Comparison of pesticide exposure and physical examination,
neurological assessment, and laboratory findings between
full-time and part-time vegetable farmers in the Philippines
Jinky Leilanie Lu
Received: 19 February 2009/Accepted: 3 August 2009/Published online: 3 September 2009
? The Japanese Society for Hygiene 2009
tices and health effects of pesticide exposure between full-
time and part-time vegetable farmers.
Data was gathered via structured personal
interview using a 9-page questionnaire, physical exami-
nation, and blood extraction for complete blood count and
Pyrethroid was the pesticide type most used by
both groups. The risk for full-time farmers was related to
both the amount of exposure and the type of pesticide.
There were more full-time farmers who complained of
falling ill because of work. This difference was statisti-
cally significant (P = 0.05). The level of those seeking
medical attention was also significantly different between
the two groups (P = 0.01). In assessing the individual
components of the neurologic examination, 5.22% of
full-time and 8.63% of part-time farmers had abnormal
cranial nerve function, and 22 (5.7%) and 9 (6.47%) had
abnormal motor strength. All farmers tested for reflexes,
meningeals, and autonomics from both groups were
normal. Based on hematologic examination, full-time
farmers had higher mean values for creatinine, white
blood cell, red blood cell, hemoglobin, and hematocrit.
Activity of cholinesterase enzymes in blood can be uti-
lized as a biomarker for the effect of organophosphates;
of the 232 blood cholinesterase results, 94 (40%) were
between full-time and part-time farmers in terms of
This study aimed to compare the work prac-
farming practices and health-related problems. Education
on safe pesticide use and handling and better health mon-
itoring of the farmers are recommended.
Pesticide exposure ? Pesticide-related health problems ?
Full-time and part-time farmers
Vegetable farmers ? Farming practices ?
Agriculture is one of the primary economic sectors in the
Philippines, contributing about 20% of the gross domestic
product. Crops comprise about 47.56% of the total agri-
cultural sector and contribute about 510 billion pesos (USD
10.6 billion) to the country’s national income.
The majority of farmers in the Philippines still use
pesticides, some of them banned in more developed
countries. However, in Philippine agriculture, there is
increasing reliance on pesticide use without thought for its
deleterious effects on community, health, and environment.
Although alternative methods for pest control, organic
farming, and integrated pesticide management have been
initiated, they are not strongly sustained.
Given that there exists an inherent risk in these farming
practices, this paper attempts to elucidate differences in
farming communities of Benguet Province with differing
levels of pesticide exposure.
Benguet is a province in the northern portion of the
Philippines belonging to the Cordillera Administrative
Region. There are about 27.5 thousand farms covering
30,000 ha of agricultural land in Benguet. The province is
known as the ‘‘salad bowl’’ of the Philippines as its major
crops are tubers, roots and bulbs, and leafy vegetables,
stems, and flowers. Of the 27,000 farms present in Benguet,
J. L. Lu (&)
National Institutes of Health, University of the Philippines
Manila, P Gil Street, 1100 Manila, Philippines
Environ Health Prev Med (2009) 14:345–352
14,349 are involved in tubers, roots, and bulbs; 11,515 are
involved in vegetables; and 9,868 are involved in legumes.
In 2005, Benguet was the top producer of broccoli and
carrots, producing about 1,200 and 13,700 metric tons,
contributing 87.4% and 81.4%, respectively, of the national
The crops grown in the study include mainly vegetables
such as cabbage, potato, carrots, wombok, lettuce, sweet
pea, onion leak, celery, beans, and tomato. The other major
crops of the Philippines, including rice, sugarcane, coco-
nut, corn, and banana, are grown elsewhere.
This study aimed to compare the health effects of pes-
ticide exposure between four municipalities identified as
having high exposure to pesticide and two municipalities
having low exposure to pesticides.
Materials and methods
Five hundred forty-two farmers from six communities
formed the target population: 73 from community 1, 104
from community 2, 52 were from community 3, 90 from
community 4, 73 from community 5, and 150 from
community 6. They were selected via cluster sampling.
