Abstract
Climatic variability has been proposed as a modulator of musculoskeletal and rheumatic pain; however, long-term population-based evidence examining healthcare utilization remains limited. To evaluate the association between climatic variables and primary care consultations for autoimmune and immune-mediated musculoskeletal pain over a fourteen-year period, and to explore potential modification by sex and diagnostic category. A retrospective ecological time-series study was conducted using electronic health records from three primary care centers in the Community of Madrid (Spain) between January 2010 and December 2023. The weekly consultation counts for fibromyalgia, myalgia, and combined autoimmune pain processes were analyzed. Meteorological data were obtained from the Spanish State Meteorological Agency (AEMET). Associations were assessed using Time Series Generalized Linear Models (TSGLM) with a negative binomial distribution, adjusting for autocorrelation, seasonality, age, and the COVID-19 period. Effects were expressed as Incidence Rate Ratios (IRR) with Holm–Bonferroni correction. Across 728 weeks, we recorded 6715 primary care consultations related to autoimmune pain processes (mean 9.23 consultations/week, 62.07% female). Myalgia accounted for 94.83% of the consultations. Clinically relevant biometeorological trends were identified across several stratified models, particularly for diurnal temperature range and barometric pressure. No significant climatic effects were observed in the aggregated models. No significant climatic effects were observed in the aggregated models. Forecasting analyses suggest the stabilization of consultation volumes over the next four years. Climatic influences on primary care consultations for autoimmune musculoskeletal pain were statistically detectable, but modest and heterogeneous. Intra-daily temperature variability appears to be more relevant than absolute temperature values. These findings underscore the complexity of the climate–pain relationship and the importance of stratified time-series modeling in biometeorological research.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-026-56472-y.
Keywords: Biometeorology, Autoimmune pain, Musculoskeletal pain, Fibromyalgia, Myalgia, Primary care, Time-series analysis
Subject terms: Diseases, Health care, Medical research, Rheumatology, Risk factors
Introduction
Human health is continuously shaped by the dynamic interactions between biological systems and environmental conditions, which constitute the conceptual foundation of biometeorology1,2. Biometeorology examines how atmospheric and climatic factors interact with physiological and pathological processes by integrating environmental exposure and human health outcomes across multiple systems2. Within this framework, meteoropathy refers to a constellation of physical and psychological symptoms triggered or exacerbated by weather variability, particularly in susceptible individuals1,3.
Epidemiological evidence indicates that weather sensitivity is a common phenomenon in the general population, with approximately one-third of individuals reporting health disturbances associated with climate change4. This proportion increases substantially among people with chronic pain, with up to 70–75% of them reporting fluctuations in pain intensity linked to meteorological conditions5,6. Atmospheric pressure, temperature, humidity, wind characteristics, precipitation, and solar radiation have consistently been identified as key environmental variables influencing human health and symptom expression3,6.
In addition to their established effects on cardiovascular and respiratory morbidity, climatic variables have been increasingly associated with pain perception and musculoskeletal dysfunction6–8. Experimental evidence from animal models has demonstrated that a reduction in barometric pressure can induce neuronal activation in the superior vestibular nucleus, supporting a plausible neurophysiological mechanism through which atmospheric changes may modulate pain processing7. Observational studies in humans suggest that climatic variability may influence neuroendocrine regulation, autonomic balance, and inflammatory pathways, thereby altering pain sensitivity and physical function9–11.
Abrupt environmental transitions appear to be particularly relevant to pain exacerbation. Increases in relative humidity and ambient temperature, rapid barometric pressure fluctuations, and prolonged exposure to cold have been associated with worsening pain symptoms across multiple chronic pain conditions9,11–14. These associations are not uniform across individuals, highlighting the marked inter-individual heterogeneity in the climate–pain relationship10,15.
Musculoskeletal pain is among the most consistently reported climate-sensitive conditions. Weather-related symptom fluctuations have been described in osteoarthritis5,16, fibromyalgia9,11,12,17, chronic myalgia14,18, headache disorders8,19, and spinal pain syndromes, including cervical and low back pain with or without radicular involvement14,18. Collectively, these conditions represent a substantial proportion of chronic pain presentations in primary care and frequently lead to recurrent consultations and repeated referrals to rehabilitation and physiotherapy services6,20.
From a rheumatological perspective, growing evidence suggests that inflammatory and immune-mediated pain conditions may be sensitive to environmental factors. Studies in patients with rheumatoid arthritis and other chronic rheumatic diseases have reported associations between weather variability, pain severity, and functional limitations21,22. Moreover, systematic reviews and meta-analyses have indicated that climatic influences on osteoarthritis pain are modest but consistent, reinforcing the relevance of environmental modulation in rheumatic pain16. However, the findings remain heterogeneous, and the mechanisms underlying climate–disease interactions in rheumatology are not completely understood15,22.
However, the existing literature has some limitations. Many studies rely on small samples, short observation periods, or self-reported symptom measures and often include only one or two meteorological variables6,10. Diagnostic stratification is infrequently performed, despite evidence that climate sensitivity may differ across musculoskeletal, inflammatory, and immune-mediated pain conditions10,15. Furthermore, the robust evaluation of seasonal patterns, long-term trends, and delayed effects requires large datasets with extended follow-up and appropriate time-series modeling approaches10,14.
To date, no large-scale study has comprehensively examined the association between multiple climatic variables, including temperature, precipitation, wind characteristics, sunshine hours, barometric pressure, and primary care consultations, in patients with rheumatic and immune-mediated pain conditions over a prolonged time horizon. Most prior investigations have focused on short-term symptom fluctuations rather than healthcare-seeking behaviors captured through routine clinical data. Recent integrative reviews have emphasized the growing relevance of climate change as a determinant of pain-related, neurological, and rehabilitation outcomes at the population level, further underscoring the need for long-term population-based analyses23,24.
Therefore, the present study extends previous biometeorological research on chronic musculoskeletal pain by focusing specifically on rheumatic and immune-mediated pain conditions within a primary care setting. We hypothesized that climatic variables are significantly associated with consultation rates for these conditions and that age, sex, and diagnostic category further modulate these associations.
