Kouamé et al. BMC Public Health (2022) 22:2443 https://doi.org/10.1186/s12889-022-14446-5 RESEARCH Open Access Education and Socio-economic status are key factors influencing use of insecticides and malaria knowledge in rural farmers in Southern Côte d’Ivoire Ruth M. A. Kouamé1,2, Federica Guglielmo3, Kouabénan Abo1, Allassane F. Ouattara2,4, Joseph Chabi5, Luigi Sedda6, Martin J. Donnelly3 and Constant Edi2* Abstract Background: Insecticides play a key role in rural farming; however, their over- or misuse has been linked with a negative impact on malaria vector control policies. This study was conducted amongst agricultural communities in Southern Côte d’Ivoire to identify which insecticides are used by local farmers and how it relates to the perception of farmers on malaria. Understanding the use of insecticides may help in designing awareness programme on mosquito control and pesticides management. Methods: A questionnaire was administered to 1399 farming households across ten villages. Farmers were inter- viewed on their education, farming practices (e.g. crops cultivated, insecticides use), perception of malaria, and the different domestic strategies of mosquito control they use. Based on some pre-defined household assets, the socio- economic status (SES) of each household was estimated. Statistical associations were calculated between different variables, showing significant risk factors. Results: The educational level of farmers was significantly associated with their SES (p < 0.0001). Most of the house- holders (88.82%) identified mosquitoes as the principal cause of malaria, with good knowledge of malaria resulting as positively related to high educational level (OR = 2.04; 95%CI: 1.35, 3.10). The use of indoor chemical compounds was strongly associated to the SES of the households, their education level, their use of ITNs and insecticide in agricultural (p < 0.0001). Indoor application of pyrethroid insecticides was found to be widespread among farmers as well as the use of such insecticide for crops protection. Conclusion: Our study shows that the education level remains the key factor influencing the use of insecticides by farmers and their awareness of malaria control. We suggest that better communication tailored to education level and including SES, controlled availability and access to chemical products, should be considered when designing cam- paigns on use of pesticides and vector borne disease control for local communities. Keywords: Insecticides, Mosquitoes, Socioeconomic status, Farmers, Malaria, Côte d’Ivoire Introduction Agriculture represents the key economic driver in many *Correspondence: constant.edi@csrs.ci West African countries. In 2018 and 2019, Côte d’Ivoire 2 Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, 01 BP 1303, was the world’s leading producer of cocoa and cashew Abidjan, Côte d’Ivoire Full list of author information is available at the end of the article nuts and the third African producer of coffee [1], with © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://c reati veco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ public doma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Kouamé et al. BMC Public Health (2022) 22:2443 Page 2 of 11 agricultural services and products representing 22% of department of the Agnéby-Tiassa region [38]. The climate the gross domestic product (GDP) [2]. Rural smallhold- is tropical with two rainy seasons (April to July and Octo- ers are the main contributors to the economic develop- ber to November) [39, 40]. Farming is the main activ- ment of this sector as owners of most farming lands [3]. ity in the area, and it is conducted by smallholders and The country has considerable agricultural potential with large agro-industrial firms. The 10 sites included Aboude 17 million ha of agricultural land and seasonal variation, Boa Vincent (323,729.62 E, 651,821.62  N), Aboude which facilitate crop diversification and the cultivation Kouassikro (326,413.09 E, 651,573.06  N), Aboude of coffee, cocoa, cashew, rubber, cotton, yam, palm, cas- Mandeke (330,633.05 E, 652,372.90  N), Amengbeu sava, rice, vegetables [2]. Intensified agriculture favours (348,477.76 E, 664,971.70 N), Grand Morie (374,039.75 E, the proliferation of pests, controlled mainly through 661,579.59 N), Guessiguié 1 (363,140.15 E, 634,256.47 N), increased application of pesticides [4] especially among Loviguie 1 (351,545.32 E, 642,062.37 N), Offa (350,924.31 rural farmers in order to protect crops and increase pro- E, 654,607.17 N), Offompo (338,578.51 E, 657,302.17 N) ductions [5], and to control mosquitoes [6]. However, and Ouanguie (363,990.74 E, 648,587.44 N). inappropriate insecticide use is among the leading causes of insecticide resistance in disease vectors, especially in agricultural areas where mosquitoes and crop pests may Study design and data collection be subject to selection pressure from the same insecti- The study was carried out between August 2018 and cides [7–10]. The use of insecticides requires attention as March 2019 and involved agricultural households. The a factor that could impact vector control strategies and total number of inhabitants per village was obtained from environment by polluting [11–15]. the local services, and 1,500 were randomly selected from Pesticides use among farmers has been investigated in this list. Participants recruited represented between 6 the past [5, 16]. Education level has been shown to be and 16% of the village population. Households included a key factor in the correct use of insecticides [17, 18], in the study were those belonging to farmers who gave although farmers pesticide use tend to rely on empiri- their consent to participate. A pre-questionnaire was cal experience or on the advice of retailers [5, 19, 20]. conducted among 20 farmers to evaluate if there was a Financial difficulties are among the most common bar- need to rewrite some questions. Then the questionnaire riers constricting access to insecticides or pesticides, was conducted by trained and paid data collectors in each steering farmers towards banned or obsolete products, village, with at least one recruited from the village itself. often found at lower prices than legal products [21, 22]. This choice ensured that at least one data collector in Similar trends have been reported in other West African each