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


Journal

PLoS medicine
ISSN: 1549-1676
Titre abrégé: PLoS Med
Pays: United States
ID NLM: 101231360

Informations de publication

Date de publication:
09 2020
Historique:
received: 06 02 2020
accepted: 03 08 2020
entrez: 8 9 2020
pubmed: 9 9 2020
medline: 21 10 2020
Statut: epublish

Résumé

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. 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.

Sections du résumé

BACKGROUND
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.
CONCLUSIONS
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.

Identifiants

pubmed: 32898152
doi: 10.1371/journal.pmed.1003245
pii: PMEDICINE-D-20-00344
pmc: PMC7478648
doi:

Substances chimiques

Drinking Water 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1003245

Subventions

Organisme : Medical Research Council
ID : MR/P021999/1
Pays : United Kingdom

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

Public Health Nutr. 2017 Aug;20(11):1963-1972
pubmed: 28367791
J Hum Nutr Diet. 2015 Oct;28(5):417-42
pubmed: 24935211
BMJ. 2019 Sep 4;366:l4786
pubmed: 31484641
Psychol Methods. 2006 Mar;11(1):36-53
pubmed: 16594766
BMC Med. 2020 Jan 13;18(1):20
pubmed: 31931800
World Health Organ Tech Rep Ser. 1995;854:1-452
pubmed: 8594834
BMJ. 2013 Oct 31;347:f6189
pubmed: 24179043
Br J Nutr. 2012 Aug;108(3):536-51
pubmed: 22186747
Obes Facts. 2017;10(6):674-693
pubmed: 29237159
Nutr Rev. 2019 Mar 1;77(3):181-196
pubmed: 30624760
Pediatrics. 2017 Jun;139(6):
pubmed: 28562300
Eur J Clin Nutr. 2012 Feb;66(2):244-52
pubmed: 21952695
Obesity (Silver Spring). 2011 Mar;19(3):652-8
pubmed: 20930718
Soc Sci Med. 2013 Sep;92:22-6
pubmed: 23849275
PLoS Med. 2020 Feb 11;17(2):e1003025
pubmed: 32045418
Am J Prev Med. 2013 Apr;44(4):351-357
pubmed: 23498100
J Epidemiol Community Health. 2013 Feb;67(2):190-3
pubmed: 22875078
Soc Sci Med. 2019 Jun;230:318-327
pubmed: 31030908
J Epidemiol Community Health. 2018 Apr;72(4):324-330
pubmed: 29363613
Soc Sci Med. 2019 Aug;235:112361
pubmed: 31262504
Am J Clin Nutr. 2019 Jan 1;109(1):79-89
pubmed: 30535176
Arch Dis Child. 2012 Sep;97(9):769-72
pubmed: 22685053
Int J Behav Nutr Phys Act. 2018 Jan 17;15(1):8
pubmed: 29343247
J Acad Nutr Diet. 2015 Jul;115(7):1109-16
pubmed: 25704262
Br J Nutr. 2015 Oct 28;114(8):1294-303
pubmed: 26299892
Front Psychol. 2019 May 29;10:1214
pubmed: 31191405
Physiol Behav. 2010 Apr 26;100(1):4-9
pubmed: 20045423

Auteurs

Nicolas Berger (N)

Population Health Innovation Lab, Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Sciensano, Brussels, Belgium.

Steven Cummins (S)

Population Health Innovation Lab, Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom.

Alexander Allen (A)

Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom.

Richard D Smith (RD)

Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom.
College of Medicine and Health, University of Exeter, Exeter, United Kingdom.

Laura Cornelsen (L)

Population Health Innovation Lab, Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom.

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