The recurrence and mortality risk in Luminal A breast cancer patients who lived in high pollution area

Pimwarat Srikummoon; Patrinee Traisathit; Wimrak Onchan; Chagkrit Ditsatham; Natthapat Thongsak; Nawapon Nakharutai; Salinee Thumronglaohapun; Titaporn Supasri; Phonpat Hemwan; Imjai Chitapanarux

doi:10.1371/journal.pone.0335140

Abstract

Luminal A is the most common subtype of breast cancer and has the best prognosis comparing to the others. The association between air pollution and survival of breast cancer have been reported but not specific to this subtype. We examined pollutant distributions over a decade in upper Northern Thailand, the area of high average annual particulate matter levels, and their impact on the mortality and recurrence risks of patients with luminal A breast cancer. Retrospective data of 1,305 luminal A breast cancer patients diagnosed from 2003 to 2018 were enrolled to this study. Cox proportional hazard models were used to identify factors associated with mortality and recurrence risks including all known risk factors and the annually averaged concentrations of pollutants. On multivariable analysis; metastatic stage (adjusted hazard ratio (aHR) =10.50; 95% confidence interval (95%CI): 7.23–15.25), smoking history (aHR = 1.72; 95% CI: 1.14–2.60), and age ≥ 50 years old (aHR = 1.46; 95% CI: 1.13–1.90) were significant factors influencing mortality risk. Factors contributing to recurrence risk included metastatic stage (aHR) 4.96 (95% CI: 2.78–8.83) and exposure to the time-updated local concentration of PM₁₀ > 55 µg/m³ (aHR = 1.68; 95% CI: 1.16–2.45). Exposure to air pollutants is one of the detrimental factors affected to recurrence and mortality in luminal A subtype breast cancer.

Citation: Srikummoon P, Traisathit P, Onchan W, Ditsatham C, Thongsak N, Nakharutai N, et al. (2025) The recurrence and mortality risk in Luminal A breast cancer patients who lived in high pollution area. PLoS One 20(10): e0335140. https://doi.org/10.1371/journal.pone.0335140

Editor: Oyelola A. Adegboye, Menzies School of Health Research: Charles Darwin University, AUSTRALIA

Received: July 12, 2024; Accepted: October 7, 2025; Published: October 17, 2025

Copyright: © 2025 Srikummoon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data cannot be shared publicly because they contain potentially identifying information. Data are available from the Chiang Mai Cancer Registry Institutional Data Access/Ethics Committee (contact via +66 5393 6641 ext. 22 or researchmed@cmu.ac.th) for researchers who meet the criteria for accessing confidential data.

Funding: This work was supported by Fundamental Fund 2023, Chiang Mai University, Thailand. The funding source has no role on this manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The luminal A breast cancer subtype comprises a substantial proportion (50–60%) of the collective occurrence of breast cancers [1]. Within the specific context of Thailand, luminal A breast cancer has been observed in 46% of reported cases [2]. The identification of this subtype is predicated upon its characteristic estrogen receptor-positive (ER+), progesterone receptor-positive (PR+), and human epidermal growth factor receptor 2-negative (HER2–) statuses [1,3]. It is noteworthy that luminal A breast cancer is associated with a favorable prognosis and exhibits a notable degree of responsiveness to hormone therapy [4,5].

Based on the existing literature, it has consistently been reported that individuals diagnosed with luminal A breast cancer demonstrate a reduced risk of recurrence [6,7] and an increased overall survival (OS) rate compared to those with other subtypes [7–10]. Noteworthy factors associated with an elevated risk of recurrence of luminal A breast cancer include advancing age [11], alcohol consumption [12], smoking habit [13], high body mass index (BMI) [14,15], and the specific stage of cancer progression [12,16].

Exposure to air pollutants, notably nitrogen dioxide (NO₂), particulate matter (PM) (PM_2.5 (PM ≤ 2.5 µm) and PM₁₀ (PM ≤ 10 µm)), has been positively correlated with an elevated risk of mortality in individuals diagnosed with breast cancer [17–21]. Nevertheless, there is a conspicuous gap in the literature concerning investigation of the connection between air pollution and mortality risk of breast cancer patients with the luminal A subtype [22]. In addition, the potential connection between air pollution and the recurrence risk in luminal A breast cancer cases has not been adequately explored. Consequently, it is imperative to thoroughly examine the effect of air pollution on both the survival and recurrence probabilities of individuals affected by luminal A breast cancer.

The levels of PM_2.5, PM₁₀, nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃) in the upper northern region of Thailand spanning Chiang Mai, Chiang Rai, Nan, Phrae, Phayao, Lampang, Lamphun, and Mae Hong Son provinces consistently exceed the guidelines established by the World Health Organization (WHO) [23]. Especially, Chiang Mai province has been acknowledged as one of the world’s most hazardous air pollution locations, particularly during the dry season (January–April) [24,25]. The heightened pollution in this region is principally ascribed to various contributors, including smoke emanating from forest fires and biomass burning [26,27], cross-border pollution [28], and pronounced transportation activity [29]. The upper northern region of Thailand is characterized by a multitude of mountain ranges that play a pivotal role in the accumulation of air pollution in the area [30,31]. Consequently, the authorities are persistently grappling with severe air pollution from PM, which has constituted an enduring issue for over a decade [31].

In this study, we primarily investigated whether exposure to air pollutants PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃ over the past decade has influenced the survival and recurrence rates of luminal A breast cancer by using time-varying covariates within a Cox proportional hazard model. In addition, we also assessed the risk of each 10-unit increase in annual concentrations of PM_2.5, PM₁₀, CO, and O₃ and each 1-unit increase in annual concentrations of NO₂ and SO₂ among luminal A breast cancer patients by utilizing Poisson regression models (types of generalized linear models (GLM)) and distributed lag models.

Materials and methods

Study population

Women diagnosed with luminal A breast cancer between January 1, 2003, and December 31, 2018, were identified from the Chiang Mai Cancer Registry located at Chiang Mai University Hospital. This registry is the primary repository for cancer statistics for the upper northern region of Thailand covering Chiang Mai, Chiang Rai, Nan, Phrae, Phayao, Lampang, Lamphun, and Mae Hong Son provinces. Our methodology involved a systematic review of the pathological diagnostic data, from which we categorized breast cancer subtypes based on the expression statuses of ER, PR, and HER2. ER and PR are considered to be positively expressed when present in at least 1% of the cell nuclei while HER2 is considered to be positively expressed when at least 1% of cells showed uniform, intense, and complete membrane staining for it [32,33]. Subsequently, cohort members were grouped according to the classification of luminal A breast cancer as ER + , PR + , and HER2– [3,34]. We access this clinical data on May 25, 2021.

Data collection and measurements

Demographic data on the patients encompassing the age at the time of diagnosis, BMI, histories of smoking and alcohol use, and precise cancer staging based on the Surveillance, Epidemiology, and End Results (SEER) classification system, which delineates stages as localized, regional, or metastatic, were used in the analysis. Since the tumor grade, histology, and treatment for each luminal A breast cancer patient were not systematically linked between hospital medical records and the Chiang Mai Cancer Registry, these variables that influenced recurrence and survival were not included in this study.

The follow-up duration is from the moment of diagnosis to the occurrence of death irrespective of the cause or until the last recorded follow-up date. In addition, instances where data were lost due to censoring were accounted for by employing the termination of the study period (December 31, 2020) as a reference point and contingent upon which event transpired first. This rigorous methodology ensured a comprehensive analysis of patient outcomes within the specified timeframe.

