Sequence for Context for Health and risk factor questionnaire in Generic DataSchema Life Habits/Behaviours Significant features of the life-style and behaviours of the participants Life Habits/Behaviours Version 1.2 - 03/09/2010 Sequence for Context for Sociodemographic characteristics in Generic DataSchema Age Currently under development Essential Under development This domain must be covered by any population study. Current age of a participant at recruitment. Domain finalized, but still evolving. Risk factor true Age This domain includes basic variables for any biobank. Currently under development Currently under development It was universally agreed that all biobanks should collect either date of birth or age or ideally both (in part as an internal check of consistency for what is such an important determinant of disease risk). Sequence for Context for Individual disease history in Generic DataSchema Individual History of Chronic Obstructive Pulmonary Disease Cigarette smoking is without doubt the most important risk factor in the development of COPD (1, 4, 10). Approximately 80 to 90 percent of COPD deaths are caused by personal smoking (11) and between 15% and 20% of long-term smokers will develop COPD (10). Other risk factors of COPD include second-hand smoke, male gender, air pollution (indoor and outdoor), history of childhood respiratory infections and heredity (12-14). The causes for COPD have different patterns according to the geographic areas. In high- and middle-income countries tobacco smoke is the biggest risk factor, meanwhile in low-income countries exposure to indoor air pollution, such as the use of biomass fuels for cooking and heating, causes the COPD burden (1). Under development In total, 6 of 13 studies (46%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Diseases of the respiratory system) A record of a subject's personal medical history relating to the occurrence of chronic obstructive pulmonary disease (COPD) and COPD-related problems and events. Domain finalized, but still evolving. Outcome true Individual History of Chronic Obstructive Pulmonary Disease Chronic obstructive pulmonary disease (COPD) is an umbrella term used to describe a group of chronic lung diseases that cause limitations to lung airflow. It is a major public health problem. COPD is a growing cause of morbidity and mortality worldwide and is projected to become the fourth leading cause of death worldwide by 2030 (1, 2). Currently, 210 million people have COPD and 3 million people died of the disease in 2005 (1). Most people with COPD are at least 40 years of age at the onset of symptoms (2), and mortality increases sharply with age. Patients living with COPD have an impaired quality of life and must necessarily adapt their activities of daily living (3). In addition, the economic burden of COPD is enormous. For example, in the USA it is estimated that, on average, COPD costs US$ 1,522 per sufferer per annum and the total national cost is US$ 23.9 billion per annum (4). These costs are attributable to a combination of direct medical care expenditures and the financial penalties associated with morbidity and premature mortality. Family (5) and twin studies (6) have shown that COPD is a complex genetic disease involving interactions between genetic and environmental determinants and it is believed that many genetic factors increase (or decrease) an individual's risk of developing COPD. Because many of these genetic, environmental and life-style determinants are shared by relatives, there is strong evidence of familial clustering of COPD (7, 8). COPD is therefore well worthy of scientific study in biobanks. The prevalence of COPD is high enough to provide adequate statistical power for epidemiological study and analysis in many contemporary biobanks (9). There is therefore a strong case for collecting information about doctor diagnosed COPD on recruitment into a cohort biobank. In particular, it enables informative cross-sectional analyses at recruitment (providing for early 'scientific wins') and also permits the subsequent discrimination of pre-existing (prevalent) cases of COPD from new (incident) diagnoses. But, it must be recognised that the thoroughness of the diagnostic process varies markedly between clinical settings and assessment errors can occur in both directions (false positives and false negatives) and this can impair statistical power (see Comments below). 1. World Health Organization. Chronic obstructive pulmonary diseases (2007) http://www.who.int/respiratory/copd/en/. 2. Buist, A.S. et al. International variation in the prevalence of COPD (The BOLD Study): a population-based prevalence study. Lancet 370, 741-750 (2007). PMID: 17765523. 3. National Heart Lung and Blood Institute. COPD (2007) http://www.nhlbi.nih.gov/health/dci/Diseases/Copd/Copd_WhoIsAtRisk.html 4. Devereux, G. ABC of chronic obstructive pulmonary disease. Definition, epidemiology, and risk factors. BMJ 332:1142-1144 (2006). PMID: 16690673. 5. The Lung Association. COPD (2007) http://www.lung.ca/diseases-maladies/copd-mpoc/what-quoi/index_e.php 6. Sullivan, S.D., Ramsey, S.D. & Lee T.A. The economic burden of COPD. Chest 117, 5S-9S (2000). PMID: 10673466. 7. Hemminki, K., Li, X., Sundquist, K., Sundquist, J. Familial risks for chronic obstructive pulmonary disease among siblings based on hospitalisations in Sweden. Journal of Epidemiology and Community Health 62:398-401 (2008). PMID: 18413451. 8. Givelber, R.J. et al. Segregation analysis of pulmonary function among families in the Framingham Study. Am. J. Respir. Crit. Care Med. 157, 1445-1451 (1998). PMID: 9603122. 9. Burton PR, Hansell A. UK Biobank: the expected distribution of incident and prevalent cases of chronic disease and the statistical power of nested casecontrol studies. Manchester, UK: UK Biobank Technical Reports, 2005. 10. Celli, B.R. et al. Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper. European Respiratory Journal 23, 932-946 (2004). PMID: 15219010. 11. Centers for Disease Control and Prevention. National Center for Chronic Disease Prevention and Health Promotion. Tobacco Information and Prevention Source (TIPS). Tobacco Use in the United States (2004). 12. University of Texas Medical Branch. Chronic Obstructive Pulmonary Disease (2006) http://www.utmbhealthcare.org/Health/Content.asp?PageID=P08595 13. Risk Factors. American Thoracic Society (2008) http://www.thoracic.org/sections/copd/for-health-professionals/epidemiology-risk-factors-and-natural-history/risk-factors.html 14. Molfino, N.A. Genetic predisposition to accelerated decline of lung function in COPD. International journal of chronic obstructive pulmonary disease 2, 117-119 (2007). PMID: 18044683. 15. Weiss, S.T., DeMeo, D.L. & Postma, D.S. COPD: problems in diagnosis and measurement. European Respiratory Journal 41, 4S-12S (2003). PMID: 12795326. The variables selected for this domain include: the occurrence of chronic obstructive pulmonary disease (COPD); age at onset; and current treatment for COPD. There was extensive discussion on the value of including this information. In particular, it was argued that it is difficult to collect scientifically valid information from a restricted set of questions to participants. Ultimately, it was decided to include COPD in the pilot phase of the DataSHaPER in order to collect further comments and to explore the value of keeping the domain. Sequence for Context for Familial disease history in Generic DataSchema Family History of Chronic Obstructive Pulmonary Disease Shared environmental determinants (particularly, personal and passive smoking) and shared genetic factors (alpha 1-antitrypsin deficiency and other genetic variants) contribute to familial aggregation in COPD (7, 8). The presence of a family history of COPD impacts on the pulmonary function in blood-relatives that can result in the development of COPD or other respiratory conditions such as asthma or 'bronchitis' (10, 11). There is also an increased risk of lung cancer in families with a history of COPD (12). Essential Under development In total, 0 of 13 studies (0%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Fam. hist.: Diseases of the respiratory system) A record of a medical history relating to the occurrence of chronic obstructive pulmonary disease (COPD) and COPD-related problems and events in a subject's blood relatives. Domain finalized, but still evolving Risk factor true Family History of Chronic Obstructive Pulmonary Disease Chronic obstructive pulmonary disease (COPD) is an umbrella term used to describe a group of chronic lung diseases that cause limitations to lung airflow. It is a major public health problem. COPD is a growing cause of morbidity and mortality worldwide and is projected to become the fourth leading cause of death worldwide by 2030 (1, 2). Currently, 210 million people have COPD and 3 million people died of the disease in 2005 (1). Most people with COPD are at least 40 years of age at the onset of symptoms (2), and mortality increases sharply with age. Patients living with COPD have an impaired quality of life and must necessarily adapt their activities of daily living (3). In addition, the economic burden of COPD is enormous. For example, in the USA it is estimated that, on average, COPD costs US$ 1,522 per sufferer per annum and the total national cost is US$ 23.9 billion per annum (4). These costs are attributable to a combination of direct medical care expenditures and to the financial penalties associated with morbidity and premature mortality. Family (5) and twin studies (6) have shown that COPD is a complex genetic disease involving interactions between genetic and environmental determinants and it is believed that many genetic factors increase (or decrease) an individual's risk of developing COPD. Because many of these genetic, environmental and life-style determinants are shared by relatives, there is strong evidence of familial clustering of COPD (7, 8). For example, the risk of COPD in subjects with two affected siblings can be between 4 and 14 times higher than that in subjects with no affected siblings (7). For these reasons, it can be useful to know whether a participant has a family history of COPD history on recruitment to a major epidemiological study. COPD exhibits a sufficiently high prevalence to provide adequate statistical power for epidemiological study and analysis in many contemporary biobanks (9). Information on COPD diagnosis can be collected at reasonable cost with a questionnaire and without prejudice to study participants. But the disease is diagnosed with varying thoroughness in the clinical setting, and there can therefore be substantial assessment error when reporting a 'doctor diagnosis' of COPD. Assessment errors can be particularly problematic when subjects report the disease status of their relatives. Consequently, although it might be recommended that family history of COPD should be collected on recruitment to a cohort biobank, it must be recognised that the data will not be error free and that this will impair statistical power. 1. World Health Organization. Chronic obstructive pulmonary diseases (COPD). (2007) http://www.who.int/respiratory/copd/en/. 2. National Heart Lung and Blood Institute. COPD. (2007) http://www.nhlbi.nih.gov/health/dci/Diseases/Copd/Copd_WhoIsAtRisk.html. 3. The Lung Association. COPD. (2007) http://www.lung.ca/diseases-maladies/copd-mpoc/what-quoi/index_e.php 4. Sullivan, S.D., Ramsey, S.D. & Lee T.A. The economic burden of COPD. Chest 117, 5S-9S (2000). PMID: 10673466. 5. Givelber, R.J. et al. Segregation analysis of pulmonary function among families in the Framingham Study. Am. J. Respir. Crit. Care Med. 157, 1445-1451 (1998). PMID: 9603122. 6. Redline, S. et al. Assessment of genetic and nongenetic influences on pulmonary function: a twin study. Am. Rev. Respir. Dis. 135, 217-222 (1987). PMID: 3800147. 7. Hemminki, K. et al. Familial risks for chronic obstructive pulmonary disease among siblings based on hospitalisations in Sweden. Journal of Epidemiology and Community Health, 62:398-401 (2008). PMID: 18413451. 8. Khoury, M.J. et al. Familial aggregation in chronic obstructive pulmonary disease: use of the loglinear model to analyze intermediate environmental and genetic risk factors. Genet. Epidemiol. 2, 155-166 (1985). PMID: 3876967. 9. Burton PR, Hansell A. UK Biobank: the expected distribution of incident and prevalent cases of chronic disease and the statistical power of nested case control studies. Manchester, UK: UK Biobank Technical Reports (2005). 10. Silverman, E.K. et al. Genetic epidemiology of severe, early-onset chronic obstructive pulmonary disease. Risk to relatives for airflow obstruction and chronic bronchitis. Am. J. Respir. Crit. Care Med. 157, 1770-1778 (1998). PMID: 9620904. 11. Rusconi, F. et al. Risk factors for early, persistent, and late-onset wheezing in young children. Am. J. Respir. Crit. Care Med. 160, 1617-1622 (1999). PMID: 10556130. 12. Schwartz, A.G., Ruckdeschel, J.C. Genetic Susceptibility and Relationship to Chronic Obstructive Pulmonary Disease. American Journal of Respiratory and Critical Care Medicine 173: 16-22 (2006). PMID: 16141445. The variables selected for this domain reflect the occurrence of Familial History of Chronic Obstructive Pulmonary Disease among the first degree relatives of a participant. There was broad consensus on the need to collect this information. This is important as it provides information relevant to the baseline risk of Familial History of Chronic Obstructive Pulmonary Disease in a participant. However, there were extensive discussions on how 'wide the net should be cast' in identifying family history. In the end, there was consensus that the relevance of the structure proposed should be evaluated during the pilot phase of the DataSHaPER. Sequence for Marital status Prefer not to answer Prefer not to answer Sequence for Context for Body Circumferences in Generic DataSchema Contraindication for taking waist circumference measurement Presence of any contraindication for taking waist circumference measurement. Contraindication for taking waist circumference measurement false Sequence for Context for Residence in Generic DataSchema Country where lived longest Country where the participant lived longest. Country where lived longest false Sequence for Context for Alcohol Use in Generic DataSchema Current quantity of liquor consumed Average number of drinks of liquor/spirits (of any size) consumed by the participant per week. Current quantity of liquor consumed false Drinks/week Sequence for Context for Working Status in Generic DataSchema Type of industry for occupation of longest duration Type of industry in which the participant worked for the longest duration. Type of industry for occupation of longest duration false Sequence for Context for Education Level in Generic DataSchema Some elements of tertiary education completed (ISCED 5-6) Indicator of whether the participant completed at least some elements of tertiary education. Some elements of tertiary education completed (ISCED 5-6) false Sequence for Context for Tobacco Use in Generic DataSchema Age cigarette smoking onset Age at which the participant began smoking cigarettes. Age cigarette smoking onset false Years Sequence for Context for Blood Pressure in Generic DataSchema Current antihypertensive medication Current treatment for high blood pressure. Current antihypertensive medication false Socioeconomic Characteristics Socio-economic characteristics of the participants Socioeconomic Characteristics Individual History of Other Major Disorders Currently under development Optional Under development In total, in the P3G Questionnaire Catalogue and based on Questionnaire Catalogue's Keywords, 6 of 13 studies (46%) recorded information (at recruitment) about Certain infectious and parasitic diseases; 6 of 13 (46%) about Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism; 12 of 13 (92%) about Endocrine, nutritional and metabolic diseases; 8 of 13 (62%) about Mental and behavioural disorders; 8 of 13 (62%) about Diseases of the nervous system; 6 of 13 (46%) about Diseases of the eye and adnexa; 3 of 13 (23%) about Diseases of the ear and mastoid process; 13 of 13 (100%) about Diseases of the circulatory system; 13 of 13 (100%) about Diseases of the respiratory system; 10 of 13 (77%) about Diseases of the digestive system; 7 of 13 (54%) about Diseases of the skin and subcutaneous tissue; 12 of 13 (92%) about Diseases of the musculoskeletal system and connective tissue; 10 of 13 (77%) about Diseases of the genitourinary system; 3 of 13 (23%) about Pregnancy, childbirth and the puerperium; 2 of 13 (15%) about Certain conditions originating in the perinatal period; 3 of 13 (23%) about Congenital malformations, deformations and chromosomal abnormalities; 3 of 13 (23%) about Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified; 6 of 13 (46%) about Injury, poisoning and certain other consequences of external causes; 1 of 13 (8%) about External causes of morbidity and mortality; 1 of 13 (8%) about Factors influencing health status and contact with health services. A record of a subject's personal medical history relating to the occurrence of any other major disorders and their related problems and events. Domain finalized, but still evolving Outcome true Individual History of Other Major Disorders Currently under development Currently under development The variables selected for this domain include: the occurrence; age at onset; and current treatment for major disorders other than those whose individual history is already included in the DataSHaPER. There was broad consensus on the need to collect this information. Information collected at recruitment on the occurrence of other major conditions was considered essential by the panel of experts to discriminate prevalent and incident cases of stroke and to identify conditions that are themselves risk factors for other diseases and conditions. Finally, it was agreed that is was essential to allow for several conditions to be collected. Information relevant for waist circumference measurement interpretation Presence of any information relevant for the interpretation of waist circumference measurement. Information relevant for waist circumference measurement interpretation false Heaviest ever alcohol consumption Average number of alcoholic drinks (of any size) consumed by the participant per week during the period when the participant drank the most. Heaviest ever alcohol consumption false Drinks/week Current quantity of cigarettes smoked Current average number of cigarettes smoked per week. Current quantity of cigarettes smoked false Cigarettes/week Sociodemographic Characteristics Demographic or social characteristics of the participants Sociodemographic Characteristics Individual History of Asthma Asthma is likely to result from the effects of environmental stimuli in genetically susceptible individuals (6). Factors that influence the risk of asthma can be divided into those that cause the development of asthma and those that trigger asthma symptoms and attacks; some do both (3). The former include host factors (which are primarily genetic, obesity, sex) and the latter are usually environmental factors (allergens - domestic mites, furred animals, cockroach, allergen, fungi, moulds, yeasts, pollens, fungi, yeasts-, infections, occupational sensitizers, tobacco smoke, air pollution, diet.) (7-9). Essential Under development In total, 9 of 13 studies (69%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Diseases of the respiratory system) A record of a subject's personal medical history relating to the occurrence of asthma and asthma-related problems and events. Domain finalized, but still evolving Outcome true Individual History of Asthma Asthma is a major global health problem. It affects people of all ages, but it is the single most common chronic disorder in childhood (1, 2). Throughout the world, the prevalence of asthma has been increasing over the past few decades, regardless of level of development (2, 3, 4). According to the World Health Organization, 300 million people suffer from asthma, although the prevalence of asthma varies from 1% to 18% in different countries (4). Without urgent action, it is projected that there will be a 20% increase in asthma prevalence over the next 10 years (2,4). In 2005, 255 000 people worldwide died from the disease (2). Eighty percent of deaths occurred in low- and middle-income countries (2). The high prevalence of asthma means that its economic impact is high and that there is a heavy burden in terms of health care system use, time off work and school, and premature retirement costs. In the United States, this is estimated to be 9.4 billion dollars per year (5). As well as absenteeism from work and school, asthma has other social consequences such limitations of physical activity, and lack of sleep (4). A positive family history of asthma is one of the strongest risk factors for asthma (6). Genes likely interact both with other genes and with environmental factors to determine asthma susceptibility (7, 8). Asthma is therefore well worthy of scientific study in biobanks. The prevalence of asthma is high enough to provide adequate statistical power for epidemiological study and analysis in many contemporary biobanks. Subjects that have been diagnosed with, and are treated for, asthma can usually report their disease status accurately, and these data can be collected by questionnaire. There is therefore a strong case for collecting information about doctor diagnosed asthma on recruitment into a cohort biobank. In particular, it enables informative cross-sectional analyses at recruitment (providing for early 'scientific wins') and also permits the subsequent discrimination of pre-existing (prevalent) cases of asthma from new (incident) diagnoses. But, it must be recognised that the thoroughness of the diagnostic process varies markedly between clinical settings and assessment errors can occur in both directions (false positives and false negatives) and this can impair statistical power. 1. The International Study of Asthma and Allergies in Childhood Steering Committee. Worldwide variation in prevalence of symptoms of asthma, allergic rhinoconjuntivitis, and atopic eczema: ISAAC. The International Study of Asthma and Allergies in Childhood (ISAAC) Steering Committee. Lancet 351, 1225-1232 (1998). PMID: 9643741. 2. World Health Organization. Asthma. (2006) http://www.who.int/mediacentre/factsheets/fs307/en/index.html 3. Global Initiative for Asthma. Global Strategy for Asthma Management and Prevention.92 p. (2006). http://www.ginasthma.com/ 4. American Academy of Allergy Asthma & Immunology. The costs of asthma and allergy. (2004) http://www.aaaai.org/patients/advocate/2004/fall/costs.stm. 5. Ober, C. Perspectives on the past decade of asthma genetics. J. Allergy Clin. Immunol. 116, 274-278 (2005). PMID: 16083779. 6. Yang, I.A. et al. Gene-environmental interaction in asthma. Curr. Opin. Allergy Clin. Immunol. 7, 75-82 (2007). PMID: 17218815. 