ABSTRACT

Objective:

The aim of this study was to analyze the spatial and temporal distribution of systemic lupus erythematosus (SLE) cases in Brazil from 2008 to 2022.

Methods:

We conducted an ecological study based on data from patients treated in the Unified Health System. SLE cases were identified using International Classification of Diseases-10 codes and analyzed by geographic region, age, and color/race. Spatial distribution was assessed to identify high and low prevalence, while temporal trends were evaluated through annual percentage change (APC).

Results:

In 2022, the national prevalence was 52.3/100,000 inhabitants, with marked geographical disparities. Southeast (68.14/100,000) and South (66.37/100,000) regions showed the highest reporting rates. Spatial analysis identified significant clustering, particularly in São Paulo and Paraná, accounting for 95.4% of the high-prevalence municipalities. Temporal analysis of the adult population revealed a consistent increase in SLE prevalence from 2008 to 2022 (APC=15.5%, p<0.001), which was most pronounced in the Northeast and South, while a slower increase was observed in the North. A correlation was observed between the number of rheumatologists and the number of cases/100,000 inhabitants (R=0.567, p=0.002).

Conclusion:

This study reveals significant geographic disparities and a rising trend in SLE prevalence across Brazil. The clustering of cases in specific municipalities and the correlation between rheumatologist availability and prevalence underscore the need for targeted healthcare resources. These findings highlight the importance of investigating how healthcare access impacts regional disparities in SLE prevalence and advancing equitable care nationwide.

Keywords:
Lupus erythematosus, systemic; Prevalence; Brazil; Spatio-temporal analysis; Public health

RESUMO

Objetivo:

Analisar a distribuição espacial e temporal dos casos de lúpus eritematoso sistêmico (LES) no Brasil de 2008 a 2022.

Métodos:

Estudo ecológico baseado em dados dos usuários do Sistema Único de Saúde. Os casos de LES foram identificados pela 10ª edição da Classificação Internacional de Doenças e Problemas Relacionados à Saúde e analisados por região, idade e etnia. A análise espacial avaliou clusters de prevalência, enquanto a variação percentual anual foi estimada por região e grupo etário.

Resultados:

Em 2022, a prevalência nacional foi de 52,3/100 mil habitantes, com disparidades regionais. As regiões Sudeste (68,14/100 mil) e Sul (66,37/100 mil) apresentaram maiores taxas. Clusters significativos foram encontrados em São Paulo e Paraná, abrangendo 95,4% dos municípios com alta prevalência. A análise temporal dos dados da população adulta revelou aumento consistente na prevalência do LES de 2008 a 2022 (VPA=15,5%, p<0,001), especialmente no nordeste e sul. O crescimento foi mais lento no norte. Houve correlação moderada entre o número de reumatologistas e casos por 100 mil habitantes (R=0,567, p=0,002).

Conclusão:

O estudo destaca disparidades regionais e a tendência crescente na prevalência de LES no Brasil. Clusters em municípios específicos e a relação com a disponibilidade de reumatologistas evidenciam a necessidade de alocar recursos de forma direcionada. Esses achados reforçam a importância de se investigar como o acesso aos cuidados de saúde influencia as disparidades e de promover equidade no atendimento em todo o país.

Palavras-chave:
Lúpus eritematoso sistêmico; Prevalência; Brasil; Análise espaço-temporal; Saúde pública

INTRODUCTION

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by a relapsing-remitting course, with periods of clinical remission and exacerbation¹1. Zucchi D, Silvagni E, Elefante E, Signorini V, Cardelli C, Trentin F, et al. Systemic lupus erythematosus: one year in review 2023. Clin Exp Rheumatol 2023; 41(5): 997-1008. https://doi.org/10.55563/clinexprheumatol/4uc7e8
https://doi.org/10.55563/clinexprheumato... . The etiology of SLE remains incompletely understood; however, it is recognized as a multifactorial condition with a heterogeneous distribution influenced by a complex interplay of genetic predisposition, racial/ethnic background, environmental exposures, hormonal factors, infectious agents, and socioeconomic determinants²2. Blaskievicz PH, Silva AMC, Fernandes V, Pinto Junior OB, Shimoya-Bittencourt W, Ferreira SMB, et al. Atmospheric pollution exposure increases disease activity of systemic lupus erythematosus. Int J Environ Res Public Health 2020; 17(6): 1984. https://doi.org/10.3390/ijerph17061984
https://doi.org/10.3390/ijerph17061984...

