METHODS: Between 2009 and 2012, a kilometre-long walk was completed by trained investigators in 462 communities across 16 countries to collect data on tobacco marketing. We interviewed community members about their exposure to traditional and non-traditional marketing in the previous six months. To examine differences in marketing between urban and rural communities and between high-, middle- and low-income countries, we used multilevel regression models controlling for potential confounders.
FINDINGS: Compared with high-income countries, the number of tobacco advertisements observed was 81 times higher in low-income countries (incidence rate ratio, IRR: 80.98; 95% confidence interval, CI: 4.15-1578.42) and the number of tobacco outlets was 2.5 times higher in both low- and lower-middle-income countries (IRR: 2.58; 95% CI: 1.17-5.67 and IRR: 2.52; CI: 1.23-5.17, respectively). Of the 11,842 interviewees, 1184 (10%) reported seeing at least five types of tobacco marketing. Self-reported exposure to at least one type of traditional marketing was 10 times higher in low-income countries than in high-income countries (odds ratio, OR: 9.77; 95% CI: 1.24-76.77). For almost all measures, marketing exposure was significantly lower in the rural communities than in the urban communities.
CONCLUSION: Despite global legislation to limit tobacco marketing, it appears ubiquitous. The frequency and type of tobacco marketing varies on the national level by income group and by community type, appearing to be greatest in low-income countries and urban communities.
Methods: We searched seven databases up to July 2020 for randomized controlled trials investigating the effectiveness of telemedicine in the delivery of diabetes care in low- and middle-income countries. We extracted data on the study characteristics, primary end-points and effect sizes of outcomes. Using random effects analyses, we ran a series of meta-analyses for both biochemical outcomes and related patient properties.
Findings: We included 31 interventions in our meta-analysis. We observed significant standardized mean differences of -0.38 for glycated haemoglobin (95% confidence interval, CI: -0.52 to -0.23; I2 = 86.70%), -0.20 for fasting blood sugar (95% CI: -0.32 to -0.08; I2 = 64.28%), 0.81 for adherence to treatment (95% CI: 0.19 to 1.42; I2 = 93.75%), 0.55 for diabetes knowledge (95% CI: -0.10 to 1.20; I2 = 92.65%) and 1.68 for self-efficacy (95% CI: 1.06 to 2.30; I2 = 97.15%). We observed no significant treatment effects for other outcomes, with standardized mean differences of -0.04 for body mass index (95% CI: -0.13 to 0.05; I2 = 35.94%), -0.06 for total cholesterol (95% CI: -0.16 to 0.04; I2 = 59.93%) and -0.02 for triglycerides (95% CI: -0.12 to 0.09; I2 = 0%). Interventions via telephone and short message service yielded the highest treatment effects compared with services based on telemetry and smartphone applications.
Conclusion: Although we determined that telemedicine is effective in improving several diabetes-related outcomes, the certainty of evidence was very low due to substantial heterogeneity and risk of bias.