This paper investigates the determinants of pesticide-related cost of illness (COI) and acute symptoms, using a balanced panel of 363 farmers interviewed from seven major vegetable producing districts of Kenya. Finding shows that the incidences of pesticide-related health impairments have increased. Variation in number of symptoms and symptom severity significantly explained COI. The personal protective equipment (PPE), education level, record keeping, and geographical location considerably determined health impairments. Encouraging the proper use of PPE and record keeping of pesticide use could greatly reduce poisoning cases and COI.
The health effects of pesticide use have become one of the major public health problems worldwide. In developing countries, frequent exposure to pesticides by farmers and farm workers is very common [
The World Health Organization (WHO) and the United Nations Environment Program (UNEP) estimate pesticide poisoning rates at 2-3 per minute [
In Kenya, pesticide use and farmers health have been documented by some empirical studies [
The objective of this paper therefore is to examine the incidences and the determinants of acute pesticide poisoning among vegetable farmers in Kenya controlling for unobserved heterogeneity.
The study was conducted in seven major vegetable producing districts of Central Province (Kiambu, Kirinyaga, Murang’a, Nyandarua, and Nyeri North) and Eastern Province (Makueni and Meru Central) of Kenya (Figure
Study sites. Source: this study based on GIS mapping of potential vegetable production areas.
The 2005 survey comprised 839 interviews from the Diamondback moth biological control impact assessment survey (“DBM” with 295 farmers) and the Global Good Agricultural Practices (“GLOBALGAP” with 544 farmers) assessment survey. GLOBALGAP (formerly known as EUREPGAP) is a private sector body that sets voluntary standards for the certification of agricultural products around the globe. In both surveys, a multistage sampling procedure was used to select districts, sublocations, and farmers, respectively. First, districts were purposely sampled according to intensity of vegetable production and agroecological zones. Lists of farmers that were compiled by extension workers at sublocation level served as sampling frame from which 839 farmers were randomly sampled by probability proportional to size (PPS) procedure.
Sampled farmers were then monitored in one cropping season and were trained in record keeping of their production activities by trained enumerators. The trained enumerators under direct supervision of the researcher visited the farmers to check the records and transferred the information to the survey questionnaire.
Due to budget constraints, the 2008 survey was a recall survey of a random subsample of 425 farmers among the 839 farmers. However, we only obtain 363 balanced data set after the 2 years of study. Table
Regional distribution of survey respondents.
Province | District | Main vegetable crops | Previous surveys (2005) | Number of farmers sampled (2008) | Balanced data (farmers) | |
---|---|---|---|---|---|---|
Domestic | Export | |||||
Central | Kiambu | Cabbages, kales, and spinach | 48 | 27 | 19 | |
Kirinyaga | Peas, tomatoes | French beans | 155 | 74 | 66 | |
Muranga | Tomatoes, kales | French beans | 51 | 24 | 21 | |
Nyandarua | Cabbages, potatoes | 119 | 52 | 49 | ||
Nyeri North | Peas, cabbage, onions, and carrots | French beans | 277 | 116 | 107 | |
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Eastern | Makueni | Cabbages, kales | Asian vegetables |
49 | 22 | 8 |
Meru Central | Peas, tomatoes, cabbage, and onions | French beans | 140 | 110 | 93 |
Source: this study.
The semistructured questionnaires employed covered a wide range of topics, such as cropping systems, demographics, common farming practices, pesticide use and handling practices, and type and quantities of pesticides sprayed. Health symptoms investigated were specified as those that only began during the spraying operation or within 24 hours after spraying. Additional information collected included the following: number of times the symptom occurred, workdays lost partially or completely due to the health impairment, medication taken by victims, and direct costs due to the symptoms, that is, pharmacy cost, consulting fees, and indirect costs such as travel expenses to and from health centre and dietary expenses resulting from illness like drinking milk or taking honey.
