Time-Series Analysis of Staphylococcus aureus and MRSA Trends, Seasonality, and Pandemic-Associated Disruptions in a Tertiary-Care University Hospital (2016–2025)

Time-Series Analysis of Staphylococcus aureus and MRSA Trends, Seasonality, and Pandemic-Associated Disruptions in a Tertiary-Care University Hospital (2016–2025) | 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 Time-Series Analysis of Staphylococcus aureus and MRSA Trends, Seasonality, and Pandemic-Associated Disruptions in a Tertiary-Care University Hospital (2016–2025) Pedro Martínez-Ayala, Judith Carolina De Arcos-Jiménez, Adolfo Gómez-Quiroz, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7228547/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Methicillin-resistant Staphylococcus aureus (MRSA) remains a leading cause of healthcareassociated infections worldwide, yet longterm trends and the COVID-19 pandemic’s impact in Latin American hospitals are inadequately described. We aimed to characterize decadelong epidemiology, seasonal patterns, and pandemicassociated changes in hospitalonset S. aureus infections at a tertiarycare referral center. Methods We conducted a retrospective timeseries analysis of all clinically significant S. aureus isolates recovered from June 30, 2016 to March 29, 2025 at our university hospital. Weekly counts and proportions of MRSA were smoothed using LOESS (span = 0.3). Monotonic trends were evaluated via the Mann–Kendall test and Theil–Sen estimator. Seasonality was assessed with SeasonalTrend decomposition using Loess (STL), generalized additive models (GAMs) with cyclic splines, and Fourier spectral analysis. Interrupted timeseries (ITS) segmented regression estimated level and slope changes on March 1, 2020 (pandemic onset) and March 1, 2022 (postpeak stabilization). Results Among 6318 clinically significant S. aureus isolates, 1308 (21.7%) were MRSA. Proportion peaked at approximately 38% in late 2017 before undergoing a sustained decline to below 10% by June 2025 (median weekly Sen’s slope = − 0.00056; Mann–Kendall z = − 9.72, p < 0.001). ITS analysis of total S. aureus counts revealed an accelerated case incidence during the high SARS‑CoV‑2 circulation phase (March 1, 2020 – Feb 28, 2022), with a significant slope increase of + 0.0311 cases/week (SE 0.0086; p = 0.0003), yielding a net drift of + 0.0416 cases/week despite an initial level drop at pandemic onset. In contrast, segmented regression of MRSA counts showed no significant level change at the pandemic’s start (β₂ = − 3.490 cases; SE 4.048; p = 0.390) nor slope modification during high viral circulation (β₃ = − 0.018 cases/month; SE 0.2498; p = 0.943); instead, only the post‑peak stabilization period (from March 1, 2022) exhibited a statistically robust downward trend (β₅ = − 0.418 cases/month; SE 0.1528; p = 0.007). STL decomposition also revealed a stable 12‑month cycle, with consistent mid‑year peaks recurring annually between April and July. Conclusions Our decade‑long surveillance demonstrated a persistent, significant MRSA decline despite stable seasonal mid‑year peaks and a COVID‑19–associated surge in overall S. aureus cases without a parallel rise in resistance. Post‑pandemic, MRSA incidence decreased even more sharply. Elucidating these mechanisms via genomic epidemiology and multicenter studies will be essential to guide continuous, seasonally targeted interventions in similar settings. Staphylococcus aureus Methicillinresistant S. aureus Timeseries analysis Seasonality COVID-19 pandemic Infection control Antimicrobial stewardship Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Staphylococcus aureus is a globally prevalent pathogen that contributes significantly to the burden of disease across diverse populations and a wide range of clinical settings [ 1 ]. In high-income countries, S. aureus remains a leading cause of healthcare-associated infections (HAIs), including bloodstream infections (BSIs), surgical site infections (SSIs), and device-related infections [ 2 ]. In acute-care hospitals, S. aureus accounts for approximately 20% of infections in intensive care units (ICUs), with methicillin-resistant S. aureus (MRSA) responsible for nearly one-third of these cases [ 2 , 3 ]. S. aureus HAIs are associated with significant excess mortality (20.2% at one year), increased risk of long-term disability, prolonged hospital stays (by an average of 12 days), and increased healthcare costs [ 4 – 6 ]. Hospital-onset MRSA infections—especially bloodstream infections—have declined markedly and consistently in high-resource settings. For instance, in the U.S. Department of Veterans Affairs medical centers, overall S. aureus infections fell by 43% between 2005 and 2017, driven chiefly by a 55% reduction in MRSA, while methicillin-susceptible S. aureus (MSSA) infections decreased by 12 [ 5 ]. Although the initial decline was substantial, more recent surveillance—both in this and other reports—indicates that the downward trend in hospital-onset MRSA infections has slowed [ 2 , 6 , 7 ]. In Latin American hospitals, recent multicenter studies indicate that MRSA continues to represent a substantial proportion of S. aureus infections, accounting for approximately 44–45% of S. aureus bloodstream infections across multiple countries [ 8 ]. However, marked heterogeneity exists in MRSA prevalence by country and region, with some areas reporting higher rates (e.g., Peru) and others lower (e.g., Venezuela) [ 9 ]. In Argentina, national surveillance data indicate that while the overall incidence of Staphylococcus aureus infections has increased—driven primarily by MSSA—the incidence of MRSA infections has remained relatively stable in recent years [ 10 ]. MRSA prevalence in Mexican hospitals has remained substantial, with recent multicenter surveillance reporting methicillin resistance rates in S. aureus as high as 21–27% in clinical isolates from diverse regions and hospital types [ 11 , 12 ]. Comparative studies of resistance patterns in trauma patients treated in Mexican hospitals versus those in the United States further highlight the higher frequency of MRSA and other multidrug-resistant organisms in Mexican healthcare settings, emphasizing the need for robust infection control and antimicrobial stewardship [ 13 ]. Prior to the COVID-19 pandemic, multiple studies documented a gradual decline in the incidence and prevalence of MRSA in hospital settings, an effect attributed to sustained infection-control practices and antimicrobial stewardship efforts [ 14 , 15 ]. During the pandemic period, MRSA trends proved heterogeneous [ 16 – 18 ]. For instance, U.S. hospitals experienced a 15% increase in MRSA BSIs between 2019 and 2020, likely reflecting pandemic-related disruptions in infection-prevention practices [ 2 ]. Several investigations have reported reductions in MRSA acquisition ranging from 12.7–41% following the implementation of enhanced infection control measures, including intensified hand hygiene and expanded use of personal protective equipment [ 19 – 25 ]. In contrast, a large multicenter cohort study observed a 31.5% increase in hospital-onset MRSA and other antimicrobial-resistant (AMR) infections during the peak of the COVID-19 pandemic (March 2020 to February 2022) compared to the pre-pandemic period [ 26 ]. Although overall AMR rates returned to baseline as the pandemic subsided, hospital-onset AMR infections—including MRSA—remained elevated above pre-pandemic levels [ 26 ]. Similar increases have been reported by other authors [ 16 , 27 ]. Because data on trends in Staphylococcus aureus infections in hospital settings are scarce in Latin America—and most available studies predate the COVID-19 pandemic—this study aims to characterize the long-term epidemiology of hospital-onset S. aureus (including MRSA) infections at our tertiary-care referral university hospital in western Mexico, to identify and model underlying seasonal patterns, and to assess the pandemic’s impact on these trends, with the goal of informing future infection-prevention and antimicrobial-stewardship strategies. Methods 2.1 Setting, registry consultation, and data extraction This retrospective study was conducted at Antiguo Hospital Civil de Guadalajara, a tertiary referral and teaching institution affiliated with the University of Guadalajara in Jalisco, Mexico. The hospital primarily serves an uninsured population from western Mexico, providing specialized care for both adults and children. We consulted the historical laboratory databases to identify all positive Staphylococcus aureus cultures. Using the electronic medical record system, we then extracted patient demographic information, source of isolate, hospital department of origin, and antibiotic-susceptibility phenotypes. 2.2 Patient inclusion and antimicrobial susceptibility testing The study population comprised hospitalized patients with at least one clinical specimen culture positive for Staphylococcus aureus and a clinical syndrome compatible with active S. aureus infection. Phenotypic classification as MRSA or MSSA was based on cefoxitin disk diffusion and oxacillin MIC results originally generated by our clinical microbiology laboratory per CLSI M100 and M07 standards [ 28 ]. Archived zone diameters (30µg cefoxitin disk on Mueller–Hinton agar, 35°C for 16–18 h) and broth microdilution MIC values (≤ 2 µg/mL susceptible; ≥ 4 µg/mL resistant) were extracted directly from the laboratory information system. Additional antibiotic susceptibility profiles had been determined at the time of culture using the Vitek 2 automated platform (bioMérieux, Marcy l’Étoile, France) were likewise retrieved and incorporated into our analyses. Quality control was reviewed by verifying archived run records for the inclusion of S. aureus ATCC 25923 and ATCC 29213 reference strains in each antimicrobial susceptibility test batch. 2.3 Data management and quality control Prior to analysis, all extracted records underwent systematic data cleaning. Duplicate entries—defined as repeated isolates from the same patient, same sample type, and within a 7-day window—were removed to ensure non-redundant inclusion. Missing values in key fields (e.g., hospital service, sample source, susceptibility results) were evaluated; incomplete records that precluded classification or trend analysis were excluded. MRSA classification required concordant results from both cefoxitin disk diffusion and oxacillin MIC testing, as per CLSI standards [ 28 ]; in cases of discordance or missing confirmatory data, the isolate was excluded from resistance-specific analyses but retained for aggregate S. aureus counts. Colonization was differentiated from infection based on clinical documentation and sample context; surveillance cultures (e.g., nasal swabs without signs of infection) were excluded. 2.4 Statistical analysis Demographic data were summarized as simple relative frequencies. The percentage of positive results was calculated by dividing the number of positive tests by the total number of tests performed during the specified period and expressed as a percentage. Data normality was assessed using the Shapiro–Wilk test. Categorical variables were compared using Pearson’s chi-square test or Fisher’s exact test, as appropriate. 2.4.1 Temporal trend assessment of MRSA To stabilize week-to-week variation in sample size, we calculated both absolute weekly counts and their corresponding proportions for MSSA and MRSA. We then applied locally estimated scatterplot smoothing (LOESS) with a reduced span (0.3) to each series, yielding nonparametric estimates of the secular trend and its 95% confidence band. At each time point, LOESS fits a low-degree polynomial within a neighborhood defined by the span, thereby revealing underlying trends, transient shifts, and recurring seasonal cycles without imposing rigid parametric assumptions. This visualization guided subsequent formal time-series analyses (e.g., decomposition, trend testing, slope estimation). To formally assess monotonic change without relying on distributional assumptions, we applied the Mann–Kendall test to the weekly proportion series. We then used the Theil–Sen estimator to derive a robust, median-based estimate of the weekly rate of change in MRSA proportion, complete with nonparametric confidence intervals. To complement these nonparametric approaches, we fitted a generalized linear model with an identity link under a Gaussian variance structure, treating time as a continuous predictor of weekly MRSA proportion. This parametric framework allowed explicit hypothesis testing of the linear trend and model diagnostics to confirm adequacy of assumptions. 2.4.2 Seasonality To assess the presence of seasonal patterns in Staphylococcus aureus and MRSA, we applied a triangulated time-series approach combining smoothing decomposition, harmonic analysis, and non-parametric regression. We aggregated microbiology records into monthly counts over a 10-year period (2016–2025), generating time series of total S. aureus and MRSA isolates. Seasonality was evaluated based on three complementary methods. First, we applied Seasonal-Trend decomposition based on Loess (STL) to visually separate long-term trends, seasonal variation, and residual components in the time series. This method allowed for the visualization of intra-annual oscillations independent of non-stationary trends. Second, we used generalized additive models (GAMs) with cyclic spline terms to estimate the smooth effect of calendar month on case counts. This approach enabled formal hypothesis testing of seasonality by evaluating the statistical significance of the smooth term, while allowing flexible modeling of nonlinear seasonal shapes. Third, we implemented Fourier analysis to decompose the series into component frequencies and identify dominant periodicities. Together, these methods allowed both statistical and visual confirmation of seasonality, enabling robust inference even in the presence of non-linear trends and variable noise levels across the time series. 2.4.3 Interrupted time series analysis of COVID-19–associated changes in S. aureus and MRSA incidence We divided the time series into three biologically and epidemiologically relevant phases: Baseline period (July 1, 2016 – February 29, 2020): represents the pre–COVID-19 era. High viral circulation period (March 1, 2020 – February 28, 2022): encompasses the pandemic’s first three waves, including the Delta surge and the Omicron peak. Post-peak stabilization period (March 1, 2022 – end of study): marks the transition to endemic COVID-19 transmission following the Omicron apex in January–February 2022. Next, we fitted a segmented (piecewise) regression model to estimate the immediate level shifts at each breakpoint and the subsequent trend changes. Finally, we verified model assumptions by inspecting residual plots and conducting autocorrelation tests. 