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- 7/8/17 - 1 Formulier, 1 Itemgroep, 36 Data-elementen, 2 Talen
Itemgroep: openEHR-EHR-EVALUATION.tobacco_smoking_summary.v1.xml
Use to record summary information about the individual's pattern of smoking of tobacco and tobacco-containing products. This archetype is to be used to record information about both current and previous smoking behaviour. The specific scope of this archetype is on documentation about the use of all types of inhaled tobacco smoke because of the associated health risks from direct inhalation of tobacco and associated chemicals. Amount of nicotine and tar, use of filters and additives has been left outside of scope for the core archetype, but could be added into the Episode SLOT if required. Please note that the scope of this archetype does not include unintentional exposure to tobacco smoke (see Misuse). The 'Per type' cluster of data elements allows for recording of specific details and episodes about each type of tobacco smoked and can be repeated once per type. The list of tobacco types listed in the 'Per type' run-time name constraint identifies the type of tobacco. This name constraint can be applied during template modelling or at run-time within a software application. In many situations the individual will only smoke one type of tobacco, such as manufactured cigarettes. If other types of tobacco are smoked, the details will be recorded in another instance of the 'Per type' cluster. The history of waxing and waning of use for each type of tobacco over time can be captured using the repeatable 'Per episode' cluster. This cluster of data elements allows for a very detailed pattern of smoking behaviour to be recorded for each type of tobacco smoked such as daily 'roll-your-own' cigarette smoking, alongside weekly cigar smoking every Friday night and occasional Bidi smoking while on holiday in Bali. Triggers for closing one episode and commencing a new one will largely reflect local data collection preferences, including if the individual: - quits for a significant period of time (which will likely be locally defined); or - significantly changes their amount of use or pattern of their smoking. If only one type of tobacco is smoked, the value for 'Pack years' will be identical to the 'Overall pack years' data element. If more than one type of tobacco is smoked, the calculation for the value for 'Overall pack years' will require a more complex algorithm such as http://smokingpackyears.com/. Use to incorporate the narrative descriptions of tobacco smoking habits within existing or legacy clinical systems into an archetyped format, using the 'Overall description' data element.
- 1/29/25 - 6 Formulieren, 1 Itemgroep, 4 Data-elementen, 1 Taal
Itemgroep: pht005036
Principal Investigator: David Weir, PhD, University of Michigan, Ann Arbor, MI, USA MeSH: Aging,Neoplasms,Arthritis,Lung Diseases, Obstructive,Dementia,Heart Diseases,Heart Failure,Hypertension,Myocardial Infarction,Diabetes Mellitus,Hypercholesterolemia,Obesity,Body Weight,Mobility Limitation,Pain,Cholesterol,Hemoglobin A, Glycosylated,C-Reactive Protein,Cystatin C,Depression,Alcohol Drinking,Smoking,Personality,Life Style,Cognition,Demography,Ethnic Groups,Health Status,Population Groups,Housing,Independent Living,Socioeconomic Factors,Career Mobility,Educational Status,Employment,Family Characteristics,Income,Occupations,Poverty,Social Change,Social Class,Social Conditions,Risk Factors https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000428 *Introduction to V2: *This data release comprises data from the V1 release combined with approximately 3,000 additional samples, collected during the HRS 2010 field period. The 2010 data include samples from a random half of the new cohort enrolled in 2010 along with a significant expansion of the minority sample. *Description:* The University of Michigan Health and Retirement Study (HRS) is a longitudinal panel study that surveys a representative sample of approximately 20,000 people in America over the age of 50 every two years. Supported by the National Institute on Aging (NIA U01AG009740) and the Social Security Administration, the HRS explores the changes in labor force participation and the health transitions that individuals undergo toward the end of their work lives and in the years that follow. The study collects information about income, work, assets, pension plans, health insurance, disability, physical health and functioning, cognitive functioning, and health care expenditures. Through its unique and in-depth interviews, the HRS provides an invaluable and growing body of multidisciplinary data that researchers can use to address important questions about the challenges and opportunities of aging. Because of its innovation and importance, the HRS has become the model and hub for a growing network of harmonized longitudinal aging studies around the world. *Origins of the HRS.