INTRODUCTION
Over the last few decades, the alarming rise in the prevalence of non-communicable diseases (NCDs) has become a major public health issue worldwide. NCDs are recognized as the leading cause of global morbidity and mortality, accounting for approximately three-quarters of deaths that occur each year. Most of these deaths are due to cardiovascular diseases, cancers, diabetes, and respiratory diseases, which are responsible for over 80% of all premature deaths related to NCDs1.
The World Health Organization (WHO) reported that 86% of all NCD-related deaths occur in low- and middle-income countries (LMICs)1. In Morocco, a lower middle-income country in North Africa, NCDs account for 84% of total deaths, making it one of the countries with the highest NCD mortality rates in the Eastern Mediterranean Region2. The leading causes of death in Morocco are cardiovascular diseases (38%), cancers (14%), diabetes (6%), and chronic respiratory diseases (4%)3.
Smoking is one of the most well-established risk factors for NCDs4. The relationship between smoking status and NCDs is complex, with smoking contributing to the pathogenesis of multiple physiological conditions. This occurs through mechanisms such as inflammation, oxidative stress, and vascular damage that play key roles in the onset of many NCDs5.
Despite widespread awareness of its detrimental health effects, tobacco consumption remains prevalent worldwide, particularly in LMICs, where more than 80% of the world’s 1.3 billion tobacco users live6. Therefore, further studies are needed to understand how smoking status (current, former, or never smoker) correlates with the broader spectrum of NCDs across different adult populations and to contribute to the development of more effective policies for tobacco control and disease prevention.
In this context, the Moroccan Ministry of Health and Social Protection conducted in 2017–2018 a nationwide cross-sectional survey on risk factors for NCDs (Morocco STEPS 2017–2018). The objectives of the current study were to estimate the prevalence of smoking and investigate its association with obesity, diabetes, hypertension, and dyslipidemia among adults aged 18–100 years.
METHODS
Study design and data source
In this secondary dataset analysis, we used data from the first national STEPS survey carried out in 2017–20187. This was a cross-sectional study designed to choose a representative sample of adults aged 18–100 years from all regions in Morocco. A multi-stage, stratified, and geographically clustered sampling design was used. The sample size was determined using the 2014 General Population Census, ensuring representativeness, with an overall response rate of 89%7. It was derived from the master sample based on four-stage selection procedure: 1) 244 primary sampling units (PSUs) were selected, with 158 from urban areas and 86 from rural areas, out of a total of 4500 PSUs in the master sample; 2) one secondary sampling unit (SSU) was chosen from each PSU, with each SSU typically containing 50 households (averaging 300 households per PSU); 3) systematic sampling was conducted to select households; and 4) in each household one adult member meeting the selection criteria was randomly selected. More details on sample size calculation are presented elsewhere7.
Data collection
Trained investigators collected the data following the standardized WHO STEPwise methodology for monitoring NCD risk factors8. The investigators recorded the data using electronic tablets and conducted measurements at the participants’ homes.
Collection of sociodemographic and behavioral data
A standardized WHO questionnaire was used to collect sociodemographic data, such as age, gender, marital status, education level, residence, and professional status, as well as behavioral information regarding tobacco and alcohol use, physical activity, and fruit and vegetable consumption. Participants were categorized as either physically active or inactive based on the WHO recommendations9. The assessment of fruit and vegetable intake followed the WHO guidelines10, which suggest a minimum daily consumption of 400 g, corresponding to 5 servings of fruit and vegetables, or 2 servings of fruits and 3 servings of vegetables, with each portion weighing 80 g.
Physical measurements
Anthropometric measurements were performed using standardized methods and equipment; more details are presented elsewhere11. Body mass index (BMI, kg/m2) was calculated as the ratio of weight (kg) to height squared (m2). Based on the WHO criteria, the weight status of each participant was categorized as: underweight <18.5, normal weight 18.5≤ to <25, overweight 25≤ to <30 or obese ≥30). Abdominal obesity was defined as waist circumference >94 cm for men and >80 cm for women.
