The Antwerp COR*-IDS database 2020 is a transformed and harmonized historical demographic database in a cross-nationally comparable format designed to be open and easy to use for international researchers. The database is constructed from the 2010 release of the Antwerp COR*-historical demographic database, which was created using a letter sample of the whole district of Antwerp (Flanders, Belgium). It has a total sample size of +/- 33,000 residents of Antwerp. The sample spans nearly seven decades.
Intermediate Data Structure
The Scanian Economic-Demographic Database (SEDD) is a high-quality longitudinal data resource spanning the period 1646−1967. It covers all individuals born in or migrated to the city of Landskrona and five rural parishes in western Scania in southern Sweden. The entire population present in the area is fully covered after 1813. At the individual level, SEDD combines various demographic and socioeconomic records, including causes of death, place of birth and geographic data on the place of residence within a parish.
It has previously been shown that infant mortality clusters in a subset of families, a phenomenon which was observed in historical populations as well as contemporary developing countries. A transmission of death clustering across generations has also been shown in Belgium, but it is unknown whether such effects are specific to the studied context or are also found in other areas.
Studies conducted in historical populations and developing countries have evidenced the existence of clustering in infant deaths, which could be related to genetic inheritance, early life exposures, and/or to social and cultural factors such as education, socioeconomic status or parental care. A transmission of death clustering has also been found across generations.
Studies conducted in historical populations and developing countries have evidenced the existence of clustering in infant deaths, which could be related to genetic inheritance and/or to social and cultural factors such as education, socioeconomic status or parental care. A transmission of death clustering has also been found across generations. One way of expanding the knowledge on intergenerational transfers in infant mortality is by conducting comparable studies across different populations.
This paper is one of a series of five studying the intergenerational transfer of infant mortality down the maternal line. All five studies share the same theoretical and methodological design, and use data derived from a standard database format: the Intermediate Data Structure (IDS). The data for the research reported in this paper were derived from a longitudinal dataset covering the 19th and 20th century population of the province of Troms in Northern Norway.
This contribution is part of an international comparative initiative with the aim to assess the analytical power of the Intermediate Data Structure (IDS) in a study of possible intergenerational transmissions of death in infancy. An evaluation of the data in applied research will be useful for further development of the IDS structure and for its future use in comparative research. An additional methodological aim for this part of the study is to evaluate and compare different models for statistical analysis of intergenerational transfers.
The burden of infant mortality is not shared equally by all families, but clusters in high risk families. As yet, it remains unclear why some families experience more infant deaths than other families. Earlier research has shown that the risk of early death among infants may at least partially be transmitted from grandmothers to mothers.
The Intermediate Data Structure (IDS) provides a standard format for storing and sharing individual-level longitudinal life-course data (Alter and Mandemakers 2014; Alter, Mandemakers and Gutmann 2009). Once the data are in the IDS format, a standard set of programs can be used to extract data for analysis, facilitating the analysis of data across multiple databases. Currently, life-course databases store information in a variety of formats, and the process of translating data into IDS can be long and tedious.
The Intermediate Data Structure (IDS) provides a common structure for storing and sharing historical demographic data. The structure also facilitates the construction of different open-access software to extract information from these tables and construct new variables. The article Using the Intermediate Data Structure (IDS) to Construct Files for Analysis (Quaranta 2015) presented a series of concepts and programs that allow the user to construct a rectangular episodes file for longitudinal statistical analysis using data stored in the IDS.