village was familiar with the environment and spoke countries, where low income was a reason for buying and the local language. In each household, a face-to-face using inappropriate pesticides [23, 24]. interview was conducted with the household head (father In Côte d’Ivoire, there is a widespread use of pesticides or mother) or, in case of their absence, with another adult in crops [25, 26], which influences agricultural prac- aged above 18. The questionnaire contained 36 questions, tices and malaria vector population [27–30]. Studies in structured into three parts: (1) demographic and socio- malaria-endemic regions, have shown a relation between economic background of the household; (2) agricultural socioeconomic position and perceptions of malaria and practices and pesticide use; and (3) knowledge of malaria risks of infection, as well as the use of insecticide-treated and use of insecticide against mosquitoes [see Additional nets (ITNs) [31–37]. Despite these studies, efforts to file 1]. establish concrete policies towards the management of mosquitoes are undermined by a lack of information Data analysis about insecticides use in rural areas and the factors that Pesticides mentioned by farmers were coded by their could promote their appropriate use. This study investi- commercial names and sorted by active ingredients gates the perception of malaria and the strategies used to and chemical families using the Ivorian phytosanitary control mosquitoes among agricultural households from index [41]. Socioeconomic status of each household was Agboville, Southern Côte d’Ivoire. assessed by calculating asset index [42]. Households assets were transformed into dichotomous variables [43]. Methods A negative factor score was associated with lower soci- Description of the study area oeconomic status (SES) while a positive one to higher The study was carried out in ten villages of the depart- SES. The asset scores were summed to a total score for ment of Agboville in Southern Côte d’Ivoire (Fig. 1). The each household [35]. According to their total scores, the department of Agboville has a population of 292,109 households were categorized into five SES quintiles, from inhabitants within 3,850 km2 and it is the most populated the poorest to the wealthiest [see Additional file 4]. K ouamé et al. BMC Public Health (2022) 22:2443 Page 3 of 11 Fig. 1 Localities surveyed in the department of Agboville To determine whether a variable varied significantly across SES, villages or educational level of the household head, either Chi square or Fisher’s exact tests was used as appropriate. Logistics regressions models were fitted with the following predictors: education level, SES (both con- Table 1 Characteristic of household heads verted into dichotomous variables), villages (including as Variables Number Frequency (%) [CI]a categorical variable), high knowledge of malaria and agri- cultural insecticide use, with as output the indoor insec- Sex ticide use (by spray can or coil); education level, SES and Male 1283 91.71 [90.14–93.10] villages, with as output the high knowledge of malaria. Female 116 8.29 [6.89–9.86] This logistic mixed regression model was performed using Educational level R package lme4 (glmer function). Statistical analyses were Illiterate 248 17.97 [15.98–20.10] conducted in R 4.1.3 (https:// www.r-p roje ct. org) and Koranic school 44 3.19 [2.33–4.26] Stata 16.0 (StataCorp, College Station, Texas). Primary 563 40.80 [38.19–43.44] Secondary 461 33.41 [30.92–35.96] University 64 4.64 [3.59–5.88] Results Presence of children (Yes) 1276 91.21 [89.60–92.64] Sociodemographic characteristics of household heads Profession Out of the 1,500 interviews conducted, 101 were Only farming 1250 89.35 [87.61–90.91] excluded from analysis due to the uncompleted question- Farming + other activities 149 10.65 [9.08–12.39] naire. The highest proportion of interviewed households a CI = Confidence intervals Kouamé et al. BMC Public Health (2022) 22:2443 Page 4 of 11 was in Grand Morie (18.87%) and the lowest in Ouan- Among prevention strategies against malaria, inter- guie (2.29%). The 1399 interviewed households included viewees mentioned the use of traditional medicines; in the analysis represented a population of 9,023 people. however, in case of illness, both biomedical and tradi- Household heads were 91.71% males and 8.29% females tional approaches to treat malaria were mentioned as as shown in Table 1. viable options (80.01%), with preferences significantly About 8.86% of household heads were from neigh- associated to SES (p < 0.0001): farmers with higher SES bouring countries such as Benin, Mali, Burkina Faso, and preferred, and were able to afford, biomedical treat- Ghana. The most represented ethnic groups were Abbey ment; at a lower SES, famers leaned towards more tra- (60.26%), Malinké (10.01%), Krobou (5.29%) and Baoulé ditional, herbal treatments. The mean yearly money (4.72%). As expected by selecting agricultural house- spent by almost half of households to treat malaria holds, farming was the only source of income for most was more than 30,000 XOF (negatively associated with of them (89.35%); cocoa crops were the most cultivated SES; p < 0.0001). According to self-reported estimates by households sampled. There were also vegetables, food of direct expenses, households with the lowest SES market crops, rice, rubber, and plantain cultivation on were more likely to spend up to 30,000 XOF (around relatively small land surfaces. The rest of household heads 50 US dollars) more for malaria treatment than house- were traders, art professionals and fishermen (Table 1). A hold with the highest SES. Furthermore, most of the summary of the household characteristics by village is respondents perceived malaria to occur more often in shown in an additional file [see Additional file 3]. children (49.11%) than in adults (6.55%) (Table 2), a per- Education category did not differ by gender (p = 0.4672). ception more frequent among households belonging to The respondents with elementary (primary) educational the poorest quintiles (p < 0.01). level were the majority (40.80%) followed by second- Against mosquito bites, most participant (85.20%) ary education level (33.41%) and illiterate (17.97%). Only reported using ITNs, which they largely received during 4.64% have reached the university (Table  