Pesticide exposure and rhinitis: A cross-sectional study among farmers in Pitsanulok, Thailand
Yuwayong Juntarawijit, Chudchawal Juntarawijit
TL;DR
This study found that farmers in Thailand who use pesticides are more likely to develop rhinitis, a nasal condition, with many types of pesticides showing a strong link.
Contribution
The study provides new evidence linking specific pesticide types and individual pesticides to rhinitis in farmers in Thailand.
Findings
Rhinitis prevalence was 6.3% among farmers in Phitsanulok, Thailand.
All pesticide types (insecticides, herbicides, etc.) were significantly associated with rhinitis.
33 out of 35 individual pesticides showed a significant association with rhinitis.
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…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
| Characteristics | Number (%),
|
|---|---|
| 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) |
| N (%),
| |
|---|---|
| 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
| 39 (6.7) |
| Not rhinitis | Rhinitis | P-value
| |
|---|---|---|---|
| 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) | |
| Pesticide | Not rhinitis | Rhinitis | OR (crude) | OR (adjusted)
|
|---|---|---|---|---|
| Any pesticide | ||||
| No | 463 (96.5) | 17 (3.5) | 1.0 | 1.0 |
| Yes | 8575 (93.5) | 592 (6.5) |
|
|
| Insecticide | ||||
| No | 1996 (98.9) | 22 (1.1) | 1.0 | 1.0 |
| Yes | 7044 (92.3) | 587 (7.7) |
|
|
| Q1 | 1837 (95.1) | 94 (4.9) | 1.0 | 1.0 |
| Q2 | 2009 (93.7) | 136 (6.3) |
|
|
| Q3 | 2048 (90.5) | 215(9.5) |
|
|
| Q4 | 1150 (89.0) | 142 (11.0) |
|
|
| P for trend | 0.206 | |||
| Herbicide | ||||
| No | 1254 (98.4) | 20 (1.6) | 1.0 | 1.0 |
| Yes | 7786 (93.0) | 586 (7.0) |
|
|
| Q1 | 2227 (95.2) | 112 (4.8) | 1.0 | 1.0 |
| Q2 | 1864 (93.9) | 122 (6.1) |
|
|
| Q3 | 2555 (92.0) | 221 (8.0) |
|
|
| Q4 | 1140 (89.5) | 134 (10.5) |
|
|
| P for trend | 0.095 | |||
| Fungicide | ||||
| No | 5107 (95.2) | 256 (4.8) | 1.0 | 1.0 |
| Yes | 3933 (91.8) | 353 (8.2) |
|
|
| 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) |
|
|
| Q4 | 959 (90.0) | 106 (10.0) |
|
|
| P for trend | 0.391 | |||
| Rodenticide | ||||
| No | 7371 (95.1) | 379 (4.9) | 1.0 | 1.0 |
| Yes | 1669 (87.9) | 230 (12.1) |
|
|
| Q1 | 514 (88.9) | 64 (11.1) | 1.0 | 1.0 |
| Q2 | 319 (83.5) | 63 (16.5) |
| 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) |
|
|
| 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 | |||
| Not
| Rhinitis | OR (Crude) | OR (Adjusted)
| |
|---|---|---|---|---|
|
| ||||
|
| ||||
| No | 1104 (97.3) | 31 (2.7) | 1.0 | 1.0 |
| Yes | 7936 (93.2) | 578 (6.8) |
|
|
| 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) |
|
|
| Q4 | 1751 (92.7) | 137 (7.3) |
|
|
| P for trend | 0.708 | |||
|
| ||||
| No | 3503 (95.6) | 160 (4.4) | 1.0 | 1.0 |
| Yes | 5537 (92.5) | 449 (7.5) |
|
|
| 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 | |||
|
| ||||
| No | 4413 (94.9) | 239 (5.1) | 1.0 | 1.0 |
| Yes | 4627 (92.6) | 370 (7.4) |
|
|
| 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) |
|
| ||||
