Changes in lifestyle behaviours have led to a rise in rates of overweight and obesity, which has both a significant health and economic impact. In this context, this chapter summarises key findings and policy recommendations following a review of 12 interventions targeting overweight and obesity risk factors – i.e. unhealthy diets and physical inactivity.
Healthy Eating and Active Lifestyles
Best Practices in Public Health
1. Key findings and recommendations
Abstract
Overweight is a major public health issue requiring effective policy action
Overweight, which includes obesity,1 is a key public health issue facing countries across the world. Advances in technology, globalisation, urbanisation and the expansion of food retail have changed the way people eat. Consequently, diets today are increasingly comprised of foods associated with weight gain (e.g. added fats and sugar) at the expense of those with healthy dietary elements (e.g. fruit and vegetables). Concurrently, people today have fewer reasons to be physically active with the rising use of less active modes of transport and sedentary jobs. Given changes in body weight are primarily due to imbalances between energy intake from diet and energy output through physical activity, it is not surprising that rates of overweight, which includes obesity, are high: as of 2019, 64% of men and 56% of women in the OECD live with overweight (OECD, 2019[1]). For further details on overweight risk factors and trends, see Chapter 2.
Overweight places both a health and economic toll on countries. Adults who live with overweight are at greater risk of developing certain non-communicable diseases (NCDs) such as type 2 diabetes and several cancers. High NCD rates, in turn, lead to a greater number of premature deaths: over the period 2020‑50, overweight is estimated to reduce average life expectancy in the OECD by 2.7 years, with this figure increasing to 3.2 years when considering healthy life years (OECD, 2019[2]). Given people living with overweight are more likely have one or multiple NCDs, demand and costs for health care are also likely to be higher among this group of people. For example, OECD estimates the cost of treating overweight and its related conditions at USD PPP (purchasing power parity) 209 per year, per person, which translates into 8.4% of total health expenditure.
OECD countries have responded to the obesity epidemic with a mixture of policy interventions. Across the OECD, over 90% of countries have implemented a national action plan to address unhealthy diets and physical inactivity (WHO, 2019[3]; WHO, 2019[4]). Action plans at the national level complement and align with those developed by the international community such as WHO’s Global Action Plan on Physical Activity, which aims to reduce the global prevalence of physical inactivity by 15% by 2030. Action plans typically include a range of interventions such as those that aim to widen the availability of healthy choices (see Chapter 2 for further details).
To assist policy makers address rising rates of overweight, this report examines a selection of strategically important prevention interventions implemented within OECD and EU27 countries. Given the significant health and economic impact of overweight, policy makers have implemented a range of interventions targeting key risk factors – i.e. unhealthy diets and physical inactivity. To assist policy makers select and transfer interventions considered “best practice”, this report details key findings and aligning policy recommendations following a review of 12 interventions submitted by OECD policy makers or selected from the EU’s Public Health Best Practice Portal (see Table 1.1 for a high-level overview of each intervention) (European Commission, 2021[5]). Due to this process, selected interventions typically target individual lifestyle behaviours. In reality, tackling overweight requires a multifaceted approach, including policies to change economic, social and physical environments. Further, the chosen interventions do not cover all overweight risk factors, but instead focus on the two predominant risk factors – diet and physical activity. Despite these limitations, selected interventions represent some of those that are of key strategic interest, therefore, countries are considering transferring them to their local context.
Each intervention was assessed against a standard methodology – namely an assessment of the intervention against five best practice criteria including effectiveness, efficiency and equity, as well as an assessment of the intervention’s transferability potential. For further methodological details, see Annex A.
Table 1.1. Overview of selected case study interventions
* The case study may operate across the country or in specific regions within that country.
Modelling estimates indicate selected interventions targeting overweight risk factors are both effective an efficient
Selected interventions evaluated using OECD’s microsimulation model estimate large declines in disease incidence. OECD’s Strategic Public Health Planning for non-communicable diseases (SPHeP-NCD) model estimated changes in disease incidence for five of the twelve interventions following an improvement in overweight risk factors: the Multimodal Training Intervention, Nutri-Score, the Combined Lifestyle Intervention, Young People at a Healthy Weight (JOGG) and Physical Activity on Prescription (see Table 1.1 for a high-level overview of interventions). Results from the model estimate that if all interventions were to be scaled-up simultaneously by 2050, 18.4 and 5.9 million cases of disease would be avoided across analysed OECD and EU27 countries, respectively. The majority of these disease cases are either musculoskeletal disorders (MSDs) or cardiovascular diseases (CVDs) (Figure 1.1).2 To put these figures into perspective, in the EU each year there are approximately CVD 6 million cases (Institute for Health Metrics and Evaluation, 2019[6]). Nutri-Score – a healthy food labelling scheme first established in France – is estimated to have the greatest impact, which is largely due its wide reach (covering everyone aged 1 year and over). Conversely, the Multimodal Training Intervention – a healthy living programme in Iceland – despite having a large health impact at the individual level, would have a relatively low impact across the population as it targets those aged 65+. A reduction in disease incidence has a subsequent positive impact on life years (LYs) and disability adjusted life years (DALYs) gained as summarised in Figure 1.2.
Figure 1.1. Disease cases avoided between 2021‑50, OECD and EU27 countries
Note: MTI = Multimodal Training Intervention, CLI = Combined Lifestyle Intervention, JOGG = Young People at a Healthy Weight, PAP = Physical Activity on Prescription.
Source: OECD analyses based on the OECD SPHeP-NCDs model, 2021.
Figure 1.2. LYs and DALYs gained annually per 100 000 people, 2021‑50
Note: MTI = Multimodal Training Intervention, CLI = Combined Lifestyle Intervention, JOGG = Young People at a Healthy Weight, PAP = Physical Activity on Prescription.
Interventions result in both health expenditure savings and improvements in workforce productivity. The impact of the same five interventions on efficiency was also measured using OECD’s SPHeP-NCD model. Results from the analysis indicate savings ranging from 0.02% (Combined Lifestyle Intervention) to 0.06% (Young People at Healthy Weight) of total health expenditure per year over the period 2021‑50. On a per capita basis, this translates into annual savings of EUR 0.41 to EUR 1.28 across OECD countries (see Figure 1.3). Interventions accrue additional savings as a result of improvements to workforce productivity – i.e. a reduction in absenteeism, presenteeism and early retirement as well as a higher employment rate. Gains in workforce productivity are also shown in Figure 1.3, which covers all interventions except the Multimodal Training Intervention given it targets those aged 65 and over and who are presumed to have already left the workforce.
Figure 1.3. Health expenditure savings and workforce gains, EUR per capita, per year, 2021‑50
Note: MTI = Multimodal Training Intervention, CLI = Combined Lifestyle Intervention, JOGG = Young People at a Healthy Weight, PAP = Physical Activity on Prescription.
Source: OECD analyses based on the OECD SPHeP-NCDs model, 2021.