7. Health Canada. "It's your health: Asthma" (2006) http://www.hc-sc.gc.ca/hl-vs/iyh-vsv/diseases-maladies/asthm-eng.php 8. American Academy of Allergy Asthma & Immunology. Tips to remember: Asthma triggers and management (2007). http://www.aaaai.org/patients/publicedmat/tips/asthmatriggersandmgmt.stm 9. Asthma UK. What causes asthma ? (2007). http://www.asthma.org.uk/all_about_asthma/asthma_basics/what_causes.html The variables selected for this domain include: the occurrence of asthma; and age at onset. There was extensive discussion on the value of including this information. In particular, it was argued that it is difficult to collect scientifically valid information from a restricted set of questions to participants. Ultimately, it was decided to include asthma in the pilot phase of the DataSHaPER in order to collect further comments and to explore the value of keeping the domain. Family History of Asthma Individuals with a positive family history for asthma have an increased risk of expressing a variety of respiratory phenotypes including: increased airways responsiveness; lower respiratory symptoms; atopy; chronic bronchitis/emphysema; pneumonia; and seasonal allergies (6, 9, 10). Essential Under development In total, 2 of 13 studies (15%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Fam. hist.: Diseases of the respiratory system) A record of a medical history relating to the occurrence of asthma and asthma-related problems and events in a subject's blood relatives. Domain finalized, but still evolving Risk factor true Family History of Asthma Asthma is a major global health problem. It affects people of all ages, but it is the single most common chronic disorder in childhood (1, 2). Throughout the world, the prevalence of asthma has been increasing over the past few decades, regardless of level of development (2, 3, 4). According to the World Health Organization, 300 million people suffer from asthma, although the prevalence of asthma varies from 1% to 18% in different countries (4). Without urgent action, it is projected that there will be a 20% increase in asthma prevalence over the next 10 years (2,4). In 2005, 255 000 people worldwide died from the disease (2). Eighty percent of deaths occurred in low- and middle-income countries (2). The high prevalence of asthma means that its economic impact is high and that there is a heavy burden in terms of health care system use, time off work and school, and premature retirement costs. In the United States, this is estimated to be 9.4 billion dollars per year (5). As well as absenteeism from work and school, asthma has other social consequences such limitations of physical activity, and lack of sleep (4). A positive family history of asthma is one of the strongest risk factors for asthma (6). Genes likely interact both with other genes and with environmental factors to determine asthma susceptibility (7, 8). Family history of asthma is therefore well worthy of scientific study in biobanks. The prevalence of asthma is high enough to provide adequate statistical power for epidemiological study and analysis in many contemporary biobanks. Subjects that have been diagnosed with, and are treated for, asthma can usually report their disease status accurately, and these data can be collected by questionnaire. There is therefore a strong case for collecting information about doctor diagnosed asthma in family members on recruitment into a cohort biobank. But, it must be recognised that the thoroughness of the diagnostic process varies markedly between clinical settings and assessment errors can occur in both directions (false positives and false negatives) and this can impair statistical power. Any assessment errors are likely to be exaggerated when a subject is reporting the disease status of his/her relatives rather than of him/herself. 1. The International Study of Asthma and Allergies in Childhood Steering Committee. Worldwide variation in prevalence of symptoms of asthma, allergic rhinoconjuntivitis, and atopic eczema: ISAAC. The International Study of Asthma and Allergies in Childhood (ISAAC) Steering Committee. Lancet 351, 1225-1232 (1998). PMID: 9643741. 2. World Health Organization. Asthma. (2006) http://www.who.int/mediacentre/factsheets/fs307/en/index.html 3. Global Initiative for Asthma. Global Strategy for Asthma Management and Prevention.92 p. (2006) http://www.ginasthma.com/. 4. American Academy of Allergy Asthma & Immunology. The costs of asthma and allergy. (2004) http://www.aaaai.org/patients/advocate/2004/fall/costs.stm. 5. Ober, C. Perspectives on the past decade of asthma genetics. J. Allergy Clin. Immunol. 116, 274-278 (2005). PMID: 16083779. 6. Jenkins, M., Hopper, J., and Giles, G. 1997. Regressive logistic modelling of familial aggregation for asthma in 7,394 population-based nuclear families. Genet. Epidemiol. 14: 317-332. PMID: 9181360. 7. Yang, I.A. et al. Gene-environmental interaction in asthma. Curr. Opin. Allergy Clin. Immunol. 7, 75-82 (2007). PMID: 17218815. 8. Health Canada. ''It's your health: Asthma'' (2006) http://www.hc-sc.gc.ca/iyh-vsv/diseases-maladies/asthme.html 9. Laprise, C. and Boulet, L. Airway responsiveness and atopy in families of patients with asthma. Clin. Invest. Med. 19: 461-469 (1996). PMID: 8959356. 10. Sears, M. et al. Parental and neonatal risk factors for atopy, airway hyper-responsiveness, and asthma. Arch. Dis. Child. 75: 392-398 (1996). PMID: 8957951. The variables selected for this domain reflect the occurrence of asthma among the first degree relatives of a participant. There was broad consensus on the need to collect this information. This is important as it provides information relevant to the baseline risk of asthma in a participant. However, there were extensive discussions on how 'wide the net should be cast' in identifying family history. In the end, there was consensus that information should target only first degree relatives (parents, siblings and offspring) but that the relevance of the structure proposed should be evaluated during the pilot phase of the DataSHaPER. Type of contraindication for waist circumference measurement Reason for waist measurement contraindication. (For example: pregnant for more than 12 weeks) Type of contraindication for waist circumference measurement true Frequency of binge drinking Frequency of binge drinking. Frequency of binge drinking false Times/month Duration of occupation held longest Duration in longest held occupation. Duration of occupation held longest false Years Age last smoked cigarettes Age at which the participant last smoked cigarettes. Age last smoked cigarettes false Years Don't know Don't know Not applicable Not applicable Context for Health and risk factor questionnaire in Generic DataSchema Context for Individual disease history in Generic DataSchema Context for Individual History of Cancer in Generic DataSchema Context for Individual History of Diabetes in Generic DataSchema Context for Individual History of High Blood Pressure in Generic DataSchema Context for Individual History of Stroke in Generic DataSchema Context for Individual History of Myocardial Infarction in Generic DataSchema Context for Individual History of Depression in Generic DataSchema Context for Individual History of Chronic Obstructive Pulmonary Disease in Generic DataSchema Context for Individual History of Asthma in Generic DataSchema Context for Individual History of Other Major Disorders in Generic DataSchema Context for Medication in Generic DataSchema Context for Medication Intake in Generic DataSchema Context for Perception of Health in Generic DataSchema Context for Women's health in Generic DataSchema Context for Menstruation in Generic DataSchema Context for Menopause in Generic DataSchema Context for Hysterectomy in Generic DataSchema Context for Oophorectomy in Generic DataSchema Context for Reproductive history in Generic DataSchema Context for Men's reproductive history in Generic DataSchema Context for Women's reproductive history in Generic DataSchema Context for Familial disease history in Generic DataSchema Context for Family History of Cancer in Generic DataSchema Context for Family History of Diabetes in Generic DataSchema Context for Family History of High Blood Pressure in Generic DataSchema Context for Family History of Stroke in Generic DataSchema Context for Family History of Myocardial Infarction in Generic DataSchema Context for Family History of Depression in Generic DataSchema Context for Family History of Chronic Obstructive Pulmonary Disease in Generic DataSchema Context for Family History of Asthma in Generic DataSchema Context for Life habits/Behaviours in Generic DataSchema Context for Tobacco Use in Generic DataSchema Context for Alcohol Use in Generic DataSchema Context for Physical Activity in Generic DataSchema Context for Sociodemographic characteristics in Generic DataSchema Context for Residence in Generic DataSchema Context for Individual Birth Location in Generic DataSchema Context for Parental Birth Location in Generic DataSchema Context for Sibling in Generic DataSchema Context for Household Status in Generic DataSchema Context for Gender in Generic DataSchema Context for Age in Generic DataSchema Context for Socioeconomic characteristics in Generic DataSchema Context for Education Level in Generic DataSchema Context for Income in Generic DataSchema Context for Working Status in Generic DataSchema Context for Physical environment in Generic DataSchema Context for Passive Smoking Exposure in Generic DataSchema Context for Physical and cognitive measures in Generic DataSchema Context for Anthropometric measures in Generic DataSchema Context for Height in Generic DataSchema Context for Standing height in Generic DataSchema Context for Contraindication for taking standing height measurement in Generic DataSchema Context for Information relevant for standing height measurement interpretation in Generic DataSchema Context for Weight in Generic DataSchema Context for Weight in Generic DataSchema Context for Contraindication for taking weight measurement in Generic DataSchema Context for Information relevant for weight measurement interpretation in Generic DataSchema Context for Body Circumferences in Generic DataSchema Context for Hip circumference in Generic DataSchema Context for Contraindication for taking hip circumference measurement in Generic DataSchema Context for Information relevant for hip circumference measurement interpretation in Generic DataSchema Context for Waist circumference in Generic DataSchema Context for Contraindication for taking waist circumference measurement in Generic DataSchema Context for Information relevant for waist circumference measurement interpretation in Generic DataSchema Context for Body composition measures in Generic DataSchema Context for Body Mass Index in Generic DataSchema Context for Body mass index in Generic DataSchema Context for Body function measures in Generic DataSchema Context for Heart Rate in Generic DataSchema Context for Heart rate at rest in Generic DataSchema Context for Contraindication for taking heart rate measurement in Generic DataSchema Context for Information relevant for heart rate measurement interpretation in Generic DataSchema Context for Blood Pressure in Generic DataSchema Context for Diastolic blood pressure at rest in Generic DataSchema Context for Systolic blood pressure at rest in Generic DataSchema Context for Contraindication for taking blood pressure measurement in Generic DataSchema Context for Information relevant for blood pressure measurement interpretation in Generic DataSchema Context for Handedness in Generic DataSchema Context for Interview administration in Generic DataSchema Context for Administrative data in Generic DataSchema Context for Administrative data in Generic DataSchema Familial Disease History Significant ill health experienced by the relatives of the participants Familial Disease History Individual Disease History Significant features of the participants' personal medical history Individual Disease History Body Function Measures Physical measurements related to the function or dysfunction of individual organs or systems of the participants body Body Function Measures Anthropometric Measures Physical measurements of the proportions, size, and/or weight of the participants' bodies Anthropometric Measures Physical Environment Physical or chemical exposures arising from the environment in which the participants play out their lives Physical Environment Body Composition Measures Physical measurements related to the proportion of fat, bone and muscle in the participants' bodies Body Composition Measures Women's Health Significant features of health specifically related to female anatomy and/or physiology Women's Health Medication Medications taken by the study participants Medication Reproductive History Significant features of the participants' reproductive history Reproductive History Perception of Health Signiticant features of the participants' quality of life Perception of Health Administrative Data Administrative information collected Administrative Data No child had depression No child had depression At least one child has had depression At least one child has had depression Family history of cancer Family history of cancer No family history of cancer No family history of cancer Father has not had cancer Father has not had cancer Father has had cancer Father has had cancer Classification based on the ATC/DDD code Classification based on the ATC/DDD code Presence of information which may have influenced the heart rate measurement. Presence of information which may have influenced the heart rate measurement. No information which may have influenced the heart rate measurement. No information which may have influenced the heart rate measurement. Presence of information which may have influenced the weight measurement. Presence of information which may have influenced the weight measurement. No presence of information which may have influenced the weight measurement. No presence of information which may have influenced the weight measurement. Presence of information which may have influenced the waist circumference measurement. Presence of information which may have influenced the waist circumference measurement. No information which may have influenced the waist circumference measurement. No information which may have influenced the waist circumference measurement. No presence od contraindication for waist circumference measurement. No presence od contraindication for waist circumference measurement. Presence of contraindication for waist circumference measurement. Presence of contraindication for waist circumference measurement. Presence of contraindication for standing height measurement. Presence of contraindication for standing height measurement. No presence of contraindication for standing height measurement. No presence of contraindication for standing height measurement. Family history of diabetes Family history of diabetes No family history of diabetes No family history of diabetes No child has had diabetes No child has had diabetes At least one child has had diabetes At least one child has had diabetes Mother has not had diabetes Mother has not had diabetes Mother has had diabetes Mother has had diabetes Father has had diabetes Father has had diabetes Father has not had diabetes Father has not had diabetes At least one sibling has had diabetes At least one sibling has had diabetes No sibling has had diabetes No sibling has had diabetes Classification based on the ISCO-88 codes Classification based on the ISCO-88 codes Is not retired Is not retired Is retired Is retired Classification based on the List of countries (ISO-3166) Classification based on the List of countries (ISO-3166) Female Female Male Male Born in a multiple birth Born in a multiple birth Not born in a multiple birth Not born in a multiple birth Single, never married Single, never married Married and / or living with a partner Married and / or living with a partner Widowed Widowed Separated Separated Divorced Divorced Doesn't live with a spouse or a partner Doesn't live with a spouse or a partner Lives with a spouse or a partner Lives with a spouse or a partner To be defined by the investigators according to the context To be defined by the investigators according to the context Mother has not had chronic obstructive pulmonary disease Mother has not had chronic obstructive pulmonary disease Mother has had chronic obstructive pulmonary disease Mother has had chronic obstructive pulmonary disease Family history of chronic obstructive pulmonary disease Family history of chronic obstructive pulmonary disease No family history of chronic obstructive pulmonary disease No family history of chronic obstructive pulmonary disease Father has had chronic obstructive pulmonary disease Father has had chronic obstructive pulmonary disease Father has not had chronic obstructive pulmonary disease Father has not had chronic obstructive pulmonary disease At least one sibling has had chronic obstructive pulmonary disease At least one sibling has had chronic obstructive pulmonary disease No sibling had chronic obstructive pulmonary disease No sibling had chronic obstructive pulmonary disease No child had chronic obstructive pulmonary disease No child had chronic obstructive pulmonary disease At least one child has had chronic obstructive pulmonary disease At least one child has had chronic obstructive pulmonary disease Never had asthma Never had asthma Ever had asthma Ever had asthma Family history of asthma Family history of asthma No family history of asthma No family history of asthma Mother has had asthma Mother has had asthma Mother has not had asthma Mother has not had asthma At least one sibling has had asthma At least one sibling has had asthma No sibling has had asthma No sibling has had asthma Father has not had asthma Father has not had asthma Father has had asthma Father has had asthma At least one child has had asthma At least one child has had asthma No child has had asthma No child has had asthma Never been exposed to passive smoking Never been exposed to passive smoking Ever been exposed to passive smoking Ever been exposed to passive smoking Not exposed to passive smoking during childhood Not exposed to passive smoking during childhood Exposed to passive smoking during childhood Exposed to passive smoking during childhood Currently exposed to passive smoking Currently exposed to passive smoking Not currently exposed to passive smoking Not currently exposed to passive smoking Classification based on the ICD-10 codes comprised between C00 and D48 (Chapter II) Classification based on the ICD-10 codes comprised between C00 and D48 (Chapter II) No presence of contraindication for weight measurement. No presence of contraindication for weight measurement. Presence of contraindication for weight measurement. Presence of contraindication for weight measurement. Ever had cancer Ever had cancer Never had cancer Never had cancer Ever had menopause Ever had menopause Never had menopause Never had menopause Ever had menstruation Ever had menstruation Never had menstruation Never had menstruation Had one or two ovary removed Had one or two ovary removed Never had oophorectomy Never had oophorectomy Had one ovary removed Had one ovary removed Had both ovaries removed Had both ovaries removed Ever had hysterectomy Ever had hysterectomy Never had hysterectomy Never had hysterectomy High level of physical activity High level of physical activity Low level of physical activity Low level of physical activity Moderate level of physical activity Moderate level of physical activity Ever consumed alcohol Ever consumed alcohol Never consumed alcohol Never consumed alcohol Never consumes alcohol Never consumes alcohol Consumes alcohol Consumes alcohol Never had depression Never had depression Ever had depression Ever had depression Ever smoked tobacco Ever smoked tobacco Never smoked tobacco Never smoked tobacco Tobacco smoker Tobacco smoker Non-tobacco smoker Non-tobacco smoker Diabetes mellitus type 1 Diabetes mellitus type 1 Diabetes mellitus type 2 Diabetes mellitus type 2 Never had diabetes Never had diabetes Ever had diabetes Ever had diabetes Never had chronic obstructive pulmonary disease Never had chronic obstructive pulmonary disease Ever had chronic obstructive pulmonary disease Ever had chronic obstructive pulmonary disease Classification based on the ISIC Rev4 codes Classification based on the ISIC Rev4 codes No paid employment and not self-employed No paid employment and not self-employed Paid employment or self-employed Paid employment or self-employed Did not complete secondary education Did not complete secondary education At least secondary education completed At least secondary education completed Did not complete first stage of tertiary education Did not complete first stage of tertiary education At least first stage of tertiary education completed At least first stage of tertiary education completed Did not complete post-secondary education Did not complete post-secondary education At least post-secondary education completed At least post-secondary education completed Classification based on the ISCED codes Classification based on the ISCED codes Did not complete primary education Did not complete primary education At least primary education completed At least primary education completed Not currently under treatment for high blood pressure Not currently under treatment for high blood pressure Currently under treatment for high blood pressure Currently under treatment for high blood pressure Ever had high blood pressure Ever had high blood pressure Never had high blood pressure Never had high blood pressure At least one sibling has ever had stroke At least one sibling has ever had stroke No sibling has had stroke No sibling has had stroke Mother has not had a stroke Mother has not had a stroke Mother has had a stroke Mother has had a stroke At least one child has had stroke At least one child has had stroke No child has had stroke No child has had stroke Family history of stroke Family history of stroke No family history of stroke No family history of stroke Father has not had a stroke Father has not had a stroke Father has had a stroke Father has had a stroke Ever had stroke Ever had stroke Never had stroke Never had stroke Presence of information which may have influenced the standing height measurement. Presence of information which may have influenced the standing height measurement. No information which may have influenced the standing height measurement. No information which may have influenced the standing height measurement. Presence of information which may have influenced the hip circumference measurement. Presence of information which may have influenced the hip circumference measurement. No information which may have influenced the hip circumference measurement. No information which may have influenced the hip circumference measurement. No presence of contraindication for hip circumference measurement. No presence of contraindication for hip circumference measurement. Presence of contraindication for hip circumference measurement. Presence of contraindication for hip circumference measurement. Presence of information which may have influenced the blood pressure measurement. Presence of information which may have influenced the blood pressure measurement. No information which may have influenced the blood pressure measurement. No information which may have influenced the blood pressure measurement. Currently using medication Currently using medication Not currently using medication Not currently using medication Presence of contraindication for heart rate measurement. Presence of contraindication for heart rate measurement. No presence of contraindication for heart rate measurement. No presence of contraindication for heart rate measurement. Presence of contraindication for blood pressure measurement. Presence of contraindication for blood pressure measurement. No presence