| No | 6868 (94.8) | 374 (5.2) | 1.0 | 1.0 |
| Yes | 2172 (90.2) | 235 (9.8) |
|
|
| 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) |
|
| ||||
| No | 8222 (93.9) | 536 (6.1) | 1.0 | 1.0 |
| Yes | 818 (91.8) | 73 (8.2) |
|
|
| 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) |
| 1.73 (0.81-3.67) |
| P for trend | 0.492 | |||
|
| ||||
| No | 8931 (93.8) | 593 (6.2) | 1.0 | 1.0 |
| Yes | 109 (87.2) | 16 (12.8) |
|
|
| 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 | |||
|
| ||||
|
| ||||
| 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) |
|
|
| Q4 | 1153 (96.1) | 47 (3.9) | 0.66 (0.47-0.95) | 0.723 (0.50-1.04) |
|
| ||||
| No | 7015 (94.5) | 408 (5.5) | 1.0 | 1.0 |
| Yes | 2025 (91.0) | 201 (9.0) |
|
|
| 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 | |||
|
| ||||
| No | 8017 (95.3) | 391 (4.7) | 1.0 | 1.0 |
| Yes | 1023 (82.4) | 218 (17.6) |
|
|
| 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) |
|
|
| Q4 | 221 (73.2) | 81 (26.8) |
|
|
| P for trend | 0.157 | |||
|
| ||||
| No | 8658 (94.0) | 553 (6.0) | 1.0 | 1.0 |
| Yes | 382 (87.2) | 56 (12.8) |
|
|
| 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) |
|
| Q4 | 0 (0.0) | 7 (100.0) | NA | NA |
|
| ||||
| No | 8856 (93.9) | 575 (6.1) | 1.0 | 1.0 |
| Yes | 184 (84.4) | 34 (15.6) |
|
|
| 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) |
| 2.14 (0.55-8.30) |
| P for trend | 0.169 | |||
|
| ||||
| No | 8937 (93.8) | 591 (6.2) | 1.0 | 1.0 |
| Yes | 103 (85.1) | 18 (14.9) |
|
|
| Low | 64 (91.4) | 6 (8.6) | 1.0 | 1.0 |
| High | 39 (76.5) | 12 (23.5) |
| 2.70 (0.64-11.32) |
|
| ||||
| No | 8868 (93.8) | 587 (6.2) | 1.0 | 1.0 |
| Yes | 172 (88.7) | 22 (11.3) |
|
|
| 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 |
|
| ||||
| No | 8951 (93.8) | 591 (6.2) | 1.0 | 1.0 |
| Yes | 89 (83.2) | 18 916.8) |
|
|
| Low | 52 (91.2) | 5 (8.8) | 1.0 | 1.0 |
| High | 36 (73.5) | 13 (26.5) |
| 5.87 (0.90-38.10) |
|
| ||||
| No | 8837 (94.2) | 545 (5.8) | 1.0 | 1.0 |
| Yes | 203 (76.0) | 64 (24.0) |
|
|
| 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 | |||
|
| ||||
| No | 8683 (93.9) | 561 (6.1) | 1.0 | 1.0 |
| Yes | 357 (88.1) | 48 (11.9) |
|
|
| 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 | |||
|
| ||||
| 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) |
|
| ||||
|
| ||||
| No | 8415 (94.3) | 511 (5.7) | 1.0 | 1.0 |
| Yes | 625 (86.4) | 98 (13.6) |
|
|
| 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) |
|
|
| P for trend | 0.070 | |||
|
| ||||
| No | 8581 (94.1) | 542 (5.9) | 1.0 | 1.0 |
| Yes | 459 (87.3) | 67 (12.7) |
|
|
| 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 | |||
|
| ||||
| No | 7945 (94.4) | 467 (5.6) | 1.0 | 1.0 |
| Yes | 1095 (88.5) | 142 (11.5) |
|
|
| 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 | |||
|
| ||||
| No | 8336 (94.4) | 497 (5.6) | 1.0 | 1.0 |
| Yes | 704 (86.3) | 112 (13.7) |
|
|
| 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 | |||
|
| ||||
|
| ||||
| No | 8083 (94.0) | 515 (6.0) | 1.0 | 1.0 |
| Yes | 957 (91.1) | 94 (8.9) |
|
|
