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Abstract Background Monitoring the time course of mortality by cause is a key public health issue. However, several mortality data production changes may affect cause-specific time trends, thus altering the interpretation. This paper proposes a statistical method that detects abrupt changes ("jumps") and estimates correction factors that may be used for further analysis. Methods The method was applied to a subset of the AMIEHS (Avoidable Mortality in the European Union, toward better Indicators for the Effectiveness of Health Systems) project mortality database and considered for six European countries and 13 selected causes of deaths. For each country and cause of death, an automated jump detection method called Polydect was applied to the log mortality rate time series. The plausibility of a data production change associated with each detected jump was evaluated through literature search or feedback obtained from the national data producers. For each plausible jump position, the statistical significance of the between-age and between-gender jump amplitude heterogeneity was evaluated by means of a generalized additive regression model, and correction factors were deduced from the results. Results Forty-nine jumps were detected by the Polydect method from 1970 to 2005. Most of the detected jumps were found to be plausible. The age- and gender-specific amplitudes of the jumps were estimated when they were statistically heterogeneous, and they showed greater by-age heterogeneity than by-gender heterogeneity. Conclusion The method presented in this paper was successfully applied to a large set of causes of death and countries. The method appears to be an alternative to bridge coding methods when the latter are not systematically implemented because they are time- and resource-consuming.

We present the results that we have obtained in analysing all death certificates notified in 2000, in France, with a mention to at least one of the ICD10 codes concerning influenza.We found 1865 death certificates with a mention of influenza. The wording used by doctors to state influenza was not standardised; for example, the ICD code J111 covers 48 different expressions. For 1576 death certificates, influenza was the underlying cause of death according to the rules of the Automated Classification of Medical Entities (ACME). Only four of these certificates correspond to virologically confirmed influenza (J101). Three hundred and fifteen were classified as J110 (influenza with pneumonia). The majority (1233) were classified as J111 (influenza with other respiratory manifestations). The specificity and sensitivity of the physician''s classification of influenza as underlying cause were low, emphasizing the need for a better education in coding, possibly new insight into the search for the underlying cause in very old patients with multiple pathologies. [Copyright &y& Elsevier]

A letter to the editor is presented in response to the article "Counting the dead and what they died from: an assessment of the global status of cause of death data," by C. D. Mathers and colleagues which appeared in a previous issue.