Multiple sclerosis treatment coverage: an analysis based on inequality indicators

ABSTRACT

OBJECTIVE  To assess territorial inequalities in the coverage of first-line pharmacological treatment for relapsing-remitting multiple sclerosis in the Brazilian Unified Health System (SUS) in 2024, analyzing the regional distribution of coverage, consumption, and spending, and quantifying socioeconomic inequalities using the Slope Index of Inequality (SII) and Relative Index of Inequality (RII) indicators.

METHODS  An observational study based on administrative data from the SUS, integrating information from Outpatient Procedure Authorizations and epidemiological estimates to calculate consumption in defined daily doses and per capita expenditure on disease-modifying drugs. The state Human Development Index was used as a socioeconomic proxy to calculate the SII and RII indicators, which quantify absolute and relative inequalities.

RESULTS  In 2024, only 35.3% of eligible patients received treatment, with coverage varying between regions (50.9% in the Central-West and 34.3% in the Northeast). The SII (0.2082) and RII (2.58) for PT/PAT_SUS indicated strong socioeconomic inequality in coverage. For per capita spending, there was no consistent socioeconomic gradient (SII = -99.18; RII = 0.96). Consumption in defined daily doses showed significant inequality (SII = 61.65; RII = 2.50), reflecting greater therapeutic intensity in states with a higher Human Development Index.

CONCLUSIONS  The study shows important limitations in the coverage and equity of treatment for relapsing-remitting multiple sclerosis in the SUS, with structural inequalities associated with regional socioeconomic position. The integrated use of administrative indicators and inequality metrics (SII and RII) provides subsidies for policy interventions focused on distributive justice and sustainability of the public health system.

DESCRIPTORS
Health Equity; Pharmaceutical Services; Multiple Sclerosis

RESUMO

OBJETIVO  Avaliar as desigualdades territoriais na cobertura do tratamento farmacológico de primeira linha para esclerose múltipla remitente-recorrente no Sistema Único de Saúde (SUS) brasileiro em 2024, analisando a distribuição regional da cobertura, consumo e gastos, e quantificando desigualdades socioeconômicas por meio dos indicadores Slope Index of Inequality (SII) e Relative Index of Inequality (RII).

MÉTODOS  Estudo observacional baseado em dados administrativos do SUS, integrando informações de Autorizações de Procedimentos Ambulatoriais e estimativas epidemiológicas para calcular o consumo em doses diárias definidas e gastos per capita com medicamentos modificadores do curso da doença. O Índice de Desenvolvimento Humano estadual foi utilizado como proxy socioeconômica para o cálculo dos indicadores SII e RII, que quantificam desigualdades absolutas e relativas.

RESULTADOS  Em 2024, apenas 35,3% dos pacientes elegíveis receberam tratamento, com cobertura variando entre regiões (50,9% no Centro-Oeste e 34,3% no Nordeste). O SII (0,2082) e o RII (2,58) para PT/PAT_SUS indicaram forte desigualdade socioeconômica na cobertura. Para gastos per capita, não se observou gradiente socioeconômico consistente (SII = -99,18; RII = 0,96). O consumo em doses diárias definidas apresentou desigualdade significativa (SII = 61,65; RII = 2,50), refletindo maior intensidade terapêutica em estados com maior Índice de Desenvolvimento Humano.

CONCLUSÕES  O estudo evidencia limitações importantes na cobertura e equidade do tratamento de esclerose múltipla remitente-recorrente no SUS, com desigualdades estruturais associadas à posição socioeconômica regional. O uso integrado de indicadores administrativos e métricas de desigualdade (SII e RII) oferece subsídios para intervenções políticas focadas em justiça distributiva e sustentabilidade do sistema público de saúde.

