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.
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.
Authors: Jean-Marie Robine; Siu Lan K Cheung; Sophie Le Roy; Herman Van Oyen; Clare Griffiths; Jean-Pierre Michel; François Richard Herrmann Journal: C R Biol Date: 2007-12-31 Impact factor: 1.583
Authors: Corrado Magnani; Danila Azzolina; Elisa Gallo; Daniela Ferrante; Dario Gregori Journal: Int J Environ Res Public Health Date: 2020-05-15 Impact factor: 3.390
Authors: David J Muscatello; Janaki Amin; C Raina MacIntyre; Anthony T Newall; William D Rawlinson; Vitali Sintchenko; Robin Gilmour; Sarah Thackway Journal: PLoS One Date: 2014-05-29 Impact factor: 3.240