Patterns of beverage purchases amongst British households: A latent class analysis

Autoři: Nicolas Berger aff001;  Steven Cummins aff001;  Alexander Allen aff003;  Richard D. Smith aff003;  Laura Cornelsen aff001
Působiště autorů: Population Health Innovation Lab, Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom aff001;  Sciensano, Brussels, Belgium aff002;  Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom aff003;  College of Medicine and Health, University of Exeter, Exeter, United Kingdom aff004
Vyšlo v časopise: Patterns of beverage purchases amongst British households: A latent class analysis. PLoS Med 17(9): e32767. doi:10.1371/journal.pmed.1003245
Kategorie: Research Article
doi: 10.1371/journal.pmed.1003245



Beverages, especially sugar-sweetened beverages (SSBs), have been increasingly subject to policies aimed at reducing their consumption as part of measures to tackle obesity. However, precision targeting of policies is difficult as information on what types of consumers they might affect, and to what degree, is missing. We fill this gap by creating a typology of beverage consumers in Great Britain (GB) based on observed beverage purchasing behaviour to determine what distinct types of beverage consumers exist, and what their socio-demographic (household) characteristics, dietary behaviours, and weight status are.

Methods and findings

We used cross-sectional latent class analysis to characterise patterns of beverage purchases. We used data from the 2016 GB Kantar Fast-Moving Consumer Goods (FMCG) panel, a large representative household purchase panel of food and beverages brought home, and restricted our analyses to consumers who purchase beverages regularly (i.e., >52 l per household member annually) (n = 8,675). Six categories of beverages were used to classify households into latent classes: SSBs; diet beverages; fruit juices and milk-based beverages; beer and cider; wine; and bottled water. Multinomial logistic regression and linear regression were used to relate class membership to household characteristics, self-reported weight status, and other dietary behaviours, derived from GB Kantar FMCG. Seven latent classes were identified, characterised primarily by higher purchases of 1 or 2 categories of beverages: ‘SSB’ (18% of the sample; median SSB volume = 49.4 l/household member/year; median diet beverage volume = 38.0 l), ‘Diet’ (16%; median diet beverage volume = 94.4 l), ‘Fruit & Milk’ (6%; median fruit juice/milk-based beverage volume = 30.0 l), ‘Beer & Cider’ (7%; median beer and cider volume = 36.3 l; median diet beverage volume = 55.6 l), ‘Wine’ (18%; median wine volume = 25.5 l; median diet beverage volume = 34.3 l), ‘Water’ (4%; median water volume = 46.9 l), and ‘Diverse’ (30%; diversity of purchases, including median SSB volume = 22.4 l). Income was positively associated with being classified in the Diverse class, whereas low social grade was more likely for households in the classes SSB, Diet, and Beer & Cider. Obesity (BMI > 30 kg/m2) was more prevalent in the class Diet (41.2%, 95% CI 37.7%–44.7%) despite households obtaining little energy from beverages in that class (17.9 kcal/household member/day, 95% CI 16.2–19.7). Overweight/obesity (BMI > 25 kg/m2) was above average in the class SSB (66.8%, 95% CI 63.7%–69.9%). When looking at all groceries, households from the class SSB had higher total energy purchases (1,943.6 kcal/household member/day, 95% CI 1,901.7–1,985.6), a smaller proportion of energy from fruits and vegetables (6.0%, 95% CI 5.8%–6.3%), and a greater proportion of energy from less healthy food and beverages (54.6%, 95% CI 54.0%–55.1%) than other classes. A greater proportion of energy from sweet snacks was observed for households in the classes SSB (18.5%, 95% CI 18.1%–19.0%) and Diet (18.8%, 95% CI 18.3%–19.3%). The main limitation of our analyses, in common with other studies, is that our data do not include information on food and beverage purchases that are consumed outside the home.


Amongst households that regularly purchase beverages, those that mainly purchased high volumes of SSBs or diet beverages were at greater risk of obesity and tended to purchase less healthy foods, including a high proportion of energy from sweet snacks. These households might additionally benefit from policies targeting unhealthy foods, such as sweet snacks, as a way of reducing excess energy intake.

