Evidence map›Paper›PMID 32343712›Full record

ArticlePloS one2020

Data linkage and computerised algorithmic coding to enhance individual clinical care for Aboriginal people living with chronic hepatitis B in the Northern Territory of Australia - Is it feasible?

Kelly Hosking, Geoffrey Stewart, Mikaela Mobsby, Steven Skov, Yuejen Zhao, Jiunn-Yih Su, Steven Tong, Peter Nihill, Joshua Davis, Christine Connors and 1 more

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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

  1. Pooled it
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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

11 authors.

Kelly HoskingPrimary Health Care Branch, Top End Health Service, Northern Territory Government, Darwin, Northern Territory, Australia.ORCID 0000-0002-6775-7089
Geoffrey StewartPrimary Health Care Branch, Top End Health Service, Northern Territory Government, Darwin, Northern Territory, Australia.
Mikaela MobsbyPrimary Health Care Branch, Top End Health Service, Northern Territory Government, Darwin, Northern Territory, Australia.
Steven SkovCentre for Disease Control, Northern Territory Government, Darwin, Northern Territory, Australia.
Yuejen ZhaoInnovation & Research, Northern Territory Government, Darwin, Northern Territory, Australia.
Jiunn-Yih SuGlobal and Tropical Health Division, Menzies School of Health Research, Darwin, Northern Territory, Australia.ORCID 0000-0003-1345-8013
Steven TongGlobal and Tropical Health Division, Menzies School of Health Research, Darwin, Northern Territory, Australia.
Peter NihillPrimary Health Care Branch, Top End Health Service, Northern Territory Government, Darwin, Northern Territory, Australia.
Joshua DavisGlobal and Tropical Health Division, Menzies School of Health Research, Darwin, Northern Territory, Australia.
Christine ConnorsPrimary Health Care Branch, Top End Health Service, Northern Territory Government, Darwin, Northern Territory, Australia.
Jane DaviesPrimary Health Care Branch, Top End Health Service, Northern Territory Government, Darwin, Northern Territory, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic hepatitis B (CHB) is endemic in the Aboriginal population of Australia's Northern Territory (NT). However, many people's hepatitis B virus (HBV) status remains unknown.

objective1. To maximise the utility of existing HBV test and vaccination data in the NT by creating a linked dataset and computerised algorithmic coding. 2. To undertake rigorous quality assurance processes to establish feasibility of using the linked dataset and computerised algorithmic coding for individual care for people living with CHB.

methodsStep 1: We used deterministic data linkage to merge information from three separate patient databases. HBV testing and vaccination data from 2008-2016 was linked and extracted for 19,314 people from 21 remote Aboriginal communities in the Top End of the NT. Step 2: A computerised algorithm was developed to allocate one of ten HBV codes to each individual. Step 3: A quality assurance process was undertaken by a clinician, using standardised processes, manually reviewing all three databases, for a subset of 5,293 Aboriginal people from five communities to check the accuracy of each allocated code.

resultsThe process of data linking individuals was highly accurate at 99.9%. The quality assurance process detected an overall error rate of 17.7% on the HBV code generated by the computerised algorithm. Errors occurred in source documentation, primarily from the historical upload of paper-based records to electronic health records. An overall HBV prevalence of 2.6% in five communities was found, which included ten cases of CHB who were previously unaware of infection and not engaged in care.

conclusionsData linkage of individuals was highly accurate. Data quality issues and poor sensitivity in the codes produced by the computerised algorithm were uncovered in the quality assurance process. By systematically, manually reviewing all available data we were able to allocate a HBV status to 91% of the study population.

Indexed as

Information Storage and RetrievalAlgorithmsDatabases, FactualElectronic Health RecordsEndemic DiseasesHepatitis B, ChronicHepatitis B VaccinesHumansNorthern TerritoryQuality Assurance, Health CareSerologic TestsHepatitis B Vaccines

Identifiers

PMID32343712
PMCPMC7188233

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Registered trials

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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.