Hourly concentrations of PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃ were obtained from the Copernicus Atmosphere Monitoring Service (CAMS) and the European Centre for Medium-Range Weather Forecasts [35–37]. The CAMS reanalysis datasets derived from global observational data assimilated through a modeling system provide atmospheric composition data at an approximate spatial resolution of 80 kilometers. These data are available as either spectral coefficients with T255 triangular truncation (approximately 80 km) or a reduced N128 Gaussian grid. The CAMS datasets (generated using a physics- and chemistry-based atmospheric model [38,39]) are appropriate for climatological calculations, trend analyses, and comparative studies with other reanalysis models [37]. The data’s reliability and validity (documented on the CAMS Quality Assurance website) indicate the integrated system’s utility for estimating observational bias and differentiating high-quality data from inaccurate data. The atmospheric model generates estimates for areas with limited data or for atmospheric constituents for which direct observations are unavailable. Reanalysis offers significant advantages in ensuring spatiotemporal data completeness at each global grid point over extended periods and in a uniform format [40]. We accessed the three-hour ambient air pollution concentration levels of PM_2.5 (kg/m³), PM₁₀ (kg/m³), NO₂ (kg/m²), SO₂ (kg/m²), CO (kg/m²), and O₃ (kg/m²) in upper northern Thailand for the study period (accessed on 27 November 2021). The concentration of each pollutant was calculated from the annual average concentrations for each district separately in the eight provinces in upper northern Thailand. Subsequently, the annual average concentrations were linked to the district of each patient’s residence based on the assumption that the district recorded in the patient’s information corresponded to their actual residence.

Ethical approval

Ethical approval was granted by the Chiang Mai University Ethics Committee (No 200/2021) in the Faculty of Medicine. Due to anonymous data recorded in the present study, the requirements for written informed consent were waived by the Research Ethics Committee of the Faculty of Medicine at Chiang Mai University.

Statistical analysis

The characteristics of patients are presented using medians and interquartile ranges (IQRs) for continuous data, while categorical data are portrayed as frequencies and percentages. Moreover, the annually averaged concentrations of PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃ were segmented into two categories using quartiles, to which dichotomization was applied when deemed suitable. The age at diagnosis was categorized as < 50 and ≥50 years old and BMI categorized was as <30 and ≥30 kg/m². These were adhered to due to the recommended cut-off point [41,42].

The overall rate of death and the rates for each variable were calculated as the number of deaths divided by the total number of person-years of follow-up (PYFU). The overall rate of recurrence was calculated as the number of recurrences divided by the total number of PYFU. Confidence intervals (CIs) for the mortality rates and recurrence rates were obtained from Poisson distributions. OS rates and recurrence-free survival (RFS) rates were created by using Kaplan–Meier curves, and log-rank tests were used to test for significant differences between the survival probabilities of the groups for each variable. Cox proportional hazard models were used to identify factors associated with mortality and cancer recurrence risks. Those with a p-value<0.25 in the univariate analysis were included in the multivariate analysis via a backward elimination procedure, except for variables with a lot of missing values or high correlation (multicollinearity), or else where the Kaplan–Meier curve intersected after the period of the study.

We also assessed the risk of each 10-unit increase in annual concentrations of PM_2.5, PM₁₀, CO, and O₃ and each 1-unit increase in annual concentrations of NO₂ and SO₂ among luminal A breast cancer patients by utilizing Poisson regression models and distributed lag models. The selection of different unit increments is based on the variability of the annual average concentration distributions. NO₂ and SO₂ show relatively low concentration variability annually, thus necessitating a 1-unit increment for exposure consideration. Conversely, PM_2.5, PM₁₀, and CO demonstrate higher variability in annual average concentrations, thus requiring a 10-unit increment for exposure assessment (S1 Fig). To control time-varying confounders, models were adjusted for long-term trends and seasonality. Both constrained and unconstrained distributed lag models were tested to determine the most appropriate effect patterns. The effect estimates are presented as the relative risk (RR) with 95% CI, representing the change in risk per 1-unit increase in annual concentrations of NO₂ and SO₂ and per 10-unit increase in annual concentrations of PM_2.5, PM₁₀, CO, and O₃. To determine the optimal lag length for the model, we considered the Likelihood Ratio (LR) test to assess whether additional lags significantly improve model fit and evaluated the Final Prediction Error (FPE), Akaike Information Criterion (AIC), Hannan-Quinn Information Criterion (HQ), and Schwarz Bayesian Information Criterion (SBIC), with lower values indicating better model performance. All the analyses were performed by using STATA (version 12).

Results

The cohort used in the study comprised 1,305 patients who received a diagnosis of luminal A breast cancer between January 2003 and December 2018. The demographic profile of the patients revealed a median age of 52 years old at diagnosis (IQR: 45–60) and a median BMI of 23.9 kg/m² (IQR: 21.5–26.4). The distribution of cancer stages exhibited 63% for localized cases, 30% for regional cases, and 5% for metastatic instances. Further exploration of the patients’ histories revealed that 5% of the cohort had a history of smoking while 8% had a history of alcohol use. These delineated statistics provide a comprehensive overview of the demographic and clinical characteristics within the specified timeframe.

Baseline characteristics and mortality rate

In Table 1, 288 patients afflicted with luminal A breast cancer had a mortality rate of 3.4 per 100 PYFU (95%CI: 3.1–3.9). Specifically, the mortality rate for older patients reached 3.9 per 100 PYFU (95%CI: 3.4–4.5), while those with a low BMI exhibited a mortality rate of 3.6 per 100 PYFU (95%CI: 2.2–5.8).

Download:

Table 1. Baseline characteristics and the associated mortality rate of the study population.

https://doi.org/10.1371/journal.pone.0335140.t001

After stratification based on the cancer stages, the group diagnosed with metastatic disease had a mortality rate of 22.6 per 100 PYFU (95% CI: 16.8–30.3). Furthermore, the mortality patterns among individuals with a history of smoking and/or alcohol use revealed heightened mortality rates (7.3 per 100 PYFU; 95%CI: 5.0–10.7 and 4.0 per 100 PYFU; 95%CI: 2.8–5.9, respectively).

Survival probabilities

Fig 1a demonstrates the survival curve of luminal A breast cancer patients, portraying a gradual decrement in survival probability over time. The survival rates at 2, 4-, 6-, 8-, and 10-years post-diagnosis were 95%, 88%, 82%, 75%, and 70%, respectively.

Download:

Fig 1. The outcome of luminal A breast cancer patients.

(a) Survival rates (b) recurrence-free survival rates.

https://doi.org/10.1371/journal.pone.0335140.g001

In Fig 2, the OS rates of luminal A breast cancer patients are stratified by fundamental baseline characteristics encompassing age, BMI, cancer stage, smoking history, and alcohol-use history. As can be seen in Fig 2a, patients aged ≥50 years old exhibited a markedly diminished survival rate in comparison to their younger counterparts. Furthermore, luminal A breast cancer patients diagnosed with the metastatic stage manifested a significantly lower survival rate relative to those diagnosed with either the regional or localized stage (Fig 2c). Notably, individuals with a history of smoking demonstrated a notably lower survival rate than their non-smoking counterparts (Fig 2d).

Download:

Fig 2. The survival rates of the luminal A breast cancer patients according to (a) age, (b) BMI, (c) cancer stage, (d) smoking history, and (e) alcohol-use history.

https://doi.org/10.1371/journal.pone.0335140.g002

Fig 3 shows the impact of air pollution on the survival rates predicated on the patients’ residences at the time of diagnosis. No differences in survival rates were observed among patients residing in areas according to the type of pollutant: PM_2.5 (Fig 3a), PM₁₀ (Fig 3b), NO₂ (Fig 3c), SO₂ (Fig 3d), CO (Fig 3e), and O₃ (Fig 3f).

