Patterns of beverage purchases amongst British households: A latent class analysis
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...
Saved in:
| Published in | PLoS medicine Vol. 17; no. 9; p. e1003245 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
01.09.2020
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1549-1676 1549-1277 1549-1676 |
| DOI | 10.1371/journal.pmed.1003245 |
Cover
Loading…
| Abstract | 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. |
|---|---|
| AbstractList | BackgroundBeverages, 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 findingsWe 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.ConclusionsAmongst 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. Nicolas Berger and colleagues report the types of beverage consumer and their socio-demographic characteristics to aid precision policy targeting. 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.BACKGROUNDBeverages, 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.METHODS AND FINDINGSWe 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.CONCLUSIONSAmongst 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. SSBs; diet beverages; fruit juices and milk-based beverages; beer and cider; wine; and bottled water. What did the researchers do and find? * We applied a data-driven method, known as latent class analysis, to food and beverage purchase data from 8,675 British households, to identify population subgroups with similar patterns of drink consumption in order to identify high-risk households that could be targets for interventions. * We found that 48% of households purchased medium-to-high volumes of sugary drinks and 16% of households purchased high volumes of diet drinks. Other households mainly purchased fruit juice, water, or alcoholic drinks such as beer or wine. * Households purchasing high volumes of sugary or diet drinks were more likely to have low socio-economic status, higher BMI, and overall less healthy food purchases, characterised by a high proportion of energy obtained from sweet snacks (approximately 18%). Abbreviations: BIC, Bayesian information criterion;BLRT, bootstrap likelihood ratio test;BMI, body mass index;FMCG, Fast-Moving Consumer Goods;GB, Great Britain;LCA, latent class analysis;NSP, non-starch polysaccharide;RRR, relative risk ratio;SDIL, Soft Drinks Industry Levy;SES, socio-economic status;SSB, sugar-sweetened beverage;VLMR-LRT, Vuong–Lo–Mendell–Rubin adjusted likelihood ratio test Introduction Beverages, such as sugar-sweetened beverages (SSBs), fruit juices, and alcohol, are an important source of excess sugar and energy intake globally [1–4]. SSBs; diet beverages; fruit juices and milk-based beverages; beer and cider; wine; and bottled water. What did the researchers do and find? * We applied a data-driven method, known as latent class analysis, to food and beverage purchase data from 8,675 British households, to identify population subgroups with similar patterns of drink consumption in order to identify high-risk households that could be targets for interventions. * We found that 48% of households purchased medium-to-high volumes of sugary drinks and 16% of households purchased high volumes of diet drinks. Other households mainly purchased fruit juice, water, or alcoholic drinks such as beer or wine. * Households purchasing high volumes of sugary or diet drinks were more likely to have low socio-economic status, higher BMI, and overall less healthy food purchases, characterised by a high proportion of energy obtained from sweet