Evidence map›Paper›PMID 39096519›Full record

ArticleHealth policy and planning2024

Comparing the effectiveness and cost-effectiveness of alternative type 2 diabetes monitoring intervals in resource limited settings.

Elton Mukonda, Maia Lesosky, Siphesihle Sithole, Diederick J van der Westhuizen, Jody A Rusch, Naomi S Levitt, Bronwyn Myers, Susan Cleary

Abstract readComparative Study
In one paragraph

Article in Health policy and planning, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

  1. Pooled it
  2. Article
  3. Review
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

8 authors.

Elton MukondaDivision of Epidemiology & Biostatistics, School of Public Health, University of Cape Town, Anzio Road, Cape Town 7925, South Africa.ORCID 0000-0002-3930-3389
Maia LesoskyNational Heart and Lung Institute, Imperial College London, 1B Manresa Road, London SW3 6LR, United Kingdom.
Siphesihle SitholeDivision of Epidemiology & Biostatistics, School of Public Health, University of Cape Town, Anzio Road, Cape Town 7925, South Africa.
Diederick J van der WesthuizenDivision of Chemical Pathology, Department of Pathology, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.
Jody A RuschDivision of Chemical Pathology, Department of Pathology, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.
Naomi S LevittChronic Disease Initiative for Africa, Department of Medicine, University of Cape Town, Observatory, Cape Town 7925, South Africa.
Bronwyn MyersCurtin enAble Institute, Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth WA 6845, Australia.ORCID 0000-0003-0235-6716
Susan ClearyHealth Economics Unit, School of Public Health, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.ORCID 0000-0002-9071-7958

Funding

Medical Research Council MR/M014290/1National Research Foundation UID 129506UK Economic and Social Research Council MR/M014290/1Wellcome TrustWellcome Trust MR/M014290/1; Myers
6 · The paper itself

Abstract

Type 2 diabetes (T2D) represents a growing disease burden in South Africa. While glycated haemoglobin (HbA1c) testing is the gold standard for long-term blood glucose management, recommendations for HbA1c monitoring frequency are based on expert opinion. This study investigates the effectiveness and cost-effectiveness of alternative HbA1c monitoring intervals in the management of T2D. A Markov model with three health states (HbA1c <7%, HbA1c ≥ 7%, Dead) was used to estimate lifetime costs and quality-adjusted life years (QALYs) of alternative HbA1c monitoring intervals among patients with T2D, using a provider's perspective and a 3% discount rate. HbA1c monitoring strategies (three-monthly, four-monthly, six-monthly and annual tests) were evaluated with respect to the incremental cost-effectiveness ratio (ICER) assessing each comparator against a less costly, undominated alternative. The scope of costs included the direct medical costs of managing diabetes. Transition probabilities were obtained from routinely collected public sector HbA1c data, while health service utilization and health-related-quality-of-life (HRQoL) data were obtained from a local cluster randomized controlled trial. Other parameters were obtained from published studies. Robustness of findings was evaluated using one-way and probabilistic sensitivity analyses. A South African indicative cost-effectiveness threshold of USD2665 was adopted. Annual and lifetime costs of managing diabetes increased with HbA1c monitoring, while increased monitoring provides higher QALYs and life years. For the overall cohort, the ICER for six-monthly vs annual monitoring was cost-effective (USD23 22.37 per QALY gained), whereas the ICER of moving from six-monthly to three-monthly monitoring was not cost effective (USD6437.79 per QALY gained). The ICER for four-monthly vs six-monthly monitoring was extended dominated. The sensitivity analysis showed that the ICERs were most sensitive to health service utilization rates. While the factors influencing glycaemic control are multifactorial, six-monthly monitoring is potentially cost-effective while more frequent monitoring could further improve patient HrQoL.

Indexed as

Cost-Benefit AnalysisDiabetes Mellitus, Type 2Glycated HemoglobinQuality-Adjusted Life YearsFemaleHumansMaleMarkov ChainsMiddle AgedQuality of LifeResource-Limited SettingsSouth AfricaGlycated Hemoglobincost-effectivenesslow- and middle-income countriesroutine monitoringType-2 diabetes

Identifiers

PMID39096519
PMCPMC11474914

What OpenQuestion holds

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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.