Monitoring of all-cause mortality in Belgium (Be-MOMO): a new and automated system for the early detection and quantification of the mortality impact of public health events

Abstract

Objectives

Be-MOMO is the monitoring of all-cause death registry data in Belgium. The new methods are described and the detection and quantification of outbreaks is presented for the period April 2006–March 2007. Sensitivity, specificity and timeliness are illustrated by means of a temporal comparison with known health events.

Methods

Relevant events are identified from important mortality risks: climate, air pollution and influenza. Baselines and thresholds for deaths by gender, age group, day and week are estimated by the method of Farrington et al. (J R Stat Soc Ser A, 159:547–563, 1996). By adding seasonal terms to the basic model, a complete 5-year reference period can be used, while a reduction of noise allows the application to daily counts.

Results

Ignoring two false positives, all flags could be classified into five distinct outbreaks, coinciding with four heat periods and an influenza epidemic. Negative deviations from expected mortality in autumn and winter might reflect a displacement of mortality by the heat waves. Still, significant positive excess was found during five influenza weeks. Correcting for the delay in registration of deaths, outbreaks could be detected as soon as 1–2 weeks after the event.

Conclusion

The sensitivity of Be-MOMO to different health threats suggests its potential usefulness in early warning: mortality thresholds and baselines might serve as rapid tools for detecting and quantifying outbreaks, crucial for public health decision-making and evaluation of measures.

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References

  1. Anonymous (2009) European monitoring of excess mortality for public health action. http://www.euromomo.eu/. Accessed 18 Dec 2009

  2. Belgian Federal Public Service Health, Food Chain Safety and Environment (2008) Ozone and heat waves. https://portal.health.fgov.be/portal/page?_pageid=56,805538&_dad=portal&_schema=PORTAL. Accessed 20 Nov 2008

  3. Be-MOMO (2009) Belgian mortality monitoring. http://www.iph.fgov.be/Epidemio/Be-Momo. Accessed 18 Dec 2009

  4. Braga ALF, Zanobetti A, Schwartz J (2000) Do respiratory epidemics confound the association between air pollution and daily deaths? Eur Respir J 16:723–728

    Article  CAS  PubMed  Google Scholar 

  5. Brookmeyer R, Stroup DF (2004) Monitoring the health of populations: statistical principles and methods for public health surveillance. Oxford University Press, New York

    Google Scholar 

  6. Brucker G (2005) Vulnerable populations: lessons learnt from the summer 2003 heat waves in Europe. Euro Surveill 10(7):147

    PubMed  Google Scholar 

  7. Centers for Disease Control and Prevention (2001) Updated guidelines for evaluating public health surveillance systems: recommendations from the Guidelines Working Group. MMWR 50(RR-13):1–35

    Google Scholar 

  8. Cox B, Wuillaume F, Maes S (2008a) Mortalité dans la population générale pendant la période de consultations ILI et IRA. In: Surveillance de la grippe saisonnière en Belgique. Saison 2007-08. Rapport annuel (FR)/Mortaliteit binnen de globale populatie tijdens de registratieperiode van ILI en ALI. In: Surveillance van seizoensgriep in België. Seizoen 2007-08. Jaarrapport (NL). National Influenza Centre Belgium, Yearly reports. http://www.iph.fgov.be/flu/EN/21EN.htm. Accessed 20 Nov 2008

  9. Cox B, Wuillaume F, Maes S, Van Oyen H (2008b) Be-MOMO: mortality by region during the hot summers of 2003 and 2006. Scientific Institute of Public Health Belgium, IPH Reports and publications. http://www.iph.fgov.be/reports.asp?Lang=EN&ReportID=3161. Accessed 20 Nov 2008

  10. Dominici F, Zeger S, Samet J (2002) On the use of generalized additive models in time-series studies of air pollution and health. Am J Epidemiol 156:1–11

    Article  Google Scholar 

  11. Farrington CP, Andrews NJ, Beale AD, Catchpole MA (1996) A statistical algorithm for the early detection of outbreaks of infectious disease. J R Stat Soc Ser A 159:547–563

    Article  Google Scholar 

  12. Haines A, Kovats RS, Campbell-Lendrum D, Corvalan C (2006) Climate change and human health: impacts, vulnerability and public health. Public Health 120(7):585–596

    Article  CAS  PubMed  Google Scholar 

  13. Hajat S, Armstrong B, Baccini M, Biggeri A, Bisanti L, Russo A, Paldy A, Menne B, Kosatsky T (2006) Impact of high temperatures on mortality: is there an added ‘heat wave’ effect? Epidemiology 17:632–638

    Article  PubMed  Google Scholar 

  14. Höhle M, Paul M, Held L (2009) Statistical approaches to the monitoring and surveillance of infectious diseases for veterinary public health. Prev Vet Med 91(1):2–10

    Article  PubMed  Google Scholar 

  15. Hutwagner LC, Maloney EK, Bean NH, Slutsker L, Martin SM (1997) Using laboratory-based surveillance data for prevention: an algorithm for detecting Salmonella outbreaks. Emerg Infect Dis 3:395–400

    Article  CAS  PubMed  Google Scholar 

  16. Johnson H, Kovats RS, McGregor G, Stedman J, Gibbs M, Walton H (2005) The impact of the 2003 heat wave on daily mortality in England and Wales and the use of rapid weekly mortality estimates. Euro Surveill 10(7):168–171