Many interventions are cost-effective, and for several countries, even cost-saving. For each of the selected case studies, the cost per DALY gained (over the period 2021‑50) revealed interventions were in many instances not only cost-effective but also cost-saving (see Figure 1.4).3 Nutri-Score, in particular, is viewed as a good financial investment as it is cost-saving across all OECD and EU27 countries. More intensive interventions such as the Multimodal Training Intervention, which offers participants access to fitness classes under the supervision of a qualified personal trainer, are more expensive and therefore, although highly effective, may not being cost-effective against standard thresholds. It is important to note that the Multimodal Training Intervention and the Combined Lifestyle Intervention (CLI) have a relatively low extent of coverage, indicating there is room to improve economies of scale and therefore possibly reduce the cost per targeted individual. For example, previous analyses by OECD (2019[2]) found physical activity prescription programmes similar to CLI are cost-effective when scaled-up to the whole population. In addition, the Multimodal Training Intervention targets older people, therefore, compared to interventions that target young people or the general population, there is limited time to see an improvement in health outcomes.
Figure 1.4. Efficiency of interventions across OECD and EU27 countries
Note: Cost per DALY gained is measured using total intervention costs less total health expenditure savings divided by total DALYs gained over the period 2021‑50. Costs and benefits have been discounted at a rate of 3%. MTI = Multimodal Training Intervention, CLI = Combined Lifestyle Intervention, JOGG = Young People at a Healthy Weight, PAP = Physical Activity on Prescription. For the purpose of this analysis, an intervention is cost-effective if the cost per DALY gained is less than or equal to EUR 50 000 (mid-way point between estimates outlined in Vallejo-Torres et al. (2016[7])).
Source: OECD analyses based on the OECD SPHeP-NCDs model, 2021.
Comprehensive policy packages are needed given interventions have a greater than additive impact when combined
Overweight is a complex health issue for which there is no “silver bullet” solution. Changes in modifiable overweight risk factors such as diet, physical activity, sleep and stress are complex and multifaceted. Therefore, it is not surprising that individual interventions, alone, are limited in their ability to markedly reverse trends in overweight and obesity. This aligns with findings among selected interventions, which often recorded small changes to key health outcomes. For instance, the StopDia Pilot – a diabetes prevention programme targeting Finland’s Somali population – recorded a statistically significant improvement in vegetable consumption and non-significant changes among remaining indicators (e.g. step count). Similarly, another diabetes prevention programme in Europe (DE‑PLAN) led to a statistically significant drop in consumption of sugars and sweets but no significant change in fruit and vegetable consumption or physical activity levels. One notable exception was the Multimodal Training Intervention, a healthy living programme in Iceland for the elderly, which reported statistically significant, favourable changes across all outcome measures (see Box 1.1).
Box 1.1. The impact of Iceland’s Multimodal Training intervention on outcome measures
The Multimodal Training Intervention is designed to improve the overall health of older individuals through exercise classes and nutritional education information sessions. To assess the impact of the intervention, data was collected from the same patients every six months over a period of two years, namely:
Adiposity measures: BMI, waist-to-hip ratio, body fat percentage, muscle mass, body fat mass
Physical activity measures: 30‑second chair stand, short physical performance battery score, hand grip, six minute walking test, minutes of exercise per week
Quality of life: measured using the widely used tool – EQ‑5D‑5L. The tool measures health across five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Participants provide self-reported data for each dimension.
Other: resting heart rate and systolic blood pressure.
An analysis of changes in outcome measures using panel data revealed statistically significant improvements in all outcome measures over the two‑year period. It is important to note the data did not include a control group. However, these findings are supported by previous evaluations of the Multimodal Training Intervention which used randomised controlled trials (RCTs) (Guðlaugsson, 2014[8]).
Note: See Chapter 6 for further details.
Countries must address the obesity epidemic using a mixture of interventions targeting both the individual and society. Policy packages comprised of several interventions can address the multiple causes of overweight. This report outlines several interventions policy makers can draw upon to create a comprehensive policy package targeting overweight. Selected interventions cover a range of settings (e.g. schools, primary care, and community facilities) and populations (e.g. younger children all the way up to the older population). However, due to the selection process based on submissions by countries (see Annex A), the majority are “downstream” interventions that focus on changing the behaviour of individuals. “Upstream” structural interventions that aim to change the environment in which people live should complement downstream ones (see Box 1.2 for information on how Australia incorporates both down and upstream policies). For example, food reformulation, food procurement, restrictions on unhealthy food purchases, changes in the urban environment, and increases in green spaces and bike/walking paths (these types of interventions will be reviewed in future best practice reports). Structural interventions are important for deconstructing the obesogenic environment by “making the healthy choice the easy choice”. Strategies to tackle overweight should also cover lesser-known risk factors such as sleep and stress.
Box 1.2. Australia’s National Preventive Health Strategy – desired policy achievements for healthy diets and physical activity by 2030
In December 2021, the Australian Government released its National Preventive Health Strategy covering years 2021‑30. The aim of the strategy is to “improve health and well-being of all Australians at all stages of life, through a systems-based approach to prevention that addresses the wider determinants of health, reduces health inequities and decreases the overall burden of disease”.
The strategy includes a number of action areas and aligning policy goals for 2030 including improving access to a healthy diet and increasing physical activity. A review of the listed policy goals highlights Australia’s multifaceted and comprehensive approach to tackling key risk factors for overweight. Several policy goals, although not all, are outlined below.
Physical activity
Mass media campaigns that link to actionable behaviour change in order to create healthier social norms and physical activity
Prioritise urban design, land use and infrastructure to support physical activity (e.g. public open spaces and active transport networks)
Increased physical activity and reduced sedentary behaviour is promoted in the workplace
Incorporate sleep and screen time recommendations for all age groups into national guidelines
Provide health care professionals with appropriate training to providing advice and support to patients regarding physical activity
Healthy diet
Healthy eating is promoted through widespread multi-media education campaigns
Ongoing access to adequate and affordable healthy food options available to all Australians
Reduced sugar, saturated fat and sodium content of relevant packaged and processed foods through reformulation and a reduction in serving sizes
Consumer choice is guided by the Health Star Rating system, which is displayed on all multi‑ingredient packaged food products
Further reduce children’s exposure to unhealthy food and drink marketing, branding and sponsorships.
Source: Australian Government Department of Health (2021[9]), “National Preventive Health Strategy 2021‑30”, https://www.health.gov.au/sites/default/files/documents/2021/12/national-preventive-health-strategy-2021-2030.pdf.
