Sociodemographic and Clinical Factors Associated with Tobacco Use among Patients Treated at the Neuro-Psycho Pathological Centre of the University of Kinshasa, Democratic Republic of Congo (DRC) ()
1. Introduction
Tobacco use is a major global public health issue. The World Health Organization (WHO) reports that tobacco kills half of its users, causing more than 8 million deaths annually. Among these deaths, more than 7 million are consumers or former consumers, and approximately 1.2 million are non-smokers exposed to second-hand smoke. More than 80% of the 1.3 billion smokers worldwide live in low- or middle-income countries [1] [2]. At the core of tobacco use is nicotine, a key component of tobacco that creates addiction. Nicotine acts on the central nervous system, creating reward and pleasure effects that reinforce consumption. This addictive substance plays a central role in maintaining smoking habits and complicates efforts to quit this harmful practice [3].
Psychiatric disorders are a major public health issue in Africa and the Democratic Republic of Congo (DRC). In Africa, it is estimated that between 5% and 7% of the population, or approximately 70 to 98 million people, suffer from psychiatric disorders [4]. The most common disorders in the population are depression, bipolar mood disorder (56%), schizophrenia (82%), anxiety disorders, and addictions [4]. Regarding the DRC, there are no reliable figures on the incidence of mental illnesses. However, according to the WHO, in 2019, nearly one billion people worldwide, including 14% of adolescents, were affected by a mental disorder. Unfortunately, the number of people with neuropsychiatric disorders increased significantly in 2020 due to the COVID-19 pandemic [2].
Epidemiological studies have shown that the prevalence of psychiatric disorders is higher among smokers than non-smokers. Moreover, the prevalence of smoking is two to three times higher among individuals with psychiatric disorders than in the general population [5]. The relationship between smoking and psychiatric disorders can be unidirectional (in one direction or the other) or bidirectional [5]. Patients with psychiatric disorders have more difficulty quitting smoking than smokers without psychiatric pathology. These difficulties are one of the main reasons for the high prevalence of smoking in this population [5].
Africa, particularly the DRC, faces several challenges in managing this comorbidity. One of the main challenges is the lack of adequate infrastructure for the treatment of psychiatric disorders. Additionally, the aggressive marketing of the tobacco industry contributes to the increase in tobacco use, particularly among young people. Furthermore, the illicit tobacco trade in the DRC leads to a significant loss of tax revenue that could be used to improve mental health infrastructure.
These challenges underscore the importance of studies like this one to inform policies and interventions aimed at reducing tobacco use and improving the management of psychiatric disorders, particularly in low-resource settings. To address this issue, this study aims to examine tobacco use habits and the factors associated with this use among patients treated at the CNPP/UNIKIN. To achieve this, we will:
- Determine the frequency of tobacco addiction among psychiatric patients;
- Identify the sociodemographic, clinical, and consumption pattern factors associated with tobacco addiction among psychiatric patients.
The analysis of these different aspects will allow for a better understanding of smoking and its factors among patients treated at the CNPP/UNIKIN, in order to develop targeted and appropriate public health actions, such as habit modification strategies.
This study specifically examines how sociodemographic, clinical factors and tobacco consumption patterns relate to dependence in a psychiatric population in Kinshasa.
2. Material and Methods
2.1. Participants and Procedure
A cross-sectional and analytical study was conducted over 3 months (from November 1, 2023, to January 31, 2024). A convenience sample of 204 psychiatric patients treated on an outpatient basis at the CNPP/UNIKIN and who used drugs was recruited. While convenience sampling was used due to logistical constraints, future studies should consider stratified random sampling to improve generalizability. Convenience sampling allowed for the recruitment of voluntary participants meeting predefined inclusion criteria, such as the presence of a confirmed psychiatric disorder, a history of current or past tobacco use, and follow-up at the centre during the study period. The main exclusion criteria were neurological pathologies, absence of tobacco use, and inability or refusal to participate in the survey. Diagnoses were confirmed by a psychiatrist using ICD-10 criteria. Inclusion required current or past tobacco use. Comorbid substance use was also assessed.
