Skip to main content
SpringerLink
Log in

Registry Data in Injury Research: Study Designs and Interpretation

  • Injury Epidemiology (A Rowhani-Rahbar, Section Editor)
  • Published:
Current Epidemiology Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Injury data is frequently captured in registries that form a census of 100% of known cases that meet specified inclusion criteria. These data are routinely used in injury research with a variety of study designs. We reviewed study designs commonly used with data extracted from injury registries and evaluated the advantages and disadvantages of each design type.

Recent Findings

Registry data are suited to 5 major design types: (1) Description, (2) Ecologic (with ecologic cohort as a particularly informative sub-type), (3) Case–control (with location-based and culpability studies as salient subtypes), (4) Case-only (including case-case and case-crossover subtypes), and (5) Outcomes.

Summary

Registries are an important resource for injury research. Investigators considering use of a registry should be aware of the advantages and disadvantages of available study designs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Haddon W, Bradess VA. Alcohol in the single vehicle fatal accident: experience of Westchester County, New York. J Am Med Assoc. 1959;169(14):1587–93.

    Article  PubMed  Google Scholar 

  2. Sleet DA, Baldwin G, Marr A, et al. History of injury and violence as public health problems and emergence of the National Center for Injury Prevention and Control at CDC. J Safety Res. 2012;43(4):233–47.

    Article  PubMed  Google Scholar 

  3. Porta M. A dictionary of epidemiology. Oxford university press; 2014.

  4. National Highway Transport Safety Administration. MMUCC guideline: Model Minimum Uniform Crash Criteria. Accessed January 28, 2022. https://www.ghsa.org/sites/default/files/publications/files/MMUCC_5thEd_web.pdf

  5. National Center for Statistics and Analysis. Traffic safety facts: pedestrians.; 2019. Accessed January 27, 2022. https://crashstats.nhtsa.dot.gov/Api/Public/Publication/813079

  6. Branas CC, Knudson MM. Helmet laws and motorcycle rider death rates. Accid Anal Prev. 2001;33(5):641–8.

    Article  CAS  PubMed  Google Scholar 

  7. Gorman DM, Huber Jr JC, Carozza SE. Evaluation of the Texas 0.08 BAC law. Alcohol Alcohol. 2006;41(2):193–199.

  8. Mooney SJ, Magee C, Dang K, et al. “Complete Streets” and adult bicyclist fatalities: applying G-computation to evaluate an intervention that affects the size of a population at risk. Am J Epidemiol. 2018;187(9):2038–45.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Li G, Brady JE, Chen Q. Drug use and fatal motor vehicle crashes: a case-control study. Accid Anal Prev. 2013;60:205–10.

    Article  PubMed  Google Scholar 

  10. Li G, Chihuri S, Brady JE. Role of alcohol and marijuana use in the initiation of fatal two-vehicle crashes. Ann Epidemiol. 2017;27(5):342–347. e1.

  11. McCartt AT, Northrup VS. Factors related to seat belt use among fatally injured teenage drivers. J Safety Res. 2004;35(1):29–38.

    Article  PubMed  Google Scholar 

  12. Amoros E, Chiron M, Thélot B, Laumon B. The injury epidemiology of cyclists based on a road trauma registry. BMC Public Health. 2011;11(1):1–12.

    Article  Google Scholar 

  13. Elvik R. Comparative analysis of techniques for identifying locations of hazardous roads. Transp Res Rec. 2008;2083(1):72–5.

    Article  Google Scholar 

  14. Tian N, Cui W, Zack M, Kobau R, Fowler KA, Hesdorffer DC. Suicide among people with epilepsy: a population-based analysis of data from the US National Violent Death Reporting System, 17 states, 2003–2011. Epilepsy Behav. 2016;61:210–7.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hemenway D, Solnick SJ. The epidemiology of homicide perpetration by children. Inj Epidemiol. 2017;4(1):1–6.

    Article  Google Scholar 

  16. Athey S, Imbens GW. Identification and inference in nonlinear difference-in-differences models. Econometrica. 2006;74(2):431–97.