DESCRITORES
Equidade em Saúde; Assistência Farmacêutica; Esclerose Múltipla

INTRODUCTION

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that promotes demyelination and progressive neurodegeneration, significantly compromising the functionality and quality of life of patients. The relapsing-remitting form (RRMS) corresponds to 85%–90% of cases at initial diagnosis, constituting the main clinical manifestation of the disease in its early stages and a reliable epidemiological indicator for monitoring public policies aimed at treating MS¹,².

In Brazil, the Unified Health System (SUS) guarantees comprehensive care for MS patients through the Clinical Protocol and Therapeutic Guidelines (PCDT), updated in 2022³. For cases of RRMS with low or moderate activity, it is recommended to start treatment with betainterferones, glatiramer acetate, teriflunomide, or dimethyl fumarate. In the event of intolerance, hypersensitivity, adverse events, therapeutic failure, or poor adherence, substitution with drugs from another class is allowed. Azathioprine, due to its lower efficacy, should be reserved for cases with low adherence to parenteral forms (intramuscular, subcutaneous, or intravenous). These first-line therapies have a favorable safety profile, moderate clinical efficacy in reducing relapses, and wide availability in the public network³.

Although these technologies have been formally incorporated into the SUS, disparities persist in actual access to treatment⁴,⁵. Several factors contribute to this inequality, including regional differences in installed capacity, distribution of specialists, supply logistics, and local decision-making processes⁴,⁶. These disparities represent an ethical and operational challenge, as they violate the principle of equity - a fundamental pillar of the Brazilian health system - and directly compromise patients’ clinical outcomes.

Equity in access to medicines transcends simple physical availability and encompasses the timely and appropriate use of therapies according to clinical need⁴. In this context, distributive cost-effectiveness analysis emerges as a fundamental tool for the rational and fair allocation of health resources, considering not only the clinical value of interventions, but also their ability to reduce historical inequalities between regions and populations¹⁰,¹¹. This perspective is especially relevant in the treatment of MS, whose progressive and heterogeneous course requires early interventions to prevent accumulative disabilities and high future costs.

Evaluating coverage of first-line therapies for RRMS makes it possible to understand the organizational effectiveness of pharmaceutical care in the SUS and provides technical support for regionalized planning of public policies. The analysis of indirect indicators of consumption, expenditure, and coverage of the eligible population, adjusted to the pattern of use of the public system, can reveal structural patterns of inequity and guide more equitable and sustainable interventions in the care of rare diseases.

The primary objective of this study is to analyze territorial inequalities in the coverage of pharmacological treatment for low- or moderate-activity RRMS in the SUS. Other objectives include determining the different levels of coverage between Brazilian regions; calculating the average expenditure per capita, the average consumption per capita, and the population treated; and applying two robust indicators of health inequality - the Slope Index of Inequality (SII) and the Relative Index of Inequality (RII).

METHODS

Study Design and Population

This is a cross-sectional study using records of patients covered by Outpatient Procedure Authorizations (APAC) undergoing treatment for RRMS during the year 2024.

Data Source

The data was extracted from the SUS Sistema de Informação Ambulatorial (SIA – Outpatient Information System), specifically from the Apac records, a platform that monitors outpatient care provided by the SUS¹². A national database was built, by Brazilian state, using as the main filter the medicines listed in Table 1 and their codes from the Sistema de Gerenciamento da Tabela de Procedimentos, Medicamentos e OPM do SUS (SIGTAP – SUS Table of Procedures, Medicines, and OPM Management System), as well as the International Disease Codes - ICD-10 (G35 and G35.0)³. The categories of variables analyzed in this registry included: (a) clinical - ICD-10 of the disease, time of treatment (12 months with registration in the APAC), identification of primary or secondary APAC, and code of the outpatient procedure; (b) demographic - federative unit and municipality of origin of the APAC; (c) medicines - number of the APAC, unit value of the procedure according to the SIGTAP Table and Databank of Prices in the Health System - approved quantity of the procedure and approved value¹³,¹⁴. Medicines without a price determined on these bases or with revoked use were excluded. For the purposes of this study, authorized APACs are considered to be a proxy for the quantity of medication dispensed. The extraction was carried out without identifying any inconsistencies in the records.

