| Peer-Reviewed

Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model

Received: 17 February 2015     Accepted: 27 March 2015     Published: 3 April 2015
Views:       Downloads:
Abstract

Objectives: The study aimed to determine the tobacco smoking patterns in Kenya. Methods: This research project used the Kenya GATS 2014 data, in which a sample of 5436 total people was interviewed. However since the research focussed on modelling tobacco smoking pattern in Kenya, data from only 4418 people was used for the analysis. Data from 1018 people in the sample was dropped because information about the individuals smoking pattern, age or work status could not be found. Data Analysis: The data was analysed using R-software version 3.0.2, and report presented in form of tables and graphs. Results: This project found out that there is likelihood of a person being a heavy smoker, light smoker or Non-smoker, if the person works in the Government and Non-government /private organization, self-employed or Unemployed. The overall effect of work status was statistically significant with a chi-square value of 129.722 (p-value<0.0001). Conclusion: The results show that a person’s working status and their age are good predictors of a specific smoking pattern. From the results we have more people smoking as they grow old.

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 3)
DOI 10.11648/j.ajtas.20150403.14
Page(s) 89-98
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), 2015. Published by Science Publishing Group

Keywords

GATS, Kenya, Tobacco Smoking

References
[1] Brenya, E. (2012). Struggling To Weaken the Giant: Litigation as a Measure to Compel the Adoption of Tobacco Control Instrument in Malawi. African Journal of Political Science and International Relations, 6 (7), 155-166.
[2] CDC. (2008). Global Youth Tobacco Surveillance, 2000–2007. Atlanta: CDC.
[3] CDC. (n.d.). Global Adult Tobacco Survey (GATS) — Overview. Retrieved January 5, 2015, from CDC website: http://nccd.cdc.gov/gtssdata/Ancillary/Documentation.aspx?SUID=4&DOCT=1
[4] CDC. (n.d.). Global Tobacco Control. Retrieved January 6, 2015, from CDC website: http://www.cdc.gov/tobacco/global/index.htm
[5] CHO. (n.d.). Non-communicable diseases in Kenya. Retrieved December 14, 2014, from Commonwealth health online: http://www.commonwealthhealth.org/africa/kenya/non_communicable_diseases_in_kenya
[6] Doll, R.; Hill , AB.;. (1954). The mortality of doctors in relation to their smoking habits. BMJ(1), 1451-5.
[7] Durham, B. (1868). Old West Durham Neighborhood Association. North Carolina.
[8] Feller, W. (1968). An Introduction to Probability Theory and Its Applications, Vol. 1, 3rd Edition. USA: Wiley.
[9] Friedman, Jerome, Hastie, Trevor, & Tibshirani, Rob. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software(33), 1.
[10] Gately, Iain. (2004). A Cultural History of How an Exotic Plant Seduced Civilization. Diane. Tobacco Control, 3–7.
[11] GYTS. (2005). Global Tobacco Surveillance System (GTSS). J Sch Health(75), 15-24.
[12] Hammond, EC; Horn, D.;. (1954). The relationship between human smoking habits and death rates: a follow-up study of 187,766 men. JAMA(155), 1316-28.
[13] Jamie , S. (2015, Feb 3). Australians stub out cigarettes in wake of plain-pack law. Australian, Australian.
[14] Japkor, P. (2012). how British American Tobacco undermines the WHO FCTC through agricultural initiatives. TobaccoControl(21), 220.
[15] Jon StarkWeather;Amanda Kay Moske(2011). Multinomial logistic regression. Retrieved March 26,2015, from statistics website. http://www.unt.edu/rss/class/jon/benchmarks/MLR_JDS_Aug2011.pdf
[16] KNBS. (2015). Global Adult Tobacco Survey. Retrieved Jan 4, 2015, from KNBS website: http://www.knbs.or.ke
[17] Kunze, M. (1989). Current smoking habits in Europe. European Conference on Tobacco priorities and strategies (pp. 1-3). The Hague, The Netherlands: international Union Against Cancer and the Dutch Foundation on Smoking and Health
[18] Lopez AD; Collishaw NE; Piha T.;. (1994). A descriptive model of the cigarette epidemic in developed countries. Tob Control(3), 242-7.
[19] MoH. (2008). Kenya Health policy 2014-2030. Nairobi-Kenya: MOH Kenya.
[20] MoH. (2010). Kenya Health Policy 2012-2030. Nairobi: MOH Kenya.
[21] Müller, F., Lungencarcinom.Zeitschrift, & Krebsforschung, f. (2011). Commentary: Lung cancer and tobacco consumption. international journal of Epidemiology.
[22] Nairobi News. (2015, Feb 2). Health ministry to introduce new scary packs for cigarettes. Nairobi, Nairobi, Kenya: Nation Media Group. Retrieved Feb 6, 2015, from http://nairobinews.co.ke/health-ministry-to-introduce-new-scary-packs-for-cigarettes/
[23] Nwhator, S. (2012). Nigeria's costly complacency and the global tobacco epidemic. J Public Health Policy, 33(1), 16-33.
[24] Robert , P. N. (2012). The history of the discovery of the cigarette-lung cancer link: evidentiary traditions, corporate denial, global toll, Tobacco Control. Tobacco Control, 87(e91), 21.
[25] WHO. (2008). Report on the Global Tobacco Epidemic. Geneva: World Health Organization.
[26] WHO. (2008). Tobacco is the single most preventable cause of death in the world today. Geneva: WHO.
[27] WHO. (2008). WHO global burden of disease report. WHO.
[28] WHO. (2010). The Abuja Declaration ten years on. Abuja: WHO.
[29] WHO. (2014). WHO tobacco treaty makes significant progress despite mounting pressure from tobacco industry. Retrieved January 4, 2015, from WHO Website.
[30] Wikipedia. (2015, Jan 22). Tobacco advertising. (Wikipedia, Producer) Retrieved Feb 5, 2015, from Tobaco advertising website: http://en.wikipedia.org
[31] Wikipedia. (2015). Tobacco advertising. Retrieved Feb 6, 2015, from http://en.wikipedia.org: http://en.wikipedia.org
[32] Zulu R; Siziya S; Nzala SH;. (2009). tobacco smoking prevalence among in-school adolescents aged 13-15 years: baseline for evaluation of the implementation of the FCTC in Lusaka district,Zambia. medical journal of Zambia, 35(3).
Cite This Article
  • APA Style

