Characteristics |
Frequency |
Percentage |
Age (years) |
||
18 - 25 |
67 |
28.88 |
25-45 |
98 |
42.24 |
46 and above |
45 |
19.40 |
Education |
||
None |
66 |
28.44 |
Primary |
106 |
45.69 |
Secondary |
44 |
18.97 |
Tertiary |
16 |
6.70 |
Marital Status |
||
Single |
34 |
14.66 |
Married |
198 |
85.34 |
Years of work experience |
||
>5 |
43 |
5.60 |
5-10 |
138 |
59.48 |
11-15 |
28 |
12.09 |
15+ |
23 |
9.91 |
Religion |
||
Muslim |
164 |
70.69 |
Christian |
53 |
22.84 |
Traditionalist |
15 |
4.47 |
decreases as the bid increases in price. This is confirmed by the fact that the lower starting bid price was more likely to generate a "Yes/Yes" response and more likely to produce a "No/No" response. Ross [40] and Ross [46] suggested that the magnitude of a price increase is the most important predictor of quit intentions. Guillaumier et al., [42] posited that larger hypothetical price increases resulted in significantly fewer smokers choosing to make no change and more endorsing quit attempts evident from cross-sectional survey of Australian socioeconomically disadvantaged smokers. In this study, larger cigarette and alcohol bid price rises motivated more smokers/drinkers to consider quitting, while price-resistant smokers appeared to have a more entrenched smoker status. Price increases large enough to see a pack of cigarettes in Nigeria cost N450.5 (US$1.2) would prompt over 36% of smokers to quit while price increases large enough of a bottle of alcohol in Nigeria cost N525.5 (US$1.4) would also prompt 41% of drinkers to quit.
Mean/median Willingness to Quit Psychoactive Substance Use
The mean and median WTQ were calculated using the estimated parameters from the constant-only bid function, which restricts all the exploratory variables except the bid variable. Thus, the parameter estimates are contained in the constant (intercept) and bid variable in the model. In order to select the appropriate probability distribution with the WTQ data, which is one with the best goodness of fit to the sample data, the value of the restricted model of log-likelihood
Table 2: Pattern of alcohol and cigarette use among respondents
Characteristics |
Frequency |
% |
Pattern of Abuse |
||
Concurrent (Alcohol & Cigarette) |
168 |
72.4 |
Alcohol only |
34 |
14.7 |
Cigarette only |
30 |
12.9 |
Number of Alcohol bottle consumed per week |
||
10 and below |
52 |
22.4 |
11 -20 |
154 |
66.4 |
21 - 30 |
22 |
9.5 |
31 and above |
4 |
0.4 |
Non-tax policies |
||
Warning labels |
93 |
40.1 |
Anti-smoking Information |
87 |
37.5 |
Non-smoking/drinking environment |
52 |
22.4 |
Self-rated health status |
||
Good |
176 |
76.5 |
Bad |
56 |
23.1 |
Awareness of cigarette warning labels |
154 |
66.4 |
Smoking status |
||
Never smoker |
6 |
2.6 |
Experimental smoker |
42 |
18.1 |
Intermittent smoker |
61 |
23.3 |
Addictive smoker |
123 |
53.0 |
Time of substance use |
||
Before driving |
188 |
81.0 |
After driving |
21 |
9.1 |
Anything |
23 |
9.9 |
Average age of drinking initiation (year) |
||
Less than 18 |
56 |
24.1 |
More than 18 |
176 |
75.9 |
Attempted alcohol and cigarette cessations |
145 |
62.5 |
Level of drinking |
||
Within reference limits of 14 units of alcohol per week |
78 |
33.6 |
Above reference limits |
154 |
66.3 |
Number of cigarette sticks consumed per week |
||
1-20 |
78 |
33.6 |
21-40 |
46 |
19.8 |
41-60 |
53 |
22.8 |
61 and above |
55 |
24.7 |
Age at smoking initiation (year) |
||
Less than 18 |
99 |
42.7 |
More than 18 |
133 |
57.3 |
Types of alcoholic beverages consumed |
||
Wine |
18 |
7.8 |
Beer |
146 |
62.9 |
Spirit (Brandy/Gin) |
68 |
29.3 |
Pattern of Alcohol use |
||
Binge Drinker |
178 |
76.7 |
Social Drinker |
54 |
23.3 |
Source: Field Survey, 2018
function, leaving only the constant and bid terms, was employed [47].
The result of the mean/median WTQ in Table 6 shows that consumers were willing to quit with a mean amount of N450.5 (US$1.2) per pack of cigarette and mean amount of N525.5 (US$1.4) per bottle of alcohol respectively. This means that consumers would be willing to quit cigarette smoking and alcohol drinking with higher cigarette price per pack of 20 sticks which is approximate 80% increment and alcohol price per bottle approximate of 55% increment. Mean price to quit cigarette was higher than alcohol reflecting the level of addiction of the two psychoactive active with cigarette having a higher level than alcohol. Hence, the stronger the level of addiction, the less likelihood the RTWs are willing to quit, given the hypothetical price increase. Ross [40] and Ross [41] stated that the magnitude of a price increase is the most important predictor of quit intentions.