3. Sestan M, Kifer N, Arsov T, Cook M, Ellyard J, Vinuesa CG, et al. The role of genetic risk factors in pathogenesis of childhoodonsetsystemic lupus erythematosus. Curr Issues Mol Biol 2023; 45(7): 5981-6002. https://doi.org/10.3390/cimb45070378
https://doi.org/10.3390/cimb45070378...

4. Charras A, Smith E, Hedrich CM. Systemic lupus erythematosus in children and young people. Curr Rheumatol Rep 2021; 23(3): 20. https://doi.org/10.1007/s11926-021-00985-0
https://doi.org/10.1007/s11926-021-00985... -⁵5. Zhao Z, Anderson AN, Kannapell CC, Kwok WW, Gaskin F, Fu SM. HLA-DR3 restricted environmental epitopes from the bacterium Clostridium tetani have T cell cross-reactivity to the SLE-related autoantigen SmD. Front Immunol 2022; 13: 928374. https://doi.org/10.3389/fimmu.2022.928374
https://doi.org/10.3389/fimmu.2022.92837... . Recent studies have implicated those environmental factors, such as air pollutants and ultraviolet B radiation, are also involved in the pathogenesis of SLE through mechanisms such as DNA hypomethylation in CD4+ T cells, which may contribute to disease activity and increased hospitalization risk in juvenile populations⁶6. Fernandes EC, Silva CA, Braga AL, Sallum AM, Campos LM, Farhat SC. Exposure to air pollutants and disease activity in juvenile-onset systemic lupus erythematosus patients. Arthritis Care Res 2015; 67(11): 1609-14. https://doi.org/10.1002/acr.22603
https://doi.org/10.1002/acr.22603...

7. Ameer MA, Chaudhry H, Mushtaq J, Khan OS, Babar M, Hashim T, et al. An overview of systemic lupus erythematosus (SLE) pathogenesis, classification, and management. Cureus 2022; 14(10): e30330. https://doi.org/10.7759/cureus.30330
https://doi.org/10.7759/cureus.30330... -⁸8. Pan Y, Fang Y, Chen Y, Chen C, Zhang RD, Fang X, et al. Associations between particulate matter air pollutants and hospitalization risk for systemic lupus erythematosus: a time-series study from Xi’an, China. Environ Geochem Health 2023; 45(6): 3317-30. https://doi.org/10.1007/s10653-022-01409-3
https://doi.org/10.1007/s10653-022-01409... .

SLE global incidence and prevalence vary across different geographic regions, largely driven by disparities in socioeconomic status, healthcare access, and diagnostic practices⁹9. Mendoza-Pinto C, Etchegaray-Morales I, Ugarte-Gil MF. Improving access to SLE therapies in low and middle-income countries. Rheumatology 2023; 62(Supl. 1): i30-i35. https://doi.org/10.1093/rheumatology/keac530
https://doi.org/10.1093/rheumatology/kea... . Historically, it was assumed that there was lower SLE prevalence in African populations compared to Europeans¹⁰10. Essouma M, Nkeck JR, Endomba FT, Bigna JJ, Singwe-Ngandeu M, Hachulla E. Systemic lupus erythematosus in Native sub-Saharan Africans: A systematic review and meta-analysis. J Autoimmun 2020; 106: 102348. https://doi.org/10.1016/j.jaut.2019.102348
https://doi.org/10.1016/j.jaut.2019.1023... . However, recent epidemiological data indicate that African/Asian descent present a two- to three-fold higher SLE prevalence when compared to Caucasians, with blacks and Hispanics experiencing greater morbidity and mortality¹¹11. Barber MRW, Falasinnu T, Ramsey-Goldman R, Clarke AE. The global epidemiology of SLE: narrowing the knowledge gaps. Rheumatology 2023; 62(Supl. 1): i4-i9. https://doi.org/10.1093/rheumatology/keac610
https://doi.org/10.1093/rheumatology/kea... . These disparities underscore the important role of social determinants of health in the burden of chronic diseases, particularly in low- and middle-income countries¹²12. Izmirly PM, Ferucci ED, Somers EC, Wang L, Lim SS, Drenkard C, et al. Incidence rates of systemic lupus erythematosus in the USA: estimates from a meta-analysis of the Centers for Disease Control and Prevention national lupus registries. Lupus Sci Med 2021; 8(1): e000614. https://doi.org/10.1136/lupus-2021-000614
https://doi.org/10.1136/lupus-2021-00061... .

