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Journal articles on the topic 'Cohort study'

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Author: Grafiati

Published: 4 June 2021

Last updated: 20 May 2024

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1

Akiba, Suminori, and Yoshihide Kinjo. "Japanese Legacy Cohorts: Six-Prefecture Cohort Study (Hirayama Cohort Study)." Journal of Epidemiology 30, no. 3 (March 5, 2020): 111–15. http://dx.doi.org/10.2188/jea.je20190249.

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van der Weijden, Jessica, Marco van Londen, Joke I. Roodnat, Marcia L. Kho, Jacqueline van de Wetering, Heinrich Kloke, Ine M. M. Dooper, et al. "Impact of measured versus estimated glomerular filtration rate-based screening on living kidney donor characteristics: A study of multiple cohorts." PLOS ONE 17, no. 7 (July 7, 2022): e0270827. http://dx.doi.org/10.1371/journal.pone.0270827.

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Background Most transplant centers in the Netherlands use estimated glomerular filtration rate (eGFR) for evaluation of potential living kidney donors. Whereas eGFR often underestimates GFR, especially in healthy donors, measured GFR (mGFR) allows more precise kidney function assessment, and therefore holds potential to increase the living donor pool. We hypothesized that mGFR-based donor screening leads to acceptance of donors with lower pre-donation eGFR than eGFR-based screening. Methods In this longitudinal cohort study, we compared eGFR (CKD-EPI) before donation in one center using mGFR-based screening (mGFR-cohort, n = 250) with two centers using eGFR-based screening (eGFR-cohort1, n = 466 and eGFR-cohort2, n = 160). We also compared differences in eGFR at five years after donation. Results Donor age was similar among the cohorts (mean±standard deviation (SD) mGFR-cohort 53±10 years, eGFR-cohort1 52±13 years, P = 0.16 vs. mGFR-cohort, and eGFR-cohort2 53±9 years, P = 0.61 vs. mGFR-cohort). Estimated GFR underestimated mGFR by 10±12 mL/min/1.73m2 (mean±SD), with more underestimation in younger donors. In the overall cohorts, mean±SD pre-donation eGFR was lower in the mGFR-cohort (91±13 mL/min/1.73m2) than in eGFR-cohort1 (93±15 mL/min/1.73m2, P<0.05) and eGFR-cohort2 (94±12 mL/min/1.73m2, P<0.05). However, these differences disappeared when focusing on more recent years, which can be explained by acceptance of more older donors with lower pre-donation eGFR over time in both eGFR-cohorts. Five years post-donation, mean±SD eGFR was similar among the centers (mGFR-cohort 62±12 mL/min/1.73m2, eGFR-cohort1 61±14 mL/min/1.73m2, eGFR-cohort2 62±11 mL/min/1.73m2, P = 0.76 and 0.95 vs. mGFR-cohort respectively). In the mGFR-cohort, 38 (22%) donors were excluded from donation due to insufficient mGFR with mean±SD mGFR of 71±9 mL/min/1.73m2. Conclusions Despite the known underestimation of mGFR by eGFR, we did not show that the routine use of mGFR in donor screening leads to inclusion of donors with a lower pre-donation eGFR. Therefore eGFR-based screening will be sufficient for the majority of the donors. Future studies should investigate whether there is a group (e.g. young donors with insufficient eGFR) that might benefit from confirmatory mGFR testing.

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Fouzia, Tebbani. "Maternal Anemia during Pregnancy: A Longitudinal Cohort Study." Women Health Care and Issues 4, no. 2 (April 9, 2021): 01–07. http://dx.doi.org/10.31579/2642-9756/041.

