Dataset Information

Using floating catchment area (FCA) metrics to predict health care utilization patterns.

ABSTRACT: BACKGROUND:Floating Catchment Area (FCA) metrics provide a comprehensive measure of potential spatial accessibility to health care services and are often used to identify geographic disparities in health care access. An unexplored aspect of FCA metrics is whether they can be useful in predicting where people actually seek care. This research addresses this question by examining the utility of FCA metrics for predicting patient utilization patterns, the flows of patients from their residences to facilities. METHODS:Using more than one million inpatient hospital visits in Michigan, we calculated expected utilization patterns from Zip Codes to hospitals using four FCA metrics and two traditional metrics (simple distance and a Huff model) and compared them to observed utilization patterns. Because all of the accessibility metrics rely on the specification of a distance decay function and its associated parameters, we conducted a sensitivity analysis to evaluate their effects on prediction accuracy. RESULTS:We found that the Three Step FCA (3SFCA) and Modified Two Step FCA (M2SFCA) were the most effective metrics for predicting utilization patterns, correctly predicting the destination hospital for nearly 74% of hospital visits in Michigan. These two metrics were also the least sensitive to changes to the distance decay functions and parameter settings. CONCLUSIONS:Overall, this research demonstrates that FCA metrics can provide reasonable predictions of patient utilization patterns and FCA utilization models could be considered as a substitute when utilization pattern data are unavailable.

SUBMITTER: Delamater PL

PROVIDER: S-EPMC6399985 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background: Geographic weights are vital in the floating catchment area (FCA) method of accessibility measurements due to their simulation of spatial barriers in various ways. When modelling population demand, geographical weights with different distance decay coefficients can reflect diverse distance tolerances in facility utilization and could lead to erratic accessibility results. Quantifying accessibility as the sum of weighted supply-demand ratios can alleviate the distance decay coefficient's influence and generate stable geographic patterns. However, the effects of weighted ratios on different FCA models and resources have not been investigated. Methods: To identify impacts of weighted ratios on various FCA variants, this study contrasted the accessibility calculated from the sum of ratios (access) and the sum of weighted ratios (access ratios) within three prevalent FCA models: enhanced two-step FCA (E2SFCA), modified two-step FCA (M2SFCA), and three-step FCA (3SFCA). In addition, the accessibilities of various resources evaluate the stability of the weighted ratios' effect. This study therefore examined the accessibilities to primary schools, job opportunities, and major hospitals in Shanghai. Shanghai is a case study that provides lessons on using big data to measure accessibility in metropolitan areas. Results: Geographic weights can not only mitigate the impact of the distance decay coefficients, but can also eliminate model features, which reduces the performance of the M2SFCA's supply decay and the 3SFCA's population demand adjustment in accessibility results. Moreover, weighted ratios tend to overestimate accessibility in marginal communities that lie within fewer catchments, regardless of the resource type. This tendency can lead to an epistemological trap that creates an inaccurate and counter-intuitive perception of resource distribution in a given area. Conclusions: The results identify a gap between the methodological logic and the empirical perception in accessibility measurements. This study concludes that the use of geographic weights needs to be cautious and epistemologically consistent.

Project description:BackgroundThere are only limited studies on access to COVID-19 vaccines and identifying the most appropriate health centres for performing vaccination in metropolitan areas. This study aimed to measure potential spatial access to COVID-19 vaccination centres in Mashhad, the second-most populous city in Iran.MethodsThe 2021 age structure of the urban census tracts was integrated into the enhanced two-step floating catchment area model to improve accuracy. The model was developed based on three different access scenarios: only public hospitals, only public healthcare centres and both (either hospitals or healthcare centres) as potential vaccination facilities. The weighted decision-matrix and analytic hierarchy process, based on four criteria (i.e. service area, accessibility index, capacity of vaccination centres and distance to main roads), were used to choose potential vaccination centres looking for the highest suitability for residents. Global Moran's index (GMI) was used to measure the spatial autocorrelation of the accessibility index in different scenarios and the proposed model.ResultsThere were 26 public hospitals and 271 public healthcare centres in the study area. Although the exclusive use of public healthcare centres for vaccination can provide the highest accessibility in the eastern and north-eastern parts of the study area, our findings indicate that including both public hospitals and public healthcare centres provide high accessibility to vaccination in central urban part. Therefore, a combination of public hospitals and public healthcare centres is recommended for efficient vaccination coverage. The value of GMI for the proposed model (accessibility to selected vaccination centres) was calculated as 0.53 (Z = 162.42, P < 0.01). Both GMI and Z-score values decreased in the proposed model, suggesting an enhancement in accessibility to COVID-19 vaccination services.ConclusionsThe periphery and poor areas of the city had the least access to COVID-19 vaccination centres. Measuring spatial access to COVID-19 vaccination centres can provide valuable insights for urban public health decision-makers. Our model, coupled with geographical information systems, provides more efficient vaccination coverage by identifying the most suitable healthcare centres, which is of special importance when only few centres are available.

