Ambient PM2.5 and respiratory tract disorder–related outpatient visits among healthcare workers: an ecological time-series study

Abstract

Introduction

Ambient fine particulate matter (PM2.5) is associated with adverse respiratory outcomes, yet evidence among healthcare workers remains limited. This study examined temporal associations between weekly ambient PM2.5 concentrations and respiratory tract disorder (RTD)-related outpatient visits among healthcare workers, an occupationally active urban population potentially affected by ambient air pollution.

Methods

An ecological time-series study was conducted among healthcare workers at a 1,000-bed tertiary-care hospital in Bangkok, Thailand, from November 2018 to April 2019. RTD- and respiratory tract symptom (RTS)-related outpatient visits were identified using ICD-10 codes (J00–J99) from electronic medical records. Weekly average ambient PM2.5 concentrations were obtained from the nearest national monitoring station. Weekly outpatient visit counts were analysed using Poisson or negative binomial regression models and expressed as incidence rate ratios (IRRs) per 10 µg/m³ increase in PM2.5 concentration, adjusted for temporal trends.

Results

Among 12,458 healthcare workers, short-term increases in weekly ambient PM2.5 concentrations were associated with higher outpatient visit counts for overall RTDs (IRR 1.139, 95% CI 1.059–1.225), common cold (1.164, 1.082–1.252), allergic rhinitis (1.240, 1.126–1.365), sinusitis (1.168, 1.017–1.342), and asthma (1.429, 1.003–2.037). Significant associations were also observed for upper respiratory symptoms including sneezing, sore throat, cough, and shortness of breath.

Conclusions

Short-term increases in ambient PM2.5 concentrations were temporally associated with higher respiratory outpatient visits among healthcare workers. These findings extend evidence regarding air pollution and respiratory health to an understudied occupational population and reinforce the public health importance of continued air quality management in urban environments.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12889-026-28580-x.

Introduction

Air pollution remains a major global public health challenge [1] and continues to impede progress toward the Sustainable Development Goals [2]. In 2007, polluted air contributed to 4.6 million deaths worldwide, increasing to 4.9 million deaths by 2017. Among these, 1.6 million deaths were attributed to household air pollution and 2.9 million to ambient particulate matter pollution [3]. Without effective intervention, ambient air pollution is projected to cause between 6 and 9 million deaths annually by 2060 [4]. In Southeast Asia, the combined effects of ambient and household air pollution accounted for an attributable mortality rate of 125.1 per 100,000 population in 2016, exceeding the global rate of 94.8 per 100,000 and reflecting persistent regional vulnerability. In Thailand, these effects contributed to mortality rates of 98 per 100,000 in men and 71 per 100,000 in women during the same year [5]. Major sources of ambient particulate pollution include motor vehicles, power plants, residential wood burning, agricultural burning, industrial activities, and other combustion-related processes [6].

Respiratory tract disorders (RTDs) represent a substantial burden on healthcare systems and contribute to morbidity, productivity loss [7], absenteeism, and presenteeism [8]. Healthcare workers may be particularly susceptible because of prolonged exposure to crowded clinical environments, workplace stress, and frequent contact with respiratory pathogens [9]. Respiratory illness among healthcare personnel may also affect workforce capacity and healthcare service delivery, particularly during periods of seasonal respiratory infection and increased environmental pollution [10].

Among air pollutants, fine particulate matter (PM2.5; aerodynamic diameter ≤ 2.5 μm) poses particular health risks due to its ability to penetrate deep into the respiratory tract and enter systemic circulation [11]. Short- and long-term exposure to PM2.5 has been associated with hypertension [12], cardiovascular disease [13, 14], stroke [15], chronic obstructive pulmonary disease [16], asthma exacerbations, allergic disorders [17], lung cancer [18], and increased respiratory mortality [19]. These effects may be further influenced by environmental factors such as heat, low humidity, and seasonal variation [20]. Mechanistically, PM2.5 induces airway inflammation, oxidative stress, epithelial injury, and impaired mucociliary clearance [21], thereby increasing susceptibility to respiratory tract symptoms and infections [22].

