Improvements in data completeness in health information systems reveal racial inequalities: longitudinal national data from hospital admissions in Brazil 2010-2022.
Int J Equity Health
; 23(1): 143, 2024 Jul 18.
Article
en En
| MEDLINE
| ID: mdl-39026324
ABSTRACT
BACKGROUND:
Race and ethnicity are important drivers of health inequalities worldwide. However, the recording of race/ethnicity in data systems is frequently insufficient, particularly in low- and middle-income countries. The aim of this study is to descriptively analyse trends in data completeness in race/color records in hospital admissions and the rates of hospitalizations by various causes for Blacks and Whites individuals.METHODS:
We conducted a longitudinal analysis, examining hospital admission data from Brazil's Hospital Information System (SIH) between 2010 and 2022, and analysed trends in reporting completeness and racial inequalities. These hospitalization records were examined based on year, quarter, cause of admission (using International Classification of Diseases (ICD-10) codes), and race/color (categorized as Black, White, or missing). We examined the patterns in hospitalization rates and the prevalence of missing data over a period of time.RESULTS:
Over the study period, there was a notable improvement in data completeness regarding race/color in hospital admissions in Brazil. The proportion of missing values on race decreased from 34.7% in 2010 to 21.2% in 2020. As data completeness improved, racial inequalities in hospitalization rates became more evident - across several causes, including assaults, tuberculosis, hypertensive diseases, at-risk hospitalizations during pregnancy and motorcycle accidents.CONCLUSIONS:
The study highlights the critical role of data quality in identifying and addressing racial health inequalities. Improved data completeness has revealed previously hidden inequalities in health records, emphasizing the need for comprehensive data collection to inform equitable health policies and interventions. Policymakers working in areas where socioeconomic data reporting (including on race and ethnicity) is suboptimal, should address data completeness to fully understand the scale of health inequalities.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Sistemas de Información en Hospital
/
Disparidades en el Estado de Salud
/
Disparidades en Atención de Salud
/
Sistemas de Información en Salud
Límite:
Female
/
Humans
/
Male
País/Región como asunto:
America do sul
/
Brasil
Idioma:
En
Revista:
Int J Equity Health
Año:
2024
Tipo del documento:
Article
País de afiliación:
Brasil
Pais de publicación:
Reino Unido