* As the population ages it is increasingly important to obtain reliable data about aging and topics that are relevant to a range of policy issues in aging. To address this need, the National Institutes on Aging (NIA) established a cooperative agreement with the University of Michigan Institute for Social Research to collect such data. The HRS launched data collection in 1992 and has re-interviewed the original sample of respondents every two years since then. By adding new cohorts and refreshing the sample, the HRS has grown to become the largest, most representative longitudinal panel study of Americans 50 years and older. *HRS Study Design.* The target population for the original HRS cohort includes all adults in the contiguous United States born during the years 1931-1941 who reside in households, with a 2:1 oversample of African-American and Hispanic populations. The original sample is refreshed with new birth cohorts (51-56 years of age) every six years. The sample has been expanded over the years to include a broader range of birth cohorts as well. The target population for the AHEAD survey consists of United States household residents who were born in 1923 or earlier. Children of the Depression (CODA) recruits households born 1924-1930, War Babies 1942-47, Early Boomers 1948-53, and Mid-Boomers 1954-59. Data collection includes a mixed mode design combining in-person, telephone, mail, and Internet. For consenting respondents, HRS data are linked at the individual level to administrative records from Social Security and Medicare claims. *Genetic Research in the HRS.* The HRS has genotyped 2.5 million single nucleotide polymorphisms (SNPs) on respondents using Illumina's Human Omni2.5-Quad (Omni2.5) BeadChip. The genotyping was performed by the NIH Center for Inherited Disease Research (CIDR). Saliva was collected on half of the HRS sample each wave starting in 2006. In 2006, saliva was collected using a mouthwash collection method. From 2008 onward, the data collection method switched to the Oragene kit. Saliva completion rates were 83% in 2006, 84% in 2008, and 80% in 2010 among new cohort enrollees. HRS Phenotypic data. Phenotypic data are available on a variety of dimensions. Health measures include physical/psychological self-report, various health conditions, disabilities, cognitive performance, health behaviors (smoking, drinking, exercise), physical performance and anthropomorphic measures, and biomarkers (HbA1c, Total Cholesterol, HDL, CRP, Cystatin-C). Data are also available on health services including utilization, insurance and out-of-pocket spending with linkage to Medicare records. Economic measures include employment status/history, earnings, disability, retirement, type of work, income by source, wealth by asset type, capital gains/debt, consumption, linkage to pensions, Social Security earnings/benefit histories. There is also extensive information on family structure, proximity, transfers to/from of money, time, social and psychological characteristics, as well as a wide range of demographics. Performance on a cognitive test combining immediate and delayed word recall was selected as an example trait for the dbGaP data release. In the immediate word recall task the interviewer reads a list of 10 nouns to the respondent and asks the respondent to recall as many words as possible from the list in any order. After approximately five minutes of asking other survey questions, the respondent is asked to recall the nouns previously presented as part of the immediate recall task. The total recall score is the sum of the correct answers to these two tasks, with a range of 0 to 20. Researchers who wish to link to other HRS measures not in dbGaP will be able to apply for access from HRS. A separate Data Use Agreement (DUA) will be required for linkage to the HRS data. See the HRS website (http://hrsonline.isr.umich.edu/gwas) for details.