1). Among the a national distribution occurred in 2017. Both adults and 116 women interviewed, more than 75% have at least the children were reported sleeping under ITNs in 90.99% primary level and the others had never gone to school. of the households. In all the villages, the frequency of Education levels of farmers varied significantly between households with ITNs use was higher than 70%, except villages (Fisher’s exact test, p < 0.0001) and the household the village of Guessiguié where only 40% households heads educational level significantly and positively associ- reported to use them. The mean number of ITNs owned ated with their SES (Fisher’s exact test p < 0.0001). In fact, by households was significantly and positively associ- the higher SES quintiles are characterised mostly by farm- ated to the dimensions of the household (Pearson’s cor- ers with higher level of education; and conversely the low- relation r = 0.41, p < 0.0001). Our findings also show that est SES quintiles by illiterate farmers. According to their households with children under 1 year old are more likely total score of assets, sampled households were ranked to use ITNs in the house compared to households with into five wealth quintiles, from the poorest (Q1) to the none or older children (Odds Ratio (OR) = 2.08, 95%CI: wealthiest (Q5), [see Additional file 4]. 1.25–3.47). The marital status of household heads was significantly different between wealth classes (p < 0.0001), with 83.62% Farmers insecticide uses being monogamous and 16.38% polygynous (with up Apart from ITNs use, farmers were asked about other to three spouses). No significant difference was found means for mosquito control in their houses and about between the wealth classes and the number of spouses. agricultural products used against crops pests. Only 36.24% of the participants mentioned spraying insecti- Knowledge of malaria, malaria treatments, and use of ITNs cides in their houses (significantly and positively asso- Most of the interviewees (88.82%) identified mosquitoes ciated to SES p < 0.0001). The chemical compounds among the causes of malaria. Only 1.65% answered that reported belonged to nine commercials brand, mostly they did not know what the cause of malaria was. Other provided by the local market and some retailers in the causes identified were drinking dirty water, exposure to form of fumigating coils (16.10%) and insecticides sprays sun, bad food, and fatigue (Table 2). At the village level, in (83.90%). The ability of farmers ability to provide the Grand Morie, most of the households identified drinking name of insecticides sprayed in their houses increased dirty water to be the main cause of malaria (statistically dif- with their educational level (12.43%; p < 0.05). The agro- ferent between villages, p < 0.0001). The two main identified chemical products in use were bought originally in symptoms of malaria were high body temperature (78.38%) cans and diluted in sprayers before application, with and yellow eyes (72.07%). Farmers also mentioned vomit- the highest proportion being generally aimed at crops ing, anaemia, and paleness (see Table 2 below). (78.84%) (Table 2). The village of Amangbeu recorded the K ouamé et al. BMC Public Health (2022) 22:2443 Page 5 of 11 Table 2 Household head perception on malaria knowledge, prevention and treatment related to their SES Poorest Very poor Poor Less poor Wealthiest Total N (%) N = 298 N = 298 N = 252 N = 273 N = 278 Malaria causes* Mosquitoes 699 (88.82) 80.1 89.89 88.72 96.58 92.44 Dirty water 375 (47.65) 15.42 57.98 50.38 72.6 52.1 Sun 251 (31.89) 23.38 31.38 33.83 42.47 31.93 Bad food 43 (5.46) 1.49 5.32 6.02 9.59 6.72 Fatigue 41 (5.21) 5.97 5.85 5.26 4.79 3.36 Unknow 13 (1.65) 2.99 0.53 2.26 0 2.52 Malaria symptoms* Hot body 609 (78.38) 60.71 79.14 78.63 86.9 95.76 Vomiting 478 (61.52) 58.67 63.64 55.73 62.76 67.8 Pale body 378 (48.65) 33.16 53.48 46.56 53.79 62.71 Yellow eyes 560 (72.07) 39.8 79.14 76.34 87.59 90.68 Yellow urine 505 (64.99) 29.08 74.87 63.36 80.69 91.53 Anaemia 407 (52.38) 30.1 56.68 48.85 61.38 75.42 Malaria frequency on adults Rarely 875 (63.64) 68.28 65.65 63.01 57.56 63.14 Often 410 (29.82) 22.76 28.57 31.3 34.69 32.48 Very often 90 (6.55) 8.97 5.78 5.69 7.75 4.38 Malaria frequency on children Rarely 118 (8.75) 6.79 9.44 6.22 5.95 15.07 Often 568 (42.14) 42.14 43.01 45.23 43.12 37.5 Very often 662 (49.11) 51.07 47.55 48.55 50.93 47.43 Way of treatment Traditional 34 (2.52) 3.94 2.06 3.28 2.23 1.12 Modern 236 (17.47) 11.47 12.71 15.16 21.93 26.49 Both 1081 (80.01) 84.59 85.22 81.56 75.84 72.39 Cost of treatment < 10 000 107 (7.76) 6.19 9.46 4.49 5.17 13.04 10 000–30 000 302 (21.9) 18.56 22.64 20.82 22.14 25.36 > 30 000 572 (41.48) 45.02 36.15 44.9 47.6 34.42 Unknow 398 (28.86) 30.24 31.76 29.8 25.09 27.17 Bed net usage 1192 (85.2) 77.85 85.23 84.92 89.38 89.21 Insecticide use in crops 1103 (78.84) 64.09 80.54 79.76 79.49 91.37 Insecticide use in houses 507 (36.24) 15.1 36.91 34.52 46.15 50.00 Weekly insecticide use in houses < 3 times 160 (32.72) 41.46 43.93 26.51 26.23 30.88 3–4 times 290 (59.3) 43.9 47.66 67.47 66.39 61.76 > 4 times 39 (7.98) 14.63 8.41 6.02 7.38 7.35 * Farmers reported more than one variable lowest proportions of farmers that use insecticides both used (Fisher exact test p < 0.0001); however, in some in houses (0.93%) and in crops (16.67%). cases, such products were found to have the same active The maximum number of declared insecticidal prod- ingredients under different commercial names. Table  2 ucts per house, as spray or coils, was three, with a posi- shows the frequency of weekly insecticides applied by tive association between SES and the number of products farmers according to their SES. Kouamé et al. BMC Public Health (2022) 22:2443 Page 6 of 11 Fig. 2 Chemical families of insecticides applied by farmers in their houses (A) and in their crops B Pyrethroids were the most represented chemical family main cause of malaria, which was negatively associated among both domestic (48.74%) and agricultural (54.74%) with insecticide use (OR = 0.07, 95%CI: 0.03, 0.13)) insecticides sprays. Products were made of each insecti- (Fig.  3). Among these positive predictors, agricul- cide or in combination with other insecticides. The com- tural insecticides use represents an interesting factor. mon combination in domestic insecticides were made of Farmers who applied insecticides to their crops were Carbamate, Organophosphate and Pyrethroid whereas 188% more likely to apply insecticides in their houses in agricultural insecticide, Neonicotinoid and Pyrethroid (95%CI: 1.12, 8.26). However, insecticide use in houses were prevalent (Additional file 5). Figure 2 shows the pro- was less likely in those households with high knowl- portion of the different insecticide families used by farm- edge of malaria transmission. Knowledge of mosqui- ers, all of which were identified as belonging to class II toes as the main cause of malaria was more likely in (moderately hazardous) or class III (slightly hazardous) high education level groups (OR = 2.04; 95%CI: 1.35, according to the WHO classification of pesticides [44]. 