Food purchases in households with and without diabetes based on consumer purchase data

OBJECTIVES

Dietary recommendations for individuals with diabetes are easy to provide, but adherence is difficult to monitor. The objective of this study was to investigate whether there was a difference in grocery purchases between households with and without diabetes.

STUDY DESIGN

Cohort study.

METHODS

Consumer purchase data in 2019 was collected from 6662 households donating their supermarket receipts via a receipt collecting service. Of these households, 718 included at least one individual with diabetes. The monetary percentages spent on specific food groups were used to characterize households using all purchases in 2019. A probability index model was used to compare households with diabetes to households without diabetes.

RESULTS

We included 405,264 shopping trips in 2019 attributed to 6662 households. Both households with and without diabetes spent the highest monetary percentage on sweets (with diabetes: 9.3%, without diabetes: 8.8%), with no statistically significant difference detected. However, compared to households without diabetes, households with diabetes had a significantly higher probability of spending a higher monetary percentage on butter, oil and dressings; non-sugary drinks; processed red meat and ready meals as well as a significantly lower probability of spending a higher monetary percentage on accessory compounds; alcoholic beverages; eggs; grains; rice and pasta, and raw vegetables.

CONCLUSIONS

Households with diabetes spent a relatively higher monetary value on several unhealthy foods and less on several healthy groceries compared to households without diabetes. There is a need for more diabetes self-management education focused on including more healthy dietary choices in their household grocery purchases.

Applying historical data in a nonlinear mixed-effects model can reduce the number of control rats required for calculation of the relative potency of insulin analogues

This paper examines how to reduce the number of control animals in preclinical hyperinsulemic glucose clamp studies if we make use of information on historical studies. A dataset consisting of 59 studies in rats to investigate new insulin analogues for diabetics, collected in the years 2000 to 2015, is analysed. A simulation experiment is performed based on a carefully built nonlinear mixed-effects model including historical information, comparing results (for the relative log-potency) with the standard approach ignoring previous studies. We find that by including historical information in the form of the mixed-effects model proposed, we can to remove between 23% and 51% of the control rats in the two studies looked closely upon to get the same level of precision on the relative log-potency as in the standard analysis. How to incorporate the historical information in the form of the mixed-effects model is discussed, where both a mixed-effect meta-analysis approach as well as a Bayesian approach are suggested. The conclusions are similar for the two approaches, and therefore, we conclude that the inclusion of historical information is beneficial in regard to using fewer control rats.