snacks (approximately 18%). Abbreviations: BIC, Bayesian information criterion;BLRT, bootstrap likelihood ratio test;BMI, body mass index;FMCG, Fast-Moving Consumer Goods;GB, Great Britain;LCA, latent class analysis;NSP, non-starch polysaccharide;RRR, relative risk ratio;SDIL, Soft Drinks Industry Levy;SES, socio-economic status;SSB, sugar-sweetened beverage;VLMR-LRT, Vuong–Lo–Mendell–Rubin adjusted likelihood ratio test Introduction Beverages, such as sugar-sweetened beverages (SSBs), fruit juices, and alcohol, are an important source of excess sugar and energy intake globally [1–4]. 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. |
| Author | Berger, Nicolas Cummins, Steven Allen, Alexander Cornelsen, Laura Smith, Richard D. |
| AuthorAffiliation | 1 Population Health Innovation Lab, Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom 4 College of Medicine and Health, University of Exeter, Exeter, United Kingdom 2 Sciensano, Brussels, Belgium Harvard Medical School, UNITED STATES 3 Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom |
| AuthorAffiliation_xml | – name: 2 Sciensano, Brussels, Belgium – name: 4 College of Medicine and Health, University of Exeter, Exeter, United Kingdom – name: 3 Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom – name: 1 Population Health Innovation Lab, Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom – name: Harvard Medical School, UNITED STATES |
| Author_xml | – sequence: 1 givenname: Nicolas orcidid: 0000-0002-4213-6040 surname: Berger fullname: Berger, Nicolas – sequence: 2 givenname: Steven orcidid: 0000-0002-3957-4357 surname: Cummins fullname: Cummins, Steven – sequence: 3 givenname: Alexander orcidid: 0000-0002-6998-447X surname: Allen fullname: Allen, Alexander – sequence: 4 givenname: Richard D. surname: Smith fullname: Smith, Richard D. – sequence: 5 givenname: Laura orcidid: 0000-0003-3769-8740 surname: Cornelsen fullname: Cornelsen, Laura |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32898152$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kl1rFDEUhoNU7If-A9EBb3qza75mkumFUIsfhYIieh3OZs7sZMlO1iRb6L83252VtohXCcn7PnnPyTklR2MYkZDXjM6ZUOz9KmzjCH6-WWM3Z5QKLutn5ITVsp2xRjVHD_bH5DSlFaW8pS19QY4F161mNT8hP75DzhjHVIW-WuAtRlhitdlGO0DCVME6jMuUq4_RZZeGagjbhEPwXbqoLisPGcdcWQ-pSEuau-TSS_K8B5_w1bSekV-fP_28-jq7-fbl-uryZmZr3uSZtpx2DUPBNAemGSqNnGsLXd1oCdjXTGMvZC2x7VrBuoZD23VCqFpyAC7OyNs9d-NDMlM7kuFStbJpSn1Fcb1XdAFWZhPdGuKdCeDM_UGISwMxO-vRKFwASoEWRSN134Ji1NpFragVnbJ9YX2YXtsuSsdtqTuCfwR9fDO6wSzDrVFS6YIsgPMJEMPvLaZs1i5Z9B5GLE0tuSXjWlCuivTdE-m_q3vzMNHfKIfPLYKLvcDGkFLE3liXIbuwC-i8YdTsJukAN7tJMtMkFbN8Yj7w_2v7A4gq0PU |
| CitedBy_id | crossref_primary_10_1017_S1368980020005029 crossref_primary_10_3390_nu14142891 crossref_primary_10_1177_27538931231182231 crossref_primary_10_4178_epih_e2022102 crossref_primary_10_1016_j_appet_2025_107894 crossref_primary_10_1038_s41406_023_0945_7 crossref_primary_10_1080_03670244_2023_2257606 crossref_primary_10_1007_s11357_023_00889_0 crossref_primary_10_1146_annurev_resource_111522_111325 crossref_primary_10_1186_s12889_024_20004_y crossref_primary_10_1111_dom_15859 |
| Cites_doi | 10.1093/ajcn/nqy123 10.1186/s12966-018-0646-8 10.1016/j.socscimed.2019.04.003 10.1037/1082-989X.11.1.36 10.1016/j.socscimed.2013.05.012 10.1080/10705511.2016.1247646 10.1080/10705511.2014.915181 10.1136/jech-2017-209791 10.1016/j.amepre.2012.11.036 10.1186/s12916-019-1477-4 10.1136/jech-2012-201257 10.1371/journal.pmed.1003025 10.1016/j.physbeh.2009.12.022 10.1017/S0007114511006465 10.1136/archdischild-2012-301818 10.1093/nutrit/nuy015 10.1111/jhn.12250 10.1038/oby.2010.228 10.1038/ejcn.2011.166 10.1159/000484566 10.1017/S0007114515002706 10.1136/bmj.l4786 10.1017/S1368980017000416 10.1016/j.jand.2014.12.013 10.1016/j.foodpol.2017.12.003 10.3389/fpsyg.2019.01214 10.1093/biomet/88.3.767 10.1093/oxrep/grv004 10.1136/bmj.f6189 10.1080/10705510701575396 10.1542/peds.2017-0967 10.1136/bmjnph-2019-000036 10.1016/j.socscimed.2019.112361 |
