| Literature DB >> 30835380 |
Ratchaneewan Sinitkul1,2, Chathaya Wongrathanandha3, Somkiat Sirirattanapruk4, Adisak Plitponkarnpim3, Richard J Maude2,5,6, Emma L Marczylo7.
Abstract
BACKGROUND: There is increasing evidence of a link between environmental pollution and preventable diseases in developing countries, including Thailand. Economic development has generated several types of pollution that can affect population health. While these environmental health effects can be observed throughout life, pregnant women and children represent particularly vulnerable and sensitive groups.Entities:
Mesh:
Substances:
Year: 2018 PMID: 30835380 PMCID: PMC6748291 DOI: 10.29024/aogh.2301
Source DB: PubMed Journal: Ann Glob Health ISSN: 2214-9996 Impact factor: 2.462
Main concept and alternative terms of PICO search strategy.
| Main concept | Alternative terms | |
|---|---|---|
| Thailand | ||
| Kid?, Pregnan*, “in utero”, newborn, neonat*, infan*, toddler, preschool, young, teenage*, pupils, student? | ||
| Chemical, Pollution?, “Air quality”, PM, “particulate matter”, particle, VOCs, “volatile organic compounds”, benzene, “polyaromatic hydrocarbon”, PAH, allergen, mold, mould, fungi, cockroach, smok???, metal?, arsenic, lead, cadmium, mercury, plasticizer, POPs, “persistent organic pollutants”, “Polychlorinated biphenyl”, Dioxins, Furans, “Polybrominated compounds”, “Polyfluorinated compounds”, “Halogenated hydrocarbons”, pesticide?, herbicide?, fungicide?, organophosphate, organochlorine, carbamate, glyphosate, chlorpyrifos, Dichlorodiphenyltrichloroethane, phthalate, BPA, “bisphenol A”, Industr*, agricultur*, waste, Hazard???, Toxic | ||
| “Birth weight”, prematurity, “birth defects” | ||
| Weight, height, BMI, “body mass index”, obes* | ||
| Development*, autistic?, autism, attention, hyperactive, ADHD, learning, cognitive, intelligen*, IQ, aggressive, pervasi*, Behavior*, Behaviour* | ||
| atop??, inflammation, sensitization, IgE, hypersensitivity, “food allergy”, “atopic dermatitis”, “allergic dermatitis”, wheezing | ||
| “pulmonary function”, “Lung function”, “respiratory symptom?”, “respiratory infection” | ||
| Thyroid, diabetes, DM, metabolic? | ||
| Coronary, hypertension, “blood pressure”, Atheroscler* | ||
| Renal, “kidney function”, “renal function”, creatinine | ||
| “Liver diseases”, cirrhosis, jaundice | ||
| “acute poisoning”, “chronic poisoning” | ||
| Malignancy, Leukaemia, Leukemia, Lymphoma, Carcinoma | ||
| “DNA damage”, epigenetic, miRNA, Histone, Methylation, Adduct | ||
Population (P) = Thai children, Indicator (I) = Exposure (environmental chemical, metal, allergen, or air pollution), Comparison (C) = no comparison, Outcome (O) = Health outcomes. Both * and/or ? were applied to widen the search result by including various word endings and spellings (* = unlimited character number, ? = limited character number (to number of ?s)).
Figure 1The article selection process.
Epidemiological studies investigating pesticide exposure in Thai children.