3.10) but not statistically associated with high SES On one occasion Deltamethrin insecticide designed for (OR = 1.51; 95%CI: 0.93, 2.46). agricultural purposes was found to be used domestically. Regarding active ingredients, Propoxur and Deltame- thrin were the most common products intended for Perception on insecticides effect on mosquito behaviour domestic and field use respectively. Details about the According to the household heads, mosquito abun- chemical products used by farmers both in their houses dance peaks during the wet season, citing the night as and in their crops are reported in Additional file 5. the time of most frequent bites (85.79%). As farmers Farmers mentioned other means of mosquito control, were questioned about their perception of the effect including fans made of leaves (pêpê in local language of insecticides spraying on mosquito malaria vector Abbey), burning some leaves, cleaning the surroundings, population, 86.59% affirmed that mosquitoes seemed removing all stagnant water, using mosquito repellents, to become resistant to insecticides. The inability to or simply chasing mosquitoes away with bed clothes. use enough chemical product(s) due to unafford- Factors associated with farmers knowledge of malaria able costs was indicated as the main contributor to and indoor insecticides spray (logistic regression insecticide resistance; product inefficacy or misuse analyses). were identified as other determinant factors. The Data showed significative associations between latter, specifically, was associated with lower educa- indoor insecticide spray use and the five predictors: tional status (p < 0.01) even when controlling for SES education level, SES, knowledge of mosquitoes as the (p < 0.0001). Mosquito robustness was identified as main cause of malaria, use of ITN, and agrochemical one possible reason of insecticide resistance by only insecticide use. The Fig.  3 shows the different OR of 12.41% of interviewees. each predictor. When clustering between villages, all There was a positive association between the fre- the predictors showed positive association with insec- quency of insecticide used in the households and the ticide spray use in houses (except knowledge of the perception on insecticide resistance in mosquitoes K ouamé et al. BMC Public Health (2022) 22:2443 Page 7 of 11 Fig. 3 Odds Ratio (OR) of the five predictors of the indoor insecticide use (p < 0.0001): insecticide resistance in mosquitoes was Household heads perception of malaria reported mostly by farmers who used insecticides in Malaria causes and symptoms were well known to the their houses 3 to 4 times per week (90.34%). Apart from participants as in many malaria endemic regions [33, the frequency, the number of insecticides used was also 46–49]. In general, awareness of children susceptibil- positively associated to insecticide resistance percep- ity to malaria was widespread [31, 34]. This awareness tion in farmers (p < 0.0001). might derive from a combination of the susceptibility of children as well as of the intensity of symptoms shown in Discussion case of malaria [50, 51]. This study focused on perceptions of malaria and uses Participants reported spending an average annual of insecticides among farmers. Our findings show that amount of 30,000 XOF for malaria treatment—half of the both education and SES play a key role in behavioural guaranteed minimum wage per month recommended habits and knowledge of malaria. Although most of in Côte d’Ivoire—and this is an underestimation: many the household heads attended primary schools, the farmers rely on traditional medicines and the figure proportion of farmers with no education was consid- addresses direct costs only, leaving aside factors such as erable, as found elsewhere [35, 45]. This observation loss of productivity, transport etc. could be explained by the fact that even if many farm- A comparison between farmers SES showed that farm- ers began education, most of them had to leave school ers with the lowest SES reported spending more money to support the family through agricultural activi- than the wealthiest one. This might be due to the lowest ties [26]. Conversely, this phenomenon highlights the SES households perceiving the expense as higher—since connection between SES and education as essential higher is its weight on the overall household finances— to explain the link between SES and the ability to act or on the collateral benefits of being employed in the upon the information received. public and private sector, as it was the case of wealthier Kouamé et al. BMC Public Health (2022) 22:2443 Page 8 of 11 households: thanks to health insurance, the money spent the model. When taking the overall population, this pre- on malaria treatment (versus the overall cost) might be dictor was positively associated with insecticide use but considerably inferior than the amount spent by house- after clustering between villages, it was negatively asso- holds who do not benefit from insurance [52]. In fact, ciated with insecticide use. This result shows the impor- wealthiest households were reported to use mainly bio- tance of anthropophagic effect on people behaviour and medical treatment when compared to poorest ones. the need to consider include the random effect in the analysis. Our study is the first which shows that farmers ITNs and insecticide use with experience of agricultural insecticide use are most Even if most farmers identified mosquitoes as the princi- inclined than others to use sprays and coils insecticides pal cause of malaria, only few of them used insecticides as a domestic strategy against malaria. in their houses (by spraying and fumigant coils), similarly Echoing previous work on SES influence on the to findings from Cameroon and Equatorial Guinea [48, attitudes of farmers towards pesticides [16, 60–63], 53]. This lack of attention to mosquitoes compared to wealthier households reported higher variability and crop pests is due to the economic values of the crops. To frequency of insecticides use. Interviewees considered limit expenditures, low-cost method like burning leaves spraying large amounts of insecticide the best mean to in their houses or simply chasing mosquitoes manually avoid mosquito resistance, in line with concerns high- were preferred. Perceived toxicity is likely to be a factor lighted elsewhere [64]. Along these lines, farmers used too: the smell of some chemical products and bad expe- domestic products that displayed the same chemical riences after their usage led some users avoiding them profile under different commercial names, which sug- [54]. The high presence (with 85.20% reporting use) of gests that farmers technical knowledge of the prod- ITNs in the households further motivates the low use of ucts and their active ingredients should be prioritised. insecticides against mosquitoes. The presence of ITNs in Attention should also be paid to retailers’ knowledge, households was also highly correlated with the presence as they are one of the main point of reference of insec- of children under 1 years old, possibly due to ante-natal ticide buyers [17, 24, 65–67]. clinic support whereby pregnant women receive ITNs To positively affect insecticide, use in rural communi- during prenatal consultation [6]. ties, policies and interventions should focus on improv- Pyrethroids being the main class of insecticides used ing communication strategies, considering educational both on ITNs [55] and by farmers for pests and mosquito level and behavioural habits within cultural and contex- control raises concerns on the surge of insecticide resist- tual adaptations, and on making safe insecticides accessi- ance [55–59]. This situation could explain the reduction ble. People will buy according to the cost (how much they of mosquito susceptibility to insecticides observed by can afford) and the quality of the product. Once quality farmers. is offered at an acceptable cost, the need for behaviour change in buying good product is expected to be greatly SES and education related to farmers knowledge improved; educating farmers on insecticides alternation of malaria and attitudes toward insecticides to break down the chain of insecticide resistance, clari- A higher SES was not related to better knowledge of fying that alternation does not mean change in product malaria and of mosquitoes as its cause. Unlike previous brand (since different brands have the same active com- findings of Ouattara and colleagues in 2011 showing that pound) but in the active ingredient. This education could wealthier people tend to better identify malaria causes be also supported by better labelling on products through because of their easy access to information via TVs and easy and comprehensible representations. radio [35]. Our analyses revealed that higher education level was the predictor which influence better knowledge Conclusion of malaria. This observation confirms that education As pesticides are widespread used among rural farmers remains a key element in knowledge of malaria in farm- in the department of Agboville, understanding knowl- ers. The low impact of SES could be explained by the fact edge gaps and attitude of farmers towards the use of that sharing TVs and radio in village are not uncommon. insecticides in their environment, appears as a prereq- However, SES should be considered when implement- uisite for designing successful awareness programmes. ing knowledge in terms of domestic strategies of malaria Our study confirms that education remains the main ele- avoidance. ment for a correct use of insecticides and knowledge of Higher SES and higher education level was positively malaria. Household socioeconomic status was identified associated with domestic insecticide use (spray, or coil). to be an important tool to be considered as well. Apart Surprisingly, the ability of farmers to identify mosquitoes from household heads SES and their educational level, as the main cause of malaria had a negative influence in other patterns like the knowledge of malaria, insecticide K ouamé et al. BMC Public Health (2022) 22:2443 Page 9 of 11 use for agricultural pest management, and perception of Funding insecticide resistance in mosquitoes were found to influ- MRC Funding Body Grant Ref: MR/P02520X/1; Royal Society Wolfson Fel- lowship, RSWF\FT\180003; Dr Edi is supported by Wellcome Trust funding ence farmers attitude towards insecticide use. 110430/Z/15/Z. Limitations Availability of data and materials The datasets used and/or analysed during the current study are available from Respondent-dependent methods such as question- the corresponding author on reasonable request. naires are vulnerable to recollection and social desira- bility bias. Using household characteristic for assessing Declarations SES is relatively easy although these indicators may be specific to the temporal and geographical context in Ethics approval and consent to participate The study protocol was approved by the Comité National d’Ethique des Sci- which they were developed and unevenly capture con- ences de la Vie et de la Santé (catalog no. 168–18/MSHP/CNESVS- km). Prior temporary realities of culture-specific items of value, the study, a written informed consent was obtained from all participants. At making comparison across studies difficult. In fact, recruitment, it was explained to the prospective participants that participation was voluntary and that they had a right to withdraw at any time. All methods there may be significant changes in household owner- were performed in accordance with the Declaration of Helsinki. ship of the index components, which may not neces- sarily translate into a reduction in material poverty. Consent for publication Not applicable. Some farmers did not remember the insecticide prod- uct’s name, therefore the number of pesticides used by Competing interests farmers can be under- or overestimated. Our study did The authors declare they have no competing interests. not consider farmers attitudes when spraying pesti- Author details cides and their perceptions of the consequences of their 1 Institut National Polytechnique Félix Houphouët Boigny, BP 1093 Yamous- behaviours on their health and environments. Retailers soukro, Côte d’Ivoire. 