| ContentType | Journal Article |
| Copyright | 2020 Berger et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 Berger et al 2020 Berger et al |
| Copyright_xml | – notice: 2020 Berger et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2020 Berger et al 2020 Berger et al |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7TK 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA CZK |
| DOI | 10.1371/journal.pmed.1003245 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Neurosciences Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea ProQuest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals PLoS Medicine |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Neurosciences Abstracts ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| DocumentTitleAlternate | Patterns of beverage purchases amongst British households: A latent class analysis |
| EISSN | 1549-1676 |
| ExternalDocumentID | 2479466981 oai_doaj_org_article_7ebae43ece3648f9a710ccb570c3d7cf PMC7478648 32898152 10_1371_journal_pmed_1003245 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GeographicLocations | United Kingdom United Kingdom--UK |
| GeographicLocations_xml | – name: United Kingdom – name: United Kingdom--UK |
| GrantInformation_xml | – fundername: Medical Research Council grantid: MR/P021999/1 – fundername: ; grantid: MR/P021999/1 |
| GroupedDBID | --- 123 29O 2WC 53G 5VS 7X7 88E 8FI 8FJ AAFWJ AAUCC AAWOE AAWTL AAYXX ABDBF ABUWG ACGFO ACIHN ACPRK ACUHS ADBBV AEAQA AENEX AFKRA AFPKN AFRAH AFXKF AHMBA AKRSQ ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS B0M BAWUL BCNDV BENPR BPHCQ BVXVI BWKFM CCPQU CITATION CS3 DIK DU5 E3Z EAP EAS EBD EBS EJD EMK EMOBN ESX F5P FPL FYUFA GROUPED_DOAJ GX1 HMCUK HYE IAO IHR IHW INH INR IOF IOV IPO ISN ISR ITC KQ8 M1P M48 MK0 O5R O5S OK1 OVT P2P PHGZM PHGZT PIMPY PQQKQ PROAC PSQYO PV9 RNS RPM RZL SV3 TR2 TUS UKHRP WOW XSB YZZ ~8M ADRAZ ADXHL CGR CUY CVF ECM EIF H13 IPNFZ NPM PJZUB PPXIY RIG WOQ 3V. 7TK 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 5PM PUEGO AAPBV ABPTK ACDSR BBAFP BCGST CZK ICW M~E UMP |
| ID | FETCH-LOGICAL-c526t-8c20d61e3182a181e78e228cad5684aef518ef3454e9d931d62a9dd337542aa23 |
| IEDL.DBID | M48 |
| ISSN | 1549-1676 1549-1277 |
| IngestDate | Sun Nov 06 00:11:05 EDT 2022 Wed Aug 27 01:27:45 EDT 2025 Thu Aug 21 13:57:31 EDT 2025 Mon Jul 21 10:55:55 EDT 2025 Fri Jul 25 21:10:11 EDT 2025 Mon Jul 21 06:04:54 EDT 2025 Thu Apr 24 23:13:01 EDT 2025 Tue Jul 01 03:27:25 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Language | English |
| License | This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Creative Commons Attribution License |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c526t-8c20d61e3182a181e78e228cad5684aef518ef3454e9d931d62a9dd337542aa23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The authors have declared that no competing interests exist. |
| ORCID | 0000-0003-3769-8740 0000-0002-4213-6040 0000-0002-3957-4357 0000-0002-6998-447X |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1371/journal.pmed.1003245 |
| PMID | 32898152 |
| PQID | 2479466981 |
| PQPubID | 1436338 |