2.5 Software All analyses were carried out in R 4.5.1 (R Foundation for Statistical Computing, Vienna, Austria) within RStudio to ensure full reproducibility. Data import/export was handled with readxl and writexl; data wrangling and aggregation (weekly and monthly) with dplyr, tidyr, lubridate and zoo. Time-series visualizations—including raw points and LOESS curves (via ggplot2), arranged in multi-panel layouts (via gridExtra)—were supplemented by non-parametric trend testing (Mann–Kendall tests using the Kendall and trend packages) and robust slope estimation (Theil–Sen estimator). Seasonal structure was evaluated with STL decomposition (forecast), generalized additive models with cyclic cubic splines (mgcv), and Fourier spectral analysis (base fft()). Piecewise regression with two a priori breakpoints was fitted via stats::lm and refined with segmented, while diagnostics (residual plots, ACF/PACF, Durbin–Watson tests) used nlme and car. Finally, parametric linear trends were quantified using generalized linear models from the stats package. Results 3.1. Demographic, Clinical, and Microbiological Characteristics of the Study Population During the study period from June 30, 2016 to March 29, 2025, a total of 6318 clinically significant, documented and verified Staphylococcus aureus isolates were retrieved. Of these, 25.4% (n = 1604) were recovered from female patients and 74.6% (n = 4714) from male patients. Isolates were obtained across multiple clinical services, most frequently nephrology (14.3%), neurosurgery (8.9%), internal medicine (8.8%) and orthopedics (7.9%), with a significantly higher proportion of orthopedic isolates in male versus female patients (8.9% vs. 5.1%; p < 0.001). Blood cultures accounted for 25.9% of specimens, surgical-wound secretions for 9.1% and bronchial aspirates for 9.1%, with no significant sex‐based difference in blood‐culture yields (p = 0.291). Clinically, bloodstream infections comprised 30.7% of cases, followed by surgical‐site infections (19.9%) and ventilator‐associated pneumonias (14.5%), with surgical‐site infections more common in male patients (21.3% vs. 16.0%; p < 0.001). Methicillin resistance was observed in 21.7% of isolates, and was significantly more prevalent among males (23.3% vs. 16.7%; p < 0.001). A detailed breakdown of patient demographics, departmental distributions, infection sources, and clinical diagnoses by sex is provided in Table 1 . Table 1 Demographic, clinical, and microbiological characteristics of hospitalized patients with Staphylococcus aureus infections, stratified by sex (2016–2025). Variable Total n, (%) Female n, (%) Male n, (%) p-value Number of patients 6318 (100.0) 1604 (25.4) 4714 (74.6) - Department - Nephrology 905 (14.3) 244 (15.2) 661 (14) 0.258 Neurosurgery 561 (8.9) 118 (7.4) 443 (9.4) 0.015 Internal Medicine 556 (8.8) 149 (9.3) 407 (8.6) 0.455 Orthopedics & Traumatology 500 (7.9) 81 (5.1) 419 (8.9) < 0.001 Adult Infectious Diseases 422 (6.7) 106 (6.6) 316 (6.7) 0.94 Adult Intensive Care Unit (ICU) 356 (5.6) 79 (4.9) 277 (5.9) 0.172 General Surgery 266 (4.2) 55 (3.4) 211 (4.5) 0.083 Gynecology and Obstetrics 211 (3.3) 75 (4.7) 136 (2.9) 0.001 Pediatric Intensive Care Unit (PICU) 206 (3.3) 74 (4.6) 132 (2.8) 0.001 Cardiology 190 (3) 67 (4.2) 123 (2.6) 0.002 Pediatric Emergency 178 (2.8) 54 (3.4) 124 (2.6) 0.147 Cardiovascular Surgery 163 (2.6) 24 (1.5) 139 (3.0) 0.002 Gastroenterology 159 (2.5) 33 (2.1) 126 (2.7) 0.205 Adult Emergency 155 (2.5) 41 (2.6) 114 (2.4) 0.831 HIV/AIDS Care Unit 154 (2.4) 20 (1.2) 134 (2.8) < 0.001 Others 1327 (21) 382 (23.8) 945 (20.1) - Department Type Surgical Department 2196 (34.8) 484 (30.2) 1712 (36.3) < 0.001 Medical Department 4122 (65.2) 1120 (69.8) 3002 (63.7) < 0.001 Age Group Department 6318 (100) 1604 (100) 4714 (100) - Pediatric Department 840 (13.3) 265 (16.5) 575 (12.2) < 0.001 Adult Department 5478 (86.7) 1339 (83.5) 4139 (87.8) < 0.001 Infection Site Blood Culture 1638 (25.9) 391 (24.8) 1247 (26.4) 0.291 Surgical Wound Culture 578 (9.1) 128 (8) 450 (9.5) 0.067 Bronchial Aspirate 575 (9.1) 178 (11.1) 397 (8.4) 0.002 Unspecified Body Fluid 484 (7.7) 146 (9.1) 338 (7.2) 0.014 Unspecified Secretion 481 (7.6) 76 (4.7) 405 (8.6) < 0.001 Tracheal Aspirate 275 (4.4) 79 (4.9) 196 (4.2) 0.219 Tissue Culture 273 (4.3) 75 (4.7) 198 (4.2) 0.46 Abscess Culture 262 (4.1) 71 (4.4) 191 (4.1) 0.564 Sputum Culture 246 (3.9) 53 (3.3) 193 (4.1) 0.181 Peritoneal Fluid Culture 199 (3.1) 53 (3.3) 146 (3.1) 0.743 Wound secretion culture 191 (3) 51 (3.2) 140 (3.0) 0.734 Catheter Tip Culture 190 (3) 60 (3.7) 130 (2.8) 0.057 Urine Culture 185 (2.9) 43 (2.7) 142 (3.0) 0.552 Cerebrospinal Fluid Culture 110 (1.7) 24 (1.5) 86 (1.8) 0.449 Others 631 (10) 176 (11) 455 (9.7) - Clinical Diagnosis - Bloodstream infections 1828 (30.7) 451 (28.1) 1377 (29.2) 0.177 Surgical site infections 1259 (19.9) 256 (16) 1003 (21.3) < 0.001 Ventilator-associated pneumonia 916 (14.5) 272 (17) 644 (13.7) 0.001 Not specified 490 (7.8) 149 (9.3) 341 (7.2) 0.009 Hospital-aquired pneumonia 404 (6.4) 95 (5.9) 309 (6.6) 0.404 Tissue not specified 272 (4.3) 74 (4.6) 198 (4.2) 0.527 Abscess site not specified 262 (4.1) 71 (4.4) 191 (4.1) 0.564 Dyalisis related peritonitis 200 (3.2) 54 (3.4) 146 (3.1) 0.653 urinary tract infection 185 (2.9) 43 (2.7) 142 (3.0) 0.552 Healthcare-associated meningitis 110 (1.7) 24 (1.5) 86 (1.8) 0.449 Septic arthritis 79 (1.3) 14 (0.9) 65 (1.4) 0.148 Pharyngitis 70 (1.1) 23 (1.4) 47 (1.0) 0.192 Pressure ulcer infection 54 (0.9) 24 (1.5) 30 (0.6) 0.002 conjunctivitis 31 (0.5) 11 (0.7) 20 (0.4) 0.277 complicated intraabdominal infection 14 (0.2) 7 (0.4) 7 (0.1) 0.07 Others 34 (0.5) 17 (1.1) 17 (0.4) 0.002 MRSA (Cefoxitin screening) 6040 (95.6) 1541 (96.1) 4499 (95.4) - Resistant 1308 (21.7) 258 (16.7) 1050 (23.3) < 0.001 MRSA: Methicillin-Resistant Staphylococcus aureus . A total of 6318 Staphylococcus aureus isolates were recovered from 38 clinical departments during the study period. Sampling volumes varied substantially across departments, with the highest number of isolates recorded in Nephrology, Neurosurgery, Internal Medicine, and Orthopedics & Traumatology. Marked interdepartmental variability was observed in MRSA prevalence. While some services, such as Nephrology, showed relatively low MRSA rates, others—including General Surgery, Cardiac Surgery, Proctology and the Intensive Care Unit—exhibited substantially higher proportions of methicillin resistance. These findings highlight the heterogeneity of both Staphylococcus aureus burden and resistance patterns across clinical departments, underscoring the need for department-specific infection control strategies. The distribution of S. aureus isolates and the corresponding MRSA proportions by department are presented in Fig. 1 . Staphylococcus aureus isolates were obtained from patients classified into 22 diagnostic categories. The most frequent clinical syndromes were bloodstream infections (n = 1828; 28.9%), surgical site infections (n = 1259; 19.9%), and ventilator-associated pneumonia (n = 916; 14.5%). MRSA prevalence varied substantially by clinical syndrome, reaching its highest level in complicated intra-abdominal infections (35.7%; 5/14), followed by surgical site infections (28.1%; 354/1259), pressure ulcer infections (27.8%; 15/54), and nosocomial meningitis (27.3%; 30/110). Intermediate MRSA rates were observed in hospital-acquired pneumonia (17.8%; 72/404), urinary tract infections (18.4%; 34/185), and bloodstream infections (16.2%; 315/1938). These patterns are visually summarized in Fig. 1 , which presents the distribution of S. aureus isolates and corresponding MRSA rates by diagnostic category. Weekly proportions of MRSA among all S. aureus isolates were calculated for 40 hospital departments, yielding a total of 3202 observations. The overall mean MRSA proportion was 20.0%, with a LOESS-smoothed average of 19.97%. Surgical specialties exhibited the highest MRSA burden. In contrast, no MRSA cases were reported in Endocrinology or Pediatric Urology (0%), while the Burn Unit and Transplant Surgery had low mean proportions of 3.3% and 5.8%, respectively. LOESS smoothing confirmed that these inter-departmental disparities remained consistent throughout the study period. These trends are illustrated in Fig. 2 , which displays weekly MRSA proportions stratified by hospital department over the 2016–2025 period. We analyzed weekly trends in the proportion of MRSA across 22 clinical syndromes over a 10-year period using LOESS smoothing to capture both temporal and seasonal dynamics. As illustrated in Fig. 3 , MRSA rates varied substantially by diagnosis. Bloodstream infections, surgical site infections, and hospital-acquired pneumonia (including ventilator-associated pneumonia), despite historically high prevalence, showed a sustained decline. Nosocomial meningitis and pressure ulcer infections exhibited intermediate resistance levels (~ 24%), while urinary tract infections and ventilator-associated pneumonia consistently demonstrated low resistance rates. 3.2 Temporal trend assessment of MRSA Overall, during the study period, the weekly proportion of MRSA declined steadily, despite some fluctuations. LOESS smoothing of the weekly MRSA proportion (see Fig. 4 ) revealed an early peak in late 2017 at 35–40%, followed by a gradual descent interrupted by a modest rise around the COVID-19 surge in 2020–2021, and then a persistent downward trajectory to below 10% by mid-2025. Interestingly, while the COVID-19 pandemic period (2020–2022) was associated with a transient surge in the absolute number of Staphylococcus aureus infections, this increase was not mirrored in MRSA rates. As shown in Fig. 4 , the weekly frequency of MRSA remained relatively stable or declined, whereas MSSA exhibited a marked rise. These findings suggest that the pandemic-related increase in S. aureus cases was driven predominantly by MSSA, pointing to a potential shift in strain dynamics or risk profiles during this period. Formal trend tests, summarized in Table 2 , confirmed this impression: the Mann–Kendall statistic (z = − 9.72, p < 0.001) demonstrated a highly significant monotonic decrease, and Sen’s slope estimated a median weekly change of − 0.00056 (95% CI − 0.00066 to − 0.00045), corresponding to approximately a 2.9 percentage-point decline per year. A simple linear regression of MRSA proportion on week index also yielded a negative slope (β = − 7.9×10⁻⁵ per week, p < 0.001). Collectively, these results provide robust and complementary evidence that MRSA prevalence in our setting has markedly decreased over the study period. Table 2 Summary of non-parametric and parametric trend analyses of weekly MRSA proportion (2016–2025). Method Parameter Estimate 95% CI p-value Mann–Kendall test z –9.72 — < 0.001 Sen’s slope β (change per week) –0.00056 –0.00066 to − 0.00045 < 0.001 Linear regression β₁ (change per week) –7.90 × 10⁻⁵ — < 0.001 3.3 Seasonality The seasonal-trend decomposition of Staphylococcus aureus cases revealed a clear annual pattern in both the overall time series and the MRSA subset (Fig. 5 ). In the full S. aureus dataset, a pronounced mid-year seasonal oscillation was visible across all years, with consistent peaks between April and July. The MRSA series, although noisier and lower in volume, exhibited a similar seasonal structure, superimposed on a long-term declining trend following a post-pandemic peak in 2021. Residual components in both decompositions were randomly distributed without systematic bias, supporting the robustness of the seasonal and trend signals. The STL decomposition (Fig. 5 ) revealed a transient increase in Staphylococcus aureus burden during the COVID-19 pandemic, predominantly driven by methicillin-susceptible strains (MSSA). While the overall S. aureus time series exhibited a pronounced mid-pandemic peak in 2021 ( Fig. 5 b), the MRSA-specific trend did not follow this pattern; instead, it remained stable or declined Fig. 5 f). This decoupling between total case volume and MRSA-specific dynamics further supports the interpretation that the observed pandemic-related surge was primarily attributable to MSSA. Generalized additive models confirmed a statistically significant seasonal effect for S. aureus (p = 0.008, adjusted R² = 0.073), but not for MRSA (p = 0.12, adjusted R² = 0.023) (Fig. 6 a–b). The effect in S. aureus followed a smooth unimodal curve with a broad peak in mid-year months, consistent with STL findings. In contrast, the MRSA model produced a weaker, non-significant seasonal curve, likely reflecting lower monthly counts and reduced statistical power. Nonetheless, both series showed similar peak timing in the estimated seasonal component. Fourier spectral analysis provided harmonic confirmation of annual seasonality in both series (Fig. 6 c–d). In both S. aureus and MRSA, the spectrum exhibited a dominant frequency at 1/12 cycles per month, corresponding to a single annual cycle. The amplitude at this frequency was markedly stronger in the total S. aureus series, further supporting the presence of a stable and recurrent seasonal signal. In MRSA, the same annual frequency was detectable but with reduced amplitude, consistent with the lower-case volume and weaker GAM fit. 3.4 COVID-19–Associated Changes in S. aureus and MRSA Incidence In the ITS analysis of weekly S. aureus counts, we observed a small but significant upward drift during the pre-COVID-19 period (July 1, 2016–February 29, 2020) at + 0.0105 cases/week (SE 0.0030; p = 0.0005). At the onset of widespread SARS-CoV-2 transmission on March 1, 2020, there was an immediate level drop of 3.606 cases (SE 1.087; p = 0.0009), yet the subsequent slope accelerated by + 0.0311 cases/week (SE 0.0086; p = 0.0003). Combined, this yielded a four-fold increase to + 0.0416 cases/week through February 28, 2022. Following the Omicron peak (March 1, 2022), S. aureus counts again declined abruptly by 2.830 cases (SE 1.349; p = 0.036) and the trend reversed, decreasing by − 0.0163 cases/week (SE 0.0048; p = 0.0007), for a net post-peak slope of − 0.0058 cases/week. When the