2 Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, 01 BP 1303, Abidjan, Côte d’Ivoire. 3 Liverpool School of Tropical Medi- also were not included in the study. Both points may be cine, Vector Biology Department, Pembroke Place, Liverpool L3 5QA, United investigated in future research. Kingdom. 4 Université Nangui Abrogoua, 02 BP 801 Abidjan, Côte d’Ivoire. 5 Noguchi Memorial Institute for Medical Research, Accra, Ghana. 6 Lancaster Ecology and Epidemiology Group, Lancaster Medical School, Lancaster Univer- Abbreviations sity, Furness Building, Lancaster LA1 4YG, United Kingdom. CI: Confidence Intervals; ITNs: Insecticidal Treated Nets; OR: Odds Ratio; SES: Received: 14 January 2022 Accepted: 25 October 2022 Socioeconomic status; WHO: World Health Organization. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/s 12889-0 22-1 4446-5. References 1. ICCO. International Cocoa Organization-Cocoa Year 2019/20. 2020. Avail- able at https://w ww.i cco. org/ aug- 2020- quart erly- bulle tin- of- cocoa-s tati Additional file 1. Questionnaire form. stics/. Additional file 2. Information sheet for volunteer householders in the 2. FAO. Adapting irrigation to climate change (AICCA). 2020. Available at socio-economic survey. https:// www. fao. org/ in- action/ aicca/ countr y-a ctiv ities/ cote-d ivoir e/ backg round/e n/. Additional file 3. Farmers household characteristics among the ten locali- 3. Sangaré A, Koffi E, Akamou F, Fall CA. Rapport national sur l’état des res- ties sampled, frequency (percentage). sources phytogénétiques pour l’alimentation et l’agriculture. Ministère de Additional file 4. Variables used in principal component analysis for l’agriculture-République de Côte d’Ivoire. Second rapport national; 2009. describing index of wealth of each household. p. 65. Additional file 5. 4. Kouamé N, N’Guessan F, N’Guessan H, N’Guessan P, Tano Y. Variations Active ingredients and chemical classes of insecticides saisonnières des populations de mirides du cacaoyer dans la région de used by farmers’ crops and houses. l’Indénié-Djuablin en Côte d’Ivoire. J Appl Biosci. 2015;83:7595. https:// doi. org/ 10. 4314/ jab. v83i1.2. Acknowledgements 5. Fan L, Niu H, Yang X, Qin W, Bento CPM, Ritsema CJ, et al. Factors affecting The authors would like to thank the researchers of GAARDIAN project, the farmers’ behaviour in pesticide use: Insights from a field study in northern village chiefs and population of Aboude Boa Vincent, Aboude Kouassikro, China. Sci Total Environ. 2015;537:360–8. https://d oi.o rg/ 10. 1016/j. scito Aboude Mandeke, Amengbeu, Grand Morie, Guessiguié 1, Loviguie 1, Offa, tenv. 2015. 07. 150. Offompo and Ouanguie, for their support. Thanks also to the investiga- 6. WHO. The “World malaria report 2019” at a glance. 2019. https://w ww. tors and the respondents, without whom the data would not have been who. int/ news-r oom/ featu re-s tori es/d etail/w orld- malar ia- report- 2019. collected. 7. Gnankiné O, Bassolé IHN, Chandre F, Glitho I, Akogbeto M, Dabiré RK, et al. Insecticide resistance in Bemisia tabaci Gennadius (Homoptera: Authors’ contributions Aleyrodidae) and Anopheles gambiae Giles (Diptera: Culicidae) could RK, FG, KA, MD, CE designed and clarified the study objective. RK collected compromise the sustainability of malaria vector control strategies in West the data. RK, AO, LS, CJ conducted the statistical analyses. RK developed the Africa. Acta Trop. 2013;128:7–17. https://d oi. org/ 10. 1016/j.a ctatr opica. manuscript draft. All authors contributed to the writing of the paper by read- 2013.0 6.0 04. ing, editing, reviewing; and approved the final manuscript. 8. Bass C, Puinean AM, Zimmer CT, Denholm I, Field LM, Foster SP, et al. The evolution of insecticide resistance in the peach potato aphid, Myzus Kouamé et al. BMC Public Health (2022) 22:2443 Page 10 of 11 persicae. Insect Biochem Mol Biol. 2014;51:41–51. https:// doi.o rg/ 10. 28. Matthys B, N’Goran EK, Koné M, Koudou BG, Vounatsou P, Cissé G, et al. 1016/j. ibmb. 2014. 05.0 03. Urban agricultural land use and characterization of mosquito larval habi- 9. Djegbe I, Missihoun AA, Djouaka R, Akogbeto M. Dynamique de la popu- tats in a medium-sized town of Côte d’Ivoire. J Vector Ecol. 2006;31:319– lation et de la résistance aux insecticides chez Anopheles gambiae sl en 33. https://d oi.o rg/ 10. 3376/ 1081-1 710(2006)3 1[319:U ALUAC] 2.0. CO;2. milieu de riziculture irriguée au Sud Bénin. J Appl Biosci. 2017;111:10934– 29. Chouaïbou MS, Fodjo BK, Fokou G, Allassane OF, Koudou BG, David J-P, 43. http://d x. doi. org/ 104314/ jab. v111i1. 10. et al. Influence of the agrochemicals used for rice and vegetable cultiva- 10. Edi CVA, Koudou BG, Jones CM, Weetman D, Ranson H. Multiple- tion on insecticide resistance in malaria vectors in southern Côte d’Ivoire. insecticide resistance in Anopheles gambiae mosquitoes Southern Côte Malar J. 2016;15:426. https:// doi. org/1 0. 1186/ s12936-0 16- 1481-5. d’Ivoire. Emerg Infect Dis. 2012;18:1508–11. https://d oi.o rg/ 10. 3201/ eid18 30. Fodjo BK, Koudou BG, Tia E, Saric J, N’dri PB, Zoh MG, et al. Insecticides 09. 120262. resistance status of an. gambiae in areas of varying agrochemical use in 11. Müller T, Prosche A, Müller C. Sublethal insecticide exposure affects Côte D’Ivoire. BioMed Res Int. 2018;2018:1–9. https://d oi. org/ 10.1 155/ reproduction, chemical phenotype as well as offspring development 2018/ 287416 0. and antennae symmetry of a leaf beetle. Environ Pollut. 2017;230:709–17. 31. Nuwaha F. People’s perception of malaria in Mbarara, Uganda. Tropical https://d oi. org/1 0.1 016/j.e nvpol.2 017.0 7. 018. Med Int Health. 2002;7:462–70. https:// doi. org/ 10. 1046/j. 1365- 3156. 2002. 12. Soro G, Amao WS, Adjiri AO, Soro N. Risques sanitaires et environne- 00877.x. mentaux liés à l’usage des produits phytosanitaires dans l’horticulture 32. Mboera LEG, Shayo EH, Senkoro KP, Rumisha SF, Mlozi MRS, Mayala BK. à Azaguié (Sud Côte d’Ivoire). J Appl Biosci. 