As discussed earlier panel data setup was used to control for the unobserved heterogeneity. In general, panel data model offers some distinct advantages over the cross-sectional data analysis. Greene [
A general panel regression model is presented as
The two main methods of dealing with
The analysis was implemented in two steps. First, the COI model was estimated to evaluate the determinants of health costs among the vegetable farmers. Cost of illness was computed as the sum of farmer-reported medical treatment costs to clinics and private physicians, the opportunity cost of workdays lost to illness, travel costs to and from health facility, time spent in traveling, and the cost of home-based health care. In the second stage, the principal factors associated with the pesticide poisoning symptoms were examined seeking ones that are relevant at policy recommendation.
In previous studies, the health costs of pesticides were modeled using a Logarithmic regression model [
For the empirical model, the explanatory factors for the model explaining health costs incorporate four broad classes of variables, namely, those related to health (number of acute symptoms and symptoms severity), farmer characteristics variables (age, education, and gender), farm management variables (farm size (proxy for wealth), GLOBALGAP certification, and record keeping), and location control (district dummies) (see (
Descriptive statistics of variables used in empirical estimations (
Variables | Definition | Unit | Mean |
|
|
---|---|---|---|---|---|
2005 | 2008 | ||||
Dependent variables | |||||
TACUTE |
Number of symptoms | Count | 1.89 (0.13) | 1.09 (0.03) |
|
TACUTE | Number of symptoms | Count | 0.38 (0.48) | 0.37 (0.03) |
|
HEALTHCOST |
Cost of illness | US$ | 4.15 (1.70) | 7.98 (1.57) | 1.57 |
HEALTHCOST | Cost of illness | US$ | 0.84 (0.35) | 2.72 (0.58) | 2.80 |
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Farmer characteristics variables | |||||
AGE | Age of the farmer | Years | 43.19 (0.66) | 46.18 (0.67) | 6.30 |
AGESQ | Age of the farmer (years squared) | Years | 2024.43 (62.80) | 2292.64 (66.85) | 65.21 |
EDUCATION | 0 = none; 1 = preprimary; 2 = primary; 3 = secondary; 4 = college | Ordinal | 2.45 (0.05) | 2.51 (0.04) | 1.09 |
GENDER | Male | 1/0 | 0.70 (0.02) | 0.70 (0.02) | 0.00 |
EXPERIENCE | Farming experience | Years | 18.42 (0.74) | 20.56 (0.07) | 2.38 |
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Health-related and pesticide exposure variables | |||||
HEALTH | Farmer reported a symptom | 1/0 | 0.20 (0.02) | 0.34 (0.02) | 4.26 |
SEVERE | 1 = mild; 2 = severe; 3 = very severe | Ordinal | 1.11 (0.08) | 1.59 (0.36) | 1.22 |
PWHOIab | WHO Ia and WHO Ib (extremely hazardous) | g | 8.79 (2.32) | 33.55 (12.79) | 1.92 |
PWHOII | WHO category II (moderately hazardous) | g | 129.87 (10.15) | 432.63 (25.20) | 10.97 |
PWHOIII | WHO category III (slightly hazardous) | g | 18.95 (3.39) | 166.12 (19.23) | 7.45 |
PWHOU | WHO category U (no hazard) | g | 79.87 (7.47) | 167.79 (16.86) | 4.87 |
PESTHA | Total amount applied | g/ha/season | 1,473.00 (201.82) | 2,124.87 (118.28) | 2.97 |
NPEST | Pesticide products | Count | 2.89 (0.09) | 3.32 (0.08) | 3.37 |
COAT | Wear coat/apron | 1/0 | 0.49 (0.03) | 0.71 (0.02) | 6.06 |
GLOVE | Wear gloves | 1/0 | 0.26 (0.02) | 0.35 (0.02) | 2.49 |
GUMBOOT | Wear boots | 1/0 | 0.26 (0.02) | 0.89 (0.02) | 17.35 |
MASK | Wear facemask | 1/0 | 0.24 (0.02) | 0.40 (0.02) | 4.36 |
TPPE | Protective equipment | Count | 2.81 (0.07) | 4.00 (0.11) | 10.85 |
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Farm management variables | |||||
FARMSIZE | Total farm size | ha | 1.46 (0.08) | 1.06 (0.05) |
|
GLOBAL-GAP | GLOBALGAP certified farmers | 1/0 | 0.07 (0.01) | 0.19 (0.02) | 0.15 |
RECORD | Records keeping | 1/0 | 0.71 (0.02) | 0.32 (0.01) |
|
All monetary variables for example health cost were adjusted (normalized) to US$ of 2008 to take account of inflation. US$ = 72 KSh (2005) and 75 KSh (2008).
