Evidence map›Paper›PMID 28095905›Full record

ArticleBMC health services research2017

Imputing HIV treatment start dates from routine laboratory data in South Africa: a validation study.

Mhairi Maskew, Jacob Bor, Cheryl Hendrickson, William MacLeod, Till Bärnighausen, Deenan Pillay, Ian Sanne, Sergio Carmona, Wendy Stevens, Matthew P Fox

Open access · goldAbstract readValidation Study
In one paragraph

Article in BMC health services research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.1field-weighted citation impact, top 14% 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

16 citing papers in PubMed, 18 citations in OpenAlex.

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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

10 authors at 6 institutions in 4 countries.

Mhairi MaskewHeath Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Jacob BorHeath Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. jbor@bu.edu.
Cheryl HendricksonHeath Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
William MacLeodHeath Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Till BärnighausenAfrica Health Research Institute, Somkhele, South Africa.
Deenan PillayAfrica Health Research Institute, Somkhele, South Africa.
Ian SanneHeath Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Sergio CarmonaNational Health Laboratory Service, Johannesburg, South Africa.
Wendy StevensNational Health Laboratory Service, Johannesburg, South Africa.
Matthew P FoxHeath Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
University of the Witwatersrand · ZABoston University · USAfrica Health Research Institute · ZANational Health Laboratory Service · ZAPerinatal HIV Research Unit · ZAUniversity College London · GB

Funding

Causal Pathways to population health impact of HIV antiretroviral treatmentR01HD084233 · NICHD · UNIVERSITY OF KWAZULU-NATAL · PI BARNIGHAUSEN, TILL, TANSER, FRANK COURTENEY · 2015 to 2021
$2.3M
Analysis of National Lab Database to evaluate the HIV treatment rollout in South AfricaR01AI115979 · NIAID · WITS HEALTH CONSORTIUM (PTY), LTD · PI FOX, MATTHEW ALEXANDER PEASE, MASKEW, MHAIRI · 2015 to 2019
$1.6M
Economic, health, and behavioral dimensions of HIV treatment scale-upK01MH105320 · NIMH · BOSTON UNIVERSITY MEDICAL CAMPUS · PI BOR, JACOB · 2015 to 2018
$613k
NIAID NIH HHS R01 AI115979NICHD NIH HHS R01 HD084233NIMH NIH HHS K01 MH105320PEPFAR 674-A-00-08-0001-00Wellcome Trust 082384/Z/07/Z
6 · The paper itself

Abstract

backgroundPoor clinical record keeping hinders health systems monitoring and patient care in many low resource settings. We develop and validate a novel method to impute dates of antiretroviral treatment (ART) initiation from routine laboratory data in South Africa's public sector HIV program. This method will enable monitoring of the national ART program using real-time laboratory data, avoiding the error potential of chart review.

methodsWe developed an algorithm to impute ART start dates based on the date of a patient's "ART workup", i.e. the laboratory tests used to determine treatment readiness in national guidelines, and the time from ART workup to initiation based on clinical protocols (21 days). To validate the algorithm, we analyzed data from two large clinical HIV cohorts: Hlabisa HIV Treatment and Care Programme in rural KwaZulu-Natal; and Right to Care Cohort in urban Gauteng. Both cohorts contain known ART initiation dates and laboratory results imported directly from the National Health Laboratory Service. We assessed median time from ART workup to ART initiation and calculated sensitivity (SE), specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) of our imputed start date vs. the true start date within a 6 month window. Heterogeneity was assessed across individual clinics and over time.

resultsWe analyzed data from over 80,000 HIV-positive adults. Among patients who had a workup and initiated ART, median time to initiation was 16 days (IQR 7,31) in Hlabisa and 21 (IQR 8,43) in RTC cohort. Among patients with known ART start dates, SE of the imputed start date was 83% in Hlabisa and 88% in RTC, indicating this method accurately predicts ART start dates for about 85% of all ART initiators. In Hlabisa, PPV was 95%, indicating that for patients with a lab workup, true start dates were predicted with high accuracy. SP (100%) and NPV (92%) were also very high.

conclusionsRoutine laboratory data can be used to infer ART initiation dates in South Africa's public sector. Where care is provided based on protocols, laboratory data can be used to monitor health systems performance and improve accuracy and completeness of clinical records.

Indexed as

Diagnostic Tests, RoutineAdolescentAdultAlgorithmsAnti-HIV AgentsCD4 Lymphocyte CountClinical ProtocolsFemaleHIV InfectionsHumansMaleMedical AuditMiddle AgedNational Health ProgramsSouth AfricaTime FactorsAnti-HIV AgentsAntiretroviral therapyChronic disease managementHealth systemsHIV/AIDSImputationLaboratoryMissing dataMonitoring and evaluationResource-limited settingsSouth AfricaValidation

Identifiers

PMID28095905
PMCPMC5240407
OpenAlexW2576413429

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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.