2.2. Measures
An ad-hoc questionnaire was used to collect numerous sociodemographic data, such as gender, age, marital status, education level, and income. This questionnaire also collected detailed information on tobacco use habits, including age of initiation, initial circumstances, evolution of quantities and frequencies, as well as quit attempts. Additionally, it collected information on associated psychiatric comorbidities, such as the onset of disorders, previous episodes, and complications. Finally, the questionnaire assessed motivations for continued tobacco use using the Horn Scale, as well as the degree of tobacco dependence using the Fagerström Scale.
The Fagerström Scale, called the “Fagerström Test for Nicotine Dependence” (FTND), determines the degree of tobacco dependence. It consists of 6 items, with the first and fourth items scored from 0 to 3, while the others are scored from 0 to 1, for a total score out of 10 points. The categorisation of dependence based on scores is as follows: Very high dependence (score between 8 and 10), High dependence (score between 6 and 7), Moderate dependence (score of 5), Low dependence (score of 3 to 4), Very low dependence (score of 0 to 2) [6].
The Horn Scale (known as the Horn-Waingrow Scale), on the other hand, is used to explore motivations for continued tobacco use. It consists of 18 items, each scored from 1 to 5. Each motivation is explored by 3 items, with a total score ranging from 3 to 15. A score above 10 indicates a high level of motivation for the consideration in question. The 6 motivations assessed are as follows: stimulation, pleasure of the gesture, relaxation, anxiety-support, absolute need, and acquired habit [7]. The Horn Test thus estimates psychological dependence on cigarettes, taking into account smoking habits and the psychological factors that drive the individual to smoke.
These two tests are commonly used in research and clinical practice to help plan interventions aimed at helping smokers quit.
2.3. Statistical Analyses
Data were analysed using frequency measures for qualitative variables, as well as mean and standard deviation for quantitative variables following a normal distribution. For quantitative variables not following a normal distribution, we used the median and quartiles. The search for determinants of tobacco addiction was conducted using multivariate analysis with a logistic regression model. Variables with p < 0.05 in univariate analysis were included in the multivariate model to control for confounding factors. The adjusted odds ratio calculated allowed us to estimate the degree of association between tobacco addiction and the independent variables. A p-value < 0.05 was considered the threshold for statistical significance. Data analysis was performed using SPSS software (version 27.0; SPSS® Inc., Chicago, IL).
2.4. Ethical Considerations
The study was approved by the ethics committee of the School of Public Health at the University of Kinshasa under number ESP/CE/167/2023. Ethically, the rules of informed consent, confidentiality, absence of harm to participants, and collective benefit were rigorously applied.
3. Results
3.1. Tobacco Addiction
Among the sample of 204 patients, 146 (71.6%) were regular tobacco smokers. The mean tobacco dependence score, measured by the FTND (Fagerström Test for Nicotine Dependence), was 4.75 (standard deviation = 1.41). Ninety-three participants (63.7%) met the criteria defining moderate to severe tobacco dependence, in accordance with the indications in Table 1.
Table 1. Mean scores and frequency of moderate to severe tobacco dependence among participants.
Variables |
Frequency or mean (SD) |
Percentage or min-max |
FTND Score (n = 146) |
4.75 (1.41) |
0.0 - 8.0 |
Moderate to severe tobacco addiction (n = 146) |
93 |
63.7 |
SD, standard deviation; FTND, Fagerstrom Test for Nicotine Dependence.
3.2. Sociodemographic Characteristics of Participants
Table 2. Sociodemographic characteristics by dependence status.
