Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-22T16:30:05.095Z Has data issue: false hasContentIssue false

Costing a healthy diet: measurement and policy implications

Published online by Cambridge University Press:  13 October 2016

Wilma E Waterlander*
Affiliation:
National Institute for Health InnovationSchool of Population HealthUniversity of AucklandAuckland, New Zealand
Sally Mackay
Affiliation:
Department of Epidemiology and BiostatisticsSchool of Population HealthUniversity of AucklandAuckland, New Zealand
Rights & Permissions [Opens in a new window]

Abstract

Type
Editorial
Copyright
Copyright © The Authors 2016 

Health-related food taxes and subsidies are a hot topic in public health research. A number of countries have recently introduced such pricing strategies with the aim to promote healthier food choices. One of the most recent examples is Mexico, which introduced an excise tax on sugar-sweetened beverages of 1 peso per litre (approximately a 10 % price increase based on 2013 prices) and a sales tax on several highly energy-dense foods (containing 1151 kJ (≥275 kcal) per 100 g)( Reference Colchero, Popkin and Rivera 1 ).

The relatively high cost of healthy food is often cited as an important argument to introduce food taxes and subsidies and thereby make healthy food (relatively) more affordable( Reference Darmon and Drewnowski 2 ). Likewise, the cost of healthy foods is listed by consumers, and particular lower socio-economic groups, as an important barrier to purchasing healthier foods( Reference Waterlander, de Mul and Schuit 3 ). However, there is much debate in the literature about whether healthy foods indeed cost more, where it has been argued that it is possible to eat healthily on a limited budget, but also that this is only possible if people are willing to divert hugely from culturally acceptable dietary patterns( Reference Darmon, Ferguson and Briend 4 ). A recent systematic review and meta-analysis revealed that, on average, healthier diets cost more than unhealthy diets( Reference Rao, Afshin and Singh 5 ); however, that review also highlights the importance of carefully considering the metric of healthfulness, intensity of contrast and unit of comparison( Reference Rao, Afshin and Singh 5 ). As it turns out, measuring the price of foods and diets is a lot more complicated than it might appear at first sight.

The current issue of Public Health Nutrition features a new systematic review that looked into measuring food prices in more detail( Reference Lewis and Lee 6 ). The aim of that review was to determine similarities and differences in metrics and results between tools, protocols and methods used for monitoring (Australian) healthy food prices and affordability. The authors reviewed thirty-nine reports and twenty-four journal articles which described fifty-nine distinct food pricing surveys undertaken in Australia. The included surveys measured the cost of healthy foods by using a food basket approach. This approach involves measuring the cost of a predetermined selection (basket) of foods by collecting prices of these foods from retail outlets (mostly supermarkets). From these surveys, the authors identified six ‘major’ food pricing tools (used in multiple areas and multiple time periods) and five ‘minor’ food pricing tools (only used in one survey or during one time period).

The authors identified large differences in the tools and protocols used in the different surveys, even when the same food pricing tool was used in different areas or time periods. They observed differences in: selection of ‘healthy’ basket contents; reference household composition; inclusion of availability and/or quality measures; household income sources; store sampling methods; season of data collection; and data collection protocols and analysis. This divergence in measurements makes it virtually impossible to make food price comparisons between countries or regions or over time and the authors argue it is important to establish a single measurement of (healthy) food costs. The INFORMAS (International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support) network is currently leading initiatives for establishing such a standardized monitoring approach( Reference Swinburn, Sacks and Vandevijvere 7 ).

A typical (healthy/unhealthy) diet

A remarkable finding from the systematic review was that only one of the reviewed surveys measured the cost of a typical (unhealthy) diet to allow comparison between the cost of a healthy diet and the current diet. Recently, standardized methods to assess the relative price difference between a healthy and current diet have been piloted in Australia( Reference Lee, Kane and Ramsey 8 ). The results of that pilot suggested that healthy diets can be more affordable than the current Australian diet, particularly because people currently spend a relatively large proportion of their household budget on alcohol and discretionary foods (a family of two adults and two children was found to spend over 58 % of its food dollars on discretionary choices). Nevertheless, the individual prices of some healthy foods, particularly meat, dairy and vegetables, were found to be relatively expensive compared with energy-dense discretionary foods( Reference Lee, Kane and Ramsey 8 ). Also, the study found that while a diet consistent with dietary guidelines was affordable for families on a median income (18 % of disposable income), it was less affordable for low-income families (28 % of their household disposable income)( Reference Lee, Kane and Ramsey 8 ).

