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L.K. is a fellow in the Canadian Institutes of Health Research (CIHR) Strategic Training in Advanced Genetic Epidemiology (STAGE) programme and is supported by the CIHR Doctoral Research Award from the Frederick Banting and Charles Best Canada Graduate Scholarships (GSD-137441). Transdisciplinary Research for Cancer in Lung (TRICL) of the International Lung Cancer Consortium (ILCCO) was supported by the National Institutes of Health (U19-CA148127, CA148127S1). Genotyping for the TRICL-ILCCO OncoArray was supported by in-kind genotyping at Centre for Inherited Disease Research (CIDR) (26820120008i-0–6800068-1). Genotyping for the Head and Neck Cancer OncoArray performed at CIDR was funded by the US National Institute of Dental and Craniofacial Research (NIDCR) grant 1X01HG007780–0. CAPUA study was supported by FIS-FEDER/Spain grant numbers FIS-01/310, FIS-PI03–0365 and FIS-07-BI060604, FICYT/Asturias grant numbers FICYT PB02–67 and FICYT IB09–133, and the University Institute of Oncology (IUOPA), of the University of Oviedo and the Ciber de Epidemiologia y Salud Publica. CIBERESP, SPAIN. The work performed in the CARET study was supported by the National Institute of Health (NIH)/National Cancer Institute (NCI): UM1 CA167462 (PI: Goodman), National Institute of Health UO1-CA6367307 (PIs Omen, Goodman); National Institute of Health R01 CA111703 (PI Chen), National Institute of Health 5R01 CA151989 (PI Doherty). The Liverpool Lung Project is supported by the Roy Castle Lung Cancer Foundation. The Harvard Lung Cancer Study was supported by the NIH (National Cancer Institute) grants CA092824, CA090578 and CA074386. The Multiethnic Cohort Study was partially supported by NIH Grants CA164973, CA033619, CA63464 and CA148127. The work performed in MSH-PMH study was supported by the Canadian Cancer Society Research Institute (020214), Ontario Institute of Cancer and Cancer Care Ontario Chair Award to R.J.H. and G.L. and the Alan Brown Chair and Lusi Wong Programs at the Princess Margaret Hospital Foundation. The Norway study was supported by Norwegian Cancer Society, Norwegian Research Council. The work in TLC study has been supported in part the James & Esther King Biomedical Research Program (09KN-15), National Institutes of Health Specialized Programs of Research Excellence (SPORE) Grant (P50 CA119997) and by a Cancer Center Support Grant (CCSG) at the H. Lee Moffitt Cancer Center and Research Institute, an NCI designated Comprehensive Cancer Center (grant number P30-CA76292). The dataset(s) used for the analyses described were obtained from Vanderbilt University Medical Center’s BioVU, which is supported by institutional funding and by the Vanderbilt CTSA grant UL1 TR000445 from NCATS/NIH. Dr Melinda Aldrich is supported by the by NIH/National Cancer Institute 5K07CA172294. The Copenhagen General Population Study (CGPS) was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council and Herlev Hospital. The NELCS study: Grant Number P20RR018787 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). Kentucky Lung Cancer Research Initiative (KLCRI) was supported by the Department of Defense (Congressionally Directed Medical Research Program, U.S. Army Medical Research and Materiel Command Program) under award number: 10153006 (W81XWH-11–1-0781). Views and opinions of, and endorsements by the author(s) do not reflect those of the US Army or the Department of Defense. This research was also supported by unrestricted infrastructure funds from the UK Center for Clinical and Translational Science, NIH grant UL1TR000117 and Markey Cancer Center NCI Cancer Center Support Grant (P30 CA177558) Shared Resource Facilities: Cancer Research Informatics, Biospecimen and Tissue Procurement, and Biostatistics and Bioinformatics. The research undertaken by M.D.T., L.V.W. and M.S.A. was partly funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. M.D.T. holds a Medical Research Council Senior Clinical Fellowship (G0902313). The Tampa study was funded by Public Health Service grants P01-CA68384 and R01-DE13158 from the National Institutes of Health. The University of Pittsburgh head and neck cancer case–control study is supported by US National Institutes of Health grants P50 CA097190 and P30 CA047904. The Carolina Head and Neck Cancer Study (CHANCE) was supported by the National Cancer Institute (R01CA90731). The Head and Neck Genome Project (GENCAPO) was supported by the Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP; grants 04/12054–9 and 10/51168–0). The authors thank all the members of the GENCAPO team. This publication presents data from the Head and Neck 5000 study. The study was a component of independent research funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research scheme (RP-PG-0707–10034). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Human papillomavirus (HPV) serology was supported by a Cancer Research UK Programme Grant, the Integrative Cancer Epidemiology Programme (grant number: C18281/A19169). The Alcohol-Related Cancers and Genetic Susceptibility Study in Europe (ARCAGE) was funded by the European Commission’s fifth framework programme (QLK1– 2001-00182), the Italian Association for Cancer Research, Compagnia di San Paolo/FIRMS, Region Piemonte and Padova University (CPDA057222). The Rome Study was supported by the Associazione Italiana per la Ricerca sul Cancro (AIRC) awards IG 2011 10491 and IG 2013 14220 to S.B. and by Fondazione Veronesi to S.B. The IARC Latin American study was funded by the European Commission INCO-DC programme (IC18-CT97–0222), with additional funding from Fondo para la Investigacion Cientifica y Tecnologica (Argentina) and the Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (01/01768–2). The IARC Central Europe study was supported by the European Commission’s INCO-COPERNICUS Program (IC15-CT98–0332), US NIH/National Cancer Institute grant CA92039 and World Cancer Research Foundation grant WCRF 99A28. The IARC Oral Cancer Multicenter study was funded by grant S06 96 202489 05F02 from Europe against Cancer; grants FIS 97/0024, FIS 97/0662 and BAE 01/5013 from Fondo de Investigaciones Sanitarias, Spain; the UICC Yamagiwa-Yoshida Memorial International Cancer Study; the National Cancer Institute of Canada; Associazione Italiana per la Ricerca sul Cancro; and the Pan-American Health Organization. Coordination of the EPIC study is financially supported by the European Commission (DG SANCO) and the International Agency for Research on Cancer.

