A geo-gender-based analysis of human health: The presence of cut flower farms can attenuate pesticide exposure in African communities, with women being the most vulnerable
Bibliographic record
Abstract
Background: The rapid expansion of the cut flower industry in Africa has led to pervasive use and potential exposure of pesticides, raising concerns for local communities. Whether the risks associated with pesticide applications are localised or have broader implications remains unclear. Methods: We measured biomarkers of real and perceived pesticide exposure in two Kenyan communities: Naivasha, where the cut flower industry is present, and Mogotio, where the cut flower industry is absent. We measured real exposure by the percentage of acetylcholinesterase (AChE) inhibition and perceived exposure by assessing hair cortisol levels, a biomarker of stress. Additionally, we conducted a demographic survey to evaluate the health and socioeconomic status of participants, as well as their perceptions of pesticide risks associated with the cut flower industry. Results: Perceived pesticide exposure was more common in Naivasha (n = 36, 56%) compared to Mogotio (n = 0, 0%), according to community surveys. However, Mogotio residents had significantly higher mean hair cortisol levels (mean (x̄) = 790 ng/g, standard deviation (SD) = 233) and percentage of AChE inhibition (x̄ = 28.5%, SD = 7.3) compared to Naivasha residents, who had lower mean hair cortisol levels (x̄ = 548 ng/g, SD = 187) and percentage of AChE inhibition (x̄ = 14.5%, SD = 10.1). Location (proximity to cut flower farms) and gender were significant factors influencing pesticide exposure, with individuals living outside the cut flower industrial complexes being at higher risk. Women in both communities were the most vulnerable demographic, showing significantly higher mean hair cortisol levels (x̄ = 646 ng/g, SD = 267.4) and percentage of AChE inhibition (x̄ = 22.5%, SD = 12.4) compared to men hair cortisol levels (x̄ = 558.2 ng/g, SD = 208.2) and percentage of AChE inhibition (x̄ = 10.4%, SD = 13.1). Conclusions: A heightened awareness of the potential risks of pesticide exposure was widespread within cut flower industrial complexes. This may have led to a reduction in exposure of both workers and non-workers living within or close to these complexes. In contrast, communities living outside these complexes showed higher levels of exposure, possibly due to limited chemical awareness and a lack of precautionary measures. Despite this contrast between communities, women remained the most vulnerable members, likely due to their socioeconomic roles in African society. Monitoring women's pesticide exposure is crucial for providing an early warning system for community exposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".