Eligibility

1 Itemgroep 6 Data-elementen

pht002612.v2.p2

1 Itemgroep 4 Data-elementen

pht002613.v2.p2

1 Itemgroep 5 Data-elementen

pht002614.v2.p2

1 Itemgroep 7 Data-elementen

pht005037.v1.p2

1 Itemgroep 5 Data-elementen
- 11/27/24 - 7 Formulieren, 1 Itemgroep, 7 Data-elementen, 1 Taal
Itemgroep: pht009405
Principal Investigator: Andrew W. Bergen, PhD, SRI International, Menlo Park, CA, USA; Oregon Research Institute, Eugene, OR, USA MeSH: Nicotine,Pharmacokinetics,Smoking Cessation,Cigarette Smoking,Smoking Cessation Agents,Bupropion,Tobacco Use Cessation Devices,Lung Neoplasms https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000931 This study (DA033813; PI: Andrew W Bergen; PMID:26132489) includes samples from two laboratory studies of nicotine metabolism. The Pharmacokinetics of Nicotine Metabolism in Twins study (PKTWIN; PI: Gary E Swan; PMID: 15527659) was based on recruitment from a twin registry (PMID: 23084148). The Integrated Research Project on Tobacco Use and Dependence (IRP; PI: Gary E Swan; PMID: 14578134) was based on recruitment from a pedigree-based longitudinal study of risk factors for substance use, the Smoking in Families study (SMOFAM; DA03706; PI: Hy Hops). These two laboratory studies (PKTWIN and IRP/SMOFAM) served as the Stage I dataset to interrogate Drug Metabolizing Enzyme and Transporter genes with a targeted SNP array for association with the Nicotine Metabolite Ratio (NMR, ratio of trans-3'-hydroxycotinine and cotinine), an established biomarker of nicotine metabolism. In addition to the laboratory studies, samples from eight RCTs (PMID: 23249876) with the NMR and smoking-related measures used to test SNPs identified in Stage I (PMID: 26132489). In a third stage, a lung cancer meta-analysis database (PMID: 24880342) was used to assess association of SNPs identified in Stage II with lung cancer. The objectives of the study were to identify novel genes and SNPs contributing to nicotine metabolism (Stage I), and to validate PK SNPs associated with the NMR from individuals participating in a clinical laboratory protocol with the NMR obtained from treatment-seeking smokers, and then to investigate association with prospective smoking cessation (Stage II). This study built upon existing studies of nicotine metabolism and randomized trials of smoking cessation therapies. Enhanced knowledge of the genes influencing nicotine metabolism and prospective abstinence may help personalize smoking cessation treatment and risk assessment for smoking-related diseases. For Stage I, both subject [fixed-dose NMR, covariates (age, BMI, ethnicity, sex, smoking status, and hormone use), and pedigree relationships] and sample (common DMET SNP genotype, genotyping quality control) data are available in this accession. The analysis protocol, quality control summaries, summary genotype, summary phenotype, and analysis results are available for Stage I, II and III samples (PMID: 26132489). Extensive discussion of the prior *CYP2A6* association literature with the NMR, abstinence, smoking heaviness and lung cancer risk is available (PMID: 26132489). The NMR has previously been associated with *CYP2A6* activity, response to smoking cessation treatments, and cigarette consumption. We searched for drug metabolizing enzyme and transporter (DMET) gene variation associated with the NMR and prospective abstinence in 2,946 participants of laboratory studies of nicotine metabolism and of clinical trials of smoking cessation therapies. Stage I was a meta-analysis of the association of 507 common single nucleotide polymorphisms (SNPs) at 173 DMET genes with the NMR in 449 participants of two laboratory studies. Nominally significant associations were identified in ten genes after adjustment for intragenic SNPs; *CYP2A6* and two *CYP2A6* SNPs attained experiment-wide significance adjusted for correlated SNPs (*CYP2A6* PsubACT/sub=4.1E-7, rs4803381 PsubACT/sub=4.5E-5, rs1137115, PsubACT/sub=1.2E-3). Stage II was mega-regression analyses of 10 DMET SNPs with pretreatment NMR and prospective abstinence in up to 2,497 participants from eight trials. rs4803381 and rs1137115 SNPs were associated with pretreatment NMR at genome-wide significance. In *post-hoc* analyses of *CYP2A6* SNPs, we observed nominally significant association with: abstinence in one pharmacotherapy arm; cigarette consumption among all trial participants; and lung cancer in four case:control studies. *CYP2A6* minor alleles were associated with reduced NMR, CPD, and lung cancer risk. We confirmed the major role that *CYP2A6* plays in nicotine metabolism, and made novel findings with respect to genome-wide significance and associations with CPD, abstinence and lung cancer risk. Additional multivariate analyses with patient variables and genetic modeling will improve prediction of nicotine metabolism, disease risk and smoking cessation treatment prognosis.br