Participants’ blood pressure was measured three times in a seated position, without their legs crossed, and the cuff was correctly placed to ensure accurate readings. The average of the measurements was used to categorize it according to the guidelines of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC7)12: normal blood pressure, prehypertension [systolic blood pressure (SBP) of 120–139 mmHg or diastolic blood pressure (DBP) of 80–89 mmHg], and hypertension (SBP ≥140 mmHg or DBP ≥90 mmHg). A participant who self-reported diagnosed hypertension with a physician’s prescription of antihypertensive medication and/or any previous diagnosis of hypertension by a health professional was considered hypertensive. In this study, high blood pressure refers to both prehypertension and hypertension.
Biochemical measurements
Blood samples were taken after a 12-h fast to measure blood glucose, total cholesterol, triglycerides, and HDL cholesterol. These samples were sent to the Reference Laboratory of the Joint Research Unit in Nutrition and Food, Regional Designed Center of Nutrition (AFRA/IAEA), Ibn Tofail University-CNESTEN, Rabat, Morocco. Fasting blood glucose level was interpreted according to the WHO criteria13 for diagnosing diabetes (≥7 mmol/L or 126 mg/dL) and prediabetes (6.1–6.9 mmol/L or 110–125 mg/dL). In this study, hyperglycemia refers to both prediabetes and diabetes. Based on the European guidelines14, dyslipidemia was defined as having at least one of the following abnormalities: high total cholesterol (≥5 mmol/L or 190 mg/dL), hyperglyceridemia or high triglycerides (≥1.7 mmol/L or 150 mg/dL), low HDL cholesterol (<1.03 mmol/L or 40 mg/dL for men, <1.29 mmol/L or 50 mg/dL for women), and high LDL cholesterol (≥115 mg/dL or 3.0 mmol/L), calculated using the following formula: LDL cholesterol = total cholesterol – HDL cholesterol – triglycerides /5 (g/L)14.
Ethical considerations
Ethical approval for this survey was obtained from the Biomedical Research Ethics Committee of the Faculty of Medicine and Pharmacy in Rabat, Morocco (Approval number: 248; Date: 22 March 2016). Before data collection, all invited participants were briefed about the survey objectives and methods, and only those who provided written informed consent were involved. A results sheet was used to inform the participants on-site about the measurement result, and those who were found to be at risk were directed to the closest health center for additional care.
Statistical analysis
The Statistical Package for Social Sciences (SPSS) software (version 27.0.1.0) was used to perform statistical analyses. Results are presented as proportions and 95% confidence intervals using descriptive statistics. The association between categorical variables was examined by the chi-squared test. Bivariate and multivariate logistic regression analyses were conducted to assess the association of current and former smoking with obesity, hyperglycemia, raised blood pressure, hypertriglyceridemia, and low HDL cholesterol. Logistic regression models are reported as crude odds ratio (OR) or adjusted odds ratio (AOR) and 95% confidence interval (95% CI). The AORs were used to control the effect of age, gender, residence, education level, marital status, physical activity level, fruit and vegetable intake, alcohol consumption, weight status, blood pressure, fasting blood glucose, triglycerides, and HDL cholesterol, as potential confounding variables. A two-tailed p<0.05 was deemed statistically significant.
RESULTS
This study involved 4580 Moroccan adults aged ≥18 years. The characteristics of the study population are shown in Table 1. About two-thirds (65%) of the participants were women, 74% were aged 30–69 years, and 60% lived in urban areas. The prevalence of being overweight, obese, having abdominal obesity, high blood pressure, hyperglycemia, low HDL cholesterol, and hypertriglyceridemia was 35.3%, 24.7%, 76.9%, 27.6%, 57.2%, and 16.4%, respectively. The proportion of individuals who met the WHO recommendations of physical activity and fruit and vegetable intake was 78.3% and 37.8%, respectively. Smoking prevalence was 7.7%. There were significant differences between men and women for all variables, except triglyceride levels.
