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Open Access Research article

Investigating linkage rates among probabilistically linked birth and hospitalization records

Jason P Bentley12, Jane B Ford1, Lee K Taylor2, Katie A Irvine3 and Christine L Roberts1*

Author Affiliations

1 Clinical and Population Perinatal Health Research, Kolling Institute of Medical Research, Sydney Medical School, University of Sydney at Royal North Shore Hospital, Building 52, St Leonards, NSW, 2006, Australia

2 Centre for Epidemiology and Evidence, NSW Ministry of Health, Locked Bag 961, North Sydney, NSW, 2059, Australia

3 Centre for Health Record Linkage, Cancer Institute, Australian Technology Park, 8 Central Avenue, Eveleigh, NSW, 2015, Australia

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BMC Medical Research Methodology 2012, 12:149  doi:10.1186/1471-2288-12-149

Published: 25 September 2012

Abstract

Background

With the increasing use of probabilistically linked administrative data in health research, it is important to understand whether systematic differences occur between the populations with linked and unlinked records. While probabilistic linkage involves combining records for individuals, population perinatal health research requires a combination of information from both the mother and her infant(s). The aims of this study were to (i) describe probabilistic linkage for perinatal records in New South Wales (NSW) Australia, (ii) determine linkage proportions for these perinatal records, and (iii) assess records with linked mother and infant hospital-birth record, and unlinked records for systematic differences.

Methods

This is a population-based study of probabilistically linked statutory birth and hospital records from New South Wales, Australia, 2001-2008. Linkage groups were created where the birth record had complete linkage with hospital admission records for both the mother and infant(s), partial linkage (the mother only or the infant(s) only) or neither. Unlinked hospital records for mothers and infants were also examined. Rates of linkage as a percentage of birth records and descriptive statistics for maternal and infant characteristics by linkage groups were determined.

Results

Complete linkage (mother hospital record – birth record – infant hospital record) was available for 95.9% of birth records, partial linkage for 3.6%, and 0.5% with no linked hospital records (unlinked). Among live born singletons (complete linkage = 96.5%) the mothers without linked infant records (1.6%) had slightly higher proportions of young, non-Australian born, socially disadvantaged women with adverse pregnancy outcomes. The unlinked birth records (0.4%) had slightly higher proportions of nulliparous, older, Australian born women giving birth in private hospitals by caesarean section. Stillbirths had the highest rate of unlinked records (3-4%).

Conclusions

This study shows that probabilistic linkage of perinatal records can achieve high, representative levels of complete linkage. Records for mother’s that did not link to infant records and unlinked records had slightly different characteristics to fully linked records. However, these groups were small and unlikely to bias results and conclusions in a substantive way. Stillbirths present additional challenges to the linkage process due to lower rates of linkage for lower gestational ages, where most stillbirths occur.

Keywords:
Probabilistic record linkage; Pregnancy; Administrative health data; International classification of diseases