pht009401.v1.p1

1 Itemgroep 7 Data-elementen

Eligibility

1 Itemgroep 7 Data-elementen

pht009400.v1.p1

1 Itemgroep 2 Data-elementen

pht009402.v1.p1

1 Itemgroep 2 Data-elementen

pht009403.v1.p1

1 Itemgroep 17 Data-elementen

pht009404.v1.p1

1 Itemgroep 11 Data-elementen
- 2/25/23 - 6 Formulieren, 1 Itemgroep, 11 Data-elementen, 1 Taal
Itemgroep: IG.elig
Principal Investigator: Kathleen Mullan Harris, PhD, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA MeSH: Adolescent Health,National Longitudinal Study of Adolescent Health,Obesity,Body Weight,Cholesterol,C-Reactive Protein,Depression,Alcohol Drinking,Smoking,Personality,Life Style,Ethnic Groups,Health Status,Population Groups,Housing,Socioeconomic Factors,Educational Status,Employment,Family Characteristics,Income,Occupations,Poverty,Risk Factors https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001367 The National Longitudinal Study of Adolescent to Adult Health [Add Health] is an ongoing longitudinal study of a nationally representative U.S. cohort of more than 20,000 adolescents in grades 7-12 (aged 12-19 years) in 1994 followed into adulthood with five interviews/surveys in 1995, 1996, 2001-02, 2008, and 2016-18. Add Health was designed to understand how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. Add Health contains unprecedented environmental, behavioral, psychosocial, biological, and genetic data from early adolescence and into adulthood on a large, nationally representative cohort with unprecedented racial, ethnic, socioeconomic, and geographic diversity. Add Health has a large, multidisciplinary user base of over 50,000 researchers around the world who have published over 3,400 research articles. Add Health is housed at the Carolina Population Center of the University of North Carolina at Chapel Hill. Add Health datasets are distributed according to a tiered data disclosure plan designed to protect the data from the risk of direct and indirect disclosure of respondent identity. Add Health's large sample size, population diversity and rich longitudinal data base of psychosocial, physical, and contextual data will permit investigation of an exceptionally broad range of phenotypes with known genetic variation. Prospective longitudinal measures are available to document change over time in each of these phenotypes, as well as change in the social environment and life experiences, making the Add Health sample ideal for understanding genetic linkages with health and behavior across the life course. The original design of Add Health included important features for understanding biological processes in health and developmental trajectories across the life course of young people, including an embedded genetic sample with more than 3,000 pairs of adolescents with varying biological resemblance (e.g., twins, full sibs, half sibs, and adolescents who grew up in the same household but have no biological relationship), testing of saliva and urine for sexually transmitted infections and HIV, and biomarkers of cardiovascular health, metabolic processes, immune function, renal function, and inflammation. Add Health therefore has critical objective indicators of health status and disease markers in young adulthood, well before chronic illness or its complications emerge in later adulthood. Because DNA has been collected on the full sample at Wave IV, it is possible to link genetic profiles with social, behavioral, and biological measures over time from adolescence into adulthood. Add Health sampled the multiple environments in which young people live their lives, including the family, peers, school, neighborhood, community, and relationship dyads, and provides independent and direct measurement of these environments over time. Add Health contains extensive longitudinal information on health-related behavior, including life histories of physical activity, involvement in risk behavior, substance use, sexual behavior, civic engagement, education, and multiple indicators of health status based on self-report (e.g., general health, chronic illness), direct measurement (e.g., overweight status and obesity), and biomarkers. No other data resource with this expanse of genotype and phenotype data on a large nationally representative longitudinal sample with race, ethnic, socioeconomic, and geographic diversity exists. A complete reference guide on study design and accomplishments can be found on the Add Health website: Design Paper: *The Add Health Study: Design and Accomplishments Kathleen Mullan Harris Carolina Population Center University of North Carolina at Chapel Hill 2013*

