Can Big Data Solve a Big Problem? Reporting the Obesity Data Landscape in Line with the Foresight Obesity System Map

Overview

Journal Int J Obes (Lond)

Specialty Endocrinology

Date 2018 Sep 23

PMID 30242238

Citations 17

Authors

Michelle A Morris

Emma Wilkins

Kate A Timmins

Maria Bryant

Mark Birkin

Claire Griffiths

Affiliations

Soon will be listed here.

Abstract

Background: Obesity research at a population level is multifaceted and complex. This has been characterised in the UK by the Foresight obesity systems map, identifying over 100 variables, across seven domain areas which are thought to influence energy balance, and subsequent obesity. Availability of data to consider the whole obesity system is traditionally lacking. However, in an era of big data, new possibilities are emerging. Understanding what data are available can be the first challenge, followed by an inconsistency in data reporting to enable adequate use in the obesity context. In this study we map data sources against the Foresight obesity system map domains and nodes and develop a framework to report big data for obesity research. Opportunities and challenges associated with this new data approach to whole systems obesity research are discussed.

Methods: Expert opinion from the ESRC Strategic Network for Obesity was harnessed in order to develop a data source reporting framework for obesity research. The framework was then tested on a range of data sources. In order to assess availability of data sources relevant to obesity research, a data mapping exercise against the Foresight obesity systems map domains and nodes was carried out.

Results: A reporting framework was developed to recommend the reporting of key information in line with these headings: Background; Elements; Exemplars; Content; Ownership; Aggregation; Sharing; Temporality (BEE-COAST). The new BEE-COAST framework was successfully applied to eight exemplar data sources from the UK. 80% coverage of the Foresight obesity systems map is possible using a wide range of big data sources. The remaining 20% were primarily biological measurements often captured by more traditional laboratory based research.

Conclusions: Big data offer great potential across many domains of obesity research and need to be leveraged in conjunction with traditional data for societal benefit and health promotion.

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References

Hewitt J, Walters M, Padmanabhan S, Dawson J . Cohort profile of the UK Biobank: diagnosis and characteristics of cerebrovascular disease. BMJ Open. 2016; 6(3):e009161. PMC: 4809076. DOI: 10.1136/bmjopen-2015-009161. View

Lobstein T, Baur L, Uauy R . Obesity in children and young people: a crisis in public health. Obes Rev. 2004; 5 Suppl 1:4-104. DOI: 10.1111/j.1467-789X.2004.00133.x. View

Boyd A, Golding J, Macleod J, Lawlor D, Fraser A, Henderson J . Cohort Profile: the 'children of the 90s'--the index offspring of the Avon Longitudinal Study of Parents and Children. Int J Epidemiol. 2012; 42(1):111-27. PMC: 3600618. DOI: 10.1093/ije/dys064. View

Rutter H, Savona N, Glonti K, Bibby J, Cummins S, Finegood D . The need for a complex systems model of evidence for public health. Lancet. 2017; 390(10112):2602-2604. DOI: 10.1016/S0140-6736(17)31267-9. View

Harrison F, Burgoine T, Corder K, van Sluijs E, Jones A . How well do modelled routes to school record the environments children are exposed to? A cross-sectional comparison of GIS-modelled and GPS-measured routes to school. Int J Health Geogr. 2014; 13:5. PMC: 3942764. DOI: 10.1186/1476-072X-13-5. View

Timmins K, Green M, Radley D, Morris M, Pearce J . How has big data contributed to obesity research? A review of the literature. Int J Obes (Lond). 2018; 42(12):1951-1962. PMC: 6291419. DOI: 10.1038/s41366-018-0153-7. View

Smith D, Cummins S, Clark C, Stansfeld S . Does the local food environment around schools affect diet? Longitudinal associations in adolescents attending secondary schools in East London. BMC Public Health. 2013; 13:70. PMC: 3567930. DOI: 10.1186/1471-2458-13-70. View

Colchero M, Popkin B, Rivera J, Ng S . Beverage purchases from stores in Mexico under the excise tax on sugar sweetened beverages: observational study. BMJ. 2016; 352:h6704. PMC: 4986313. DOI: 10.1136/bmj.h6704. View

Wadsworth M, Kuh D, Richards M, Hardy R . Cohort Profile: The 1946 National Birth Cohort (MRC National Survey of Health and Development). Int J Epidemiol. 2005; 35(1):49-54. DOI: 10.1093/ije/dyi201. View

10.

Silver L, Ng S, Ryan-Ibarra S, Smith Taillie L, Induni M, Miles D . Changes in prices, sales, consumer spending, and beverage consumption one year after a tax on sugar-sweetened beverages in Berkeley, California, US: A before-and-after study. PLoS Med. 2017; 14(4):e1002283. PMC: 5395172. DOI: 10.1371/journal.pmed.1002283. View

11.

Cade J, Burley V, Alwan N, Hutchinson J, Hancock N, Morris M . Cohort Profile: The UK Women's Cohort Study (UKWCS). Int J Epidemiol. 2015; 46(2):e11. PMC: 5837471. DOI: 10.1093/ije/dyv173. View

12.

Diaz K, Krupka D, Chang M, Peacock J, Ma Y, Goldsmith J . Fitbit®: An accurate and reliable device for wireless physical activity tracking. Int J Cardiol. 2015; 185:138-40. PMC: 4406840. DOI: 10.1016/j.ijcard.2015.03.038. View

13.

Connelly R, Playford C, Gayle V, Dibben C . The role of administrative data in the big data revolution in social science research. Soc Sci Res. 2016; 59:1-12. DOI: 10.1016/j.ssresearch.2016.04.015. View

14.

Cetateanu A, Jones A . Understanding the relationship between food environments, deprivation and childhood overweight and obesity: evidence from a cross sectional England-wide study. Health Place. 2014; 27:68-76. PMC: 4018665. DOI: 10.1016/j.healthplace.2014.01.007. View

15.

Albar S, Alwan N, Evans C, Greenwood D, Cade J . Agreement between an online dietary assessment tool (myfood24) and an interviewer-administered 24-h dietary recall in British adolescents aged 11-18 years. Br J Nutr. 2016; 115(9):1678-86. DOI: 10.1017/S0007114516000593. View

16.

Heesch K, Langdon M . The usefulness of GPS bicycle tracking data for evaluating the impact of infrastructure change on cycling behaviour. Health Promot J Austr. 2016; 27(3):222-229. DOI: 10.1071/HE16032. View

17.

Razak F, Anand S, Vuksan V, Davis B, Jacobs R, Teo K . Ethnic differences in the relationships between obesity and glucose-metabolic abnormalities: a cross-sectional population-based study. Int J Obes (Lond). 2005; 29(6):656-67. DOI: 10.1038/sj.ijo.0802937. View

18.

Thornton L, Crawford D, Ball K . Neighbourhood-socioeconomic variation in women's diet: the role of nutrition environments. Eur J Clin Nutr. 2010; 64(12):1423-32. DOI: 10.1038/ejcn.2010.174. View

19.

Carter M, Albar S, Morris M, Mulla U, Hancock N, Evans C . Development of a UK Online 24-h Dietary Assessment Tool: myfood24. Nutrients. 2015; 7(6):4016-32. PMC: 4488770. DOI: 10.3390/nu7064016. View

20.

Jarman B . Identification of underprivileged areas. Br Med J (Clin Res Ed). 1983; 286(6379):1705-9. PMC: 1548179. DOI: 10.1136/bmj.286.6379.1705. View