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Health and Quality of Life Outcomes
Published in: Health and Quality of Life Outcomes 1/2018

Open Access 01-12-2018 | Research

Development and validation of a new instrument to measure perceived risks associated with the use of tobacco and nicotine-containing products

Authors: Stefan Cano, Christelle Chrea, Thomas Salzberger, Thomas Alfieri, Gerard Emilien, Nelly Mainy, Antonio Ramazzotti, Frank Lüdicke, Rolf Weitkunat

Published in: Health and Quality of Life Outcomes | Issue 1/2018

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Abstract

Background

Making tobacco products associated with lower risks available to smokers who would otherwise continue smoking is recognized as an important strategy towards addressing smoking-related harm. Predicting use behavior is an important major component of product risk assessment. In this context, risk perception is a possible factor driving tobacco product uptake and use. As prior to market launch real-world actual product use cannot be observed, assessing risk perception can provide predictive information. Considering the lack of suitable validated self-report instruments, the development of a new instrument was undertaken to quantify perceived risks of tobacco and nicotine-containing products by adult smokers, former smokers and never-smokers.

Methods

Initial items were constructed based on a literature review, focus groups and expert opinion. Data for scale formation and assessment were obtained through two successive US-based web surveys (n = 2020 and 1640 completers, respectively). Psychometric evaluation was based on Rasch Measurement Theory and Classical Test Theory.

Results

Psychometric evaluation supported the formation of an 18-item Perceived Health Risk scale and a 7-item Perceived Addiction Risk scale: item response option thresholds were ordered correctly for all items; item locations in each scale were spread out (coverage range 75–87%); scale reliability was supported by high person separation indices > 0.93, Cronbach’s alpha > 0.98 and Corrected Item-Total Correlations > 0.88; and no differential item functioning was present. Construct validity evaluations met expectations through inter-scale correlations and findings from known-group comparisons.

Conclusions

The Perceived Risk Instrument is a psychometrically robust instrument applicable for general and personal risk perception measurement, for use in different types of products (including cigarettes, nicotine replacement therapy, potential Modified Risk Tobacco Products), and for different smoking status groups (i.e., current smokers with and without intention to quit, former smokers, never smokers).
Footnotes
1
The three experts involved in the literature review have strong expertise in public health and quality of life, consumer risk perception, qualitative and quantitative research in scale development. They currently hold, or previously held, positions at governmental regulatory bodies, universities or contract research organisations.
 
2
The four experts involved at this stage were subject matter key opinion leaders (KOLs) in fields of nicotine and other addictions, motivational aspects of consumer perception, in epidemiologic study design, data management, and evaluation, measurement of clinical concepts, evidence-based medicine, and statistical analysis in health. All experts hold leading positions at universities or organisations in health care in the USA or Canada. The KOLs have a long record of publications in the healthcare sector and have contributed to governmental programs at treating various kinds of addictions in public health. None of the four experts was involved in the previous literature review.
 
3
Reduced Risk Products (“RRPs”) is the term used by Philip Morris Products S.A. to refer to products with the potential to reduce individual risk and population harm in comparison to smoking cigarettes. More details are available on www.​pmiscience.​com.
 