| Pesticides | Authors (published year) | Type of epidemiological study | Population (age) | Sample size | Exposure assessment | Level measured | Comments |
|---|---|---|---|---|---|---|---|
| Panuwat et al. (2009) [ | Cross-sectional | Student | 207 | Urinary 18 specific pesticide metabolites | 14 metabolites of chlorpyrifos, permethrin, pyrethroids were found | 4 groups of parental occupations (farmers, merchants and trader, government and company employees, and laborers) Farmers group: significantly higher urinary pyrethroid metabolites levels | |
| Kongtip et al. (2017) [ | Birth cohort | 82 pairs | Maternal and cord blood serum glyphosate levels Interviews (questionnaire) | Median glyphosate levels in the pregnant women’s serum were 17.5 ng/mL (0.2–189.1 ng/mL), significantly higher than those in umbilical cord serum (median 0.2 (0.2–94.9 ng/mL) | Factors for glyphosate exposure were working in agriculture or living in families that work in agriculture. | ||
| Hanchenlaksh et al. (2011) [ | Cross-sectional | Preschool and school child (2–12 years) | 16 | Urinary DAP metabolites (DEP, DETP, DEDTP, DMP and DMTP) | Metabolites
Geometric mean of DAP levels was 7.6 mg/g. | Exposure for farmers’ families seems to be through transfer from the farmer to family members or contamination of the home environment | |
| Liu et al. (2015) [ | Cross-sectional | Preschool and school child (2–12 years) | 72 | Urinary DAP metabolites% Self-administered questionnaires | Metabolites
Geometric mean of DAPs levels was 3.12 μg of creatinine | Children in Thai farming | |
| Panuwat et al. (2009) [ | Cross-sectional | School child (10–15 years) | 306 | Urinary parathion metabolites (PNP, DMP, DMAP, DMTP) | Metabolites
Geometric mean of PNP, DMP, DMTP, and DMAP were 3.5 ng/mL, 1.9 ng/mL, 1.6 ng/mL and 29.9 nmol/mL, respectively | 25–60% of the PNP (metabolites of | |
| Petchuay et al. (2006) [ | Case-controlled | Preschool | 54 | Urinary DAP metabolites (DMP, DEP, DMTP, DETP, and DEDTP) | Metabolites | Farm children tended to have significantly higher DAP concentrations compared with children living outside the farming area, particularly during the dry vegetable-planting season. | |
| Rohitrattana et al. (2014) [ | Cohort | 86, 87, 51 (28 weeks pregnancy, delivery, 2 months post- partum) | Urinary DAP metabolites (DMP, DEP, DETP, DEDTP, total DEP, and DAPs) Interviewed questionnaires (agricultural activities) | Metabolites: 28 weeks pregnancy 37.7: Delivery 36.2: 2 months postpartum 38.0: | The levels of urinary OP metabolites at 28 weeks, delivery, and 2 months postpartum fluctuated depending on their pesticide exposures both at home and agricultural activities. | ||
| Kongtip et al. (2017) [ | Birth cohort | 82 pairs | Maternal and cord blood serum paraquat levels Interviews (questionnaire) | Median paraquat levels in pregnant women’s serum were <0.4 ng/mL (0.2–58.3 ng/mL) and similar to those in umbilical cord serum (median 0.2 (0.2–47.6 ng/mL) | Factors for paraquat exposure were work in agriculture or live in families that work in agriculture. | ||
| Panuwat et al. (2009) [ | Cross-sectional | Student | 207 | Urinary 18 specific pesticide metabolites | 14 metabolites of chlorpyrifos, permethrin, pyrethroids were found. | 4 groups of parental occupations (farmers, merchants and trader, government and company employees, and laborers) Farmers group: significantly higher urinary pyrethroid metabolites levels | |
| Rohitrattana et al. (2014) [ | Case-controlled | School child | 53 | Urinary PYR metabolites (ie, 3-PBA DCCA) Hand wipe samples Structured-interviews | Median urinary PYR metabolites were not significantly between both group, neither wet nor dry seasons. | Positive correlation (r = 0.40–0.46) between PYR residues collected from the hands and urinary PYR metabolites | |
| Asawasinsopon et al. (2006) [ | Cross-sectional | 39 pairs | Maternal and cord blood serum DDT metabolites | p,p’-DDE) was the highest level in both maternal and cord serum (geometric mean of 1,191 ng/g lipids in maternal serum and 742 ng/g lipids in cord serum) | Negative association between cord serum total T4 levels and DDT metabolites | ||
| Fielder et al. (2015) [ | Case-controlled | School child | 53 | Urinary metabolites of OPs (6 DAP%, TCPy) | Rice farm had significantly higher total DAP and TCPy | No significant adverse neurobehavioral effects were observed between participant groups during either the high or low pesticide use season | |
| Kongtip et al. (2017) [ | Birth cohort | 50 pairs (20–35 years of pregnant women and 5 months old of their child) | Maternal urinary DAP metabolites: DMP, DEP, DETP, DEDTP | Median adjusted urinary DAP levels of 28 weeks gestational age of pregnant women were 36.83, 15.15, 0.07, and 0.15 nmol/L for DMP, DEP, DETP, and DEDTP, respectively. | Higher total DEP and total DAP metabolite level from the 28 weeks GA of pregnant women were significantly associated with reduced cognitive and motor composite scores on the Bayley-III at five months old of their child. | ||
| Fielder et al. (2015) [ | Case-controlled | School child | 53 | Urinary metabolites of PYR (DCCA) | Rice farm had significantly higher DCCA | No significant adverse neurobehavioral effects were observed between participant groups during either the high or low pesticide use season | |
was banned in 2007; data was collected in 2006.
