Improving Cohort-Hospital Matching Accuracy through Standardization and Validation of Participant Identifiable Information
Bibliographic Data
| ID | 15713862 |
|---|---|
| Authors | Yanhong Hu (0000-0002-0923-7923, Royal Children's Hospital), Anna Fedyukova (0000-0002-1861-812X, Royal Children's Hospital), Jing Wang (0000-0002-7594-8539, Royal Children's Hospital), Joanne Said (0000-0001-6263-0030, Royal Children's Hospital), Joanne M Said (Murdoch Children’s Research Institute, The Royal Children’s Hospital, Parkville, VIC 3052, Australia), Niranjan Thomas (0000-0002-1651-3201, The University of Melbourne), Elizabeth Noble (0000-0001-5334-4649, Sunshine Hospital), Jeanie L Y Cheong (0000-0001-5901-0455, Royal Children's Hospital), Bill Karanatsios (0000-0002-0681-5986, Western Health), Sharon Goldfeld (0000-0001-6520-7094, Royal Children's Hospital), Melissa Wake (0000-0001-7501-9257, Royal Children's Hospital, corresponding author) |
| Year | 2022 |
| Volume | 9 |
| Issue | 12 |
| Pages | 1916-1916 |
| Publication date | 2022-12-07 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Children (JOURNAL) |
| Journal identifiers | ISSN: 2227-9067 • E-ISSN: 2227-9067 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/children9121916 |
| PMID | 36553359 |
| OpenAlex | W4311809066 |
| Language | EN |
| Citations received | 1 |
| References cited | 29 |
Linking very large, consented birth cohorts to birthing hospitals clinical data could elucidate the lifecourse outcomes of health care and exposures during the pregnancy, birth and newborn periods. Unfortunately, cohort personally identifiable information (PII) often does not include unique identifier numbers, presenting matching challenges. To develop optimized cohort matching to birthing hospital clinical records, this pilot drew on a one-year (December 2020-December 2021) cohort for a single Australian birthing hospital participating in the whole-of-state Generation Victoria (GenV) study. For 1819 consented mother-baby pairs and 58 additional babies (whose mothers were not themselves participating), we tested the accuracy and effort of various approaches to matching. We selected demographic variables drawn from names, DOB, sex, telephone, address (and birth order for multiple births). After variable standardization and validation, accuracy rose from 10% to 99% using a deterministic-rule-based approach in 10 steps. Using cohort-specific modifications of the Australian Statistical Linkage Key (SLK-581), it took only 3 steps to reach 97% (SLK-5881) and 98% (SLK-5881.1) accuracy. We conclude that our SLK-5881 process could safely and efficiently achieve high accuracy at the population level for future birth cohort-birth hospital matching in the absence of unique identifier numbers
Cohort · Cohort study · Environmental health · Family medicine · Identifier · Matching (statistics · Population · Record linkage · Standardization · Unique identifier · Birth, Development, and Health · Computer Science · Demography · Health disparities and outcomes · Health, Environment, Cognitive Aging · Medicine
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |