Evidence map›Paper›PMID 32993610›Full record

ArticleBMC public health2020

Modelling improved efficiency in healthcare referral systems for the urban poor using a geo-referenced health facility data: the case of Sylhet City Corporation, Bangladesh.

Alayne M Adams, Rushdia Ahmed, Shakil Ahmed, Sifat Shahana Yusuf, Rubana Islam, Ruman M Zakaria Salam, Rocco Panciera

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 16% of its field
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. A Bibliometric Analysis and Visualization of Decision Support Systems for Healthcare Referral Strategies.International journal of environmental research and public health · 2022
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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

7 authors at 3 institutions in 4 countries.

Alayne M AdamsDepartment of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, 5858 Cote des Neiges, Room 332, Montréal, H3S 1Z1, Québec, Canada. alayne.adams@gmail.com.ORCID http://orcid.org/0000-0002-0961-9825
Rushdia AhmedHealth Systems and Population Studies Division, icddr,b, Dhaka, Bangladesh.
Shakil AhmedHealth Systems and Population Studies Division, icddr,b, Dhaka, Bangladesh.
Sifat Shahana YusufHealth Systems and Population Studies Division, icddr,b, Dhaka, Bangladesh.
Rubana IslamSchool of Public Health & Community Medicine, University of New South Wales, Sydney, Australia.
Ruman M Zakaria SalamHealth Systems and Population Studies Division, icddr,b, Dhaka, Bangladesh.
Rocco PancieraImplementation Research and Delivery Science Unit, Health Section, UNICEF, New York, NY, USA.
International Centre for Diarrhoeal Disease Research · BDUnited Nations Children's Fund · USUNSW Sydney · AU

Funding

Deutsche Gesellschaft für Internationale Zusammenarbeit GR-01147
6 · The paper itself

Abstract

backgroundAn effective referral system is critical to ensuring access to appropriate and timely healthcare services. In pluralistic healthcare systems such as Bangladesh, referral inefficiencies due to distance, diversion to inappropriate facilities and unsuitable hours of service are common, particularly for the urban poor. This study explores the reported referral networks of urban facilities and models alternative scenarios that increase referral efficiency in terms of distance and service hours.

methodsRoad network and geo-referenced facility census data from Sylhet City Corporation were used to examine referral linkages between public, private and NGO facilities for maternal and emergency/critical care services, respectively. Geographic distances were calculated using ArcGIS Network Analyst extension through a "distance matrix" which was imported into a relational database. For each reported referral linkage, an alternative referral destination was identified that provided the same service at a closer distance as indicated by facility geo-location and distance analysis. Independent sample t-tests with unequal variances were performed to analyze differences in distance for each alternate scenario modelled.

resultsThe large majority of reported referrals were received by public facilities. Taking into account distance, cost and hours of service, alternative scenarios for emergency services can augment referral efficiencies by 1.5-1.9 km (p < 0.05) compared to 2.5-2.7 km in the current scenario. For maternal health services, modeled alternate referrals enabled greater referral efficiency if directed to private and NGO-managed facilities, while still ensuring availability after working-hours. These referral alternatives also decreased the burden on Sylhet City's major public tertiary hospital, where most referrals were directed. Nevertheless, associated costs may be disadvantageous for the urban poor.

conclusionsFor both maternal and emergency/critical care services, significant distance reductions can be achieved for public, NGO and private facilities that avert burden on Sylhet City's largest public tertiary hospital. GIS-informed analyses can help strengthen coordination between service providers and contribute to more effective and equitable referral systems in Bangladesh and similar countries.

Indexed as

Health Services AccessibilityMaternal Health ServicesBangladeshFemaleHealth FacilitiesHumansPregnancyReferral and ConsultationBangladeshGISHealth systemsReferral inefficienciesReferral mechanismReferral systemUrban

Identifiers

PMID32993610
PMCPMC7526238
OpenAlexW3089962160

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.