was banned in early 1983; data was collected in 2003–2004.
: DMP, DEP, DMT, DMDTP, DETP, and DEDTP.
Abbreviations: DAP: dialkyl phosphate, DCCA: cis/trans-2,2-(dichloro)-2-dimethylvinylcyclopropane carboxylic acid, DDT: dichlrodiphenyltrichloroethane, DEDTP: diethyl dithiophosphate, DEP: diethyl phosphate, DETP: diethyl thiophosphate, DMAP: dimethylalkylphosphate, DMDTP: dimethyldithiophosphate, DMP: dimethylphosphate, DMTP: dimethylthiophosphate, OPs: organophosphates, PNP: paranitrophenol, p,p’, DDE: 1,1-dichloro-2,2-di(4-chlorophenyl)ethylene, PYR: pyrethroid, TCPy: 3,5,6-trichloropyridinol, 3-PBA: 3-phenoxybenzoic acid.
Epidemiological studies investigating heavy metal exposure in Thai children.
| Heavy metals | Authors (published year) | Type of epidemiological study | Population (age)* | Sample size | Exposure assessment | Level measured | Comments |
|---|---|---|---|---|---|---|---|
| Chaiwonga et al. (2013) [ | Cross-sectional | School child (9–12 years and 13–15 years) | 748 | Urinary cadmium Questionnaires | All urine samples had cadmium of more than 1 μg/g creatinine (Thai general population is 0.5 μg/c Cr) with 2% had higher than 5 μg/g Cr. | The likely exposure sources was dietary (approximately 20–50% of them consume home-grown rice) | |
| Chomchai et al. (2005) [ | Case | Infant and preschool child | 296 (exposure = 114, controlled 149) | Blood lead Questionnaires | Average BLL was 5.65 ± 3.05 μg The overall prevalence of children with EBLL > 10 μg | Predictors factors of EBLL: peeling paint, eating paint chips, and geographical location Saliva was not suitable for the biomarker of lead exposure | |
| Maharachpong et al. (2006) [ | Cross | Preschool child to adolescent (4–14 years) | 319 | House dust and soil lead level spatial distribution) | Mean house dust lead was 210 mg/kg (<ATSDR concerning and a dust-lead standard for London (500 mg/kg)). | The lead content level in soil was exponentialy declined with distance from boat-repair yards (point source contamination) | |
| Mitchell et al. (2012) [ | Cross-sectional | Child | 642 | Capillary blood lead Questionnaires | 5.1% had EBLL (cBLL ≥ 10 μg/dL) with highest prevalence in children younger than 2 years. | The risk factors of EBLL were anemia (Hb < 10 g/dL), exposure to car batteries, and taking traditional medicines | |
| Neesanan et al. (2011) [ | Retrospective study | Preschool child (3–7 years) | 213 | Blood lead Interviewed the primary care providers (identify risk factors of EBLL) | Mean BLL was 7.71 ± 4.62 μg/dL (3–25 μg/dL) 26% had BLL ≥ 10 μg/dL. | Risk factors of EBLL were male gender and and source of drinking water from either tap or canal | |
| Ruangkanchanasetr et al. (1999) [ | Cross-sectional | Infant to school child | 511 | Blood lead Questionnaires | Mean BLL 5.57 ± 2.31 μg EBLL were 1–35%. | Mean BLL and prevalence of EBLL (>10 μg/dL) increased with age Risk factors of EBLL: older age group, larger family size, and male gender | |
| Swaddiwudhipong et al. (2013) [ | Cross-sectional | Child | 254 | Blood lead Questionnaires Lead environmental survey: water, vegetables grown in the area | Mean BLL was 9.8 ± 5.1 μg/dL. The overall prevalence of EBLL (≥10 μg/dL) was 43.3% | Lead contamination was found in house floor dust, drinking water kept in household containers. 50.8% of EBLL children lived in vented lead-acid batteries, while 23.3% lived in the house without vented lead-acid batteries. | |
| Swaddiwudhipong et al. (2014) [ | Cross-sectional | Child | 695 | Blood lead Questionnaires Lead environmental survey: floor dust, drinking water, metal pot | Geometric mean BLL was 9.16 μg/dL. Overall prevalence of EBLL was 47.1% | The metal pots were safe for cooking rice but might be unsafe for acidic food preparation. | |
| Thaweboon et al. (2005) [ | Cross | Preschool child | 8 | Salivary and blood lead | All children subjects had BLL > 10 μg/dL The geometric mean for BLL was found to be 24.03 µg/dl (11.80–46.60 µg/dl), lead in saliva was 5.69 µg/dl (1.82–25.28 µg/dl). | Saliva was not correlated with BLL (γ =–0.025). | |