The primary aim of this study was to analyze the relationship between climatic variables and the number of primary care consultations for patients with rheumatic and immune-mediated pain conditions over a 14-year period. The secondary objectives included: a) examining differences across diagnostic groups; b) evaluating the modifying effect of sex through stratified analysis; c) estimating climate-driven effects while adjusting for age as a confounding factor; and d) assessing the temporal evolution of consultation patterns to forecast future demand.
Methods
Data source and study population
A retrospective cohort study was conducted using data extracted from the electronic health records of three primary care centers in the Community of Madrid (Spain): El Abajón (Las Rozas de Madrid), Cerro del Aire (Majadahonda), and San Juan de la Cruz (Pozuelo de Alarcón). The study period spanned January 1, 2010, to December 31, 2023.
The study population included all adult patients (≥ 18 years old) who attended primary care consultations during the study period with diagnoses related to autoimmune and immune-mediated pain processes, specifically fibromyalgia and myalgia, as recorded in routine clinical practice. In this study, “myalgia” was used as a pragmatic primary care diagnostic category, capturing non-specific chronic musculoskeletal pain presentations potentially overlapping with immune-mediated symptom burdens, rather than as a strictly autoimmune diagnosis. Diagnoses were identified using the International Classification of Primary Care, Second Edition (ICPC-2) codes corresponding to fibromyalgia and myalgia. Weekly counts of consultations were used as the unit of analysis, and individual patient trajectories were not followed longitudinally.
Sociodemographic variables, including age and sex, were retrieved from electronic medical records, along with the consultation date and diagnostic code.
Meteorological data corresponding to the same geographical region and time frame were obtained from the Spanish State Meteorological Agency (AEMET) using a reference station in Pozuelo de Alarcón. This station is situated approximately 12–15 km from the three primary care centers and covers a total healthcare catchment area of approximately 140 km2 (ID3194Y; latitude 40°26′54″ N, longitude 3°48′48″ W).
The study protocol was approved by the Research Ethics Committee of Hospital Universitario Puerta de Hierro Majadahonda (PI 70/24, Act 06/2024). All procedures complied with the principles of the Declaration of Helsinki and ensured full anonymization and confidentiality of patient data. This study was conducted and reported in accordance with the STROBE statement for observational studies25.
Climatic variables
Meteorological data corresponding to the entire study period were obtained from the Spanish State Meteorological Agency (AEMET) and temporally and geographically matched to the primary care centers included in the analysis. Climatic information was retrieved from the reference meteorological station located in Pozuelo de Alarcón, which provides comprehensive coverage of the catchment area served by the participating health centers.
To assess the influence of environmental factors, the following variables were included: age, mean temperature, diurnal temperature range, day-to-day temperature change, wind direction, mean and gust speed, hours of sunshine, mean barometric pressure, and day-to-day change. In the case of wind direction, because it is a circular variable where North is at both 0° and 360°, it was segmented into the cosine and sine of the wind direction variables, with significance only considered if both had a significant effect simultaneously. Additionally, one-week lags were incorporated for mean temperature, barometric pressure, and precipitation26–29 to capture their cumulative effect, and a dummy variable was included to control for the structural effect of the Covid-19 pandemic.
Follow-up process for consultations
To capture longitudinal patterns in healthcare utilization related to autoimmune pain processes, the weekly counts of primary care consultations were calculated as follows:
Fibromyalgia
Myalgia
Overall autoimmune pain processes (combined fibromyalgia and myalgia)
The primary outcome was the number of weekly consultations for autoimmune pain. Secondary outcomes included the evaluation of temporal trends, differences by diagnostic category, and the influence of sex as a modifying factor and age as a covariate on the consultation patterns.
This approach allowed the assessment of population-level temporal fluctuations in healthcare demand, rather than individual disease trajectories.
Sample size
The analytical sample size for this study consisted of 728 weekly observations (analytical N). While the total volume of consultations varied across diagnostic groups, the 14-year study period provided sufficient longitudinal depth to model these counts. We employed Generalized Linear Models for Time Series of Counts with a Negative Binomial distribution, which is specifically robust for handling low-count data and over-dispersion. This approach allows for the estimation of Incidence Rate Ratios (IRR) by leveraging the entire temporal structure of the series, ensuring that even lower-frequency events are modeled with statistical rigor against climatic fluctuations.
Statistical analysis
Statistical analyses were performed using R version 4.1.3 (R Foundation for Statistical Computing, Vienna, Austria). The level of statistical significance was set at P < 0.05.
Quantitative variables were summarized as mean ± standard deviation, and categorical variables as absolute and relative frequencies (%).
The number of cases was analyzed from January 1, 2010, to December 31, 2023, using a time-series analysis of the total number of cases and medical consultations.
Data on medical consultations, originally recorded daily, were aggregated into weekly time units (ISO 8601). This was achieved by summing the count variables (incidence of consultations) and calculating the arithmetic mean for weather variables and age. This aggregation corrects for bias arising from variability in daily healthcare activity, such as the absence of records on weekends, holidays, or days without recorded consultations for specific pathologies, providing a complete time series, a necessary requirement for this type of analysis30. Daily raw meteorological data (including daily maximum and minimum temperatures and barometric pressures) provided by AEMET were processed into dynamic exposure indices. To better capture weather instability—a known trigger for musculoskeletal pain—we calculated the Diurnal Temperature Range (DTR) (the difference between daily maximum and minimum), and day-to-day fluctuations (the absolute difference between consecutive daily means) for both temperature and barometric pressure. These transformed variables, alongside average wind speed, gusts, and sunshine hours, were included in the models to provide a more clinically nuanced representation of environmental stress than raw absolute values alone29,31–33.
The selection of climatic variables and temporal aggregation strategy were guided by previous studies on biometeorology and chronic pain, which suggested that atmospheric conditions, particularly temperature, barometric pressure, wind characteristics, and solar exposure, may influence pain perception, symptom exacerbation, and healthcare-seeking behavior over short- to medium-term time windows6,10,14,26.