Table 1
Disease-modifying drugs in the PCDT, used in the first-line treatment of relapsing-remitting multiple sclerosis, with their respective SIGTAP codes, ATC classification, DDD, and prices.

SIGTAP code and procedure	ATC	DDD	DDD presentation	Price (R$)
0604390106 - betainterferone 1A 12,000,000 IU (44 mcg) injectable (per filled syringe)	L03AB07	4.30 mcg	1.00	368.1513
0604390084 - betainterferone 1A 6,000,000 IU (22 mcg) injectable (per filled syringe)	L03AB07	4.30 mcg	0.50	320.3213
0604390092 - betainterferone 1A 6,000,000 IU (30 mcg) injectable (per vial, filled syringe or filled pen)	L03AB07	4.30 mcg	0.68	1.019.6614
0604390114 - betainterferon 1B 9,600,000 IU (300 mcg) for injection (per vial)	L03AB08	4.00 UM	1.00	161.6413
0604520026 - glatiramer acetate 40 mg for injection (per filled syringe)	L03AX13	20.00 mg	1.00	352.3414
0604540043 - teriflunomide 14 mg (per tablet)	L04AK02	14.00 mg	1.00	161.6314
0604540027 - dimethyl fumarate 120 mg (capsule)	L04AX07	0.48 g	0.50	27.5714
0604540035 - dimethyl fumarate 240 mg (per tablet)	L04AX07	0.48 g	1.00	106.4514
0604530013 - azathioprine 50 mg (per tablet)	L04AX01	0.15 g	0.50	1.0314

• DDD: defined daily dose; mcg: microgram; mg: milligram; g: gram; IU: international units; UM : unit of measurement; ATC: Anatomical-Chemical-Classification; PCDT: Clinical Protocol and Therapeutic Guidelines; SIGTAP: SUS Table of Procedures, Medicines, and OPM Management System.
• Source: SIGTAP¹³, Databank of Prices in the Health System¹⁴; Norwegian Institute of Public Health, 2024¹⁵.

The Institute for Health Metrics and Evaluation (IHME), through the Global Health Data Exchange¹⁵, a catalog of health-related censuses and vital statistics, provided data on disease prevalence for 2021, in the absence of national data. A database was created based on the APACs collected and the categories of variables proposed. Extractions were made by state for the year 2024. For the analysis, the states were aggregated into macro-regions, which allows for the identification of structural patterns of inequality that reflect historical conditions of socio-economic development in Brazil, as well as providing larger samples for analysis statistics, reducing sample variability. Considering that 85%–90% of MS cases have the relapsing-remitting form at onset³,⁶, the population indicators calculated for RRMS are very close to the national prevalence and impact of MS. In addition, the adjusted estimate data - such as those from Global Burden of Disease (GBD)¹⁵ are compatible with the universe studied for RRMS in the SUS, justifying the use of the term RRMS as a proxy for assessing access to and coverage of treatment.

The Human Development Index (HDI), obtained from the Atlas of Human Development in Brazil¹⁶.

“Tracer” treatment

According to the disease’s PCDT³, the first line of therapy for the current treatment of low- or moderate-activity RRMS (G35, G35.0) is betainterferon, glatiramer or teriflunomide or dimethyl fumarate or azathioprine. We used the Anatomical-Chemical-Classification (ATC) and the Defined Daily Dose (DDD), obtained from the website of the Norwegian Institute of Public Health¹⁷. The SIGTAP and CATMAT codes of the drugs, their dosage, classification, DDD and DDD per presentation, and prices for 2024 are shown in Table 1.

