Pesticide exposure and rhinitis: A cross-sectional study among farmers in&nbsp;Pitsanulok, Thailand

Yuwayong Juntarawijit; Chudchawal Juntarawijit

doi:10.12688/f1000research.53257.1

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Research Article

Pesticide exposure and rhinitis: A cross-sectional study among farmers in Pitsanulok, Thailand

[version 1; peer review: awaiting peer review]

Yuwayong Juntarawijit¹, Chudchawal Juntarawijit ²

PUBLISHED 15 Jun 2021

Author details Author details

¹ Faculty of Nursing, Naresuan University, Phitsanulok, 65000, Thailand
² Faculty of Agriculture, Natural Resources and Environment, Naresuan University, Phitsanulok, 65000, Thailand

Yuwayong Juntarawijit
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Supervision, Validation, Writing – Original Draft Preparation

Chudchawal Juntarawijit
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Global Public Health gateway.

This article is included in the Agriculture, Food and Nutrition gateway.

Abstract

Background: Pesticide exposure has been suspected to cause rhinitis, a common disease that affects the health and wellbeing of millions of people around the world. The objective of this cross-sectional study was to examine the association between pesticide use and rhinitis prevalence among farmers in Phitsanulok province, Thailand.
Methods: Data on pesticide use and rhinitis use were collected by an in-person interview questionnaire. Data from 9,649 participants were included in the analysis. The association between pesticide exposure and rhinitis was determined by multiple variable logistic regression, adjusted for potential confounding factors.
Results: It was found that 6.3% of study participants had rhinitis, which was defined as having one or more of the following symptoms: sneezing, runny nose, and nasal congestion, within the past year. The study found each type of pesticide (insecticides, herbicides, fungicides, rodenticides, molluscicides) to be significantly associated with rhinitis, with the odds ratio ranging from 1.67 (95% CI 1.41-1.99) for the fungicide group to 7.19 (95% CI 4.67-11.06) for insecticide. For individual pesticides, the study found 33 out of 35 to be significantly associated with rhinitis with the OR values ranging from 1.30 (95% CI 1.00-1.69) for alachlor to 5.67 (95% CI 3.16-10.16) for Bordeaux mixture.
Conclusions: This study found a prevalence of rhinitis among farmers in Phitsanulok, Thailand to be associated with the long-term exposure of many individual pesticides. This finding was consistent with other literature. Effects of pesticide on rhinitis should receive more attention from the public health and other organizations responsible for the farmers’ health.

Keywords

pesticide exposure, insecticide effect, herbicide effect, fungicide effect, rhinitis, farmer health

Corresponding author: Chudchawal Juntarawijit

Competing interests: No competing interests were disclosed.

Grant information: National Science Research and Innovation Fund (NSRF) of Thailand
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2021 Juntarawijit Y and Juntarawijit C. 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 work is properly cited.

How to cite: Juntarawijit Y and Juntarawijit C. Pesticide exposure and rhinitis: A cross-sectional study among farmers in Pitsanulok, Thailand [version 1; peer review: awaiting peer review]. F1000Research 2021, 10:474 (https://doi.org/10.12688/f1000research.53257.1) First published: 15 Jun 2021, 10:474 (https://doi.org/10.12688/f1000research.53257.1) Latest published: 05 Dec 2023, 10:474 (https://doi.org/10.12688/f1000research.53257.3)

Background

Rhinitis is a common disease that affects general health, and quality of life. Approximately, 10% to 30% of the different populations worldwide suffer from this disease¹. One study in Bangkok, Thailand, has reported a prevalence of chronic rhinitis to be approximately 13%². In clinical terms, rhinitis refers to the inflammatory disease of the nasal mucosa, which can cause the following symptoms: nasal congestion, rhinorrhea, and sneezing³. Although rhinitis does not have a strict classification criterion, it can be classified into allergic rhinitis (AR), and nonallergic rhinitis (NAR). Both have the same nasal symptoms, with the difference that AR is triggered by allergens. Several factors can trigger NAR, including cold air, climate change, cooking smells, chemical odour, cigarette smoke, volatile organic chemicals, exercise, alcohol ingestion⁴, and cooking fumes⁵. NAR can be further classified by their pathological mechanisms into several subtypes, including occupation rhinitis, hormonal rhinitis, drug-induced rhinitis, food-induced rhinitis, emotion-induced rhinitis, etc⁶. Approximately 43% of all rhinitis cases are AR, and 23% are NAR, while 34% of the cases are a mixture of both⁴.

Studies found pesticide exposure to increase the risk of several respiratory problems, e.g., asthma, chronic bronchitis⁷, and rhinitis⁸. One study among grape farmers in Greece reported a higher prevalence of AR among those who used pesticides⁹. Another study in France found that children living in areas surrounding vineyards had a higher rate of rhinitis symptoms (OR=3.56; 95% CI 1.04–12.12)¹⁰. In an occupational setting, a study found number of hours working in the greenhouse per day to be associated with rhinitis (OR, 1.85; 95% CI, 1.05–3.23)¹¹. A large survey of farmworkers in the United States of America (U.S.A) found that insecticide and herbicide use has significantly increased the risk of allergic rhinitis and asthma¹².

Currently, studies on the effects of individual pesticides are limited. Pesticides that were found to have a positive association with rhinitis were bipyridyl herbicides such as paraquat, and the broad-spectrum herbicides 2,4-D, glyphosate, dithiocarbamate fungicides including benomyl, and insecticide diazinon^9,13,14. In one study, pesticides, especially cholinesterase inhibitors, e.g., organophosphates and carbamates pesticides, were found to cause neurogenic inflammation and muscarinic effects of the respiratory airway, as well as modification of the inflammatory response to other allergens¹⁵.

The main objective of this cross-sectional study was to determine the association between pesticide use and rhinitis among farmers in Phitsanulok, Thailand. The use of a large sample size in this study has provided the opportunity to assess also the risks that different groups and subgroups of pesticide exposure have had on these individuals. The study results would be useful for disease prevention, and comparison with other studies.

Methods

Study design and setting

This study was a cross-sectional study. Participants were farmers in Phitsanulok province, located about 370 km north of Bangkok, Thailand. In 2019, the province had approximately 865,368 people (342,787 households) from nine districts. The major crops in the province are rice, sugarcane, and maize^16,17.

Study participants and sampling procedure

Multistage sampling was used for the random selection of the participants. The three districts were randomly selected from nine districts in the Phitsanulok province. From all the selected districts, 18 out of 26 (69.2%) sub-districts were further selected. In each sub-district, all local hospitals participated in the study and provided support for data collection. In each sub-district, farmers were selected by village health volunteers (VHV), who were working in the hospitals. The data from the local authority and personal contacts were used by the VHV for selecting the farmers. Sample size (n=9,649) was achieved with the snowball sampling technique. From each family, one adult aged 20 years or older who does agricultural work, was interviewed.

The minimum sample size was calculated to be 10,002, based on the following assumptions: significance level = 95%; power of detection = 80%; ratio of unexposed/exposed = 1; percent of unexposed with outcome = 10%²; odds ratio = 1.2.

Study questionnaire and data collection

Data were collected by using an in-person interview questionnaire (provided as Extended data in English and Thai)¹⁸. Data on rhinitis was collected by using a modified form of SFAR (Score For Allergic Rhinitis) questionnaire which is recommended for population studies, where medical diagnosis and objective measurements were absent or difficult to obtain¹⁹. Self-reported rhinitis was defined as the participant answering “yes” to the question: “during the last 12 months, have you ever had symptoms such as sneezing, or a runny, or blocked nose when you did not have a cold or the flu?”. Allergic rhinitis was also determined on a “yes” response to the question: “Have you ever been told by a doctor that you had allergic rhinitis?”.

For pesticide exposure, the data was collected by a questionnaire used in our previous study²⁰. Data on the long-term use of pesticides, either by types of pesticides (insecticide, herbicide, fungicide, rodenticide, and molluscicide), or by specific individual pesticides, were collected. A list of 35 individual pesticides were chosen from those that were commonly used in Thailand and were reported to cause adverse health effects^20,21. Participants were asked whether they have ever used pesticides, defined as a mixture, or spray pesticides, in their lifetime. Participants were also asked to provide data on the duration (days/year and total years) of pesticide use. This information was used to calculate total days, and quartiles of days using each pesticide in the farmers’ lifetime.

Lifetime pesticide use measured in days = [Number of days per year] × [total years]

Data was collected from October 2020 to February 2021, by 210 VHV. Before data collection, these volunteers had to attend a one-day training program to be informed on the purpose of the study and to learn how to properly interview and collect data by using an online questionnaire. The interview mostly took place in the participant’s home. However, sometimes it was done in a local temple or hospital.

Statistical analysis

Demographic data were analysed using descriptive statistics. Comparison of categorical data was analysed using the Chi-square test. The association between pesticide exposure and rhinitis prevalence was analysed using multivariable logistic regression, and both crude and adjusted odds ratios (OR) and 95% confidence interval (CI) were reported. Adjusted variables were gender (male, female), age (continuous), marital status (married, single, divorced/widow/separated), education (non-educated, primary school, secondary school, college degree or higher), family income (<5000 THB, 5001-10000 THB, 10001-30000THB, > 30000 THB), cigarette smoking (non-smoker, ex-smoker, current smoker), alcohol consumption (non- drinker, ex- drinker, regular- drinker). Data analysis was performed using IBM SPSS version 26, and OpenEpi (online version 3.01). All statistical values were two-tailed, and a p-value < 0.05 was considered as statistically significant.