Determinants of Willingness to Quit Psychoactive Substance Use
Table 7 revealed the results of the logit regression model. The results show that the bid price, age, initiation age, marital status, perceived health status, level of addiction, ever attempt to quit, perceived exposures to non-tax policies especially anti-smoking and anti-drinking information were related to WTQ cigarette and alcohol use among RTWs.
Discussion
This study found all RTWs drivers to be males. A similar report had been documented by studies done among long distance commercial vehicle drivers in Nigeria [7,20,51]. Empirical evidence further shows that RTWs in Nigeria are essentially dominated by males. The mean age (38years) of the RTWs in this study was close to the findings from other studies [8, 20, 52]. This is a reflection of the age characteristics of commercial vehicle drivers in general. To be a RTW, it is must for one to meet some conditions which include a minimum age of 18 years. This may partly account for why young people in their second to third decade of life being the majority of those involved in Road Transport Work. Most of the subjects were married and had no formal or primary education. This finding is similar to studies done in other parts of this country [7, 51]. The prevalence of psychoactive substance use in this study is 72.4%.
Mean price to quit cigarette was higher than alcohol reflecting the level of addiction of the two psychoactive active with cigarette having a higher level than alcohol. Hence, the stronger the level of addiction, the less likelihood the RTWs are willing to quit, given the hypothetical price increase. The bid price was positive and this is in conformity with the a-priori expectation. The implication of this is that as the bid price increases, the respondents’ willingness to quit increases. The higher purchased cigarette and alcohol price were related to a higher price to quit. In response to 10% and 20% hypothetical price rises, Guillaumier [42] found a significantly more participants endorsed trying to quit in response to the larger increase scenario (P<0.001), and fewer selected no change to their smoking (P<0.001).
Table 3. Bid System for alcohol and cigarette
Variables |
Conventional prices ( |
First bid ( |
Second bid ( |
|
Higher amount (75% increment) |
Lower amount (50% increment) |
|||
Alcohol |
350 |
437.5 |
700.0 |
525.0 |
Cigarette |
250 |
312.5 |
500.0 |
375.0 |
Source: Field Survey, 2018
Table 4. Distribution of WTQ response of Consumers
Variable |
Alcohol |
Cigarette |
||
Frequency |
Percent |
Frequency |
Percent |
|
Yes |
99 |
42.67 |
112 |
48.28 |
No |
133 |
57.33 |
120 |
51.72 |
Total |
232 |
100 |
232 |
100 |
Source: Field Survey, 2018
Table 5. Double-Bounded WTQ Responses Distribution
Type of Substance |
Percentage of Respondents |
||||
Yes– Yes |
Yes–No |
No–Yes |
No–No |
Total |
|
Alcohol |
15.52.% |
28.88% |
36.21% |
19.40% |
100% |
Cigarette |
9.480% |
23.14% |
41.38% |
25.00% |
100% |
Source: Field Survey, 2018
Note: "Yes/Yes" indicates Yes and Yes response in the first and second bid, respectively.
"Yes/No" indicates Yes and No response in the first and second bid, respectively.
"No/No" indicates No and No response in the first and second bid, respectively.
"No/Yes" indicates No and Yes response in the first and second bid, respectively.
