Temporal Associations Between Ambient Air Pollutants and Inhaled Beclomethasone Dispensing in Brazil

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Figueiredo, Isis Morais, Marisa Treglia, Wender Aparecido Oliveira, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8959512/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Rationale : Air pollution is a recognized trigger of asthma exacerbations, but its influence on population-level demand for controller medications remains insufficiently quantified, particularly in middle-income countries. We assessed whether temporal variations in ambient air pollutants are associated with national dispensing of inhaled beclomethasone in Brazil. Methods : This nationwide ecological time-series study analyzed monthly data from January 2018 to December 2024. Aggregated beclomethasone dispensing data were obtained from the Brazilian Farmácia Popular program and standardized as doses per 100 inhabitants. Monthly mean concentrations of PM₂.₅, nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and black carbon were derived from the Copernicus Atmosphere Monitoring Service reanalysis. Seasonal ARIMA models with exogenous regressors (SARIMAX) were fitted, adjusting for relative humidity and including an indicator for the COVID-19 pandemic period. AIC/BIC and residual diagnostics guided model selection. Results : A total of 4.22 billion beclomethasone doses were dispensed during the study period, with consistent winter peaks. In multivariable SARIMAX models, higher concentrations of hydrophobic black carbon (β = 5.89×10⁹; p < 0.001) and SO₂ (β = 2.19×10⁹; p < 0.001) were positively associated with increased dispensing of beclomethasone canisters. In contrast, NO₂ (β = −2.24×10⁹; p < 0.001) and PM₂.₅ (β = −4.09×10⁸; p < 0.001) showed inverse associations, potentially explained by temporal lag and anticipatory medication renewal. The pandemic period was associated with a marked reduction in dispensing (β = −8.78; 95% CI − 10.81 to − 6.75). Conclusion : Air pollution was significantly associated with patterns of inhaled corticosteroid dispensing in Brazil, indicating that population-level therapeutic needs for asthma control mirror fluctuations in air quality. Air Pollution Environmental Exposure Asthma Public Health Beclomethasone Figures Figure 1 Figure 2 Figure 3 Introduction Asthma is a heterogeneous chronic respiratory disease characterized by airway inflammation, airflow limitation, and mucus hypersecretion. Genetic predisposition and environmental exposures are known to influence disease onset and can serve as triggers for asthma exacerbations ( 1 , 2 ). Air pollution promotes the release of epithelial-derived cytokines that drive T helper 2 (Th2) immune responses, while concurrently inducing oxidative stress and the production of proinflammatory mediators ( 3 ); together with pollution-related disruption of airway epithelial barrier integrity, these mechanisms amplify type 2 inflammation and are strongly implicated in the exacerbation of asthma ( 4 ). The prevalence of asthma is expected to fall between 1% and 29% across several countries. According to the Global Asthma Report, the global prevalence of asthma is around 9.1%–11% among children and adolescents and 6.6% among adults. In Brazil, the prevalence is estimated at 4.6% in children and 12.1% in adults, with a mortality rate of approximately 0.03 deaths/100,000 inhabitants ( 1 , 4 , 5 ). Asthma’s phenotypic expression is characterized by variable respiratory symptoms, including wheezing, dyspnea, chest tightness, and cough, which may fluctuate in intensity over time ( 6 ). Most patients develop symptoms that usually begin in childhood, and many also display additional atopic manifestations, including eczema and allergic rhinitis. Symptoms often follow an intermittent pattern, varying in severity with exposure to aeroallergens, viral infections, or other environmental factors ( 7 , 8 ). Asthma is a clinical diagnosis established by the presence of characteristic variable respiratory symptoms, treatment responsiveness, and supported by objective evidence of variable expiratory airflow limitation ( 5 ). Spirometry is recommended to evaluate airflow obstruction and to assess disease severity ( 9 ). When spirometry is normal, but clinical suspicion remains high, bronchial challenge testing may be considered to demonstrate airway hyperresponsiveness ( 10 ). Additionally, fractional exhaled nitric oxide (FeNO) and blood eosinophils may support the presence of type 2 airway inflammation, particularly in difficult diagnostic cases ( 6 , 7 ). Several environmental features can trigger asthma symptoms and exacerbations. Common triggers include aeroallergens such as house dust mites, pollens, and mold spores; respiratory infections; occupational irritants; exercise; and climatic factors such as atmospheric pollutants, cold, dry air ( 11 , 12 ). Air pollutants—particularly particulate matter (PM)₂.₅, PM 10 , nitrogen dioxide (NO₂), and ozone (O₃)—have been consistently implicated in both acute worsening of symptoms and long-term deterioration of lung function ( 13 – 15 ). Management of asthma follows a stepwise approach focused on controlling airway inflammation and relieving bronchoconstriction. Inhaled corticosteroids (ICS) are the cornerstone of controller therapy, with beclomethasone dipropionate widely used ( 6 ). Short-acting β₂-agonists (SABA) and formoterol, when used with ICS, relieve acute symptoms. Long-acting β₂-agonists (LABA), long-acting muscarinic (LAMA), leukotriene receptor antagonists, and, in severe cases, biologics may be added based on control level ( 6 , 7 , 16 ). Despite substantial evidence linking short-term exposure to major air pollutants—such as particulate matter, nitrogen dioxide, and sulfur dioxide—to asthma exacerbations and emergency visits, few studies have examined whether these environmental variations translate into measurable changes in asthma medication demand at a national scale. In particular, no previous investigation has assessed this association in Brazil, where climatic diversity, uneven pollution monitoring, and broad public access to subsidized inhaled corticosteroids offer a unique natural setting for ecological analyses. This study, therefore, aimed to evaluate the temporal relationship between monthly variations in ambient air pollutant concentrations and national dispensing patterns of beclomethasone canisters within the Brazilian Farmácia Popular program from 2018 to 2024, providing new insights into how environmental factors may influence population-level asthma control and inhaled asthma medication use. Methods This ecological time-series study evaluated whether monthly fluctuations in ambient air pollutants are associated with national demand for beclomethasone, an inhaled corticosteroid widely prescribed for asthma control in Brazil. Because Brazilian patients commonly intensify medication use during symptomatic periods, the volume of beclomethasone dispensed was adopted as a population-level proxy of asthma morbidity. De-identified, aggregated dispensing records for beclomethasone canisters were obtained under the Brazilian Freedom of Information Act for the period from January 2018 to December 2024. Each record corresponded to the number of the inhaler device (200 doses each) provided by the Programa Farmácia Popular in every municipality. Municipal counts were divided by population estimates from the Brazilian Institute of Geography and Statistics (IBGE, in Portuguese Instituto Brasileiro de Geografia