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Data Linkage Research Conversation Series SA NT DataLink 2015 update and beyond Friday, 4 December 2015 SAHMRI Building, North Terrace Adelaide Chris Radbone,

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Presentation on theme: "Data Linkage Research Conversation Series SA NT DataLink 2015 update and beyond Friday, 4 December 2015 SAHMRI Building, North Terrace Adelaide Chris Radbone,"— Presentation transcript:

1 Data Linkage Research Conversation Series SA NT DataLink 2015 update and beyond Friday, 4 December 2015 SAHMRI Building, North Terrace Adelaide Chris Radbone, Associate Director, SA NT DataLink Miro Palfy, Senior Statistical Analyst, SA NT DataLink The Australian Government has provided financial support to SA NT DataLink through the National Collaborative Research Infrastructure Strategy (NCRIS).

2 What is the Master Linkage File?

3 HEALTH DATA Inventory of Linked Data

4 EDUCATION DATA Inventory of Linked Data

5 REGISTRIES Inventory of Linked Data

6 SOCIAL DATA Inventory of Linked Data

7 SA CHILD HEALTH CHECK Data Subsets SA PUBLIC SCHOOL ENROLMENT CENSUS NT CLIENT MASTER INDEX

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10 1 Birth Registry Record 1 Perinatal Record by Child Records 1 Public Hospital Emergency Dept. Records 1 Public Hospital Inpatient Data Record 1 Public School Student Record 1 Child Health Development Record Image from: http://imgs.steps.dragoart.com/how-to-draw-a-simple-girl-step-7_1_000000024971_5.jpg Source Mean number of records Child Health Development Records 0.88 Child Protection Data 0.45 Australian Early Development Census 2009, 2012,.... 2015, 2018 0.00 Birth Registry 0.92 Cancer Registry 0.00 Death Registry 0.00 Public School Student ID 1.27 Public Hospital Emergency Department 2.31 Public Hospital Inpatient Data 0.98 Perinatal Records by Child 0.77 Dental Records 0.07 Source Percentage of children with at least one record Child Health Development Records87% Child Protection Data19% Australian Early Development Census 2009, 2012,.... 2015, 2018 0% Birth Registry81% Cancer Registry0% Death Registry0% Public School Student ID58% Public Hospital Emergency Department67% Public Hospital Inpatient Data44% Perinatal Records by Child76% Dental Records6% Jenny MLF born in 2006

11 1 Birth Registry Record 1 Perinatal Record by Child Record 2 Public Hospital Emergency Dept. Records 1 Public Hospital Inpatient Data Record 1 Public School Student Record 1 Child Health Development Record Image from: http://imgs.steps.dragoart.com/how-to-draw-a-simple-boy-step-7_1_000000024947_5.jpg Source Mean number of records Child Health Development Records 0.87 Child Protection Data 0.46 Australian Early Development Census 2009, 2012,.... 2015, 2018 0.00 Birth Registry 0.91 Cancer Registry 0.00 Death Registry 0.00 Public School Student ID 1.31 Public Hospital Emergency Department 2.75 Public Hospital Inpatient Data 1.27 Perinatal Records by Child 0.76 Dental Records 0.08 Source Percentage of children with at least one record Child Health Development Records 86% Child Protection Data 20% Australian Early Development Census 2009, 2012,.... 2015, 2018 0% Birth Registry 79% Cancer Registry 0% Death Registry 0% Public School Student ID 59% Public Hospital Emergency Department 72% Public Hospital Inpatient Data 50% Perinatal Records by Child 76% Dental Records 7% Johnny MLF born in 2006

12 Image from: http://sketchbooknation.com/draw-girl-sketchbook-challenge-17/ 5 Public School Student Records SourceMean number of records Child Health Development Records0.00 Child Protection Data0.60 Australian Early Development Census0.00 Birth Registry0.00 Cancer Registry0.00 Cervical Cancer Screening0.00 Death Registry0.00 Public School Student ID5.82 Public Hospital Emergency Department1.51 Public Hospital Inpatient Data0.65 Perinatal Records by Child0.00 Perinatal Records by Mother0.00 Dental Records0.50 Source Percentage of women with at least one record Child Health Development Records 0% Child Protection Data 22% Australian Early Development Census 0% Birth Registry 0% Cancer Registry 0% Cervical Cancer Screening 0% Death Registry 0% Public School Student ID 73% Public Hospital Emergency Department 48% Public Hospital Inpatient Data 28% Perinatal Records by Child 0% Perinatal Records by Mother 0% Dental Records 38% Mary MLF born in 1995

