Historical Databases Now and in the Future

Kees Mandemakers has enriched historical databases in the Netherlands and internationally through the development of the Historical Sample of the Netherlands, the Intermediate Data Structure, a practical implementation of rule-based record linking (LINKS) and personal encouragement of high quality longitudinal data in a number of countries.

Measuring Migration Status Based on the Place of Marriage Overestimates the Share of Male Migrants in Historical Populations. Evidence From Dutch Marriage Certificates

Thanks to the construction of large databases such as LINKS and GENLIAS based on Dutch civil certificates, our knowledge of individual demographic behavior in the past has improved significantly. However, the use of such research infrastructures also introduces some potential pitfalls, as these databases do not contain all information available from the original sources. For instance, variables that are available on the original source but lacking in LINKS are the places of residence of the bride and the groom at marriage. A common practice among researchers using LINKS and GENLIAS is therefore to identify migrants by comparing an individual’s birth place with the place of marriage. The place of marriage, however, is not necessarily identical to the place of residence, because couples traditionally contracted their marriage in the bride's or bride's parents' municipality of residence. It is therefore particularly likely that grooms are erroneously considered as migrants even though they had never moved before marriage. In this paper we explore whether this poses a problem to studies using the place of marriage as an equivalent to the place of residence. This will be achieved with the help of the marriage certificates release from the Historical Sample of the Netherlands (HSN), which, unlike LINKS, contains both the place of marriage of the couple and the residence of the bride and groom, and allows us to compare the findings derived from both approaches. The analyses show that identifying migrants based on place of marriage causes indeed a significant overestimation of male migrants, but not of female migrants. We therefore suggest the use of a couple's place of first childbirth as a robustness check to avoid overestimating male migration in the past.

Surviving in Overijssel. An Analysis of Life Expectancy, 1812–1912.

The rise in life expectancy is one of the main processes of social change in the 19th century. In the Netherlands, regional differences in life expectancy, and their development, were huge. Therefore, studies on average life expectancy or studies, which examine the whole of the Netherlands do not fully capture the differential determinants of this process. This study focuses on social, economic, and geographic differences in life expectancy in 19th-century Overijssel using the Historical Sample of the Netherlands (HSN). Exploiting Cox regression, the influence of several factors on life expectancy are investigated. The article shows that birth cohort, urbanisation, and gender had an important relation with life expectancy in 19th-century Overijssel, while industrialisation, religion, and inheritance customs were not associated with age at death.

Criminal Life Courses in Context

The future of the Historical Sample of the Netherlands (HSN) will certainly include the enrichment of the foundational database with additional, new sources of information. In general, the HSN would highly benefit from current mass digitization projects involving citizen science. This essay proposes a pilot in linking 19th- and early 20th-century criminal records to HSN. In spite of the extensive state and parish registration documenting individual and family lives in close systematic detail, life course approaches to historical crime are less common. The large datasets necessary to conduct longitudinal life course research into deviant behaviour will facilitate both the analysis of criminality as an event and the scrutiny of the trajectories of individuals' lives leading up to their involvement in crime.

Enriching the HSN With Individual Causes of Death. A Database for a Life-Course Analysis of Victims and Survivors

The focus of this article is on how a newly created database on causes of death in Amsterdam (1854–1940) may offer innovative insights by combining it with the available information from the Historical Sample of the Netherlands (HSN). By doing so, it illustrates how future research can help to provide new perspectives on ongoing debates on historical and contemporary infectious diseases by combining information from several historical sources and databases.

The Life Span of Large Historical Databases

Large historical databases, although intended to last for a long time, can become obsolete for a variety of reasons. In this essay these reasons are explored and used for a 'health check' of the Historical Sample of the Netherlands (HSN). The HSN leaders are examined for their visionary qualities and their sense of ownership, and the database for its complementarity, versatility and consistency. The essay concludes that, despite challenges ahead, HSN is sound of mind and body.