Interventions that target children are of particular importance when designing policy packages to tackle overweight. Tackling overweight in children is of critical importance given its link to obesity in adulthood, which is associated with more complex health issues. In addition, overweight is connected with high levels of stigma among children leading to poor mental health and well-being (see Box 1.3 for further details). Despite the well-established link between overweight in childhood and poor physical and mental health, some interventions targeting children may be overlooked given the lack of evidence on their long-term impact. For example, compared to interventions targeting adults, those based in schools may look unfavourable given diseases associated with overweight (e.g. cardiovascular diseases) do not usually appear until middle adulthood, and are therefore not captured within usual evaluation timelines (OECD, 2022[10]). This is supported by previous OECD analyses which showed school-based overweight interventions are effective once a sufficient number of children exposed to the intervention are adults who are susceptible to developing chronic diseases (OECD, 2019[2]). The importance of evaluating prevention interventions targeting children and teenagers is highlighted in Switzerland, where it is a top priority within the Federal Office of Public Health.
Box 1.3. The health and well-being impact of overweight in childhood
Overweight in childhood is associated with various health and well-being issues, as highlighted in this box. Therefore investing now in children will lead to significant benefits in the future.
Health: overweight in childhood is a key determinant of obesity in adulthood, which is associated with a range of NCDs. Children living with overweight may also experience health issues at an early age including breathing difficulties, insulin resistance and hypertension. (See Chapter 2 for further details.)
Bullying: children who live with overweight are at greater risk of being bullied at school. According to the Rudd Centre for Food Policy and Obesity, school-aged children living with overweight or obesity are 63% more likely to be bullied by their peers, friends, and even family (WHO, 2017[11]). Girls are particularly vulnerable to bullying if overweight or obese (OECD, 2019[2]).
Educational outcomes: compared to their peers with a healthy weight, children who live with overweight perform worse at school. An analysis of Health Behaviour in School-aged Children (HBSC) survey data by OECD found healthy-weight children are 13% more likely to report good performance at school than children living with obesity (OECD, 2019[2]). There are various reasons for this relationship such as poor health and mental well-being, which makes children less likely to engage in class.
Policy packages covering individual and structural overweight prevention interventions can have a greater than additive effect on health outcomes and be cost saving. As outlined above, policy packages are necessary for addressing the multiple causes of overweight and for ensuring the whole population is reached. Policy packages are also attractive given their potential to have a more than additive effect on health outcomes. As an example, interventions to boost physical activity, such as through lifestyle programmes, will be more effective among people living in environments that promote exercise, such as those with green spaces and walking paths. Similarly, interventions to improve health literacy regarding nutrition will be more effective if people have better access to healthy food that is affordable.
Interventions had limited impact on reducing health inequalities; therefore, policy makers should focus on interventions that target the needs of disadvantaged groups
Only two of the twelve selected interventions targeted a disadvantaged group in an effort to reduce health inequalities – the StopDia Pilot and SI!. The StopDia Pilot is an adaption of the nationwide Finnish diabetes prevention programme, StopDia. Specifically, StopDia was adapted to suit the needs of Finland’s Somali population who are at greater risk of developing diabetes. For example, participants were recruited from their local mosque and group education sessions were run by a health professional with a Somali background. Regarding SI! – a school-based obesity prevention intervention – the process for recruiting schools ensured children with an immigrant background and/or in a low-income area were included.
In addition to the StopDia Pilot, four interventions reported outcomes specific to a disadvantaged group, which found mixed results:
StopDia Pilot: initial evidence from a before‑and-after evaluation of the StopDia Pilot indicate the intervention achieved small improvements in lifestyle‑related diabetes risk factors such as increased vegetable consumption and physical activity, as well as weight loss (Hussein et al., 2020[12]).
Young People at a Healthy Weight (JOGG): a review of JOGG, a community-based intervention for those aged 0‑19, found the intervention had a greater impact on reducing overweight prevalence in low-SES municipalities compared to middle‑ to high-SES municipalities. Further, the results were only significant for low-SES municipalities (Kobes, Kretschmer and Timmerman, 2021[13]).
Nutri-Score: a study of Nutri-Score, a healthy food labelling scheme, found people with a lower education level were more likely than higher educated people to change purchasing behaviours (Sarda, Ducrot and Serry, 2020[14]). The same study, however, found Nutri-Score was more likely to change the behaviour of middle‑income people relative to those on low-incomes.
ToyBox: an evaluation of ToyBox, a kindergarten based overweight prevention intervention, found it had a greater impact on reducing sedentary behaviour among children attending kindergartens in high-SES areas (De Craemer et al., 2014[15]).
SI!: a randomised control trial (RCT) of SI!, another kindergarten based overweight prevention intervention, found larger observed effects among children whose parents had at least a high school education and a relatively high income – specifically, in terms of changes in knowledge, attitude and habits in relation to diet and physical activity (Peñalvo et al., 2015[16]).
Literature reviews of similar interventions were the basis for assessing equity among those that neither targeted nor reported outcomes for disadvantaged population groups. Findings are summarised in Box 1.4.
Box 1.4. The impact of different overweight interventions on reducing health inequalities
Not all intervention evaluations analysed in this report included information on equity. In these instances, evidence from the wider literature pertaining to similar interventions was used. These interventions are grouped into one of three overweight prevention themes – primary care, food labelling and digital.
Primary care
Personalised Approach to Obesity Management in Children, DE‑PLAN, Physical Activity on Prescription and the Combined Lifestyle Intervention
Obesity interventions delivered in a primary care setting risk widening existing health inequalities given low-income children and adults are less likely to visit their general practitioner (GP). For example, after adjusting for needs, 70% of people living in the poorest quintile across OECD countries visited their GP in the past year compared to 73% in the richest quintile (OECD, 2019[17]). The difference isn’t large, however, these figures represent the OECD average with the gap markedly wider for certain countries. For example, in Greece, which operates the primary-care diabetes prevention intervention, DE‑PLAN, 55% of those in the poorest quintile accessed a GP compared to 66% in the richest quintile.
Food labelling
Danish Whole Grain Partnership
Food labelling can both widen and reduce health inequalities. Evidence has shown that products with a healthy food label are often more expensive, which may reflect a higher willingness-to-pay among consumers for “healthier products” and/or greater production costs (Sumanac, Mendelson and Tarasuk, 2013[18]). For example, Romania, a country in the process of transferring the Whole Grain Partnership, found products high in whole grain would be more expensive than refined grain products. However, this is not the case in Denmark, where the intervention was originally implemented. Conversely, easy-to‑understand food labels can promote health equity as they are easily understood by all people.
Digital
Let Food Be Your Medicine
Mhealth apps, such as Let Food Be Your Medicine, have the potential to widen existing health inequalities given they are more popular among younger, higher educated populations (Bol, Helberger and Weert, 2018[19]; Azzopardi-Muscat and Sørensen, 2019[20]; Baragwanath, 2021[21]). Conversely, they may promote health equity by making care more accessible, in particular, for those who living in regional/rural areas.
The limited impact of selected interventions on health equity aligns with the broader literature. For example, Brown and colleagues’ (2019[22]) systematic review of overweight prevention interventions for children found studies “rarely” reported on outcomes by factors that affect equity (e.g. by socio‑economic or migrant status). Further, for studies that did report by different population groups, results were mixed and often found more favourable outcomes among privileged populations.
