Age-specific climate sensitivity of respiratory hospitalizations in a tropical coastal city: A 20-year machine learning analysis

Age-specific climate sensitivity of respiratory hospitalizations in a tropical coastal city: A 20-year machine learning analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Age-specific climate sensitivity of respiratory hospitalizations in a tropical coastal city: A 20-year machine learning analysis Marcos Paulo dos Santos Pereira, Rafaela Lisboa Costa, Fabrício Daniel dos Santos Silva, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9014481/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Respiratory diseases remain a major public health challenge in tropical coastal cities, where persistent heat-humidity coupling and climate variability create vulnerability patterns. We investigated associations between atmospheric conditions and respiratory disease hospitalizations in Maceió, Brazil, a coastal tropical city, using 20 years of data (2000–2019). Weekly hospitalization rates stratified by age (children 0–4 years, adults 5–59 years, elderly ≥ 60 years) were analyzed against meteorological variables including temperature, humidity, precipitation, atmospheric pressure, and solar radiation at 0-, 1-, and 2-week lags. Random Forest models were applied to forecast weekly respiratory hospitalization rates. Minimum temperature showed a strong inverse correlation with hospitalizations across all age groups (ρ = −0.65, p < 0.001), with effects persisting up to 2 weeks. Children exhibited immediate sensitivity to thermal and precipitation variables, while elderly populations showed delayed responses to barometric pressure and evaporation. The Random Forest model achieved excellent-to-good predictive accuracy (R² = 0.83–0.90 for children and adults; Symmetric Mean Absolute Percentage Error = 13–25% across all groups). Long-term declining trends in children and adults contrasted with stabilization and subsequent increases among elderly populations after 2010, reflecting demographic aging and heightened climate sensitivity. These findings provide a transferable framework for climate-informed respiratory risk assessment and early warning systems in tropical coastal environments, supporting age-sensitive public health planning under ongoing climate change. Respiratory diseases Climate–health interactions Tropical climate Random Forest modeling Lag effects Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Respiratory diseases remain a major global public health concern, contributing substantially to morbidity, mortality, and disability-adjusted life years worldwide. The Global Burden of Disease Study 2019 estimated more than 100 million annual incident cases of lower respiratory infections, resulting in approximately 2.6 million deaths (GBD 2019 Diseases and Injuries Collaborators 2020 ). Although mortality rates have declined in recent decades, the burden remains disproportionately high in low- and middle-income countries, where structural inequalities, environmental exposures, and limited healthcare access exacerbate vulnerability (World Health Organization 2023 ; Landrigan et al. 2018 ). Poor housing conditions, high population density, and exposure to climatic extremes further amplify respiratory morbidity and reinforce health inequities (Ebi et al. 2021 ). Chronic respiratory diseases affect an estimated 545 million individuals globally (Soriano et al. 2020 ), and many are highly sensitive to climatic variability. Hospital admissions and symptom exacerbations have been linked to temperature extremes, elevated humidity, heavy precipitation, and abrupt weather transitions (Li et al. 2022 ; Silva et al. 2014 ; Xu et al. 2020 ). Both heat and cold exposure are associated with increased respiratory hospitalizations and mortality, particularly during extreme events such as heatwaves (Achebak et al. 2023 ; Gasparrini et al. 2015 ). High humidity can impair thermoregulation and mucociliary clearance while enhancing pathogen survival, and intense rainfall promotes mold growth and aeroallergen dispersion, further aggravating respiratory conditions (Duan et al. 2021 ; Wu et al. 2016 ; World Health Organization 2020 ). These climate–health interactions are especially pronounced in tropical coastal cities, where persistent heat-humidity coupling and limited nocturnal cooling create conditions of chronic thermal stress. In environments such as Maceió, northeastern Brazil, sustained relative humidity exceeding 80% and marine aerosol exposure may intensify airway inflammation and respiratory vulnerability (Field et al. 2021 ; Edwards et al. 2021 ). Rising wet-bulb temperatures, often exceeding 29°C in urban areas, represent an additional physiological stressor, particularly for children and older adults (Raymond et al. 2020 ; Mora et al. 2017 ). Despite these risks, relatively few climate–health modeling studies focus on tropical coastal settings, leaving important gaps in understanding continuous exposure to compound heat and humidity (Hajat et al. 2023 ). Addressing these gaps requires locally contextualized analyses that integrate long-term meteorological variability with demographic and health data. In this study, we employed a retrospective ecological time-series design to examine associations between atmospheric conditions and respiratory disease hospitalizations in Maceió over a 20-year period (2000–2019). Using a Random Forest framework, we developed predictive models to forecast weekly hospitalization rates while evaluating age-specific vulnerability patterns. This approach provides a transferable basis for climate-informed respiratory risk assessment and early warning systems in tropical coastal environments. Materials and Methods Study area and design This retrospective ecological time-series study was conducted in Maceió (09°39′S, 35°44′W), capital of Alagoas State, northeastern Brazil. The city is characterized by a tropical coastal climate with persistently high temperatures (annual mean ~ 25.5°C), elevated relative humidity (~ 80%), and a pronounced rainy season from March to May. The study period spanned from 1 January 2000 to 31 December 2019, enabling assessment of short-term meteorological effects and long-term climate–health interactions under evolving demographic conditions. Health, population, and meteorological data Hospitalization data were obtained from the Hospital Information System of the Brazilian Unified Health System (SIH/SUS), accessed via DATASUS. Records were restricted to respiratory diseases (ICD-10 codes J00–J99) and aggregated by date of admission, municipality of residence, and age group. Three age categories were defined: children (0–4 years), adults (5–59 years), and elderly (≥ 60 years), allowing evaluation of age-specific vulnerability patterns, consistent with evidence of differential climatic sensitivity across life stages (Gasparrini et al. 2015 ). Annual population estimates were retrieved from the Brazilian Institute of Geography and Statistics (IBGE), and age-specific weekly denominators were derived using linear interpolation between census years. Weekly hospitalization rates per 100,000 inhabitants were calculated to ensure temporal and demographic comparability. Daily meteorological data for the same period were obtained from the National Institute of Meteorology (INMET), including minimum, mean, and maximum air temperature (°C), relative humidity (%), atmospheric pressure (hPa), wind speed (m s⁻¹), solar radiation (h), evaporation (mm), and precipitation (mm). Quality control procedures included removal of implausible values and temporal alignment with hospitalization records. Missing data were treated using linear interpolation for gaps ≤ 3 days and multiple imputation for longer gaps, following established approaches for environmental time series (Little and Rubin 2019 ). Daily observations were aggregated into weekly means (temperature, humidity, pressure, wind speed, radiation) or weekly totals (precipitation, evaporation) to match the temporal resolution of health outcomes. To account for delayed physiological and epidemiological responses, meteorological variables were evaluated at three lag structures: concurrent week (lag 0), 1-week delay (lag 1), and 2-week delay (lag 2). This approach captures delayed climatic effects and typical incubation periods associated with respiratory infections (Xu et al. 2013 ; Hajat et al. 2024 ). Statistical analysis and predictive modeling Spearman rank-order correlation coefficients (ρ) were computed between weekly meteorological variables and age-stratified hospitalization rates at each lag. To control for multiple testing, Bonferroni correction was applied (α = 0.005). Predictive modeling was conducted using Random Forest regression to estimate weekly respiratory hospitalization rates for each age group. Random Forest was selected due to its capacity to model nonlinear exposure–response relationships, handle multicollinearity among predictors, and capture complex meteorological–temporal interactions without parametric assumptions. Data were partitioned using a time-aware strategy preserving temporal structure: 2000–2018 for model development (training and testing subsets) and 2019 as an independent validation year. Hyperparameters were optimized through grid search with 5-fold cross-validation. Model performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and symmetric mean absolute percentage error (SMAPE), selected for its robustness and interpretability in forecasting applications (Hyndman and Koehler 2006 ). All analyses were performed in Python 3.8 using Pandas, NumPy, SciPy, and Scikit-learn. Results Meteorological conditions and temporal hospitalization patterns Meteorological conditions in Maceió during 2000–2019 were characterized by persistently high temperatures (mean maximum: 29.9°C) and elevated relative humidity (mean: 80.1%; 75th percentile: 84%), with minimal diurnal thermal variation (minimum temperature median: 21.9°C) (Table 1 ). Precipitation exhibited marked variability (maximum: 187.8 mm), whereas atmospheric pressure remained relatively stable (mean: 1004.9 hPa). Table 1 Descriptive statistics of meteorological variables in Maceió, Brazil (2000–2019). Data include mean, minimum (Min), maximum (Max), median, and 25th/75th percentiles (P25/P75) for temperature, humidity, wind speed, solar radiation, evaporation, precipitation, and atmospheric pressure. Variable Mean Min. Max. Median P25 P75 Min. Temperature (°C) 21.58 15 26 21.9 20.4 22.9 Max. Temperature (°C) 29.91 23 36.8 30 28.5 31.4 Mean Temperature (°C) 25.54 20.1 30.1 25.6 24.5 26.6 Mean Humidity (%) 80.08 57.5 99 79.8 75.8 84 Min Humidity (%) 69.79 45 97 69 64 75 Wind Speed (m s − 1 ) 2.71 0 7.8 2.6 2.1 3.3 Radiation (h) 6.74 0 12 7.4 4.6 9.3 Evaporation (mm) 2.84 0 13 2.8 2.3 3.2 Precipitation (mm) 5.23 0 187.8 0.2 0 5.3 Pressure (hPa) 1004.94 998 1012.6 1004.7 1003.3 1006.6 The 20-year time series revealed a significant declining trend in