a) Number of patients to be treated (PATsus) per year

The number of patients to be treated at SUS (PAT_sus), i.e. the number of prevalent cases in each state that should be treated at SUS, was obtained from the Global Health Data Exchange 2021¹⁵. An adjustment was made to this prevalence using the rate at which Brazilians use public health facilities, assuming that 71% of Brazilians use the SUS as a reference¹⁸, multiplying the prevalence of the disease¹⁵by this coverage rate, where:

${\mathrm{PAT}}_{\text{ous}}=P\times 0.71$

b) Annual consumption in DDD

To calculate annual consumption, the approved quantities of the procedure (e.g. tablets, filled syringe) were used as internal variables of the APACs. Many APACs had zero. Even so, the values found were added up, divided by the prevalence per state and then grouped by region.

This indicator is determined by the ratio between the sum of the approved quantities of the procedure multiplied by the DDD of the drug presentation and divided by the PAT_SUS in the state/region.

$\begin{array}{c}\text{Annual consumption in DDD per}{\text{PAT}}_{\text{sus otate}}=(\sum \text{approved quantity of}\\ \text{procedure}{}_{\text{otate}})\text{* DDD/presentation/}{\text{PAT}}_{\text{sus}}\text{EMRR}{2024}_{\text{etate.}}\end{array}$

c) Patients treated per year

The number of patients treated (PT) per year is the product of the ratio of the sum of the annual consumption in DDD and the DDD/patient/year of each drug (expressing the intensity of treatment according to the defined period¹⁹,²¹). This result is equivalent to the number of PT by the SUS in 2024, per state, if the DDD had been applied in full to an individual. Thus:

$\mathrm{PT}/\text{year}=\sum \text{annual consumption in}{\mathrm{DDD}}_{\text{etate}}/\mathrm{DDD}/\text{patient}/\text{year}$

The ratio between PT and PAT_SUS was calculated in percentage terms, obtaining the percentage of PT in SUS, to get closer to equity of access.

d) Average annual per capita expenditure

The average per capita expenditure was calculated by the ratio between the sum of the approved number of APACs authorized in the states for the year 2024, multiplied by the average price of the drug registered in SIGTAP or the Databank of Prices in the Health System (BPS) for the same year¹³,¹⁴, divided by the number of individuals treated (PT) per Brazilian state.

$\text{Average expenditure per capita}{}_{\text{atate}}\left(R\$\right)=\left(\sum _i^{11}(n^0\text{de}{\mathrm{APAC}}_i\times {\text{preço}}_i)\right)/{\mathrm{PT}}_{\text{atate}}$

e) Slope Index of Inequality (SII) and Relative Index of Inequality (RII)

The SII or absolute inequality index represents an absolute measure of inequality, expressing the difference in the outcome of interest (for example: spending, consumption or access) between the extremes of the socioeconomic distribution, based on an ordinal scale. Positive values indicate a greater concentration of the outcome among the more favored states, while negative values indicate a greater occurrence among the less favored. Values close to zero suggest no significant inequality in the dimension being assessed⁷.

The RII or Relative Inequality Index expresses the ratio between the outcomes observed at the extremes of socioeconomic distribution, reflecting a relative measure. An RII value > 1 indicates a greater outcome among the more favored; RII < 1, among the less favored; and RII ≈ 1, relative equality between the groups⁷,¹⁰.

To calculate the SII and RII, the 2021 state HDI was used as the socioeconomic ranker¹⁶. The outcomes evaluated were: ratio of treated patients to adjusted prevalence (PT/PAT_SUS), as a proxy for territorial equity of coverage; per capita expenditure on disease-modifying drugs (as a cost indicator); and DDD consumption (as a measure of intensity of use).

Data Analysis

Descriptive data analysis was carried out, including frequency distribution for the variables analyzed and linear regression to calculate SII and RII. All the analyses were carried out using Excel^® software.

Ethical Issues

This study used secondary data from public domain sources, without any nominal identification, and respected the ethical principles established in National Health Council Resolution No. 466 of December 12, 2012²⁰, with no need for analysis by the Research Ethics Committee. The analyses were carried out using the Excel^® program.

