases Ischaemic heart disease I24-I25 Apoplexy I60-I69, G45, G46 Acute myocardial infarction I21,I22 Angina pectoris I20 Heart failure I50, I11.0, I13.0, I13.2 Cardiac valve diseases I08, I09, I38, I39, Z95 Atrial fibrillation I48 Cancer Cancer ex. breast cancer and C00-C99 (ex. C50 and C44) non-malignant neoplasms of skin Hypertension Hypertension I10-I15 Chronic mental illness Schizophrenia F20 Psychotic disorders F22-F25, F28-F29 Affective disorders incl. depression F30-39 Dementia F00-F03, F05.1, G30 Anxiety F40-F41 Eating disorder F50.0, F50.2 Chronic lung disease COPD DJ40, DJ409, DJ41, DJ410, DJ411, DJ418, DJ42, DJ429, DJ429A, DJ429B, DJ43, DJ430, DJ430A, DJ431, DJ431A, DJ432, DJ438, DJ439, DJ439A, DJ44, DJ440, DJ441, DJ448, DJ448A, DJ448B, DJ449, DJ47, DJ479, DJ96, DJ960, DJ961, DJ969 Chronic neurological disorders Epilepsy G40 (ex. G40.4), G41 Parkinson s disease G20-G22 Multiple sclerosis G35 Chronic arthritis Rheumatoid arthritis M05, M06, M790 Inflammatory bowel disease/chronic bowel disease Colitis ulcerosa K51 Mb. Crohn K50 Chronic liver disease Chronic viral hepatitis B18 Chronic liver disease K70, K71.3-K71.5, K71.7, K72.1, K72.7, K72.9, K73-K74, K76 Chronic kidney disease Chronic kidney disease N18, N19 39

40 Patterns of non-participation in breast cancer screening Multimorbidity (Paper II) To assess multimorbidity, we included the CDGs described in Table 3; and also here, we only included diagnoses registered in the hospital system. Multimorbidity was operationalised as: Multimorbidity : the co-occurrence of two or more chronic diseases from two or more of the CDGs; severe multimorbidity : the co-occurrence of three or more chronic diseases from three or more of the CDGs; physical multimorbidity : the co-occurrence of two or more physical CDGs, but without the mental CDG; physical-mental multimorbidity : the co-occurrence of at least one physical CDG and the mental CDG. Thus, each woman could be defined as having more than one kind of multimorbidity; e.g., it was possible for a woman to be defined as having both severe multimorbidity and physical multimorbidity. However, it was not possible for a woman with physical-mental multimorbidity to be defined as having physical multimorbidity as well. Finally, disease counts was measured using the categories: 0, 1, 2, 3 CDGs. Psychiatric morbidity Psychiatric morbidity was assessed using registry-based data from the PCRR and the NPR and included hospital-registered diagnosis, prescribed psychoactive drug in the RMPS and registered consultations with private psychiatrists in the HSR. Psychiatric diseases were identified by hospital-related contacts with the following diseases: schizophrenia, affective disorders, anxiety-related disorders, eating disorder and substance abuse-related disorders. Due to few women with eating disorders (n=22), this group was omitted when assessing the diseases individually. The inclusion criteria are listed in Table 4. To capture patients with milder psychiatric morbidity than the ones requiring hospital-related contacts, psychoactive drugs were studied including antidepressants, anti-anxiety drugs and anti-psychotics. Women were categorised as users of psychoactive drugs if more than two prescriptions of one of the three prescriptions types had been redeemed within 8 years before the scheduled screening date. Finally, data on contacts with private psychiatrists were studied as well. 40

41 Results Table 4: Included psychiatric diseases and ICD-10 codes Psychiatric diseases Schizophrenia (incl. psychotic episodes) Affective disorders (incl. depression, bipolar disorders) Anxiety-related disorders ICD-10 codes F20-F29 F30-F39 F40-F41 Eating disorder F50.0 and F50.2 Substance abuse-related disorders F10-F19 Health-Related Quality of Life (HRQoL) (Paper IV) HRQoL was measured with a Danish version of the generic quality of life measure, SF-12, which includes a measure of both a Physical Component Summary (PCS) and a Mental Component Summary (MCS) (116). In the physical component, respondents are asked, e.g., to state if and how much various activities and levels of activities limit them on a daily basis. The mental component contained questions asking, e.g., if the respondent had felt calm and peaceful, downhearted and blue during the past 4 weeks. On the basis of these questions, a score was calculated for each component ranging from with a score of 50 representing average health (116, 117). The questionnaire has been used numerous times and has been validated in both Danish and international settings (e.g. 118, 119). Perceived stress (Paper V) Perceived stress was measured by a Danish version of the 10-item Perceived Stress Scale (PSS) (120) asking, e.g., if the woman has found her life uncontrollable during the past month. Each item was rated on a five-point Likert scale (0=never to 4=very often). The total score was generated by summarising the answers after reverse-scoring. Scores range from 0 to 40, with high score indicating high perceived stress. Studies examining the psychometric properties of the PSS have established that the scale is a valid measure of perceived stress (e.g. 121, 122). 41

