The Use of Names for Linking Personal Records

Newcombe, Howard B.; Fair, Martha E.; Lalonde, Pierre

doi:10.2307/2290660

Cited by 10 publications

(5 citation statements)

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“…Routine data are primarily collected for administrative purposes and their accuracy has been questioned [ 68 ]. Population-based, administrative data sets have been used to assess service utilization - among other outcomes - for many years, and the data linkage and analysis procedures have been validated and well-established [ 69 , 70 ]. The use of administrative data eliminates biases associated with the use of self-report data and attrition problems common in studies involving long-term follow-up.…”

Section: Discussionmentioning

confidence: 99%

Is the health of people living in rural areas different from those in cities? Evidence from routine data linked with the Scottish Health Survey

Teckle

Hannaford

Sutton

2012

BMC Health Serv Res

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BackgroundTo examine the association between rurality and health in Scotland, after adjusting for differences in individual and practice characteristics.MethodsDesign: Mortality and hospital record data linked to two cross sectional health surveys. Setting: Respondents in the community-based 1995 and 1998 Scottish Health Survey who consented to record-linkage follow-up. Main outcome measures: Hypertension, all-cause premature mortality, total hospital stays and admissions due to coronary heart disease (CHD).ResultsOlder age and lower social class were strongly associated with an increased risk of each of the four health outcomes measured. After adjustment for individual and practice characteristics, no consistent pattern of better or poorer health in people living in rural areas was found, compared to primary cities. However, individuals living in remote small towns had a lower risk of a hospital admission for CHD and those in very remote rural had lower mortality, both compared with those living in primary cities.ConclusionThis study has shown how linked data can be used to explore the possible influence of area of residence on health. We were unable to find a consistent pattern that people living in rural areas have materially different health to that of those living in primary cities. Instead, we found stronger relationships between compositional determinants (age, gender and socio-economic status) and health than contextual factors (including rurality).

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Section: Discussionmentioning

confidence: 99%

Is the health of people living in rural areas different from those in cities? Evidence from routine data linked with the Scottish Health Survey

Teckle

Hannaford

Sutton

2012

BMC Health Serv Res

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“…16 The information on mortality was mainly provided by record linkage searches conducted by Statistics Canada using the Generalized Record Linkage System as applied to the Canadian Mortality Data Base that records deaths in Canada from 1950 onward. [30][31][32] Searches were also made through the United States and the United Kingdom as needed. As of the common closing date of December 31, 1992, death information was complete for the 1952 sample but vital status remained unknown for 4 people in the 1970 sample.…”

Section: Data Analyzedmentioning

confidence: 99%

Time Trends in Mortality Associated with Depression: Findings from the Stirling County Study

et al. 2010

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Objective The question addressed is whether a mortality risk associated with depression in a 1952 representative sample of Stirling County adults changed in a new sample of 1970 and whether there was a change in relationships to cigarette smoking and alcoholism. Method Sample members were interviewed about depression and cigarette smoking. General physicians were interviewed by psychiatrists regarding alcoholism. Information about death as of December 31, 1992 was provided by Statistics Canada. Proportional hazards models were fitted in the two samples to assess the mortality risks associated with depression among men and women over 20 years of follow-up, and additionally among men with heavy smoking and alcoholism. Specific causes of death were investigated. Results Hazard ratios representing the association between depression and premature death among men were 2.6 (95%CI 1.4 to 4.9) and 2.8 (95%CI 1.5 to 5.1) respectively in the 1952 and 1970 samples for the first 10 years of follow-up. Hazard ratios for women were 1.4 (95%CI 0.6 to 3.2) and 1.2 (95%CI 0.5 to 2.9). The risk associated with depression among men was independent of alcoholism and heavy smoking. Depression and alcoholism were significantly associated with death due to external causes and to circulatory disease; heavy smoking with malignant neoplasms. Conclusion The mortality associated with depression did not change over the period of 1952 to 1970. Depressed men experienced a significant mortality risk that was not matched among depressed women and also was not due to alcoholism and heavy smoking.

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“…Implementation of this algorithm could conceivably make it possible to merge student records even if a unique student ID were not available at all. Similar name-based matching algorithms have been proposed elsewhere (Cohen, Ravikumar, & Fienberg, 2003;Newcombe, Fair, & Lalonde, 1992). In this case, the LND could be computed by comparing all N 1 names in the year 1 data file to all N 2 names in the year 2 data file.…”

Section: Merges When Unique Id Is Unavailablementioning

confidence: 99%

Application of the Levenshtein Distance Metric for the Construction of Longitudinal Data Files

Doran

Wamelen

2010

Educational Measurement

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The analysis of longitudinal data in education is becoming more prevalent given the nature of testing systems constructed for No Child Left Behind Act (NCLB). However, constructing the longitudinal data files remains a significant challenge. Students move into new schools, but in many cases the unique identifiers (ID) that should remain constant for each student change. As a result, different students frequently share the same ID, and merging records for an ID that is erroneously assigned to different students clearly becomes problematic. In small data sets, quality assurance of the merge can proceed through human reviews of the data to ensure all merged records are properly joined. However, in data sets with hundreds of thousands of cases, quality assurance via human review is impossible. While the record linkage literature has many applications in other disciplines, the educational measurement literature lacks details of formal protocols that can be used for quality assurance procedures for longitudinal data files. This article presents an empirical quality assurance procedure that may be used to verify the integrity of the merges performed for longitudinal analysis. We also discuss possible extensions that would permit merges to occur even when unique identifiers are not available.

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The Use of Names for Linking Personal Records

Cited by 10 publications

References 0 publications

Is the health of people living in rural areas different from those in cities? Evidence from routine data linked with the Scottish Health Survey

Is the health of people living in rural areas different from those in cities? Evidence from routine data linked with the Scottish Health Survey

Time Trends in Mortality Associated with Depression: Findings from the Stirling County Study

Application of the Levenshtein Distance Metric for the Construction of Longitudinal Data Files

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