Back

Common Acute Pesticide Poisoning in a Single Centre Study in Bangladesh

Majumder, S. N.; Amin, M. R.; Hasan, M. J.; Islam, M. R.; Faiz, M.

2025-03-26 transplantation
10.1101/2025.03.24.25324472 medRxiv
Show abstract

BackgroundAcute pesticide poisoning is a primary public health concern in Bangladesh, contributing to significant morbidity and mortality, primarily due to deliberate self-harm. Accurate and timely identification of the ingested pesticide is essential for effective management, but this remains challenging due to limited toxicological resources and inadequate information at the point of care. MethodsA cross-sectional observational study was conducted at Dhaka Medical College Hospital from June 2017 to February 2018. Patients aged 18 years and older presenting with features of acute pesticide poisoning were enrolled if they could provide a sample of the ingested substance, either physically or digitally (via photos or messaging apps). Clinical characteristics, toxidromes, and outcomes were recorded using a standardized case record form. Data was analyzed using SPSS version 17 to identify toxicological patterns and predictors of mortality. ResultsAmong 236 patients, organophosphate compounds (OPCs) accounted for 52.2% of cases, followed by miticides (21.1%), herbicides (9.1%), and carbamates (3.4%). Intentional ingestion accounted for 93% of cases. The overall mortality rate was 12.7% (30 deaths), with OPCs responsible for 66.7% of fatalities. A Glasgow Coma Scale score below 9 (OR 12.12; p<0.001) and constricted pupils at admission were significant predictors of poor outcomes. Illiteracy was also associated with increased mortality (OR 3.78; p=0.002). Identifying the ingested agent through physical or digital samples substantially aided clinical decision-making. ConclusionAcute pesticide poisoning in Bangladesh is primarily due to OPCs and affects predominantly young adults. Collection of ingested agent samples, including digital images, can facilitate targeted treatment and improve outcomes in resource-limited healthcare settings.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.