od contraindication for blood pressure measurement. No presence od contraindication for blood pressure measurement. Use both hands Use both hands Left handed Left handed Right handed Right handed Never had myocardial infarction Never had myocardial infarction Ever had myocardial infarction Ever had myocardial infarction Father has not had a myocardial infarction Father has not had a myocardial infarction Father has had a myocardial infarction Father has had a myocardial infarction Mother has had a myocardial infarction Mother has had a myocardial infarction Mother has not had a myocardial infarction Mother has not had a myocardial infarction No child has had myocardial infarction No child has had myocardial infarction At least one child has had myocardial infarction At least one child has had myocardial infarction At least one sibling has had myocardial infarction At least one sibling has had myocardial infarction No sibling has had myocardial infarction No sibling has had myocardial infarction No family history of myocardial infarction No family history of myocardial infarction Family history of myocardial infarction Family history of myocardial infarction Classification based on the ICD-10 codes Classification based on the ICD-10 codes Ever had other major disorders Ever had other major disorders Never had other major disorders Never had other major disorders No family history of high blood pressure No family history of high blood pressure Family history of high blood pressure Family history of high blood pressure Father has not had high blood pressure Father has not had high blood pressure Father has had high blood pressure Father has had high blood pressure At least one child has had high blood pressure At least one child has had high blood pressure No child had high blood pressure No child had high blood pressure Mother has not had high blood pressure Mother has not had high blood pressure Mother has had high blood pressure Mother has had high blood pressure At least one sibling has had high blood pressure At least one sibling has had high blood pressure No sibling has had high blood pressure No sibling has had high blood pressure Mother has not had depression Mother has not had depression Mother has had depression Mother has had depression Father has had depression Father has had depression Father has not had depression Father has not had depression No sibling had cancer No sibling had cancer At least one sibling has had cancer At least one sibling has had cancer Mother has not had cancer Mother has not had cancer Mother has had cancer Mother has had cancer No family history of depression No family history of depression Family history of depression Family history of depression At least one sibling has had depression At least one sibling has had depression No sibling has had depression No sibling has had depression No child had cancer No child had cancer At least one child has had cancer At least one child has had cancer Ever had any major disorder Ever had any major disorder Never had any major disorder Never had any major disorder Current smoker of tobacco products other than cigarettes Current smoker of tobacco products other than cigarettes Current non-smoker of tobacco products other than cigarettes Current non-smoker of tobacco products other than cigarettes Never smoked cigarettes Never smoked cigarettes Ever smoked cigarettes Ever smoked cigarettes Never smoked other tobacco products than cigarettes Never smoked other tobacco products than cigarettes Ever smoked other tobacco products than cigarettes Ever smoked other tobacco products than cigarettes Non-cigarettes smoker Non-cigarettes smoker Cigarettes smoker Cigarettes smoker Currently under treatment for high blood pressure Currently under treatment for high blood pressure Not currently under treatment for high blood pressure Not currently under treatment for high blood pressure Generic DataSchema Generic Baseline Principally prospective harmonization The "Generic DataSchema" is the first DataSchema developed under the DataSHaPER project. Its content was chosen so as to provide a minimal dataset with broad international applicability. Generic DataSchema Middle-aged participants Population-based Generic purpose Occurrence of depression among children Occurrence of depression at any point during the life of the biological children of the participant. Occurrence of depression among children false Number of live births mothered Number of living births a female participant has had. Number of live births mothered false Live births Current quantity of wine consumed Average number of glasses of wine (include all types of wine, of any size) consumed by the participant per week. Current quantity of wine consumed false Glasses of wine /week Occurrence of cancer in the family Presence of cancer amongst members of the biological family of the participant. Could be constructed using the occurrence among mother, father, siblings and children. Occurrence of cancer in the family false Occurrence of father cancer Occurrence of cancer at any point during the life of the participant's biological father. Occurrence of father cancer false Current prescribed medications Current prescribed medications. Current prescribed medications true Type of contraindication for heart rate measurement Reason for heart rate measurement contraindication. (For example: Arm limitation such as the presence of wounds, rashes, oedema, open sores, burns, tubes, scars, gauze / dressings, paralysis, a cast, amputation or where both arms are withered). Type of contraindication for heart rate measurement true Information relevant for heart rate measurement interpretation Presence of any information relevant for the interpretation of heart rate measurement. Information relevant for heart rate measurement interpretation false Information relevant for weight measurement interpretation Presence of any information relevant to the interpretation of weight measurement. Information relevant for weight measurement interpretation false Waist circumference Measured distance around waist. Waist circumference false Centimetres Type of interpretative information for waist circumference measurement Type of information relevant for the interpretation of waist circumference measurement (For example: number of weeks pregnant). Type of interpretative information for waist circumference measurement true Contraindication for taking standing height measurement Presence of any contraindication for taking standing height measurement. Contraindication for taking standing height measurement false Occurrence of diabetes in the family Presence of diabetes amongst members of the biological family of the participant. Could be constructed using the occurrence among mother, father, siblings and children. Occurrence of diabetes in the family false Occurrence of diabetes among children Occurrence of diabetes at any point during the life of the biological children of the participant. Occurrence of diabetes among children false Occurrence of mother diabetes Occurrence of diabetes at any point during the life of the participant's biological mother. Occurrence of mother diabetes false Occurrence of father diabetes Occurrence of diabetes at any point during the life of the participant's biological father. Occurrence of father diabetes false Occurrence of diabetes among siblings Occurrence of diabetes at any point during the life of the biological siblings (one or two common parents) of the participant. Occurrence of diabetes among siblings false Number of siblings with diabetes Total number of participant's biological siblings (one or two common parents) that have ever had diabetes. Number of siblings with diabetes false Siblings Number of children with diabetes Total number of participant's biological children that have ever had diabetes. Number of children with diabetes false Children Number of years of education Number of years of education. Number of years of education false Years Job title for occupation of longest duration Title of job the participant had for the longest duration. Job title for occupation of longest duration false Retirement status Indicator of whether the participant is currently retired. Retirement status false Current country of residence Country where the participant currently lives. Current country of residence false Age at recruitment Participant's age at recruitment. Age at recruitment = (Date of interview- Birth date) / 365 Age at recruitment false Years Birth date Participant's birth date. Birth date false YYYY/MM/DD Birth year Participant's year of birth. Birth year false YYYY Gender Gender of the participant. Gender false Biological mother's country of birth Country where the participant's biological mother was born. Biological mother's country of birth false Biological father's mother's country of birth Country where the biological mother of the participant's father was born. Biological father's mother's country of birth false Biological father's country of birth Country where the participant's biological father was born. Biological father's country of birth false Biological mother's father's country of birth Country where the biological father of the participant's mother was born. Biological mother's father's country of birth false Biological father's father's country of birth Country where the biological father of the participant's father was born. Biological father's father's country of birth false Biological mother's mother's country of birth Country where the biological mother of the participant's mother was born. Biological mother's mother's country of birth false Current postal code Postal code where the participant currently lives. Current postal code false Number of biological siblings Number of biological (one or two common parents) brothers and sisters of the participant. Number of biological siblings false Siblings Born in a multiple birth Participant born in a multiple birth. Born in a multiple birth false Birth rank Birth rank of the participant. Birth rank false Marital status Current marital status. Marital status false Number of people in the household Number of people who live with the participant in the same household. Number of people in the household false People Living with partner Status of living, or not, with a spouse or a partner in a common household. Living with partner false Current region of residence Region (province, state, territory, etc.) where the participant currently lives. Current region of residence false Region where lived longest Region (province, state, territory, etc.) where the participant lived longest. Region where lived longest false Region of birth Region (province, state, territory, etc.) where the participant was born. Region of birth false Town of birth City, town or village where the participant was born. Town of birth false Household income Average current annual income, before taxes, of the participant's entire household . Household income false Highest level of education Highest level of education completed by the participant. Highest level of education false Body mass index Weight (in kg) divided by height (in m) squared. Body mass index = (Weight) / (Standing height * 0.01)^2 Body mass index false BMI in kg/m2 Occurrence of mother chronic obstructive pulmonary disease Occurrence of chronic obstructive pulmonary disease at any point during the life of the participant's biological mother. Occurrence of mother chronic obstructive pulmonary disease false Occurrence of chronic obstructive pulmonary disease in the family Presence of chronic obstructive pulmonary disease amongst members of the biological family of the participant. Could be constructed using the occurrence among mother, father, siblings and children. Occurrence of chronic obstructive pulmonary disease in the family false Number of children with chronic obstructive pulmonary disease Total number of participant's biological children that have ever had chronic obstructive pulmonary disease. Number of children with chronic obstructive pulmonary disease false Children Occurrence of father chronic obstructive pulmonary disease Occurrence of chronic obstructive pulmonary disease at any point during the life of the participant's biological father. Occurrence of father chronic obstructive pulmonary disease false Occurrence of chronic obstructive pulmonary disease among siblings Occurrence of chronic obstructive pulmonary disease at any point during the life of the biological siblings (one or two common parents) of the participant. Occurrence of chronic obstructive pulmonary disease among siblings false Occurrence of chronic obstructive pulmonary disease among children Occurrence of chronic obstructive pulmonary disease at any point during the life of the biological children of the participant. Occurrence of chronic obstructive pulmonary disease among children false Asthma onset Age of the participant at asthma diagnosis. Asthma onset false Years Occurrence of asthma Occurrence of asthma at any point during the life of the participant. Occurrence of asthma false Occurrence of asthma in the family Presence of asthma amongst members of the biological family of the participant. Could be constructed using the occurrence among mother, father, siblings and children. Occurrence of asthma in the family false Occurrence of mother asthma Occurrence of asthma at any point during the life of the participant's biological mother. Occurrence of mother asthma false Occurrence of asthma among siblings Occurrence of asthma at any point during the life of the biological (one or two common parents) siblings of the participant. Occurrence of asthma among siblings false Occurrence of father asthma Occurrence of asthma at any point during the life of the participant's biological father. Occurrence of father asthma false Number of children with asthma Total number of participant's biological children that have ever had asthma. Number of children with asthma false Children Occurrence of asthma among children Occurrence of asthma at any point during the life of the biological children of the participant. Occurrence of asthma among children false Number of siblings with asthma Total number of participant's biological (one or two common parents) siblings that have ever had asthma. Number of siblings with asthma false Siblings Ever exposed to passive smoking Occurrence of passive smoking exposure during the life of the participant. Ever exposed to passive smoking false Occurrence of passive smoking exposure during childhood Occurrence of passive smoking exposure during childhood. Occurrence of passive smoking exposure during childhood false Current passive smoking exposure Current passive smoking exposure. Current passive smoking exposure false Current duration of passive smoking exposure Number of hours per week of passive smoking exposure. Current duration of passive smoking exposure false Hours/week Number of siblings with chronic obstructive pulmonary disease Total number of participant's biological siblings (one or two common parents) that have ever had chronic obstructive pulmonary disease. Number of siblings with chronic obstructive pulmonary disease false Siblings Type of cancer Type of cancer diagnosed. Type of cancer true Type of contraindication for weight measurement Reason for weight measurement contraindication (For example: unable to stand unassisted) Type of contraindication for weight measurement true Contraindication for taking weight measurement Presence of any contraindication for taking weight measurement. Contraindication for taking weight measurement false Weight Weight of the participant. Weight false Kilograms Type of interpretative information for weight measurement Type of Information relevant to the interpretation of weight measurement. (For example, number of weeks pregnant). Type of interpretative information for weight measurement true Type of cancer onset Age of the participant at cancer diagnosis. Type of cancer onset true Years Occurrence of cancer Occurrence of cancer at any point during the life of the participant. Occurrence of cancer false Occurrence of menopause Occurrence of menopause at any point during the life of the participant. Occurrence of menopause false Occurrence of menstruation Occurrence of menarche at any point during the life of the participant. Occurrence of menstruation false Menstruation onset Age of the participant at menarche. Menstruation onset false Years Oophorectomy onset Age of the participant at the most recent oophorectomy. Oophorectomy onset false Years Occurrence of oophorectomy Occurrence of oophorectomy at any point during the life of the participant. Occurrence of oophorectomy false Number of ovaries removed Number of ovaries removed during the life of the participant. Number of ovaries removed false Menopause onset Age of the participant at menopause. Menopause onset false Years Occurrence of hysterectomy Occurrence of hysterectomy at any point during the life of the participant. Occurrence of hysterectomy false Hysterectomy onset Age of the participant at hysterectomy. Hysterectomy onset false Years Age last smoked tobacco Age at which the participant last smoked tobacco. Age last smoked tobacco false Years Total walking activity Quantitative indicator of the walking activities in metabolic equivalent (MET)-minutes/week. Total walking activity false MET minutes per week Level of physical activity Categorical indicator of the participant's level of physical activity. Level of physical activity false Total vigorous intensity activity Quantitative indicator of the vigorous activities in metabolic equivalent (MET)-minutes/week. Total vigorous intensity activity false MET minutes per week Total moderate intensity activity Quantitative indicator of the moderate intensity activities in metabolic equivalent (MET)-minutes/week. Total moderate intensity activity false MET minutes per week Total physical activity Quantitative indicator of global physical activity in metabolic equivalent (MET)-minutes per week. Total physical activity false MET minutes per week Ever alcohol consumption Occurrence of alcohol consumption at any point during the life of the participant. Ever alcohol consumption false Current quantity of other drink consumed Average number of drinks (of any size) of other kinds of alcohol (not beer, liquor/spirit or wine) consumed by the participant per week. Current quantity of other drink consumed false Drinks/week Current use of alcohol Indicator of whether the participant currently consumes alcohol. Current use of alcohol false Current quantity of beer consumed Average number of beers (of any size) consumed by the participant per week. Current quantity of beer consumed false Beers/week Occurrence of depression Occurrence of depression at any point during the life of the participant. Occurrence of depression false Depression onset Age of the participant at depression diagnosis. Depression onset false Years Current quantity of red wine consumed Average number of glasses of red wine (of any size) consumed by the participant per week. Current quantity of red wine consumed false Glasses of red wine /week Current quantity of white wine consumed Average number of glasses of white wine (of any size) consumed by the participant per week. Current quantity of white wine consumed false Glasses of white wine /week Current quantity of total alcohol consumed Milligrams of alcohol consumed on average per week. Current quantity of total alcohol taken = (Current quantity of red wine taken * Average quantity of alcohol in a glass (size) of red wine) + (Current quantity of white wine taken * Average quantity of alcohol in a glass (size) of white wine) + (Current quantity of beer taken * Average quantity of alcohol in a bottle (size) of beer) + (Current quantity of liquor taken * Average quantity of alcohol in a measure (size) of liquor) + (Current quantity of other drink taken * Average quantity of alcohol in a measure (size) of other drink) Current quantity of total alcohol consumed false mg of alcohol/week Heaviest ever quantity of pipes Average use of pipes per week during the period the participant consumed the most. Heaviest ever quantity of pipes false Pipes/week Ever smoked tobacco Indicator of whether a participant has ever smoked tobacco. Ever smoked tobacco false Current tobacco smoker Indicator of whether a participant currently smokes tobacco. Current tobacco smoker false Current quantity of total nicotine Current average quantity of nicotine consumed per week. Current quantity of total nicotine false Milligrams of nicotine per week Heaviest ever quantity cigarettes Average number of cigarettes smoked per week during the period the participant smoked the most. Heaviest ever quantity cigarettes false Cigarettes/week Age of tobacco smoking onset Age at which the participant began smoking tobacco. Age of tobacco smoking onset false Years Current quantity of pipes smoked Current average use of pipes per week. Current quantity of pipes smoked false Pipes/week Heaviest ever quantity of cigars/small cigars Average number of cigars/small cigars smoked per week during the period the participant smoked the most. Heaviest ever quantity of cigars/small cigars false Cigars/small cigars/week Duration of smoking Total number of years of smoking. Duration of smoking false Years Current quantity of cigars/small cigars smoked Current average number of cigars/small cigars smoked per week. Current quantity of cigars/small cigars smoked false Cigars/small cigars/week Heaviest quantity of total nicotine Average quantity of nicotine consumed per week during the period the participant smoked the most. Heaviest quantity of total nicotine false Milligrams of nicotine per week Type of diabetes Type of diabetes diagnosed. Type of diabetes false Diabetes onset Age of the participant at diabetes diagnosis. Diabetes onset false Years Occurrence of diabetes Occurrence of diabetes at any point during the life of the participant. Occurrence of diabetes false Occurrence of chronic obstructive pulmonary disease Occurrence of chronic obstructive pulmonary disease at any point during the life of the participant. Occurrence of chronic obstructive pulmonary disease false Chronic obstructive pulmonary disease onset Age of the participant at chronic obstructive pulmonary disease diagnosis. Chronic obstructive pulmonary disease onset false Years Country of birth Country where the participant was born. Country of birth false Town where lived longest City, town or village where the participant lived longest. Town where lived longest false Current town of residence City, town or village where the participant currently lives. Current town of residence false Current type of industry Type of industry in which the participant currently works. Current type of industry false Employment status Indicator of whether the participant is currently in paid employment or is self-employed. Employment status false Current job title Participant's current job title. Current job title false Duration in the current job Duration in current occupation. Duration in the current job false Years Secondary education completed (ISCED 3-6) Indicator of whether the participant completed at least his secondary education/second stage of basic education. Secondary education completed (ISCED 3-6) false Some elements of post-secondary education completed (ISCED 4-6) Indicator of whether the participant completed at least some elements of post-secondary education. Some elements of post-secondary education completed (ISCED 4-6) false Standard level of education