2019;138:14072. https:// doi. Knowledge, perceptions and practices of farming communities on link- org/ 10.4 314/ jab.v 138i1.7. ages between malaria and agriculture in Mvomero district. Tanzania Acta 13. Eddleston M, Karalliedde L, Buckley N, Fernando R, Hutchinson G, Isbister Tropica. 2010;113:139–44. https:// doi. org/ 10. 1016/j.a ctat ropica. 2009. 10. G, et al. Public health Pesticide poisoning in the developing world — a 008. minimum pesticides list. Lancet. 2002;360:1163–7. https://d oi.o rg/ 10. 33. Dike N, Onwujekwe O, Ojukwu J, Ikeme A, Uzochukwu B, Shu E. Influence 1016/ S0140-6 736(02) 11204-9. of education and knowledge on perceptions and practices to control 14. Devine GJ, Furlong MJ. Insecticide use: contexts and ecological conse- malaria in Southeast Nigeria. Soc Sci Med. 2006;63:103–6. https:// doi. org/ quences. Agric Hum Values. 2007;24:281–306. https:// doi.o rg/ 10.1 007/ 10.1 016/j.s ocsci med.2 005.1 1. 061. s10460- 007- 9067-z. 34. Chase C, Sicuri E, Sacoor C, Nhalungo D, Nhacolo A, Alonso PL, et al. 15. Coscolla C, Yusa V. Pesticides and agricultural air quality. Compr Anal Determinants of household demand for bed nets in a rural area of Chem. 2016:423–90. https://d oi.o rg/ 10.1 016/b s.c oac. 2016. 04. 012. southern Mozambique. Malar J. 2009;8:132. https:// doi. org/ 10.1 186/ 16. Li J, He R. Relationships among socioeconomic factors, rice planting 1475- 2875-8- 132. method and pesticide use. Environ Dev Sustainability. 2021;23:7358–72. 35. Ouattara AF, Raso G, Edi CV, Utzinger J, Tanner M, Dagnogo M, et al. https:// doi. org/1 0.1 007/ s10668- 020- 00920-w. Malaria knowledge and long-lasting insecticidal net use in rural com- 17. Ali MdP, Kabir MMdM, Haque SS, Qin X, Nasrin S, Landis D, et al. Farmer’s munities of central Côte d’Ivoire. Malar J. 2011;10:288. https:// doi. org/ 10. behavior in pesticide use: Insights study from smallholder and intensive 1186/1 475- 2875- 10- 288. agricultural farms in Bangladesh. Sci Total Environ. 2020;747:141160. 36. Essé C, Utzinger J, Tschannen AB, Raso G, Pfeiffer C, Granado S, et al. Social https://d oi. org/1 0. 1016/j.s citot env.2 020.1 41160. and cultural aspects of “malaria” and its control in central Côte d’Ivoire. 18. Mekonnen Y, Agonafir T. Pesticide sprayers’ knowledge, attitude and Malar J. 2008;7:224. https://d oi. org/1 0. 1186/ 1475- 2875-7-2 24. practice of pesticide use on agricultural farms of Ethiopia. Occup Med. 37. Knoblauch AM, Winkler MS, Archer C, Divall MJ, Owuor M, Yapo RM, 2002;52:311–5. https:// doi. org/ 10. 1093/ occmed/5 2.6.3 11. et al. The epidemiology of malaria and anaemia in the Bonikro mining 19. Tijani AA. Factors influencing pesticide use among cocoa farmers in area, central Côte d’Ivoire. Malar J. 2014;13:194. https:// doi. org/ 10. 1186/ Ondo State, Nigeria. Second RUFORUM Biennial Meeting 2010:361–4. 1475-2 875-1 3-1 94. 20. Sonwa DJ, Weise S, Adesina A, Nkongmeneck AB, Tchatat M, Ndoye 38. Institut National de la Statistique (INS). Récensement Général de la O. Production constraints on cocoa agroforestry systems in West and Population et de l’Habitat (RGPH) 2014. Répertoire des localités, Région Central Africa: The need for integrated pest management and multi- de l’Agnéby-Tiassa. République de Côte d’Ivoire; 2015. p. 37. institutional approaches. For Chron. 2005;81:345–9. https:// doi.o rg/ 10. 39. Ahoussi KE, Koffi YB, Kouassi AM, Soro G, Soro N, Biémi J. Étude de la 5558/ tfc813 45-3. variabilité hydroclimatique et de ses conséquences sur les ressources en 21. Wesseling C, Mcconnell R, Partanen T, Hogstedt C. Agricultural eau du Sud forestier et agricole de la Côte d ’ Ivoire : cas de la région d ’ pesticide use in developping countries : health effects and research Abidjan-Agboville. Int J Pure Appl Biosci. 2013;1:30–50. needs. Int J Health Serv. 1997;27:273–308. https:// doi. org/ 10. 2190/ 40. World Medical Association. World Medical Association Declaration of E259- N3AH-T A1Y-H 591. Helsinki. Ethical principles for medical research involving human subjects. 22. Mahob R, Ndoumbe-Nkeng M, Ten Hoopen G, Dibog L, Nyasse S, Ruther- Bull World Health Organ. 2001;79:373–4. Epub 2003 Jul 2. ford M, et al. Pesticides use in cocoa sector in Cameroon: characterization 41. Ministère de l’Agriculture (MINAGRI). Index Phytosanitaire 2015. Répub- of supply source, nature of actives ingredients, fashion and reasons for lique de Côte d’Ivoire; 2015. p. 536. their utilization. Int J Biol Chem Sci. 2015;8:1976–89. https:// doi. org/ 10. 42. Howe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, Onwu- 4314/i jbcs. v8i5.3. jekwe O, et al. Measuring socio-economic position for epidemiological 23. Aminu FO, Ayinde IA, Sanusi RA, Olaiya AO. Determinants of pesticide studies in low- and middle-income countries : a methods of measure- use in cocoa production in Nigeria. Can J Agri Crops. 2019;4:101–10. ment in epidemiology paper. Int J Epidemiol. 2012;41:871–86. https:// doi. https:// doi. org/1 0. 20448/ 803.4. 2. 101. 110. org/1 0. 1093/i je/ dys037. 24. Aniah P, Kaunza-Nu-Dem MK, Dong-Uuro PP, Ayembilla JA, Osumanu 43. Filmer D, Pritchett LH. Estimating wealth effects without expenditure IK. Vegetable farmers’ knowledge on pesticides use in Northwest data—or tears: an application to educational enrollments in states of India. Ghana. Environ Dev Sustain. 2021;23:7273–88. https:// doi. org/1 0. 1007/ Demography. 2001;38:115–32. https://d oi.o rg/ 10.1 353/ dem. 2001. 0003. s10668-0 20- 00916-6. 44. WHO. The WHO recommended classification of pesticides by hazard and 25. Ano EJ, Tahiri A, Diby YKS, Siapo Y. Évaluation des pratiques phy- guidelines to classification: 2009. 2010. p. 78. tosanitaires paysannes dans les cacaoyères : Cas du département 45. Yêyinou Loko LE, Orobiyi A, Agre P, Dansi A, Tamò M, Roisin Y. Farmers’ d’Abengourou (Est, Côte d’Ivoire). J Animal Plant Sci. 2018;38:6159–74. perception of termites in agriculture production and their indigenous 26. Siapo YM, Tahiri A, Ano EJ, Diby YKS. Évaluation des pratiques phytosani- utilization in Northwest Benin. J Ethnobiol Ethnomed. 2017;13:64. https:// taires paysannes dans les vergers de cacao dans le département de doi. org/ 10. 1186/ s13002- 017- 0187-2. Daloa, Côte d’Ivoire. Euro Sci J. 2018;14:267–80. https://d oi.o rg/1 0. 19044/ 46. Deressa W, Ali A. Malaria-related perceptions and practices of women esj.2 018.v 14n3 3p267. with children under the age of five years in rural Ethiopia. BMC Public 27. Koudou BG, Doumbia M, Janmohamed N, Tschannen AB, Tanner M, Health. 