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Introduction: Anemia is a public health problem, prevalent among women of childbearing age. The aim was to determine the frequency of anemia in the first, second and third trimesters of pregnancy and to determine the associating factors in Algerian pregnant women. Methods: We conducted a prospective and longitudinal cohort study of 300 pregnant women from December 2013 to July 2016. All consenting women attending antenatal clinics and having undergone complete blood count (CBC) were included in the study. Sociodemographic characteristics, individual’s obstetrical history and the results of the CBC were collected. Anemia was defined according to the WHO criteria. After some descriptive statistics, we performed a bivariate analysis using the Chi-square test and Fisher exact probability test in order to determine the factors associated with gestational anemia. Results: The rate of anemia was 28.0 % in the first trimester, 32.3 % in the second and 54.2 % in the third one. It was more frequently observed during the third trimester of pregnancy (P < 0.05). No significant difference was found between gestational anemia and socio-demographic factors. Women with inadequate gain were more anemic (p = 0.01). The average concentration of hemoglobin, hematocrit, VGM and platelets were lower in anemic pregnant women (p < 0.0001). Conclusion: The prevalence of anemia during pregnancy remains high. A better management of chronic diseases in pregnant women and of postpartum follow-up is necessary to treat anemia before a subsequent pregnancy.

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Mary-Krause, Murielle, Joel José Herranz Bustamante, Camille Bolze, Cédric Galéra, Eric J. Fombonne, and Maria Melchior. "Cohort Profile: The TEMPO Cohort Study." International Journal of Epidemiology 50, no. 4 (May 20, 2021): 1067–68. http://dx.doi.org/10.1093/ije/dyab026.

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5

Fotouhi, A., H. Hashemi, M. Shariati, M. H. Emamian, K. Yazdani, E. Jafarzadehpur, H. Koohian, et al. "Cohort Profile: Shahroud Eye Cohort Study." International Journal of Epidemiology 42, no. 5 (October 17, 2012): 1300–1308. http://dx.doi.org/10.1093/ije/dys161.

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6

Syddall, HE, A. Aihie Sayer, EM Dennison, HJ Martin, DJP Barker, and C. Cooper. "Cohort Profile: The Hertfordshire Cohort Study." International Journal of Epidemiology 34, no. 6 (June 17, 2005): 1234–42. http://dx.doi.org/10.1093/ije/dyi127.

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7

Goldberg, Marcel, Annette Leclerc, Sébastien Bonenfant, Jean François Chastang, Annie Schmaus, Nadine Kaniewski, and Marie Zins. "Cohort profile: the GAZEL Cohort Study." International Journal of Epidemiology 36, no. 1 (November 12, 2006): 32–39. http://dx.doi.org/10.1093/ije/dyl247.

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8

Verschuren, W., A. Blokstra, H. Picavet, and H. Smit. "Cohort Profile: The Doetinchem Cohort Study." International Journal of Epidemiology 37, no. 6 (January 31, 2008): 1236–41. http://dx.doi.org/10.1093/ije/dym292.

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9

Ghalib, Hawar Hasan Ali. "COMPLICATIONS AFTER SURGERY FOR INVASIVE BREAST CANCER: COHORT STUDY." Journal of Sulaimani Medical College 6, no. 1 (June 1, 2016): 1–7. http://dx.doi.org/10.17656/jsmc.10082.

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10

Wesseling, Janet, Maarten Boers, Max A. Viergever, Wim KHA Hilberdink, Floris PJG Lafeber, Joost Dekker, and Johannes WJ Bijlsma. "Cohort Profile: Cohort Hip and Cohort Knee (CHECK) study." International Journal of Epidemiology 45, no. 1 (August 29, 2014): 36–44. http://dx.doi.org/10.1093/ije/dyu177.

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11

MACKERRAS, Dorothy E. M., Gurmeet R. SINGH, and Susan SAYERS. "The Aboriginal Birth Cohort Study: When is a cohort study not a cohort design?" Nutrition & Dietetics 67, no. 3 (August 25, 2010): 171–76. http://dx.doi.org/10.1111/j.1747-0080.2010.01451.x.

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12

Becker, James T., Lawrence A. Kingsley, Samantha Molsberry, Sandra Reynolds, Aaron Aronow, Andrew J. Levine, Eileen Martin, et al. "Cohort Profile: Recruitment cohorts in the neuropsychological substudy of the Multicenter AIDS Cohort Study." International Journal of Epidemiology 44, no. 5 (April 24, 2014): 1506–16. http://dx.doi.org/10.1093/ije/dyu092.

