Key Points
Question
Does residing near a commercial pesticide application site increase the residents’ risk of being diagnosed with chronic rhinosinusitis (CRS)?
Finding
This retrospective cohort study of 310 patients who were evaluated for CRS at a tertiary institution found that residential proximity (within a 2000-m radius buffer) to a commercial pesticide application site was independently associated with an approximately 2.5-fold increase in the odds of being diagnosed with CRS.
Meaning
The association of residential proximity to a commercial pesticide application site with CRS diagnosis may have substantial implications for public health; additional research should further investigate this relationship.
Abstract
Importance
Environmental and occupational toxicants have been shown to be associated with an increased prevalence of chronic rhinosinusitis (CRS). However, few to no studies have evaluated patients for CRS using objective testing and workup protocols that fulfill guidelines for CRS diagnostic criteria. Furthermore, no study, to our knowledge, has investigated the risks of CRS in the context of residential exposure through proximity to a commercial pesticide application site.
Objectives
To evaluate associations of residential proximity to a commercial pesticide application site and the prevalence of CRS with nasal polyps (CRSwNP) and without nasal polyps (CRSwoNP).
Design, Setting, and Participants
This was a retrospective cohort study of patients who presented to a tertiary care institution for rhinology evaluation between March 1, 2018, and December 31, 2022.
Main Outcomes and Measures
The outcome variable was the clinical diagnosis of CRS (CRSwNP, CRSwoNP, or non-CRS control). Patients’ residential addresses were utilized to determine pesticide exposure status based on a validated computational geographic information algorithm based on data from the California Pesticide Use Report System. The dichotomous independent variable of exposure status (exposed or non-exposed) was determined by assessing reports of any pesticide applications within 2000 m of each participant’s residence in 2017. Multivariable logistic regressions assessing CRS status and CRS subtypes were conducted with pesticide exposure as the primary covariate of interest. The primary study outcome and measurements as well as study hypothesis were all formulated before data collection.
Results
Among a total of 310 patients (90 CRSwNP, 90 CRSwoNP, and 130 control), the mean (SD) age was 50 (17) years; 164 (53%) were female. Race and ethnicity information was not considered. Controlling for patient demographic information, smoking history, county of residence, and medical comorbidities, pesticide exposure was associated with an approximately 2.5-fold increase in odds of CRS (adjusted odds ratio, 2.41; 95% CI, 1.49-3.90). Pesticide exposure was associated with similar risks for CRSwNP (adjusted relative risk ratio [aRRR], 2.34; 95% CI, 1.31-4.18) and CRSwoNP (aRRR, 2.42; 95% CI, 1.37-4.30).
Conclusions and Relevance
The findings of this retrospective cohort study and analysis revealed that residential exposure to commercial pesticide application within a 2000-m buffer was independently associated with an approximately 2.5-fold increase in odds of being diagnosed with CRS. If validated by additional research, this association would have substantial implications for public health.
This retrospective cohort study explores the association between residential proximity to a commercial pesticide application site and chronic rhinosinusitis.
Introduction
Chronic rhinosinusitis (CRS) is a persistent inflammatory state of the nasal and paranasal sinus mucosa characterized by nasal congestion, purulent drainage, facial pain or fullness, and olfactory dysfunction for 12 weeks or longer.1 This condition is categorized into 2 phenotypes based on the presence of nasal polyps: chronic rhinosinusitis with nasal polyps (CRSwNP) and chronic rhinosinusitis without nasal polyps (CRSwoNP). Although CRS is highly prevalent worldwide, its causes remain an ongoing topic of debate.2,3 A variety of causes have been postulated to play a role in the pathogenesis of CRS, including epithelial dysfunction, microbiome dysbiosis, anatomic factors, and immunodeficiencies.2,4,5,6
Modifiable factors, such as environmental and occupational toxicants, have also been studied as potential causative agents of CRS.7,8,9,10,11 Particularly, commercial pesticide use among farmers has been examined, with several studies demonstrating that prolonged pesticide exposure was associated with higher rates of rhinitis and asthma.12,13,14,15,16 However, research has been mostly limited by the self-reported nature of exposure status, which was shown to be highly susceptible to environmental and psychological bias.17 Furthermore, prior studies have used mostly self-reported information for diagnostic determination of CRS, with few to no studies to date that have evaluated patients for CRS using objective testing and workup protocols that fulfill guidelines for CRS diagnostic criteria. A prior systematic review concluded that existing evidence in the literature does not permit any robust conclusion pertaining to the role of environmental toxicants in the pathogenesis of CRS18 owing to the reasons highlighted previously.