    Article  Google Scholar 

  17. Donald SG, Lang K. Inference with difference-in-differences and other panel data. Rev Econ Stat. 2007;89(2):221–33.

    Article  Google Scholar 

  18. Goodman-Bacon A. Difference-in-differences with variation in treatment timing. J Econom. 2021;225(2):254–77.

    Article  Google Scholar 

  19. Schwartz S. The fallacy of the ecological fallacy: the potential misuse of a concept and the consequences. Am J Public Health. 1994;84(5):819–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ward JW, Michalek JJ, Azevedo IL, Samaras C, Ferreira P. Effects of on-demand ridesourcing on vehicle ownership, fuel consumption, vehicle miles traveled, and emissions per capita in US States. Transp Res Part C Emerg Technol. 2019;108:289–301.

    Article  Google Scholar 

  21. Branas CC, Cheney RA, MacDonald JM, Tam VW, Jackson TD, Ten Have TR. A difference-in-differences analysis of health, safety, and greening vacant urban space. Am J Epidemiol. 2011;174(11):1296–306.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Abadie A, Diamond A, Hainmueller J. Synthetic control methods for comparative case studies: estimating the effect of California’s tobacco control program. J Am Stat Assoc. 2010;105(490):493–505.

    Article  CAS  Google Scholar 

  23. Robinson WS. Ecological correlations and the behavior of individuals. Am Sociol Rev. 1950;15(3):351–7.

    Article  Google Scholar 

  24. Piantadosi S, Byar DP, Green SB. The ecological fallacy. Am J Epidemiol. 1988;127(5):893–904.

    Article  CAS  PubMed  Google Scholar 

  25. Selvin HC. Durkheim’s suicide and problems of empirical research. Am J Sociol. 1958;63(6):607–19.

    Article  Google Scholar 

  26. Fotheringham AS, Wong DW. The modifiable areal unit problem in multivariate statistical analysis. Environ Plan A. 1991;23(7):1025–44.

    Article  Google Scholar 

  27. Mark Anderson D, Hansen B, Rees DI. Medical marijuana laws, traffic fatalities, and alcohol consumption. J Law Econ. 2013;56(2):333–69.

    Article  Google Scholar 

  28. Morgenstern H. Ecologic studies in epidemiology: concepts, principles, and methods. Annu Rev Public Health. 1995;16(1):61–81.

    Article  CAS  PubMed  Google Scholar 

  29. Brenner H, Savitz DA, Jöckel KH, Greenland S. Effects of nondifferential exposure misclassification in ecologic studies. Am J Epidemiol. 1992;135(1):85–95.

    Article  CAS  PubMed  Google Scholar 

  30. Mooney SJ, Richards CA, Rundle AG. There goes the neighborhood effect: bias due to non-differential measurement error in the construction of neighborhood contextual measures. Epidemiol Camb Mass. 2014;25(4):528.

    Article  Google Scholar 

  31. Aydelotte JD, Brown LH, Luftman KM, et al. Crash fatality rates after recreational marijuana legalization in Washington and Colorado. Am J Public Health. 2017;107(8):1329–31.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Wacholder S. Design issues in case-control studies. Stat Methods Med Res. 1995;4(4):293–309.

    Article  CAS  PubMed  Google Scholar 

  33. Wacholder S, McLaughlin JK, Silverman DT, Mandel JS. Selection of controls in case-control studies: I. Principles. Am J Epidemiol. 1992;135(9):1019–28.

    Article  CAS  PubMed  Google Scholar 

  34. Kim JH, Mooney SJ. The epidemiologic principles underlying traffic safety study designs. Int J Epidemiol. 2016;45(5):1668–75.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Branas CC, Richmond TS, Ten Have TR, Wiebe DJ. Acute alcohol consumption, alcohol outlets, and gun suicide. Subst Use Misuse. 2011;46(13):1592–603.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Begg CB, Zhang ZF. Statistical analysis of molecular epidemiology studies employing case-series. Cancer Epidemiol Prev Biomark. 1994;3(2):173–5.

