Evidence map›Paper›PMID 18647873›Full record

ArticleSexually transmitted infections2008

Estimating the number of HIV infections averted: an approach and its issues.

L M Heaton, R Komatsu, D Low-Beer, T B Fowler, P O Way

Abstract read
In one paragraph

Article in Sexually transmitted infections, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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. Article
  2. Article
  3. Article
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

5 authors.

L M HeatonUS Census Bureau, 4600 Silver Hill Road, Washington, USA. Laura.M.Heaton@census.gov
R Komatsu
D Low-Beer
T B Fowler
P O Way

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo propose a methodology to estimate the number of new HIV infections averted. Knowledge of HIV infection has increased tremendously and modelling tools to project current epidemics into the future have greatly improved. Different types of models can be used to estimate HIV infections averted, although the number of new HIV infections averted cannot be measured directly.

methodUsing cohort-component population projections, a disease modelling-based approach was used to compare the observed epidemiology of a disease after programme initiation with an expected epidemiology from past trends before programme initiation. The concept of modelling infections averted in a disease modelling-based approach involves a comparison between an "expected" or baseline epidemic with an "estimated" one. A hypothetical example was featured in order to demonstrate the proposed methodology. Using both the Estimation and Projection Package (EPP) and the Spectrum demographic modelling program, the underlying annual incidence levels implied by both the baseline and estimated epidemics were examined.

resultsThe difference between baseline and estimated incidence levels is interpreted as "infections averted". Strengths and limitations of the approach are discussed.

conclusionsIn this study an expected epidemiological approach was compared to one based on observation. Once sufficient data become available, the validation of various country data including HIV prevalence, mortality, and behaviour must be done. Additional information related to behaviour change may be critical to further support arguments for a change in disease trend. It is therefore important to use all available data, consequently strengthening findings from a disease modelling-based approach on HIV infections averted.

Indexed as

AdolescentAdultData CollectionDisease OutbreaksEpidemiologic MethodsFemaleHIV InfectionsHIV SeroprevalenceHumansMaleMiddle AgedPrevalence

Identifiers

PMID18647873
PMCPMC2569155

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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.