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Journal articles on the topic 'Data linkage'

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Published: 4 June 2021
Last updated: 20 February 2023

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1

Schwartz, Harvey A. "Data linkage." Statistics in Medicine 14, no. 5-7 (March 15, 1995): 687–89. http://dx.doi.org/10.1002/sim.4780140525.

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2

Bergstra, J. A., and C. A. Middelburg. "Data Linkage Algebra, Data Linkage Dynamics, and Priority Rewriting." Fundamenta Informaticae 128, no. 4 (2013): 367–412. http://dx.doi.org/10.3233/fi-2013-950.

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3

Neil, Martin, and Richard Bache. "Data linkage maps." Journal of Software Maintenance: Research and Practice 5, no. 3 (1993): 155–64. http://dx.doi.org/10.1002/smr.4360050304.

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4

Fix, Jonathan, Amy I. Ising, Scott K. Proescholdbell, Dennis M. Falls, Catherine S. Wolff, Antonio R. Fernandez, and Anna E. Waller. "Linking Emergency Medical Services and Emergency Department Data to Improve Overdose Surveillance in North Carolina." Public Health Reports 136, no. 1_suppl (November 2021): 54S—61S. http://dx.doi.org/10.1177/00333549211012400.

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Introduction Linking emergency medical services (EMS) data to emergency department (ED) data enables assessing the continuum of care and evaluating patient outcomes. We developed novel methods to enhance linkage performance and analysis of EMS and ED data for opioid overdose surveillance in North Carolina. Methods We identified data on all EMS encounters in North Carolina during January 1–November 30, 2017, with documented naloxone administration and transportation to the ED. We linked these data with ED visit data in the North Carolina Disease Event Tracking and Epidemiologic Collection Tool. We manually reviewed a subset of data from 12 counties to create a gold standard that informed developing iterative linkage methods using demographic, time, and destination variables. We calculated the proportion of suspected opioid overdose EMS cases that received International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes for opioid overdose in the ED. Results We identified 12 088 EMS encounters of patients treated with naloxone and transported to the ED. The 12-county subset included 1781 linkage-eligible EMS encounters, with historical linkage of 65.4% (1165 of 1781) and 1.6% false linkages. Through iterative linkage methods, performance improved to 91.0% (1620 of 1781) with 0.1% false linkages. Among statewide EMS encounters with naloxone administration, the linkage improved from 47.1% to 91.1%. We found diagnosis codes for opioid overdose in the ED among 27.2% of statewide linked records. Practice Implications Through an iterative linkage approach, EMS–ED data linkage performance improved greatly while reducing the number of false linkages. Improved EMS–ED data linkage quality can enhance surveillance activities, inform emergency response practices, and improve quality of care through evaluating initial patient presentations, field interventions, and ultimate diagnoses.
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5

Untoro, Meida Cahyo. "MWMOTE optimization for imbalanced data using complete linkage." Jurnal Teknologi dan Sistem Komputer 9, no. 2 (January 18, 2021): 77–82. http://dx.doi.org/10.14710/jtsiskom.2021.13748.

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Imbalanced data can result in classification errors, such as in WMMOTE, and can decrease its performance and accuracy. Clustering in MWMOTE can be optimized to improve synthetic data generation and improve MWMOTE performance. This study aims to optimize the MWMOTE algorithm's performance in the clustering process in making synthetic data with complete linkage (CL). The dataset used a variety of data ratios to handle imbalanced data. The decision tree was used to determine the performance of MWMOTE and CL-MWMOTE oversampling. CL-MWMOTE evaluation results provide better and optimal performance than MWMOTE and increase the precision, recall, f-measure, and accuracy of 0.53 %, 0.67 %, 0.66 %, and 0.67 %, respectively.
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Kamimura, Tetsuo, Yoshihiko Abe, and Hideo Ikegami. "US-Japan Data Linkage." Kakuyūgō kenkyū 53, no. 1 (1985): 67–88. http://dx.doi.org/10.1585/jspf1958.53.67.

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7

Renwick, J. H., and M. M. Izatt. "Linkage data on monilethrix." Cytogenetic and Genome Research 47, no. 1-2 (1988): 108. http://dx.doi.org/10.1159/000132522.

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8

Greiver, Michelle, Karen Tu, Steven Bernard, Babak Aliarzadeh, Sumeet Kalia, Conrad Pow, Tao Chen, and Rahim Moineddin. "Data on Patient Record Trajectory for Linkage (DataPRinT Linkage)." Canadian Journal of Diabetes 45, no. 7 (November 2021): S22. http://dx.doi.org/10.1016/j.jcjd.2021.09.065.

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9

Sukarso, Aso, Asep Yusup Hanapia, and Chandra Budhi.L.S. "Analisis Keterkaitan antar Sektor Kunci Berdasarkan Data Input Output Kabupaten Tasikmalaya Tahun 2012." WELFARE Jurnal Ilmu Ekonomi 2, no. 2 (January 21, 2022): 148–58. http://dx.doi.org/10.37058/wlfr.v2i2.3627.