Policy makers should prioritise and support overweight interventions that promote healthy equity. Specifically, interventions that target disadvantaged population groups or have processes in places to ensure the needs of these groups are addressed, for example by:
Adapting the intervention to the needs of the disadvantaged, employing a diversified recruitment and communication strategy, as well as stratifying evaluation indicators by different population groups (see Box 1.5 for further details).
Making funding conditional on proving how the intervention promotes health equity and/or prioritise scaling-up/transfer efforts for interventions with a proven impact on reducing health inequalities.
Providing additional financial resources given the difficulties in accessing, engaging and retaining disadvantaged population groups in public health interventions. For example, extra funding may be necessary to recruit a sufficiently large number of participants, which is important for rigorous evaluations (Bonevski et al., 2014[23]). Similarly, additional funding may be necessary to ensure participants with a low SES have equal access to interventions, in particular among those that require participants to pay a proportion of fees out-of-pocket (such as the Multimodal Training Interventions and Physical Activity on Prescription).
Improving levels of health literacy (HL), which refers to an individual’s knowledge, motivation and skills to access, understand, evaluate and apply health information (Stormacq, Wosinski and Van den Broucke, 2016[24]). Levels of HL are low in the population (HLS‐EU Consortium, 2012[25]; Moreira, 2018[26]), in particular among disadvantaged population groups. For example, a nation-wide study of HL in Denmark found immigrants and individuals with a basic education and below average income had lower levels of HL (Svendsen et al., 2020[27]). Higher levels of HL among disadvantaged groups will ultimately improve population health and reduce inequalities. See Box 1.6 for an overview of policy options to boost HL rates.
Box 1.5. Policies to promote health equity among overweight interventions
This box sets out example policy options to improve the performance of overweight interventions in reducing health inequalities. It is important to note that this list is non-exhaustive.
Adapt the intervention to the needs of disadvantaged population groups
Before designing an intervention, it is important to undertake a situational analysis and needs assessment involving local community members. Findings from this research should subsequently inform the design of the intervention to increase salience, acceptability and uptake among disadvantaged groups. Public Health England’s National Health Inequalities Team, for example, have developed a Health Equity Assessment Tool (HEAT) to help public health professionals “systematically address health inequalities” and “identify what action can be taken to reduce health inequalities”. See the following citation to see how HEAT was applied to a local weight loss management service (Public Health England, 2020[28]).
The design of the intervention should also be adapted if results from a process and/or outcome evaluation reveal the intervention is not having the desired impact among disadvantaged population groups.
Employ a diversified recruitment and communication strategy
Disadvantaged population groups typically have worse access to health care. This is problematic particularly if eligible participants are identified within a health care setting – e.g. a GP’s office.
To increase participation among disadvantaged groups, various strategies can be employed such as:
Partnering with community organisations that have close ties with disadvantaged population groups to promote the intervention (e.g. social services and charities)
Involving trusted community members during the recruitment stage
Expanding recruitment opportunities beyond a primary care setting, for example, to faith-based and other community events.
It is also important to be wary that promotional material delivered by government may reduce uptake among disadvantaged groups who are typically more anxious and suspicious of prevention messaging from public health authorities (Peretti-Watel and Constance, 2009[29]).
Stratify evaluation indicators according to disadvantaged population groups
When studying the impact of overweight prevention interventions, it is important to look at their effect on inequalities. Therefore, pertinent factors for inequalities among the implementation population should be identified and included in the data collection process. Results can subsequently be analysed across different population groups to see whether the intervention increased or decreased inequalities.
Researchers can use the PROGRESS framework, which outlines factors contributing to health inequity, when choosing how to stratify data: Place of residence; Race/ethnicity/culture/language; Occupation; Gender; Religion; Education; Socio‑economic status; Social capital (O’Neill et al., 2014[30]).
Box 1.6. Boosting health literacy to reduce health inequalities
Improving levels of health literacy (HL) will ultimately improve population health. Taking selected interventions in this report as examples, higher levels of HL can enhance their effectiveness by:
Engaging parents in school-based interventions: school-based diet and physical activity interventions, such as ToyBox and SI!, require parental engagement. Parents who are health literate are more likely to engage in school-based interventions as well ensure good behaviours learnt in schools are transferred to the home environment.
Increasing access to preventative care services: people who are health literate are more likely to use preventative care services, such as regular visits to their GP. At-risk patients are often recruited to prevention interventions via their GP. For example, in the Netherlands, patients who are identified as living with overweight, obesity or with certain risk factors by their GP are referred to the Combined Lifestyle Intervention, which offers patients healthy eating and physical activity advice as part of the country’s basic health insurance package. Similarly, in Sweden, GPs offer eligible patients prescriptions for physical activity based on their individual circumstance.
Improving understanding of health information: higher levels of HL helps people better understand and therefore act on health nutrition labels such as Nutri-Score and the Danish Whole Grain Partnership logo (Campos, Doxey and Hammond, 2011[31]).
To address low rates of HL, OECD have outlined a four‑pronged policy approach (Moreira, 2018[26]), as outlined below. In addition, countries could consider actions directly targeting individuals, for example, encouraging HL at schools, and providing HL counselling and training in community and workplace settings.
Strengthen the health system role: establish national strategies and frameworks designed to address HL
Acknowledge the importance of HL through research: measure and monitor the progress of HL interventions to better understand what policies work
Improve data infrastructure: improve international comparisons of HL as well as monitoring HL levels over time
Strengthen international collaboration: share best practice interventions to boost HL across countries.
Levels of uptake and retention are low among selected interventions indicating further strategies to boost participation are needed to ensure their long-term effectiveness
Uptake and retention issues limit the effectiveness of overweight prevention interventions. Changing people’s behaviour is complex, particularly in relation to behaviours that affect rates of overweight given they are shaped by cultural, socio‑economic and environmental factors (Rogers et al., 2016[32]). Therefore, it is not surprising that weight loss interventions suffer from recruitment and retention issues (Lam, Partridge and Allman-Farinelli, 2015[33]), including interventions in this report. For example, the Combined Lifestyle Intervention in the Netherlands, which offers participants advice on diet and physical activity, has a GP referral rate of 1.03% (it is important to note that this intervention is in its infancy). Further, only half of all people prescribed physical activity as part of Sweden’s Physical Activity on Prescription intervention followed-up after six months.
There are various reasons explaining low rates of uptake and retention. These include, but are not limited to: issues with making initial contact, particularly for certain groups such as ethnic minorities, a lack of interest and time among eligible participants, poor intervention design, and limited confidence in the intervention (Lam, Partridge and Allman-Farinelli, 2015[33]). Failing to improve uptake and retention ultimately has a negative impact on an intervention’s long-term effectiveness.
To improve uptake and retention in overweight prevention interventions, policy makers should use a multi-pronged evidence‑based approach. Several strategies are available to policy makers to increase uptake and retention in overweight prevention interventions, including those outlined in Box 1.7.
