METHODS: The study employs a cross-sectional design using respondent driven sampling methods. The sample includes 406 fishermen from Pahang state, Malaysia. Using multivariate logistic regressions, we examined the relationship between individual (depression), social (adverse interactions with the police), and structural (poverty-related) stressors and injection drug use and risky injection drug use (e.g.., receptive and non-receptive needle sharing, frontloading and back-loading, or sharing drugs from a common container).
RESULTS: Participants below the poverty line had significantly lower odds of injection drug use (OR 0.52, 95 % CI: 0.27-0.99, p = 0.047) and risky injection drug use behavior (OR 0.48, 95 % CI: 0.25-0.93, p = 0.030). In addition, participants with an arrest history had higher odds of injection use (OR 19.58, 95 % CI: 9.81-39.10, p
METHODS: We studied the health of 636 OA from seven sub-tribes in the Peninsular. Parameters that were assessed included height, weight, BMI and waist circumference whilst blood pressure, cholesterols, fasting blood glucose and HbA1c levels were recorded. We then analysed cardio-metabolic risk factor prevalences and performed multiple pair-wise comparisons among different sub-tribes and socio-economic clusters.
RESULTS: Cardio-metabolic risk factors were recorded in the seven sub-tribes.. Prevalence for general and abdominal obesity were highest in the urbanized Orang Seletar (31 · 6 ± 5 · 7%; 66 · 1 ± 5 · 9%). Notably, hunter gatherer Jehai and Batek tribes displayed the highest prevalence for hypertension (43 · 8 ± 9 · 29% and 51 · 2 ± 15 · 3%) despite being the leanest and most remote, while the Mendriq sub-tribe, living in the same jungle area with access to similar resources as the Batek were less hypertensive (16.3 ± 11.0%), but displayed higher prevalence of abdominal obesity (27.30 ± 13.16%).
CONCLUSIONS: We describe the cardio-metabolic risk factors of seven indigenous communities in Malaysia. We report variable prevalence of obesity, cholesterol, hypertension and diabetes in the OA in contrast to the larger ethnic majorities such as Malays, Chinese and Indians in Malaysia These differences are likely to be due to socio-economic effects and lifestyle changes. In some sub-tribes, other factors including genetic predisposition may also play a role. It is expected that the cardio-metabolic risk factors may worsen with further urbanization, increase the health burden of these communities and strain the government's resources.
METHODS: This was a cross-sectional population based study with data on occupational social class, educational level obtained using a detailed health and lifestyle questionnaire. A total of 10,147 men and 12,304 women aged 45-80 years living in Norfolk, United Kingdom, were recruited using general practice age-sex registers as part of the European Prospective Investigation into Cancer (EPIC-Norfolk). Plasma levels of cholesterol and triglycerides were measured in baseline samples. Social class was classified according to three classifications: occupation, educational level, and area deprivation score according to Townsend deprivation index. Differences in lipid levels by socio-economic status indices were quantified by analysis of variance (ANOVA) and multiple linear regression after adjusting for body mass index and alcohol consumption.
RESULTS: Total cholesterol levels were associated with occupational level among men, and with educational level among women. Triglyceride levels were associated with educational level and occupational level among women, but the latter association was lost after adjustment for age and body mass index. HDL-cholesterol levels were associated with both educational level and educational level among men and women. The relationships with educational level were substantially attenuated by adjustment for age, body mass index and alcohol use, whereas the association with educational class was retained upon adjustment. LDL-cholesterol levels were not associated with social class indices among men, but a positive association was observed with educational class among women. This association was not affected by adjustment for age, body mass index and alcohol use.
CONCLUSIONS: The findings of this study suggest that there are sex differences in the association between socio-economic status and serum lipid levels. The variations in lipid profile with socio-economic status may be largely attributed to potentially modifiable factors such as obesity, physical activity and dietary intake.