Another remarkable finding from the systematic review featured in this issue( Reference Lewis and Lee 6 ) was that ten of eleven identified ‘healthy basket’ food pricing tools did not fully align with the principles of the most recent Australian Dietary Guidelines. This is because studies added discretionary foods and/or commonly consumed unhealthy foods to the ‘healthy’ basket apparently to ‘adjust the energy content of the basket’. The authors do not discuss this topic in depth, other than saying that most healthy baskets do not constitute a diet consistent with dietary guidelines; however, this is a very remarkable observation. Adding energy-dense foods (e.g. sugar, oil, processed meat, snacks) to healthy food baskets to obtain the required energy levels seems highly controversial. It makes it look as if, by following the dietary guidelines, it is impossible to consume the required energy amount (say, 8368 kJ/d (2000 kcal/d)). This is odd, as one could argue the opposite is true. Meeting all daily micro- and macronutrient requirements while ensuring a good energy balance is relatively difficult and leaves little room for discretionary foods (i.e. you need all 8368 kJ (2000 kcal) to get the required nutrients from healthy foods). Most people consume more kilojoules than they need without meeting recommended intakes for a number of nutrients. Therefore, people should choose foods that are high in nutrients but low to moderate in energy content; that is, meeting nutrient recommendations must go hand in hand with keeping kilojoules under control( 9 ). Another reason for adding discretionary foods to healthy food baskets could be to make the basket more realistic and in line with what people are currently eating. While this makes sense, it would be better to have a separate basket containing foods from the current diet and compare this with the healthy diet that fully aligns with dietary guidelines. Authors could also opt to add some sensitivity analyses (e.g. have varying baskets meeting 50–100% of dietary guidelines).

Price metrics

Another gnawing issue in measuring food costs is the metrics used for food price. There are three different potential metrics, often leading to very different results: price per weight ($/g); price per serving ($/serving); and price per energy unit ($/kJ or kcal). A recent systematic review and meta-analysis revealed that the most striking example for difference in findings between the used metrics can be observed in dairy products, where healthier options were found to be $US 0.004 less expensive per serving but $US 0.21 more expensive per 837 kJ (200 kcal)( Reference Rao, Afshin and Singh 5 ). The authors clarify that this divergence in findings can be explained by the fact that whole milk contains nearly twice the energy as fat-free milk, meaning that nearly double the amount of fat-free milk must be purchased to achieve equivalent energy. As explained in their paper, these findings highlight the dangers of so-called ‘circular reasoning’ (e.g. selecting a metric based on energy content and then evaluating price differences per unit of energy) and the importance of identifying the most accurate unit of comparison for any individual or public health decision about price differences of foods( Reference Rao, Afshin and Singh 5 ).

The issue of price metrics has long been a hot topic of debate in the literature. In the early 2000s, Drewnowski, Darmon and others started publishing a series of papers examining the issue of energy density and energy cost, looking either at individual foods( Reference Drewnowski 10 , Reference Drewnowski 11 ) or dietary patterns( Reference Darmon, Briend and Drewnowski 12 Reference Drewnowski, Monsivais and Maillot 14 ). These studies produced the well-known diagrams as displayed in Fig. 1, showing that foods high in energy density (e.g. fats, sugar) are relatively cheaper per kilojoule than foods with a low energy density (e.g. fruits, vegetables). In 2009, this finding was challenged by Lipsky, who argued that this result is merely a mathematical artefact (or circular reasoning as explained above)( Reference Lipsky 15 ). To prove the point, Lipsky used a random-number generator to create numbers for three variables: energy (A), grams (B) and total price (C), and then used these variables to create A/B (energy density) and C/A (energy cost). Also, she measured actual food price data from US supermarkets. Subsequently, she drew scatterplots using the random data and observational data and compared the two. The author found that the same relationship was found in the randomly generated data and the genuinely observed data, meaning that the ‘energy density/energy cost’ observation is caused primarily by having energy in both the independent and dependent variable( Reference Lipsky 15 ).