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This study aims to determine the backward linkages and forward linkages between key sectors in Tasikmalaya Regency. The data used is the Table of Input Output (BPS) of Tasikmalaya Regency in 2012. The data analysis in this study uses standard deviation processed by Excel. This study uses several approaches, namely: backward linkage and direct backward spreads; direct and indirect total backward linkage; direct, indirect, and induced total backward linkage; forward linkage and direct forward spreads; direct and indirect total forward linkages; direct, indirect, and induced total forward linkages. Based on the results of the study, it was found that sector 3 (processing industry) is a key sector that is directly, indirectly, and affected, this sector asks for inputs in other sectors equally. Meanwhile, sector 6 (trade, hotels and restaurants) is a key sector because directly, indirectly, and affected, this sector demands output in other sectors equally.Penelitian ini bertujuan untuk mengetahui keterkaitan ke belakang dan keterkaitan ke depan antar sektor kunci di Kabupaten Tasikmalaya. Data yang digunakan adalah tabel Input Output (BPS) Kabupaten Tasikmalaya Tahun 2012. Analisis data pada penelitian ini menggunakan standar deviasi yang diolah dengan Excel. Penelitian ini menggunakan beberapa pendekatan, yaitu: keterkaitan ke belakang dan penyebaran ke belakang langsung; keterkaitan ke belakang total langsung dan tidak langsung; keterkaitan ke belakang total langsung, tidak langsung, dan terimbas; keterkaitan ke depan dan penyebaran ke depan langsung; keterkaitan ke depan total langsung dan tidak langsung; keterkaitan ke depan total langsung, tidak langsung, dan terimbas. Berdasarkan hasil penelitian, diperoleh bahwa sektor 3 (industri pengolahan) adalah sektor kunci karena secara langsung, tidak langsung, dan terimbas, sektor ini meminta input atau faktor produksi pada sektor-sektor lain secara merata. Adapun sektor 6 (perdagangan, hotel dan restoran) adalah sektor kunci oleh karena secara langsung, tidak langsung, dan terimbas, sektor ini meminta output atau hasil produksi pada sektor-sektor lain secara merata.
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10

Gillespie, T., P. Dhillon, K. Ward, A. Aggarwal, D. Bumb, D. Kondal, N. Kaushik, et al. "Feasibility and Results of Cancer Registry and Noncommunicable Disease Cohort Data Linkages in India." Journal of Global Oncology 4, Supplement 2 (October 1, 2018): 65s. http://dx.doi.org/10.1200/jgo.18.53600.

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Background: Cancer registries worldwide are vital to determine cancer burden, plan cancer control measures, and facilitate research. Population-based cancer registries are a priority for LMICs by the UICC; the National Cancer Registry Program (NCRP) of India oversees 28 such registries. A primary function of registries is to combine data for the same individual from multiple sources. For other disease cohorts where cancer is an outcome of interest, registries can potentially connect information by linking datasets together. Barriers to successful registration and linkages include systems in which cancer is not a notifiable disease, no universal unique individual identifier exists, and lack of trained personnel. This study utilizes technology and infrastructure to develop better linkages, surveillance, and outcomes. Aim: To assess the feasibility of linking large cohorts designed for cardio-metabolic disease research with cancer registries in New Delhi and Chennai; determine additional steps required for linkage accuracy and completeness; and develop detailed protocols for future applications. Methods: A pilot protocol for linkage between a large diabetes cohort and cancer registries in Delhi and Chennai was developed using MatchPro, a probabilistic record linkage program developed for cancer registries. Probabilistic software links datasets together in the presence of uncertainty (eg misspelled or abbreviated names) to identify record pairs with high probability of representing the same individual. For this study, algorithms were developed to address unique aspects of names and demographics in India. The software and algorithms focused on: detecting duplicates in cancer registries; and linking registries with external files from diabetes cohorts. In Delhi, 3 1-year datasets covering 3 years (2010, 2011, 2012) were linked with the diabetes cohort; in Chennai, the linkage included 3 5-year datasets covering 15 years (2000-04, '05-'09, '10-'14). The unique ID (Aadhaar) is not collected or linked systematically between different systems at this point in time. Results: Linkage attempts yielded potential matches ranked according to probabilistic scores; highest scores were reviewed to determine true matches. In Chennai, this process yielded: (2010-2014) 21% self-reported (SR) cases matching perfectly, 36% requiring follow-up, 13 nonreported (NR) cases found; 2005-2009: 33% SR cases matched perfectly, 1 NR case found; 2000-2004: 1 NR case. Also, 2 training workshops on data linkages and software were held. Conclusion: Linkages between cancer registries and other data sources are feasible in LMICs using probabilistic record linkage software augmented by manual matching. Future efforts to use existing epidemiologic resources (cohorts) and cancer research infrastructure (registries and clinical centers) can enhance research including understanding shared risk factors and pathophysiologic mechanisms e.g., between cancer and other NCD.
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11

Grath-Lone, Louise Mc, Nicolás Libuy, David Etoori, Ruth Blackburn, Ruth Gilbert, and Katie Harron. "Ethnic bias in data linkage." Lancet Digital Health 3, no. 6 (June 2021): e339. http://dx.doi.org/10.1016/s2589-7500(21)00081-9.

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12

Harron, Katie. "Data linkage in medical research." BMJ Medicine 1, no. 1 (March 2022): e000087. http://dx.doi.org/10.1136/bmjmed-2021-000087.

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13

Bergstra, J. A., and C. A. Middelburg. "Data Linkage Dynamics with Shedding." Fundamenta Informaticae 103, no. 1-4 (2010): 31–52. http://dx.doi.org/10.3233/fi-2010-317.

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14

Suh, Young Ju, Taesung Park, and Soo Yeon Cheong. "Linkage analysis of longitudinal data." BMC Genetics 4, Suppl 1 (2003): S27. http://dx.doi.org/10.1186/1471-2156-4-s1-s27.

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15

LAMM, ROBERT. "LINKAGE CALCULATIONS FROM SEMISTERILITY DATA." Hereditas 36, no. 4 (July 9, 2010): 457–69. http://dx.doi.org/10.1111/j.1601-5223.1950.tb03389.x.

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16

Scambler, P. J., G. Bell, E. Watson, M. Farrall, G. Bates, K. Davies, N. Lench, et al. "Cystic fibrosis linkage exclusion data." Cytogenetic and Genome Research 41, no. 1 (1986): 62–63. http://dx.doi.org/10.1159/000132200.

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17

Schwartz, Marianne, and Thomas Rosenberg. "Åland eye disease: Linkage data." Genomics 10, no. 2 (June 1991): 327–32. http://dx.doi.org/10.1016/0888-7543(91)90315-6.

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18

Smith, Merran, James Semmens, Diana Rosman, Jane Ford, Catherine Storey, C. D'Arcy Holman, Emma Fuller, and Victoria Gray. "International Health Data Linkage Network." Healthcare Policy | Politiques de Santé 6, SP (February 1, 2011): 94–96. http://dx.doi.org/10.12927/hcpol.2011.22127.