Education level according to the International Standard Classification of Education (ISCED). Standard level of education false Primary education completed (ISCED 1-6) Indicator of whether the participant completed at least his primary education/first stage of basic education. Primary education completed (ISCED 1-6) false Current treatment for high blood pressure Current treatment of the participant for high blood pressure. Current treatment for high blood pressure false Occurrence of high blood pressure Occurrence of high blood pressure at any point during the life of the participant. Occurrence of high blood pressure false High blood pressure onset Age of the participant at high blood pressure diagnosis. High blood pressure onset false years Number of children with stroke Total number of participant's biological children that have ever had stroke. Number of children with stroke false Children Occurrence of stroke among siblings Occurrence of stroke at any point during the life of the biological siblings (one or two common parents) of the participant. Occurrence of stroke among siblings false Number of siblings with stroke Total number of participant's biological siblings (one or two common parents) that have ever had stroke. Number of siblings with stroke false Siblings Occurrence of mother stroke Occurrence of stroke at any point during the life of the participant's biological mother. Occurrence of mother stroke false Occurrence of stroke among children Occurrence of stroke at any point during the life of the biological children of the participant. Occurrence of stroke among children false Occurrence of stroke in the family Presence of stroke amongst members of the biological family of the participant. Could be constructed using the occurrence among mother, father, siblings and children. Occurrence of stroke in the family false Occurrence of father stroke Occurrence of stroke at any point during the life of the participant's biological father. Occurrence of father stroke false Occurrence of stroke Occurrence of stroke at any point during the life of the participant. Occurrence of stroke false Stroke onset Age of the participant when the stroke occurred. Stroke onset false Years Type of interpretative information for standing height measurement Type of information relevant to the interpretation of standing height measurement. (For example: a physical condition such as: severe kyphosis, scoliosis or ankylosing spondytilis). Type of interpretative information for standing height measurement true Information relevant for standing height measurement interpretation Presence of any information relevant to the interpretation of standing height measurement. Information relevant for standing height measurement interpretation false Standing height The vertical measurement or distance from the foot to the head of the participant when he/she is standing. Standing height false Centimetres Type of contraindication for standing height measurement Reason for standing height measurement contraindication. (For example: unable to stand unassisted) Type of contraindication for standing height measurement true Information relevant for hip circumference measurement interpretation Presence of any information relevant for the interpretation of hip circumference measurement. Information relevant for hip circumference measurement interpretation false Type of interpretative information for hip circumference measurement Type of information relevant to the interpretation of hip circumference measurement (For example, number of weeks pregnant). Type of interpretative information for hip circumference measurement true Hip circumference Measured distance around hips. Hip circumference false Centimetres Type of contraindication for hip circumference measurement Reason for hip circumference measurement contraindication. (For example: pregnant for more than 12 weeks) Type of contraindication for hip circumference measurement true Contraindication for taking hip circumference measurement Presence of any contraindication for taking hip circumference measurement. Contraindication for taking hip circumference measurement false Type of contraindication for blood pressure measurement Reason for blood pressure measurement contraindication (For example: Arm limitation such as the presence of wounds, rashes, oedema, open sores, burns, tubes, scars, gauze / dressings, paralysis, a cast, amputation or where both arms are withered). Type of contraindication for blood pressure measurement true Systolic blood pressure at rest Systolic blood pressure measured at rest Systolic blood pressure at rest false mmHg Diastolic blood pressure at rest Diastolic blood pressure measured at rest Diastolic blood pressure at rest false mmHg Information relevant for blood pressure measurement interpretation Presence of any information relevant for the interpretation of blood pressure measurement. Information relevant for blood pressure measurement interpretation false Type of interpretative information for blood pressure measurement Type of information relevant for the interpretation of blood pressure measurement (For example: arm used to take blood pressure, consumption of alcohol, caffeine or tobacco in the previous 24 hours). Type of interpretative information for blood pressure measurement true Current prescribed medication intake status Indicator of whether the participant currently uses prescribed medication. Current prescribed medication intake status false Heart rate at rest Number of heart beats per minute measured at rest. Heart rate at rest false Beats/min Type of interpretative information for heart rate measurement Type of information relevant for the interpretation of heart rate measurement (For example: arm used to take blood pressure, consumption of alcohol, caffeine or tobacco in the previous 24 hours). Type of interpretative information for heart rate measurement true Contraindication for taking heart rate measurement Presence of any contraindication for taking heart rate measurement. Contraindication for taking heart rate measurement false Contraindication for taking blood pressure measurement Presence of any contraindication for taking blood pressure measurement. Contraindication for taking blood pressure measurement false Handedness Dominant use or manifestations of one side of the body versus the other. Handedness false Occurrence of myocardial infarction Occurrence of myocardial infarction at any point during the life of the participant. Occurrence of myocardial infarction false Occurrence of father myocardial infarction Occurrence of myocardial infarction at any point during the life of the participant's biological father. Occurrence of father myocardial infarction false Number of siblings with myocardial infarction Total number of participant's biological siblings (one or two common parents) that have ever had myocardial infarction. Number of siblings with myocardial infarction false Siblings Occurrence of mother myocardial infarction Occurrence of myocardial infarction at any point during the life of the participant's biological mother. Occurrence of mother myocardial infarction false Occurrence of myocardial infarction among children Occurrence of myocardial infarction at any point during the life of the biological children of the participant. Occurrence of myocardial infarction among children false Occurrence of myocardial infarction among siblings Occurrence of myocardial infarction at any point during the life of the biological siblings (one or two common parents) of the participant. Occurrence of myocardial infarction among siblings false Number of children with myocardial infarction Total number of participant's biological children that have ever had myocardial infarction. Number of children with myocardial infarction false Children Occurrence of myocardial infarction in the family Presence of myocardial infarction amongst members of the biological family of the participant. Could be constructed using the occurrence among mother, father, siblings and children. Occurrence of myocardial infarction in the family false Myocardial infarction onset Age of the participant at myocardial infarction. Myocardial infarction onset false Years Type of major disorders Name of major disorder diagnosed. Type of major disorders true Occurrence of any major disorders Occurrence, at any point during the life of the participant, of any major disorder that is (are) not included in the current list of disorders. Occurrence of any major disorders false Onset of major disorders Age of the participant at major disorder diagnosis. Onset of major disorders true Years Number of siblings with high blood pressure Total number of participant's biological siblings (one or two common parents) that have ever had high blood pressure. Number of siblings with high blood pressure false Siblings Occurrence of high blood pressure in the family Presence of high blood pressure amongst members of the biological family of the participant. Could be constructed using the occurrence among mother, father, siblings and children. Occurrence of high blood pressure in the family false Occurrence of father high blood pressure Occurrence of high blood pressure at any point during the life of the participant's biological father. Occurrence of father high blood pressure false Occurrence of high blood pressure among children Occurrence of high blood pressure at any point during the life of the biological children of the participant. Occurrence of high blood pressure among children false Occurrence of mother high blood pressure Occurrence of high blood pressure at any point during the life of the participant's biological mother. Occurrence of mother high blood pressure false Occurrence of high blood pressure among siblings Occurrence of high blood pressure at any point during the life of the biological siblings (one or two common parents) of the participant. Occurrence of high blood pressure among siblings false Number of children with high blood pressure Total number of participant's biological children that have ever had high blood pressure. Number of children with high blood pressure false Children Occurrence of mother depression Occurrence of depression at any point during the life of the participant's biological mother. Occurrence of mother depression false Occurrence of father depression Occurrence of depression at any point during the life of the participant's biological father. Occurrence of father depression false Number of children with depression Total number of participant's biological children that have ever had depression. Number of children with depression false Children Occurrence of cancer among siblings Occurrence of cancer at any point during the life of the biological siblings (one or two common parents) of the participant. Occurrence of cancer among siblings false Occurrence of mother cancer Occurrence of cancer at any point during the life of the participant's biological mother. Occurrence of mother cancer false Occurrence of depression in the family Presence of depression amongst members of the biological family of the participant. Could be constructed using the occurrence among mother, father, siblings and children. Occurrence of depression in the family false Occurrence of depression among siblings Occurrence of depression at any point during the life of the biological siblings (one or two common parents) of the participant. Occurrence of depression among siblings false Number of siblings with depression Total number of participant's biological siblings (one or two common parents) that have ever had depression. Number of siblings with depression false Siblings Occurrence of cancer among children Occurrence of cancer at any point during the life of the biological children of the participant. Occurrence of cancer among children false Number of live births fathered Number of living births (biological children only) a male participant has fathered. Number of live births fathered false Live births Gravidity Total number of times a female participant has been pregnant. Gravidity false Pregnancies Age at first pregnancy Age of female participant when she first became pregnant. Age at first pregnancy false Years Date of interview Date of the interview. Date of interview false YYYY/MM/DD Year of interview Year of the interview. Year of interview false YYYY Language of interview Language of the interview. Language of interview false Location of interview Location of the interview Location of interview false Unique identifier Identification number of the participant. Unique identifier false Current other tobacco smoker Indication of whether a participant currently smokes tobacco products other than cigarettes. Current other tobacco smoker false Ever smoked cigarettes Indication of whether a participant has ever smoked cigarettes. Ever smoked cigarettes false Ever smoked other tobacco Indication of whether a participant has ever smoked other tobacco products than cigarettes. Ever smoked other tobacco false Current cigarette smoker Indicator of whether a participant currently smokes cigarettes. Current cigarette smoker false Body Circumferences Currently under development Essential Under development In total, 6 of 9 studies (66%) in the Physical and Cognitive Measures Catalogue recorded information (at recruitment) about the domain of interest. (Based on Physical and Cognitive Measures Catalogue 's Keyword : Circumference) Measured distance around the body at an anatomically defined position. Domain finalized, but still evolving Risk factor true Body Circumferences Currently under development Currently under development The variables selected for this domain include: hip and waist circumference. There was consensus that these variables are important for a general purpose biobank. Family History of Depression Major depression is a family disorder and this mostly or entirely results from genetic influences. The probability of having depression increases if a blood relative already presents with this disorder (10). Under development In total, 1 of 13 studies (8%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Fam. hist.: Mental and behavioural disorders) A record of a medical history relating to the occurrence of depression and depression-related problems and events in a subject's blood relatives. Domain finalized, but still evolving Risk factor true Family History of Depression Major depression, a common (1), costly (2), and recurrent disorder (3), is associated with considerable morbidity (4) and excess mortality (5). Major depression is associated with psychological distress and suffering, and it can lead to impairments in physical, mental, and social functioning. It has been projected to become the second leading cause of disability worldwide by 2020 (second to ischemic heart disease) (6). The average age for onset of depression is between 20 and 40 years, but it may affect people in childhood (7). In Canada in 2006, the lifetime prevalence for major depression was found to be 12.2% in the population and major depression occurring in the past 12 months was found in 4.8% of the population (8). These findings are similar to those from the United States (9). Major depression is a familial disorder and this mostly or entirely results from genetic influences (10). The prevalence of major depression is high enough to provide adequate statistical power for epidemiological study and analysis in many contemporary biobanks. Subjects that have been diagnosed with, and are treated for, major depression can usually report their disease status accurately, and these data can be collected by questionnaire at a reasonable cost. There is therefore a strong case for collecting information about a family history of major depression on recruitment into a cohort biobank. But, it must be recognised that the thoroughness of the diagnostic process varies markedly between clinical settings and that assessment errors can occur in both directions (false positives and false negatives) and this can impair statistical power. Furthermore, affected individuals may not inform their close relatives of their diagnosis and so there is a substantial prospect of assessment error in determining family history of major depression. 1. Kessler, R.C. et al. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey. Arch. Gen. Psychiatry 51: 8-19 (1994). PMID: 8279933. 2. Greenberg, P.E. et al. The economic burden of depression in 1990. J. Clin. Psychiatry 54:405-418 (1993). PMID: 8270583. 3. Piccinelli, M., Wilkinson, G. Outcome of depression in psychiatric settings. Br. J. Psychiatry 164:297-304 (1994). PMID: 8199782. 4. Ustun, T.B. et al. (WHO/NIH Joint Project CAR Study Group). Multiple-informant ranking of the disabling effects of different health conditions in 14 countries. Lancet 354:111-115 (1999). PMID: 10408486. 5. Harris, E.C., Barraclough, B.B. Excess mortality of mental disorder. Br. J. Psychiatry 173:11-53 (1998). PMID: 9850203. 6. Murray, C.J.L., Lopez, A.D. Evidence-based health policy: lessons from the Global Burden of Disease Study. Science 274: 740-743 (1996). PMID: 8966556. 7. Gater, R. et al. Sex differences in the prevalence and detection of depressive and anxiety disorders in general health care settings. Arch. Gen. Psychiatry 55:405-413 (1998). PMID: 9596043. 8. Patten, S.B. et al. Descriptive epidemiology of major depression in Canada. Can. J. Psychiatry 51, 84-90 (2006). PMID: 16989107. 9. Olfson, M. et al. National trends in the outpatient treatment of depression. Journal of the American Medical Association 287, 203-209 (2002). PMID: 11779262. 10. Sullivan, P.F. et al. Genetic Epidemiology of Major Depression: Review and Meta-Analysis. Am. J. Psychiatry 157, 1552-1562 (2000). PMID: 11007705. The variables selected for this domain reflect the occurrence of major depression among the first degree relatives of a participant. There was broad consensus on the need to collect this information. This is important as it provides information relevant to the baseline risk of major depression in a participant. However, there were extensive discussions on how 'wide the net should be cast' in identifying family history. In the end, there was consensus that information should target only first degree relatives (parents, siblings and offspring) but that the relevance of the structure proposed should be evaluated during the pilot phase of the DataSHaPER. Women's Reproductive History Currently under development Extended Under development In total, 8 of 13 studies (62%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Pregnancies (outcomes/characteristics)) Information related to the participant's reproductive history, including those with a normal outcome and those associated with problems. Domain finalized, but still evolving true Women's Reproductive History Currently under development Currently under development The variables selected for this domain include: the number of births, the number of living births and the age of female participants when they first gave birth. There was broad consensus on the need to collect this information and agreement that it could be collected with acceptable validity. But, there was extensive discussion about the content and format of the information that ideally ought to be collected. Ultimately, it was decided to include the chosen variables. Family History of Cancer Individuals with a positive family history for many types of cancer have an increased risk of developing cancer themselves with typical recurrence risk ratios amongst 1st degree relatives of order two or greater (2, 3, 4). Increased risks for breast cancer (8) and ovarian cancer (9) have been found in women with affected first degree relatives. Similarly, higher risks have been noted amongst relatives for endometrial (10), colorectal (11), and prostate cancers (12). Significant associations have also been observed between a family history of hepatocellular carcinoma and primary liver cancer, between a family history of pancreatic cancer and pancreatic cancer, and between a family history of gallbladder cancer and gallbladder cancer (14). Interestingly, the risk of lung cancer has been found to be increased among subjects who had two or more first-degree relatives with any type of cancer (13). Optional Under development In total, 10 of 13 studies (77%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Fam. hist.: Neoplasms) A record of a medical history relating to the occurrence of cancer and cancer-related problems and events in a subject's blood relatives. Domain finalized, but still evolving Risk factor true Family History of Cancer Cancer is of great public health relevance because it is a leading cause of morbidity and mortality across the world. Of the 58 million deaths worldwide in 2005 from all causes, cancer accounted for 7.6 million (or 13%) (1). Individuals with a positive family history for certain types of cancer have an increased risk of developing cancer themselves (2, 3, 4). Familial cancer may be attributed to shared genes, common life-style, or shared environment among family members (3). The relatively high prevalence of many cancers means that there is adequate statistical power for epidemiological study and analysis of many of the commoner cancers on the basis of the typical sample sizes provided by many contemporary biobanks (5). Patient-reported family cancer histories for first-degree relatives are usually accurate and valuable (6, 7), but it is desirable to collect these data before cancer develops in the subject him/herself as recall bias can otherwise arise. 1. World Health Organization. Cancer (2007) http://www.who.int/mediacentre/factsheets/fs297/en/index.html 2. Lynch, H.T. et al. Familial risk and cancer control. JAMA 236, 582-584 (1976). PMID: 947240. 3. Burton P.R., Tobin MD, Hopper JL. Key concepts in genetic epidemiology. Lancet 366(9489):941-951 (2005). PMID: 16154023. 4. Weber, W., Gencik, A. & Muller, H. Familial cancer: consequences for the oncological practice. Cancer Detect. 9, 455-458 (1986). PMID: 3779708. 5. Burton P.R., Hansell A. UK Biobank: the expected distribution of incident and prevalent cases of chronic disease and the statistical power of nested casecontrol studies. Manchester, UK: UK Biobank Technical Reports (2005). 6. Murff. H.J., Spigel, D.R., Syngal, S. Does This Patient Have a Family History of Cancer? An Evidence-Based Analysis of the Accuracy of Family Cancer History. JAMA 292, 1480-1489 (2004). PMID: 15383520. 7. Ziogas, A. & Anton-Culver, H. Validation of family history data in cancer family registries. Am. J. Prev. Med. 24:190-198 (2003). PMID: 12568826. 8. Pharoah, P.D. et al. Family history and the risk of breast cancer: a systematic review and meta-analysis. Int. J. Cancer. 71, 800-809 (1997). PMID: 9180149. 9. Stratton, J.F. et al. A systematic review and meta-analysis of family history and risk of ovarian cancer. Br. J. Obstet. Gynaecol. 105, 493-499 (1998). PMID: 9637117. 10. Parslov, M. et al. Risk factors among young women with endometrial cancer: a Danish case-control study. Am. J. Obstet. Gynecol. 182, 23-29 (2000). PMID: 10649152. 11. Fuchs, C.S. et al. A prospective study of family history and the risk of colorectal cancer. N. Engl. J. Med. 331, 1669-1674 (1994). PMID: 7969357. 12. Kalish, L.A., McDougal, W.S. & McKinlay, J.B. Family history and the risk of prostate cancer. Urology 56, 803-806 (2000). PMID: 11068306. 13. Zhang, Y. et al. Family history of cancer and risk of lung cancer among non smoking Chinese women. Cancer Epidemiol. Biomarkers Prev. 16, 2432-2435 (2007). PMID: 18006933. 