| ParticipantIDs | plos_journals_2479466981 doaj_primary_oai_doaj_org_article_7ebae43ece3648f9a710ccb570c3d7cf pubmedcentral_primary_oai_pubmedcentral_nih_gov_7478648 proquest_miscellaneous_2441283027 proquest_journals_2479466981 pubmed_primary_32898152 crossref_citationtrail_10_1371_journal_pmed_1003245 crossref_primary_10_1371_journal_pmed_1003245 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2020-09-01 |
| PublicationDateYYYYMMDD | 2020-09-01 |
| PublicationDate_xml | – month: 09 year: 2020 text: 2020-09-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: San Francisco – name: San Francisco, CA USA |
| PublicationTitle | PLoS medicine |
| PublicationTitleAlternate | PLoS Med |
| PublicationYear | 2020 |
| Publisher | Public Library of Science Public Library of Science (PLoS) |
| Publisher_xml | – name: Public Library of Science – name: Public Library of Science (PLoS) |
| References | Y Lo (pmed.1003245.ref036) 2001; 88 P Scarborough (pmed.1003245.ref015) 2020; 17 T Lorenc (pmed.1003245.ref016) 2013; 67 KC Mathias (pmed.1003245.ref043) 2013; 44 KJ Duffey (pmed.1003245.ref001) 2012; 66 KM Hashem (pmed.1003245.ref013) 2019; 77 pmed.1003245.ref037 R van de Schoot (pmed.1003245.ref039) 2017; 24 SW Ng (pmed.1003245.ref002) 2012; 108 KJ Petersen (pmed.1003245.ref021) 2019; 10 pmed.1003245.ref032 T Asparouhov (pmed.1003245.ref041) 2014; 21 PFD Scheelbeek (pmed.1003245.ref027) 2019; 366 JR Carpenter (pmed.1003245.ref034) 2012 pmed.1003245.ref011 pmed.1003245.ref033 NA VanKim (pmed.1003245.ref019) 2015; 115 R Griffith (pmed.1003245.ref024) 2015; 31 N Berger (pmed.1003245.ref010) 2019; 73 EJM Joy (pmed.1003245.ref026) 2017; 20 RG Watt (pmed.1003245.ref006) 2012; 97 BM Popkin (pmed.1003245.ref017) 2010; 100 LK Bandy (pmed.1003245.ref014) 2020; 18 L Cornelsen (pmed.1003245.ref012) 2018; 74 M Luger (pmed.1003245.ref005) 2018; 10 J Huh (pmed.1003245.ref020) 2011; 19 pmed.1003245.ref028 pmed.1003245.ref007 R Pechey (pmed.1003245.ref030) 2013; 92 MB Heyman (pmed.1003245.ref004) 2017; 139 D Quirmbach (pmed.1003245.ref025) 2018; 72 pmed.1003245.ref042 MA Mendez (pmed.1003245.ref008) 2019; 109 KL Nylund (pmed.1003245.ref038) 2007; 14 K Bolt-Evensen (pmed.1003245.ref009) 2018; 15 pmed.1003245.ref003 JR Hipp (pmed.1003245.ref040) 2006; 11 KE Masyn (pmed.1003245.ref035) 2013 pmed.1003245.ref044 K Murakami (pmed.1003245.ref045) 2015; 114 ADM Briggs (pmed.1003245.ref018) 2013; 347 L Cornelsen (pmed.1003245.ref029) 2019; 230 AE Özen (pmed.1003245.ref022) 2015; 28 N Berger (pmed.1003245.ref023) 2019; 2 L Cornelsen (pmed.1003245.ref031) 2019; 235 |
| References_xml | – volume: 109 start-page: 79 year: 2019 ident: pmed.1003245.ref008 article-title: Persistent disparities over time in the distribution of sugar-sweetened beverage intake among children in the United States publication-title: Am J Clin Nutr doi: 10.1093/ajcn/nqy123 – volume: 15 start-page: 8 year: 2018 ident: pmed.1003245.ref009 article-title: Consumption of sugar-sweetened beverages and artificially sweetened beverages from childhood to adulthood in relation to socioeconomic status—15 years follow-up in Norway publication-title: Int J Behav Nutr Phys Act doi: 10.1186/s12966-018-0646-8 – volume: 230 start-page: 318 year: 2019 ident: pmed.1003245.ref029 article-title: How price increases and decreases affect the energy and nutrient content of food and beverage purchases in Great Britain publication-title: Soc Sci Med doi: 10.1016/j.socscimed.2019.04.003 – volume: 11 start-page: 36 year: 2006 ident: pmed.1003245.ref040 article-title: Local