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WHO-5 - WHO (Five) Well-Being Index

- 12/13/24 - 1 form, 1 itemgroup, 5 items, 30 languages
Itemgroup: Over the last two weeks
WHO (Five) Well-Being Index (1998 version) Also called : WHO Five, WHO 5 [Topp CW, Østergaard SD, Søndergaard S, Bech P. The WHO-5 Well-Being Index: a systematic review of the literature. Psychother Psychosom. 2015;84(3):167-76. doi: 10.1159/000376585]

HADS - Hospital Anxiety and Depression Scale (HADS)

- 11/16/18 - 1 form, 1 itemgroup, 14 items, 1 language
Itemgroup: In the past week
Hospital Anxiety and Depression Scale (HADS) was originally developed by [Zigmond, AS; Snaith, RP (1983). "The hospital anxiety and depression scale". Acta Psychiatrica Scandinavica 67 (6): 361–370] and is commonly used by doctors to determine the levels of anxiety and depression that a patient is experiencing. Also used for routine documentation at the University Hospital Muenster (UKM) see http://en.wikipedia.org/wiki/Hospital_Anxiety_and_Depression_Scale

dbGaP phs000428 Health and Retirement Study (HRS) - pht005036.v1.p2

- 1/29/25 - 6 forms, 1 itemgroup, 4 items, 1 language
Itemgroup: 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 itemgroup 6 items

pht002612.v2.p2

1 itemgroup 4 items

pht002613.v2.p2

1 itemgroup 5 items

pht002614.v2.p2

1 itemgroup 7 items

pht005037.v1.p2

1 itemgroup 5 items

dbGaP phs000944 eMERGE Clinical Center at Partners HealthCare - Eligibility

- 11/27/24 - 6 forms, 2 itemgroups, 11 items, 1 language
Itemgroups: IG.elig, IG.elig
Principal Investigator: Scott T. Weiss, MD, MS, Partners HealthCare System, Boston, MA, USA MeSH: Hypercholesterolemia,Asthma,Arthritis, Rheumatoid,Attention Deficit Disorder with Hyperactivity,Bipolar Disorder,Coronary Disease,Depression,Heart Failure,Inflammatory Bowel Diseases,Multiple Sclerosis,Schizophrenia,Stroke https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000944 The Partners HealthCare Biobank is a large research data and sample repository working within the framework of Partners Personalized Medicine. It provides researchers access to high quality, consented samples to help foster research, advance understanding of the causes of common diseases, and advance the practice of medicine. The Partners Biobank provides banked samples (plasma, serum and DNA) collected from consented patients. These samples are available for distribution to Partners HealthCare investigators with appropriate approval from the Partners Institutional Review board (IRB). They are linked to clinical data that originates in the Electronic Medical Record (EMR), as well as additional health information collected in a self-reported survey. The Partners Biobank will be genotyping 25,000 subjects with the Illumina Multiethnic Beadchip 1.6 million SNPs with exome and custom content ( 60,000 LoFs). Of the participants genotyped so far, 4929 of 4962 (99.3%) individuals have genotype data that passed the default quality thresholds for the Infinium array (call rate = 0.99). We are submitting the genotype data to dbGaP for 4929 subjects with 12 phenotypes (based on icd9 codes). We will do annual releases until we reach the full 25,000 genotyped subjects.

pht004847.v1.p1

1 itemgroup 5 items

pht005288.v1.p1

1 itemgroup 6 items

pht004844.v1.p1

1 itemgroup 2 items

pht004845.v1.p1

1 itemgroup 3 items

pht004846.v1.p1

1 itemgroup 18 items

dbGaP phs001367 Add Health: The National Longitudinal Study of Adolescent to Adult Health (Add Health) - Eligibility

- 2/25/23 - 6 forms, 1 itemgroup, 11 items, 1 language
Itemgroup: 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 itemgroup 5 items

pht008245.v1.p1

1 itemgroup 2 items

pht008246.v1.p1

1 itemgroup 6 items

pht008247.v1.p1

1 itemgroup 3 items

pht008248.v1.p1

1 itemgroup 8 items

dbGaP phs000682 Genetics of Neuropsychiatric and Neurodevelopmental Disorders - Eligibility