| Untimanon et al. (2011) [ | Case-controlled | Child | 24 | Lead at on skin(wipe) Household floor and dust lead content Questionnaire-based interview | Mean lead contamination in case vs. controlled
Household floor loading: 109.9 vs. 40.1 μg/m2 Household dust content: 434.8 vs. 80.8 μg/g Hand loading: 64.4 vs. 36.2 μg/m2 Foot loading: 77.8 vs. 43.8 μg/m2 | Hand lead loading in children was higher than adults Skin lead levels were elevated in family members living in a lead-exposed worker’s house and were related to the levels of household lead contamination. | |
| Hinhumpatc et al. (2013) [ | Case-controlled | School child (5–8 years) | 60 | Arsenic in toenails, fingernails, saliva, and urine Salivary and urinary 8-OHdG (DNA damage) and | The DNA damage (salivary and urinary 8-OHdG) was increase but DNA repair capacity (hOGG1) was decreased in exposed group, | A follow-up study of prenatally arsenic exposure children and continuing exposure until the age of 5–7 years with matched case controls Potential risk for mutation and cancer from biomarkers. | |
| Intarasunanont et al. (2012) [ | Case-controlled | 71 (case 55, controlled 16) | Arsenic in drinking water Arsenic in cord blood, nails, and hair DNA methylation in cord blood lymphocyte and Arsenic in drinking and non-drinking water | Arsenic levels in case vs. controlled were
– Cord blood: 5.79 ± 0.5 vs. 1.97 ± 0.64 μg/g – Toenails: 1.52 ± 0.38 vs. 0.12 ± 0.04 μg/g – Fingernails: 1.91 ± 0.38 vs. 0.08 ± 0.05 μg/g – Hair: 0.05 ± 0.01 vs. 0.01 ± 0.003 μg/g. | |||
| Phookphan et al. (2017) [ | Case-controlled (follow-up study) | School child | 81 | Arsenic in toenails | Arsenic in toenails in exposed and unexposed children was 8.08 ± 1.47 μg/g and 0.76 ± 0.14 μg/g, respectively. | A follow-up study from arsenic exposure | |
| Vitayavirasak et al. (2005) [ | Case-controlled | School child | 130 (high exposure area 50, low exposure area 50, controlled 30) | Urinary iAs and its metabolites (MMA, DMA) Questionnaire-based interview Arsenic environmental monitoring: surface soil, ambient air, drinking water, fruit, vegetables and meat | Geometric mean of iAs + metabolites were 54.21, 36.61, and 17.50 μg/g creatinine in high exposure, low exposure and control group, respectively. Average of arsenic in agricultural product was within standard acceptance of US FDA at 2 mg/kg. except the freshwater snail ( | Source of exposure was one of food chain (fresh water snail), water (surface and ground) and soil Individual risk behaviors of exposure were soil playing and without hand washing before eating. Probable risk of developing cancer (between 10–5 –10–6) | |
| Swaddiwudhipong et al. (2015) [ | Case-controlled | School child | 594 | Urinary cadmium Questionnaire Urinary β2-MG and calcium | 19% of children in this study had urinary cadmium ≥ 1 μg/g creatinine, which was higher in girls and in those consuming rice grown in cadmium-contaminated areas. | Significantly higher geometric mean levels of urinary excretion of β2-MG and calcium (early renal effects) were found among children in contaminated areas compared to those in comparison (non-contaminated) areas | |
| Pusapukdepob et al. (2007) [ | Cross-sectional | Child | 126 | Lead in ‘soil vegetables’ and meat Lead in blood and teeth | Mean BLL in children of case and controlled groups were 25.75 ± 13.89 μg/dL and 7.68 ± 2 μg/dL, respectively. 72% (89 children and 9 adults) of case group had high BLL (>10 μg/dL) while there were no cases of high BLL in controlled group. Lead concentration in soil vegetables and meat (fish and shellfish) exceeded the recommended standard | IQ scores of the case group (vicinity < 30 km from mining) were significantly lower than controlled group (82.7 ± 8.27 vs 96.14 ± 7.8) | |
| Youravong et al. (2006) [ | Cross-sectional | Preschool to school child (6–10 years) | 292 | Blood lead | Children with BLL ≥ 10 μg/dL was 21%. | Lead exposure was associated with carries in deciduous teeth but not in permanent teeth (cariogenicity) in dose-response relationship. | |
