A systems framework for implementing healthy food retail in grocery settings.

BACKGROUND: Food retailers can be reluctant to initiate healthy food retail activities in the face of a complex set of interrelated drivers that impact the retail environment. The Systems Thinking Approach for Retail Transformation (START) is a determinants framework created using qualitative systems modelling to guide healthy food retail interventions in community-based, health-promoting settings. We aimed to test the applicability of the START map to a suite of distinct healthy food marketing and promotion activities that formed an intervention in a grocery setting in regional Victoria, Australia. METHODS: A secondary analysis was undertaken of 16 previously completed semi-structured interviews with independent grocery retailers and stakeholders. Interviews were deductively coded against the existing START framework, whilst allowing for new grocery-setting specific factors to be identified. New factors and relationships were used to build causal loop diagrams and extend the original START systems map using Vensim. RESULTS: A version of the START map including aspects relevant to the grocery setting was developed ("START-G"). In both health-promoting and grocery settings, it was important for retailers to 'Get Started' with healthy food retail interventions that were supported by a proof-of-concept and 'Focus on the customer' response (with grocery-settings focused on monitoring sales data). New factors and relationships described perceived difficulties associated with disrupting a grocery-setting 'Supply-side status quo' that promotes less healthy food and beverage options. Yet, most grocery retailers discussed relationships that highlighted the potential for 'Healthy food as innovation' and 'Supporting cultural change through corporate social responsibility and leadership'. CONCLUSIONS: Several differences were found when implementing healthy food retail in grocery compared to health promotion settings. The START-G map offers preliminary guidance for identifying and addressing commercial interests in grocery settings that currently promote less healthy foods and beverages, including by starting to address business outcomes and supplier relationships.

Establishing a sustainable childhood obesity monitoring system in regional Victoria.

Issue addressed Childhood obesity poses a significant immediate and long-term burden to individuals, societies and health systems. Infrequent and inadequate monitoring has led to uncertainty about trends in childhood obesity prevalence in many countries. High-quality data, collected at regular intervals, over extended timeframes, with high response rates and timely feedback are essential to support prevention efforts. Our aim was to establish a sustainable childhood obesity monitoring system in regional Australia to collect accurate anthropometric and behavioural data, provide timely feedback to communities and build community engagement and capacity. Methods All schools from six government regions of South-West Victoria were invited to participate. Passive (opt-out) consent was used to collect measured anthropometric and self-reported behavioural data from children in years 2, 4, and 6, aged 7-12 years. Results We achieved a 70% school participation rate (n=46) and a 93% student response rate (n=2198) among government and independent schools. Results were reported within 10 weeks post data collection. Harnessing high levels of community engagement throughout the planning, data collection and reporting phases increased community capacity and data utility. Conclusions The monitoring system achieved high response rates, community engagement and community capacity building, and delivered results back to the community in a timely manner. So what? This system has the potential to provide sustainable monitoring of childhood obesity that is not dependent on external funding. The results of this monitoring will likely inform health promotion efforts in communities across the region.

Spillover effects of childhood obesity prevention interventions: A systematic review.

BACKGROUND: Childhood obesity prevention initiatives are complex interventions that aim to improve children's obesity-related behaviors and provide health promoting environments. These interventions often impact individuals, communities, and outcomes not primarily targeted by the intervention or policy. To accurately capture the effectiveness and cost-effectiveness of childhood obesity prevention interventions, an understanding of the broader impacts (or spillover effects) is required. This systematic review aims to assess the spillover effects of childhood obesity prevention interventions. METHODS: Six academic databases and two trial registries were searched (2007-2023) to identify studies reporting quantifiable obesity-related and other outcomes in individuals or communities not primarily targeted by an obesity prevention intervention. Critical appraisal was undertaken for studies that reported statistically significant findings, and a narrative synthesis of the data was undertaken. RESULTS: Twenty academic studies and 41 trial records were included in the synthesis. The most commonly reported spillovers were diet or nutrition-related, followed by BMI and physical activity/sedentary behavior. Spillovers were mostly reported in parents/caregivers followed by other family members. Nine of the 20 academic studies reported statistically significant spillover effects. CONCLUSION: Limited evidence indicates that positive spillover effects of childhood obesity prevention interventions can be observed in parents/caregivers and families of targeted participants.

Comprehensive application of a systems approach to obesity prevention: a scoping review of empirical evidence.

A systems approach to obesity prevention is increasingly urged. However, confusion exists on what a systems approach entails in practice, and the empirical evidence on this new approach is unclear. This scoping review aimed to identify and synthesise studies/programmes that have comprehensively applied a systems approach to obesity prevention in intervention development, delivery/implementation, and evaluation. By searching international databases and grey literature, only three studies (10 publications) met inclusion criteria, which might be explained partially by suboptimal reporting. No conclusion on the effectiveness of this approach can be drawn yet due to the limited evidence base. We identified common features shared by the included studies, such as measuring ongoing changes, in addition to endpoint outcomes, and supporting capacity building. Some facilitators and barriers to applying a comprehensive systems approach in practice were identified. More well-designed and reported studies are needed, especially from low- and middle-income countries.