RESULTS

According to GBD 2021¹⁵, the estimated prevalence of MS in Brazil in 2021 was 54,710 people. The states with the highest absolute number of cases were: São Paulo (14,066), Minas Gerais (5,677), and Rio Grande do Sul (4,934), while the lowest prevalence rates were in: Roraima (88), Amapá (133), and Acre (140). Table 2 shows the prevalence, PT and PAT_SUS values for the Brazilian states.

Table 2
Prevalence, PATSUS, and PT with MS treatment, by Federative Unit of Brazil, 2024.

State	Prevalencea	PATSUS	PT
Acre	140	100	21
Alagoas	577	409	75
Amapá	133	94	5
Amazonas	545	387	46
Bahia	2,928	2,079	526
Ceará	1,659	1,178	390
Federal District	1,111	789	164
Holy Spirit	1,120	795	173
Goias	1,785	1,267	299
Maranhão	1,096	778	101
Mato Grosso	796	565	125
Mato Grosso do Sul	791	562	83
Minas Gerais	5,677	4,031	1,434
Pará	1,242	882	109
Paraíba	698	495	154
Paraná	4,119	2,924	1,491
Pernambuco	1,856	1317	405
Piauí	639	454	79
Rio de Janeiro	4,184	2,971	977
Rio Grande do Norte	609	432	139
Rio Grande do Sul	4,934	3,503	1,041
Rondônia	397	282	37
Roraima	88	62	6
Santa Catarina	2,781	1,974	829
São Paulo	14,066	9,987	4,955
Sergipe	426	303	35
Tocantins	314	223	23
Total	54,710	38,844	13,721

• PAT_SUS: patients to be treated; PT: patients treated; MS: multiple sclerosis.
• ^aData from GBD: Global Burden of Disease 2021¹⁵.

By adjusting the estimated prevalence with the exclusive utilization rate of SUS (71%), it was found that 38,844 people with low or moderate activity RRMS should receive drug treatment through the public system in 2024 (PAT_SUS). However, consumption data shows that only 13,721 patients were treated (PT), resulting in national coverage of 35.3%. This result reveals a significant gap between the estimated need for treatment and the effective coverage of pharmacological care, with important implications for equity in the SUS. In absolute terms, approximately 25,123 patients with RRMS remained without first-line pharmacological treatment in 2024, despite being included in the public system’s target population.

The analysis by region shows marked disparities. The Central-West had the highest coverage (50.9%), followed by the North (48.5%), South (42.3%), Southeast (39.0%), and Northeast (34.3%). Although the national average indicates that just over a third of the eligible population has been effectively assisted, regional variations reveal systemic inequalities in the coverage of specialized treatment.

Table 3 shows the ratio between PT/PAT_SUS, as well as spending and consumption related to the pharmacological treatment of MS.

Table 3
Ratio between PT and PATSUS, expenditure, and consumption with MS treatment, by Federative Unit of Brazil, 2024.

State	PT/PATSUS	Average annual per capita expenditure (R$)	Annual consumption in DDD
Acre	0.21	2,105.4	65.4
Alagoas	0.18	3,553.6	56.8
Amapá	0.06	3,259.9	19.5
Amazonas	0.12	1,305.9	41.0
Bahia	0.25	1,432.7	81.2
Ceará	0.33	1,184.7	109.9
Federal District	0.21	2,190.3	67.0
Holy Spirit	0.22	1,491.8	71.6
Goiás	0.24	1,733.1	74.2
Maranhão	0.13	1,478.9	40.1
Mato Grosso	0.22	1,745.4	76.3
Mato Grosso do Sul	0.15	1,913.9	44.2
Minas Gerais	0.36	2,417.1	99.9
Pará	0.12	2,498.7	39.2
Paraíba	0.31	1,977.2	94.1
Paraná	0.51	2,259.2	154.1
Pernambuco	0.31	3,138.4	89.3
Piauí	0.17	2,971.3	48.2
Rio de Janeiro	0.33	2,296.4	107.1
Rio Grande do Norte	0.32	3,124.2	102.4
Rio Grande do Sul	0.30	3,000.8	89.2
Rondônia	0.13	4,028.4	32.8
Roraima	0.10	3,349.4	23.6
Santa Catarina	0.42	3,099.0	122.4
São Paulo	0.50	2,041.1	152.3
Sergipe	0.12	3,751.2	34.6
Tocantins	0.10	4,586.0	24.6
Average	0.35	2,556.5	107.8