Brazil, as the fifth largest country in the world, presents extensive geographical and ethnic diversity¹³13. Instituto Brasileiro de Geografia e Estatística. Portal [Internet]. Instituto Brasileiro de Geografia e Estatística; 2024 [accessed on Nov. 8, 2024]. Available at: https://www.ibge.gov.br/
https://www.ibge.gov.br/... . Despite this, comprehensive national data on the epidemiology of SLE are scarce, with few studies limited to localized areas and small populations. This lack of data jeopardizes the development of effective regional health strategies and public policies tailored to address the specific needs of the Brazilian population. To fill this gap, the present study aims to provide a detailed analysis of the spatial and temporal distribution of SLE cases in Brazil from 2008 to 2022. By elucidating the epidemiological landscape across the country’s federative units, this study seeks to support the development of targeted interventions and improve healthcare planning and resource allocation for SLE management in Brazil.

METHODS

Study design

We carried out an ecological study using data from patients treated within the Unified Health System (SUS). The study aimed to assess the prevalence of systemic lupus erythematosus (SLE) based on administrative and health records over a 15-year period. Brazil spans approximately 8.5 million square kilometers and has a population of around 203 million. The country is administratively divided into five macro-regions (North, Northeast, Central-West, Southeast, and South), 27 federative units, and 5,570 municipalities.

Variables and data sources

Data analysis

Variables are reported as absolute counts and relative frequencies. Case rates and physician numbers were standardized per 100,000 population. The Shapiro-Wilk test was applied to guide test selection. Pearson’s correlation test assessed the relationship between the number of rheumatologists and SLE cases. In-hospital mortality rates for SLE patients were analyzed using the χ²2. Blaskievicz PH, Silva AMC, Fernandes V, Pinto Junior OB, Shimoya-Bittencourt W, Ferreira SMB, et al. Atmospheric pollution exposure increases disease activity of systemic lupus erythematosus. Int J Environ Res Public Health 2020; 17(6): 1984. https://doi.org/10.3390/ijerph17061984
https://doi.org/10.3390/ijerph17061984... test. Kruskal–Wallis test compared outpatient treatment rates for SLE patients across age groups: children, adolescents, adults, and the elderly.

Spatial distribution maps were generated using R software (version 4.3.2) and the cartographic data provided by IBGE. Spatial dependence, which refers to the extent to which SLE prevalence in one area influences neighboring areas, was assessed using spatial autocorrelation metrics.

A first-order contiguity matrix was constructed, where municipalities sharing borders were considered neighbors (value=1) and those without shared borders were considered non-neighbors (value=0). Spatial autocorrelation was initially evaluated using Global Moran’s I statistic. Local spatial clusters were identified using Local Indicators of Spatial Association (LISA), enabling the detection of regions with significant spatial patterns, such as “high-high” (clusters with high prevalence) and “low-low” (clusters with low prevalence) areas. Statistical significance was set at p≤0.05 for all analyses.

Temporal trends in SLE prevalence were assessed using joinpoint regression analysis, which models data using segmented regression with variance estimation via Poisson regression. The significance of trend changes was evaluated using the Monte Carlo permutation method, assuming constant variance (homoscedasticity) and first-order autocorrelation. For each trend segment, the annual percentage change (APC) was calculated and tested against the null hypothesis of no change. Joinpoints represented statistically significant shifts in the direction of the trend, with each segment characterized by its respective APC.