14. Fernandez, E. et al. Family history and the risk of liver, gallbladder, and pancreatic cancer. Cancer Epidemiol. Biomarkers Prev. 3, 209-212 (1994). PMID: 8019368. The variables selected for this domain reflect the occurrence of cancer among the first degree relatives of a participant. There was broad consensus on the need to collect this information. This is important as it provides information relevant to the baseline risk of cancer in a participant. However, there were extensive discussions on how 'wide the net should be cast' in identifying family history. In the end, there was consensus that information should target only first degree relatives (parents, siblings and offspring) and that information collected from more distant relatives was less reliable and potentially biasing in the current context. Individual History of Myocardial Infarction MI aggregates in families and risk factors seem to include both genetic and environmental factors (3-7). Risk factors include older age, tobacco use, high blood pressure, high blood cholesterol or triglyceride levels, excessive alcohol consumption, the abuse of certain drugs, lack of physical activity, obesity, diabetes, chronically high levels of stress in certain persons, and family history of heart attack (3-7). Previous history of vascular disease such as atherosclerotic coronary heart disease and/or angina, a previous heart attack or stroke, any previous episodes of abnormal heart rhythms or syncope also increase the risk of recurrent infarctions (5). There are now a number of genetic variants that have been identified and reliably replicated as being associated with coronary artery disease (8-9-10-11). Essential Under development In total, 8 of 13 studies (62%) in the Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Diseases of the circulatory system) A record of a subject's personal medical history relating to the occurrence of myocardial infarction (MI) and MI-related problems and events. Domain finalized, but still evolving. Outcome true Individual History of Myocardial Infarction Myocardial infarction (MI - widely called a 'heart attack' by the general public), is a form of ischemic heart disease. It is the leading cause of death in the United States as well as in most industrialized nations throughout the world. It is the third ranked cause of death in developing countries (1, 2). In 2005, 11.8 million people worldwide died of heart attacks and other heart diseases. It is projected that by 2015, an estimated 20 million people will die from cardiovascular disease annually, mainly from heart attack and stroke (1). Although the death rate from coronary diseases has decreased in most developed countries, the prevalence of ischaemic heart disease is still increasing across the world (2). MI aggregates in families and the risk factors seem to include both genetic and environmental determinants (3, 4, 5). After an infarction, many people have permanent heart damage and survivors are at an increased risk of recurrent infarctions, heart failure, abnormalities of cardiac rhythm and sudden death (5). The prevalence of MI is high enough to provide adequate statistical power for epidemiological study and analysis in many contemporary biobanks (6). Subjects that have been diagnosed with MI can usually report their disease status accurately, and these data can be collected by questionnaire. There is therefore a strong case for collecting information about doctor diagnosed MI on recruitment into a cohort biobank. In particular, it enables informative cross-sectional analyses at recruitment (providing for early 'scientific wins') and also permits the subsequent discrimination of pre-existing (prevalent) cases of diabetes from new (incident) diagnoses. But, it must be recognised that the thoroughness of the diagnostic process varies markedly between clinical settings and assessment errors can occur in both directions (false positives and false negatives) and this can impair statistical power. 1. World Health Organization. New WHO pocket-charts will save lives by predicting heart attack and stroke. (2007) http://www.who.int/mediacentre/news/releases/2007/pr47/en/. 2. Boersma, E. et al. Acute myocardial infarction. The Lancet 361, 847-858 (2003). PMID: 12642064 3. Heller, D.A. et al. Genetic and environmental influences on serum lipid levels in twins. N. Engl. J. Med. 328: 1150-1156 (1993). PMID: 8455681. 4. Hong, Y. et al. Genetic and environmental influences on blood pressure in elderly twins. Hypertension 24: 663-670 (1994). PMID: 7995622. 5. Andresdottir, M.B. et al. Fifteen percent of myocardial infarctions and coronary revascularizations explained by family history unrelated to conventional risk factors. The Reykjavik Cohort Study. European Heart Journal 23, 1655-1663 (2002). PMID: 12398822 6. Burton PR, Hansell A. UK Biobank: the expected distribution of incident and prevalent cases of chronic disease and the statistical power of nested casecontrol studies. Manchester, UK: UK Biobank Technical Reports (2005). 7. Ciruzzi, M. et al. Frequency of family history of acute myocardial infarction in patients with acute myocardial infarction. Argentine FRICAS (Factores de Riesgo Coronario en America del Sur) Investigators. Am. J. Cardiol. 80: 122-127 (1997). PMID: 9230145. 8. Wellcome_Trust_Case_Control_Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661-678 (2007). PMID: 17554300. 9. Samani, N.J. et al. Analysis of two genome-wide association studies identifies and validates novel gene loci for myocardial infarction. New England Journal of Medicine 2007: in press. 9. Samani, N.J. et al., Genomewide association analysis of coronary artery disease. N. Engl. J. Med. 357: 443-453 (2007). PMID: 17634449. 10. Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A, et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 316:1491-1493 (2007). PMID: 17478679. 11. McPherson, R. et al. A common allele on chromosome 9 associated with coronary heart disease. Science 316:1488-1491 (2007). PMID: 17478681. The variables selected for this domain include: the occurrence of myocardial infarction (MI); age at onset. There was broad consensus on the need to collect this information and general agreement that it could be collected with acceptable validity. Information collected at recruitment on the occurrence of MI was considered essential by the panel of experts because it will be important to discriminate prevalent and incident cases of MI. Medication Intake Currently under development Optional Under development In total, 9 of 13 studies (69%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Medication intake) Information regarding medications currently taken. This includes any drugs or medicines that treat, prevent or alleviate symptoms of disease. Domain finalized, but still evolving Risk factor true Medication Intake This section should be ideally completed by a nurse or a trained interviewer with direct access to the medications. Currently under development Currently under development The variables selected for this domain represent a record of the medications currently taken by each participant. There was broad consensus on the need to collect this information. It was also agreed that the list of medication should ideally be completed by a nurse or a trained interviewer and that, where possible, each participant should be asked to bring all of his/her medications to the assessment clinic. It was agreed that is was essential to allow for a potentially large number of medications to be collected. There was some debate on whether the DataSHaPER should collect information solely about prescription medication or all medication. The correct decision to make here will depend both on the disease focus, if any, of the biobank concerned, and on the balance between prescription sales and over-the-counter sales in the particular nation in which a biobank is to be based. Information on medication at recruitment was considered important because: (1) the receipt of certain medications can sometimes be used as a proxy for prevalent disease status; (2) certain medications modify the risk of developing key conditions; (3) certain medications directly alter measured phenotypes and must therefore be taken into appropriate account in analysing those phenotypes. Height Height is positively associated with better-than-average health and longevity, and several studies have shown adult height to be negatively associated with morbidity and mortality, particularly from respiratory and cardiovascular disease (10,11). There is also seems to be a weak, though variable association, between height and risk of cancer (10-12); Davey Smith et al, found mortality from smoking related cancers to have no association with height, non-smoking related cancers, particularly haematopoetic malignancies, increased with height, while stomach cancer was negatively related (10). Further research is required to determine the underlying mechanism of why this might be so. Research in this area has concentrated on investigating whether early childhood nutrition affects a hormonal change within the individual resulting in a change in both their height and their susceptibility to the development of cancer ((12) discussion). All cause mortality falls as height increases (10,11,13). Essential Under development In total, 9 of 13 studies (69%) in the Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Actual height) In total, 9 of 9 studies (100%) in the Physical and Cognitive Measures Catalogue recorded information (at recruitment) about the domain of interest. (Based on Physical and Cognitive Measures Catalogue 's Keyword: Standing height) Height of the subject in a defined position or stance. Domain finalized, but still evolving Risk factor true Height Height is a widely accepted biomarker for health status (1). Final adult height is known to be under the influence of both genetic and environmental factors (2-4). Studies have revealed that the environmental factors affecting height include the effects on the individual in utero from the age of mother, and whether the mother smoked during the pregnancy. Smoking is known to reduce foetal birth weight and length (5), two factors which act as a predictor for final adult height. Postnatal conditions are also known to affect height; these include nutritional quality/availability, family size, socioeconomic status, parental education and housing conditions (6, 7) and childhood psychosocial stress (8). When comparing children with no siblings, to those with four siblings one study showed those with four siblings had a reduced birth length and a reduced rate of growth per year during childhood, resulting in an overall reduced height by age 10 (9). Height is of therefore of scientific interest both as an outcome in its own right and as an explanatory variable or confounder on the causal pathways leading to a host of health states. It is also fundamental to the calculation of BMI (weight/height2) which is an important independent measure of body size. In addition, height is an important qualifying variable, for example, leading to an altered interpretation of a number of spirometry measures (which may be standardised for height) and of weight. Height has been linked to various diseases as discussed under associations. Height can easily and accurately be measured in a clinical setting and can also be collected via self report in a questionnaire (2); however, self report is subject to a moderate increase in random measurement error and systematic upward bias, particularly in some population subgroups (2). It can be recorded for all people and therefore provides adequate statistical power for epidemiological study and analysis. 1. Deaton A. Height, health, and development. Proc Natl Acad Sci U S A 2007; 104: 13232-7. 2. Macgregor, S. et al. Bias, precision and heritability of self-reported and clinically measured height in Australian twins. Hum. Genet. 120, 571-580 (2006). PMID: 16933140. 3. Silventoinen, K. et al. Heritability of adult body height: A comparative study of twin cohorts in eight countries. Twin Res. 6, 399-408 (2003). PMID: 14624724. 4. Weedon, M.N. et al. A common variant of HMGA2 is associated with adult and childhood height in the general population. Nat Genet. 39, 1245-1250 (2007). PMID: 17767157. 5. Li L, Manor O, Power C. Early environment and child-to-adult growth trajectories in the 1958 British birth cohort. Am J Clin Nutr 2004; 80: 185-92. 6. Martin RM, Smith GD, Mangtani P et al. Association between breast feeding and growth: the Boyd-Orr cohort study. Arch Dis Child Fetal Neonatal Ed 2002; 87: F193-201. 7. Webb E, Kuh D, Peasey A et al. Childhood socioeconomic circumstances and adult height and leg length in central and eastern Europe. J Epidemiol Community Health 2008; 62: 351-7. 8. Pine DS, Cohen P, Brook J. Emotional problems during youth as predictors of stature during early adulthood: results from a prospective epidemiologic study. Pediatrics 1996; 97: 856-63. 9. Lawson DW, Mace R. Sibling configuration and childhood growth in contemporary British families. Int J Epidemiol 2008; 37: 1408-21. 10. Davey Smith G, Hart C, Upton M et al. Height and risk of death among men and women: aetiological implications of associations with cardiorespiratory disease and cancer mortality. J Epidemiol Community Health 2000; 54: 97-103. 11. Leon DA, Smith GD, Shipley M et al. Adult height and mortality in London: early life, socioeconomic confounding, or shrinkage? J Epidemiol Community Health 1995; 49: 5-9. 12. Gunnell D, Okasha M, Smith GD et al. Height, leg length, and cancer risk: a systematic review. Epidemiol Rev 2001; 23: 313-42. 13. Waaler HT. Height, weight and mortality. The Norwegian experience. Acta Med Scand Suppl 1984; 679: 1-56. There was broad consensus that general purpose biobanks should, where possible, include a measurement of standing height. Individual History of Stroke Risk factors of stroke include tobacco use, hypertension, obesity, elevated cholesterol and lipids, physical inactivity, excessive alcohol intake, illegal drug use, male sex, increasing age, as well as heredity and ethnicity (1, 2). Ten or more years will often elapse between exposure to risk factors and stroke. Essential Under development In total, 10 of 13 studies (77%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Diseases of the circulatory system) A record of a subject's personal medical history relating to the occurrence of stroke and stroke-related problems and events. Domain finalized, but still evolving Outcome true Individual History of Stroke Stroke is common and is the cause of extensive mortality and morbidity. It remains the third leading cause of death, behind heart disease and cancer, and is the leading cause of serious, long-term disability in the United States (1). Annually, 15 million people worldwide suffer from stroke. Of these, 5 million die and another 5 million are left permanently disabled. Stroke is a medical emergency as active treatment is most effective if a stroke is recognized early (1). The characteristics of the disease impose a heavy burden both on families and on the community (2). Economic burden due to stroke is estimated to be between US $6.5 and 11.2 billion each year in the USA (3). Some genes and life-style exposures are important determinants of stroke (1, 2). The prevalence of stroke is high enough to provide adequate statistical power for useful epidemiological study and analysis in many contemporary biobanks (4). Subjects that have been diagnosed with stroke can usually report their disease status accurately, and these data can be collected by questionnaire. There is therefore a strong case for collecting information about doctor diagnosed stroke on recruitment into a cohort biobank. In particular, it enables informative cross-sectional analyses at recruitment (providing for early 'scientific wins') and also permits the subsequent discrimination of pre-existing (prevalent) cases of stroke from new (incident) diagnoses. But, it must be recognised that the thoroughness of the diagnostic process varies markedly between clinical settings and assessment errors can occur in both directions (false positives and false negatives) and this can impair statistical power. 1. World Health Organization. The Atlas of Heart Disease and Stroke (2008). http://www.who.int/cardiovascular_diseases/resources/atlas/en/ (2008) 2. Feigin, V. Stroke epidemiology in the developing world. The Lancet 365, 2160-2161 (2005). PMID: 15978910 3. Kaste, M., Fogelholm, R. & Rissanen, A. Economic burden of stroke and the evaluation of new therapies. Public Health 112, 103-112 (1998). PMID: 95814524. 4. Burton, P.R. & Hansell, A. UK Biobank: the expected distribution of incident and prevalent cases of chronic disease and the statistical power of nested casecontrol studies. Manchester, UK: UK Biobank Technical Reports, 2005. The variables selected for this domain include: the occurrence of stroke; and age at onset. There was broad consensus on the need to collect this information and general agreement that it could be collected with acceptable validity. Information collected at recruitment on the occurrence of stroke was considered essential by the panel of experts because it will be important to discriminate prevalent and incident cases of stroke. Family History of Diabetes Family history of specific diseases reflects the consequences of genetic susceptibilities, shared environment, and common behaviours. Epidemiologic studies have shown that individuals with 1 or more first-degree relatives affected with diabetes are 2 to 6 times more likely to have the disease compared with people who have no affected relatives (7). The contribution of family history to this excess risk seems to be independent of that conferred by other common risk factors (8). Moreover, a family history of diabetes appears to increase the risk of coronary heart disease (CHD) even in non-diabetic subjects (9). Optional Under development In total, 7 of 13 studies (54%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Fam. hist.: Endocrine, nutritional and metabolic diseases) A record of a medical history relating to the occurrence of diabetes mellitus and diabetes-related problems and events in a subject's blood relatives. The World Health Organization recognizes three main forms of diabetes mellitus: type 1, type 2, and gestational diabetes (occurring during pregnancy). Domain finalized, but still evolving Risk factor true Family History of Diabetes Diabetes (strictly, diabetes mellitus) is a significant cause of social, psychological and financial burden in populations worldwide (1, 2). The worldwide prevalence of diabetes across all age-groups was estimated to be 2.8% in 2000 and is projected to increase to 4.4% in 2030 (3). In 2002, the prevalence of diabetes within the adult population of the United States was estimated to be 8.7% (4). Ninety percent of all cases are type 2 diabetes (5). Although less common, type 1 diabetes is the third most prevalent chronic disease of childhood, affecting up to 0.4% of children in some populations by age 30 years, with an overall lifetime risk of nearly 1% (6). Most people with diabetes in low and middle income countries are middle-aged (45-64 years old) (7). All forms of diabetes can have serious negative health effects. Undiagnosed, or poorly controlled, diabetes can lead to long-term complications which include abnormalities in the cardiovascular (heart, central and peripheral vasculature), ocular, neurological and renal systems. Diabetes leads to a decrease in life expectancy (1, 2). In fact, it causes about 5% of all deaths globally each year, and the number of these deaths is projected to increase by more than 50% over the next 10 years if remedial intervention is not instituted. The risk factors for the different types of diabetes vary. Type 1 diabetes is a complex, autoimmune disease involving interactions between genes and environment/life-style (8) with an important etiological contribution arising from variants in the HLA system (9). Type 2 diabetes is strongly associated with environmental factors (5) such as lifestyle and diet as well as with genetic factors (10). The prevalence of diabetes is high enough to provide adequate statistical power for epidemiological study and analysis in many contemporary biobanks (11). Subjects that have been diagnosed with diabetes can usually report their disease status accurately, and these data can be collected by questionnaire. In particular, patient-reported family diabetes histories are usually accurate and valuable (6). There is therefore a strong case for collecting information about a family history of doctor diagnosed diabetes at recruitment into a cohort biobank. But, it must be recognised that type 2 diabetes can remain undiagnosed for a prolonged period of time and in consequence, the sensitivity of the diagnosis can be low and, although sensitivity is less critical than specificity, it can nevertheless impair statistical power. 1. Wild, S. et al. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27, 1047-1053 (2004). PMID: 15111519. 2. Boyle, J.P. et al. Projection of diabetes burden through 2050: impact of changing demography and disease prevalence in the U.S. Diabetes Care 24, 1936-1940 (2001). PMID: 11679460. 3. World Health Organization. Genetics and Diabetes http://www.who.int/genomics/about/Diabetis-fin.pdf 4. Wiley, J. & Sons, Ltd. Current literature in diabetes. Diabetes Metab. Res. Rev. 23, i-ix. (2007). PMID: 17960862. 5. Watanabe, R.M. et al. Genetics of gestational diabetes mellitus and type 2 diabetes. Diabetes Care 30 Suppl 2, S134-140 (2007). PMID: 17596461. 6. Murabito, J.M. et al. Accuracy of offspring reports of parental cardiovascular disease history: the framingham offspring study. Ann. Intern. Med. 16, 434-440 (2004). PMID: 15023709. 7. Harrison, T.A. et al. Family history of diabetes as a potential public health tool. American Journal of Preventive Medicine 24, 152-159 (2003). PMID: 12568821. 8. Dallo, F.J. & Weller, S.C. Effectiveness of diabetes mellitus screening recommendations [published correction appears in Proc Natl Acad Sci U S A. 2003;100:13116]. Proc. Natl. Acad. Sci. USA. 100, 10574-10579 (2003). PMID: 12925739. 9. Wellcome_Trust_Case_Control_Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661-678 (2007). PMID: 17554300. 10. Pierce, M., Keen, H. & Bradley, C. Risk of diabetic in offspring of parents with non-insulin-dependent diabetes. Diabet. Med. 12, 6-13 (1995). PMID: 7712706. 