Evidence map›Paper›PMID 24573538›Full record

ArticleAmerican journal of epidemiology2014

From exposures to population interventions: pregnancy and response to HIV therapy.

Daniel Westreich

Abstract read
In one paragraph

Article in American journal of epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing 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

50 citing papers in PubMed.

  1. Article
  2. Effect measure modification in mixtures and public health.American journal of epidemiology · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Different effects for different questions: An illustration using short cervix and the risk of preterm birth.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2023
    Article
  10. A Framework for Descriptive Epidemiology.American journal of epidemiology · 2022
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

1 author.

Daniel Westreich

Funding

Social and Behavioral Sciences CoreP30AI064518 · NIAID · DUKE UNIVERSITY · PI Nwora Lance Okeke · 2005 to 2026
$50.5M
Wits HIV Research Group CLINICAL TRIAL UNIT (CTU) reapplicationUM1AI069463 · NIAID · WITS HEALTH CONSORTIUM (PTY), LTD · PI REES, HELEN, SANNE, IAN MATTHIAS · 2012 to 2025
$31.8M
Pregnancy and Response to Antiretroviral Therapy in South AfricaR00HD063961 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI WESTREICH, DANIEL J · 2010 to 2012
$687k
NIAID NIH HHS 5UM1-AI-06-9463NIAID NIH HHS P30 AI064518NIAID NIH HHS P30-AI-064518NICHD NIH HHS R00 HD063961NICHD NIH HHS R00-HD-06-3961PEPFAR
6 · The paper itself

Abstract

Many epidemiologic studies identify contrasts between an "always-exposed" population and a "never-exposed" population. Such "exposure effects" are perhaps most valuable in discussing individual lifestyle changes, or in clinical care; they may be less valuable in estimating the potential effects of realistic public health interventions. Various methods, among them population attributable fractions and generalized impact fractions, attempt to obtain more policy-relevant estimates of "population intervention" effects, but such methods remain rare in the epidemiologic literature. Here, we describe the use of the parametric g-formula as a tool for the estimation of population intervention effects in longitudinal data. Our discussion is motivated by a previous study of the effect of incident pregnancy on time to virological failure among human immunodeficiency virus-positive women initiating antiretroviral therapy in South Africa between 2004 and 2011. We show that 1) interventional estimates of effect can be estimated in longitudinal data using the parametric g-formula and 2) exposure effects and population interventional effects can have dramatically different interpretations and magnitudes in real-world data. Epidemiologists should consider estimating interventional effects in addition to exposure effects; doing so would allow the results of epidemiologic studies to be more immediately relevant to policy-makers and to implementation science efforts.

Indexed as

AdultAntiretroviral Therapy, Highly ActiveContraceptionData Interpretation, StatisticalFemaleHIV InfectionsHumansLikelihood FunctionsLongitudinal StudiesMonte Carlo MethodPregnancyPregnancy Complications, InfectiousRisk FactorsSouth AfricaTime FactorsViral Loadcausal inferencegeneralized impact fractionimplementation sciencepopulation attributable fractionpopulation intervention effects

Identifiers

PMID24573538
PMCPMC3969531

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.