The ICD10h. An international Coding Scheme for Individual Level Causes of Death

Angelique Janssens, Radboud University
Tim Riswick, Radboud University

In 2017, the research network SHiP (Studying the history of Health in Port cities) was established. It is a network of scholars studying the dynamics of health and mortality change in port cities across Europe in the 19th and 20th centuries. The network uses individual-level cause-of-death data for the entire population of these cities for ca. 1850–1950. These datasets are truly unique as they enable us to go beyond what was captured in highly-aggregated national statistics which make use of extremely limited 19th-century disease classifications. We can thus reconstruct the epidemiological 'fingerprints' of European port cities and the way these changed in a period in which life expectancy nearly doubled and infectious diseases sharply declined in Europe. These ambitious aims imply the construction of joint methodological tools, the most important of which is a joint coding scheme which can be used to code historical causes of death in a strictly comparative fashion. The ICD10h coding scheme assigns codes to a large number of causes of death in a systematic way. The ICD10h scheme is however not a classification scheme. A classification scheme places causes of death in a small number of groups of diseases for analytical purposes, whereas a coding scheme is nested within a classification scheme and can be used to be built up into different classification schemes. A coding scheme is therefore not tied to a particular classification scheme. The most important features of the ICD10h coding scheme are that the system is flexible and detailed so that it can deal well with large numbers of historical disease descriptions, from different linguistic areas in Europe, while at the same time it is able to connect to current day disease patterns.In our presentation we will focus on the process of constructing the ICD10h, and the challenges we faced.

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 Presented in Session 95. Vocabularies. Exploring Shared Names for Default Variables across Databases