Download:

Fig 3. The survival rates of the luminal A breast cancer patients according to the annually averaged concentrations of (a) PM_2.5, (b) PM₁₀, (c) NO₂, (d) SO₂, (e) CO, and (f) O₃.

https://doi.org/10.1371/journal.pone.0335140.g003

Risk factors associated with death

Cox proportional hazard models were utilized to elucidate the pertinent risk factors contributing to mortality within the cohort of luminal A breast cancer patients, the results of which are available in Table 2. The last iteration of the multivariable analysis affirmed that age, cancer stage, and smoking history maintained significant associations with mortality risk (p-value<0.05). Of particular significance is that the metastatic cancer stage attained the highest adjusted hazard ratio (aHR) of 10.50 (95%CI: 7.23–15.25). Furthermore, the regional cancer stage (aHR = 2.04; 95%CI: 1.55–2.69), an age of ≥ 50 years old (aHR = 1.46; 95%CI: 1.13–1.90), and a history of smoking (aHR = 1.72; 95%CI: 1.14–2.60) all emerged as important factors augmenting the mortality risk within the luminal A breast cancer patient cohort.

Download:

Table 2. Risk factors associated with mortality among the luminal A breast cancer patients.

https://doi.org/10.1371/journal.pone.0335140.t002

Baseline characteristics and recurrence rate

Table 3 shows the recurrence patterns observed among 135 luminal A breast cancer patients. The cumulative recurrence rate for this cohort was computed as 1.7 per 100 PYFU (95%CI: 1.4–2.0). Furthermore, the recurrence rate for patients aged < 50 years old exhibited was 1.8 per 100 PYFU (95%CI: 1.4–2.3), while that for a high BMI was higher at 3.4 per 100 PYFU (95%CI: 2.0–5.6). Upon stratification by cancer stage, the metastatic stage group exhibited the highest recurrence rate of 7.7 per 100 PYFU (95%CI: 4.5–13.3). Furthermore, patients with a history of smoking and/or alcohol use demonstrated elevated recurrence rates of 3.2 per 100 PYFU (95%CI: 1.7–5.8) and 2.5 per 100 PYFU (95%CI: 1.6–4.1), respectively.

Download:

Table 3. Baseline characteristics and the associated cancer recurrence rate for the study population.

https://doi.org/10.1371/journal.pone.0335140.t003

Recurrence-free survival probabilities

The RFS rate among the luminal A breast cancer patients gradually declined over time as shown in Fig 1b. The RFS rate at 2, 4, 6, 8, and 10 years were 97%, 93%, 90%, 87%, and 85%, respectively. Fig 4 demonstrates the RFS rate of luminal A breast cancer patients across the various baseline characteristics included in the study. Fig 4b unveils a notable reduction in the RFS rate among patients with a high BMI. In addition, the luminal A breast cancer patients diagnosed with the metastatic stage exhibited a significantly diminished RFS rate in comparison to those diagnosed with either the regional or localized stage (Fig 4c). Fig 5 shows the temporal effects of air pollution on the cancer RFS rate based on the patients’ residences. Statistically significant effects were found for patients who had high annual averaged concentrations of PM₁₀ (Fig 5b) and CO (Fig 5e).

Download:

Fig 4. The recurrence-free survival rates of the luminal A breast cancer patients according to (a) age, (b) BMI, (c) cancer stage, (d) smoking history, and (e) alcohol-use history.

https://doi.org/10.1371/journal.pone.0335140.g004

Download:

Fig 5. The recurrence-free survival rates of the luminal A breast cancer patients according to the annually averaged concentrations of (a) PM_2.5, (b) PM₁₀, (c) NO₂, (d) SO₂, (e) CO, and (f) O₃.

https://doi.org/10.1371/journal.pone.0335140.g005

Risk factors associated with luminal A breast cancer recurrence

The findings delineating risk factors pertinent to recurrence risk among luminal A breast cancer patients derived from Cox proportional hazard models are provided in Table 4. In the multivariable analysis, pollutants correlated with PM₁₀ concentration were deliberately excluded, thereby refining the precision of the model. Subsequently, only cancer stage, smoking history, and the time-updated local concentration of PM₁₀ were included in the multivariate framework. In the ultimate model, all the parameters except for smoking history demonstrated independent associations with mortality risk (p-values < 0.05). Metastatic cancer stage attained the highest aHR of 4.96 (95%CI: 2.78–8.83). Furthermore, exposure to the time-updated local concentration of PM₁₀ above 55 µg/m³ was found to be linked with an augmented recurrence risk among the luminal A breast cancer patients (aHR = 1.68; 95%CI: 1.16–2.45). Maps that represent trends in PM₁₀ levels over time shown in S2 Fig.

Download:

Table 4. Risk factors associated with recurrence among the luminal A breast cancer patients.

https://doi.org/10.1371/journal.pone.0335140.t004

Modeling lagged (delayed) associations between air pollution exposure and the mortality and recurrence of luminal A breast cancer

Lag 5 shows the lowest values for AIC (11.00), HQ (10.80), and SBIC (11.95), and the sequential modified LR test suggests that a 5-lag specification is optimal (S1 Table). Therefore, we selected lag 5 as the optimal lag length, indicating superior overall model performance. RRs with 95% CIs for recurrence and mortality outcomes for luminal A breast cancer per unit increase in air pollutant concentration were estimated using a distributed lag model (S2 Table). An increase in NO₂ concentration of 1 ppb was associated with a higher risk of total deaths at lag 0 years (RR: 1.98, 95% CI: 1.15–3.41) (Fig 6c). A 1 ppb increase in SO₂ concentration at lag 1 year was significantly associated with a 3.5-fold increase in recurrence risk (RR: 3.51, 95% CI: 1.45–8.45) (Fig 7d), while a 1 ppb increase in SO₂ concentration at lag 5 years was associated with a higher mortality risk (RR: 1.85, 95% CI: 1.06–3.21) (Fig 6d). However, the cumulative risk (lag 0–5 years) per 10-unit increase in annual concentrations of PM_2.5, PM₁₀, CO, and O₃, and per 1-unit increase in annual concentrations of NO₂ and SO₂ was not associated with mortality from and recurrent luminal A breast cancer (S3 Fig).

Download:

Fig 6. Lag-specific relative risks of luminal A breast cancer-associated mortality per 10-unit increase in annual concentrations of (a) PM_2.5, (b) PM₁₀, (e) CO, and (f) O₃ and per 1-unit increase in annual concentrations of (c) NO₂ and (d) SO₂.

https://doi.org/10.1371/journal.pone.0335140.g006

Download:

Fig 7. Lag-specific relative risks of luminal A breast cancer recurrence per 10-unit increase in annual concentrations of (a) PM_2.5, (b) PM₁₀, (e) CO, and (f) O3 and per 1-unit increase in annual concentrations of (c) NO₂ and (d) SO₂.

https://doi.org/10.1371/journal.pone.0335140.g007

Discussion

Existing studies have primarily focused on widely-known risk factors of luminal A breast cancer such as age, alcohol consumption, smoking habit, BMI, and stage of cancer [11–16], but not much about the effects of air pollution has been explored. Since luminal A breast cancer has favorable prognosis with many long-term survivors [6,7], analyzing the impact of prolonged exposure to air pollutants on the recurrence and survival risks of these patients is necessary for the improvements of healthcare and public health strategies. In the present study, we explored potential correlations between factors that could affect luminal A breast cancer 10-year recurrence rates and mortality of a cohort of patients in the upper northern region of Thailand. While we established a connection between exposure to air pollution and luminal A breast cancer mortality/recurrence risks, it is important to emphasize that we identified an association rather than a causal relationship. The findings suggest that patients living in areas with higher levels of PM₁₀ exposure had shorter recurrence-free times. However, we cannot definitively conclude that air pollution directly caused these outcomes. This distinction is critical to avoid insinuating any causality and provide a more balanced interpretation of our findings. The study encompassed all women who had been diagnosed with luminal A breast cancer in Thailand during 2003–2018. Most of the luminal A breast cancers were of the local stage (62.8%; 820/1,305 cases), which is in line with it frequently having a favorable prognosis relative to other subtypes attributable to its characteristic traits of restrained growth rate and consistent responsiveness to hormone therapy.