- 1/24/23 - 6 forms, 1 itemgroup, 15 items, 1 language
Itemgroup: IG.elig
Principal Investigator: David Goldstein, PhD, Duke University, Durham, NC, USA MeSH: Schizophrenia,Schizoaffective disorder,Attention Deficit Hyperactivity Disorder,Seizures,Oppositional defiant disorder,Anxiety,Depression,Autism,Autism Spectrum Disorders,Bipolar Disorder,Developmental Disabilities,Ataxia,Migraine,Paranoid schizophrenia,Obsessive compulsive disorder,Kluver-Bucy syndrome,Intellectual disability https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000682 Although schizophrenia is a highly heritable disease, relatively little progress had been made in securely identifying the genetic causes of this disorder, and most instances of schizophrenia in the general population remain unexplained. One avenue of explanation for the genetic basis of schizophrenia, however, has been effectively closed by recent research. Genome-wide association studies (GWAS) have now shown that common variation makes at most a modest contribution to the risk of schizophrenia. At the same time that the role of common variation has been circumscribed by GWAS, however, the analysis of copy number variants that are detectable on a genome-wide scale has revealed and replicated a number of very rare variants that associate with schizophrenia. These rare copy number variants that have been implicated in schizophrenia, however, have one striking feature in common: they are all risk factors for other brain related disorders beyond schizophrenia such as mental retardation, autism and epilepsy. These findings argue that genetic risk factors may confer a highly penetrant vulnerability to neuropsychiatric disorder, which is then further modified by interacting genetic or environmental factors to determine the ultimate manifestation. Most schizophrenia collections that are being studied today, however, have been selected precisely for their homogeneity: including only schizophrenia patients with no comorbidities, or schizophrenia patients with relatives who have schizophrenia but no other neuropsychiatric conditions. These selection criteria are inconsistent with what we now know about the bulk of the genetic differences that have been associated with disease. The central hypothesis of this project is that there are rare genetic variants that strongly elevate the risk of various neuropsychiatric diseases, and that these risk factors can be identified most readily in families segregating multiple neuropsychiatric conditions.

pht003594.v1.p1

1 itemgroup 5 items

pht003595.v1.p1

1 itemgroup 6 items

pht003596.v1.p1

1 itemgroup 5 items

pht003597.v1.p1

1 itemgroup 5 items

pht003598.v1.p1

1 itemgroup 3 items

dbGaP phs000713 GDNF and Anxiety - Eligibility

- 1/3/23 - 5 forms, 1 itemgroup, 1 item, 1 language
Itemgroup: IG.elig
Principal Investigator: Anna Szekely, PhD, Institute of Psychology, Eötvös Loránd University University, Budapest, Hungary MeSH: Anxiety,Depression https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000713 GDNF gene variants were studied as possible risk factors of depression or anxiety on a young sample. The association study involved eight (rs1981844, rs3812047, rs3096140, rs2973041, rs2910702, rs1549250, rs2973050 and rs11111) GDNF single nucleotide polymorphisms and anxiety and depression scores measured by the Hospital Anxiety and Depression Scale (HADS) on 708 Caucasian young adults with no psychiatric history. Results provided significant effects of two single nucleotide polymorphisms on anxiety scores following the Bonferroni correction for multiple testing (p=0.00070 and p=0.00138 for rs3812047 and rs3096140, respectively). Haplotype analysis confirmed the role of these SNPs (p=0.00029). A significant sex-gene interaction was also observed since the effect of the rs3812047 A allele as a risk factor of anxiety was more pronounced in males. This is the first demonstration of a significant association between the GDNF gene and mood characteristics demonstrated by the association of two SNPs of the GDNF gene (rs3812047 and rs3096140) and individual variability of anxiety using self-report data from a non-clinical sample. *Reprinted from Kotyuk et. al., 2013* (Kotyuk, E., Keszler, G., Nemeth, N., Ronai, Z., Sasvari-Szekely, M., and Szekely, A. (2013). Glial Cell Line-Derived Neurotrophic Factor (GDNF) as a Novel Candidate Gene of Anxiety. PLoS One,8, (12) PMID: 24324616), *with permission from Publisher* *(All content of articles published in PLOS journals is open access. You can read about our open access license here: http://www.plos.org/about/open-access/. To summarize, this license allows you to download, reuse, reprint, modify, distribute, and/or copy articles or images in PLOS journals, so long as the original creators are credited (e.g., including the article's citation and/or the image credit); Laura Perry; Staff EO;PLOS ONE)*

