Improvements in Data Completeness in Health Information Systems Reveal Racial Inequalities: Longitudinal National Data from Hospital Admissions in Brazil 2010-2022

Overview

Journal Int J Equity Health

Publisher Biomed Central

Specialties Health Services
Public Health

Date 2024 Jul 18

PMID 39026324

Authors

Rony Coelho

Rudi Rocha

Thomas Hone

Affiliations

Soon will be listed here.

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.

References

Camelo L, Giatti L, Ladeira R, Griep R, Mill J, Chor D . Racial disparities in renal function: the role of racial discrimination. The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). J Epidemiol Community Health. 2018; 72(11):1027-1032. DOI: 10.1136/jech-2018-210665. View

Constante H, Marinho G, Bastos J . The door is open, but not everyone may enter: racial inequities in healthcare access across three Brazilian surveys. Cien Saude Colet. 2021; 26(9):3981-3990. DOI: 10.1590/1413-81232021269.47412020. View

Reboucas P, Goes E, Pescarini J, Ramos D, Ichihara M, Sena S . Ethnoracial inequalities and child mortality in Brazil: a nationwide longitudinal study of 19 million newborn babies. Lancet Glob Health. 2022; 10(10):e1453-e1462. PMC: 9638038. DOI: 10.1016/S2214-109X(22)00333-3. View

Beach M, Gary T, Price E, Robinson K, Gozu A, Palacio A . Improving health care quality for racial/ethnic minorities: a systematic review of the best evidence regarding provider and organization interventions. BMC Public Health. 2006; 6:104. PMC: 1525173. DOI: 10.1186/1471-2458-6-104. View

Knight H, Deeny S, Dreyer K, Engmann J, Mackintosh M, Raza S . Challenging racism in the use of health data. Lancet Digit Health. 2021; 3(3):e144-e146. DOI: 10.1016/S2589-7500(21)00019-4. View

The Lancet . Advancing racial and ethnic equity in health. Lancet. 2022; 400(10368):2007. DOI: 10.1016/S0140-6736(22)02533-8. View

Geraldo R, Oliveira J, Alexandre L, Aguiar M, Vieira A, Germani A . Filling out the race/skin color item in the patient identification form: aspects of its implementation in a university hospital. Cien Saude Colet. 2022; 27(10):3871-3880. DOI: 10.1590/1413-812320222710.08822022. View

Moorthie S, Peacey V, Evans S, Phillips V, Roman-Urrestarazu A, Brayne C . A Scoping Review of Approaches to Improving Quality of Data Relating to Health Inequalities. Int J Environ Res Public Health. 2022; 19(23). PMC: 9740714. DOI: 10.3390/ijerph192315874. View

ONeil S, Taylor S, Sivasankaran A . Data Equity to Advance Health and Health Equity in Low- and Middle-Income Countries: A Scoping Review. Digit Health. 2022; 7:20552076211061922. PMC: 8725220. DOI: 10.1177/20552076211061922. View

10.

The Lancet . Medicine and medical science: Black lives must matter more. Lancet. 2020; 395(10240):1813. PMC: 7289575. DOI: 10.1016/S0140-6736(20)31353-2. View

11.

Branham D, Finegold K, Chen L, Sorbero M, Euller R, Elliott M . Trends in Missing Race and Ethnicity Information After Imputation in HealthCare.gov Marketplace Enrollment Data, 2015-2021. JAMA Netw Open. 2022; 5(6):e2216715. PMC: 9187953. DOI: 10.1001/jamanetworkopen.2022.16715. View

12.

Castro M, Massuda A, Almeida G, Menezes-Filho N, Andrade M, de Souza Noronha K . Brazil's unified health system: the first 30 years and prospects for the future. Lancet. 2019; 394(10195):345-356. DOI: 10.1016/S0140-6736(19)31243-7. View

13.

Chen Y, Lin H, Tseng T, Wen H, DeVivo M . Racial Differences in Data Quality and Completeness: Spinal Cord Injury Model Systems' Experiences. Top Spinal Cord Inj Rehabil. 2018; 24(2):110-120. PMC: 5915102. DOI: 10.1310/sci2402-110. View

14.

Hone T, Stokes J, Trajman A, Saraceni V, Coeli C, Rasella D . Racial and socioeconomic disparities in multimorbidity and associated healthcare utilisation and outcomes in Brazil: a cross-sectional analysis of three million individuals. BMC Public Health. 2021; 21(1):1287. PMC: 8252284. DOI: 10.1186/s12889-021-11328-0. View

15.

Medeiros S, Coelho R, Millett C, Saraceni V, Coeli C, Trajman A . Racial inequalities in mental healthcare use and mortality: a cross-sectional analysis of 1.2 million low-income individuals in Rio de Janeiro, Brazil 2010-2016. BMJ Glob Health. 2023; 8(12). PMC: 10693873. DOI: 10.1136/bmjgh-2023-013327. View

16.

Nashef S . What is a near miss?. Lancet. 2003; 361(9352):180-1. DOI: 10.1016/S0140-6736(03)12218-0. View

17.

Muniz J, Bastos J . Classificatory volatility and (in)consistency of racial inequality. Cad Saude Publica. 2017; 33Suppl 1(Suppl 1):e00082816. DOI: 10.1590/0102-311X00082816. View

18.

Kressin N . Race/ethnicity identification: vital for disparities research, quality improvement, and much more than "meets the eye". Med Care. 2015; 53(8):663-5. DOI: 10.1097/MLR.0000000000000409. View

19.

Johnson J, Moore B, Hwang E, Hickner A, Yeo H . The accuracy of race & ethnicity data in US based healthcare databases: A systematic review. Am J Surg. 2023; 226(4):463-470. DOI: 10.1016/j.amjsurg.2023.05.011. View

20.

Pavao A, Ploubidis G, Werneck G, Campos M . Racial discrimination and health in Brazil: evidence from a population-based survey. Ethn Dis. 2012; 22(3):353-9. View