Enhancing informal workers' tools to reduce workplace injuries: a quasi-randomized control trial of electronic waste recyclers in Thailand
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Updated: Jan 11, 2026

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Abas Shkembi, Emma Linhart, Suzanne Chou
1Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA 48109. rneitzel@umich.edu.
Objectives:
In low- and middle-income countries (LMIC), there is mixed evidence on the effectiveness of interventions in improving workplace conditions among hazardous industries. In Thailand, a particularly hazardous industry with high injuries is informal electronic waste (e-waste) recycling. We investigated whether developing an optimized tool to dismantle e-waste would reduce injuries.
Methods:
We conducted a quasi-randomized control trial to determine the perceptions and efficacy of the optimized tool in reducing worker injuries over three months among 89 workers. The optimized tool for dismantling e-waste was designed following employee and business owner input using conjoint analysis. Workers were quasi-randomized into an intervention (ie, receiving the tool) or control (ie, not receiving) group from an auction. We conducted differences-in-differences Poisson regression to examine differences in self-reported injuries and near misses over three months follow-up between the intervention and control groups.
Results:
Among 44 workers who received the tool, workers self-reported that the tool created a safer work environment and reduced near misses, hammer danger, hand vibrations and hand pain. Among 42 workers (21 treatments, 21 controls) with complete information, the intervention reduced self-reported injuries over three months [difference-in-differences: -58%, 95% confidence interval (CI) -19- -79%]. Similar reductions in near misses were observed but not statistically significant (-53%, 95% CI -92-173%).
Conclusions:
Our study suggests that meaningful reductions in injury risk for specific types of work can be achieved with co-designed tools optimized to consider inputs from multiple stakeholders. This approach can be especially useful in resource-constrained environments, including working conditions in LMIC.
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