11. Burton PR, Hansell A. UK Biobank: the expected distribution of incident and prevalent cases of chronic disease and the statistical power of nested casecontrol studies. Manchester, UK: UK Biobank Technical Reports (2005). The variables selected for this domain reflect the occurrence of diabetes among the first degree relatives of a participant. There was broad consensus on the need to collect this information. This is important as it provides information relevant to the baseline risk of diabetes in a participant. However, there were extensive discussions on how 'wide the net should be cast' in identifying family history. In the end, there was consensus that information should target only first degree relatives (parents, siblings and offspring) and that information collected from more distant relatives was less reliable and potentially biasing in the current context. Individual History of High Blood Pressure Currently under development Essential Under development In total, 8 of 13 studies (62%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Diseases of the circulatory system) A record of a subject's personal medical history relating to the occurrence of high blood pressure and high blood pressure-related problems and events. Domain finalized, but still evolving Outcome true Individual History of High Blood Pressure Currently under development Currently under development The variables selected for this domain include: the occurrence of high blood pressure; age at onset; and current treatment for high blood pressure. There was broad consensus on the need to collect this information. Information collected at recruitment on the occurrence of high blood pressure was considered essential by the panel of experts because it will be important to discriminate prevalent and incident cases of high blood pressure. It was also noted that pre-existing high blood pressure alters a subject's later risk of developing a number of key conditions including ischaemic heart disease, stroke and renal disease. The decision to collect information about high blood pressure was made despite the fact that it was recognised that there is no agreed and universal definition of 'high blood pressure' and that it may be difficult for a participant to report whether they truly had high blood pressure. Education Level Currently under development Essential Under development In total, 9 of 13 questionnaires (69%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Subject education level) An Indication of the education received by the participant.. Domain finalized, but still evolving Risk factor true Education Level Currently under development Currently under development The variables selected for this domain include: the highest level of education attained; the number of years of education received; and the standard level of education (ISCED classification: http://www.uis.unesco.org/ev.php?ID=3813_201&ID2=DO_TOPIC). There was broad consensus on the need to collect this. Education level is often used as an indicator of the socio-economic status, but it was recognised that important challenges must be faced in harmonizing the information collected across different educational systems. Working Status Working status is associated with a broad spectrum of mortality and morbidity measures. In both men and women, for example, there are clear differences between different employment groups in the incidence of angina and chronic bronchitis as well as notable differences in health-related behaviours such as smoking and dietary choices (5). There are also differences in all cause mortality risks. The highest death rates are seen amongst those in the unskilled manual groups (3, 5, 6). Unemployment is associated with increases in smoking behaviour and alcohol consumption as well as increased psychological pressures, which in turn increase the risk of both morbidity and mortality for diseases related to these exposures (1). Optional Under development In total, 11 of 13 studies (85%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Working status/history) The principal activity that a participant does to earn money or income. Domain finalized, but still evolving Risk factor true Working Status Working status can be used as a measure of socioeconomic status, conveying information on household income, lifestyle, and social status of the participant, which can all have an impact on health measures and health related behaviours (1, 2). Alongside other indicators of socioeconomic status, occupation or job specific exposures are important determinants of health (3). As with other measures of socioeconomic status, working status can therefore act as a primary explanatory variable of interest (e.g. differential exposure to environmental risks) or as a confounder on the causal pathways leading to a host of health states. Information regarding working status can reliably be obtained by questionnaire and participants are able to report their status accurately. It should be noted that reverse causality may complicate etiological inferences: that is, poor health may lead to a decline in occupational status, especially in an aging population (4). 1. Wilson, S.H. and G.M. Walker, Unemployment and health: a review. Public Health, 1993. 107(3): p. 153-62. 2. Macintyre, S., et al., Socio-economic position and health: what you observe depends on how you measure it. J Public Health Med, 2003. 25(4): p. 288-94. 3. Lax, M.B., et al., Recognizing occupational disease--taking an effective occupational history. Am Fam Physician, 1998. 58(4): p. 935-44. 4. Daly, M.C., et al., Optimal indicators of socioeconomic status for health research. Am J Public Health, 2002. 92(7): p. 1151-7. 5. Marmot, M.G., et al., Health inequalities among British civil servants: the Whitehall II study. Lancet, 1991. 337(8754): p. 1387-93. 6. Power, C., E. Hypponen, and G.D. Smith, Socioeconomic position in childhood and early adult life and risk of mortality: a prospective study of the mothers of the 1958 British birth cohort. Am J Public Health, 2005. 95(8): p. 1396-402. The variables selected for this domain include: current working status, employment status, retirement status, current title of work (using ISCO-88 (International Standard Classification of Occupations)), current type of industry (using the ISIC Rev.3.1 (International Standard Industrial Classification of All Economic Activities Revision 3.1)), duration in the current job, title of job for occupation of longest duration (using ISCO-88), type of industry for occupation of longest duration (using the ISIC Rev.3.1), and duration of occupation held longest. There was broad consensus: (1) on including this particular set of information; (2) that the variables generated would be of acceptable validity; (3) that the proposed classification indexes were appropriate. However, difficulties could arise in the collection of these information by the studies. Residence A variety of studies have also shown that rates of diabetes, myocardial infarction, hypertension, and stroke can differ depending on a persons' geographical location when comparing rates of disease in North America, the Caribbean, Europe, and Asia (5). Other work has shown an increase of cancer rates in the developed world, and this is thought to be partly due to longer life expectancy and partly to the development of unhealthy lifestyle behaviours (6). It is clear that where a person resides and therefore knowledge of what environmental factors they're exposed to can play a major role in uncovering causes of disease, making this domain of particular importance. Essential Under development In total, 4 of 13 studies (31%) in the Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword: Citizenship/residency status) The location (address, region or nation) in which a subject dwells more than transiently. Domain finalized, but still evolving Risk factor true Residence This domain concerns the place of residence in terms of the region (province, state, territory, etc) where the participant lived longest and where they live at the time of assessment. Place of residence is known to indicate a person's overall risk of exposure to disease, and their subsequent access to treatment and other health care facilities (including screening and immunisation programs) (1, 2). The place where the participant has lived for the longest is likely to have had the greatest influence on their health status over their life-span as a whole, and their current residence on contemporary measures and assessments of factors that fluctuate markedly over time. Residence is known to play a major role in terms of life expectancy. People residing within a developed country have a greater overall life expectancy when compared to people living in a developing country (1). This difference in overall life expectancy is known to be due to a complex combination of exposure to disease, disease risk factors, and access to health care (3). In terms of likelihood of disease, specific diseases can be more prevalent in certain geographical locations, and are not always negatively correlated to wealth, as discussed in associations. In addition, information on a participant's place of residence provides potential indirect indicators of their socioeconomic status, not only on a global scale but also when considering the type of neighbourhood in which a participant resides. The manner in which neighbourhoods of different economic status might affect a person's potential risk of disease and their actual disease state is of great importance when considering the distribution of health care resources within a country. Many studies have been undertaken to outline the discrepancies between different residential areas under the same health care provider (4). Many developed nations have sophisticated geographical information systems allowing linkage of a postcode (or other marker) of residence to a wide variety of measures of social class, and environmental exposures. Regarding the collection of information, current residence and residence of longest duration can both be obtained accurately and with ease using a questionnaire format for all participants. This provides adequate statistical power for analysis within the study. Residence can therefore act as a primary explanatory variable of interest (e.g. differential exposure to environmental risks) or as a confounder on the causal pathways leading to a host of health states. 1. World Health Organization. (1999). The world health report 1999: Making a difference, Geneva, Accessed: March 25, 2009, http://www.who.int/whr/1999/en/. 2. World Health Organization. (Last update: 04 September 2006). Regional Health Forum. Regional Health for All: The Challenge of the New Millennium, Geneva, Accessed: March 25, 2009, http://www.searo.who.int/en/Section1243/Section1310/Section1343/Section1344/Section1352_5253.htm. 3. Seale C. Changing patterns of death and dying. Soc Sci Med 2000; 51: 917-30. 4. Auchincloss AH, Diez Roux AV. A new tool for epidemiology: the usefulness of dynamic-agent models in understanding place effects on health. Am J Epidemiol 2008; 168: 1-8. 5. Zineh I, Cooper-Dehoff RM, Wessel TR et al. Global differences in blood pressure control and clinical outcomes in the INternational VErapamil SR-Trandolapril STudy (INVEST). Clin Cardiol 2005; 28: 321-8. 6. Jones LA, Chilton JA, Hajek RA et al. Between and within: international perspectives on cancer and health disparities. J Clin Oncol 2006; 24: 2204-8. The variables selected for this domain include: country/nation of current residence; town/city and region of current residence; postal code (or other GIS measure) of current residence in nations where such information exists. There was broad consensus on the need to collect this information which provides - potential indirect - indicators of socioeconomic status and environmental exposure. Given the time commitment implicit to obtaining a comprehensive residential history from each participant, it has been decided in the context of the current DataSchema to limit the information to current residence and birth location. Gender Prevalence and incidence of many diseases, even those not related to reproductive systems are often different between men and women, for example, the incidence of stroke is higher in men and men are at increased risk of death from stroke (2), and there are distinct differences across the range of mental health disorders both in terms of incidence and prevalence and in terms of health-seeking behaviour (3). These differences arise not only from a range of genetic and physiological differences, but also from environmental and social factors. In summary, there are key differences between the sexes in the prevalence of exposure to disease risk factors, response to those factors, the development of specific diseases and their sequalae, and also in health related behaviours and response to health modifying agents (1). Essential Under development This domain must be covered by any population study. Term used to distinguish men and women on the basis of their biological characteristics. Domain finalized, but still evolving Risk factor true Gender Biological sex and socially-constructed gender affect both normal and pathological physiological functions, exposure to health risks and access to public health services. Gender should be considered as an essential item of information for any Biobank as these differences can often be a source of bias and confounding that must be addressed (1). Information regarding gender is easily and accurately measurable by questionnaire. Information regarding sex/gender is of central scientific relevance. A growing body of evidence suggests the role of sex in the biological basis of disease is much broader than previously thought (1). Many analyses must therefore be stratified by sex (sex acting as a qualifying variable), and interactions with sex are often of specific interest. Sex/gender can also act as a primary explanatory variable of interest in its own right or as a confounder on the causal pathways leading to a host of health states. Finally, knowledge of sex/gender can be important in determining the SOPs that should be adopted in physical measurement and sample collection. 1. Institute of Medicine (U.S.). Committee on Understanding the Biology of Sex and Gender Differences., Wizemann TM, Pardue ML, Exploring the biological contributions to human health does sex matter? 2001, National Academy Press: Washington, D.C. p. 267 p. 2. Turtzo LC, McCullough LD. Sex differences in stroke. Cerebrovasc Dis 2008; 26: 462-74. 3. Afifi M. Gender differences in mental health. Singapore Med J 2007; 48: 385-91. Gender was universally considered as an essential item of information for any biobank. It was however recognised that in social theory there is a subtle difference between biological sex and gender (an individual's self-conception as being either male or female). Parental Birth Location Currently under development Essential Under development In total, 4 of 13 studies (31%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about this domain. (Based on Questionnaire Catalogue's Keyword: Subject's family's birth location). The locations (usually towns, regions or nations) at which a subject's parents and grandparents were born. Domain finalized, but still evolving Risk factor true Parental Birth Location Currently under development Currently under development The variables selected for this domain include: the country/nation of birth of the participant's parents and grand-parents. There was broad consensus on the need to collect this information which is widely recommended as providing an indirect indicator of the ethnicity of participants. It can also be used to contribute to the control of analytic biases arising from population substructure in ancestry. Sibling It has been hypothesized (4) that the presence of older siblings and/or being a member of a multiple birth (5) protects against the development of asthma under a mechanism involving an increased incidence of early respiratory infections and consequent 'T-cell switching' (6). This is sometimes known as the hygiene hypothesis, but more recent work (including a review of 53 individual studies) leaves this hypothesis open to debate (7). In general, older siblings are thought to facilitate the transmission of infectious agents either by direct contact with the child or via contact with their mother (4) and this has obvious implications for any study involving an infectious agent. In addition, lower socio-economic conditions in childhood may have a modest effect on the risk of coronary heart disease in later life (8). Twins (9) and higher-order multiple births (10) show an overall reduced risk of childhood cancer, and debate around why this might be continues in an effort to further understand and utilize the mechanisms involved (9, 11). Finally, number of siblings has been found to be related (via deprivation in childhood) to an adult risk of stomach cancer and haemorrhagic stroke. In the case of stomach cancer it is thought to occur due to childhood infection, which as discussed earlier is more prevalent in families with a greater number of siblings (1). Under development In total, 4 of 13 studies (31%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Familial and social environment) Indicator of a participant sibship status. Domain finalized, but still evolving Risk factor true Sibling Knowledge of family structure can be of direct relevance to any study involving an investigation of genetic determinants of disease. In addition, it has been demonstrated that there is a strong correlation between number of siblings and the socioeconomic and health status of an individual (1). An increase in sibling number causes a dilution in the parental and environmental resources available to each child (2), which in turn results in a lowering of socioeconomic status (3). Later-born children are shown to be most affected by resource division and interestingly all these effects have been shown to occur even in relatively wealthy Western well-nourished families (2). This domain is of interest as it provides a reflection of an individual's history of childhood socioeconomic status, which may intrinsically be linked to later health. This measure of socioeconomic status can therefore act as an explanatory variable of interest (e.g. differential exposure to environmental risk) or as a confounder on the causal pathways leading to a host of health states. Sibling status has also been linked to various diseases of interest as discussed in associations. Information collected in this domain may also help in the interpretation of disease when investigating the prevalence of certain disorders within a family. As sibling status can be collected accurately via questionnaire and potentially be recorded for all persons, information obtained is generally of adequate quality and statistical power for epidemiological study and analysis. 1. Hart CL, Davey Smith G. Relation between number of siblings and adult mortality and stroke risk: 25 year follow up of men in the Collaborative study. J Epidemiol Community Health 2003; 57: 385-91. 2. Lawson DW, Mace R. Sibling configuration and childhood growth in contemporary British families. Int J Epidemiol 2008; 37: 1408-21. 3. Jongbloet PH. Siblings and adult mortality and stroke risk. J Epidemiol Community Health 2004; 58: 803. 4. Strachan DP. Hay fever, hygiene, and household size. BMJ 1989; 299: 1259-60. 5. McKeever TM, Lewis SA, Smith C et al. Siblings, multiple births, and the incidence of allergic disease: a birth cohort study using the West Midlands general practice research database. Thorax 2001; 56: 758-62. 6. Poulsen LK, Hummelshoj L. Triggers of IgE class switching and allergy development. Ann Med 2007; 39: 440-56. 7. Karmaus W, Botezan C. Does a higher number of siblings protect against the development of allergy and asthma? A review. J Epidemiol Community Health 2002; 56: 209-17. 8. Ramsay SE, Whincup PH, Morris RW et al. Are childhood socio-economic circumstances related to coronary heart disease risk? Findings from a population-based study of older men. Int J Epidemiol 2007; 36: 560-6. 9. Murphy MF, Bunch KJ, Chen B et al. Reduced occurrence of childhood cancer in twins compared to singletons: protection but by what mechanism? Pediatr Blood Cancer 2008; 51: 62-5. 10. Puumala SE, Carozza SE, Chow EJ et al. Childhood cancer among twins and higher order multiples. Cancer Epidemiol Biomarkers Prev 2009; 18: 162-8. 11. Neale RE, Mineau G, Whiteman DC et al. Childhood and adult cancer in twins: evidence from the Utah genealogy. Cancer Epidemiol Biomarkers Prev 2005; 14: 1236-40. The variables selected for this domain include: the number of biological brothers and sisters; the birth rank of the participant and whether the participant was born in a multiple birth. There was broad consensus on the need to collect this information and general agreement that it could be collected with acceptable validity. The collection of this information is particularly useful if it is linked to the family history of disease. It can also be helpful in identifying informative families that can usefully be extended by recruiting additional members in a later study. Some diseases have a birth-rank related risk profile - e.g. asthma under the 'hygiene hypothesis'. Household Status The marital status of an individual affects their longevity, with current marriage being associated with longer survival (3). Social isolation is a significant risk factor when considering all cause mortality risk and specific risks of, for example, coronary heart disease. Those who have never been married have the highest overall mortality risk when compared to those who are married, divorced or widowed (3, 4). Finally, overcrowding within the home has been associated with incidence of mental illness, respiratory infection and chronic respiratory disease (5, 6) and also functions as a stressor which can result in elevated blood pressure and neuroendocrine abnormalities (2). Essential Under development In total, 4 of 13 (31%) and 8 of 13 studies (62%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based respectively on Questionnaire Catalogue's Keywords: Familial and social environment and Marital status) A social unit comprised of one or more individuals living together (all of whom need not be related) in the same dwelling, who aggregate and share their incomes. Domain finalized, but still evolving Risk factor true Household Status This domain concerns the marital and co-habitation status of individuals and the number of individuals within a given household. These variables provide a measure of social isolation and socioeconomic status both of which are intrinsically linked to health. Marriage and co-habitation are used as a rough proxy for social connectedness, and have associated economic and psychosocial benefits which have positive effects on health (1). Residential crowding is linked to income which is reflected in requirements for and ability to access health care (2). These measures of socioeconomic status can therefore act as primary explanatory variables of interest (e.g. differential exposure to environmental risks) or as a confounder on the causal pathways leading to a host of health states. 1. Liu H, Umberson DJ. The times they are a changin': marital status and health differentials from 1972 to 2003. J Health Soc Behav 2008; 49: 239-53. 2. Evans GW, Kantrowitz E. Socioeconomic status and health: the potential role of environmental risk exposure. Annu Rev Public Health 2002; 23: 303-31. 3. Kaplan RM, Kronick RG. Marital status and longevity in the United States population. J Epidemiol Community Health 2006; 60: 760-5. 4. Lynch JJ (1977). The broken heart : the medical consequences of loneliness. New York, Basic Books. 5. Kellett J. Invited review health and housing. Journal of Psychosomatic Research 1989; 33: 255-268. 