same segmented regression was applied to monthly MRSA case counts, the only statistically robust shift occurred after March 1, 2022: a significant downward trend of − 0.4183 cases/month (SE 0.1528; p = 0.0073). Neither the initial pandemic level change (–3.490 cases; SE 4.049; p = 0.391) nor its slope modification (–0.0181 cases/month; SE 0.2498; p = 0.943) reached significance, nor did the pre-Omicron level change (–7.759 cases; SE 5.136; p = 0.134). These findings indicate that, unlike total S. aureus , MRSA incidence did not surge during peak viral circulation but instead entered a sustained decline only in the post-peak stabilization phase. Table 3 Segmented regression coefficients (β) from Interrupted Time Series Analysis of Staphylococcus aureus and MRSA incidence across COVID-19 phases. β index Parameter Estimate (β) Std. Error t value p-value Component Staphylococcus aureus β₀ Intercept 4.668 0.635 7.346 < 0.001 Intercept β₁ Pre-COVID slope ( time ) 0.010 0.003 3.489 < 0.001 Baseline trend β₂ Level change on March 1, 2020 ( high_circ ) –3.606 1.087 –3.317 < 0.001 Level change β₃ Slope change during high circulation ( t_high ) 0.031 0.008 3.634 < 0.001 Trend change β₄ Level change on March 1, 2022 ( post_peak ) –2.829 1.348 –2.098 0.036 Level change β₅ Slope change post-peak ( t_post ) –0.016 0.004 –3.399 < 0.001 Trend change MRSA β₀ Intercept 12.024 2.410 4.988 < 0.001 Baseline level β₁ Baseline trend ( time ) 0.130 0.093 1.396 0.165 Baseline trend β₂ Level change on March 1, 2020 ( high_circ ) –3.490 4.048 –0.862 0.390 Level change β₃ Trend change during high circulation ( t_high ) –0.018 0.249 –0.072 0.942 Trend change β₄ Level change on March 1, 2022 ( post_peak ) –7.758 5.136 –1.511 0.134 Level change β₅ Trend change post-peak ( t_post ) –0.418 0.152 –2.737 0.007 Trend change βindex definitions Time – Baseline trend before the COVID-19 pandemic. High_circ – Level change at the onset of high SARS-CoV-2 circulation (March 1, 2020). T_high – Trend change during the high SARS-CoV-2 circulation period. Post_peak – Level change after the post-peak SARS-CoV-2 stabilization period (March 1, 2022). T_post – Trend change after the post-peak SARS-CoV-2 stabilization period. Discussion Over a ten-year surveillance period, we documented a marked and statistically significant decline in methicillin-resistant Staphylococcus aureus within our institution. MRSA accounted for approximately 38% of S. aureus isolates at its peak in late 2017 but declined steadily to less than 10% by June 2025. Using the non-parametric Theil–Sen estimator, we observed a median weekly reduction of 0.00056 in MRSA proportion—equivalent to an average annual decrease of approximately 2.9 percentage points. This downward monotonic trend was confirmed by the Mann–Kendall test (z = − 9.72, p < 0.001). Weekly case counts also exhibited a consistent seasonal pattern. Total S. aureus isolations increased predictably from April through July each year, forming a broad mid-year peak. A similar annual harmonic was observed in the MRSA subset, although the effect did not reach conventional statistical significance, likely due to the smaller number of resistant isolates. The timeline additionally revealed a perturbation associated with the COVID-19 pandemic. During the period of intense viral transmission (March 2020 to February 2022), overall S. aureus incidence increased, primarily driven by methicillin-susceptible strains. Notably, MRSA did not exhibit the surge reported in many high-income settings. Instead, following the peak associated with the Omicron wave, MRSA incidence declined more rapidly (post-peak slope: −0.418 cases per month, p = 0.007), suggesting that pandemic-related pressures did not reverse—and may have even accelerated—the pre-existing downward trend. These aggregate trends masked substantial clinical heterogeneity. At the departmental level, MRSA accounted for 40% of isolates in General Surgery, compared to only 12% in Nephrology. At the syndromic level, MRSA prevalence ranged from 29% in surgical site infections to 18% in bloodstream infections—the two most frequently observed infectious syndromes. Throughout the decade, male patients contributed the majority of cases and consistently exhibited higher resistance rates than females. Together, these findings depict a hospital where MRSA is broadly in decline, yet persists within specific high-risk departments and clinical syndromes, necessitating targeted infection-control strategies even as the overall trend remains favorable. Published reports indicate that, prior to the COVID-19 pandemic, MRSA incidence in hospital settings was either stable or declining, but during 2020–2021 many institutions experienced divergent trends—some documenting reductions of 28–41% associated with intensified hand hygiene and PPE use, while others reported increases in MRSA detection, reflecting heterogeneous pandemic impacts [ 16 , 18 , 29 , 30 ]. In contrast, our ten-year surveillance at a Mexican university hospital revealed a consistent decline in MRSA prevalence, with no significant surge during the COVID-19 period. Notably, following the Omicron peak, MRSA incidence entered a markedly accelerated decline (βpost-peak = − 0.418 cases·month⁻¹; p = 0.007). These findings underscore a sustained post-pandemic decline in MRSA incidence within a resource-limited Latin American setting. The progressive decline of MRSA may be multifactorial, involving both epidemiological and microbiological mechanisms. A primary driver is the implementation and intensification of infection control measures in healthcare settings [ 15 , 31 , 32 ]. These include improved hand hygiene, contact precautions, active surveillance, decolonization protocols, and enhanced environmental cleaning [ 32 , 33 ]. Such interventions have been temporally associated with marked reductions in hospital-onset MRSA infections, particularly in intensive care units, and have been shown to reduce transmission of healthcare-associated MRSA clones such as USA100 in the United States and ST228-I in Europe [ 31 , 33 ]. Clonal replacement is another important mechanism. Over time, certain epidemic MRSA clones have been supplanted by others with different fitness characteristics [ 34 , 35 ]. For example, in several European and North American hospitals, older clones (e.g., ST228-I, CC45-MRSA-IV) have been replaced by more successful clones (e.g., CC22-MRSA-IV, CC5-MRSA-II, CC8-IV), which may have altered virulence, transmissibility, or antimicrobial susceptibility profiles [ 33 , 34 ]. Some of these newer clones exhibit lower levels of antimicrobial resistance, possibly reflecting a fitness advantage in the absence of strong antibiotic selection pressure [ 36 , 37 ]. Microevolutionary changes within MRSA lineages may also play a role. There is evidence that the maintenance of methicillin resistance, conferred by the mecA gene, imposes a fitness cost on S. aureus in the absence of selective antibiotic pressure [ 38 , 39 ]. Loss of resistance determinants (e.g., mecA or SCCmec elements) has been observed in certain lineages, leading to the re-emergence of methicillin-susceptible S. aureus (MSSA) from previously resistant backgrounds, particularly when the selective advantage of resistance diminishes due to reduced antibiotic use or effective infection control [ 38 ]. The absence of a pronounced MRSA spike may reflect the influence of several mitigating factors. Broad antibiotic de-escalation guidelines implemented during the pandemic likely helped limit unnecessary use of broad-spectrum antibiotics [ 2 , 40 ]. Additionally, the early adoption of SARS-CoV-2–specific therapeutics may have reduced empiric antibiotic prescribing [ 41 ]. Continued MRSA admission screening for patients with severe pneumonia, alongside the sustained implementation of contact precautions, may also have contributed to the stable MRSA proportion during this period [ 40 , 42 ]. This combination of measures likely preserved previous gains in antimicrobial resistance control and facilitated the accelerated decline in MRSA incidence observed after 2022. This study has several limitations inherent to its retrospective, single-center design. First, as data were obtained exclusively from laboratory records and clinical registries, the findings may not be generalizable to other institutions, which may differ in epidemiological profiles, diagnostic capacities, or infection control practices. Second, although robust time-series analytical methods and microbiological quality control protocols were employed, no molecular characterization or clonal typing of MRSA strains was conducted, limiting insights into the genetic dynamics underlying the observed decline. Third, case classification was based on electronic medical records, which may introduce misclassification bias or non-systematic data loss. Moreover, although isolates deemed colonizing were excluded, distinguishing colonization from active infection remains inherently challenging in complex clinical contexts. Finally, although significant shifts associated with the COVID-19 pandemic were identified, the potential influence of concurrent external factors—such as changes in clinical workflows, antimicrobial pressure, or hospital occupancy patterns—could not be fully accounted for and may have independently influenced MRSA incidence. Future research should integrate high-resolution genomic and epidemiologic approaches to deepen our understanding of MRSA dynamics. Whole-genome sequencing of archived isolates will elucidate post-2022 clonal shifts, including potential USA300 incursions, while a multicenter network spanning Western Mexico can validate our seasonal and secular trends across diverse climates. Incorporating antimicrobial-use density metrics into interrupted-time-series analyses will quantify the precise impact of stewardship policies, and patient-level modeling—leveraging variables such as length of stay, device days, and immunosuppression—can refine risk-stratified prevention bundles. Finally, targeted qualitative audits of infection-control practices during surge versus baseline periods will contextualize pandemic-related resilience and inform adaptive strategies for future healthcare crises. Conclusions In a decade of continuous surveillance at our tertiary-care hospital in western Mexico, we observed a sustained decline in MRSA alongside persistent seasonal peaks of S. aureus and notable variability between clinical units, underscoring the enduring impact of robust infection‐control and antimicrobial‐stewardship measures even under pandemic pressures. These results highlight the need for unit‐specific reinforcement of hygiene and isolation practices during high‐transmission periods and support the resilience of our prevention protocols during healthcare crises. To further advance MRSA control, future efforts should focus on integrating genomic epidemiology, quantifying antimicrobial usage, and extending surveillance to multicenter networks, thereby guiding more precise, data‐driven interventions across diverse hospital settings. Abbreviations AMR Antimicrobial-Resistance ATCC American Type Culture Collection BSIs Bloodstream Infections CI Confidence Interval CLSI Clinical and Laboratory Standards Institute COVID-19 Coronavirus Disease 2019 GAM Generalized Additive Model HAIs Healthcare-Associated Infections ICUs Intensive Care Units LOESS Locally Estimated Scatterplot Smoothing MIC Minimum Inhibitory Concentration MRSA Methicillin-Resistant Staphylococcus aureus MSSA Methicillin-Susceptible Staphylococcus aureus PACF Partial Autocorrelation Function PPE Personal Protective Equipment PICU Pediatric Intensive Care Unit STL Seasonal-Trend Decomposition using Loess SSIs Surgical Site Infections VAP Ventilator-Associated Pneumonia Declarations Ethics approval and consent to participate This study involving humans was approved by the "Comité de Ética en Investigación en Ciencias de la Salud del Centro Universitario de Tlajomulco, Universidad de Guadalajara" (ethical approval number CUTLAJO/DS/CEICS/23/25) and conducted in accordance with the Helsinki declaration, national legislation, and institutional requirements. As the study was performed retrospectively and only deidentified data were used, informed consent was waived. Availability of data and materials The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Competing interests All authors declare that they have no financial or non-financial conflicts of interest in relation to this study. None of the institutions to which the authors are affiliated received payment or other support from commercial entities in connection with this work. Funding This research received no external funding. Authors' contributions Pedro Martínez-Ayala: Conceptualization, Investigation, Data Curation, Writing - Original Draft. Judith Carolina De Arcos-Jiménez : Conceptualization, Investigation, Data Curation, Writing - Original Draft. Adolfo Gómez-Quiroz : Investigation, Data curation, Writing- Original draft preparation. Brenda Berenice Avila-Cardenas : Investigation, Data curation, Writing- Original draft preparation. Roberto Miguel Damian-Negrete: Investigation, Data curation, Writing- Original draft preparation. Ana María López-Yáñez : Investigation, Methodology, Writing - Review & Editing. Leonardo García-Miranda : Investigation, Data Curation, Formal analysis, Writing - Original Draft. Jaime Briseno-Ramírez : Conceptualization, Methodology, Software, Formal analysis, Writing - Reviewing and Editing, Project administration. Acknowledgements The authors have no acknowledgments to report. References Chen H, Song S, Cui R, Feng Y-W, Ge P. Global trends in staphylococcus aureus-related lower respiratory infections from 1990 to 2021: findings from the 2021 global burden of disease report. Eur J Clin Microbiol Infect Dis. 2025;44:1455–69. Popovich KJ, Aureden K, Ham DC, Harris AD, Hessels AJ, Huang SS et al. 