Assessing Public Awareness about the Health Effects of Nicotine and Cigarettes Using Negative Binomial Regression

Awoke Seyoum Tegegne

doi:doi:10.11648/j.sjams.20140203.11

| Peer-Reviewed

Assessing Public Awareness about the Health Effects of Nicotine and Cigarettes Using Negative Binomial Regression

Awoke Seyoum Tegegne

Published in Science Journal of Applied Mathematics and Statistics (Volume 2, Issue 3)

Received: 19 May 2014 Accepted: 9 June 2014 Published: 20 June 2014

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Both the public and private sectors have acted responsibly to help decrease smoking-related deaths by putting health warnings on all cigarette packages. This study investigated the social or demographic factors associated with public awareness of health warnings on the harmful effects of environmental tobacco smoke based on baseline data collected by the health bureau of Amhara Region (in Ethiopia). Respondents in the survey were asked to recall the number of anti-smoking messages which appeared as warning messages on cigarette advertisements. The number of anti-smoking messages recalled ranged from 0 to 7 with a mean of 2.90 (variance of 3.11) and a median of 3.00. Because the variance (3.11) was different from mean (2.9), the negative binomial regression model provided an improved fit to the data and accounted better for over dispersion than the Poisson regression model, which assumed that the mean and variance are the same. The level of education was found to be the most significant factors. Moreover, the lower income socio-economic class nonsmokers’ anti-smoking messages recalling rate was 2.5 times that of the lower socio-economic class smokers. Unlike men, women’s anti-smoking message response rate increased with income.

Published in	Science Journal of Applied Mathematics and Statistics (Volume 2, Issue 3)
DOI	10.11648/j.sjams.20140203.11
Page(s)	60-65
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Environmental Tobacco Smoke, Negative Binomial Regression, Over Dispersion, Poisson Regression, Rate Ratios, Smoking

References

[1]	Allison, P. (1999). Logistic regression using the SAS sys-tem: Theory and applications. Cary, NC:SAS Insti-tute.
[2]	Agresti, A. (2002). Categorical data analy-sis. New York: Wiley.
[3]	Barron, D. (1992). The analysis of count data: Overdispersion and autocorrela-tion. In V. Peter (Ed.), Sociological methodology. Mars-den: Blackwell.
[4]	Bennett, W.P, Alavanja, M.C., Blomeke, B., Vahakangas, K.H., Castren K., & Welsh, J.A. (1999).Environmental tobacco smoke, genetic suscepti-bility, and risk of lung cancer in neversmoking women. Journal of the National Cancer Institute, 91, 14–2009.
[5]	Bjartveit, K., & Tverdal, A. (2005). Health consequences of smoking 1–4 cigarettes per day. TobaccoControl, 14, 315–320.
[6]	Bjorn, E., & Niel-sen, P.V. (1996). Passive smoking in a displacement ven-tilated room. Indoor Air 1996. Proceedings from: The 7th International Conference on Indoor Air Quality and Climate, July 21–26, Nagoya, Japan, 92–887.
[7]	Brook, R.D., Franklin, B., & Cascio, W. (2004). Air pollution and cardiovascular disease: As-tatement for healthcare professionals from the Expert Panel on Population and Prevention Science of the American Heart Association. Circulation, 109, 2655–2671.
[8]	Brundtland, G.H. (2000). Address at the opening of World No Tobacco Day, World Health Organization. Retrieved June 18, 2007, from http://www.who.int/directorgeneral/speeches/2000/english/20000531_bangkok.html
[9]	Cameron, A.C., & Trivedi, P.K. (1998). Regression analysis of count data. New York: Cambridge University Press.
[10]	Environmental Protection Agency (EPA). (1992). Respiratory health effects of passive smoking: Lung cancer and other disorders. EPA/600/6-90/006F. Retrieved May 4, 2007, from http://
[11]	Fienburg, S.E. (1980). The analysis of cross classified data. Cam-bridge, MA: MIT Press.
[12]	Firth, D. (1991). Genera-lised Linear Models. In D.V. Hinkley, N. Reid & E.J. Snell (Eds.), Statistical theory and modelling: In honour of Sir David Cox. London: Chapman and Hall.
[13]	Fontham, E.T., Correa, P., Reynolds, P., Wu-Williams, A., Buffler, P.A., & Greenberg, R.S. (1994).Environmental tobacco smoke and lung cancer in nonsmoking women. The Journal of the American Medical Association, 271(22), 1752–1759.
[14]	Groenewald, P., Vos, T., Normann, R., Laubscher, R, van Walbeek, C., Saloojee, Y., et al. and the South African Comparative Risk Assessment Colla-borating Group. (2007). Estimating the burden of dis-ease attributable to smoking in South Africa in 2000. South African Medical Journal, 97(8), 674–681.
[15]	Hammond, S.K. (2002). The efficacy of strategies to reduce environmental tobacco smoke concentrations in homes, workplaces, restaurants and correctional facilities. In H. Levin (Ed.), Proceedings from: The 9th International Conference on Indoor Air Quality and Climate, June 30–July 5, Monterey, California, Santa Cruz.
[16]	Hanke, W., Kalinka, J., Florek, E., & Sobala, W. (1999). Passive smoking and pregnancy outcome in central Poland. Human Experimental Toxicology, 18, 265–271.
[17]	Lindsey, J.K. (1995). Modelling fre-quency and count data. Oxford: Oxford University Press.
[18]	Malats, N., Camus-Radon, A.M., Nyberg, F., Ahrens, W., Constantinescu, V., & Mukeria, A. (2000).Lung cancer risk in nonsmokers and GSTM1 and GSTT1 genetic polymorphism. Cancer Epidemiological Biomarkers Preview, 9, 827–833.
[19]	Mc Cullagh, P., & Nelder, J.A. (1989). Generalised linear models (2nd ed.). London: Chapman and Hall.
[20]	McCulloch, C.E., & Searle, S.R. (2001). Generalized, linear, and mixed models. New York: John Wiley Inc.
[21]	Miller, D.P., De Vivo, I., Neuberg, D., Wain, J.C., Lynch, T.J., & Su, L. (2003). Association between self-reported environmental tobacco smoke exposure and lung cancer: Modification by GSTP1 polymorphism. International Journal of Cancer, 104, 758–763.
[22]	National Cancer Institute. (2007). Health effects of exposure to environmental tobacco smoke.Report of the California Environmental Protection Agency. Retrieved May 4, 2007, from http://www.cancercontrol.cancer.gov/tcrb/monographs/10/m10_complete.pdf
[23]	Repace, J., Kawachi, I., & Glantz, S. (1999). Fact sheet on secondhand smoke: The 2nd European Conference on Tobacco or Health, the 1st Iberoamerican Conference on Tobacco or Health, February 23–27, Canary Islands, Spain. Retrieved May 22, 2007, from http://www.repace.com/SHSFactsheet.pdf
[24]	SAS Institute Inc. (2004). SAS/STAT User’s Guide (Version 9.1, Chap. 29, Proc GENMOD). Cary (NC): SAS Institute Inc.
[25]	Sitas, F., Bradshow, D., Bah, S., & Peto, R. (2004). Tobacco attributable deaths in South Afri-ca.Tobacco Control, 13, 396–399.
[26]	Suh, H.H., Bahadori, T., Vallarino, J., & Spengler, J.D. (2000). Crite-ria for air pollutants and toxic air pollutants. Environ-mental Health Perspectives, 108, 625–633.
[27]	Witorsch, P. (1998). Effects of envi-ronmental tobacco smoke exposure on pulmonary func-tion andrespiratory health in adults: Update 1997. In-door Build Environment, 7, 4–17.