pht004271.v1.p1

1 itemgroup 2 items

pht004272.v1.p1

1 itemgroup 3 items

pht004273.v1.p1

1 itemgroup 31 items

pht004274.v1.p1

1 itemgroup 6 items

dbGaP phs000020 Major Depression: Stage 1 Genomewide Association in Population-Based Samples - pht000668.v1.p1

- 10/12/22 - 4 forms, 1 itemgroup, 2 items, 1 language
Itemgroup: pht000668
Principal Investigator: MeSH: Major Depressive Disorder https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000020 Identification of genomic regions that confer susceptibility to or protection from major depressive disorder (MDD) via genomewide association. *Consent groups and participant set* - Psychiatric and Related Somatic Conditions (PRSC): 3741 (1821 cases, 1822 controls, 98 others)

pht000070.v1.p1

1 itemgroup 228 items

Eligibility

1 itemgroup 2 items

pht000667.v1.p1

1 itemgroup 2 items

dbGaP phs000397 NIA Long Life Family Study (LLFS) - Eligibility

- 10/12/22 - 6 forms, 1 itemgroup, 3 items, 1 language
Itemgroup: IG.elig
Principal Investigator: Michael A. Province, PhD, Washington University School of Medicine, St. Louis, MO, USA MeSH: Longevity,Aging,Cardiovascular Diseases,Neoplasms,Stroke,Inflammation,Immune System,Diabetes Mellitus,Hypertension,Dyslipidemias,Lipids,Osteoporosis,Pulmonary Function Tests,Kidney Function Tests,Alzheimer Disease,Depression,Personality,Executive Function,Reproductive History https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000397 The Long Life Family Study (LLFS) is an international collaborative study of the genetics and familial components of exceptional survival, longevity, and healthy aging. Families were recruited through elderly probands (generally in their 90s) who self-reported on the survival history of their parents and siblings, and on the basis of this information, families which showed clustering of exceptional survival were recruited. [Specifically, a Family Longevity Selection Score (FLOSS) ≥7 was required. The FLOSS measures the average excess Observed lifespan over that Expected based upon lifetables, while adding a bonus term for still-living individuals. Thus FLOSS is a useful tool for scoring and selecting families for inclusion in a research study of exceptional survival (Sebastiani et al., 2009, PMID: 19910380)]. Probands resided in the catchment areas of four Field Centers (Boston University, Columbia University, University of Pittsburgh, and University of Southern Denmark). Recruited family members were phenotyped through extensive in-home visits by teams of technicians who traveled all over the USA and Denmark. Blood assays were centrally processed at a Laboratory Core (University of Minnesota) and protocols were standardized, monitored and coordinated through a Data Management Coordinating Center (Washington University). We examined and extensively phenotyped in all major domains of healthy aging, 4,953 individuals in 539 families through comprehensive in-home visits. Of these, 4,815 gave dbGaP sharing permission and had sufficient quantity/quality of DNA for GWAS genotyping. This large collection of families, selected on the basis of clustering for exceptional survival, is a unique resource for the study of human longevity and healthy aging. We estimate that less than 1% of the Framingham Heart Study (FHS) families (a roughly random population family sample) would meet the minimal entrance criteria for exceptional survival required in the LLFS (Sebastiani et al., 2009, PMID: 19910380). Thus, the least exceptional LLFS families show more clustering for exceptional longevity than 99% of the FHS families. Although the LLFS pedigrees were selected on the basis of longevity per se in the upper generation (and the generation above that), the children's generation have significantly lower rates of many major diseases and have better healthy aging profiles for many disease phenotypes (Newman et al., 2011, PMID: 21258136). The participants had their first in-person visit between 2006 and 2009. After that visit, they were contacted annually by telephone to update vital status, medical history, and general health. Between 2014 and 2017, willing participants completed a second in-person visit. The second visit followed the same protocols and centralized training as the first visit. During the second visit, a portable carotid ultrasound exam was added. Again, participants were continuously contacted annually for telephone follow-up during the period of the second in-person visit and after that. Annual telephone follow-ups currently ongoing, and plans for a third in-person visit are in progress.