solutions in the estimation of growth mixture models publication-title: Psychol Methods doi: 10.1037/1082-989X.11.1.36 – volume: 92 start-page: 22 year: 2013 ident: pmed.1003245.ref030 article-title: Socioeconomic differences in purchases of more vs. less healthy foods and beverages: analysis of over 25,000 British households in 2010 publication-title: Soc Sci Med doi: 10.1016/j.socscimed.2013.05.012 – volume: 24 start-page: 451 year: 2017 ident: pmed.1003245.ref039 article-title: The GRoLTS-Checklist: guidelines for reporting on latent trajectory studies publication-title: Struct Equ Modeling doi: 10.1080/10705511.2016.1247646 – volume: 21 start-page: 329 year: 2014 ident: pmed.1003245.ref041 article-title: Auxiliary variables in mixture modeling: three-step approaches using Mplus publication-title: Struct Equ Modeling doi: 10.1080/10705511.2014.915181 – volume: 72 start-page: 324 year: 2018 ident: pmed.1003245.ref025 article-title: Effect of increasing the price of sugar-sweetened beverages on alcoholic beverage purchases: an economic analysis of sales data publication-title: J Epidemiol Community Health doi: 10.1136/jech-2017-209791 – ident: pmed.1003245.ref042 – ident: pmed.1003245.ref044 – volume: 44 start-page: 351 year: 2013 ident: pmed.1003245.ref043 article-title: Foods and beverages associated with higher intake of sugar-sweetened beverages publication-title: Am J Prev Med doi: 10.1016/j.amepre.2012.11.036 – start-page: 551 volume-title: The Oxford handbook of quantitative methods: statistical analysis year: 2013 ident: pmed.1003245.ref035 – ident: pmed.1003245.ref037 – volume: 18 start-page: 20 year: 2020 ident: pmed.1003245.ref014 article-title: Reductions in sugar sales from soft drinks in the UK from 2015 to 2018 publication-title: BMC Med doi: 10.1186/s12916-019-1477-4 – volume: 67 start-page: 190 year: 2013 ident: pmed.1003245.ref016 article-title: What types of interventions generate inequalities? Evidence from systematic reviews publication-title: J Epidemiol Community Health doi: 10.1136/jech-2012-201257 – volume: 17 start-page: e1003025 issue: 2 year: 2020 ident: pmed.1003245.ref015 article-title: 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 publication-title: PLoS Med doi: 10.1371/journal.pmed.1003025 – volume: 100 start-page: 4 year: 2010 ident: pmed.1003245.ref017 article-title: Patterns of beverage use across the lifecycle publication-title: Physiol Behav doi: 10.1016/j.physbeh.2009.12.022 – ident: pmed.1003245.ref033 – volume: 108 start-page: 536 year: 2012 ident: pmed.1003245.ref002 article-title: Patterns and trends of beverage consumption among children and adults in Great Britain, 1986–2009 publication-title: Br J Nutr doi: 10.1017/S0007114511006465 – volume: 97 start-page: 769 year: 2012 ident: pmed.1003245.ref006 article-title: Dental caries, sugars and food policy publication-title: Arch Dis Child doi: 10.1136/archdischild-2012-301818 – volume: 77 start-page: 181 year: 2019 ident: pmed.1003245.ref013 article-title: Effects of product reformulation on sugar intake and health—a systematic review and meta-analysis publication-title: Nutr Rev doi: 10.1093/nutrit/nuy015 – volume: 28 start-page: 417 year: 2015 ident: pmed.1003245.ref022 article-title: Fluid intake from beverages across age groups: a systematic review publication-title: J Hum Nutr Diet doi: 10.1111/jhn.12250 – volume: 19 start-page: 652 year: 2011 ident: pmed.1003245.ref020 article-title: Identifying patterns of eating and physical activity in children: a latent class analysis of