Recent in vitro experiments have demonstrated that commercial pesticide contents, such as N, N-Diethyl-Meta-Toluamide (DEET) and permethrin, can elicit dose-dependent cytotoxic damage of sinonasal epithelia.19,20 These findings further propel the need to explore the effects of pesticide use on the prevalence of CRS on a population level. In this study, we analyzed the residential pesticide exposure among a group of adults who presented to a tertiary academic institution for rhinologic evaluation between March 1, 2018, and December 31, 2022. Using a retrospective cohort design, we investigated whether residential proximity to a commercial pesticide application site was associated with an increased prevalence of CRS diagnoses.
Methods
This study was reviewed and approved by the institutional review board of the University of California Los Angeles (No. 17-000024); informed consent was waived because its retrospective design. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
Study Population and Study Design
This was a retrospective cohort study of patients who presented to a tertiary care institution for rhinology evaluation between March 1, 2018, and December 31, 2022. Inclusion criteria were patients who underwent formal evaluation for CRS and had a precise residential address recorded in their electronic health record (EHR). We randomly selected several dates throughout the study period. At each selected date, patients on the surgery schedule were assessed for possible inclusion in the study. Patients were considered to be CRS group participants if they underwent endoscopic sinus surgery after being diagnosed with CRS by a rhinologist according to the diagnostic criteria described in the American Academy of Otolaryngology−Head and Neck Surgery Foundation clinical practice guidelines for adult CRS.1,21 Patients were considered to be control group participants if they had been formally evaluated for CRS (with negative findings) and underwent non-CRS procedures (eg, transnasal transsphenoidal approach to pituitary, septoplasty, turbinate reduction, eustachian tube balloon dilation). Control patients were confirmed to have no CRS based on clinical history, findings of nasal endoscopy, and diagnostic imaging results. Approximately 90 participants from each of the diagnostic end points (CRSwNP and CRSwoNP) and 130 control participants were included. Residential (home) addresses of study participants were retrieved from their EHRs and were used to evaluate for possible commercial pesticide exposure. Patient age, sex, smoking history, year of evaluation, and medical comorbidities were also collected from the EHR.
Pesticide Exposure
Ambient pesticide exposure was estimated by using a validated geographic information system (GIS)-based computational model.22 This GIS model incorporates data from the California State Pesticide Use Reports23 (CA-PUR), which has legally mandated the declaration of all commercial pesticide applications in California since 1989. Data from land use surveys of the California Department of Water Resources detailing the precise locations of each crop and geocoded residential address information were used to refine the CA-PUR locations. Using this GIS algorithm, estimates of commercial pesticide application within a 2000-m radius buffer of each participant’s residential address were computed. Participants were considered to be exposed to a particular pesticide when its pounds per acre exceeded 0 during the study period. Given the small exposure prevalence sample sizes and correlations across exposure for the individual pesticides, we determined an overall dichotomous pesticide exposure measure for analysis. Participants meeting the exposure criteria for any pesticide were considered to be exposed in the final analysis. This method has been delineated in greater detail and validated in previous biomarker studies.24,25,26,27
Variable Characterization
The primary outcome variable of interest was CRS status, which was assessed as both a dichotomous variable (CRS vs control) and a nominal variable with 3 levels (CRSwNP vs CRSwoNP vs control). American Board of Otolaryngology-certified otolaryngologists specializing in rhinology determined each participant’s CRS status through clinical examination, nasal endoscopy, and diagnostic imaging. The clinical workup, standard of care, and diagnosis of CRS were made in accordance with the clinical practice guidelines for adult CRS of the American Academy of Otolaryngology-Head and Neck Surgery Foundation.1,21
The primary independent variable of interest was residential pesticide exposure. We evaluated a dichotomous variable assessing whether the residential address was within 2000 m of any commercial pesticide application according to the GIS algorithm in 2017, the most recent year with available CA-PUR data. The radius buffer of 2000 m was used because it was previously shown to be an important range for which substantial amounts of pesticides were detected in insects.28 Prior research has also shown that prenatal environmental pesticide exposure within 2000 m of a pregnant woman’s residence was associated with an increased risk of autism spectrum disorder in the offspring.29 Pesticide application data used to compute exposure status captured residential exposure before the formal CRS evaluation of participants was performed.