Project description:BackgroundLyme disease is the most frequently reported vector borne infection in the United States. The Centers for Disease Control have estimated that approximately 10% to 20% of individuals may experience Post-Treatment Lyme Disease Syndrome - a set of symptoms including fatigue, musculoskeletal pain, and neurocognitive complaints that persist after initial antibiotic treatment of Lyme disease. Little is known about the impact of Lyme disease or post-treatment Lyme disease symptoms (PTLDS) on health care costs and utilization in the United States.Objectives1) to examine the impact of Lyme disease on health care costs and utilization, 2) to understand the relationship between Lyme disease and the probability of developing PTLDS, 3) to understand how PTLDS may impact health care costs and utilization.MethodsThis study utilizes retrospective data on medical claims and member enrollment for persons aged 0-64 years who were enrolled in commercial health insurance plans in the United States between 2006-2010. 52,795 individuals treated for Lyme disease were compared to 263,975 matched controls with no evidence of Lyme disease exposure.ResultsLyme disease is associated with $2,968 higher total health care costs (95% CI: 2,807-3,128, p<.001) and 87% more outpatient visits (95% CI: 86%-89%, p<.001) over a 12-month period, and is associated with 4.77 times greater odds of having any PTLDS-related diagnosis, as compared to controls (95% CI: 4.67-4.87, p<.001). Among those with Lyme disease, having one or more PTLDS-related diagnosis is associated with $3,798 higher total health care costs (95% CI: 3,542-4,055, p<.001) and 66% more outpatient visits (95% CI: 64%-69%, p<.001) over a 12-month period, relative to those with no PTLDS-related diagnoses.ConclusionsLyme disease is associated with increased costs above what would be expected for an easy to treat infection. The presence of PTLDS-related diagnoses after treatment is associated with significant health care costs and utilization.

Project description:Healthcare facilities (HF) may identify catchment areas (CA) by selecting criteria that depend on various factors. These refer to hospital activities, geographical definition, patient covariates, and more. The analyses that were traditionally pursued have a limiting factor in the consideration of only static conditions. Instead, some of the CA determinants involve influences occurring at both temporal and spatial scales. The study of CA in the cancer context means choosing between HF, usually divided into general hospitals versus oncological centers (OCs). In the CA context, electronic health records (EHRs) promise to be a valuable source of information, one driving the next-generation patient-driven clinical decision support systems. Among the challenges, digital health requires the re-definition of a role of stochastic modeling to deal with emerging complexities from data heterogeneity. To model CA with cancer EHR, we have chosen a computational framework centered on a logistic model, as a reference, and on a multivariate statistical approach. We also provided a battery of tests for CA assessment. Our results indicate that a more refined CA model's structure yields superior discrimination power between health facilities. The increased significance was also visualized by comparative evaluations with ad hoc geo-localized maps. Notably, a cancer-specific spatial effect can be noticed, especially for breast cancer and through OCs. To mitigate the data distributional influences, bootstrap analysis was performed, and gains in some cancer-specific and spatially concentrated regions were obtained. Finally, when the temporal dynamics are assessed along a 3-year timeframe, negligible differential effects appear between predicted probabilities observed between standard critical values and bootstrapped values. In conclusion, for interpreting CA in terms of both spatial and temporal dynamics, sophisticated models are required. The one here proposed suggests that bootstrap can improve test accuracy. We recommend that evidences from stochastic modeling are merged with visual analytics, as this combination may be exploited by policy-makers in support to quantitative CA assessment.

Dataset Information

Using floating catchment area (FCA) metrics to predict health care utilization patterns.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets