Surveillance and outbreak report Open Access
Like 0


Accurate case-based surveillance data remain the key data source for estimating HIV burden and monitoring prevention efforts in Europe. We carried out a literature review and exploratory analysis of surveillance data regarding two crucial issues affecting European surveillance for HIV: missing data and reporting delay. Initial screening showed substantial variability of these data issues, both in time and across countries. In terms of missing data, the CD4+ cell count is the most problematic variable because of the high proportion of missing values. In 20 of 31 countries of the European Union/European Economic Area (EU/EEA), CD4+ counts are systematically missing for all or some years. One of the key challenges related to reporting delays is that countries undertake specific one-off actions in effort to capture previously unreported cases, and that these cases are subsequently reported with excessive delays. Slightly different underlying assumptions and effectively different models may be required for individual countries to adjust for missing data and reporting delays. However, using a similar methodology is recommended to foster harmonisation and to improve the accuracy and usability of HIV surveillance data at national and EU/EEA levels.


Article metrics loading...

Loading full text...

Full text loading...



  1. European Centre for Disease Prevention and Control (ECDC). European Network for HIV/AIDS Surveillance. Stockholm: ECDC. [Accessed 29 May 2018]. Available from: https://ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/european-network-hivaids-1
  2. European Centre for Disease Prevention and Control (ECDC). Long-term surveillance strategy 2014– 2020. Stockholm: ECDC; 2013. Available from: https://ecdc.europa.eu/sites/portal/files/media/en/publications/Publications/long-term-surveillance-strategy-2014-2020.pdf
  3. Little RJA, Rubin DB. Statistical Analysis with Missing Data. 2nd ed. Hoboken, New Jersey: John Wiley & Sons; 2002. 381 p.
  4. Carpenter JR, Kenward MG. Missing data in randomised controlled trials - a practical guide. Birmingham: London School of Hygiene and Tropical Medicine (LSHTM) Research Online; 2007. Available from: http://researchonline.lshtm.ac.uk/4018500/
  5. Lawless JF. Adjustments for Reporting Delays and the Prediction of Occurred but Not Reported Events. Can J Stat. 1994;22(1):15-31.  https://doi.org/10.2307/3315826.n1 
  6. Noufaily A, Ghebremichael-Weldeselassie Y, Enki DG, Garthwaite P, Andrews N, Charlett A, et al. Modelling reporting delays for outbreak detection in infectious disease data. J R Stat Soc Ser A Stat Soc. 2015;178(1):205-22.  https://doi.org/10.1111/rssa.12055 
  7. Midthune DN, Fay MP, Clegg LX, Feuer EJ. Modeling Reporting Delays and Reporting Corrections in Cancer Registry Data. J Am Stat Assoc. 2005;100(469):61-70.  https://doi.org/10.1198/016214504000001899 
  8. Heisterkamp SH, Jager JC, Ruitenberg EJ, Van Druten JAM, Downs AM. Correcting reported AIDS incidence: a statistical approach. Stat Med. 1989;8(8):963-76.  https://doi.org/10.1002/sim.4780080807  PMID: 2799125 
  9. European Centre for Disease Prevention and Control, World Health Organization Regional Office for Europe. (ECDC, WHO/Europe). HIV/AIDS surveillance in Europe 2015. Stockholm: ECDC; 2016. Available from: https://ecdc.europa.eu/en/publications-data/hivaids-surveillance-europe-2015
  10. European Centre for Disease Prevention and Control (ECDC). The European Surveillance System (TESSy). Stockholm: ECDC. [Accessed 9 May 2018]. Available from: http://ecdc.europa.eu/en/activities/surveillance/TESSy/Pages/TESSy.aspx
  11. Seaman SR, White IR. Review of inverse probability weighting for dealing with missing data. Stat Methods Med Res. 2013;22(3):278-95.  https://doi.org/10.1177/0962280210395740  PMID: 21220355 
  12. Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York: John Wiley & Sons; 1987. 258 p.
  13. Schafer JL. Analysis of Incomplete Multivariate Data. 1st ed. 1. CRC Press reprint. Boca Raton, Florida: Chapman & Hall/CRC; 1999. 430 p.