Table 6. Mean Bid Price of WTQ (N)
Alcohol |
Cigarette |
|||||
Measures |
WTP |
Lower Bound |
Upper Bound |
WTP |
Lower Bound |
Upper Bound |
Mean |
525.5 |
375.7 |
655.5 |
450.5 |
255.2 |
575.5 |
Medium |
502.0 |
330.5 |
625.0 |
415.0 |
225.5 |
550.7 |
Source: Field Survey, 2018
Age is a positive significant predictor of the willingness to quit smoking and drinking according to the result. This implies that older RTWs are more willing to quit than their younger counterpart. Osler and Prescott [53] found that older smokers may have higher cessation rates because they are more likely to experience health problems, which make the risks associated with smoking more apparent, and health problems were often reported as a reason for stopping smoking. Kaleta et al., [54] found that in men adult tobacco Poland survey, the long-term quit rates increased with age. The quit rates were highest among the male subjects of 60 years of age or older compared to those aged 25–29 years. Also, age at first initiation was found to be significantly negatively associated with willingness to quit. It is important to note RTWs whose age at first initiation is above 18years of alcohol drinking are more willing to quit than those who were initiated earlier in life. Besides, RTWs who are married and have attempt quitting were willing to quit than their counterpart. This study found that education level seems not to be associated with willingness to quit cigarette smoking and alcohol consumption. Similar result was found in Georgiadou et al., [55]. However, this is inconsistent with the findings of Kaleta et al., [54] and Marti [56]. This difference may be attributed to different research. RTWs who perceived that their health status is poor are willing to quit. Also, the level of cigarette smoking and alcohol drinking addiction is negatively related to WTQ. This implies that nicotine dependent smokers have more difficulty quitting and are therefore less likely to quit successfully than non-dependent smokers. Breslau et al. [57], found that smokers with nicotine dependence was 40% less likely to quit than smokers who were not dependent. Marquese-Valdez et al. [58], stated that difficulty to quit increased with increasing nicotine dependence and the number of previous quitting attempts. The presence of a smoking family member has been reported to decrease the willingness to quit alcohol drinking. Kardia et al., [59] stated that partner’s support can help to combat anxiety and stress generated during the effort to quit smoking, and that the partner’s opposition to smoking is a factor that acts positively towards quitting. The non –tax policy of non-drinking environment effect of WTQ was positively significant implying that RTWs who are aware of this non –tax policy of non-drinking environment was more willing to quit than their unaware counterpart.
Conclusion
Following the results of the analysis above, this study concludes that previous attempts to quit were significantly associated with the willingness to quit smoking and drinking. These findings suggest the necessity of identifying past unsuccessful strategies, re-evaluating smoking prevention programs, and focusing more on non –tax policy of non-smoking/drinking environment with a view to seriously ban psychoactive substance use in public places especially in motor parks with a view to achieving better psychoactive substances cessation outcomes. Also, it is recommended that enlightening programs should be developed for RTWs to assist them in their quest to quit smoking and maintain cessation.
Table 7. Estimated Double Bounded Logit Model for Determinants of WTQ
Cigarette (Model 1) |
Alcohol (Model 2) |
||||
Variables |
Coefficient |
SE |
Variables |
Coefficient |
SE |
Bid Price ( |
.052** |
.022 |
Bid Price ( |
.087*** |
.033 |
Age (years) |
.630** |
.312 |
Age (years) |
.849* |
.434 |
Age at first initiation (?18years=1, ?18years=0) |
-.832*** |
.274 |
Age at first initiation(?18years=1, ?18years=0) |
-.494*** |
.183 |
Marital status (married=1, 0=single) |
.776* |
.435 |
Marital status (married=1, 0=single) |
.327** |
.158 |
Perceived Health status (Good =1, Poor=0) |
-.119* |
.039 |
Perceived Health status (Good =1, Poor=0) |
-.502* |
.296 |
Ever attempting quitting (Yes=1, No=0) |
.776* |
.437 |
Ever attempting quitting (Yes=1, No=0) |
.496* |
.276 |
Level of Education (years) |
-.175 |
.349 |
Level of Education (years) |
-.179 |
.299 |
Status of addiction (addictive =1, non-addictive) |
-.496* |
.276 |
Status of addiction (binge drinker =1, social drinker =0) |
-.724** |
.324 |
Monthly Income ( |
.124 |
.324 |
Monthly Income ( |
-.318 |
.288 |
Warning labels (Yes=1, No=0) |
-.096 |
.353 |
Warning labels (Yes=1, No=0) |
.011 |
.334 |
Non-smoking environment (Yes=1, No=0) |
-.496* |
.276 |
Non-drinking environment (Yes=1, No=0) |
.357*** |
.096 |
Noticing anti-smoking advertising or information (Yes=1, No=0) |
-.143 |
.318 |
Noticing anti-drinking advertising or information (Yes=1, No=0) |
.324 |
.241 |
Smoking status of family members (Yes=1, No=0) |
.257 |
.198 |
Drinking status of family members (Yes=1, No=0) |
-.608* |
.321 |
Constant |
2.145 |
2.084 |
Constant |
2.535 |
8.265 |
Number of Observation |
232 |
232 |
|||
Log likelihood |
206.12 |
188.75 |
|||
Wald [endif]--> |
102.56*** |
87.23*** |
Source: Field Survey, 2018
Author Contributions: This work was carried out in collaboration between all authors. Author CPA is the corresponding author who conducted the research with authors CIA, SOA and FMO. Author CPA, CIA, and SOA collected the data and drew up the econometric models while authors CPA and SOA did the necessary statistical analysis. FMO drew up the conceptual framework. Authors CIA, SOA, and FMO assisted in literature review and several editing done on this work. All the authors read and approved the manuscript.
Conflict of interest: None declared
Funding: None
Acknowledgement: My sincere gratitude goes to the officials and the entire members of the National Union of Road Transport Workers, Abeokuta, Ogun State chapter for their cooperation and assistance during the period of data collection.
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