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19

Langley, J. D., and J. C. Alsop. "Determining First Admissions in a Hospital Discharge File via Record Linkage." Methods of Information in Medicine 37, no. 01 (1998): 32–37. http://dx.doi.org/10.1055/s-0038-1634496.

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Abstract:The aim of this study was to identify first admissions in a public hospital discharge file with the greatest possible accuracy. Computerised data linkage was used to link injury events. This involved the use of “internal” data linkage (unduplication) which, in data linkage terms, is equivalent to matching two identical files. Admission status indicators obtained from deterministic and probabilistic linkages were compared with those obtained from a manual review. Small absolute reductions in error were obtained using a probabilistic linkage over a deterministic linkage. However, these reflected large relative reductions in error. A validity check confirmed initial results and discounted against possible bias due to the subjective nature of the probabilistic linking procedure.
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20

Roos, L. L., and A. Wajda. "Record Linkage Strategies." Methods of Information in Medicine 30, no. 02 (1991): 117–23. http://dx.doi.org/10.1055/s-0038-1634828.

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AbstractRecord linkage techniques can help identify the same patient for matching diverse files (hospital discharge abstracts, insurance claims, registries, Vital Statistics data) which contain similar identifiers. Prior knowledge of whether a linkage is feasible is important to prevent wasted effort (additional data collection or data manipulation), which decreases the cost-effectiveness of the linkage. Using examples generated by linking the Manitoba Health Services Commission data with Vital Statistics files, a method of estimating the information in each data set is presented first. Further, the feasibility of several different record linkage strategies is described and tested, given varying amounts of information. At the margin, relatively small amounts of information (having just one more variable to match with) can make a great difference. Probabilistic linkage’s great advantage was found in those situations where only a moderate amount of extra information was available.By using the above techniques when working with one or both files in a proposed record linkage project, a much more informed judgement can now be made as to whether a linkage will or will not work. In facilitating record linkage, flexibility of both software and the strategy for matching is very important.
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21

Hagger-Johnson, Gareth, Katie Harron, Harvey Goldstein, Robert Aldridge, and Ruth Gilbert. "Probabilistic linkage to enhance deterministic algorithms and reduce data linkage errors in hospital administrative data." Journal of Innovation in Health Informatics 24, no. 2 (June 30, 2017): 234. http://dx.doi.org/10.14236/jhi.v24i2.891.

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BackgroundThe pseudonymisation algorithm used to link together episodes of care belonging to the same patients in England (HESID) has never undergone any formal evaluation, to determine the extent of data linkage error.ObjectiveTo quantify improvements in linkage accuracy from adding probabilistic linkage to existing deterministic HESID algorithms.MethodsInpatient admissions to NHS hospitals in England (Hospital Episode Statistics, HES) over 17 years (1998 to 2015) for a sample of patients (born 13/28th of months in 1992/1998/2005/2012). We compared the existing deterministic algorithm with one that included an additional probabilistic step, in relation to a reference standard created using enhanced probabilistic matching with additional clinical and demographic information. Missed and false matches were quantified and the impact on estimates of hospital readmission within one year were determined.ResultsHESID produced a high missed match rate, improving over time (8.6% in 1998 to 0.4% in 2015). Missed matches were more common for ethnic minorities, those living in areas of high socio-economic deprivation, foreign patients and those with ‘no fixed abode’. Estimates of the readmission rate were biased for several patient groups owing to missed matches, which was reduced for nearly all groups. ConclusionProbabilistic linkage of HES reduced missed matches and bias in estimated readmission rates, with clear implications for commissioning, service evaluation and performance monitoring of hospitals. The existing algorithm should be modified to address data linkage error, and a retrospective update of the existing data would address existing linkage errors and their implications.
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22

Brook, Emma L., Diana L. Rosman, and C. D'Arcy J. Holman. "Public good through data linkage: measuring research outputs from the Western Australian Data Linkage System." Australian and New Zealand Journal of Public Health 32, no. 1 (February 2008): 19–23. http://dx.doi.org/10.1111/j.1753-6405.2008.00160.x.

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23

Jette, Nathalie, Kristin Atwood, Mark Hamilton, Rachel Hayward, Lundy Day, Theo Mobach, Colleen Maxwell, et al. "Linkage Between Neurological Registry Data and Administrative Data." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 40, S2 (July 2013): S32—S34. http://dx.doi.org/10.1017/s0317167100017145.

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Ong, Toan C., Michael V. Mannino, Lisa M. Schilling, and Michael G. Kahn. "Improving record linkage performance in the presence of missing linkage data." Journal of Biomedical Informatics 52 (December 2014): 43–54. http://dx.doi.org/10.1016/j.jbi.2014.01.016.

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Bu, Jianwei, Wei Liu, Zhao Pan, and Kang Ling. "Comparative Study of Hydrochemical Classification Based on Different Hierarchical Cluster Analysis Methods." International Journal of Environmental Research and Public Health 17, no. 24 (December 18, 2020): 9515. http://dx.doi.org/10.3390/ijerph17249515.

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Traditional methods for hydrochemical analyses are effective but less diversified, and are constrained to limited objects and conditions. Given their poor accuracy and reliability, they are often used in complement or combined with other methods to solve practical problems. Cluster analysis is a multivariate statistical technique that extracts useful information from complex data. It provides new ideas and approaches to hydrogeochemical analysis, especially for groundwater hydrochemical classification. Hierarchical cluster analysis is the most widely used method in cluster analysis. This study compared the advantages and disadvantages of six hierarchical cluster analysis methods and analyzed their objects, conditions, and scope of application. The six methods are: The single linkage, complete linkage, median linkage, centroid linkage, average linkage (including between-group linkage and within-group linkage), and Ward’s minimum-variance. Results showed that single linkage and complete linkage are unsuitable for complex practical conditions. Median and centroid linkages likely cause reversals in dendrograms. Average linkage is generally suitable for classification tasks with multiple samples and big data. However, Ward’s minimum-variance achieved better results for fewer samples and variables.
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Secolin, Rodrigo, Cristiane S. Rocha, Fábio R. Torres, Marilza L. Santos, Cláudia V. Maurer-Morelli, Neide F. Santos, and Iscia Lopes-Cendes. "LINKGEN: A new algorithm to process data in genetic linkage studies." Genomics 91, no. 6 (June 2008): 544–47. http://dx.doi.org/10.1016/j.ygeno.2008.02.001.