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13

SASAKI, Noriyuki, and Tatsuo KAGIMURA. "Case Cohort Study." Japanese Journal of Pharmacoepidemiology/Yakuzai ekigaku 18, no. 2 (2014): 84–89. http://dx.doi.org/10.3820/jjpe.18.84.

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14

WEICH, SCOTT. "The cohort study." International Review of Psychiatry 10, no. 4 (January 1998): 284–90. http://dx.doi.org/10.1080/09540269874637.

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Luo, Miyang, Linda Wei Lin Tan, Xueling Sim, Milly Khiam Hoon Ng, Rob Van Dam, E. Shyong Tai, Kee Seng Chia, Wern Ee Tang, Darren EJ Seah, and Kavita Venkataraman. "Cohort profile: the Singapore diabetic cohort study." BMJ Open 10, no. 5 (May 2020): e036443. http://dx.doi.org/10.1136/bmjopen-2019-036443.

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PurposeThe diabetic cohort (DC) was set up to study the determinants of complications in individuals with type 2 diabetes and examine the role of genetic, physiological and lifestyle factors in the development of complications in these individuals.ParticipantsA total of 14 033 adult participants with type 2 diabetes were recruited from multiple public sector polyclinics and hospital outpatient clinics in Singapore between November 2004 and November 2010. The first round of follow-up was conducted for 4131 participants between 2012 and 2016; the second round of follow-up started in 2016 and is expected to end in 2021. A questionnaire survey, physical assessments, blood and urine sample collection were conducted at recruitment and each follow-up visit. The data set also includes genetic data and linkage to medical and administrative records for recruited participants.Findings to dateData from the cohort have been used to identify determinants of diabetes and related complications. The longitudinal data of medical records have been used to analyse diabetes control over time and its related outcomes. The cohort has also contributed to the identification of genetic loci associated with type 2 diabetes and diabetic kidney disease in collaboration with other large cohort studies. About 25 scientific papers based on the DC data have been published up to May 2019.Future plansThe rich data in DC can be used for various types of research to study disease-related complications in patients with type 2 diabetes. We plan to further investigate disease progression and new biomarkers for common diabetic complications, including diabetic kidney disease and diabetic neuropathy.

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Connelly, R., and L. Platt. "Cohort Profile: UK Millennium Cohort Study (MCS)." International Journal of Epidemiology 43, no. 6 (February 17, 2014): 1719–25. http://dx.doi.org/10.1093/ije/dyu001.

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Clavel-Chapelon, Françoise. "Cohort Profile: The French E3N Cohort Study." International Journal of Epidemiology 44, no. 3 (September 10, 2014): 801–9. http://dx.doi.org/10.1093/ije/dyu184.

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18

Goldberg, Marcel, Annette Leclerc, and Marie Zins. "Cohort Profile Update: The GAZEL Cohort Study." International Journal of Epidemiology 44, no. 1 (November 23, 2014): 77–77. http://dx.doi.org/10.1093/ije/dyu224.

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19

Obel, N., F. N. Engsig, L. D. Rasmussen, M. V. Larsen, L. H. Omland, and H. T. Sorensen. "Cohort Profile: The Danish HIV Cohort Study." International Journal of Epidemiology 38, no. 5 (September 17, 2008): 1202–6. http://dx.doi.org/10.1093/ije/dyn192.

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Jacobs, J. M., A. Cohen, M. Bursztyn, D. Azoulay, E. Ein-Mor, and J. Stessman. "Cohort Profile: the Jerusalem longitudinal cohort study." International Journal of Epidemiology 38, no. 6 (December 4, 2008): 1464–69. http://dx.doi.org/10.1093/ije/dyn252.

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Schoeni-Affolter, F., B. Ledergerber, M. Rickenbach, C. Rudin, H. F. Gunthard, A. Telenti, H. Furrer, S. Yerly, and P. Francioli. "Cohort Profile: The Swiss HIV Cohort Study." International Journal of Epidemiology 39, no. 5 (November 30, 2009): 1179–89. http://dx.doi.org/10.1093/ije/dyp321.