• DDD: defined daily dose; PAT_SUS: patients to be treated; PT: patients treated; MS: multiple sclerosis.

The ratio between PT and PAT_SUS shows that the states of the South and Southeast concentrate a significant portion of PT, especially São Paulo (PT = 4,955; PT/PAT_SUS= 0.50), Paraná (PT = 1,491; PT/PAT_SUS= 0.51), and Santa Catarina (PT = 829; PT/PAT_SUS= 0.42). On the other hand, states in the North and Northeast regions, such as Amapá (PT/PAT_SUS= 0.06), Tocantins (0.10) and Roraima (0.10), had the lowest coverage, with absolute numbers of PT substantially lower than the population estimate.

In 2024, the average annual per capita expenditure on disease-modifying drugs varied between the states, from R$1,184.70 in Ceará to R$ 4,586.00 in Tocantins. This variation probably reflects differences in the composition of the therapeutic mix prescribed – with greater or lesser use of drugs with a higher unit cost – the epidemiological profile of patients and the relative volume of treatment carried out in each state, as well as possible variations in the administrative recording of information.

The average consumption in DDD/patient/year, calculated based on the quantities approved and adjusted by the population treated, was 107.8 DDD in the country. The regions with the highest consumption were the North (DDD = 136.1) and Central-West (DDD = 131.9), while the Northeast recorded the lowest average (DDD = 97.2). Although high consumption may indicate greater adherence and continuity of treatment, it may also suggest prescribing above the expected average, related to the dosage regimen and regional clinical practices.

Based on the 2021 HDI¹⁶of the Brazilian states, the SII and RII inequality indicators were calculated, as shown in Table 4.

Table 4
Slope Index of Inequality (SII) and Relative Index of Inequality (RII), according to the HDI of the Brazilian states.

Indicator	SII	RII
PT/PATSUS	0.208	2.582
Expenditure per capita	-99.182	0.961
DDD per capita	61.645	2.497

• DDD: defined daily dose; PAT_SUS: patients to be treated; PT: patients treated; HDI: Human Development Index.

For the PT/PAT_SUS indicator, both the positive SII (0.208) and the high RII (2.58) show a significant socioeconomic gradient, indicating a higher proportion of PT in states with a higher HDI.

Regarding per capita spending, the negative SII (-99.18) and the RII close to unity (0.96) suggest no systematic inequality related to the socioeconomic position of the states. These results may reflect variations in the therapeutic profile adopted by the federal units, rather than structural inequalities in financing.

The results for DDD consumption per capita show a positive SII (61.65) and an RII of 2.50, indicating that the average volume of medicines used is considerably higher in states with a higher HDI. This pattern may reflect greater adherence, continuity or intensity of treatment in the more socio-economically developed regions.

Joint analysis of the data reveals a worrying scenario of widespread under-treatment, associated with inadequate allocation of resources and heterogeneity in therapeutic intensity between states. The lack of standardization and the poor performance of some regions, even with the availability of medicines in the SUS, show flaws in management and implementation of care.

The findings reinforce the urgent need for public policies aimed at territorial equity in rare disease care, including a review of funding criteria, strengthening federative coordination, and continuous monitoring of effective access to treatment.

