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1.  Optimal strategy for linkage of datasets containing a statistical linkage key and datasets with full personal identifiers 
Linkage of aged care and hospitalisation data provides valuable information on patterns of health service utilisation among aged care service recipients. Many aged care datasets in Australia contain a Statistical Linkage Key (SLK-581) instead of full personal identifiers. We linked hospital and death records using a full probabilistic strategy, the SLK-581, and three combined strategies; and compared results for each strategy.
Linkage of Admitted Patient Data for 2000–01 to 2008–09 and Registry of Births, Deaths and Marriages death registration data for 2008–09 for New South Wales, Australia, was carried out using probabilistic methods and compared to links created using four strategies incorporating a SLK-581. The Basic SLK-581 strategy used the SLK-581 alone. The Most Recent SLK-581, Most Frequent SLK-581, and Any Match SLK-581 strategies leveraged probabilistic links between hospital records drawn from the Centre for Health Record Linkage Master Linkage Key. Rates of hospitalisations among people who died were calculated for each strategy and a range of health conditions.
Compared to full probabilistic linkage, the basic SLK-581 strategy produced substantial rates of missed links that increased over the study period and produced underestimates of hospitalisation rates that varied by health condition. The Most Recent SLK-581, Most Frequent SLK-581, and Any Match SLK-581 strategies resulted in substantially lower rates of underestimation than the Basic SLK-581. The Any Match SLK-581 strategy gave results closest to full probabilistic linkage.
Hospitalisation rates prior to death are substantially underestimated by linkage using a SLK-581 alone. Linkage rates can be increased by combining deterministic methods with probabilistically created links across hospital records.
PMCID: PMC4236530  PMID: 25257549
Data linkage; Record linkage; SLK-581; Linkage methods
2.  Potential prevention of small for gestational age in Australia: a population-based linkage study 
Small for gestational age (SGA) infants are at increased risk of morbidity and mortality. We sought to identify risk factors associated with SGA and examined the potential for reducing the proportion of infants with SGA at a population level.
Birth and hospital records were linked for births occurring in 2007–2010 in New South Wales, Australia. The analysis was stratified into three groups: preterm births, term births to non-diabetic mothers and term births to diabetic mothers. Logistic regression was used to examine the association between SGA and a range of socio-demographic and behavioural factors and health conditions, with generalised estimating equations to account for correlation among births to the same mother. Model-based population attributable fractions (PAFs) were calculated for risk factors that were considered causative and potentially modifiable.
Of 28,126 SGA infants, the largest group was term infants of non-diabetic mothers (88.5%), followed by term infants of diabetic mothers (6.3%) and preterm infants (5.3%). The highest PAFs were for smoking: 12.4% for preterm SGA and 10.3% for term SGA infants of non-diabetic mothers. Other risk factors for SGA that were considered modifiable included: illicit drug dependency or abuse in pregnancy in all three groups, and pregnancy hypertension and late commencement of antenatal care in term infants of non-diabetic mothers, but PAFs were less than 3%.
There are opportunities for modest reduction of the prevalence of SGA through reduction in smoking in pregnancy, and possibly earlier commencement of antenatal care and improved management of high-risk pregnancies.
PMCID: PMC3835866  PMID: 24246011
Small for gestational age; Population attributable fraction; Record linkage
3.  Investigating linkage rates among probabilistically linked birth and hospitalization records 
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.
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.
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%).
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.
PMCID: PMC3533905  PMID: 23009079
Probabilistic record linkage; Pregnancy; Administrative health data; International classification of diseases
4.  Enhanced reporting of deaths among Aboriginal and Torres Strait Islander peoples using linked administrative health datasets 
Aboriginal and Torres Strait Islander peoples are under-reported in administrative health datasets in NSW, Australia. Correct reporting of Aboriginal and Torres Strait Islander peoples is essential to measure the effectiveness of policies and programmes aimed at reducing the health disadvantage experienced by Aboriginal and Torres Strait Islander peoples. This study investigates the potential of record linkage to enhance reporting of deaths among Aboriginal and Torres Strait Islander peoples in NSW, Australia.
Australian Bureau of Statistics death registration data for 2007 were linked with four population health datasets relating to hospitalisations, emergency department attendances and births. Reporting of deaths was enhanced from linked records using two methods, and effects on patterns of demographic characteristics and mortality indicators were examined.
Reporting of deaths increased by 34.5% using an algorithm based on a weight of evidence of a person being Aboriginal or Torres Strait Islander, and by 56.6% using an approach based on 'at least one report' of a person being Aboriginal or Torres Strait Islander. The increase was relatively greater in older persons and those living in less geographically remote areas. Enhancement resulted in a reduction in the urban-remote differential in median age at death and increases in standardised mortality ratios particularly for chronic conditions.
Record linkage creates a statistical construct that helps to correct under-reporting of deaths and potential bias in mortality statistics for Aboriginal and Torres Strait Islander peoples.
PMCID: PMC3413579  PMID: 22747900

Results 1-4 (4)