RESULTS

In 2022, a total of 106,156 patients were registered in the outpatient system for an SLE diagnosis in Brazil, corresponding to a prevalence of approximately 52.3 cases per 100,000 inhabitants, based on a total population of 203,062,512. The majority of patients were female (N=92,843; 87.5%), with a median age of 44 years (interquartile range: 33–55 years). Regarding racial identity, 36,424 (34.3%) individuals identified as white, 32,684 (30.8%) as mixed races, and 37,069 (34.9%) did not report their racial or ethnic background. Age-specific analysis revealed a lower prevalence of SLE among children under 9 years old, with an estimation of three cases per 100,000 inhabitants. We observed a higher prevalence of SLE, peaking at 90–95 cases per 100,000 inhabitants among individuals aged 45–54 years (Figure 1). Adult and elderly patients showed significantly higher rates of SLE compared to other age groups (p<0.0001).

In 2022, the prevalence of SLE and lupus erythematosus (LE) significantly varied across the Brazilian macro-regions. The highest prevalence rates were observed in the Southeast (68.14 cases per 100,000 inhabitants) and South (66.37 cases per 100,000 inhabitants), followed by the Midwest (43.92 cases per 100,000 inhabitants) and Northeast (33.33 cases per 100,000 inhabitants). In contrast, the North region had the lowest prevalence, with 15.38 cases per 100,000 inhabitants (Figure 2). The Moran’s I statistic, calculated to assess spatial autocorrelation of SLE prevalence per 10,000 inhabitants, indicated a significant positive spatial autocorrelation (Moran’s I=0.3177, p<0.0001), suggesting a non-random geographical distribution of cases.

Further analysis using LISA identified distinct clusters of SLE. Six states (Maranhão, Minas Gerais, Rio de Janeiro, São Paulo, Paraná, and Rio Grande do Sul) and 240 municipalities exhibited “high-high” clusters, indicating areas with high prevalence positively influencing neighboring areas. Conversely, five states (Pernambuco, São Paulo, Paraná, Santa Catarina, and Rio Grande do Sul) and 39 municipalities showed “low-low” clusters, representing regions with low prevalence negatively influencing neighboring areas. Notably, two states—São Paulo and Paraná—accounted for 95.4% (n=229) of the municipalities classified as “high-high” clusters.

When the analysis was restricted to SLE cases alone (ICD-10 M320, M321, M328, and M329), the regional distribution pattern remained similar. The Southeast and South regions showed the highest prevalence rates (60.64 and 60.46 cases per 100,000 inhabitants, respectively), followed by the Midwest (39.98 cases per 100,000 inhabitants), Northeast (30.32 cases per 100,000 inhabitants), and North (14.64 cases per 100,000 inhabitants).

The temporal analysis of SLE prevalence in adults from 2008 to 2022 revealed a significant upward trend in Brazil, with an APC of 15.5% (p<0.0001). The Northeast and South regions displayed similar trends, with APCs of 20.6 and 17.4%, respectively (both p<0.0001). In contrast, the North region exhibited a more gradual increase over the same period, with an APC of 7.8% (p<0.0001). The Midwest region experienced a notable increase from 2008 to 2021 (APC=11.8%; p<0.0001), which was disrupted in 2020 with a sharp decline (APC=-53.6%; p=0.0060). In the Southeast, a marked rise was observed between 2012 and 2015 (APC=49.6%; p=0.46), followed by a modest decrease in SLE cases between 2015 and 2022 (APC=-8.5%; p=0.0040) (Figure 3A).

Among individuals under 19 years of age, an increasing trend in SLE prevalence was observed between 2010 and 2015 (APC=22.9%; p=0.0400), sustained from 2015 onwards. The temporal patterns in this younger age group varied across the macro-regions. In the Midwest, there was an initial exponential increase (APC=-22.9%; p=0.0400), which plateaued after 2019. Similarly, the Northeast showed an exponential rise in cases (APC=21.3%; p<0.0001) (Figure 3B).