Ethical considerations

The study was approved by the Ethical Committee of Naresuan University (COA No. 657/2019) and written informed consent from the participants was obtained before the interview process.

Results

It was found that the proportion of female participants was slightly higher than that of the male participants (Table 1 and the underlying data²²). Most of these individuals were aged 40 years and older with an average age of 55 (± 12 years), married (78.0%), finished primary school or with lower education (77.2), and had an average family income of 10,000 THB or less. A total of 8.7% of these females are cigarette smokers, and 13.7% consume alcohol.

Table 1. Demographic data of study participants.

Characteristics	Number (%), N = 9649
Gender
Male	4163 (43.1)
Female	5486 (56.9)
Age, years
20–30	306 (3.2)
31–40	840 (8.7)
41–50	2000 (20.7)
51–60	3174 (32.9)
61+	3329 (34.5)
Mean ± SD = 55 ± 12
Min-Max = 20-92
Marital status
Married	7523 (78.0)
Single	737 (7.6)
Divorce/widow/separated	1389 (14.4)
Education
Non-educated	402 (4.2)
Primary school	7046 (73.0)
Secondary school	2063 (21.4)
College degree or higher	138 (1.4)
Family income, THB/month
<5000	3168 (32.8)
5001–10000	5739 (59.5)
10001–30000	668 (6.9)
>30000	74 (0.8)
Cigarette smoking
Non-smoker	8511 (88.2)
Ex-smoker	303 (3.1)
Current smoker	835 (8.7)
Alcohol consumption
Non-drinker	7865 (81.5)
Ex-drinker	461 (4.8)
Regular-drinker	1323 (13.7)

The prevalence of rhinitis was found to be 6.3% (609/9649) (Table 2). Of the three symptoms, sneezing was found to be the most common (4.0%), followed by nasal congestion (3.0%), and runny nose (2.9%). Only 16.3% had eye irritations together with rhinitis symptoms. The months with the highest frequency of symptoms were March to July (summer season in Thailand), and November to February (winter season). The prevalence of rhinitis was significantly associated with marital status, education, family income, cigarette smoking, and alcohol consumption (Table 3).

Table 2. Prevalence of rhinitis and its characteristics.

	N (%), N=9649
Having symptoms in the past year
Sneezing	390 (4.0)
Stuffy nose	294 (3.0)
Runny nose	282 (2.9)
Rhinitis (having one of the above three symptoms)	609 (6.3)
Having eyes irritation while having rhinitis	99 (16.3)
Month of having the symptoms (n=609)
January	177 (29.3)
February	103 (17.0)
March	76 (12.6)
April	100 (16.5)
May	258 (42.6)
June	272 (44.9)
July	245 (40.5)
August	95 (15.7)
September	61 (10.1)
October	53 (8.8)
November	99 (16.4)
December	137 (22.6)
Allergens activated the symptoms (n=609)
Dust	467 (76.7)
Mites	282 (46.3)
Smoke	275 (45.2)
Cooking fume	190 (31.4)
Straw or grass	101 (16.6)
Pet	33 (5.4)
Pollen	34 (5.6)
Patients who believed they have allergy	104 (17.1)
Patients that had allergic testing	36 (5.9)
Patients with positive result	24 (3.9)
Patients who were diagnosed with asthma or allergy	39 (6.7)

Table 3. Association between demographic data and rhinitis prevalence.

	Not rhinitis	Rhinitis	P-value^a
Gender			0.176
Male	3884 (93.3)	279 (6.7)
Female	5156 (94.0)	330 (6.0)
Age			0.488
20–30	285 (93.1)	21 (6.9)
31–40	777 (92.5)	63 (7.5)
41–50	1867 (93.4)	133 (6.7)
51–60	2983 (94.0)	191 (6.0)
>60	3128 (94.0)	201 (6.0)
Marital status			0.010*
Married	7021 (93.3)	502 (6.7)
Single	693 (94.0)	44 (6.0)
Divorce/widow/separated	1326 (95.5)	63 (4.5)
Education complete			<0.001*
Non-educated	381 (94.8)	21 (5.2)
Primary school	6655 (94.5)	391 (5.5)
Secondary school	1877 (91.0)	186 (9.0)
College degree or higher	127 (92.0)	11 (8.0)
Family income, THB/month			<0.001*
<5000	3067 (96.8)	101 (3.2)
5001–10000	5330 (92.9)	409 (7.1)
10001–30000	576 (86.2)	92 (13.8)
>30000	67 (90.5)	7 (9.5)
Cigarette smoking			0.001*
Non-smoker	7997 (94.0)	514 (6.0)
Ex-smoker	269 (88.8)	34 (11.2)
Current smoker	774 (92.7)	61 (7.3)
Alcohol consumption			<0.001*
Non-drinker	7415 (94.3)	450 (5.7)
Ex-drinker	406 (88.1)	55 (11.9)
Regular-drinker	1219 (92.1)	104 (7.9)

^a Chi-square test.

* Statistically significant difference with p value <0.05

In this study, among the five groups of pesticides all were significantly associated with rhinitis. The OR values ranged between 1.67 (95%CI 1.41-1.99) for fungicide and 7.19 (95%CI 4.67-11.06) for insecticide (Table 4).

Table 4. The associations between type of pesticides use and rhinitis prevalence.

Pesticide	Not rhinitis	Rhinitis	OR (crude)	OR (adjusted)^a
Any pesticide
No	463 (96.5)	17 (3.5)	1.0	1.0
Yes	8575 (93.5)	592 (6.5)	1.88 (1.15-3.07)^b	1.79 (1.09-2.94)
Insecticide
No	1996 (98.9)	22 (1.1)	1.0	1.0
Yes	7044 (92.3)	587 (7.7)	7.56 (4.93-11.61)	7.19 (4.67-11.06)
Q1	1837 (95.1)	94 (4.9)	1.0	1.0
Q2	2009 (93.7)	136 (6.3)	1.32 (1.01-1.73)	1.36 (1.03-1.79)
Q3	2048 (90.5)	215(9.5)	2.05 (1.60-2.64)	2.18 (1.68-2.82)
Q4	1150 (89.0)	142 (11.0)	2.41 (1.84-3.16)	2.65 (2.00-3.52)
P for trend				0.206
Herbicide
No	1254 (98.4)	20 (1.6)	1.0	1.0
Yes	7786 (93.0)	586 (7.0)	4.74 (3.03-7.44)	4.25 (2.71-6.69)
Q1	2227 (95.2)	112 (4.8)	1.0	1.0
Q2	1864 (93.9)	122 (6.1)	1.30 (1.00-1.69)	1.34 (1.03-1.76)
Q3	2555 (92.0)	221 (8.0)	1.72 (1.36-2.17)	1.77 (1.38-2.25)
Q4	1140 (89.5)	134 (10.5)	2.34 (1.80-3.03)	2.55 (1.94-3.36)
P for trend				0.095
Fungicide
No	5107 (95.2)	256 (4.8)	1.0	1.0
Yes	3933 (91.8)	353 (8.2)	1.79 (1.52-2.11)	1.67 (1.41-1.99)
Q1	1006 (93.7)	68 (6.3)	1.0	1.0
Q2	1020 (92.3)	100 (7.7)	1.23 (0.89-1.69)	1.23 (0.89-1.70)
Q3	766 (90.7)	79 (9.3)	1.53 (1.09-2.14)	1.49 (1.05-2.11)
Q4	959 (90.0)	106 (10.0)	1.64 (1.19-2.25)	1.53 (1.10-2.13)
P for trend				0.391
Rodenticide
No	7371 (95.1)	379 (4.9)	1.0	1.0
Yes	1669 (87.9)	230 (12.1)	2.68 (2.26-3.18)	2.63 (2.20-3.14)
Q1	514 (88.9)	64 (11.1)	1.0	1.0
Q2	319 (83.5)	63 (16.5)	1.59 (1.09-2.31)	1.47 (0.99-2.17)
Q3	448 (90.3)	48 (9.7)	0.86 (0.58-1.28)	0.79 (0.53-1.19)
Q4	388 (87.6)	55 (12.4)	1.14 (0.78-1.67)	0.95 (0.64-1.43)
P for trend				0.758
Molluscicide
No	7096 (95.0)	371 (5.0)	1.0	1.0
Yes	1944 (889.1)	238 (10.9)	2.34 (1.98-2.78)	2.29 (1.92-2.73)
Q1	573 (86.6)	89 (13.4)	1.0	1.0
Q2	404 (87.6)	57 (12.4)	0.91 (0.64-1.30)	0.90 (0.62-1.30)
Q3	544 (92.5)	44 (7.5)	0.52 (0.36-0.76)	0.50 (0.34-0.73)
Q4	423 (89.8)	48 (10.2)	0.73 (0.50-1.06)	0.67 (0.45-0.98)
P for trend				0.687

^a Adjusted variables were gender (male, female), age (continuous), marital status (married, single, divorce/willow/separated), education (non-educated, primary school, secondary school, college degree or higher), family income (<5000 THB, 5001-10000 THB, 10001-30000 THB, >30000 THB), cigarette smoking (non-smoker, ex-smoker, current smoker), alcohol consumption (non-drinker, ex-drinker, regular-drinker). ^b Significant OR were indicated in bold numbers.