respiratory hospitalizations in the total population (− 1.24 hospitalizations/day/year; 95% CI: −1.42 to − 1.03), corresponding to a cumulative reduction of approximately 24.8 hospitalizations/day (Fig. 1 a). Structural declines in 2006 and 2012 coincided with expansion of the Family Health Strategy and pneumococcal vaccination programs (Domingues et al. 2020 ; Macinko and Harris 2015 ). Age-stratified analyses showed heterogeneous trajectories. Children exhibited the strongest decline (− 0.92/day/year; 95% CI: −1.03 to − 0.79) and the highest seasonal amplitude (Fig. 1 b), adults demonstrated a moderate reduction (− 0.38/day/year; 95% CI: −0.44 to − 0.31) (Fig. 1 c), and the elderly showed no significant overall trend (+ 0.04/day/year; 95% CI: −0.02 to + 0.09), with a downward phase (2000–2009) followed by an upward trajectory (2010–2019), consistent with demographic aging and increased climate sensitivity (Miranda et al. 2016 ; Silva et al. 2020 ) (Fig. 1 d). Pronounced seasonality was observed across all groups (Fig. 2 ), with a unimodal peak during March–May (rainy season) in the total population and children. Seasonal variability increased by 23% in the total population and 18% in children, based on interquartile range (IQR) estimates, a robust dispersion metric (Wilcox 2017 ). Adults displayed more distributed peaks (March, August, January), whereas the elderly showed delayed maxima in August–September. Meteorological correlates and lag structure Spearman analyses demonstrated consistent age-specific and lag-dependent associations between meteorological variables and hospitalization rates (Fig. 3 ). At lag 0, minimum temperature exhibited the strongest inverse correlation in the total population (ρ = −0.654, p < 0.001), followed by mean temperature and solar radiation, while precipitation showed weak positive associations. Children mirrored this temperature sensitivity (ρ = −0.602), with stronger precipitation effects than adults. Adults showed greater sensitivity to minimum humidity, whereas the elderly exhibited distinct associations with mean humidity and atmospheric pressure. At lag 1, correlation magnitudes remained comparable, with peak atmospheric pressure effects in the elderly (ρ = +0.215, p < 0.001) and pronounced inverse associations with evaporation. At lag 2, correlations weakened but remained significant; minimum temperature continued to show strong inverse associations across groups, and pressure and evaporation effects persisted in the elderly, suggesting cumulative environmental stress. Overall, the results indicate heterogeneous temporal responses to meteorological exposures, with minimum temperature emerging as the dominant predictor across age groups and pressure-related variables showing particular relevance in older adults. Random Forest performance and validation Random Forest models demonstrated strong predictive capacity (Table 2 ; Fig. 4 ). For the total population, the highest performance occurred at lag 2 (R² = 0.90; RMSE = 2.49), indicating delayed meteorological effects. Children showed consistent performance across lags (R² = 0.82–0.83), while adults achieved optimal predictions at lag 2 (R² = 0.83; MAE = 0.95). Performance in the elderly was moderate (R² = 0.45–0.51), likely reflecting additional clinical and behavioral determinants not captured by meteorological predictors. Independent validation using 2019 data confirmed robust predictive accuracy (Fig. 5 ). Forecast skill, assessed using SMAPE (Hyndman and Koehler 2006 ), indicated excellent performance (< 20%) for the total population across all lags, with progressive improvement at lag 2 (13.46%). Children and adults showed good-to-excellent performance, particularly at lag 2, supporting delayed-response hypotheses. The elderly maintained good predictive skill (~ 21–22%), despite greater complexity. All SMAPE values were within excellent-to-good ranges (< 25%), confirming the stability and generalizability of the age-stratified, lag-structured forecasting framework for respiratory disease surveillance in tropical coastal environments. Table 2 Random Forest model performance metrics (training/testing: 2000–2018). Group Lag MAE RMSE R² Total Population 0 1.89 2.74 0.88 Total Population 1 1.88 2.71 0.87 Total Population 2 1.87 2.49 0.90 Children 0 15.98 22.98 0.82 Children 1 15.61 22.08 0.82 Children 2 16.34 22.05 0.83 Adults 0 0.89 1.22 0.81 Adults 1 1.01 1.41 0.77 Adults 2 0.95 1.22 0.83 Elderly 0 4.52 5.91 0.51 Elderly 1 4.16 5.34 0.45 Elderly 2 4.15 5.41 0.51 Discussion Respiratory disease hospitalizations in Maceió were systematically modulated by local atmospheric conditions, with age-specific and lag-dependent responses. The long-term decline observed in the total population, children, and adults is consistent with national trends in Brazil (Victora et al. 2011 ; Barreto et al. 2011 ) and likely reflects the expansion of the Family Health Strategy and the introduction of pneumococcal conjugate vaccines (Domingues et al. 2020 ; Macinko and Harris 2015 ). These structural public health interventions appear to have reduced baseline respiratory morbidity, even within climate-sensitive environments. In contrast, the elderly exhibited stabilization followed by increasing hospitalization rates after 2010, paralleling Brazil’s rapid demographic aging (Miranda et al. 2016 ). Age-related physiological frailty and increased thermosensitivity (Silva et al. 2020 ), combined with the growing burden of chronic conditions (Guimarães et al. 2021 ), likely contribute to this divergence. Similar patterns of elevated climate vulnerability among older adults have been documented in European settings (Åström et al. 2016 ), underscoring that reductions in overall mortality do not necessarily translate into reduced morbidity among aging populations exposed to chronic environmental stress. Seasonally, Maceió differs from temperate regions, exhibiting a dominant peak during the rainy season (March–May) rather than winter. This pattern aligns with tropical pathogen ecology, where humidity and rainfall enhance viral circulation and indoor crowding (Tamerius et al. 2011 ; Chowdhury et al. 2018 ), and coincides with regional precipitation dynamics (Pereira et al. 2024 ; Quesado et al. 2023 ). Children showed the highest seasonal amplitude, consistent with pediatric susceptibility to humidity-driven viral transmission (Paynter et al. 2010 ; Nenna et al. 2017 ), whereas delayed peaks among the elderly may reflect influenza B circulation patterns (Alonso et al. 2015 ) and behavioral differences in healthcare-seeking (Chipato and Ranganathan 2022 ; Sun and Smith 2017 ). Meteorological drivers and physiological pathways Minimum temperature emerged as the dominant meteorological correlate across all age groups, with strong inverse associations persisting up to 2-week lags. Even modest nocturnal cooling within tropical thermal ranges may influence airway reactivity, immune function, and viral stability, as observed in other low-latitude cities (Gasparrini et al. 2015 ; Zhao et al. 2019 ). These findings highlight that in tropical settings, relative thermal variability rather than extreme cold may be sufficient to trigger respiratory exacerbations. Precipitation and humidity demonstrated consistent positive associations, particularly among children and the elderly, supporting mechanisms involving post-rain indoor dampness, fungal proliferation, and allergen exposure (Mendell et al. 2011 ; Harley et al. 2009 ). Persistent associations between atmospheric pressure, evaporation, and elderly hospitalizations suggest heightened sensitivity to barometric and moisture-related stress, likely reflecting reduced cardiopulmonary reserve and impaired thermoregulation in advanced age. Collectively, these results indicate that compound climatic stressors, rather than temperature alone, shape respiratory morbidity in tropical coastal environments. Predictive modeling and climate change implications The Random Forest framework demonstrated robust predictive skill (R² > 0.80 for total population, children, and adults; SMAPE < 25%), supporting the utility of machine learning approaches for modeling nonlinear climate–health interactions (Xu et al. 2024 ). Improved performance at longer lags reinforces hypotheses regarding delayed physiological responses, incubation periods, and healthcare-seeking behavior (Hajat et al. 2024 ; Xu et al. 2013 ). The more moderate performance in the elderly (R² = 0.45–0.51) suggests that meteorological predictors alone may be insufficient in this demographic, consistent with evidence that geriatric outcomes are shaped by complex clinical and social determinants (Schinasi et al. 2018 ). Maceió’s climatic regime, defined by sustained high temperatures, persistently elevated humidity, and frequent heavy rainfall, reflects conditions of continuous thermal load rather than isolated extreme events. Increasing evidence suggests that prolonged heat-humidity exposure can generate cumulative respiratory stress in tropical coastal populations (Hajat et al. 2023 ). Despite this, fewer than 15% of climate–health modeling studies explicitly examine such environments (Hajat et al. 2023 ), highlighting a critical gap in the literature. Our findings therefore reinforce the need for locally calibrated risk models tailored to compound tropical stressors, rather than reliance on extrapolations from temperate regions. In coastal settings, marine aerosol interactions may further influence airway inflammation and respiratory vulnerability (Field et al. 2021 ; Edwards et al. 2021 ). Public health implications and study limitations The identified age-specific sensitivities and lag structures support integration of meteorological surveillance into early warning systems for respiratory diseases in tropical coastal cities. Forecast-based alerts 1–2 weeks in advance, hospital surge planning, and anticipatory vaccination or risk communication strategies could enhance climate-resilient health planning. This study has limitations inherent to ecological designs, precluding individual-level causal inference. Hospitalization data reflect severe cases and may underestimate total community morbidity. Additionally, air pollution was not included due to data constraints, despite well-documented interactions between atmospheric conditions and air quality (Silva et al. 2014 ). Future research should incorporate pollutant data, indoor environmental conditions, and clinical covariates to refine predictive accuracy, particularly among elderly populations. Conclusions This 20-year analysis demonstrates that respiratory disease hospitalizations in Maceió are systematically shaped by local atmospheric conditions through age-specific and lag-dependent pathways. Minimum temperature was the dominant meteorological correlate across all groups, with persistent inverse associations up to two weeks post-exposure, while precipitation, humidity, and barometric pressure exerted additional age-differentiated effects, particularly