Fig. 1 Relationship between energy density and energy cost (from Waterlander et al.( Reference Waterlander, de Haas and van Amstel 46 ))

As explained above, using energy in both the numerator and denominator causes problems. On top of that, it can be argued that people don’t buy food per kilojoule, providing a second reason why this metric is invalid. While we appreciate these problems, we would like to highlight that this doesn’t mean that energy cost is not a valid measure at all. Arguably, when people buy food they do inherently think of energy density one way or the other. For example, if you are hungry and have $US 3 to spend, would you buy a bag of apples or a Big Mac? While this example shows that $US 3 might provide you both a healthy and unhealthy option (showing that you can buy healthily for little money), apples are not filling you up the same way as a Big Mac does. This importance was also highlighted by Darmon and Maillot in their response to Lipsky’s criticism: ‘price is a well-known determinant of food choice and because the need for energy is probably the only nutritional requirement specifically and acutely perceived by individuals, we continue to believe that energy cost may influence dietary behaviour’( Reference Darmon and Maillot 16 ).

To date, there is no consensus in the literature on what the best price metric is. However, there are a number of papers that argue (using price per gram or per serving) you can eat healthily on a limited budget( Reference Carlson and Frazao 17 , Reference Temple and Steyn 18 ). Using the cost of the diet, some observational studies found that certain population groups can achieve higher-quality diets at a lower cost than other groups( Reference Drewnowski 19 , Reference Marty, Dubois and Gaubard 20 ). Others report that if foods are carefully selected it is possible to consume a low-cost diet relatively low in energy density and high in nutrient density( Reference Drewnowski 21 Reference Primavesi, Caccavelli and Ciliberto 23 ) but this requires a motivated and knowledgeable shopper( Reference Temple and Steyn 18 ). While, strictly speaking, it might be true that people can eat healthily on a limited budget, we have to be careful in interpreting this finding correctly. The fact that people can, in theory, buy healthy food for little money does not mean that the price of food is not a barrier for healthy eating. First, it is still quite hard to eat a healthy and varied diet on a limited budget; you can’t expect people to eat lentils every day. But, more importantly, it is a lot easier to eat unhealthily on a limited budget. Arguably, if people are restrained by other factors (e.g. job stress, financial stress, etc.), consuming a healthy diet might not be on top of their priority list. Even when healthy and unhealthy foods are the same price at point of purchase, if it’s easier and more convenient to choose unhealthy foods (think of all the unhealthy foods you can buy for a few dollars), healthy foods will still not be the choice of preference.

Cost of convenience

Not only the absolute cost, but also the perceived inconvenience is a major barrier to healthy food choices when choosing meals( Reference Glanz, Basil and Maibach 24 , Reference McDermott and Stephens 25 ). Households perceive a lack of time for food preparation and food manufacturers use this sentiment to develop and promote convenience foods( Reference Celnik, Gillespie and Lean 26 , Reference Luiten, Steenhuis and Eyles 27 ). Indeed, global food manufacturers have a vested interest in the production and sale of ultra-processed foods because production costs are low and highly processed foods have a long shelf-life and a high retail value( Reference Stuckler, McKee and Ebrahim 28 , Reference Moodie, Stuckler and Monteiro 29 ). Unfortunately, there is a clear trade-off between nutritional quality and convenience, where consuming meals prepared with wholesome foods is associated with good health and low risk of disease while consumption of ultra-processed energy-dense foods is associated with increased risk of obesity and non-communicable diseases( Reference Monteiro, Levy and Claro 30 , 31 ).