pht008249.v1.p1

1 Itemgroep 5 Data-elementen

pht008245.v1.p1

1 Itemgroep 2 Data-elementen

pht008246.v1.p1

1 Itemgroep 6 Data-elementen

pht008247.v1.p1

1 Itemgroep 3 Data-elementen

pht008248.v1.p1

1 Itemgroep 8 Data-elementen
- 12/13/22 - 4 Formulieren, 1 Itemgroep, 1 Data-element, 1 Taal
Itemgroep: IG.elig
Principal Investigator: Cora E. Lewis, MD, University of Alabama at Birmingham, Birmingham, AL, USA MeSH: Cardiovascular Diseases,Hypertension,Atherosclerosis,Obesity,Lipids,Diabetes Mellitus,Smoking,Pulmonary Function Test,Physical Activities,Energy Intake,Diet https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000309 *For the GENEVA CARDIA project, three genotype call sets were generated from a single set of array scans as a consequence of DNA sample quality problems. These call sets are designated "Birdsuite-1", "Birdsuite-2" and "Beaglecall". ("Beaglecall" used both Birdseed and BEAGLECALL calling algorithms.) An analysis-ready genotypic data set is provided in PLINK format for the "Beaglecall" set only, because it performs very well in QC analyses. Only raw CHP and ALLELE_SUMMARY files are provided for the two Birdsuite call sets because they have significant quality issues. Use of the Beaglecall set is highly recommended. Users of the other two call sets should proceed with caution. More details are given in the genotypic QC report.* The CARDIA study, sponsored by the National Heart, Lung and Blood Institute (NHLBI), is a prospective, multi-center investigation of the natural history and etiology of cardiovascular disease in African-Americans and Whites 18-30 years of age at the time of initial examination. The initial examination included 5,115 participants selectively recruited to represent proportionate racial, gender, age, and education groups from 4 communities: Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA. Participants from the Birmingham, Chicago, and Minneapolis centers were recruited from the total community or from selected census tracts. Participants from the Oakland center were randomly recruited from the Kaiser-Permanente health plan membership. From the time of initiation of the study in 1985-1986, five follow-up examinations have been conducted at years 2, 5, 7, 10, 15, and 20. The Year 25 examination is scheduled to begin in 2010. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors associated with variation in longitudinal blood pressure profiles during the critical transition period from young adulthood to early middle-age; and to characterize their interactions with relevant environmental factors, such as body weight profiles. Genotyping was performed at the Broad Institute of MIT and Harvard, a GENEVA genotyping center. Data cleaning and harmonization were performed at the GEI-funded GENEVA Coordinating Center at the University of Washington.