obesity risk publication-title: Obesity doi: 10.1038/oby.2010.228 – volume-title: Multiple imputation and its application year: 2012 ident: pmed.1003245.ref034 – volume: 66 start-page: 244 year: 2012 ident: pmed.1003245.ref001 article-title: Beverage consumption among European adolescents in the HELENA study publication-title: Eur J Clin Nutr doi: 10.1038/ejcn.2011.166 – volume: 10 start-page: 674 year: 2018 ident: pmed.1003245.ref005 article-title: Sugar-sweetened beverages and weight gain in children and adults: a systematic review from 2013 to 2015 and a comparison with previous studies publication-title: Obes Facts doi: 10.1159/000484566 – volume: 114 start-page: 1294 year: 2015 ident: pmed.1003245.ref045 article-title: Prevalence and characteristics of misreporting of energy intake in US adults: NHANES 2003–2012 publication-title: Br J Nutr doi: 10.1017/S0007114515002706 – volume: 366 start-page: l4786 year: 2019 ident: pmed.1003245.ref027 article-title: Potential impact on prevalence of obesity in the UK of a 20% price increase in high sugar snacks: modelling study publication-title: BMJ doi: 10.1136/bmj.l4786 – volume: 20 start-page: 1963 year: 2017 ident: pmed.1003245.ref026 article-title: Dietary patterns and non-communicable disease risk in Indian adults: secondary analysis of Indian Migration Study data publication-title: Public Health Nutr doi: 10.1017/S1368980017000416 – volume: 115 start-page: 1109 year: 2015 ident: pmed.1003245.ref019 article-title: Food shopping profiles and their association with dietary patterns: a latent class analysis publication-title: J Acad Nutr Diet doi: 10.1016/j.jand.2014.12.013 – volume: 74 start-page: 138 year: 2018 ident: pmed.1003245.ref012 article-title: Viewpoint: Soda taxes—four questions economists need to address publication-title: Food Policy doi: 10.1016/j.foodpol.2017.12.003 – ident: pmed.1003245.ref028 – ident: pmed.1003245.ref003 – ident: pmed.1003245.ref007 – volume: 10 start-page: 1214 year: 2019 ident: pmed.1003245.ref021 article-title: The application of latent class analysis for investigating population child mental health: a systematic review publication-title: Front Psychol doi: 10.3389/fpsyg.2019.01214 – volume: 88 start-page: 767 year: 2001 ident: pmed.1003245.ref036 article-title: Testing the number of components in a normal mixture publication-title: Biometrika doi: 10.1093/biomet/88.3.767 – volume: 31 start-page: 116 year: 2015 ident: pmed.1003245.ref024 article-title: Relative prices, consumer preferences, and the demand for food publication-title: Oxford Rev Econ Policy doi: 10.1093/oxrep/grv004 – volume: 347 start-page: f6189 year: 2013 ident: pmed.1003245.ref018 article-title: 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 publication-title: BMJ doi: 10.1136/bmj.f6189 – volume: 14 start-page: 535 year: 2007 ident: pmed.1003245.ref038 article-title: Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study publication-title: Struct Equ Modeling doi: 10.1080/10705510701575396 – volume: 139 start-page: e20170967 year: 2017 ident: pmed.1003245.ref004 article-title: Fruit juice in infants, children, and adolescents: current recommendations publication-title: Pediatrics doi: 10.1542/peds.2017-0967 – volume: 73 start-page: A3 issue: Suppl 1 year: 2019 ident: pmed.1003245.ref010 article-title: Changes in the sugar content of food purchases and socio-economic inequalities: a longitudinal study of British households, 2014–2017 publication-title: J Epidemiol Community Health – ident: pmed.1003245.ref011 – volume: 2 year: 2019 ident: pmed.1003245.ref023 article-title: 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 publication-title: BMJ Nutr Prev Health doi: 10.1136/bmjnph-2019-000036 – volume: 235 start-page: 112361 year: 2019 ident: pmed.1003245.ref031 article-title: Socio-economic patterning of expenditures on ‘out-of-home’ food and non-alcoholic beverages by product and place of purchase in Britain publication-title: Soc Sci Med doi: 10.1016/j.socscimed.2019.112361 – ident: pmed.1003245.ref032 |
