ArticleBMC health services research2015
An optimization framework for measuring spatial access over healthcare networks.
Article in BMC health services research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Access to specialty healthcare in urban versus rural US populations: a systematic literature review.BMC health services research · 2019Pooled it
- Evaluating access to psychosocial services for the medicaid-insured children in Georgia.BMC public health · 2025Article
- Dental care access for children in the United States.Journal of public health dentistry · 2024Article
- Can we optimize locations of hospitals by minimizing the number of patients at risk?BMC health services research · 2023Article
- A comparison of methods for measuring spatial access to health care.Health services research · 2021Article
- Impact of nonphysician providers on spatial accessibility to primary care in Iowa.Health services research · 2020Article
- Mapping mammography in Arkansas: Locating areas with poor spatial access to breast cancer screening using optimization models and geographic information systems.Journal of clinical and translational science · 2020Article
- Optimal allocation of HIV resources among geographical regions.BMC public health · 2019Article
- Quantifying Disparities in Accessibility and Availability of Pediatric Primary Care across Multiple States with Implications for Targeted Interventions.Health services research · 2018Article
- Disparities in Preventive Dental Care Among Children in Georgia.Preventing chronic disease · 2017Article
- Quantifying and explaining accessibility with application to the 2009 H1N1 vaccination campaign.Health care management science · 2017Article
- Small-Area Estimation of Spatial Access to Care and Its Implications for Policy.Journal of urban health : bulletin of the New York Academy of Medicine · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMeasurement of healthcare spatial access over a network involves accounting for demand, supply, and network structure. Popular approaches are based on floating catchment areas; however the methods can overestimate demand over the network and fail to capture cascading effects across the system.
methodsOptimization is presented as a framework to measure spatial access. Questions related to when and why optimization should be used are addressed. The accuracy of the optimization models compared to the two-step floating catchment area method and its variations is analytically demonstrated, and a case study of specialty care for Cystic Fibrosis over the continental United States is used to compare these approaches.
resultsThe optimization models capture a patient's experience rather than their opportunities and avoid overestimating patient demand. They can also capture system effects due to change based on congestion. Furthermore, the optimization models provide more elements of access than traditional catchment methods.
conclusionsOptimization models can incorporate user choice and other variations, and they can be useful towards targeting interventions to improve access. They can be easily adapted to measure access for different types of patients, over different provider types, or with capacity constraints in the network. Moreover, optimization models allow differences in access in rural and urban areas.
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