| 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 | |||
|
| ||||
|
| ||||
| No | 7814 (94.6) | 447 (5.4) | 1.0 | 1.0 |
| Yes | 1226 (88.3) | 162 (11.7) |
|
|
| 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 | |||
|
| ||||
| No | 8979 (93.9) | 585 (6.1) | 1.0 | 1.0 |
| Yes | 61 (71.8) | 24 (28.2) |
|
|
| Low | 46 (90.2) | 5 (9.8) | 1.0 | 1.0 |
| High | 15 (44.1) | 19 (55.9) |
|
|
|
| ||||
| No | 8992 (93.9) | 589 (6.1) | 1.0 | 1.0 |
| Yes | 48 (70.6) | 20 (29.4) |
|
|
| Low | 33 (89.2) | 4 (10.8) | 1.0 | 1.0 |
| High | 15 (48.4) | 16 (51.6) |
| NA |
|
| ||||
| No | 8759 (94.4) | 524 (5.6) | 1.0 | 1.0 |
| Yes | 281 (76.8) | 85 (23.2) |
|
|
| 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) |
| 1.52 (0.67-3.47) |
| P for trend | 0.372 | |||
|
| ||||
| No | 8948 (93.9) | 586 (96.2) | 1.0 | 1.0 |
| Yes | 92 (80.0) | 23 (20.0) |
|
|
| Low | 57 (87.7) | 8 (12.3) | 1.0 | 1.0 |
| High | 35 (70.0) | 15 (30.0) |
| 2.25 (0.48-10.63) |
|
| ||||
| No | 8941 (93.9) | 584 (6.1) | 1.0 | 1.0 |
| Yes | 99 (79.8) | 25 (20.2) |
|
|
| Low | 61 (91.0) | 6 (9.0) | 1.0 | 1.0 |
| High | 38 (66.7) | 19 (33.3) |
| 0.81 (0.10-6.84) |
|
| ||||
|
| ||||
| No | 8315 (93.9) | 544 (6.1) | 1.0 | 1.0 |
| Yes | 725 (91.8) | 65 (8.2) |
|
|
| 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) |
| 1.71 (0.79-3.71) |
| P for trend | 0.084 | |||
|
| ||||
| No | 8407 (94.1) | 525 (5.9) | 1.0 | 1.0 |
| Yes | 633 (88.3) | 84 (11.7) |
|
|
| 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 | |||
|
| ||||
| 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 | |||
|
| ||||
| No | 8624 (93.9) | 560 (6.1) | 1.0 | 1.0 |
| Yes | 398 (89.0) | 49 (11.0) |
|
|
| 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) |
| 2.51 (0.91-6.93) |
| P for trend | 0.301 | |||
|
| ||||
| No | 7856 (94.2) | 484 (5.8) | 1.0 | 1.0 |
| Yes | 1184 (90.5) | 125 (9.5) |
|
|
| 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 | |||
|
| ||||
| No | 8828 (93.8) | 582 (6.2) | 1.0 | 1.0 |
| Yes | 212 (88.7) | 27 (11.3) |
|
|
| 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 | |||
|
| ||||
| No | 8916 (93.8) | 590 (6.2) | 1.0 | 1.0 |
| Yes | 124 (86.7) | 19 (13.3) |
|
|
| Low | 72 (92.3) | 6 (7.7) | 1.0 | 1.0 |
| High | 52 (80.0) | 13 (20.0) |
| 1.28 (0.30-5.54) |
|
| ||||
| No | 8996 (93.8) | 591 (6.2) | 1.0 | 1.0 |
| Yes | 44 (71.0) | 18 (29.0) |
|
|
| 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) |
| 0.62 (0-360.35) |
- —Thailand Science Research and Innovation (SRI)
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPesticide Exposure and Toxicity · Allergic Rhinitis and Sensitization · Air Quality and Health Impacts
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 disease ^ 1 ^. 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 sneezing ^ 3 ^. 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 ingestion ^ 4 ^, and cooking fumes ^ 5 ^. 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, etc ^ 6 ^. Approximately 43% of all rhinitis cases are AR, and 23% are NAR, while 34% of the cases are a mixture of both ^ 4 ^.
