Assessing transport accessibility for healthcare facility reconfiguration using GIS and multilevel modelling

Transport accessibility to healthcare facilities is a major issue in the United Kingdom, as recently demonstrated by the shift away from providing healthcare in acute hospitals to care closer to home . Common measures of accessibility focus on the creation of distance or travel time contours around a destination and devote less attention to individual differences such as user perceptions, their transport usage, and area-wide factors including income deprivation, safety and security. Failure to account for such factors may result in imperfect decision making in terms of healthcare relocation and reconfiguration. This thesis therefore aims to develop a user-based accessibility model by focusing on both individual socio-economic (e.g. age, gender, access to transport modes) and area-wide characteristics (e.g. income deprivation, public transport provision, safety and security). In order to identify important factors that affect accessibility and to develop the user-based accessibility model, two revealed preference questionnaire surveys were undertaken at Loughborough and Hinckley. The purpose of the first questionnaire was to understand underlying factors affecting accessibility to a healthcare facility. The results revealed that both individual and area-wide factors affect transport accessibility to a healthcare facility. The purpose of the second questionnaire was conducted to capture data relating to users perception of accessibility and their socio-economic factors so as to develop a user-perception based accessibility model. Network-based travel time and travel distance as well as public transport provision data from a respondent home to a healthcare facility were generated using a GIS technique. Individual-level questionnaire data were then integrated with the other secondary datasets (e.g. Census, Index of Multiple Deprivation, Accidents) using postcodes of survey respondents. Both single-level and multilevel mixed-effects linear regression models were employed to develop a relationship between user-perceptions relating to accessibility and the factors influencing accessibility. Multilevel models that can control data from the two levels (i.e. individuals nested within local areas) provided better goodness-of-fit statistics compared with those of single-level regression models. The results indicate that travel distance by car, number of available direct bus services, age, and destination choices affect user-perceptions of accessibility to a healthcare facility. For instance, if travel distance by car increases by one mile then the perception of accessibility to a healthcare facility decreases by four units (on a scale of 0-100). Surprisingly, many area-wide factors such as security and safety, income deprivation were found to be statistically insignificant. In order to see which healthcare facility is more accessible, calibrated multilevel models along with number of people within the catchment area were then employed to predict the overall accessibility score related to a healthcare facility. This is important for policy makers in healthcare facility relocation and reconfiguration with respect to user perception of transport accessibility. Also it would be valuable to organisations that need to make decisions based on their users perceptions who are the real decision makers as to whether to use a facility or not