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22

Low, Gary Kim Kuan, Seng Chiew Gan, and Shu Cheow Ho. "Biomarkers in differentiating clinical dengue cases: A prospective cohort study." Journal of Coastal Life Medicine 3, no. 12 (December 2015): 967–70. http://dx.doi.org/10.12980/jclm.3.2015j5-141.

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23

Kaprio, Jaakko. "The Finnish Twin Cohort Study: An Update." Twin Research and Human Genetics 16, no. 1 (January 8, 2013): 157–62. http://dx.doi.org/10.1017/thg.2012.142.

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In 2002 and 2006, review papers have described the Finnish Twin Cohort and studies conducted on these population-based, longitudinal data sets with extensive follow-up data. Three cohorts have been established: the older twin cohort in the 1970s, and the Finntwin12 and Finntwin16 studies initiated in the 1990s. The present review provides on update on the latest data collections conducted since the previous review. These cover the fourth waves of data collection in the older cohort (twins born before 1958) and Finntwin12 (twins born 1983–1987). The fifth wave of data collection in Finntwin16 (twins born 1975–1979) also included assessments of their spouses/partners. An analysis of mortality in the older cohort from 1975 to 2009 indicates that the mortality of adult twins (as individuals) does not differ from the population at large. Based on the cohorts, many sub-studies with more detailed phenotyping and collection of omics data have been conducted or are in progress. We also contribute to numerous national and international collaborations.

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Tao, Luo, Tian Tian, Lirong Liu, Zewen Zhang, Qi Sun, Gaofeng Sun, Jianghong Dai, and Hong Yan. "Cohort profile: The Xinjiang Multiethnic Cohort (XMC) study." BMJ Open 12, no. 5 (May 2022): e048242. http://dx.doi.org/10.1136/bmjopen-2020-048242.

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PurposeTo investigate the potential causal link between heredity, geographical environment, diet and other lifestyle factors with long-term health consequences, we established the Xinjiang Multiethnic Cohort Study (XMC), the first large-scale prospective cohort in Xinjiang, China.ParticipantsXMC commenced in 2018 and enrolled participants from three study sites (Urumqi, Hotan and Ili) in Xinjiang, China. Data collected include standard baseline questionnaire, physical measurement, biological specimen. In addition, about one-third of participants were assessed habitual diet by a more detailed semiquantitative food frequency questionnaire which included 127 foods items at baselineFindings to dateFinally, a total of 30 949 participants, with 32.37% from Urumqi, 41.75% from Hotan, and 25.88% from Ili were recruited in XMC. The average age of participants was 56.21 years for men, and 54.75 years for women. More than 60% of participants in all three survey sites reported an average consumption of fruit and vegetable three or more times per week. In Hotan and Ili, the staple food was wheaten food, whereas, in Urumqi, rice and wheaten food was the food staples. Consumption of white meat, such as fish and poultry, was lower in the three survey locations. Based on self-reported disease from study participants, the five most common chronic diseases among participants across all three survey locations were dyslipidaemia, hypertension, cholecystitis, diabetes, ischaemic heart disease.Future plansFirst, we will collect all health-related records of the study participants in January each year for the previous year. Second, 10% of subjects were randomly selected for telephone follow-up in the final year of cohort building. Finally, as planned, we will revisit the study subjects on site every 2–3 years. Again, we will conduct face-to-face questionnaires and collect biological specimens such as blood and urine from the study subjects.

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25

Lee, Wanhyung, Yongho Lee, Junhyeong Lee, Uijin Kim, Eunsun Han, Seunghon Ham, Won-Jun Choi, and Seong-Kyu Kang. "Cohort Profile: Gachon Regional Occupational Cohort Study (GROCS)." Safety and Health at Work 13, no. 1 (March 2022): 112–16. http://dx.doi.org/10.1016/j.shaw.2021.11.005.

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26

Hu, Pian, Azhu Han, Yan Hu, Yuqi Wen, Jingjing Liang, Wanqi Xiao, Suifang Lin, et al. "Cohort protocol: Guangzhou High-Risk Infant Cohort study." BMJ Open 10, no. 10 (October 2020): e037829. http://dx.doi.org/10.1136/bmjopen-2020-037829.