| Youravong et al. (2013) [ | Cross-sectional | Preschool to school child (6–10 years) | 120 | Salivary and blood lead | The salivary lead level low correlated with blood lead level (R2 = 0.18, p = 0.05) | There was no association between salivary lead and dental caries. | |
| Umbangtalad et al. (2007) [ | Case-controlled | School child | 59 | Hair and urinary mercury Environmental monitoring of mercury on water, sediment, | The urinary mercury was 15.82 and 9.95 µg/g creatinine in ‘involved’ and ‘not involved in mining activities’, respectively. Average Hg hair level in all schoolchildren (0.93 µg/g) with no significant difference between each group | The hazard quotient (HQ) based on the inorganic mercury exposure was < 1 (no risk). | |
*Age group reference: newborn: birth–1 month, infant: 1–23 months, preschool child: 2–5 years, school child: 6–12 years, adolescent: 13–18 years, child: birth–18 years.
Abbreviations: BLL: blood lead level, cBLL: DMA: dimethylarsinic acid, EBLL: elevated blood lead level, Hb: haemoglobin, iAs: inorganic arsenic, MG: macroglobulin, MMA: monomethylarsonic acid.
Epidemiological studies investigating air pollution exposure in Thai children.
| Air pollution | Authors (published year) | Type of epidemiological study | Population (age)* | Sample size | Exposure assessment | Level measured | Comments |
|---|---|---|---|---|---|---|---|
| Lee et al. (2006) [ | Cross-sectional | Dust from young child’s home | 50 | Endotoxin level and dust mite in dust from mattress of young child’s home | Endotoxin level in rural Thailand was higher than urban Singapore, however, dust mite allergen was higher in urban Singapore Endotoxin level was also prominent in agricultural area independent of farming environment | Cleaning practice and mattress type influenced the endotoxin level Air-conditioning also influenced the dust mite allergen level (positive correlation) | |
| Pumhirun et al. (1997) [ | Cross-sectional | School child and adolescent (10–18 years) | Total 100 | Skin prick test (30 aeroallergens) | Allergic rhinitis patients were mostly sensitised to house dust mite (D. | 85% of patients sensitive to house dust mite were positive to both D. pteronyssinus and D. farina (substantial cross-reactivity) | |
| Ruchirawat et al. (2005) [ | Case-controlled | School child | 69 | Benzene ambient air monitoring (personal) Blood benzene, and urinary t,t-MA levels | The benzene levels on the main roads ranged from 16.35 to 49.25 ppb. Benzene exposure were 4.71 ± 0.25 and 2.10 ± 0.16 ppb in exposure and control group, respectively. | School children in Bangkok were exposed in total Benzene more than control group (living outside the city) | |
| Anuntaseree et al. (2008) [ | Cross-sectional | Parent of infants | 3,256 | Questionnaires | Prevalence of at least one smoker in household was 47.2%, paternal smoking in the present of their infant was 35.1%, maternal smoking 0.3% | The significant association with paternal smoking was age (25–34 years), education less than secondary school, and Muslim father. This study was the part of the “Prospective Cohort Study of Thai Children”; PCTC | |
| Thongthai et al. (2008) [ | Cross-sectional | Adolescent | 2,596 | Questionnaires | Tobacco smoke affected 60% of population | Smokers were more likely to be male and older, but those exposed to secondhand smoke tend to be female and younger. | |
| Ruchirawat et al. (2005) [ | Case-controlled | School child | 69 | PAH ambient air monitoring (personal) Urinary 1-OHP | Total PAHs on the main roads ranged from 7.10 to 83.04 ng/m3. Total PAHs were 6.70 ± 0.47 and 1.25 ± 0.24 ng/m3 in exposure and control group, respectively | School children in Bangkok were exposed in total PAH more than control group (living outside the city) | |