Other variables that could potentially influence the relationship between pesticide exposure and CRS status were also accounted for in the analyses. Patient age was considered as a linear variable; patient sex, as a dichotomous variable; smoking history, as a 3-level ordinal variable (none, prior, or current [within 4 weeks of surgery]); history of diabetes, as a dichotomous variable; history of hypertension, as a dichotomous variable; county of residence, as a dichotomous variable (Los Angeles vs others); and the year of evaluation, as a linear variable.
Statistical Analyses
To evaluate the association between pesticide exposure and CRS status, we constructed a multivariable binary logistic regression model with CRS status as the dichotomous outcome variable (CRS vs control) and pesticide exposure (yes or no) as the primary independent variable of interest. Furthermore, we evaluated CRS status as a nominal variable (CRSwNP vs CRSwoNP vs control) in a multivariable multinomial logistic regression model to assess whether the association was influenced by CRS phenotype. Patient age and sex, history of smoking, year of evaluation, county of residence, history of diabetes, and history of hypertension were thought to be potential confounders; therefore, they were considered as potential covariates for the models. We computed 3 multivariable models with different combinations of the covariates—controlling for specific potential confounders—to clearly demonstrate any associations between pesticide exposure and the risk of being diagnosed with CRS. Adjusted model 1 included baseline demographic covariates, such as patient age and sex. Adjusted model 2 incorporated the addition of smoking history, year of evaluation, and county of residence. Adjusted model 3 incorporated the addition of medical comorbidities, ie, diabetes and hypertension. Data analyses were performed from April 20 to May 7, 2023, using with R, version 4.2.1 (The R Foundation for Statistical Computing) and Stata, version 17 (StataCorp LLC).
Results
The total study sample comprised 310 participants (mean [SD] age, 50 [17] years; 146 [47.1%] men and 164 [52.9%] women; race and ethnicity were not considered). Table 1 presents a summary of the study population’s characteristics. Most participants (227; 73.2%) had never smoked, 69 (22.3%) were prior smokers, and 14 (4.5%) were current smokers. Most participants (178; 57.4%) were residents of Los Angeles County and 155 participants (50%) resided within 2000 m of any reported commercial pesticide application. There were 130 patients in the control group, 90 patients in the CRSwNP group, and 90 patients in the CRSwoNP group. The numbers exposed to pesticides were 48 (36.9%) control patients, 53 (58.9%) CRSwNP patients, and 54 (60.0%) CRSwoNP patients. A geographic illustration of patients’ residences, stratified by CRS and exposure status, is displayed in the Figure.
Table 1. Participant Characteristics by Study Group.
| Characteristic | Control group, No. (%) | CRSwNP group, No. (%) | CRSwoNP group, No. (%) | Total, No. (%) |
|---|---|---|---|---|
| Study participants, No. | 130 | 90 | 90 | 310 |
| Age, mean (SD), y | 49 (17) | 48 (15) | 53 (18) | 50 (17) |
| Sex | ||||
| Female | 81 (62.3) | 32 (35.6) | 51 (56.7) | 164 (52.9) |
| Male | 49 (37.7) | 58 (64.4) | 39 (43.3) | 146 (47.1) |
| Smoking | ||||
| Never | 103 (79.2) | 66 (73.3) | 58 (64.4) | 227 (73.2) |
| Prior | 24 (18.5) | 20 (22.2) | 25 (27.8) | 69 (22.3) |
| Current | 3 (2.3) | 4 (4.4) | 7 (7.8) | 14 (4.5) |
| Diabetes | ||||
| No | 115 (88.5) | 80 (88.9) | 77 (85.6) | 272 (87.7) |
| Yes | 15 (11.5) | 10 (11.1) | 13 (14.4) | 38 (12.3) |
| Hypertension | ||||
| No | 95 (73.1) | 71 (78.9) | 62 (68.9) | 228 (73.5) |
| Yes | 35 (26.9) | 19 (21.1) | 28 (31.1) | 82 (26.5) |
| County of residence | ||||
| Other county | 51 (39.2) | 39 (43.3) | 42 (46.7) | 132 (42.6) |
| Los Angeles County | 79 (60.8) | 51 (56.7) | 48 (53.3) | 178 (57.4) |
| Year of evaluation | ||||
| 2018 | 30 (23.1) | 28 (31.1) | 26 (28.9) | 84 (27.1) |
| 2019 | 27 (20.8) | 28 (31.1) | 20 (22.2) | 75 (24.2) |
| 2020 | 15 (11.5) | 5 (5.6) | 2 (2.2) | 22 (7.1) |
| 2021 | 36 (27.7) | 9 (10.0) | 12 (13.3) | 57 (18.4) |
| 2022 | 22 (16.9) | 20 (22.2) | 30 (33.3) | 72 (23.2) |
| Pesticide exposure | ||||
| No | 82 (63.1) | 37 (41.1) | 36 (40.0) | 155 (50.0) |
| Yes | 48 (36.9) | 53 (58.9) | 54 (60.0) | 155 (50.0) |
Abbreviations: CRS, chronic rhinosinusitis; wNP, with nasal polyps; woNP, without nasal polyps.