42 Patterns of non-participation in breast cancer screening STATISTICAL ANALYSES All analyses were carried out using Stata In all papers, we used generalised linear models (GLM) (123, 124) from the binominal family. Prevalence ratio (PR) was chosen over odds ratio in this thesis since PR has been suggested to be a reliable alternative when the outcome of interest is frequent (40, 124) (here 21% non-participants). In all analysis, we applied robust variance estimates to adjust for clustering of patients in general practices. All estimates were given with 95% CI. In all papers, an unadjusted model was presented followed by multivariate analysis adjusting for possible confounders (see the individual papers for further detail). Cubic splines In Papers IV and V, we modelled the associations using cubic splines allowing for flexible relationships between the exposure and outcome. This is an advantage when hypothesising that the association under study is not linear. We used the method proposed by Orsini and Greenland (125). Knots were set at 5, 27.5, 50, 72.5 and 95 percentiles as recommended by Harrel (126). APPROVALS According to Danish legislation and the Central Denmark Region Committees on Biomedical Research Ethics (j.no.: 181/2011), the study was exempt from formal ethical approval as it was based only on registry and survey data. The project was approved by the Danish Data Protection Agency (j. no.: and j. no.: ). 42

43 Results CHAPTER 3: RESULTS This section presents the main results of the included papers. A detailed presentation of the results can be seen in the individual papers. 43

44 Patterns of non-participation in breast cancer screening SOCIO-DEMOGRAPHY (PAPER I) The study on socio-demography and socio-economy included 144,264 women, and the distribution of selected socio-economic variables is illustrated in Table 5. Both unadjusted and adjusted regression analysis indicated differences in participation according to socio-economic and socio-demographic status. Compared with participants, non-participants were statistically significantly more likely to be of non-danish origin, not married, between 65 and 69 years, have low income and not own their home or a vehicle (see Paper 1, p. 107). The association between education and screening participation revealed a U- shaped association which indicated that women with both low education and high education were more likely not to participate than were women with a middle-level education (see Paper I). Women outside the workforce were less likely to participate than employed women. This applied to the following groups: unemployed women and women on benefits (e.g. sick leave), retired women and women on social welfare. One exception was that women who were self-employed or chief executives were also more likely not to participate than employed women (see Paper I). We further studied non-participants by stratifying this group into active nonparticipants (i.e. women who called and declined participation) and passive non-participants (i.e. women who did not show up for their scheduled screening appointment). Passive non-participants were significantly more likely to be single, immigrants, low educated and have low income, be social welfare recipients, or be tenants and not to own a vehicle (see Paper I). 44

45 Results Table 5. Distribution of socio-demographic and socio-economic status among participants and non-participants (n=144,264, numbers vary due to missing data) Participants (n=113,811) Non-participants (n=30,453) n (%) n (%) P-value Age (years) < ,965 (80.4) 7,536 (19.6) ,722 (80.2) 7,580 (19.8) ,532 (79.2) 7,998 (20.8) ,592 (74.6) 7,339 (25.4) Ethnicity <0.001 Danish 110,018 (79.6) 28,201 (20.4) Western immigrants 2,024 (68.8) 918 (31.2) Non-western immigrants 1,749 (57.3) 1,306 (42.7) Marital status <0.001 Married 80,748 (83.3) 16,208 (16.6) Registered partnership 96 (67.6) 46 (32.4) Cohabiting 7,746 (77.6) 2,230 (22.4) Single 25,183 (67.9) 11,924 (32.1) Occupation <0.001 Employed 63,169 (83.7) 12,270 (16.3) Self-employed/chief executive 4,536 (76.7) 1,283 (23.3) Unemployed/benefits* 14,103 (67.4) 6,817 (32.6) Retired women 28,596 (77.6) 8,275 (22.4) Social welfare recipients 609 (53.4) 531 (46.6) Others 2,760 (69.2) 1,228 (30.8) Education (years) < ,214 (75.6) 12,651 (24.4) ,661 (81.8) 10,624 (18.2) >15 25,549 (80.2) 6,286 (19.7) OECD-adjusted household income** <0.001 Low 33,484 (69.8) 14,476 (30.2) Middle 39,255 (81.6) 8,880 (18.4) High 41,034 (85.3) 7,048 (14.7) Home ownership <0.001 Home owners 86,309 (82.8) 17,978 (17.2) Tenants 25,648 (68.6) 11,751 (31.4) Access to vehicle <0.001 No 99,597 (82.4) 21,302 (17.6) Yes 14,176 (60.9) 9,106 (39.1) * State benefits in relation to sickness, education, leave benefits, disability retirement and student grants ** Divided into tertiles based on income distribution 45