    CAS  PubMed  Google Scholar 

  17. Josseran L, Nicolau J, Caillere N, Astagneau P, Bruecker G (2006) Syndromic surveillance based on emergency department activity and crude mortality: two examples. Euro Surveill 11(12):225–229

    CAS  PubMed  Google Scholar 

  18. Maes S, Wuillaume F, Cox B, Van Oyen H (2007) Mortalité en Belgique pendant l’été 2006 (FR)/Mortaliteit in België in de zomer van 2006 (NL). Scientific Institute of Public Health Belgium, IPH Reports and Publications. http://www.iph.fgov.be/reports.asp?Lang=EN&ReportID=2913 (FR)/http://www.iph.fgov.be/reports.asp?Lang=EN&ReportID=2914 (NL). Accessed 20 Nov 2008

  19. Maes S, Cox B, Wuillaume F (2008) Mortalité dans la population générale pendant la période de consultations ILI et IRA. In: Surveillance de la grippe en Belgique. Saison 2006–07. Rapport annuel (FR)/Mortaliteit binnen de globale populatie tijdens de registratieperiode van ILI en ALI. In: Surveillance van griep in België. Seizoen 2006–07. Jaarrapport (NL). National Influenza Centre Belgium, Yearly reports. http://www.iph.fgov.be/flu/EN/21EN.htm. Accessed 20 Nov 2008

  20. Mazick A, Participants of a Workshop on Mortality Monitoring in Europe (2007) Monitoring excess mortality for public health action: potential for a future European network. Euro Surveill 12(1). Article id 3107

  21. Milne EM (2005) Mortality spike at New Year but not Christmas in North East England. Eur J Epidemiol 20(10):849–854

    Article  PubMed  Google Scholar 

  22. O’Neill M, Hajat S, Zanobetti A, Ramirez AM, Schwartz J (2005) Impact of control for air pollution and respiratory epidemics on the estimated associations of temperature and daily mortality. Int J Biometeorol 50:121–129

    Article  PubMed  Google Scholar 

  23. Pattenden S, Nikiforov B, Armstrong B (2003) Mortality and temperature in Sofia and London. J Epidemiol Commun Health 57:628–633

    Article  CAS  Google Scholar 

  24. Phillips DP, Jarvinen JR, Abramson IS, Phillips RR (2004) Cardiac mortality is higher around Christmas and New Year’s than at any other time: the holidays as a risk factor for death. Circulation 110(25):3781–3788

    Article  PubMed  Google Scholar 

  25. Robine JM, Cheung SL, Le Roy S, Van Oyen H, Griffiths C, Michel JP, Herrmann F (2008) Death toll exceeded 70000 in Europe during summer 2003. C R Biol 301:171–178

    Article  Google Scholar 

  26. Royal Meteorological Institute (2008) Vague de chaleur, vague de froid—IRM (FR)/Hittegolf, koudegolf—KMI (NL). http://www.meteo.be/meteo/view/fr/91313-Woordenlijst+A-Z.html?view=134870 (FR)/http://www.meteo.be/meteo/view/nl/91313-DicoMeteo.html?view=134870 (NL). Accessed 20 Nov 2008

  27. Sartor F (2004) La surmortalité en Belgique au cours de l’été 2003 (FR)/Oversterfte in België tijdens de zomer 2003 (NL). Scientific Institute of Public Health Belgium, IPH Reports and publications. http://www.iph.fgov.be/reports.asp?Lang=EN&ReportID=2550 (FR)/http://www.iph.fgov.be/reports.asp?Lang=EN&ReportID=2551 (NL). Accessed 20 Nov 2008

  28. Sartor F, Snacken R, Demuth C, Walckiers D (1995) Temperature, ambient ozone levels, and mortality during summer, 1994, in Belgium. Environ Res 70:105–113

    Article  CAS  PubMed  Google Scholar 

  29. Sartorius B, Jacobsen H, Torner A, Giesecke J (2006) Description of a new all cause mortality surveillance system in Sweden as a warning system using threshold detection algorithms. Eur J Epidemiol 21(3):181–189

    Article  CAS  PubMed  Google Scholar 

  30. Simonsen L, Clarke MJ, Stroup DF, Williamson GD, Arden NH, Cox NJ (1997) A method for timely assessment of influenza-associated mortality in the United States. Epidemiology 8(4):390–395

    Article  CAS  PubMed  Google Scholar 

  31. Sonesson C, Bock D (2003) A review and discussion of prospective statistical surveillance in public health. J R Stat Soc Ser A 166(1):5–12

    Article  Google Scholar 

  32. Stroup DF, Williamson GD, Herndon JL, Karon J (1989) Detection of aberrations in the occurrence of notifiable diseases surveillance data. Stat Med 8:323–329

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

Belgian real-time mortality monitoring (Be-MOMO) is financed by the federal public service health, food chain safety and environment (DG2). The authors would like to thank collaborators from the National Register (Stefan Van de Venster, Luc Coppens and Marc Ruymen), RMI (Olivier Latinne) and IRCEL-CELINE (Olivier Brasseur and Frans Fierens) for their support and cooperation.

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Correspondence to Bianca Cox.

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Cox, B., Wuillaume, F., Van Oyen, H. et al. Monitoring of all-cause mortality in Belgium (Be-MOMO): a new and automated system for the early detection and quantification of the mortality impact of public health events. Int J Public Health 55, 251–259 (2010). https://doi.org/10.1007/s00038-010-0135-6

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Keywords

  • All-cause mortality monitoring
  • Outbreak detection
  • Threshold
  • Baseline
  • Excess