For individual pesticides, the study found 33 out of 35 to be significantly associated with rhinitis, including six herbicides [glyphosate, paraquat, 2,4-Dichlorophenoxyacetic acid butachlor (2,4-D), butachlor, alachlor, and diuron], nine organophosphates (OP) insecticides [chlorpyrifos, folidol (parathion), methamidophos, monocrotophos, mevinphos, dicrotophos, dichlorvos, O-Ethyl O-(4-nitrophenyl) phenylphosphonothioate (EPN), and imidacloprid], four carbamate insecticides [carbaryl, methomyl, carbosulfan, and carbofuran], one pyrethroid insecticides – permethrin, six OC insecticides [endosulfan, dieldrin, aldrin, Dichlorodiphenyltrichloroethane (DDT), chlordane and heptachlor], and seven fungicides [metalaxyl, mancozeb, propineb, carbendazim, thiophanate, benomyl, and bordeaux mixture] (Table 5).

Table 5. The associations between individual pesticide and rhinitis prevalence.

	Not rhinitis	Rhinitis	OR (Crude)	OR (Adjusted)^a
Herbicides
Glyphosate
No	1104 (97.3)	31 (2.7)	1.0	1.0
Yes	7936 (93.2)	578 (6.8)	2.59 (1.80-3.74)^b	2.43 (1.68-3.52)
Q1	2463 (94.6)	141 (5.4)	1.0	1.0
Q2	1853 (93.5)	129 (6.5)	1.22 (0.95-1.56)	1.29 (0.99-1.66)
Q3	1869 (91.6)	171 (8.4)	1.60 (1.27-2.01)	1.67 (1.32-2.12)
Q4	1751 (92.7)	137 (7.3)	1.36 (1.07-1.74)	1.46 (1.13-1.89)
P for trend				0.708
Paraquat
No	3503 (95.6)	160 (4.4)	1.0	1.0
Yes	5537 (92.5)	449 (7.5)	1.78 (1.48-2.14)	1.54 (1.28-1.87)
Q1	1723 (93.4)	121 (6.6)	1.0	1.0
Q2	1233 (93.1)	91 (6.9)	1.05 (0.79-1.39)	1.10 (0.83-1.47)
Q3	1309 (90.4)	139 (9.6)	1.51 (1.17-1.95)	1.60 (1.23-2.08)
Q4	1272 (92.8)	98 (7.2)	1.10 (0.83-1.45)	1.16 (0.87-1.55)
P for trend				0.909
2,4-D
No	4413 (94.9)	239 (5.1)	1.0	1.0
Yes	4627 (92.6)	370 (7.4)	1.48 (1.25-1.75)	1.38 (1.61-1.63)
Q1	1301 (92.3)	109 (7.7)	1.0	1.0
Q2	1161 (91.5)	108 (8.5)	1.11 (0.84-1.47)	1.09 (0.81-1.44)
Q3	1077 (92.4)	89 (7.6)	0.99 (0.74-1.32)	0.99 (0.72-1.33)
Q4	1089 (94.4)	64 (5.6)	0.70 (0.51-0.97)	0.71 (0.51-1.00)
Butachlor
No	6868 (94.8)	374 (5.2)	1.0	1.0
Yes	2172 (90.2)	235 (9.8)	1.99 (1.68-2.36)	1.82 (1.52-2.17)
Q1	577 (90.3)	62 (9.7)	1.0	1.0
Q2	635 (90.1)	70 (9.9)	1.03 (0.72-1.47)	1.00 (0.69-1.45)
Q3	525 (89.8)	59 (10.1)	1.05 (0.72-1.52)	0.96 (0.65-1.42)
Q4	435 (90.8)	44 (9.2)	0.94 (0.63-1.41)	0.85 (0.55-1.30)
Alachlor
No	8222 (93.9)	536 (6.1)	1.0	1.0
Yes	818 (91.8)	73 (8.2)	1.37 (1.06-1.77)	1.30 (1.00-1.69)
Q1	259 (92.8)	20 (7.2)	1.0	1.0
Q2	147 (87.5)	21 (12.5)	1.85 (0.97-3.53)	1.76 (0.89-3.51)
Q3	302 (95.3)	15 (4.7)	0.64 (0.32-1.28)	0.61 (0.29-1.28)
Q4	110 (86.6)	17 (13.4)	2.00 (1.01-3.97)	1.73 (0.81-3.67)
P for trend				0.492
Diuron
No	8931 (93.8)	593 (6.2)	1.0	1.0
Yes	109 (87.2)	16 (12.8)	2.21 (1.30-3.76)	1.73 (1.00-2.99)
Q1	38 (97.4)	1 (2.6)	1.0	1.0
Q2	30 (93.8)	2 (6.3)	2.533 (0.22-29.29)	5.62 (0.10-318.16)
Q3	21 (87.5)	3 (12.5)	5.43 (0.53-55.52)	20.38 (0.40-103.23)
Q4	20 (66.7)	10 (33.3)	19.00 (2.27-159.20)	13.59 (0.53-350.81)
P for trend				0.706
Organophosphate insecticides
Abamectin
No	3952 (93.2)	290 (6.8)	1.0	1.0
Yes	5088 (94.1)	319 (5.9)	0.85 (0.73-1.01)	0.82 (0.69-0.97)
Q1	1630 (94.2)	100 (5.8)	1.0	1.0
Q2	1108 (93.7)	75 (6.3)	1.10 (0.81-1.50)	1.11 (0.81-1.52)
Q3	1191 (92.5)	97 (7.5)	1.33 (1.00-1.78)	1.35 (1.00-1.82)
Q4	1153 (96.1)	47 (3.9)	0.66 (0.47-0.95)	0.723 (0.50-1.04)
Chlorpyrifos
No	7015 (94.5)	408 (5.5)	1.0	1.0
Yes	2025 (91.0)	201 (9.0)	1.71 (1.43-2.04)	1.53 (1.28-1.84)
Q1	535 (90.8)	54 (9.2)	1.0	1.0
Q2	658 (91.3)	63 (8.7)	0.95 (0.65-1.39)	0.96 (0.65-1.42)
Q3	393 (89.5)	46 (10.5)	1.16 (0.77-1.76)	1.22 (0.79-1.87)
Q4	439 (92.0)	38 (8.0)	0.86 (0.56-1.32)	0.84 (0.53-1.32)
P for trend				0.324
Folidol/parathion
No	8017 (95.3)	391 (4.7)	1.0	1.0
Yes	1023 (82.4)	218 (17.6)	4.37 (3.66-5.22)	3.95 (3.29-4.74)
Q1	298 (89.2)	36 (10.8)	1.0	1.0
Q2	277 (86.8)	42 (13.2)	1.26 (0.78-2.02)	1.43 (0.87-2.35)
Q3	227 (79.4)	59 (20.6)	2.15 (1.37-3.37)	2.19 (1.37-3.50)
Q4	221 (73.2)	81 (26.8)	3.03 (1.98-4.66)	2.93 (1.85-4.65)
P for trend				0.157
Methamidophos
No	8658 (94.0)	553 (6.0)	1.0	1.0
Yes	382 (87.2)	56 (12.8)	2.30 (1.71-3.08)	2.06 (1.522-2.79)
Q1	319 (89.6)	37 (10.4)	1.0	1.0
Q2	58 (87.9)	8 (12.1)	1.19 (0.53-2.68)	0.73 (0.29-1.81)
Q3	4 (50.0)	4 (50.0)	8.62 (2.07)	8.22 (1.30-52.01)
Q4	0 (0.0)	7 (100.0)	NA	NA
Monocrotophos
No	8856 (93.9)	575 (6.1)	1.0	1.0
Yes	184 (84.4)	34 (15.6)	2.85 (1.96-4.13)	2.44 (1.65-3.61)
Q1	52 (88.1)	7 (11.9)	1.0	1.0
Q2	49 (84.5)	9 (15.5)	1.36 (0.47-3.95)	1.12 (0.30-4.11)
Q3	57 (96.6)	2 (3.4)	0.26 (0.05-1.31)	0.20 (0.03-1.24)
Q4	26 (61.9)	16 (38.1)	4.57 (1.67-12.49)	2.14 (0.55-8.30)
P for trend				0.169
Mevinphos
No	8937 (93.8)	591 (6.2)	1.0	1.0
Yes	103 (85.1)	18 (14.9)	2.64 (1.59-4.39)	2.32 (1.36-3.94)
Low	64 (91.4)	6 (8.6)	1.0	1.0
High	39 (76.5)	12 (23.5)	3.28 (1.14-9.45)	2.70 (0.64-11.32)
Dicrotophos
No	8868 (93.8)	587 (6.2)	1.0	1.0
Yes	172 (88.7)	22 (11.3)	1.93 (1.23-3.04)	1.70 (1.07-2.71)
Low	99 (88.4)	13 (11.6)	1.0	1.0
High	73 (89.0)	9 (11.0)	0.94 (0.38-2.31)	0.70 (0.24-2.11
Dichlorvos
No	8951 (93.8)	591 (6.2)	1.0	1.0
Yes	89 (83.2)	18 916.8)	3.06 (1.83-5.12)	2.77 (1.63-4.70)
Low	52 (91.2)	5 (8.8)	1.0	1.0
High	36 (73.5)	13 (26.5)	3.76 (1.23-11.46)	5.87 (0.90-38.10)
EPN
No	8837 (94.2)	545 (5.8)	1.0	1.0
Yes	203 (76.0)	64 (24.0)	5.11 (3.81-6.86)	4.15 (3.06-5.62)
Q1	60 (74.1)	21 (25.9)	1.0	1.0
Q2	49 (87.5)	7 (12.5)	0.41 (0.16-1.04)	0.45 (0.16-1.25)
Q3	48 (75.0)	16 (25.0)	0.95 (0.45-2.02)	0.98 (0.42-2.30)
Q4	46 (69.7)	20 (30.3)	1.24 (0.63-2.56)	0.98 (0.42-2.28)
P for trend				0.677
Imidacloprid
No	8683 (93.9)	561 (6.1)	1.0	1.0
Yes	357 (88.1)	48 (11.9)	2.08 (1.52-2.85)	1.88 (1.36-2.61)
Q1	88 (83.0)	18 (37.5)	1.0	1.0
Q2	103 (91.2)	10 (8.8)	0.78 (0.21-1.08)	0.40 (0.15-1.03)
Q3	82 (94.3)	5 (5.7)	0.30 (0.11-1.08)	0.31 (0.10-1.01)
Q4	84 (84.8)	15 (15.2)	0.87 (0.41-1.84)	1.07 (0.45-2.56)
P for trend				0.394
Profenofos