Background/Aims There is evidence for non-pharmacological interventions to support patients to self-manage fatigue, however implementation in clinical practice is a challenge. LIFT (Lessening the Impact of Fatigue in inflammatory rheumatic diseases: a randomised Trial) is a multi-centre three-arm randomised trial using a remotely delivered cognitive-behavioural approach (CBA) or personalized exercise programme (PEP) interventions, in addition to usual care, compared to normal care alone. Interventions were delivered to patients by rheumatology health professionals using a manual, after training. The aim of this nested qualitative evaluation was to understand their perspectives of delivering the interventions. Methods A subgroup of rheumatology healthcare professionals who had delivered the CBA and PEP interventions took part in semi-structured telephone interviews to explore their experiences of training and delivery, the challenges and benefits of learning new skills, and the barriers and facilitators to supporting patients remotely (mainly by telephone) using the LIFT manual. Results A total of 17 rheumatology healthcare professionals (13 women, 4 men) from the CBA (n = 9) and PEP (n = 8) arms contributed. SB conducted an inductive thematic analysis of the data set. ED, CA, AW and KL reviewed a sub-set of transcripts. Five main themes were identified: The benefits of informative, structured training: Rheumatology healthcare professionals reflected how training, including role-play, helped them to practice their skills, even though this could feel uncomfortable. Those allocated shorter four-hour training sessions would have liked more time to practice. Many felt anxious before meeting patients for the first time but liked the manual to refer to. Getting into the swing of it: Practice gave rheumatology healthcare professionals the confidence to tailor content to individual patients’ requirements. Clinical supervision in the PEP and CBA arm supported rheumatology healthcare professionals to query their own practice, gain valuable feedback, and request assistance where needed. Benefits of telephone delivery: The initial face-to-face session enabled rheumatology healthcare professionals to build rapport with patients. Thereafter, patients seemed engaged and valued the opportunity to address their fatigue and challenge their own beliefs via the telephone. Some patients not ready to change: Rheumatology healthcare professionals struggled to work collaboratively with a minority of patients who were not willing to make changes, lacked motivation to complete tasks or stopped engaging with the intervention. LIFT developing clinical skills: Rheumatology healthcare professionals were confident that they were doing the ‘right thing’ for patients with fatigue and gained professional satisfaction seeing patients’ fatigue improve. Many felt that the skills they acquired and their experiences of remote delivery were helping them to respond to the current COVID-19 related changes in service provision. Conclusion Findings support the value of skills training for rheumatology health professionals to deliver fatigue management interventions remotely. These insights can inform service provision and clinical practice. Disclosure S.E. Bennett: None. E. Dures: None. C. Almeida: None. E. Bachmair: None. K. Lovell: None. L. Paul: None. A. Wearden: None. G.J. Macfarlane: None. N. Basu: None.

