Large-scale loyalty card data in health research

Cohort profile: The Health, Food, Purchases and Lifestyle (SMIL) cohort - a Danish open cohort

PURPOSE

The Health, Food, Purchases and Lifestyle (SMIL) cohort is a prospective open Danish cohort that collects electronic consumer purchase data, which can be linked to Danish nationwide administrative health and social registries. This paper provides an overview of the cohort's baseline characteristics and marginal differences in the monetary percentage spent on food groups by sex, age and hour of the day.

PARTICIPANTS

As of 31 December 2022, the cohort included 11 214 users of a smartphone-based receipt collection application who consented to share their unique identification number for linkage to registries in Denmark. In 2022, the composition of the cohort was as follows: 62% were men while 24% were aged 45-55. The cohort had a median of 63 (IQR 26-116) unique shopping trips. The cohort included participants with a range of health statuses. Notably, 21% of participants had a history of cardiovascular disease and 8% had diabetes before donating receipts.

FINDINGS TO DATE

The feasibility of translating consumer purchase data to operationalisable food groups and merging with registers has been demonstrated. We further demonstrated differences in marginal distributions which revealed disparities in the amount of money spent on various food groups by sex and age, as well as systematic variations by the hour of the day. For example, men under 30 spent 8.2% of their total reported expenditure on sugary drinks, while women under 30 spent 6.5%, men over 30 spent 4.3% and women over 30 spent 3.9%.

FUTURE PLANS

The SMIL cohort is characterised by its dynamic, continuously updated database, offering an opportunity to explore the relationship between diet and disease without the limitations of self-reported data. Currently encompassing data from 2018 to 2022, data collection is set to continue. We expect data collection to continue for many years and we are taking several initiatives to increase the cohort.

Tensorial Principal Component Analysis in Detecting Temporal Trajectories of Purchase Patterns in Loyalty Card Data: Retrospective Cohort Study

BACKGROUND

Loyalty card data automatically collected by retailers provide an excellent source for evaluating health-related purchase behavior of customers. The data comprise information on every grocery purchase, including expenditures on product groups and the time of purchase for each customer. Such data where customers have an expenditure value for every product group for each time can be formulated as 3D tensorial data.

OBJECTIVE

This study aimed to use the modern tensorial principal component analysis (PCA) method to uncover the characteristics of health-related purchase patterns from loyalty card data. Another aim was to identify card holders with distinct purchase patterns. We also considered the interpretation, advantages, and challenges of tensorial PCA compared with standard PCA.

METHODS

Loyalty card program members from the largest retailer in Finland were invited to participate in this study. Our LoCard data consist of the purchases of 7251 card holders who consented to the use of their data from the year 2016. The purchases were reclassified into 55 product groups and aggregated across 52 weeks. The data were then analyzed using tensorial PCA, allowing us to effectively reduce the time and product group-wise dimensions simultaneously. The augmentation method was used for selecting the suitable number of principal components for the analysis.

RESULTS

Using tensorial PCA, we were able to systematically search for typical food purchasing patterns across time and product groups as well as detect different purchasing behaviors across groups of card holders. For example, we identified customers who purchased large amounts of meat products and separated them further into groups based on time profiles, that is, customers whose purchases of meat remained stable, increased, or decreased throughout the year or varied between seasons of the year.

CONCLUSIONS

Using tensorial PCA, we can effectively examine customers' purchasing behavior in more detail than with traditional methods because it can handle time and product group dimensions simultaneously. When interpreting the results, both time and product dimensions must be considered. In further analyses, these time and product groups can be directly associated with additional consumer characteristics such as socioeconomic and demographic predictors of dietary patterns. In addition, they can be linked to external factors that impact grocery purchases such as inflation and unexpected pandemics. This enables us to identify what types of people have specific purchasing patterns, which can help in the development of ways in which consumers can be steered toward making healthier food choices.