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Clin Infect Dis. 2023;77:1381–6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Guadalajara","correspondingAuthor":false,"prefix":"","firstName":"Brenda","middleName":"Berenice","lastName":"Avila-Cardenas","suffix":""},{"id":496266093,"identity":"e906e242-5642-4446-b736-ae97efbbd068","order_by":4,"name":"Roberto Miguel Damian-Negrete","email":"","orcid":"","institution":"Antiguo Hospital Civil de Guadalajara “Fray Antonio Alcalde”. Guadalajara","correspondingAuthor":false,"prefix":"","firstName":"Roberto","middleName":"Miguel","lastName":"Damian-Negrete","suffix":""},{"id":496266094,"identity":"619cfec6-743c-44dc-9dbe-8fccbb198312","order_by":5,"name":"Ana María López-Yáñez","email":"","orcid":"","institution":"Hospital Civil de Oriente","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"María","lastName":"López-Yáñez","suffix":""},{"id":496266095,"identity":"a82b5d59-f375-479a-b866-d67162182563","order_by":6,"name":"Leonardo García-Miranda","email":"","orcid":"","institution":"University of Guadalajara","correspondingAuthor":false,"prefix":"","firstName":"Leonardo","middleName":"","lastName":"García-Miranda","suffix":""},{"id":496266096,"identity":"8a98d4d8-f97b-4309-92bd-f32482c0de2f","order_by":7,"name":"Carlos Roberto Álvarez-Alba","email":"","orcid":"","institution":"Antiguo Hospital Civil de Guadalajara “Fray Antonio Alcalde”. Guadalajara","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"Roberto","lastName":"Álvarez-Alba","suffix":""},{"id":496266097,"identity":"f0aebb66-8438-41df-b15f-c0b241739ef0","order_by":8,"name":"Jaime Briseno-Ramirez","email":"data:image/png;base64,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","orcid":"","institution":"Hospital Civil de Oriente","correspondingAuthor":true,"prefix":"","firstName":"Jaime","middleName":"","lastName":"Briseno-Ramirez","suffix":""}],"badges":[],"createdAt":"2025-07-27 23:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7228547/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7228547/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88775460,"identity":"4950c80c-30fd-4b14-8ed4-9343ddc5c517","added_by":"auto","created_at":"2025-08-11 10:05:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":266622,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e Isolates and MRSA Proportion by Clinical Department (a) and Diagnostic Syndrome (b).\u003c/p\u003e\n\u003cp\u003eBar length reflects total number of isolates per category; color intensity denotes the corresponding MRSA rate (%).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7228547/v1/43dba4525067028af4aa172d.png"},{"id":88775461,"identity":"60371ca5-6a0c-4f4d-908f-9b5a79838f1c","added_by":"auto","created_at":"2025-08-11 10:05:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":377823,"visible":true,"origin":"","legend":"\u003cp\u003eWeekly MRSA proportions by hospital department (LOESS-Smoothed Trends, 2016–2025)\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7228547/v1/31a2fc61d6a27a1d9cd77838.png"},{"id":88775462,"identity":"1792bf40-6856-4cc6-b0a0-86ee140fbaf9","added_by":"auto","created_at":"2025-08-11 10:05:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":327560,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal dynamics of MRSA proportions by clinical syndrome (2016–2025)\u003c/p\u003e\n\u003cp\u003eWeekly proportions of MRSA among \u003cem\u003eS. aureus\u003c/em\u003e isolate for 22 diagnostic categories.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7228547/v1/2b1e28cd993341914b22c282.png"},{"id":88778148,"identity":"637d5458-c341-4416-81a2-aa8b4334f719","added_by":"auto","created_at":"2025-08-11 10:21:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":788490,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends in weekly counts and proportions of MSSA and MRSA isolates with LOESS Smoothing (2016–2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel a\u003c/strong\u003eshows the total weekly counts of S. aureus isolates with a LOESS‐smoothed curve; \u003cstrong\u003ePanel b\u003c/strong\u003e presents weekly counts disaggregated by MSSA and MRSA; and \u003cstrong\u003ePanel c\u003c/strong\u003edepicts the weekly proportions of each type, again with LOESS smoothing\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7228547/v1/e32d4258fbabd2328bb7a877.png"},{"id":88777546,"identity":"a326079d-ae45-444d-95c8-42269e996c4f","added_by":"auto","created_at":"2025-08-11 10:13:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":767446,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal-trend decomposition of \u003cem\u003eS. aureus\u003c/em\u003e and MRSA time series (2016–2025).\u003c/p\u003e\n\u003cp\u003ePanels a–d show the decomposition of weekly \u003cem\u003eS. aureus\u003c/em\u003e cases from 2016 to 2025 into (a) the observed series, (b) long-term trend, (c) seasonal component, and (d) residuals. Panels e–h present the STL decomposition of monthly MRSA cases from 2016 to 2025 into (e) the observed series, (f) long-term trend, (g) seasonal component, and (h) residuals. The STL method allows separation of time series into interpretable components to facilitate detection of structural changes or anomalies in temporal patterns.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7228547/v1/685be449a398953856df896c.png"},{"id":88777548,"identity":"f4c4215f-c15a-419a-bd14-7b3263c71d02","added_by":"auto","created_at":"2025-08-11 10:13:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":195340,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of seasonal modeling using GAM and Fourier analysis for \u003cem\u003eS. aureus\u003c/em\u003e and MRSA.\u003c/p\u003e\n\u003cp\u003e(a–b) Estimated seasonal effects from generalized additive models (GAMs) using cyclic splines for calendar month. A significant seasonal pattern was detected for \u003cem\u003eS. aureus\u003c/em\u003e (p = 0.008), with peak activity during mid-year months. No statistically significant seasonality was observed for MRSA (p = 0.12), though the estimated curve suggests a similar timing of peak incidence. (c–d) Fourier spectra of monthly \u003cem\u003eS. aureus\u003c/em\u003e and MRSA cases, respectively, showing a dominant annual frequency at 1/12 cycles per month in both series. The amplitude at this frequency is notably higher for \u003cem\u003eS. aureus\u003c/em\u003e, supporting stronger seasonal periodicity.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7228547/v1/8b52f4a59960ff8a25b39853.png"},{"id":101204682,"identity":"53915740-44e5-4d57-898a-4f884397a1eb","added_by":"auto","created_at":"2026-01-27 09:43:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4100388,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7228547/v1/18803c48-1911-4136-85cb-491614265775.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time-Series Analysis of Staphylococcus aureus and MRSA Trends, Seasonality, and Pandemic-Associated Disruptions in a Tertiary-Care University Hospital (2016–2025)","fulltext":[{"header":"Background","content":"\u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e is a globally prevalent pathogen that contributes significantly to the burden of disease across diverse populations and a wide range of clinical settings [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In high-income countries, \u003cem\u003eS. aureus\u003c/em\u003e remains a leading cause of healthcare-associated infections (HAIs), including bloodstream infections (BSIs), surgical site infections (SSIs), and device-related infections [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In acute-care hospitals, \u003cem\u003eS. aureus\u003c/em\u003e accounts for approximately 20% of infections in intensive care units (ICUs), with methicillin-resistant \u003cem\u003eS. aureus\u003c/em\u003e (MRSA) responsible for nearly one-third of these cases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. \u003cem\u003eS. aureus\u003c/em\u003e HAIs are associated with significant excess mortality (20.2% at one year), increased risk of long-term disability, prolonged hospital stays (by an average of 12 days), and increased healthcare costs [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHospital-onset MRSA infections\u0026mdash;especially bloodstream infections\u0026mdash;have declined markedly and consistently in high-resource settings. For instance, in the U.S. Department of Veterans Affairs medical centers, overall \u003cem\u003eS. aureus\u003c/em\u003e infections fell by 43% between 2005 and 2017, driven chiefly by a 55% reduction in MRSA, while methicillin-susceptible \u003cem\u003eS. aureus\u003c/em\u003e (MSSA) infections decreased by 12 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although the initial decline was substantial, more recent surveillance\u0026mdash;both in this and other reports\u0026mdash;indicates that the downward trend in hospital-onset MRSA infections has slowed [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Latin American hospitals, recent multicenter studies indicate that MRSA continues to represent a substantial proportion of \u003cem\u003eS. aureus\u003c/em\u003e infections, accounting for approximately 44\u0026ndash;45% of \u003cem\u003eS. aureus\u003c/em\u003e bloodstream infections across multiple countries [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, marked heterogeneity exists in MRSA prevalence by country and region, with some areas reporting higher rates (e.g., Peru) and others lower (e.g., Venezuela) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In Argentina, national surveillance data indicate that while the overall incidence of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infections has increased\u0026mdash;driven primarily by MSSA\u0026mdash;the incidence of MRSA infections has remained relatively stable in recent years [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. MRSA prevalence in Mexican hospitals has remained substantial, with recent multicenter surveillance reporting methicillin resistance rates in \u003cem\u003eS. aureus\u003c/em\u003e as high as 21\u0026ndash;27% in clinical isolates from diverse regions and hospital types [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Comparative studies of resistance patterns in trauma patients treated in Mexican hospitals versus those in the United States further highlight the higher frequency of MRSA and other multidrug-resistant organisms in Mexican healthcare settings, emphasizing the need for robust infection control and antimicrobial stewardship [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrior to the COVID-19 pandemic, multiple studies documented a gradual decline in the incidence and prevalence of MRSA in hospital settings, an effect attributed to sustained infection-control practices and antimicrobial stewardship efforts [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. During the pandemic period, MRSA trends proved heterogeneous [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For instance, U.S. hospitals experienced a 15% increase in MRSA BSIs between 2019 and 2020, likely reflecting pandemic-related disruptions in infection-prevention practices [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Several investigations have reported reductions in MRSA acquisition ranging from 12.7\u0026ndash;41% following the implementation of enhanced infection control measures, including intensified hand hygiene and expanded use of personal protective equipment [\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In contrast, a large multicenter cohort study observed a 31.5% increase in hospital-onset MRSA and other antimicrobial-resistant (AMR) infections during the peak of the COVID-19 pandemic (March 2020 to February 2022) compared to the pre-pandemic period [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Although overall AMR rates returned to baseline as the pandemic subsided, hospital-onset AMR infections\u0026mdash;including MRSA\u0026mdash;remained elevated above pre-pandemic levels [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Similar increases have been reported by other authors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBecause data on trends in \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infections in hospital settings are scarce in Latin America\u0026mdash;and most available studies predate the COVID-19 pandemic\u0026mdash;this study aims to characterize the long-term epidemiology of hospital-onset \u003cem\u003eS. aureus\u003c/em\u003e (including MRSA) infections at our tertiary-care referral university hospital in western Mexico, to identify and model underlying seasonal patterns, and to assess the pandemic\u0026rsquo;s impact on these trends, with the goal of informing future infection-prevention and antimicrobial-stewardship strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Setting, registry consultation, and data extraction\u003c/h2\u003e\u003cp\u003eThis retrospective study was conducted at Antiguo Hospital Civil de Guadalajara, a tertiary referral and teaching institution affiliated with the University of Guadalajara in Jalisco, Mexico. The hospital primarily serves an uninsured population from western Mexico, providing specialized care for both adults and children. We consulted the historical laboratory databases to identify all positive \u003cem\u003eStaphylococcus aureus\u003c/em\u003e cultures. Using the electronic medical record system, we then extracted patient demographic information, source of isolate, hospital department of origin, and antibiotic-susceptibility phenotypes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Patient inclusion and antimicrobial susceptibility testing\u003c/h2\u003e\u003cp\u003eThe study population comprised hospitalized patients with at least one clinical specimen culture positive for \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and a clinical syndrome compatible with active \u003cem\u003eS. aureus\u003c/em\u003e infection. Phenotypic classification as MRSA or MSSA was based on cefoxitin disk diffusion and oxacillin MIC results originally generated by our clinical microbiology laboratory per CLSI M100 and M07 standards [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Archived zone diameters (30\u0026micro;g cefoxitin disk on Mueller\u0026ndash;Hinton agar, 35\u0026deg;C for 16\u0026ndash;18 h) and broth microdilution MIC values (\u0026le;\u0026thinsp;2 \u0026micro;g/mL susceptible; \u0026ge; 4 \u0026micro;g/mL resistant) were extracted directly from the laboratory information system. Additional antibiotic susceptibility profiles had been determined at the time of culture using the Vitek 2 automated platform (bioM\u0026eacute;rieux, Marcy l\u0026rsquo;\u0026Eacute;toile, France) were likewise retrieved and incorporated into our analyses. Quality control was reviewed by verifying archived run records for the inclusion \u003cem\u003eof S. aureus\u003c/em\u003e ATCC 25923 and ATCC 29213 reference strains in each antimicrobial susceptibility test batch.