Literature
1.
U.S. Department of Health and Human Services. The health consequences of smoking - a report of the surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2004.
2.
U.S. Department of Health and Human Services. The health benefits of smoking cessation. Rockville: US Department of Health and Human Services Public Health Service Centers for Disease Control Center for Chronic Disease Prevention and Health Promotion Office on Smoking and Health; 1990. DHHS Publication No. (CDC)9O-8416
3.
Stratton K, Shetty P, Wallace R, Bondurant S. Clearing the smoke: the science base for tobacco harm reduction-executive summary. Tob Control. 2001;10:189–95.CrossRef
4.
WHO (World Health Organization). Monograph: advancing knowledge on regulating tobacco products. Geneva: World Health Organization; 2009.
5.
FDA (Food and Drug Administration). Guidance for industry - Modified risk tobacco product applications - Draft Guidance. Silver Springs, Maryland: FDA; 2012.
6.
Hatsukami DK, Biener L, Leischow SJ, Zeller MR. Tobacco and nicotine product testing. Nicotine Tob Res. 2012;14:7–17.CrossRef
7.
Bandura A. A social cognitive theory of action. In: Forgas JP, Innes JM, Science IUoP, Society AP, editors. Recent advances in social psychology: an international perspective. North-Holland: Elsevier; 1989. p. 127–38.
8.
Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50:179–211.CrossRef
9.
Rees VW, Kreslake JM, Cummings KM, O’Connor RJ, Hatsukami DK, Parascandola M, Shields PG, Connolly GN. Assessing consumer responses to potential reduced-exposure tobacco products: a review of tobacco industry and independent research methods. Cancer Epidemiol Biomark Prev. 2009;18:3225–40.CrossRef
10.
Salzberger T, Chrea C, Cano SJ, Martin M, Atkison M, Emilien G, Mainy N, Ramazzotti A, Weitkunat R, Lüdicke F. Perceived risks associated with the use of tobacco and nicotine-containing products: findings from qualitative research. Tob Sci Technol. 2017;50:32–42.
11.
Rasch G. Probabilistic models for some intelligence and attainment tests. (Expanded edition (1980) with foreword and afterword by B.D. Wright, Chicago, Illinois: The University of Chicago Press). Copenhagen: Danish Institute for Education Research; 1960. Reprinted by MESA Press, 1980
12.
Rasch G. On specific objectivity: an attempt at formalizing the request for generality and validity of scientific statements. Dan Yearb Philos. 1977;14:58–94.
13.
Fisher WP, Stenner AJ. Theory-based metrological traceability in education: a reading measurement network. Measurement. 2016;92:489–96.CrossRef
14.
Andrich D, Pedler P. On a law of ordinal error. J Phys Conf Ser. 2018;1044:012055.CrossRef
15.
Rindfleisch A, Crockett DX. Cigarette smoking and perceived risk : a multidimensional investigation. J Public Policy Mark. 1999;18:159–71.
16.
Rodu B, Plurphanswat N, Hughes JR, Fagerstrom K. Associations of proposed relative-risk warning labels for snus with perceptions and behavioral intentions among tobacco users and nonusers. Nicotine Tob Res. 2015;18(5):809–16.CrossRef
17.
Hamilton WL, Norton G, Ouellette TK, Rhodes WM, Kling R, Connolly GN. Smokers’ responses to advertisements for regular and light cigarettes and potential reduced-exposure tobacco products. Nicotine Tob Res. 2004;6(Suppl 3):S353–62.CrossRef
18.
Biener L, Bogen K, Connolly G. Impact of corrective health information on consumers’ perceptions of “reduced exposure” tobacco products. Tob Control. 2007;16:306–11.CrossRef
19.
O’Connor RJ, Hyland A, Giovino GA, Fong GT, Cummings KM. Smoker awareness of and beliefs about supposedly less-harmful tobacco products. Am J Prev Med. 2005;29:85–90.CrossRef
20.
Shadel WG, Lerman C, Cappella J, Strasser AA, Pinto A, Hornik R. Evaluating smokers’ reactions to advertising for new lower nicotine quest cigarettes. Psychol Addict Behav. 2006;20:80–4.CrossRef
21.
