Risk factors associated with an outbreak of dengue fever/dengue haemorrhagic fever in Hanoi, Vietnam

Dengue fever/dengue haemorrhagic fever (DF/DHF) appears to be emerging in Hanoi in recent years. A case-control study was performed to investigate risk factors for the development of DF/DHF in Hanoi. A total of 73 patients with DF/DHF and 73 control patients were included in the study. The risk factor analysis indicated that living in rented housing, living near uncovered sewers, and living in a house discharging sewage directly into to ponds were all significantly associated with DF/DHF. People living in rented houses were 2·2 times more at risk of DF/DHF than those living in their own homes [adjusted odds ratio (aOR) 2·2, 95% confidence interval (CI) 1·1–4·6]. People living in an unhygienic house, or in a house discharging sewage directly to the ponds were 3·4 times and 4·3 times, respectively, more likely to be associated with DF/DHF (aOR 3·4, 95% CI 1–11·7; aOR 4·3, 95% CI 1·1–16·9). These results contribute to the understanding of the dynamics of dengue transmission in Hanoi, which is needed to implement dengue prevention and control programmes effectively and efficiently.

Predicting intentions and behaviours in populations with or at-risk of diabetes: A systematic review

Purpose To systematically review the Theory of Planned Behaviour studies predicting self-care intentions and behaviours in populations with and at-risk of diabetes. Methods A systematic review using six electronic databases was conducted in 2013. A standardised protocol was used for appraisal. Studies eligibility included a measure of behaviour for healthy eating, physical activity, glucose monitoring, medication use (ii) the TPB variables (iii) the TPB tested in populations with diabetes or at-risk. Results Sixteen studies were appraised for testing the utility of the TPB. Studies included cross-sectional (n=7); prospective (n=5) and randomised control trials (n=4). Intention (18% – 76%) was the most predictive construct for all behaviours. Explained variance for intentions were similar across cross-sectional (28 -76%); prospective (28 -73%); and RCT studies (18 - 63%). RCTs (18 - 43%) provided slightly stronger evidence for predicting behaviour. Conclusions Few studies tested predictability of the TPB in populations with or at-risk of diabetes. This review highlighted differences in the predictive utility of the TPB suggesting that the model is behaviour and population specific. Findings on key determinants of specific behaviours contribute to a better understanding of mechanisms of behaviour change and are useful in designing targeted behavioural interventions for different diabetes populations.

Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis

Background: Preventing risk factor exposure is vital to reduce the high burden from lung cancer. The leading risk factor for developing lung cancer is tobacco smoking. In Australia, despite apparent success in reducing smoking prevalence, there is limited information on small area patterns and small area temporal trends. We sought to estimate spatio-temporal patterns for lung cancer risk factors using routinely collected population-based cancer data. Methods: The analysis used a Bayesian shared component spatio-temporal model, with male and female lung cancer included separately. The shared component reflected exposure to lung cancer risk factors, and was modelled over 477 statistical local areas (SLAs) and 15 years in Queensland, Australia. Analyses were also run adjusting for area-level socioeconomic disadvantage, Indigenous population composition, or remoteness. Results: Strong spatial patterns were observed in the underlying risk factor exposure for both males (median Relative Risk (RR) across SLAs compared to the Queensland average ranged from 0.48-2.00) and females (median RR range across SLAs 0.53-1.80), with high exposure observed in many remote areas. Strong temporal trends were also observed. Males showed a decrease in the underlying risk across time, while females showed an increase followed by a decrease in the final two years. These patterns were largely consistent across each SLA. The high underlying risk estimates observed among disadvantaged, remote and indigenous areas decreased after adjustment, particularly among females. Conclusion: The modelled underlying exposure appeared to reflect previous smoking prevalence, with a lag period of around 30 years, consistent with the time taken to develop lung cancer. The consistent temporal trends in lung cancer risk factors across small areas support the hypothesis that past interventions have been equally effective across the state. However, this also means that spatial inequalities have remained unaddressed, highlighting the potential for future interventions, particularly among remote areas.