2009;9:259. https:// doi. org/1 0. 1186/1 471- 2458-9-2 59. Hemingway J, et al. Effects of seasonality and irrigation on malaria 47. Forero DA, Chaparro PE, Vallejo AF, Benavides Y, Gutiérrez JB, Arévalo-Her- transmission in two villages in Côte d’Ivoire. Ann Trop Med Parasitol. rera M, et al. Knowledge, attitudes and practices of malaria in Colombia. 2010;104:109–21. https://d oi.o rg/1 0.1 179/ 13648 5910X 12607 01237 4154. Malar J. 2014;13:165. https:// doi. org/ 10. 1186/ 1475- 2875- 13- 165. K ouamé et al. BMC Public Health (2022) 22:2443 Page 11 of 11 48. Romay-Barja M, Ncogo P, Nseng G, Santana-Morales MA, Herrador Z, 66. Khan M, Mahmood HZ, Damalas CA. Pesticide use and risk percep- Berzosa P, et al. Caregivers’ Malaria knowledge, beliefs and attitudes, tions among farmers in the cotton belt of Punjab, Pakistan. Crop Prot. and related factors in the Bata district Equatorial Guinea. PLoS ONE. 2015;67:184–90. https:// doi.o rg/1 0. 1016/j. cropro. 2014. 10. 013. 2016;11:e0168668. https:// doi.o rg/ 10. 1371/j ourn al. pone.0 1686 68. 67. Oesterlund A, Thomsen J, Sekimpi D, Maziina J, Racheal A, Jørs E. Pesticide 49. Talipouo A, Ngadjeu CS, Doumbe-Belisse P, Djamouko-Djonkam L, Sonha- knowledge, practice and attitude and how it affects the health of fouo-Chiana N, Kopya E, et al. Malaria prevention in the city of Yaoundé: small-scale farmers in Uganda: a cross-sectional study. African Health Sci. knowledge and practices of urban dwellers. Malar J. 2019;18:167. https:// 2014;14:420–33. https:// doi. org/1 0. 4314/a hs. v14i2.1 9. doi.o rg/1 0. 1186/ s12936- 019- 2799-6. 50. Beiersmann C, Sanou A, Wladarsch E, De Allegri M, Kouyaté B, Mül- ler O. Malaria in rural Burkina Faso: local illness concepts, patterns of Publisher’s Note traditional treatment and influence on health-seeking behaviour. Malar J. Springer Nature remains neutral with regard to jurisdictional claims in pub- 2007;6:106. https:// doi. org/ 10. 1186/1 475-2 875-6- 106. lished maps and institutional affiliations. 51. Tolhurst R, Amekudzi YP, Nyonator FK, Bertel Squire S, Theobald S. “He will ask why the child gets sick so often”: the gendered dynamics of intra-household bargaining over healthcare for children with fever in the Volta Region of Ghana. Soc Sci Med. 2008;66:1106–17. https://d oi.o rg/ 10. 1016/j. socsc imed. 2007. 11. 032. 52. Sekhri N, Savedoff W. Private health insurance: implications for develop- ing countries. Bull World Health Organ. 2005;83:127–34. 53. Mbongue R, Akono P, Ngo Hondt O, Magne Tamdem G, Nopowo N, Offono L, et al. Knowledges, attitudes and practices of household heads on Malaria in urban and rural areas of Kribi, South-Cameroon. Austin J Public Health Epidemiol. 2020;7:1088. 54. Calliera M, Luzzani G, Sacchettini G, Capri E. Residents perceptions of non dietary pesticide exposure risk. Knowledge gaps and challenges for tar- geted awareness-raising material in Italy. Sci Total Environ. 2019;685:775– 85. https:// doi.o rg/ 10.1 016/j. scitot env.2 019.0 6. 223. 55. Meredith HR, Furuya-Kanamori L, Yakob L. Optimising systemic insecti- cide use to improve malaria control. BMJ Glob Health. 2019;4:e001776. https://d oi.o rg/1 0. 1136/ bmjgh- 2019- 001776. 56. Kudom AA, Mensah BA, Froeschl G, Rinder H, Boakye D. DDT and pyre- throid resistance status and laboratory evaluation of bio-efficacy of long lasting insecticide treated nets against Culex quinquefasciatus and Culex decens in Ghana. Acta Trop. 2015;150:122–30. https:// doi. org/ 10. 1016/j. actat ropica. 2015. 07. 009. 57. Matiya DJ, Philbert AB, Kidima W, Matowo JJ. Dynamics and monitoring of insecticide resistance in malaria vectors across mainland Tanzania from 1997 to 2017: a systematic review. Malar J. 2019;18:102. https:// doi. org/ 10. 1186/ s12936- 019- 2738-6. 58. Philbert A, Lyantagaye SL, Nkwengulila G. Farmers’ pesticide usage prac- tices in the malaria endemic region of North-Western Tanzania: implica- tions to the control of malaria vectors. BMC Public Health. 2019;19:1456. https://d oi.o rg/1 0. 1186/ s12889- 019- 7767-0. 59. Simma EA, Dermauw W, Balabanidou V, Snoeck S, Bryon A, Clark RM, et al. Genome-wide gene expression profiling reveals that cuticle altera- tions and P450 detoxification are associated with pyrethroid resistance in Anopheles arabiensis populations from Ethiopia. Pest Manag Sci. 2019;75:1808–18. https:// doi. org/ 10.1 101/4 51336. 60. Clarke EEK, Levy LS, Spurgeon A, Calvert IA. The problems associated with pesticide use by irrigation workers in Ghana. Occup Med. 1997;47:301–8. https:// doi. org/1 0. 1093/ occmed/4 7.5. 301. 61. Faelker T, Pickett W, Brison RJ. Socioeconomic differences in childhood injury: a population based epidemiologic study in Ontario Canada. Inj Prev. 2000;6:203–8. https:// doi.o rg/ 10.1 136/i p.6.3 .2 03. 62. Sun S, Chen J, Johannesson M, Kind P, Xu L, Zhang Y, et al. Population health status in China: EQ-5D results, by age, sex and socio-economic status, from the national health services survey 2008. Qual Life Res. 2011;20:309–20. https:// doi.o rg/ 10. 1007/ s11136- 010-9 762-x. Ready to submit your research ? Choose BMC and benefit from: 63. Gesesew HA, Woldemichael K, Massa D, Mwanri L. Farmers knowledge, • fast, convenient online submission attitudes, practices and health problems associated with pesticide use in rural irrigation villages Southwest Ethiopia. PLoS ONE. 2016;11:e0162527. • thorough peer review by experienced rese archers in your field https:// doi. org/1 0. 1371/j ourna l. pone. 016252 7. • rapid publication on acceptance 64. Barraza D, Jansen K, van WendeldeJoode B, Wesseling C. Pesticide use in • support for research data, including large and complex data types banana and plantain production and risk perception among local actors in Talamanca, Costa Rica. Environ Res. 2011;111:708–17. https:// doi. org/ • gold Open Access which fosters wider collaboration and increased citations 10. 1016/j. envres. 2011. 02.0 09. • maximum visibility for your research: over 100M website views per year 65. Jin S, Bluemling B, Mol APJ. Information, trust and pesticide overuse: inter- actions between retailers and cotton farmers in China. NJAS - Wagenin- At BMC, research is always in progress. gen J Life Sci. 2015;72:23–32. https:// doi. org/1 0. 1016/j. njas. 2014.1 0. 003. Learn more biomedcentral.com/submissions