pht001997.v2.p2

1 Itemgroep 4 Data-elementen

pht001999.v2.p2

1 Itemgroep 151 Data-elementen

pht001998.v2.p2

1 Itemgroep 4 Data-elementen
- 10/12/22 - 3 Formulieren, 1 Itemgroep, 4 Data-elementen, 1 Taal
Itemgroep: pht001035.v1.p1
https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000090 The Atherosclerosis Risk in Communities (ARIC) Study, sponsored by the National Heart, Lung and Blood Institute (NHLBI), is a prospective epidemiologic study conducted in four U.S. communities. The four communities are Forsyth County, NC; Jackson, MS; the northwest suburbs of Minneapolis, MN; and Washington County, MD. ARIC is designed to investigate the etiology and natural history of atherosclerosis, the etiology of clinical atherosclerotic diseases, and variation in cardiovascular risk factors, medical care and disease by race, gender, location, and date. ARIC includes two parts: the Cohort Component and the Community Surveillance Component. The Cohort Component began in 1987, and each ARIC field center randomly selected and recruited a cohort sample of approximately 4,000 individuals aged 45-64 from a defined population in their community. A total of 15,792 participants received an extensive examination, including medical, social, and demographic data. These participants were reexamined every three years with the first screen (baseline) occurring in 1987-89, the second in 1990-92, the third in 1993-95, and the fourth and last exam was in 1996-98. Follow-up occurs yearly by telephone to maintain contact with participants and to assess health status of the cohort. In the Community Surveillance Component, currently ongoing, these four communities are investigated to determine the community-wide occurrence of hospitalized myocardial infarction and coronary heart disease deaths in men and women aged 35-84 years. Hospitalized stroke is investigated in cohort participants only. Starting in 2006, the study conducts community surveillance of inpatient (ages 55 years and older) and outpatient heart failure (ages 65 years and older) for heart failure events beginning in 2005. ARIC is currently funded through January 31, 2012. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to atherosclerosis and cardiovascular disease through large-scale genome-wide association studies of well-characterized cohorts of adults in four defined populations. Genotyping was performed at the Broad Institute of MIT and Harvard, a GENEVA genotyping center. Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

pht000114.v1.p1

1 Itemgroep 362 Data-elementen

pht001036.v1.p1

1 Itemgroep 4 Data-elementen
- 10/12/22 - 4 Formulieren, 1 Itemgroep, 3 Data-elementen, 1 Taal
Itemgroep: pht002218
Principal Investigator: Maria Teresa Landi, Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA MeSH: Lung Neoplasms,Smoking https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000336 Three genetic loci for lung cancer risk have been identified by genome-wide association studies (GWAS), but inherited susceptibility to specific histologic types of lung cancer is not well established. We conducted a GWAS of lung cancer and its major histologic types genotyping 515,922 single nucleotide polymorphisms (SNPs) in 5,739 incident lung cancer cases and 5,848 controls from one population-based case-control study and three cohort studies. Results were combined with summary data from 10 additional studies for a total of 13,300 cases and 19,666 controls of European descent. Four studies also provided histology data for replication resulting in 3,333 adenocarcinomas (AD), 2,589 squamous cell carcinomas (SQ), and 1,418 small cell carcinomas (SC). In analyses by histology, rs2736100 (*TERT*) on chromosome 5p15.33, was associated with risk of adenocarcinoma (OR=1.23, 95%CI=1.13-1.33, P=3.02x10sup-7/sup), but not other histologic types (OR=1.01, P=0.84, and OR=1.00, P=0.93, for SQ and SC, respectively). This finding was confirmed in each replication study and overall meta-analysis (OR=1.24, 95%CI=1.17-1.31, P=3.74x10sup-14/sup for AD and OR=0.99, P=0.69 and OR=0.97, P=0.48, for SQ and SC, respectively). Other previously reported association signals on 15q25 and 6p21 were also refined, but no additional loci reached genome-wide significance. In conclusion, a lung cancer GWAS identified a distinct hereditary contribution to adenocarcinoma. *Note: The lung study dataset to be released to dbGaP and caBIG excludes 47 individuals from the PLCO cohort who consented to participate only in cancer research projects and 22 individuals because of updated QC. Thus, the released dataset is derived from 11517 subjects, 5699 cases and 5818 controls. After the updated QC, the dataset to be released to dbGaP and caBIG includes 506062 SNPs.*

pht002219.v1.p1

1 Itemgroep 2 Data-elementen

pht002220.v1.p1

1 Itemgroep 9 Data-elementen

Eligibility

1 Itemgroep 5 Data-elementen
- 11/19/20 - 1 Formulier, 2 Itemgroepen, 4 Data-elementen, 1 Taal
Itemgroepen: Administrative Data, Smoking Status
- 11/11/20 - 1 Formulier, 5 Itemgroepen, 13 Data-elementen, 1 Taal
Itemgroepen: Administrative Data, Disease Duration COPD, History of COPD Exacerbations, COPD type, Smoking History

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