| Buthbumrung et al. (2008) [ | Case-controlled | School child | 171 | Personal air and area sampling of benzene Blood benzene, urinary benzene and metabolite (MA) levels 8-OHdG in leukocyte and urine (oxidative DNA damage markers) DNA isolation and identification of metabolic genotype | Mean benzene level in ambient air at roadsides adjacent to the Bangkok schools were 17.75 ± 2.23 ppb and in rural school were 4.49 ± 0.59 ppb Level of 9-OHdG in exposure vs control group were 0.25 ± 0.02/105 dG vs 0.08 ± 0.06/105 dG | Children living in high traffic density areas were exposed to higher level of benzene than those living in rural area Benzene contribute to oxidative DNA damage. | |
| Navasumrit et al. (2005) [ | Case-controlled | School child (12–14 years) | 71 | Bangkok school children (5.50 ppb) were exposed to significantly higher levels of benzene than provincial school children (2.54 ppb; p < 0.01). Increased DNA damage and a decreased DNA repair capacity in benzene exposed group were observed, compared to unexposed children. | |||
| Ruchirawat et al. (2007) [ | Case-controlled | School child | 184 | Ambient benzene monitoring DNA strand breaks and DNA capacity Genetic polymorphisms of SGTs and CTP450 | Benzene levels on children studying in Bangkok were significantly higher than (3.5 times) students studying in rural school (control group) | DNA strand breaks were significantly higher, but DNA repair capacity was significantly lower in Bangkok children. Genetic polymorphisms of GSTs and CYP450 enzymes were detected, involved in metabolism of benzene and PAHs, but no significant effects on the biomarkers of PAH exposure. | |
| Ruchirawat et al. (2010) [ | Case-controlled | School child | 276 | Ambient benzene and 1,3-butadiene, particle associated PAHs DNA-adduct of peripheral mononuclear white blood cells 8-OHdG in leukocytes DNA strand break and DNA repair capacity | School children in city and rural areas were exposed to 19.38 and 8.4 μg/m3 benzene respectively | Low level of benzene exposure, alone or concurrently with other carcinogens, resulted in early biological effect in the study. | |
| Sriyaraj et al. (2008) [ | Cross-sectional | School child | 511 | Standard ISAAC questionnaires | Prevalence of cigarette smoking for mother was 4.5%, father was 49.3% and mother during the 1st year of child’s life was 5.3% | Potential environmental factors that were significantly positively correlated with the allergic disorders were diesel engine vehicles, antibiotic and paracetamol use, nuts consumed, contact with dogs and cats in the first year of life, contact with farm animal by mother while pregnant, and maternal cigarette smoking Prevalence of Rhinitis (24.3% vs. 15.8%), hay fever (23.2% vs. 13.9%), and atopic dermatitis (12.5% vs. 7.2%) were more common in urban than in suburban areas. The prevalence of asthma was not different between both areas (5.5%). | |
| Sritippayawan et al. (2006) [ | Case-controlled | Preschool child | 71 | Urinary cotinine/creatinine, Questionnaires | 28% of patients had history of in-house smoking. The median urinary cotinine level was 0.5 μg/mg Cr. | ETS exposure increased the risk of desaturation (SpO2 < 9%) in RSV-LRI but was not associated with RSV-LSI itself | |
| Pongpiachan et al. (2015) [ | Cross-sectional | Preschool child | Not applicable (risk assessment from environmental monitoring results) | PM2.5-bound PAHs | The average values of Σ3,4-ring PAHs and B[a]P equivalent concentrations in world urban cities were significantly much higher than those in samples collected from northern provinces during both sampling periods. | The cancer risk related to exposure through inhalation appears to be minor, while direct ingestion could potentially be a significant pathway for children due to their hand-to-mouth activities. | |
| Ruchirawat et al. (2006) [ | Case-controlled | School child | 69 | Ambient PAHs monitoring PAH-DNA adduct levels in lymphocytes | Ambient levels of PAHs are relatively high in Bangkok. | PAH-DNA adduct levels in lymphocytes were 5-fold higher in Bangkok | |
| Ruchirawat et al. (2007) [ | Case-controlled | School child | 184 | Ambient PAHs monitoring | PAHs of children studying in Bangkok were significantly higher than students studying in rural school (control group) | DNA strand breaks were significantly higher, but DNA repair capacity was significantly lower in Bangkok children Genetic polymorphisms of GSTs and CYP450 enzymes were detected, involved in metabolism of benzene and PAHs, but no significant effects on the biomarkers of PAH exposure. | |