Variable |
Overall (n = 204) |
Non-dependent |
Dependent
(n = 93) |
p-value |
Age (years) |
32.1 ± 10.5 |
31.6 ± 9.27 |
32.8 ± 10.8 |
0.477 |
Sexe |
|
|
|
0.996 |
Male |
172 (84.3) |
49 (36.3) |
86 (63.7) |
|
Female |
32 (15.7) |
4 (36.4) |
7 (63.6) |
|
Marital status |
|
|
|
0.674 |
Unmarried |
147 (72.1) |
41 (39.0) |
64 (61.0) |
|
married |
36 (17.6) |
8 (32.0) |
17 (68.0) |
|
divorced |
14 (6.9) |
3 (23.1) |
10 (76.9) |
|
widower |
7 (3.4) |
1 (33.3) |
2 (66.7) |
|
Educational level |
|
|
|
0.973 |
primary and secondary |
160 (78.4) |
44 (36.4) |
16 (64.0) |
|
High School |
44 (21.6) |
9 (36.0) |
77 (63.6) |
|
Gainfully employed |
|
|
|
0.864 |
Yes |
157 (770) |
40 (36.7) |
69 (63.3) |
|
No |
47 (23.0) |
13 (35.1) |
24 (64.9) |
|
Source of income |
|
|
|
0.630 |
Oneself |
137 (67.2) |
35 (35.0) |
65 (65.0) |
|
Others |
67 (32.8) |
18 (39.1) |
28 (60.9) |
|
Presence of legal issues |
|
|
|
0.025 |
Yes |
65 (31.9) |
14 (25.0) |
42 (75.0) |
|
No |
139 (68.1) |
39 (43.3) |
51 (56.7) |
|
According to the data in Table 2, participants with moderate to severe tobacco dependence were more likely to encounter legal problems than those who were not dependent on tobacco (p = 0.025). However, the same table indicates that there was no statistically significant difference between dependent and non-dependent participants regarding other sociodemographic factors such as age, gender, marital status, education level, income source, and paid employment.
3.3. Clinical Characteristics of Participants
Table 3. Clinical characteristics by dependence status.
Variables |
Overall (n = 204) |
Dependent
(n = 93) |
Non-dependent
(n = 53) |
p-value |
Insight |
|
|
|
0.685 |
Good |
200 (98.0) |
52 (36.1) |
92 (63.9) |
|
Bad |
4 (2.0) |
1 (50.0) |
1 (50.0) |
Number of previous mental disorder episodes |
|
|
|
0.630 |
Single episode |
31 (15.5) |
7 (31.8) |
15 (68.2) |
|
Two or more episodes |
169 (84.5) |
45 (37.2) |
76 (62.8) |
|
Age at first episode (years) |
25.3 ± 8.50 |
23.5 ± 6.18 |
26.2 ± 9.07 |
0.062 |
Being on psychotropic treatment |
|
|
|
0.927 |
Yes |
181 (88.7) |
47 (36.4) |
82 (63.6) |
|
No |
23 (11.3) |
6 (35.3) |
11 (64.7) |
|
Presence of family history of mental disorder |
|
|
|
0.337 |
Yes |
118 (57.8) |
27 (32.9) |
55 (67.1) |
|
No |
86 (4.2) |
26 (40.6) |
38 (59.4) |
|
Similarity between the nature of psychiatric disorders in the family and that of the patient |
|
|
|
0.309 |
Yes |
113 (55.4) |
25 (32.) |
52 (67.5) |
|
No |
91 (44.6) |
28 (40.6) |
41 (59.4) |
|
Rehabilitation during periods between crises |
|
|
|
0.492 |
Yes |
161 (78.9) |
42 (37.8) |
69 (62.2) |
|
No |
43 (21.1) |
11 (31.4) |
24 (68.6) |
|
Change or cessation of the occupation following the disorder |
|
|
|
0.273 |
Yes |
164 (80.4) |
46 (38.3) |
74 (61.7) |
|
No |
40 (19.6) |
7 (26.9) |
19 (73.1) |
|
According to the data in Table 3, individuals with moderate to severe tobacco dependence did not show statistically significant differences compared to those who were not dependent regarding various clinical characteristics. These characteristics include insight, the number of previous episodes of mental illness, age at first episode, use of psychotropic drugs, family history of mental disorders, similarity of psychiatric disorders between the patient and their family, participation in rehabilitation between crises, as well as job change or complete cessation of occupation due to health status.
