Evidence map›Paper›PMID 31254122›Full record

ArticleEnvironmental monitoring and assessment2019

Teaming up census and patient data to delineate fine-scale hospital service areas and identify geographic disparities in hospital accessibility.

Peng Jia, Xinyu Shi, Imam M Xierali

Abstract read
In one paragraph

Article in Environmental monitoring and assessment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Neighborhood supermarket access and childhood obesity: A systematic review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2021
    Pooled it
  2. Article
  3. Article
  4. Article
  5. Community-based social determinants of three measures of mortality in Rhode Island cities and towns.Archives of public health = Archives belges de sante publique · 2020
    Article
  6. Article
  7. Geospatial technology in environmental health applications.Environmental monitoring and assessment · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Peng JiaFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500, Enschede, The Netherlands. p.jia@utwente.nl.ORCID http://orcid.org/0000-0003-0110-3637
Xinyu ShiInternational Initiative on Spatial Lifecourse Epidemiology (ISLE), 7500, Enschede, The Netherlands.
Imam M XieraliDepartment of Family and Community Medicine, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.

Funding

China Medical Board 16-262Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education JOF201704Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University 2017LSDMIS08
6 · The paper itself

Abstract

The number of hospital beds per capita, an important measure of equity in healthcare availability and resource allocation, was found to vary across geographic areas in many countries, including the USA. The hospital service areas (HSAs) have proven to be more meaningful spatial units for studying health-seeking behaviors and health resource allocation and service utilization. However, when evaluating the geographical balance in ratios of hospital beds to population (HBtP), no existing HSA delineation methods directly consider the underlying population distribution. Using Geographic Information Systems (GIS), this study incorporated the State Inpatient Database with census data to develop a population-based HSA delineation method. The census-derived HSAs were produced for Florida and were validated by aggregating and comparing with the traditional flow-based HSAs. The difference in current ratios of HBtP between the most over- and under-served HSAs was approximately 60 times. Significant clusters of high and low ratios were found in Miami and Jacksonville metropolitan areas, respectively. Such results may be of interest to relevant stakeholders and contribute to planning and optimization of hospital resource allocation and healthcare policy-making. Furthermore, the discovery of a strong correlation between the numbers of hospital discharges and the population at ZIP code level holds a remarkable potential for affordable population estimation, especially in non-census years.

Indexed as

CensusesAdultCatchment Area, HealthFloridaGeographic Information SystemsGeographyHealth Services AccessibilityHospitalizationHospitalsHumansAccessibilityCensusFloridaGISHCUPHospital dischargeHospital service areaRegionalization

Identifiers

PMID31254122
PMCPMC6598966

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.