| Ruchirawat et al (2010) [ | Cross-sectional | School child | 276 | Ambient benzene and 1,3-butadiene, particle associated PAHs DNA-adduct of peripheral mononuclear white blood cells 8-OHdG in leukocytes DNA strand break and DNA repair capacity | School children in city and rural, respectively were exposed to:
1,3-butadiene–2.42 and 0.65 μg/m3 (city and rural) Total PAHs–4.13 and 1.18 (city and rural) B[a]P equivalents–1.50 and 0.43 ng/m3 | Low level of benzene exposure, alone or concurrently with other carcinogens, resulted in early biological effect in the study populations. | |
| Tuntawiroon et al. (2007) [ | Case-controlled | School child | 184 | Urinary 1-HOP Ambient air monitoring DNA adduct level in peripheral lymphocytes | Concentration of urinary 1-HOP was significantly higher in Bangkok schoolchildren. | Bulky carcinogen-DNA adduct levels in peripheral lymphocytes were also significantly higher Significantly higher level of DNA strand breaks were significantly higher DNA repair capacity significantly lower in children in Bangkok. | |
| Aekplakorn et al. (2003) [ | Cohort | School child | 175 | PM10 ambient air monitoring Pulmonary function test Respiratory symptom questionnaires | A 10 µg/m3 increment was associated with changes in the highest FVC (–6.3 ml, 95% CI: –9.8, –2.8), FEV1 (–6.0 ml, 95% CI: –9.2, 2.7), PEFR (–18.9 ml.sec–1, 95% CI: –28.5, –9.3) and forced expiratory flow 25 to 75% of the FVC (FEF25–75%) (–3.7 ml.sec–1, 95% CI: –10.9, 3.5) in asthmatic children. | Declines in pulmonary function among asthmatic children were | |
| Langkulsen et al. (2006) [ | Cross-sectional | School child | 878 | PM10 monitoring Questionnaire PFT | Prevalence of respiratory symptoms increased significantly in high polluted area [OR 2.44 (95% CI, 1.21–4.93) and 2.60 (1.38–4.91), in road side and general area, respectively]. | Increased chronic respiratory symptom and impaired lung function in high-pollution area | |
| Preutthipan et al. (2004) [ | Cohort | School child | 133 | PM10 air monitoring | PM10 levels exceeded 120 mg/m3 (Thai national standard at that time) for 14 days of 31 days records 4-hr average PM10 levels ranged between 46–201 mg/m3. PM10 levels exceeded 120 mg/m3 for 14 days. When PM10 levels were >120 mg/m3, the daily reported nasal irritation of asthmatic children was significantly higher than when PM10 levels were ≤ 120 mg/m3 | PEFR did not change with different ambient PM10 levels in both groups. Elevated levels of PM10 concentrations in Bangkok affected respiratory symptoms of schoolchildren with and without asthma. | |
| Aekplakorn et al. (2003) [ | Cohort | School child | 175 | SO2 air monitoring Pulmonary function test Respiratory symptom questionnaires | The ambient SO2 levels were relatively low except for a few days. The daily 24-hour mean SO2 concentrations were lower than the Thai ambient standards (300 µg/m3). | ||
| Aungudornpukdee et al. (2009) [ | Cross-sectional | School child | 2,956 | GIS Neurobehavioral test | The distance to industrial park from residential areas was not statistically different between children who had and had not motor coordination deficit. | The associated factors of visual-motor coordination deficit were gender, monthly parental income, children’s age, residential period, and household ETS | |
| Singkaew et al. (2013) [ | Case-controlled | School child | 6 | Ambient air monitoring of VOCs | The average VOCs during 2006–2010 that exceeded Thai annual standards were 1,2-Dichloroethane, 1,3-Butadiene, and Benzene. | The lifetime cancer and non-cancer risk in all high-risk group including children were in acceptable range based on the US EPA health risk assessment. | |
*Age group reference: newborn: birth–1 month, infant: 1–23 months, preschool child: 2–5 years, school child: 6–12 years, adolescent: 13–18 years, child: birth–18 years.
