Modeling the Influence of Social Ties and Transportation Choice on Access to Oral Healthcare for Older Adults

Abstract

The current U.S. demographic shift toward an older population and the importance of intervening before conditions become severe warrant a concerted effort to ease the burden of access to healthcare for older adults. With regard to oral healthcare, more integrated services for older adults are needed to effectively serve their complex medical and dental needs. Using an agent-based simulation model, this paper examines the influence of social ties and transportation mode choices on opportunities for older adults to participate in community-based preventive screening events and access needed oral healthcare. This approach accounts for the heterogeneity of behavior that arises for a population exhibiting diversity in terms of social factors, including socioeconomic means and social support. In the context of older adults living in urban environments, the availability of different transportation modes ought to be taken into consideration. To explore alternative scenarios for the accessibility of preventive screening events offered at senior centers in northern Manhattan, an agent-based model (ABM) was created with a geographic information system (GIS) to simulate the influence of social ties and transportation choices on older adults seeking preventive screening services and oral healthcare. Results of simulation experiments indicate preferences for public transportation and inequities in accessibility that may be mitigated with social support. This simulation model offers a way to explore social support as an important factor in making transportation mode choices that mediate oral healthcare accessibility and thus oral health outcomes for older adults.

Keywords: Agent-based modeling; Geographic information science; Healthcare accessibility; Multi-modal transportation analysis; Social influence.

Fig. 1

Causal loop diagram of feedback mechanisms in model

Fig. 2

A screenshot of the study area during model simulation

Fig. 3

Daily routine of older adult agents in the model

Fig. 4

Sensitivity analysis for the threshold value of health seeking behavior

Fig. 5

The simulated trend in the number of screening program participants for a socially involved population, comparing the output from the baseline scenario (blue) to the “no car support” scenario (red). The histogram in the inset depicts the transportation mode choice distributions after a year, comparing percentages for the baseline scenario to the “no car support” scenario.

Fig. 6

Comparison of the “no car support” scenario with the “no social network” scenario

Fig. 7

Average travel distance for screening program participants in the baseline scenario, the “no car support” scenario, and the “no social network” scenario

Fig. 8

Model simulation results using different screening program assumptions: baseline (blue; one screening event held per week), one screening event held per day (green), ten screening events held per week (yellow), and no screening program (red). The top graphic provides results for the average oral health status of the older adult agents, the middle graphic provides results for the density of their social networks, and the bottom graphic provides results for the average travel distance to attend a screening event.