Recent time-series and case-crossover studies have reported that short-term increases in ambient PM2.5 concentrations are associated with increased outpatient visits [23], emergency department presentations, and hospital admissions [24]. Associations have been reported particularly for upper airway and allergic responses. For example, elevated PM2.5 concentrations were associated with increased outpatient visits for allergic rhinitis in Beijing [25] and delayed respiratory outpatient effects in Chongqing [26], while multi-city analyses have demonstrated positive associations between PM2.5 peaks and respiratory hospitalisations using both case-crossover and time-series approaches [27].

Beyond its health implications, air pollution may also adversely affect workforce productivity and occupational functioning. Previous studies have demonstrated that improvements in air quality are associated with increased productivity across multiple occupational sectors, including manufacturing, agriculture, and service industries [28]. However, evidence regarding respiratory health effects associated with ambient PM2.5 exposure among healthcare workers remains limited. Healthcare workers represent an important occupationally active urban population who frequently work in high-density environments and may experience repeated exposure to urban air pollution during commuting and daily activities. In addition, institutional electronic medical record systems provide an opportunity to examine respiratory outpatient visits using routinely collected clinical data. Compared with previous studies focusing primarily on the general population, children, or older adults, the present study applies an ecological time-series analytical approach within a defined occupational population to evaluate temporal associations between ambient PM2.5 concentrations and RTD-related outpatient visits.

Therefore, the objectives of this study were to describe the incidence of RTD-related outpatient visits among healthcare workers at a tertiary-care hospital and to evaluate the temporal associations between weekly average ambient PM2.5 concentrations and weekly RTD- and respiratory tract symptom (RTS)-related outpatient visit counts.

Methodology

Research design

This retrospective ecological time-series study was conducted at a 1,000-bed tertiary-care university hospital in Bangkok, Thailand, with a large and diverse healthcare workforce and comprehensive electronic medical record systems that enabled evaluation of respiratory outpatient visit patterns within a defined occupational population over time.

The study examined temporal associations between ambient PM2.5 concentrations and respiratory tract disorder (RTD)-related outpatient visits. The analysis focused on the weekly aggregated counts of RTD-related outpatient visits among healthcare workers from November 2018 to April 2019.

Study population

The study population included hospital employees involved in both direct patient care (e.g., physicians and nurses) and indirect clinical or support services (e.g., laboratory technicians, aides, and medical waste handlers) who attended outpatient or emergency units during the study period.

The study evaluated RTD-related outpatient visit patterns among healthcare workers in relation to short-term ambient PM2.5 exposure. Only visits representing new respiratory-related clinical presentations were included. Follow-up visits were defined as scheduled revisits for previously diagnosed conditions without new or worsening acute respiratory symptoms and were excluded. Duplicate visits were defined as multiple records from the same individual on the same day with similar respiratory diagnoses or symptom presentations; in such cases, only the initial visit was retained. Routine health examinations without abnormal respiratory findings and records with incomplete clinical information were also excluded.

Outcome definition

Respiratory tract diseases and symptoms were identified using physician-documented diagnoses recorded in the hospital electronic medical record (EMR) system. RTDs were classified according to the International Classification of Diseases, 10th Revision (ICD-10), primarily within codes J00–J99. Major outcome categories included acute upper respiratory infections (J00–J06), allergic rhinitis (J30–J31), sinusitis (J01–J32), acute laryngitis (J04), bronchitis (J20–J40), asthma (J45–J46), pneumonia (J12–J18), influenza (J09–J11), and other respiratory disorders. Influenza was analyzed separately because of its distinct seasonal epidemiology and potential influence on respiratory outpatient patterns. RTSs were identified from documented symptom records, including cough, sore throat, shortness of breath, and wheezing.