Beyond Last Words: Patterns of Linguistic and Interactional Behavior in a Historical Sample of Dying Hospital Patients

Patterns of linguistic and interactional behavior by people at the very end of their lives are not well described, partly because data is difficult to obtain. This paper analyzes descriptions of 486 deaths gathered from 1900 to 1904 in the first-ever clinical study of dying by noted Canadian physician, Sir William Osler. Only 16 patients were noted speaking, and only four canonical last words were reported. The most frequent observation by medical staff was that the deaths were quiet ( n = 30), though range of other behaviors were noted (e.g., moaning, delirium, seeming intention to speak). Osler's problematic study left behind data whose analysis is a small step toward empirically characterizing the linguistic and interactional details of a previously under-described phenomena as well as the importance of the social context in which they occur.

Improved selection of participants in genetic longevity studies: family scores revisited

Background Although human longevity tends to cluster within families, genetic studies on longevity have had limited success in identifying longevity loci. One of the main causes of this limited success is the selection of participants. Studies generally include sporadically long-lived individuals, i.e. individuals with the longevity phenotype but without a genetic predisposition for longevity. The inclusion of these individuals causes phenotype heterogeneity which results in power reduction and bias. A way to avoid sporadically long-lived individuals and reduce sample heterogeneity is to include family history of longevity as selection criterion using a longevity family score. A main challenge when developing family scores are the large differences in family size, because of real differences in sibship sizes or because of missing data. Methods We discussed the statistical properties of two existing longevity family scores: the Family Longevity Selection Score (FLoSS) and the Longevity Relatives Count (LRC) score and we evaluated their performance dealing with differential family size. We proposed a new longevity family score, the mLRC score, an extension of the LRC based on random effects modeling, which is robust for family size and missing values. The performance of the new mLRC as selection tool was evaluated in an intensive simulation study and illustrated in a large real dataset, the Historical Sample of the Netherlands (HSN). Results Empirical scores such as the FLOSS and LRC cannot properly deal with differential family size and missing data. Our simulation study showed that mLRC is not affected by family size and provides more accurate selections of long-lived families. The analysis of 1105 sibships of the Historical Sample of the Netherlands showed that the selection of long-lived individuals based on the mLRC score predicts excess survival in the validation set better than the selection based on the LRC score . Conclusions Model-based score systems such as the mLRC score help to reduce heterogeneity in the selection of long-lived families. The power of future studies into the genetics of longevity can likely be improved and their bias reduced, by selecting long-lived cases using the mLRC.

Linking the Historical Sample of the Netherlands with the USA Censuses, 1850–1940

During the 19th and early 20th century about 220,000 Dutch born persons migrated to the USA. The Historical Sample of the Netherlands (HSN) contains about 85,500 persons born in the Netherlands between 1812 and 1922. In this article we report the way we have matched persons from the HSN with the American censuses from the period 1850 till 1940. For this purpose, a linking process was designed, comprising of three stages: harmonization, matching and validation. The different nature of the two datasets (HSN and the USA Censuses) asked for some harmonization prior to the matching. Once the data had been properly prepared, two strategies were applied in order to link the data sets. The first one, called Similarity Approach, matched individuals from both datasets by comparing on the basis of resemblance of first and last names. The second approach, called Transformation Approach, made use of dictionaries with Anglicized versions of Dutch first and last names and their most common or most likely Dutch original(s). Because of the sample character of the HSN even exact matches showed ambiguity that needs to be resolved. For this reason, a validation process comparing the household context was run to provide a more trustworthy result. In the end we identified 484 individuals present in the HSN database with reliable links to the American censuses. We also evaluated the result in the light of what we know from emigration patterns to the USA over time and period and we concluded that our efforts have produced a reasonable result. Nevertheless, we are aware that we may have missed links. We also found that at least 45% of the emigrants returned to the Netherlands at some point during their life course.

Assessment of the Couple between the Historical Sample and the Theoretical Probability Distributions for Maximum flow Values Based on a Fuzzy Methodology

In this article, an adjustment of the extreme theoretical probability distributions upon the sample is proposed, based on the conventional fuzzy linear regression model of Tanaka [1], where all the data must be included within the produced fuzzy band. This is achieved by using the quintile approach, which relates the observed return period with the theoretical cumulative probability. A new contribution of this work is the use of the fuzzified maximum likelihood, as a measure of goodness of fit. The model is applied for real data from the Strymonas River, regarding the annual maximum flow, and finally, useful conclusions are made.