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27

Douglas, M. M., D. Gardner, D. Hucker, and S. W. Kendrick. "Best-Link Matching of Scottish Health Data Sets." Methods of Information in Medicine 37, no. 01 (1998): 64–68. http://dx.doi.org/10.1055/s-0038-1634494.

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Abstract:Methods are described used to link the Community Health Index and the National Health Service Central Register (NHSCR) in Scotland to provide a basis for a national patient index. The linkage used a combination of deterministic and probability matching techniques. A best-link principle was used by which each Community Health Index record was allowed to link only to the NHSCR record with which it achieved the highest match weight. This strategy, applied in the context of two files which each covered virtually the entire population of Scotland, increased the accuracy of linkage approximately a thousand-fold compared with the likely results of a less structured probability matching approach. By this means, 98.8% of linkable records were linked automatically with a sufficient degree of confidence for administrative purposes.
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CHAMBERS, Georgina M., Stephanie K. Y. CHOI, Katie IRVINE, Christos VENETIS, Katie HARRIS, Alys HAVARD, Robert J. NORMAN, Kei LUI, William LEDGER, and Louisa R. JORM. "ANZARD Data Linkage – Agreement Between Births Recorded by Clinics and in NSW Perinatal Data Collection." Fertility & Reproduction 04, no. 03n04 (September 2022): 176. http://dx.doi.org/10.1142/s2661318222740875.

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Background: Fertility clinics submit treatment data on all ART cycles to the Australian and New Zealand Assisted Reproductive Technology Database (ANZARD) as part of their accreditation. The National Perinatal Epidemiology and Statistics Unit (NPESU), who manages ANZARD, is undertaking a study involving the linkage of ANZARD to state and commonwealth datasets to investigate health outcomes of infants born from fertility treatments. Aim: To describe the creation and performance of the linked dataset and to evaluate the agreement between births recorded by clinics and those recorded in state perinatal data collections (PDC). Method: The linked dataset was created by linking the ANZARD to NSW and ACT administrative datasets (performed by NSW Centre for Health Record Linkage (CHeReL)) and to Medicare Benefits Scheme and Pharmaceutical Benefits Schedule (performed by AIHW). The CHeReL’s Master Linkage Key (MLK) was used as a bridge between ANZARD’s statistical linkage key and state administrative datasets. Linkage rates and concordance between births recorded in ANZARD and PDCs was evaluated. Results: A 96.7% linkage rate was achieved between women recorded in ANZARD and CHeReL’s MLKs. A reconciliation of ANZARD-recorded births among NSW residents found that 94.2% (95% CI: 93.9-94.4%) of births were also recorded in NSW/ACT PDCs. A proportion of the missing births could be to women who had ART treatment in NSW but birthed in a different Australian state or country. A high concordance rate (>99%) was found in plurality status and birth outcome between ANZARD and PDCs. Conclusion: High linkage rates can be achieved with partially identifiable data and population spines, such as the CHeReL’s MLK, can be successfully used to link clinical registries and administrative datasets. This linkage resource will provide invaluable information on the safety of the ART and non-ART treatment, and the role of subfertility on the fertility treatments for Australia and beyond.
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Ellis, T. H., L. Turner, R. P. Hellens, D. Lee, C. L. Harker, C. Enard, C. Domoney, and D. R. Davies. "Linkage maps in pea." Genetics 130, no. 3 (March 1, 1992): 649–63. http://dx.doi.org/10.1093/genetics/130.3.649.

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Abstract We have analyzed segregation patterns of markers among the late generation progeny of several crosses of pea. From the patterns of association of these markers we have deduced linkage orders. Salient features of these linkages are discussed, as is the relationship between the data presented here and previously published genetic and cytogenetic data.
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Harron, Katie. "What's the big idea? Data linkage." Significance 18, no. 2 (March 26, 2021): 38–39. http://dx.doi.org/10.1111/1740-9713.01511.

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31

Reiter, Jerome. "Data Quality and Record Linkage Techniques." Journal of the American Statistical Association 103, no. 482 (June 1, 2008): 881. http://dx.doi.org/10.1198/jasa.2008.s229.

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Hassanzadeh, Oktie, Ken Q. Pu, Soheil Hassas Yeganeh, Renée J. Miller, Lucian Popa, Mauricio A. Hernández, and Howard Ho. "Discovering linkage points over web data." Proceedings of the VLDB Endowment 6, no. 6 (April 2013): 445–56. http://dx.doi.org/10.14778/2536336.2536345.

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33

Kivlin, Jane D., George E. Sanborn, Eileen Wright, Lisa Cannon, John Carey, John M. Opitz, and James F. Reynolds. "Further linkage data on Norrie disease." American Journal of Medical Genetics 26, no. 3 (March 1987): 733–36. http://dx.doi.org/10.1002/ajmg.1320260329.

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34

Zhang, Shuanglin, and Hongyu Zhao. "Linkage disequilibrium mapping with genotype data." Genetic Epidemiology 22, no. 1 (December 7, 2001): 66–77. http://dx.doi.org/10.1002/gepi.1044.

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35

Joda, Tim, Tuomas Waltimo, Christiane Pauli-Magnus, Nicole Probst-Hensch, and Nicola Zitzmann. "Population-Based Linkage of Big Data in Dental Research." International Journal of Environmental Research and Public Health 15, no. 11 (October 25, 2018): 2357. http://dx.doi.org/10.3390/ijerph15112357.