From a study conducted in China with a median follow-up of 5.7 years, luminal A tumors had RFS and OS rates of 87.3% and 94.2%, respectively [43]. In comparison, we determined 10-year RFS and OS rates of 85% and 70%, respectively. Luminal A breast cancer could have a greater tendency to recur later so at least 10 years of follow-up when studying recurrence is important [7,25,44,45]. Ten-year RFS of Dutch study was 87.5% which was in line with our study [7].

We discovered that patients diagnosed with luminal A breast cancer aged 50 years old or older face a mortality rate risk approximately 1.5 times higher than those below this age. These observations align with those from earlier research [10], implying an association between luminal A breast cancer and an augmented risk of death in older women. Moreover, we found that patients with the regional or metastatic cancer stage exhibited a significantly elevated risk of death (aHR: 2.04; 95%CI: 1.55–2.69 vs. aHR:10.50; 95%CI: 7.23–15.25). Moreover, our findings also reveal that patients with both luminal A breast cancer and a history of smoking had a mortality rate risk of approximately 1.7 times greater. This observation agrees with recent investigations conducted on women aged 20–69 years old diagnosed with primary invasive luminal breast cancer in the Seattle–Puget Sound region [46], in which the risk of mortality rate was 1.6 times higher. Our study is no significant difference between the mortality rate among patients residing in high pollution. This observation is inconsistent with Cheng et al. [20] found that PM_2.5 and PM₁₀ (per 10 µg/m³) had a mortality rate risk of approximately 1.17 and 1.13 times, respectively, including the study of Hwang et al. [19] that found PM₁₀ (per 10 µg/m³) associated with breast cancer mortality rate about 1.05 times. However, we could not perform our analysis at WHO guidance levels since the PM data are left-skewed. In other words, the PM in our study areas were higher than the standard levels. Therefore, in our analysis, we grouped them using quartiles and by choosing a suitable dichotomization.

In the context of recurrence rates, we found that 85% of luminal A breast cancer patients survived 10 years post-diagnosis regardless of the residential concentration of air pollution. This finding is consistent with that of Mudduwa et al. [47], who discovered that 83.6% of luminal A breast cancer cases in Southern Sri Lanka from 2006 to 2015 were recurrence-free over a five-year post-diagnosis period. However, our results uncover a noteworthy disparity in recurrence times among luminal A breast cancer patients residing in districts with annually averaged PM₁₀ concentrations either below or equal to or above 55 µg/m³. Specifically, a lower recurrence-free rate was observed when PM₁₀ levels were equal to or exceeded 55 µg/m³. Thus, individuals residing in areas severely impacted by high levels of air pollution experienced a shorter recurrence-free time. To the best of our knowledge, the relationship between air pollution and recurrence rates in luminal A breast cancer patients has not previously been explored. It is imperative to emphasize that luminal A breast cancer patients constitute a vulnerable group residing in areas with PM₁₀ concentrations surpassing the WHO guidance level. Our findings underscore the critical necessity to address the severe issue of air pollution, particularly in northern Thailand. To our knowledge, this is first study to evaluate the effects of air pollution and the RFS rate among the luminal A breast cancer patients, so it is a strength of this study.

Our findings on air pollution exposure and risk of mortality and recurrence indicate that a 1 ppb increase in NO₂ was associated with a 1.98-fold increase in mortality within the same year. This suggests that in the short term, higher NO₂ levels are associated with an elevated mortality risk. This is in keeping with Reding et al. [22], who found that NO₂ increased the risk of ER + /PR+ breast cancer, with an RR of 1.10 (95% CI: 1.02–1.19 per IQR of 5.8 ppb) using a Land use regression model. Furthermore, we discovered that SO₂ was associated with an increase in recurrence risk at lag 1 year and mortality risk at lag 5 years. This finding is consistent with Hwang et al. [19], who found that an increment in SO₂ concentration of 1ppb increased the risk of breast cancer (OR = 1.04; 95% CI: 1.02–1.05). Conversely, Khorrami et al. [48] found that the concentration of SO₂ inversely affected the severity of breast cancer among women over 50 years old. However, these study populations were not limited to luminal A breast cancer patients. Understanding the delayed effects of air pollution exposure enhances the ability to assess long-term health risks and provides crucial information for future surveillance and healthcare management of luminal A breast cancer patients.

The study’s findings have important implications for policies concerning pollution and patient care. The association between higher air pollution levels and the recurrence of luminal A breast cancer underscores the importance of addressing air quality in regions with high pollution, particularly in northern Thailand. Policymakers should consider the potential health impacts of air pollution on vulnerable populations such as breast cancer patients, and they might want to explore air quality regulations or stricter pollution controls to mitigate these risks. For healthcare providers, these results suggest that breast cancer patients living in high-pollution areas might benefit from additional lifestyle recommendations, such as reducing outdoor activities during high-pollution periods, utilizing air filtration systems at home, or considering other preventive measures that could reduce exposure to environmental pollutants. For clinical practice, we recommend adapting breast cancer follow-up protocols to include environmental risk stratification. Breast cancer patients residing in high-pollution areas should undergo more frequent clinical evaluations. This should be accompanied by comprehensive health education for patients, caregivers, and healthcare personnel on the signs and symptoms of breast cancer recurrence, including both locoregional and distant recurrence, to facilitate earlier detection. Additionally, primary care providers should be trained to consider environmental exposure history when planning long-term management and survivorship care. To effectively apply these findings to regions with different air pollution profiles, it would be beneficial to conduct comparative studies across different regions and analyze area-specific pollutant compositions. Furthermore, consideration should be given to local environmental and demographic factors, along with the potential for meta-analyses that combine data from multiple regions. This comprehensive approach would provide a more robust understanding of how air pollution impacts health outcomes across different geographical and environmental contexts.

Our study has some limitations. First, this study was conducted hospital-based nature, which may potentially compromise the generalizability of the results. However, this concern was addressed by the 10-year follow-up period with a commendable completeness rate of 73.6% (961/1,305). Moreover, the tumor grade, histology, and treatment for each luminal A breast cancer patient, while present in hospital medical records, were not systematically linked with the Chiang Mai Cancer Registry. Consequently, we were unable to investigate these factors in our analysis. If they had been linked, we could have conducted a more comprehensive analysis that included exploring the influence of tumor grade, histology, and treatment on luminal A breast cancer patient recurrence and mortality. Finally, due to a 10-year patient follow-up period, we were unable to examine associations with exposure lags exceeding 5 years as this would have rendered the lag analysis unreliable. In addition, the low number of deaths and recurrences observed annually in luminal-A breast cancer cases resulted in wide 95% CI when applying Poisson regression models and distributed lag models. Therefore, future studies should consider employing these methodologies to larger sample sizes to enhance the precision of the analysis.

Conclusion

Not only BMI and cancer stage, but exposure to PM₁₀ and CO had also adverse associations to recurrence risk in luminal A breast cancer patients in upper northern Thailand. An elevated breast cancer recurrence risk was associated with the time-updated local concentration of PM₁₀ over 55 µg/m³. These findings are crucial for encouraging public health and allows the people who are related in the decision-making process to consider the women health impacts. While we identified an association between air pollution and luminal A breast cancer recurrence/mortality, further research is needed to establish whether a causal relationship exists. Emphasizing that there is an association rather than a causal relationship ensures a more balanced and cautious interpretation of our findings, thereby guiding future studies in this area.

Supporting information

S1 Fig. Annual average concentrations of (a) PM_2.5, (b) PM₁₀, (c) NO₂, (d) SO₂, (e) CO, and (f) O₃.

https://doi.org/10.1371/journal.pone.0335140.s001

(DOCX)

S2 Fig. PM₁₀ levels in the study area from 2003–2020.

