From trials to communities: implementation and scale-up of health behaviour interventions

BACKGROUND

To maximise their potential benefits to communities, effective health behaviour interventions need to be implemented, ideally 'at scale', and are often adapted as part of this. To inform future implementation and scale-up efforts, this study broadly sought to understand (i) how often health behaviour interventions are implemented in communities, (ii) the adaptations that occur; (iii) how frequency it occurred 'at scale'; and (iv) factors associated with 'scale-up'.

METHODS

A cross-sectional survey was conducted of corresponding authors of trials (randomised or non-randomised) assessing the effects of preventive health behaviour interventions. Included studies of relevant Cochrane reviews served as a sampling frame. Participants were asked to report on the implementation and scale-up (defined as investment in large scale delivery by a (non)government organisation) of their intervention in the community following trial completion, adaptations made, and any research dissemination strategies employed. Information was extracted from published reports of the trial including assessments of effectiveness and risk of bias.

RESULTS

Authors of 104 trials completed the survey. Almost half of the interventions were implemented following trial completion (taking on average 19 months), and 54% of those were adapted prior to doing so. The most common adaptations were adding intervention components, and adapting the intervention to fit within the local service setting. Scale-up occurred in 33% of all interventions. There were no significant associations between research trial characteristics such as intervention effectiveness, risk of bias, setting, involvement of end-user, and incidence of scale-up. However the number of research dissemination strategies was positively associated to the odds of an intervention being scaled-up (OR = 1.50; 95% CI: 1.19, 1.88; p < 0.001).

CONCLUSIONS

Adaptation of implemented trials is often undertaken. Most health behaviour interventions are not implemented or scaled-up following trial completion. The use of a greater number of dissemination strategies may increase the likelihood of scaled up.

Estimating the potential impact of the Australian government's reformulation targets on household sugar purchases

BACKGROUND

Countries around the world are putting in place sugar reformulation targets for packaged foods to reduce excess sugar consumption. The Australian government released its voluntary sugar reformulation targets for nine food categories in 2020. We estimated the potential impact of these targets on household sugar purchases and examined differences by income. For comparison, we also modelled the potential impact of the UK sugar reduction targets on per capita sugar purchases as the UK has one of the most comprehensive sugar reduction strategies in the world.

METHODS

Grocery purchase data from a nationally representative consumer panel (n=7,188) in Australia was linked with a large database (FoodSwitch) with product-specific sugar content information for packaged foods (n=25,261); both datasets were collected in 2018. Potential reductions in per capita sugar purchases were calculated overall and by food category. Differences in sugar reduction across income level were assessed by analysis of variance.

RESULTS

In 2018, the total sugar acquired from packaged food and beverage purchases consumed at-home was 56.1 g/day per capita. Australia's voluntary reformulation targets for sugar covered 2,471/25,261 (9.8%) unique products in the FoodSwitch dataset. Under the scenario that all food companies adhered to the voluntary targets, sugar purchases were estimated to be reduced by 0.9 g/day per capita, which represents a 1.5% reduction in sugar purchased from packaged foods. However, if Australia adopted the UK targets, over twice as many products would be covered (n=4,667), and this would result in a more than four times greater reduction in sugar purchases (4.1 g/day per capita). It was also estimated that if all food companies complied with Australia's voluntary sugar targets, reductions to sugar would be slightly greater in low-income households compared with high-income households by 0.3 g/day (95%CI 0.2 - 0.4 g/day, p<0.001).

CONCLUSIONS

Sugar-reduction policies have the potential to substantially reduce population sugar consumption and may help to reduce health inequalities related to excess sugar consumption. However, the current reformulation targets in Australia are estimated to achieve only a small reduction to sugar intakes, particularly in comparison to the UK's sugar reduction program.

Management of multivessel coronary artery disease in patients with non-ST-elevation myocardial infarction: a complex path to precision medicine

Recent analyses suggest the incidence of acute coronary syndrome is declining in high- and middle-income countries. Despite this, overall rates of non-ST-elevation myocardial infarction (NSTEMI) continue to rise. Furthermore, NSTEMI is a greater contributor to mortality after hospital discharge than ST-elevation myocardial infarction (STEMI). Patients with NSTEMI are often older, comorbid and have a high likelihood of multivessel coronary artery disease (MVD), which is associated with worse clinical outcomes. Currently, optimal treatment strategies for MVD in NSTEMI are less well established than for STEMI or stable coronary artery disease. Specifically, in relation to percutaneous coronary intervention (PCI) there is a paucity of randomized, prospective data comparing multivessel and culprit lesion-only PCI. Given the heterogeneous pathological basis for NSTEMI with MVD, an approach of complete revascularization may not be appropriate or necessary in all patients. Recognizing this, this review summarizes the limited evidence base for the interventional management of non-culprit disease in NSTEMI by comparing culprit-only and multivessel PCI strategies. We then explore how a personalized, precise approach to investigation, therapy and follow up may be achieved based on patient-, disease- and lesion-specific factors.