    CAS  Google Scholar 

  37. Mooney SJ, DiMaggio CJ, Lovasi GS, et al. Use of Google Street View to assess environmental contributions to pedestrian injury. Am J Public Health. 2016;106(3):462–9.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Krumkamp R, Reintjes R, Dirksen-Fischer M. Case–case study of a Salmonella outbreak: an epidemiologic method to analyse surveillance data. Int J Hyg Environ Health. 2008;211(1–2):163–7.

    Article  PubMed  Google Scholar 

  39. Brubacher J, Chan H, Asbridge M. Culpability analysis is still a valuable technique. Int J Epidemiol. 2014;43(1):270–2.

    Article  PubMed  Google Scholar 

  40. Romano E, Torres-Saavedra P, Voas RB, Lacey JH. Marijuana and the risk of fatal car crashes: what can we learn from FARS and NRS data? J Prim Prev. 2017;38(3):315–28.

    Article  PubMed  Google Scholar 

  41. Khoury MJ, Flanders WD. Nontraditional epidemiologic approaches in the analysis of gene environment interaction: case-control studies with no controls! Am J Epidemiol. 1996;144(3):207–13.

    Article  CAS  PubMed  Google Scholar 

  42. Gatto NM, Campbell UB, Rundle AG, Ahsan H. Further development of the case-only design for assessing gene–environment interaction: evaluation of and adjustment for bias. Int J Epidemiol. 2004;33(5):1014–24.

    Article  PubMed  Google Scholar 

  43. Kaplan MS, McFarland BH, Huguet N. Characteristics of adult male and female firearm suicide decedents: findings from the National Violent Death Reporting System. Inj Prev. 2009;15(5):322–7.

    Article  CAS  PubMed  Google Scholar 

  44. Härmä M, Koskinen A, Sallinen M, Kubo T, Ropponen A, Lombardi DA. Characteristics of working hours and the risk of occupational injuries among hospital employees: a case-crossover study. Scand J Work Environ Health. 2020;46(6):570.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Fröbert O, Lagerqvist B, Olivecrona GK, et al. Thrombus aspiration during ST-segment elevation myocardial infarction. N Engl J Med. 2013;369:1587–97.

    Article  PubMed  Google Scholar 

  46. Johnson EA, Carrington JM. Clinical research integration within the electronic health record: a literature review. CIN Comput Inform Nurs. 2021;39(3):129–35.

    Google Scholar 

  47. Rubinger L, Ekhtiari S, Gazendam A, Bhandari M. Registries: big data, bigger problems? Injury: Published online; 2021.

    Google Scholar 

  48. Zhang JM, Harman M, Ma L, Liu Y. Machine learning testing: survey, landscapes and horizons. IEEE Trans Softw Eng: Published online; 2020.

    Google Scholar 

  49. Sato N, Cameron P, Mclellan S, Beck B, Gabbe B. Association between anticoagulants and mortality and functional outcomes in older patients with major trauma. Emerg Med J: Published online; 2021.

    Google Scholar 

Download references

Funding

This work was supported by grant R00LM012868 from the National Library of Medicine, grants R01AA028552 and K01AA026327 from the National Institute on Alcohol Abuse and Alcoholism, and grants R49-CE003094 and R49CE003087 from the Centers for Disease Control and Prevention.

Author information

Authors and Affiliations

  1. Department of Epidemiology, University of Washington, Box 357236, Seattle, WA, 98195, USA

    Stephen J. Mooney

  2. Harborview Injury Prevention and Research Center, University of Washington, Seattle, WA, USA

    Stephen J. Mooney

  3. Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA

    Andrew G. Rundle & Christopher N. Morrison

  4. Center for Injury Science and Prevention, Columbia University, New York, NY, USA

    Andrew G. Rundle & Christopher N. Morrison

  5. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia

    Christopher N. Morrison

Authors
  1. Stephen J. Mooney
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew G. Rundle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christopher N. Morrison
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stephen J. Mooney.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Injury Epidemiology

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mooney, S.J., Rundle, A.G. & Morrison, C.N. Registry Data in Injury Research: Study Designs and Interpretation. Curr Epidemiol Rep 9, 263–272 (2022). https://doi.org/10.1007/s40471-022-00311-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40471-022-00311-x

Keywords

Search

Navigation