Studies found pesticide exposure to increase the risk of several respiratory problems, e.g., asthma, chronic bronchitis ^ 7 ^, and rhinitis ^ 8 ^. One study among grape farmers in Greece reported a higher prevalence of AR among those who used pesticides ^ 9 ^. 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 asthma ^ 12 ^.
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 allergens ^ 15 ^.
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 obtain ^ 19 ^. 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 study ^ 20 ^. 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 data ^ 22 ^). 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.
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).
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).
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).
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 York ^ 23 ^, and 31% of greenhouse flower and ornamental plant growers in Europe ^ 24 ^. Though it is difficult to differentiate between AR and NAR ^ 4 ^, 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 symptoms ^ 25 ^.
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, metolachlor ^ 26 ^, diazinon, carbofuran, malathion, and carbaryl ^ 21 ^. 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 rhinitis ^ 27 ^. Research on carbamate insecticides has found this type of insecticides to increase the risk of asthma, a disease closely related to rhinitis ^ 28 ^. 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 tightness ^ 27 ^.
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 airway ^ 29 ^. In addition, pesticides can also modify inflammatory responses of other allergens ^ 30 ^. Pesticides and the solvent composition of the mixture that contains them are lipid-soluble, therefore could irritate the nose and throat when inhaled ^ 31 ^. OPs, and carbamate insecticides are cholinesterase inhibitors that can inhibit acetylcholinesterase and cause more secretion of mucus in the nose and airway system ^ 32 ^. Studies among the asthmatic group, found OP exposure to induce bronchospasm and airway hyperreactivity ^ 33 ^. In vitro study showed the cytotoxic effects of fungicides on bronchial epithelial cells ^ 34 ^. Evidence from both animal and human studies showed that herbicides can cause airway inflammation ^ 35 ^. A recent study found herbicide 2,4-D to increase the mast cells in the nasal epithelium ^ 36 ^.
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 exposures ^ 21 ^. 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 reproducible ^ 37 ^. 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 association ^ 38 ^. 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.14524326 ^ 22 ^.
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.v1 ^ 18 ^.
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).
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1de Shazo RD : Allergic rhinitis: Clinical manifestations, epidemiology, and diagnosis - Up To Date. Up To Date. 2021; Published online 2020: 1–30. Accessed February 8, 2021. Reference Source
- 2Bunnag C Jareoncharsri P Voraprayoon S : Epidemiology of rhinitis in Thais: characteristics and risk factors. Asian Pac J Allergy Immunol. 2000;18(1):1–7. 12546051 · pubmed ↗
- 3Poddighe D Gelardi M Licari A : Non-allergic rhinitis in children: Epidemiological aspects, pathological features, diagnostic methodology and clinical management. World J Methodol. 2016;6(4):200. 10.5662/wjm.v 6.i 4.200 28074172 PMC 5183989 · doi ↗ · pubmed ↗
- 4Kushnir 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
- 5Kong X Zhang Y Sun Y : Household Fuels for Cooking and Allergies of Preschool Children in Tianjin, China: A Cross-Sectional Study.In: Procedia Eng. 2015;121:446–449. 10.1016/j.proeng.2015.08.1091 · doi ↗
- 6Bachert C : Persistent rhinitis - Allergic or nonallergic?In: Allergy. 2004;59:11–15. 10.1111/j.0108-1675.2004.00389.x 14984551 · doi ↗ · pubmed ↗
- 7Mamane A Baldi I Tessier JF : Occupational exposure to pesticides and respiratory health. Eur Respir Rev. 2015;24(136):306–319. 10.1183/16000617.00006014 26028642 PMC 9487813 · doi ↗ · pubmed ↗
- 8Schyllert C Rönmark E Andersson M : 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. 10.1136/oemed-2016-103595 27466615 · doi ↗ · pubmed ↗