among children and older adults. The Random Forest framework showed strong predictive performance (SMAPE: 13–25%; R² > 0.80 for total population, children, and adults), with optimal forecasts at 2-week lags for the general and adult populations, supporting delayed-response mechanisms. More moderate performance among the elderly (R² = 0.45–0.51) indicates that meteorological variables alone may be insufficient to fully capture geriatric vulnerability, underscoring the importance of integrating clinical and socioeconomic determinants in future models. Long-term declines in hospitalizations among children and adults likely reflect the impact of vaccination programs and primary healthcare expansion, whereas stabilization followed by increasing trends in older adults highlight the interaction between demographic aging and climate sensitivity. These divergent trajectories emphasize the need for age-stratified climate–health surveillance in rapidly aging tropical populations. By addressing the underrepresentation of tropical coastal cities in climate–health research, this study provides a transferable, meteorologically informed forecasting framework for early warning systems, hospital surge planning, and targeted prevention. As climate change intensifies compound heat-humidity stress in low-latitude regions, locally calibrated, age-sensitive predictive models will be essential for climate-resilient public health planning and adaptive healthcare systems. Declarations Acknowledgements The authors would like to thank the National Council for Scientific and Technological Development (CNPq) (grant no. 445363/2023-1) and the Department of Science and Technology (DECIT) of the Brazilian Ministry of Health for the financial support of this research. Funding The Article Processing Charge (APC) for the publication of this research was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (ROR identifier: 00x0ma614). Author Contributions M.P.S.P.: Conceptualization; Methodology; Formal analysis; Data curation; Writing – original draft. R.L.C., J.S.R. and J.O.A.A.: Data curation (meteorological data); Methodology. G.L.M. and F.D.S.S.: Methodology; Investigation; Supervision. H.B.G.: Supervision; Project administration. H.B.G. and D.F.S.: Literature review; Statistical methodology. E.M.M.V. and B.C.B.: Software development. F.R.C.S.P., R.J.L., and F.M.R.S.J.: Writing – review & editing. M.F.A.: Conceptualization; Supervision; Project administration; Funding acquisition. All authors read and approved the final manuscript. Corresponding author Correspondence to Fabrício Daniel dos Santos Silva Competing Interests The authors declare that they have no conflict of interest. Data Availability The datasets analyzed during the current study are publicly available from DATASUS (Brazilian Ministry of Health) and INMET meteorological databases. Ethics approval This study used secondary, publicly available, anonymized data from DATASUS and meteorological databases to the INMET. Ethical approval was not required according to Brazilian regulations. References Achebak H, Devolder D, García-Aymerich J, Rey G, Chen Z, Méndez-Turrubiates RF, et al (2023) Heat-related mortality in Europe during the summer of 2022. Nat Med 29:1857–1866. https://doi.org/10.1038/s41591-023-02419-z Alonso WJ, Guillebaud J, Viboud C, Gunther S, et al (2015) Seasonality of influenza in Brazil: a traveling wave from the Amazon to the subtropics. Am J Epidemiol 181:944–951. https://doi.org/10.1093/aje/kwu467 Åström C, Orru H, Rocklöv J, Strandberg G, Ebi KL, Forsberg B (2016) Heat-related respiratory hospital admissions in Europe in a changing climate: a health impact assessment. BMJ Open 6:e009684. https://doi.org/10.1136/bmjopen-2015-009684 Barreto ML, Teixeira MG, Bastos FI, Ximenes RAA, Barata RB, Rodrigues LC (2011) Successes and failures in the control of infectious diseases in Brazil: social and environmental context, policies, interventions, and research needs. Lancet 377:1877–1889. https://doi.org/10.1016/S0140-6736(11)60202-X Chipato P, Ranganathan M (2022) Climate, seasonality and health-seeking behaviour for fever in adults in southern Malawi: a qualitative study. Glob Public Health 17:3081–3096. https://doi.org/10.1080/17441692.2021.2015616 Chowdhury FR, Ibrahim QSU, Bari MS, Alam MMJ, Dunachie SJ, Rodriguez-Morales AJ, et al (2018) The association between temperature, rainfall and humidity with common climate-sensitive infectious diseases in Bangladesh. PLoS One 13:e0199579. https://doi.org/10.1371/journal.pone.0199579 Domingues CMAS, Maranhão AGK, Teixeira AMS, Fantinato FFS (2020) The Brazilian National Immunization Program: 46 years of achievements and challenges. Cad Saude Publica 36:e00103920. https://doi.org/10.1590/0102-311X00103920 Duan Y, Liao Y, Li H, Yan S, Zhao Z, Yu S, et al (2021) Effect of changes in season and temperature on mortality associated with air pollution in Seoul, Korea. Sci Total Environ 761:143226. https://doi.org/10.1016/j.scitotenv.2020.143226 Ebi KL, Vanos J, Baldwin JW, Bell JE, Hondula DM, Errett NA, et al (2021) Extreme weather and climate change: population health and health system implications. Annu Rev Public Health 42:293–315. https://doi.org/10.1146/annurev-publhealth-012420-105026 Edwards DA, Ausiello D, Salzman J, Devlin T, Langer R, Beddingfield BJ, et al (2021) Exhaled aerosol increases with COVID-19 infection, age, and obesity. Proc Natl Acad Sci U S A 118:e2021830118. https://doi.org/10.1073/pnas.2021830118 Field RD, Moelis N, Salzman J, Bax A, Ausiello D, Woodward SM, et al (2021) Inhaled water and salt suppress respiratory droplet generation and COVID-19 incidence and death on US coastlines. Mol Front J 5:17–29. https://doi.org/10.1142/S2529732521400058 Gasparrini A, Guo Y, Hashizume M, Lavigne E, Zanobetti A, Schwartz J, et al (2015) Mortality risk attributable to high and low ambient temperature: a multicountry observational study. Lancet 386:369–375. https://doi.org/10.1016/S0140-6736(14)62114-0 GBD 2019 Diseases and Injuries Collaborators (2020) Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019. Lancet 396:1204–1222. https://doi.org/10.1016/S0140-6736(20)30925-9 Guimarães RM, França EB, Ishitani LH, Marinho F, Vasconcelos AMN (2021) The burden of disease in the elderly population of Brazil: an analysis of mortality and disability. Rev Bras Epidemiol 24:e210004. https://doi.org/10.1590/1980-549720210004.supl.2 Hajat S, Proestos Y, Araya-Lopez L, Barrera-Gómez J, Chersich MF, Luchters S, et al (2024) Health effects of climate change: an overview of systematic reviews. Lancet Planet Health 8:e2–e12. https://doi.org/10.1016/S2542-5196(23)00223-6 Hajat S, Quijal-Zamorano M, Petkova EP, Gasparrini A, Schneider R, Vicedo-Cabrera AM, et al (2023) A comparative analysis of the temperature-mortality risks using different weather datasets across heterogeneous regions. Geohealth 7:e2022GH000776. https://doi.org/10.1029/2022GH000776 Harley KG, Macher JM, Lipsett M, et al (2009) Fungi and pollen exposure in the first months of life and risk of early childhood wheezing. Thorax 64:353–358. https://doi.org/10.1136/thx.2007.090241 Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22:679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001 Landrigan PJ, Fuller R, Acosta NJR, Adeyi O, Arnold R, Baldé AB, et al (2018) The Lancet Commission on pollution and health. Lancet 391:462–512. https://doi.org/10.1016/S0140-6736(17)32345-0 Li T, Zhang Y, Wang J, Xu D, Yin Z, Chen H, et al (2022) All-cause and cause-specific mortality in relation to extreme and non-extreme temperatures. Environ Int 169:107537. https://doi.org/10.1016/j.envint.2022.107537 Li T, Horton RM, Bader DA, Zhou M, Liang S, et al (2018) Ambient temperature and risk of COPD hospitalizations. Environ Int 121:140–146. https://doi.org/10.1016/j.envint.2018.08.064 Little RJA, Rubin DB (2019) Statistical analysis with missing data, 3rd edn. Wiley, Hoboken Macinko J, Harris MJ (2015) Brazil's family health strategy—delivering community-based primary care in a universal health system. N Engl J Med 372:2177–2181. https://doi.org/10.1056/NEJMp1501140 Mendell MJ, Mirer AG, Cheung K, Tong M, Douwes J (2011) Respiratory and allergic health effects of dampness, mold, and dampness-related agents. Environ Health Perspect 119:748–756. https://doi.org/10.1289/ehp.1002410 Miranda GMD, Mendes ACG, Silva ALAD (2016) Population aging in Brazil: current and future social challenges and consequences. Rev Bras Geriatr Gerontol 19:507–519. https://doi.org/10.1590/1809-98232016019.150140 Mora C, Dousset B, Caldwell IR, Powell FE, Geronimo RC, Bielecki CR, et al (2017) Global risk of deadly heat. Nat Clim Chang 7:501–506. https://doi.org/10.1038/nclimate3322 Nenna R, Evangelisti M, Frassanito A, Scagnolari C, Pierangeli A, Antonelli G, et al (2017) Respiratory syncytial virus bronchiolitis, weather conditions and air pollution in an Italian urban area. Environ Res 158:188–193. https://doi.org/10.1016/j.envres.2017.06.014 Paynter S (2015) Humidity and respiratory virus transmission in tropical and temperate settings. Epidemiol Infect 143:1110–1118. https://doi.org/10.1017/S0950268814002702 Paynter S, Ware RS, Weinstein P, Williams G, Sly PD (2010) Childhood pneumonia: a neglected, climate-sensitive disease? Lancet 376:1804–1805. https://doi.org/10.1016/S0140-6736(10)61040-7 Pereira MPS, Couto F, Schumacher V, Silva FDS, Gomes HB, Costa RL, et al (2024) Rainy season migration across the northeast coast of Brazil related to sea surface temperature patterns. Atmosphere 15:713. https://doi.org/10.3390/atmos15060713 Quesado EML, Souza TMO, Venancio LPR (2023) Effects of climate variability on respiratory diseases in the Western Region of Bahia, Brazil. Public Health 222:1–6. https://doi.org/10.1016/j.puhe.2023.06.030 Raymond C, Matthews T, Horton RM (2020) The emergence of heat and humidity too severe for human tolerance. Sci Adv 6:eaaw1838. https://doi.org/10.1126/sciadv.aaw1838 Reis JSd, Costa RL, Silva FDdS, de Souza EDF, Cortes TR, Coelho RH, et al (2025) Predicting asthma hospitalizations from climate and air pollution data. Climate 13:23. https://doi.org/10.3390/cli13020023 Requia WJ, Damasceno da Silva RM, Hoinaski L, Amini H (2024) Thermal comfort conditions and mortality in Brazil. Int J Environ Res Public Health 21:1248. https://doi.org/10.3390/ijerph21091248 Schinasi LH, Benmarhnia T, De Roos AJ (2018) Temperature, precipitation, and dementia hospitalizations in the United States, 2005–2015. Environ Epidemiol 2:e028. https://doi.org/10.1097/EE9.0000000000000028 Silva DL, et al (2021) Seasonality of respiratory viruses in a region with a tropical climate. J Pediatr (Rio J) 97:513–520. https://doi.org/10.1016/j.jped.2020.11.005 Silva DR, Viana VP, Müller AM, Livi FP, Dalcin PTR (2014) Respiratory viral infections and effects of meteorological parameters and air pollution in adults with respiratory symptoms. Influenza Other Respir Viruses 8:42–52. https://doi.org/10.1111/irv.12158 Silva FA, Mambrini