Weekly RTD-related outpatient visit counts were extracted and aggregated from the EMR system. Weekly incidence rates were calculated using newly recorded RTD-related outpatient visits as the numerator and the total hospital workforce obtained from institutional administrative records as the denominator. These estimates were used to describe RTD-related outpatient visit patterns within the workforce during the study period.

No exclusions were made for seasonal influenza or other viral respiratory infections because the study aimed to evaluate overall respiratory outpatient visit patterns in relation to ambient PM2.5 concentrations.

Air pollution exposure

Ambient PM2.5 concentrations were obtained from a national air quality monitoring station operated by the Pollution Control Department located approximately 2 km northeast of the hospital. The station was situated within the same urban, traffic-influenced metropolitan environment and was used to represent background ambient PM2.5 conditions across the study area.

The study period (November 2018 to April 2019) coincided with Thailand’s seasonal period of elevated PM2.5 concentrations, commonly influenced by traffic emissions, biomass burning, meteorological conditions, and regional atmospheric accumulation during the dry season [29].

Weekly average PM2.5 concentrations were calculated to reduce short-term variability in ambient air pollution measurements and to capture cumulative short-term exposure relevant to sub-acute respiratory responses. Weekly exposure estimates were temporally linked to aggregated RTD- and RTS-related outpatient visit counts within the same calendar week. Weekly aggregation is commonly applied in environmental epidemiology to evaluate temporal associations between ambient particulate matter exposure and respiratory outpatient visits, particularly for irritative and allergic respiratory conditions that may develop or worsen over several days following exposure [30, 31].

Statistical methods

Descriptive statistics were used to summarize weekly ambient PM2.5 concentrations and weekly RTD-related outpatient visit counts among healthcare workers.

Weekly RTD- and RTS-related outpatient visit counts were initially analyzed using Poisson regression models for count data. Overdispersion was assessed by comparing residual deviance with the corresponding degrees of freedom. Outcomes demonstrating overdispersion were subsequently analyzed using negative binomial regression models.

Ambient PM2.5 concentration was included as a continuous exposure variable, expressed per 10 µg/m³ increase in weekly average concentration. Calendar week was included as a linear temporal term to account for temporal trends and seasonal variation during the study period. Regression models were specified using a weekly aggregated ecological framework to evaluate temporal associations between ambient PM2.5 concentrations and RTD-related outpatient visit counts. Lagged exposure analyses and additional meteorological variables were not included.

Stratified analyses were conducted by sex, age group, and occupational category using separate regression models to explore potential heterogeneity in associations. These analyses were considered exploratory. Results are presented as incidence rate ratios (IRRs) with 95% confidence intervals (CIs). Statistical significance was defined as p < 0.05. All analyses were performed using STATA version 18.0 (StataCorp, College Station, TX, USA). This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [32].

Results

RTD-related outpatient visits among healthcare workers

From November 2018 to April 2019, the total hospital workforce comprised 12,458 employees. Among these, 2,090 healthcare workers (16.8%) had at least one RTD-related outpatient visit. A total of 2,964 RTD-related outpatient visits were recorded, indicating that some individuals sought care more than once.

The overall incidence proportion of healthcare workers with at least one RTD-related outpatient visit was 16.8%, corresponding to approximately 23.8 visits per 100 employees over six months.

Table 1 presents the demographic and occupational distribution of the workforce and the proportion of employees within each category who had at least one RTD-related outpatient visit. Female healthcare workers accounted for the majority of RTD-related outpatient visits, consistent with their predominance in the workforce. Visits were most commonly observed among workers aged 31–60 years, with the highest proportion occurring in the 51–60-year age group.

Table 1.