e Estatística ) and expressed as doses per 100 inhabitants; monthly national means were then calculated by averaging across all municipalities. The linkage in this process was performed using the municipality’s ID, with unsuccessful links omitted. Atmospheric data were retrieved from the Copernicus Atmosphere Monitoring Service EAC4 re-analysis through its API. For each grid cell spanning 0.75° × 0.75° within Brazilian territory (5.75° N–34° S, 74° W–34° W), we retrieved 15:00 UTC surface concentrations of particulate matter ≤ 2.5 µm, hydrophilic and hydrophobic black carbon, sulfur dioxide, nitrogen dioxide, and carbon monoxide, along with relative humidity at 1000 hPa. Grid cells outside a secondary mesh that delineated Brazilian territory were excluded, and monthly national means were computed for every variable. Both the medication and pollutant series were decomposed into trend, seasonal, and residual components, and autocorrelation function (ACF) and partial autocorrelation function (PACF) plots guided the specification of candidate seasonal autoregressive integrated moving-average models. The augmented Dickey–Fuller test was performed to verify stationarity. Because routine healthcare utilization was profoundly disrupted during the COVID-19 pandemic, a binary indicator captured the interval from 1 May 2020 to 1 September 2021, when lockdowns and resource reallocation markedly altered non-COVID service demand. This covariate was incorporated to disentangle pandemic shocks from pollutant effects. A baseline seasonal ARIMA model with the pandemic dummy alone was first fitted; its order was selected based on ACF and PACF plots and aimed to minimize the Akaike Information Criterion (AIC). Subsequently, the pollutants were introduced into the seasonal ARIMA model simultaneously as exogenous regressors, with relative humidity retained in all specifications to control for meteorological confounding. Model adequacy was judged by changes in AIC greater than two units, corroborated by the Bayesian information criterion, Ljung–Box tests of residual independence, and graphical diagnostics. Two-sided p-values < 0.05 denoted statistical significance. Given the inferential focus and uninterrupted national series, cross-validation was not performed. Analyses were executed in Python (v 3.11) with PySpark, xdarray, cdsapi, pandas, numpy, scipy, statsmodels, scikit-learn, and matplotlib. All scripts and anonymized data are available from the corresponding author upon reasonable request, ensuring reproducibility. As only public, aggregate information devoid of personal identifiers was analyzed, research-ethics committee approval was not required under Brazilian regulations. Results Between 2018 January 1st and 2024 December 31st, 4,216,161,800 doses of beclomethasone were dispensed in the Farmacia Popular program. 2023 was the year with the highest demand during the period, accounting for over 700 million dispensed doses (Table 1 ). This number refers to 3,505,086 bottles dispensed in that year. The Southeast had the highest number of dispensed doses, with over 300 million doses each year. Table 1 Distribution of demand for inhaled beclomethasone across Brazilian regions Region 2018 2019 2020 2021 2022 2023 2024 Midwest 38,115,200 27,028,800 31,291,400 23,308,800 30,854,000 43,986,000 33,054,600 Northeast 59,892,600 64,624,400 70,573,800 68,925,000 79,664,400 95,369,000 109,873,200 North 11,801,000 7,585,200 10,530,400 8,772,800 11,957,400 12,892,200 13,271,800 Southeast 313,977,400 323,447,000 325,718,400 312,783,200 373,380,400 399,428,400 381,404,400 South 120,854,400 125,523,800 120,701,800 118,903,400 137,512,800 149,341,600 159,812,800 Total 544,640,600 548,209,200 558,815,800 532,693,200 633,369,000 701,017,200 697,416,800 Among inhaled beclomethasone products, the 250 µg dose had the highest demand, while the jet-device formulation had the lowest. Throughout the period analyzed, demand for the 200 µg dose rose steadily after 2019. As shown in Fig. 1 , most peaks of demand for beclomethasone products coincide with the start of winter. Dotted vertical red line represents the beginning of winter in the Southern Hemisphere. The blue dotted vertical line represents the first cases of COVID-19 in Brazil. As shown in Fig. 2 , outdoor air pollutant levels usually start to increase a few months before winter begins, reaching their peak a couple of months after winter begins. Notably, the levels of all air pollutants assessed in this study reached the highest levels in the series during the winter of 2024, approximately 4 times those of the previous year. The time series decomposition highlights a strong seasonal component, with peaks around the middle of every year – coinciding with the beginning of winter in Brazil. Also, the trend component shows an increasing trend in this data, abruptly decreased between May 2020 and October 2021 – possibly affected by the COVID-19 pandemic in Brazil (Fig. 3 ). A SARIMAX model was built using an order of [2, 0, 0] (p, q, d) and a seasonal order of [0, 1, 1, ( 12 )] (P, Q, D, s). ADF test indicated the series is stationary (p = 0.02). The model mentioned above had an AIC of 386; adding the binary pandemic exogenous variable further improved the AIC to 365.43. In multivariable SARIMAX models, higher concentrations of hydrophobic black carbon (β = 5.89×10⁹; 95% CI 5.89×10⁹ to 5.89×10⁹; p < 0.001) and SO₂ (β = 2.19×10⁹; 95% CI 2.19×10⁹ to 2.19×10⁹; p < 0.001) were positively associated with increased dispensing of beclomethasone canisters, suggesting a contemporaneous rise in controller medication use in response to worsening air quality. In contrast, NO₂ (β = −2.24×10⁹; 95% CI − 2.24×10⁹ to − 2.24×10⁹; p < 0.001) and PM₂.₅ (β = −4.09×10⁸; 95% CI − 4.09×10⁸ to − 4.09×10⁸; p < 0.001) exhibited inverse associations, likely reflecting temporal displacement between exposure and medication redemption. In addition to the SARIMAX model, enhanced with pandemic controls, all pollutant levels were included as additional exogenous features. This led to an increased (worst) AIC of 367.54, but the delta was found to be threshold-bound to an acceptable delta (2 units). Carbon monoxide and Hydrophilic black carbon were removed from the model, as their inclusion resulted in an unacceptable increase in AIC (369.53). The results show that air pollutants were statistically significant (p < 0.001) in explaining the expected levels of demand for inhaled beclomethasone (Table 2 ). Table 2 Results of the SARIMAX model Variable Coeficient Std error p-value CI95% LB CI95% UB pandemic -8.7751 1.036 0.001 -10.805 -6.745 aermr09* 5.888e + 09 3.86e-05 0.001 5.89e + 09 5.89e + 09 so2* 2.19e + 09 6.99e-06 0.001 2.19e + 09 2.19e + 09 no2* -2.235e + 09 1.33e-05 0.001 -2.24e + 09 -2.24e + 09 pm2p5* -4.09e + 08 0.001 0.001 -4.09e + 08 -4.09e + 08 Relative humidity 0.1803 0.098 0.067 -0.012 0.373 ar.L1 0.7177 0.141 0.000 0.442 0.994 ar.L2 0.1489 0.138 0.282 -0.122 0.420 ma.S.L12 -0.9989 58.201 0.986 -115.070 113.072 sigma2 5.2301 303.972 0.986 -590.544 601.004 * results statistically significant for pollutant; aermr09: Hydrophobic black carbon; CI95%: Confidence interval 95%; LB: Lower bound; NO2: Nitrogen dioxide; pm2p5: Particulate matter 2.5µm; SO2: Sulphur dioxide; Std: Standard; UB: Upper bound. The Ljung-Box test yielded a Probability(Q) of 0.79, indicating that the model residuals behave as white noise. The BIC for the model was 390.31 Discussion The present study revealed a predominantly positive short-term association between air concentrations of