This map was created with QGIS using open data (shapefile) from the Geo-Informatics and Space Technology Development Agency (https://gistdaportal.gistda.or.th/data/rest/services/EEC_Public).

https://doi.org/10.1371/journal.pone.0335140.s002

(DOCX)

S3 Fig. Cumulative relative risks (lag 0–5 years) for mortality from (a) and recurrence of (b) luminal A breast cancer per 10-unit increase in annual concentrations of PM_2.5, PM₁₀, CO, and O₃ and per 1-unit increase in annual concentrations of NO₂ and SO₂.

https://doi.org/10.1371/journal.pone.0335140.s003

(DOCX)

S1 Table. Lag order selection criteria.

https://doi.org/10.1371/journal.pone.0335140.s004

(DOCX)

S2 Table. Relative risks (RRs) with 95% confidence intervals (CIs) for luminal A breast cancer-associated deaths and recurrences with per unit increase in air pollutant concentrations estimated using a distributed lag model.

https://doi.org/10.1371/journal.pone.0335140.s005

(DOCX)

Acknowledgments

We would like to acknowledge Chiang Mai Cancer Registry, Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine and Chiang Mai University for supporting the current study (Fundamental Fund 2023).

References

1. Yersal O, Barutca S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J Clin Oncol. 2014;5(3):412–24. pmid:25114856
- View Article
- PubMed/NCBI
- Google Scholar
2. Sripan P, Sriplung H, Pongnikorn D, Bilheem S, Virani S, Waisri N, et al. Clinical subtypes of breast cancer in Thai women: a population-based study of Chiang Mai province. Asian Biomed. 2019;13(1):11–7.
- View Article
- Google Scholar
3. Orrantia-Borunda E, Anchondo-Nunez P, Acuna-Aguilar LE, Gomez-Valles FO, Ramirez-Valdespino CA. Subtypes of Breast Cancer. In: Mayrovitz HN, editor. Breast Cancer. Brisbane (AU); 2022. p. 31–42.
4. Fisher B, Costantino JP, Wickerham DL, Cecchini RS, Cronin WM, Robidoux A, et al. Tamoxifen for the prevention of breast cancer: current status of the National Surgical Adjuvant Breast and Bowel Project P-1 study. J Natl Cancer Inst. 2005;97(22):1652–62. pmid:16288118
- View Article
- PubMed/NCBI
- Google Scholar
5. Guarneri V, Conte P. Metastatic breast cancer: therapeutic options according to molecular subtypes and prior adjuvant therapy. Oncologist. 2009;14(7):645–56. pmid:19608638
- View Article
- PubMed/NCBI
- Google Scholar
6. Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thürlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013;24(9):2206–23. pmid:23917950
- View Article
- PubMed/NCBI
- Google Scholar
7. van Maaren MC, de Munck L, Strobbe LJA, Sonke GS, Westenend PJ, Smidt ML, et al. Ten-year recurrence rates for breast cancer subtypes in the Netherlands: A large population-based study. Int J Cancer. 2019;144(2):263–72. pmid:30368776
- View Article
- PubMed/NCBI
- Google Scholar
8. Mohammed AA. The clinical behavior of different molecular subtypes of breast cancer. Cancer Treat Res Commun. 2021;29:100469. pmid:34624832
- View Article
- PubMed/NCBI
- Google Scholar
9. Minicozzi P, Berrino F, Sebastiani F, Falcini F, Vattiato R, Cioccoloni F, et al. High fasting blood glucose and obesity significantly and independently increase risk of breast cancer death in hormone receptor-positive disease. Eur J Cancer. 2013;49(18):3881–8. pmid:24011933
- View Article
- PubMed/NCBI
- Google Scholar
10. Fallahpour S, Navaneelan T, De P, Borgo A. Breast cancer survival by molecular subtype: a population-based analysis of cancer registry data. CMAJ Open. 2017;5(3):E734–9. pmid:28951445
- View Article
- PubMed/NCBI
- Google Scholar
11. Abubakar M, Sung H, Bcr D, Guida J, Tang TS, Pfeiffer RM, et al. Breast cancer risk factors, survival and recurrence, and tumor molecular subtype: analysis of 3012 women from an indigenous Asian population. Breast Cancer Res. 2018;20(1):114. pmid:30227867
- View Article
- PubMed/NCBI
- Google Scholar
12. Hirko KA, Chen WY, Willett WC, Rosner BA, Hankinson SE, Beck AH, et al. Alcohol consumption and risk of breast cancer by molecular subtype: Prospective analysis of the nurses’ health study after 26 years of follow-up. Int J Cancer. 2016;138(5):1094–101. pmid:26384849
- View Article
- PubMed/NCBI
- Google Scholar
13. Loroña NC, Othus M, Malone KE, Linden HM, Tang M-TC, Li CI. Alcohol, Smoking, and Risks of Breast Cancer Recurrence and Mortality among Women with Luminal, Triple-Negative, and HER2-Overexpressing Breast Cancer. Cancer Epidemiol Biomarkers Prev. 2024;33(2):288–97. pmid:38019269
- View Article
- PubMed/NCBI
- Google Scholar
14. Cespedes Feliciano EM, Kwan ML, Kushi LH, Chen WY, Weltzien EK, Castillo AL, et al. Body mass index, PAM50 subtype, recurrence, and survival among patients with nonmetastatic breast cancer. Cancer. 2017;123(13):2535–42. pmid:28295245
- View Article
- PubMed/NCBI
- Google Scholar
15. García-Estévez L, Cortés J, Pérez S, Calvo I, Gallegos I, Moreno-Bueno G. Obesity and Breast Cancer: A Paradoxical and Controversial Relationship Influenced by Menopausal Status. Front Oncol. 2021;11:705911. pmid:34485137
- View Article
- PubMed/NCBI
- Google Scholar
16. Ntirenganya F, Twagirumukiza JD, Bucyibaruta G, Rugwizangoga B, Rulisa S. Premenopausal Breast Cancer Risk Factors and Associations with Molecular Subtypes: A Case-Control Study. Int J Breast Cancer. 2021;2021:5560559. pmid:34659834
- View Article
- PubMed/NCBI
- Google Scholar
17. Zhang Z, Yan W, Chen Q, Zhou N, Xu Y. The relationship between exposure to particulate matter and breast cancer incidence and mortality: A meta-analysis. Medicine (Baltimore). 2019;98(50):e18349. pmid:31852135
- View Article
- PubMed/NCBI
- Google Scholar
18. White AJ, Bradshaw PT, Hamra GB. Air pollution and Breast Cancer: A Review. Curr Epidemiol Rep. 2018;5(2):92–100. pmid:30271702
- View Article
- PubMed/NCBI
- Google Scholar
19. Hwang J, Bae H, Choi S, Yi H, Ko B, Kim N. Impact of air pollution on breast cancer incidence and mortality: a nationwide analysis in South Korea. Sci Rep. 2020;10(1):5392. pmid:32214155
- View Article
- PubMed/NCBI
- Google Scholar
20. Cheng I, Yang J, Tseng C, Wu J, Conroy SM, Shariff-Marco S, et al. Outdoor ambient air pollution and breast cancer survival among California participants of the Multiethnic Cohort Study. Environ Int. 2022;161:107088. pmid:35063793
- View Article
- PubMed/NCBI
- Google Scholar
21. Tagliabue G, Borgini A, Tittarelli A, van Donkelaar A, Martin RV, Bertoldi M, et al. Atmospheric fine particulate matter and breast cancer mortality: a population-based cohort study. BMJ Open. 2016;6(11):e012580. pmid:28076275
- View Article
- PubMed/NCBI
- Google Scholar
22. Reding KW, Young MT, Szpiro AA, Han CJ, DeRoo LA, Weinberg C, et al. Breast Cancer Risk in Relation to Ambient Air Pollution Exposure at Residences in the Sister Study Cohort. Cancer Epidemiol Biomarkers Prev. 2015;24(12):1907–9. pmid:26464427
- View Article
- PubMed/NCBI