JVM, Malta DC, Lima-Costa MF (2020) Prevalence of frailty in Brazilian older adults: a systematic review and meta-analysis. J Aging Health 32:1130–1144. https://doi.org/10.1177/0898264319894556 Soriano JB, Kendrick PJ, Paulson KR, Gupta V, Abrams EM, Adedoyin RA, et al (2020) Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017. Lancet Respir Med 8:585–596. https://doi.org/10.1016/S2213-2600(20)30105-3 Sun JK, Smith J (2017) Self-perceptions of aging and perceived barriers to care: reasons for health care delay. Gerontologist 57:S216–S226. https://doi.org/10.1093/geront/gnx014 Tamerius J, Nelson MI, Zhou SZ, Viboud C, Miller MA, Alonso WJ (2011) Global influenza seasonality: reconciling patterns across temperate and tropical regions. Environ Health Perspect 119:439–445. https://doi.org/10.1289/ehp.1002383 Victora CG, Aquino EML, do Carmo Leal M, Monteiro CA, Barros FC, Szwarcwald CL (2011) Maternal and child health in Brazil: progress and challenges. Lancet 377:1863–1876. https://doi.org/10.1016/S0140-6736(11)60138-4 Wilcox RR (2017) Introduction to robust estimation and hypothesis testing, 4th edn. Academic Press, London. World Health Organization (2020) WHO global strategy on health, environment and climate change. WHO, Geneva. World Health Organization (2023) Operational framework for building climate resilient and low carbon health systems. WHO, Geneva. Wu X, Lu Y, Zhou S, Chen L, Xu B (2016) Impact of climate change on human infectious diseases. Environ Int 86:14–23. https://doi.org/10.1016/j.envint.2015.09.007 Xu R, Yu P, Abramson MJ, Li S, Guo Y (2024) Artificial intelligence for climate health readiness. Lancet Planet Health 8:e271–e281. https://doi.org/10.1016/S2542-5196(24)00019-2 Xu Z, Etzel RA, Su H, Huang C, Guo Y, Tong S (2013) Impact of ambient temperature on children's health: a systematic review. Environ Health Perspect 121:785–790. https://doi.org/10.1289/ehp.1204912 Xu Z, Tong S, Cheng J, Zhang Y, Wang N, Zhang Y, et al (2020) Heatwaves, hospital admissions for respiratory diseases, and disease-specific mortality. BMJ Open 10:e038137. https://doi.org/10.1136/bmjopen-2020-038137 Zhao Q, Li S, Coelho MSZS, et al (2019) The association between heatwaves and risk of hospitalization in Brazil. PLoS Med 16:e1002753. https://doi.org/10.1371/journal.pmed.1002753 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 Mar, 2026 Reviewers invited by journal 19 Mar, 2026 Editor assigned by journal 05 Mar, 2026 First submitted to journal 04 Mar, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9014481","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609076872,"identity":"cabd5ab1-25ea-4883-9c1b-a9d425274cbd","order_by":0,"name":"Marcos Paulo dos Santos Pereira","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Marcos","middleName":"Paulo dos Santos","lastName":"Pereira","suffix":""},{"id":609076873,"identity":"0ca4154e-fc9b-4579-99e7-294b8807659d","order_by":1,"name":"Rafaela Lisboa Costa","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rafaela","middleName":"Lisboa","lastName":"Costa","suffix":""},{"id":609076874,"identity":"0372c489-6924-4be9-b0b3-86130a7784d1","order_by":2,"name":"Fabrício Daniel dos Santos Silva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie2QMUvEMBiGv1JIltiu39T+hZaCusj9lR6Bu6UVXI4OR+mUm07X/gwn50qgXaq/4UDQRSQu4uBgetwdCrm6OuQh5COBh/dNACyWfwitGEAKeLgIyG6CB8yosOan0gAkWnEqPYGMKQe0Mq3+VOhDqzbL8wB8+fykinJ+TeXLxhFRSOCkVSaFXfI6bTEBnJ1FTS9zwWZxpZVYgMdrgzKBLNFlUC84xXvR5AJhUL4cASwxFvNfd4rffWilnBOk70PK5KiC+xTIhhQ3Jci2xabHlTcOw1sIZgvsexkLll3V6WPEhetxc7FcOp/LMvD97g6LogzDVXer1CK6uKFrafzlPeTXKR02d1SwWCwWywjfGihVxqKAUEQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-3185-6413","institution":"Universidade Federal de Alagoas","correspondingAuthor":true,"prefix":"","firstName":"Fabrício","middleName":"Daniel dos Santos","lastName":"Silva","suffix":""},{"id":609076875,"identity":"8ffc8d6b-34dc-4b6e-8757-405008c5ef97","order_by":3,"name":"Glauber Lopes Mariano","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Glauber","middleName":"Lopes","lastName":"Mariano","suffix":""},{"id":609076876,"identity":"8102947f-ffc6-41d3-8116-220da6b0b193","order_by":4,"name":"Heliofabio Barros Gomes","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Heliofabio","middleName":"Barros","lastName":"Gomes","suffix":""},{"id":609076877,"identity":"131118c2-801e-4612-929f-0f29324ae0b2","order_by":5,"name":"Helber Barros Gomes","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Helber","middleName":"Barros","lastName":"Gomes","suffix":""},{"id":609076878,"identity":"e407d481-fbe5-47b1-8037-734cf10270e4","order_by":6,"name":"Djane Fonseca da Silva","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Djane","middleName":"Fonseca da","lastName":"Silva","suffix":""},{"id":609076879,"identity":"34002059-1ee0-4f7c-8c1b-68efcb0b2e04","order_by":7,"name":"João Otávio Alves Accioly","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"João","middleName":"Otávio Alves","lastName":"Accioly","suffix":""},{"id":609076880,"identity":"0aa33fb0-8ee5-4e4b-be9e-1c00907701c7","order_by":8,"name":"Jean Souza dos reis","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jean","middleName":"Souza dos","lastName":"reis","suffix":""},{"id":609076881,"identity":"caa683c0-7773-4b25-a18d-10f7ccfca96f","order_by":9,"name":"Eva Maria Mollinedo Veneros","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"Maria Mollinedo","lastName":"Veneros","suffix":""},{"id":609076882,"identity":"6ebf223c-7f0e-459e-bda8-b6c9c9ece2d3","order_by":10,"name":"Bruno Coelho Bulcão","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bruno","middleName":"Coelho","lastName":"Bulcão","suffix":""},{"id":609076883,"identity":"67bd9d72-ff03-4fb5-825a-9c10ce4f6e31","order_by":11,"name":"Marcelo Félix Alonso","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Marcelo","middleName":"Félix","lastName":"Alonso","suffix":""},{"id":609076884,"identity":"a1be4e0e-cea8-4ca4-af61-43d1571f8c3a","order_by":12,"name":"Flavio Manoel Rodrigues da Silva Júnior","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Flavio","middleName":"Manoel Rodrigues da Silva","lastName":"Júnior","suffix":""},{"id":609076885,"identity":"2f8f2d5a-572a-4ac3-921b-e7c41a91b1d1","order_by":13,"name":"Richard James Ladle","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"James","lastName":"Ladle","suffix":""},{"id":609076886,"identity":"448771e7-bc16-486c-a5e7-87a2a7240585","order_by":14,"name":"Fabiana Rita do Couto Santos Pereira","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fabiana","middleName":"Rita do Couto Santos","lastName":"Pereira","suffix":""}],"badges":[],"createdAt":"2026-03-03 01:07:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9014481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9014481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105319560,"identity":"e7a8083f-9ef4-4ab2-9f6b-e68b37aebb04","added_by":"auto","created_at":"2026-03-24 17:05:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":490348,"visible":true,"origin":"","legend":"\u003cp\u003eDaily respiratory disease hospitalizations in Maceió, Brazil (2000–2019), showing long-term temporal trends by age group: (a) total population (G0); (b) children aged 0–4 years (G1); (c) adults aged 5–59 years (G2); and (d) elderly aged ≥60 years (G3). Black lines represent observed daily hospitalizations, and the red line denotes the 30-day moving average. The figure highlights long-term trends, including marked declines in 2006 and 2012 and divergent trajectories among age groups over the study period. Data source: DATASUS.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9014481/v1/6a43ebada48503e021dce95f.png"},{"id":105319562,"identity":"0f2243e5-6a26-4e53-b153-df66b7644b4b","added_by":"auto","created_at":"2026-03-24 17:05:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128703,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal distribution of daily respiratory disease hospitalizations in Maceió, Brazil (2000–2019), by age group: (a) total population (G0); (b) children aged 0–4 years (G1); (c) adults aged 5–59 years (G2); and (d) elderly aged ≥60 years (G3). Curves illustrate intra-annual variability, highlighting peak hospitalizations during March–May (rainy season) for most groups and a delayed seasonal peak (August–September) among the elderly. The red line denotes the 30-day moving average. Data source: DATASUS.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9014481/v1/df3cc2ee32942895749e1ab9.png"},{"id":105319558,"identity":"1c24461b-10ab-4f34-a8d9-c3d7d8e2abed","added_by":"auto","created_at":"2026-03-24 17:05:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":179506,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlations (ρ) between meteorological variables and age-stratified hospitalization rates at lag 0 (same week), lag 1 (1-week delay) and lag 2 (2-week delay). Color intensity reflects ρ values (blue: positive, red: negative). White background indicates non-significant associations (p ≥ 0.005 Bonferroni-adjusted).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9014481/v1/badf3601b223f324794c3d0c.png"},{"id":105319561,"identity":"f89ff33e-9459-403a-b81c-3e8acd7d02ed","added_by":"auto","created_at":"2026-03-24 17:05:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":267792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePredicted versus observed weekly respiratory hospitalization rates (per 100,000 inhabitants) in Maceió, Brazil, from 2000 to 2018, using the Random Forest model. Panels are organized by age group (rows) and exposure-response lag period (columns). Rows represent: (G0) Total population (4a–c), (G1) Children aged 0–4 years (4d–f), (G2) Adults aged 5–59 years (4g–i), and (G3) Elderly aged ≥60 years (4j–l). Columns correspond to lag intervals between meteorological exposure and hospitalizations: lag 0 (same week; 4a,d,g,j), lag 1 (1-week delay; 4b,e,h,k), and lag 2 (2-week delay; 4c,f,i,l).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9014481/v1/6a66370702aeac654cd619e6.png"},{"id":105319559,"identity":"4a02e8f2-825b-43f7-b09e-791de096e5e9","added_by":"auto","created_at":"2026-03-24 17:05:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":467896,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between observed and predicted weekly respiratory hospitalization rates in Maceió, Brazil, for the independent validation year 2019. Panels correspond to age groups: \u003cstrong\u003e(5a)\u003c/strong\u003e total population (G0), \u003cstrong\u003e(5b)\u003c/strong\u003echildren aged 0–4 years (G1), \u003cstrong\u003e(5c)\u003c/strong\u003e adults aged 5–59 years (G2), and \u003cstrong\u003e(5d)\u003c/strong\u003eelderly aged ≥60 years (G3). The black line represents observed hospitalization rates, while colored lines indicate Random Forest model predictions using different exposure–response lag structures: red for lag 0 (same-week meteorological exposure), bluefor lag 1 (1-week delay), and greenfor lag 2 (2-week delay). The figure illustrates the model’s ability to capture temporal variability and age-specific dynamics in respiratory disease hospitalizations, highlighting differences in predictive accuracy across lag periods and population groups.