Box 1.7. Strategies to improve uptake and retention in overweight prevention interventions
This box includes a non-exhaustive list of strategies to increase uptake and retention in overweight prevention interventions:
Provide social support for participants. Interventions can improve social support by offering “buddies” or peer coaches as well as involving family and friends in activities (Public Health England, 2018[34]).
Set goals targeting behaviours. Higher retention has been show in interventions that encourage participants to set goals targeting behaviours (e.g. amount of physical activity), not just outcomes (e.g. weight loss) (Public Health England, 2018[34]).
Supervise and monitor adherence. A review by Rogers et al. (2016[32]) found weight loss interventions that supervise and monitor attendance have adherence rates 65% higher than those that do not.
Design interventions that are convenient and enjoyable. For people to participate in overweight prevention interventions they must be convenient (e.g. easy-to‑access location) and be perceived as fun and interesting (e.g. by offering group activities).
Explore the use of financial incentives. There is evidence indicating financial incentives increase uptake and retention thereby improving the overall effectiveness of overweight prevention interventions – e.g. cash payments, discounts from participating brands (Ananthapavan, Peterson and Sacks, 2017[35]). However, the impact of incentives typically only lasts for the duration of the incentive.
Raise awareness of this public health issue: not only can this help increase uptake in overweight prevention interventions, it can also help reduce social stigma, which may in turn improve retention rates. Raising awareness is currently a top priority for the Swiss Federal office of Public Health.
The following strategies aim to increase uptake and retention in overweight prevention interventions by targeting health care professionals, and were developed as part of the Physical Activity on Prescription intervention in Sweden (Gustavsson et al., 2018[36]).
Increase knowledge and affirmative attitude among the health care professionals. This includes knowledge on how to talk about health behaviours in patient consultations, and knowledge of and belief in overweight prevention interventions. Ireland for example is in the process of implementing training programmes for health professionals as well as developing an overarching Model of Care for the treatment of overweight.
Provide clear, supportive management. Policies and clinical guidelines need to be developed, shared and approved at all levels, central management of health care organisations need to show clear support as well as earmark time and resources for the intervention.
Provide supporting structures. This includes a centralised or local support function, such as a central co‑ordinator, tailored written routines at health care centres on when and how to do referrals, and the availability of an up-to-date list of intervention providers in the area.
Any attempt to transfer or scale‑up a best practice intervention should be adequately resourced given the complexity of this task
Several countries transferred selected interventions to their local setting. Over half of all selected interventions have been transferred from their original “owner” country to another “target” country (Table 1.2). The majority of transfers took place among European countries. Of these interventions, only two reported changes in outcomes in the target country, which is largely due to the infancy of several transfer projects, many of which were delayed due to COVID‑19. One further intervention – StopDia, a diabetes prevention intervention in Finland – was adapted and transferred to the country’s Somali population. The remaining four interventions exist only in the owner country.
Table 1.2. Transfer status of selected interventions
*Case study interventions reported the impact on outcomes in countries in which the intervention was transferred.
Transferring interventions is a complex task that requires an in-depth understanding of contextual factors. Public health interventions, such as those targeting overweight risk factors, are complex given they involve several interacting components and multiple stakeholders in areas such as health, education, community and environment. They also target heterogeneous populations and have outcomes influenced by various direct and indirect factors (Craig et al., 2008[37]; Norris et al., 2019[38]). Transferring validated interventions is therefore complex as it requires an in-depth understanding of the owner and target setting. This information is often missing leading to a “significant gap between research, practice and transfer” (Barnfield, Savolainen and Lounamaa, 2020[39]). For example, transferring the kindergarten based healthy living intervention, ToyBox, to Malta was “much harder to implement in practice than it look[ed] on paper” for various reasons including a resistance from teachers to “excessively regulate” the school environment as well as an unwillingness among teachers and parents “to act as role models” (Stegeman et al., 2020[40])
Policy makers are providing increasing support to assist the spread of best practice interventions. Policy makers are eager to transfer interventions supported by a strong evidence base. Given the complexity of transferring an intervention, it is increasingly common for policy makers to implement multi-year projects designed to assist the transfer process. For example, as part of the Joint Action (JA) CHRODIS-PLUS (a European JA on Chronic Diseases), the following interventions were transferred to other European countries over a period of three years (2017‑20): the Multimodal Training Intervention, ToyBox and Young People at a Healthy Weight (JOGG). Further details on JA CHRODIS-PLUS and other European JAs and projects related to intervention transfers are in Box 1.8. In general, these JAs and projects promote extensive mapping of contextual factors as well as building close ties between individuals responsible for implementation in the owner and target setting.
Box 1.8. European JAs and projects that support the transfer of best practice interventions
Several European JAs and projects supported the transfer of best practice interventions across EU Member States.
A European Action on Whole Grain Partnerships (WholEUGrain)
The European Action on Whole Grain Partnerships’ (WholEUGrain) will run between 2019‑22. The project is designed to assist countries transfer and adapt the Danish Whole Grain Partnership to their local setting. Three countries are involved in WholEUGrain – Romania, Slovenia, and Bosnia and Herzegovina (European Commission, 2019[41]).
As part of WholeEUGrain, a “Toolbox” to guide countries through the implementation process was developed. In addition, representatives from the Danish Whole Grain Partnership run a three‑day spring or summer school each year. Each event consists of several webinars providing answers to questions such as “what are the pre‑requisites for a well-functioning Partnership”?
A European Physical Activity on Prescription Model (EUPAP)
A transfer of the Swedish Physical Activity on Prescription intervention to other European countries has begun under the EU-funded European Physical Activity on Prescription Model project (EUPAP). This three‑year project (2019‑22), co‑ordinated by the Public Health Agency of Sweden, will transfer the Swedish intervention to a further nine countries: Portugal, Romania, Lithuania, Spain, Germany, Denmark, Belgium, Italy and Malta.
EUPAP will provide education and training initiatives according to the needs of different partner countries, targeting both health care professionals responsible for prescribing as well as trainers or educators. In addition, several tools will be developed in English, which countries can translate into their local language.
Another important part of the EUPAP project is the feasibility study (EUPAP, 2020[42]). Each of the target countries was analysed to understand the context in which Physical Activity on Prescription would operate and how this compares to the Swedish situation. At the macro level, the feasibility study looked at the political context, past experiences with Physical Activity on Prescription schemes, relevant regulations and budget. At the micro level, the study assessed the preparedness for implementation among four groups: stakeholders, health care settings, prescribers and end-users/patients.
JA CHRODIS-PLUS (Joint Action for Chronic Diseases) (2017‑20)
JA CHRODIS-PLUS (2017‑20) was designed to promote the transfer and implementation of interventions covering a range of areas including health promotion and primary prevention, and multimorbidity. As part of this JA, chosen interventions developed an implementation strategy to support the transfer process. In addition, implementers from the target setting were required to participate in site visits to gain an in-depth understanding of the intervention as well as develop ties with administrators from the owner setting (Stegeman et al., 2020[40]).
