Network-Based Delineation of Health Service Areas: A Comparative Analysis of Community Detection Algorithms

Pinheiro, Diego; Hartman, Ryan; Romero, Erick; Menezes, Ronaldo; Cadeiras, Martín

doi:10.1007/978-3-030-40943-2_30

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Each delineated SCA consists of a set of ZCTAs in which hospitals are embedded. A set of four diverse community detection algorithms were considered to decrease both variability and bias [ 23 ]. The algorithms were Louvain [ 24 ] with resolution equal to 1, Stochastic Block Model [ 25 , 26 ] with degree corrected, Infomap [ 27 ] with two levels, and Speaker-Listener Label Propagation [ 28 ] with postprocessing threshold equal to 0.5…”

Section: Methodsmentioning

confidence: 99%

The Association of Shared Care Networks With 30-Day Heart Failure Excessive Hospital Readmissions: Longitudinal Observational Study

Pinheiro¹,

Hartman²,

Mai³

et al. 2022

JMIRx Med

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Background Higher-than-expected heart failure (HF) readmissions affect half of US hospitals every year. The Hospital Reduction Readmission Program has reduced risk-adjusted readmissions, but it has also produced unintended consequences. Shared care models have been advocated for HF care, but the association of shared care networks with HF readmissions has never been investigated. Objective This study aims to evaluate the association of shared care networks with 30-day HF excessive readmission rates using a longitudinal observational study. Methods We curated publicly available data on hospital discharges and HF excessive readmission ratios from hospitals in California between 2012 and 2017. Shared care areas were delineated as data-driven units of care coordination emerging from discharge networks. The localization index, the proportion of patients who reside in the same shared care area in which they are admitted, was calculated by year. Generalized estimating equations were used to evaluate the association between the localization index and the excessive readmission ratio of hospitals controlling for race/ethnicity and socioeconomic factors. Results A total of 300 hospitals in California in a 6-year period were included. The HF excessive readmission ratio was negatively associated with the adjusted localization index (β=–.0474, 95% CI –0.082 to –0.013). The percentage of Black residents within the shared care areas was the only statistically significant covariate (β=.4128, 95% CI 0.302 to 0.524). Conclusions Higher-than-expected HF readmissions were associated with shared care networks. Control mechanisms such as the Hospital Reduction Readmission Program may need to characterize and reward shared care to guide hospitals toward a more organized HF care system.

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Section: Methodsmentioning

confidence: 99%

The Association of Shared Care Networks With 30-Day Heart Failure Excessive Hospital Readmissions: Longitudinal Observational Study

Pinheiro¹,

Hartman²,

Mai³

et al. 2022

JMIRx Med

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Section: Discharge Datamentioning

confidence: 99%

Association of Shared Care Networks with Heart Failure Excessive Hospital Readmissions

Pinheiro

Hartman

Mai

et al. 2021

Preprint

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Objectives: This study aimed to evaluate the impact of shared care networks on heart failure readmission rates. Background: Higher-than-expected heart failure (HF) readmissions affect half of US hospitals every year. The Hospital Reduction Readmission Program (HRRP) has reduced risk-adjusted readmissions, but it has also produced unintended consequences. Shared care models have been advocated for HF care, but the association of shared care networks with HF readmissions has never been investigated. Methods: We curated publicly available data on hospital discharges and HF excessive readmission ratios (ERRs) from hospitals in California between 2012 and 2017. Shared Care Areas (SCAs) were delineated as data-driven units of care coordination emerging from discharge networks. The localization index (LI), the proportion of patients who reside in the same SCA in which they are admitted, were calculated by year. Generalized estimating equations (GEE) were used to evaluate the association between the LI and the ERR of hospitals controlling for race/ethnicity and socioeconomics factors. Results: A total of 300 hospitals in California in a 6-yr period were included. The HF excessive readmission ratio (ERR) was negatively associated with the localization index (beta: -0.0474; 95% CI: -0.082 to -0.013). The percentage of Black residents within the SCAs was the only statistically significant covariate (beta: 0.4128; 95% CI: 0.302 to 0.524). Conclusions: Higher-than-expected HF readmissions were associated with shared care networks. Control mechanisms such as the HRRP may need to characterize and reward shared care to guide hospitals towards a more organized HF care system.

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“…Whether it is discrete or overlapping is determined by whether a node belongs to one or multiple communities. Although the concept can be traced back to the 1970s (Ball et al., 2011), discrete community detection algorithms have dominated the literature (Blondel et al., 2008; Rosvall & Bergstrom, 2008; Traag et al., 2019), including those used to define hospital service areas (HSAs) and CSAs (Hu et al., 2018; Pinheiro et al., 2020; Wang & Wang, 2022; Wang, Wang, et al., 2022). In contrast, the development of overlapping community detection algorithms lags behind.…”

Section: Introductionmentioning

confidence: 99%

Overlapping cancer service areas: Delineation and implications

Wang,

Onega,

Wang

2024

Transactions in GIS

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Prior research has defined cancer service areas (CSAs) anchored by major cancer centers in the U.S., such as members of the Association of American Cancer Institutes (AACI). Those CSAs have discrete boundaries and may not capture the increasingly interwoven cancer care markets. This is the first attempt to delineate possible overlapping CSAs. Specifically, we integrate the concept of shared memberships and other spatial attributes into the Speaker‐Listener Label Propagation (SLPA) algorithm, termed the spatially constrained SLPA (or ScSLPA), and apply it to six representative areas in the U.S. The results show that overlapping CSAs tend to form in areas that are more urbanized, with higher localization index (LI) values, larger populations, and shorter travel times than discrete CSAs. Two CSAs in Los Angeles and San Diego are consistent with the catchment areas (CAs) of the National Cancer Institute (NCI)‐designated cancer centers, and other CSAs are much smaller than the CAs of their anchoring cancer centers. The study has important implications for public health policy to advance cancer control and prevention efforts.

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Network-Based Delineation of Health Service Areas: A Comparative Analysis of Community Detection Algorithms

Cited by 6 publications

References 9 publications

The Association of Shared Care Networks With 30-Day Heart Failure Excessive Hospital Readmissions: Longitudinal Observational Study

The Association of Shared Care Networks With 30-Day Heart Failure Excessive Hospital Readmissions: Longitudinal Observational Study

Association of Shared Care Networks with Heart Failure Excessive Hospital Readmissions

Overlapping cancer service areas: Delineation and implications

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