Physical examination was carried out for 533 respon-
dents. Complete blood count was done for 510 respon-
dents, while serum creatinine was performed for 404
The full-time farmers consisted of communities 1 and 6
(both located in the central part of the province) and
communities 2 and 5 (both located in the northeastern part
of the province). They are identified as full-time farmers, as
farming is done as a full-time job, vegetable production is
produced for commercial purposes, and harvest is year-
round. The crops produced by this group included cabbage,
potato, carrots, wombok, lettuce, sweet pea, onion leak,
celery, beans, and tomato. Meanwhile, the part-time
farmers came from communities 3 (located in the south-
eastern part of the province) and 4 (located in the mid-
western part). These are part-time farmers, as farming is a
secondary occupation, and vegetables are grown for per-
sonal rather than commercial use, i.e., the farmers had the
side-job of seasonal vegetable harvesting. The crops pro-
duced by this group included cabbage, lettuce, coffee, and
Data collection methods
Methods included structured survey interviews by field
workers, who interviewed farmers in situ using a prepared
questionnaire. The9-page questionnaire contained
information on demographics, past and present medical
histories, family medical histories, obstetric and gyneco-
logical history for females, pesticide use and practices, and
Physical, hematologic, and neurological examinations
were also carried out, adopted from the standard form used
by the National Poisons Control and Information Service
(NPCIS) of the UP-Philippine General Hospital in its
The physical examination, including examination of
various body parts (head, eyes, ears, nose, throat, oral
cavity, neck, lungs, heart, abdomen, extremities, and
integument), were performed by 20–30 doctors. The neu-
rologic examination assessed all cranial nerves (I–XII).
These cranial nerves were assessed by a neurologist by
classifying clinical findings as abnormal or normal.
For the hematologic examination, the following blood
indices were measured and mean readings were referenced
to the standard reading for such parameters: red blood
cells, hemoglobin, hematocrit, mean corpuscular volume
(MCV), mean corpuscular hemoglobin (MCH), mean cel-
lular hemoglobin concentration (MCHC), white blood
cells, platelets, creatinine clearance, and red blood cell
Table 1 presents the hematologic parameters measured
and their corresponding normal values. Blood extraction
was done by a licenced medical technician.
Hematocrit reflects concentration of packed red blood
cell (RBC) volume. It increases in cases of dehydration or
increased blood cellularity. mean corpuscular volume
(MCV) is an index of RBC size and is computed by
dividing the hematocrit by the RBC count. MCHC is the
average concentration of hemoglobin in a given volume of
red blood cells; MCH is the average weight of hemoglobin
of red blood cells. All three give an insight into the type of
anemia a person has, if present.
Table 1 Hematologic parameters and corresponding normal values
used for this study
Hematologic parametersNormal values
White blood cell (G/L)
Red blood cell (T/L) 4.69–61.3
Hemoglobin (g/L) 140–181
Mean corpuscular volume (fL)80–97
MCH (pg) 27–31.2
RBC cholinesterase (D ph/h)
346Environ Health Prev Med (2009) 14:345–352
White blood cells are part of the immune system. Their
values tend to increase during infectious or allergic pro-
cesses and decrease in cases of typhoid fever; a decrease in
production by bone marrow can also occur secondary to
diseases such as neoplasm. Creatinine is a byproduct of the
metabolism of muscles and is produced in a constant
amount in the body. As such it is often used as a marker for
renal function, wherein its clearance is used to estimate the
glomerular filtration rate of the kidneys. Some pesticides
adversely affect renal function by producing acute tubular
Based on the blood parameters, certain types of anemia
were analyzed. Microcytic anemia is most commonly
caused by iron deficiency through inadequate intake, poor
absorption, excessive iron requirements or chronic blood
loss. Normocytic anemia is seen among patients experi-
encing acute blood loss, hemolytic disorders or suffering
from a chronic disease.
Red blood cell cholinesterase was also measured in
this study. Two hundred thirty-two respondents submitted
for this examination while the others refused to partici-
pate. Cholinesterase corresponds to two enzymes: ace-
tylcholinesterase and butyrylcholinesterase (also called
enzymes in the blood can be utilized as a biomarker for
the effect of organophosphate and carbamate exposure.
The normal value for cholinesterase reference used is
D 0.75–1.0 ph/h.
Blood (10–15 ml) was extracted from each respondent for
blood cholinesterase, blood count, and serum creatinine by
a licenced medical technician. Blood vials were transported
on ice and analyzed within 24 h of extraction in the
The health and work practices data, including the
physical and neurological examinations and blood exami-
nation results, were encoded and analyzed using SPSS 13.0
software. Data analysis included descriptive and inferential
Participants were informed about the nature of study,
including the research objectives, purposes, and goals.
They were also informed about the blood extraction pro-
cedure and its purposes and risks. They have written
informed consent. Participants were also assured of data
confidentiality. The informed consent form was duly
approved by the Ethics Review Board of the University of
the Philippines-National Institutes of Health and accom-
panied each interview schedule.