OECD’s transferability analysis revealed several good transfer candidate countries for selected interventions. OECD developed a methodology to cluster countries based on their potential to transfer selected interventions to their own countries. High-level methodological details are in Annex A, with further details available in an upcoming Health Working Paper. The points below, as well as Box 1.9, summarise key findings from an analysis of transferability results for all 12 interventions:
Several countries have population, sector specific, political and economic arrangements in place that are likely to increase the transferability potential of selected interventions including Austria, Finland, Belgium, Germany, the United Kingdom and Ireland. Details on good transfer candidate countries according to the type of intervention are provided in Table 1.3, which covers school-based, primary care based and nutrition labelling interventions.
Countries such as Hungary and Greece should consider undertaking further analysis to ensure interventions align with overarching political priorities, which is a key transfer facilitator. For example, these countries do not have a national plan to address unhealthy diets, which is a key overweight risk factor.
Prior to transferral, several countries should ensure whether selected interventions are affordable in the long term, in particular, for interventions that require participants to pay out-of-pocket (i.e. Iceland’s Multimodal Training Intervention and Sweden’s Physical Activity on Prescription).
It is important to note that there are limitations with this analysis, most importantly:
It should not be assumed that selected interventions will fail in countries where it is recommended that the political and/or economic feasibility be ensured. Instead, the results indicate areas these countries should pay particular attention to, but are not necessarily pre‑requisites for transferral.
The analysis relied on publically available data that covered a large number of OECD and EU27 countries. Therefore, the data is high level and does not capture all relevant indicators for assessing transferability. Further, the data covers information at the national level, and therefore does not take into account regional differences.
Table 1.3. Findings from OECD’s transferability clustering analysis for selected interventions
Note: As a result of the methodology, not all countries are represented in the table above (e.g. countries which were neither considered to have high transfer potential nor fell into a cluster of countries were further considerations should be taken into account before transferral). Further, countries can appear more than once in the table indicating there is more than one area to consider before transferring interventions.
Box 1.9. Transferability clustering analysis – key findings for specific interventions
Given the breadth of selected interventions, this report also summarises key findings from the transferability assessment according to type of intervention, namely school based, primary care based and nutrition labelling.
School based interventions
Half of all OECD and non-OECD European countries are considered candidates with high transfer potential for interventions addressing overweight and obesity in schools – such as ToyBox and SI! Good transfer countries covered all parts of Europe, Latin America and the Middle East indicating school-based interventions can operate successfully across the world.
Primary care interventions
Countries such as Italy, France, Belgium, Germany, the United Kingdom and Ireland show high transfer potential for overweight interventions implemented in a primary care setting – such as the Combined Lifestyle Intervention and Physical Activity on Prescription. Most of the aforementioned countries have a healthy lifestyle curriculum for health professionals, a national physical inactivity strategy, and a population who regularly see their GP.
Nutrition labelling
Half of all OECD and non-OECD European countries have a high transfer potential for food labelling interventions such as Nutri-Score and the Danish Whole Grain Partnership. These countries already have front-of-pack labelling arrangements (either voluntary or mandatory) indicating regulatory and legal arrangements are in place to support these interventions, as well as a national strategy for addressing unhealthy diets.
Policy makers can help spread best practice interventions targeting overweight by dedicating resources to the transfer and/or scaling-up process. Given the effort needed to transfer an intervention, policy makers should provide dedicated support to this process. In Europe, this is increasingly common with the European Commission funding multi-year projects dedicated to transferring one or multiple public health interventions to several countries (see Box 1.8 for example projects). Projects that support the transfer process should ultimately depend on factors such as the size, significance and complexity of the intervention, as well as the number of countries adopting the intervention. However, for any transfer project, it is important to promote the following transfer “facilitators”:
Close ties between implementers in the owner and target setting. A review of health promotion and disease prevention intervention transfers as part of JA CHRODIS-PLUS found building a “strong relationship between the good practice owner and implementer” was a key transfer facilitator (Stegeman et al., 2020[40]). For example, by organising visits to the owner setting to understand the “ins and outs” of the intervention and meeting with stakeholders face‑to-face.
An understanding of the context in the owner and target setting, and how the two differ. The owner and target setting will differ, in particular for cross-country transfers – e.g. differences in cultural diversity, regulations, and workforce skills. It is important to understand these differences at the outset and adapt the intervention accordingly. OECD’s Transferability Framework (see Annex A) provides guidance on what types of contextual factors to consider when undertaking this assessment. Further information is in OECD’s Guidebook on Best Practices in Public Health (OECD, 2022[10]), including publically available indicators and databases to assess differences in contexts. It is unlikely publically available data can fully describe the two contexts, which reinforces the importance of establishing close ties between the owner and target setting to fill in any knowledge gaps.
Practical materials and guides that assist transfers across multiple settings. Such information will help spread the intervention to multiple settings in an efficient manner. For example, as part of WholEUGrain – a project to transfer the Danish Whole Grain Partnership to several European countries – administrators of the Partnership developed a systematic implementation guide (i.e. the “Toolbox”). See Box 1.10 for further details and for other publically available transfer material.
Provide evidence supporting the impact of the intervention in a different setting. Securing buy in from policy makers and stakeholders is necessary when transferring best practice interventions to other countries. One way to secure buy in is to show evidence of the intervention’s estimated impact in the target setting. Such an analysis was carried out for a selection of interventions in this report using OECD’s SPHeP-NCD model (see Box 1.11). Where such information is not available, it is possible to draw upon evidence from previous transfers to similar countries.
Box 1.10. Resources for transferring and scaling-up best practice interventions targeting overweight
This box provides a list of publically available resources to help policy makers transfer best practice public health interventions.
OECD’s Guidebook on Best Practices in Public Health. Step 1b of the Guidebook outlines a process for assessing the transferability of an intervention (OECD, 2022[10]).
JA CHRODIS-PLUS: Building on what works – transferring and implementing good practice to strengthen health promotion and disease prevention in Europe. This document summarises the experience of transferring several best practice interventions, and includes recommendations to help future intervention transfers (Stegeman et al., 2020[40]).
WholEUGrain – A European Action on Whole Grain Partnerships. The WholEUGrain project has created a website dedicated to the transfer of the Whole Grain Partnership across Europe. The website provides a good example of materials and activities developed to assist the transfer process. See: https://www.gzs.si/wholeugrain/.
EU Physical Activity on Prescription (EUPAP). Similarly, EUPAP developed a website including materials and activities to help countries implement Physical Activity on Prescription, which was first developed in Sweden. See: https://www.eupap.org/.
Box 1.11. Estimated impact of scaling-up and transferring interventions using OECD’s SPHeP-NCD model
Coverage – as defined as the proportion of eligible participants who participate in the intervention – was low for certain interventions. For example, the Multimodal Training Intervention operates in just 5 of Iceland’s 72 municipalities.
