Health-Seeking Behaviors After Acute Respiratory Infection Among Urban and Rural Residents and Different Age Groups: Cross-Sectional Questionnaire Study.

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 aimed 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 multistate Markov model to quantify transition probabilities and intensities between various health-seeking behaviors, using beta regression models to assess differences in state transition probabilities across urban-rural groups and age groups. Results: This analysis included 2340 patients with ARI from sampled areas of Chengdu. Subgroup analysis revealed that the rural population was more likely to go directly to a hospital after ARI onset, while the urban population was more likely to purchase medicine directly. Furthermore, there were no significant differences in information seeking and self-monitoring (ISM) use between urban and rural residents after ARI onset. However, following ISM, rural residents were significantly less likely than urban residents to visit a hospital or purchase medicine. Minors and older adults were significantly less likely than adults to engage in ISM after ARI onset. Minors were more likely than adults to directly visit hospitals after ARI onset. After ISM use, adults were more inclined to purchase medicine than minors and older adults, while older adults were more inclined to visit hospitals. Conclusions: The findings 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 diverged when using ISM. The findings suggested that health interventions should leverage ISM to effectively drive offline actions, implementing targeted strategies tailored to the decision-making characteristics of different populations.