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9014481/v1/9db48d74a1fadba4fcaf0b63.png"},{"id":105319567,"identity":"7b3cde3c-d4ce-425f-a707-66503621cd6b","added_by":"auto","created_at":"2026-03-24 17:05:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2267148,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9014481/v1/c7e8ba61-688c-4d38-8e4e-82b77d860739.pdf"}],"financialInterests":"","formattedTitle":"Age-specific climate sensitivity of respiratory hospitalizations in a tropical coastal city: A 20-year machine learning analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRespiratory diseases remain a major global public health concern, contributing substantially to morbidity, mortality, and disability-adjusted life years worldwide. The Global Burden of Disease Study 2019 estimated more than 100\u0026nbsp;million annual incident cases of lower respiratory infections, resulting in approximately 2.6\u0026nbsp;million deaths (GBD 2019 Diseases and Injuries Collaborators \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although mortality rates have declined in recent decades, the burden remains disproportionately high in low- and middle-income countries, where structural inequalities, environmental exposures, and limited healthcare access exacerbate vulnerability (World Health Organization \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Landrigan et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Poor housing conditions, high population density, and exposure to climatic extremes further amplify respiratory morbidity and reinforce health inequities (Ebi et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChronic respiratory diseases affect an estimated 545\u0026nbsp;million individuals globally (Soriano et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and many are highly sensitive to climatic variability. Hospital admissions and symptom exacerbations have been linked to temperature extremes, elevated humidity, heavy precipitation, and abrupt weather transitions (Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Both heat and cold exposure are associated with increased respiratory hospitalizations and mortality, particularly during extreme events such as heatwaves (Achebak et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gasparrini et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). High humidity can impair thermoregulation and mucociliary clearance while enhancing pathogen survival, and intense rainfall promotes mold growth and aeroallergen dispersion, further aggravating respiratory conditions (Duan et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; World Health Organization \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese climate\u0026ndash;health interactions are especially pronounced in tropical coastal cities, where persistent heat-humidity coupling and limited nocturnal cooling create conditions of chronic thermal stress. In environments such as Macei\u0026oacute;, northeastern Brazil, sustained relative humidity exceeding 80% and marine aerosol exposure may intensify airway inflammation and respiratory vulnerability (Field et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Edwards et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Rising wet-bulb temperatures, often exceeding 29\u0026deg;C in urban areas, represent an additional physiological stressor, particularly for children and older adults (Raymond et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mora et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite these risks, relatively few climate\u0026ndash;health modeling studies focus on tropical coastal settings, leaving important gaps in understanding continuous exposure to compound heat and humidity (Hajat et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAddressing these gaps requires locally contextualized analyses that integrate long-term meteorological variability with demographic and health data. In this study, we employed a retrospective ecological time-series design to examine associations between atmospheric conditions and respiratory disease hospitalizations in Macei\u0026oacute; over a 20-year period (2000\u0026ndash;2019). Using a Random Forest framework, we developed predictive models to forecast weekly hospitalization rates while evaluating age-specific vulnerability patterns. This approach provides a transferable basis for climate-informed respiratory risk assessment and early warning systems in tropical coastal environments.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area and design\u003c/h2\u003e \u003cp\u003eThis retrospective ecological time-series study was conducted in Macei\u0026oacute; (09\u0026deg;39\u0026prime;S, 35\u0026deg;44\u0026prime;W), capital of Alagoas State, northeastern Brazil. The city is characterized by a tropical coastal climate with persistently high temperatures (annual mean\u0026thinsp;~\u0026thinsp;25.5\u0026deg;C), elevated relative humidity (~\u0026thinsp;80%), and a pronounced rainy season from March to May. The study period spanned from 1 January 2000 to 31 December 2019, enabling assessment of short-term meteorological effects and long-term climate\u0026ndash;health interactions under evolving demographic conditions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHealth, population, and meteorological data\u003c/h3\u003e\n\u003cp\u003eHospitalization data were obtained from the Hospital Information System of the Brazilian Unified Health System (SIH/SUS), accessed via DATASUS. Records were restricted to respiratory diseases (ICD-10 codes J00\u0026ndash;J99) and aggregated by date of admission, municipality of residence, and age group. Three age categories were defined: children (0\u0026ndash;4 years), adults (5\u0026ndash;59 years), and elderly (\u0026ge;\u0026thinsp;60 years), allowing evaluation of age-specific vulnerability patterns, consistent with evidence of differential climatic sensitivity across life stages (Gasparrini et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnnual population estimates were retrieved from the Brazilian Institute of Geography and Statistics (IBGE), and age-specific weekly denominators were derived using linear interpolation between census years. Weekly hospitalization rates per 100,000 inhabitants were calculated to ensure temporal and demographic comparability.\u003c/p\u003e \u003cp\u003eDaily meteorological data for the same period were obtained from the National Institute of Meteorology (INMET), including minimum, mean, and maximum air temperature (\u0026deg;C), relative humidity (%), atmospheric pressure (hPa), wind speed (m s⁻\u0026sup1;), solar radiation (h), evaporation (mm), and precipitation (mm). Quality control procedures included removal of implausible values and temporal alignment with hospitalization records. Missing data were treated using linear interpolation for gaps\u0026thinsp;\u0026le;\u0026thinsp;3 days and multiple imputation for longer gaps, following established approaches for environmental time series (Little and Rubin \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Daily observations were aggregated into weekly means (temperature, humidity, pressure, wind speed, radiation) or weekly totals (precipitation, evaporation) to match the temporal resolution of health outcomes.\u003c/p\u003e \u003cp\u003eTo account for delayed physiological and epidemiological responses, meteorological variables were evaluated at three lag structures: concurrent week (lag 0), 1-week delay (lag 1), and 2-week delay (lag 2). This approach captures delayed climatic effects and typical incubation periods associated with respiratory infections (Xu et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hajat et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eStatistical analysis and predictive modeling\u003c/h3\u003e\n\u003cp\u003eSpearman rank-order correlation coefficients (ρ) were computed between weekly meteorological variables and age-stratified hospitalization rates at each lag. To control for multiple testing, Bonferroni correction was applied (α\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e \u003cp\u003ePredictive modeling was conducted using Random Forest regression to estimate weekly respiratory hospitalization rates for each age group. Random Forest was selected due to its capacity to model nonlinear exposure\u0026ndash;response relationships, handle multicollinearity among predictors, and capture complex meteorological\u0026ndash;temporal interactions without parametric assumptions.\u003c/p\u003e \u003cp\u003eData were partitioned using a time-aware strategy preserving temporal structure: 2000\u0026ndash;2018 for model development (training and testing subsets) and 2019 as an independent validation year. Hyperparameters were optimized through grid search with 5-fold cross-validation. Model performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R\u0026sup2;), and symmetric mean absolute percentage error (SMAPE), selected for its robustness and interpretability in forecasting applications (Hyndman and Koehler \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). All analyses were performed in Python 3.8 using Pandas, NumPy, SciPy, and Scikit-learn.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMeteorological conditions and temporal hospitalization patterns\u003c/h2\u003e \u003cp\u003eMeteorological conditions in Macei\u0026oacute; during 2000\u0026ndash;2019 were characterized by persistently high temperatures (mean maximum: 29.9\u0026deg;C) and elevated relative humidity (mean: 80.1%; 75th percentile: 84%), with minimal diurnal thermal variation (minimum temperature median: 21.9\u0026deg;C) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Precipitation exhibited marked variability (maximum: 187.8 mm), whereas atmospheric pressure remained relatively stable (mean: 1004.9 hPa).\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\u003eDescriptive statistics of meteorological variables in Macei\u0026oacute;, Brazil (2000\u0026ndash;2019). Data include mean, minimum (Min), maximum (Max), median, and 25th/75th percentiles (P25/P75) for temperature, humidity, wind speed, solar radiation, evaporation, precipitation, and atmospheric pressure.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP75\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin. Temperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax. Temperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Temperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Humidity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin Humidity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind Speed (m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiation (h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaporation (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressure (hPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1004.