A key way to encourage policy makers and stakeholders scale‑up an intervention is to provide evidence of the intervention’s estimated impact in the target setting. Such an analysis was carried out for the following interventions using OECD’s SPHeP-NCD model – Nutri-Score, Combined Lifestyle Intervention, Prescription on Physical Activity, JOGG (Young People at a Healthy Weight) and the Multimodal Training Intervention (Chapters 3 to 7).
For each of the aforementioned interventions, OECD estimated the health and economic impact of scaling-up the intervention across the entire owner setting (i.e. where the intervention currently operates) and to all other OECD and non-OECD European countries.
Such analyses can encourage policy makers to invest in cost-effective interventions that address the obesity epidemic.
Incentives that strengthen the evidence‑base concerning intervention effectiveness and efficiency are needed to support the transfer and scale‑up process
Several selected interventions employed gold standard evaluation designs, which are not commonplace in public health. In various fields, randomised control trials (RCTs) are the gold standard in evaluation given their potential to reduce many sources of bias (Deconinck et al., 2021[43]). RCTs, however, are not always appropriate when evaluating public health interventions such as those targeting overweight risk factors. For example, it may not be ethical or possible to exclude certain groups from accessing an intervention, further, given the high cost associated with RCTs, they are not always affordable. For these reasons, overweight prevention interventions are frequently evaluated using other study designs such as observational studies (Barnish and Turner, 2017[44]). Among selected interventions, 55% relied on findings from prospective or retrospective cohort studies (observational studies), while the remaining 45% employed a RCT.
Other factors used to assess the quality of evidence show selected interventions performed well in several areas. Evaluations that use the same study design aren’t necessarily of the same quality. For this reason, the quality of evidence supporting selected interventions was assessed against the Quality Assessment Tool for Quantitative Studies (Effective Public Health Practice Project, 1998[45]). This tool evaluates the internal validity of studies using six criteria: selection bias; study design; controlling for confounders; blinding; data collection methods; and withdrawals and dropouts. For each criterion, a study is awarded either a “strong”, “moderate” or “weak” score. Results across the 12 interventions are summarised in Figure 1.5, which show positive results in several areas. In particular, the majority of studies used strong data collection methods to measure outcomes and recorded low levels of withdrawal and dropout, thereby reducing bias. Conversely, studies infrequently controlled for all confounding factors or blinded participants and researchers. This is not surprising given overweight is a complex issue influenced by many factors for which there is not always readily available data (e.g. socio‑economic status), further, participants must often be made aware of the intervention in order to participate.
Figure 1.5. Selected interventions – quality of evidence summary
Note: Results reflect findings from the selected case study interventions. “N/A” = not applicable.
Source: Effective Public Health Practice Project (1998[45]), “Quality assessment tool for quantitative studies”, https://www.nccmt.ca/knowledge-repositories/search/14.
Despite the use of relatively strong evaluation methods, there is room to improve the quality of evidence base supporting the effectiveness and efficiency of overweight prevention interventions, as outlined below. Ultimately, a strong evidence base helps policy makers make better decisions on which interventions to support.
Effectiveness evidence base
Incentives to encourage rigorous evaluations will improve the quality of evidence supporting overweight prevention interventions. Types of incentives may include:
Requiring applicants to submit an evaluation study when applying for funding. At a minimum, the evaluation study should include a logic model, proposed evaluation indicators, chosen study design and data collection methods. Developing an evaluation study is an important first step for any new intervention.
Setting minimum evidence standards when deciding which interventions to scale‑up and transfer. For example, the Irish Physical Activity Research Collaboration developed a standardised evaluation framework to help funders and policy makers make evidence‑based decisions on whether to continue or discontinue publically funded interventions. It is important to note that it is not plausible to rigorously evaluate all public health interventions, further, it is not always ethically feasible to apply gold standard evaluation designs, such as RCTs. For this reason, minimum evidence standards should depend on intervention characteristics such as their overall priority, risk and evidence to date. See Box 1.12 for an example of where this principle is applied.
Box 1.12. Evidence standards framework for digital health technologies – England
It is not possible to apply rigorous “gold standard” evaluation designs to all public health interventions. Therefore, it is necessary to assign evaluation resources based on intervention characteristics such as:
Priority: the overall strategic importance of the intervention in achieving high level objectives as well as the intervention’s importance against competing priority areas
Funding: the overall funding the intervention receives
Risk: risk associated with the intervention, such as difficulty in assessing intervention outcomes
Impact: how likely the intervention will affect population health
Evidence: the existing evidence‑base for the intervention to date.
The National Institute for Health and Care Excellence (NICE), England, takes such an approach having set out a three‑tiered evidence standards framework for digital health technologies (DHTs). DHTs are allocated into tiers based on their potential risk to users – specifically:
Tier A – system impact: refers to DHTs that have no measurable impact on patient outcomes, but which help the health and social care system perform.
Tier B – understanding and communicating: refers to DHTs that provide information to the public, patients and clinicians, general health monitoring, and communication between patients and professionals.
Tier C – interventions: refers to DHTs offering services such as preventative behaviour change, self-management of diseases, treatment guidelines, active monitoring of patients, and diagnosis.
The minimum evidence standard differs across tiers, with standards more rigorous for DHTs that fall under Tier C (e.g. a requirement showing the DHT showed improvements in relevant patient outcomes using experimental or quasi‑experimental study designs).
Source: NICE (2021[46]), “Evidence standards framework for digital health technologies”, https://www.nice.org.uk/corporate/ecd7/chapter/section-a-evidence-for-effectiveness-standards.
Providing technical and financial support will also help strengthen the evidence base. Evaluating public health interventions, such as those targeting overweight, is a complex task that may require the development of a long-term assessment strategy. Therefore, it is necessary to involve people with experience in programme evaluations as well as ensure sufficient funding to undertake an evaluation. Some examples of how this can be achieved include:
Continuing to build workforce skills and competencies for example by offering short courses to workers involved in the evaluation who do not have prior experience in this area (Pettman et al., 2012[47]), as well as promoting existing material outlining the steps involved in undertaking an evaluation (Box 1.13).
Box 1.13. Publically available resources to support evaluations of public health interventions
This box outlines publically available materials to assist those undertaking intervention evaluations:
OECD’s Guidebook on Best Practices in Public Health. Step 3 of the Guidebook outlines a step-by-step process for undertaking rigorous evaluations (OECD, 2022[10]).
CDC’s Introduction to Program Evaluation for Public Health Programs: A Self-Study Guide (Centers for Disease Control and Prevention, 2012[48]).
Developing a logic model: Logic Model Workbook from Innovation Network (Innovation Network, n.d.[49]).
Stakeholder engagement and other steps in programme evaluation: CDC’s Introduction to Program Evaluation for Public Health Programs: A Self-Study Guide (Centers for Disease Control and Prevention, 2012[48]).