Objective To examine trends of spontaneous very preterm birth (vPTB) and its relationship with maternal socioeconomic status and smoking. Methods This was a population-based cohort study in Aberdeen Maternity Hospital, UK. The cohort was restricted to spontaneous singleton deliveries occurring in Aberdeen from 1985 to 2010. The primary outcome was very preterm birth which was defined as Results There was an increased likelihood of vPTB in those with unskilled-occupations compared to professional-occupations [aOR:2.77 (95%CI:1.54–4.99)], in those who lived in the most deprived areas compared to those in the most affluent [aOR: 2.16 (95% CI: 1.27–3.67)] and in women who smoked compared to those who did not [aOR: 1.74 (95% CI: 1.36-2.21)]. The association with Carstairs index was no longer statistically significant when restricted to smokers but remained significant when restricted to non-smokers. Conclusion The strongest risk factor for vPTB was maternal smoking while socioeconomic deprivation showed a strong association in non-smokers. Smoking cessation interventions may reduce vPTB. Modifiable risk factors should be explored in deprived areas.

A key aspect of epidemiology is about understanding and exploring variations in disease. In order to do so it is important that the same definitions of disease occurrence are used, no matter where a study is being conducted, and that the same approach to measurement is taken. The epidemiologist needs to be aware of which measure to use in specific circumstances. Measuring disease occurrence is the basic activity of epidemiology, and the following section provides the background to choosing the most appropriate measure(s) of disease occurrence for the aims of the study. The term ‘disease’ can also be taken in this context to describe any personal attribute. Thus, the concepts described in this chapter apply equally well to assessing the occurrence of a symptom, the development of a particular disability, or the frequency of a medical intervention.

This chapter builds on the previous one on the analysis of descriptive epidemiological studies and illustrates statistical methods appropriate for analysis of analytical epidemiological studies. It mainly focuses on data obtained from case–control and cohort studies, but also considers other study designs presented in Chapter 6. There are also several practical examples to help with the analysis and interpretation of the results of analytical epidemiological studies. In practice, relatively little mathematical calculation is done without computers. In this chapter, however, formulae are presented for the main measures of effect together with worked examples. Indeed, when data are available in tabulated form, as opposed to raw data files, it is frequently an easy task to calculate the important measures ‘by hand’. The formulae presented will permit the reader, for example, to check or further explore data published by others.

Having conducted a specific study design, the task is then to use the information collected to provide an effect measure which quantifies the magnitude of the association between the exposure(s) of interest and the disease under study. Epidemiological studies, and in particular their subsequent analysis, are therefore aimed at quantifying the level of increased risk when exposed to a particular factor, as this chapter explains. The effect measure which can be obtained to quantify the strength of the association, varies according to the type of study conducted. Just because there is a relationship between exposure and disease (even a strong one) does not mean that the relationship is causal.

Epidemiology can be considered as a suite of methodologies designed to inform on aspects of health and disease in human populations. They have been designed to cope with the variability between people in the risk for diseases and their course and response to treatments. There are many aspects of epidemiological study that are challenging and the successful study requires attention to the design, conduct of the study, and analysis of the data generated. There is another view of epidemiology, which is that it is a tool for studying diseases as they occur in human populations. The focus can also be on diagnosis, outcome, and results of interventions, as well as on causes. This broader definition allows a greater scope for the kinds of question that can be addressed both by those studying the health of populations and by those whose main focus is the study of disease in patient groups.

Although epidemiological studies are increasingly based on the analysis of existing data sets (including linked data sets), many studies still require primary data collection. Such data may come from patient questionnaires, interviews, abstraction from records, and/or the results of tests and measures such as weight or blood test results. The next stage is to analyse the data gathered from individual subjects to provide the answers required. Before commencing with the statistical analysis of any data set, the data themselves must be prepared in a format so that the detailed statistical analysis can achieve its goals. Items to be considered include the format the data are initially collected in and how they are transferred to an appropriate electronic form. This chapter explores how errors are minimized and the quality of the data set ensured. These tasks are not trivial and need to be planned as part of a detailed study methodology.