Finally, we analyzed the distribution of rheumatology services across Brazilian states. The number of rheumatologists per 100,000 inhabitants in the federative units ranges from 0.24 to 1.98. The Federal District (DF) has the highest density, with 1.99 rheumatologists per 100,000 inhabitants, followed by Rio de Janeiro (RJ) with 1.31 and Espírito Santo (ES) with 1.15. Conversely, the states with the lowest density of rheumatologists include Acre (AC) with 0.24, Amapá (AP) with 0.27, and Rondônia (RO) with 0.47.

The distribution of rheumatologists in Brazil is uneven, with higher concentrations in the South and Southeast and lower access in the North and Northeast, reflecting healthcare inequities for rheumatic disease patients (Figure 4). The Pearson correlation coefficient between rheumatologist density and patient numbers per 100,000 inhabitants was 0.567 (p=0.0020), indicating a moderate positive correlation. States with more rheumatologists tend to have more patients, with a 95% confidence interval of 0.238–0.779. Regional disparities in mortality rates are significant (p=0.0080), with the Midwest having the highest rate (6.72%), followed by the North (4.47%). Brazil’s overall mortality rate is 3.87% (Table 1; Figure 4).

DISCUSSION

A national prevalence of 52.3 cases per 100,000 inhabitants was observed, with marked heterogeneity among the five macro-regions. The variation in SLE prevalence in Brazil aligns with findings from other countries, such as Argentina (58.6 cases per 100,000 inhabitants), Turkey (51.7 cases per 100,000 inhabitants), and several African nations (60 cases per 100,000 inhabitants)¹⁵15. Fatoye F, Gebrye T, Mbada C. Global and regional prevalence and incidence of systemic lupus erythematosus in low-and-middle income countries: a systematic review and meta-analysis. Rheumatol Int 2022; 42: 2097-107. https://doi.org/10.1007/s00296-022-05183-4
https://doi.org/10.1007/s00296-022-05183...

16. Ekwom PE. Systemic lupus erythematosus (SLE) at the Kenyatta National Hospital. Clin Rheumatol 2013; 32(8): 1215-7. https://doi.org/10.1007/s10067-013-2217-3
https://doi.org/10.1007/s10067-013-2217-...

17. Gbané-Koné M, Ouattara B, Djaha KJM, Megne E, Ngandeu AN, Coulibaly AK, et al. Autoantibodies in systemic lupus erythematosus, on Black African subject, in Abidjan. Open J Rheumatol Autoimmune Dis 2015; 5(2): 28. https://doi.org/10.4236/ojra.2015.52006
https://doi.org/10.4236/ojra.2015.52006... -¹⁸18. Essouma M, Noubiap JJ, Singwe-Ngandeu M, Hachulla E. Epidemiology of Sjögren syndrome in Africa: a scoping review. J Clin Rheumatol 2022; 28(1): e240-e244. https://doi.org/10.1097/RHU.0000000000001708
https://doi.org/10.1097/RHU.000000000000... . The age-stratified prevalence was also consistent with reports in the literature, showing lower rates in children compared to adults¹⁹19. Charras A, Smith E, Hedrich C. Systemic lupus erythematosus in children and young people. Curr Rheumatol Rep 2021; 23: 20. https://doi.org/10.1007/s11926-021-00985-0
https://doi.org/10.1007/s11926-021-00985... . The density of rheumatologists in Brazil highlights disparities that affect SLE detection and treatment. A moderate positive correlation between rheumatologist density and SLE prevalence shows that areas with more specialists report more cases. This suggests that increased specialist availability may improve diagnosis, contributing to regional variations in SLE prevalence.