Data collection methods and other steps in programme evaluation: Public Health Ontario’s Evaluating Health Promotion Programmes: introductory workbook (Public Health Ontario, 2016[50])
Developing an interview protocol: “Preparing for Interview Research: The Interview Protocol Refinement Framework” by Milagros Castillo-Montoya (2016[51])
Collecting and analysing data: Collecting and analysing evaluation data – Planning and Evaluating Health Information Outreach Projects Booklet 3, by Olney and Barnes (2013[52])
Acting on the results and other elements of community project evaluation: The Community Tool Box from the Center for Community Health and Development at the University of Kansas (Center for Community Health and Development, n.d.[53]).
Bridging the gap between academic and public health practice and policies by encouraging collaborative research. In Quebec, Canada, for example, a 4P Training Programme – Prevention, Promotion and Public Policies – was implemented, which partners doctoral and post-doctoral students with public health organisations to undertake applied public health research (Box 1.14) (Hamelin and Paradis, 2018[54]). Similarly, the United States promotes alliances between academic associations and the Centers for Disease Control and Prevention (CDC) via the “Academic Partnerships to Improve Health” initiative.
Box 1.14. The 4P Programme in Quebec, Canada
In 2003, Quebec, Canada, developed the Transdisciplinary Research Training Programme in Public Health Interventions: Promotion, Prevention and Public Policy – i.e. the 4P Programme.
The primary objective of the 4P Programme is to prepare doctoral and post-doctoral students from universities in Quebec undertake research regarding population and public health interventions within a public health organisation (PHO).
Under the Programme, each student is assigned a public health mentor within a PHO. Depending on whether the student is doctoral or post-doctoral, they will spend between 60‑80% of their time over a two‑to-three year period at that PHO.
A review of the 4P Programme in 2017 found it successfully “enhanced the research skills” and the “abilities of [students] to navigate the complex interactions between research and public health practice”.
Source: Paradis et al. (2017[55]), “The University – Public Health Partnership for Public Health Research Training in Quebec, Canada”, https://doi.org/10.2105/AJPH.2016.303529.
Encouraging funding agencies to dedicate a specific proportion of funds to monitor and evaluate the intervention – for example, public health agencies often set aside 15% of total funding for this purpose (Pettman et al., 2012[47]).
Providing funding for long-term research projects. At present, much of the research on overweight interventions focuses on short-term impacts, including those in this report. This is problematic given the benefits of overweight interventions can take years or even decades to be realised, particularly those targeting children. For this reason, policy makers are encouraged to prioritise long-term research projects. The Dutch Public Health Institute, RIVM, for example, are in the process of developing a data registry for participants of the Combined Lifestyle Intervention, which provides participants with advice on diet and physical activity. The registry is designed to assess the long-term impact of this intervention (Box 1.15).
Box 1.15. Monitoring of the Combined Lifestyle Intervention in the Netherlands
The Combined Lifestyle Intervention is a Dutch programme designed to help people living with overweight or other risk factors improve their lifestyle. GPs refer eligible patients to a local Combined Lifestyle Intervention programme where they receive advice on diet, physical activity training, and counselling on behavioural change over a period of two years. The country’s basic health insurance coverage covers the cost of the intervention.
To monitor the long-term effectiveness of this intervention, the country’s public health institute, RIVM, are in the process of developing a data registry covering past and present participants. The registry will collect long-term outcome measures including BMI, waist circumference and quality of life, as well as demographic and socio‑economic data. Data from the registry will be linked to claims data from health insurers, which includes information on costs and health care utilisation.
RIVM is working with a data storage provider and the government to set up the necessary infrastructure and data sharing agreements for the registry.
Note: See Chapter 5 for further details.
Efficiency evidence base
Demonstrating efficiency is increasingly important in public health. Governments today face tight budgetary constraints concurrent with growing demand for services to meet population needs. Consequently, the health sector “competes” with itself and other sectors for funding (World Health Organization and ExpandNet, 2009[56]). Demonstrating efficiency is therefore increasingly important, particularly among prevention interventions given the low proportion of funding dedicated to this area of health – as of 2019, less than 3% of total health care expenditure in the OECD is spent on prevention (OECD, 2021[57]).
Despite the importance of demonstrating efficiency, studies related to overweight prevention interventions are limited. Economic evaluations to assess efficiency among selected interventions were rare. This finding aligns with the broader literature where the cost-effectiveness of overweight prevention interventions are “infrequently studied” (Wang et al., 2015[58]), particularly among children (Brown et al., 2019[22]). The dearth of efficiency studies is not surprising given the difficulty in choosing which costs to include, particularly for interventions targeting society as a whole.
Policies to promote efficiency studies will further enhance the evidence base for overweight prevention interventions. Similar incentives and support mechanisms to promote high-quality effectiveness evaluations can be applied to economic evaluations to demonstrate efficiency. Compared to outcome evaluations, economic evaluations typically require a greater level of expertise, therefore, it is of particular importance to bridge the gap between academia (e.g. health economists) and intervention administrators in order to promote collaborative research. For example, Australia established the ACE Obesity Policy, a priority-setting study led by academics and obesity experts to evaluate the “economic credentials” of several obesity prevention policies (Box 1.16).
Box 1.16. Australia’s Assessing Cost-Effectiveness (ACE)-Obesity Policy
The National Health and Medical Research Council (NHMRC) is Australia’s leading health and medical research body. Between 2012‑18, NHMRC funded the Centre of Research Excellence (CRE) in Obesity Policy and Food Systems, which comprised a multidisciplinary team of researchers with backgrounds in epidemiology, nutrition, health economics, food policy, health services research and advocacy.
As part of the CRE, researchers undertook the ACE‑Obesity Policy study to find the “most effective, cost effective, affordable and implementable policy options to prevent obesity across a range of settings”. This involved undertaking full economic evaluations of 16 obesity interventions such as food labelling, media campaigns on sugar-sweetened beverages, reformulation and advertising regulations.
Source: Ananthapavan et al. (2018[59]), “Assessing Cost-effectiveness of Obesity Prevention Policies in Australia”, https://secureservercdn.net/192.169.221.188/y97.516.myftpupload.com/wp-content/uploads/2018/12/ACE-Obesity-Report_Final.pdf.
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Notes
← 1. For adults, WHO define overweight and obesity as having a BMI >=25Kg/m2 and 30Kg/m2, respectively. BMI is the most widely used proxy for body adiposity to assess population-level rates of overweight, as it is easily derived from a person’s weight and height (i.e. weight (kg) divided by height in metres squared) (WHO, 2019[60]).
← 2. These figures assume interventions have a purely additive effect, which has not been verified.
← 3. An intervention is cost-effective if the cost per DALY gained falls below international thresholds used to define a country’s willingness to pay for one year of life in good health (this threshold typically ranges between EUR 22 000-80 000 (Vallejo-Torres et al., 2016[7]). An intervention is cost saving if the cost per DALY gained is negative.