Characteristics of healthcare workers with RTD-related outpatient visits, November 2018–April 2019

Variables	Total Population, N (%)a	Employees with ≥ 1 RTD-related outpatient visit, N
		Yes (%)a
Gender
Male	2613 (21.0)	351 (13.4)
Female	9845 (79.0)	1739 (17.7)
Age
Less than 30	5019 (40.3)	653 (13.0)
31 to 40	3979 (31.9)	764 (19.2)
41 to 50	2021 (16.2)	385 (19.0)
51 to 60	1210 (9.7)	263 (21.7)
More than 60	229 (1.8)	25 (10.9)
BMIb
Less than 18.5	328 (2.6)	115 (35.1)
18.5–22.9	2739 (22.0)	1440 (52.6)
23–24.9	969 (7.8)	181 (18.7)
25–29.9	1347 (10.8)	222 (16.5)
More than 30	592 (4.8)	132 (22.3)
Occupations
Administrative Officer	2344 (18.8)	555 (23.7)
Physician/Medical staff	2092 (16.6)	296 (14.1)
Dentist/Dental staff	44 (0.4)	17 (38.6)
Pharmacist/Assistant pharmacist	457 (3.6)	107 (23.4)
Nurse	5016 (39.8)	729 (14.5)
Psychologist	15 (0.1)	3 (20.0)
Medical technician	156 (1.2)	72 (46.2)
Radiologic technologist	165 (1.3)	28 (17.0)
Physical/Occupational therapist	49 (0.4)	24 (49.0)
Engineering technician	995 (7.9)	20 (2.0)
Security guard	84 (0.7)	19 (22.6)
Service staff	894 (7.1)	192 (21.5)
Hospital porter	61 (0.5)	9 (14.8)
Hospital driver	28 (0.2)	3 (10.7)
Janitor/gardener/Maid	28 (0.2)	7 (25.0)
Laundry worker	14 (0.1)	6 (42.9)
Cooking staff	16 (0.1)	3 (18.8)
Underlying diseaseb
No	4489 (36.0)	705 (15.7)
Yes	1484 (11.9)	1385 (93.3)
Smoking statusb
Never/Former	3324 (26.7)	2055 (61.8)
Current smoker	87 (0.7)	33 (37.9)

^a Percentages represent the proportion of employees within each category who had at least one RTD-related outpatient visit during the study period

^b Information regarding BMI, underlying disease, and smoking status was incomplete for some employees; percentages for these variables were therefore calculated using available data (BMI n = 5,975; underlying disease n = 5,973; smoking status n = 3,411). Underlying disease status included hypertension, dyslipidaemia, diabetes mellitus, obesity, allergic rhinitis, asthma, cardiovascular disease, and other chronic conditions

Across occupational categories, relatively higher proportions of RTD-related outpatient visits were observed among medical technicians and physical or occupational therapists, whereas physicians and nurses accounted for substantial numbers of visits because of their larger workforce representation. Underlying disease status included hypertension, dyslipidaemia, diabetes mellitus, obesity, allergic rhinitis, asthma, cardiovascular disease, and other chronic conditions. Employees with at least one recorded chronic condition were classified as having an underlying disease, which may partly explain the relatively high proportion of RTD-related outpatient visits observed in this group. RTD-related outpatient visits were also more frequently observed among employees with underlying diseases. Most visits occurred among never or former smokers, reflecting the distribution of smoking status within the workforce.

Information regarding BMI, underlying disease, and smoking status was incomplete for some employees; therefore, percentages for these variables were calculated using available records for each category.

Temporal patterns of ambient PM2.5 and RTD-related outpatient visits

Figure 1 illustrates weekly average ambient PM2.5 concentrations alongside weekly RTD-related outpatient visit counts. PM2.5 concentrations demonstrated temporal variability consistent with seasonal air pollution patterns in Bangkok. Periods of elevated PM2.5 concentrations generally corresponded with increased RTD-related outpatient visits. Statistical associations were further evaluated using count-based regression models (Table 2).

Fig. 1.

Weekly Ambient PM2.5 Concentrations and RTD-Related Outpatient Visits. *The red line represents weekly average ambient PM2.5 concentrations during the study period

Table 2.