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IntroductionDespite the increase in the survival rate of high-risk infants (HRIs) worldwide, the prevalence of motor and neurodevelopmental sequelae in such newborns has not shown concomitant improvement. Meanwhile, there are few cohorts that explore factors related to the development of HRIs in China. Therefore, the Guangzhou High-Risk Infant Cohort (GHRIC) has been designed to examine the complex relationships among a myriad of factors influencing growth and development in such children.Methods and analysisThe GHRIC study is a prospective cohort study that by the year 2023 will enrol an estimated total of 3000 HRIs from Guangzhou Women and Children’s Medical Center (GWCMC) in Guangzhou, China. This study is designed to assess the growth and cognitive characteristics of HRIs and the risk factors affecting their development and prognoses. Data on risk factors, neurodevelopmental and cognitive-function evaluations, laboratory results, and specimens will be collected and analysed. Information on perinatal and clinical interventions for these infants will also be recorded during regular follow-up visits until age 6.Ethics and disseminationThe protocol for this study has been approved by the Research Ethics Committee of GWCMC, which accepted responsibility for supervising all of the aspects of the study (No. 2017102712). Study outcomes will be disseminated through conference presentations, peer-reviewed publications, the Internet and social media.Trial registration numberChiCTR-EOC-17013236

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27

Tao, Fang-Biao, Jia-Hu Hao, Kun Huang, Pu-Yu Su, Dai-Juan Cheng, Xiu-Ya Xing, Zhao-Hui Huang, Jing-Li Zhang, and Shi-Lu Tong. "Cohort Profile: The China-Anhui birth cohort study." International Journal of Epidemiology 42, no. 3 (June 21, 2012): 709–21. http://dx.doi.org/10.1093/ije/dys085.

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Amigo, Hugo, Patricia Bustos, Elinor Zumelzú, and Roberto J. Rona. "Cohort Profile: The Limache, Chile, birth cohort study." International Journal of Epidemiology 43, no. 4 (December 23, 2013): 1031–39. http://dx.doi.org/10.1093/ije/dyt091.

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Manczuk, Marta, Paolo Boffetta, Samantha Sartori, Dana Hashim, Lars J. Vatten, and Witold A. Zatonski. "Cohort Profile: The Polish-Norwegian Study (PONS) cohort." International Journal of Epidemiology 46, no. 2 (May 6, 2015): e5-e5. http://dx.doi.org/10.1093/ije/dyv037.

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Steegers-Theunissen, Régine PM, Jennifer JFM Verheijden-Paulissen, Evelyne M. van Uitert, Mark F. Wildhagen, Niek Exalto, Anton HJ Koning, Alex J. Eggink, et al. "Cohort Profile: The Rotterdam Periconceptional Cohort (Predict Study)." International Journal of Epidemiology 45, no. 2 (July 29, 2015): 374–81. http://dx.doi.org/10.1093/ije/dyv147.

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Cade, Janet E., Victoria J. Burley, Nisreen A. Alwan, Jayne Hutchinson, Neil Hancock, Michelle A. Morris, Diane E. Threapleton, and Darren C. Greenwood. "Cohort Profile: The UK Women’s Cohort Study (UKWCS)." International Journal of Epidemiology 46, no. 2 (October 1, 2015): e11-e11. http://dx.doi.org/10.1093/ije/dyv173.

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32

Karlsson, Linnea, Mimmi Tolvanen, Noora M. Scheinin, Henna-Maria Uusitupa, Riikka Korja, Eeva Ekholm, Jetro J. Tuulari, et al. "Cohort Profile: The FinnBrain Birth Cohort Study (FinnBrain)." International Journal of Epidemiology 47, no. 1 (September 14, 2017): 15–16. http://dx.doi.org/10.1093/ije/dyx173.

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Emamian, Mohammad Hassan, Hassan Hashemi, Mehdi Khabazkhoob, Sarvenaz Malihi, and Akbar Fotouhi. "Cohort Profile: Shahroud Schoolchildren Eye Cohort Study (SSCECS)." International Journal of Epidemiology 48, no. 1 (December 6, 2018): 27–27. http://dx.doi.org/10.1093/ije/dyy250.