There were more males than females in both groups. The
full-time farmers (N = 398) had lower mean age than the
part-time farmers (N = 192): 46.67 ± 11.978 years and
48.44 ± 12.97 years, respectively. The full-time farmers
had an age range of 15–78 years, with the majority
belonging to the 36–50 years age range. The part-time
farmers had an age range of 21–78 years, with an almost
equal distributionof respondents
36–50 years and 51–65 years age groups. Most of the
respondents for both groups were married (79.3% and
Household sizes ranged from farmers living alone to
families as large as 22 members for full-time farmers, and
12 for part-time farmers. Most had lived in their current
residence for more than 5 years (95.2% and 92.5%) with
the majority of full-time farmers living less than or equal to
about 50 m away from the plantation (44.3% and 35.8%).
Most of the respondents from both groups lived in homes
less than 50 m away from the highway. The demographic
profile of the two groups was similar, except for the age
distribution (P = 0.029), educational attainment (P =
0.001), number of household members (P = 0.039), and
distance from plantation (P = 0.001).
The majority of the respondents from both groups were
mainly agricultural workers (85–87%). The remaining 13–
pesticide distributors, students or government employees.
There were a total of 831 and 266 pregnancies among
the female full-time and part-time farmers, respectively;
the majority of outcomes for both groups were full term
(92.06% and 98.12%). There were five preterm outcomes
among full-time female farmers compared with none
among part-time female farmers. Some congenital abnor-
malities and pregnancy disorders reported among full-time
female farmers were microcephaly, mental retardation,
clubfoot, hydatiform mole, and ectopic pregnancy. There
were about 10 times more spontaneous abortions among
full-time female farmers than among part-time female
farmers. However, these differences were not statistically
significant (P = 0.99).
Pesticide use and exposure
Of full-time and part-time farmers, 96.5% and 95%,
respectively, were pesticide users. About 58.7% of full-
time farmers and 46.8% of part-time farmers had family
members working with pesticides. For the full-time farm-
ers, 18.27% were under the age of 15 years, and for the
part-time farmers this value was 44.44%.
The majority of the farmers in both groups used pyre-
throids (71.1%, 73%). For full-time farmers, this was
Environ Health Prev Med (2009) 14:345–352347
followed by organophosphates (67.8%) and carbamates
(57%). For part-time farmers, carbamate (48%) was the
second most commonly used. Organophosphate was the
least used pesticide (31%). Other pesticides used by both
groups included organochlorides and nitrites (Table 2).
The two groups were similar in terms of pyrethroid, car-
bamate, and other pesticide use. However, their use of
organophosphate was significantly different (P = 0.001),
such that about 2/3 of the full-time farmers used organo-
phosphate, while only 1/3 of the part-time farmers used it.
In terms of the specific active ingredients for each pesticide
type, Table 3 shows the usage by the two groups.
Both groups were similar in terms of pesticide use,
pyrethroid, carbamate, and other pesticide use, but differed
significantly in terms of organophosphate use (P = 0.001).
Organophosphate was used more frequently by the full-
time farmers compared to the part-time farmers.
Full-time farmers spent a longer mean time of
110.33 min spraying per load of pesticide compared with
85.43 min for part-time farmers, this difference being
Farmers from both groups were involved in mixing,
applying or loading pesticides or combination of the
three. For full-time farmers, there were more farmers
involved in mixing (93.9%), while 88.6% of part-time
farmers were involved in either mixing or loading.
Practices were similar in the two groups except for
mixing (P = 0.045), which was more prevalent among
Most of the farmers from both groups used a knapsack
or backpack sprayer (88.2%, 88.98%). Of full-time farm-
ers, 11.8% used power sprayer, hand spray (0.5%), or
mechanical, tank or compressor sprayer (0.25% each).
Of the full-time and part-time farmers, 91% and 87.8%,
respectively, reported that they wore protective clothing
but, when itemized, the majority did not wear coveralls,
goggles, face shield or mask, or use respirators. Slightly
more than half (50.3%) of the full-time farmers and 1/3 of
the part-time framers used gloves. Of the full-time and
part-time farmers, 95% and 75%, respectively, used boots.
Both groups differed significantly in their use of face mask,
gloves, and boots.
Farmers from both groups used makeshift protective
clothing in the form of a handkerchief around the face, hats
or bonnets, jackets or pants used as arm covers, shirts used
alone or wrapped in plastic, or raincoats or plastics.
There are also practices that should be done after pes-
ticide use to minimize risk of intoxication or poisoning.