6. Krieger J, Higgins DL. Housing and health: time again for public health action. Am J Public Health 2002; 92: 758-68. The variables selected for this domain include: 'household status'; marital status and number of people living in the household. To obtain relevant information, it was decided to use 'household status' (living with a spouse or a partner) as well as marital status, because the latter can be culturally specific. When it can be combined with an indicator of household income, the number of people living in the household is a useful indirect measure of resource-based socioeconomic status. There was broad consensus on the need to collect this information and general agreement that it could be collected with acceptable validity. Individual Birth Location Currently under development Essential Under development In total, 7 of 13 studies (54%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Subject's birth location) Location where the participant was born. Domain finalized, but still evolving Risk factor true Individual Birth Location Currently under development Currently under development The variables selected for this domain include: the country/nation of birth of the participant; the town/city and region of birth of the participant. There was broad consensus on the need to collect this information which provides - potential indirect - indicators of socioeconomic status and environmental exposure and also provides a useful means of partially controlling analytic biases arising from population substructure in ancestry. Income All cause mortality is strongly associated with income (2) with risk of death greatest for those in the low income bracket (3). Low household income during pre-retirement years confers a higher mortality risk for both women (4) and men (1). Income affects mortality and morbidity associated with cardiovascular disease, increasing the incidence and prevalence of cardiovascular disease risk factors such as high blood pressure and elevated serum cholesterol (3). Extended Under development In total, 4 of 13 studies (31%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Income) An indication of the income of the participant. Domain finalized, but still evolving Risk factor true Income As one of a set of socioeconomic status indicators, income is known to be correlated with a variety of health outcomes (1). Although unrepresentative of the socioeconomic status of retired individuals, household income is indicative of the standard of living of the majority of individuals and reflects access to such things as quality education and medical care. A whole range of health related measures are affected by income as discussed in associations and may therefore act as primary explanatory variables of interest (e.g. differential exposure to risks) or as confounders on the causal pathways leading to a host of health states. Data in this domain can easily be collected by questionnaire and can be measured per period of time (e.g. per month or per year). It should be noted that issues exist surrounding reverse causation and inequality in access to household income; long-term sickness may force a drop in income and females may be disadvantaged in the share of household income (1). 1. Daly MC, Duncan GJ, McDonough P et al. Optimal indicators of socioeconomic status for health research. Am J Public Health 2002; 92: 1151-7. 2. Deaton A, Paxson CH, National Bureau of Economic Research. (1999). Mortality, education, income, and inequality among American cohorts. Cambridge, MA, National Bureau of Economic Research. 3. Kaplan GA, Keil JE. Socioeconomic factors and cardiovascular disease: a review of the literature. Circulation 1993; 88: 1973-98. 4. Power C, Hypponen E, Smith GD. Socioeconomic position in childhood and early adult life and risk of mortality: a prospective study of the mothers of the 1958 British birth cohort. Am J Public Health 2005; 95: 1396-402. There was broad consensus on the need to collect family income as an indicator of socioeconomic status. It was decided to consider this variable as open because differences between the currencies and social contexts of different nations make it difficult to define income ranges that are universally applicable and meaningful. Individual History of Diabetes Although our understanding of the etiology of type 1 diabetes remains incomplete, it is recognized that both genetic and environmental determinants are important in defining disease risk for type 1 diabetes (6). There are now a number of genetic variants that have been identified and reliably replicated (9, 12) - including important variants in the HLA system (9). A number of key risk factors have also been identified for type 2 diabetes: age, obesity, family history of diabetes, lack of physical activity, race/ethnicity, high blood pressure, high cholesterol or other fats in the blood and history of vascular disease. There are also now a number of genetic variants that have reliably been identified and replicated (9, 12-14). Type 2 diabetes occurs more frequently in individuals with hypertension or dyslipidemia and in women with earlier pregnancy diabetes or polycystic ovary syndrome (4). Essential Under development In total, 11 of 13 studies (85%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Endocrine, nutritional and metabolic diseases) A record of a subject's personal medical history relating to the occurrence of diabetes mellitus and diabetes-related problems and events. The World Health Organization recognizes three main forms of diabetes mellitus: type 1, type 2, and gestational diabetes (occurring during pregnancy). Domain finalized, but still evolving Outcome true Individual History of Diabetes Diabetes (strictly, diabetes mellitus) is a significant cause of social, psychological and financial burden in populations worldwide (1, 2). The worldwide prevalence of diabetes across all age-groups was estimated to be 2.8% in 2000 and is projected to increase to 4.4% in 2030 (3). In 2002, the prevalence of diabetes within the adult population of the United States was estimated to be 8.7% (4). Ninety percent of all cases are type 2 diabetes (5). Although less common, type 1 diabetes is the third most prevalent chronic disease of childhood, affecting up to 0.4% of children in some populations by age 30 years, with an overall lifetime risk of nearly 1% (6). Most people with diabetes in low and middle income countries are middle-aged (45-64 years old) (7). All forms of diabetes can have serious negative health effects. Undiagnosed, or poorly controlled, diabetes can lead to long-term complications which include abnormalities in the cardiovascular (heart, central and peripheral vasculature), ocular, neurological and renal systems. Diabetes leads to a decrease in life expectancy (1,2). In fact, it causes about 5% of all deaths globally each year, and the number of these deaths is projected to increase by more than 50% over the next 10 years if remedial intervention is not instituted. The risk factors for the different types of diabetes vary. Type 1 diabetes is a complex, autoimmune disease involving interactions between genes and environment/life-style (8) with an important etiological contribution arising from variants in the HLA system (9). Type 2 diabetes is strongly associated with environmental factors (5) such as lifestyle and diet as well as with genetic factors (10). The prevalence of diabetes is high enough to provide adequate statistical power for epidemiological study and analysis in many contemporary biobanks (11). Subjects that have been diagnosed with diabetes can usually report their disease status accurately, and these data can be collected, very effectively, by questionnaire. There is therefore a strong case for collecting information about doctor diagnosed diabetes on recruitment into a cohort biobank. In particular, it enables informative cross-sectional analyses at recruitment (providing for early 'scientific wins') and also permits the subsequent discrimination of pre-existing (prevalent) cases of diabetes from new (incident) diagnoses. But, it must be recognised that type 2 diabetes can remain undiagnosed for a prolonged period of time and in consequence, the sensitivity of the diagnosis can be low and, although sensitivity is less critical than specificity, it can nevertheless impair statistical power. 1. World Health Organization. WHO's Human Genetics areas of work, Genetics and Diabetes. http://www.who.int/genomics/about/Diabetis-fin.pdf. 2. Wiley, J. & Sons, Ltd. Current literature in diabetes. Diabetes Metab. Res. Rev. 23, i-ix (2007) PMID: 17960862. 3. Wild, S. et al. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27, 1047-1053 (2004). PMID: 15111519. 4. American Diabetes Association. Screening for type 2 diabetes. Diabetes Care 27, S11-S14 (2004). PMID: 14693922. 5. Zimmet, P., Alberti, K.G., Shaw, J., Global and societal implications of the diabetes epidemic. Nature 414, 782-787 (2001). PMID: 11742409 6. Rewers, M.J. The changing face of the epidemiology of insulin-dependent diabetes mellitus (IDDM): research designs and models of disease causation. Ann Med 23:419-426 (1991). PMID: 1930939 7. World Health Organization. Diabetes. http://www.who.int/diabetes/en/. 8. Bartsocas, C.S. & Gerasimidi-Vazeou, A. Genetics of type 1 diabetes mellitus. Pediatr. Endocrinol. Rev. Suppl 3: 508-513 (2006). PMID: 17551474. 9. Wellcome_Trust_Case_Control_Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447: 661-678 (2007). PMID: 17554300. 10. McCarthy, M.I., Froguel, P. Genetic approaches to the molecular understanding of type 2 diabetes. Am. J. Physiol. Endocrinol. Metab. 283 E217-E225 (2002). PMID: 12110525. 11. Burton, P.R., Hansell, A. UK Biobank: the expected distribution of incident and prevalent cases of chronic disease and the statistical power of nested case control studies. Manchester, UK: UK Biobank Technical Reports (2005). 12. Hattersley, A.T., McCarthy MI. What makes a good genetic association study? Lancet 366:1315-1323 (2005). PMID: 16214603. 13. Diabetes Genetics Initiative. Genome-Wide Association Analysis Identifies Loci for Type 2 Diabetes and Triglyceride Levels. Science 316:1331-1336 (2007). PMID: 17463246. 14. Zeggini, E., Weedon, M.N., Lindgren, C.M. et al. Replication of Genome-Wide Association Signals in U.K. Samples Reveals Risk Loci for Type 2 Diabetes. Science 316:1336-1341 (2007). PMID: 17463249. The variables selected for this domain include: the occurrence of diabetes; the type of diabetes (type 1, type 2 or gestational); age at onset; There was broad consensus on the need to collect this information and general agreement that it could be collected with acceptable validity. Information collected at recruitment on the occurrence of diabetes was considered essential by the panel of experts because it will be important to discriminate prevalent and incident cases of diabetes. It was also noted that pre-existing diabetes alters a subject's later risk of developing a number of key conditions including ischaemic heart disease, stroke and renal failure. Body Mass Index Currently under development Essential Under development In total, 0 of 9 (0%) and 2 of 9 studies (22%) in the Physical and Cognitive Measures Catalogue recorded information (at recruitment) about the domain of interest. (Based respectively on Physical and Cognitive Measures Catalogue 's Keywords: Bioimpedance and Other body composition measures) Weight (in kilogrammes) divided by height squared (in metres). Domain finalized, but still evolving Risk factor true Body Mass Index Currently under development Currently under development There was broad consensus that general purpose biobanks should, where possible, include body mass index as a variable. Calculation of the BMI is based on weight and height which are variables and data items under the theme 'anthropometric measures'. Individual History of Cancer Cancer is caused by both external factors (tobacco use, environmental tobacco use, physical inactivity, obesity, poor diet, exposure to ultraviolet radiation, exposure to certain viruses and bacteria, certain hormones, certain chemicals and other substances, ionizing radiation, and alcohol ) and intrinsic factors (inherited mutations, heredity), hormones, immune conditions, and mutations that occur from metabolism and age (6, 7). These causal factors may act together or in sequence to initiate or promote carcinogenesis. Ten or more years often pass between exposure to external factors and detectable cancer (5). Essential Under development In total, 12 of 13 studies (92%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Neoplasms) A record of a subject's personal medical history relating to the occurrence of cancer and cancer-related problems and events. Domain finalized, but still evolving Outcome true Individual History of Cancer Cancer is of great public health relevance. It is a leading cause of death across the world and causes substantial morbidity. Of the 58 million deaths worldwide in 2005, cancer accounted for 7.6 million (or 13%) (1). In industrialized nations more than 25% of people will die from cancer (2) and the number of these deaths is projected to keep rising across the world. It is estimated that 9 million people will die from cancer in 2015 and 11.4 million in 2030 (1). Cancer may affect people at all ages, but for most common cancers the risk increases markedly with age. Nearly two thirds of cases are diagnosed in people aged 65 and over (3). Many genes and life-style/environmental exposures are important determinants of cancer. Many of these determinants are potentially subject to intervention and are therefore, well worthy of scientific study within a biobank. Cancer presents a prevalence that allows adequate statistical power. Evaluation by questionnaire provides reliable measures. There is therefore a strong case for collecting information about cancer per se in a biobank and also for ensuring that information about cancer status is collected at recruitment into a cohort biobank. 1. World Health Organization. Cancer (2006) http://www.who.int/mediacentre/factsheets/fs297/en/index.html 2. World Health Organization. World Cancer Report (2003) 3. Cancer Research UK. UK cancer incidence statistics by age (2007) http://info.cancerresearchuk.org/cancerstats/incidence/age/ 4. National Cancer Institute. Facing Forward, Life After Cancer Treatment. NIH Publication No. 06-2424 (2007) http://www.cancer.gov/cancertopics/life-after-treatment.pdf 5. American Cancer Society. Cancer Facts & Figures (2007) http://www.cancer.org/downloads/STT/CAFF2007PWSecured.pdf 6. Canadian Cancer Society. Environmental risk factors for cancer (2006) http://cancer.ca/ccs/internet/standard/0,3182,3172_1435019236__langId-en,00.html 7. University of Texas M.D. Anderson Cancer Center. Causes of cancer (2008) http://www.mdanderson.org/patients_public/about_cancer/display.cfm?id=411A2531-77B9-11D4-AEC300508BDCCE3A&method=displayFull The variables selected for this domain include: the occurrence of cancer; the type and age at onset of cancers. There was broad consensus on the need to collect this information and general agreement that it could be collected with acceptable validity. Information collected at recruitment on the occurrence of cancer was considered essential by the panel of experts because it is important to discriminate prevalent and incident malignancies. It was also noted that the occurrence of a malignancy, and the consequences of the diagnostic and/or therapeutic interventions that may therefore be required, can alter a subject's later risk of developing another malignancy and/or a range of non-malignant conditions. It was finally agreed that is was essential to allow for several malignancies. Passive Smoking Exposure Currently under development Extended Under development In total, 8 of 13 studies (62%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on the Questionnaire Catalogue's Keyword :Passive smoking exposure) Exposure to tobacco smoke derived from the smoking of other people. Domain finalized, but still evolving Risk factor true Passive Smoking Exposure Currently under development Currently under development The variables selected for this domain include: the occurrence of current passive smoking exposure; the current duration of passive smoking exposure; and the occurrence of passive smoke exposure during childhood. There was broad consensus on the need to collect this information. Passive smoking exposure was considered to be an important environmental determinant that can potentially modify the risk of a number of key conditions including lung cancer, ischaemic heart disease and asthma. Weight Currently under development Essential Under development In total, 10 of 13 studies (77%) in the Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Actual weight) In total, 9 of 9 studies (100%) in the Physical and Cognitive Measures Catalogue recorded information (at recruitment) about the domain of interest. (Based on Physical and Cognitive Measures Catalogue 's Keyword : Weight) Weight of the subject. Domain finalized, but still evolving Risk factor true Weight Currently under development Currently under development There was broad consensus that general purpose biobanks should, where possible, include a measurement of weight. Hysterectomy Currently under development Under development In total, 7 of 13 studies (54%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Medical care system/procedures) Record of a participant's history of surgical procedures to remove the uterine body (partial hysterectomy) or the uterine body and cervix (total hysterectomy). Domain finalized, but still evolving Risk factor true Hysterectomy Ask only if the participant is a woman. Currently under development Currently under development The variables selected for this domain include: the occurrence of, and age at, hysterectomy. There was broad consensus on the need to collect this information and general agreement that it could be collected with acceptable validity. This information is important because it indicates: (1) loss of fertility; (2) the possibility that the participant has a key condition that has required hysterectomy (e.g. carcinoma of the uterus or endometriosis); (3) that the subject is no longer 'at risk' of certain conditions (e.g. carcinoma of the uterus, pregnancy). Menopause Aside from the symptoms of affecting quality of life in peri-menopausal women, there are long term health effects associated with menopause. The hormonal changes associated with menopause are strongly associated with an increased risk of osteoporosis (4); late onset menopause is positively associated with both ovarian and breast cancer (5); and despite the effects of aging and menopause being inextricably linked there is evidence to suggest that menopause removes the broadly protective effect of female hormones on cardiovascular disease risk (3, 6). Extended Under development In total, 8 of 13 studies (62%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Reproductive history) Record of a participant's history of permanent cessation of menses, usually defined by 6 to 12 months of amenorrhea in a woman over 45 years of age. Domain finalized, but still evolving Risk factor true Menopause Ask only if the participant is a woman and if the participant ever had menstrual periods. Menopause is marked by significant hormonal changes, a reduction in oestrogen, followed by a rise in follicle stimulating hormone & luteinizing hormone, ultimately giving way to a reduction in progesterone and permanent amenorrhea (1). The standard definition of menopause is the date of last menstruation, and age at menopause varies between late 40's and early 50's with 51 being the average age at onset (2). Using age 50 as a proxy for menopause, the world wide population of peri- and post-menopausal women was estimated to be 467 million in 1990, with this population expected to increase to 1.2 million by 2030 (3). With an increasing number of women spending a larger proportion of their lives in the postmenopausal state, this will be reflected in demands for health care and the planning of health care provision. The menopause is not a discrete event but happens over an ill defined period of time and typically relies on retrospective self-reporting (2). This should be taken into consideration when collecting and analysing onset of menopause. Information in this domain is of scientific interest because age at menopause may be studied as an outcomes in its own right (e.g. as a measure of the hormonal milieu), and as a primary explanatory variable or confounder on the causal pathways leading to a variety of conditions some of which are outlined in associations. 1. Jones GS Hormonal changes in perimenopause, in The Menopause, comprehensive management, Eskin BA, Editor. 1980, Masson Pub. USA: New York. vi, 214 p. 2. Boldsen JL, Jeune B. Distribution of age at menopause in two Danish samples. Hum Biol 1990; 62: 291-300. 3. Hill K. The demography of menopause. Maturitas 1996; 23: 113-127. 4. North American Menopause Society. Management of postmenopausal osteoporosis: position statement of the North American Menopause Society,. Menopause 2002; 9: 84-101. 5. Zografos GC, Panou M, Panou N. Common risk factors of breast and ovarian cancer: recent view. Int J Gynecol Cancer 2004; 14: 721-40. 6. The ESHRE Capri Workshop Group. Hormones and cardiovascular health in women. Hum Reprod Update 2006; 12: 483-97. The variables selected for this domain include the occurrence of, and age at, menopause. There was broad consensus on the need to collect this information and agreement that it could be collected with acceptable validity, though it was recognised that there can be some ambiguity as to when and whether menopause has actually occurred. This information is important to collect, because the profile of disease risk in females changes substantially at the menopause. Menstruation Early menarche has been demonstrated to be associated with increased adult body mass index (4) and cardiovascular disease risk factors (5). Epidemiological studies have also shown that age at menarche can act as a predictor for breast cancer within a population (6, 7). One explanation for this is that early menarche may result in an increased number of ovulatory cycles in the lifetime of the individual, as the number of ovulatory cycles before the first pregnancy has been linked to an increase in risk of breast cancer (8). Extended Under development In total, 10 of 13 studies (77%) in the P3G Questionnaires Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keywords: Reproductive history) Record of a participant's history of menstruation and related events. Domain finalized, but still evolving Risk factor true Menstruation Ask only if the participant is a woman. Menstruation is the periodic shedding of the uterine lining and occurs approximately every 28 days in the female reproductive cycle. The first menstrual period, and the time at which this occurs in the life of the female is termed menarche (1). Historical data suggests menarche is not only determined by genetics factors, but also by environmental factors. Diminished body fat can lead to a delay in menarche, whereas excessive body fat is associated to a decrease in the average menarcheal age (2). Women worldwide are aware of menarche which means that occurrence can be accurately collected. The accuracy of recall age of menarche has been discussed at length in the literature, but studies have shown that it can be measured in a relatively accurate manner (3), however, this should be taken into consideration when collecting and analysing data regarding menarche onset. Therefore, information in this domain is of scientific interest because age at menarche and occurrence of menstruation may be studied as outcomes in their own right (e.g. as measures of the hormonal milieu), and as a primary explanatory variable or confounder on the causal pathways leading to a variety of conditions which are outlined in associations. 1. Oxford University Press. (2004). Oxford English dictionary. 2nd. Oxford ; New York, Oxford University Press. 2. Kaplowitz PB. Link between body fat and the timing of puberty. Pediatrics 2008; 121 Suppl 3: S208-17. 