Subtopic Deep Dive

Quality of Perinatal Health Information Systems
Research Guide

What is Quality of Perinatal Health Information Systems?

Quality of Perinatal Health Information Systems evaluates completeness, validity, accuracy, and underreporting in Brazilian SINASC and SIH databases for maternal and neonatal health data.

Researchers assess SINASC (Live Birth Information System) and SIH (Hospital Information System) for data quality issues like missing variables and inconsistencies. Studies develop audit protocols and correction methods to improve perinatal surveillance. Over 10 papers from 2001-2018 analyze these systems, with foundational works citing up to 2311 times.

Curated Papers

Key Challenges

Why It Matters

Accurate SINASC and SIH data enable reliable surveillance of preterm birth rates and maternal complications, informing SUS policymaking (Leal et al., 2016; Leal et al., 2012). Lima et al. (2009) identify quality dimensions like completeness missing in Brazilian SIS, leading to biased infant mortality estimates (Victora and Barros, 2001; França et al., 2017). Bittencourt et al. (2006) demonstrate SIH applications in collective health analyses, supporting interventions that reduced perinatal mortality disparities.

Key Research Challenges

Data Completeness Gaps

SINASC and SIH often lack socioeconomic and demographic variables, with Romero and Cunha (2006) reporting inconsistencies in infant death records from 1996-2001. Lima et al. (2009) review shows no systematic monitoring of SIS quality. This hinders equity analysis in maternal-neonatal outcomes.

Validity and Underreporting

Hospital data in SIH underreports complications due to coding errors, as Bittencourt et al. (2006) found in collective health studies. Leal et al. (2012) national survey reveals cesarean rate discrepancies with SINASC. Correction methods remain underdeveloped.

Regional Disparity Audits

Quality varies by Brazilian federative unit, with Victora and Barros (2001) noting perinatal mortality trends distorted by poor data. Paim et al. (2011) highlight regional inequalities in SUS systems. Standardized audit protocols are needed for scalable assessments.

Essential Papers

The Brazilian health system: history, advances, and challenges

Jairnilson Silva Paim, Cláudia Travassos, C.M.V.B. Almeida et al. · 2011 · The Lancet · 2.3K citations

Brazil is a country of continental dimensions with widespread regional and social inequalities. In this report, we examine the historical development and components of the Brazilian health system, ...

Prevalence and risk factors related to preterm birth in Brazil

María do Carmo Leal, Ana Paula Esteves‐Pereira, Marcos Nakamura‐Pereira et al. · 2016 · Reproductive Health · 245 citations

The high proportion of provider-initiated preterm birth and its association with prior cesarean deliveries and all of the studied maternal/fetal pathologies suggest that a reduction of this type of...

Complications in adolescent pregnancy: systematic review of the literature

Walter Fernandes de Azevedo, Michèle Baffi Diniz, Eduardo Sérgio Valério Borges da Fonseca et al. · 2015 · Einstein (São Paulo) · 242 citations

Sexual activity during adolescence can lead to unwanted pregnancy, which in turn can result in serious maternal and fetal complications. The present study aimed to evaluate the complications relate...

Birth in Brazil: national survey into labour and birth

María do Carmo Leal, Antônio Augusto Moura da Sílva, Marcos Augusto Bastos Dias et al. · 2012 · Reproductive Health · 240 citations

Abstract Background Caesarean section rates in Brazil have been steadily increasing. In 2009, for the first time, the number of children born by this type of procedure was greater than the number o...

Saúde reprodutiva, materna, neonatal e infantil nos 30 anos do Sistema Único de Saúde (SUS)

María do Carmo Leal, Célia Landmann Szwarcwald, Paulo Vicente Bonilha Almeida et al. · 2018 · Ciência & Saúde Coletiva · 234 citations

Resumo Este estudo apresenta um sumário das intervenções realizadas no âmbito do setor público e os indicadores de resultado alcançados na saúde de mulheres e crianças, destacando-se os avanços no ...

Principais causas da mortalidade na infância no Brasil, em 1990 e 2015: estimativas do estudo de Carga Global de Doença

Elisabeth Barboza França, Sônia Lansky, Maria Albertina Santiago Rego et al. · 2017 · Revista Brasileira de Epidemiologia · 216 citations

RESUMO: Objetivo: Analisar as taxas de mortalidade e as principais causas de morte na infância no Brasil e estados, entre 1990 e 2015, utilizando estimativas do estudo Carga Global de Doença (Globa...

