Belgian Mortality Monitoring Be-MoMo





These are the observed all-cause mortality in Belgium outputted from the Be-MOMO model over the last five years. The observed numbers are the average of the day and the 6 previous days. Because of a delay in the registration of deaths, counts for the most recent weeks are preliminary and numbers are visualised with a 4 week time lag. You are free to use the results produced by this application on the sole condition that the source is mentioned:
Belgian Mortality Monitoring (Be-MOMO), Scientific Institute of Public Health (WIV-ISP), Brussels, Belgium.

* P.I. = Prediction Interval

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This graph shows the observed all-cause mortality in Belgium and the curves of the environmental risks such as meteorological data (temperature) and air pollution data (ozone and PM10). It allows to visualize the correlations between mortality and extreme temperatures (above 25°C or below 0°C) and high air-pollutant concentrations (ozone: above 120µg/m³ and PM10: above 50µg/m³, corresponding to European thresholds). The vertical light-orange bands highlight the days with statistically significant excess mortality. All values are smoothed with a 7-day centered moving average. Meteorological data are provided by the Royal Meteorological Institute of Belgium (RMI). Ozone and PM10 data are provided by The Belgian Interregional Environment Agency (IRCEL-CELINE).

Abbreviations:
Exp. Mort.
Expected Mortality
Obs. Mort.
Observed Mortality
Tmin
Minimum temperature
Tmax
Maximum temperature
PM10
Particulate Matter < 10µm
Dynamic Interactions
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In Belgium, surveillance of all-cause mortality is carried out on a weekly basis by the Infectious Diseases Epidemiology Unit of the Scientific Institute of Public Health. The mortality monitoring model is designed to serve as a tool for rapid detection and quantification of unusual mortality which might result from disease epidemics such as influenza or from extreme environmental conditions such as heat waves. A timely assessment of the impact on mortality may be useful to guide or reinforce new or existing public health measures, e.g. vaccinations for influenza and the national heat action plan. Moreover, mortality monitoring can be used to evaluate possible effects of public health measures by comparing periods before and after the implementation of the intervention.

Data

Data are updated on a weekly basis, except population sizes for which the official numbers at the 1st of January are used. Mortality and population data are provided by the National Register. The mortality file contains information on all deaths that were registered by the Belgian municipalities during the week before (starting on Saturday and ending on Friday). The data comprise the date of birth, date of death, gender, nationality, place of residence and place of death. The causes of death are unknown. Because of a considerable variation in the rapidness of death registration (ranging from a few days to many weeks after the actual date of death), figures for recent periods are incomplete. Around 95% of mortality data are available after one month. Deaths taking place abroad are removed from the analyses since these deaths are assumed to be independent of the concurrent environmental conditions in Belgium. Foreigners who die in Belgium are included.

Statistical Methods

Observed deaths are aggregated by day. To be able to detect and quantify important increases in mortality, observed death counts are compared to 2 types of reference lines, obtained by modelling past 5-year mortality data:

  • Expected deaths are the model predictions and represent normal/average mortality levels. They are used for the calculation of the excess number of deaths (observed – expected).
  • The threshold is the upper limit of the prediction interval around expected mortality, calculated by a 2/3-power transformation to correct for skewness in the Poisson distribution (Farrington et al, 1996). Threshold values represent critical mortality levels and are used to detect unusual or significant mortality outbreaks. The confidence level for the upper threshold was chosen as the optimal compromise between sensitivity and specificity of alert detection. It was set at 99.5% for daily-level data.

The statistical model is a modification of the log-linear Farrington model, originally developed for the detection of infectious diseases outbreaks based on weekly disease counts (Farrington et al, 1996). The model was adapted in order to be applicable to both daily- and weekly-level mortality data. While the original method limits the amount of reference data by using only historical data from similar weeks, a sine and cosine wave component was added to capture the seasonal pattern of mortality. This enables modelling the complete 5-year time series and reduces random variation in the predicted baseline, especially for daily-level data.

An automated analyses procedure is implemented in STATA version 13. Statistical methods and performance of the Be-MOMO system are described in more detail in Cox et al (2010).

Cox B, Wuillaume F, Van Oyen H, Maes S. 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. International Journal of Public Health 2010, 55(4):251-259.