DISCUSSION

The results of this study show significant territorial inequalities in coverage of first-line treatment for RRMS in the SUS, with a gradient favoring federal units with a higher HDI (SII > 0; RII > 1). In 2024, only 35.3% of patients estimated to be eligible for pharmacological treatment with disease-modifying drugs were actually treated. This percentage is in line with the international literature on Latin America, where coverage of disease-modifying therapies ranges from 9.5% to 42.8%, reflecting significant structural, economic, and logistical barriers to adequate treatment of the disease⁵. The findings reinforce the urgent need for public policies aimed at territorial equity in the care of rare diseases, including the revision of funding criteria, the strengthening of federative coordination and the continuous monitoring of effective access to treatments.

Regional analysis shows that there are profound disparities. The Central-West (50.9%) and North (48.5%) had the highest relative coverage, while the Northeast (34.3%) and Southeast (39.0%) had the worst. This pattern contradicts expectations based on aggregate socio-economic indicators, such as the HDI, and may indicate flaws in local regulation, structuring of lines of care and fragmentation of pharmaceutical care management²¹. The estimate of need used in this study was derived from the GBD 2021 total multiple sclerosis prevalence, which does not discriminate clinical phenotypes (such as the RRMS form) nor is it available at a sub-national level by state¹⁵. This methodological option, combining an aggregate denominator with state PT numerators (APAC/SUS), may explain part of the discrepancies observed between the analysis by macro-regions and the analysis by states. Thus, the results should be interpreted with caution and seen as a starting point for future research exploring clinical-social microdata and, when available, more specific sub-national estimates.

The variability in average annual per capita spending, which ranged from R$ 1,184.70 (Ceará) to R$ 4,586.00 (Tocantins), points to possible inequalities in the composition of the therapeutic arsenal used. States with higher spending may be prioritizing drugs with a higher unit cost, such as injectable interferons, or facing logistical and operational limitations that increase costs. Lower figures may reflect greater prescription of oral drugs, such as dimethyl fumarate or teriflunomide, or local cost rationalization strategies²¹.

The average annual DDD consumption per patient was 107.8, with values ranging from 97.2 (Northeast) to 136.1 (North). These discrepancies may indicate both differences in the intensity of treatment and supply failures and interruptions in therapy, which is worrying, considering that discontinuation of treatment is associated with worse clinical outcomes, a greater number of relapses and an increase in the indirect and direct costs of care⁴,⁶.

From an epidemiological point of view, the estimated prevalence of MS in Brazil is approximately 14.5 per 100,000 inhabitants, with RRMS accounting for around 80% of cases³,²¹. This data is consistent with the GBD estimates¹⁵, which identified 54,710 people living with MS in Brazil in 2021. The use of this data, adjusted by the SUS utilization rate, contributed to a plausible estimate of the population eligible for pharmacological treatment in the public sector.

The results of the SII and RII inequality indicators deepen this analysis by quantifying the magnitude and direction of inequalities. For the PT/PAT_SUS indicator, the SII and RII inequality indicators confirm the existence of a strong socioeconomic gradient: the proportion of PT grows consistently with the HDI of the states. These findings indicate a pattern consistent with territorial inequalities in treatment coverage, indicated by SII/RII, to be further investigated with access/use microdata. Previous studies reinforce the usefulness of these indicators in measuring inequalities in health and in monitoring public policies, making it possible to identify distributional asymmetries which ultimately compromise the principle of equity in the SUS²². In contrast, the results for average annual per capita spending suggest the absence of a consistent socio-economic gradient and may reflect the greater influence of operational variables, such as the composition of the available therapeutic arsenal or strategies for acquiring and dispensing medicines. States with higher spending may be prioritizing drugs with a higher unit cost, such as injectable interferons, while lower values may indicate the adoption of oral therapies or greater rationalization of costs²². Regarding DDD consumption per capita, the indicators show significant inequality in the average volume of drugs used, concentrated in states with a higher HDI. This pattern may be related to greater adherence, continuity or intensity of treatment in more developed regions, or even to a lower occurrence of interruptions and supply failures, aspects which have a direct impact on the clinical and economic outcomes of RRMS²,⁴,⁵.

