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Volume 18, Issue 3 (2026)
Iran J War Public Health 2026, 18(3): 323-329 |
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Received: 2026/07/6 | Accepted: 2026/09/6 | Published: 2026/09/20
Received: 2026/07/6 | Accepted: 2026/09/6 | Published: 2026/09/20
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Al-Eqabi Q. Technology-Enabled Infection Risk Surveillance for Accident-Affected Clients Through Community Health Workers’ Early Threat Detection. , Iran J War Public Health. 2026; 18 (3) :323-329
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URL: http://ijwph.ir/article-1-1834-en.html
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Authors
Q.A.K. Al-Eqabi *
Department of Community Health Techniques, Al-Kut Technical College, Middle Technical University, Baghdad, Iraq
Abstract (554 Views)
Aims: Increased incidence of infectious diseases and disruptions to existing health systems are likely to occur among populations living in or affected by areas affected by accidents and disasters, where exposure risks may be elevated. This study aimed to examine the role of technology-enabled risk surveillance in infection surveillance by assessing the mediating effect of early threat detection capacity.
Materials and Methods: This quantitative cross-sectional study was conducted among 250 community health workers in Kut City, Iraq, in 2026. Data were collected using established measures of technology-enabled infection risk surveillance, early threat detection capacity, and infection risk surveillance. Pearson correlation, multiple linear regression, and Hayes PROCESS macro Model 4 with 5,000 bootstrap samples were applied.
Findings: Technology-enabled surveillance was positively and significantly associated with early threat detection capacity (r=0.62; p<0.001) and the risk of detecting infections (r=0.58; p<0.001). There was also a strong association between the capacity to detect potential threats early and infection risk surveillance (r=0.65; p<0.001). In the multiple linear regression model, technology-enabled surveillance was independently associated with infection risk surveillance (B=0.39; p<0.001). Mediation analysis indicated a significant indirect effect (B=0.30; 95% bootstrap CI: 0.20–0.42), supporting partial mediation.
Conclusion: Technology-enabled surveillance is positively associated with infection-risk surveillance among community health workers in Kut City, Iraq, with early threat-detection capacity partly mediating this relationship.
Materials and Methods: This quantitative cross-sectional study was conducted among 250 community health workers in Kut City, Iraq, in 2026. Data were collected using established measures of technology-enabled infection risk surveillance, early threat detection capacity, and infection risk surveillance. Pearson correlation, multiple linear regression, and Hayes PROCESS macro Model 4 with 5,000 bootstrap samples were applied.
Findings: Technology-enabled surveillance was positively and significantly associated with early threat detection capacity (r=0.62; p<0.001) and the risk of detecting infections (r=0.58; p<0.001). There was also a strong association between the capacity to detect potential threats early and infection risk surveillance (r=0.65; p<0.001). In the multiple linear regression model, technology-enabled surveillance was independently associated with infection risk surveillance (B=0.39; p<0.001). Mediation analysis indicated a significant indirect effect (B=0.30; 95% bootstrap CI: 0.20–0.42), supporting partial mediation.
Conclusion: Technology-enabled surveillance is positively associated with infection-risk surveillance among community health workers in Kut City, Iraq, with early threat-detection capacity partly mediating this relationship.