No	8822 (93.7)	590 (6.3)	1.0	1.0
Yes	218 (92.0)	19 (8.0)	1.30 (0.81-2.10)	1.26 (0.78-2.06)
Low	112 (94.1)	7 (5.9)	1.0	1.0
High	106 (89.9)	12 (10.2)	1.30 (0.81-2.10)	1.29 (0.79-2.11)
Carbamate insecticides
Carbaryl/Sevin
No	8415 (94.3)	511 (5.7)	1.0	1.0
Yes	625 (86.4)	98 (13.6)	2.58 (2.05-3.23)	2.31 (1.82-2.93)
Q1	170 (89.9)	19 (10.1)	1.0	1.0
Q2	150 (86.2)	24 (13.8)	1.43 (0.75-2.72)	1.47 (0.74-2.93)
Q3	171 (88.1)	23 (11.9)	1.20 (0.63-2.30)	1.07 (0.54-2.12)
Q4	134 (80.7)	32 (19.3)	2.14 (1.16-3.94)	2.23 (1.31-4.39)
P for trend				0.070
Methomyl
No	8581 (94.1)	542 (5.9)	1.0	1.0
Yes	459 (87.3)	67 (12.7)	2.31 (1.76-3.03)	2.02 (1.53-2.68)
Q1	120 (81.1)	28 (18.9)	1.0	1.0
Q2	107 (91.5)	10 (8.5)	0.40 (0.19-0.86)	0.45 (0.20-1.01)
Q3	131 (87.9)	18 (12.1)	0.59 (0.31-1.12)	0.63 (0.32-1.24)
Q4	101 (90.2)	11 (9.8)	0.47 (0.22-0.98)	0.49 (0.22-1.09)
P for trend				0.576
Carbosulfan
No	7945 (94.4)	467 (5.6)	1.0	1.0
Yes	1095 (88.5)	142 (11.5)	2.21 (1.81-2.69)	2.09 (1.70-2.56)
Q1	130 (82.8)	27 (17.2)	1.0	1.0
Q2	165 (88.7)	21 (11.3)	0.61 (0.33-1.13)	0.62 (0.33-1.17)
Q3	399 (88.5)	52 (11.5)	0.63 (0.38-1.04)	0.61 90.36-1.04)
Q4	401 (90.5)	42 (9.5)	0.50 (0.20-0.85)	0.49 (0.28-0.85)
P for trend				0.410
Carbofuran
No	8336 (94.4)	497 (5.6)	1.0	1.0
Yes	704 (86.3)	112 (13.7)	2.67 (2.14-3.32)	2.34 (1.86-2.93)
Q1	239 (83.9)	46 (16.1)	1.0	1.0
Q2	112 (86.8)	17 (13.2)	0.79 (0.43-1.44)	0.66 (0.35-1.26)
Q3	184 (86.4)	29 (13.6)	0.82 (0.50-1.35)	0.71 (0.41-1.22)
Q4	169 (89.4)	20 (10.6)	0.62 (0.35-1.08)	0.50 (0.27-0.90)
P for trend				0.296
Pyrethroid insecticides
Permethrin
No	8083 (94.0)	515 (6.0)	1.0	1.0
Yes	957 (91.1)	94 (8.9)	1.54 (1.23-1.94)	1.60 (1.27-2.03)
Q1	304 (88.1)	41 (11.9)	1.0	1.0
Q2	169 (91.4)	16 (8.6)	0.70 (0.38-1.29)	0.73 (0.38-1.40)
Q3	349 (92.6)	28 (7.4)	0.60 (0.36-0.99)	0.82 (0.47-1.43)
Q4	135 (93.8)	9 (6.3)	0.49 (0.23-1.05)	0.36 (0.16-0.82)
P for trend				0.084
Organochlorine insecticides
Endosulfan
No	7814 (94.6)	447 (5.4)	1.0	1.0
Yes	1226 (88.3)	162 (11.7)	2.31 (1.91-2.79)	2.17 (1.78-2.64)
Q1	378 (84.4)	70 (15.6)	1.0	1.0
Q2	300 (93.5)	21 (6.5)	0.38 (0.23-0.63)	0.36 (0.21-0.62)
Q3	261 (88.5)	34 (11.5)	0.70 (0.45-1.09)	0.60 (0.38-0.95)
Q4	287 (88.6)	37 (11.4)	0.70 (0.45-1.07)	0.60 (0.38-0.95)
P for trend				0.888
Dieldrin
No	8979 (93.9)	585 (6.1)	1.0	1.0
Yes	61 (71.8)	24 (28.2)	6.04 (3.74-9.76)	5.08 (3.07-8.43)
Low	46 (90.2)	5 (9.8)	1.0	1.0
High	15 (44.1)	19 (55.9)	11.65 (3.71-36.61)	9.23 (1.20-71.11)
Aldrin
No	8992 (93.9)	589 (6.1)	1.0	1.0
Yes	48 (70.6)	20 (29.4)	6.36 (3.75-10.79)	4.83 (2.78-8.39)
Low	33 (89.2)	4 (10.8)	1.0	1.0
High	15 (48.4)	16 (51.6)	8.80 (2.51-30.84)	NA
DDT
No	8759 (94.4)	524 (5.6)	1.0	1.0
Yes	281 (76.8)	85 (23.2)	5.05 (3.91-6.55)	4.57 (3.43-5.99)
Q1	74 (78.7)	20 (21.3)	1.0	1.0
Q2	86 (78.9)	23 (21.1)	0.99 (0.50-1.94)	1.39 (0.64-2.98)
Q3	87 (83.7)	17 (16.3)	0.72 (0.35-1.48)	0.71 (0.32-1.58)
Q4	34 (57.6)	25 (42.4)	2.72 (1.33-5.56)	1.52 (0.67-3.47)
P for trend				0.372
Chlordane
No	8948 (93.9)	586 (96.2)	1.0	1.0
Yes	92 (80.0)	23 (20.0)	3.82 (2.40-6.07)	3.43 (2.11-5.58)
Low	57 (87.7)	8 (12.3)	1.0	1.0
High	35 (70.0)	15 (30.0)	3.05 (1.17-7.94)	2.25 (0.48-10.63)
Heptachlor
No	8941 (93.9)	584 (6.1)	1.0	1.0
Yes	99 (79.8)	25 (20.2)	3.87 (2.47-6.04)	3.29 (2.07-5.24)
Low	61 (91.0)	6 (9.0)	1.0	1.0
High	38 (66.7)	19 (33.3)	5.08 (1.86-13.86)	0.81 (0.10-6.84)
Fungicide
Metalaxyl
No	8315 (93.9)	544 (6.1)	1.0	1.0
Yes	725 (91.8)	65 (8.2)	1.37 (1.05-1.79)	1.35 (1.03-1.78)
Q1	210 (92.9)	16 (7.1)	1.0	1.0
Q2	186 (95.4)	9 (4.6)	0.64 (0.27-1.47)	0.53 (0.21-1.31)
Q3	205 (92.3)	17 (7.7)	1.09 (0.54-2.21)	0.87 (0.40-1.89)
Q4	124 (84.4)	23 (15.6)	2.43 (1.24-4.78)	1.71 (0.79-3.71)
P for trend				0.084
Mancozeb
No	8407 (94.1)	525 (5.9)	1.0	1.0
Yes	633 (88.3)	84 (11.7)	2.13 (1.67-2.71)	1.99 (1.55-2.55)
Q1	166 (86.5)	26 (13.5)	1.0	1.0
Q2	182 (91.5)	17 (8.5)	0.60 (0.31-1.14)	0.57 (0.28-1.15)
Q3	138 (90.8)	14 (9.2)	0.65 (0.33-1.29)	0.53 (0.25-1.11)
Q4	147 (84.5)	27 (15.5)	1.17 (0.66-2.10)	0.90 (0.46-1.74)
P for trend				0.596
Maneb
No	8625 (93.8)	575 (6.3)	1.0	1.0
Yes	415 (92.4)	34 (7.6)	1.23 (0.56-1.76)	1.24 (0.86-1.79)
Q1	109 (94.8)	6 (5.2)	1.0	1.0
Q2	113 (91.9)	10 (8.1)	1.61 (0.57-4.58)	1.90 (0.54-6.71)
Q3	101 (95.3)	5 (4.7)	0.90 (0.27-3.04)	0.76 (0.18-3.19)
Q4	92 (87.6)	13 (12.4)	2.57 (0.94-7.02)	1.04 (0.28-3.87)
P for trend				0.808
Propineb
No	8624 (93.9)	560 (6.1)	1.0	1.0
Yes	398 (89.0)	49 (11.0)	1.90 (1.40-2.59)	1.84 (1.34-2.53)
Q1	125 (31.4)	9 (18.4)	1.0	1.0
Q2	80 (88.9)	10 (11.1)	1.74 (0.68-4.46)	1.98 (0.68-5.76)
Q3	109 (87.9)	15 (12.1)	1.91 (0.80-4.54)	1.97 (0.73-5.33)
Q4	84 (84.8)	15 (15.2)	2.48 (1.04-5.93)	2.51 (0.91-6.93)
P for trend				0.301
Carbendazim
No	7856 (94.2)	484 (5.8)	1.0	1.0
Yes	1184 (90.5)	125 (9.5)	1.71 (1.40-2.11)	1.57 (1.27-1.94)
Q1	316 (88.0)	43 (12.0)	1.0	1.0
Q2	328 (91.6)	30 (8.4)	0.67 (0.41-1.10)	0.72 (0.43-1.19)
Q3	311 (89.6)	36 (10.4)	0.85 (0.53-1.36)	0.80 (0.49-1.31)
Q4	229 (93.5)	16 (6.5)	0.51 (0.28-0.93)	0.47 (0.25-0.870
P for trend				0.148
Thiophanate
No	8828 (93.8)	582 (6.2)	1.0	1.0
Yes	212 (88.7)	27 (11.3)	1.93 (1.28-2.91)	1.90 (1.25-2.90)
Q1	52 (86.7)	8 (13.3)	1.0	1.0
Q2	60 (95.2)	3 (4.8)	0.33 (0.08-1.29)	0.22 (0.04-1.06)
Q3	53 (91.4)	5 (8.6)	0.61 (0.19-2.00)	0.66 (0.15-2.79)
Q4	47 (81.0)	11 (19.0)	1.52 (0.56-4.10)	1.16 (0.31-4.31)
P for trend				0.365
Benomyl
No	8916 (93.8)	590 (6.2)	1.0	1.0
Yes	124 (86.7)	19 (13.3)	2.32 (1.42-3.78)	2.03 (1.22-3.36)
Low	72 (92.3)	6 (7.7)	1.0	1.0
High	52 (80.0)	13 (20.0)	3.00 (1.07-8.41)	1.28 (0.30-5.54)
Bordeaux mixture
No	8996 (93.8)	591 (6.2)	1.0	1.0
Yes	44 (71.0)	18 (29.0)	6.23 (34.58-10.84)	5.67 (3.16-10.16)
Q1	13 (76.5)	4 (23.5)	1.0	1.0
Q2	14 (100.0)	0	NA	NA
Q3	14 (73.7)	5 (26.3)	1.61 (0.26-5.29)	0.63 (0.02-21.11)
Q4	3 (25.0)	9 (75.0)	9.75 (1.74-54.53)	0.62 (0-360.35)

