Evaluation of a hospital-based injury surveillance system for monitoring road traffic deaths in Phuket, Thailand
DOI: 10.1080/15389588.2019.1581924
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Summary
This study evaluates the effectiveness of Thailand’s hospital-based Injury Surveillance (IS) system in monitoring road traffic deaths in Phuket, a province with a high burden of such fatalities. Motivated by Thailand’s high road traffic death rates and the fragmentation of surveillance data across multiple government agencies, the researchers aimed to determine if the IS system could accurately capture the magnitude of road traffic deaths and whether integrating it with other data sources would improve coverage. The study focused on Phuket due to its historical challenges in merging surveillance data and its status as a high-burden province. The evaluation employed both qualitative and quantitative methods based on U.S. Centers for Disease Control and Prevention guidelines. Qualitatively, researchers conducted semi-structured interviews with key hospital stakeholders, including doctors, nurses, and epidemiologists, to assess the system’s usefulness, simplicity, flexibility, acceptability, and stability. Quantitatively, the team performed active case finding of all road traffic deaths occurring in 2014 at the provincial hospital by reviewing paper and electronic records, including emergency room logs, medical records, and electronic insurance claims. This comprehensive dataset served as the reference standard to calculate the IS system’s sensitivity, positive predictive value, and data quality. Additionally, electronic data matching software was used to merge IS data with provincial death certificates and electronic vehicle insurance claims to assess overall surveillance coverage. The results indicated that while the IS system was useful, flexible, acceptable, and stable, it suffered from low sensitivity, capturing only 55% of road traffic deaths identified through active case finding. However, the system demonstrated a high positive predictive value of 99%, meaning that cases recorded in IS were highly likely to be true positives. Data accuracy and completeness varied significantly by variable, with physical exam metrics like Glasgow Coma Scale scores showing lower accuracy (39–48%) compared to demographic data. The complexity of data collection, particularly the reliance on paper forms and duplicate entry, hindered simplicity and contributed to data loss. Crucially, combining IS data with active case finding, death certificates, and insurance claims more than doubled the number of identified road traffic deaths, increasing coverage from 41% (IS alone) to a combined total that detected 142% more cases than IS independently. The study concludes that while the IS system is a valuable tool for monitoring road traffic deaths and assessing risk behaviors, its limited sensitivity and complex data collection processes prevent it from fully representing the burden of road traffic injuries in Phuket. The findings suggest that no single surveillance source is sufficient for accurate monitoring. Instead, integrating data from multiple sources—such as hospital records, death certificates, and insurance claims—is critical for obtaining a comprehensive picture of road traffic fatalities. The authors recommend improving data quality and considering data linkage strategies to enhance surveillance coverage, noting that such integration requires consistent stakeholder participation and rigorous quality assessment to ensure accurate record matching.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-19 |
| archive | success | semantic_scholar | — | — | 6 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Applied Guidance: countermeasure evaluation
- Empirical Findings: crash risk outcomes