Poisson and negative binomial regression analyses of ambient PM2.5 and weekly RTD-related outpatient visits

Outcome category	Outcome	Model	IRR per 10 µg/m³ increase in PM2.5 (95% CI)	p-value
Respiratory Tract Diseases (RTDs)a	Total RTD-related outpatient visits	Negative binomial	1.139 (1.059,1.225)	< 0.001*
	Common cold	Negative binomial	1.164 (1.082,1.252)	< 0.001*
	Allergic rhinitis	Negative binomial	1.240 (1.126,1.365)	< 0.001*
	Acute laryngitis	Negative binomial	0.923 (0.509,1.671)	0.79
	Sinusitis	Poisson	1.168 (1.017,1.342)	0.028*
	Epistaxis	Poisson	1.182 (0.734,1.904)	0.492
	Bronchitis	Poisson	1.009 (0.867,1.174)	0.909
	Asthma	Poisson	1.429 (1.003,2.037)	0.048*
	Influenza	Negative binomial	0.981 (0.953,1.010)	0.200
	Pneumonia	Poisson	0.719 (0.379,1.365)	0.314
	Othersb	Poisson	0.971 (0.817,1.155)	0.742
Respiratory Tract Symptoms (RTSs)	Shortness of breath	Poisson	1.234 (1.018,1.494)	0.032*
	Sore throat	Negative binomial	1.115 (1.027,1.211)	0.010*
	Epistaxis	Poisson	1.149 (0.799,1.651)	0.454
	Fever	Negative binomial	0.981 (0.863,1.115)	0.765
	Cough	Negative binomial	1.112 (1.014,1.220)	0.024*
	Phlegm	Negative binomial	1.135 (1.019,1.264)	0.022*
	Sneezing	Negative binomial	1.184 (1.099,1.276)	< 0.001*
	Eye/ear symptoms	Poisson	1.212 (1.028,1.429)	0.022*
	Hoarseness	Negative binomial	0.923 (0.509,1.671)	0.790

^aRTD categories were classified according to ICD-10 codes, including acute upper respiratory infections (J00–J06), allergic rhinitis (J30–J31), sinusitis (J01–J32), acute laryngitis (J04), bronchitis (J20–J40), asthma (J45–J46), influenza (J09–J11), and pneumonia (J12–J18)

^bOther respiratory diagnoses included cough (R05), abnormalities of breathing (R06), pain in throat and chest (R07), nasal polyp (J33), other disorders of the nose and nasal sinuses (J34), diseases of vocal cords and larynx (J38), and other respiratory disorders (J98)

*p ≤ 0.05

Association between ambient PM2.5 and RTD-related outpatient visits

Table 2 presents the results of Poisson and negative binomial regression analyses examining associations between weekly average ambient PM2.5 concentrations and weekly RTD- and RTS-related outpatient visits.

Overall, each 10 µg/m³ increase in weekly average PM2.5 concentration was associated with a 13.9% increase in total RTD-related outpatient visits (IRR = 1.139, 95% CI: 1.059–1.225, p < 0.001). Among specific respiratory tract diseases, the strongest positive associations were observed for asthma (IRR = 1.429, 95% CI: 1.003–2.037), allergic rhinitis (IRR = 1.240, 95% CI: 1.126–1.365), common cold (IRR = 1.164, 95% CI: 1.082–1.252), and sinusitis (IRR = 1.168, 95% CI: 1.017–1.342). These findings indicate that higher ambient PM2.5 concentrations were more consistently associated with upper respiratory and allergic conditions.

In contrast, no statistically significant associations were identified for acute laryngitis, epistaxis, bronchitis, influenza, pneumonia, or other respiratory diagnoses.