- Google Scholar
23. Sukkhum S, Lim A, Ingviya T, Saelim R. Seasonal Patterns and Trends of Air Pollution in the Upper Northern Thailand from 2004 to 2018. Aerosol Air Qual Res. 2022;22(5):210318.
- View Article
- Google Scholar
24. IQAir. 2024 World Air Quality Report. 2025 [cited 2025 May 5]. Available from: https://www.greenpeace.org/static/planet4-chile-stateless/2025/03/edf90b7a-2024_world_air_quality_report_vf.pdf
25. Lalitaporn P, Boonmee T. Analysis of Tropospheric Nitrogen Dioxide Using Satellite and Ground Based Data over Northern Thailand. Eng J. 2019;23(6):19–35.
- View Article
- Google Scholar
26. Johnston HJ, Mueller W, Steinle S, Vardoulakis S, Tantrakarnapa K, Loh M, et al. How Harmful Is Particulate Matter Emitted from Biomass Burning? A Thailand Perspective. Curr Pollution Rep. 2019;5(4):353–77.
- View Article
- Google Scholar
27. Khodmanee S, Amnuaylojaroen T. Impact of Biomass Burning on Ozone, Carbon Monoxide, and Nitrogen Dioxide in Northern Thailand. Front Environ Sci. 2021;9.
- View Article
- Google Scholar
28. Marks D, Miller MA. A transboundary political ecology of air pollution: Slow violence on Thailand’s margins. Env Pol Gov. 2022;32(4):305–19.
- View Article
- Google Scholar
29. Phairuang W, Suwattiga P, Chetiyanukornkul T, Hongtieab S, Limpaseni W, Ikemori F, et al. The influence of the open burning of agricultural biomass and forest fires in Thailand on the carbonaceous components in size-fractionated particles. Environ Pollut. 2019;247:238–47. pmid:30685664
- View Article
- PubMed/NCBI
- Google Scholar
30. Department PC. Prevention and corrective action plan Northern region [in Thail]. 2017 [cited 2024 June 10]. Available from: https://www.pcd.go.th/wp-content/uploads/2020/04/pcdnew-2020-04-23_05-14-39_156051.pdf
31. Department PC. The state of Air and Noise Pollution in Thailand [in Thai]. 2021 [cited 2024 June 8]. Available from: https://www.pcd.go.th/publication/27805
32. Brenton JD, Carey LA, Ahmed AA, Caldas C. Molecular classification and molecular forecasting of breast cancer: ready for clinical application? J Clin Oncol. 2005;23(29):7350–60. pmid:16145060
- View Article
- PubMed/NCBI
- Google Scholar
33. Nguyen PL, Taghian AG, Katz MS, Niemierko A, Abi Raad RF, Boon WL, et al. Breast cancer subtype approximated by estrogen receptor, progesterone receptor, and HER-2 is associated with local and distant recurrence after breast-conserving therapy. J Clin Oncol. 2008;26(14):2373–8. pmid:18413639
- View Article
- PubMed/NCBI
- Google Scholar
34. Tang P, Tse GM. Immunohistochemical Surrogates for Molecular Classification of Breast Carcinoma: A 2015 Update. Arch Pathol Lab Med. 2016;140(8):806–14. pmid:27472239
- View Article
- PubMed/NCBI
- Google Scholar
35. Benedetti A, Morcrette J ‐J., Boucher O, Dethof A, Engelen RJ, Fisher M, et al. Aerosol analysis and forecast in the European Centre for Medium‐Range Weather Forecasts Integrated Forecast System: 2. Data assimilation. J Geophys Res. 2009;114(D13).
- View Article
- Google Scholar
36. Morcrette J ‐J., Boucher O, Jones L, Salmond D, Bechtold P, Beljaars A, et al. Aerosol analysis and forecast in the European Centre for Medium‐Range Weather Forecasts Integrated Forecast System: Forward modeling. J Geophys Res. 2009;114(D6).
- View Article
- Google Scholar
37. Nakharutai N, Traisathit P, Thongsak N, Supasri T, Srikummoon P, Thumronglaohapun S, et al. Impact of Residential Concentration of PM2.5 Analyzed as Time-Varying Covariate on the Survival Rate of Lung Cancer Patients: A 15-Year Hospital-Based Study in Upper Northern Thailand. Int J Environ Res Public Health. 2022;19(8):4521. pmid:35457386
- View Article
- PubMed/NCBI
- Google Scholar
38. Reungwetwattana T, Oranratnachai S, Puataweepong P, Tangsujaritvijit V, Cherntanomwong P. Lung Cancer in Thailand. J Thorac Oncol. 2020;15(11):1714–21. pmid:33148410
- View Article
- PubMed/NCBI
- Google Scholar
39. Wiriya W, Prapamontol T, Chantara S. PM10-bound polycyclic aromatic hydrocarbons in Chiang Mai (Thailand): Seasonal variations, source identification, health risk assessment and their relationship to air-mass movement. Atmos Res. 2013;124:109–22.
- View Article
- Google Scholar
40. Copernicus. EQA reports of global services. 2023 [cited 2025 Feb 15]. Available from: https://atmosphere.copernicus.eu/eqa-reports-global-services
41. Brenner H, Hakulinen T. Are patients diagnosed with breast cancer before age 50 years ever cured? J Clin Oncol. 2004;22(3):432–8. pmid:14691121
- View Article
- PubMed/NCBI
- Google Scholar
42. Weir CB, Jan A. BMI Classification percentile and cut off points. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025. Available from: https://www.ncbi.nlm.nih.gov/books/NBK541070/
43. Wang Y, Yin Q, Yu Q, Zhang J, Liu Z, Wang S, et al. A retrospective study of breast cancer subtypes: the risk of relapse and the relations with treatments. Breast Cancer Res Treat. 2011;130(2):489–98. pmid:21837481
- View Article
- PubMed/NCBI
- Google Scholar
44. Pan H, Gray R, Braybrooke J, Davies C, Taylor C, McGale P, et al. 20-Year Risks of Breast-Cancer Recurrence after Stopping Endocrine Therapy at 5 Years. N Engl J Med. 2017;377(19):1836–46. pmid:29117498
- View Article
- PubMed/NCBI
- Google Scholar
45. Intrieri T, Manneschi G, Caldarella A. 10-year survival in female breast cancer patients according to ER, PR and HER2 expression: a cancer registry population-based analysis. J Cancer Res Clin Oncol. 2023;149(8):4489–96. pmid:36129548
- View Article
- PubMed/NCBI
- Google Scholar
46. Goldvaser H, Gal O, Rizel S, Hendler D, Neiman V, Shochat T, et al. The association between smoking and breast cancer characteristics and outcome. BMC Cancer. 2017;17(1):624. pmid:28874120
- View Article
- PubMed/NCBI
- Google Scholar
47. Mudduwa LKB, Peiris HH, Liyanage T. Predictive Factors of Recurrence Free Survival of Patients with Luminal Breast Cancer in Southern Sri Lanka. Bengal J Cancer. 2021;1(2):78–85.
- View Article
- Google Scholar
48. Khorrami Z, Pourkhosravani M, Karamoozian A, Jafari-Khounigh A, Akbari ME, Rezapour M, et al. Ambient air pollutants and breast cancer stage in Tehran, Iran. Sci Rep. 2024;14(1):3873.
- View Article
- Google Scholar

[ref1] 1. Yersal O, Barutca S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J Clin Oncol. 2014;5(3):412–24. pmid:25114856
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Sripan P, Sriplung H, Pongnikorn D, Bilheem S, Virani S, Waisri N, et al. Clinical subtypes of breast cancer in Thai women: a population-based study of Chiang Mai province. Asian Biomed. 2019;13(1):11–7.
View Article
Google Scholar

[6] View Article

[7] Google Scholar

[ref3] 3. Orrantia-Borunda E, Anchondo-Nunez P, Acuna-Aguilar LE, Gomez-Valles FO, Ramirez-Valdespino CA. Subtypes of Breast Cancer. In: Mayrovitz HN, editor. Breast Cancer. Brisbane (AU); 2022. p. 31–42.

