Chronic diseases and musculoskeletal conditions have a significant global burden and frequently co-occur. Musculoskeletal conditions may contribute to the development of chronic disease; however, this has not been systematically synthesised. We aimed to investigate whether the most common musculoskeletal conditions, namely neck or back pain or osteoarthritis of the knee or hip, contribute to the development of chronic disease. We searched CINAHL, Embase, Medline, Medline in Process, PsycINFO, Scopus and Web of Science to February 8, 2018, for cohort studies reporting adjusted estimates of the association between baseline musculoskeletal conditions (neck or back pain or osteoarthritis of the knee or hip) and subsequent diagnosis of a chronic disease (cardiovascular disease, cancer, diabetes, chronic respiratory disease or obesity). Two independent reviewers performed data extraction and assessed study quality. Adjusted hazard ratios were pooled using the generic inverse variance method in random effect models, regardless of the type of musculoskeletal condition or chronic disease. PROSPERO: CRD42016039519. There were 13 cohort studies following 3,086,612 people. In the primary meta-analysis of adjusted estimates, osteoarthritis (n = 8 studies) and back pain (n = 2) were the exposures and cardiovascular disease (n = 8), cancer (n = 1) and diabetes (n = 1) were the outcomes. Pooled adjusted estimates from these 10 studies showed that people with a musculoskeletal condition have a 17% increase in the rate of developing a chronic disease compared to people without (hazard ratio 1.17, 95% confidence interval 1.13–1.22; I2 52%, total n = 2,686,113 people). This meta-analysis found that musculoskeletal conditions may increase the risk of chronic disease. In particular, osteoarthritis appears to increase the risk of developing cardiovascular disease. Prevention and early treatment of musculoskeletal conditions and targeting associated chronic disease risk factors in people with long standing musculoskeletal conditions may play a role in preventing other chronic diseases. However, a greater understanding about why musculoskeletal conditions may increase the risk of chronic disease is needed.

Background: Despite the known benefits of healthy eating in childhood, few Australian childcare services provide food that is consistent with dietary guidelines. The effectiveness of a web-based menu planning intervention to increase childcare service provision of healthy foods and decrease provision of discretionary foods in long day-care services in Australia was assessed in a randomised controlled trial. Here we consider the costs, consequences, cost-effectiveness and budget impact of the intervention using data collected within the trial. Methods: The prospective trial-based economic evaluation involved 54 childcare services across New South Wales (NSW), Australia. Services were randomised to a 12-month intervention or usual care. The intervention involved access to a web-based menu planning and decision support tool and online resources. Effectiveness measures included mean number of food groups, overall menu and individual food group compliance with dietary guidelines, and mean servings of food groups at 12 months. Costs (reported in $AUD, 2017/18) were evaluated from both health sector and societal perspectives. The direct cost to support uptake of the intervention was calculated, as were costs to each childcare service. The incremental cost of the intervention was calculated as the net difference in the cost to undertake menu planning and review plus the direct cost of the intervention. Incremental cost-effectiveness ratios (ICERs) including uncertainty intervals were estimated for differences in costs and effects between intervention and control groups. A relative value index was calculated to determine overall value for money. Results: Over the 12 months of the trial, we calculated a difference in cost between usual practice and intervention groups of − $482 (95% UI − $859, − $56). While the measured increase in menu and food group compliance within the trial did not reach statistical significance, there were significant improvements in mean servings of fruit and discretionary food, represented in the cost-consequence analysis. The calculated relative value index of 1.1 suggests that the intervention returns acceptable value for money for the outcomes generated. Conclusion: Compared to usual practice, web-based programmes may offer an efficient and sustainable alternative for childcare services to improve the provision of healthy foods to children in their care. Trial registration: Australian New Zealand Clinical Trials Registry ACTRN12616000974404