Figure. Geographic Distribution of Participants’ Home Residences by Study Group and Pesticide Exposure Status.
Maps are not drawn to scale and not all residences are visible.
There are currently 1061 different pesticide active ingredients registered for use in California.30 Of these, 234 were applied within 2000 m of the residences of 2 or more study participants and 65 active ingredients were applied near the residences of 25 or more participants during the study period (Table 2). The individual pesticides with the highest exposure prevalence were glyphosate isopropyl amine salt, myclobutanil, imidacloprid, potassium bicarbonate, pyraclostrobin, boscalid, and abamectin.
Table 2. Pesticides, by Code and Chemical Name, With Highest Exposure Prevalence Among Study Participants.
| CA-PUR code | Participants exposed, No. (%) | Chemical name |
|---|---|---|
| 1855 | 84 (27.1) | Glyphosate, isopropylamine salt |
| 2245 | 83 (26.8) | Myclobutanil |
| 3849 | 78 (25.2) | Imidacloprid |
| 5037 | 75 (24.2) | Potassium bicarbonate |
| 5759 | 75 (24.2) | Pyraclostrobin |
| 5790 | 74 (23.9) | Boscalid |
| 2254 | 71 (22.9) | Abamectin |
| 1685 | 65 (21.0) | Acephate |
| 2106 | 65 (21.0) | Petroleum distillates, refined |
| 4000 | 65 (21.0) | Cyprodinil |
| 151 | 62 (20.0) | Copper hydroxide |
| 5321 | 60 (19.4) | Trifloxystrobin |
| 5955 | 59 (19.0) | Spirotetramat |
| 4037 | 53 (17.1) | Azoxystrobin |
| 5027 | 51 (16.5) | Fludioxonil |
| 3983 | 50 (16.1) | Spinosad |
| 4032 | 50 (16.1) | Fenhexamid |
| 401 | 47 (15.2) | Mineral oil |
| 5787 | 45 (14.5) | Quinoxyfen |
| 2195 | 44 (14.2) | Tau-fluvalinate |
| 2300 | 44 (14.2) | Bifenthrin |
| 4011 | 42 (13.5) | Mefenoxam |
| 5762 | 41 (13.2) | Acetamiprid |
| 677 | 40 (12.9) | Chlorothalonil |
| 5820 | 40 (12.9) | Glyphosate, potassium salt |
| 6004 | 40 (12.9) | Fluopyram |
| 5698 | 39 (12.6) | Methoxyfenozide |
| 5822 | 39 (12.6) | Dinotefuran |
| 560 | 38 (12.3) | Sulfur |
| 2276 | 38 (12.3) | Propiconazole |
| 5598 | 38 (12.3) | Thiamethoxam |
| 5946 | 38 (12.3) | Spinetoram |
| 510 | 37 (11.9) | Pyrethrins |
| 1973 | 36 (11.6) | Oxyfluorfen |
| 3956 | 36 (11.6) | Beta-cyfluthrin |
| 1696 | 33 (10.6) | Thiophanate-methyl |
| 5024 | 33 (10.6) | Difenoconazole |
| 5939 | 33 (10.6) | Tetraconazole |
| 5964 | 33 (10.6) | Chlorantraniliprole |
| 5657 | 31 (10.0) | Bifenazate |
| 6003 | 31 (10.0) | Cyflufenamid |
Abbreviation: CA-PUR, California State Pesticide Use Reports.23
Table 3 presents results from the multivariable logistic regression analyses. In the crude model, pesticide exposure was associated with an approximate 2.5-fold increase in odds for CRS (odds ratio [OR], 2.50; 95% CI, 1.57-3.98). Adjusting for patient age, sex, smoking history, year of evaluation, and county of residence (model 2), pesticide exposure was independently associated with an approximate 2.5-fold increase in odds for CRS, with 95% statistical confidence that this increase was from at least 1.5-fold to as much as 3.9-fold (adjusted OR [aOR], 2.42; 95% CI, 1.49-3.92). The strength of this association was similar after adjusting for diabetes and hypertension in model 3 (aOR, 2.41; 95% CI, 1.49-3.90).