particulate hydrophobic black carbon and SO2 and the monthly dispensation of beclomethasone canisters. Nevertheless, NO 2 and PM 2.5 showed paradoxical inverse associations at later lags. Specifically, early poor asthma control following initial increases in these pollutants may prompt patients to refill prescriptions before peak concentrations are reached ( 17 ), resulting in negative coefficients at later lags rather than a true protective effect. After visual inspection of the NO₂ distribution, concentration peaks are observed to occur approximately one to two months after the onset of winter in the Southern Hemisphere and, consequently, about two months after the peak in beclomethasone dispensation. Clinically, this interval is consistent with the expected lag between the initial deterioration in air quality and the period when pollutant concentrations typically peak in the study region. Patients who experience early worsening of respiratory symptoms are therefore likely to redeem prescriptions shortly after the initial rise in pollutant levels, rather than at their peak. As a result, when NO₂ concentrations reach their maximum, a substantial proportion of susceptible individuals may have already renewed their medication. This temporal mismatch can generate an apparent inversion of the curves and negative coefficients at longer lags, particularly under the plausible assumption of intermittent use of inhaled beclomethasone. Similar anticipatory dispensing patterns have been described in community pharmacy datasets from Northern Europe, where the largest increases in asthma medication sales occur shortly after pollutant levels begin to rise, rather than at their peak ( 18 ). From a statistical perspective, negative signs at longer lags can also reflect over-differencing or residual collinearity between closely spaced pollutant lags. We addressed both issues by confirming stationarity after differencing using the augmented Dickey–Fuller test, which assesses whether a time series is free of unit roots and therefore suitable for regression-based time-series modeling (p < 0.05). The resulting pattern—positive immediate effects coupled with adverse late effects—thus appears robust and biologically plausible, mirroring the time course of pollutant-induced eosinophilic airway inflammation, which typically peaks days after exposure and wanes as controller therapy is intensified ( 19 ). Although such increments might be interpreted as a deterioration in model parsimony, information-theoretic guidelines suggest that a ΔAIC of less than 2 indicates virtually equivalent empirical support for competing models. Accordingly, we retained these variables because each displayed a significant Wald statistic (p < 0.05), which tests whether an individual regression coefficient differs significantly from zero given its estimated variance, indicating an independent contribution to the model. These variables also contributed to clinically meaningful effect estimates, even in the absence of a sizable improvement in overall model fit ( 20 , 21 ). Our results have tangible public-health implications. The observed association between dispensation of beclomethasone canisters and ambient pollutant concentrations underscores the need for policies that simultaneously strengthen pharmaceutical assistance and improve air-quality standards. Air pollution—amplified by ongoing climate change—should therefore be recognized not merely as an environmental concern but as a pressing determinant of respiratory health and an overall public health issue. Notably, recent studies highlight that short- and long-term exposure to black carbon and fine particulate matter is associated with asthma exacerbations, emergency visits, and hospitalizations, particularly in vulnerable populations ( 19 ). Acute respiratory infections, particularly viral infections, have been consistently implicated as major triggers of asthma exacerbations in both adults and children, underscoring the multifactorial nature of exacerbation risk ( 14 , 15 ). Emerging evidence suggests that airborne pollutants such as PM₂.₅, PM₁.₀, ultrafine particulate matter (UFPM), and black carbon can interact with viral and bacterial particles, potentially enhancing their adhesion to airway surfaces and thereby amplifying the risk and severity of exacerbations ( 22 ). Several limitations of this study merit consideration. Dispensing records capture medication acquisition, not adherence; we may therefore under- or over-estimate actual usage. Second, pollutant measurements were derived from fixed-site monitors and may not fully reflect individual exposure heterogeneity. Third, potential confounders such as viral epidemics or allergen loads were unavailable, although our residual diagnostics did not indicate substantial unmodelled seasonality. Additionally, while SARIMA-based inference is robust for regularly spaced aggregates, future work should explore mixed-frequency or hierarchical Bayesian formulations to incorporate additional clinical covariates and patient-level effect modifiers. Finally, dispensation registries may not directly reflect uncontrolled asthma or acute exacerbations. Further studies incorporating clinical outcomes, such as hospitalizations or emergency department visits, would therefore be essential to better contextualize these findings. By linking large-scale pharmaceutical and atmospheric datasets, this study provides novel evidence of the association of asthma medication dispensing and the impact of air pollution on respiratory health. Integrated public health policies aimed at improving air quality should be prospectively evaluated for their potential to enhance asthma control in environmentally vulnerable populations. Conclusion This nationwide ecological analysis identified a significant temporal association between air pollutant concentrations and national dispensing of beclomethasone canisters for asthma treatment in Brazil. Increases in hydrophobic black carbon and sulfur dioxide were followed by higher inhaled betamethasone demand. In contrast, nitrogen dioxide and fine particulate matter showed inverse associations at later lags, likely reflecting temporal and behavioral dynamics in prescription renewal. These findings indicate that variations in air quality are mirrored by population-level changes in the use of inhaled corticosteroids, suggesting that pollutant exposure contributes to symptomatic worsening and increased therapeutic needs among individuals with asthma. Declarations Competing Interests The authors declare the following financial interests/personal relationships, which may be considered potential competing interests: Marisa Treglia and Wender Aparecido Oliveira employees at Chiesi Farmacêutica LTDA. Ricardo G. Figueiredo, Isis Morais, Tulio Tadeu Rocha Sarmento, Juan Calderón , Ivan Chérrez-Ojeda and Eddy Oliveira declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This research received no external funding. Author Contribution RGF, MT, and EO designed the study. IM, WAO, and TTRS performed the literature review. RGF, MT, WAO, JC, and ICO wrote the manuscript. All authors have read and approved the final manuscript. Acknowledgement During the preparation of this work, the author(s) used artificial intelligence tools (Grammarly) in order to improve language and readability. The authors have reviewed and edited the final version for accuracy and take full responsibility for the content of the published paper. Data Availability SARIMAX model or other relevant data would be available from the corresponding author upon reasonable request. Ethics declarations This study analyzed publicly available, aggregated ecological data and did not involve individual-level human participants, identifiable personal information, or biological specimens. Therefore, institutional review board approval was not required under local regulations (CNS 466/2012, 510/2016), and informed consent to participate and consent for publication are not required. 