Cite This Article

Plain Text BibTeX RIS

APA Style

Awoke Seyoum Tegegne. (2014). Assessing Public Awareness about the Health Effects of Nicotine and Cigarettes Using Negative Binomial Regression. Science Journal of Applied Mathematics and Statistics, 2(3), 60-65. https://doi.org/10.11648/j.sjams.20140203.11

Copy | Download

ACS Style

Awoke Seyoum Tegegne. Assessing Public Awareness about the Health Effects of Nicotine and Cigarettes Using Negative Binomial Regression. Sci. J. Appl. Math. Stat. 2014, 2(3), 60-65. doi: 10.11648/j.sjams.20140203.11

Copy | Download

AMA Style

Awoke Seyoum Tegegne. Assessing Public Awareness about the Health Effects of Nicotine and Cigarettes Using Negative Binomial Regression. Sci J Appl Math Stat. 2014;2(3):60-65. doi: 10.11648/j.sjams.20140203.11

Copy | Download

@article{10.11648/j.sjams.20140203.11,
  author = {Awoke Seyoum Tegegne},
  title = {Assessing Public Awareness about the Health Effects of Nicotine and Cigarettes Using Negative Binomial Regression},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {2},
  number = {3},
  pages = {60-65},
  doi = {10.11648/j.sjams.20140203.11},
  url = {https://doi.org/10.11648/j.sjams.20140203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20140203.11},
  abstract = {Both the public and private sectors have acted responsibly to help decrease smoking-related deaths by putting health warnings on all cigarette packages. This study investigated the social or demographic factors associated with public awareness of health warnings on the harmful effects of environmental tobacco smoke based on baseline data collected by the health bureau of Amhara Region (in Ethiopia). Respondents in the survey were asked to recall the number of anti-smoking messages which appeared as warning messages on cigarette advertisements. The number of anti-smoking messages recalled ranged from 0 to 7 with a mean of 2.90 (variance of 3.11) and a median of 3.00. Because the variance (3.11) was different from mean (2.9), the negative binomial regression model provided an improved fit to the data and accounted better for over dispersion than the Poisson regression model, which assumed that the mean and variance are the same. The level of education was found to be the most significant factors. Moreover, the lower income socio-economic class nonsmokers’ anti-smoking messages recalling rate was 2.5 times that of the lower socio-economic class smokers. Unlike men, women’s anti-smoking message response rate increased with income.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Assessing Public Awareness about the Health Effects of Nicotine and Cigarettes Using Negative Binomial Regression
AU  - Awoke Seyoum Tegegne
Y1  - 2014/06/20
PY  - 2014
N1  - https://doi.org/10.11648/j.sjams.20140203.11
DO  - 10.11648/j.sjams.20140203.11
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 60
EP  - 65
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20140203.11
AB  - Both the public and private sectors have acted responsibly to help decrease smoking-related deaths by putting health warnings on all cigarette packages. This study investigated the social or demographic factors associated with public awareness of health warnings on the harmful effects of environmental tobacco smoke based on baseline data collected by the health bureau of Amhara Region (in Ethiopia). Respondents in the survey were asked to recall the number of anti-smoking messages which appeared as warning messages on cigarette advertisements. The number of anti-smoking messages recalled ranged from 0 to 7 with a mean of 2.90 (variance of 3.11) and a median of 3.00. Because the variance (3.11) was different from mean (2.9), the negative binomial regression model provided an improved fit to the data and accounted better for over dispersion than the Poisson regression model, which assumed that the mean and variance are the same. The level of education was found to be the most significant factors. Moreover, the lower income socio-economic class nonsmokers’ anti-smoking messages recalling rate was 2.5 times that of the lower socio-economic class smokers. Unlike men, women’s anti-smoking message response rate increased with income.
VL  - 2
IS  - 3
ER  -

