Examining the quality of record linkage process using nationwide Brazilian administrative databases to build a large birth cohort.

BACKGROUND: Research using linked routine population-based data collected for non-research purposes has increased in recent years because they are a rich and detailed source of information. The objective of this study is to present an approach to prepare and link data from administrative sources in a middle-income country, to estimate its quality and to identify potential sources of bias by comparing linked and non-linked individuals. METHODS: We linked two administrative datasets with data covering the period 2001 to 2015, using maternal attributes (name, age, date of birth, and municipally of residence) from Brazil: live birth information system and the 100 Million Brazilian Cohort (created using administrative records from over 114 million individuals whose families applied for social assistance via the Unified Register for Social Programmes) implementing an in house developed linkage tool CIDACS-RL. We then estimated the proportion of highly probably link and examined the characteristics of missed-matches to identify any potential source of bias. RESULTS: A total of 27,699,891 live births were submited to linkage with maternal information recorded in the baseline of the 100 Million Brazilian Cohort dataset of those, 16,447,414 (59.4%) children were found registered in the 100 Million Brazilian Cohort dataset. The proportion of highly probably link ranged from 39.3% in 2001 to 82.1% in 2014. A substantial improvement in the linkage after the introduction of maternal date of birth attribute, in 2011, was observed. Our analyses indicated a slightly higher proportion of missing data among missed matches and a higher proportion of people living in an urban area and self-declared as Caucasian among linked pairs when compared with non-linked sets. DISCUSSION: We demonstrated that CIDACS-RL is capable of performing high quality linkage even with a limited number of common attributes, using indexation as a blocking strategy in larg e routine databases from a middle-income country. However, residual records occurred more among people under worse living conditions. The results presented in this study reinforce the need of evaluating linkage quality and when necessary to take linkage error into account for the analyses of any generated dataset.

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.

Code sharing in ecology and evolution increases citation rates but remains uncommon.

Biologists increasingly rely on computer code to collect and analyze their data, reinforcing the importance of published code for transparency, reproducibility, training, and a basis for further work. Here, we conduct a literature review estimating temporal trends in code sharing in ecology and evolution publications since 2010, and test for an influence of code sharing on citation rate. We find that code is rarely published (only 6% of papers), with little improvement over time. We also found there may be incentives to publish code: Publications that share code have tended to be low-impact initially, but accumulate citations faster, compensating for this deficit. Studies that additionally meet other Open Science criteria, open-access publication, or data sharing, have still higher citation rates, with publications meeting all three criteria (code sharing, data sharing, and open access publication) tending to have the most citations and highest rate of citation accumulation.

Administrative Data Linkage in Brazil: Potentials for Health Technology Assessment.

Health technology assessment (HTA) is the systematic evaluation of the properties and impacts of health technologies and interventions. In this article, we presented a discussion of HTA and its evolution in Brazil, as well as a description of secondary data sources available in Brazil with potential applications to generate evidence for HTA and policy decisions. Furthermore, we highlighted record linkage, ongoing record linkage initiatives in Brazil, and the main linkage tools developed and/or used in Brazilian data. Finally, we discussed the challenges and opportunities of using secondary data for research in the Brazilian context. In conclusion, we emphasized the availability of high quality data and an open, modern attitude toward the use of data for research and policy. This is supported by a rigorous but enabling legal framework that will allow the conduct of large-scale observational studies to evaluate clinical, economical, and social impacts of health technologies and social policies.

Genome-wide burden and association analyses implicate copy number variations in asthma risk among children and young adults from Latin America.

The genetic architecture of asthma was relatively well explored. However, some work remains in the field to improve our understanding on asthma genetics, especially in non-Caucasian populations and with regards to commonly neglected genetic variants, such as Copy Number Variations (CNVs). In the present study, we investigated the contribution of CNVs on asthma risk among Latin Americans. CNVs were inferred from SNP genotyping data. Genome wide burden and association analyses were conducted to evaluate the impact of CNVs on asthma outcome. We found no significant difference in the numbers of CNVs between asthmatics and non-asthmatics. Nevertheless, we found that CNVs are larger in patients then in healthy controls and that CNVs from cases intersect significantly more genes and regulatory elements. We also found that a deletion at 6p22.1 is associated with asthma symptoms in children from Salvador (Brazil) and in young adults from Pelotas (Brazil). To support our results, we conducted an in silico functional analysis and found that this deletion spans several regulatory elements, including two promoter elements active in lung cells. In conclusion, we found robust evidence that CNVs could contribute for asthma susceptibility. These results uncover a new perspective on the influence of genetic factors modulating asthma risk.