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data management and quality control\u003c/h2\u003e\u003cp\u003ePrior to analysis, all extracted records underwent systematic data cleaning. Duplicate entries\u0026mdash;defined as repeated isolates from the same patient, same sample type, and within a 7-day window\u0026mdash;were removed to ensure non-redundant inclusion. Missing values in key fields (e.g., hospital service, sample source, susceptibility results) were evaluated; incomplete records that precluded classification or trend analysis were excluded. MRSA classification required concordant results from both cefoxitin disk diffusion and oxacillin MIC testing, as per CLSI standards [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; in cases of discordance or missing confirmatory data, the isolate was excluded from resistance-specific analyses but retained for aggregate S. aureus counts. Colonization was differentiated from infection based on clinical documentation and sample context; surveillance cultures (e.g., nasal swabs without signs of infection) were excluded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\u003cp\u003eDemographic data were summarized as simple relative frequencies. The percentage of positive results was calculated by dividing the number of positive tests by the total number of tests performed during the specified period and expressed as a percentage. Data normality was assessed using the Shapiro\u0026ndash;Wilk test. Categorical variables were compared using Pearson\u0026rsquo;s chi-square test or Fisher\u0026rsquo;s exact test, as appropriate.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1 Temporal trend assessment of MRSA\u003c/h2\u003e\u003cp\u003eTo stabilize week-to-week variation in sample size, we calculated both absolute weekly counts and their corresponding proportions for MSSA and MRSA. We then applied locally estimated scatterplot smoothing (LOESS) with a reduced span (0.3) to each series, yielding nonparametric estimates of the secular trend and its 95% confidence band. At each time point, LOESS fits a low-degree polynomial within a neighborhood defined by the span, thereby revealing underlying trends, transient shifts, and recurring seasonal cycles without imposing rigid parametric assumptions. This visualization guided subsequent formal time-series analyses (e.g., decomposition, trend testing, slope estimation).\u003c/p\u003e\u003cp\u003eTo formally assess monotonic change without relying on distributional assumptions, we applied the Mann\u0026ndash;Kendall test to the weekly proportion series. We then used the Theil\u0026ndash;Sen estimator to derive a robust, median-based estimate of the weekly rate of change in MRSA proportion, complete with nonparametric confidence intervals.\u003c/p\u003e\u003cp\u003eTo complement these nonparametric approaches, we fitted a generalized linear model with an identity link under a Gaussian variance structure, treating time as a continuous predictor of weekly MRSA proportion. This parametric framework allowed explicit hypothesis testing of the linear trend and model diagnostics to confirm adequacy of assumptions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2 Seasonality\u003c/h2\u003e\u003cp\u003eTo assess the presence of seasonal patterns in \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and MRSA, we applied a triangulated time-series approach combining smoothing decomposition, harmonic analysis, and non-parametric regression. We aggregated microbiology records into monthly counts over a 10-year period (2016\u0026ndash;2025), generating time series of total \u003cem\u003eS. aureus\u003c/em\u003e and MRSA isolates. Seasonality was evaluated based on three complementary methods.\u003c/p\u003e\u003cp\u003eFirst, we applied Seasonal-Trend decomposition based on Loess (STL) to visually separate long-term trends, seasonal variation, and residual components in the time series. This method allowed for the visualization of intra-annual oscillations independent of non-stationary trends.\u003c/p\u003e\u003cp\u003eSecond, we used generalized additive models (GAMs) with cyclic spline terms to estimate the smooth effect of calendar month on case counts. This approach enabled formal hypothesis testing of seasonality by evaluating the statistical significance of the smooth term, while allowing flexible modeling of nonlinear seasonal shapes. Third, we implemented Fourier analysis to decompose the series into component frequencies and identify dominant periodicities. Together, these methods allowed both statistical and visual confirmation of seasonality, enabling robust inference even in the presence of non-linear trends and variable noise levels across the time series.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3 Interrupted time series analysis of COVID-19\u0026ndash;associated changes in \u003cem\u003eS. aureus\u003c/em\u003e and MRSA incidence\u003c/h2\u003e\u003cp\u003eWe divided the time series into three biologically and epidemiologically relevant phases:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eBaseline period (July 1, 2016 \u0026ndash; February 29, 2020): represents the pre\u0026ndash;COVID-19 era.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHigh viral circulation period (March 1, 2020 \u0026ndash; February 28, 2022): encompasses the pandemic\u0026rsquo;s first three waves, including the Delta surge and the Omicron peak.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePost-peak stabilization period (March 1, 2022 \u0026ndash; end of study): marks the transition to endemic COVID-19 transmission following the Omicron apex in January\u0026ndash;February 2022.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eNext, we fitted a segmented (piecewise) regression model to estimate the immediate level shifts at each breakpoint and the subsequent trend changes. Finally, we verified model assumptions by inspecting residual plots and conducting autocorrelation tests.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Software\u003c/h2\u003e\u003cp\u003eAll analyses were carried out in R 4.5.1 (R Foundation for Statistical Computing, Vienna, Austria) within RStudio to ensure full reproducibility. Data import/export was handled with readxl and writexl; data wrangling and aggregation (weekly and monthly) with dplyr, tidyr, lubridate and zoo. Time-series visualizations\u0026mdash;including raw points and LOESS curves (via ggplot2), arranged in multi-panel layouts (via gridExtra)\u0026mdash;were supplemented by non-parametric trend testing (Mann\u0026ndash;Kendall tests using the Kendall and trend packages) and robust slope estimation (Theil\u0026ndash;Sen estimator). Seasonal structure was evaluated with STL decomposition (forecast), generalized additive models with cyclic cubic splines (mgcv), and Fourier spectral analysis (base fft()). Piecewise regression with two a priori breakpoints was fitted via stats::lm and refined with segmented, while diagnostics (residual plots, ACF/PACF, Durbin\u0026ndash;Watson tests) used nlme and car. Finally, parametric linear trends were quantified using generalized linear models from the stats package.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Demographic, Clinical, and Microbiological Characteristics of the Study Population\u003c/h2\u003e\u003cp\u003eDuring the study period from June 30, 2016 to March 29, 2025, a total of 6318 clinically significant, documented and verified \u003cem\u003eStaphylococcus aureus\u003c/em\u003e isolates were retrieved. Of these, 25.4% (n\u0026thinsp;=\u0026thinsp;1604) were recovered from female patients and 74.6% (n\u0026thinsp;=\u0026thinsp;4714) from male patients. Isolates were obtained across multiple clinical services, most frequently nephrology (14.3%), neurosurgery (8.9%), internal medicine (8.8%) and orthopedics (7.9%), with a significantly higher proportion of orthopedic isolates in male versus female patients (8.9% vs. 5.1%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Blood cultures accounted for 25.9% of specimens, surgical-wound secretions for 9.1% and bronchial aspirates for 9.1%, with no significant sex‐based difference in blood‐culture yields (p\u0026thinsp;=\u0026thinsp;0.291). Clinically, bloodstream infections comprised 30.7% of cases, followed by surgical‐site infections (19.9%) and ventilator‐associated pneumonias (14.5%), with surgical‐site infections more common in male patients (21.3% vs. 16.0%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Methicillin resistance was observed in 21.7% of isolates, and was significantly more prevalent among males (23.3% vs. 16.7%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A detailed breakdown of patient demographics, departmental distributions, infection sources, and clinical diagnoses by sex is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eDemographic, clinical, and microbiological characteristics of hospitalized patients with \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infections, stratified by sex (2016\u0026ndash;2025).\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eTotal\u003c/p\u003e\u003cp\u003en, (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003cp\u003en, (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003en, (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6318 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1604 (25.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4714 (74.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepartment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNephrology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e905 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e244 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e661 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeurosurgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e561 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e443 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternal Medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e556 (8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e407 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrthopedics \u0026amp; Traumatology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e500 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e419 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdult Infectious Diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e422 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e316 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdult Intensive Care Unit (ICU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e356 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e277 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral Surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e266 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e211 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGynecology and Obstetrics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e211 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePediatric Intensive Care Unit (PICU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e206 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e132 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e190 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePediatric Emergency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e178 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular Surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e163 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastroenterology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e159 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdult Emergency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e114 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.831\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIV/AIDS Care Unit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e154 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e134 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1327 (21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e382 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e945 (20.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepartment Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2196 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e484 (30.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1712 (36.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4122 (65.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1120 (69.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3002 (63.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Group Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6318 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1604 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4714 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePediatric Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e840 (13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e265 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e575 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdult Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5478 (86.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1339 (83.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4139 (87.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection Site\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1638 (25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e391 (24.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1247 (26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical Wound Culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e578 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e450 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBronchial Aspirate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e575 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e178 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e397 (8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnspecified Body Fluid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e484 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e338 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnspecified Secretion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e481 (7.