CIDACS-RL: a novel indexing search and scoring-based record linkage system for huge datasets with high accuracy and scalability.

BACKGROUND: Record linkage is the process of identifying and combining records about the same individual from two or more different datasets. While there are many open source and commercial data linkage tools, the volume and complexity of currently available datasets for linkage pose a huge challenge; hence, designing an efficient linkage tool with reasonable accuracy and scalability is required. METHODS: We developed CIDACS-RL (Centre for Data and Knowledge Integration for Health - Record Linkage), a novel iterative deterministic record linkage algorithm based on a combination of indexing search and scoring algorithms (provided by Apache Lucene). We described how the algorithm works and compared its performance with four open source linkage tools (AtyImo, Febrl, FRIL and RecLink) in terms of sensitivity and positive predictive value using gold standard dataset. We also evaluated its accuracy and scalability using a case-study and its scalability and execution time using a simulated cohort in serial (single core) and multi-core (eight core) computation settings. RESULTS: Overall, CIDACS-RL algorithm had a superior performance: positive predictive value (99.93% versus AtyImo 99.30%, RecLink 99.5%, Febrl 98.86%, and FRIL 96.17%) and sensitivity (99.87% versus AtyImo 98.91%, RecLink 73.75%, Febrl 90.58%, and FRIL 74.66%). In the case study, using a ROC curve to choose the most appropriate cut-off value (0.896), the obtained metrics were: sensitivity = 92.5% (95% CI 92.07-92.99), specificity = 93.5% (95% CI 93.08-93.8) and area under the curve (AUC) = 97% (95% CI 96.97-97.35). The multi-core computation was about four times faster (150 seconds) than the serial setting (550 seconds) when using a dataset of 20 million records. CONCLUSION: CIDACS-RL algorithm is an innovative linkage tool for huge datasets, with higher accuracy, improved scalability, and substantially shorter execution time compared to other existing linkage tools. In addition, CIDACS-RL can be deployed on standard computers without the need for high-speed processors and distributed infrastructures.

Effectiveness of rotavirus vaccine against hospitalized rotavirus diarrhea: A case-control study.

Rotavirus is one of the leading cause of hospitalization and outpatients visits among children under five years. This study evaluated overall and genotype-specific vaccine effectiveness of oral monovalent rotavirus vaccine (G1P[8] strain) in preventing hospital admission of Brazilian children with rotavirus acute diarrhea. A hospital based case-control study was conducted in five Regions of Brazil using the National Rotavirus Acute Diarrhea Surveillance System from July 2008 to August 2011. A total of 215 cases (aged 4-24 months) admitted with confirmed rotavirus diarrhea were recruited and 1961 controls hospitalized without diarrhea were frequency matched by sex and age group to cases. Two-dose adjusted vaccine effectiveness (adjusted by year of birth and the frequency matching variables) was 76% (95%CI: 58-86) lasting for two years. Effectiveness controlled by the available potential confounders was 72% (95%CI: 44-85), suggesting no appreciable confounding by those factors for which adjustment was made. In a half of the cases the rotavirus genotype was G2P[4] and in 15% G1P[8]. Genotype-specific VE (two doses) was 89% (95%CI: 78-95), for G1P[8] and 76% (95%CI: 64-84) for G2P[4]. For all G1, it was 74% (95%CI: 35-90), for all G2, 76% (95%CI: 63-84), and for all non G1/G2 genotypes, 63% (95%CI: -27-99). Effectiveness for one dose was 62% (95%CI: 39-97). Effectiveness of two-dose monovalent rotavirus vaccine in preventing hospital admission with rotavirus diarrhea was high, lasted for two years and it was similar against both G1P[8] and G2P[4]. Based on the findings of the study we recommend the continued use of rotavirus in the Brazilian National Immunization Program and the monitoring of the early emergence of unusual and novel rotavirus genotypes.