Evidence map›Paper›PMID 29866161›Full record

ArticleBMC research notes2018

Repeat participation in annual cross-sectional surveys of drug users and its implications for analysis.

P A Agius, C K Aitken, C Breen, P M Dietze

Abstract readMulticenter Study
In one paragraph

Article in BMC research notes, 2018. 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

4 authors.

P A AgiusBurnet Institute, 85 Commercial Rd, Melbourne, VIC, 3004, Australia.
C K AitkenBurnet Institute, 85 Commercial Rd, Melbourne, VIC, 3004, Australia. campbell.aitken@burnet.edu.au.
C BreenNational Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia.
P M DietzeBurnet Institute, 85 Commercial Rd, Melbourne, VIC, 3004, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveWe sought to establish the extent of repeat participation in a large annual cross-sectional survey of people who inject drugs and assess its implications for analysis.

resultsWe used "porn star names" (the name of each participant's first pet followed by the name of the first street in which they lived) to identify repeat participation in three Australian Illicit Drug Reporting System surveys. Over 2013-2015, 2468 porn star names (96.2%) appeared only once, 88 (3.4%) twice, and nine (0.4%) in all 3 years. We measured design effects, based on the between-cluster variability for selected estimates, of 1.01-1.07 for seven key variables. These values indicate that the complex sample is (e.g.) 7% less efficient in estimating prevalence of heroin use (ever) than a simple random sample, and 1% less efficient in estimating number of heroin overdoses (ever). Porn star names are a useful means of tracking research participants longitudinally while maintaining their anonymity. Repeat participation in the Australian Illicit Drug Reporting System is low (less than 5% per annum), meaning point-prevalence and effect estimation without correction for the lack of independence in observations is unlikely to seriously affect population inference.

Indexed as

Drug OverdoseIllicit DrugsSurveys and QuestionnairesAustraliaCitiesCross-Sectional StudiesDrug UsersHumansPrevalenceSubstance-Related DisordersIllicit DrugsCross-sectional surveyDesign effect estimationPoint prevalence estimationPopulation inferenceRepeat participation

Identifiers

PMID29866161
PMCPMC5987568

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.