In this section, we describe the methods we used to integrate two major nationwide databases, the Live Birth Information System (SINASC) and the baseline of the 100 Million Brazilian Cohort from 2001 to 2015.
Datasets
1) SINASC (Sistema de Informação Sobre Nascidos Vivos/ Live Birth Information System)
The Brazilian Ministry of Health defines live births as the complete expulsion or extraction from the body of the pregnant woman of a product of conception, independent of the duration of pregnancy, who, after the separation, breathes or shows any other signs of life, such as heartbeat, umbilical cord pulsation, or definite movement of voluntary muscles, whether or not the cord is cut and whether or not the placenta is attached. SINASC records live births in Brazil, and this system is updated using the registration of live birth. It is a compulsory document, completed by a health professional who assisted the delivery. This form is divided into eight blocks. I -characteristics of the newborn; II- identification of the place of birth; III- characteristics of the mother; IV- identification of the father; V- characteristics of pregnancy and delivery; VI- characteristics of congenital anomalies: this block should be filled in when congenital anomalies are identified at birth using the ICD-10 code. VII- identification of the professional completing the notification. VIII- registry office identification 14. Between 2001 to 2015 this system recorded 44.485.274 births.
Data completeness is very high, with 97% of Brazilian births registered,15 and most variables were >90% complete.
2) The baseline of the 100 Million Brazilian Cohort
The baseline of the 100 million Brazilian cohort was built using information from the application of families and their family members for social assistance programmes in Brazil through the registration with the Unified Register for Social Programs (CadÚnico). The CadÚnico is the main instrument used by the Brazilian government to assess the inclusion criteria of potential beneficiaries of social programs. To be enrolled in CadÚnico, one person in the family must provide information and required documents of all family members to an interviewer. This person must be at least 16 years old and, preferably, be a woman. The information available in the 100 million cohort is collected for each member of the family in a standardized form that includes individual (ie, sex, age, race or ethnicity, education, and work status) and familial (ie, familial income, household density, and housing characteristics) sociodemographic information. The information is renewed periodically as long as the person is a candidate to receive one of the several Brazilian government social protection programs16. The Centre for Data and Knowledge Integration for Health - CIDACS has the custody of several snapshots of CadÚnico. Each snapshot file refers to a year backup from 2001 to 2015. The efforts to build the 100 Million Brazilian Cohort were concentrated in three main steps. The first was the harmonization of variables from three different versions of CadÚnico. Second, the data cleaning to ensure the standardization of the categories. The third step aims to find the first appearance of each individual in the CadÚnico backup file.
Data completeness depend on the variable, but name and municipality of residence are available for all individual registered. Once registered, each family receive a unique code.
The process of linking
Data pre-processing
During the data pre-processing phase, first, we searched automatically for invalid names (e.g., "unknown" or "newborn"), by comparing the recorded name with a standardized list of possible Brazilian names. All names considered invalid are submitted to a clerical review. In this review, the potential invalid terms are analysed to see if they are valid but were not recognized because they had typos, different spelling, or foreign name, among other reasons, or if in fact; they are invalid (such as RN from, unknown, ignored). And so, any term that deviates from what is known as "valid" is excluded. We removed punctuation, deleted consecutive spaces; middle initials, prefixes, and suffixes were maintained as recorded to retain the discriminatory power of the name variable.
Blocking/ Indexing
The complexity of the record linkage task is quadratic. We have to find the best match, on database B, for each record in database A, |A| X |B|. 'To enable the record linkage is efficient when massive datasets are involve, we need to use methods capable of avoiding unnecessary comparisons, whilst keeping the accuracy. The total number of pairwise comparisons between SINASC and CadUnico would otherwise be prohibitively high 44,485,267 x 114,007,705=5,07166e15. To meet these challenges, we use the CIDACS-RL 16 (Centre for Data and Knowledge Integration for Health- record linkage); a novel record linkage tool developed to link big administrative datasets at the CIDACS (Centre for Data and Knowledge Integration for Health).