Source: this study.
It is hypothesized that the number of acute symptoms, symptom severity, age, and farm size are positively associated with the health costs, while a negative association is expected for level of education, GLOBALGAP certification, and record keeping. The direction of the effect of gender on health costs is not clear a priori.
It is anticipated that young farmers may have a higher tendency to protect against pesticides exposure and consequently reduce the pesticide-related acute symptoms and associated health costs. Increased education is also expected to reduce health costs because farmers are more likely to read pesticide labels and follow the recommendation, again reducing the exposure and the acute symptoms. Likewise, GLOBALGAP certification and record keeping can result in a more judicious use of pesticide and higher tendency to protect against pesticide intoxication resulting in reduced acute symptoms:
A Negative Binomial Regression model is a count data model and a good facet of the model is that the Poisson model is nested within it [
For the empirical model, the acute symptom model aggregates skin irritation, diarrhea, sneezing, headache, dizziness, vomiting, stomach poisoning, blurred vision, eye irritation, and backache episodes incurred by the farmer during and/or soon after spraying pesticide as the dependent variable. For the explanatory variables, the medical literature indicates that the type and severity of pesticide poisoning depend on the toxicity of the pesticides, amount of pesticides involved in the exposure, and route of exposure [
A priori, it is anticipated that WHO class Ia, Ib, and II pesticides are positively correlated with incidences of acute poisoning whereas negative correlation can be expected with category III and U pesticide. Pesticides in WHO Ia, WHO Ib, and WHO II are very harmful, while WHO III and WHO U are less harmful [
Age could increase acute symptoms, as older farmers may be less concerned about health effects of pesticides. As already mentioned in cost of illness model it is expected that pesticide-related acute symptoms decrease with the increase in level of education, GLOBALGAP certification, record keeping of production activities, and appropriate use of personal protective equipment:
Table
A total of 62 pesticides products, comprising 36 active ingredients formulated singly or in mixture, were used to control various vegetable pests in 2005. The number increased slightly to 66 products in 2008 with 44 active ingredients in the formulations. However, close analysis showed that 19 new products were applied in 2008, implying that 15 products of those used in 2005 were dropped. The commonly used products included dimethoate (WHO II), used by 48% of farmers, lambda cyhalothrin (WHO II, 27%), cymoxanil (WHO II, 22%), cypermethrin (WHO II, 22%), cyfluthrin (WHO Ib, 20%), mancozeb (WHO U, 18%), and deltamethrin (WHO II, 14%).
For minor poisoning, many farmers used home remedies such as milk, lemon juices, honey, and herbs. The medicines from the local pharmacy shops which were sometimes painkillers were bought in cases where the symptoms of illness were mild and farmers visited the health clinic if the symptoms either persisted or became serious; that is, the victim was unable to talk, walk, or see or vomited continuously. This evidence seems to suggest that many farmers treat acute pesticide effects as minor problems that do not warrant medical attention. Although in only about a quarter of the poisoning cases a physician was consulted, this cost component accounts for the largest share of the total cost of treatment.
The health cost almost doubled in 2008 as compared to 2005. On average, health cost was estimated at US$ 6.55/farmer/season for 28% of the farmers who reported pesticide-related illnesses. These costs equal 47% of mean household pesticides expenditures in 2008. Considering all the farmers this translates to a mean of US$ 1.77/farmer/season and assuming two crop seasons per year the costs amount to US$ 3.54/farmer/year. However, the true health costs are likely to be much higher because the costs arising from chronic diseases resulting from long-term pesticides exposure were not considered, as this would have required more detailed medical assessments. Moreover, only costs directly involving family members were reported; costs occurring to hired farm laborers were not included. Furthermore, other “costs” to restore health status completely and nonmonetary costs like suffering and income lost by family members assisting in seeking treatment were not captured [
Comparison with other studies conducted in developing countries shows similar results. Pingali et al. [
Pesticide application rate/hectare/season also increased by 47%. Comparison between the years for the specific farmers who participated in the DBM survey showed that many farmers had reduced the pesticides application rate by 8%, while the GLOBALGAP surveyed farmer had increased by 40%. Similar findings in support of the reduction of pesticide use were reported by Jankowski [
The estimation results of the Tobit models with the health costs as dependent variable are reported in Table
Tobit model for cost of illness estimations.