The economic burden of ill health due to diet, physical inactivity, smoking, alcohol and obesity in the UK: an update to 2006-07 NHS costs.

BACKGROUND: Estimates of the economic cost of risk factors for chronic disease to the NHS provide evidence for prioritization of resources for prevention and public health. Previous comparable estimates of the economic costs of poor diet, physical inactivity, smoking, alcohol and overweight/obesity were based on economic data from 1992-93. METHODS: Diseases associated with poor diet, physical inactivity, smoking, alcohol and overweight/obesity were identified. Risk factor-specific population attributable fractions for these diseases were applied to disease-specific estimates of the economic cost to the NHS in the UK in 2006-07. RESULTS: In 2006-07, poor diet-related ill health cost the NHS in the UK £5.8 billion. The cost of physical inactivity was £0.9 billion. Smoking cost was £3.3 billion, alcohol cost £3.3 billion, overweight and obesity cost £5.1 billion. CONCLUSION: The estimates of the economic cost of risk factors for chronic disease presented here are based on recent financial data and are directly comparable. They suggest that poor diet is a behavioural risk factor that has the highest impact on the budget of the NHS, followed by alcohol consumption, smoking and physical inactivity.

Generating political commitment for ending malnutrition in all its forms: A system dynamics approach for strengthening nutrition actor networks.

Generating political commitment for ending all forms of malnutrition represents a key challenge for the global nutrition community. Without commitment, the policies, programs, and resources needed to improve nutrition are unlikely to be adopted, effectively implemented, nor sustained. One essential driver of commitment is nutrition actor network (NAN) effectiveness, the web of individuals and organizations operating within a given country who share a common interest in improving nutrition and who act collectively to do so. To inform new thinking and action towards strengthening NAN effectiveness, we use a systems dynamics theoretical approach and literature review to build initial causal loop diagrams (CLDs) of political commitment and NAN effectiveness and a qualitative group model building (GMB) method involving an expert workshop to strengthen model validity. First, a "nutrition commitment system" CLD demonstrates how five interrelated forms of commitment-rhetorical, institutional, operational, embedded, and system-wide-can dynamically reinforce or diminish one another over time. Second, we present CLDs demonstrating factors shaping NAN effectiveness organized into three categories: actor features, resources, and capacities; framing strategies, evidence, and norms; and institutional, political, and societal contexts. Together, these models generate hypotheses on how political commitment and NAN effectiveness could be strengthened in future and may provide potential starting points for country-specific conversations for doing so.

Healthy together Victoria and childhood obesity-a methodology for measuring changes in childhood obesity in response to a community-based, whole of system cluster randomized control trial.

BACKGROUND: Healthy Together Victoria (HTV) - a complex 'whole of system' intervention, including an embedded cluster randomized control trial, to reduce chronic disease by addressing risk factors (physical inactivity, poor diet quality, smoking and harmful alcohol use) among children and adults in selected communities in Victoria, Australia (Healthy Together Communities). OBJECTIVES: To describe the methodology for: 1) assessing changes in the prevalence of measured childhood obesity and associated risks between primary and secondary school students in HTV communities, compared with comparison communities; and 2) assessing community-level system changes that influence childhood obesity in HTC and comparison communities. METHODS: Twenty-four geographically bounded areas were randomized to either prevention or comparison (2012). A repeat cross-sectional study utilising opt-out consent will collect objectively measured height, weight, waist and self-reported behavioral data among primary [Grade 4 (aged 9-10y) and Grade 6 (aged 11-12y)] and secondary [Grade 8 (aged 13-14y) and Grade 10 (aged 15-16y)] school students (2014 to 2018). Relationships between measured childhood obesity and system causes, as defined in the Foresight obesity systems map, will be assessed using a range of routine and customised data. CONCLUSION: This research methodology describes the beginnings of a state-wide childhood obesity monitoring system that can evolve to regularly inform progress on reducing obesity, and situate these changes in the context of broader community-level system change.

Composition of objectively measured physical activity and sedentary behaviour participation across the school-day, influence of gender and weight status: cross-sectional analyses among disadvantaged Victorian school children.

BACKGROUND: The after-school period has been described as the 'critical window' for physical activity (PA) participation. However, little is known about the importance of this window compared with the before and during-school period among socioeconomically disadvantaged children, and influence of gender and weight status. METHODS: 39 out of 156 (RR=25%) invited primary schools across 26 local government areas in Victoria, Australia, consented to participate with 856 children (RR=36%) participating in the wider study. The analysis sample included 298 Grade 4 and Grade 6 children (mean age: 11.2±1.1; 44% male) whom met minimum accelerometry wear-time criteria and had complete height, weight and health-behaviours questionnaire data. Accelerometry measured duration in daily light-intensity PA (LPA), moderate-to-vigorous PA (MVPA) and sedentary time (ST) was calculated for before-school=8-8:59, during-school=9:00-15:29 and after-school=15:30-18:00. Bivariate and multivariable linear regression analyses were conducted. RESULTS: During-school represented the greatest accumulation of LPA and MVPA compared with the before and after-school periods. Boys engaged in 102 min/day of LPA (95% CI 98.5 to 104.9) and 62 min/day of MVPA (95% CI 58.9 to 64.7) during-school; girls engaged in 103 min/day of LPA (95% CI 99.7 to 106.5) and 45 min/day of MVPA (95% CI 42.9 to 47.4). Linear regression models indicated that girls with overweight or obesity engaged in significantly less LPA, MVPA and more time in ST during-school. CONCLUSIONS: This study highlights the importance of in-school PA compared with after-school PA among socioeconomically disadvantage children whom may have fewer resources to participate in after-school PA.