Among respiratory tract symptoms, higher PM2.5 concentrations were associated with increased outpatient visits for shortness of breath, sore throat, cough, phlegm, sneezing, and eye or ear symptoms, with IRRs ranging from 1.112 to 1.234 per 10 µg/m³ increase in PM2.5 concentration. Sneezing and shortness of breath demonstrated relatively stronger associations among symptom-based outcomes. No statistically significant associations were observed for fever, epistaxis, or hoarseness.

Overall, the associations between ambient PM2.5 concentrations and RTD-related outpatient visits demonstrated greater consistency for upper respiratory and allergic conditions than for lower respiratory or systemic respiratory outcomes.

Discussion

Impact of PM2.5 on respiratory health

This ecological time-series study examined temporal associations between ambient PM2.5 concentrations and RTD-related outpatient visits among healthcare workers in Bangkok, Thailand. Short-term increases in weekly average PM2.5 concentrations were associated with higher weekly outpatient visit counts for selected respiratory diseases and symptoms, particularly upper respiratory and allergic conditions including common cold, allergic rhinitis, sinusitis, asthma, sneezing, sore throat, cough, and shortness of breath.

These findings are consistent with previous environmental epidemiology studies demonstrating associations between short-term PM2.5 exposure and increased respiratory outpatient visits and hospital admissions, particularly for allergic and upper respiratory conditions in urban environments. Similar associations have been reported in both time-series and case-crossover studies conducted in heavily polluted settings [25–27]. The stronger associations observed for upper respiratory outcomes in the present study are biologically plausible because particulate matter can induce airway irritation, oxidative stress [33], epithelial inflammation, and enhanced allergen sensitisation [34]. PM2.5 may also act as a carrier for allergens, potentially intensifying allergic responses [35, 36].

The magnitude and statistical significance of associations varied across outcomes. Stronger and more consistent associations were observed for upper respiratory and allergic conditions, including allergic rhinitis, common cold, sinusitis, sneezing, and sore throat. These conditions may be particularly sensitive to short-term fluctuations in PM2.5 because particulate matter initially contacts the upper airway mucosa, where inflammatory and allergic responses may develop rapidly. In addition, these symptoms may be more likely to prompt outpatient healthcare visits during periods of worsening air quality.

In contrast, no statistically significant associations were observed for influenza, pneumonia, bronchitis, or certain systemic symptoms. These outcomes may be more strongly influenced by viral pathogens, host susceptibility, chronic disease, or delayed disease processes than by short-term fluctuations in ambient PM2.5 alone. Some diagnostic categories also involved relatively small numbers of outpatient visits, resulting in wider confidence intervals and reduced statistical precision.

The weekly exposure aggregation framework also has important implications for interpretation. Weekly averaging was used to evaluate cumulative short-term exposure patterns relevant to sub-acute respiratory responses over time. This approach may be particularly suitable for upper respiratory and allergic conditions that fluctuate over several days following exposure, although it may be less sensitive for outcomes with longer latency periods or multifactorial etiologies.

The study period coincided with Thailand’s seasonal high-PM2.5 period, commonly influenced by traffic emissions, biomass burning, atmospheric stagnation, and regional transboundary pollution during the dry season. Seasonal increases in PM2.5 concentrations may have contributed to increased respiratory symptom burden and outpatient visits, particularly for upper respiratory and allergic conditions [37]. Seasonal viral circulation may also have influenced outpatient visit patterns during the study period.

This study contributes to the literature by examining respiratory outpatient visit patterns within a defined occupational population. Healthcare workers represent an important urban working population who may experience repeated exposure to air pollution during commuting and daily activities. The use of institutional electronic medical records enabled evaluation of respiratory outpatient visit patterns in relation to temporal variation in ambient PM2.5 concentrations within a real-world healthcare setting.