3.4. Characteristics Related to the History of Tobacco Use by
Participants According to Dependence Status
Compared to non-dependent participants, those with tobacco dependence more frequently reported that their motivation for smoking was related to seeking relaxation, anxiolysis, support, and an acquired habit. Other characteristics, such as the age at which they started smoking, smoking alone or in a group, smoking preceding psychiatric symptoms, legal problems related to tobacco use associated with one or more illicit substances, quit attempts, the phase of the illness where tobacco use was significant, seeking stimulation and pleasure of the gesture, as well as absolute need as a motivation, did not show significant differences between participants with and without moderate to severe tobacco dependence (Table 4).
Table 4. Characteristics of tobacco use habits among participants according to their dependence status.
Variables |
Overall
(n = 204) |
Dependent
(n = 93) |
Non-dependent
(n = 53) |
p-value |
Age at beginning of tobacco consumption (years) |
21.1 ± 4.54 |
20.8 ± 4.30 |
21.3 ± 4.70 |
0.525 |
Mode of consumption |
|
|
|
|
Alone |
95 (65.1%) |
33 (34.7%) |
62 (65.3%) |
0.697 |
In a group |
51 (34.9%) |
19 (38.0%) |
31 (62.0%) |
|
Tobacco use prior to onset of psychiatric disorder |
|
|
|
|
Yes |
110 (75.3%) |
37 (33.6%) |
73 (66.4%) |
0.322 |
No |
36 (24.7%) |
15 (42.9%) |
20 (57.1%) |
|
Problems with police following tobacco consumption |
|
|
|
|
Yes |
38 (26.0%) |
11 (29.7%) |
26 (70.3%) |
0.367 |
No |
108 (74.0%) |
41 (38.0%) |
67 (62.0%) |
|
Multiple drugs use |
|
|
|
|
Yes |
108 (74.0%) |
35 (32.7%) |
72 (67.3%) |
0.184 |
No |
38 (26.0%) |
17 (44.7%) |
21 (55.3%) |
|
Attempts to stop tobacco consumption |
|
|
|
|
Yes |
107 (73.3%) |
41 (38.7%) |
65 (61.3%) |
0.244 |
No |
39 (26.7%) |
11 (28.2%) |
28 (71.8%) |
|
Phase of illness where tobacco consumption is highest |
|
|
|
|
Acute-phase |
22 (15.1%) |
5 (23.8%) |
16 (76.2%) |
0.213 |
stabilization phase |
124 (84.9%) |
47 (37.9%) |
77 (62.1%) |
|
Seeking stimulation as a motivation to smoke |
|
|
|
|
High level of seeking stimulation |
131 (89.1%) |
44 (83.0%) |
86 (92.5%) |
0.079 |
Low level of seeking stimulation |
16 (10.9%) |
9 (17.0%) |
7 (7.5%) |
|
Pleasure of the gesture as a motivation to smoke |
|
|
|
|
High level of pleasure of the gesture |
111 (75.5%) |
36 (67.9%) |
75 (80.6%) |
0.083 |
Low level of pleasure of the gesture |
36 (24.5%) |
17 (32.1%) |
18 (19.4%) |
|
Seeking relaxation as a motivation to smoke |
|
|
|
|
High level of seeking relaxation |
111 (75.5%) |
35 (66.0%) |
76 (81.7%) |
0.033 |
Low level of seeking relaxation |
36 (24.5%) |
18 (34.0%) |
17 (18.3%) |
|
Seeking anxiolysis and support as a motivation to smoke |
|
|
|
|
High level of seeking anxiolysis and support |
105 (71.4%) |
33 (62.3%) |
72 (77.4%) |
0.050 |
Low level of seeking anxiolysis and support |
42 (28.6%) |
20 (37.7%) |
2 (22.6%) |
|
Absolute need as a motivation to smoke |
|
|
|
|
High level of absolute need |
120 (81.6%) |
40 (75.5%) |
79 (84.9%) |
0.156 |
Low level of absolute need |
27 (18.4%) |
13 (24.5%) |
14 (15.1%) |
|
Acquired habit as a motivation to smoke |
|
|
|
|
High level of acquired habit |
75 (51.0%) |
20 (37.7%) |
55 (59.7%) |
0.013 |
Low level of acquired habit |
72 (49.0%) |
33 (62.3%) |
38 (40.9%) |
|
3.5. Factors Associated with Tobacco Dependence among Participants
In Table 5, we find the results of univariate and multivariate analyses. Univariate analysis revealed that two elements were associated with moderate to severe tobacco dependence: seeking relaxation as a motivation to smoke and acquired habit. In multivariate analysis, only acquired habit as a motivation to smoke was significantly associated with moderate to severe tobacco dependence (aOR = 2.13, 95% CI: 1.04 - 4.33, p = 0.037). Seeking relaxation lost significance after adjusting for confounders (Table 5).