The CIDACS-RL applies the combination of indexing and searching algorithms implemented in Apache Lucene solution as the blocking strategy to reduce the number of comparisons during the linkage. The indexation strategy allows the CIDACS-RL to search the most similar records from the Indexed baseline of the 100 Million Brazilian Cohort for each record in SINASC and submit them to the pairwise comparisons step, instead of restricting the comparison group as an ordinary blocking step. This search was performed in two ways, (i) using the mothers' name, municipality, and mothers date of birth records as attributes, from 2011 to 2015 (ii) using mothers name and municipality, from 2001-2010, because the mothers' date of birth was not registered before 2011. This search strategy uses a mixture of exact, semi fuzzy and fuzzy queries to return the 1000 best candidates from the indexed baseline of the 100 Million Brazilian Cohort. The exact queries return only records with equal attributes in every querying, while the semi-fuzzy and fuzzy approaches permit more flexibility by retrieving candidates where one (semi-fuzzy) or more attributes differ (fuzzy). In cases where the name of the mother was not the same, the Damerau-Levenshtein distance is used as a string comparator to estimate the similarity between comparison pairs, and values above 0.5 are considered 17.
Pairwise Comparison
The most discriminant variables available on the live birth database to identify a child are a maternal name, maternal municipality, and maternal age. For those records from 2011 to 2015, the mothers' date of birth attribute becomes available, and its filling increases gradually across the years. For 2001-2010, where the mothers date of birth is not available, we proceeded with the search using only two attributes (mothers name and municipality) then, we create a new variable by subtracting the date of birth of the child information recorded in SINASC from the date of birth of the mother recorded in baseline of the 100 Million Brazilian Cohort, and this value was compared with the age of the mother registered in SINASC, only the candidates with exacted same value were considered as possible candidates and submitted to the pairwise comparison step. This step was also executed for records from 2011 to 2015 with missing values in the mothers' date of birth.
Figure 1 describes the two different approaches for each set of available variables. Then CIDACS-RL set weights according to the discriminatory power of the attributes ( name of the mother: 1 maternal age or date of birth: 1 state of birth: 0.008, municipality of birth: 0.16). At that moment, a combined scoring and query modules are used to perform the record linkage.
The similarities between names recorded in SINASC and the 1000 best candidates from the baseline of the 100 Million Brazilian Cohort were compared using the Jaro-Winkler string comparator 18. The Jaro-Winkler string comparator19 counts the number of common characters between two strings and the number of transpositions of these common characters, producing similarity values varying between 0 and 1 (perfectly similar). To compare the date attributes, we applied the Hamming distance 16, which measures the minimum number of substitutions required to change one string into the other. Then a linkage score is generated, and the function returns all pairs matched along with the score obtained.
Selection of the threshold
Candidate linking records were ordered by the scores achieved; only the comparison pair with the highest score is retained as a potential link. All remaining candidate records are discarded. If two people received the same candidate as a potential link, we retained only the 'best candidate' as a comparison pair. We removed this candidate as a possible match for all other comparison pair. Then a sample of 2000 pairs stratified in three categories of linkage score (high score – above 0.95, intermediate score – values between 0.90 and 0.95, and low score - below 0.90) is evaluated manually, and the records pairs are classified as likely true pairs or likely false pairs. Based on the training dataset of 2000, the receiver operating curve (ROC) is built to choose the best cut off point, and calculating the area under the curves (AUC), balancing between sensitivity and specificity values. Records were therefore classified as links or non-links based on a single threshold. The software R is used to generate accurate results.
Evaluation of the linkage error
Since we expected that all births registered in the baseline of the 100 Million Brazilian Cohort overlapped with the births existing at SINASC databases, we were able to identify the number of missed matches (record from the same mother-baby pair that failed to link) of the linkage. We then examined which characteristics were associated with missed matches. We examined race, sex, place of residence, sewage treatment, water supply, garbage collection.
The process described above identifies maternal links between the SINASC and the 100 million cohort dataset. After the mother is identified, we searched for the registry of the offspring in the 100 million cohorts using the child date of birth and sex.