Table 3. Logistic Regression Assessing Risks of Chronic Rhinosinusitis (CRS) Diagnosis Associated With Pesticide Exposure.
| Variable | Logistic regression outcome: CRS (yes/no) | |||
|---|---|---|---|---|
| Crude model, OR (95% CI) | Model 1,a aOR (95% CI) | Model 2,b aOR (95% CI) | Model 3,c aOR (95% CI) | |
| Pesticide exposure | ||||
| No | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Yes | 2.50 (1.57-3.98)d | 2.38 (1.49-3.82)d | 2.42 (1.49-3.92)d | 2.41 (1.49-3.90)d |
| Age, year prior | NA | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Age, yearly increase | NA | 1.00 (0.99-1.02) | 1.00 (0.99-1.02) | 1.01 (0.99-1.02) |
| Female sex | NA | 0.54 (0.34-0.87)d | 0.59 (0.36-0.95)d | 0.58 (0.36-0.95)d |
| Male sex | NA | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Smoking | ||||
| Never | NA | NA | 1 [Reference] | 1 [Reference] |
| Prior | NA | NA | 1.27 (0.70-2.31) | 1.29 (0.70-2.36) |
| Current | NA | NA | 3.02 (0.80-11.48) | 2.90 (0.76-11.08) |
| Evaluation year | ||||
| Year prior | NA | NA | 1 [Reference] | 1 [Reference] |
| Yearly increase | NA | NA | 0.94 (0.81-1.10) | 0.94 (0.81-1.10) |
| Other county | NA | NA | 1 [Reference] | 1 [Reference] |
| Los Angeles County | NA | NA | 0.83 (0.51-1.35) | 0.81 (0.50-1.32) |
| Diabetes | ||||
| No | NA | NA | NA | 1 [Reference] |
| Yes | NA | NA | NA | 1.07 (0.49-2.31) |
| Hypertension | ||||
| No | NA | NA | NA | 1 [Reference] |
| Yes | NA | NA | NA | 0.77 (0.41-1.45) |
Abbreviations: aOR, adjusted odds ratio; NA, not applicable; OR, odds ratio.
Adjusted for age and sex.
Adjusted for age, sex, smoking, year, and county.
Adjusted for age, sex, smoking, year, county, diabetes, and hypertension.
95% CI does not include 1.
Table 4 presents results from the multivariable multinomial logistic regression analysis. In the crude model, pesticide exposure was associated with a 2.5-fold increase in odds of CRSwNP (relative risk ratio [RRR], 2.45; 95% CI, 1.41-4.24) and a 2.6-fold increase in odds of CRSwoNP (RRR, 2.56; 95% CI, 1.48-4.45) compared with the control group. Controlling for patient age, sex, smoking history, year of evaluation, and county of residence (model 2), residing within 2000 m of any commercial pesticide application site was independently associated with a 2.4-fold increase in odds of CRSwNP, with 95% statistical confidence that this increase was from at least 1.3-fold to as much as 4.2-fold (adjusted RRR [aRRR], 2.37; 95% CI, 1.33-4.21), and a 2.4-fold increase in odds of CRSwoNP, with 95% statistical confidence that this increase was at least 1.4-fold to as much as 4.3-fold (aRRR, 2.42; 95% CI, 1.37-4.30). The strength of these associations was similar after adjusting for diabetes and hypertension (CRSwNP aRRR, 2.34; 95% CI, 1.31-4.18; and CRSwoNP aRRR, 2.42; 95% CI, 1.37-4.30). Pesticide exposure appeared to be independently associated with similar risks for CRSwNP and CRSwoNP in all models (Table 4).
