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BMC Public Health
Published in: BMC Public Health 1/2019

Open Access 01-12-2019 | Smoking and Nicotine Detoxification | Research article

Factors affecting tobacco smoking in Ethiopia: evidence from the demographic and health surveys

Authors: Harminder Guliani, Samuel Gamtessa, Monika Çule

Published in: BMC Public Health | Issue 1/2019

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Abstract

Background

Tobacco smoking is growing at an alarming rate in the developing world and sub-Saharan Africa. Although Ethiopia has a relatively low rate in the region, it is not immune to the tobacco epidemic. The government of Ethiopia passed an anti-tobacco bill in 2015 that includes measures governing tobacco consumption, advertising, packaging, and labeling. To effectively address the challenge of tobacco control, the government should consider a number of aspects of tobacco production and consumption, such as local production in rural areas, as well as the complementarity nature of tobacco and khat use.

Methods

Using the World Bank’s Demographic and Health Surveys (2011 and 2016), this paper analyzes the key determinants of tobacco smoking in Ethiopia, emphasizing possible differences in various social contexts, across regions. More specifically, we assess the association between khat use and tobacco smoking while controlling for various observed individual-level, household-level, and community-level covariates. Using GPS data, we are able to capture the neighboring effects of smoking behavior in community clusters bordering other administrative regions as well as differences in smoking patterns between lowland and highland residents. We utilize a multilevel modeling framework and use a two-stage residual inclusion estimation method that accounts for the endogeneity of khat and tobacco use.

Results

The results suggest that chewing khat and geographic regions are statistically significant determinants of tobacco smoking even after controlling for various socioeconomic and demographic factors. Altitude information analysis suggests that people living in lowlands are more likely to smoke compared to those living in highland areas. Additional analysis including interactions between regions and khat use indicate wide inter-regional variations in tobacco smoking by khat users. We also extend our analysis by interacting khat use with religious adherence. Results indicate a wide variation in tobacco smoking by khat chewers across different religious groups.

Conclusions

To effectively control tobacco smoking of the diverse communities in Ethiopia, policymakers should consider a multi-pronged policy approach that combines various policy tools that account for regional variation, the local social contexts, as well as the complementary nature of smoking and khat chewing practices.
Appendix
Available only for authorised users
Footnotes
3
It should be noted that we restricted our analysis to age group 15–49 as the DHS did not collect data for females aged 50–59.
 
4
Detailed information on survey methodology can be found elsewhere [see reference [34, 35].
 
5
These types of models have been previously employed using the DHS (See reference [51]). The details of the statistical notations are provided in the Additional file 1 for interested readers
 
6
We decided to exclude the use of smokeless tobacco such as chewing tobacco, snuff by mouth or nose etc. from the analysis since less than 1% of the respondents reported to use smokeless tobacco in combined 2011 and 2016 survey data.
 
7
The Eastern region comprises of Dire Dawa, Harari and Somali. Dire Dawa and Harari administrative regions are cities (although delineated as independent regions) adjacent to the Somali region. The dwellers of these cities share many social aspects with people residing in Somali region in terms of their religion, ethnic backgrounds and khat consumption practices.
 
8
DHS measures the cluster’s elevation/altitude (in meters) from the SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model) for the specified coordinate location. Elevations are regularly spaced at 30-arc sec or approximately 1 km. The altitude variable is converted into log values for our analysis. It should be noted that altitude information is missing for 47 community clusters (N = 1988). As a result, observations from these clusters were not included in the analysis.
 
9
As noted before the size-based categorization of rural/urban areas may provide some misleading results which should be taken with caution.
 
10
It should be noted that only 4% (N = 2453) of the total sample (N = 56,644) had missing information and was excluded from the analysis. Out of 2453 missing cases, 1988 observations were excluded for missing altitude information (see footnote 8) and only 23 observations had missing smoking information. Hence, the remaining missing data constitutes less than 1% of the total sample size.
 
11
We also ran the baseline model separately for 2011 and 2016 survey periods. We found no qualitative differences as most of the coefficients had the same sign and statistical significance in both survey periods. The results can be found in the additional file 2. In addition, we ran a test for the statistical difference of coefficients across the two surveys using a seemingly unrelated estimation method. The test results suggest that there is no statistically significant difference between the coefficients of the two regression equations, with the exception of age and occupation category. Therefore, we report the pooled cross-section analysis which includes a time dummy that captures time effects in tobacco smoking between the two survey years. As expected with the larger sample size, the pooled data yields higher precision of statistical estimates by reducing standard errors.
 