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Population-based linkage of patient-level information opens new strategies for dental research to identify unknown correlations of diseases, prognostic factors, novel treatment concepts and evaluate healthcare systems. As clinical trials have become more complex and inefficient, register-based controlled (clinical) trials (RC(C)T) are a promising approach in dental research. RC(C)Ts provide comprehensive information on hard-to-reach populations, allow observations with minimal loss to follow-up, but require large sample sizes with generating high level of external validity. Collecting data is only valuable if this is done systematically according to harmonized and inter-linkable standards involving a universally accepted general patient consent. Secure data anonymization is crucial, but potential re-identification of individuals poses several challenges. Population-based linkage of big data is a game changer for epidemiological surveys in Public Health and will play a predominant role in future dental research by influencing healthcare services, research, education, biotechnology, insurance, social policy and governmental affairs.
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Geleta, Negash. "Urban- Rural Marketing Linkage Practices and Challenges Evidences from Ambo Town West Shoa Zone, Orimia Regional State, Ethiopia." International Journal of Marketing Research Innovation 4, no. 2 (August 19, 2020): 9–22. http://dx.doi.org/10.46281/ijmri.v4i2.718.

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The objective of study was to assess the practices and challenges of rural-urban marketing linkages in Ambo Town. The study may be used to show the gaps between urban and rural on the marketing of agricultural products. The study was used descriptive research design. The populations of the study were some selected retailers and farmers those participate on the marketing of agricultural products. For sample size determination Yemane Taro formula was used and accordingly 187 sample size was employed. To identify this sample random sampling was used to select the research subjects from the population. As the source of information primary data was employed using structured questionnaire. For data analysis descriptive statistics was employed. Based on the data collected the findings of the study show that the production linkages were very weak which was reflected mainly in case of poor connection in the between urban and rural. However, a strong consumption linkage was observed as farmers tend to purchase goods and services from the Town. The insufficient crop production had made the existing marketing linkage to be expressed only in the form of exchange of products. Financial linkage was further strengthened even through all are not connected with rural farmers. Finally, the research recommended that urban and rural oriented linkage must be practiced which need further strengthen for the existing financial linkages and production linkage as it provides opportunity for existing traders in the Town.
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37

Clarke, Geraldine M., and Lon R. Cardon. "Disentangling Linkage Disequilibrium and Linkage From Dense Single-Nucleotide Polymorphism Trio Data." Genetics 171, no. 4 (August 22, 2005): 2085–95. http://dx.doi.org/10.1534/genetics.105.047431.

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38

Tromp, Miranda, Anita C. Ravelli, Gouke J. Bonsel, Arie Hasman, and Johannes B. Reitsma. "Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage." Journal of Clinical Epidemiology 64, no. 5 (May 2011): 565–72. http://dx.doi.org/10.1016/j.jclinepi.2010.05.008.

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39

Bennett, T. D., J. M. Dean, H. T. Keenan, M. H. McGlincy, A. M. Thomas, and L. J. Cook. "Linked Records of Children with Traumatic Brain Injury." Methods of Information in Medicine 54, no. 04 (2015): 328–37. http://dx.doi.org/10.3414/me14-01-0093.

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SummaryObjective: Record linkage may create powerful datasets with which investigators can conduct comparative effectiveness studies evaluating the impact of tests or interventions on health. All linkages of health care data files to date have used protected health information (PHI) in their linkage variables. A technique to link datasets without using PHI would be advantageous both to preserve privacy and to increase the number of potential linkages.Methods: We applied probabilistic linkage to records of injured children in the National Trauma Data Bank (NTDB, N = 156,357) and the Pediatric Health Information Systems (PHIS, N = 104,049) databases from 2007 to 2010. 49 match variables without PHI were used, many of them administrative variables and indicators for procedures recorded as International Classification of Diseases, 9th revision, Clinical Modification codes. We validated the accuracy of the linkage using identified data from a single center that submits to both databases.Results: We accurately linked the PHIS and NTDB records for 69% of children with any injury, and 88% of those with severe traumatic brain injury eligible for a study of intervention effectiveness (positive predictive value of 98%, specificity of 99.99%). Accurate linkage was associated with longer lengths of stay, more severe injuries, and multiple injuries.Conclusion: In populations with substantial illness or injury severity, accurate record linkage may be possible in the absence of PHI. This methodology may enable linkages and, in turn, comparative effectiveness studies that would be unlikely or impossible otherwise.
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40

Boyd, James H., Sean Randall, Adrian P. Brown, Max Maller, Davie Botes, Margo Gillies, and Anna Ferrante. "Population Data Centre Profiles: Centre for Data Linkage." International Journal of Population Data Science 4, no. 2 (March 11, 2020). http://dx.doi.org/10.23889/ijpds.v4i2.1139.

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The Centre for Data Linkage (CDL) was established at Curtin University, Western Australia, to develop infrastructure to enable cross-jurisdictional record linkage in Australia. The CDL’s operating model makes use of the ‘separation principle’, with content data typically provided to researchers directly by the data custodian; jurisdictional linkage where available are used within the linkage process. Along with conducting record linkage, the team has also invested in establishing a research programme in record linkage methodology and in developing modern record linkage software which can handle the size and complexity of today’s workloads. The Centre has been instrumental in the development of practical methods for privacy-preserving record linkage, with this methodology now regularly used for real-world linkages. While the promise of a nation-wide linkage system in Australia has yet to be met, distributed models provide a potential solution.
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41

Bhatt, Hitesh, Robert Jagodzinski, Allison N. Scott, Leslie Twilley, and Xinjie Cui. "Linkage Quality Assessment for Anonymously linked Administrative Data." International Journal of Population Data Science 3, no. 4 (August 24, 2018). http://dx.doi.org/10.23889/ijpds.v3i4.679.