Meal preparation is influenced by the cost of purchasing food and the cost of time as well as taste, culture and other influences. For some people, the provision of home-cooked meals provides benefits such as enjoyment of cooking, social interaction, relaxing, a nurturing role and the opportunity cost of time for other activities. For others cooking is considered a chore, with little time available or prioritized for cooking( Reference Mancino and Newman 32 ). Time is not usually factored into the price of food preparation but can be a barrier to preparing meals( Reference Davis and You 33 ). The evidence on how to incorporate time is limited( Reference Davis and You 33 ), with very few studies in the literature incorporating external costs. An American study evaluated the cost of various pre-prepared and home-made ingredients (e.g. apple sauce) and meals (e.g. lasagne). The time to prepare the items was calculated and the hourly wage of a food preparer was used as the cost of time. When the cost of time was included, the processed items cost less than the home recipe for all items, particularly grains, vegetables and fruit( Reference Yang, Davis and Muth 34 ). The cost to prepare the US Department of Agriculture’s Thrifty Food Plan was met by 62 % of low-income households, but when the time costs were included only 13 % could purchase the required foods( Reference Davis and You 33 ).

Conclusion

The systematic review featured in this issue of Public Health Nutrition ( Reference Lewis and Lee 6 ) and other literature show that measuring the cost of food both accurately and meaningfully is very challenging. Nevertheless, it is clear that the cost of food is a major issue in public health nutrition and should therefore be seriously considered for policy intervention. There is growing evidence suggesting that health-related food taxes and subsidies (e.g. a soft drink tax or a fruit and vegetable subsidy) are effective in improving population diets( Reference An 35 Reference Thow, Downs and Jan 40 ). Likewise, there is evidence that fiscal policy might not only work to improve the health profile of population diets, but also improve the environmental footprint( Reference Briggs, Kehlbacher and Tiffin 41 ), showing the important link between agriculture, health and global greenhouse gas emissions( Reference Foley, Ramankutty and Brauman 42 ).

Health-related food taxes or subsidies are, however, not the only mechanism to alter food prices, where we should not forget that certain existing policies heavily influence the price of food. For example, the European Common Agricultural Policy (CAP) currently heavily subsidizes beef and milk production which leads to an oversupply of cheap meat in the market. Similarly, CAP keeps fruit and vegetable prices high by implementing price guarantees, product withdrawal and import tariffs for fruit and vegetables from outside the EU( Reference Birt 43 ). The history of subsidies – both direct and indirect – has created an agricultural system focused on creating cheap beef and other animal-source foods in most developing nations( Reference Popkin 44 ). This is problematic as a recent American study found that, among US adults, higher consumption of energy from subsidized food commodities was associated with a greater probability of certain cardiometabolic risks, including a high BMI and high non-HDL cholesterol( Reference Siegel, McKeever Bullard and Imperatore 45 ). There are clear opportunities to better align agricultural policies with public health outcomes by reducing or eliminating subsidies on beef and milk and increasing the subsidies on fruit and vegetables( Reference Birt 43 , Reference Popkin 44 ). This also shows that food prices are indeed a policy concern (not only that of the free market) where we rely on solid food policy initiatives (covering health, environmental sustainability and the economy) to ensure a nutritious food supply now and for future generations.