In Brazil, the highest SLE prevalence rates were found in the Southeast (68.14 cases per 100,000) and South (66.37 cases per 100,000), reflecting geographical disparities influenced by socio-economic factors. These regions, with higher gross domestic product and better healthcare access, likely see higher detection rates. São Paulo and Paraná, key economic centers, accounted for 95.4% of municipalities classified as “high-high” clusters, highlighting the role of economic and healthcare resources in SLE prevalence reporting²⁰20. Klumb EM, Scheinberg M, Souza VA, Xavier RM, Azevedo VF, McElwee E, et al. The landscape of systemic lupus erythematosus in Brazil: An expert panel review and recommendations. Lupus 2021; 30(10): 1684-95. https://doi.org/10.1177/09612033211030008
https://doi.org/10.1177/0961203321103000... ,²¹21. Abreu MM, Monticielo OA, Fernandes V, Rodrigues DLAS, Silva CAL, Maiorano AC, et al. Characterization of the patterns of care, access, and direct cost of systemic lupus erythematosus in Brazil: findings from the Macunaíma study. Adv Rheumatol 2024; 64(1): 30. https://doi.org/10.1186/s42358-024-00369-9
https://doi.org/10.1186/s42358-024-00369... .

Environmental factors like ultraviolet (UV) radiation, air pollution, and genetics are triggers for SLE. Prolonged UV exposure creates a pro-inflammatory environment and triggers apoptosis in SLE patients²²22. Chen J, Liao S, Pang W, Guo F, Yang L, Liu HF, et al. Life factors acting on systemic lupus erythematosus. Front Immunol 2022; 13: 986239. https://doi.org/10.3389/fimmu.2022.986239
https://doi.org/10.3389/fimmu.2022.98623... . Caucasians exposed to the sun for over 24 months have three times the risk of developing SLE. In Brazil, high UV radiation levels are common, especially in the summer¹⁵15. Fatoye F, Gebrye T, Mbada C. Global and regional prevalence and incidence of systemic lupus erythematosus in low-and-middle income countries: a systematic review and meta-analysis. Rheumatol Int 2022; 42: 2097-107. https://doi.org/10.1007/s00296-022-05183-4
https://doi.org/10.1007/s00296-022-05183... ,²³23. Reid S, Alexsson A, Frodlund M, Morris D, Sandling JK, Bolin K, et al. High genetic risk score is associated with early disease onset, damage accrual and decreased survival in systemic lupus erythematosus. Ann Rheum Dis 2020; 79(3): 363-9. https://doi.org/10.1136/annrheumdis-2019-216227
https://doi.org/10.1136/annrheumdis-2019... .

From 2008 to 2022, SLE prevalence steadily increased in Brazil, unlike in Europe, where studies in Denmark²⁴24. Laustrup H, Voss A, Green A, Junker P. Occurrence of systemic lupus erythematosus in a Danish community: an 8-year prospective study. Scand J Rheumatol 2009; 38(2): 128-32. https://doi.org/10.1080/03009740802419073
https://doi.org/10.1080/0300974080241907... and Norway²⁵25. Lerang K, Gilboe I, Garen T, Thelle DS, Gran JT. High incidence and prevalence of systemic lupus erythematosus in Norway. Lupus 2012; 21(12): 1362-9. https://doi.org/10.1177/0961203312458168
https://doi.org/10.1177/0961203312458168... showed stable incidence over 8 and 10 years, respectively. A long-term study in Spain noted an increase before stabilization²⁶26. Alonso MD, Llorca J, Martinez-Vazquez F, Miranda-Filloy JA, Diaz de Teran T, Dierssen T, et al. Systemic lupus erythematosus in northwestern Spain: a 20-year epidemiologic study. Medicine 2011; 90(5): 350-8. https://doi.org/10.1097/MD.0b013e31822edf7f
https://doi.org/10.1097/MD.0b013e31822ed... , and a 43-year study in the U.S. found a modest 2% rise, with five cases per 100,000, much lower than Brazil²⁷27. Duarte-García A, Hocaoglu M, Valenzuela-Almada M, Osei-Onomah SA, Dabit JY, Sanchez-Rodriguez A, et al. Rising incidence and prevalence of systemic lupus erythematosus: a population-based study over four decades. Ann Rheum Dis 2022; 81(9): 1260-6. https://doi.org/10.1136/annrheumdis-2022-222276
https://doi.org/10.1136/annrheumdis-2022... (52.3 cases per 100,000). This suggests that improved diagnostics and awareness may partly explain the rise, but genetic, environmental, and socio-economic factors also play a role. The trend highlights the need for targeted public health interventions and further research.