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1012.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1004.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1003.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1006.6\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\u003eThe 20-year time series revealed a significant declining trend in respiratory hospitalizations in the total population (\u0026minus;\u0026thinsp;1.24 hospitalizations/day/year; 95% CI: \u0026minus;1.42 to \u0026minus;\u0026thinsp;1.03), corresponding to a cumulative reduction of approximately 24.8 hospitalizations/day (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Structural declines in 2006 and 2012 coincided with expansion of the Family Health Strategy and pneumococcal vaccination programs (Domingues et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Macinko and Harris \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAge-stratified analyses showed heterogeneous trajectories. Children exhibited the strongest decline (\u0026minus;\u0026thinsp;0.92/day/year; 95% CI: \u0026minus;1.03 to \u0026minus;\u0026thinsp;0.79) and the highest seasonal amplitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), adults demonstrated a moderate reduction (\u0026minus;\u0026thinsp;0.38/day/year; 95% CI: \u0026minus;0.44 to \u0026minus;\u0026thinsp;0.31) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), and the elderly showed no significant overall trend (+\u0026thinsp;0.04/day/year; 95% CI: \u0026minus;0.02 to +\u0026thinsp;0.09), with a downward phase (2000\u0026ndash;2009) followed by an upward trajectory (2010\u0026ndash;2019), consistent with demographic aging and increased climate sensitivity (Miranda et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003ePronounced seasonality was observed across all groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with a unimodal peak during March\u0026ndash;May (rainy season) in the total population and children. Seasonal variability increased by 23% in the total population and 18% in children, based on interquartile range (IQR) estimates, a robust dispersion metric (Wilcox \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Adults displayed more distributed peaks (March, August, January), whereas the elderly showed delayed maxima in August\u0026ndash;September.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMeteorological correlates and lag structure\u003c/h2\u003e \u003cp\u003eSpearman analyses demonstrated consistent age-specific and lag-dependent associations between meteorological variables and hospitalization rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt lag 0, minimum temperature exhibited the strongest inverse correlation in the total population (ρ = \u0026minus;0.654, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by mean temperature and solar radiation, while precipitation showed weak positive associations. Children mirrored this temperature sensitivity (ρ = \u0026minus;0.602), with stronger precipitation effects than adults. Adults showed greater sensitivity to minimum humidity, whereas the elderly exhibited distinct associations with mean humidity and atmospheric pressure.\u003c/p\u003e \u003cp\u003eAt lag 1, correlation magnitudes remained comparable, with peak atmospheric pressure effects in the elderly (ρ = +0.215, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and pronounced inverse associations with evaporation. At lag 2, correlations weakened but remained significant; minimum temperature continued to show strong inverse associations across groups, and pressure and evaporation effects persisted in the elderly, suggesting cumulative environmental stress.\u003c/p\u003e \u003cp\u003eOverall, the results indicate heterogeneous temporal responses to meteorological exposures, with minimum temperature emerging as the dominant predictor across age groups and pressure-related variables showing particular relevance in older adults.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRandom Forest performance and validation\u003c/h3\u003e\n\u003cp\u003eRandom Forest models demonstrated strong predictive capacity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For the total population, the highest performance occurred at lag 2 (R\u0026sup2; = 0.90; RMSE\u0026thinsp;=\u0026thinsp;2.49), indicating delayed meteorological effects. Children showed consistent performance across lags (R\u0026sup2; = 0.82\u0026ndash;0.83), while adults achieved optimal predictions at lag 2 (R\u0026sup2; = 0.83; MAE\u0026thinsp;=\u0026thinsp;0.95). Performance in the elderly was moderate (R\u0026sup2; = 0.45\u0026ndash;0.51), likely reflecting additional clinical and behavioral determinants not captured by meteorological predictors.\u003c/p\u003e \u003cp\u003eIndependent validation using 2019 data confirmed robust predictive accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Forecast skill, assessed using SMAPE (Hyndman and Koehler \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), indicated excellent performance (\u0026lt;\u0026thinsp;20%) for the total population across all lags, with progressive improvement at lag 2 (13.46%). Children and adults showed good-to-excellent performance, particularly at lag 2, supporting delayed-response hypotheses. The elderly maintained good predictive skill (~\u0026thinsp;21\u0026ndash;22%), despite greater complexity.\u003c/p\u003e \u003cp\u003eAll SMAPE values were within excellent-to-good ranges (\u0026lt;\u0026thinsp;25%), confirming the stability and generalizability of the age-stratified, lag-structured forecasting framework for respiratory disease surveillance in tropical coastal environments.\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\u003eRandom Forest model performance metrics (training/testing: 2000\u0026ndash;2018).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLag\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMAE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\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 \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRespiratory disease hospitalizations in Macei\u0026oacute; were systematically modulated by local atmospheric conditions, with age-specific and lag-dependent responses. The long-term decline observed in the total population, children, and adults is consistent with national trends in Brazil (Victora et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Barreto et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and likely reflects the expansion of the Family Health Strategy and the introduction of pneumococcal conjugate vaccines (Domingues et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Macinko and Harris \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These structural public health interventions appear to have reduced baseline respiratory morbidity, even within climate-sensitive environments.\u003c/p\u003e \u003cp\u003eIn contrast, the elderly exhibited stabilization followed by increasing hospitalization rates after 2010, paralleling Brazil\u0026rsquo;s rapid demographic aging (Miranda et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Age-related physiological frailty and increased thermosensitivity (Silva et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), combined with the growing burden of chronic conditions (Guimar\u0026atilde;es et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), likely contribute to this divergence. Similar patterns of elevated climate vulnerability among older adults have been documented in European settings (\u0026Aring;str\u0026ouml;m et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), underscoring that reductions in overall mortality do not necessarily translate into reduced morbidity among aging populations exposed to chronic environmental stress.\u003c/p\u003e \u003cp\u003eSeasonally, Macei\u0026oacute; differs from temperate regions, exhibiting a dominant peak during the rainy season (March\u0026ndash;May) rather than winter. This pattern aligns with tropical pathogen ecology, where humidity and rainfall enhance viral circulation and indoor crowding (Tamerius et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chowdhury et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and coincides with regional precipitation dynamics (Pereira et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Quesado et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Children showed the highest seasonal amplitude, consistent with pediatric susceptibility to humidity-driven viral transmission (Paynter et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nenna et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), whereas delayed peaks among the elderly may reflect influenza B circulation patterns (Alonso et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and behavioral differences in healthcare-seeking (Chipato and Ranganathan \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sun and Smith \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMeteorological drivers and physiological pathways\u003c/h2\u003e \u003cp\u003eMinimum temperature emerged as the dominant meteorological correlate across all age groups, with strong inverse associations persisting up to 2-week lags. Even modest nocturnal cooling within tropical thermal ranges may influence airway reactivity, immune function, and viral stability, as observed in other low-latitude cities (Gasparrini et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These findings highlight that in tropical settings, relative thermal variability rather than extreme cold may be sufficient to trigger respiratory exacerbations.