Differential effects of distance decay on hospital inpatient visits among subpopulations in Florida, USA

Abstract

Understanding patients’ travel behavior for seeking hospital care is fundamental for understanding healthcare market and planning for resource allocation. However, few studies examined the issue comprehensively across populations by geographical, demographic, and health insurance characteristics. Based on the 2011 State Inpatient Database in Florida, this study modeled patients’ travel patterns for hospital inpatient care across geographic areas (by average affluence, urbanicity) and calendar seasons, and across subpopulations (by age, gender, race/ethnicity, and health insurance status). Overall, travel patterns for all subpopulations were best captured by the log-logistic function. Patients in more affluent areas and rural areas tended to travel longer for hospital inpatient care, so did the younger, whites, and privately insured. Longer travel distances may be a necessity for rural patients to cope with lack of accessibility for local hospital care, but for the other population groups, it may indicate rather better mobility and more healthcare choices. The results can be used in various healthcare analyses such as accessibility assessment, hospital service area delineation, and healthcare resource planning.

Inpatient Flow Distribution Patterns at Shanghai Hospitals

“…According to the distance attenuation law, if geographical phenomena interact with each other, the action decreases with increasing distance. In the area of health, understanding patient travel behaviors for seeking hospital care is fundamental for analyzing the healthcare market and planning resource allocation [6]. However, patient flow analysis is complex and requires actual traffic flow data, which are difficult to obtain.…”

“…In addition, some studies have begun to focus on the heterogeneity of medical services [10]. One study examined the differences in medical treatment patterns among different ages, genders, and ethnic groups and determined the specific distance decay parameter on hospital inpatient visits among subpopulations [6,11]. Factors, such as age and gender, are not direct factors influencing the choice of medical treatment.…”

Orchestrating performance of healthcare networks subjected to the compound events of natural disasters and pandemic

“…The number of ER visits and inpatient admission are collected from the California Department of Health 60 , while the data for the acute care admission are obtained from the California Health and Human Services Open Data Portal (OSHPD) 42 for the first half of the year 2018. We use these data to validate the patient-driven model (see Supplementary Note 2 ) utilized in this study to obtain the demand for each hospital in Butte county, which is then compared with the model developed by Jia et al 63 (see Supplementary Fig. 8 ).…”

How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data

“…For instance, individuals' travel frequency and the spatial dispersion of destinations visited are found to be related to employment status and income level (Limtanakool, Dijst, & Schwanen, 2006), and people exhibit different distance decay patterns according to their race, sex, and trip purpose, etc. (Jia, Wang, & Xierali, 2019). Yet, difficulties in data collection have limited the scope of these studies to a small number of participants for a short period of time, and posed additional challenges to perform comparative analysis across socio-demographic groups (Lu & Liu, 2012;Xie, 2013).…”