    Samwel N. Mwenda, Anthony Kibira Wanjoya, Anthony Gichuhi Waititu. (2015). Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model. American Journal of Theoretical and Applied Statistics, 4(3), 89-98. https://doi.org/10.11648/j.ajtas.20150403.14

    Copy | Download

    ACS Style

    Samwel N. Mwenda; Anthony Kibira Wanjoya; Anthony Gichuhi Waititu. Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model. Am. J. Theor. Appl. Stat. 2015, 4(3), 89-98. doi: 10.11648/j.ajtas.20150403.14

    Copy | Download

    AMA Style

    Samwel N. Mwenda, Anthony Kibira Wanjoya, Anthony Gichuhi Waititu. Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model. Am J Theor Appl Stat. 2015;4(3):89-98. doi: 10.11648/j.ajtas.20150403.14

    Copy | Download

  • @article{10.11648/j.ajtas.20150403.14,
      author = {Samwel N. Mwenda and Anthony Kibira Wanjoya and Anthony Gichuhi Waititu},
      title = {Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {4},
      number = {3},
      pages = {89-98},
      doi = {10.11648/j.ajtas.20150403.14},
      url = {https://doi.org/10.11648/j.ajtas.20150403.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150403.14},
      abstract = {Objectives: The study aimed to determine the tobacco smoking patterns in Kenya. Methods: This research project used the Kenya GATS 2014 data, in which a sample of 5436 total people was interviewed. However since the research focussed on modelling tobacco smoking pattern in Kenya, data from only 4418 people was used for the analysis. Data from 1018 people in the sample was dropped because information about the individuals smoking pattern, age or work status could not be found. Data Analysis: The data was analysed using R-software version 3.0.2, and report presented in form of tables and graphs. Results: This project found out that there is likelihood of a person being a heavy smoker, light smoker or Non-smoker, if the person works in the Government and Non-government /private organization, self-employed or Unemployed. The overall effect of work status was statistically significant with a chi-square value of 129.722 (p-value<0.0001). Conclusion: The results show that a person’s working status and their age are good predictors of a specific smoking pattern. From the results we have more people smoking as they grow old.},
     year = {2015}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model
    AU  - Samwel N. Mwenda
    AU  - Anthony Kibira Wanjoya
    AU  - Anthony Gichuhi Waititu
    Y1  - 2015/04/03
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ajtas.20150403.14
    DO  - 10.11648/j.ajtas.20150403.14
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
    SP  - 89
    EP  - 98
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20150403.14
    AB  - Objectives: The study aimed to determine the tobacco smoking patterns in Kenya. Methods: This research project used the Kenya GATS 2014 data, in which a sample of 5436 total people was interviewed. However since the research focussed on modelling tobacco smoking pattern in Kenya, data from only 4418 people was used for the analysis. Data from 1018 people in the sample was dropped because information about the individuals smoking pattern, age or work status could not be found. Data Analysis: The data was analysed using R-software version 3.0.2, and report presented in form of tables and graphs. Results: This project found out that there is likelihood of a person being a heavy smoker, light smoker or Non-smoker, if the person works in the Government and Non-government /private organization, self-employed or Unemployed. The overall effect of work status was statistically significant with a chi-square value of 129.722 (p-value<0.0001). Conclusion: The results show that a person’s working status and their age are good predictors of a specific smoking pattern. From the results we have more people smoking as they grow old.
    VL  - 4
    IS  - 3
    ER  - 

    Copy | Download

Author Information
  • Department of data processing, ICT Directorate, Kenya National Bureau of Statistics, Nairobi, Kenya

  • Department statistics and actuarial science, Jomo Kenyatta university of Agriculture and technology, Nairobi, Kenya

  • Department statistics and actuarial science, Jomo Kenyatta university of Agriculture and technology, Nairobi, Kenya

  • Sections