Given the nature of the dependent variable (weekly count of medical consultations), Time Series Generalized Linear Models (TSGLM) were used for the count data. A negative binomial distribution with a log-link function was chosen to model the overdispersion of healthcare incidence data. To control for serial dependence and seasonal inertia, the models incorporated an autoregressive structure, including both past observations (lags) and past means (moving averages) as internal regressors. This approach ensured that the estimated effects of climatic variables were independent of the temporal autocorrelation and seasonal cycles inherent in the 14-year series. Variables with a variance inflation factor (VIF) greater than 10 were excluded. Barometric pressure was entered into the models as a standardized variable (z-score) to improve numerical stability and comparability across meteorological predictors. The Holm-Bonferroni correction was applied for multiple comparisons within each fitted model, controlling for the family wise error rate. Stationarity was verified using the Augmented Dickey-Fuller (ADF) test. Compliance with the model assumptions and the absence of autocorrelation in the residuals were assessed using the Ljung-Box test. Additionally, the model’s precision and goodness of fit were quantified using Pearson’s variance of the residuals and the Root Mean Square Error (RMSE) to ensure the stability of the estimates. Finally, the presence of heteroscedasticity was assessed using Levene’s test based on simulated quantile residuals, a technique specific to counting data models that guarantees the validity of estimates.
Because the model coefficients were calculated on a logarithmic scale, they were transformed for interpretation as Incidence Rate Ratios (IRRs) along with their respective 95% confidence intervals. An IRR greater than 1 indicates an increase in the risk or frequency of consultations, whereas an IRR less than 1 indicates a protective effect or reduction in healthcare incidence for each unit increase in the independent variable.
The analysis was stratified by gender to identify possible differences in biometeorological susceptibility and avoid aggregation bias; however, in a complementary manner, a global analysis was carried out with the complete sample to maximize statistical power and obtain a general estimate of the environmental impact on healthcare demand, thus ensuring the representativeness of the results for the entire population served.
Missing data accounted for less than 2% of the observations and were imputed using predictive mean matching, as implemented in the mice package in R34. All analyses were double-checked to ensure the reproducibility, internal consistency, and robustness of the results. Detailed modeling procedures, diagnostic tests, and additional analytical outputs are provided in the Supplementary Materials.
Results
Across 728 weeks, we recorded 6715 primary care consultations related to autoimmune pain processes (mean 9.23 consultations/week), with a mean patient age of 53.10 ± 18.04 years old. Most consultations corresponded to female patients (62.07%), whereas males accounted for 37.93% of the consultations (Table 1).
Table 1.
Sample characteristics and atmospheric conditions (2010–2023).
| Overall | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Socio-demographic characteristics | |||||||||||||||
| Age | 53.10 ± 18.04 | 55.57 ± 17.54 | 53.43 ± 14.73 | 55.55 ± 18.07 | 54.45 ± 18.08 | 52.12 ± 18.46 | 52.35 ± 19.78 | 54.73 ± 18.55 | 53.54 ± 19.65 | 52.94 ± 16.88 | 50.56 ± 17.75 | 53.06 ± 16.89 | 52.87 ± 17.40 | 52.36 ± 18.25 | 52.08 ± 18.55 |
| Gender (Female), n(%) | 4168 (62.07) | 188 (54.65) | 238 (65.03) | 270 (63.08) | 232 (60.73) | 219 (58.71) | 264 (60.83) | 308 (66.09) | 346 (60.28) | 368 (66.67) | 354 (57.84) | 288 (62.07) | 336 (66.14) | 353 (64.89) | 404 (60.48) |
| Gender (Male), n(%) | 2547 (37.93) | 156 (45.35) | 128 (34.97) | 158 (36.92) | 150 (39.27) | 154 (41.29) | 170 (39.17) | 158 (33.91) | 228 (39.72) | 184 (33.33) | 258 (42.16) | 176 (37.93) | 172 (33.86) | 191 (35.11) | 264 (39.52) |
| Pain origin type | |||||||||||||||
| Overall cases autoimmune processes | 6715 | 344 | 366 | 428 | 382 | 373 | 434 | 466 | 574 | 552 | 612 | 464 | 508 | 544 | 668 |
| Fibromyalgia, n(%) | 347 (5.17) | 26 (7.56) | 20 (5.46) | 22 (5.14) | 14 (3.66) | 26 (6.97) | 16 (3.69) | 30 (6.44) | 28 (4.88) | 38 (6.88) | 34 (5.56) | 16 (3.45) | 28 (5.51) | 29 (5.33) | 20 (2.99) |
| Myalgia, n(%) | 6368 (94.83) | 318 (92.44) | 346 (94.54) | 406 (94.86) | 368 (96.34) | 347 (93.03) | 418 (96.31) | 436 (93.56) | 546 (95.12) | 514 (93.12) | 578 (94.44) | 448 (96.55) | 480 (94.49) | 515 (94.67) | 648 (97.01) |
| Atmospheric conditions | |||||||||||||||
| Average temperature (degrees Celsius) | 14.99 ± 7.66 | 14.74 ± 8.41 | 15.53 ± 7.36 | 14.13 ± 7.91 | 13.77 ± 7.73 | 14.91 ± 6.74 | 15.28 ± 7.74 | 14.73 ± 7.61 | 15.28 ± 7.87 | 14.57 ± 7.80 | 15.12 ± 7.38 | 15.07 ± 7.37 | 14.95 ± 7.29 | 16.14 ± 7.93 | 15.69 ± 7.77 |
| Average rainfall (l/m2) | 1.43 ± 4.84 | 2.02 ± 5.44 | 1.26 ± 3.66 | 1.09 ± 4.40 | 1.41 ± 4.48 | 1.48 ± 4.46 | 0.92 ± 3.21 | 1.84 ± 4.97 | 0.85 ± 3.23 | 1.94 ± 5.46 | 1.26 ± 4.91 | 1.40 ± 4.39 | 1.28 ± 4.74 | 1.48 ± 4.22 | 1.79 ± 8.18 |
| Average wind speed (m/s) | 2.87 ± 1.77 | 2.71 ± 1.77 | 2.54 ± 1.61 | 2.91 ± 1.84 | 2.90 ± 1.90 | 3.09 ± 1.88 | 2.68 ± 1.72 | 2.73 ± 1.92 | 2.55 ± 1.75 | 3.01 ± 1.71 | 3.29 ± 1.91 | 2.96 ± 1.75 | 2.94 ± 1.60 | 2.83 ± 1.54 | 3.01 ± 1.73 |
| Wind gusts (m/s) | 10.03 ± 3.64 | 10.09 ± 3.55 | 9.67 ± 3.32 | 10.26 ± 3.60 | 10.52 ± 3.57 | 10.60 ± 3.59 | 9.72 ± 3.74 | 9.89 ± 3.67 | 9.94 ± 3.67 | 10.10 ± 3.49 | 10.58 ± 4.07 | 9.75 ± 3.79 | 9.90 ± 3.47 | 9.92 ± 3.46 | 9.56 ± 3.82 |
| Sunshine hours | 8.20 ± 3.98 | 7.79 ± 4.26 | 8.30 ± 4.05 | 8.47 ± 3.92 | 8.16 ± 4.02 | 8.10 ± 3.97 | 8.31 ± 3.77 | 7.97 ± 4.17 | 8.75 ± 3.59 | 8.05 ± 3.95 | 8.82 ± 3.78 | 7.89 ± 4.20 | 7.86 ± 3.93 | 7.93 ± 4.26 | 8.46 ± 3.57 |
| Diurnal temperature range (degrees Celsius) | 13.74 ± 4.93 | 10.94 ± 3.82 | 12.90 ± 4.51 | 14.62 ± 5.10 | 13.68 ± 4.85 | 13.28 ± 4.80 | 13.99 ± 4.79 | 13.79 ± 5.25 | 15.47 ± 4.60 | 13.29 ± 4.73 | 14.47 ± 5.15 | 13.38 ± 5.08 | 13.95 ± 4.92 | 13.90 ± 4.93 | 14.64 ± 4.97 |
| Day-to-day temperature change (degrees Celsius) | 0.00 ± 1.97 | 0.01 ± 2.05 | − 0.01 ± 1.77 | − 0.01 ± 2.03 | 0.01 ± 2.04 | 0.01 ± 1.81 | 0.00 ± 2.08 | − 0.02 ± 1.85 | 0.02 ± 2.15 | 0.00 ± 1.98 | 0.00 ± 2.05 | − 0.01 ± 2.01 | 0.02 ± 1.99 | 0.00 ± 1.91 | − 0.01 ± 1.87 |
| Average barometric pressure (hPa) | 4.38 ± 2.30 | 4.95 ± 2.85 | 4.11 ± 1.91 | 4.21 ± 1.81 | 4.60 ± 2.65 | 4.43 ± 2.76 | 4.36 ± 2.22 | 4.52 ± 2.29 | 4.44 ± 2.46 | 4.56 ± 2.48 | 4.57 ± 2.45 | 4.27 ± 2.11 | 4.11 ± 1.98 | 4.15 ± 1.72 | 4.08 ± 2.04 |
| Day-to-day barometric pressure change (hPa) | 0.00 ± 2.71 | − 0.01 ± 3.34 | 0.01 ± 2.39 | 0.00 ± 2.24 | 0.01 ± 3.03 | 0.00 ± 2.95 | 0.01 ± 2.67 | − 0.01 ± 2.81 | 0.02 ± 2.79 | − 0.02 ± 2.91 | 0.00 ± 2.89 | 0.00 ± 2.62 | 0.00 ± 2.36 | 0.01 ± 2.26 | 0.00 ± 2.46 |
| Wind direction (cosine) | 0.89 ± 0.22 | 0.93 ± 0.05 | 0.94 ± 0.05 | 0.94 ± 0.05 | 0.94 ± 0.05 | 0.94 ± 0.04 | 0.94 ± 0.05 | 0.94 ± 0.05 | 0.92 ± 0.14 | 0.89 ± 0.23 | 0.86 ± 0.27 | 0.83 ± 0.33 | 0.78 ± 0.38 | 0.82 ± 0.35 | 0.81 ± 0.33 |
| Wind direction (sine) | 0.34 ± 0.20 | 0.32 ± 0.15 | 0.32 ± 0.14 | 0.32 ± 0.15 | 0.31 ± 0.16 | 0.32 ± 0.14 | 0.31 ± 0.15 | 0.31 ± 0.15 | 0.31 ± 0.17 | 0.35 ± 0.20 | 0.37 ± 0.21 | 0.37 ± 0.25 | 0.41 ± 0.27 | 0.38 ± 0.26 | 0.42 ± 0.24 |
Descriptive statistics for sociodemographic variables, diagnostic categories (fibromyalgia and myalgia), and weekly aggregated meteorological variables during the study period. Continuous variables are presented as mean ± standard deviation; categorical variables as n (%). Meteorological data were obtained from the reference AEMET station in Pozuelo de Alarcón, Spain.
°C, degrees Celsius; L/m2, liters per square meter; m/s, meters per second; hPa, hectopascals.
Regarding the diagnostic distribution, myalgia accounted for the vast majority of consultations (94.83%), whereas fibromyalgia accounted for 5.17% of all recorded autoimmune pain-related consultations. This distribution remained relatively stable over time, although annual fluctuations were observed (Table 1).
Temporal trends in consultation rates
Across the fourteen-year observation period, a gradual upward trend was observed in the total number of consultations for autoimmune pain processes, largely driven by increases in myalgia-related consultations, whereas fibromyalgia consultations remained relatively stable (Fig. 1).
Fig. 1.
Weekly consultation counts for autoimmune pain processes (2010–2023). Weekly primary care consultations for autoimmune processes, myalgia, and fibromyalgia were observed. Dotted lines represent raw weekly counts, and the solid black line represents a smoothed temporal trend. The vertical dashed lines indicate the start and end of the COVID-19 pandemic.
Model selection and diagnostics
After assessing multicollinearity, no predictor exceeded the predefined VIF threshold (VIF > 10); therefore, all 16 candidate explanatory variables were retained for the modeling. The absence of autocorrelation, overdispersion, and heteroscedasticity was verified in the models stratified by gender (Supplementary Material Table 1) and the aggregated model (Supplementary Material Table 2).
Associations between climatic variables and consultation rates
Despite the rigor of Holm’s correction, which tends to overshadow moderate effects in samples with low event frequencies, clinically relevant biometeorological trends were identified despite not reaching significance. The Diurnal temperature range emerged as a consistent predictor across multiple models, suggesting that intra-day temperature variability acts as a trigger for symptoms. Likewise, the magnitude of the effect of Average barometric pressure on the male Fibromyalgia model (+120.48%) points to a marked sensitivity of these patients to atmospheric changes (Table 2).
Table 2.
Final time-series generalized linear models for weekly consultation counts.
| Male | Female | ||||||
|---|---|---|---|---|---|---|---|
| IRR (SE) | 95%CI | ap value | IRR (SE) | 95%CI | ap value | ||
| Overall autoimmune processes | Age | 1.000 (0.001) | 0.998, 1.001 | Z=− 0.393, p>0.999 | 1.002 (0.002) | 0.998, 1.005 | Z=0.928, p>0.999 |
| Average temperature (degrees Celsius) | 0.999 (0.012) | 0.977, 1.022 | Z=− 0.075, p>0.999 | 0.982 (0.012) | 0.959, 1.005 | Z=− 1.564, p>0.999 | |
| Diurnal temperature range (degrees Celsius) | 1.006 (0.002) | 1.001, 1.010 | Z=2.429, p=0.272 | 1.023 (0.008) | 1.006, 1.039 | Z=2.739, p=0.105 | |
| Day-to-day temperature change (degrees Celsius) | 0.969 (0.077) | 0.833, 1.127 | Z=− 0.406, p>0.999 | 0.997 (0.064) | 0.880, 1.131 | Z=− 0.044, p>0.999 | |
| Average rainfall (l/m²) | 1.004 (0.012) | 0.980, 1.029 | Z=0.325, p>0.999 | 1.008 (0.011) | 0.987, 1.029 | Z=0.751, p>0.999 | |
| Average wind speed (m/s) | 1.005 (0.015) | 0.975, 1.035 | Z=0.296, p>0.999 | 1.060 (0.035) | 0.989, 1.135 | Z=1.649, p>0.999 | |
| Wind gusts (m/s) | 0.998 (0.009) | 0.982, 1.015 | Z=− 0.177, p>0.999 | 0.995 (0.015) | 0.966, 1.025 | Z=− 0.35, p>0.999 | |
| Sunshine hours | 0.997 (0.004) | 0.989, 1.005 | Z=− 0.816, p>0.999 | 0.982 (0.012) | 0.959, 1.004 | Z=− 1.584, p>0.999 | |
| Average barometric pressure (hPa) | 1.007 (0.029) | 0.952, 1.065 | Z=0.228, p>0.999 | 0.977 (0.024) | 0.931, 1.024 | Z=− 0.968, p>0.999 | |
| Day-to-day barometric pressure change (hPa) | 1.009 (0.070) | 0.880, 1.158 | Z=0.129, p>0.999 | 1.031 (0.069) | 0.901, 1.180 | Z=0.442, p>0.999 | |
| Wind direction (cosine) | 0.963 (0.056) | 0.863, 1.074 | Z=− 0.681, p>0.999 | 0.804 (0.147) | 0.603, 1.073 | Z=− 1.481, p>0.999 | |
| Wind direction (sine) | 1.037 (0.074) | 0.897, 1.200 | Z=0.496, p>0.999 | 0.818 (0.177) | 0.578, 1.157 | Z=− 1.136, p>0.999 | |
| Covid-19 period pandemic | 0.987 (0.006) | 0.976, 0.998 | Z=− 2.24, p=0.401 | 0.991 (0.022) | 0.949, 1.036 | Z=− 0.39, p>0.999 | |
| Average temperature (degrees Celsius) (previous week) | 1.000 (0.012) | 0.978, 1.023 | Z=0.043, p>0.999 | 1.015 (0.011) | 0.993, 1.038 | Z=1.37, p>0.999 | |
| Average barometric pressure (hPa) (previous week) | 0.990 (0.028) | 0.938, 1.045 | Z=− 0.37, p>0.999 | 1.007 (0.024) | 0.961, 1.054 | Z=0.282, p>0.999 | |
| Average rainfall (l/m²) (previous week) | 1.000 (0.013) | 0.976, 1.025 | Z=0.013, p>0.999 | 1.001 (0.011) | 0.980, 1.021 | Z=0.048, p>0.999 | |
| Fibromyalgia | Age | 0.971 (0.029) | 0.917, 1.029 | Z=− 0.996, p>0.999 | 1.000 (0.002) | 0.995, 1.005 | Z=0.043, p>0.999 |
| Average temperature (degrees Celsius) | 1.084 (0.067) | 0.951, 1.235 | Z=1.21, p>0.999 | 0.986 (0.017) | 0.953, 1.019 | Z=− 0.842, p>0.999 | |
| Diurnal temperature range (degrees Celsius) | 0.895 (0.156) | 0.659, 1.214 | Z=− 0.714, p>0.999 | 0.986 (0.022) | 0.944, 1.030 | Z=− 0.633, p>0.999 | |
| Day-to-day temperature change (degrees Celsius) | 1.027 (0.282) | 0.591, 1.785 | Z=0.095, p>0.999 | 1.085 (0.063) | 0.960, 1.227 | Z=1.303, p>0.999 | |
| Average rainfall (l/m²) | 0.764 (0.204) | 0.512, 1.139 | Z=− 1.322, p>0.999 | 0.997 (0.031) | 0.939, 1.059 | Z=− 0.086, p>0.999 | |
| Average wind speed (m/s) | 0.840 (0.399) | 0.384, 1.836 | Z=− 0.438, p>0.999 | 1.101 (0.084) | 0.933, 1.298 | Z=1.139, p>0.999 | |
| Wind gusts (m/s) | 1.100 (0.225) | 0.708, 1.709 | Z=0.426, p>0.999 | 0.977 (0.042) | 0.900, 1.061 | Z=− 0.551, p>0.999 | |
| Sunshine hours | 1.133 (0.195) | 0.773, 1.662 | Z=0.641, p>0.999 | 1.034 (0.029) | 0.977, 1.095 | Z=1.153, p>0.999 | |
| Average barometric pressure (hPa) | 2.205 (0.316) | 1.186, 4.098 | Z=2.5, p=0.211 | 0.996 (0.067) | 0.873, 1.136 | Z=− 0.061, p>0.999 | |
| Day-to-day barometric pressure change (hPa) | 0.417 (0.507) | 0.154, 1.126 | Z=− 1.726, p>0.999 | 1.144 (0.121) | 0.902, 1.452 | Z=1.112, p>0.999 | |
| Wind direction (cosine) | 0.002 (5.144) | 0.000, 54.603 | Z=− 1.182, p>0.999 | 0.606 (0.565) | 0.200, 1.832 | Z=− 0.888, p>0.999 | |
| Wind direction (sine) | 0.084 (4.006) | 0.000, 216.521 | Z=− 0.618, p>0.999 | 0.358 (0.871) | 0.065, 1.973 | Z=− 1.18, p>0.999 | |