Table 1
Sociodemographic, health and lifestyle characteristics of community-dwelling adults in Morocco, STEPS 2017 cross-sectional survey (N=4580)
| Characteristics | Total | Men | Women | p* | |||
|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | ||
| Total | 4580 | 100.0 | 1604 | 35.0 | 2976 | 65.0 | |
| Sociodemographic | |||||||
| Age (years) | <0.001 | ||||||
| 18–29 | 801 | 17.5 | 263 | 16.4 | 538 | 18.1 | |
| 30–44 | 1457 | 31.8 | 427 | 26.6 | 1030 | 34.6 | |
| 45–59 | 1316 | 28.7 | 465 | 29.0 | 851 | 28.6 | |
| 60–69 | 617 | 13.5 | 283 | 17.6 | 334 | 11.2 | |
| ≥70 | 389 | 8.5 | 166 | 10.3 | 223 | 7.5 | |
| Residence | 0.001 | ||||||
| Rural | 1818 | 39.7 | 689 | 43.0 | 1129 | 37.9 | |
| Urban | 2762 | 60.3 | 915 | 57.0 | 1847 | 62.1 | |
| Education level | <0.001 | ||||||
| No formal education | 2392 | 52.2 | 624 | 38.9 | 1769 | 59.4 | |
| Primary school | 948 | 20.6 | 418 | 26.1 | 528 | 17.7 | |
| Middle school | 541 | 11.8 | 228 | 14.2 | 314 | 10.6 | |
| High school | 367 | 8.0 | 167 | 10.4 | 200 | 6.7 | |
| University | 332 | 7.2 | 167 | 10.4 | 165 | 5.5 | |
| Marital status | <0.001 | ||||||
| Single | 634 | 13.8 | 305 | 19.0 | 329 | 11.1 | |
| Married | 3363 | 73.4 | 1247 | 77.7 | 2120 | 71.2 | |
| Separated/divorced | 135 | 2.9 | 23 | 1.4 | 108 | 3.6 | |
| Widowed | 448 | 9.8 | 29 | 1.8 | 419 | 14.1 | |
| Health and lifestyle | |||||||
| Weight status | 0.022 | ||||||
| Underweight | 199 | 4.3 | 69 | 4.3 | 130 | 4.4 | |
| Normal weight | 1637 | 35.7 | 552 | 34.4 | 1085 | 38.5 | |
| Overweight | 1615 | 35.3 | 583 | 36.3 | 1032 | 34.7 | |
| Obese | 1129 | 24.7 | 400 | 24.9 | 729 | 24.5 | |
| Abdominal obesity | <0.001 | ||||||
| No | 1355 | 29.8 | 492 | 31.1 | 863 | 29.1 | |
| Yes | 3197 | 70.2 | 1092 | 68.9 | 2105 | 70.9 | |
| High blood pressure | 0.019 | ||||||
| Yes | 3523 | 76.9 | 1205 | 75.1 | 2318 | 77.9 | |
| No | 1057 | 23.1 | 399 | 24.9 | 658 | 22.1 | |
| Hyperglycemia | <0.001 | ||||||
| Yes | 1262 | 27.6 | 370 | 23.1 | 892 | 30.0 | |
| No | 3318 | 72.4 | 1234 | 76.9 | 2084 | 70.0 | |
| Low HDL cholesterol | <0.001 | ||||||
| Yes | 2620 | 57.2 | 850 | 53.0 | 1770 | 59.5 | |
| No | 1960 | 42.8 | 754 | 47.0 | 1206 | 40.5 | |
| Hypertriglyceridemia | 0.117 | ||||||
| Yes | 753 | 16.4 | 249 | 15.5 | 504 | 16.9 | |
| No | 3827 | 83.6 | 1355 | 84.5 | 2472 | 83.1 | |
| Physical activity levels | <0.001 | ||||||
| Active | 3588 | 78.3 | 1310 | 81.7 | 2278 | 76.5 | |
| Inactive | 992 | 21.7 | 294 | 18.3 | 688 | 23.5 | |
| Fruit and vegetable intake | 0.031 | ||||||
| Sufficient | 1731 | 37.8 | 336 | 39.7 | 1095 | 36.8 | |
| Insufficient | 2849 | 62.2 | 968 | 60.3 | 1881 | 63.2 | |
| Alcohol consumption | <0.001 | ||||||
| No | 4348 | 94.9 | 1384 | 86.3 | 2964 | 99.6 | |
| Yes | 232 | 5.1 | 220 | 113.7 | 12 | 0.4 | |