Limitations and future directions

This study has some limitations. First, the study was based on electronic medical records and therefore reflects outpatient visits and hospital presentations rather than true population-level disease incidence. Healthcare-seeking behaviour may have influenced observed visit patterns. Second, ambient PM2.5 concentrations were obtained from a single monitoring station and used as indicators of background air quality conditions within the study area. Healthcare workers spend substantial time indoors during working hours, where ventilation systems and indoor environmental conditions may differ from outdoor PM2.5 levels. Variability related to indoor air quality, commuting, and individual activity patterns could not be assessed and may have contributed to exposure misclassification. Third, although models included temporal adjustment, residual confounding from viral activity, meteorological conditions, or indoor environmental factors cannot be excluded. Finally, the single-center study design may limit generalisability to other healthcare settings. The weekly exposure framework may also have been less sensitive for outcomes involving longer latency periods or more complex etiologies.

Despite these limitations, the findings provide evidence that short-term increases in ambient PM2.5 concentrations were temporally associated with higher outpatient visit counts for selected respiratory conditions among healthcare workers. These findings reinforce broader evidence linking ambient air pollution with RTD-related visits and support the importance of continued air quality management in urban environments.

Public health and policy implications

Although this study was ecological in design, the observed temporal associations between ambient PM2.5 concentrations and respiratory outpatient visits among healthcare workers are consistent with broader epidemiological evidence linking air pollution to adverse respiratory outcomes. These findings reinforce the importance of continued efforts to reduce ambient air pollution in urban environments.

Improvements in air quality may contribute to reductions in respiratory presentations across working populations. During periods of elevated PM2.5 concentrations, preventive strategies may be particularly relevant for individuals with underlying respiratory or allergic conditions.

Healthcare facilities may consider monitoring ambient air quality trends and promoting preventive strategies during high-pollution periods, including respiratory health education and appropriate protective behaviors. Future studies incorporating individual-level exposure assessment and indoor air quality measurements would help inform more targeted occupational and environmental health policies.

Conclusion

In this ecological time-series study conducted among healthcare workers at a tertiary-care hospital in Bangkok, short-term increases in weekly average ambient PM2.5 concentrations were temporally associated with higher outpatient visit counts for selected respiratory tract diseases and symptoms, particularly upper respiratory and allergic conditions. The use of institutional electronic medical records enabled evaluation of respiratory outpatient visit patterns within a real-world healthcare workforce setting.

These findings align with existing evidence linking ambient PM2.5 exposure to adverse respiratory outcomes and extend this understanding to healthcare workers, an important yet understudied occupational population. The findings reinforce the public health relevance of ambient air pollution in urban environments and support the importance of continued air quality management efforts.

Future studies incorporating longitudinal designs, multi-center settings, and assessment of indoor air quality may further improve understanding of exposure–response relationships within occupational settings.

Abbreviations

CI

Confidence Interval

IRRs

Incidence rate ratios

PM2.5

Ambient fine particulate matter

RTDs

respiratory tract disorders

RTSs

respiratory tract symptoms

Authors’ contributions

CT contributed to the conceptualization, study design, data acquisition, supervision, data interpretation, manuscript drafting, and final approval of the manuscript. SS contributed to the data management, literature review, statistical support, and drafted the manuscript. CW designed to the data collection, data verification, and critical revision of the manuscript. NA contributed to the study design consultation, statistical analysis planning, data interpretation, methodological oversight, and critical revision of the manuscript for important intellectual content. All authors contributed to the interpretation of the results, reviewed the manuscript critically, approved the final version, and agree to be accountable for all aspects of the work.

Funding

Open access funding provided by Faculty of Medicine Ramathibodi Hospital, Mahidol University. No funding was received for conducting this study.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to ethical and privacy restrictions to protect the confidentiality of study participants.

Declarations

Ethics approval and consent to participate

Ethical approval was obtained on May 8, 2024, from the Human Research Ethics Committee, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand (reference: COA No. MURA2024/307, Ref. 2025/516). The requirement for informed consent was waived by the IRB due to the retrospective nature of the study and the use of de-identified electronic medical record data. The study was conducted in accordance with the Declaration of Helsinki and relevant institutional ethical guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.