The methodological approach adopted in this study – based on the integrated analysis of access indicators (PT), estimated need (PAT_SUS), DDD consumption and per capita expenditure – constitutes a relevant contribution to the field of Pharmacoeconomics and health equity assessment. The inclusion of SII and RII indicators strengthens the robustness of the analysis and allows for a more precise understanding of inequalities in multiple dimensions (coverage, use, and cost), guiding specific strategies for regulation, financing, and reorganization of pharmaceutical care for rare diseases.

This study has limitations inherent to the use of administrative bases. The APACs record authorized and billed procedures, but do not guarantee effective dispensing or adherence to treatment. In addition, they do not capture relevant clinical variables, such as disease severity, history of relapses or therapeutic response. The prevalence estimate used, based on data from 2021, may not accurately reflect real demand in 2024, due to possible demographic and epidemiological variations.

Moreover, because we used the 71% SUS dependency adjustment, it is possible that the PAT_SUS estimate is underestimated, since high-cost drugs, such as those for RRMS, are often withdrawn from the Specialized Component of Pharmaceutical Assistance (CEAF) even by users with private plans. We also don’t have individual social attributes (income, race/color, gender), which limits the evaluation of equity of access. Our findings should therefore be interpreted as territorial inequalities in coverage, at an aggregate level.

Unlike databases such as CEAF or the Horus system, which enable individualized patient follow-up and dispensation tracking, the APAC does not include clinical or sociodemographic variables, limiting multivariate analysis of access determinants¹,²¹. Despite these restrictions, its use is widely recognized in population studies and provides important information for the management and evaluation of public policies²⁵,²⁶.

Comparisons between different studies looking at public health spending are made difficult by methodological heterogeneity, the diversity of indicators used and different levels of aggregation (by state, drug or disease). Even so, the findings presented here corroborate previously described trends of under-treatment and unequal distribution of therapeutic resources within the SUS⁴,⁵,²¹.

This study reveals a scenario of limited and unequal coverage of first-line treatment for RRMS in the SUS. In 2024, only 35.3% of eligible patients were treated, showing a significant coverage deficit in relation to the estimated demand. The formal availability of therapies in the SUS does not translate into universal access, compromising the principle of equity.

Regional inequalities – with higher coverage in the North and Central-West regions and lower in the Southeast and Northeast regions – reflect historical asymmetries in the structuring of services and local management of pharmaceutical care. The significant variability in per capita spending and consumption indicators in DDD reinforces this diagnosis, indicating inefficiencies in the organization of care and possible gaps in adherence or continuity of treatment.

The application of the SII and RII indicators confirmed the presence of a significant socioeconomic gradient, especially in relation to coverage (PT/PAT_SUS) and consumption (DDD per capita), with a concentration of favorable outcomes in states with a higher HDI. These findings point not only to absolute inequalities, but also to structural inequities that require systemic responses.

In this context, we recommend adoption of structuring measures that combine equitable funding, clinical standardization, qualification of the workforce and strengthening of the diagnosis and treatment network. Regional compensation strategies, linked to coverage targets and continuous monitoring through inequality indicators, can promote greater distributive justice in therapy coverage.

Acknowledgements

To the University of La Rioja and the State University of Rio de Janeiro for the work infrastructure.

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• Data Availability:
  The data are available upon request to the authors.
• Funding:
  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - process 406465/2024-0).

Edited by

• Associate Editor:
  Paulo Frazão https://orcid.org/0000-0002-3224-0020

Data availability

The data are available upon request to the authors.

Publication Dates

• Publication in this collection
  02 Feb 2026
• Date of issue
  2026

History

• Received
  25 June 2025
• Accepted
  16 Oct 2025