In 2020, the World Health Organization declared the COVID-19 pandemic, which may have contributed to either an increase or a decrease in the estimated prevalence of SLE. A Greek study found an increase in autoimmune rheumatic diseases from 2020 to 2023 compared to previous years²⁸28. Bournia VK, Fragoulis GE, Mitrou P, Tsolakidis A, Mathioudakis K, Vassilopoulos D, et al. Increased prevalence of inflammatory arthritis, systemic lupus erythematosus and systemic sclerosis, during 2020–2023 versus 2016–2019 in a Nation-Wide Cohort Study. Rheumatol Int 2024; 44: 2837-46. https://doi.org/10.1007/s00296-024-05733-y
https://doi.org/10.1007/s00296-024-05733... . Factors such as stress and viral infections may have triggered the development of SLE²⁹29. Doria A, Canova M, Tonon M, Zen M, Rampudda E, Bassi N, et al. Infections as triggers and complications of systemic lupus erythematosus. Autoimmun Rev 2008; 8(1): 24-8. https://doi.org/10.1016/j.autrev.2008.07.019
https://doi.org/10.1016/j.autrev.2008.07... , potentially contributing to the high rate of cases during this period in Brazil’s public health system.

The COVID-19 pandemic impacted Brazil’s healthcare system differently across regions, with the North and Northeast experiencing greater challenges due to socioeconomic disparities and health inequalities. These regions were identified as major risk clusters for COVID-19 mortality, likely affecting the diagnosis of chronic diseases like SLE, which require specialized care and advanced health technologies³⁰30. Santos VS, Siqueira TS, Atienzar AIC, Rocha Santos MAR, Vieira SCF, Lopes ADSA, et al. Spatial clusters, social determinants of health and risk of COVID-19 mortality in Brazilian children and adolescents: A nationwide population-based ecological study. Lancet Reg Health Am 2022; 13: 100311. https://doi.org/10.1016/j.lana.2022.100311
https://doi.org/10.1016/j.lana.2022.1003... . Higher SLE rates were observed in the South and Southeast, while lower rates in the North and Northeast may be attributed to limited federal funding and a shortage of rheumatologists. The pandemic also led to an uneven distribution of financial resources, with states like Minas Gerais and São Paulo receiving more support. In contrast, Northern states had fewer resources, forcing them to prioritize COVID-19 management³¹31. Brasil. Recursos Federais destinados ao combate da pandemia de Coronavírus (COVID-19) [Internet]. Brasil; 2022 [accessed on Feb. 7, 2025]. Available at: https://portaldatransparencia.gov.br/coronavirus?ano=2020
https://portaldatransparencia.gov.br/cor... . This reallocation of resources, coupled with the increased mortality risk among SLE patients due to COVID-19, may explain the significant decline in reported SLE cases in the Central-West region between 2020 and 2022. This observation highlights the complex interplay between the pandemic, healthcare access, and SLE incidence, suggesting that while COVID-19 may biologically increase the risk of SLE, it may also lead to an underestimation of its prevalence due to healthcare disruptions.