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IntroductionLinked datasets are important resources for research, but linkage errors can lead to incorrect results. For data security and privacy concerns, when linkage of personal identifiers is performed anonymously, it is difficult to assess the quality of the linked dataset. We describe the method used to perform linkage quality. Objectives and ApproachWe explored how to check the quality of linkages while preserving the privacy of individuals. We also adopted an approach that minimized time and burden on data providers involved in physical verification using randomly-generated appropriate sample sizes. To validate these linkages, data providers were given random samples of 50 unique records from both linked and unlinked individuals across two other Government programs. Data providers were asked to look at the records associated with those individuals in their original datasets. Three types of linkage results were validated: cross-program linkages, cross-program non-linkages, and within-program linkages. Proportions of false-matches and missed-matches were estimated. ResultsTwenty data providers checked their samples with two other programs which gave us a sample of 2000 individuals. The linkage process, based on anonymized personal identifiers, resulted in high true positive and high true negative rates. Agreement between human judges and the linkage software was strong. Results of this exercise and other linkage validation examinations provided confidence in the accuracy of the linkage process. With false matches occurring approximately only 3% of the time and virtually no missed-matches occurring, no adjustments were deemed necessary. Although linkage rates were reassuring, the sample sizes used for comparison were small, so it is expected that there would be significant variation associated with this 3% estimate; caution is advised in its use. Conclusion/ImplicationsProportions of false-matches and missed-matches determine linkage quality which is the base for research when linkages are performed anonymously. A low proportion of false-matches and an absence of missed-matches was an indication of robust linkages.
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Xue, Li. "Linked Administrative Data at Statistics Canada – new data resources for horizontal research." International Journal of Population Data Science 4, no. 3 (November 8, 2019). http://dx.doi.org/10.23889/ijpds.v4i3.1209.

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There has been an increasing demand for analytics and research related to cross-cutting and horizontal issues in Canada, such as in the domains of housing, aging and immigration. Very often policy makers and stakeholders are posing a full spectrum of questions around a specific topic, requiring multidisciplinary evidence and data. Statistics Canada has a long history of record linkage. Over the past decade, the number of record linkage projects has increased exponentially. Several established platforms have been developed to facilitate linkage – Canadian Employer and Employer Database which brings together tax and employment records from both employees and employers; the Social Data Linkage Environment created to support linkages at the individuals level across a broad spectrum of social data (health, justice, education, socio-economic); and the Linkable File Environment for business data. The breadth of our data holdings married with record linkage capabilities allows the creation of data sets that crosses disciplines and areas or research. This presentation will showcase the innovative data integration approaches that Statistics Canada has advanced to meet the inter-disciplinary data needs. Statistics Canada are pioneering in some innovative linkages across various domains to help answer cross-cutting questions. For example, Longitudinal Administrative Databank linking longitudinal tax records to numerous other data files including tax records of spouses and children in the household, longitudinal Immigration Database linkage key and health records, is used to study economic impact of hospitalization, as well as better understand health outcomes of immigrants by various dimensions including socio-economic status. Other examples include the pilot projects linking Canadian Financial Capability Survey to tax records, to gauge the relationship between financial literacy and annual retirement savings behavior and Intergenerational Income Database being linked to Census to understand socio-economic factors affecting the intergenerational mobility. Rapid growth in data availability for research also poses new challenges on IM/IT, governance, access, capacity building, etc. As Statistics Canada has moved on a path of modernization, data integration is key to the development of new data sources to fill information gaps as we move forward.
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Mirel, Lisa, Dean Resnick, Scott Campbell, and Cordell Golden. "Quality of linked data: Linking the National Hospital Care Survey Data to the National Death Index." International Journal of Population Data Science 3, no. 4 (September 6, 2018). http://dx.doi.org/10.23889/ijpds.v3i4.912.

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IntroductionData linkages can produce rich data resources to address a variety of research topics. However, assessing linkage quality can be challenging given that there are many approaches and no clear best practices. Objectives and ApproachThrough its Data Linkage Program, the National Center for Health Statistics (NCHS) links national survey data with vital and administrative records. A recent linkage of the National Hospital Care Survey data with the National Death Index employed a new linkage methodology, which included a first time approach for validating the results within the linkage algorithm. ResultsThe new methodology includes two passes: a deterministic linkage, followed by a probabilistic approach based on the Fellegi-Sunter methodology. In the second pass, a key identifier, Social Security Number (SSN), was not used as a linkage variable but instead to determine link accuracy, when available on the patient record. A model was then built to predict link accuracy status according to the computed Fellegi-Sunter total pair weight and then used to estimate it for those patient records without an SSN. Results from this new approach were compared with results from prior linkage methodologies and generated higher match rates and lower error rates. The linkage methodology designed for this study is now being tested on other types of input data such as data from household surveys. Conclusion/ImplicationsThe linkage approach may be incorporated into additional linkages conducted by NCHS. This talk will describe the input sources for this linkage, the methodology used, the error rate assessment and then discuss conclusions and implications for precision and efficiency.
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Jarrett, Michael, Brent Hills, Yinshan Zhao, Adrian Brown, Sean Randall, James Boyd, Anna Ferrante, and Kimberlyn McGrail. "Evaluating PPRL Vs Clear Text Linkage with Real-World Data." International Journal of Population Data Science 5, no. 5 (December 7, 2020). http://dx.doi.org/10.23889/ijpds.v5i5.1542.

