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Record W2142983533 · doi:10.1186/1472-6963-13-274

Cost-effectiveness of a nurse-based intervention (AIMS) to improve adherence among HIV-infected patients: design of a multi-centre randomised controlled trial

2013· article· en· W2142983533 on OpenAlexaff
E. Oberjé, Marijn de Bruin, Silvia Evers, Wolfgang Viechtbauer, Hans-Erik Nobel, Herman P. Schaalma, Jim McCambridge, Luuk Gras, Eric Tousset, Jan M. Prins

Bibliographic record

VenueBMC Health Services Research · 2013
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsInstitute of Infection and Immunity
FundersAmsterdam School of Communication Research, University of AmsterdamLeids Universitair Medisch CentrumWageningen University and ResearchNederlandse Organisatie voor Wetenschappelijk OnderzoekErasmus Medisch CentrumUniversiteit MaastrichtZonMwCare and Public Health Research Institute, Universiteit MaastrichtUniversiteit van AmsterdamUniversiteit Leiden
KeywordsMedicineRandomized controlled trialCost effectivenessPsychological interventionHealth administrationNursing researchEconomic evaluationQuality of life (healthcare)Intervention (counseling)Quality-adjusted life yearPhysical therapyResearch designFamily medicineNursingPublic healthSurgeryRisk analysis (engineering)

Abstract

fetched live from OpenAlex

BACKGROUND: Non-adherence to HIV-treatment can have a negative impact on patients treatment success rates, quality of life, infectiousness, and life expectancy. Few adherence interventions have shown positive effects on adherence and/or virologic outcomes. The theory- and evidence-based Adherence Improving self-Management Strategy (AIMS) is an intervention that has been demonstrated to improve adherence and viral suppression rates in a randomised controlled trial. However, evidence of its cost-effectiveness is lacking. Following a recent review suggesting that cost-effectiveness evaluations of adherence interventions for chronic diseases are rare, and that the methodology of such evaluations is poorly described in the literature, this manuscript presents the study protocol for a multi-centre trial evaluating the effectiveness and cost-effectiveness of AIMS among a heterogeneous sample of patients. METHODS/DESIGN: The study uses a multi-centre randomised controlled trial design to compare the AIMS intervention to usual care from a societal perspective. Embedded in this RCT is a trial-based and model-based economic evaluation. A planned number of 230 HIV-infected patients are randomised to receive either AIMS or usual care. The relevant outcomes include changes in adherence, plasma viral load, quality of life, and societal costs. The time horizon for the trial-based economic evaluation is 12-15 months. Costs and effects are extrapolated to a lifetime horizon for the model-based economic evaluation. DISCUSSION: The present multicentre RCT is designed to provide sound methodological evidence regarding the effectiveness and cost-effectiveness of a nurse-based counselling intervention (AIMS) to support treatment adherence among a large and heterogeneous sample of HIV-infected patients in the Netherlands. The objective of the current paper is to describe the trial protocol in sufficient detail to allow full evaluation of the quality of the study design. It is anticipated that, if proven cost-effective, AIMS can contribute to improved evidence-based counselling guidelines for HIV-nurses and other health care professionals. TRIAL REGISTRATION: The study has been registered on clinicaltrials.gov (Identifier: NCT01429142).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.078
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.078
Threshold uncertainty score0.413

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0780.086
Meta-epidemiology (narrow)0.0060.002
Meta-epidemiology (broad)0.0130.011
Bibliometrics0.0030.002
Science and technology studies0.0020.005
Scholarly communication0.0050.004
Open science0.0030.004
Research integrity0.0080.004
Insufficient payload (model declined to judge)0.0130.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.064
GPT teacher head0.431
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2013
Admission routes1
Has abstractyes

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