Park ER, Ostroff JS, Rakowski W, Gareen IF, Diefenbach MA, Feibelmann S, Rigotti NA. Risk perceptions among participants undergoing lung cancer screening: baseline results from the National Lung Screening Trial. Ann Behav Med. 2009;37:268–79.CrossRef
22.
Linacre JM. Sample size and item calibration stability. Rasch Meas Trans. 1994;7:328–9.
23.
Budd GM, Preston DB. College student’s attidudes and beliefs about the consequences of smoking: development and normative scores of a new scale. J Am Acad Nurse Pract. 2001;13:421–7.CrossRef
24.
Jeffries SK, Catley D, Okuyemi KS, Nazir N, McCarter KS, Grobe JE, Ahluwalia JS. Use of a brief smoking consequences questionnaire for adults (SCQ-A) in African American smokers. Psychol Addict Behav. 2004;18:74–7.CrossRef
25.
Gilliard J, Bruchon-Schweitzer M. Development and validation of a multidimensional smoking behaviour questionnaire. Psychol Rep. 2001;89:499–509.CrossRef
26.
Smith SY, Curbow B, Stillman FA. Harm perception of nicotine products in college freshmen. Nicotine Tob Res. 2007;9:977–82.CrossRef
27.
O’Connor RJ, McNeill A, Borland R, Hammond D, King B, Boudreau C, Cummings KM. Smokers’ beliefs about the relative safety of other tobacco products: findings from the ITC collaboration. Nicotine Tob Res. 2007;9:1033–42.CrossRef
28.
IOM (Institute of Medicine). Scientific standards for studies on modified risk tobacco products. Washington, DC: The National Academies Press; 2012.
29.
WHO (World Health Organization). Monitoring tobacco use. In: Guidelines for Controlling and Monitoring the Tobacco Epidemic. Geneva: WHO; 1998. p. 76–101.
30.
Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol. 1983;51:390–5.CrossRef
31.
Prochaska JO, Di Clemente CC. Transtheoretical therapy: toward a more integrative model of change. Psychol Psychother Theory Res Pract. 1982;19:276–88.CrossRef
32.
Wright BD, Stone MH. Best test design. Rasch measurement. Chicago, IL, USA: MESA Press; 1979.
33.
Hobart JC, Cano SJ, Warner TT, Thompson AJ. What sample sizes for reliability and validity studies in neurology? J Neurol. 2012;259:2681–94.CrossRef
34.
Weitkunat R, Coggins CRE, Sponsiello-Wang Z, Kallischnigg G, Dempsey R. Assessment of cigarette smoking in epidemiologic studies. Beiträge zur Tabakforschung Int. 2013;25:638–48.CrossRef
35.
Slovic P. What does it mean to know a cumulative risk? Adolescents’ perceptions of short-term and long-term consequences of smoking. J Behav Decis Mak. 2000;13:259–66.CrossRef
36.
Fischer GH, Molenaar IW. Rasch models: foundations, recent developments, and applications. New York: Springer-Verlag; 1995.CrossRef
37.
Andrich D. Rasch models for measurement. Beverly Hills: Sage Publications; 1988.CrossRef
38.
Hagquist C, Bruce M, Gustavsson JP. Using the Rasch model in nursing research: an introduction and illustrative example. Int J Nurs Stud. 2009;46:380–93.CrossRef
39.
Weinstein ND. Accuracy of smokers’ risk perception. Nicotine Tob Res. 1999;1(Suppl 1):S123–30.CrossRef
40.
Weinstein ND, Marcus SE, Moser RP. Smokers’ unrealistic optimism about their risk. Tob Control. 2005;14:55–9.CrossRef
41.
Borland R, Yong HH, Balmford J, Fong GT, Zanna MP, Hastings G. Do risk-minimizing beliefs about smoking inhibit quitting? Findings from the international tobacco control (ITC) four-country survey. Prev Med. 2009;49:219–23.CrossRef
42.
Wellek S, Blettner M. On the proper use of the crossover design in clinical trials: part 18 of a series on evaluation of scientific publications. Dtsch Arztebl Int. 2012;109:276–81.PubMedPubMedCentral
43.
Spearman C. The proof and measurement of association between two things. Int J Epidemiol. 1904;39:1137–50.CrossRef
44.
Warm TA. Weighted likelihood estimation of ability in item response theory. Psychometrika. 1989;54:427–50.CrossRef
45.
Andrich D. A rating formulation for ordered response categories. Psychometrika. 1978;43:561–73.CrossRef
46.
Andrich D. A general form of Rasch’s extended logistic model for partial credit scoring. Appl Meas Educ. 1988;1:363–78.CrossRef
