Design and evaluation of probabilistic record linkage methods supporting the Brazilian 100-million cohort initiative.

Abstract

Background and aimsA cooperation Brazil-UK was set in mid-2013 aiming at to build a huge cohort comprised by individuals registered in CadastroÚnico (CADU), a socioeconomic database used in social programmes of the Brazilian government. Epidemiologists and statisticians wish to assess the impact of Bolsa Família (PBF), a conditional cash transfer programme, on the incidence of several diseases (tuberculosis, leprosy, HIV etc). The cohort must contain all individuals who received at least one payment from PBF between 2007 and 2012, which results in a 100-million records according to our preliminary analysis. These individuals must be probabilistically linked with databases from the Unified Health System (SUS), such as hospitalization (SIH), notifiable diseases (SINAN), mortality (SIM), live births (SINASC), to produce data marts (domain-specific data) to the proposed studies. Within this cooperation, our first goal was to design and evaluate probabilistic methods to routine link the cohort, PBF, and SUS outcomes.
 ApproachWe implemented two probabilistic linkage methods: a full probabilistic, based on the Dice similarity (Sorensen index) of Bloom filters; and an hybrid approach, based on rules to deterministic and probabilistic matching. We performed linkages involving CADU (2011 extraction) and SUS outcomes (SIH, SINAN, and SIM) with samples from 3 states (Sergipe, Santa Catarina and Bahia) with an increasing size (from 1,447,512 to 12,036,010).
 ResultsUsing a Dice between 0.90 and 0.92, our methods retrieved more than 95% of true positive pairs amongst the linked pairs. For Sergipe, we obtained as <linked pairs,true positives>: <23,22>, <315,300>, <32,32>, respectively for SIH, SINAN, and SIM. For Bahia: <771,593>, <2677,2626>, <208,207>. Another linkage between CADU (1,447,512 records) and SINAN (624 records), for tuberculosis in Sergipe, returned 397 (full probabilistic) and 311 (hybrid) linked pairs, being 306 and 300 true positives. Another execution considering CADU (1,988,599 records) and SINAN (2,094 records), for tuberculosis in Santa Catarina, returned 791 (full probabilistic) and 500 (hybrid) linked pairs, with 667 and 472 true positives. Linking CADU (1.685,697 records) and SIM, for mortality of children under-4, returned 18 linked pairs, all of them true positives, for a Dice between 0.90 and 0.92 and with 100% of sensitivity, specificity, and positive predictive value.
 ConclusionDue to the absence of gold standards, we use samples with increasing sizes and manual review when adequate. Our results are quite accurate, although obtained with an unique extraction of CADU. We are starting to run linkages with the entire cohort.

The effect of primary health care on tuberculosis in a nationwide cohort of 7·3 million Brazilian people: a quasi-experimental study

“…Demographic and socioeconomic adjusting variables showed an overall protective effect of being a woman, having a higher level of education, and having a higher income per capita, and an increased risk among Black people, older people, and those living in households built with precarious materials (table 3). The associations for all adjusting variables for the models in table 4 are shown in the appendix 2 (pp [15][16][17][18].…”

“…The quality of each linkage between CadUnico, SINAN, and SIM (for all causes) have been widely evaluated and validated. 16 , 17 …”

“…The quality of each linkage between CadUnico, SINAN, and SIM (for all causes) have been widely evaluated and validated. 16,17 We evaluated all relevant indicators on the pathway from infection to mortality from tuberculosis (defined by ICD-10 codes A15-19) as follows: tuberculosis incidence (number of new tuberculosis cases divided by person-years × 100 000), tuberculosis cure rate (number of tuberculosis cases with treatment success minus proven cure plus complete treatment divided by the number of tuberculosis cases × 100), tuberculosis case-fatality (number of deaths from tuberculosis divided by the number of tuberculosis cases × 100), and tuberculosis mortality rates (number of deaths from tuberculosis divided by person-years × 100 000), for the general population and for strata defined by demographic and socioeconomic characteristics, such as income, sex, and age. Further stratifications for education are shown in appendix 2 (p 10).…”

Evaluating the impact of social determinants, conditional cash transfers and primary health care on HIV/AIDS: Study protocol of a retrospective and forecasting approach based on the data integration with a cohort of 100 million Brazilians

“…The CIDACS has developed algorithms and codes to perform efficient, highly sensitive and specific probabilistic linkages using each individual’s name, date of birth, sex, and municipality of residence between these systems, and has successfully tested linkages [ 44 , 45 ]. The quality of the each linkage between CadUnico and SIM (for all causes) have been extensively evaluated and validated [ 46 , 47 ]. SINAN-AIDS, SICLOM and SISCEL, which have not been previously linked to the 100 Million Cohort because only HIV/AIDS specific, will be linked using the same algorithms and the linkages will be validated using the same protocols.…”