Klíčová slova:

Alcohol consumption – Beverages – Diet – Food – Milk – Nutrition – Obesity – Wine


1. Duffey KJ, Huybrechts I, Mouratidou T, Libuda L, Kersting M, De Vriendt T, et al. Beverage consumption among European adolescents in the HELENA study. Eur J Clin Nutr. 2012;66:244–52. doi: 10.1038/ejcn.2011.166 21952695

2. Ng SW, Ni Mhurchu C, Jebb SA, Popkin BM. Patterns and trends of beverage consumption among children and adults in Great Britain, 1986–2009. Br J Nutr. 2012;108:536–51. doi: 10.1017/S0007114511006465 22186747

3. Scientific Advisory Committee on Nutrition. Carbohydrates and health. London: Scientific Advisory Committee on Nutrition; 2015 [cited 2020 Aug 6].

4. Heyman MB, Abrams SA. Fruit juice in infants, children, and adolescents: current recommendations. Pediatrics. 2017;139:e20170967. doi: 10.1542/peds.2017-0967 28562300

5. Luger M, Lafontan M, Bes-Rastrollo M, Winzer E, Yumuk V, Farpour-Lambert N. Sugar-sweetened beverages and weight gain in children and adults: a systematic review from 2013 to 2015 and a comparison with previous studies. Obes Facts. 2018;10:674–93. doi: 10.1159/000484566 29237159

6. Watt RG, Rouxel PL. Dental caries, sugars and food policy. Arch Dis Child. 2012;97:769–72. doi: 10.1136/archdischild-2012-301818 22685053

7. Public Health England. Sugar reduction: achieving the 20%. London: Public Health England; 2019 [cited 2020 Aug 6].

8. Mendez MA, Miles DR, Poti JM, Sotres-Alvarez D, Popkin BM. Persistent disparities over time in the distribution of sugar-sweetened beverage intake among children in the United States. Am J Clin Nutr. 2019;109:79–89. doi: 10.1093/ajcn/nqy123 30535176

9. Bolt-Evensen K, Vik FN, Stea TH, Klepp KI, Bere E. Consumption of sugar-sweetened beverages and artificially sweetened beverages from childhood to adulthood in relation to socioeconomic status—15 years follow-up in Norway. Int J Behav Nutr Phys Act. 2018;15:8. doi: 10.1186/s12966-018-0646-8 29343247

10. Berger N, Cummins S, Smith RD, Cornelsen L. Changes in the sugar content of food purchases and socio-economic inequalities: a longitudinal study of British households, 2014–2017. J Epidemiol Community Health. 2019;73(Suppl 1):A3–4. doi: 10.1136/jech-2019-SSMabstracts.7

11. World Health Organization. Fiscal policies for diet and prevention of noncommunicable diseases. Geneva: World Health Organization; 2016 [cited 2020 Aug 6].

12. Cornelsen L, Smith RD. Viewpoint: Soda taxes—four questions economists need to address. Food Policy. 2018;74:138–42. doi: 10.1016/j.foodpol.2017.12.003

13. Hashem KM, He FJ, MacGregor GA. Effects of product reformulation on sugar intake and health—a systematic review and meta-analysis. Nutr Rev. 2019;77:181–96. doi: 10.1093/nutrit/nuy015 30624760

14. Bandy LK, Scarborough P, Harrington RA, Rayner M, Jebb SA. Reductions in sugar sales from soft drinks in the UK from 2015 to 2018. BMC Med. 2020;18:20. doi: 10.1186/s12916-019-1477-4 31931800

15. Scarborough P, Adhikari V, Harrington RA, Elhussein A, Briggs A, Rayner M, et al. Impact of the announcement and implementation of the UK Soft Drinks Industry Levy on sugar content, price, product size and number of available soft drinks in the UK, 2015–18: controlled interrupted time series analysis. PLoS Med. 2020;17(2):e1003025. doi: 10.1371/journal.pmed.1003025 32045418