In comparing rates between populations, it is important that one is comparing ‘like with like’. One population may be considerably older than a population to which it is compared and therefore it would not be surprising that mortality rates were higher. Instead it is more useful to make comparisons taking account of differences in characteristics such as age or gender. The same considerations apply to examining disease rates over time in a given population. If the characteristics of the population change over time (e.g. the population gets older), this needs to be considered. To formulate hypotheses, the rate of a disease under study in a population may be compared with the rate in other populations, or in the same population at difierent time points. If the rates vary significantly between populations or are changing within a population, then this provides impetus for investigating the reasons underlying these differences or changes.

Although the definition of measures of disease occurrence, such as incidence and prevalence, may be relatively straightforward, there will be issues of how to operationalize these. The solutions will depend on what systems exist in the location of study such as clinical records, a prospective registration system. Alternatively, if there are no such systems, then alternative solutions will need to be investigated. This could include a proxy measure of disease or could involve undertaking one or more surveys to identify prevalence or new onset cases. Overall, the decision would need to be made on the basis of specific needs relating to an individual project where actual incidence data were not available, to ascertain whether an alternative (proxy) measure may be suitable.

Designing a study ‘on paper’ is no guarantee it will work; for example, individuals agree to be recruited or the method of data acquisition is acceptable and provides the data in the format and quality required. For this reason it is, especially when planning large/expensive studies, to test the feasibility of the different components in separate small-scale studies. Alternatively, or in addition it may be useful to do a pilot study, which reproduces the planned methods for the proposed full study, to test ‘the whole system’. A successful pilot study enhances the likelihood of grant success; indeed, grant-awarding bodies will often expect to see evidence that they have been conducted.

Collecting accurate and comprehensive information both direct from the participants, or indirectly from records or other data is one of the major challenges to a successful epidemiological study. Epidemiological information comes from a variety of sources. These may be conveniently divided into those that are available from previously documented data and those that require the gathering of new information. Examples of the former include extracting information about individuals from their medical records, occupational records, and similar data sources. Design and choice of delivery of patient data capture forms by direct interview or telephone, by post, email, or other electronic means all require considerable thought and pilot testing. Attention to the specific wording of certain questions is crucial. This chapter therefore focuses on the issues surrounding the collection of information that otherwise would not be available: primary data collection.

The preceding chapters have focused on the generation of scientific evidence using epidemiological studies. Here the emphasis is on bringing together evidence (evidence synthesis) to inform future research and health policies. The chapter considers levels and quality of scientific evidence and describes in detail how to conduct a systematic review. It reviews the ways of summarizing and evaluating evidence from epidemiological studies. Summary of evidence is needed in everyday clinical practice and for public health. We live in a time of information overload, and it is impossible to read all the available scientific journals, even on a narrow scientific topic. Simply using search terms such as ‘cancer’ will result in millions of results in Google Scholar or PubMed (a service of the US National Library of Medicine®) database. Given the ever-increasing volume of medical literature and time constraints, summary of evidence plays a big role in decision-making.

A key issue in epidemiology is to choose an appropriate study design. The design chosen will depend on the question of interest and some designs will offer clear advantages over others. However, other factors such as current state of knowledge will be important. The decision will be based not only on methodological but also on practical considerations, including the resources available, and the speed with which a result is required. For example, the most appropriate study may be too expensive or take too long to provide an answer. In such circumstances a compromise will need to be made—to undertake a study which can be conducted within the budget and time available, and which delivers information that is suitable for addressing the question of interest.

Primary data collection is challenging and with increasing electronic data capture in routine healthcare and other aspects of life, it is possible to address several epidemiological questions by robust analysis of such ‘secondary data’. There are considerable advantages in terms of scope, size, and speed of study to be balanced against the quality and depth of using primary data. Even when such direct contact is not required, there is often the need to extract necessary information from individual subject records such as medical files. There is often no alternative source of information, although the greater digitization of information is changing that scenario with the potential that the availability of such information might preclude the need for primary data.

The most important aspect that will influence the validity of any epidemiological study is the careful selection of the subjects for investigation. Separate issues relate to the sampling of subjects for disease status in case–control studies, and sampling by exposure status in cohort studies. In simplest terms, the issues are who should be the cases and, given that, who should be the controls. Thus, in each instance the needs are to identify the sampling frame and then what should be the process for selecting the specific sample or subsamples needed for study. In addition to consideration of who to study, other factors such as how to identify and verify plus the size of the planned study are all topics to be addressed.