| Smoking status | <0.001 | ||||||
| Never | 3899 | 85.1 | 938 | 58.5 | 2961 | 99.5 | |
| Former | 329 | 7.2 | 324 | 20.2 | 5 | 0.2 | |
| Current | 352 | 7.7 | 342 | 21.3 | 10 | 0.3 | |
The proportion of current and former smokers was 7.7% and 7.2%, respectively. There was a significant association of smoking status with sex (p<0.001), age (p=0.001), education level (p<0.001), marital status (p<0.001), weight status (p=0.016), abdominal obesity (p<0.001), hyperglycemia (p<0.001), HDL cholesterol (p=0.002), and triglyceride (p=0.024), as well as physical activity level (p<0.001) and alcohol consumption (p<0.001). Current smoking prevalence increased with age, with the highest rates among individuals aged 30–44 years and 45–59 years compared to their younger and older age groups. However, it decreased as education level increased, with the highest rates among the illiterate and those who completed primary education than those with high level of education. Individuals who were single/married, non-drinkers, and physically active showed a higher prevalence of smoking compared to those who were separated/widowed, alcohol drinkers, and physically inactive. Participants who were urban dwellers and had insufficient fruit/vegetable intake tended to have higher prevalence of smoking than those who were rural dwellers and had sufficient fruit/vegetable intake, respectively, but these differences did not reach statistical significance (Table 2).
Table 2
Smoking status among adults according to demographic, social and behavioral factors in Morocco, STEPS 2017 cross-sectional survey (N=4580)
| Variables | Smoking status | pa | pb | |||||
|---|---|---|---|---|---|---|---|---|
| Never | Former | Current | ||||||
| n | % | n | % | n | % | |||
| Total | 3899 | 85.1 | 329 | 7.2 | 352 | 7.7 | ||
| Sociodemographic | ||||||||
| Gender | <0.001 | <0.001 | ||||||
| Men | 938 | 24.1 | 324 | 98.5 | 342 | 97.2 | ||
| Women | 2961 | 75.9 | 5 | 1.5 | 10 | 2.8 | ||
| Age (years) | 0.001 | 0.001 | ||||||
| 18–29 | 732 | 18.8 | 19 | 5.8 | 50 | 14.2 | ||
| 30–44 | 1281 | 32.9 | 54 | 16.4 | 122 | 34.7 | ||
| 45–59 | 1066 | 27.3 | 134 | 40.7 | 116 | 33.0 | ||
| 60–69 | 480 | 12.3 | 85 | 25.8 | 52 | 14.8 | ||
| >70 | 340 | 8.7 | 37 | 11.2 | 12 | 3.4 | ||
| Residence | 0.550 | 0.269 | ||||||
| Rural | 1532 | 39.3 | 141 | 52.9 | 145 | 41.2 | ||
| Urban | 2367 | 60.7 | 188 | 57.1 | 207 | 58.8 | ||
| Education level | <0.001 | <0.001 | ||||||
| No formal education | 2133 | 54.7 | 164 | 49.8 | 96 | 27.3 | ||
| Primary school | 740 | 19.0 | 79 | 24.0 | 127 | 36.1 | ||
| Middle school | 446 | 11.4 | 36 | 10.9 | 60 | 17.0 | ||
| High school | 301 | 7.7 | 27 | 8.2 | 39 | 11.1 | ||
| University | 279 | 7.2 | 23 | 7.0 | 30 | 8.5 | ||
| Marital status | <0.001 | <0.001 | ||||||