16. Lorenc T, Petticrew M, Welch V, Tugwell P. What types of interventions generate inequalities? Evidence from systematic reviews. J Epidemiol Community Health. 2013;67:190–3. doi: 10.1136/jech-2012-201257 22875078

17. Popkin BM. Patterns of beverage use across the lifecycle. Physiol Behav. 2010;100:4–9. doi: 10.1016/j.physbeh.2009.12.022 20045423

18. Briggs ADM, Mytton OT, Kehlbacher A, Tiffin R, Rayner M, Scarborough P. Overall and income specific effect on prevalence of overweight and obesity of 20% sugar sweetened drink tax in UK: econometric and comparative risk assessment modelling study. BMJ. 2013;347:f6189. doi: 10.1136/bmj.f6189 24179043

19. VanKim NA, Erickson DJ, Laska MN. Food shopping profiles and their association with dietary patterns: a latent class analysis. J Acad Nutr Diet. 2015;115:1109–16. doi: 10.1016/j.jand.2014.12.013 25704262

20. Huh J, Riggs NR, Spruijt-Metz D, Chou C-P, Huang Z, Pentz M. Identifying patterns of eating and physical activity in children: a latent class analysis of obesity risk. Obesity. 2011;19:652–8. doi: 10.1038/oby.2010.228 20930718

21. Petersen KJ, Qualter P, Humphrey N. The application of latent class analysis for investigating population child mental health: a systematic review. Front Psychol. 2019;10:1214. doi: 10.3389/fpsyg.2019.01214 31191405

22. Özen AE, Bibiloni M del M, Pons A, Tur JA. Fluid intake from beverages across age groups: a systematic review. J Hum Nutr Diet. 2015;28:417–42. doi: 10.1111/jhn.12250 24935211

23. Berger N, Cummins S, Smith RD, Cornelsen L. Recent trends in energy and nutrient content of take-home food and beverage purchases in Great Britain: an analysis of 225 million food and beverage purchases over 6 years. BMJ Nutr Prev Health. 2019;2:bmjnph-2019-000036. doi: 10.1136/bmjnph-2019-000036

24. Griffith R, O’Connell M, Smith K. Relative prices, consumer preferences, and the demand for food. Oxford Rev Econ Policy. 2015;31:116–30. doi: 10.1093/oxrep/grv004

25. Quirmbach D, Cornelsen L, Jebb SA, Marteau T, Smith R. Effect of increasing the price of sugar-sweetened beverages on alcoholic beverage purchases: an economic analysis of sales data. J Epidemiol Community Health. 2018;72:324–30. doi: 10.1136/jech-2017-209791 29363613

26. Joy EJM, Green R, Agrawal S, Aleksandrowicz L, Bowen L, Kinra S, et al. Dietary patterns and non-communicable disease risk in Indian adults: secondary analysis of Indian Migration Study data. Public Health Nutr. 2017;20:1963–72. doi: 10.1017/S1368980017000416 28367791

27. Scheelbeek PFD, Cornelsen L, Marteau TM, Jebb SA, Smith RD. Potential impact on prevalence of obesity in the UK of a 20% price increase in high sugar snacks: modelling study. BMJ. 2019;366:l4786. doi: 10.1136/bmj.l4786 31484641

28. Department of Health. Nutrient profiling technical guidance. London: Department of Health; 2011 [cited 2020 Aug 6].

29. Cornelsen L, Mazzocchi M, Smith RD. Fat tax or thin subsidy? How price increases and decreases affect the energy and nutrient content of food and beverage purchases in Great Britain. Soc Sci Med. 2019;230:318–27. doi: 10.1016/j.socscimed.2019.04.003 31030908

30. Pechey R, Jebb SA, Kelly MP, Almiron-Roig E, Conde S, Nakamura R, et al. Socioeconomic differences in purchases of more vs. less healthy foods and beverages: analysis of over 25,000 British households in 2010. Soc Sci Med. 2013;92:22–6. doi: 10.1016/j.socscimed.2013.05.012 23849275

31. Cornelsen L, Berger N, Cummins S, Smith RD. Socio-economic patterning of expenditures on ‘out-of-home’ food and non-alcoholic beverages by product and place of purchase in Britain. Soc Sci Med. 2019;235:112361. doi: 10.1016/j.socscimed.2019.112361 31262504

32. Ipsos MORI. Social grade: a classification tool. London: Ipsos MORI; 2009 [cited 2020 Aug 6].

33. World Health Organization. Physical status: the use of and interpretation of anthropometry. Report of a WHO Expert Committee. Geneva: World Health Organization; 1995 [cited 2020 Aug 6].