Table 5. Univariate and multivariate logistic regression of characteristics associated with tobacco addiction.
Variable |
Univariate analysis |
Multivariate analysis |
p-value |
OR (95% CI) |
p-value |
aOR (95% CI) |
Presence of legal issues |
|
|
|
|
No |
|
1 |
|
|
Yes |
0.369 |
1.45 (0.65 - 3.24) |
|
|
Seeking relaxation as a motivation to smoke |
|
|
|
|
Low level |
|
1 |
|
|
High level |
0.035 |
2.29 (1.06 - 4.99) |
0.106 |
1.93 (0.87 - 4.30) |
Seeking anxiolysis and support as a motivation to smoke |
|
|
|
|
Low level |
|
1 |
|
|
High level |
0.052 |
2.95 (0.99 - 4.35) |
|
|
Acquired habit as a motivation to smoke |
|
|
|
|
Low level |
|
1 |
|
|
High level |
0.014 |
2.39 (1.20 - 4.77) |
0.037 |
2.13 (1.04 - 4.33) |
4. Discussion
4.1. Tobacco Addiction
This high rate of tobacco dependence among psychiatric patients aligns with international findings. Studies in similar African contexts report comparable or higher rates of tobacco use in schizophrenia and mood disorders (e.g., 65 - 85%) [8].
4.2. Tobacco and Legal Issues, Absence of Sociodemographic Distinction
Our study also revealed a significant association between moderate to severe tobacco dependence and legal problems. However, no significant differences were observed regarding other variables such as age, gender, marital status, education level, and employment. This result is consistent with previous studies that have also established links between substance use (including tobacco) and deviant behaviours or legal problems. For example, a 2009 study showed that individuals dependent on nicotine are more likely to engage in criminal behaviour and encounter legal problems [9]. The correlation between legal problems and tobacco dependence may indicate underlying factors such as stress, personality disorders, or unfavourable socioeconomic conditions that warrant particular attention. The absence of significant differences in other sociodemographic factors is consistent with some studies indicating the transversality of tobacco dependence across different demographic groups without marked distinction by age, gender, or economic status [10] [11].
4.3. Complexity of Interactions between Tobacco Dependence and Clinical Characteristics in Psychiatric Patients
Our results indicate that there is no statistically significant difference between participants with moderate to severe tobacco dependence and those who are not dependent regarding various clinical characteristics. For example, insight, often associated with treatment adherence and mental illness management, does not appear to be affected by tobacco dependence. The onset of mental disorders may be influenced by various genetic and environmental factors, and tobacco use may often begin after the onset of these disorders. Additionally, family history, a well-established risk factor for mental illness, is not influenced by tobacco use. Severe mental illness can lead to occupational disability, and tobacco dependence does not appear to influence this factor. These findings are consistent with several previous studies that have also shown that the clinical characteristics of patients with severe mental disorders are not significantly influenced by tobacco use [12] [13].