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Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviews received at journal 17 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 15 Mar, 2026 Editor assigned by journal 25 Feb, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 24 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Figueiredo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIie2SsUoDQRCG/2ULmzFpJ9zJvcJK4IIQ9VWEg6tMFbCUQGDSJNim9wnyBhsGUklsLQNpLZJCSBcXRMFmL6XgfrDLX+zHPzALJBJ/ErPx4aZwHHZA+ytHse5HMXOgMzpF+U7O0ilK7+nZLD+kn/cm48X2WvpcjM5Xu5iSv22hF1JT/rIadgdSs/Otah5TmF+9dkSJ+b7MBqKPDtSNDsasCMqRuHgvsys5hsGalPYYy7340EJlZsQzfGOLhWJdEVM9vJyuK3baqhpa1O4PDze3fKaLTQhcTGYaVcB3sL8XYeNC+CAe5tD0KJFIJP43n6RVQqlaRk1vAAAAAElFTkSuQmCC","orcid":"","institution":"Universidade Estadual de Feira de Santana (UEFS)","correspondingAuthor":true,"prefix":"","firstName":"Ricardo","middleName":"G.","lastName":"Figueiredo","suffix":""},{"id":607100695,"identity":"b4462930-5f47-467a-80ef-ca08d40fa851","order_by":1,"name":"Isis Morais","email":"","orcid":"","institution":"Universidade Estadual de Feira de Santana (UEFS)","correspondingAuthor":false,"prefix":"","firstName":"Isis","middleName":"","lastName":"Morais","suffix":""},{"id":607100696,"identity":"a269e81c-c4d5-46dc-9fe4-8fef38281d1e","order_by":2,"name":"Marisa Treglia","email":"","orcid":"","institution":"Chiesi Farmacêutica","correspondingAuthor":false,"prefix":"","firstName":"Marisa","middleName":"","lastName":"Treglia","suffix":""},{"id":607100697,"identity":"afdb215d-fd0d-4adc-957d-1c5bda57007e","order_by":3,"name":"Wender Aparecido Oliveira","email":"","orcid":"","institution":"Chiesi Farmacêutica","correspondingAuthor":false,"prefix":"","firstName":"Wender","middleName":"Aparecido","lastName":"Oliveira","suffix":""},{"id":607100698,"identity":"fbe70e77-442e-476a-a0f6-5718126e5bde","order_by":4,"name":"Tulio Tadeu Rocha Sarmento","email":"","orcid":"","institution":"Universidade Federal de Minas Gerais (UFMG)","correspondingAuthor":false,"prefix":"","firstName":"Tulio","middleName":"Tadeu Rocha","lastName":"Sarmento","suffix":""},{"id":607100699,"identity":"f0700d8c-71e5-4269-be5d-7ce05e35e20b","order_by":5,"name":"Juan Calderón","email":"","orcid":"","institution":"Respiralab","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Calderón","suffix":""},{"id":607100700,"identity":"8921d75b-1c49-4b5a-9963-00288f8d46ab","order_by":6,"name":"Ivan Chérrez-Ojeda","email":"","orcid":"","institution":"Respiralab","correspondingAuthor":false,"prefix":"","firstName":"Ivan","middleName":"","lastName":"Chérrez-Ojeda","suffix":""},{"id":607100701,"identity":"987384bb-5092-472d-86fb-d7d9893e7269","order_by":7,"name":"Eddy Oliveira","email":"","orcid":"","institution":"Universidade Estadual de Feira de Santana (UEFS)","correspondingAuthor":false,"prefix":"","firstName":"Eddy","middleName":"","lastName":"Oliveira","suffix":""}],"badges":[],"createdAt":"2026-02-24 16:23:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8959512/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8959512/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105033775,"identity":"ec4f486d-4869-4576-a5e5-149a0b2c78ba","added_by":"auto","created_at":"2026-03-20 07:21:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87028,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIndexed dispensed inhaled beclomethasone canisters per 100 inhabitants.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDotted vertical red line represents the beginning of winter in the Southern Hemisphere. The blue dotted vertical line represents the first cases of COVID-19 in Brazil.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8959512/v1/2e92da3dd9de34d991bb3816.png"},{"id":105562613,"identity":"cc998ce5-ffac-41bf-ae40-5fa059a19c46","added_by":"auto","created_at":"2026-03-27 12:43:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":287541,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMonthly standardized concentrations of outdoor air pollutants in Brazil from 2018 to 2024. \u003c/strong\u003eNO2: Nitrogen dioxide; SO2: Sulphur dioxide.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8959512/v1/6f220f9cf06d934f443111d6.png"},{"id":104841893,"identity":"0e457879-3f38-4c5d-88e6-79958b27771a","added_by":"auto","created_at":"2026-03-17 19:54:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50909,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime-series decomposition reveals a pronounced, recurrent seasonal pattern, with annual peaks around mid-year, corresponding to the onset of winter in Brazil. \u003c/strong\u003eThe long-term trend shows a gradual increase, interrupted by a marked decline between May 2020 and October 2021, likely reflecting disruptions in healthcare utilization and environmental exposures during the COVID-19 pandemic.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8959512/v1/833b29c73824cc9a1d57a2d0.png"},{"id":105571888,"identity":"2bb82133-310a-4ad1-9e68-303db6a39e4a","added_by":"auto","created_at":"2026-03-27 13:24:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1090621,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8959512/v1/057055a9-6f5c-429e-a2c6-3db1244851ab.pdf"}],"financialInterests":"Competing interest reported. The authors declare the following financial interests/personal relationships, which may be considered potential competing interests: Marisa Treglia and Wender Aparecido Oliveira employees at Chiesi Farmacêutica LTDA. Ricardo G. Figueiredo, Isis Morais, Tulio Tadeu Rocha Sarmento, Juan Calderón , Ivan Chérrez-Ojeda and Eddy Oliveira declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","formattedTitle":"Temporal Associations Between Ambient Air Pollutants and Inhaled Beclomethasone Dispensing in Brazil","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAsthma is a heterogeneous chronic respiratory disease characterized by airway inflammation, airflow limitation, and mucus hypersecretion. Genetic predisposition and environmental exposures are known to influence disease onset and can serve as triggers for asthma exacerbations (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Air pollution promotes the release of epithelial-derived cytokines that drive T helper 2 (Th2) immune responses, while concurrently inducing oxidative stress and the production of proinflammatory mediators (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e); together with pollution-related disruption of airway epithelial barrier integrity, these mechanisms amplify type 2 inflammation and are strongly implicated in the exacerbation of asthma (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe prevalence of asthma is expected to fall between 1% and 29% across several countries. According to the Global Asthma Report, the global prevalence of asthma is around 9.1%\u0026ndash;11% among children and adolescents and 6.6% among adults. In Brazil, the prevalence is estimated at 4.6% in children and 12.1% in adults, with a mortality rate of approximately 0.03 deaths/100,000 inhabitants (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAsthma\u0026rsquo;s