The reporting of studies conducted using observational routinely-collected health data (RECORD) statement_Chinese Journal of Evidence-Based Medicine

Authors：

Translated by: NIE Xiaolu ,  PENG Xiaoxia , 譯

Clinical Epidemjology & Evidence-based Medicine Center, Beijing Children's Hospital, Capital Medical University, Beijing, 100045, P. R. China;

Corresponding?author：

PENG Xiaoxia, Email: pengxiaoxia@bch.com.cn

Keywords：

RECORD 規范; 指南

DOI：

10.7507/1672-2531.201702009

Video：

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Abstract Full text Figures/Tables Video References Cited by

常規收集衛生數據是指基于管理和臨床目的且事先沒有特定研究目標而收集的數據，已被越來越多地用于研究。此類數據發展迅速，可及性好，但相關注意事項并未在現有的報告規范中被提及，如加強觀察性流行病學研究報告的聲明（strengthening the reporting of observational studies in epidemiology statement，STROBE）。使用常規收集衛生數據開展觀察性研究的報告規范（the report of studies conducted using observational routinely collected data，RECORD）可填補該空缺。RECORD 規范是 STROBE 規范擴展版，其可用來提出針對使用常規收集衛生數據開展觀察性研究有關報告的條件要求。RECORD 清單擴展了包括題目、摘要、前言、方法、結果、討論和其他內容等需要在此類研究報告中包含的 13 個條目內容。該規范包括了清單、詳盡的解釋性信息以提高清單的使用。該規范還給出每條 RECORD 清單條目良好的報告實例。本文及其官網和留言板（http://www.record-statement.org）可提高 REDORD 規范的應用和理解。通過應用 RECORD，作者、期刊編輯和同行評議者可促進研究報告的質量。

Citation： Translated by: NIE Xiaolu, PENG Xiaoxia. The reporting of studies conducted using observational routinely-collected health data (RECORD) statement. Chinese Journal of Evidence-Based Medicine, 2017, 17(4): 475-487. doi: 10.7507/1672-2531.201702009 Copy