46 Patterns of non-participation in breast cancer screening CHRONIC DISEASES AND MULTIMORBIDITY (PAPER II) In total, 28.6% women with one of the chronic diseases did not participate in the first screening round compared with 20.3% of women without a chronic disease (see Paper II, p. 117). The adjusted regression analysis showed that having cancer, mental disease, COPD, neurological disorders and kidney disease was significantly more likely not to participate in both time periods than women without the disease in question. The associations were strongest if the woman was registered with a diagnosis 0-2 years before the screening date. Having chronic bowel disease significantly increased the propensity to participate in both time periods (see Paper II). The disease-count variable showed a dose-response association where nonparticipation increased for each additional disease compared with having no disease (Table 6). Having multimorbidity including both mental and physical diseases was more strongly associated with non-participation (PRadj 1.54, 95% CI: ) than physical multimorbidity (PRadj 1.37, 95% CI: ) (Table 6). The estimates were strongest in the time period closest to the screening date for all definitions of multimorbidity (see Paper II). Analysing time since latest hospital contact with one of the CDGs, we found a dose-response relation between the likelihood of non-participation and the latest hospital contact before screening: the closer the hospital contact was before screening, the higher the likelihood of non-participation. For instance, it was found that a hospital contact with any of the CDGs 0-1 year before the screening date yielded a 34% increased likelihood of non-participation (95% CI: 30-38%) compared with having no registered diseases. The likelihood of nonparticipation was 8% if the latest hospital contact took place 5-10 years before the screening date (95% CI: 3-13%) (Table 7). 46

47 Results Table 6: Distribution of multimorbidity and adjusted prevalence ratio of nonparticipation in breast cancer screening for women with registered multimorbidity 0-2 years before the screening date (n=144,264) Participants Non-participants n=113,811 (%) n=30,453 (%) PR (95% CI)* Disease count 0 104,012 (79.7) 26,533 (20.3) 1 (ref) 1 8,887 (72.4) 3,383 (27.6) 1.20 ( ) (63.4) 488 (36.6) 1.47 ( ) 3 67 (57.8) 49 (42.2) 1.58 ( ) Multimorbidity ( 2 diseases) No 112,899 (79.0) 29,916 (21.0) 1 (ref) Yes 912 (62.9) 537 (37.1) 1.38 ( ) Severe multimorbidity ( 3 diseases) No 113,744 (78.9) 30,404 (21.1) 1 (ref) Yes 67 (57.8) 49 (42.2) 1.53 ( ) Physical multimorbidity No 113,043 (79.0) 30,032 (21.0) 1 (ref) Yes 768 (64.6) 421 (35.4) 1.37 ( ) Physical-mental multimorbidity No 113,657 (78.9) 30,320 (21.1) 1 (ref) Yes 154 (53.7) 133 (46.3) 1.54 ( ) * Adjusted for age, ethnicity, marital status, education and for having multimorbidity 2-10 years before screening Statistically significant results in bold Table 7: Prevalence ratio of non-participation in breast cancer screening and time between the screening date and latest hospital-related contact with one of the CDGs PR (95% CI)* Time from screening date until last hospital contact No diagnoses of the CDGs 1 (ref) 0 1 year since latest contact 1.34 ( ) 1 2 years since latest contact 1.20 ( ) 2 5 years since latest contact 1.13 ( ) 5 10 years since latest contact 1.08 ( ) Statistically significant results in bold *Adjusted for age, ethnicity, marital status and education 47