Validity in respect of information that is collected in an epidemiological study is a measure of how accurate the data collected are, relative to an underlying truth. Classical approaches aim to ensure that the approach is both sensitive (low false negatives) and specific (low false positives) as, for example, in determining the presence or absence of a disease. A modified approach is needed where the value of an attribute is collected. Underpinning both these approaches is the need to consider what is the test of the underlying truth. The reader may already be aware of the application of validity tests in assessing the performance of diagnostic and screening tests. It is conventional to evaluate this performance in terms of the two indicators, sensitivity and specificity.

There are several reasons (e.g. sources of bias), why a study’s results may deviate from the truth. Recruitment and/or follow-up, which is non-random as to who does not provide data is a major source of bias. Other sources include observer preferences and the subject’s answers being informed, for example, by their current or prior status. It is not easy to undertake the perfect, bias-free, study. The challenge is to design the study to minimize the likelihood of bias and to undertake the necessary subanalyses, where appropriate, to obtain a sense of how important a particular bias might have been in explaining the results obtained.

An apparent relationship between a disease and a risk factor may be explained (confounded) by their joint association with an intermediate third ‘true’ risk factor. The issue here is not one of impaired validity; if the study had been carefully conducted then the relationship observed was correct and the problem of confounding is therefore one of interpretation. Confounding can only be proved after appropriate analysis. Further confounding as an explanation of an association is not (or is only extremely rarely) an all-or-nothing phenomenon. The effect of confounding will be, more usually, to alter the strength of an apparent relationship between two variables (e.g. risk factor and disease).

This chapter reviews the options for population selection in undertaking investigations aimed at estimating the occurrence of a disease. In terms of measuring disease occurrence, an important issue will be determining the appropriate target population. The choice will depend on being able to access the population, how representative it is, the size of the population, and the population data accuracy. It will not always be possible to use an ‘ideal’ population, there will be different options and the advantages and disadvantages of each will need to be determined. The same principles apply, however, if the object of the study is to investigate the occurrence of a risk factor, such as cigarette smoking, or other human attributes.

This volume provides a practical, hands on guide to the design, planning, conduct, analysis, and interpretation of epidemiological studies. The learning points are applicable to all human studies of health and disease that require the collection and analysis of data to answer questions on disease risk, health outcomes, and the effects of interventions in ‘real world’ populations. It summarizes the main study designs used in epidemiological from purely quantitative studies to assess incidence and prevalence, to the range of observational studies used in the modern practice of epidemiology. A substantial section is devoted to the practical aspects of conduct of epidemiological studies, balancing scientific quality with practicality: the latter covering issues such as costs and ethics. This book also provides a detailed menu of activities that takes the investigator through all the necessary steps following the collection of individual subject data through to generating the statistically robust results necessary to reach conclusions about the questions asked. It provides insights into how to use existing data (secondary data analysis) to answer epidemiological questions, an increasing activity in this era of ‘big data’. Similarly, with a growing epidemiological literature, with multiple studies seemingly addressing the same question, the volume explores how practically to synthesize the results of such multiple investigations and the role of meta-analysis. The book’s ultimate goal is to provide a practical toolkit to enable the successful completion of questions appropriate for applying epidemiological methods.

There are several major ethical issues that face an epidemiological study. There is always the challenge in studying free-living individuals in the modern society, of balancing the needs for robust methods with individual freedoms. The key concerns reflect ensuring an appropriate consent process, maintaining confidentiality, and minimizing any negative consequences for a participant. The most commonly collected information for an epidemiological study come either from material already available in databases, material such as hard copies of records that can have key data items extracted, or data that is gathered directly from the subject. Occasionally a limited physical examination is undertaken. Much less often, there is a requirement to take samples of biological fluid such as blood and urine, or to undergo simple investigations such as electrocardiography or plain radiography, but even such investigations are typically associated with trivial risk to health.

Epidemiological studies can only show associations they cannot prove that a link is causative. Even in the bias free study with minimal confounding, a strong association does not mean that, for example, the presence of the risk factor has a direct biological link to the disease in question. There are several tests that can be used to increase the confidence that an association has biological meaning and needs to be considered. It is interesting always to differentiate factors that are on the ‘path’ to disease but not the underlying cause. The appropriateness of all these steps is dependent on the validity of the association.