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1. ?Spasoff RA. Epidemiologic Methods for Health Policy. New York: Oxford University Press, 1999.
2. Morrato EH, Elias M, Gericke CA. Using population-based routine data for evidence-based health policy decisions: lessons from three examples of setting and evaluating national health policy in Australia, the UK and the USA. J Public Health, 2007, 29(4): 463-471.
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4. Hemkens LG, Benchimol EI, Langan SM,et al. Reporting of studies using routinely collected health data: systematic literature analysis (oral abstract presentation). Edinburgh, UK: Reward/equator conference, 2015.
5. Benchimol EI, Manuel DG, To T,et al. Development and use of reporting guidelines for assessing the quality of validation studies of health administrative data. J Clin Epidemiol, 2011, 64(8): 821-829.
6. Herrett E, Thomas SL, Schoonen WM,et al. Validation and validity of diagnoses in the General Practice Research Database: a systematic review. Br J Clin Pharmacol, 2010, 69(1): 4-14.
7. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology, 3rd edition. Philadelphia: Lippincott Williams & Wilkins, 2008.
8. Plint AC, Moher D, Morrison A,et al. Does the CONSORT checklist improve the quality of reports of randomised controlled trials? A systematic review. Med J Aust, 2006, 185(5): 263-267.
9. Enhancing the quality and transparency of health research (EQUATOR) network library 2015. Available at: www.equator-network.org/library/.
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12. Sorensen AA, Wojahn RD, Manske MC,et al. Using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement to assess reporting of observational trials in hand surgery. J Hand Surg Am, 2013, 38(8): 1584-1589.
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14. Benchimol EI, Langan S, Guttmann A. Call to RECORD: the need for complete reporting of research using routinely collected health data. J Clin Epidemiol, 2013, 66(7): 703-705.
15. Langan SM, Benchimol EI, Guttmann A,et al. Setting the RECORD straight: developing a guideline for the REporting of studies Conducted using Observational Routinely collected Data. Clin Epidemiol, 2013, 5: 29-31.
16. Nicholls SG, Quach P, von Elm E,et al. The REporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) Statement: Methods for Arriving at Consensus and Developing Reporting Guidelines. PLOS One, 2015, 10(5): e0125620.
17. Moher D, Schulz KF, Simera I,et al. Guidance for developers of health research reporting guidelines. PLOS Med, 2010, 7(2): e1000217.
18. Glasziou P, Altman DG, Bossuyt P,et al. Reducing waste from incomplete or unusable reports of biomedical research. Lancet, 2014, 383(9913): 267-276.
19. Blotiere PO, Weill A, Ricordeau P,et al. Perforations and haemorrhages after colonoscopy in 2010: a study based on comprehensive French health insurance data (SNIIRAM). Clin Res Hepatol Gastroenterol, 2014, 38(1): 112-117.
20. Siregar S, Pouw ME, Moons KG,et al. The Dutch hospital standardised mortality ratio (HSMR) method and cardiac surgery: benchmarking in a national cohort using hospital administration data versus a clinical database. Heart, 2014, 100(9): 702-710.
21. Price SD, Holman CD, Sanfilippo FM,et al. Use of case-time-control design in pharmacovigilance applications: exploration with high-risk medications and unplanned hospital admissions in the Western Australian elderly. Pharmacoepidemiol Drug Saf, 2013, 22(11): 1159-1170.
22. Gross CP, Andersen MS, Krumholz HM,et al. Relation between Medicare screening reimbursement and stage at diagnosis for older patients with colon cancer. JAMA, 2006, 296(23): 2815-2822.
23. Vandenbroucke JP. Observational research, randomised trials, and two views of medical science. PLOS Med, 2008, 5(3): e67.
24. Smith GD, Ebrahim S. Data dredging, bias, or confounding. BMJ, 2002, 325(7378): 1437-1438.
25. Prokosch HU, Ganslandt T. Perspectives for medical informatics. Reusing the electronic medical record for clinical research. Methods Inf Med, 2009, 48(1): 38-44.
26. Benchimol EI, Manuel DG, Guttmann A,et al. Changing age demographics of inflammatory bowel disease in ontario, canada: a population-based cohort study of epidemiology trends. Inflamm Bowel Dis, 2014, 20(10): 1761-1769.
27. Ducharme R, Benchimol EI, Deeks SL,et al. Validation of diagnostic codes for intussusception and quantification of childhood intussusception incidence in ontario, Canada: a population-based study. J Pediatr, 2013, 163(4): 1073-1079.
28. Herrett E, Shah AD, Boggon R,et al. Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study. BMJ, 2013, 346: f2350.
29. van Herk-Sukel MP, van de Poll-Franse LV, Lemmens VE,et al. New opportunities for drug outcomes research in cancer patients: the linkage of the Eindhoven Cancer Registry and the PHARMO Record Linkage System. Eur J Cancer, 2010, 46(2): 395-404.
30. Fosbol EL, Granger CB, Peterson ED,et al. Prehospital system delay in STsegment elevation myocardial infarction care: a novel linkage of emergency medicine services and in hospital registry data. Am Heart J, 2013, 165(3): 363-370.
31. Manuel DG, Rosella LC, Stukel TA. Importance of accurately identifying disease in studies using electronic health records. BMJ, 2010, 341: c4226.
32. Narcolepsy in association with pandemic influenza vaccination (a multi-country European epidemiological investigation). Stockholm: ECDC, 2012.
33. Lewis JD, Schinnar R, Bilker WB,et al. Validation studies of the health improvement network (THIN) database for pharmacoepidemiology research. Pharmacoepidemiol Drug Saf, 2007, 16(4): 393-401.
34. Sorensen HT, Sabroe S, Olsen J. A framework for evaluation of secondary data sources for epidemiological research. Int J Epidemiol, 1996, 25(2): 435-442.
35. Baron JA, Lu-Yao G, Barrett J,et al. Internal validation of Medicare claims data. Epidemiology, 1994, 5(5): 541-544.
36. Marston L, Carpenter JR, Walters KR,et al. Smoker, ex-smoker or non-smoker? The validity of routinely recorded smoking status in UK primary care: a cross-sectional study. BMJ open, 2014, 4(4): e004958.
37. Hardelid P, Dattani N, Gilbert R. Estimating the prevalence of chronic conditions in children who die in England, Scotland and Wales: a data linkage cohort study. BMJ Open, 2014, 4(8): e005331.
38. Murray J, Bottle A, Sharland M,et al. Risk factors for hospital admission with RSV bronchiolitis in England: a population-based birth cohort study. PLOS One, 2014, 9(2): e89186.
39. Berry JG, Hall M, Hall DE,et al. Inpatient growth and resource use in 28 children's hospitals: a longitudinal, multi-institutional study. JAMA pediatrics, 2013, 167(2): 170-177.
40. Shahian DM, Wolf RE, Iezzoni LI,et al. Variability in the measurement of hospital-wide mortality rates. N Engl J Med, 2010, 363(26): 2530-2539.
41. Springate DA, Kontopantelis E, Ashcroft DM,et al. ClinicalCodes: an online clinical codes repository to improve the validity and reproducibility of research using electronicmedical records. PLOS One, 2014, 9(6): e99825.
42. Dommett RM, Redaniel MT, Stevens MC,et al. Features of childhood cancer in primary care: a population-based nested case-control study. Br J Cancer, 2012, 106(5): 982-987.
43. Tsang C, Bottle A, Majeed A,et al. Adverse events recorded in English primary care: observational study using the General Practice Research Database. Br J Gen Pract, 2013, 63(613): e534-42.
44. Harron K, Goldstein H, Wade A,et al. Linkage, evaluation and analysis of national electronic healthcare data: application to providing enhanced blood-stream infection surveillance in paediatric intensive care. PLOS One, 2013, 8(12): e85278.
45. Adams MM, Wilson HG, Casto DL,et al. Constructing reproductive histories by linking vital records. Am J Epidemiol, 1997, 145(4): 339-348.
46. Ford JB, Roberts CL, Taylor LK. Characteristics of unmatched maternal and baby records in linked birth records and hospital discharge data. Paediatr Perinat Epidemiol, 2006, 20(4): 329-337.
47. Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc, 2013, 20(1): 144-151.
48. Sandall J, Murrells T, Dodwell M,et al. The efficient use of the maternity workforce and the implications for safety and quality in maternity care: a population-based, cross-sectional study. Health Serv Deliv Res, 2014, 2: 38. Available at: .
49. Welch C, Petersen I, Walters K,et al. Two-stage method to remove population- and individual-level outliers from longitudinal data in a primary care database. Pharmacoepidemiol Drug Saf, 2012, 21(7): 725-732.
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