Suchergebnisse

IntroductionPregnancy at age extremes and low maternal schooling are associated with under-five mortality. However, we found no study in the literature that evaluated the interaction between mother's age and schooling on under-five mortality measured in both multiplicative and additive scales.
Objectives and ApproachTo analyze the interaction between mother's age and schooling on under-five mortality in a birth cohort in Rio de Janeiro City (Brazil) (N=153,527 live births). We carried out a non-concurrent cohort study using probabilistic record linkage of birth and death records for Rio de Janeiro (Brazil). The association between under-five mortality according to mother's age (15-19, 20-34 and 35 years or more) and schooling (less than 4 years; 4 to 11 years and 12 or more years of schooling) were estimated using generalized linear models with binomial distribution and Neperian logarithm function. The interaction was evaluated in both scales.
ResultsThe under-five mortality rate was 14.0 per thousand live births. Higher rates were observed among women with less than 4 years' schooling and in age extremes. When compared with women aged 20 to 34 years and with more than 12 years' schooling, these women had the following risk ratios: 15 to 19 years – RR = 3.66 (95% CI 2.39; 5.62); 35 years or more – RR = 2.74 (95% CI 1.83; 4.11). We did not observe statistically significant interactions in the additive and multiplicative scales.
Conclusion/ImplicationsAlthough we did not observe an interaction between mother's age and schooling, under-five mortality was more frequent among women with less the four years' schooling, especially at the age extremes, suggests these are more vulnerable groups that demand greater attention in care.

IntroductionThe National Disease Surveillance Data System (SINAN) is the main tool used by tuberculosis control programs to measure control actions and disease incidence rates in Brazil. Therefore, data quality is essential to support health surveillance and management.
Objectives and ApproachThe aim of this study was to propose an algorithm for the qualification of the variables type of entry, treatment outcome, HIV test, HIV/AIDS co-infection and diabetes comorbidity.
We conduct a probabilistic record linkage between three databases of the city of Rio de Janeiro: SINAN-Tuberculosis, 2004-2012; the Mortality Information System (SIM), 2009-2013; and AIDS unified database, until 2012 (SINAN-AIDS, SIM registries that AIDS was the basic cause of death, drug distribution and laboratory databases). The criteria for qualifying the variables were based on technical guidelines, published by the Brazilian Ministry of Health. We implemented a script using Structured Query Language.
ResultsThe script had 103 rules. There was an 115% increase in the treatment output classified as "dropout" due to a decrease in: i) the number of records with patient transference but that they were not followed by another health care unit; output treatment classified as "ignored"/missing values; records with treatment output classified as "cure" before the end of the treatment. There was a 28% decrease in the number of records with treatment output classified as "death due to tuberculosis" and a 64% increase in those classified as "death due to other causes". There was a 2.4% increase in the number of records with diabetes as a comorbidity, a 5.3% increase in the number of registries with HIV-positive test and a 8.7% increase in TB-HIV/AIDS co-infection.
Conclusion/ImplicationsLinking databases is an important strategy for retrieving information from other systems to improve the quality of information to support decision making for TB control.

IntroductionRepeat pregnancy among adolescents is associated with preexisting disadvantages related to the adolescent's social and cultural background. In Brazil, there is a lack of studies that evaluate repeat pregnancy during the entire adolescence period. Record linkage is a useful tool for following adolescents after the birth of their first child.
Objectives and ApproachTo estimate pregnancy repetition during adolescence and to evaluate the effect of inadequate schooling during the first pregnancy on repetition in a cohort of adolescents in Rio de Janeiro City who had their first live-born child between the ages of 15 and 18. We carried out a non-concurrent cohort study using probabilistic record linkage techniques. The association between mother's schooling and at least one repeat pregnancy during adolescence was evaluated through risk ratios adjusted for mother's age, estimated using generalized linear models with binomial distribution and Neperian logarithm function. We also evaluated the interdelivery interval.
Results9,285 adolescents were included in the study. Of these, 29.4% had inadequate schooling at the time their first child was born and 9.9% had at least on repeat pregnancy during adolescence. The risk of repeat pregnancy was significantly higher in the group with inadequate schooling (adjusted RR=1.19; 95% CI= 1.05-1.34), when compared with the group with adequate schooling. Half of the teenage mothers had an interval between the first and the second pregnancy of less than 24 months, with data showing no statistically significant difference between the two groups with respect to adequacy of mother's schooling. Adolescents with more than one repeat pregnancy had the worst prenatal and labor care indicators, as well as the highest incidence of premature births.
Conclusion/ImplicationsInadequate schooling at the time of the first child's birth increases the risk of repeat pregnancies during adolescence. Additionally, inadequate schooling and a history of repeat pregnancies during adolescence are markers of populations that accumulate disadvantages in access to care and in pregnancy outcomes.

IntroductionRecord linkage has been increasingly used in Brazil. However, only a few studies report the quality of the linkage process. Synthetic test data can be used to evaluate the quality of data linkage.
Objectives and ApproachTo develop a synthetic data generator that creates test datasets with similar attributes and error characteristics found in the Brazilian databases.
We analyzed the 2013 mortality database from Rio de Janeiro State to know the characteristics and frequency distribution of the database attributes (name, mother's name, sex, date of birth and address).
We used Python and C++ to customize and add routines to GeCo (http://dlrep.org/dataset/GeCo), a personal data generation tool developed by Tran et al. (DOI:10.1145/2505515.2508207).
ResultsBrazilian names have specific characteristics that distinguish them from other countries' patterns: multiple family names are usual, as are composite first names, and, despite that, homonyms are frequent. Family names may include the full extension or only parts of either the father and mother's respective family names, or both, so there is a wide variation in progeny family names and not necessarily a common family name for all family members.
Conclusion/ImplicationsDue to the specific national characteristics of name building in Brazil, modeling synthetic data is particularly challenging and needs to have more flexible rules in order to generate databases that will actually allow assessing the quality of data linkage processes.