References

1. Colchero, MA, Popkin, BM, Rivera, JA et al. (2016) Beverage purchases from stores in Mexico under the excise tax on sugar sweetened beverages: observational study. BMJ 352, h6704.CrossRefGoogle ScholarPubMed
2. Darmon, N & Drewnowski, A (2015) Contribution of food prices and diet cost to socioeconomic disparities in diet quality and health: a systematic review and analysis. Nutr Rev 73, 643660.CrossRefGoogle Scholar
3. Waterlander, WE, de Mul, A, Schuit, AJ et al. (2010) Perceptions on the use of pricing strategies to stimulate healthy eating among residents of deprived neighbourhoods: a focus group study. Int J Behav Nutr Phys Act 7, 44.Google Scholar
4. Darmon, N, Ferguson, EL & Briend, A (2006) Impact of a cost constraint on nutritionally adequate food choices for French women: an analysis by linear programming. J Nutr Educ Behav 38, 8290.CrossRefGoogle ScholarPubMed
5. Rao, M, Afshin, A, Singh, G et al. (2013) Do healthier foods and diet patterns cost more than less healthy options? A systematic review and meta-analysis. BMJ Open 3, 12.Google Scholar
6. Lewis, M & Lee, A (2016) Costing ‘healthy’ food baskets in Australia: a systematic review of food price and affordability monitoring tools, protocols and methods. Public Health Nutr 19, 28722886.Google Scholar
7. Swinburn, B, Sacks, G, Vandevijvere, S et al. (2013) INFORMAS (International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support): overview and key principles. Obes Rev 14, Suppl., 1, 112.Google Scholar
8. Lee, AJ, Kane, S, Ramsey, R et al. (2016) Testing the price and affordability of healthy and current (unhealthy) diets and the potential impacts of policy change in Australia. BMC Public Health 16, 315.Google Scholar
9. US Department of Health and Human Services & US Department of Agriculture (2005) Dietary Guideline for Americans, 2005, 7th ed. Washington, DC: US Government Printing Office.Google Scholar
10. Drewnowski, A (2003) Fat and sugar: an economic analysis. J Nutr 133, issue 3, 838S840S.Google Scholar
11. Drewnowski, A (2004) Obesity and the food environment: dietary energy density and diet costs. Am J Prev Med 27, 3 Suppl., 154162.Google Scholar
12. Darmon, N, Briend, A & Drewnowski, A (2004) Energy-dense diets are associated with lower diet costs: a community study of French adults. Public Health Nutr 7, 2127.Google Scholar
13. Drewnowski, A, Darmon, N & Briend, A (2004) Replacing fats and sweets with vegetables and fruits – a question of cost. Am J Public Health 94, 15551559.Google Scholar
14. Drewnowski, A, Monsivais, P, Maillot, M et al. (2007) Low-energy-density diets are associated with higher diet quality and higher diet costs in French adults. J Am Diet Assoc 107, 10281032.Google Scholar
15. Lipsky, LM (2009) Are energy-dense foods really cheaper? Reexamining the relation between food price and energy density. Am J Clin Nutr 90, 13971401.Google Scholar
16. Darmon, N & Maillot, M (2010) In foods, energy is cheap where it is abundant and expensive where it is scarce: this is a fact, not an artifact. Am J Clin Nutr 91, 10681069.Google Scholar
17. Carlson, A & Frazao, E (2014) Food costs, diet quality and energy balance in the United States. Physiol Behav 134, 2031.Google Scholar
18. Temple, NJ & Steyn, NP (2009) Food prices and energy density as barriers to healthy food patterns in Cape Town, South Africa. J Hunger Environ Nutr 4, 203213.Google Scholar
19. Drewnowski, A (2015) Nutrition economics: how to eat better for less. J Nutr Sci Vitaminol (Tokyo) 61, Suppl., S69S71.CrossRefGoogle ScholarPubMed
20. Marty, L, Dubois, C, Gaubard, MS et al. (2015) Higher nutritional quality at no additional cost among low-income households: insights from food purchases of ‘positive deviants’. Am J Clin Nutr 102, 190198.Google Scholar
21. Drewnowski, A (2010) The Nutrient Rich Foods Index helps to identify healthy, affordable foods. Am J Clin Nutr 91, issue 4, 1095S1101S.Google Scholar
22. Maillot, M, Ferguson, EL, Drewnowski, A et al. (2008) Nutrient profiling can help identify foods of good nutritional quality for their price: a validation study with linear programming. J Nutr 138, 11071113.Google Scholar
23. Primavesi, L, Caccavelli, G, Ciliberto, A et al. (2015) Nutrieconomic model can facilitate healthy and low-cost food choices. Public Health Nutr 18, 827835.Google Scholar