[ref4] 4. Fisher B, Costantino JP, Wickerham DL, Cecchini RS, Cronin WM, Robidoux A, et al. Tamoxifen for the prevention of breast cancer: current status of the National Surgical Adjuvant Breast and Bowel Project P-1 study. J Natl Cancer Inst. 2005;97(22):1652–62. pmid:16288118
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref5] 5. Guarneri V, Conte P. Metastatic breast cancer: therapeutic options according to molecular subtypes and prior adjuvant therapy. Oncologist. 2009;14(7):645–56. pmid:19608638
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref6] 6. Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thürlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013;24(9):2206–23. pmid:23917950
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref7] 7. van Maaren MC, de Munck L, Strobbe LJA, Sonke GS, Westenend PJ, Smidt ML, et al. Ten-year recurrence rates for breast cancer subtypes in the Netherlands: A large population-based study. Int J Cancer. 2019;144(2):263–72. pmid:30368776
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref8] 8. Mohammed AA. The clinical behavior of different molecular subtypes of breast cancer. Cancer Treat Res Commun. 2021;29:100469. pmid:34624832
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref9] 9. Minicozzi P, Berrino F, Sebastiani F, Falcini F, Vattiato R, Cioccoloni F, et al. High fasting blood glucose and obesity significantly and independently increase risk of breast cancer death in hormone receptor-positive disease. Eur J Cancer. 2013;49(18):3881–8. pmid:24011933
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref10] 10. Fallahpour S, Navaneelan T, De P, Borgo A. Breast cancer survival by molecular subtype: a population-based analysis of cancer registry data. CMAJ Open. 2017;5(3):E734–9. pmid:28951445
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref11] 11. Abubakar M, Sung H, Bcr D, Guida J, Tang TS, Pfeiffer RM, et al. Breast cancer risk factors, survival and recurrence, and tumor molecular subtype: analysis of 3012 women from an indigenous Asian population. Breast Cancer Res. 2018;20(1):114. pmid:30227867
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref12] 12. Hirko KA, Chen WY, Willett WC, Rosner BA, Hankinson SE, Beck AH, et al. Alcohol consumption and risk of breast cancer by molecular subtype: Prospective analysis of the nurses’ health study after 26 years of follow-up. Int J Cancer. 2016;138(5):1094–101. pmid:26384849
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref13] 13. Loroña NC, Othus M, Malone KE, Linden HM, Tang M-TC, Li CI. Alcohol, Smoking, and Risks of Breast Cancer Recurrence and Mortality among Women with Luminal, Triple-Negative, and HER2-Overexpressing Breast Cancer. Cancer Epidemiol Biomarkers Prev. 2024;33(2):288–97. pmid:38019269
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref14] 14. Cespedes Feliciano EM, Kwan ML, Kushi LH, Chen WY, Weltzien EK, Castillo AL, et al. Body mass index, PAM50 subtype, recurrence, and survival among patients with nonmetastatic breast cancer. Cancer. 2017;123(13):2535–42. pmid:28295245
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref15] 15. García-Estévez L, Cortés J, Pérez S, Calvo I, Gallegos I, Moreno-Bueno G. Obesity and Breast Cancer: A Paradoxical and Controversial Relationship Influenced by Menopausal Status. Front Oncol. 2021;11:705911. pmid:34485137
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref16] 16. Ntirenganya F, Twagirumukiza JD, Bucyibaruta G, Rugwizangoga B, Rulisa S. Premenopausal Breast Cancer Risk Factors and Associations with Molecular Subtypes: A Case-Control Study. Int J Breast Cancer. 2021;2021:5560559. pmid:34659834
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref17] 17. Zhang Z, Yan W, Chen Q, Zhou N, Xu Y. The relationship between exposure to particulate matter and breast cancer incidence and mortality: A meta-analysis. Medicine (Baltimore). 2019;98(50):e18349. pmid:31852135
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref18] 18. White AJ, Bradshaw PT, Hamra GB. Air pollution and Breast Cancer: A Review. Curr Epidemiol Rep. 2018;5(2):92–100. pmid:30271702
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref19] 19. Hwang J, Bae H, Choi S, Yi H, Ko B, Kim N. Impact of air pollution on breast cancer incidence and mortality: a nationwide analysis in South Korea. Sci Rep. 2020;10(1):5392. pmid:32214155
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref20] 20. Cheng I, Yang J, Tseng C, Wu J, Conroy SM, Shariff-Marco S, et al. Outdoor ambient air pollution and breast cancer survival among California participants of the Multiethnic Cohort Study. Environ Int. 2022;161:107088. pmid:35063793
View Article
PubMed/NCBI
Google Scholar

[74] View Article

[75] PubMed/NCBI

[76] Google Scholar

[ref21] 21. Tagliabue G, Borgini A, Tittarelli A, van Donkelaar A, Martin RV, Bertoldi M, et al. Atmospheric fine particulate matter and breast cancer mortality: a population-based cohort study. BMJ Open. 2016;6(11):e012580. pmid:28076275
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref22] 22. Reding KW, Young MT, Szpiro AA, Han CJ, DeRoo LA, Weinberg C, et al. Breast Cancer Risk in Relation to Ambient Air Pollution Exposure at Residences in the Sister Study Cohort. Cancer Epidemiol Biomarkers Prev. 2015;24(12):1907–9. pmid:26464427
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref23] 23. Sukkhum S, Lim A, Ingviya T, Saelim R. Seasonal Patterns and Trends of Air Pollution in the Upper Northern Thailand from 2004 to 2018. Aerosol Air Qual Res. 2022;22(5):210318.
View Article
Google Scholar

[86] View Article

[87] Google Scholar

[ref24] 24. IQAir. 2024 World Air Quality Report. 2025 [cited 2025 May 5]. Available from: https://www.greenpeace.org/static/planet4-chile-stateless/2025/03/edf90b7a-2024_world_air_quality_report_vf.pdf

[ref25] 25. Lalitaporn P, Boonmee T. Analysis of Tropospheric Nitrogen Dioxide Using Satellite and Ground Based Data over Northern Thailand. Eng J. 2019;23(6):19–35.
View Article
Google Scholar

[90] View Article

[91] Google Scholar

[ref26] 26. Johnston HJ, Mueller W, Steinle S, Vardoulakis S, Tantrakarnapa K, Loh M, et al. How Harmful Is Particulate Matter Emitted from Biomass Burning? A Thailand Perspective. Curr Pollution Rep. 2019;5(4):353–77.
View Article
Google Scholar

[93] View Article

[94] Google Scholar

[ref27] 27. Khodmanee S, Amnuaylojaroen T. Impact of Biomass Burning on Ozone, Carbon Monoxide, and Nitrogen Dioxide in Northern Thailand. Front Environ Sci. 2021;9.
View Article
Google Scholar

[96] View Article

[97] Google Scholar

[ref28] 28. Marks D, Miller MA. A transboundary political ecology of air pollution: Slow violence on Thailand’s margins. Env Pol Gov. 2022;32(4):305–19.
View Article
Google Scholar

[99] View Article

[100] Google Scholar

[ref29] 29. Phairuang W, Suwattiga P, Chetiyanukornkul T, Hongtieab S, Limpaseni W, Ikemori F, et al. The influence of the open burning of agricultural biomass and forest fires in Thailand on the carbonaceous components in size-fractionated particles. Environ Pollut. 2019;247:238–47. pmid:30685664
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref30] 30. Department PC. Prevention and corrective action plan Northern region [in Thail]. 2017 [cited 2024 June 10]. Available from: https://www.pcd.go.th/wp-content/uploads/2020/04/pcdnew-2020-04-23_05-14-39_156051.pdf