12
For instance, a considerably higher percentage of respondent that belong to Islam chewed khat (80% of 9464 responders who chewed khat) or used both khat and tobacco (76% of 2009 respondents who used both).
 
13
Testing for the equality of coefficients on 40–44 and 45–49 age groups suggest that they could be aggregated.
 
15
The distance variable attempts to capture whether the social context similarities of the Oromia’s communities bordered with the Eastern region affect the likelihood of smoking. Harari is used as the focal point in the Eastern region since it has the second highest prevalence of smoking after Gambela.
 
16
In addition, the descriptive statistics from the two DHS surveys used in this study suggest that the lowest income groups (the bottom three quintiles) report a higher prevalence of smoking in 2016 than in 2011. This is concerning, since the elevated health risk from smoking among the poorest population can further affect their economic situations adversely, due to potential loss in income if they become ill and are unable to work.
 
17
According to the Global Adult Tobacco Survey (GATS) in 2016, there were around 3.4 million tobacco users and 1.9 million adult manufactured cigarette smokers in Ethiopia [48]. According to the World Bank’s World Development Indicators, Ethiopia’s population with ages 15–64 were estimated at 56.7 million in 2016 and 47.5 million in 2011. This indicates a prevalence rate of 6% for tobacco users and 3% for manufactured cigarette smoking.
 
18
See the Ethiopian media article http://​addisstandard.​com/​commentary-khat-chat-ethiopia-criminalize-not/​ to gain some further insights on khat use [50].
 
Literature
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
Ambaye G. Production and consumption trends of Khat in Ethiopia: a big business or a big worry. Adv Agric Sci Eng Res. 2012;2(10):414–27.
14.
15.
16.
17.
Damena T, Mossie A, Tesfaye M. Khat chewing and mental distress: a community based study, in Jimma City, southwestern Ethiopia. Ethiop J Health Sci. 2011;21(1):37–45.CrossRef
18.
19.
Nigussie T, Gobena T, Mossie A. Association between khat chewing and gastrointestinal disorders: a cross-sectional study. Ethiop J Health Sci. 2013;23(2):123–30.PubMedPubMedCentral
20.
Aden A, Dimba EA, Ndolo UM, Chindia ML. Socio-economic effects of khat chewing in northeastern Kenya. East Afr Med J. 2006;83(3):69–73.PubMed
21.
Teni FS, Surur AS, Hailemariam A, Aye A, Mitiku G, Gurmu A, Tessema B. Prevalence, reasons, and perceived effects of Khat chewing among students of a College in Gondar Town, northwestern Ethiopia: a cross-sectional study. Ann Med Health Sci Res. 2015;5(6):454–60.CrossRef
22.
23.
Nakajima M, Dokam A, Khalil NS, Alsoofi M, al'Absi M. Correlates of concurrent Khat and tobacco use in Yemen. Subst Use Misuse. 2016;51(12):1535–41.CrossRef
24.
Kebede Y. Cigarette smoking and Khat chewing among college students in Northwest Ethiopia. East Afr Med J. 2002;79(5):274–8.CrossRef
25.
26.
27.
28.
29.
30.
31.
Schoenmaker N, Hermanides J, Davey G. Prevalence and predictors of smoking in Butajira town, Ethiopia. Ethiop J Health Dev. 2005;19(3):182–7.
32.
33.
34.
35.
36.
Rabe-Hesketh S, Skrondal A. Multilevel and longitudinal modeling using Stata. Texas: Stata Corporation; 2005.
37.
38.
39.
40.
41.
42.
43.
44.
Bobak M, Jha P, Nguyen S, Jarvis M. Poverty and smoking. In: Jha P, Chaloupka F, editors. Tobacco control in developing countries. New York: Oxford University Press; 2000. p. 41-61.
45.
Efroymson D, Ahmed S, Townsend J, Alam SM, Dey AR, Saha R, et al. Hungry for tobacco: an analysis of the economic impact of tobacco consumption on the poor in Bangladesh. Tob Control. 2001;10(3):212–7.CrossRef
46.
47.
48.
49.
50.
51.
52.
Metadata
Title
Factors affecting tobacco smoking in Ethiopia: evidence from the demographic and health surveys
Authors
Harminder Guliani
Samuel Gamtessa
Monika Çule
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2019
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-019-7200-8

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