A map of community-based obesity prevention initiatives in Australia following obesity funding 2009-2013.

OBJECTIVE: Obesity is the single biggest public health threat to developed and developing economies. In concert with healthy public policy, multi-strategy, multi-level community-based initiatives appear promising in preventing obesity, with several countries trialling this approach. In Australia, multiple levels of government have funded and facilitated a range of community-based obesity prevention initiatives (CBI), heterogeneous in their funding, timing, target audience and structure. This paper aims to present a central repository of CBI operating in Australia during 2013, to facilitate knowledge exchange and shared opportunities for learning, and to guide professional development towards best practice for CBI practitioners. METHODS: A comprehensive search of government, non-government and community websites was undertaken to identify CBI in Australia in 2013. This was supplemented with data drawn from available reports, personal communication and key informant interviews. The data was translated into an interactive map for use by preventive health practitioners and other parties. RESULTS: We identified 259 CBI; with the majority (84%) having a dual focus on physical activity and healthy eating. Few initiatives, (n=37) adopted a four-pronged multi-strategy approach implementing policy, built environment, social marketing and/or partnership building. CONCLUSION: This comprehensive overview of Australian CBI has the potential to facilitate engagement and collaboration through knowledge exchange and information sharing amongst CBI practitioners, funders, communities and researchers. IMPLICATIONS: An enhanced understanding of current practice highlights areas of strengths and opportunities for improvement to maximise the impact of obesity prevention initiatives.

Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

Web search activity data accurately predict population chronic disease risk in the USA.

BACKGROUND: The WHO framework for non-communicable disease (NCD) describes risks and outcomes comprising the majority of the global burden of disease. These factors are complex and interact at biological, behavioural, environmental and policy levels presenting challenges for population monitoring and intervention evaluation. This paper explores the utility of machine learning methods applied to population-level web search activity behaviour as a proxy for chronic disease risk factors. METHODS: Web activity output for each element of the WHO's Causes of NCD framework was used as a basis for identifying relevant web search activity from 2004 to 2013 for the USA. Multiple linear regression models with regularisation were used to generate predictive algorithms, mapping web search activity to Centers for Disease Control and Prevention (CDC) measured risk factor/disease prevalence. Predictions for subsequent target years not included in the model derivation were tested against CDC data from population surveys using Pearson correlation and Spearman's r. RESULTS: For 2011 and 2012, predicted prevalence was very strongly correlated with measured risk data ranging from fruits and vegetables consumed (r=0.81; 95% CI 0.68 to 0.89) to alcohol consumption (r=0.96; 95% CI 0.93 to 0.98). Mean difference between predicted and measured differences by State ranged from 0.03 to 2.16. Spearman's r for state-wise predicted versus measured prevalence varied from 0.82 to 0.93. CONCLUSIONS: The high predictive validity of web search activity for NCD risk has potential to provide real-time information on population risk during policy implementation and other population-level NCD prevention efforts.

The inter-section of political history and health policy in Asia--the historical foundations for health policy analysis.

One of the challenges for health reform in Asia is the diverse set of socio-economic and political structures, and the related variability in the direction and pace of health systems and policy reform. This paper aims to make comparative observations and analysis of health policy reform in the context of historical change, and considers the implications of these findings for the practice of health policy analysis. We adopt an ecological model for analysis of policy development, whereby health systems are considered as dynamic social constructs shaped by changing political and social conditions. Utilizing historical, social scientific and health literature, timelines of health and history for five countries (Cambodia, Myanmar, Mongolia, North Korea and Timor Leste) are mapped over a 30-50 year period. The case studies compare and contrast key turning points in political and health policy history, and examines the manner in which these turning points sets the scene for the acting out of longer term health policy formation, particularly with regard to the managerial domains of health policy making. Findings illustrate that the direction of health policy reform is shaped by the character of political reform, with countries in the region being at variable stages of transition from monolithic and centralized administrations, towards more complex management arrangements characterized by a diversity of health providers, constituency interest and financing sources. The pace of reform is driven by a country's institutional capability to withstand and manage transition shocks of post conflict rehabilitation and emergence of liberal economic reforms in an altered governance context. These findings demonstrate that health policy analysis needs to be informed by a deeper understanding and questioning of the historical trajectory and political stance that sets the stage for the acting out of health policy formation, in order that health systems function optimally along their own historical pathways.