Copy | Download

Author Information

Awoke Seyoum Tegegne

Statistics department, College of Science, Bahir Dar University, Ethiopia

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Awoke Seyoum Tegegne. (2014). Assessing Public Awareness about the Health Effects of Nicotine and Cigarettes Using Negative Binomial Regression. Science Journal of Applied Mathematics and Statistics, 2(3), 60-65. https://doi.org/10.11648/j.sjams.20140203.11

Copy | Download

ACS Style

Awoke Seyoum Tegegne. Assessing Public Awareness about the Health Effects of Nicotine and Cigarettes Using Negative Binomial Regression. Sci. J. Appl. Math. Stat. 2014, 2(3), 60-65. doi: 10.11648/j.sjams.20140203.11

Copy | Download

AMA Style

Awoke Seyoum Tegegne. Assessing Public Awareness about the Health Effects of Nicotine and Cigarettes Using Negative Binomial Regression. Sci J Appl Math Stat. 2014;2(3):60-65. doi: 10.11648/j.sjams.20140203.11

Copy | Download

@article{10.11648/j.sjams.20140203.11,
  author = {Awoke Seyoum Tegegne},
  title = {Assessing Public Awareness about the Health Effects of Nicotine and Cigarettes Using Negative Binomial Regression},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {2},
  number = {3},
  pages = {60-65},
  doi = {10.11648/j.sjams.20140203.11},
  url = {https://doi.org/10.11648/j.sjams.20140203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20140203.11},
  abstract = {Both the public and private sectors have acted responsibly to help decrease smoking-related deaths by putting health warnings on all cigarette packages. This study investigated the social or demographic factors associated with public awareness of health warnings on the harmful effects of environmental tobacco smoke based on baseline data collected by the health bureau of Amhara Region (in Ethiopia). Respondents in the survey were asked to recall the number of anti-smoking messages which appeared as warning messages on cigarette advertisements. The number of anti-smoking messages recalled ranged from 0 to 7 with a mean of 2.90 (variance of 3.11) and a median of 3.00. Because the variance (3.11) was different from mean (2.9), the negative binomial regression model provided an improved fit to the data and accounted better for over dispersion than the Poisson regression model, which assumed that the mean and variance are the same. The level of education was found to be the most significant factors. Moreover, the lower income socio-economic class nonsmokers’ anti-smoking messages recalling rate was 2.5 times that of the lower socio-economic class smokers. Unlike men, women’s anti-smoking message response rate increased with income.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Assessing Public Awareness about the Health Effects of Nicotine and Cigarettes Using Negative Binomial Regression
AU  - Awoke Seyoum Tegegne
Y1  - 2014/06/20
PY  - 2014
N1  - https://doi.org/10.11648/j.sjams.20140203.11
DO  - 10.11648/j.sjams.20140203.11
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 60
EP  - 65
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20140203.11
AB  - Both the public and private sectors have acted responsibly to help decrease smoking-related deaths by putting health warnings on all cigarette packages. This study investigated the social or demographic factors associated with public awareness of health warnings on the harmful effects of environmental tobacco smoke based on baseline data collected by the health bureau of Amhara Region (in Ethiopia). Respondents in the survey were asked to recall the number of anti-smoking messages which appeared as warning messages on cigarette advertisements. The number of anti-smoking messages recalled ranged from 0 to 7 with a mean of 2.90 (variance of 3.11) and a median of 3.00. Because the variance (3.11) was different from mean (2.9), the negative binomial regression model provided an improved fit to the data and accounted better for over dispersion than the Poisson regression model, which assumed that the mean and variance are the same. The level of education was found to be the most significant factors. Moreover, the lower income socio-economic class nonsmokers’ anti-smoking messages recalling rate was 2.5 times that of the lower socio-economic class smokers. Unlike men, women’s anti-smoking message response rate increased with income.
VL  - 2
IS  - 3
ER  -

Copy | Download