| SSID | ssj0029090 |
| Score | 2.3962324 |
| Snippet | Beverages, especially sugar-sweetened beverages (SSBs), have been increasingly subject to policies aimed at reducing their consumption as part of measures to... SSBs; diet beverages; fruit juices and milk-based beverages; beer and cider; wine; and bottled water. What did the researchers do and find? * We applied a... Nicolas Berger and colleagues report the types of beverage consumer and their socio-demographic characteristics to aid precision policy targeting. BackgroundBeverages, especially sugar-sweetened beverages (SSBs), have been increasingly subject to policies aimed at reducing their consumption as part of... SSBs; diet beverages; fruit juices and milk-based beverages; beer and cider; wine; and bottled water. What did the researchers do and find? * We applied a... |
| SourceID | plos doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | e1003245 |
| SubjectTerms | Adult Alcohol use Alcoholic beverages Animals Artificially Sweetened Beverages Bayesian analysis Beer Beverages Beverages - economics Biology and Life Sciences Body mass index Body Weight Cider Commerce - trends Consumer Behavior - economics Consumer Behavior - statistics & numerical data Cross-Sectional Studies Diet Drinking Water Energy intake Family Characteristics Female Fruit and Vegetable Juices Fruit juices Fruits Households Humans Income Latent Class Analysis Male Medicine and Health Sciences Milk Nutrition Surveys Obesity - psychology Polysaccharides Purchasing Soft drinks Starch Sugar Sweet taste United Kingdom Wine |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1La9wwEBYlh9BL6Ttu06BCr25WL0vOLQkJodBSSgO5GVkakcLiXWLn_3dG1i7ZEsilV0vG1szI841m_A1jX6KAgCg21cGaVGsBuKWSxyglCQ1KRuq5TdUWP5qra_3txtw8aPVFNWEzPfAsuGMLvQetIIBqtEutR5cYQm_sIqhoQ6KvL_q8TTBVQq12kU9XiH-sFtLa8tOcsuK46OjrGr0N1QggojA7Tilz9xPX6XI1PoY7_y2ffOCPLl-yFwVI8tN5Aa_YMxhes_3vJVX-hv36mZkzh5GvEu8BLRa_HHxNvZ_QcY08dxkaJ15Yjfjt6n4EykWNJ_yULxGCDhMPhK25L8Qlb9n15cXv86u6NFCog5HNVLsgF7ERdMwpPbpysA6kdMFH0zjtIRnhICltNLSxVSI20rcxqtwW13up3rG9YTXAAeOiT0bGhKowUUcXeulVACGT6S2CBlUxtZFgFwq7ODW5WHY5ZWYxypjl0pHcuyL3itXbu9Yzu8YT889IOdu5xI2dL6DFdMViuqcspmIHpNrNA8ZOziz7rRMVO9yo-_Hhz9th3IWUWvEDoHpwjhaZSs1W7P1sHduXVBjTOoRJFbM7drOzit2R4c9tZvqm5ga4hg__Y9kf2XNJZwW5Pu6Q7U139_AJAdXUH-W98xfFMyE8 priority: 102 providerName: Directory of Open Access Journals – databaseName: Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV1Nb9QwELWgSIgL4ruhBRmJa-j6K056QQVRVUgghKi0t8ixx7TSKlma9P8z43i3LKrgGnsVx2N7nmdm32PsbRDgEcXG0lsTSy0At1R0eEuJQoOSgTS3qdria3V2rj8vzTIH3MZcVrk5E9NBHQZPMfIjOVOhN7V4v_5VkmoUZVezhMZddo-oy6ikyy5vLlzNIsVYiIWsFNLa_Nc5ZcVRttS7NfocqhRAXGF2XFNi8CfG09Uw3oY-_y6i_MMrnT5iDzOc5Cez_R-zO9A_Yfe_5IT5U_b9W-LP7Ec-RN4Brls8P_iaFKDQfY08aQ2NE8_cRvxiuB6BMlLjMT_hKwSi_cQ9IWzuMn3JM3Z--unHx7MyyyiU3shqKmsvF6ESFOyUDh062BqkrL0Lpqq1g2hEDVFpo6EJjRKhkq4JQSVxXOekes72-qGHfcZFF40M0YE2QYfad9IpD0JG01mEDqpgajODrc8c4yR1sWpT4sziXWOel5bmvc3zXrBy-6v1zLHxn_4fyDjbvsSQnR4MVz_bvOFaCx0OU4EHVek6Ng6hlPedsQuvgvWxYPtk2s0LxvZmgRXscGPu25vfbJtxL1KCxfWA5sE-WiRCNVuwF_Pq2A5S4c22RrBUMLuzbna-Yrelv7xIfN8kcYDf8PLfwzpgDyTFAlL92yHbm66u4RUCpql7nXbFb8QAF0E priority: 102 providerName: ProQuest |
