Desigualdade sócio-espacial expressa por indicadores do Sistema de Informações sobre Nascidos Vivos (SINASC)

Abstract

Variables from the Information System on Live Births (SINASC) were geocoded for municipalities from Greater Metropolitan Maringá, Paraná State, Brazil, for 19 population expansion areas (PEA). Thematic maps and the Moran I statistic and local indicators of spatial autocorrelation (LISA) were used to evaluate autocorrelation between teenage motherhood, low schooling, black/brown race/color, insufficient number of prenatal visits, cesarean delivery, prematurity, low birth weight, and 5-minute Apgar less than 8. Low schooling, black/brown race/color, and 5-minute Apgar < 8 were concentrated in the peripheral PEA, with significant autocorrelation (Moran I: 0.50; 0.67; and 0.63, respectively), while high cesarean rates were concentrated in the central PEA (Moran I = 0.59), where "low-low" patterns were observed, with black or brown teenage mothers with low schooling. High-risk clusters were identified in the peripheral PEA of Greater Metropolitan Maringá, showing that PEA is a feasible methodological alternative, together with the SINASC, for monitoring socio-spatial inequalities in maternal and child health.

Factors associated to maternal and child's health in Rio Grande do Sul, Brazil

“…20 A study using the data from Sinasc in Paraná State did not observe a direct correlation between the proportion of newborns with low birth weight in areas with adverse social conditions, however, this association may not be considered to occur in studies with other research methods. 24 The significant associations found in this study corroborate with other consulted studies. 7,10,11,13,[28][29][30] It was observed that the low maternal schooling is associated to negative outcomes for the maternal and child health, such as inadequate prenatal care with a low number of consultations and a high risk for low birth weight and prematurity.…”

“…22 In relation to the result variables, it was found that in the 19 CRS, most pregnancies had a length of 37 to 42 weeks, however, the proportions of prematurity presented were larger than the estimated 9.2% of prematurity in Brazil, described in a report by the World Health Organization in 2010. 23 Yet, the results for prematurity were high when compared to other studies of the data from Sinasc in Paraná State, which presented 8.1%, 24 in Paraíba Valley in São Paulo with 6.4% 13 and in the South Region, in Santa Catarina State with 10.6% of prematurity in 2012. 19 The highest concentration of prematurity was observed in the CRS in Santa Maria, an medium level region in of the socioeconomic development, 14 this finding seems to be in favor with the description in the study of the data from Sinasc in Santa Catarina, which found a higher prevalence of prematurity among women with a less privileged socioeconomic level.…”

A morte materna no Brasil: razão e sensibilidade

“…O estudo mostrou que são importantes as diferenças nos indicadores de saúde reprodutiva das mulheres entre os municípios, tanto na tipologia socioeconômica como na inserção social das mulheres, para mais de 40% da população total do Estado, cerca de 1,4 milhões de pessoas 10 . Usando as variáveis do Sistema de Informações sobre Nascidos Vivos (SINASC), Predebone et al 11 encontraram diferenças significativas na Região Metropolitana de Maringá em resultados maternos e perinatais: mãe adolescente, baixa escolaridade, raça/cor preta e parda, número insuficiente de consultas de pré-natal, parto cesáreo, prematuridade, baixo peso ao nascer e Apgar <8 no quinto minuto 11 . Na discussão da mortalidade materna no Brasil e em seus estados, o reconhecimento dos determinantes sociais é fundamental porque, como demonstram estes dois estudos, as iniquidades são marcantes e podem explicar parte importante do problema.…”

Preventable infant mortality: Spatial distribution and main causes in three Brazilian municipalities

“…Also, public services such as clinics and hospitals are located in the central areas of Maringá, facilitating access by residents to these services. Studies developed to evaluate health inequalities in this same region, using data from SINASC [12,13] showed the municipalities of Sarandi and Paiçandu as vulnerable areas, as they featured higher coefficients of infant mortality and inferior socioeconomic indicators, with higher rates of teenage mothers, mothers with low education, children of black or brown color/race, mothers with insufficient prenatal appointments and newborns with low vitality scores at birth. These results indicated vulnerable areas that coincide with the areas with higher preventability percentages found in the present study.…”