Record linkage

Herzog, Thomas; Scheuren, Fritz; Winkler, William E.

doi:10.1002/wics.108

Cited by 42 publications

(24 citation statements)

References 16 publications

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“…"the bringing together of information from two records that are believed to relate to the same entity". 197 This corresponds well with the definition of Newcombe et al who, 50 years before, referred to it as "the bringing together of two or more separately recorded pieces of information concerning a particular individual or family". 188 The concept of 'identifying information' is added in Howe's 94 definition, defining record linkage as "the process of comparing two or more records which contain identifying information to determine whether those records refer to the same entity".…”

Section: Origin and Definition Of Record Linkagesupporting

confidence: 73%

“…For example, by 2008, the Western Australia Data Linkage System has supported over 400 studies, and more than 250 journal publications and 35 research higher degrees, 175 and there have been identifiable advances in population health arising from the research based on linkage. 175,177,191,197 …”

Section: Wide and Increased Use Of Data Linkage For Research In Develmentioning

confidence: 99%

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Availability and quality of vital registration mortality data in South Africa: a national and surveillance site perspective

Joubert¹

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The importance of valid and reliable mortality data as a key input for public and population health planning and research, and development programmes, is globally acknowledged.However, few low-and middle-income countries have such data. Even fewer have assessed the quality of their mortality data. Despite the particular utility of periodic, independent evaluations of national mortality statistics by countries themselves, such an evaluation has not yet been conducted for South Africa. Neither has a review been conducted of the sources of mortality data in South Africa since 1995. Regarding causes of death, a few small-scale studies assessed data quality in urban settings, but no study yet in civil registration mortality data in a rural setting. In addition, notwithstanding district health managers' critical need to know the health care needs of their district population current district mortality profiles are accompanied by strong warnings of invalid data.These gaps in essential public-and population-health intelligence present important reasons for research on the availability and quality of mortality data in South Africa.The primary aim of this body of research was to assess the quality of mortality data in the national CRVS system of South Africa. As alternative mortality data sources can complement or evaluate the quality of CRVS data, a secondary aim was to identify and review alternative mortality data sources, and assess the availability, strengths, and limitations of their data. A further aim was to conduct a field study to link and compare CRVS and Agincourt HDSS mortality data for the same individuals with the purpose of quantifying the level of completeness of death registration into the CRVS system, and assess the level of agreement between CRVS and verbal autopsy cause-of-death data. Seven research questions have been addressed towards achieving these aims. Different approaches were followed, including: (a) a mortality data source review and appraisal of the availability, strengths and limitations of different sources; (b) a comprehensive evaluation of national CRVS mortality data based on demographic and epidemiological principles; (c) a detailed record-linkage study applying both deterministic and probabilistic approaches; and (d) an application of adjustment factors from the linkage study to improve district-level cause-of-death data quality.iii The data source review found a rich and varied list of mortality data sources. Recent transformation in South Africa has enhanced mortality data availability from CRVS. Detailed cause-of-death data are available from civil registration and demographic surveillance.Additionally, a range of mortality data items are available in three population censuses, three demographic surveillance systems, and various national surveys, mortality audits and disease-notification programmes. Employing nine criteria to evaluate the quality of national CRVS mortality data, the data were rated satisfactory for coverage and completeness of death registration, temporal c...

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Section: Origin and Definition Of Record Linkagesupporting

confidence: 73%

Section: Wide and Increased Use Of Data Linkage For Research In Develmentioning

confidence: 99%

Availability and quality of vital registration mortality data in South Africa: a national and surveillance site perspective

Joubert¹

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“…This problem is known in various fields under different names, such as "data matching" Herzog et al 2010), "entity resolution" Bhattacharya and Getoor 2006), "deduplication" , and "reference matching" (McCallum et al 2000). This problem is known in various fields under different names, such as "data matching" Herzog et al 2010), "entity resolution" Bhattacharya and Getoor 2006), "deduplication" , and "reference matching" (McCallum et al 2000).…”

Section: Record Linkagementioning

confidence: 99%

Population Reconstruction

Kirby¹,

Carson²,

Dunlop³

et al. 2015

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PrefacePeople shape societies. They are linked to each other by family ties and networks with social, economic and religious dimensions. People live together in households and form communities. Some own a house, land and other properties, often related to their profession. And all this is in continuous change. People are born, marry, have children and die, and they change houses and addresses, and build careers. For the study of a society in all aspects, people are at the heart of the problem and should be known in the context of their complex relationships. Even today, it is not easy to get this information in an all-enfolding way, but for populations in the past, it is a real challenge. And that is what this book is about. The book addresses the problems that are encountered, and solutions that have been proposed, when we aim to identify people and to reconstruct populations under conditions where information is scarce, ambiguous, fuzzy and sometimes erroneous.It is not a single discipline that is involved in such an endeavour. Historians, social scientists, and linguists represent the humanities through their knowledge of the complexity of the past, the limitations of sources and the possible interpretations of information. The availability of big data from digitised archives and the need of complex analyses to identify individuals require the involvement of computer scientists. With contributions from all these fields, often in direct cooperation, this book is at the heart of digital humanities and hopefully a source of inspiration for future investigations.The process from handwritten registers to a reconstructed digitised population has three major phases which shape the three sections of this book. The first phase is that of data transcription and digitisation while structuring the information in a meaningful and efficient way. Little of this phase can be automated. With archives that comprise easily tens of millions of records, the help of volunteers for transcription and digitisation is indispensable, but requires a rigorous management. Experiences from Denmark demonstrate the complexity of this task in Chap. 1. Spelling variation, aliases, abbreviations, errors and typos all generate difficulties in further processing and require data cleaning. Similarity measures can be helpful to v

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“…This problem is known in various fields under different names, such as "data matching" (Winkler 1995;Herzog et al 2010), "entity resolution" (Singla and Domingos 2006;Bhattacharya and Getoor 2006), "deduplication" (Sarawagi and Bhamidipaty 2002;Christen 2012), and "reference matching" (McCallum et al 2000). In this section, we will focus on the formalization of the concepts and techniques of record linkage in the context of historic data.…”

Section: Record Linkagementioning

confidence: 99%

Record Linkage in Medieval and Early Modern Text

Georgala

Burgh

Meeng

et al. 2015

Population Reconstruction

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This chapter covers the topic of record linkage in historic texts, specifically documents from the Middle Ages and Early Modern period. The challenge of record linkage, in general, is to analyze large collections of data recording people, with the aim of recognizing links between these people, and deciding whether multiple mentions of people actually refer to one and the same person. The typical record linkage application, for example, involving birth and marriage certificates, deals with well-structured descriptions of people in terms of their first and last name, date and place of birth, and so on. In historic texts, however, specifically the medieval ones, people are not identified systematically, and one has to include a lot of the context of the occurrences in order to decide whether two descriptions actually refer to the same historic person. Here, we report on two recent projects, ChartEx and Traces Through Time, related to these challenges. We have been developing automatic techniques for recognizing links between documents, and determining the confidence that we have in the correctness of these links, based on the evidence provided in the text. Much of the work deals with the varied nature of the evidence, specifically with the role of first and last name being much more limited in the periods involved. We thus had to include identifying properties such as titles, professions, provenances, and family relationships, to determine confident

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Record linkage

Cited by 42 publications

References 16 publications

Availability and quality of vital registration mortality data in South Africa: a national and surveillance site perspective

Availability and quality of vital registration mortality data in South Africa: a national and surveillance site perspective

Population Reconstruction

Record Linkage in Medieval and Early Modern Text

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