^b Significant OR were indicated in bold numbers.

Discussion

The results of this study indicate that 6.3% of the farmers in Phitsanulok province had rhinitis. This prevalence rate was much lower than those found in previous studies, such as the 67% of the farmers in the Agriculture Health Study (AHS)²¹, 57% of farm residents in New York²³, and 31% of greenhouse flower and ornamental plant growers in Europe²⁴. Though it is difficult to differentiate between AR and NAR⁴, the rhinitis among the study participants was more likely to be NAR because the study results found that only 16.3% of the participants had eye irritation, most of them reported having symptoms during the summer season (March-July), or the winter season (November-February) (Table 2). Besides, only 104 farmers (17.1%) believed they have an allergy, and 24 (3.9%) had allergic test positive. These were consistent with the facts that NAR is a type of rhinitis that can be triggered by changes in the weather, without having any involvement from the immune system or allergens, or rarely exhibiting itchy eye symptoms²⁵.

In this study, the prevalence of rhinitis was significantly associated with a history of using several pesticides. Among five groups of pesticides surveyed in this study, insecticides, herbicides, fungicides, rodenticides, and molluscicides, all had a positive association with rhinitis, with the OR from 1.67 (95% CI 1.41-1.99) for the fungicide group to 7.19 (95% CI 4.67-11.06) for insecticide (Table 4). Among the insecticide group, those individual pesticides that are associated with rhinitis were herbicides (glyphosate, paraquat, 2,4-D, butachlor, alachlor, and diuron), OP insecticide (chlorpyrifos, folidol (parathion), methamidophos, monocrotophos, mevinphos, dicrotophos, dichlorvos, EPN, and imidacloprid), carbamate insecticides (carbaryl, methomyl, carbosulfan, and carbofuran), pyrethroid insecticides (ermethrin; OC insecticides - endosulfan, dieldrin, aldrin, DDT, chlordane, and heptachlor), and fungicides (metalaxyl, mancozeb, propineb, carbendazim, thiophanate, benomyl, and bordeaux mixture) (Table 5). This was consistent with the literature, although a small number of the studies have focused on pesticide use and its association with rhinitis. A previous study with grape farmers in Crete, Greece reported a higher prevalence of allergic rhinitis symptoms among herbicide users, especially glyphosate when compared to the control group (OR, 3.0; 95% CI, 1.4 to 6.2)⁹. The study also observed that the herbicide paraquat and the insecticide carbamate can increase the risk of rhinitis. The AHS in the U.S.A has shown that the elevated risk of rhinitis was observed among those who were exposed to pesticides such as glyphosate, petroleum oil, 2,4-D, metolachlor²⁶, diazinon, carbofuran, malathion, and carbaryl²¹. Pyrethroid insecticide exposure, however, has been reported to involve respiratory symptoms e.g., irritation of the respiratory tract, and nose or throat irritation accompanied by sneezing or coughing, as appose to rhinitis²⁷. Research on carbamate insecticides has found this type of insecticides to increase the risk of asthma, a disease closely related to rhinitis²⁸. In AHS, the fungicide captan was significantly associated with an increased number of rhinitis episodes (OR = 1.32; 95% CI = 1.17–1.49) among those with 7–12 symptoms per year)²¹. Pesticide exposure has also been associated with some respiratory symptoms, including wheezing airway irritation, dry/sore throat, cough, breathlessness, and chest tightness²⁷.

One possible biomechanism that can explain this effect was that pesticides could increase susceptibility to allergens or other stimuli, by causing irritation, inflammation and immune suppression in the airway²⁹. In addition, pesticides can also modify inflammatory responses of other allergens³⁰. Pesticides and the solvent composition of the mixture that contains them are lipid-soluble, therefore could irritate the nose and throat when inhaled³¹. OPs, and carbamate insecticides are cholinesterase inhibitors that can inhibit acetylcholinesterase and cause more secretion of mucus in the nose and airway system³². Studies among the asthmatic group, found OP exposure to induce bronchospasm and airway hyperreactivity³³. In vitro study showed the cytotoxic effects of fungicides on bronchial epithelial cells³⁴. Evidence from both animal and human studies showed that herbicides can cause airway inflammation³⁵. A recent study found herbicide 2,4-D to increase the mast cells in the nasal epithelium³⁶.

This study has some limitations. Information on other pollutant exposure, such as grain and hay handling, environmental pesticide exposure, and maintenance activities, e.g., repairing engines and pesticide equipment, were not collected and used to control for confounding bias. However, it is more likely that both the control and study groups will have a similar risk to those factors. A previous AHS study found that the association between pesticide exposure and rhinitis was independent of crop, animal, or farm maintenance exposures²¹. In this study, self-reported rhinitis and not excluding other respiratory diseases could have resulted in the overestimation of rhinitis prevalence. However, this would have been unlikely to occur as just 6.3% of the farmers reported having rhinitis (Table 2). Recall bias and reliability of pesticide exposure data might be another concern. However, a study found that the questionnaire and data collection technique used in the AHS which uses, a similar questionnaire and data collection technique, to be reliable and reproducible³⁷. If recall bias had occurred, it would have been equally distributed among the study and control groups, and it would minimize rather than increase the association³⁸. Moreover, the use of cross-sectional design for this study does not allow for the results to be interpreted in terms of cause-and-effect association.

To our knowledge, in Thailand, this was the first study that links pesticide exposure to rhinitis. The use of large sample size, and detailed data on pesticide exposure in the study has identified new pesticides along with the association of many individual pesticides to rhinitis.

Conclusion

The prevalence of rhinitis among farmers in Phitsanulok province, Thailand was significantly associated with pesticide exposure, either by type or individual pesticide. This information should receive more public attention and be incorporated into the rhinitis prevention program. More studies should be conducted to confirm the result and identify more individual pesticides to cause rhinitis.

Data availability

Underlying data

Figshare: Dataset for study on pesticide exposure and rhinitis in Phitsanulok Thailand https://doi.org/10.6084/m9.figshare.14524326²².

This project contains the following underlying data:

• Dataset pesticide and rhinitis-Thailand (SAV and CSV).
• Data Dictionary (DOCX).

Extended data

Figshare: Questionnaire-pesticide and rhinitis-Thailand. https://doi.org/10.6084/m9.figshare.14524335.v1¹⁸.

This project contains the following extended data:

• Questionnaire-pesticide and rhinitis-English (DOCX). (Study questionnaire in English.)
• Questionnaire-pesticide and rhinitis-Thai (DOCX). (Study questionnaire in Thai.)