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e405 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTracheal Aspirate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e275 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e196 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTissue Culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e273 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e198 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbscess Culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e262 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e191 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSputum Culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e246 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e193 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeritoneal Fluid Culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e199 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e146 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWound secretion culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e191 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.734\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatheter Tip Culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e190 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine Culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e185 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e142 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.552\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrospinal Fluid Culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e631 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e176 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e455 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical Diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBloodstream infections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1828 (30.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e451 (28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1377 (29.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical site infections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1259 (19.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e256 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1003 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVentilator-associated pneumonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e916 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e272 (17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e644 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e490 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e341 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital-aquired pneumonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e404 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e309 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTissue not specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e272 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e198 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbscess site not specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e262 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e191 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyalisis related peritonitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e146 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eurinary tract infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e185 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e142 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.552\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthcare-associated meningitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptic arthritis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePharyngitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePressure ulcer infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003econjunctivitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecomplicated intraabdominal infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRSA (Cefoxitin screening)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6040 (95.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1541 (96.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4499 (95.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResistant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1308 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e258 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1050 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eMRSA: Methicillin-Resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eA total of 6318 \u003cem\u003eStaphylococcus aureus\u003c/em\u003e isolates were recovered from 38 clinical departments during the study period. Sampling volumes varied substantially across departments, with the highest number of isolates recorded in Nephrology, Neurosurgery, Internal Medicine, and Orthopedics \u0026amp; Traumatology. Marked interdepartmental variability was observed in MRSA prevalence. While some services, such as Nephrology, showed relatively low MRSA rates, others\u0026mdash;including General Surgery, Cardiac Surgery, Proctology and the Intensive Care Unit\u0026mdash;exhibited substantially higher proportions of methicillin resistance. These findings highlight the heterogeneity of both \u003cem\u003eStaphylococcus aureus\u003c/em\u003e burden and resistance patterns across clinical departments, underscoring the need for department-specific infection control strategies. The distribution of \u003cem\u003eS. aureus\u003c/em\u003e isolates and the corresponding MRSA proportions by department are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e isolates were obtained from patients classified into 22 diagnostic categories. The most frequent clinical syndromes were bloodstream infections (n\u0026thinsp;=\u0026thinsp;1828; 28.9%), surgical site infections (n\u0026thinsp;=\u0026thinsp;1259; 19.9%), and ventilator-associated pneumonia (n\u0026thinsp;=\u0026thinsp;916; 14.5%). MRSA prevalence varied substantially by clinical syndrome, reaching its highest level in complicated intra-abdominal infections (35.7%; 5/14), followed by surgical site infections (28.1%; 354/1259), pressure ulcer infections (27.8%; 15/54), and nosocomial meningitis (27.3%; 30/110). Intermediate MRSA rates were observed in hospital-acquired pneumonia (17.8%; 72/404), urinary tract infections (18.4%; 34/185), and bloodstream infections (16.2%; 315/1938). These patterns are visually summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which presents the distribution of \u003cem\u003eS. aureus\u003c/em\u003e isolates and corresponding MRSA rates by diagnostic category.\u003c/p\u003e\u003cp\u003eWeekly proportions of MRSA among all \u003cem\u003eS. aureus\u003c/em\u003e isolates were calculated for 40 hospital departments, yielding a total of 3202 observations. The overall mean MRSA proportion was 20.0%, with a LOESS-smoothed average of 19.97%. Surgical specialties exhibited the highest MRSA burden. In contrast, no MRSA cases were reported in Endocrinology or Pediatric Urology (0%), while the Burn Unit and Transplant Surgery had low mean proportions of 3.3% and 5.8%, respectively. LOESS smoothing confirmed that these inter-departmental disparities remained consistent throughout the study period. These trends are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which displays weekly MRSA proportions stratified by hospital department over the 2016\u0026ndash;2025 period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe analyzed weekly trends in the proportion of MRSA across 22 clinical syndromes over a 10-year period using LOESS smoothing to capture both temporal and seasonal dynamics. As illustrated in \u003cb\u003eFig.\u0026nbsp;3\u003c/b\u003e, MRSA rates varied substantially by diagnosis. Bloodstream infections, surgical site infections, and hospital-acquired pneumonia (including ventilator-associated pneumonia), despite historically high prevalence, showed a sustained decline. Nosocomial meningitis and pressure ulcer infections exhibited intermediate resistance levels (~\u0026thinsp;24%), while urinary tract infections and ventilator-associated pneumonia consistently demonstrated low resistance rates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Temporal trend assessment of MRSA\u003c/h2\u003e\u003cp\u003eOverall, during the study period, the weekly proportion of MRSA declined steadily, despite some fluctuations. LOESS smoothing of the weekly MRSA proportion (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed an early peak in late 2017 at 35\u0026ndash;40%, followed by a gradual descent interrupted by a modest rise around the COVID-19 surge in 2020\u0026ndash;2021, and then a persistent downward trajectory to below 10% by mid-2025.\u003c/p\u003e\u003cp\u003eInterestingly, while the COVID-19 pandemic period (2020\u0026ndash;2022) was associated with a transient surge in the absolute number of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infections, this increase was not mirrored in MRSA rates. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the weekly frequency of MRSA remained relatively stable or declined, whereas MSSA exhibited a marked rise. These findings suggest that the pandemic-related increase in \u003cem\u003eS. aureus\u003c/em\u003e cases was driven predominantly by MSSA, pointing to a potential shift in strain dynamics or risk profiles during this period.\u003c/p\u003e\u003cp\u003eFormal trend tests, summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, confirmed this impression: the Mann\u0026ndash;Kendall statistic (z = \u0026minus;\u0026thinsp;9.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) demonstrated a highly significant monotonic decrease, and Sen\u0026rsquo;s slope estimated a median weekly change of \u0026minus;\u0026thinsp;0.00056 (95% CI \u0026minus;\u0026thinsp;0.00066 to \u0026minus;\u0026thinsp;0.00045), corresponding to approximately a 2.9 percentage-point decline per year. A simple linear regression of MRSA proportion on week index also yielded a negative slope (β = \u0026minus;\u0026thinsp;7.9\u0026times;10⁻⁵ per week, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Collectively, these results provide robust and complementary evidence that MRSA prevalence in our setting has markedly decreased over the study period.\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\u003eSummary of non-parametric and parametric trend analyses of weekly MRSA proportion (2016\u0026ndash;2025).\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=\"left\" 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=\"left\" 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\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMann\u0026ndash;Kendall test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;9.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSen\u0026rsquo;s slope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ (change per week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.00056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.00066 to \u0026minus;\u0026thinsp;0.00045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLinear regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ₁ (change per week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;7.90 \u0026times; 10⁻⁵\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Seasonality\u003c/h2\u003e\u003cp\u003eThe seasonal-trend decomposition of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e cases revealed a clear annual pattern in both the overall time series and the MRSA subset (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the full \u003cem\u003eS. aureus\u003c/em\u003e dataset, a pronounced mid-year seasonal oscillation was visible across all years, with consistent peaks between April and July. The MRSA series, although noisier and lower in volume, exhibited a similar seasonal structure, superimposed on a long-term declining trend following a post-pandemic peak in 2021. Residual components in both decompositions were randomly distributed without systematic bias, supporting the robustness of the seasonal and trend signals.\u003c/p\u003e\u003cp\u003eThe STL decomposition (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e) revealed a transient increase in \u003cem\u003eStaphylococcus aureus\u003c/em\u003e burden during the COVID-19 pandemic, predominantly driven by methicillin-susceptible strains (MSSA). While the overall \u003cem\u003eS. aureus\u003c/em\u003e time series exhibited a pronounced mid-pandemic peak in 2021 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), the MRSA-specific trend did not follow this pattern; instead, it remained stable or declined Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). This decoupling between total case volume and MRSA-specific dynamics further supports the interpretation that the observed pandemic-related surge was primarily attributable to MSSA.\u003c/p\u003e\u003cp\u003eGeneralized additive models confirmed a statistically significant seasonal effect for \u003cem\u003eS. aureus\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.008, adjusted R\u0026sup2; = 0.073), but not for MRSA (p\u0026thinsp;=\u0026thinsp;0.12, adjusted R\u0026sup2; = 0.023) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea\u0026ndash;b). The effect in \u003cem\u003eS. aureus\u003c/em\u003e followed a smooth unimodal curve with a broad peak in mid-year months, consistent with STL findings. In contrast, the MRSA model produced a weaker, non-significant seasonal curve, likely reflecting lower monthly counts and reduced statistical power. Nonetheless, both series showed similar peak timing in the estimated seasonal component.\u003c/p\u003e\u003cp\u003eFourier spectral analysis provided harmonic confirmation of annual seasonality in both series (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ec\u0026ndash;d). In both \u003cem\u003eS. aureus\u003c/em\u003e and MRSA, the spectrum exhibited a dominant frequency at 1/12 cycles per month, corresponding to a single annual cycle. The amplitude at this frequency was markedly stronger in the total \u003cem\u003eS. aureus\u003c/em\u003e series, further supporting the presence of a stable and recurrent seasonal signal. In MRSA, the same annual frequency was detectable but with reduced amplitude, consistent with the lower-case volume and weaker GAM fit.