Our study has several limitations. First, we relied on administrative data, which limited access to clinical and epidemiological variables, hindering the development of a more detailed patient profile³²32. Viana SW, Faleiro MD, Mendes ALF, Torquato AC, Tavares CPO, Feres B. Limitations of using the DATASUS database as a primary source of data in surgical research: a scoping review. Rev Col Bras Cir 2023; 50: e20233545. https://doi.org/10.1590/0100-6991e-20233545-en
https://doi.org/10.1590/0100-6991e-20233... . Additionally, we focused only on patients treated within the public healthcare system, which may underestimate the true prevalence of SLE, as it is not a notifiable disease and data on private healthcare patients are unavailable. However, since 71.5% of the Brazilian population uses the public system for chronic disease treatment, our findings still capture a significant portion of SLE cases in Brazil²⁰20. Klumb EM, Scheinberg M, Souza VA, Xavier RM, Azevedo VF, McElwee E, et al. The landscape of systemic lupus erythematosus in Brazil: An expert panel review and recommendations. Lupus 2021; 30(10): 1684-95. https://doi.org/10.1177/09612033211030008
https://doi.org/10.1177/0961203321103000... ,²¹21. Abreu MM, Monticielo OA, Fernandes V, Rodrigues DLAS, Silva CAL, Maiorano AC, et al. Characterization of the patterns of care, access, and direct cost of systemic lupus erythematosus in Brazil: findings from the Macunaíma study. Adv Rheumatol 2024; 64(1): 30. https://doi.org/10.1186/s42358-024-00369-9
https://doi.org/10.1186/s42358-024-00369... . Secondary data are also prone to errors, and the SIH aggregates data by total admissions without distinguishing individual patients³²32. Viana SW, Faleiro MD, Mendes ALF, Torquato AC, Tavares CPO, Feres B. Limitations of using the DATASUS database as a primary source of data in surgical research: a scoping review. Rev Col Bras Cir 2023; 50: e20233545. https://doi.org/10.1590/0100-6991e-20233545-en
https://doi.org/10.1590/0100-6991e-20233... , potentially impacting the accuracy of our results. Previous studies have shown that data quality varies by region, particularly in low-income areas³³33. Silva RA, Hoffmann GN, Fernandez-Llimos F, Lima CE. Data quality review of the Brazilian nosocomial infections surveillance system. J Infect Public Health 2024; 17(4): 687-95. https://doi.org/10.1016/j.jiph.2024.02.013
https://doi.org/10.1016/j.jiph.2024.02.0... . The lower SLE rates observed in the North may be explained by this disparity in data quality. SLE treatment is government-funded and impacts the national health budget; the data in this study can inform health policy planning and highlight areas requiring further investigation³⁴34. Marques NP, Marques NCT, Lucena EHG, Martelli DRB, Oliveira EA, Martelli-Junior H. The continuous increase in the number of systemic lupus erythematosus cases in Brazil in the COVID-19 era. Braz Oral Res 2023; 37: e066. https://doi.org/10.1590/1807-3107bor-2023.vol37.0066
https://doi.org/10.1590/1807-3107bor-202... ,³⁵35. Reis-Neto ETD, Seguro LPC, Sato EI, Borba EF, Klumb EM, Costallat LTL, et al. II Brazilian Society of Rheumatology consensus for lupus nephritis diagnosis and treatment. Adv Rheumatol 2024; 64(1): 82. https://doi.org/10.1186/s42358-024-00423-6
https://doi.org/10.1186/s42358-024-00423... .

Furthermore, the statistical methods employed have inherent limitations. Joinpoint regression provides a robust framework for detecting significant changes in temporal trends by identifying specific points (joinpoints) where the direction or magnitude of a trend shifts. This method is particularly useful for analyzing temporal patterns, such as periods of increase, decrease, or stability, and for calculating the APC within each segment. However, it assumes that the data follow a Poisson distribution and that variance remains constant (homoscedasticity), which may not always align with the characteristics of all datasets. Additionally, while effective in identifying trend changes, this method does not account for external covariates or confounding factors that may influence the observed patterns. Despite these limitations, joinpoint regression remains a widely used tool for describing temporal changes in epidemiological data and supporting public health analyses³⁶36. Liu B, Kim H-J, Feuer EJ, Graubard BI. Joinpoint regression methods of aggregate outcomes for complex survey data. J Surv Statist Methodol 2023; 11(4): 967-89. https://doi.org/10.1093/jssam/smac014
https://doi.org/10.1093/jssam/smac014... .

Despite the limitations, our study presents the first comprehensive analysis of the epidemiology of SLE in Brazil, revealing significant geographic and temporal disparities that warrant further investigation. From 2008 to 2022, the prevalence of SLE increased consistently, with the highest rates observed in the Southeast and South, where areas of high prevalence were concentrated. The moderate correlation between the density of rheumatologists and the prevalence of SLE underscores the role of access to healthcare and highlights regional inequalities that may impact patient outcomes.

ETHICAL ASSESSMENT:

The study utilized anonymized, publicly available secondary data and therefore did not require evaluation by an ethics and research committee.

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