Data are available under the terms of the Creative Commons Zero “No right reserved” data waiver (CC0 1.0 Public domain dedication).

Acknowledgements

We would like to thank all study participants for the valuable information provided. Thanks also to the village health volunteers and local hospital staff for data collection. We would like also to thank Mr. Kevin Mark Roebl for language editing.

Faculty Opinions recommended

References

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8. Schyllert C, Rönmark E, Andersson M, et al.: Occupational exposure to chemicals drives the increased risk of asthma and rhinitis observed for exposure to vapours, gas, dust and fumes: a cross-sectional population-based study. Occup Environ Med. 2016; 73(10): 663–669. PubMed Abstract | Publisher Full Text
9. Chatzi L, Alegakis A, Tzanakis N, et al.: Association of allergic rhinitis with pesticide use among grape farmers in Crete, Greece. Occup Environ Med. 2007; 64(6): 417–421. PubMed Abstract | Publisher Full Text | Free Full Text
10. Raherison C, Baldi I, Pouquet M, et al.: Pesticides Exposure by Air in Vineyard Rural Area and Respiratory Health in Children: A pilot study. Environ Res. 2019; 169: 189–195. PubMed Abstract | Publisher Full Text
11. Riu E, Monśo E, Marin A, et al.: Occupational risk factors for rhinitis in greenhouse flower and ornamental plant growers. Am J Rhinol. 2008; 22(4): 361–364. PubMed Abstract | Publisher Full Text
12. Patel O, Syamlal G, Henneberger PK, et al.: Pesticide use, allergic rhinitis, and asthma among US farm operators. J Agromedicine. 2018; 23(4): 327–335. PubMed Abstract | Publisher Full Text | Free Full Text
13. Koureas M, Rachiotis G, Tsakalof A, et al.: Increased frequency of rheumatoid arthritis and allergic rhinitis among pesticide sprayers and associations with pesticide use. Int J Environ Res Public Health. 2017; 14(8): 865. PubMed Abstract | Publisher Full Text | Free Full Text
14. Slager RE, Poole JA, LeVan TD, et al.: Rhinitis associated with pesticide exposure among commercial pesticide applicators in the Agricultural Health Study. Occup Environ Med. 2009; 66(11): 718–724. PubMed Abstract | Publisher Full Text | Free Full Text
15. Jeebhay MF, Dalvie MA, Ndlovu V: Pesticides and the airways - a review of the literature : allergies in the workplace Current Allergy & Clinical Immunology. Curr Allergy Clin Immunol. 2011; 24(4). Accessed March 29, 2021. Reference Source
16. Phitsanulok Provincial Agriculture and Cooperatives Office: Basic Agricultural Data, Phitsanulok (In Thai). 2021. Reference Source
17. Thailand Information Center: Phitsanulok province. Accessed February 22, 2021. Reference Source
18. Juntarawijit C, Juntarawijit Y: Questionnaire-pesticide and rhinitis-Thailand. Figshare. Dataset. 2021. http://www.doi.org/10.6084/m9.figshare.14524335.v1
19. Annesi-Maesano I, Didier A, Klossek M, et al.: The score for allergic rhinitis (SFAR): A simple and valid assessment method in population studies. Allergy. 2002; 57(2): 107–114. PubMed Abstract | Publisher Full Text
20. Juntarawijit C, Juntarawijit Y: Association between diabetes and pesticides: A case-control study among Thai farmers. Environ Health Prev Med. 2018; 23(1): 3. PubMed Abstract | Publisher Full Text | Free Full Text
21. Slager RE, Simpson SL, Levan TD, et al.: Rhinitis associated with pesticide use among private pesticide applicators in the agricultural health study. J Toxicol Environ Health A. 2010; 73(20): 1382–1393. PubMed Abstract | Publisher Full Text | Free Full Text
22. Juntarawijit C, Juntarawijit Y: Dataset for study on pesticide exposure and rhinitis in Phitsanulok Thailand. figshare. Dataset. 2021. http://www.doi.org/10.6084/m9.figshare.14524326
23. Gomez MI, Hwang SA, Lin S, et al.: Prevalence and predictors of respiratory symptoms among New York farmers and farm residents. Am J Ind Med. 2004; 46(1): 42–54. PubMed Abstract | Publisher Full Text
24. Riu E, Monśo E, Marin A, et al.: Occupational risk factors for rhinitis in greenhouse flower and ornamental plant growers. Am J Rhinol. 2008; 22(4): 361–364. PubMed Abstract | Publisher Full Text
25. Quillen DM, Feller DB: Diagnosing Rhinitis: Allergic vs. Nonallergic. Am Fam Physician. 2006; 73(9): 1583–1590. PubMed Abstract
26. Slager RE, Poole JA, LeVan TD, et al.: Rhinitis associated with pesticide exposure among commercial pesticide applicators in the Agricultural Health Study. Occup Environ Med. 2009; 66(11): 718–724. PubMed Abstract | Publisher Full Text | Free Full Text
27. Ye M, Beach J, Martin JW, et al.: Occupational pesticide exposures and respiratory health. Int J Environ Res Public Health. 2013; 10(12): 6442–6471. PubMed Abstract | Publisher Full Text | Free Full Text
28. Senthilselvan A, McDuffie HH, Dosman JA: Association of asthma with use of pesticides: Results of a cross-sectional survey of farmers. Am Rev Respir Dis. 1992; 146(4): 884–887. PubMed Abstract | Publisher Full Text
29. Tarmure S, Alexescu TG, Orasan O, et al.: Influence of pesticides on respiratory pathology – A literature review. Ann Agric Environ Med. 2020; 27(2): 194–200. PubMed Abstract | Publisher Full Text
30. Carey JL, Dunn C, Gaspari RJ: Central respiratory failure during acute organophosphate poisoning. Respir Physiol Neurobiol. 2013; 189(2): 403–410. PubMed Abstract | Publisher Full Text
31. Pope JV, Skurky-Thomas M, Rosen C: Toxicity, organochlorine pesticides. In: Medscape Education. 1994. Accessed April 1, 2021. Reference Source
32. Doust E, Ayres JG, Devereux G, et al.: Is pesticide exposure a cause of obstructive airways disease? Eur Respir Rev. 2014; 23(132): 180–192. PubMed Abstract | Publisher Full Text
33. Shaffo FC, Grodzki AC, Fryer AD, et al.: Mechanisms of organophosphorus pesticide toxicity in the context of airway hyperreactivity and asthma. Am J Physiol Lung Cell Mol Physiol. 2018; 315(4): L485–L501. PubMed Abstract | Publisher Full Text | Free Full Text
34. Canal-Raffin M, L’Azou B, Jorly J, et al.: Cytotoxicity of folpet fungicide on human bronchial epithelial cells. Toxicology. 2008; 249(2-3): 160–166. PubMed Abstract | Publisher Full Text
35. Peillex C, Pelletier M: The impact and toxicity of glyphosate and glyphosate-based herbicides on health and immunity. J Immunotoxicol. 2020; 17(1): 163–174. PubMed Abstract | Publisher Full Text
36. Rossi RC, Mello FDA, Quinallia G, et al.: Evaluation of nasal and pulmonary epithelium of mice submitted to acute exposure to herbicide 2,4 dichlorophenoxyacetic acid. In: European Respiratory Journal. European Respiratory Society (ERS); 2018; 52: PA1193. Publisher Full Text
37. Hoppin JA, Yucel F, Dosemeci M, et al.: Accuracy of self-reported pesticide use duration information from licensed pesticide applicators in the Agricultural Health Study. J Expo Anal Environ Epidemiol. 2002; 12(5): 313–318. PubMed Abstract | Publisher Full Text
38. Kesmodel US: Information bias in epidemiological studies with a special focus on obstetrics and gynecology. Acta Obstet Gynecol Scand. 2018; 97(4): 417–423. PubMed Abstract | Publisher Full Text

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Version 3

VERSION 3 PUBLISHED 15 Jun 2021

Author details Author details

¹ Faculty of Nursing, Naresuan University, Phitsanulok, 65000, Thailand
² Faculty of Agriculture, Natural Resources and Environment, Naresuan University, Phitsanulok, 65000, Thailand

Yuwayong Juntarawijit
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Supervision, Validation, Writing – Original Draft Preparation

Competing interests

No competing interests were disclosed.

Grant information

National Science Research and Innovation Fund (NSRF) of Thailand
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (3)

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Revised

Published: 05 Dec 2023, 10:474

https://doi.org/10.12688/f1000research.53257.3

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Published: 26 May 2023, 10:474

https://doi.org/10.12688/f1000research.53257.2

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Published: 15 Jun 2021, 10:474

https://doi.org/10.12688/f1000research.53257.1

© 2021 Juntarawijit Y and Juntarawijit C. 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 work is properly cited.

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Juntarawijit Y and Juntarawijit C. Pesticide exposure and rhinitis: A cross-sectional study among farmers in Pitsanulok, Thailand [version 1; peer review: awaiting peer review]. F1000Research 2021, 10:474 (https://doi.org/10.12688/f1000research.53257.1)

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Version 1 15 Jun 21

Bente Elisabeth Moen, University of Bergen, Bergen, Norway
Patricia Segura-Medina, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Calzada de Tlalpan 4502, CP, Mexico

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02 Sep 2024 | for Version 3

Bente Elisabeth Moen, University of Bergen, Bergen, Norway

16 Views Cite this report Responses(0)

Approved

The authors have revised the manuscript nicely.