[ref31] 31. Department PC. The state of Air and Noise Pollution in Thailand [in Thai]. 2021 [cited 2024 June 8]. Available from: https://www.pcd.go.th/publication/27805

[ref32] 32. Brenton JD, Carey LA, Ahmed AA, Caldas C. Molecular classification and molecular forecasting of breast cancer: ready for clinical application? J Clin Oncol. 2005;23(29):7350–60. pmid:16145060
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

[ref33] 33. Nguyen PL, Taghian AG, Katz MS, Niemierko A, Abi Raad RF, Boon WL, et al. Breast cancer subtype approximated by estrogen receptor, progesterone receptor, and HER-2 is associated with local and distant recurrence after breast-conserving therapy. J Clin Oncol. 2008;26(14):2373–8. pmid:18413639
View Article
PubMed/NCBI
Google Scholar

[112] View Article

[113] PubMed/NCBI

[114] Google Scholar

[ref34] 34. Tang P, Tse GM. Immunohistochemical Surrogates for Molecular Classification of Breast Carcinoma: A 2015 Update. Arch Pathol Lab Med. 2016;140(8):806–14. pmid:27472239
View Article
PubMed/NCBI
Google Scholar

[116] View Article

[117] PubMed/NCBI

[118] Google Scholar

[ref35] 35. Benedetti A, Morcrette J ‐J., Boucher O, Dethof A, Engelen RJ, Fisher M, et al. Aerosol analysis and forecast in the European Centre for Medium‐Range Weather Forecasts Integrated Forecast System: 2. Data assimilation. J Geophys Res. 2009;114(D13).
View Article
Google Scholar

[120] View Article

[121] Google Scholar

[ref36] 36. Morcrette J ‐J., Boucher O, Jones L, Salmond D, Bechtold P, Beljaars A, et al. Aerosol analysis and forecast in the European Centre for Medium‐Range Weather Forecasts Integrated Forecast System: Forward modeling. J Geophys Res. 2009;114(D6).
View Article
Google Scholar

[123] View Article

[124] Google Scholar

[ref37] 37. Nakharutai N, Traisathit P, Thongsak N, Supasri T, Srikummoon P, Thumronglaohapun S, et al. Impact of Residential Concentration of PM2.5 Analyzed as Time-Varying Covariate on the Survival Rate of Lung Cancer Patients: A 15-Year Hospital-Based Study in Upper Northern Thailand. Int J Environ Res Public Health. 2022;19(8):4521. pmid:35457386
View Article
PubMed/NCBI
Google Scholar

[126] View Article

[127] PubMed/NCBI

[128] Google Scholar

[ref38] 38. Reungwetwattana T, Oranratnachai S, Puataweepong P, Tangsujaritvijit V, Cherntanomwong P. Lung Cancer in Thailand. J Thorac Oncol. 2020;15(11):1714–21. pmid:33148410
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref39] 39. Wiriya W, Prapamontol T, Chantara S. PM10-bound polycyclic aromatic hydrocarbons in Chiang Mai (Thailand): Seasonal variations, source identification, health risk assessment and their relationship to air-mass movement. Atmos Res. 2013;124:109–22.
View Article
Google Scholar

[134] View Article

[135] Google Scholar

[ref40] 40. Copernicus. EQA reports of global services. 2023 [cited 2025 Feb 15]. Available from: https://atmosphere.copernicus.eu/eqa-reports-global-services

[ref41] 41. Brenner H, Hakulinen T. Are patients diagnosed with breast cancer before age 50 years ever cured? J Clin Oncol. 2004;22(3):432–8. pmid:14691121
View Article
PubMed/NCBI
Google Scholar

[138] View Article

[139] PubMed/NCBI

[140] Google Scholar

[ref42] 42. Weir CB, Jan A. BMI Classification percentile and cut off points. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025. Available from: https://www.ncbi.nlm.nih.gov/books/NBK541070/

[ref43] 43. Wang Y, Yin Q, Yu Q, Zhang J, Liu Z, Wang S, et al. A retrospective study of breast cancer subtypes: the risk of relapse and the relations with treatments. Breast Cancer Res Treat. 2011;130(2):489–98. pmid:21837481
View Article
PubMed/NCBI
Google Scholar

[143] View Article

[144] PubMed/NCBI

[145] Google Scholar

[ref44] 44. Pan H, Gray R, Braybrooke J, Davies C, Taylor C, McGale P, et al. 20-Year Risks of Breast-Cancer Recurrence after Stopping Endocrine Therapy at 5 Years. N Engl J Med. 2017;377(19):1836–46. pmid:29117498
View Article
PubMed/NCBI
Google Scholar

[147] View Article

[148] PubMed/NCBI

[149] Google Scholar

[ref45] 45. Intrieri T, Manneschi G, Caldarella A. 10-year survival in female breast cancer patients according to ER, PR and HER2 expression: a cancer registry population-based analysis. J Cancer Res Clin Oncol. 2023;149(8):4489–96. pmid:36129548
View Article
PubMed/NCBI
Google Scholar

[151] View Article

[152] PubMed/NCBI

[153] Google Scholar

[ref46] 46. Goldvaser H, Gal O, Rizel S, Hendler D, Neiman V, Shochat T, et al. The association between smoking and breast cancer characteristics and outcome. BMC Cancer. 2017;17(1):624. pmid:28874120
View Article
PubMed/NCBI
Google Scholar

[155] View Article

[156] PubMed/NCBI

[157] Google Scholar

[ref47] 47. Mudduwa LKB, Peiris HH, Liyanage T. Predictive Factors of Recurrence Free Survival of Patients with Luminal Breast Cancer in Southern Sri Lanka. Bengal J Cancer. 2021;1(2):78–85.
View Article
Google Scholar

[159] View Article

[160] Google Scholar

[ref48] 48. Khorrami Z, Pourkhosravani M, Karamoozian A, Jafari-Khounigh A, Akbari ME, Rezapour M, et al. Ambient air pollutants and breast cancer stage in Tehran, Iran. Sci Rep. 2024;14(1):3873.
View Article
Google Scholar

[162] View Article

[163] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Study population

Data collection and measurements

Ethical approval

Statistical analysis

Results

Baseline characteristics and mortality rate

Survival probabilities

Risk factors associated with death

Baseline characteristics and recurrence rate

Recurrence-free survival probabilities

Risk factors associated with luminal A breast cancer recurrence

Modeling lagged (delayed) associations between air pollution exposure and the mortality and recurrence of luminal A breast cancer

Discussion

Conclusion

Supporting information

S1 Fig. Annual average concentrations of (a) PM2.5, (b) PM10, (c) NO2, (d) SO2, (e) CO, and (f) O3.

S2 Fig. PM10 levels in the study area from 2003–2020.

S3 Fig. Cumulative relative risks (lag 0–5 years) for mortality from (a) and recurrence of (b) luminal A breast cancer per 10-unit increase in annual concentrations of PM2.5, PM10, CO, and O3 and per 1-unit increase in annual concentrations of NO2 and SO2.

S1 Table. Lag order selection criteria.

S2 Table. Relative risks (RRs) with 95% confidence intervals (CIs) for luminal A breast cancer-associated deaths and recurrences with per unit increase in air pollutant concentrations estimated using a distributed lag model.

Acknowledgments

References

S1 Fig. Annual average concentrations of (a) PM_2.5, (b) PM₁₀, (c) NO₂, (d) SO₂, (e) CO, and (f) O₃.

S2 Fig. PM₁₀ levels in the study area from 2003–2020.

S3 Fig. Cumulative relative risks (lag 0–5 years) for mortality from (a) and recurrence of (b) luminal A breast cancer per 10-unit increase in annual concentrations of PM_2.5, PM₁₀, CO, and O₃ and per 1-unit increase in annual concentrations of NO₂ and SO₂.