pht002407.v3.p3

1 itemgroup 4 items

pht002408.v3.p3

1 itemgroup 6 items

pht002410.v3.p3

1 itemgroup 106 items

pht003356.v3.p3

1 itemgroup 4 items

pht002409.v3.p3

1 itemgroup 3 items

Hamilton Depression Rating Scale - F.1

- 4/11/22 - 1 form, 1 itemgroup, 19 items, 2 languages
Itemgroup: IG.1
Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry 1960; 23:56–62 CIPS(1977) Rating: Clinician-rated Administration time: 20–30 minutes Main purpose: To assess severity of, and change in, depressive symptoms Population: Adults

DASS Depressions-Angst-Stress-Skalen – deutschsprachige Kurzfassung - DASS

- 12/28/21 - 1 form, 1 itemgroup, 21 items, 1 language
Itemgroup: Fragen zum Befinden
Die deutschsprachige Kurzfassung der Depressions-Angst-Stress-Skalen (DASS) wird in der Schmerzforschung und -behandlung verwendet und wurde entwickelt, um psychologische Kernaspekte von Depression, Angst und Stress zu erfassen. Sie umfasst drei Skalen mit jeweils sieben Items. Die Erfassung psychischer Belastung hat einen zentralen Stellenwert in der Schmerzforschung und -behandlung. Für Depression als Komorbidität bei Schmerz bestehen dabei besondere methodische und konzeptionelle Schwierigkeiten. Reliabilität: Cronbachs Alpha lag bei Alpha = .91 (Depression), Alpha = .78-.82 (Angst) bzw. Alpha = .81-.89 (Stress). Validität: Konstruktvalidität des DASS wurden mit HADS (Hospital Anxiety and Depression Scale) und ADS (Allgemeine Depressionsskala) verglichen. Spezifität und Sensitivität für Depression wurden anhand eines strukturierten klinischen Interviews für DSM-IV (SKID) ermittelt und ebenfalls mit ADS und HADS verglichen. Nilges, P. & Essau, C. (2021). DASS. Depressions-Angst-Stress-Skalen - deutschsprachige Kurzfassung [Verfahrensdokumentation und Fragebogen mit Auswertung]. In Leibniz-Institut für Psychologie (ZPID) (Hrsg.), Open Test Archive. Trier: ZPID. https://doi.org/10.23668/psycharchives.4579 Disclaimer: After completion of my work with the measure, I will inform the test authors about the use of the measure and the results I have obtained by means of the feedback form.

Depression - Adult PhenX Toolkit

- 9/27/21 - 1 form, 1 itemgroup, 36 items, 2 languages
Itemgroup: PhenX - depression - adult protocol
This protocol is divided into two parts. Part I consists of the depression section from the Composite International Diagnostic Interview - Short Form (CIDI-SF), developed for the World Health Organization, and is a screening tool for the general population. Part II consists of the Quick Inventory of Depressive Symptoms (QIDS) that captures the severity of depressive symptoms in the last seven days. ODM derived from: https://cde.nlm.nih.gov Primary source: https://www.phenxtoolkit.org/ Recent publication: Hendershot, T., Pan, H., Haines, J., Harlan, W.R., Marazita, M.L., McCarty, C.A., Ramos, E.M., and Hamilton, C.M. (2015) Using the PhenX toolkit to add standard measures to a study. Curr. Protoc. Hum. Genet. 86:1.21.1-1.21.17. doi: 10.1002/0471142905.hg0121s86 German translation of CIDI-SF not validated. German translation of QIDS: http://www.ids-qids.org/translations/german/QIDS-SR_AU1.0_ger-DE.pdf Permission to publish granted by Carol M. Hamilton.

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