| Title | Patterns of beverage purchases amongst British households: A latent class analysis |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/32898152 https://www.proquest.com/docview/2479466981 https://www.proquest.com/docview/2441283027 https://pubmed.ncbi.nlm.nih.gov/PMC7478648 https://doaj.org/article/7ebae43ece3648f9a710ccb570c3d7cf http://dx.doi.org/10.1371/journal.pmed.1003245 |
| Volume | 17 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV1Za9wwEB5yQOlL6R2n6aJCXx1Wl2UXSklKQmhJSEMX9s3IOprCYm_XG0j_fUfyQbds6IsfLAnLI43nk0b-PoD3ljqDKNanRkmfCurQpbzGVYqnwnFmg-Z2OG1xlV3MxJe5nO_AoNnaG7DdurQLelKz1eL4_tfvT-jwH6Nqg6JDo-Mlxo-Q9UeMIHdhH2OTCq56Kca8AiumxbT_ge6hlhsBKvL4B97TRdNuw6D_HqX8KzadP4UnPagkJ90seAY7rn4Ojy77tPkLuLmOLJp1SxpPKoezF78iZBl0oDCItSQqDrVr0jMckdvmrnUhL9V-ICdkgXC0XhMTcDbRPYnJS5idn33_fJH2YgqpkSxbp7lhU5vRsOXJNIZ1p3LHWG60lVkutPOS5s5zIYUrbMGpzZgurOVRIldrxl_BXt3U7gAIrbxk1msnpBU2NxXT3DjKvKwUAgieAB8sWJqeaTwIXizKmD5TuOLo7FIGu5e93RNIx1bLjmnjP_VPw-CMdQNPdrzRrH6UvduVylXYTe6M45nIfaERUBlTSTU13CrjEzgIQzs8oC1Zx7hf5DSBo2G4txe_G4vRI0OaRdcOhwfrCBpp1VQCr7vZMXaS4_o2R8iUgNqYNxtvsVlS_7yNrN9B6ADf4fDhHr-BxyzsBsQTcEewt17dubcImdbVBHbVXE1g__Ts6vpmEjce8Pr1Wz6J_vEHGIcdrw |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELaqVgIuiHcDBYwEx9D1K06QEGqh1Za2q6pqpd6C40eLtEqWZivEn-I3MpM4C4sqOPW6drTOPOxvPJNvCHntmLeAYkNqtQqpZB5cKhiIUgKTXnCHPbex2mKSjU_l5zN1tkJ-Dt_CYFnlsCd2G7VrLN6Rb_KeCr3I2YfZtxS7RmF2dWih0ZvFvv_xHUK29v3eJ9DvG853d04-jtPYVSC1imfzNLd85DKGd3_cwPnmde45z61xKsul8UGx3AchlfSFKwRzGTeFc6LrFWsMEh3Alr8mBYQyq2Rte2dydLwI8YpRd6uDvGcp41rHj_WEZpvRNt7O4JTD2gRAMmrpMOx6BiDH6rRpr8O7f5dt_nEO7t4jdyOApVu9xd0nK75-QG4dxhT9Q3J81DF21i1tAq08eArsWHSGPafgwGxp192ondPIpkQvmqvWYw6sfUe36BSgbz2nFjE9NZEw5RE5vRERPyardVP7dUJZFRR3wXipnHS5rbgR1jMeVKUBrIiEiEGCpY2s5thcY1p2qToN0U0vlxLlXka5JyRdPDXrWT3-M38blbOYi5zc3Q_N5XkZXbzUvoJlCm-9yGQeCgPgzdpK6ZEVTtuQkHVU7fAHbfnbpBOyMaj7-uFXi2HwfkzpmNqDemCOZB2Fm07Ik946FosUEEvnAM8SopfsZuktlkfqrxcdwzg2VYB3ePrvZb0kt8cnhwflwd5k_xm5w_Emoqu-2yCr88sr_xzg2rx6EX2Eki837Za_AFXJVI4 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELaqIlVcEO8GChgJjmHXdhwnSAgVyqqlUFWISnsLjh8UaZUsTSrEX-PXMeM4WxZVcOp17WideY9n8g0hzyxzBqJYnxolfZoxByrlNWQpnmVOcIszt7Hb4ijfP8nez-V8g_wav4XBtsrRJgZDbVuDd-QTPkChlwWb-NgWcbw3e738nuIEKay0juM0BhE5dD9_QPrWvTrYA14_53z27vPb_TROGEiN5HmfFoZPbc7wHpBr8HVOFY7zwmgr8yLTzktWOC8ymbnSloLZnOvSWhHmxmqNoAdg_q8pIRnqmJpfJHvlNNzvIAJayrhS8bM9odgkSsmLJfg77FKAmEauucUwPQDRVhdtd1nk-3cD5x8ecXaT3IihLN0dZO8W2XDNbbL1MRbr75BPxwG7s-lo62ntQGfAdtElTp8C19nRMOeo62nEVaKn7XnnsBrWvaS7dAFBcNNTg9E91RE65S45uRIC3yObTdu4bUJZ7SW3XrtM2swWpuZaGMe4l7WCsEUkRIwUrEzEN8cxG4sqFO0U5DkDXSqkexXpnpB09dRywPf4z_43yJzVXkTnDj-0Z1-rqOyVcjUcUzjjRJ4VvtQQxhlTSzU1wirjE7KNrB3_oKsuhDshOyO7L19-uloGO4DFHd04YA_syVgAc1MJuT9Ix-qQArLqAgK1hKg1uVl7i_WV5ttpwBrH8QrwDg_-fawnZAuUsfpwcHT4kFzneCUR2vB2yGZ_du4eQdzW14-DglDy5ao18jdNFlde |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Patterns+of+beverage+purchases+amongst+British+households%3A+A+latent+class+analysis&rft.jtitle=PLoS+medicine&rft.au=Berger%2C+Nicolas&rft.au=Cummins%2C+Steven&rft.au=Allen%2C+Alexander&rft.au=Smith%2C+Richard&rft.date=2020-09-01&rft.pub=Public+Library+of+Science&rft.eissn=1549-1676&rft.volume=17&rft.issue=9&rft_id=info:doi/10.1371%2Fjournal.pmed.1003245&rft.externalDocID=2479466981 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1549-1676&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1549-1676&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1549-1676&client=summon |