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Ando, Shuntaro, Atsushi Nishida, Syudo Yamasaki, Shinsuke Koike, Yuko Morimoto, Aya Hoshino, Sho Kanata, et al. "Cohort Profile: The Tokyo Teen Cohort study (TTC)." International Journal of Epidemiology 48, no. 5 (March 16, 2019): 1414–1414. http://dx.doi.org/10.1093/ije/dyz033.

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Elser, Holly, Andreas M. Neophytou, Erika Tribett, Deron Galusha, Sepideh Modrek, Elizabeth M. Noth, Valerie Meausoone, Ellen A. Eisen, Linda F. Cantley, and Mark R. Cullen. "Cohort Profile: The American Manufacturing Cohort (AMC) study." International Journal of Epidemiology 48, no. 5 (April 9, 2019): 1412–22. http://dx.doi.org/10.1093/ije/dyz059.

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Olsen, Else Marie, Charlotte Ulrikka Rask, Hanne Elberling, Pia Jeppesen, Lars Clemmensen, Anja Munkholm, Xiao Qiang Li, et al. "Cohort Profile: The Copenhagen Child Cohort Study (CCC2000)." International Journal of Epidemiology 49, no. 2 (December 26, 2019): 370–71. http://dx.doi.org/10.1093/ije/dyz256.

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Liu, J., L. A. McCauley, Y. Zhao, H. Zhang, and J. Pinto-Martin. "Cohort Profile: The China Jintan Child Cohort Study." International Journal of Epidemiology 39, no. 3 (May 11, 2009): 668–74. http://dx.doi.org/10.1093/ije/dyp205.

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McCaw-Binns, A., D. Ashley, M. Samms-Vaughan, R. Wilks, T. Ferguson, N. Younger, J. A. Reece, M. Tulloch-Reid, and K. Foster-Williams. "Cohort Profile: The Jamaican 1986 Birth Cohort Study." International Journal of Epidemiology 40, no. 6 (August 30, 2010): 1469–76. http://dx.doi.org/10.1093/ije/dyq149.

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Pasdar, Yahya, Farid Najafi, Mehdi Moradinazar, Ebrahim Shakiba, Hosain Karim, Behrooz Hamzeh, Michael Nelson, and Annette Dobson. "Cohort Profile: Ravansar Non-Communicable Disease cohort study: the first cohort study in a Kurdish population." International Journal of Epidemiology 48, no. 3 (February 6, 2019): 682–83. http://dx.doi.org/10.1093/ije/dyy296.

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Ghosh, Shaurav. "Early Total Body CT for Trauma Patients: A Randomized Cohort Study." New Indian Journal of Surgery 11, no. 4 (December 15, 2020): 515–22. http://dx.doi.org/10.21088/nijs.0976.4747.11420.11.

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Introduction: The purpose of our study was to compare Total-Body Computed Tomography (TBCT) with selective scanning in adults with poly-trauma and assess outcomes as a function of scan time, cost, radiation exposure and length of hospital stay. Methodology: A retrospective analysis was performed with data derived from the trauma registry of the Emergency department of a Quaternary care hospital. Admissions from January, 2017 to December, 2017 were considered. Patients were selected based on their Injury Severity Score (ISS). Descriptive and inferential statistical analysis was done using this data. Results: Outcomes were independent of gender and age distribution. Most patients belonged to the Young Adult (18–35 years) age group. The average time for scanning was 43.88m. Radiation Exposure was found to be increased after TBCT imaging compared with selective imaging. Scan-time and cost of investigation were less for the TBCT group. In the case of the selective scanning group, cost increased as re-imaging and further extended imaging was used. LOS was less for the TBCT imaging group. Subsequent re-visits post hospitalization were more in the case of the selective imaging group. Conclusions: The results from this study suggest that application of Total Body CT significantly reduces overall time spent in the emergency department, with higher exposure to radiation, but with an overall benefit in terms of lower cost.