48 Patterns of non-participation in breast cancer screening PSYCHIATRIC DISEASES (PAPER III) In total, 47,648 women had an indication of a psychiatric disease defined by one or more contacts to a psychiatric department up to 10 years before the screening date, and/or minimum two prescriptions of psychoactive drugs and/or a first consultation with a private psychiatrist up to 8 years before the screening date. After adjustment for SEP, this group had an increased likelihood of nonparticipation of 17% (95% CI: 14-19%) compared with women without indication of a psychiatric disease (Table 8). We studied the included psychiatric diseases separately and found the strongest associations for women with schizophrenia (PRadj 1.63, 95% CI: ) and women with substance abuse (PRadj 1.69, 95% CI: ). Psychoactive drugs were also independently associated with non-participation, with the highest non-participation likelihood for women with antipsychotic prescriptions (PRadj 1.30, 95% CI: ). Women with minimum one consultation with a private psychiatrist were also more likely not to participate (Table 8). The chronicity of the psychiatric disease was also associated with nonparticipation. Women who had indications of psychiatric morbidity both 1 year before screening and more than 1 year before screening (i.e. persistent psychiatric diseased) were 1.34 times (95% CI: ) more likely not to participate in screening than women with no indication of a psychiatric disease. Women who were registered only with psychiatric morbidity up to 1 year before screening (i.e. only recently active psychiatric diseased) were PRadj 1.20 (95% CI: ) times more likely not to participate than women with no registered psychiatric morbidity. Finally, women with registered psychiatric morbidity more than 1 year before the screening date (i.e. inactive psychiatric diseased) had a 4% statistically significantly increased likelihood of non-participation (95% CI: 2-8%) (Table 8) (Paper III, p. 143). 48

49 Results Table 8. Distribution of psychiatric morbidity and adjusted prevalence ratios of nonparticipation in breast cancer screening (n=144,264) Participants Non-participants n=113,811 (%) n=30,453 (%) PR (95% CI) Indication of psychiatric disease* No 78,317 (81.1) 18,299 (18.9) 1 (ref) Yes 35,494 (74.5) 12,154 (25.5) 1.17 ( ) Schizophrenia diagnosis No 113,306 (79.1) 29,936 (20.9) 1 (ref) Yes 505 (49.4) 517 (50.6) 1.63 ( ) Affective disorders diagnosis ** No 111,478 (79.2) 29,333 (20.8) 1 (ref) Yes 2,333 (67.6) 1,120 (32.4) 1.19 ( ) Anxiety diagnosis ** No 113,403 (79.0) 30,191 (21.0) 1 (ref) Yes 408 (60.7) 262 (39.3) 1.35 ( ) Substance abuse diagnosis ** No 113,072 (79.2) 29,765 (20.8) 1 (ref) Yes 739 (51.7) 688 (48.3) 1.69 ( ) Antidepressive medicine* No 89,090 (80.1) 22,084 (19.9) 1 (ref) Yes 21,769 (76.4) 6,712 (23.6) 1.09 ( ) Antianxiety medicine * No 94,082 (80.3) 23,042 (19.7) 1 (ref) Yes 17,550 (74.4) 6,033 (25.6) 1.18 ( ) Antipsychotic medicine* No 108,805 (79.6) 27,885 (20.4) 1 (ref) Yes 3,193 (69.6) 1,392 (30.4) 1.30 ( ) Consultation with private psychiatrist ** No 110,484 (79.1) 29,145 (20.9) 1 (ref) Yes 2,531 (73.0) 934 (27.0) 1.17 ( ) Chronicity of psychiatric morbidity** No psychiatric disease 78,317 (81.1) 18,299 (18.9) 1 (ref) Inactive disease 16,007 (78.3) 4,440 (23.5) 1.04 ( ) Only recently active 2,003 (76.0) 634 (24.0) 1.20 ( ) Persistent disease 17,484 (71.2) 7,080 (28.8) 1.34 ( ) *Regression analyses adjusted for age, ethnicity, marital status and education **Regression analyses adjusted for age, ethnicity, marital status, education and the other included diagnoses Only included women who were not registered in the PCRR or the NPR with the included psychiatric diagnoses 49