Table 4. Multinomial Logistic Regression Assessing Risks of Chronic Rhinosinusitis Subtypes Associated With Pesticide Exposure.
| Study group | Covariates adjusted for exposure | Multinomial regression outcome, CRS subtype | |||
|---|---|---|---|---|---|
| Crude model | Adjusted model 1a | Adjusted model 2b | Adjusted model 3c | ||
| Control group | |||||
| RRR (95% CI) | Not exposed | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Exposed | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | |
| Predicted probability (95% CI) | Not exposed | 0.53 (0.45-0.61) | 0.52 (0.44-0.60) | 0.52 (0.44-0.60) | 0.52 (0.44-0.60) |
| Exposed | 0.31 (0.24-0.38) | 0.32 (0.24-0.39) | 0.32 (0.24-0.39) | 0.32 (0.25-0.39) | |
| CRSwNP group | |||||
| RRR (95% CI) | Not exposed | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Exposed | 2.45 (1.41-4.24)d | 2.32 (1.32-4.08)d | 2.37 (1.33-4.21)d | 2.34 (1.31-4.18)d | |
| Predicted probability (95% CI) | Not exposed | 0.24 (0.17-0.31) | 0.24 (0.18-0.31) | 0.24 (0.18-0.31) | 0.24 (0.18-0.31) |
| Exposed | 0.34 (0.27-0.42) | 0.34 (0.26-0.41) | 0.34 (0.27-0.41) | 0.34 (0.26-0.41) | |
| CRSwoNP group | |||||
| RRR (95% CI) | Not exposed | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Exposed | 2.56 (1.48-4.45)d | 2.42 (1.38-4.22)d | 2.42 (1.37-4.30)d | 2.42 (1.37-4.30)d | |
| Predicted probability (95% CI) | Not exposed | 0.23 (0.17-0.30) | 0.24 (0.17-0.30) | 0.24 (0.17-0.30) | 0.24 (0.17-0.30) |
| Exposed | 0.35 (0.27-0.42) | 0.35 (0.27-0.42) | 0.34 (0.27-0.42) | 0.34 (0.27-0.42) | |
Abbreviations: CRS, chronic rhinosinusitis; wNP, with nasal polyps; woNP, without nasal polyps; RRR, relative risk ratio.
Adjusted for age and sex.
Adjusted for age, sex, smoking, year, and county.
Adjusted for age, sex, smoking, year, county, diabetes, and hypertension.
95% CI does not include 1.
Discussion
Among this regional sample of predominantly residents of southern California, we found that commercial pesticide application occurring near residences of study participants was independently associated with an approximate 2.5-fold increase in odds of being diagnosed with CRS. After conducting a stratified analysis by CRS phenotype, we found associated risks for CRSwNP and CRSwoNP, with residential pesticide exposure to be similar.
Previous research on the relationship between pesticide exposure and upper respiratory illness was mainly conducted among farmers in occupational settings, with most studies relying on cross-sectional analyses of self-reported data. For example, several studies have found an association between pesticide exposure and higher rates of self-reported rhinitis symptoms (eg, sneezing, runny nose, and nasal congestion).12,15 Farmers with pesticide usage have also been found to report allergic rhinitis and sinusitis at higher rates compared with nonfarmers or farmers without pesticide exposure.13,16 Although substantial evidence in the literature supports that occupational exposure to pesticides may be a precursor to upper respiratory illnesses, the self-reported nature of these studies can introduce a significant level of bias and mislabeling, as noted in a prior systematic reivew.18 Therefore, the shortage of objective diagnostic evaluation limits the conclusions that can be reasonably drawn from these findings.
Indirect pesticide exposure among nonfarmers has also been studied as a potential environmental risk factor for upper respiratory illness. A prior investigation on residents of households with prior chlordane (a termite control pesticide) treatment found a dose-response relationship between indoor chlordane concentration and rates of self-reported sinusitis symptoms.31 More recently, a 2019 study was conducted among school children in a French vineyard rural area during summer months when pesticides were used in nearby farms. The study found a significant association between urinary concentration of ethylene thiourea and self-reported allergic rhinitis symptoms; however, ambient pesticide in the air did not correlate significantly with allergic rhinitis symptoms.32 A subsequent 2022 meta-analysis including 24 studies similarly found no significant association between pesticide exposure and allergic rhinitis among children and adolescents.14 Therefore, the relationship between environmental pesticide exposure and risks for upper respiratory illness has been unclear to date.