  14. von Hippel PT. Should a Normal Imputation Model be Modified to Impute Skewed Variables? Sociol Methods Res. 2013;42(1):105-38.  https://doi.org/10.1177/0049124112464866 
  15. Hughes RA, Sterne J, Tilling K. Comparison of imputation variance estimators. Stat Methods Med Res. 2016;25(6):2541-57.  https://doi.org/10.1177/0962280214526216  PMID: 24682265 
  16. Bodner TE. What Improves with Increased Missing Data Imputations? Struct Equ Model Multidiscip J. 2008;15(4):651-75.
  17. Carpenter JR, Goldstein H, Kenward MG. REALCOM-IMPUTE Software for Multilevel Multiple Imputation with Mixed Response Types. J Stat Softw. 2011;45(5).  https://doi.org/10.18637/jss.v045.i05 
  18. van Buuren S. Multiple imputation of discrete and continuous data by fully conditional specification. Stat Methods Med Res. 2007;16(3):219-42.  https://doi.org/10.1177/0962280206074463  PMID: 17621469 
  19. Bartlett JW, Seaman SR, White IR, Carpenter JR, Alzheimer’s Disease Neuroimaging Initiative*. Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model. Stat Methods Med Res. 2015;24(4):462-87.  https://doi.org/10.1177/0962280214521348  PMID: 24525487 
  20. Quartagno M, Carpenter JR. Multiple imputation for IPD meta-analysis: allowing for heterogeneity and studies with missing covariates. Stat Med. 2016;35(17):2938-54.  https://doi.org/10.1002/sim.6837  PMID: 26681666 
  21. Jolani S, Debray TPA, Koffijberg H, van Buuren S, Moons KGM. Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE. Stat Med. 2015;34(11):1841-63.  https://doi.org/10.1002/sim.6451  PMID: 25663182 
  22. Collins LM, Schafer JL, Kam CM. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychol Methods. 2001;6(4):330-51.  https://doi.org/10.1037/1082-989X.6.4.330  PMID: 11778676 
  23. Carpenter JR, Kenward MG, White IR. Sensitivity analysis after multiple imputation under missing at random: a weighting approach. Stat Methods Med Res. 2007;16(3):259-75.  https://doi.org/10.1177/0962280206075303  PMID: 17621471 
  24. Brookmeyer R, Liao JG. The analysis of delays in disease reporting: methods and results for the acquired immunodeficiency syndrome. Am J Epidemiol. 1990;132(2):355-65.  https://doi.org/10.1093/oxfordjournals.aje.a115665  PMID: 2372012 
  25. Brookmeyer R, Damiano A. Statistical methods for short-term projections of AIDS incidence. Stat Med. 1989;8(1):23-34.  https://doi.org/10.1002/sim.4780080105  PMID: 2919244 
  26. Harris JE. Reporting Delays and the Incidence of AIDS. J Am Stat Assoc. 1990;85(412):915-24.  https://doi.org/10.1080/01621459.1990.10474962 
  27. Lagakos SW, Barraj LM, Gruttola VD. Nonparametric analysis of truncated survival data, with application to AIDS. Biometrika. 1988;75(3):515-23.  https://doi.org/10.1093/biomet/75.3.515 
  28. Pagano M, Tu XM, De Gruttola V, MaWhinney S. Regression analysis of censored and truncated data: estimating reporting-delay distributions and AIDS incidence from surveillance data. Biometrics. 1994;50(4):1203-14.  https://doi.org/10.2307/2533459  PMID: 7787003 
  29. Kalbfleisch JD, Lawess JF. Regression models for right truncated data with applications to AIDS incubation times and reporting lags. Stat Sin. 1991;1:19-32.
  30. Shen P. Nonparametric analysis of doubly truncated data. Ann Inst Stat Math. 2010;62(5):835-53.  https://doi.org/10.1007/s10463-008-0192-2 
  31. Efron B, Petrosian V. Nonparametric Methods for Doubly Truncated Data. J Am Stat Assoc. 1999;94(447):824-34.  https://doi.org/10.1080/01621459.1999.10474187 
  32. Kirwan PD, Chau C, Brown AE, Gill ON, Delpech VC. HIV in the UK - 2016 report. London: Public Health England; Dec 2016. Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/602942/HIV_in_the_UK_report.pdf
  33. Eurostat. Population on 1 January. Luxembourg: Eurostat. [Accessed 9 May 2018]. Available from: http://ec.europa.eu/eurostat/tgm/table.do?tab=table&init=1&language=en&pcode=tps00001&plugin=1

Data & Media loading...

Submit comment
Comment moderation successfully completed
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error