phenotypic expression is characterized by variable respiratory symptoms, including wheezing, dyspnea, chest tightness, and cough, which may fluctuate in intensity over time (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Most patients develop symptoms that usually begin in childhood, and many also display additional atopic manifestations, including eczema and allergic rhinitis. Symptoms often follow an intermittent pattern, varying in severity with exposure to aeroallergens, viral infections, or other environmental factors (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAsthma is a clinical diagnosis established by the presence of characteristic variable respiratory symptoms, treatment responsiveness, and supported by objective evidence of variable expiratory airflow limitation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Spirometry is recommended to evaluate airflow obstruction and to assess disease severity (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). When spirometry is normal, but clinical suspicion remains high, bronchial challenge testing may be considered to demonstrate airway hyperresponsiveness (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Additionally, fractional exhaled nitric oxide (FeNO) and blood eosinophils may support the presence of type 2 airway inflammation, particularly in difficult diagnostic cases (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral environmental features can trigger asthma symptoms and exacerbations. Common triggers include aeroallergens such as house dust mites, pollens, and mold spores; respiratory infections; occupational irritants; exercise; and climatic factors such as atmospheric pollutants, cold, dry air (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Air pollutants\u0026mdash;particularly particulate matter (PM)₂.₅, PM\u003csub\u003e10\u003c/sub\u003e, nitrogen dioxide (NO₂), and ozone (O₃)\u0026mdash;have been consistently implicated in both acute worsening of symptoms and long-term deterioration of lung function (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eManagement of asthma follows a stepwise approach focused on controlling airway inflammation and relieving bronchoconstriction. Inhaled corticosteroids (ICS) are the cornerstone of controller therapy, with beclomethasone dipropionate widely used (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Short-acting β₂-agonists (SABA) and formoterol, when used with ICS, relieve acute symptoms. Long-acting β₂-agonists (LABA), long-acting muscarinic (LAMA), leukotriene receptor antagonists, and, in severe cases, biologics may be added based on control level (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite substantial evidence linking short-term exposure to major air pollutants\u0026mdash;such as particulate matter, nitrogen dioxide, and sulfur dioxide\u0026mdash;to asthma exacerbations and emergency visits, few studies have examined whether these environmental variations translate into measurable changes in asthma medication demand at a national scale. In particular, no previous investigation has assessed this association in Brazil, where climatic diversity, uneven pollution monitoring, and broad public access to subsidized inhaled corticosteroids offer a unique natural setting for ecological analyses. This study, therefore, aimed to evaluate the temporal relationship between monthly variations in ambient air pollutant concentrations and national dispensing patterns of beclomethasone canisters within the Brazilian \u003cem\u003eFarm\u0026aacute;cia Popular\u003c/em\u003e program from 2018 to 2024, providing new insights into how environmental factors may influence population-level asthma control and inhaled asthma medication use.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis ecological time-series study evaluated whether monthly fluctuations in ambient air pollutants are associated with national demand for beclomethasone, an inhaled corticosteroid widely prescribed for asthma control in Brazil. Because Brazilian patients commonly intensify medication use during symptomatic periods, the volume of beclomethasone dispensed was adopted as a population-level proxy of asthma morbidity.\u003c/p\u003e \u003cp\u003eDe-identified, aggregated dispensing records for beclomethasone canisters were obtained under the Brazilian Freedom of Information Act for the period from January 2018 to December 2024. Each record corresponded to the number of the inhaler device (200 doses each) provided by the \u003cem\u003ePrograma Farm\u0026aacute;cia Popular\u003c/em\u003e in every municipality. Municipal counts were divided by population estimates from the Brazilian Institute of Geography and Statistics (IBGE, in Portuguese \u003cem\u003eInstituto Brasileiro de Geografia e Estat\u0026iacute;stica\u003c/em\u003e) and expressed as doses per 100 inhabitants; monthly national means were then calculated by averaging across all municipalities. The linkage in this process was performed using the municipality\u0026rsquo;s ID, with unsuccessful links omitted.\u003c/p\u003e \u003cp\u003eAtmospheric data were retrieved from the Copernicus Atmosphere Monitoring Service EAC4 re-analysis through its API. For each grid cell spanning 0.75\u0026deg; \u0026times; 0.75\u0026deg; within Brazilian territory (5.75\u0026deg; N\u0026ndash;34\u0026deg; S, 74\u0026deg; W\u0026ndash;34\u0026deg; W), we retrieved 15:00 UTC surface concentrations of particulate matter\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026micro;m, hydrophilic and hydrophobic black carbon, sulfur dioxide, nitrogen dioxide, and carbon monoxide, along with relative humidity at 1000 hPa. Grid cells outside a secondary mesh that delineated Brazilian territory were excluded, and monthly national means were computed for every variable.\u003c/p\u003e \u003cp\u003eBoth the medication and pollutant series were decomposed into trend, seasonal, and residual components, and autocorrelation function (ACF) and partial autocorrelation function (PACF) plots guided the specification of candidate seasonal autoregressive integrated moving-average models. The augmented Dickey\u0026ndash;Fuller test was performed to verify stationarity.\u003c/p\u003e \u003cp\u003eBecause routine healthcare utilization was profoundly disrupted during the COVID-19 pandemic, a binary indicator captured the interval from 1 May 2020 to 1 September 2021, when lockdowns and resource reallocation markedly altered non-COVID service demand. This covariate was incorporated to disentangle pandemic shocks from pollutant effects.\u003c/p\u003e \u003cp\u003eA baseline seasonal ARIMA model with the pandemic dummy alone was first fitted; its order was selected based on ACF and PACF plots and aimed to minimize the Akaike Information Criterion (AIC). Subsequently, the pollutants were introduced into the seasonal ARIMA model simultaneously as exogenous regressors, with relative humidity retained in all specifications to control for meteorological confounding. Model adequacy was judged by changes in AIC greater than two units, corroborated by the Bayesian information criterion, Ljung\u0026ndash;Box tests of residual independence, and graphical diagnostics. Two-sided p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 denoted statistical significance. Given the inferential focus and uninterrupted national series, cross-validation was not performed.