Prior research on the relationship between environmental toxicants and risks for CRS has been even more limited. This is likely because CRS is a diagnosis that warrants extensive evaluation and objective evidence supporting chronic inflammation, an endeavor that is difficult to accomplish in larger database studies. Furthermore, most studies that included CRS as a comorbidity were designed to study general or respiratory health rather than CRS specifically. Therefore, the nuances involved in diagnosing CRS were often not carefully considered. A 2015 systematic review found that most studies determined CRS status based on self-reported symptoms, without accounting for objective evidence of inflammation.18 Furthermore, that review found that none of the existing studies in the literature at the time adhered to the guidelines for the appropriate diagnosis of CRS. More recently, a 2022 systematic review found similar limitations among studies on the association between pesticide exposure and CRS.33 In fact, both reviews found few to no studies differentiating among CRS subtypes (ie, CRSwNP and CRSwoNP) in their analyses.18,33
The present study was, to our knowledge, the first to investigate the association between residential pesticide exposure and risks for CRS. Rather than relying on self-reported data, the determination of CRS status was based on clinical history and objective evidence of sinonasal inflammation per endoscopy and/or imaging results in accordance with the diagnostic guidelines for CRS diagnosis. Our study results showed that commercial pesticide application within a 2000 m buffer of study participants’ residences was associated with greater risks for CRS. Prior laboratory research has demonstrated the in vitro cytotoxic effects of pesticide ingredients, such as permethrin and DEET on sinonasal epithelia.19,20 Specifically, these chemicals induced the production of reactive oxygen species and cellular damage, and thereby, proinflammatory pathways, which may contribute to the pathogenesis of CRS.19,20 Our study adds to the literature, demonstrating that patients residing as far as 2000 m away from any commercial pesticide application site experienced increased odds for CRS pathologies by a factor of 2.5. Although the exact mechanism underlying this association is difficult to delineate and our study design does not permit causal inference, there exists a reasonable basis for concern given the results from prior in vitro experiments and clinical research. Furthermore, inhaled toxicants have been shown to elicit chronic cellular dysfunction of the upper respiratory epithelia, which could be a factor in the development of CRS.34,35,36 Therefore, our findings suggest that the potential role of pesticides in the pathogenesis of CRS may not only apply to applicators but also to individuals who reside in the surrounding areas. With additional evidence from further research, these findings may contribute to important public health implications.
This study also compared the risks for different phenotypes of CRS with regard to pesticide exposure. Although CRSwNP and CRSwoNP have been shown to exhibit different inflammatory profiles, we did not find the risks associated with pesticide exposure to differ between phenotypes.37 Our study’s findings showed that residential proximity to commercial pesticide application was associated with greater risks for both CRSwNP and CRSwoNP. The most similar study that adhered to diagnostic guidelines for CRS was a 2019 investigation that compared the associated effects of environmental pollutants—eg, black carbon and PM2.5—on CRS severity between CRS phenotypes.38 That study similarly did not find a significant difference between CRSwNP and CRSwoNP. Therefore, it appears that the association between ambient pesticide exposure and CRS is independent of the specific inflammatory profile of the disease. However, additional studies with larger patient cohorts may be necessary to determine whether there is any correlation between CRS subtype and pesticide exposure.
Limitations
Although the present study reveals novel and important findings, there admittedly exist several limitations. First, the sample is heavily predominated by patients of a tertiary care institution, and conclusions should be cautiously generalized in conjunction with findings from future reports. Second, we cannot ascertain the directionality of the association between pesticide exposure and CRS status because the residential addresses are neither manipulable nor randomizable. Our findings demonstrated that residential pesticide exposure was associated with the diagnosis of CRS, not the development of CRS. As such, we are unable to ascertain whether pesticide exposure transpired before the development of CRS. Lastly, although our analyses attempted to control for a variety of potential confounders, there could be additional explanatory variables that may have been missed. Hence, it must be emphasized that our findings indicate an association, not a causative link between residential pesticide exposure and CRS. Nevertheless, to our knowledge, this study is the first to highlight the association between residential proximity to commercial pesticide application and risks for CRS. Although preliminary, this study’s findings serve as a springboard for additional research to investigate an important yet understudied topic.
Conclusions
This retrospective cohort study demonstrated that application of commercial pesticides within a 2000-m radius of participants’ residences was associated with an approximate 2.5-fold increase in odds of being diagnosed with CRS. Pesticide exposure was associated with similar increases in risks for CRSwNP and CRSwoNP phenotypes compared with the control group. On validation in future cohorts, these findings may advance the current understanding of the pathogenesis of CRS and highlight important public health implications that warrant policy considerations.
Data Sharing Statement.
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Data Sharing Statement.