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IntroductionPrivacy-preserving Record Linkage (PPRL) is a record linkage technique that can increase the security of personal information. PPRL uses techniques of either hashing identifiers (where exact matches are required) or Blooming identifiers (where partial matches are of interest before they are provided for linkage. Objectives and ApproachWe use LinXmart software to evaluate performance of PPRL linkage compared to linkage using clear text identifiers. The test linkage dataset is one that is routinely linked (N=2,672,257) at our linkage centre. The population spine (N=8,440,442) includes a record for every person who has resided in British Columbia, Canada over the past 30 years. Weights were determined using LinXmart’s implementation of the Expectation Maximization (EM) algorithm. For both linkages, accepted links were the highest-weighted candidate link with a weight above the threshold suggested by EM estimation. We compare linkage rates and quality and differences in weight and threshold estimations between clear-text and PPRL linkages ResultsClear-text and PPRL methods resulted in 97% and 90% linkage rates, respectively. Approximately 67% of records in the linked datasets contained a nominally unique ID. Records with a unique ID linked at higher rates (>99% for both clear-text and PPRL) while the linkage rate for records missing the ID differed substantially (92% /70% for clear-text/PPRL). Comparing PPRL linkage to the clear-text linkage, we obtain F-measures of 0.99 and 0.80 for records with and without the unique ID, respectively. Conclusion / ImplicationsLinkage performance may be attributable to differences in comparison operators between the two methods. Bloomed fields compared with Dice coefficient allow for partial matching but may not be as sensitive as clear-text string comparisons. Numerical comparisons in PPRL are exact matches while clear-text comparisons allow for more sophisticated matching. Further refinements in PPRL are being explored to improve these results.
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Randall, Sean, Anna Ferrante, James Boyd, and Adrian Brown. "How do socio-demographic differences in administrative records affect the quality (accuracy) of data linkage?" International Journal of Population Data Science 3, no. 4 (September 4, 2018). http://dx.doi.org/10.23889/ijpds.v3i4.852.

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IntroductionRecord linkage is inherently uncertain, with all linkages containing some amount of false positive and false negative errors. Previous results have suggested that linkage error may not be evenly distributed throughout the population, with particular subgroups exhibiting higher rates of linkage error. Record linkage is inherently uncertain, with all linkages containing Objectives and ApproachThis study investigated the distribution of linkage error using four large-scale Australian administrative datasets; hospital admissions datasets from Western Australia and New South Wales, and emergency presentation datasets from New South Wales and South Australia. Each dataset had been previously de-duplicated to a very high standard, with large scale manual review taking place; these results were used as our truth set. Each dataset was linked using probabilistic record linkage with results (precision and recall) compared by gender, age, geographic indices of remoteness and socioeconomic status. ResultsResults were highly dataset dependent. Consistent findings were lower linkage quality found for individuals living in remote locations, and lower linkage quality in those in the youngest category (those born after 1980). Some datasets showed lower linkage quality for females, for those in middle age as compared to the elderly, and for those with lower socioeconomic status. The differences in linkage quality found were typically small. Changes in threshold settings had generally no effect on the relationship between sociodemographic characteristics and linkage quality. Conclusion/ImplicationsLinkage studies focussing on younger individuals and those in remote areas may have greater uncertainty regarding their results. Targeting efforts by linkage units may be required to ensure even distribution of linkage errors. Further research is required into investigating how linkage errors effect research outcomes.
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Trudeau, Richard. "Social Data Linkage Environment." International Journal of Population Data Science 1, no. 1 (April 13, 2017). http://dx.doi.org/10.23889/ijpds.v1i1.76.

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ABSTRACTObjectivesThe Social Data Linkage Environment (SDLE) at Statistics Canada promotes the innovative use of existing administrative and survey data to address important research questions and inform socio-economic policy through record linkage. It expands the potential of data integration across multiple domains, such as health, justice, education and income, through the creation of linked analytical data files without the need to collect additional data from Canadians.ApproachAt the core of the SDLE is a Derived Record Depository (DRD), essentially a national dynamic relational data base containing only basic personal identifiers. The DRD is created by linking selected Statistics Canada source index files for the purpose of producing a list of unique individuals. These files are brought into the environment, processed and linked only once to the DRD. Each individual in the DRD is assigned an SDLE identifier. Some of the source index files used to build the DRD include tax records, vital statistics registration records (births and deaths), and immigrant data. Updates to these data files are linked to the DRD on an ongoing basis. Only basic personal identifiers are stored in the DRD. Examples of personal identifiers stored in the DRD include surnames, given names, date of birth, sex, insurance numbers, parents' names, marital status, addresses (including postal codes), telephone numbers, immigration date, emigration date and date of death. The paired SDLE identifiers and source index file record IDs resulting from the record linkage are stored in a Key Registry. To reduce the risk of privacy intrusiveness and to minimize the risk of disclosure, source files are separated into source index files and source data files. Employees performing the record linkages in SDLE have access to only the basic personal identifiers needed for linkage. Employees who build the analytical files for research have access only to the data stripped of personal identifiers.ResultsThe SDLE is a highly secure environment that facilitates the creation of linked population data files for social analysis. It is not a large integrated data base.ConclusionThe SDLE program facilitates pan-Canadian social and economic statistical research. It is a record linkage environment that: increases the relevance of existing surveys without collecting new data; substantially increases the use of administrative data; generates new information without additional data collection; maintains the highest privacy and data security standards; and promotes a standardized approach to record linkage processes and methods.
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Boyd, James, Yuen Ai Lee, Adrian Brown, Sean Randall, and Anna Ferrante. "Unlocking the potential of health systems using privacy preserving record linkage: A pilot project exploring the research potential of developing a linkable general practice dataset." International Journal of Population Data Science 4, no. 3 (November 18, 2019). http://dx.doi.org/10.23889/ijpds.v4i3.1231.