3. Must A, Phillips SM, Naumova EN et al. Recall of early menstrual history and menarcheal body size: after 30 years, how well do women remember? Am J Epidemiol 2002; 155: 672-9. 4. Pierce MB, Leon DA. Age at menarche and adult BMI in the Aberdeen children of the 1950s cohort study. Am J Clin Nutr 2005; 82: 733-9. 5. Remsberg KE, Demerath EW, Schubert CM et al. Early menarche and the development of cardiovascular disease risk factors in adolescent girls: the Fels Longitudinal Study. J Clin Endocrinol Metab 2005; 90: 2718-24. 6. Bernstein L, Ross RK. Endogenous hormones and breast cancer risk. Epidemiol Rev 1993; 15: 48-65. 7. Bernstein L. Epidemiology of endocrine-related risk factors for breast cancer. J Mammary Gland Biol Neoplasia 2002; 7: 3-15. 8. Colditz GA, Frazier AL. Models of breast cancer show that risk is set by events of early life: prevention efforts must shift focus. Cancer Epidemiol Biomarkers Prev 1995; 4: 567-71. The variables selected for this domain include the occurrence of, and age-at-onset of, menstrual periods. There was broad consensus on the need to collect this information. Age at menarche is considered to be a risk factor for breast cancer. Oophorectomy Oophorectomy, particularly in premenopausal women, is associated with increased risk of coronary heart disease and osteoporosis as well as a range of conditions affecting quality of life (1, 3, 4). Under development In total, 7 of 13 studies (54%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Medical care system/procedures) Record of a participant's history of partial or complete removal of one or both of the ovaries. Domain finalized, but still evolving Risk factor true Oophorectomy Ask only if the participant is a woman. Oophorectomy is commonly undertaken either as part of a planned response to a specific genetic risk of ovarian or breast cancer, as a prophylactic measure in the prevention of ovarian cancer in women not known to be at increased risk (1) or as direct intervention to manage ovarian disease (including ovarian cancer) or to modulate the hormonal conditions in women with hormone sensitive disease (including breast cancer). Large numbers of women undergo oophorectomy each year. In the United States, for example, over a quarter of a million women underwent oophorectomy, half of which were performed before the median age of natural menopause (2). The prevalence of oophorectomy is therefore high enough to provide adequate statistical power for epidemiological study and analysis in many contemporary biobanks. Participants who have undergone this procedure can usually report their status accurately, and these data can be collected by questionnaire. Information in this domain is of scientific interest not only as a primary explanatory variable or as a confounder on the causal pathways leading to a variety of conditions (described below) but as a qualifying variable to modify interpretation of other variables (e.g. you can't interpret menopause after oophorectomy; and survival with breast cancer may need to be interpreted differently). 1. Clarke A, Chang YM, McPherson K. Removing organs "just in case"--is prophylactic removal of the ovaries a good thing? J Epidemiol Community Health 2006; 60: 186-7. 2. Wild RA. Introduction to special issue on surgical menopause. Menopause 2007; 14: 556-561. 3. Parker WH, Broder MS, Liu Z et al. Ovarian conservation at the time of hysterectomy for benign disease. Clin Obstet Gynecol 2007; 50: 354-61. 4. Shuster LT, Gostout BS, Grossardt BR et al. Prophylactic oophorectomy in premenopausal women and long-term health. Menopause Int 2008; 14: 111-6. The variables selected for this domain include: the occurrence of, and age at, oopherectomy. There was broad consensus on the need to collect this information and general agreement that it could be collected with acceptable validity. This information is important because it indicates: (1) loss of fertility; (2) the possibility that the participant has a key condition that has required oopherectomy (e.g. carcinoma of the ovary); (3) that the subject is no longer at risk of certain conditions (e.g. carcinoma of the ovary, pregnancy). Tobacco Use Tobacco use (including environmental tobacco smoke) is the single largest preventable risk factor for public health across the world. Both active and passive smoking lead to serious and fatal health consequences. Tobacco is known to cause many types of cancer including lung, throat, mouth, pancreas, bladder, stomach, liver and kidney. There is also well-established evidence for a causal link between cigarette smoking and cardiovascular damage, ischaemic heart disease, stroke and limb-threatening peripheral vascular disease. Moreover, tobacco consumption provokes respiratory diseases including chronic obstructive pulmonary disease and lower respiratory infections (1-3). Tobacco use is also a primary cause of many oral diseases and adverse oral conditions (periodontal disease, tooth loss...). Finally, it is an important cause of low birth weight and congenital defects (8). Essential Under development In total, 13 of 13 studies (100%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on the Questionnaire Catalogue's Keyword :Smoking/tobacco use) Use of tobacco in any form, including smoking, chewing or sniffing. Domain finalized, but still evolving Risk factor true Tobacco Use Tobacco use is of great public health relevance. It is a known risk factor for lung and other cancers, cardiovascular diseases and for a number of respiratory conditions (1, 2). The World Health Organization (WHO) attributes approximately 5 million deaths a year to tobacco, and the number is expected to exceed 10 million deaths by 2020. Approximately 70% of these deaths will occur in developing countries (3). Tobacco use is a preventable risk factor and strategies that target this risk can provide substantial - often underestimated - public health gains (1, 4). Over the past decade, research on the genetics of tobacco use has strongly suggested that genetic factors do play a role in different stages of tobacco use and dependence, as well as on vulnerability to cancer (5, 6). Tobacco use is a common habit with the number of smokers worldwide estimated to be at least 100 million (7). This ensures adequate statistical power for epidemiological study and analysis in many contemporary biobanks. Data on smoking and tobacco use can be collected by questionnaire. Because of problems associated with recall bias once a disease process has begun, it is recommended that information on tobacco exposure should be collected before any disease process starts. 1. Ezzati, M. et al. Selected major risk factors and global and regional burden of disease. Lancet 360, 1347-1360 (2002). PMID: 12423980 2. Peto, R. et al. Mortality from tobacco in developed countries: indirect estimation from national vital statistics. Lancet 339, 1268-1278 (1992). PMID: 1349675 3. Peto, R. et al. Mortality from smoking in developed countries 1950--2000; indirect estimation from National Vital Statistics (Oxford University Press, Oxford, 1994) http://www.deathsfromsmoking.net/download%20files/Original%20research/Mortality%20from%20smoking%20in%20developed%20countries%201950-2000%20(2nd%20ed.).pdf 4. Jha, P. & Chaloupka, F.J. Tobacco control in developing countries (Oxford University Press, Oxford, 2000), ISBN 0-19-263250-7. 5. Niu, T. et al. Nicotine dependence and its familial aggregation in Chinese. Int. J. Epidemiol. 29, 248-252 (2000). PMID: 10817120 6. Bitton, A. et al. The p53 tumour suppressor gene and the tobacco industry: research, debate and conflict of interest. Lancet 365, 531-540 (2005). PMID: 15705463 7. World Bank. Curbing the epidemic: Governments and economics of tobacco control (World Bank, Washington, DC, 1999). 8. Reibel, J. Tobacco and oral diseases: An update on the evidence, with recommendations. Med. Princ. Pract. 12, 22-32 (2003). PMID: 12707498 The variables selected for this domain include: ever used tobacco; current user of tobacco; quantification of current and past (non-passive) exposure to cigarettes, cigars and pipes, duration of the use of tobacco, duration of the non smoking period. There was broad consensus on the need to collect this information. Exposure to tobacco smoke was considered to be an important environmental determinant that can potentially modify the risk of a number of key conditions including lung cancer, ischaemic heart disease and asthma. Because this information is so critical, it will be important to review the structure and flow of the questions in the light of the findings of the DataSHaPER pilot. Nutrition Currently under development Currently under development Under development In total, 11 of 13 studies (85%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword: Nutrition) Record of the consumption of food and other nourishing material. Scientific information being collected. A Dataschema will be developed specifically on nutrition. Risk factor false No variables are currently listed for this domain, even if the collection of valid nutritional information was universally viewed as critical, but it was also recognised that nutritional study is a very specialist field with many theoretical and practical pitfalls. A specialist working group for nutritional assessment under the DataSHaPER is therefore to be set up and this domain is therefore under development. Nutrition Currently under development Currently under development Currently under development Currently under development Currently under development. Physical Activity CNCDs include chronic heart disease, stroke (8), colon and breast cancer (2, 8), asthma, chronic obstructive pulmonary disease, and type 2 diabetes (3, 8). The protective effects of adequate physical activity against CNCDs such as cardiovascular disease, colon and breast cancer, and type 2 diabetes, have been extensively reviewed (9-11). CNCDs currently pose the greatest public health problem in most countries around the world (2), and have a large negative effect on many economies (12), accounting for 44% of premature deaths worldwide (8). Presently the majority of these deaths occur in low- and middle-income countries; however, in the coming decades the burden of CNCDs is set to rise in the developing world (8). Optional Under development In total, 10 of 13 questionnaires (77%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Physical activity) A record of a participant's exercise, physical activity and/or mobility. Physical activity may include planned activity such as walking, running, basketball, or other sports. Physical activity may also include other daily activities such as household chores, yard work, walking the dog, etc. Domain finalized, but still evolving Risk factor true Physical Activity This domain concerns the time the participant spends doing different types of physical activity in a typical week. The participant should answer these questions even if he does not consider himself to be a physically active person. The domain of Physical Activity concerns the total amount of physical activity regularly undertaken by the participant. Regular and adequate physical activity is a major factor in the promotion and maintenance of good health, even in the presence of a healthy diet. Adequate activity requires an individual to accumulate thirty minutes of moderate-intensity physical activity, such as brisk walking, on most days (1). At least 60% of the world's population falls below this recommended level (2), and inactivity continues to rise across the globe as populations adopt more automated occupations and motorized methods of transport (3, 4). Physical inactivity increases risk of premature death independent of obesity (5), and acts as a modifiable risk factor for chronic non-communicable diseases (CNCDs) (6, 7) as discussed in associations. Physical activity is therefore of scientific interest as an explanatory variable or confounder on the causal pathways leading to a host of health states. Levels of physical activity can be measured via self report within a questionnaire. They can be recorded for all people and therefore provide adequate statistical power for epidemiological study and analysis. 1. World Health Organization. (2004). Global strategy on diet, physical activity, and health. Geneva, Switzerland, World Health Organization. 2. World Health Organization. (2008). Myths about Physical Activity, Accessed: May 12, 2009, http://www.who.int/dietphysicalactivity/factsheet_myths/en/index.html. 3. Bell AC, Ge K, Popkin BM. The road to obesity or the path to prevention: motorized transportation and obesity in China. Obes Res 2002; 10: 277-83. 4. Prentice AM, Jebb SA. Obesity in Britain: gluttony or sloth? BMJ 1995; 311: 437-9. 5. Mathur N, Pedersen BK. Exercise as a mean to control low-grade systemic inflammation. Mediators Inflamm 2008; 2008: 109502. 6. Waxman A, Norum KR. Why a global strategy on diet, physical activity and health? The growing burden of non-communicable diseases. Public Health Nutr 2004; 7: 381-3. 7. World Health Organization. (2008). Benefits of physical activity Accessed: May 12, 2009, http://www.who.int/dietphysicalactivity/factsheet_benefits/en/. 8. Daar AS, Singer PA, Persad DL et al. Grand challenges in chronic non-communicable diseases. Nature 2007; 450: 494-6. 9. Thune I, Furberg AS. Physical activity and cancer risk: dose-response and cancer, all sites and site-specific. Med Sci Sports Exerc 2001; 33: S530-50; discussion S609-10. 10. Wilund KR. Is the anti-inflammatory effect of regular exercise responsible for reduced cardiovascular disease? Clin Sci (Lond) 2007; 112: 543-55. 11. LaMonte MJ, Blair SN, Church TS. Physical activity and diabetes prevention. J Appl Physiol 2005; 99: 1205-13. 12. Suhrcke M, Oxford Health Alliance. (2006). Chronic disease : an economic perspective. London, Oxford Health Alliance. Global Physical Activity Questionnaire (GPAQ) Analysis Guide, developed by the World Health Organization: http://www.who.int/chp/steps/resources/GPAQ_Analysis_Guide.pdf; International the Physical Activity questionnaire (IPAQ) Website:http://www.ipaq.ki.se/ipaq.htm There was broad consensus on the need to collect information about physical activity, and general agreement that it could be collected with acceptable validity. Several tools were considered, and on the basis of the discussion, the Global Physical Activity Questionnaire (GPAQ) and International the Physical Activity questionnaire (IPAQ) developed by the World Health Organization where suggested as appropriate collection tools for physical activity. They include variables reflecting: total physical activity; average physical activity; the intensity of physical activity; the context under which physical activity takes place (work/ transport/ recreation); the median duration of moderate and vigorous physical activity at work; the median duration of moderate and vigorous physical activity as recreation; and the total duration of sedentary activities. There was broad consensus on the selection of the IPAQ in the context of the current DataSchema. Alcohol Use Besides the direct effects of intoxication and addiction, alcohol use is responsible for about 20% to 30% (each) of oesophageal cancer, liver disease, and epileptic seizures. It can also be associated with depression and behavioural disorders (3). Heavy alcohol use increases the risk of cardiovascular disease (6) and stroke (7). Moreover, alcohol consumption during pregnancy is related to various risks to the foetus. These not only include disorders in the Fetal Alcohol Spectrum (8), but also spontaneous abortion, low birth weight, prematurity and intra-uterine growth retardation (9, 10). Essential Under development In total, 11 of 13 questionnaires (85%) in the P3G Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Alcohol use) Consumption of liquids containing ethanol, including the behaviors associated with drinking alcohol. Domain finalized, but still evolving Risk factor true Alcohol Use The disease burden attributable to alcohol consumption is significant. In many countries, public health problems caused by harmful use of alcohol represent a substantial health, social and economic burden (1). In 2000, alcohol consumption caused 3.2% of deaths (1.8 million) worldwide and 4% of the global disease burden (2). It currently represents the third largest risk factor for death in developed countries (3). In developed countries the proportion of disease burden associated with alcohol consumption is between 2.6% and 9.8% for males, and between 0.5% and 2% for females (4). There is compelling evidence for genetic predisposing factors for susceptibility to alcoholism (5). Alcohol consumption is a common habit, and this generates adequate statistical power for epidemiological study and analysis in many contemporary biobanks. Because of problems associated with recall bias once a disease process has begun, it is recommended that information on alcohol consumption should be collected using a questionnaire, before any disease process starts. 1. WHO Expert Committee on Problems Related to Alcohol Consumption. Second report. World Health Organ. Tech. Rep. Ser. (944):1-53, 55-57 (2007). PMID: 17970166 2. World Health Organization. The World Health Report- Reducing Risks, Promoting Healthy Life. Geneva (2002). 3. World Health Organization. Global Status Report on Alcohol. Geneva (2004). 4. Rehm, J. et al. In: Comparative Quantification of Health Risks: Global and Regional Burden of Disease Due to Selected Major Risk Factors: Alcohol (eds Ezzati, M. et al.) (Geneva, World Health Organization, 2004). 5. Li, T.K. Pharmacogenetics of responses to alcohol and genes that influence alcohol drinking. J. Stud. Alcohol 61, 5-12 (2000). PMID: 10627090 6. Cargiulo, T. Understanding the health impact of alcohol dependence. Am. J. Health Syst. Pharm.64 (5 Suppl 3): S5-11 (2007). PMID: 17322182 7. Berger, K. et al. Light to moderate alcohol consumption and risk of stroke among US male physicians. New England Journal of Medicine 341, 1557-1564 (1999). PMID: 10564684 8. Alvear, J., Andreani, S. & Cortes, F. Fetal alcohol syndrome and fetal alcohol effects: importance of early diagnosis and nutritional treatment. Revista Medica de Chile 126, 407-412 (1998). PMID: 9699371 9. Passaro, K. T. & Little, R. E. In: Gender and Alcohol: Individual and Social Perspectives: Childbearing and alcohol use (eds Wilsnack, R.W. & Wilsnack, S.C.) 90-113 (Rutgers Center of Alcohol Studies, New Brunswick, 1997). 10. Bradley, K. A. et al. Medical risks for women who drink alcohol. Journal of General Internal Medicine 13, 627-639 (1998). PMID: 9754520 The variables selected for this domain include: ever drunk alcohol; current and past consumer of alcohol; quantification of current intake of red wine, white wine, beer, liquor and other drinks; frequency of binge drinking. There was broad consensus on the need to collect this information, even taking into account the biases that are known to impact on the assessment of alcohol exposure. There was also general agreement not to attempt to quantify past alcohol consumption because it was felt that this is very difficult to assess retrospectively and is likely to be subject to major bias. Exposure to alcohol was considered to be an important factor that can potentially modify the risk of a number of key conditions including cirrhosis, liver failure and liver cancer. Because this information is so critical, it will be important to review the structure and flow of the questions in the light of the findings of the DataSHaPER pilot. Individual History of Depression Major depression is a familial disorder and this mostly or entirely results from genetic influences (10). Many risks factors have been associated with the onset of depression including gender (women have a greater risk (7), marital status (separated and divorced persons have the highest risk, while single and married persons carry the lowest risk) (11), past depression (12, 13), presence of other mental disorders (13), sleep disturbance and disability (12). Moreover, social stressors (life events such as parental loss in early childhood or bereavement; chronic stress, and daily hassles) may play a significant role as risk factors for depression at any age in a person's life (11). The Wellcome Trust Case Control Consortium recently reported one strong association with a genetic variant for bipolar depression (14). Under development In total, 4 of the 13 studies (31%) in the Questionnaire Catalogue recorded information (at recruitment) about the domain of interest. (Based on Questionnaire Catalogue's Keyword : Mental and behavioural disorders) A record of a subject's personal medical history relating to the occurrence of depression and depression-related problems and events. Domain finalized, but still evolving Outcome true Individual History of Depression Major depression, a common (1), costly (2), and recurrent disorder (3), is associated with considerable morbidity (4) and excess mortality (5). Major depression is associated with psychological distress and suffering, and it can lead to impairments in physical, mental, and social functioning. It has been projected to become the second leading cause of disability worldwide by 2020 (second to ischemic heart disease) (6). The average age for onset of depression is between 20 and 40 years, but it may affect people in childhood (7). In Canada in 2006, the lifetime prevalence for major depression was found to be 12.2% in the population and major depression occurring in the past 12 months was found in 4.8% of the population (8). These findings are similar to those from the United States (9). Major depression is a familial disorder and this mostly or entirely results from genetic influences (10). The prevalence of major depression is high enough to provide adequate statistical power for epidemiological study and analysis in many contemporary biobanks. Subjects that have been diagnosed with, and are treated for, major depression can usually report their disease status accurately, and these data can be collected by questionnaire at a reasonable cost. There is therefore a strong case for collecting information about major depression on recruitment into a cohort biobank. In particular, it enables informative cross-sectional analyses at recruitment (providing for early 'scientific wins') and also permits the subsequent discrimination of pre-existing (prevalent) cases of major depression from new (incident) diagnoses. But, it must be recognised that the thoroughness of the diagnostic process varies markedly between clinical settings and assessment errors can occur in both directions (false positives and false negatives) and this can impair statistical power. 1. Kessler, R.C. et al. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey. Arch. Gen. Psychiatry 51:8-19 (1994). PMID: 8279933. 2. Greenberg, P.E. et al. The economic burden of depression in 1990. J. Clin. Psychiatry 54:405-418 (1993). PMID: 8270583. 3. Piccinelli, M., Wilkinson, G. Outcome of depression in psychiatric settings. Br. J. Psychiatry 164:297-304 (1994). PMID: 8199782. 4. Ustun, T.B. et al. (WHO/NIH Joint Project CAR Study Group): Multiple-informant ranking of the disabling effects of different health conditions in 14 countries. Lancet 354:111-115 (1999). PMID: 10408486. 5. Harris, E.C., Barraclough, B.B. Excess mortality of mental disorder. Br. J. Psychiatry 173:11-53(1998). PMID: 9850203. 6. Murray, C.J.L., Lopez, A.D. Evidence-based health policy: lessons from the Global Burden of Disease Study. Science 274: 740-7