These results corroborate the conclusions of several previous studies showing that the clinical characteristics of patients with severe mental disorders are not significantly influenced by tobacco use. For example, in schizophrenic patients, insight is related to the severity of the illness rather than tobacco use [14]. The frequency of episodes of mental illness and the use of psychotropic drugs are primarily influenced by the nature and severity of the illness, rather than factors such as tobacco use [15].
These results highlight that, although tobacco dependence is associated with certain problematic behaviours, it does not necessarily affect the fundamental clinical characteristics of mental disorders.
4.4. Specific Motivations Driving Tobacco Use
Our study identified that seeking relaxation, anxiolysis and support, as well as acquired habit, are significantly higher motivations among dependent smokers. Other motivations did not show significant differences.
These results confirm that the mode of tobacco consumption is often influenced by social and cultural factors rather than the level of dependence. The search for relaxation is particularly high among dependents, with tobacco often used to manage stress and anxiety [16]. Similarly, anxiolysis and the search for psychological support are common motivations among dependent smokers [16] [17]. Acquired habit, a significantly higher motivation among dependents, becomes more entrenched over time, making dependence more difficult to overcome [18]. These observations are consistent with several previous studies that have explored motivations and behaviours related to tobacco use [19] [20].
4.5. Factors Associated with Tobacco Dependence
Although several factors were identified as significantly associated with tobacco dependence in univariate analysis, only one factor remained significant in multivariate analysis: acquired habit as a motivation to smoke. Seeking relaxation, although associated with tobacco dependence in univariate analysis (p = 0.035, OR = 2.29, 95% CI = 1.06 - 4.99), lost its statistical significance in multivariate analysis (p = 0.106, aOR = 1.93, 95% CI = 0.87 - 4.30). This may be explained by the influence of other confounding variables not accounted for in the univariate analysis (16). Seeking anxiolysis and support is also close to statistical significance in univariate analysis (p = 0.052, OR = 2.95, 95% CI = 0.99 - 4.35) but is not included in the final multivariate analysis, likely due to moderating or mediating factors [20]. Acquired habit as a motivation to smoke is significantly associated with tobacco dependence (p = 0.014, OR = 2.39, 95% CI = 1.20 - 4.77) in univariate analysis and remains significantly associated with tobacco dependence (p = 0.037, aOR = 2.13, 95% CI = 1.04 - 4.33) in multivariate analysis. This means that acquired habit is an important and well-documented risk factor for the development and persistence of tobacco dependence. Habits are often reinforced by repetitive behaviours and social contexts, making dependence more difficult to overcome [18]. The results of this study are in line with several previous studies on motivations and habits related to tobacco use. Zvolensky et al. showed that stress and anxiety management are common motivations among smokers, but their direct link to dependence may be complicated by other factors such as tolerance and physical dependence [16]. The literature highlights that acquired habits are major predictors of tobacco dependence. Chaiton et al. demonstrated that habits acquired at a young age are particularly difficult to change and are strongly associated with persistent tobacco dependence [18].
The study’s limitations include its cross-sectional design, reliance on self-report, and convenience sampling, which may limit generalizability. Future studies should employ longitudinal designs and more representative sampling to confirm these findings.
5. Conclusions
This study conducted at the CNPP in Kinshasa revealed a high prevalence of tobacco use among psychiatric patients, with 71.6% of the 204 surveyed patients being regular smokers. Tobacco dependence, measured by the FTND score, is significant among these patients, as 63.7% of them presented moderate to severe dependence. The results show that psychiatric patients with moderate to severe tobacco dependence do not differ significantly from non-dependent patients in terms of sociodemographic or clinical characteristics, except for legal problems. On the other hand, their motivation to smoke is often related to seeking relaxation and acquired habits. Among the many factors explored, acquired habit was found to be the only factor significantly associated with moderate to severe dependence according to multivariate analysis.
This study highlights the importance of targeting modifiable habits such as acquired routine smoking. Practical interventions may include cognitive-behavioral therapy (CBT), group-based cessation programs, and community-based peer support. These should be adapted to the psychiatric context in Kinshasa.