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Araújo, Natália, Isa Silva, Patrícia Campos, Rita Correia, Margarida Calejo, Pedro Freitas, Mariana Seco, et al. "Long-term neurological complications in COVID-19 survivors: study protocol of a prospective cohort study (NeurodegCoV-19)." BMJ Open 13, no. 7 (July 2023): e072981. http://dx.doi.org/10.1136/bmjopen-2023-072981.

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BackgroundEvidence suggests an association between SARS-CoV-2 infection and worse performance on cognitive tests, and a higher risk of Parkinson’s disease (PD) and dementia up to 6 and 12 months after infection, respectively. Longer follow-ups with comparison groups are needed to clarify the potentially increased risk of neurodegenerative diseases in COVID-19 survivors, namely those infected before mass vaccination.MethodsA prospective study started in July 2022 with four cohorts of 150 individuals each, defined according to SARS-CoV-2 infection and hospitalisation status between March 2020 and February 2021: cohort 1—hospitalised due to SARS-CoV-2 infection; cohort 2—hospitalised, COVID-19-free; cohort 3—infected, not hospitalised; cohort 4—not infected, not hospitalised. Cohort 2 will be matched to cohort 1 according to age, sex, level of hospitalisation care and length of stay; cohort 4 will be age-matched and sex-matched to cohort 3. Baseline, 1-year and 2-year follow-up evaluations will include: cognitive performance assessed with the Montreal Cognitive Assessment (MoCA) and neuropsychological tests; the assessment of prodromal markers of PD with Rapid Eye Movement Sleep Behaviour Disorder single-question Screen and self-reported olfactory and gustative alterations; screening of PD with the 9-item PD screening questionnaire; gait evaluation with Timed Up&Go test. Suspected cases of cognitive impairment and PD will undergo a clinical evaluation by a neurologist. Frequency measures of neurological complications, prodromal markers and diagnoses of dementia and PD, will be presented. The occurrence of cognitive decline—the difference between baseline and 1-year MoCA scores 1.5 SD below the mean of the distribution of the variation—will be compared between cohorts 1 and 2, and cohorts 3 and 4 with OR estimated using multivariate logistic regression.Ethics and disseminationThis study received ethics approval from the Ethics Committees of the health units Unidade Local de Saúde de Matosinhos and Centro Hospitalar de Entre Douro e Vouga, and informed consent is signed for participating. Results will be disseminated among the scientific community and the public.

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42

Kreuzer, M., M. Schnelzer, A. Tschense, L. Walsh, and B. Grosche. "Cohort Profile: The German uranium miners cohort study (WISMUT cohort), 1946-2003." International Journal of Epidemiology 39, no. 4 (June 3, 2009): 980–87. http://dx.doi.org/10.1093/ije/dyp216.

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43

Bickenbach, J., A. Tennant, and G. Stucki. "The SwiSCI Cohort Study." Journal of Rehabilitation Medicine 48, no. 2 (2016): 117–19. http://dx.doi.org/10.2340/16501977-2056.

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Nishimura, Masaharu, and Hironi Makita. "Hokkaido COPD Cohort Study." Nihon Naika Gakkai Zasshi 102, no. 2 (2013): 463–70. http://dx.doi.org/10.2169/naika.102.463.

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Azizova, T. V., V. Fedirko, Y. Tsareva, F. Tretyakov, C. Funch Lassen, S. Friis, and J. Schüz. "Mayak Workers Study Cohort." Methods of Information in Medicine 51, no. 02 (2012): 144–49. http://dx.doi.org/10.3414/me11-01-0049.

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Summary Background: The cause-of-death register at the Southern Urals Biophysics Institute (SUBI), Ozyorsk, Russia, was established to document the number and causes of deaths in the Mayak workers cohort, which includes all persons (N = 22,377) employed at Mayak nuclear facility between 1948 and 1982. Most workers were occupationally exposed to high doses of ionizing radiation and have been shown to have increased risks of various chronic diseases including cancer. Objectives: To investigate the quality of cause of death coding in the SUBI register. Methods: A random sample of 246 deaths (~1% of the total) was coded independently at the SUBI and the Danish Cancer Society using the International Classification of Diseases 9 (ICD-9). Proportions of matching codes were calculated. Results: Overall, 233 deaths (95%) were identically classified using the ICD-9 main category matching. Excluding mismatches that were considered to be incorrectly coded during validation, the validity of the register increased to 98%. Using the specific ICD-9 first three-digit matching, 182 deaths were identically coded (74%) and the respective validity of the register was 85%. There were also some non-resolvable discrepancies demonstrating limitations of assigning one code for each death or using language-adapted ICD-9 version. Conclusions: This validation study was an important quality check of a register used for mortality follow-up in a highly influential epidemiological study on radiation-related health effects. The results of the inter-institutional comparison were generally favourable; however, since the comparison revealed individual mismatches and some systematically differing coding practices, it is essential to repeat it on a regular basis in order to maintain a high quality.