\u003c/p\u003e \u003cp\u003ePrecipitation and humidity demonstrated consistent positive associations, particularly among children and the elderly, supporting mechanisms involving post-rain indoor dampness, fungal proliferation, and allergen exposure (Mendell et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Harley et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Persistent associations between atmospheric pressure, evaporation, and elderly hospitalizations suggest heightened sensitivity to barometric and moisture-related stress, likely reflecting reduced cardiopulmonary reserve and impaired thermoregulation in advanced age. Collectively, these results indicate that compound climatic stressors, rather than temperature alone, shape respiratory morbidity in tropical coastal environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePredictive modeling and climate change implications\u003c/h2\u003e \u003cp\u003eThe Random Forest framework demonstrated robust predictive skill (R\u0026sup2; \u0026gt; 0.80 for total population, children, and adults; SMAPE\u0026thinsp;\u0026lt;\u0026thinsp;25%), supporting the utility of machine learning approaches for modeling nonlinear climate\u0026ndash;health interactions (Xu et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Improved performance at longer lags reinforces hypotheses regarding delayed physiological responses, incubation periods, and healthcare-seeking behavior (Hajat et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The more moderate performance in the elderly (R\u0026sup2; = 0.45\u0026ndash;0.51) suggests that meteorological predictors alone may be insufficient in this demographic, consistent with evidence that geriatric outcomes are shaped by complex clinical and social determinants (Schinasi et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMacei\u0026oacute;\u0026rsquo;s climatic regime, defined by sustained high temperatures, persistently elevated humidity, and frequent heavy rainfall, reflects conditions of continuous thermal load rather than isolated extreme events. Increasing evidence suggests that prolonged heat-humidity exposure can generate cumulative respiratory stress in tropical coastal populations (Hajat et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite this, fewer than 15% of climate\u0026ndash;health modeling studies explicitly examine such environments (Hajat et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), highlighting a critical gap in the literature. Our findings therefore reinforce the need for locally calibrated risk models tailored to compound tropical stressors, rather than reliance on extrapolations from temperate regions. In coastal settings, marine aerosol interactions may further influence airway inflammation and respiratory vulnerability (Field et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Edwards et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePublic health implications and study limitations\u003c/h2\u003e \u003cp\u003eThe identified age-specific sensitivities and lag structures support integration of meteorological surveillance into early warning systems for respiratory diseases in tropical coastal cities. Forecast-based alerts 1\u0026ndash;2 weeks in advance, hospital surge planning, and anticipatory vaccination or risk communication strategies could enhance climate-resilient health planning.\u003c/p\u003e \u003cp\u003eThis study has limitations inherent to ecological designs, precluding individual-level causal inference. Hospitalization data reflect severe cases and may underestimate total community morbidity. Additionally, air pollution was not included due to data constraints, despite well-documented interactions between atmospheric conditions and air quality (Silva et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Future research should incorporate pollutant data, indoor environmental conditions, and clinical covariates to refine predictive accuracy, particularly among elderly populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis 20-year analysis demonstrates that respiratory disease hospitalizations in Macei\u0026oacute; are systematically shaped by local atmospheric conditions through age-specific and lag-dependent pathways. Minimum temperature was the dominant meteorological correlate across all groups, with persistent inverse associations up to two weeks post-exposure, while precipitation, humidity, and barometric pressure exerted additional age-differentiated effects, particularly among children and older adults.\u003c/p\u003e \u003cp\u003eThe Random Forest framework showed strong predictive performance (SMAPE: 13\u0026ndash;25%; R\u0026sup2; \u0026gt; 0.80 for total population, children, and adults), with optimal forecasts at 2-week lags for the general and adult populations, supporting delayed-response mechanisms. More moderate performance among the elderly (R\u0026sup2; = 0.45\u0026ndash;0.51) indicates that meteorological variables alone may be insufficient to fully capture geriatric vulnerability, underscoring the importance of integrating clinical and socioeconomic determinants in future models.\u003c/p\u003e \u003cp\u003eLong-term declines in hospitalizations among children and adults likely reflect the impact of vaccination programs and primary healthcare expansion, whereas stabilization followed by increasing trends in older adults highlight the interaction between demographic aging and climate sensitivity. These divergent trajectories emphasize the need for age-stratified climate\u0026ndash;health surveillance in rapidly aging tropical populations.\u003c/p\u003e \u003cp\u003eBy addressing the underrepresentation of tropical coastal cities in climate\u0026ndash;health research, this study provides a transferable, meteorologically informed forecasting framework for early warning systems, hospital surge planning, and targeted prevention. As climate change intensifies compound heat-humidity stress in low-latitude regions, locally calibrated, age-sensitive predictive models will be essential for climate-resilient public health planning and adaptive healthcare systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the National Council for Scientific and Technological Development (CNPq) (grant \u003cstrong\u003eno.\u003c/strong\u003e 445363/2023-1) and the Department of Science and Technology (DECIT) of the Brazilian Ministry of Health for the financial support of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Article Processing Charge (APC) for the publication of this research was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (ROR identifier: 00x0ma614).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.P.S.P.: Conceptualization; Methodology; Formal analysis; Data curation; Writing – original draft. R.L.C., J.S.R. and J.O.A.A.: Data curation (meteorological data); Methodology. G.L.M. and F.D.S.S.: Methodology; Investigation; Supervision. H.B.G.: Supervision; Project administration. H.B.G. and D.F.S.: Literature review; Statistical methodology. E.M.M.V. and B.C.B.: Software development. F.R.C.S.P., R.J.L., and F.M.R.S.J.: Writing – review \u0026amp; editing. M.F.A.: Conceptualization; Supervision; Project administration; Funding acquisition. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Fabrício Daniel dos Santos Silva\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are publicly available from DATASUS (Brazilian Ministry of Health) and INMET meteorological databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used secondary, publicly available, anonymized data from DATASUS and meteorological databases to the INMET. Ethical approval was not required according to Brazilian regulations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAchebak H, Devolder D, Garc\u0026iacute;a-Aymerich J, Rey G, Chen Z, M\u0026eacute;ndez-Turrubiates RF, et al (2023) Heat-related mortality in Europe during the summer of 2022. Nat Med 29:1857\u0026ndash;1866. https://doi.org/10.1038/s41591-023-02419-z\u003c/li\u003e\n \u003cli\u003eAlonso WJ, Guillebaud J, Viboud C, Gunther S, et al (2015) Seasonality of influenza in Brazil: a traveling wave from the Amazon to the subtropics. Am J Epidemiol 181:944\u0026ndash;951. https://doi.org/10.1093/aje/kwu467\u003c/li\u003e\n \u003cli\u003e\u0026Aring;str\u0026ouml;m C, Orru H, Rockl\u0026ouml;v J, Strandberg G, Ebi KL, Forsberg B (2016) Heat-related respiratory hospital admissions in Europe in a changing climate: a health impact assessment. BMJ Open 6:e009684. https://doi.org/10.1136/bmjopen-2015-009684\u003c/li\u003e\n \u003cli\u003eBarreto ML, Teixeira MG, Bastos FI, Ximenes RAA, Barata RB, Rodrigues LC (2011) Successes and failures in the control of infectious diseases in Brazil: social and environmental context, policies, interventions, and research needs. Lancet 377:1877\u0026ndash;1889. https://doi.org/10.1016/S0140-6736(11)60202-X\u003c/li\u003e\n \u003cli\u003eChipato P, Ranganathan M (2022) Climate, seasonality and health-seeking behaviour for fever in adults in southern Malawi: a qualitative study. Glob Public Health 17:3081\u0026ndash;3096. https://doi.org/10.1080/17441692.2021.2015616\u003c/li\u003e\n \u003cli\u003eChowdhury FR, Ibrahim QSU, Bari MS, Alam MMJ, Dunachie SJ, Rodriguez-Morales AJ, et al (2018) The association between temperature, rainfall and humidity with common climate-sensitive infectious diseases in Bangladesh. PLoS One 13:e0199579. https://doi.org/10.1371/journal.pone.0199579\u003c/li\u003e\n \u003cli\u003eDomingues CMAS, Maranh\u0026atilde;o AGK, Teixeira AMS, Fantinato FFS (2020) The Brazilian National Immunization Program: 46 years of achievements and challenges. Cad Saude Publica 36:e00103920. https://doi.org/10.1590/0102-311X00103920\u003c/li\u003e\n \u003cli\u003eDuan Y, Liao Y, Li H, Yan S, Zhao Z, Yu S, et al (2021) Effect of changes in season and temperature on mortality associated with air pollution in Seoul, Korea. Sci Total Environ 761:143226. https://doi.org/10.1016/j.scitotenv.2020.143226\u003c/li\u003e\n \u003cli\u003eEbi KL, Vanos J, Baldwin JW, Bell JE, Hondula DM, Errett NA, et al (2021) Extreme weather and climate change: population health and health system implications. Annu Rev Public Health 42:293\u0026ndash;315. https://doi.org/10.1146/annurev-publhealth-012420-105026\u003c/li\u003e\n \u003cli\u003eEdwards DA, Ausiello D, Salzman J, Devlin T, Langer R, Beddingfield BJ, et al (2021) Exhaled aerosol increases with COVID-19 infection, age, and obesity. Proc Natl Acad Sci U S A 118:e2021830118. https://doi.org/10.1073/pnas.2021830118\u003c/li\u003e\n \u003cli\u003eField RD, Moelis N, Salzman J, Bax A, Ausiello D, Woodward SM, et al (2021) Inhaled water and salt suppress respiratory droplet generation and COVID-19 incidence and death on US coastlines. Mol Front J 5:17\u0026ndash;29. https://doi.org/10.1142/S2529732521400058\u003c/li\u003e\n \u003cli\u003eGasparrini A, Guo Y, Hashizume M, Lavigne E, Zanobetti A, Schwartz J, et al (2015) Mortality risk attributable to high and low ambient temperature: a multicountry observational study. Lancet 386:369\u0026ndash;375. https://doi.org/10.1016/S0140-6736(14)62114-0\u003c/li\u003e\n \u003cli\u003eGBD 2019 Diseases and Injuries Collaborators (2020) Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019. Lancet 396:1204\u0026ndash;1222. https://doi.org/10.1016/S0140-6736(20)30925-9\u003c/li\u003e\n \u003cli\u003eGuimar\u0026atilde;es RM, Fran\u0026ccedil;a EB, Ishitani LH, Marinho F, Vasconcelos AMN (2021) The burden of disease in the elderly population of Brazil: an analysis of mortality and disability. Rev Bras Epidemiol 24:e210004. https://doi.org/10.1590/1980-549720210004.supl.2\u003c/li\u003e\n \u003cli\u003eHajat S, Proestos Y, Araya-Lopez L, Barrera-G\u0026oacute;mez J, Chersich MF, Luchters S, et al (2024) Health effects of climate change: an overview of systematic reviews. Lancet Planet Health 8:e2\u0026ndash;e12. https://doi.org/10.1016/S2542-5196(23)00223-6\u003c/li\u003e\n \u003cli\u003eHajat S, Quijal-Zamorano M, Petkova EP, Gasparrini A, Schneider R, Vicedo-Cabrera AM, et al (2023) A comparative analysis of the temperature-mortality risks using different weather datasets across