\u003c/p\u003e \u003cp\u003eAnalyses were executed in Python (v 3.11) with PySpark, xdarray, cdsapi, pandas, numpy, scipy, statsmodels, scikit-learn, and matplotlib. All scripts and anonymized data are available from the corresponding author upon reasonable request, ensuring reproducibility. As only public, aggregate information devoid of personal identifiers was analyzed, research-ethics committee approval was not required under Brazilian regulations.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBetween 2018 January 1st and 2024 December 31st, 4,216,161,800 doses of beclomethasone were dispensed in the \u003cem\u003eFarmacia Popular\u003c/em\u003e program. 2023 was the year with the highest demand during the period, accounting for over 700\u0026nbsp;million dispensed doses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This number refers to 3,505,086 bottles dispensed in that year. The Southeast had the highest number of dispensed doses, with over 300\u0026nbsp;million doses each year.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of demand for inhaled beclomethasone across Brazilian regions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e 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colname=\"c8\"\u003e \u003cp\u003e33,054,600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59,892,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64,624,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70,573,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68,925,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79,664,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e95,369,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e109,873,200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,801,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,585,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,530,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,772,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,957,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12,892,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13,271,800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoutheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e313,977,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e323,447,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e325,718,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e312,783,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e373,380,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e399,428,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e381,404,400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120,854,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125,523,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120,701,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e118,903,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e137,512,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e149,341,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e159,812,800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e544,640,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e548,209,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e558,815,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e532,693,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e633,369,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e701,017,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e697,416,800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong inhaled beclomethasone products, the 250 \u0026micro;g dose had the highest demand, while the jet-device formulation had the lowest. Throughout the period analyzed, demand for the 200 \u0026micro;g dose rose steadily after 2019. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, most peaks of demand for beclomethasone products coincide with the start of winter.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDotted vertical red line represents the beginning of winter in the Southern Hemisphere. The blue dotted vertical line represents the first cases of COVID-19 in Brazil.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, outdoor air pollutant levels usually start to increase a few months before winter begins, reaching their peak a couple of months after winter begins. Notably, the levels of all air pollutants assessed in this study reached the highest levels in the series during the winter of 2024, approximately 4 times those of the previous year.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe time series decomposition highlights a strong seasonal component, with peaks around the middle of every year \u0026ndash; coinciding with the beginning of winter in Brazil. Also, the trend component shows an increasing trend in this data, abruptly decreased between May 2020 and October 2021 \u0026ndash; possibly affected by the COVID-19 pandemic in Brazil (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA SARIMAX model was built using an order of [2, 0, 0] (p, q, d) and a seasonal order of [0, 1, 1, (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)] (P, Q, D, s). ADF test indicated the series is stationary (p\u0026thinsp;=\u0026thinsp;0.02). The model mentioned above had an AIC of 386; adding the binary pandemic exogenous variable further improved the AIC to 365.43. In multivariable SARIMAX models, higher concentrations of hydrophobic black carbon (β\u0026thinsp;=\u0026thinsp;5.89\u0026times;10⁹; 95% CI 5.89\u0026times;10⁹ to 5.89\u0026times;10⁹; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SO₂ (β\u0026thinsp;=\u0026thinsp;2.19\u0026times;10⁹; 95% CI 2.19\u0026times;10⁹ to 2.19\u0026times;10⁹; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were positively associated with increased dispensing of beclomethasone canisters, suggesting a contemporaneous rise in controller medication use in response to worsening air quality. In contrast, NO₂ (β = \u0026minus;2.24\u0026times;10⁹; 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;2.24\u0026times;10⁹ to \u0026minus;\u0026thinsp;2.24\u0026times;10⁹; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PM₂.₅ (β = \u0026minus;4.09\u0026times;10⁸; 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;4.09\u0026times;10⁸ to \u0026minus;\u0026thinsp;4.09\u0026times;10⁸; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) exhibited inverse associations, likely reflecting temporal displacement between exposure and medication redemption. In addition to the SARIMAX model, enhanced with pandemic controls, all pollutant levels were included as additional exogenous features. This led to an increased (worst) AIC of 367.54, but the delta was found to be threshold-bound to an acceptable delta (2 units). Carbon monoxide and Hydrophilic black carbon were removed from the model, as their inclusion resulted in an unacceptable increase in AIC (369.53). The results show that air pollutants were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in explaining the expected levels of demand for inhaled beclomethasone (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the SARIMAX model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoeficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCI95% LB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI95% UB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epandemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.7751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaermr09*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.888e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.86e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.89e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.89e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eso2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.19e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.99e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.19e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.19e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.235e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.24e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.24e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epm2p5*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.09e\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.09e\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.09e\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative humidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ear.L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ear.L2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ema.S.L12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.9989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-115.