There are two major reasons to maximize participation in an epidemiological study. Firstly, low participation can lead to bias and secondly, inadequate numbers recruited can limit the power of the study. All other things being equal, lower participation will increase the influence of non-response bias in a study. However, it should be noted that even in studies with high participation there exists a possibility of selection bias if non-participants are markedly different from participants in aspects that are key to the conditions being studied. There are several ways of boosting participation which should be embedded into the study design from the start, since chasing up to increase participation is resource-expensive.

There are many sources of variability in measures in epidemiology. Subjects can vary in their response because of real change. Alternatively, the method of collecting data may be subject to variability because of differences in techniques or the inherent difficulty in some measures in ensuring consistency. Too much variability can introduce ‘noise’, making it difficult to find differences between populations. There may be a variety of reasons for failure to obtain consistent or reproducible results. This chapter describes various methods which can be employed to calculate the level of variability in measures used and how to minimize the sources of this.

Epidemiological studies can be very expensive, especially from large populations with multicentre recruitment. The researcher will need to ensure that there are adequate resources, allowing for the fact that things will not always go to plan, but making sure that the research is value for money. What is considered a reasonable cost will also depend on how strong the rationale is for conducting the study. Although in theory the study design influences the costs, in practice the resources available will often constrain the methodological choices. Costing an epidemiological study accurately at the start is vital. There are several ways to maximize the use of resources to ensure the study is efficient.

Objectives. To test the hypothesis that individuals with regional and widespread pain disorders have an increased risk of mortality. Methods. We conducted a prospective cohort study of 4515 adults. Subjects were an age- and sex-stratified sample who had participated in a population study of pain occurrence during 1996. Based on those reports subjects were classified as having no pain, regional pain or widespread pain. All subjects were identified on the National Health Service Central Register and followed up until April 2005, a total of 8.2 yrs, at which time information was obtained on vital status, and if applicable, date and cause of death. The relationship between pain status and subsequent death is expressed as mortality rate ratios with 95% CIs, adjusted for age, gender, ethnicity and practice. Results. A total of 35.2% reported regional pain and 16.9% satisfied criteria for widespread pain. In comparison with those without pain, there was a 20% and 30% increased risk of dying over the follow-up period among subjects with regional and widespread pain, respectively. The specific causes of death in excess were cancer and cardiovascular disease. In addition, the mortality risk from both cancer and cardiovascular deaths was found to increase as the number of pain sites that subjects reported increased. Conclusions. This study supports a previous observation that persons with regional and widespread pain are at an increased risk of cancer death. Possible mechanisms should be explored.

Objectives. Poor sleep is associated with chronic widespread pain (CWP). Conversely, good-quality sleep may play a role in the resolution of pain symptoms. Sleep is a multidimensional construct, comprising a number of diverse components. The aims of the current study were to examine the hypotheses that: (i) good sleep quality would predict the resolution of CWP, (ii) restorative sleep would predict the resolution of CWP and (iii) that these relationships would be independent of confounding psychological factors. Methods. Subjects in a population-based prospective study completed a pain questionnaire at baseline from which subjects with CWP were identified. Baseline sleep was measured using the Estimation of Sleep Problems Scale which measures sleep onset, maintenance, early wakening and restorative sleep. The questionnaire also contained scales examining psychosocial status. Subjects were followed up 15 months later and pain status was assessed. Results. A total of 1061 subjects reported CWP at baseline of whom 679 (75% of eligible subjects) responded at follow-up. Of those, a total of 300 (44%) no longer satisfied criteria for CWP. Univariate analysis revealed that three of the four sleep components were associated with the resolution of CWP: rapid sleep onset, odds ratio (OR) ¼ 1.7, 95% CI 1.2, 2.5; absence of early wakening, OR ¼ 1.6, 95% CI 1.1, 2.4; and restorative sleep, OR ¼ 2.7, 95% CI 1.5, 4.8. After adjusting for the effect of psychosocial factors, which may have confounded the relationship, only restorative sleep (OR ¼ 2.0, 95% CI 1.02, 3.8) was associated. Conclusions. Self-reported restorative sleep was independently associated with the resolution of CWP and return to musculoskeletal health.