13 Image from: http://sketchbooknation.com/wp-content/uploads/2013/09/manstandingthumb.png 6 Public School Student Records 1 Public Hospital Emergency Dept. Record Source Mean number of records Child Health Development Records 0.00 Child Protection Data 0.58 Australian Early Development Census 0.00 Birth Registry 0.01 Cancer Registry 0.00 Death Registry 0.00 Public School Student ID 6.24 Public Hospital Emergency Department 1.58 Public Hospital Inpatient Data 0.70 Perinatal Records by Child 0.00 Dental Records 0.54 Source Percentage of men with at least one record Child Health Development Records 0% Child Protection Data 22% Australian Early Development Census 0% Birth Registry 0% Cancer Registry 0% Death Registry 0% Public School Student ID 73% Public Hospital Emergency Department 48% Public Hospital Inpatient Data 28% Perinatal Records by Child 0% Dental Records 38% Mark MLF born in 1995

14 Image from: http://cdn.toonvectors.com/images/35/72344/toonvectors-72344-940.jpg 2 Public Hospital Emergency Dept. Records 1 Public Hospital Inpatient Data Record 3 Cervical Cancer Screening Records Sue MLF born in 1945 SourceMean number of records Child Health Development Records0.00 Child Protection Data0.00 Australian Early Development Census0.00 Birth Registry0.00 Cancer Registry0.13 Cervical Cancer Screening2.94 Death Registry0.08 Public School Student ID0.08 Public Hospital Emergency Department1.72 Public Hospital Inpatient Data3.58 Perinatal Records by Child0.00 Perinatal Records by Mother0.00 Dental Records0.25 Source Percentage of women with at least one record Child Health Development Records0% Child Protection Data0% Australian Early Development Census0% Birth Registry0% Cancer Registry13% Cervical Cancer Screening72% Death Registry7% Public School Student ID3% Public Hospital Emergency Department52% Public Hospital Inpatient Data61% Perinatal Records by Child0% Perinatal Records by Mother0% Dental Records18%

15 Image from: http://static.hsw.com.br/gif/how-to-draw-cartoons-102.jpg 2 Public Hospital Emergency Dept. Records 1 Public Hospital Inpatient Data Record Steve MLF born in 1945 Source Mean number of records Child Health Development Records0.00 Child Protection Data0.00 Australian Early Development Census0.00 Birth Registry0.00 Cancer Registry0.20 Death Registry0.15 Public School Student ID0.06 Public Hospital Emergency Department2.64 Public Hospital Inpatient Data5.49 Perinatal Records by Child0.00 Dental Records0.26 Source Percentage of men with at least one record Child Health Development Records 0% Child Protection Data 0% Australian Early Development Census 0% Birth Registry 0% Cancer Registry 20% Death Registry 15% Public School Student ID 2% Public Hospital Emergency Department 72% Public Hospital Inpatient Data 85% Perinatal Records by Child 0% Dental Records 20%

16 Vietnamese Names First name records matching the Vietnamese sex indicators “Thi” or “Van” were extracted from the Master Linkage File. Records matching the top 20 surnames associated with the initial extract were subsequently extracted. Finally, 86,589 records matching the top 20 surnames were extracted from the top 20 surnames. SurnameNum. Records First NameNum. Records NGUYEN28,416 THI6,359 TRAN12,889 2,903 LE6,908 VAN1,754 PHAM4,739 THANH1,316 HUYNH4,721 NGOC1,130 LAM3,315 KIM1,068 LY2,508 MINH983 PHAN2,392 THUY879 VO2,279 PHUONG867 HO2,135 ANH674 TRUONG2,122 HUONG603 DANG1,994 HOANG532 DO1,982 THAO522 NGO1,937 HONG521 VU1,835 THU454 BUI1,626 TRANG445 DUONG1,356 TAM444 HOANG1,266 DAVID444 DINH1,126 MY431 THACH1,043 ANDY429 Data Quality Assessment: 2,903 (3.4%) first names were missing. 6,359 (7.3%) first names were comprised of the sex indicator “Thi”. 1,754 (2.0%) first names were comprised of the sex indicator “Van”. 64 Dates of Birth (0.07%) were missing. There were 2,231 records (2.6%) where likely default Dates of Births were observed (e.g. Day =1 and Month =1). For example, 1/01/1929, 1/01/1955 etc. In general Vietnamese names are quite short in length, which increases the likelihood that string comparison algorithms (which count the number of differences between two names) will result in erroneous matches.


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