heterogeneous regions. Geohealth 7:e2022GH000776. https://doi.org/10.1029/2022GH000776\u003c/li\u003e\n \u003cli\u003eHarley KG, Macher JM, Lipsett M, et al (2009) Fungi and pollen exposure in the first months of life and risk of early childhood wheezing. Thorax 64:353\u0026ndash;358. https://doi.org/10.1136/thx.2007.090241\u003c/li\u003e\n \u003cli\u003eHyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22:679\u0026ndash;688. https://doi.org/10.1016/j.ijforecast.2006.03.001\u003c/li\u003e\n \u003cli\u003eLandrigan PJ, Fuller R, Acosta NJR, Adeyi O, Arnold R, Bald\u0026eacute; AB, et al (2018) The Lancet Commission on pollution and health. Lancet 391:462\u0026ndash;512. https://doi.org/10.1016/S0140-6736(17)32345-0\u003c/li\u003e\n \u003cli\u003eLi T, Zhang Y, Wang J, Xu D, Yin Z, Chen H, et al (2022) All-cause and cause-specific mortality in relation to extreme and non-extreme temperatures. Environ Int 169:107537. https://doi.org/10.1016/j.envint.2022.107537\u003c/li\u003e\n \u003cli\u003eLi T, Horton RM, Bader DA, Zhou M, Liang S, et al (2018) Ambient temperature and risk of COPD hospitalizations. Environ Int 121:140\u0026ndash;146. https://doi.org/10.1016/j.envint.2018.08.064\u003c/li\u003e\n \u003cli\u003eLittle RJA, Rubin DB (2019) Statistical analysis with missing data, 3rd edn. Wiley, Hoboken\u003c/li\u003e\n \u003cli\u003eMacinko J, Harris MJ (2015) Brazil\u0026apos;s family health strategy\u0026mdash;delivering community-based primary care in a universal health system. N Engl J Med 372:2177\u0026ndash;2181. https://doi.org/10.1056/NEJMp1501140\u003c/li\u003e\n \u003cli\u003eMendell MJ, Mirer AG, Cheung K, Tong M, Douwes J (2011) Respiratory and allergic health effects of dampness, mold, and dampness-related agents. Environ Health Perspect 119:748\u0026ndash;756. https://doi.org/10.1289/ehp.1002410\u003c/li\u003e\n \u003cli\u003eMiranda GMD, Mendes ACG, Silva ALAD (2016) Population aging in Brazil: current and future social challenges and consequences. Rev Bras Geriatr Gerontol 19:507\u0026ndash;519. https://doi.org/10.1590/1809-98232016019.150140\u003c/li\u003e\n \u003cli\u003eMora C, Dousset B, Caldwell IR, Powell FE, Geronimo RC, Bielecki CR, et al (2017) Global risk of deadly heat. Nat Clim Chang 7:501\u0026ndash;506. https://doi.org/10.1038/nclimate3322\u003c/li\u003e\n \u003cli\u003eNenna R, Evangelisti M, Frassanito A, Scagnolari C, Pierangeli A, Antonelli G, et al (2017) Respiratory syncytial virus bronchiolitis, weather conditions and air pollution in an Italian urban area. Environ Res 158:188\u0026ndash;193. https://doi.org/10.1016/j.envres.2017.06.014\u003c/li\u003e\n \u003cli\u003ePaynter S (2015) Humidity and respiratory virus transmission in tropical and temperate settings. Epidemiol Infect 143:1110\u0026ndash;1118. https://doi.org/10.1017/S0950268814002702\u003c/li\u003e\n \u003cli\u003ePaynter S, Ware RS, Weinstein P, Williams G, Sly PD (2010) Childhood pneumonia: a neglected, climate-sensitive disease? Lancet 376:1804\u0026ndash;1805. https://doi.org/10.1016/S0140-6736(10)61040-7\u003c/li\u003e\n \u003cli\u003ePereira MPS, Couto F, Schumacher V, Silva FDS, Gomes HB, Costa RL, et al (2024) Rainy season migration across the northeast coast of Brazil related to sea surface temperature patterns. Atmosphere 15:713. https://doi.org/10.3390/atmos15060713\u003c/li\u003e\n \u003cli\u003eQuesado EML, Souza TMO, Venancio LPR (2023) Effects of climate variability on respiratory diseases in the Western Region of Bahia, Brazil. Public Health 222:1\u0026ndash;6. https://doi.org/10.1016/j.puhe.2023.06.030\u003c/li\u003e\n \u003cli\u003eRaymond C, Matthews T, Horton RM (2020) The emergence of heat and humidity too severe for human tolerance. Sci Adv 6:eaaw1838. https://doi.org/10.1126/sciadv.aaw1838\u003c/li\u003e\n \u003cli\u003eReis JSd, Costa RL, Silva FDdS, de Souza EDF, Cortes TR, Coelho RH, et al (2025) Predicting asthma hospitalizations from climate and air pollution data. Climate 13:23. https://doi.org/10.3390/cli13020023\u003c/li\u003e\n \u003cli\u003eRequia WJ, Damasceno da Silva RM, Hoinaski L, Amini H (2024) Thermal comfort conditions and mortality in Brazil. Int J Environ Res Public Health 21:1248. https://doi.org/10.3390/ijerph21091248\u003c/li\u003e\n \u003cli\u003eSchinasi LH, Benmarhnia T, De Roos AJ (2018) Temperature, precipitation, and dementia hospitalizations in the United States, 2005\u0026ndash;2015. Environ Epidemiol 2:e028. https://doi.org/10.1097/EE9.0000000000000028\u003c/li\u003e\n \u003cli\u003eSilva DL, et al (2021) Seasonality of respiratory viruses in a region with a tropical climate. J Pediatr (Rio J) 97:513\u0026ndash;520. https://doi.org/10.1016/j.jped.2020.11.005\u003c/li\u003e\n \u003cli\u003eSilva DR, Viana VP, M\u0026uuml;ller AM, Livi FP, Dalcin PTR (2014) Respiratory viral infections and effects of meteorological parameters and air pollution in adults with respiratory symptoms. Influenza Other Respir Viruses 8:42\u0026ndash;52. https://doi.org/10.1111/irv.12158\u003c/li\u003e\n \u003cli\u003eSilva FA, Mambrini JVM, Malta DC, Lima-Costa MF (2020) Prevalence of frailty in Brazilian older adults: a systematic review and meta-analysis. J Aging Health 32:1130\u0026ndash;1144. https://doi.org/10.1177/0898264319894556\u003c/li\u003e\n \u003cli\u003eSoriano JB, Kendrick PJ, Paulson KR, Gupta V, Abrams EM, Adedoyin RA, et al (2020) Prevalence and attributable health burden of chronic respiratory diseases, 1990\u0026ndash;2017. Lancet Respir Med 8:585\u0026ndash;596. https://doi.org/10.1016/S2213-2600(20)30105-3\u003c/li\u003e\n \u003cli\u003eSun JK, Smith J (2017) Self-perceptions of aging and perceived barriers to care: reasons for health care delay. Gerontologist 57:S216\u0026ndash;S226. https://doi.org/10.1093/geront/gnx014\u003c/li\u003e\n \u003cli\u003eTamerius J, Nelson MI, Zhou SZ, Viboud C, Miller MA, Alonso WJ (2011) Global influenza seasonality: reconciling patterns across temperate and tropical regions. Environ Health Perspect 119:439\u0026ndash;445. https://doi.org/10.1289/ehp.1002383\u003c/li\u003e\n \u003cli\u003eVictora CG, Aquino EML, do Carmo Leal M, Monteiro CA, Barros FC, Szwarcwald CL (2011) Maternal and child health in Brazil: progress and challenges. Lancet 377:1863\u0026ndash;1876. https://doi.org/10.1016/S0140-6736(11)60138-4\u003c/li\u003e\n \u003cli\u003eWilcox RR (2017) Introduction to robust estimation and hypothesis testing, 4th edn. Academic Press, London.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization (2020) WHO global strategy on health, environment and climate change. WHO, Geneva.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization (2023) Operational framework for building climate resilient and low carbon health systems. WHO, Geneva.\u003c/li\u003e\n \u003cli\u003eWu X, Lu Y, Zhou S, Chen L, Xu B (2016) Impact of climate change on human infectious diseases. Environ Int 86:14\u0026ndash;23. https://doi.org/10.1016/j.envint.2015.09.007\u003c/li\u003e\n \u003cli\u003eXu R, Yu P, Abramson MJ, Li S, Guo Y (2024) Artificial intelligence for climate health readiness. Lancet Planet Health 8:e271\u0026ndash;e281. https://doi.org/10.1016/S2542-5196(24)00019-2\u003c/li\u003e\n \u003cli\u003eXu Z, Etzel RA, Su H, Huang C, Guo Y, Tong S (2013) Impact of ambient temperature on children\u0026apos;s health: a systematic review. Environ Health Perspect 121:785\u0026ndash;790. https://doi.org/10.1289/ehp.1204912\u003c/li\u003e\n \u003cli\u003eXu Z, Tong S, Cheng J, Zhang Y, Wang N, Zhang Y, et al (2020) Heatwaves, hospital admissions for respiratory diseases, and disease-specific mortality. BMJ Open 10:e038137. https://doi.org/10.1136/bmjopen-2020-038137\u003c/li\u003e\n \u003cli\u003eZhao Q, Li S, Coelho MSZS, et al (2019) The association between heatwaves and risk of hospitalization in Brazil. PLoS Med 16:e1002753. https://doi.org/10.1371/journal.pmed.1002753\u003c/li\u003e\n\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Respiratory diseases, Climate–health interactions, Tropical climate, Random Forest modeling, Lag effects","lastPublishedDoi":"10.21203/rs.3.rs-9014481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9014481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRespiratory diseases remain a major public health challenge in tropical coastal cities, where persistent heat-humidity coupling and climate variability create vulnerability patterns. We investigated associations between atmospheric conditions and respiratory disease hospitalizations in Macei\u0026oacute;, Brazil, a coastal tropical city, using 20 years of data (2000\u0026ndash;2019). Weekly hospitalization rates stratified by age (children 0\u0026ndash;4 years, adults 5\u0026ndash;59 years, elderly\u0026thinsp;\u0026ge;\u0026thinsp;60 years) were analyzed against meteorological variables including temperature, humidity, precipitation, atmospheric pressure, and solar radiation at 0-, 1-, and 2-week lags. Random Forest models were applied to forecast weekly respiratory hospitalization rates. Minimum temperature showed a strong inverse correlation with hospitalizations across all age groups (ρ = \u0026minus;0.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with effects persisting up to 2 weeks. Children exhibited immediate sensitivity to thermal and precipitation variables, while elderly populations showed delayed responses to barometric pressure and evaporation. The Random Forest model achieved excellent-to-good predictive accuracy (R\u0026sup2; = 0.83\u0026ndash;0.90 for children and adults; Symmetric Mean Absolute Percentage Error\u0026thinsp;=\u0026thinsp;13\u0026ndash;25% across all groups). Long-term declining trends in children and adults contrasted with stabilization and subsequent increases among elderly populations after 2010, reflecting demographic aging and heightened climate sensitivity. These findings provide a transferable framework for climate-informed respiratory risk assessment and early warning systems in tropical coastal environments, supporting age-sensitive public health planning under ongoing climate change.\u003c/p\u003e","manuscriptTitle":"Age-specific climate sensitivity of respiratory hospitalizations in a tropical coastal city: A 20-year machine learning analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 17:05:37","doi":"10.21203/rs.3.rs-9014481/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-03-19T19:21:18+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T18:42:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-06T00:23:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Biometeorology","date":"2026-03-04T21:39:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1f1248be-389b-427b-a963-1018369531d2","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-25T09:33:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 17:05:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9014481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9014481","identity":"rs-9014481","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}