34. Carpenter JR, Kenward MG. Multiple imputation and its application. Hoboken (NJ): John Wiley & Sons; 2012.

35. Masyn KE. Latent class analysis and finite mixture modeling. In: Little TD, editor. The Oxford handbook of quantitative methods: statistical analysis. New York: Oxford University Press; 2013. pp. 551–611.

36. Lo Y, Mendell NR, Rubin DB. Testing the number of components in a normal mixture. Biometrika. 2001;88:767–78. doi: 10.1093/biomet/88.3.767

37. Asparouhov T, Muthén B. Using Mplus TECH11 and TECH14 to test the number of latent classes. Mplus Web Notes No. 14. Los Angeles: Mplus; 2012 [cited 2020 Aug 6].

38. Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Modeling. 2007;14:535–69.

39. van de Schoot R, Sijbrandij M, Winter SD, Depaoli S, Vermunt JK. The GRoLTS-Checklist: guidelines for reporting on latent trajectory studies. Struct Equ Modeling. 2017;24:451–67. doi: 10.1080/10705511.2016.1247646

40. Hipp JR, Bauer DJ. Local solutions in the estimation of growth mixture models. Psychol Methods. 2006;11:36–53. doi: 10.1037/1082-989X.11.1.36 16594766

41. Asparouhov T, Muthén B. Auxiliary variables in mixture modeling: three-step approaches using Mplus. Struct Equ Modeling. 2014;21:329–41. doi: 10.1080/10705511.2014.915181

42. Asparouhov T, Muthen B. Auxiliary variables in mixture modeling: using the BCH method in mplus to estimate a distal outcome model and an arbitrary secondary model. Mplus Web Notes No. 21. Los Angeles: Mplus; 2020 [cited 2020 Aug 6].

43. Mathias KC, Slining MM, Popkin BM. Foods and beverages associated with higher intake of sugar-sweetened beverages. Am J Prev Med. 2013;44:351–57. doi: 10.1016/j.amepre.2012.11.036 23498100

44. Lennox A, Bluck L, Page P, Pell D, Cole D, Ziauddeen N, et al. Misreporting in the National Diet and Nutrition Survey Rolling Programme (NDNS RP): summary of results and their interpretation. London: Food Standards Agency; 2012 [cited 2020 Aug 6].

45. Murakami K, Livingstone MBE. Prevalence and characteristics of misreporting of energy intake in US adults: NHANES 2003–2012. Br J Nutr. 2015;114:1294–303. doi: 10.1017/S0007114515002706 26299892

Článek vyšel v časopise

PLOS Medicine

2020 Číslo 9

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…


Zvyšte si kvalifikaci online z pohodlí domova

Antiseptika a prevence ve stomatologii
nový kurz
Autoři: MUDr. Ladislav Korábek, CSc., MBA

Citikolin v neuroprotekci a neuroregeneraci: od výzkumu do klinické praxe nejen očních lékařů
Autoři: MUDr. Petr Výborný, CSc., FEBO

Zánětlivá bolest zad a axiální spondylartritida – Diagnostika a referenční strategie
Autoři: MUDr. Monika Gregová, Ph.D., MUDr. Kristýna Bubová

Diagnostika a léčba deprese pro ambulantní praxi
Autoři: MUDr. Jan Hubeňák, Ph.D

Význam nemocničního alert systému v době SARS-CoV-2
Autoři: doc. MUDr. Helena Lahoda Brodská, Ph.D., prim. MUDr. Václava Adámková

Všechny kurzy
Kurzy Doporučená témata Časopisy
Zapomenuté heslo

Nemáte účet?  Registrujte se

Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.


Nemáte účet?  Registrujte se