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e113.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esigma2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e303.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-590.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e601.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* results statistically significant for pollutant; aermr09: Hydrophobic black carbon; CI95%: Confidence interval 95%; LB: Lower bound; NO2: Nitrogen dioxide; pm2p5: Particulate matter 2.5\u0026micro;m; SO2: Sulphur dioxide; Std: Standard; UB: Upper bound.\u003c/p\u003e \u003cp\u003eThe Ljung-Box test yielded a Probability(Q) of 0.79, indicating that the model residuals behave as white noise. The BIC for the model was 390.31\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study revealed a predominantly positive short-term association between air concentrations of particulate hydrophobic black carbon and SO2 and the monthly dispensation of beclomethasone canisters. Nevertheless, NO\u003csub\u003e2\u003c/sub\u003e and PM 2.5 showed paradoxical inverse associations at later lags. Specifically, early poor asthma control following initial increases in these pollutants may prompt patients to refill prescriptions before peak concentrations are reached (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), resulting in negative coefficients at later lags rather than a true protective effect. After visual inspection of the NO₂ distribution, concentration peaks are observed to occur approximately one to two months after the onset of winter in the Southern Hemisphere and, consequently, about two months after the peak in beclomethasone dispensation. Clinically, this interval is consistent with the expected lag between the initial deterioration in air quality and the period when pollutant concentrations typically peak in the study region.\u003c/p\u003e \u003cp\u003ePatients who experience early worsening of respiratory symptoms are therefore likely to redeem prescriptions shortly after the initial rise in pollutant levels, rather than at their peak. As a result, when NO₂ concentrations reach their maximum, a substantial proportion of susceptible individuals may have already renewed their medication. This temporal mismatch can generate an apparent inversion of the curves and negative coefficients at longer lags, particularly under the plausible assumption of intermittent use of inhaled beclomethasone. Similar anticipatory dispensing patterns have been described in community pharmacy datasets from Northern Europe, where the largest increases in asthma medication sales occur shortly after pollutant levels begin to rise, rather than at their peak (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a statistical perspective, negative signs at longer lags can also reflect over-differencing or residual collinearity between closely spaced pollutant lags. We addressed both issues by confirming stationarity after differencing using the augmented Dickey\u0026ndash;Fuller test, which assesses whether a time series is free of unit roots and therefore suitable for regression-based time-series modeling (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The resulting pattern\u0026mdash;positive immediate effects coupled with adverse late effects\u0026mdash;thus appears robust and biologically plausible, mirroring the time course of pollutant-induced eosinophilic airway inflammation, which typically peaks days after exposure and wanes as controller therapy is intensified (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough such increments might be interpreted as a deterioration in model parsimony, information-theoretic guidelines suggest that a ΔAIC of less than 2 indicates virtually equivalent empirical support for competing models. Accordingly, we retained these variables because each displayed a significant Wald statistic (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which tests whether an individual regression coefficient differs significantly from zero given its estimated variance, indicating an independent contribution to the model. These variables also contributed to clinically meaningful effect estimates, even in the absence of a sizable improvement in overall model fit (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results have tangible public-health implications. The observed association between dispensation of beclomethasone canisters and ambient pollutant concentrations underscores the need for policies that simultaneously strengthen pharmaceutical assistance and improve air-quality standards. Air pollution\u0026mdash;amplified by ongoing climate change\u0026mdash;should therefore be recognized not merely as an environmental concern but as a pressing determinant of respiratory health and an overall public health issue.\u003c/p\u003e \u003cp\u003eNotably, recent studies highlight that short- and long-term exposure to black carbon and fine particulate matter is associated with asthma exacerbations, emergency visits, and hospitalizations, particularly in vulnerable populations (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Acute respiratory infections, particularly viral infections, have been consistently implicated as major triggers of asthma exacerbations in both adults and children, underscoring the multifactorial nature of exacerbation risk (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Emerging evidence suggests that airborne pollutants such as PM₂.₅, PM₁.₀, ultrafine particulate matter (UFPM), and black carbon can interact with viral and bacterial particles, potentially enhancing their adhesion to airway surfaces and thereby amplifying the risk and severity of exacerbations (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral limitations of this study merit consideration. Dispensing records capture medication acquisition, not adherence; we may therefore under- or over-estimate actual usage. Second, pollutant measurements were derived from fixed-site monitors and may not fully reflect individual exposure heterogeneity. Third, potential confounders such as viral epidemics or allergen loads were unavailable, although our residual diagnostics did not indicate substantial unmodelled seasonality. Additionally, while SARIMA-based inference is robust for regularly spaced aggregates, future work should explore mixed-frequency or hierarchical Bayesian formulations to incorporate additional clinical covariates and patient-level effect modifiers. Finally, dispensation registries may not directly reflect uncontrolled asthma or acute exacerbations. Further studies incorporating clinical outcomes, such as hospitalizations or emergency department visits, would therefore be essential to better contextualize these findings.