Background: Nudge interventions are those that seek to modify the social and physical environment to enhance capacity for subconscious behaviours that align with the intrinsic values of an individual, without actively restricting options. This study sought to describe the application and effects of nudge strategies on clinician implementation of health-related guidelines, policies and practices within studies included in relevant Cochrane systematic reviews. Methods: As there is varied terminology used to describe nudge, this study examined studies within relevant systematic reviews. A two-stage screening process was undertaken where, firstly, all systematic reviews published in the Cochrane Library between 2016 and 2018 were screened to identify reviews that included quantitative studies to improve implementation of guidelines among healthcare providers. Secondly, individual studies within relevant systematic reviews were included if they were (i) randomised controlled trials (RCTs), (ii) included a nudge strategy in at least one intervention arm, and (iii) explicitly aimed to improve clinician implementation behaviour. We categorised nudge strategies into priming, salience and affect, default, incentives, commitment and ego, and norms and messenger based on the Mindspace framework. Synthesis: The number and percentage of trials using each nudge strategy was calculated. Due to substantial heterogeneity, we did not undertake a meta-analysis. Instead, we calculated within-study point estimates and 95% confidence intervals, and used a vote-counting approach to explore effects. Results: Seven reviews including 42 trials reporting on 57 outcomes were included. The most common nudge strategy was priming (69%), then norms and messenger (40%). Of the 57 outcomes, 86% had an effect on clinician behaviour in the hypothesised direction, and 53% of those were statistically significant. For continuous outcomes, the median effect size was 0.39 (0.22, 0.45), while for dichotomous outcomes the median Odds Ratio was 1.62 (1.13, 2.76). Conclusions: This review of 42 RCTs included in Cochrane systematic reviews found that the impact of nudge strategies on clinician behaviour was at least comparable to other interventions targeting implementation of evidence-based guidelines. While uncertainty remains, the review provides justification for ongoing investigation of the evaluation and application of nudge interventions to support provider behaviour change. Trial registration: This review was not prospectively registered.

While an extensive array of existing evidence-based practices (EBPs) have the potential to improve patient outcomes, little is known about how to implement EBPs on a larger scale. Therefore, we sought to identify effective strategies for scaling up EBPs in primary care. We conducted a systematic review with the following inclusion criteria: (i) study design: randomized and non-randomized controlled trials, before-and-after (with/without control), and interrupted time series; (ii) participants: primary care-related units (e.g., clinical sites, patients); (iii) intervention: any strategy used to scale up an EBP; (iv) comparator: no restrictions; and (v) outcomes: no restrictions. We searched MEDLINE, Embase, PsycINFO, Web of Science, CINAHL, and the Cochrane Library from database inception to August 2016 and consulted clinical trial registries and gray literature. Two reviewers independently selected eligible studies, then extracted and analyzed data following the Cochrane methodology. We extracted components of scaling-up strategies and classified them into five categories: infrastructure, policy/regulation, financial, human resources-related, and patient involvement. We extracted scaling-up process outcomes, such as coverage, and provider/patient outcomes. We validated data extraction with study authors. We included 14 studies. They were published since 2003 and primarily conducted in low-/middle-income countries (n = 11). Most were funded by governmental organizations (n = 8). The clinical area most represented was infectious diseases (HIV, tuberculosis, and malaria, n = 8), followed by newborn/child care (n = 4), depression (n = 1), and preventing seniors’ falls (n = 1). Study designs were mostly before-and-after (without control, n = 8). The most frequently targeted unit of scaling up was the clinical site (n = 11). The component of a scaling-up strategy most frequently mentioned was human resource-related (n = 12). All studies reported patient/provider outcomes. Three studies reported scaling-up coverage, but no study quantitatively reported achieving a coverage of 80% in combination with a favorable impact. We found few studies assessing strategies for scaling up EBPs in primary care settings. It is uncertain whether any strategies were effective as most studies focused more on patient/provider outcomes and less on scaling-up process outcomes. Minimal consensus on the metrics of scaling up are needed for assessing the scaling up of EBPs in primary care. This review is registered as PROSPERO CRD42016041461 .