Literature DB >> 29332004

Host-agent-vector-environment measures for electronic cigarette research used in NIH grants.

Mary L Garcia-Cazarin1, Rachel J Mandal1, Rachel Grana2, Kay L Wanke1, Helen I Meissner1.   

Abstract

OBJECTIVE: The purpose of this study is to describe the focus and comprehensiveness of domains measured in e-cigarette research.
METHODS: A portfolio analysis of National Institutes of Health grants focusing on e-cigarette research and funded between the fiscal years 2007 and 2015 was conducted. Grant proposals were retrieved using a government database and coded using the Host-Agent-Vector-Environment (HAVE) model as a framework to characterise the measures proposed. Eighty-one projects met the criteria for inclusion in the analysis.
RESULTS: The primary HAVE focus most commonly found was Host (73%), followed by Agent (21%), Vector (6%) and Environment (0%). Intrapersonal measures and use trajectories were the most common measures in studies that include Host measures (n=59 and n=51, respectively). Product composition was the most common area of measurement in Agent studies (n=24), whereas Marketing (n=21) was the most common (n=21) area of Vector measurement. When Environment measures were examined as secondary measures in studies, they primarily focused on measuring Peer, Occupation and Social Networks (n=18). Although all studies mentioned research on e-cigarettes, most (n=52; 64%) did not specify the type of e-cigarette device or liquid solution under study.
CONCLUSIONS: This analysis revealed a heavy focus on Host measures (73%) and a lack of focus on Environment measures. The predominant focus on Host measures may have the unintended effect of limiting the evidence base for tobacco control and regulatory science. Further, a lack of specificity about the e-cigarette product under study will make comparing results across studies and using the outcomes to inform tobacco policy difficult. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2020. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Entities:  

Keywords:  electronic nicotine delivery devices; non-cigarette tobacco products; prevention

Mesh:

Year:  2018        PMID: 29332004     DOI: 10.1136/tobaccocontrol-2017-054032

Source DB:  PubMed          Journal:  Tob Control        ISSN: 0964-4563            Impact factor:   7.552


  3 in total

1.  PhenX: Host: Biobehavioral measures for tobacco regulatory research.

Authors:  Gary A Giovino; Gary E Swan; Ben Blount; Stephanie O'Malley; Darigg C Brown; Tabitha P Hendershot
Journal:  Tob Control       Date:  2020-01       Impact factor: 7.552

2.  PhenX: Vector measures for tobacco regulatory research.

Authors:  Kurt M Ribisl; Frank J Chaloupka; Thomas R Kirchner; Lisa Henriksen; Destiney S Nettles; Rebecca C Geisler; Tabitha P Hendershot; Gary E Swan
Journal:  Tob Control       Date:  2020-01       Impact factor: 7.552

3.  Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX.

Authors:  Huaqin Pan; Vesselina Bakalov; Lisa Cox; Michelle L Engle; Stephen W Erickson; Michael Feolo; Yuelong Guo; Wayne Huggins; Stephen Hwang; Masato Kimura; Michelle Krzyzanowski; Josh Levy; Michael Phillips; Ying Qin; David Williams; Erin M Ramos; Carol M Hamilton
Journal:  Sci Data       Date:  2022-09-01       Impact factor: 8.501

  3 in total

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