| Single | 534 | 13.7 | 32 | 9.7 | 68 | 19.3 | ||
| Married | 2817 | 72.2 | 287 | 87.2 | 263 | 74.7 | ||
| Separated/divorced | 108 | 2.8 | 8 | 2.4 | 15 | 4.3 | ||
| Widowed | 440 | 11.3 | 2 | 0.6 | 6 | 1.7 | ||
| Health variables | ||||||||
| Weight status | <0.001 | 0.016 | ||||||
| Non-overweight | 1549 | 39.7 | 148 | 45.0 | 139 | 39.5 | ||
| Overweight/obese | 2350 | 60.3 | 181 | 55.0 | 213 | 60.5 | ||
| Abdominal obesity | <0.001 | <0.001 | ||||||
| No | 1142 | 29.5 | 102 | 31.2 | 111 | 31.8 | ||
| Yes | 2734 | 70.5 | 225 | 68.8 | 238 | 68.2 | ||
| High blood pressure | <0.001 | 0.297 | ||||||
| Yes | 2984 | 76.5 | 258 | 78.4 | 281 | 79.8 | ||
| No | 1057 | 23.5 | - | 21.6 | - | 20.2 | ||
| Hyperglycemia | <0.001 | <0.001 | ||||||
| Yes | 1108 | 28.4 | 95 | 28.9 | 59 | 16.8 | ||
| No | 2791 | 71.6 | 234 | 71.1 | 293 | 83.2 | ||
| Low HDL cholesterol | 0.157 | 0.002 | ||||||
| Yes | 2247 | 57.6 | 159 | 48.3 | 214 | 60.8 | ||
| No | 1652 | 42.4 | 170 | 51.7 | 138 | 39.2 | ||
| Hypertriglyceridemia | 0.018 | 0.024 | ||||||
| Yes | 647 | 18.6 | 64 | 19.5 | 42 | 11.9 | ||
| No | 3252 | 83.4 | 265 | 80.5 | 310 | 88.1 | ||
| Fruit and vegetable intake | 0.728 | 0.573 | ||||||
| Sufficient | 1468 | 37.7 | 133 | 40.4 | 89 | 25.3 | ||
| Insufficient | 2431 | 62.3 | 196 | 59.6 | 263 | 74.7 | ||
| Physical activity levels | 0.006 | <0.001 | ||||||
| Active | 3012 | 77.3 | 280 | 85.1 | 296 | 84.1 | ||
| Inactive | 887 | 22.7 | 49 | 14.9 | 56 | 15.9 | ||
| Alcohol consumption | <0.001 | <0.001 | ||||||
| No | 3862 | 99.1 | 223 | 67.8 | 263 | 74.7 | ||
| Yes | 37 | 0.9 | 106 | 32.2 | 89 | 25.3 | ||
The prevalence of overweight/obesity and high blood pressure was higher among former and current smokers compared to never smokers. However, the prevalence of hyperglycemia and hypertriglyceridemia was lower in never smokers than former and current smokers (Figure 1).
Figure 1
Proportion of adults being overweight/obese, having high blood pressure, hyperglycemia or hypertriglyceridemia, according to smoking status, Morocco STEPS 2017 cross-sectional survey (N=4580)
After adjusting for potential confounding variables, logistic regression analysis showed that both current and former smokers tended to have higher odds of being overweight/obese (AOR=1.08; 95% CI: 0.81–1.64 and AOR=1.22; 95% CI: 0.87–1.56, respectively). They also had significantly a higher likelihood of low HDL cholesterol (AOR=1.59; 95% CI: 1.19–2.10, and AOR=1.40; 95% CI: 1.13–1.85, respectively). However, they were significantly less likely than never smokers to have hyperglycemia (AOR=0.64; 95% CI: 0.42–0.89, and AOR=0.60; 95% CI: 0.37–0.80, respectively) and hypertriglyceridemia (AOR=0.72; 95% CI: 0.39–1.00, and AOR=0.69; 95% CI: 0.46–0.90, respectively) (Table 3).