\u003c/p\u003e \u003cp\u003eBy linking large-scale pharmaceutical and atmospheric datasets, this study provides novel evidence of the association of asthma medication dispensing and the impact of air pollution on respiratory health. Integrated public health policies aimed at improving air quality should be prospectively evaluated for their potential to enhance asthma control in environmentally vulnerable populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis nationwide ecological analysis identified a significant temporal association between air pollutant concentrations and national dispensing of beclomethasone canisters for asthma treatment in Brazil. Increases in hydrophobic black carbon and sulfur dioxide were followed by higher inhaled betamethasone demand. In contrast, nitrogen dioxide and fine particulate matter showed inverse associations at later lags, likely reflecting temporal and behavioral dynamics in prescription renewal. These findings indicate that variations in air quality are mirrored by population-level changes in the use of inhaled corticosteroids, suggesting that pollutant exposure contributes to symptomatic worsening and increased therapeutic needs among individuals with asthma.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare the following financial interests/personal relationships, which may be considered potential competing interests: Marisa Treglia and Wender Aparecido Oliveira employees at Chiesi Farmac\u0026ecirc;utica LTDA. Ricardo G. Figueiredo, Isis Morais, Tulio Tadeu Rocha Sarmento, Juan Calder\u0026oacute;n , Ivan Ch\u0026eacute;rrez-Ojeda and Eddy Oliveira declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRGF, MT, and EO designed the study. IM, WAO, and TTRS performed the literature review. RGF, MT, WAO, JC, and ICO wrote the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eDuring the preparation of this work, the author(s) used artificial intelligence tools (Grammarly) in order to improve language and readability. The authors have reviewed and edited the final version for accuracy and take full responsibility for the content of the published paper.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSARIMAX model or other relevant data would be available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch3\u003eEthics declarations\u003c/h3\u003e\n\u003cp\u003eThis study analyzed publicly available, aggregated ecological data and did not involve individual-level human participants, identifiable personal information, or biological specimens. Therefore, institutional review board approval was not required under local regulations (CNS 466/2012, 510/2016), and informed consent to participate and consent for publication are not required.\u003c/p\u003e\n\u003ch3\u003eData availability declaration\u003c/h3\u003e\n\u003cp\u003eSARIMAX model or other relevant data would be available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrum M, Henz J, Boeira M, Soares S, Friedrich F, M\u0026aacute;rcio Pitrez P. Recent increase in asthma mortality in Brazil: a warning sign for the public health system. 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Atmos Pollut Res mar\u0026ccedil;o de. 2024;15(3):102012. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.apr.2023.102012\u003c/span\u003e\u003cspan address=\"10.1016/j.apr.2023.102012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Air Pollution, Environmental Exposure, Asthma, Public Health, Beclomethasone","lastPublishedDoi":"10.21203/rs.3.rs-8959512/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8959512/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eRationale\u003c/strong\u003e: Air pollution is a recognized trigger of asthma exacerbations, but its influence on population-level demand for controller medications remains insufficiently quantified, particularly in middle-income countries. We assessed whether temporal variations in ambient air pollutants are associated with national dispensing of inhaled beclomethasone in Brazil.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This nationwide ecological time-series study analyzed monthly data from January 2018 to December 2024. Aggregated beclomethasone dispensing data were obtained from the Brazilian \u003cem\u003eFarmácia Popular\u003c/em\u003e program and standardized as doses per 100 inhabitants. Monthly mean concentrations of PM₂.₅, nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and black carbon were derived from the Copernicus Atmosphere Monitoring Service reanalysis. Seasonal ARIMA models with exogenous regressors (SARIMAX) were fitted, adjusting for relative humidity and including an indicator for the COVID-19 pandemic period. AIC/BIC and residual diagnostics guided model selection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 4.22\u0026nbsp;billion beclomethasone doses were dispensed during the study period, with consistent winter peaks. In multivariable SARIMAX models, higher concentrations of hydrophobic black carbon (β = 5.89×10⁹; p \u0026lt; 0.001) and SO₂ (β = 2.19×10⁹; p \u0026lt; 0.001) were positively associated with increased dispensing of beclomethasone canisters. In contrast, NO₂ (β = −2.24×10⁹; p \u0026lt; 0.001) and PM₂.₅ (β = −4.09×10⁸; p \u0026lt; 0.001) showed inverse associations, potentially explained by temporal lag and anticipatory medication renewal. The pandemic period was associated with a marked reduction in dispensing (β = −8.78; 95% CI − 10.81 to − 6.75).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Air pollution was significantly associated with patterns of inhaled corticosteroid dispensing in Brazil, indicating that population-level therapeutic needs for asthma control mirror fluctuations in air quality.\u003c/p\u003e","manuscriptTitle":"Temporal Associations Between Ambient Air Pollutants and Inhaled Beclomethasone Dispensing in Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 19:54:39","doi":"10.21203/rs.3.rs-8959512/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-23T20:12:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T10:50:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T20:01:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214873565308121845193613312625649220990","date":"2026-03-16T17:08:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155114814419137876038649429831281560424","date":"2026-03-16T15:37:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65787447245162634659668625565245443961","date":"2026-03-16T13:54:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25975020603970824328926972181009873939","date":"2026-03-16T07:55:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T01:19:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-25T16:24:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-25T13:51:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Respiratory Research","date":"2026-02-24T16:10:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20842f45-15bf-4113-93a5-019a1e8bbd7f","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T20:23:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 19:54:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8959512","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8959512","identity":"rs-8959512","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}