METHODS AND STUDY DESIGN: A case-control study was conducted involving 57 acne vulgaris patients and 57 age-, gender- and ethnicity-matched controls. All participants were aged 14 and above. The Comprehensive Acne Severity Scale (CASS) was used to categorise patients (grades 2 to 5) and controls (grades 0 to 1). Information such as the demographics, family history, smoking habits and dietary intake were collected using a self-administered questionnaire.
RESULTS: In the patient arm, the gender ratio of male to female was 1.5:1. 43 patients (75.4%) had a family history of acne vulgaris. No significant association was found for acne in patients with a history of smoking. Milk consumption was significantly higher in patients (63.2%, n=36) versus controls (43.9%, n=25), (OR=2.19, p<0.05). In addition, chocolate consumption was also significantly higher in patients (43.9%, n=25) versus controls (24.6%, n=14), (OR=2.4, p<0.05). No significant association was found with the intakes of sweets, potatoes, chips, nuts, yoghurt, ice-cream or carbonated drinks.
CONCLUSIONS: Dietary intake of milk and chocolate may play a role in acne vulgaris. Prospective cohort and intervention studies are recommended to explore whether a causal relationship might obtain.
OBJECTIVE: We aimed to measure leptin and calorie intake among different nicotine dependent groups.
DESIGN: Cross-sectional study.
SETTING: Research department in school of medical sciences.
PATIENTS AND METHODS: Subjects were selected by purposive (non-probability) sampling and categorized as having low, moderate and high nicotine dependency based on the Fagerstrom Test for Nicotine Dependence (FTND) score. Diet was recorded by interview. Anthropometry, blood pressure, body composition, lipid profile, and physical activity level were measured accordingly. Fasting serum leptin was measured using a commercial ELISA kit.
MAIN OUTCOME MEASURE(S): Nicotine dependency, 24-hour diet, clinical anthropometric and clinical measurements.
RESULTS: In 107 Malay male smokers leptin concentration was inversely correlated with nicotine dependence. However, body weight, smoking period, blood pressure, body composition, lipid profile and physical activity level were not significantly different among low, moderately and highly dependent smoking groups. Leptin concentration and total calorie intake were also not significantly different among these groups.
CONCLUSION: Leptin concentration was inversely correlated with nicotine dependence, but leptin concentration and total calorie intake status were not significantly different among our different nicotine dependency subjects.
LIMITATIONS: Purposive sampling for subject recruitment and inaccurate information in the self-administered questionnaire.
METHODS: We analysed sequential Global Adult Tobacco Survey (GATS) data done at least at five years interval in 10 countries namely India, Bangladesh, China, Mexico, Philippines, Russia, Turkey, Ukraine, Uruguay, and Vietnam. We estimated weighted prevalence rates of smoking behaviors namely current smoking (both daily and non-daily), prevalence of hardcore smoking (HCS) among current smokers (HCSs%) and entire surveyed population (HCSp%), quit ratios (QR), and the number of cigarettes smoked per day (CPD). We calculated absolute and relative (%) change in rates between two surveys in each country. Using aggregate data, we correlated relative change in current smoking prevalence with relative change in HCSs% and HCSp% as well as explored the relationship of MPOWER score with relative change in smoking behaviors using Spearman' rank correlation test.
RESULTS: Overall daily smoking has declined in all ten countries lead by a 23% decline in Russia. In India, Bangladesh, and Philippines HCSs% decreased as the smoking rate decreased while HCSs% increased in Turkey (66%), Vietnam (33%) and Ukraine (15%). In most countries, CPD ranged from 15 to 20 sticks except in Mexico (7.8), and India (10.4) where CPD declined by 18 and 22% respectively. MPOWER scores were moderately correlated with HCSs% in both sexes (r = 0.644, p = 0.044) and HCSp% (r = 0.632, p = 0.05) and among women only HCSs% (r = 0.804, p = 0.005) was significantly correlated with MPOWER score.
CONCLUSION: With declining smoking prevalence, HCS had also decreased and quit rates improved. Ecologically, a positive linear relationship between changes in smoking and HCS is a possible evidence against 'hardening'. Continued monitoring of the changes in quitting and hardcore smoking behaviours is required to plan cessation services.
METHODS: We analysed Demographic and Health Survey data on tobacco use collected from large nationally representative samples of men and women in 54 LMICs. We estimated the weighted prevalence of any current tobacco use (including smokeless tobacco) in each country for 4 educational groups and 4 wealth groups. We calculated absolute and relative measures of inequality, that is, the slope index of inequality (SII) and relative index of inequality (RII), which take into account the distribution of prevalence across all education and wealth groups and account for population size. We also calculated the aggregate SII and RII for low-income (LIC), lower-middle-income (lMIC) and upper-middle-income (uMIC) countries as per World Bank classification.
FINDINGS: Male tobacco use was highest in Bangladesh (70.3%) and lowest in Sao Tome (7.4%), whereas female tobacco use was highest in Madagascar (21%) and lowest in Tajikistan (0.22%). Among men, educational inequalities varied widely between countries, but aggregate RII and SII showed an inverse trend by country wealth groups. RII was 3.61 (95% CI 2.83 to 4.61) in LICs, 1.99 (95% CI 1.66 to 2.38) in lMIC and 1.82 (95% CI 1.24 to 2.67) in uMIC. Wealth inequalities among men varied less between countries, but RII and SII showed an inverse pattern where RII was 2.43 (95% CI 2.05 to 2.88) in LICs, 1.84 (95% CI 1.54 to 2.21) in lMICs and 1.67 (95% CI 1.15 to 2.42) in uMICs. For educational inequalities among women, the RII varied much more than SII varied between the countries, and the aggregate RII was 14.49 (95% CI 8.87 to 23.68) in LICs, 3.05 (95% CI 1.44 to 6.47) in lMIC and 1.58 (95% CI 0.33 to 7.56) in uMIC. Wealth inequalities among women showed a pattern similar to that of men: the RII was 5.88 (95% CI 3.91 to 8.85) in LICs, 1.76 (95% CI 0.80 to 3.85) in lMIC and 0.39 (95% CI 0.09 to 1.64) in uMIC. In contrast to men, among women, the SII was pro-rich (higher smoking among the more advantaged) in 13 of the 52 countries (7 of 23 lMIC and 5 of 7 uMIC).
INTERPRETATION: Our results confirm that socioeconomic inequalities tobacco use exist in LMIC, varied widely between the countries and were much wider in the lowest income countries. These findings are important for better understanding and tackling of socioeconomic inequalities in health in LMIC.