Model | Unrestricted | Restricted | ||
---|---|---|---|---|
Variables | (Coefficient) |
|
(Coefficient) |
|
|
||||
TACUTE | 7.45 (4.08) |
1.83 | 6.20 (2.00) |
3.10 |
SEVERE | 9.01 (2.52) |
3.58 | 11.07 (2.17) |
5.12 |
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AGE | −0.48 (1.09) | −0.44 | ||
AGESQ | 0.01 (0.01) | 0.61 | ||
EDUCATION | 1.46 (2.36) | 0.62 | ||
GENDER | −2.84 (4.33) | −0.66 | ||
FARMSIZE | 3.25 (2.88) | 1.13 | ||
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GLOBALGAP | −21.75 (3.40) |
−1.62 | −18.71 (7.47) |
−2.50 |
RECORD | −1.08 (4.89) | −0.22 | ||
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KIAMBU | 2.50 (10.31) | 0.24 | ||
MAKUENI | −15.08 (17.25) | −0.87 | ||
MERU CENTRAL | 1.65 (8.71) | 0.19 | ||
MURANGA | −5.48 (11.75) | −0.47 | ||
NYANDARUA | −5.61 (8.81) | −0.64 | ||
NYERI NORTH | 6.62 (8.47) | 0.78 | ||
YEAR 2008 | 7.93 (9.15) | 0.87 | ||
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Constant | −23.23 (29.41) | −0.79 | −19.54 (4.45) |
−4.39 |
Log likelihood | −464.10 | −549.55 | ||
Wald |
40.18 |
43.22 |
Source: this study.
The finding that the GLOBALGAP certification tends to decrease the health cost could indicate that the certified farmers use adequate safety precautions or use low toxic pesticides, which generally reduce the health impairments and thus decrease costs. It could also be that these farmers are able to use the minimum treatment possibilities.
Among the farmers’ characteristics variables, that is, age, education, and gender, none had any discernible effect on health costs. In addition, farm size though considered as an indicator of wealth does not have a direct effect on health costs, though it has the correct sign. Perhaps it could be because farms do not present “liquid cash” that can be accessed immediately in time of need. In addition, no direct association was found between record keeping and the health costs.
District controls are insignificant, so location does not directly affect the health costs. When the model was reestimated (restricted) by dropping insignificant variables, the estimates of the coefficients were robust.
Given the critical contribution of pesticide-related acute symptoms to the health costs as indicated in Table
Binomial Regression model for the acute symptoms estimations.
Model | Unrestricted | Restricted | ||
---|---|---|---|---|
Variables | (Coefficient) |
|
(Coefficient) |
|
|
||||
AGE | 0.04 (0.04) | 1.13 | ||
AGESQ | −0.00 (0.00) | −1.21 | ||
EDUCATION | −0.16 (0.07) |
−2.13 | −0.14 (0.07) |
−1.94 |
GENDER | −0.10 (0.16) | −0.67 | ||
GLOBALGAP | −0.33 (0.29) | −1.11 | ||
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RECORD | −0.44 (0.17) |
−2.57 | −0.55 (0.15) |
−3.77 |
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NPEST | 0.09 (0.05) |
1.88 | 0.10 (0.05) |
2.39 |
PWHOIab | 0.00 (0.00) | 1.28 | ||
PWHOII | 0.00 (0.00) | 0.68 | ||
PWHOIII | −0.00 (0.00) | −0.28 | ||
PWHOU | −0.00 (0.00) | −0.13 | ||
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COAT | −0.29 (0.16) |
−1.82 | −0.29 (0.15) |
−2.03 |
GLOVE | −0.26 (0.21) | −1.23 | ||
GUMBOOT | 0.32 (0.23) | 1.36 | ||
MASK | −0.35 (0.20) |
−1.74 | −0.39 (0.17) |
−2.30 |
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KIAMBU | 1.69 (0.36) |
4.67 | 1.63 (0.32) |
5.20 |
MAKUENI | 1.74 (0.49) |
3.55 | 1.50 (0.46) |
3.35 |
MERU CENTRAL | 1.18 (0.31) |
3.82 | 0.95 (0.25) |
3.77 |
MURANGA | 0.64 (0.46) | 1.40 | ||
NYANDARUA | 0.90 (0.34) |
2.66 | 0.80 (0.28) |
2.81 |
NYERI NORTH | 0.93 (0.30) |
3.07 | 0.79 (0.24) |
3.26 |
YEAR 2008 | −0.05 (0.21) | −0.23 | ||
Constant | −1.22 (1.06) | −1.15 | −0.01 (0.48) | −0.02 |
Log likelihood | −518.85 | −535.52 | ||
Wald |
73.74 |
60.96 |
Source: this study.