24. Glanz, K, Basil, M, Maibach, E et al. (1998) Why Americans eat what they do: taste, nutrition, cost, convenience, and weight control concerns as influences on food consumption. J Am Diet Assoc 98, 11181126.Google Scholar
25. McDermott, AJ & Stephens, MB (2010) Cost of eating: whole foods versus convenience foods in a low-income model. Fam Med 42, 280284.Google Scholar
26. Celnik, D, Gillespie, L & Lean, MEJ (2012) Time-scarcity, ready-meals, ill-health and the obesity epidemic. Trends Food Sci Technol 27, 411.Google Scholar
27. Luiten, CM, Steenhuis, IH, Eyles, H et al. (2016) Ultra-processed foods have the worst nutrient profile, yet they are the most available packaged products in a sample of New Zealand supermarkets. Public Health Nutr 19, 530538.Google Scholar
28. Stuckler, D, McKee, M, Ebrahim, S et al. (2012) Manufacturing epidemics: the role of global producers in increased consumption of unhealthy commodities including processed foods, alcohol, and tobacco. PLoS Med 9, 6.Google Scholar
29. Moodie, R, Stuckler, D, Monteiro, C et al. (2013) Profits and pandemics: prevention of harmful effects of tobacco, alcohol, and ultra-processed food and drink industries. Lancet 381, 670679.Google Scholar
30. Monteiro, CA, Levy, RB, Claro, RM et al. (2010) A new classification of foods based on the extent and purpose of their processing. Cad Saude Publica 26, 20392049.Google Scholar
31. Pan American Health Organization & World Health Organization (2015) Ultra-Processed Food and Drink Products in Latin America: Trends, Impact on Obesity, Policy Implications. Washington, DC: PAHO/WHO.Google Scholar
32. Mancino, L & Newman, C (2007) Who Has Time to Cook? How Family Resources Influence Food Preparation, Economic Research Report no. ERR-40]. Washington, DC: US Department of Agriculture, Economic Research Service.Google Scholar
33. Davis, GC & You, W (2011) Not enough money or not enough time to satisfy the Thrifty Food Plan? A cost difference approach for estimating a money–time threshold. Food Policy 36, 101107.Google Scholar
34. Yang, Y, Davis, GC & Muth, MK (2015) Beyond the sticker price: including and excluding time in comparing food prices. Am J Clin Nutr 102, 165171.Google Scholar
35. An, R (2013) Effectiveness of subsidies in promoting healthy food purchases and consumption: a review of field experiments. Public Health Nutr 16, 12151228.Google Scholar
36. Epstein, LH, Jankowiak, N, Nederkoorn, C et al. (2012) Experimental research on the relation between food price changes and food-purchasing patterns: a targeted review. Am J Clin Nutr 95, 789809.Google Scholar
37. Eyles, H, Ni Mhurchu, C, Nghiem, N et al. (2012) Food pricing strategies, population diets, and non-communicable disease: a systematic review of simulation studies. PLoS Med 9, e1001353.Google Scholar
38. Finkelstein, EA, Strombotne, KL, Zhen, C et al. (2014) Food prices and obesity: a review. Adv Nutr 5, 818821.Google Scholar
39. Powell, LM, Chriqui, JF, Khan, T et al. (2013) Assessing the potential effectiveness of food and beverage taxes and subsidies for improving public health: a systematic review of prices, demand and body weight outcomes. Obes Rev 14, 110128.CrossRefGoogle ScholarPubMed
40. Thow, AM, Downs, S & Jan, S (2014) A systematic review of the effectiveness of food taxes and subsidies to improve diets: understanding the recent evidence. Nutr Rev 72, 551565.Google Scholar
41. Briggs, AD, Kehlbacher, A, Tiffin, R et al. (2016) Simulating the impact on health of internalising the cost of carbon in food prices combined with a tax on sugar-sweetened beverages. BMC Public Health 16, 107.Google Scholar
42. Foley, JA, Ramankutty, N, Brauman, KA et al. (2011) Solutions for a cultivated planet. Nature 478, 337342.Google Scholar
43. Birt, C (2007) A CAP on Health? The Impact of the EU Common Agricultural Policy on Public Health. London: Faculty of Public Health.Google Scholar
44. Popkin, BM (2009) Reducing meat consumption has multiple benefits for the world’s health. Arch Intern Med 169, 543545.Google Scholar
45. Siegel, KR, McKeever Bullard, K, Imperatore, G et al. (2016) Association of higher consumption of foods derived from subsidized commodities with adverse cardiometabolic risk among US adults. JAMA Intern Med 176, 11241132.Google Scholar
46. Waterlander, WE, de Haas, WE, van Amstel, I et al. (2010) Energy density, energy costs and income – how are they related? Public Health Nutr 13, 15991608.Google Scholar
Figure 0

Fig. 1 Relationship between energy density and energy cost (from Waterlander et al.(46))