However, it is recommended to delete the second sentence of the Conclusions in the Abstract - it is difficult to understand and it is not in the conclusion at the end of the discussion.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Occupational health and neurotoxicology

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21 Nov 2023 | for Version 2

Patricia Segura-Medina, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Calzada de Tlalpan 4502, CP, Mexico

7 Views Cite this report Responses(1)

Approved

Is an Interesting study large based on a cross-sectional study aimed to determine the association between pesticide use and rhinitis prevalence among farmers.

Data were obtained form data bases of pesticide use and correlated with rhinitis reported cases by interview questionnaire. Data from 9,649 participants were included in the analysis. The association between pesticide exposure and rhinitis was determined by multiple variable logistic regression, adjusted for potential confounding factors.

Results are interesting, as well as the associations founded is a Local study that can be applied for other latitudes.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Asthma, COPD, Air pollution and health effects, Organophosphates and Asthma, Respiratory Toxicology

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Reviewer Report

12 Views

13 Oct 2023 | for Version 2

Bente Elisabeth Moen, University of Bergen, Bergen, Norway

12 Views Cite this report Responses(1)

Approved With Reservations

This is an interesting study about rhinitis and pesticide exposure. It is a cross-sectional study among 9 649 Thailand farmers, interviewed about rhinitis and pesticide exposure. Regression analyses show associations between the exposure to different pesticides and the rhinitis.

The manuscript is well written and easy to understand. However, there are several methodological challenges in the study:

There are no clear hypotheses in the study. Pesticides consist of many substances, and they are from different chemical classes. There is no logic in all of these to be causing rhinitis. Rhinitis might be caused by allergens or irritants, and the authors should have commented on this in the introduction and tried to make specific hypotheses related to specific pesticides. When this is not done, it should be added as a discussion point in the Discussion.
The selection of workers is not totally clear. How were the farmers addressed? How did the researchers meet them, and how was the circumstances when the interviews were conducted? Did they answer in a group of people, or in rooms where no one heard the answer. At work or at home? This should be described and the influence of the situation should be discussed in the Discussion.

The snowball sampling must be described in detail, how did this happen?
The sample calculations described must have references to where the figures came from.
The interview was based on a questionnaire that originally was French, and the different cultures in France and Thailand should be mentioned as a challenge in the Discussion.
Workers seldom know a lot about pesticides they used. How could the workers know exactly which pesticides they had been exposed to? This part of the interview should have been described better.
In the Discussion, the authors must add text on discussion of 2 important weaknesses:

1. Common method bias: This study collected data on the outcome rhinitis at the same time as exposure information (if understood correctly, it is not totally clear described). This might cause a bias, and make workers report what they think is the situation, more than what is true.

2. Multiple testing. Very many statistical tests have been performed, and this makes the likelihood of having significant results high. Testing should have been done with corrections, adjusting p-values derived from these tests, to avoid false positive results.

At least this should be clearly addressed in the discussion.
When all these methodological weaknesses exist in a study, the authors need to revise the conclusions. The results are far from clear due to these weaknesses.

At the start of the discussion, the text is: The study found a strong association between the history of pesticide exposure and the prevalence of rhinitis.

This must be changed to: The study shows an association between the history of pesticide exposure and rhinitis. However, the results must be interpreted with caution due to several methodological weaknesses.

In conclusion: - remove the word ‘ strongly’ in the first sentence. The next sentence should be deleted: The study was the first to confirm that most individual pesticides from various groups are associated with rhinitis, especially the NAR type.

These details are not likely to be true.
The discussion should add more about why it is important to study rhinitis. This symptom/disease might be the start of a more serious health problem, asthma for instance, and by observing the existence of rhinitis, one can prevent more serious health problems. This is the most important message from this study and must be highlighted and not forgotten.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Occupational health and neurotoxicology

Respond to this report

Responses (1)

Author Response

13 Apr 2024

Chudchawal Juntarawijit, Faculty of Agriculture, Natural Resources and Environment, Naresuan University, Phitsanulok, 65000, Thailand

Comments and suggestions

Comment:
This is an interesting study about rhinitis and pesticide exposure. It is a cross-sectional study among 9 649 Thailand farmers, interviewed about rhinitis and pesticide exposure. Regression analyses show associations between the exposure to different pesticides and the rhinitis.

The manuscript is well written and easy to understand. However, there are several methodological challenges in the study:

There are no clear hypotheses in the study. Pesticides consist of many substances, and they are from different chemical classes. There is no logic in all of these to be causing rhinitis. Rhinitis might be caused by allergens or irritants, and the authors should have commented on this in the introduction and tried to make specific hypotheses related to specific pesticides. When this is not done, it should be added as a discussion point in the Discussion.

Response:
We appreciate your thoughtful comments and suggestions. They are very useful, and we hope we do enough to address the issues and to improve the paper’s quality.

Yes, I agree that rhinitis might be caused by any allergens or irritants, and there is no logic to believe that all pesticides will equally affect rhinitis. As you know, with limited literature, there was not enough data to hypothesize on the association between specific pesticide and rhinitis. Therefore, our main objective was to affirm the results of previous studies and to explore some more specific potential predictive pesticides. Another point was that the effects of allergens and irritants as potential confounders has been further discussed, and more information has been added to the Introduction as suggested.

Comment:
The selection of workers is not totally clear. How were the farmers addressed? How did the researchers meet them, and how were the circumstances when the interviews were conducted? Did they answer in a group of people, or in rooms where no one heard the answer. At work or at home? This should be described, and the influence of the situation should be discussed in the Discussion.

The snowball sampling must be described in detail, how did this happen?

Response:
In this study, we use village health volunteers (VHV) to collect the data. The VHVs work as public health officers and they normally know every household in their community. The process begins with the VHV contacting the target household and visiting the household to conduct the interview. After finishing, they moved on to the next house and repeated this process until the target number was reached. We did not allow group interviews so there would not be a problem where interviewees influence each other. Sorry for not being aware of the issue and not clearly presenting the information. More information on sampling has been added in the Methods section. Since a large number of villagers participated in the study, so there were no concerns about the representation of the group.

Comment:
The sample calculations described must have references to where the figures came from.

Response:
The relevant references have been added.

Comment:
The interview was based on a questionnaire that originally was French, and the different cultures in France and Thailand should be mentioned as a challenge in the Discussion.

Response:
Although, the original was in French, most of the questions are simple yes or no questions, and asking a common symptom such as, sneezing, runny, and blocked nose. The statement has been added to the discussion as suggested.

Comment:
Workers seldom know a lot about pesticides they use. How could the workers know exactly which pesticides they had been exposed to? This part of the interview should have been described better.

Response:
Thank you for mentioning the issue. That was exactly what happened and we tried to minimize the problem by including all the names of each specific pesticide, especially commercial names in the question and read all of them to the workers when interviewed. We expected that this action could minimize recall bias to some extent. The information has been added to the Methods.

Comment:
In the Discussion, the authors must add text on discussion of 2 important weaknesses:

1. Common method bias: This study collected data on the outcome of rhinitis at the same time as exposure to the information (if understood correctly, it is not totally clear as described). This might cause a bias, and make workers report what they think is the situation, more than what is true.

Response:
Thank you for the insightful suggestions and the two biases have been acknowledged in the study limitations.
In this study, data on pesticides used in the past and the data on rhinitis were collected at the same visit and using the same questionnaire method for self-report data collection. This could cause common method bias and affect the validity of the association of the two variables. To minimize the bias, first we use a face-to-face interview instead of a self-administer questionnaire. The survey questionnaire was designed so that exposure information was collected before some health effects and rhinitis. In addition, as the information is rather new, the workers were unlikely to be aware of the relationship between pesticide exposure and rhinitis.

Comment:
2. Multiple testing. Very many statistical tests have been performed, and this makes the likelihood of having significant results high. Testing should have been done with corrections, adjusting p-values derived from these tests, to avoid false positive results.

At least this should be clearly addressed in the discussion.

Response:
Yes, we agree that the results might subject to the problem of multiple testing as several individual pesticides were tested against the disease. To acknowledge it, a statement has been added to the study limitations.

Comment:
When all these methodological weaknesses exist in a study, the authors need to revise the conclusions. The results are far from clear due to these weaknesses.

At the start of the discussion, the text is: The study found a strong association between the history of pesticide exposure and the prevalence of rhinitis.

This must be changed to: The study shows an association between the history of pesticide exposure and rhinitis. However, the results must be interpreted with caution due to several methodological weaknesses.

In conclusion: - remove the word ‘strongly’ in the first sentence. The next sentence should be deleted: The study was the first to confirm that most individual pesticides from various groups are associated with rhinitis, especially the NAR type.

These details are not likely to be true.

Response:
Thank you very much for your kind suggestions. All of them are accepted and the statements have been revised as suggested.

Comment:
The discussion should add more about why it is important to study rhinitis. This symptom/disease might be the start of a more serious health problem such as asthma, and by observing the existence of rhinitis, one can prevent more serious health problems. This is the most important message from this study and must be highlighted and not forgotten.

Response:
More information on the study's strength and importance of study rhinitis have been added to the discussion.