Table 3
Association of being overweight/obese, having high blood pressure, hyperglycemia, low HDL cholesterol, or hypertriglyceridemia, by smoking status in adults, Morocco STEPS 2017 cross-sectional survey (N=4580)
| Smoking status | Overweight/obese | High blood pressure | Hyperglycemia | Low HDL cholesterol | Hypertriglyceridemia | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| OR (95% CI) | AOR (95% CI) | OR (95% CI) | AOR (95% CI) | OR (95% CI) | AOR (95% CI) | OR (95% CI) | AOR (95% CI) | OR (95% CI) | AOR (95% CI) | |
| Never ® | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Former | 1.44 (1.13–1.78)** | 1.22 (0.87–1.56) | 1.21 (0.90–1.62) | 1.15 (0.85–1.61) | 0.47 (0.40–0.74)** | 0.60 (0.37–0.80)** | 1.14 (0.86–1.41) | 1.40 (1.13–1.90)** | 0.74 (0.45–0.90)* | 0.69 (0.46–0.90)* |
| Current | 1.49 (1.09–2.00)* | 1.08 (0.81–1.64) | 1.13 (0.76–1.60) | 1.11 (0.74–1.67) | 0.51 (0.28–0.65)** | 0.64 (0.42–0.89)* | 1.72 (1.17–2.22)** | 1.59 (1.19–2.10)** | 0.63 (0.35–0.89)** | 0.72 (0.39–1.00)* |
AOR: adjusted odds ratio; adjusted for age, gender, residence, education level, marital status, physical activity level, fruit and vegetable intake, alcohol consumption, weight status, blood pressure, fasting blood glucose, triglycerides, and HDL cholesterol as confounding variables. ® Reference category.
DISCUSSION
This study estimated the prevalence of tobacco smoking and examined its association with various sociodemographic, behavioral, and health factors among adults in Morocco. Overall, 7.7% reported smoking tobacco. Our estimate of current smoking prevalence is lower than what has been reported in other countries. For instance, the current smoking prevalence found in a large study of 11734 participants from 12 European countries was 28.3%15. Similar studies also reported a high prevalence of smoking in other European countries such as Kosovo (25.7%)16, and Georgia (27.1%)17, as well as in some African countries, including Algeria (21.8%)18, Libya (21.5%)19 and South Africa (27.6%)20.
However, other studies found an approximately comparable prevalence of smoking in large samples of 10703 participants from four Sub-Saharan African countries (10.8%)21, 4301 adults from Zambia (11.0%)22 and 2047 participants from Saudi Arabia (12.2%)23. In Morocco, Taheri et al.24 reported a prevalence of tobacco smoking of 15.1% in 2010 and 11.4% in 2023; and Peltzer et al.25 found that 10.5% of adults were current smokers in 2017. Another study conducted in 2020 among 3883 Moroccan adolescents, showed that 11.1% had already tried smoking cigarettes and 22.2% had at least one smoking parent26. Although our findings should be interpreted with caution, since they are based on self-reported information that may lead to underestimating smoking prevalence, they support the decreasing trend in the prevalence of tobacco smoking in Morocco.
The current study showed that gender, age, education level, marital status, physical activity, and alcohol consumption were significantly associated with smoking status. The prevalence of current smoking was higher in men, and married persons, compared to women and single/separated/widowed persons, respectively. These findings are aligned with previous surveys that have shown a significantly higher prevalence of tobacco smoking in men than in women17,21 and in married group than in other marital status groups24. Unsurprisingly, tobacco use is less common among women than men, given the existing social norms and taboos that discourage women from smoking27.
The results of this study also showed that the proportion of smokers increases with age with the highest proportion among adults aged 30–44 years and aged 45–59 years, compared to their younger (aged 18–29 years) and older (aged >60 years) counterparts, this is in conformity with previous studies20,22. This may be due to the financial stability that enables one to afford tobacco products28, and to the older adults’ self-perceived health that was found to be linked to smoking behavior29. It could also be attributed to a lack of specific smoking cessation interventions targeting these sections of the population in Morocco.