Such practices include washing of hands, keeping a dis-
tance from recently sprayed areas (especially if the area is
poorly ventilated), and avoiding spraying against the wind
Table 2 Percentage of type of pesticide use among farmers
Type of pesticide Full-time farmers
N = 398
N = 137
Freq.% Freq.% Total
Pyrethroid281 71.110073 381
Carbamate 227 57.0 66 48293
Other pesticide 211 52.862 45 273
Table 3 Percentage of type of pesticide by brand name used by farmers
Brand name Active ingredientChemical grouping Full-time farmers
N = 376
N = 130
TamaronMethamidophos Organophosphate 15641.537 28.5
Dithane MancozebCarbamate130 34.651 1.5
Sumicidine FenvaleratePyrethroid 13736.4 1612.3
Selecron ProfenofosOrganophosphate 10628.3 2720.9
Karate LambdacyhalothrinPyrethroid 70 18.619 14.6
ManzateMancozeb Carbamate61 16.2125.47
Bida Lambdacyhalothrin Pyrethroid55 14.62 1.5
Cartap CartapCarbamate 5213.81 0.8
SabedongCypermethrin Pyrethroid4913.0 18 13.8
MagnumCypermethrinPyrethroid 44 11.715 11.6
LannateMethomyl Carbamate 36 9.645 39.2
Success Spinosad349.00 3.1
Malathion MalathionOrganophosphate21 5.67 5.47
348Environ Health Prev Med (2009) 14:345–352
Slightly more than half (53%) of the full-time farmers,
and approximately 2/3 (64.22%) of the part-time farmers
used contaminated cloth to wipe sweat from their faces.
This difference between the two groups was significant
(P = 0.031). Of the high- and low-exposure groups, 31.7%
and 36.3%, respectively, reentered recently sprayed areas.
Used pesticide containers were either buried (34.8%),
thrown away (31.4%), sold (20.2%) or burned (6.9%). The
majority of the farmers from both groups buried the used
pesticide containers, followed by destroying or throwing
the container away. Of full-time farmers, 20.2% sold the
pesticide container, while 12.8% of the part-time farmers
burned it. Other respondents from both groups kept their
pesticides in storage areas, while the remaining left them
either beside or inside the house; in the backyards, garden
or fields; or stored the used containers inside their store-
houses. The two groups differed significantly in terms of
disposal method for used pesticide containers. More full-
time farmers sold or destroyed their used pesticide con-
tainers than did part-time farmers. In contrast, there was a
higher percentage of part-time farmers who burned or
buried their used pesticide containers.
Pesticide exposure and illness
The difference between the full-time and part-time farmers
is shown in Table 5. There were more full-time farmers
who complained of falling ill because of work. This dif-
ference was statistically significant (P = 0.05). However,
only about 31% of the full-time farmers, and 17% of the
part-time farmers reported receiving or seeking medical
attention for their illness and/or exposure; this behavior
was significantly different between the two groups
(P = 0.01).
Exposure commonly occurred in the farm or field (65%
for high-exposure, 55% for low-exposure groups). The
majority of the farmers from both groups were exposed to
insecticides, and the two groups were significantly different
in this regard.
Muscle pain was the most common general symptom felt
by both groups of farmers. This was followed by weakness
and easy fatiguability. Paresthesia and localized fascicu-
lation were the top motor symptoms experienced by the
full-time farmers, with 9.8% affected by each symptom,
followed by tremors (7.8%) and convulsion (5.6%). For the
part-time farmers, localized fasciculation was the top
motor symptom, followed by paresthesia and tremors.
Physical and neurologic examination
Two hundred and seventy-nine (69.8%) of the full-time
farmers, and 100 (71.94%) of the part-time farmers had
at least one abnormal physical examination finding.
However, only 42.6% of the full-time farmers, and 35.8%
of the part-time farmers had an abnormal clinical diag-
nosis. Both groups were similar in all physical exami-
nation findings except for the nose, throat, chest, and
lungs (P = 0.05).
A neurologic examination was conducted. Of the full-
time and part-time farmers, 22 and 2, respectively, had
abnormal neurologic diagnosis.
Table 4 Percentage distribution of practices in pesticide use among farmers
Unsafe work practicesFull-time farmers Part-time farmers
Wipe sweat from face with contaminated piece of fabric18553 34979 64.22123
Reenter recently sprayed area 118 31.7372 4536.3124
Spray against the wind 123 42.42904945 109
Wash hands after applying pesticides and before eating369 93.6 373 12699.2126
Take a bath after applying pesticides36898.4374 127100127
Table 5 Comparison between full-time and part-time farmers in terms of pesticide exposure and illness
Pesticide exposure and illnessFull-time farmers (%) Part-time farmers (%) Statistical difference between the two groups
Getting ill because of work 5344 Statistically significant difference at P = 0.05
Receiving medical attention because of illness31 17Statistically significant difference at P = 0.01
Pesticide exposure during application in the field 8065 Statistically significant difference at P = 0.01
Exposure in farms and gardens65 55 Statistically significant difference at P = 0.05
Insecticide exposure95 88 Statistically significant difference at P = 0.01
Environ Health Prev Med (2009) 14:345–352349