JMIR Publications

ABSTRACT

Background:

The choice of pathways in health-seeking behavior following acute respiratory infection (ARI) is critical for health-care resource allocation, yet traditional logistic regression methods struggle to capture the dynamic evolution of such behaviors.

Objective:

This study aims to quantify the dynamic process of changes in health-seeking behaviors following the onset of ARI and to identify differences in behavioral pathways associated with key factors such as urban-rural status and age.

Methods:

This study used a multi-state Markov model to quantify transition probabilities and intensities between various health-seeking behaviors, utilizing beta regression models to assess differences in state transition probabilities across urban-rural groups and age groups.

Results:

This analysis included 2,340 patients with ARI. Subgroup analysis revealed that rural population were more likely to go directly to a hospital after ARI onset, while urban population were more likely to purchase medicine directly. Furthermore, there were no significant differences in internet usage between urban and rural residents after ARI onset. However, after using the internet, rural residents were significantly less likely than urban residents to visit a hospital or purchase medicine. Minors and the elderly were significantly less likely than adults to use the internet after ARI onset. Minors were more likely than adults to directly visit hospitals after ARI onset. After internet use, adults were more inclined to purchase medicine than minors and the elderly, while the elderly were more inclined to visit hospitals.

Conclusions:

Research indicated that urban and rural populations, as well as different age groups, exhibited distinct patterns of seeking medical care after ARI onset, and their subsequent behaviors diverge when utilizing the internet. Findings suggested that health interventions should leverage the internet to effectively drive offline actions, implementing targeted strategies tailored to the decision-making characteristics of different populations.