View more View less

Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

[1] 1. deShazo RD: Allergic rhinitis: Clinical manifestations, epidemiology, and diagnosis - UpToDate. UpToDate. 2021; Published online 2020: 1–30. Accessed February 8, 2021. Reference Source

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[3] 3. Poddighe D, Gelardi M, Licari A, et al.: Non-allergic rhinitis in children: Epidemiological aspects, pathological features, diagnostic methodology and clinical management. World J Methodol. 2016; 6(4): 200. PubMed Abstract | Publisher Full Text | Free Full Text

[4] 4. Kushnir NM, Kaliner MA, Scarupa MD: In-depth review of allergic rhinitis. World Allergy Organization Web site. 2018; Published 2015. Accessed November 13, 2018. Reference Source

[5] 5. Kong X, Zhang Y, Sun Y, et al.: Household Fuels for Cooking and Allergies of Preschool Children in Tianjin, China: A Cross-Sectional Study. In: Procedia Eng. 2015; 121: 446–449. Publisher Full Text

[6] 6. Bachert C: Persistent rhinitis - Allergic or nonallergic? In: Allergy. 2004; 59: 11–15. PubMed Abstract | Publisher Full Text

[7] 7. Mamane A, Baldi I, Tessier JF, et al.: Occupational exposure to pesticides and respiratory health. Eur Respir Rev. 2015; 24(136): 306–319. PubMed Abstract | Publisher Full Text

[8] 8. Schyllert C, Rönmark E, Andersson M, et al.: Occupational exposure to chemicals drives the increased risk of asthma and rhinitis observed for exposure to vapours, gas, dust and fumes: a cross-sectional population-based study. Occup Environ Med. 2016; 73(10): 663–669. PubMed Abstract | Publisher Full Text

[9] 9. Chatzi L, Alegakis A, Tzanakis N, et al.: Association of allergic rhinitis with pesticide use among grape farmers in Crete, Greece. Occup Environ Med. 2007; 64(6): 417–421. PubMed Abstract | Publisher Full Text | Free Full Text

[10] 10. Raherison C, Baldi I, Pouquet M, et al.: Pesticides Exposure by Air in Vineyard Rural Area and Respiratory Health in Children: A pilot study. Environ Res. 2019; 169: 189–195. PubMed Abstract | Publisher Full Text

[11] 11. Riu E, Monśo E, Marin A, et al.: Occupational risk factors for rhinitis in greenhouse flower and ornamental plant growers. Am J Rhinol. 2008; 22(4): 361–364. PubMed Abstract | Publisher Full Text

[12] 12. Patel O, Syamlal G, Henneberger PK, et al.: Pesticide use, allergic rhinitis, and asthma among US farm operators. J Agromedicine. 2018; 23(4): 327–335. PubMed Abstract | Publisher Full Text | Free Full Text

[13] 13. Koureas M, Rachiotis G, Tsakalof A, et al.: Increased frequency of rheumatoid arthritis and allergic rhinitis among pesticide sprayers and associations with pesticide use. Int J Environ Res Public Health. 2017; 14(8): 865. PubMed Abstract | Publisher Full Text | Free Full Text

[14] 14. Slager RE, Poole JA, LeVan TD, et al.: Rhinitis associated with pesticide exposure among commercial pesticide applicators in the Agricultural Health Study. Occup Environ Med. 2009; 66(11): 718–724. PubMed Abstract | Publisher Full Text | Free Full Text

[15] 15. Jeebhay MF, Dalvie MA, Ndlovu V: Pesticides and the airways - a review of the literature : allergies in the workplace Current Allergy & Clinical Immunology. Curr Allergy Clin Immunol. 2011; 24(4). Accessed March 29, 2021. Reference Source

[16] 16. Phitsanulok Provincial Agriculture and Cooperatives Office: Basic Agricultural Data, Phitsanulok (In Thai). 2021. Reference Source

[17] 17. Thailand Information Center: Phitsanulok province. Accessed February 22, 2021. Reference Source

[18] 18. Juntarawijit C, Juntarawijit Y: Questionnaire-pesticide and rhinitis-Thailand. Figshare. Dataset. 2021. http://www.doi.org/10.6084/m9.figshare.14524335.v1

[19] 19. Annesi-Maesano I, Didier A, Klossek M, et al.: The score for allergic rhinitis (SFAR): A simple and valid assessment method in population studies. Allergy. 2002; 57(2): 107–114. PubMed Abstract | Publisher Full Text

[20] 20. Juntarawijit C, Juntarawijit Y: Association between diabetes and pesticides: A case-control study among Thai farmers. Environ Health Prev Med. 2018; 23(1): 3. PubMed Abstract | Publisher Full Text | Free Full Text

[21] 21. Slager RE, Simpson SL, Levan TD, et al.: Rhinitis associated with pesticide use among private pesticide applicators in the agricultural health study. J Toxicol Environ Health A. 2010; 73(20): 1382–1393. PubMed Abstract | Publisher Full Text | Free Full Text

[22] 22. Juntarawijit C, Juntarawijit Y: Dataset for study on pesticide exposure and rhinitis in Phitsanulok Thailand. figshare. Dataset. 2021. http://www.doi.org/10.6084/m9.figshare.14524326

[23] 23. Gomez MI, Hwang SA, Lin S, et al.: Prevalence and predictors of respiratory symptoms among New York farmers and farm residents. Am J Ind Med. 2004; 46(1): 42–54. PubMed Abstract | Publisher Full Text

[24] 24. Riu E, Monśo E, Marin A, et al.: Occupational risk factors for rhinitis in greenhouse flower and ornamental plant growers. Am J Rhinol. 2008; 22(4): 361–364. PubMed Abstract | Publisher Full Text

[25] 25. Quillen DM, Feller DB: Diagnosing Rhinitis: Allergic vs. Nonallergic. Am Fam Physician. 2006; 73(9): 1583–1590. PubMed Abstract

[26] 26. Slager RE, Poole JA, LeVan TD, et al.: Rhinitis associated with pesticide exposure among commercial pesticide applicators in the Agricultural Health Study. Occup Environ Med. 2009; 66(11): 718–724. PubMed Abstract | Publisher Full Text | Free Full Text

[27] 27. Ye M, Beach J, Martin JW, et al.: Occupational pesticide exposures and respiratory health. Int J Environ Res Public Health. 2013; 10(12): 6442–6471. PubMed Abstract | Publisher Full Text | Free Full Text

[28] 28. Senthilselvan A, McDuffie HH, Dosman JA: Association of asthma with use of pesticides: Results of a cross-sectional survey of farmers. Am Rev Respir Dis. 1992; 146(4): 884–887. PubMed Abstract | Publisher Full Text

[29] 29. Tarmure S, Alexescu TG, Orasan O, et al.: Influence of pesticides on respiratory pathology – A literature review. Ann Agric Environ Med. 2020; 27(2): 194–200. PubMed Abstract | Publisher Full Text

[30] 30. Carey JL, Dunn C, Gaspari RJ: Central respiratory failure during acute organophosphate poisoning. Respir Physiol Neurobiol. 2013; 189(2): 403–410. PubMed Abstract | Publisher Full Text

[31] 31. Pope JV, Skurky-Thomas M, Rosen C: Toxicity, organochlorine pesticides. In: Medscape Education. 1994. Accessed April 1, 2021. Reference Source

[32] 32. Doust E, Ayres JG, Devereux G, et al.: Is pesticide exposure a cause of obstructive airways disease? Eur Respir Rev. 2014; 23(132): 180–192. PubMed Abstract | Publisher Full Text

[33] 33. Shaffo FC, Grodzki AC, Fryer AD, et al.: Mechanisms of organophosphorus pesticide toxicity in the context of airway hyperreactivity and asthma. Am J Physiol Lung Cell Mol Physiol. 2018; 315(4): L485–L501. PubMed Abstract | Publisher Full Text | Free Full Text

[34] 34. Canal-Raffin M, L’Azou B, Jorly J, et al.: Cytotoxicity of folpet fungicide on human bronchial epithelial cells. Toxicology. 2008; 249(2-3): 160–166. PubMed Abstract | Publisher Full Text

[35] 35. Peillex C, Pelletier M: The impact and toxicity of glyphosate and glyphosate-based herbicides on health and immunity. J Immunotoxicol. 2020; 17(1): 163–174. PubMed Abstract | Publisher Full Text

[36] 36. Rossi RC, Mello FDA, Quinallia G, et al.: Evaluation of nasal and pulmonary epithelium of mice submitted to acute exposure to herbicide 2,4 dichlorophenoxyacetic acid. In: European Respiratory Journal. European Respiratory Society (ERS); 2018; 52: PA1193. Publisher Full Text

[37] 37. Hoppin JA, Yucel F, Dosemeci M, et al.: Accuracy of self-reported pesticide use duration information from licensed pesticide applicators in the Agricultural Health Study. J Expo Anal Environ Epidemiol. 2002; 12(5): 313–318. PubMed Abstract | Publisher Full Text

[38] 38. Kesmodel US: Information bias in epidemiological studies with a special focus on obstetrics and gynecology. Acta Obstet Gynecol Scand. 2018; 97(4): 417–423. PubMed Abstract | Publisher Full Text

Pesticide exposure and rhinitis: A cross-sectional study among farmers in Pitsanulok, Thailand

Abstract

Keywords

Background

Methods

Study design and setting

Study participants and sampling procedure

Study questionnaire and data collection

Statistical analysis

Ethical considerations

Results

Table 1. Demographic data of study participants.

Table 2. Prevalence of rhinitis and its characteristics.

Table 3. Association between demographic data and rhinitis prevalence.

Table 4. The associations between type of pesticides use and rhinitis prevalence.

Table 5. The associations between individual pesticide and rhinitis prevalence.

Discussion

Conclusion

Data availability

Underlying data

Extended data

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