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4 COVID-19\u0026ndash;Associated Changes in \u003cem\u003eS. aureus\u003c/em\u003e and MRSA Incidence\u003c/h2\u003e\u003cp\u003eIn the ITS analysis of weekly \u003cem\u003eS. aureus\u003c/em\u003e counts, we observed a small but significant upward drift during the pre-COVID-19 period (July 1, 2016\u0026ndash;February 29, 2020) at +\u0026thinsp;0.0105 cases/week (SE 0.0030; p\u0026thinsp;=\u0026thinsp;0.0005). At the onset of widespread SARS-CoV-2 transmission on March 1, 2020, there was an immediate level drop of 3.606 cases (SE 1.087; p\u0026thinsp;=\u0026thinsp;0.0009), yet the subsequent slope accelerated by +\u0026thinsp;0.0311 cases/week (SE 0.0086; p\u0026thinsp;=\u0026thinsp;0.0003). Combined, this yielded a four-fold increase to +\u0026thinsp;0.0416 cases/week through February 28, 2022. Following the Omicron peak (March 1, 2022), \u003cem\u003eS. aureus\u003c/em\u003e counts again declined abruptly by 2.830 cases (SE 1.349; p\u0026thinsp;=\u0026thinsp;0.036) and the trend reversed, decreasing by \u0026minus;\u0026thinsp;0.0163 cases/week (SE 0.0048; p\u0026thinsp;=\u0026thinsp;0.0007), for a net post-peak slope of \u0026minus;\u0026thinsp;0.0058 cases/week.\u003c/p\u003e\u003cp\u003eWhen the same segmented regression was applied to monthly MRSA case counts, the only statistically robust shift occurred after March 1, 2022: a significant downward trend of \u0026minus;\u0026thinsp;0.4183 cases/month (SE 0.1528; p\u0026thinsp;=\u0026thinsp;0.0073). Neither the initial pandemic level change (\u0026ndash;3.490 cases; SE 4.049; p\u0026thinsp;=\u0026thinsp;0.391) nor its slope modification (\u0026ndash;0.0181 cases/month; SE 0.2498; p\u0026thinsp;=\u0026thinsp;0.943) reached significance, nor did the pre-Omicron level change (\u0026ndash;7.759 cases; SE 5.136; p\u0026thinsp;=\u0026thinsp;0.134). These findings indicate that, unlike total \u003cem\u003eS. aureus\u003c/em\u003e, MRSA incidence did not surge during peak viral circulation but instead entered a sustained decline only in the post-peak stabilization phase.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSegmented regression coefficients (β) from Interrupted Time Series Analysis of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and MRSA incidence across COVID-19 phases.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003eβ index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimate (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eComponent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eβ₀\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eβ₁\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-COVID slope (\u003cem\u003etime\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBaseline trend\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eβ₂\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevel change on March 1, 2020 (\u003cem\u003ehigh_circ\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;3.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;3.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLevel change\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eβ₃\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlope change during high circulation (\u003cem\u003et_high\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTrend change\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eβ₄\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevel change on March 1, 2022 (\u003cem\u003epost_peak\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;2.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLevel change\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eβ₅\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlope change post-peak (\u003cem\u003et_post\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;3.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTrend change\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eMRSA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eβ₀\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBaseline level\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eβ₁\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBaseline trend (\u003cem\u003etime\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBaseline trend\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eβ₂\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevel change on March 1, 2020 (\u003cem\u003ehigh_circ\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;3.490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLevel change\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eβ₃\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTrend change during high circulation (\u003cem\u003et_high\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTrend change\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eβ₄\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevel change on March 1, 2022 (\u003cem\u003epost_peak\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;7.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;1.511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLevel change\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eβ₅\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTrend change post-peak (\u003cem\u003et_post\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;2.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTrend change\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\u003cstrong\u003eβindex definitions\u003c/strong\u003e\u003cp\u003e\u003cem\u003eTime\u003c/em\u003e \u0026ndash; Baseline trend before the COVID-19 pandemic. \u003cem\u003eHigh_circ\u003c/em\u003e \u0026ndash; Level change at the onset of high SARS-CoV-2 circulation (March 1, 2020). \u003cem\u003eT_high\u003c/em\u003e \u0026ndash; Trend change during the high SARS-CoV-2 circulation period. \u003cem\u003ePost_peak\u003c/em\u003e \u0026ndash; Level change after the post-peak SARS-CoV-2 stabilization period (March 1, 2022). \u003cem\u003eT_post\u003c/em\u003e \u0026ndash; Trend change after the post-peak SARS-CoV-2 stabilization period.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver a ten-year surveillance period, we documented a marked and statistically significant decline in methicillin-resistant Staphylococcus aureus within our institution. MRSA accounted for approximately 38% of \u003cem\u003eS. aureus\u003c/em\u003e isolates at its peak in late 2017 but declined steadily to less than 10% by June 2025. Using the non-parametric Theil\u0026ndash;Sen estimator, we observed a median weekly reduction of 0.00056 in MRSA proportion\u0026mdash;equivalent to an average annual decrease of approximately 2.9 percentage points. This downward monotonic trend was confirmed by the Mann\u0026ndash;Kendall test (z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;9.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eWeekly case counts also exhibited a consistent seasonal pattern. Total \u003cem\u003eS. aureus\u003c/em\u003e isolations increased predictably from April through July each year, forming a broad mid-year peak. A similar annual harmonic was observed in the MRSA subset, although the effect did not reach conventional statistical significance, likely due to the smaller number of resistant isolates.\u003c/p\u003e\u003cp\u003eThe timeline additionally revealed a perturbation associated with the COVID-19 pandemic. During the period of intense viral transmission (March 2020 to February 2022), overall \u003cem\u003eS. aureus\u003c/em\u003e incidence increased, primarily driven by methicillin-susceptible strains. Notably, MRSA did not exhibit the surge reported in many high-income settings. Instead, following the peak associated with the Omicron wave, MRSA incidence declined more rapidly (post-peak slope: \u0026minus;0.418 cases per month, p\u0026thinsp;=\u0026thinsp;0.007), suggesting that pandemic-related pressures did not reverse\u0026mdash;and may have even accelerated\u0026mdash;the pre-existing downward trend.\u003c/p\u003e\u003cp\u003eThese aggregate trends masked substantial clinical heterogeneity. At the departmental level, MRSA accounted for 40% of isolates in General Surgery, compared to only 12% in Nephrology. At the syndromic level, MRSA prevalence ranged from 29% in surgical site infections to 18% in bloodstream infections\u0026mdash;the two most frequently observed infectious syndromes. Throughout the decade, male patients contributed the majority of cases and consistently exhibited higher resistance rates than females. Together, these findings depict a hospital where MRSA is broadly in decline, yet persists within specific high-risk departments and clinical syndromes, necessitating targeted infection-control strategies even as the overall trend remains favorable.\u003c/p\u003e\u003cp\u003ePublished reports indicate that, prior to the COVID-19 pandemic, MRSA incidence in hospital settings was either stable or declining, but during 2020\u0026ndash;2021 many institutions experienced divergent trends\u0026mdash;some documenting reductions of 28\u0026ndash;41% associated with intensified hand hygiene and PPE use, while others reported increases in MRSA detection, reflecting heterogeneous pandemic impacts [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In contrast, our ten-year surveillance at a Mexican university hospital revealed a consistent decline in MRSA prevalence, with no significant surge during the COVID-19 period. Notably, following the Omicron peak, MRSA incidence entered a markedly accelerated decline (βpost-peak\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.418 cases\u0026middot;month⁻\u0026sup1;; p\u0026thinsp;=\u0026thinsp;0.007). These findings underscore a sustained post-pandemic decline in MRSA incidence within a resource-limited Latin American setting.\u003c/p\u003e\u003cp\u003eThe progressive decline of MRSA may be multifactorial, involving both epidemiological and microbiological mechanisms. A primary driver is the implementation and intensification of infection control measures in healthcare settings [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These include improved hand hygiene, contact precautions, active surveillance, decolonization protocols, and enhanced environmental cleaning [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Such interventions have been temporally associated with marked reductions in hospital-onset MRSA infections, particularly in intensive care units, and have been shown to reduce transmission of healthcare-associated MRSA clones such as USA100 in the United States and ST228-I in Europe [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eClonal replacement is another important mechanism. Over time, certain epidemic MRSA clones have been supplanted by others with different fitness characteristics [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. For example, in several European and North American hospitals, older clones (e.g., ST228-I, CC45-MRSA-IV) have been replaced by more successful clones (e.g., CC22-MRSA-IV, CC5-MRSA-II, CC8-IV), which may have altered virulence, transmissibility, or antimicrobial susceptibility profiles [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Some of these newer clones exhibit lower levels of antimicrobial resistance, possibly reflecting a fitness advantage in the absence of strong antibiotic selection pressure [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMicroevolutionary changes within MRSA lineages may also play a role. There is evidence that the maintenance of methicillin resistance, conferred by the \u003cem\u003emecA\u003c/em\u003e gene, imposes a fitness cost on \u003cem\u003eS. aureus\u003c/em\u003e in the absence of selective antibiotic pressure [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Loss of resistance determinants (e.g., mecA or SCCmec elements) has been observed in certain lineages, leading to the re-emergence of methicillin-susceptible \u003cem\u003eS. aureus\u003c/em\u003e (MSSA) from previously resistant backgrounds, particularly when the selective advantage of resistance diminishes due to reduced antibiotic use or effective infection control [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe absence of a pronounced MRSA spike may reflect the influence of several mitigating factors. Broad antibiotic de-escalation guidelines implemented during the pandemic likely helped limit unnecessary use of broad-spectrum antibiotics [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, the early adoption of SARS-CoV-2\u0026ndash;specific therapeutics may have reduced empiric antibiotic prescribing [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Continued MRSA admission screening for patients with severe pneumonia, alongside the sustained implementation of contact precautions, may also have contributed to the stable MRSA proportion during this period [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This combination of measures likely preserved previous gains in antimicrobial resistance control and facilitated the accelerated decline in MRSA incidence observed after 2022.\u003c/p\u003e\u003cp\u003eThis study has several limitations inherent to its retrospective, single-center design. First, as data were obtained exclusively from laboratory records and clinical registries, the findings may not be generalizable to other institutions, which may differ in epidemiological profiles, diagnostic capacities, or infection control practices. Second, although robust time-series analytical methods and microbiological quality control protocols were employed, no molecular characterization or clonal typing of MRSA strains was conducted, limiting insights into the genetic dynamics underlying the observed decline. Third, case classification was based on electronic medical records, which may introduce misclassification bias or non-systematic data loss. Moreover, although isolates deemed colonizing were excluded, distinguishing colonization from active infection remains inherently challenging in complex clinical contexts. Finally, although significant shifts associated with the COVID-19 pandemic were identified, the potential influence of concurrent external factors\u0026mdash;such as changes in clinical workflows, antimicrobial pressure, or hospital occupancy patterns\u0026mdash;could not be fully accounted for and may have independently influenced MRSA incidence.