O Sistema de Informação Hospitalar e sua aplicação na saúde coletiva

Sonia Azevedo Bittencourt, Luiz Antônio Bastos Camacho, María do Carmo Leal · 2006 · Cadernos de Saúde Pública · 202 citations

O trabalho teve como objetivo levantar a produção científica das aplicações dos dados do Sistema de Informação Hospitalar do SUS (SIH/SUS) em análises de questões relevantes de Saúde Coletiva. Para...

Reading Guide

Foundational Papers

Start with Paim et al. (2011) for SUS context (2311 cites), then Bittencourt et al. (2006) on SIH applications and Lima et al. (2009) for quality dimensions review.

Recent Advances

Study Leal et al. (2018) on 30-year SUS perinatal indicators and França et al. (2017) on child mortality trends using GBD estimates.

Core Methods

Core techniques include record linkage audits, completeness metrics, and validity checks via primary data comparison (Romero and Cunha, 2006; Lima et al., 2009).

How PapersFlow Helps You Research Quality of Perinatal Health Information Systems

Discover & Search

Research Agent uses searchPapers and exaSearch to find SINASC/SIH quality studies, then citationGraph on Lima et al. (2009) reveals 195-cited connections to Bittencourt et al. (2006) and Romero et al. (2006). findSimilarPapers expands to regional audits from Leal et al. (2012).

Analyze & Verify

Analysis Agent applies readPaperContent to extract quality metrics from Lima et al. (2009), then verifyResponse with CoVe checks claims against Paim et al. (2011). runPythonAnalysis processes SIH completeness data with pandas for statistical verification; GRADE grades evidence on SINASC validity.

Synthesize & Write

Synthesis Agent detects gaps in correction methods across Bittencourt et al. (2006) and Victora et al. (2001), flagging contradictions in regional reporting. Writing Agent uses latexEditText, latexSyncCitations for SUS policy reports, latexCompile with exportMermaid for quality dimension flowcharts.

Use Cases

"Assess SINASC data completeness for preterm births in Northeast Brazil 2010-2020"

Research Agent → searchPapers + exaSearch → Analysis Agent → readPaperContent (Leal et al., 2016) + runPythonAnalysis (pandas completeness stats) → CSV export of underreporting rates by region.

"Write LaTeX review on SIH quality challenges citing Lima 2009 and Bittencourt 2006"

Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations + latexCompile → PDF with diagram of quality dimensions via exportMermaid.

"Find code for auditing perinatal data validity in Brazilian health systems"

Research Agent → paperExtractUrls → Code Discovery → paperFindGithubRepo + githubRepoInspect → Python scripts for SIH validation metrics.

Automated Workflows

Deep Research workflow conducts systematic review of 50+ SINASC/SIH papers: searchPapers → citationGraph → GRADE grading → structured report on quality trends. DeepScan applies 7-step analysis to Leal et al. (2012): readPaperContent → CoVe verification → runPythonAnalysis on cesarean discrepancies. Theorizer generates hypotheses for correction models from Lima et al. (2009) and Romero et al. (2006).

Try Doxa for Quality of Perinatal Health Information Systems Research

Frequently Asked Questions

What defines quality in perinatal health information systems?

Quality encompasses completeness, validity, accuracy, and timeliness in SINASC and SIH data (Lima et al., 2009). Evaluations focus on underreporting of maternal complications and infant outcomes.

What methods assess SINASC and SIH quality?

Audits compare records against primary sources; statistical methods detect inconsistencies (Romero and Cunha, 2006). Lima et al. (2009) review dimensions like proportionality and externality checks.

What are key papers on this topic?

Foundational: Paim et al. (2011, 2311 cites), Bittencourt et al. (2006, 202 cites), Lima et al. (2009, 195 cites). Recent: Leal et al. (2018, 234 cites) on SUS perinatal advances.

What open problems exist?

Lack of systematic SIS monitoring and scalable correction methods persists (Lima et al., 2009). Regional disparities in data quality challenge national perinatal surveillance (Victora and Barros, 2001).