The model shows that pesticide-related acute symptoms increase significantly with the number of pesticide products handled. This is not surprising, given that different pesticide products require different application rates and have different levels of toxicity. In addition, handling different pesticide products can increase incidences of symptoms since an interaction between pesticides can lead to unknown toxic chemical reactions [
The level of education reduces the probability of reported symptoms, which implies that farmers with a higher education level are more knowledgeable and therefore have a better understanding of the dangers posed by pesticides. In previous studies, however, the contrary effect was found because respondents with higher knowledge were more likely to report more health symptoms [
The use of personal protective equipment particularly the use of a coat/apron and facemask significantly reduced the number of symptoms. Exposure to pesticides is often attributed to a failure to use protective equipment [
Location control for agroecology and differences in institutional settings shows that farmers in the districts of Kiambu, Meru Central, Makueni, Nyandarua, and Nyeri North experience significantly high cases of pesticide ascribed health symptoms as compared to Kirinyaga (base). Perhaps this is due to the use of protective equipment by farmers located in Kirinyaga.
Contrary to the expectations, the analysis does not support the hypothesis of a significant influence of GLOBALGAP certification on the outcome of health, but the variable has the correct signs. Once again, the low number of farmers who were certified and the failure of the certified farmers to maintain their certification may be the cause of the insignificance. The hypothesis that gender and age have a stronger relation to the acute symptoms is also not supported by the results.
The likelihood ratio test used to assess the statistical quality of the model showed that the model was statistically valid, that is, dispersion parameter alpha was greater than zero. The reduced model with only the variables that had a significant effect on the dependent variable shows that the statistical quality of the model does not differ much and the directions of the coefficient are identical, suggesting the robustness of the model.
The results of the study give indications of increase of pesticide-related health impairments with over 70% new episodes. The most frequently reported symptoms were sneezing, dizziness, headache and blurred vision, and skin irritations. The result further shows that farmer loses on average US$ 3.54/farmer/year on pesticide-related indirect health costs. These costs are significantly explained by variation in number of symptoms and severity of the symptoms. Pesticide-related acute symptoms increase significantly with the number of pesticide products handled and considerably reduce with level of education, use of PPE, and record keeping. These findings hint at some important points for policies aiming to reduce pesticide poisoning among vegetable farmers. Firstly, the results support the already widely known notion that proper use of PPE (coat/apron and facemask) reduces the pesticide-related impairment. Encouraging of PPE and record keeping of pesticide use activities by farmer is thus recommended.
Future efforts to measure pesticide-related health costs should cover the health costs of all individuals exposed to pesticides, for example, entire public, consumers, and hired workers, and also incorporate pesticide-induced chronic illnesses and deaths.
The author declares that there is no conflict of interests regarding the publication of this paper.
This research was supported by DAAD and an ICIPE-GTZ/BMZ funded project. The author expresses appreciation to Professor Dagmar Mithöfer, Professor Hermann Waibel, farmers, and staff members of the Ministry of Agriculture and other individuals who provided valuable assistance.