50 Patterns of non-participation in breast cancer screening HRQOL AND PERCEIVED STRESS (PAPER IV) The proportions of non-participants with a high or a low Physical Component Summary (PCS) were larger among non-participants than among participants (Table 9). Similarly, in the Mental Component Summary (MCS), the proportion of women scoring among the lowest 25% was larger among non-participants than among participants (Table 9); and the proportion of women reporting selfassessed stress in the highest quartile was larger for non-participants than for participants (Table 9) (Paper IV, p. 165). Both the regression analysis and the cubic spline models indicated a U-shaped association between PCS in 2006 and non-participation in screening in , which indicated that women with both high and low PCS were significantly less likely to participate (Table 9 and Figure 5). Women with low MCS in 2006 had a significant 43% increased likelihood of non-participation in compared with women rating their mental health in the middle level after adjustments for SEP and chronic diseases (Table 9). Having high stress in 2006 was associated with higher non-participation in than having middle-level stress (Table 9). This association was confirmed in the cubic spline analysis (Figure 5). Table 9: Distribution of HRQoL and perceived stress and adjusted prevalence ratios with 95% CI for the association between PCS, MCS and PSS in 2006 and nonparticipation in breast cancer screening in (n=4,512) Participants Nonparticipants Model 1* Model 2** N (%) N (%) PR (95% CI)* PR (95% CI)* PCS 25% lowest score 813 (82.9) 167 (17.1) 1.31 ( ) 1.29 ( ) >25-75% score 1,737 (87.8) 241 (12.2) 1 (ref.) 1 (ref.) >75% highest score 799 (83.1) 163 (16.9) 1.40 ( ) 1.41 ( ) MCS 25% lowest score 793 (80.9) 187 (19.1) 1.44 ( ) 1.43 ( ) >25-75% score 1,717 (87.5) 246 (12.5) 1 (ref.) 1 (ref.) >75% highest score 839 (85.9) 138 (14.1) 1.10 ( ) 1.10 ( ) PSS 25% lowest score 868 (85.8) 144 (14.2) 1.11 ( ) 1.11 ( ) >25-75% score 1,815 (86.9) 273 (13.1) 1 (ref.) 1 (ref.) >75% highest score 934 (82.4) 199 (17.6) 1.29 ( ) 1.28 ( ) *adjusted for age, ethnicity, marital status and education *adjusted for age, ethnicity, marital status and education and for having minimum one chronic disease 50

51 Results 51

52 Patterns of non-participation in breast cancer screening DISTANCE TO THE SCREENING SITE (PAPER V) Half of the women included in the analyses lived 20.3 km or less from their affiliated screening site, and the distance was 35.7 km or more for one in four women. A U-shaped association was seen in the unadjusted analysis where higher likelihood of non-participation was observed among women living closer than 10 km to or more than 15 km from the screening site (Table 10 and Figure 6). When adjusting for SEP, this U-shaped association disappeared, and the probability of non-participation rose with longer distance to the screening site, but flattened after approximately 45 km (Figure 6). The adjusted analyses showed that the 61% (n=78,002) of the population who lived 15 km or more from the screening site were significantly more likely not to participate than women living 0-10 km from the screening site (Table 10). Women who lived >35 km from their affiliated screening site, corresponding to 26% (n=33,252), had a significant 30% higher likelihood of non-participation than women living 0-10 km from the screening site (Table 10). Further analyses revealed that women without access to a vehicle formed a group with a higher likelihood of non-participation. Cubic spline models indicated that women without access to a vehicle were more likely not to participate regardless of their distance to the screening site, and that their likelihood of non-participation increased more per km than it did for women with access to a vehicle (see Paper V, p. 185). 52

53 Results Table 10: Distribution of participation together with unadjusted and adjusted prevalence ratio for associations between distance to the screening site and screening nonparticipation (n=127,648) Participants Non-participants PR (95% CI) PR (95% CI) n (%) n (%) Unadjusted Adjusted * Distance in km ,991 (79.2) 8,155 (20.8) 1 (ref) 1 (ref) > ,538 (81.5) 1,942 (18.5) 0.89 ( ) 1.04 ( ) > ,850 (79.6) 5,091 (20.4) 0.98 ( ) 1.11 ( ) > ,623 (78.9) 4,186 (21.1) 1.01 ( ) 1.16 ( ) > ,859 (76.0) 3,431 (24.0) 1.15 ( ) 1.30 ( ) > ,272 (74.8) 2,454 (25.2) 1.21 ( ) 1.35 ( ) > ,485 (75.0) 1,825 (25.0) 1.20 ( ) 1.30 ( ) >65-max 1,430 (74.2) 496 (25.7) 1.24 ( ) 1.36 ( ) * Adjusted for age, ethnicity, education, income, marital status and access to vehicle Figure 6. Association between distance to the screening site and non-participation as estimated using cubic splines models (reference point: 0 km, solid line represents the association, the grey area represents 95% CI), left: unadjusted, right: adjusted for age, ethnicity, education, income and marital status. Spikes per 1000 women illustrate the distance distribution in the population at the bottom of the figure 53