Abbreviations: Cr: creatinine, CYP450: cytochrome P450, DEP: diesel exhaust particle, ETS: environmental tobacco smoke, FEV1: forced expiratory volume at 1 second, FVC: forced vital capacity, GIS: Geographical Information System, GSTs: glutathione-S-transferases, MA: muconic acid, OR: odds ratios, PAHs: Polyaromatic hydrocarbons, PEFR: peak expiratory flow rate, PFT: pulmonary function test, PM: Particulate matter, SO2: Sulphur dioxide, t,t-MA: t,t-muconic acid, 1-HOP: 1-hydroxypyrene, VOCs: volatile organic compounds, 8-OHdG: 8-oxo-7, 8-dihydro-2’-deoxyguanosine, 95% CI: 95% confidence interval.
Health effects related to environmental exposure of particular geographical areas in Thailand.
| Region | District, province | Hazards | Health effects association/health risk | References | |
|---|---|---|---|---|---|
| Yes | No | ||||
| Umpang, Tak | Lead | NA | NA | [ | |
| Mae Sot, Tak | Cadmium | Early renal effect | NA | [ | |
| Phonom Pha, Phichit | Mercury (inorganic) | NA | Hazard quotient | [ | |
| Mae Moh, Lampang | Air pollution (SO2, PM) | Decline of pulmonary function (PM) | Decline of pulmonary function (SO2) | [ | |
| Chiang Mai | Air pollution (DEPS) | Allergic diseases (rhinitis, atopic dermatitis) | NA | [ | |
| Mae rim, Chiang Mai | Organochlorine | Negative association of cord serum total T4 levels with DDT metabolites | NA | [ | |
| Amnatchareon | Organophosphate | Cognitive and motor development in infant | NA | [ | |
| Bangkok | Lead (some areas) | NA | NA | [ | |
| Air pollution (PM, methane, traffic related PAHs, benzene aeroallergen, ETS) | Respiratory symptom | Upper respiratory infection | [ | ||
| Greater Bangkok (rice farming) | Organophosphates, chlorpyrifos, and pyrethroid | NA | Neurobehavioral in school age children | [ | |
| Klity village, Kanchanaburi | Lead | Dental caries, IQ deterioration | NA | [ | |
| Kanchanaburi | Organophosphate | Cognitive and motor development in infant | NA | [ | |
| Map Ta Phut, Rayong | VOCs (petrochemical industrial area) | NA | Visual-motor coordination deficits | [ | |
| Nakhon Sawan | Organophosphate | Cognitive and motor development in infant | NA | [ | |
| Singhanakorn district, Songkhla | Lead | Dental caries | Dental morphologic change | [ | |
| Ron Phibul district, Nakhon Sri Thammarat | Arsenic | Increased DNA damage and decreased DNA capacity | NA | [ | |
Abbreviations: DDT: Dichlorodiphenyltrichloroethane, DEPs: diesel exhaust particles, ETS: environmental tobacco smoke, IQ: intelligent quotient, NA: not applicable, PAH: polyaromatic hydrocarbons, PM: particulate matter, SO2: Sulphur dioxide, VOCs: volatile organic compounds.