47.
Muraki E. A generalized partial credit model. In: van der Linden WJ, Hambleton RK, editors. Handbook of modern item response theory. New York, NY: Springer New York; 1997. p. 153–64.CrossRef
48.
Schonlau M, van Soest A, Kapteyn A, Couper M. Selection bias in web surveys and the use of propensity scores. Sociol Methods Res. 2009;37:291–318.CrossRef
49.
Salzberger T. Reporting a Rasch analysis. In: Christensen KB, Kreiner S, Mesbah M, editors. Rasch Models in Health. NJ: iSTE London - Wiley Hoboken; 2013. p. 347–61.CrossRef
50.
Wright BD, Masters GN. Rating Scale Analysis: Pluribus Press; 1982.
51.
Andrich D, de Jong JHAL, Sheridan BE. Diagnostic opportunities with the Rasch model for ordered response categories. In: Rost J, Langeheine R, editors. Applications of latent trait and latent class models in the social sciences. New York: Waxmann Publishing Co.; 1997. p. 59–70.
52.
Yen WM. Effects of Local Item Dependence on the Fit and Equating Performance of the Three-Parameter Logistic Model. Applied Psychological Measurement. 1984;8:125–45.CrossRef
53.
Christensen KB, Makransky G, Horton M. Critical Values for Yen’s Q3: Identification of Local Dependence in the Rasch Model Using Residual Correlations. Applied Psychological Measurement. 2017;41:178–94.CrossRef
54.
Marais I. Local dependence. In: Rasch models in health Edited by Christensen KB, Kreiner S, Mesbah M. London, UK: Wiley-ISTE Ltd.; 2013. p. 111–30.CrossRef
55.
Andrich D. An elaboration of Guttman scaling with Rasch models for measurement. Sociological Methodology. 1985;15:33–80.CrossRef
56.
Andrich D. An index of person separation in latent trait theory, the traditional KR-20 index, and the Guttman scale response pattern. Edu Res Pers. 1982;9(1):95–104.
57.
Andrich D, Hagquist C. Real and artificial differential item functioning. Journal of Educational and Behavioral Statistics. 2012.
58.
WHOQOL Group. The World Health Organistion Quality of Life Assessment (WHOQOL): Development and general psychometric properties. Social science & medicine. 1998;46:1569–85.CrossRef
59.
Hays RD, Hayashi T. Beyond internal consistency reliability: rationale and user’s guide for Multitrait Analysis Program on the microcomputer. Behavior Research Methods, Instruments, & Computers. 1990;22:167–75.CrossRef
60.
DeVellis RF. Scale development: theory and applications. USA: Sage Publications; 2003.
61.
Likert R. A technique for the measurement of attitudes. Archives of Psychology. 1932;140:5–53.
62.
McHorney CA, Ware JEJ, Lu JF, Sherbourne CD. The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. Medical Care. 1994;32:40–66.CrossRef
63.
Guttman L. Some necessary conditions for common-factor analysis. Psychometrika. 1954;19:149–61.CrossRef
64.
Ware JE, Harris WJ, Gandek B, Rogers BW, Reese PR. MAP-R for Windows: Multitrait / multi-item analysis program-revised users' guide version 1. Boston MA: Health Assessment Lab; 1997.
65.
McHorney CA, Ware JE, Raczek AE. The MOS 36-Item Short-Form Health Survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs. Med Care. 1993;31:247–63.CrossRef
66.
Hays RD, Anderson R, Revicki D. Psychometric considerations in evaluating health-related quality of life measures. Qual Life Res. 1993;2:441–9.CrossRef
67.
Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16:297–334.CrossRef
Metadata
Title
Development and validation of a new instrument to measure perceived risks associated with the use of tobacco and nicotine-containing products
Authors
Stefan Cano
Christelle Chrea
Thomas Salzberger
Thomas Alfieri
Gerard Emilien
Nelly Mainy
Antonio Ramazzotti
Frank Lüdicke
Rolf Weitkunat
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Health and Quality of Life Outcomes / Issue 1/2018
Electronic ISSN: 1477-7525
DOI
https://doi.org/10.1186/s12955-018-0997-5

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