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&NA;. "A Retrospective Cohort Study." Endocrinologist 9, no. 1 (January 1999): 72. http://dx.doi.org/10.1097/00019616-199901000-00020.

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Sobel, W., G. G. Bond, T. W. Parsons, and F. E. Brenner. "Acrylamide cohort mortality study." Occupational and Environmental Medicine 43, no. 11 (November 1, 1986): 785–88. http://dx.doi.org/10.1136/oem.43.11.785.

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Krieg, M. A., D. Stoll, B. Aubry-Rozier, M. Metzger, D. Hans, and O. Lamy. "The OsteoLaus Cohort Study." Osteologie 21, no. 02 (2012): 77–82. http://dx.doi.org/10.1055/s-0037-1621671.

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ZusammenfassungEine indirekte Beurteilung der Mikroarchitektur (MA) ist in der täglichen Praxis anhand des TBS (Trabecular Bone Score) näherungsweise möglich. Das Ziel der OsteoLaus-Kohorte besteht darin, klinische Risikofaktoren und Informationen aus der DXA (Knochenmineraldichte [BMD], TBS und Wirbelkörperfrakturerkennung [VFA]) zu kombinieren, um Frauen mit hohem Frakturrisiko leichter zu erkennen. Wir nahmen 631 Frauen im mittleren Alter von 67,4 ± 6,7 J. und mit einem BMI von 26,1 ± 4,6 auf. Es bestand eine schwache Korrelation zwischen BMD und Zentrums-gematchtem TBS (r2 = 0,16). Die Prävalenz von Wirbelfrakturen (VFx) Grad 2/3, größeren osteoporotischen (OP) Frakturen und allen OP-Frakturen betrug 8,4 %, 17,0 % bzw. 26,0 %. Alters- und BMI-adjustierte OR (nach abnehmender SD) lagen bei 1,8 (1,2–2,5), 1,6 (1,2–2,1) bzw. 1,3 (1,1–1,6) für BMD und 2,0 (1,4–3,0), 1,9 (1,4–2,5) bzw. 1,4 (1,1–1,7) für TBS. Die TBS OR (nach abnehmender SD), adjustiert nach Alter, BMI und Wirbelsäulen-BMD, für VFx Grad 2/3, größere und alle OP-Frakturen betrugen 1,7 (1,1–2,7), 1,6 (1,2–2,2) bzw. 1,3 (1,0–1,7). Nur 35 bis 44 % der Frauen mit OP-Frakturen hatten eine BMD <−2,5 SD oder einen TBS < 1.200. Durch Kombination eines BMD < −2,5 SD oder TBS < 1,200 werden 54 bis 60 % der Frauen mit OP-Fraktur erkannt. Somit können wir anhand von VFA, BMD und TBS aus einem einfachen und strahlenarmen Röntgenverfahren, der DXA, Zusatzinformationen gewinnen, die für den Patienten im Praxisalltag von Nutzen sind.

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Nakano, Makiko, Kazuyuki Omae, Kazuhiko Uchida, Takehiro Michikawa, Noriyuki Yoshioka, Miyuki Hirata, and Akiyo Tanaka. "Five-Year Cohort Study." Chest 146, no. 5 (November 2014): 1166–75. http://dx.doi.org/10.1378/chest.13-2484.

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Donnell, Robert F. "Chronic prostatitis cohort study." Current Urology Reports 4, no. 4 (July 2003): 310. http://dx.doi.org/10.1007/s11934-003-0090-3.

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