Disproportionate Emergency Room Use as an Indicator of Community Health

Bemis, Kelley; Gray, Samantha; Patel, Megan T.; Christiansen, Demian

doi:10.5210/ojphi.v8i1.6409

Cited by 2 publications

(2 citation statements)

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“…Geographic variation in health factors and outcomes at the small-area level, including zip codes and census tracts, has been noted in many contexts [2]. Moreover, the use of small area analysis has become an important tool in the effective targeting of limited public health resources [3]. Despite the ability of spatial data analytics to detect patterns of clustering of events in small geographies across an urban region, small geographies may display data on health conditions that are very sparse, which can lead to cross-sectional analysis being biased by small-n designs.…”

Section: Introductionmentioning

confidence: 99%

Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments

2019

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Spatial data analytics can detect patterns of clustering of events in small geographies across an urban region. This study presents and demonstrates a robust research design to study the longitudinal stability of spatial clustering with small case numbers per census tract and assess the clustering changes over time across the urban environment to better inform public health policy making at the community level. We argue this analysis enables the greater efficiency of public health departments, while leveraging existing data and preserving citizen personal privacy. Analysis at the census tract level is conducted in Mecklenburg County, North Carolina, on hypertension during pregnancy compiled from 2011–2014 birth certificates. Data were derived from per year and per multi-year moving counts by aggregating spatially to census tracts and then assessed for clustering using global Moran’s I. With evidence of clustering, local indicators of spatial association are calculated to pinpoint hot spots, while time series data identified hot spot changes. Knowledge regarding the geographical distribution of diseases is essential in public health to define strategies that improve the health of populations and quality of life. Our findings support that spatial aggregation at the census tract level contributes to identifying the location of at-risk “hot spot” communities to refine health programs, while temporal windowing reduces random noise effects on spatial clustering patterns. With tight state budgets limiting health departments’ funds, using geographic analytics provides for a targeted and efficient approach to health resource planning.

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Section: Introductionmentioning

confidence: 99%

Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments

2019

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“…5 Generally, fewer data are available at the sub-county level. 6,7 The current geographic resolution of data collected by the Tracking Program is limited mostly to the county or state level. The Tracking Program is working to add more sub-county data to fill this void, creating a system of sub-county data.…”

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confidence: 99%

Lessons Learned From the Environmental Public Health Tracking Sub-County Data Pilot Project

Werner

Strosnider

Kassinger

et al. 2018

Journal of Public Health Management and Practice

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National standards for generating, analyzing, and sharing sub-county data should be established to build a system of sub-county data that allow for comparison of outcomes, geographies, and time. Increasing the availability and accessibility of small area data will not only enhance the Tracking Network's capabilities but also contribute to an improved understanding of environmental health and informed decision making at a local level.

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Disproportionate Emergency Room Use as an Indicator of Community Health

Abstract: Objective To identify geographic clustering of elevated emergency room (ER) usage rates for incorporation into community health assessments (CHA) in suburban Cook County and to validate this metric as a potential sub-county level community health indicator.

Cited by 2 publications

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Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments

Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments

Lessons Learned From the Environmental Public Health Tracking Sub-County Data Pilot Project

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