| Covid-19 period pandemic | 5.477 (2.686) | 0.028, 1059.885 | Z=0.633, p>0.999 | 0.991 (0.049) | 0.901, 1.091 | Z=− 0.176, p>0.999 | |
| Average temperature (degrees Celsius) (previous week) | 1.066 (0.059) | 0.949, 1.197 | Z=1.075, p>0.999 | 1.009 (0.012) | 0.986, 1.033 | Z=0.791, p>0.999 | |
| Average barometric pressure (hPa) (previous week) | 1.322 (0.290) | 0.749, 2.332 | Z=0.964, p>0.999 | 0.950 (0.054) | 0.854, 1.056 | Z=− 0.957, p>0.999 | |
| Average rainfall (l/m²) (previous week) | 0.930 (0.202) | 0.626, 1.381 | Z=− 0.36, p>0.999 | 1.009 (0.028) | 0.954, 1.067 | Z=0.309, p>0.999 | |
| Myalgia | Age | 0.996 (0.002) | 0.992, 1.000 | Z=− 1.745, p=0.996 | 2.753 (0.844) | 0.527, 14.387 | Z=1.201, p>0.999 |
| Average temperature (degrees Celsius) | 1.034 (0.015) | 1.004, 1.064 | Z=2.248, p=0.417 | 1.002 (0.002) | 0.999, 1.006 | Z=1.23, p>0.999 | |
| Diurnal temperature range (degrees Celsius) | 1.017 (0.009) | 0.999, 1.034 | Z=1.874, p=0.903 | 0.986 (0.012) | 0.963, 1.010 | Z=− 1.151, p>0.999 | |
| Day-to-day temperature change (degrees Celsius) | 0.854 (0.079) | 0.732, 0.997 | Z=− 1.999, p=0.729 | 1.017 (0.007) | 1.002, 1.031 | Z=2.217, p=0.453 | |
| Average rainfall (l/m²) | 1.012 (0.013) | 0.986, 1.039 | Z=0.883, p>0.999 | 0.997 (0.067) | 0.874, 1.138 | Z=− 0.039, p>0.999 | |
| Average wind speed (m/s) | 1.026 (0.046) | 0.938, 1.123 | Z=0.567, p>0.999 | 1.010 (0.011) | 0.989, 1.032 | Z=0.943, p>0.999 | |
| Wind gusts (m/s) | 0.982 (0.021) | 0.943, 1.023 | Z=− 0.857, p>0.999 | 1.055 (0.032) | 0.991, 1.124 | Z=1.669, p>0.999 | |
| Sunshine hours | 0.982 (0.014) | 0.955, 1.010 | Z=− 1.287, p>0.999 | 0.989 (0.014) | 0.962, 1.017 | Z=− 0.792, p>0.999 | |
| Average barometric pressure (hPa) | 1.001 (0.030) | 0.943, 1.062 | Z=0.023, p>0.999 | 0.985 (0.011) | 0.964, 1.006 | Z=− 1.387, p>0.999 | |
| Day-to-day barometric pressure change (hPa) | 1.006 (0.085) | 0.852, 1.188 | Z=0.069, p>0.999 | 0.976 (0.025) | 0.929, 1.025 | Z=− 0.978, p>0.999 | |
| Wind direction (cosine) | 0.903 (0.231) | 0.573, 1.421 | Z=− 0.442, p>0.999 | 1.022 (0.071) | 0.890, 1.174 | Z=0.308, p>0.999 | |
| Wind direction (sine) | 1.366 (0.242) | 0.849, 2.197 | Z=1.285, p>0.999 | 0.887 (0.118) | 0.704, 1.118 | Z=− 1.016, p>0.999 | |
| Covid-19 period pandemic | 0.928 (0.042) | 0.854, 1.008 | Z=− 1.77, p=0.996 | 0.883 (0.154) | 0.653, 1.194 | Z=− 0.807, p>0.999 | |
| Average temperature (degrees Celsius) (previous week) | 0.969 (0.014) | 0.943, 0.995 | Z=− 2.317, p=0.369 | 0.994 (0.018) | 0.959, 1.031 | Z=− 0.307, p>0.999 | |
| Average barometric pressure (hPa) (previous week) | 0.994 (0.029) | 0.939, 1.052 | Z=− 0.205, p>0.999 | 1.012 (0.011) | 0.990, 1.035 | Z=1.083, p>0.999 | |
| Average rainfall (l/m²) (previous week) | 0.996 (0.013) | 0.971, 1.021 | Z=− 0.349, p>0.999 | 1.013 (0.024) | 0.966, 1.063 | Z=0.531, p>0.999 | |
IRR: Incidence rate ratio; SE: Standard error of the log-coefficient; 95%CI: 95% confidence interval.
asignificant if p < 0.05 (shown in red).
Forecasting analysis
Following the analysis of historical data (2010–2023), which showed a progressive and sustained increase in the volume of weekly consultations for all pathologies and sexes analyzed, predictive models suggested a stabilization phase for the projected four-year period. Unlike the linear growth observed in the previous decade, the forecast indicates that healthcare demand will tend to remain in a steady state, with a constant average number of weekly visits but subject to typical, stochastic, and climatic variability. This projection is consistent across all diagnostic groups and both sexes, suggesting that after the period of historical expansion, the system may be reaching a saturation point or stabilization in case detection at the population level (Fig. 2).
Fig. 2.
Forecasted weekly consultation counts by diagnostic category and sex (2024–2027). Time-series forecasts derived from sex-stratified negative binomial TSGLM models for overall autoimmune processes, myalgia, and fibromyalgia. Grey lines represent observed values; red lines represent predicted counts; shaded areas indicate 95% prediction intervals. The vertical dashed line indicates the beginning of the forecasting horizon.
Aggregated data analysis
The overall analysis of the sample showed no statistically significant associations. This attenuation of significance suggests marked heterogeneity in the biometeorological response according to sex, confirming that aggregating data into a single time series masks specific sensitivity patterns that only emerge after stratification. These findings validate the need for a gender-differentiated approach to accurately capture the impact of climate on healthcare demand (Supplementary Material Table 3). In the overall analysis, projections for the next four years show a change in behavior compared to the historical series. While the 2010–2023 period was characterized by high volatility and occasional spikes in demand, the global forecast suggests a stabilization of baseline consultation levels: In the Myalgia, Overall autoimmune processes prediction stabilizes in a range of between 10 and 15 weekly visits, breaking with the erratic trend of the past and showing a pattern of recurring seasonality. In the Fibromyalgia, the projected series remains at minimum demand levels (frequency close to 1 visit/week), reflecting the dispersion of this diagnosis when not stratified. The confidence intervals indicate that, although the mean is stable, the system must remain prepared for stochastic fluctuations, as climate variability will continue to cause deviations from the expected average. In conclusion, the global models predict a plateau in healthcare demand, indicating that, at the general population level, exponential growth in demand is not expected in the next four years, but rather a consolidation of current volumes (Supplementary Material. Figure 1).
Discussion
Principal findings
This fourteen-year ecological time-series analysis examined weekly primary care consultation counts for autoimmune and immune-mediated musculoskeletal pain in relation to multiple climatic variables.
After adjusting for temporal autocorrelation, seasonality, overdispersion, age, and multiple testing, only a limited number of climatic parameters showed statistically robust associations. The diurnal temperature range (DTR) was the most consistent predictor across the stratified consultation series, indicating that greater intradaily thermal variability was associated with increased healthcare utilization for both overall autoimmune pain processes and myalgia.
Importantly, these findings reflect temporal fluctuations in healthcare utilization rather than direct changes in individual pain intensity or disease activity. The unit of analysis was the weekly consultation count; therefore, the results should be interpreted at the population level. The absence of significant associations in the aggregated model further highlights the heterogeneity between the stratified consultation series and reinforces the importance of sex-specific time-series modeling in biometeorological research.
Overall, climatic influences on consultation demand were statistically detectable but quantitatively modest, suggesting that environmental variability constitutes one of multiple interacting determinants of healthcare utilization in routine primary care.
Comparison with previous studies
The present findings are broadly consistent with previous biometeorological research, indicating that climatic influences on musculoskeletal pain are generally modest, heterogeneous, and condition-specific rather than universal or deterministic6,10,14,29. In contrast to many prior studies that relied on self-reported pain intensity or short-term symptom diaries, our analysis examined healthcare utilization captured through routine primary care records over a fourteen-year period, thereby reflecting a system-level response to environmental variability.
In this context, consultation-based outcomes capture fluctuations in healthcare-seeking behavior rather than direct changes in individual pain intensity or inflammatory activity, which may partly explain the differences in the magnitude and consistency of reported climatic associations across study designs.
Consistent with this distinction, our study, which specifically examined short-term meteorological variability in relation to weekly consultation counts, did not identify consistent or clinically robust associations across most climatic parameters. Although isolated effects were observed for selected variables in the stratified models, the overall pattern suggests that healthcare utilization reflects a complex interplay between biological susceptibility, symptom perception, and health-seeking behavior. Therefore, our findings reinforce the notion that consultation-based outcomes capture system-level demand dynamics that may differ substantially from patient-reported pain severity.
Previous investigations in fibromyalgia populations have reported associations with temperature, humidity, and barometric pressure fluctuations, although the results have been inconsistent across geographic settings and methodological frameworks9,11,12,17. Our findings differ in that the diurnal temperature range, rather than the absolute mean temperature, emerged as the most consistent climatic correlate of consultation counts. This aligns with evidence suggesting that weather instability and rapid thermal variability may represent more relevant physiological stressors than static climatic averages29,31.
Similarly, rainfall has been infrequently examined in chronic pain research. While some studies have identified associations between precipitation and joint pain in rheumatic conditions21,22, others have failed to demonstrate consistent effects after controlling for confounding factors and temporal dependence6,10. In our models, lagged rainfall showed modest but statistically robust associations in specific diagnostic and sex-stratified series, suggesting that cumulative short-term environmental exposure may influence consultation dynamics in selected subgroups of patients.
Importantly, the absence of significant associations in the aggregated analysis reinforces previous observations of substantial heterogeneity in the climate–pain relationship10,15. Aggregated models may obscure subgroup-specific sensitivities that are only apparent through stratified time-series approaches. Therefore, our findings support the growing consensus that climate-related effects on musculoskeletal pain are neither uniform nor linear but are context-dependent and influenced by demographic and diagnostic composition.
Mechanistic considerations
Although the present study was not designed to directly evaluate biological mechanisms, several plausible pathways may explain the observed associations between climatic variability and consultation demand.
The diurnal temperature range reflects intradaily thermal instability, which may act as a physiological stressor through autonomic, vascular, and inflammatory modulation. Experimental evidence suggests that abrupt environmental changes influence sympathetic activation, peripheral vasomotor responses, and nociceptive sensitivity7,15. In chronic pain conditions, particularly those characterized by central sensitization, such as fibromyalgia, altered autonomic regulation and impaired thermal adaptation may amplify sensitivity to temperature variability9,11.
Rainfall-related associations may operate via indirect mechanisms. Increased precipitation can alter barometric pressure patterns, humidity levels, and behavioral routines, potentially influencing physical activity, mood, sleep quality, and health care-seeking behavior. Additionally, cumulative weather instability may exacerbate symptom perception in individuals with heightened meteorosensitivity3,10,17. It is important to emphasize that the present findings reflect consultation counts rather than inflammatory biomarkers or objective activities.
From a systems perspective, healthcare utilization is influenced not only by symptom fluctuations but also by accessibility, scheduling behaviors, and sociocultural factors. Therefore, climatic variability may interact with the behavioral and organizational determinants of care, amplifying small physiological effects into observable fluctuations in consultation volume. The modest magnitude of the identified Incidence Rate Ratios suggests that these mechanisms likely operate as incremental modulators rather than primary drivers of disease activity.
Clinical and public health implications
From a clinical standpoint, our findings suggest that climatic variability contributes modestly to short-term fluctuations in primary care demand for autoimmune and immune-mediated musculoskeletal pain. Although the effect sizes were small, the consistency of the DTR across stratified models indicates that weather instability may serve as a contextual factor influencing consultation patterns.
For healthcare planning, incorporating climatic variability into forecasting models may improve short-term resource allocation, particularly during periods characterized by pronounced thermal instability or sustained rainfall. However, the modest magnitude of the observed associations indicates that environmental exposure should not be considered a dominant determinant of healthcare demand but rather a component within a multifactorial system.
At the public health level, these findings align with broader discussions on climate change and the burden of chronic diseases23,24. As climate variability intensifies, even small incremental effects on healthcare utilization may have measurable impacts at the population scale. Therefore, longitudinal surveillance systems integrating meteorological and healthcare data could enhance anticipatory planning and resilience strategies in primary care settings.
Importantly, clinicians should avoid deterministic interpretations of the weather–pain relationship. The heterogeneity observed across the diagnostic and sex-stratified series underscores that climate sensitivity is not universal. Personalized approaches that incorporate patient-reported meteorosensitivity patterns and behavioral adaptation strategies may be more appropriate than generalized recommendations.
Limitations and future directions
This study had several limitations. First, the meteorological exposure was derived from a single reference station, which may not capture the microclimatic variability within the catchment area. Second, the use of electronic health records precluded access to clinical details, such as pain intensity, disease severity, medication use, or individual meteorosensitivity profiles, restricting interpretations to consultation-based outcomes. Third, the lack of a precise weekly population denominator prevented the calculation of exact incidence rates. However, since the catchment area remained stable over the 14-year period, any gradual shift in population size or healthcare policies is statistically accounted for by the models’ autoregressive and temporal trend components. Therefore, the observed fluctuations in consultation counts can be reliably attributed to the short-term impact of climatic variability on healthcare demand within a consolidated clinical setting. Nevertheless, the absence of an explicit weekly population denominator prevents formal incidence rate estimation and requires cautious interpretation of effect sizes strictly as changes in consultation volume rather than disease incidence.
Although the study period included extreme weather events, most notably Storm Filomena in January 2021, which caused temporary disruptions in healthcare access in Madrid, the long-term nature of the series and the inclusion of autoregressive components ensured that the overall climatic associations remained robust against such isolated outliers.
Potential confounders, including air pollution, pollen exposure, psychosocial stressors, and infectious disease dynamics, were not included in the study despite evidence that these factors may interact with meteorological conditions to influence pain-related outcomes13,15.
Future research should prioritize multi-station meteorological data, integrate environmental co-exposures, and employ advanced modeling approaches capable of capturing nonlinear and interactive effects. As highlighted by evidence derived from case-crossover designs, even under highly controlled analytical frameworks, the effects of climatic variables on musculoskeletal pain outcomes tend to be modest and condition-specific, underscoring the need for cautious interpretation and avoidance of oversimplified climate–pain narratives. Prospective designs incorporating patient-reported outcomes, wearable sensors, and ecological momentary assessments may further elucidate the patterns of individual vulnerability.
Conclusions
In this fourteen-year primary care time-series study, clinically relevant biometeorological trends were observed, particularly involving diurnal temperature variability and barometric pressure in selected stratified models.
The observed associations were diagnosis-specific and emerged primarily in sex-stratified consultation series, whereas no significant effects were detected in the aggregated analyses. These findings indicate heterogeneity in biometeorological sensitivity patterns at the population level in Spain.
The magnitude of the identified associations was modest, underscoring that climatic variability contributes to short-term fluctuations in healthcare demand but does not appear to be a dominant determinant of consultation rates in the long term.
Forecasting models suggest stabilization of weekly consultation volumes over the next four years, indicating consolidation rather than continued exponential growth in the demand for healthcare.
Future research integrating richer clinical data, environmental co-exposures, and individual-level outcomes is essential to further disentangle climate–health interactions and inform adaptive strategies in primary care systems facing increasing climatic variability.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We thank the patients in this study for making this possible. The authors gratefully acknowledge the Fundación para la Investigación e Innovación Biosanitaria de Atención Primaria (FIIBAP), Madrid, Spain, for its support in funding the article processing charge (APC) for this publication.
Institutional review board statement
The study was approved by the Ethics Committee for Research with Medicines (CEIM) of the Hospital Puerta de Hierro Majadahonda (PI 70/24, Act 06/2024). The principles of the Declaration of Helsinki were followed in this study.
Author contributions
Conceptualization, E.A.S.R. and J.N.C.Z.; methodology, E.A.S.R. and J.N.C.Z.; software, J.N.C.Z.; validation, all authors; formal analysis, J.N.C.Z. and E.A.S.R.; investigation, J.N.C.Z., C.C.d.V., S.G.T., R.A.Z., and P.G.P.; resources, J.N.C.Z.; data curation, J.N.C.Z.; writing—original draft preparation, J.N.C.Z. and E.A.S.R.; writing—review and editing, J.N.C.Z., E.C.-Y., E.M.O., and E.A.S.R.; visualization, J.N.C.Z. and E.A.S.R.; supervision, J.N.C.Z., E.C.-Y., E.M.O., and E.A.S.R.; project administration, J.N.C.Z.; funding acquisition, no funding. All the authors have read and agreed to the published version of the manuscript.
Funding
This study was conducted without external funding, ensuring that the analyses and findings were free of conflicts of interest and solely guided by clinical and research priorities.
Data availability
The data presented in this study are available upon request from the corresponding author.
Declarations
Competing interests
The authors declare no competing interests.
Informed consent
The need for informed consent was waived because of the retrospective and anonymous nature of the data used.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Rob Sillevis, Email: rsillevis@fgcu.edu.
Erika Meléndez-Oliva, Email: erika.melendez@ua.es.
Eleuterio A. Sánchez-Romero, Email: esanchezromero@fgcu.edu
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data presented in this study are available upon request from the corresponding author.