Estatísticas da vida

Waldvogel, Bernadette Cunha; Ferreira, Carlos Eugênio de Carvalho

doi:10.1590/s0102-88392003000300007

Cited by 5 publications

(9 citation statements)

References 4 publications

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“…Despite the risk of information bias, the data provided by SEADE Foundation is based on improved deterministic linkage method of death and live birth certificates, ensuring consistent and detailed information for epidemiologic studies. 10 On the other hand, the linkage process has a manual component, and it is time consuming; therefore, 2015 is the most recent year data available for the study.…”

Section: Discussionmentioning

confidence: 99%

“…The following demographic characteristics were studied for preterm live births that survived the neonatal period and those with RDS-associated neonatal deaths: maternal age (<20, 20-34, and !35 years), marital status (married or unmarried), maternal schooling ( 7,(8)(9)(10)(11), and !12 years), parity (primiparous or multiparous), number of prenatal care visits (0-3 and !4), pregnancy (single or multiple), delivery mode (vaginal or cesarean section), gestational age (22-27, 28-31, and 32-36 weeks), birth weight (<1,000, 1,000-1,499 g, and !1,500 g), sex (male or female), and 5minute Apgar score (0-3, 4-6, and !7).…”

Section: Methodsmentioning

confidence: 99%

“…9 Besides the federal government information system, it has its own vital statistics system that captures 99.5% of the data present in civil records. 10 Data on RDS frequency and impact in São Paulo, as in the rest of the country, are sparse. In a reference center in São Paulo city, RDS increased the risk of death in preterm live births (68%).…”

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confidence: 99%

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Temporal Trend and Risk Factors for Respiratory Distress Syndrome–Associated Neonatal Mortality in Preterm Infants: A Population-Based Study in a Middle-Income Country

et al. 2022

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Objective This article aimed to report a temporal series of respiratory distress syndrome (RDS)-associated neonatal mortality rates in preterm live births in São Paulo state, Brazil, and to identify social, maternal, and neonatal characteristics associated with these deaths. Study Design This is a population-based study of all live births with gestational age (GA) between 22 and 36 weeks, birth weight ≥400 g, without congenital anomalies from mothers living in São Paulo state during 2004 to 2015. RDS-associated neonatal mortality was defined as death up to 27 days after birth with ICD-10 codes P22.0 or P28.0. RDS-associated neonatal mortality rate (annual percent change [APC] with 95% confidence intervals [95% CIs]) was analyzed by Prais–Winsten. Kaplan–Meier estimator identified the time after birth that the RDS-associated neonatal death occurred. Poisson's regression model compared social maternal and neonatal characteristics between preterm live births that survived the neonatal period and those with RDS-associated neonatal deaths, with results expressed in incidence rate ratio and 95% CI. Results A total of 645,276 preterm live births were included in the study, of which 612,110 survived and 11,078 had RDS-associated neonatal deaths. RDS-associated neonatal mortality rate was 17.17 per thousand preterm live births, with a decreasing annual trend (APC: −6.50%; 95% CI: −9.11 to −3.82%). The median time of these deaths was 48 hours after birth. The following risk factors for RDS-associated neonatal death were identified: maternal schooling ≤7 years (1.18; 1.09–1.29), zero to three prenatal care visits (1.25; 1.18–1.32), multiple pregnancy (1.24; 1.16–1.33), vaginal delivery (1.29; 1.22–1.36), GA 22 to 27 weeks (106.35; 98.36–114.98), GA 28 to 31 weeks (20.12; 18.62–21.73), male sex (1.16; 1.10–1.22), and 5-minute Apgar scores of 0 to 3 (6.74; 6.08–7.47) and 4 to 6 (3.97; 3.72–4.23). Conclusion During the study period, RDS-associated neonatal mortality rates showed significant reduction. The relationship between RDS-associated neonatal deaths and social, maternal, and neonatal factors suggests the need for perinatal strategies to reduce prematurity and to improve the initial management of preterm infants. Key Points

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Temporal Trend and Risk Factors for Respiratory Distress Syndrome–Associated Neonatal Mortality in Preterm Infants: A Population-Based Study in a Middle-Income Country

et al. 2022

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Section: The Binding Processmentioning

confidence: 99%

“…The "Deterministic Binding" technique was used, [15] which was extensively used by researchers from Fundação SEADE (Waldvogel et al [2019] and Waldvogel [2020]) . [16] This method identifies pairs of individuals who were treated at the Hospital and died inside São Paulo State. In the binding process, the following variables were used: patient name, date of birth, sex, husband (wife) name, mother's name, father's name, patient's death date (when available in the Hospital record) and patient's ID card number.…”

Section: The Binding Processmentioning

confidence: 99%

Mortality after discharge from a public tertiary cardiovascular referral hospital

et al. 2023

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It is critically important for stakeholders with distinct foci of attention on healthcare to understand patient evolution in the presence of an established diagnosis or with a suspected diagnosis of various diseases, specially considering death as an outcome. To study the long-term mortality of patients at a cardiovascular referral hospital. Deterministic binding (selection of pairs of registers from the hospital electronic health records and the mortality records of São Paulo state) from 2002 to 2017 was performed. Studied variables were: age, sex, hospital treatment unit where the first visit occurred (Emergency Unit, Outpatient Unit, Hospital Admissions, Diagnostics Services), treatment type, elapsed time between the first visit and death, diagnosis at first and last visits and variables related to death. Statistical Methods: descriptive, survival (with Kaplan–Meier method), correspondence and competitive risks analyses; in case of nonoccurrence of death until the end of 2017, the patients were considered alive. Statistical significance was set at values of P < .05. Median age at the first visit to the Hospital was 51.9 years. Birth locations included 4496 cities, 17.33% in São Paulo, 0.41% in Rio de Janeiro, 0.40% in Osasco, 24.04% in other cities. Sex included females (46.7%), males (44.2%), not defined (6.3%), and other (2.8%). We observed an association between diseases in ICD-10 Chapter 16 (certain conditions originating in the perinatal period) and Chapter 17 (congenital malformations, deformations, and chromosomal abnormalities), both as diagnoses and underlying causes of death, as well as between neoplasms as diagnoses and as the underlying cause of death. In this sample, there was an association between admission diagnoses and underlying causes of death, such as neoplasms, cardiovascular diseases, and congenital heart malformations. Additionally, patients who underwent a cardiac intervention had a smaller less mortality rate than those who were not operated on. There were also differences in cardiovascular mortality between distinct treatment units of the hospital ((Emergency Unit, Outpatient Unit, Hospital Admissions, Diagnostic Services).

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Mortalidade no longo prazo de pacientes que receberam atendimento de hospital acadêmico da rede SUS voltado para o atendimento médico cardiológico com o emprego de bases de dados assistenciais obtidas de prontuários eletrônicos

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Estatísticas da vida

Cited by 5 publications

References 4 publications

Temporal Trend and Risk Factors for Respiratory Distress Syndrome–Associated Neonatal Mortality in Preterm Infants: A Population-Based Study in a Middle-Income Country

Temporal Trend and Risk Factors for Respiratory Distress Syndrome–Associated Neonatal Mortality in Preterm Infants: A Population-Based Study in a Middle-Income Country

Mortality after discharge from a public tertiary cardiovascular referral hospital

Mortalidade no longo prazo de pacientes que receberam atendimento de hospital acadêmico da rede SUS voltado para o atendimento médico cardiológico com o emprego de bases de dados assistenciais obtidas de prontuários eletrônicos

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