Our study not only revealed a significant relationship between education level and tobacco smoking but also found that smoking prevalence decreased as education level increased. This finding aligned with previous research in both industrialized and developing nations30,31, which suggests that socioeconomic status may impact access to information and interventions designed to reduce smoking. Therefore, smoking control strategies should focus on less educated and poor individuals, as they represent the most vulnerable social groups.
Current smoking prevalence tended to be more common among urban residents compared to rural residents (4.5% vs 3.2%). Although our results are not consistent with some previous studies that reported a higher prevalence of smoking among rural than urban residents18,22, they align with those of other studies20,23. This could be explained by differences in cultural attitudes toward smoking and tobacco control policies and regulations across countries. While some countries have introduced comprehensive tobacco control measures, such as smoking bans in public places and restrictions on advertising, others, like Morocco, consider smoking as socially acceptable. In some communities, the sale of tobacco products and smoking in social settings like cafes and gatherings are even encouraged. On the other hand, in some countries, rural smokers are more likely to access tobacco and other products that are primarily grown in rural areas.
Previous studies have demonstrated a significant association of smoking with alcohol consumption and physical activity22,32,33. In contrast, our findings showed a lower prevalence of current smoking among alcohol drinkers and physically inactive individuals compared to non-drinkers and physically active subjects (1.9% vs 5.7% and 1.2% vs 6.5%, respectively). Although additional large-scale and longitudinal studies are needed, our findings highlight the importance of considering alcohol consumption, physical activity, and smoking habits in nutritional counseling and smoking cessation strategies.
The strong link between tobacco smoking and non-communicable diseases, including diabetes mellitus, and dyslipidemia, has been well demonstrated in previous studies4,34-36. However, in this study, after adjusting for sociodemographic, behavioral, and other clinical factors, both current and former smokers were significantly less likely to have hyperglycemia or hypertriglyceridemia compared to never smokers. Although our findings are partially consistent with those of some previous studies that showed an inverse relationship between hyperglycemia and smoking22,25, they should be interpreted with caution. The inverse association of smoking with hypertriglyceridemia and hyperglycemia could be due to behavioral bias as smokers diagnosed with hyperglycemia or hypertriglyceridemia may quit smoking, leading to underrepresentation in the ‘current smoker’ category. Measurements relying solely on self-reported smoking data without biochemical validation (e.g. cotinine or carbon monoxide levels) may also underestimate the true prevalence of smokers and therefore affect these associations. Also, despite the adjusting for sociodemographic, behavioral and clinical factors, such a reverse association could be related to sample-specific factors that were not explored including socioeconomic status, dietary habits, and stress levels, as well as the presence of residual confounding of various comorbidities. Further studies are required to explore mechanisms underlying the observed associations of smoking with hyperglycemia and hypertriglyceridemia in large community-based surveys that consider biomarkers (e.g. inflammatory markers, insulin levels) and biochemical methods to validate smoking status and improve data accuracy.
Strengths and limitations
The current study has several strengths, including the use of standardized data collection methods based on the WHO STEPwise approach and large sample size, which enable reasonably accurate data on smoking and associated factors. However, there are some limitations to consider. First, due to the cross-sectional nature of the data, causal relationships underlying the reported associations cannot be established, and our findings may not be generalizable to other countries. Second, the relatively low proportion of men in the study population limits the ability to assess gender differences accurately. Third, the presence of residual confounding effects of other variables can result in biased estimates of the association between smoking and targeted health disorders. Fourth, there is a lack of data on biochemical markers to provide accurate estimates of smoking prevalence rates. Finally, information bias may arise from self-reported demographic and lifestyle factors, particularly smoking and alcohol consumption.
CONCLUSIONS
Smoking was common among the study population and was associated with various sociodemographic, behavioral, and health factors. Both current and former smoking were associated with a higher likelihood of low HDL cholesterol and a reduced likelihood of hyperglycemia or hypertriglyceridemia. Although further larger and longitudinal studies are recommended to better ascertain causal relationships and provide us with deeper insights, our findings underscore the need for effective and well-designed public health interventions to improve smoking cessation and reduce smoking-related comorbidities, particularly among vulnerable groups.