\u003c/p\u003e\u003cp\u003eFuture research should integrate high-resolution genomic and epidemiologic approaches to deepen our understanding of MRSA dynamics. Whole-genome sequencing of archived isolates will elucidate post-2022 clonal shifts, including potential USA300 incursions, while a multicenter network spanning Western Mexico can validate our seasonal and secular trends across diverse climates. Incorporating antimicrobial-use density metrics into interrupted-time-series analyses will quantify the precise impact of stewardship policies, and patient-level modeling\u0026mdash;leveraging variables such as length of stay, device days, and immunosuppression\u0026mdash;can refine risk-stratified prevention bundles. Finally, targeted qualitative audits of infection-control practices during surge versus baseline periods will contextualize pandemic-related resilience and inform adaptive strategies for future healthcare crises.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn a decade of continuous surveillance at our tertiary-care hospital in western Mexico, we observed a sustained decline in MRSA alongside persistent seasonal peaks of \u003cem\u003eS. aureus\u003c/em\u003e and notable variability between clinical units, underscoring the enduring impact of robust infection‐control and antimicrobial‐stewardship measures even under pandemic pressures. These results highlight the need for unit‐specific reinforcement of hygiene and isolation practices during high‐transmission periods and support the resilience of our prevention protocols during healthcare crises. To further advance MRSA control, future efforts should focus on integrating genomic epidemiology, quantifying antimicrobial usage, and extending surveillance to multicenter networks, thereby guiding more precise, data‐driven interventions across diverse hospital settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAMR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Antimicrobial-Resistance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eATCC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;American Type Culture Collection\u003c/p\u003e\n\u003cp\u003eBSIs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Bloodstream Infections\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Confidence Interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCLSI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Clinical and Laboratory Standards Institute\u003c/p\u003e\n\u003cp\u003eCOVID-19\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Coronavirus Disease 2019\u003c/p\u003e\n\u003cp\u003eGAM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Generalized Additive Model\u003c/p\u003e\n\u003cp\u003eHAIs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Healthcare-Associated Infections\u003c/p\u003e\n\u003cp\u003eICUs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intensive Care Units\u003c/p\u003e\n\u003cp\u003eLOESS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Locally Estimated Scatterplot Smoothing\u003c/p\u003e\n\u003cp\u003eMIC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Minimum Inhibitory Concentration\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMRSA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Methicillin-Resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMSSA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Methicillin-Susceptible Staphylococcus aureus\u003c/p\u003e\n\u003cp\u003ePACF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Partial Autocorrelation Function\u003c/p\u003e\n\u003cp\u003ePPE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Personal Protective Equipment\u003c/p\u003e\n\u003cp\u003ePICU\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Pediatric Intensive Care Unit\u003c/p\u003e\n\u003cp\u003eSTL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Seasonal-Trend Decomposition using Loess\u003c/p\u003e\n\u003cp\u003eSSIs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Surgical Site Infections\u003c/p\u003e\n\u003cp\u003eVAP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ventilator-Associated Pneumonia\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involving humans was approved by the \u0026quot;Comit\u0026eacute; de \u0026Eacute;tica en Investigaci\u0026oacute;n en Ciencias de la Salud del Centro Universitario de Tlajomulco, Universidad de Guadalajara\u0026quot; (ethical approval number CUTLAJO/DS/CEICS/23/25) and conducted in accordance with the Helsinki declaration, national legislation, and institutional requirements. As the study was performed retrospectively and only deidentified data were used, informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no financial or non-financial conflicts of interest in relation to this study. None of the institutions to which the authors are affiliated received payment or other support from commercial entities in connection with this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePedro Mart\u0026iacute;nez-Ayala:\u003c/strong\u003e Conceptualization, Investigation, Data Curation, Writing - Original Draft. \u003cstrong\u003eJudith Carolina De Arcos-Jim\u0026eacute;nez\u003c/strong\u003e: Conceptualization, Investigation, Data Curation, Writing - Original Draft. \u003cstrong\u003eAdolfo G\u0026oacute;mez-Quiroz\u003c/strong\u003e: Investigation, Data curation, Writing- Original draft preparation. \u003cstrong\u003eBrenda Berenice Avila-Cardenas\u003c/strong\u003e: Investigation, Data curation, Writing- Original draft preparation. \u003cstrong\u003eRoberto Miguel Damian-Negrete:\u003c/strong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eInvestigation, Data curation, Writing- Original draft preparation. \u003cstrong\u003eAna Mar\u0026iacute;a L\u0026oacute;pez-Y\u0026aacute;\u0026ntilde;ez\u003c/strong\u003e: Investigation, Methodology, Writing - Review \u0026amp; Editing. \u003cstrong\u003eLeonardo Garc\u0026iacute;a-Miranda\u003c/strong\u003e: Investigation, Data Curation, Formal analysis, Writing - Original Draft. \u003cstrong\u003eJaime Briseno-Ram\u0026iacute;rez\u003c/strong\u003e: Conceptualization, Methodology, Software, Formal analysis, Writing - Reviewing and Editing, Project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no acknowledgments to report.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen H, Song S, Cui R, Feng Y-W, Ge P. 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Antibiot (Basel). 2023;12:974.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKelly G, Hudson M, Apple B, Bundage D, Lembcke B, Lasco T, et al. Discontinuation of contact precautions in patients with hospital-acquired MRSA and VRE infections during the COVID-19 pandemic: A multi-center experience. J Infect Prev. 2024;25:33\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHirabayashi A, Kajihara T, Yahara K, Shibayama K, Sugai M. Impact of the COVID-19 pandemic on the surveillance of antimicrobial resistance. J Hosp Infect. 2021;117:147\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCole J, Barnard E. The impact of the COVID-19 pandemic on healthcare acquired infections with multidrug resistant organisms. Am J Infect Control. 2021;49:653\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMadden G, Bielskas M, Kamruzzaman M, Bhattacharya P, Lewis B, Klein E, et al. 173. Deciphering COVID-19-Associated Effects on Hospital MRSA Transmission and Social Networks. Open Forum Infect Dis. 2021;8:S104\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYek C, Mancera AG, Diao G, Walker M, Neupane M, Chishti EA, et al. Impact of the COVID-19 Pandemic on Antibiotic Resistant Infection Burden in U.S. Hospitals: Retrospective Cohort Study of Trends and Risk Factors. Ann Intern Med. 2025;178:796\u0026ndash;807.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWolford H, McCarthy NL, Baggs J, Hatfield KM, Maillis A, Olubajo B, et al. Antimicrobial-Resistant Infections in Hospitalized Patients. JAMA Netw Open. 2025;8:e2462059.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCLSI. Performance Standards for Antimicrobial Susceptibility Testing. 34th ed. CLSI supplement M100. Clinical and Laboratory Standards Institute; 2024. Print ISBN 978-1-68440-220-5; Electron ISBN 978-1-68440-221-2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParriott AM, Kazerouni NN, Epson EE. Association of the coronavirus disease 2019 (COVID-19) pandemic with the incidence of healthcare-associated infections in California hospitals. Infect Control Hosp Epidemiol. 2023;44:1429\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbubakar U, Al-Anazi M, alanazi Z, Rodr\u0026iacute;guez-Ba\u0026ntilde;o J. Impact of COVID-19 pandemic on multidrug resistant gram positive and gram negative pathogens: A systematic review. J Infect Public Health. 2023;16:320\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim H, Kim ES, Lee SC, Yang E, Kim HS, Sung H, et al. Decreased Incidence of Methicillin-Resistant Staphylococcus aureus Bacteremia in Intensive Care Units: a 10-Year Clinical, Microbiological, and Genotypic Analysis in a Tertiary Hospital. 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Evolving MRSA: High-level β-lactam resistance in Staphylococcus aureus is associated with RNA Polymerase alterations and fine tuning of gene expression. PLoS Pathog. 2020;16:e1008672.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePettit NN, Nguyen CT, Lew AK, Pisano J. Impact of the sequential implementation of a pharmacy-driven methicillin-resistant Staphylococcus aureus (MRSA) nasal-swab ordering policy and vancomycin 72-hour restriction protocol on standardized antibiotic administration ratio (SAAR) data for antibiotics used for resistant gram-positive infections. Infect Control Hosp Epidemiol. 2024;45:196\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePettit NN, Nguyen CT, Lew AK, Bhagat PH, Nelson A, Olson G, et al. Reducing the use of empiric antibiotic therapy in COVID-19 on hospital admission. BMC Infect Dis. 2021;21:516.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEvans ME, Simbartl LA, McCauley BP, Flarida LK, Jones MM, Harris AD, et al. Active Surveillance and Contact Precautions for Preventing Methicillin-Resistant Staphylococcus aureus Healthcare-Associated Infections During the COVID-19 Pandemic. Clin Infect Dis. 2023;77:1381\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Staphylococcus aureus, Methicillinresistant S. aureus, Timeseries analysis, Seasonality, COVID-19 pandemic, Infection control, Antimicrobial stewardship","lastPublishedDoi":"10.21203/rs.3.rs-7228547/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7228547/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMethicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (MRSA) remains a leading cause of healthcareassociated infections worldwide, yet longterm trends and the COVID-19 pandemic\u0026rsquo;s impact in Latin American hospitals are inadequately described. We aimed to characterize decadelong epidemiology, seasonal patterns, and pandemicassociated changes in hospitalonset \u003cem\u003eS. aureus\u003c/em\u003e infections at a tertiarycare referral center.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective timeseries analysis of all clinically significant \u003cem\u003eS. aureus\u003c/em\u003e isolates recovered from June 30, 2016 to March 29, 2025 at our university hospital. Weekly counts and proportions of MRSA were smoothed using LOESS (span\u0026thinsp;=\u0026thinsp;0.3). Monotonic trends were evaluated via the Mann\u0026ndash;Kendall test and Theil\u0026ndash;Sen estimator. Seasonality was assessed with SeasonalTrend decomposition using Loess (STL), generalized additive models (GAMs) with cyclic splines, and Fourier spectral analysis. Interrupted timeseries (ITS) segmented regression estimated level and slope changes on March 1, 2020 (pandemic onset) and March 1, 2022 (postpeak stabilization).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 6318 clinically significant \u003cem\u003eS. aureus\u003c/em\u003e isolates, 1308 (21.7%) were MRSA. Proportion peaked at approximately 38% in late 2017 before undergoing a sustained decline to below 10% by June 2025 (median weekly Sen\u0026rsquo;s slope = \u0026minus;\u0026thinsp;0.00056; Mann\u0026ndash;Kendall z = \u0026minus;\u0026thinsp;9.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ITS analysis of total \u003cem\u003eS. aureus\u003c/em\u003e counts revealed an accelerated case incidence during the high SARS‑CoV‑2 circulation phase (March 1, 2020 \u0026ndash; Feb 28, 2022), with a significant slope increase of +\u0026thinsp;0.0311 cases/week (SE 0.0086; p\u0026thinsp;=\u0026thinsp;0.0003), yielding a net drift of +\u0026thinsp;0.0416 cases/week despite an initial level drop at pandemic onset. In contrast, segmented regression of MRSA counts showed no significant level change at the pandemic\u0026rsquo;s start (β₂ = \u0026minus;\u0026thinsp;3.490 cases; SE 4.048; p\u0026thinsp;=\u0026thinsp;0.390) nor slope modification during high viral circulation (β₃ = \u0026minus;\u0026thinsp;0.018 cases/month; SE 0.2498; p\u0026thinsp;=\u0026thinsp;0.943); instead, only the post‑peak stabilization period (from March 1, 2022) exhibited a statistically robust downward trend (β₅ = \u0026minus;\u0026thinsp;0.418 cases/month; SE 0.1528; p\u0026thinsp;=\u0026thinsp;0.007). STL decomposition also revealed a stable 12‑month cycle, with consistent mid‑year peaks recurring annually between April and July.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur decade‑long surveillance demonstrated a persistent, significant MRSA decline despite stable seasonal mid‑year peaks and a COVID‑19\u0026ndash;associated surge in overall \u003cem\u003eS. aureus\u003c/em\u003e cases without a parallel rise in resistance. Post‑pandemic, MRSA incidence decreased even more sharply. Elucidating these mechanisms via genomic epidemiology and multicenter studies will be essential to guide continuous, seasonally targeted interventions in similar settings.\u003c/p\u003e","manuscriptTitle":"Time-Series Analysis of Staphylococcus aureus and MRSA Trends, Seasonality, and Pandemic-Associated Disruptions in a Tertiary-Care University Hospital (2016–2025)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-11 10:05:33","doi":"10.21203/rs.3.rs-7228547/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f935f48-b75e-4d3f-a93d-3e66af280204","owner":[],"postedDate":"August 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-24T14:10:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-11 10:05:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7228547","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7228547","identity":"rs-7228547","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}