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BackgroundGeneral practice is a rich source of health data for research. It is an important resource which can be used to improve patient management, reduce costs and improve patient outcomes. Traditionally, the challenge has been around access to general practice data which remains hard to ‘join up’. This abstract describes technology developed to support aspirations of the MedicineInsight program to provide linked de-identified general practice data that can be used to derive insights to enable better patient outcomes. Main AimThe aim of this project was to use real-world data to identify technical, logistical and analytical requirements throughout the linkage process. Logistical aims covered the negotiation, approval and data acquisition processes, as well as data linkage and data delivery aspects performed by technical and data service stakeholders. Methods/Approach Given the sensitivity of the information involved, the project employed a privacy preserving record linkage methodology. This method uses encrypted personal identifying information (Bloom filters) in a probability-based linkage framework to help mitigate risk while maximising linkage quality. Existing MedicineInsight systems were extended to automatically generate encoded linkage data at each general practice. Pilot linkages were then used to validate the capability/capacity of CDL infrastructure to create secure extensible linked general practice datasets. ResultsThe project has successfully developed interoperable technology to create a transparent data catalogue which is linkable to other datasets. This technology has been embedded with MedicineInsight systems and results of the pilot linkages are being evaluated. The project will make recommendations to enable consistent delivery of linkage services across care settings. ConclusionOutcomes from the project will improve delivery of record linkage services to the health and broader research community. Using linked data from across the care continuum, researchers will be able to evaluate the effectiveness of service delivery and provide evidence for policy and programme development.
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Babyak, Colin, and Abdelnasser Saidi. "Record Linkage Methodology for the Social Data Linkage Environment at Statistics Canada." International Journal of Population Data Science 1, no. 1 (April 13, 2017). http://dx.doi.org/10.23889/ijpds.v1i1.49.

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ABSTRACTObjectivesThe objectives of this talk are to introduce Statistics Canada’s Social Data Linkage Environment (SDLE) and to explain the methodology behind the creation of the central depository and how both deterministic and probabilistic record linkage techniques are used to maintain and expand the environment.ApproachWe will start with a brief overview of the SDLE and then continue with a discussion of how both deterministic linkages and probabilistic linkages (using Statistic Canada’s generalized record linkage software, G-Link) have been combined to create and maintain a very large central depository, which can in turn be linked to virtually any social data source for the ultimate end goal of analysis.ResultsAlthough Canada has a population of about 36 million people, the central depository contains some 300 million records to represent them, due to multiple addresses, names, etc. Although this allows for a significant reduction in missing links, it raises the spectre of additional false positive matches and has added computational complexity which we have had to overcome.ConclusionThe combination of deterministic and probabilistic record linkage strategies has been effective in creating the central depository for the SDLE. As more and more data are linked to the environment and we continue to refine our methodology, we can now move on to the ultimate goal of the SDLE, which is to analyze this vast wealth of linked data.
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Kindermann, Bindi, Sarah Hinde, and Michael Abbondante. "Multi-Agency Data Integration Project - Linking Data – Improving Lives." International Journal of Population Data Science 1, no. 1 (April 18, 2017). http://dx.doi.org/10.23889/ijpds.v1i1.216.

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ABSTRACTObjectivesThe Australian Government’s new public sector data management agenda is initiating a national system for integrating public data and opening up access for policy makers and researchers. The Multi-agency Data Integration Project (‘the project’) is central to achieving these goals by bringing together nationally significant population datasets with the aim of streamlining the safe sharing of integrated government data. The project provides policy makers and researchers with safe access to linked, longitudinal information about the delivery of the Australian tax and transfer system, health services, along with rich demographic information. The project has been an essential step towards better enabling the Australian Government and research community to develop evidence-based policy and target services effectively, within a tight fiscal environment. The project has prompted government agencies to find new and more streamlined ways to work collaboratively to share and make best use of public data. ApproachThe first step of the project was to link a 2011 snapshot of four national administrative datasets with the 2011 Census. A cross-agency team of data analysts from five government agencies collaborated to evaluate the datasets and test whether the linked data could be used to answer policy questions. The linkage project included experimentation with different linking methodologies, linking strategies and information models for structuring the linkage. The evaluation tested whether the linked data was representative of key population groups of interest, and explored the validity of the content variables for measuring outcomes of interest. ResultsHigh linkage rates (between 80-95%) were achieved for the two-way linkages, and many population groups of interest were well represented. The work is confirming the value of the linkage for answering policy questions that had been difficult to address using existing approaches. The project developed ways of describing linkage quality to policy users and approaches to addressing linkage bias for different policy uses. ConclusionPublic sector data held by government has the power to improve life course outcomes for Australian people, households and businesses. The project has generated confidence and support for continued development of a central and streamlined integrated data system. It has also generated valuable insights about governance and how to scale up the linkage and dissemination system to support additional datasets and longitudinal data. This will maximise the value and utility of public data to support policy and research, in order to achieve a better understanding of, and deliver better outcomes for, the Australian community.
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Zhao, Yinshan, Mike Jarrett, Kimberlyn McGail, and Brent Hills. "A proposed approach for standardized reporting of data linkage processes and results." International Journal of Population Data Science 7, no. 3 (August 25, 2022). http://dx.doi.org/10.23889/ijpds.v7i3.1962.

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ObjectivesPopulation Data BC (PopData) is an agency in British Columbia, Canada, that routinely performs linkages of various administrative and researcher-collected data to a population spine. We developed a linkage report template in order to increase transparency of linkage process and outcome for end users and data providers. ApproachPopData performs probabilistic and deterministic data linkage using an in-house software. A literature review identified existing guidelines and examples of linkage reporting. A survey collected input from a wide range of end users about their interest in receiving linkage reports and specific information that is important to their work. A draft template was developed by PopData’s linkage experts and data scientists which then was reviewed by PopData staff and external partners. Privacy requirements, mode of delivery, readability to the intended audience and operational feasibility were carefully considered. ResultsThe resulting template built on our existing internal linkage summaries. The report follows a framework suggested in the literature with three key components: 1) information on the data source and linkage fields, 2) data pre-processing and linkage methodology, and 3) linkage results, presented in tables and figures, including overall linkage rates, detail on matched fields, and the distribution of linkage weights of linked and unliked pairs. In addition, an appendix describes the linkage methods and population spine in detail, and supplementary notes will comment on unique issues related to the data, when those are applicable. Educational materials to aid understanding of linkage methodologies and reporting are also under development. ConclusionLinked data are increasingly used in research, making it important to provide information on linkage process and performance to the research community. Rigorous and standardized linkage reports produced by data centres can facilitate evaluation of the impact of linkage performance on research findings and enable transparent reporting in peer-reviewed research.
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