Assessing household lifestyle exposures from consumer purchases, the My Purchases cohort

Consumer purchase data (CPD) is a promising instrument to assess the impact of purchases on health, but is limited by the need for manual scanning, a lack of access to data from multiple retailers, and limited information on product data and health outcomes. Here we describe the My Purchases cohort, a web-app enabled, prospective collection of CPD, covering several large retail chains in Denmark, that enables linkage to health outcomes. The cohort included 459 participants as of July 03, 2023. Up to eight years of CPD have been collected, with 2,225,010 products purchased, comprising 223,440 unique products. We matched 88.5% of all products by product name or item number to one generic food database and three product databases. Combined, the databases enable analysis of key exposures such as nutrients, ingredients, or additives. We found that increasing the number of retailers that provide CPD for each consumer improved the stability of individual CPD profiles and when we compared kilojoule information from generic and specific product matches, we found a median modified relative difference of 0.23. Combined with extensive product databases and health outcomes, CPD could provide the basis for extensive investigations of how what we buy affects our health.

Supermarket Transaction Records In Dietary Evaluation: the STRIDE study: validation against self-reported dietary intake

OBJECTIVE

Scalable methods are required for population dietary monitoring. The Supermarket Transaction Records In Dietary Evaluation (STRIDE) study compares dietary estimates from supermarket transactions with an online FFQ.

DESIGN

Participants were recruited in four waves, accounting for seasonal dietary variation. Purchases were collected for 1 year during and 1 year prior to the study. Bland-Altman agreement and limits of agreement (LoA) were calculated for energy, sugar, fat, saturated fat, protein and sodium (absolute and relative).

SETTING

This study was partnered with a large UK retailer.

PARTICIPANTS

Totally, 1788 participants from four UK regions were recruited from the retailer's loyalty card customer database, according to breadth and frequency of purchases. Six hundred and eighty-six participants were included for analysis.

RESULTS

The analysis sample were mostly female (72 %), with a mean age of 56 years (sd 13). The ratio of purchases to intakes varied depending on amounts purchased and consumed; purchases under-estimated intakes for smaller amounts on average, but over-estimated for larger amounts. For absolute measures, the LoA across households were wide, for example, for energy intake of 2000 kcal, purchases could under- or over-estimate intake by a factor of 5; values could be between 400 kcal and 10000 kcal. LoA for relative (energy-adjusted) estimates were smaller, for example, for 14 % of total energy from saturated fat, purchase estimates may be between 7 % and 27 %.

CONCLUSIONS

Agreement between purchases and intake was highly variable, strongest for smaller loyal households and for relative values. For some customers, relative nutrient purchases are a reasonable proxy for dietary composition indicating utility in population-level dietary research.

Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models

The COVID-19 pandemic led to unparalleled pressure on healthcare services. Improved healthcare planning in relation to diseases affecting the respiratory system has consequently become a key concern. We investigated the value of integrating sales of non-prescription medications commonly bought for managing respiratory symptoms, to improve forecasting of weekly registered deaths from respiratory disease at local levels across England, by using over 2 billion transactions logged by a UK high street retailer from March 2016 to March 2020. We report the results from the novel AI (Artificial Intelligence) explainability variable importance tool Model Class Reliance implemented on the PADRUS model (Prediction of Amount of Deaths by Respiratory disease Using Sales). PADRUS is a machine learning model optimised to predict registered deaths from respiratory disease in 314 local authority areas across England through the integration of shopping sales data and focused on purchases of non-prescription medications. We found strong evidence that models incorporating sales data significantly out-perform other models that solely use variables traditionally associated with respiratory disease (e.g. sociodemographics and weather data). Accuracy gains are highest (increases in R2 (coefficient of determination) between 0.09 to 0.11) in periods of maximum risk to the general public. Results demonstrate the potential to utilise sales data to monitor population health with information at a high level of geographic granularity.

Real-life experiments in supermarkets to encourage healthy dietary-related behaviours: opportunities, challenges and lessons learned

BACKGROUND

Supermarkets are the primary source of food for many people yet their full potential as a setting to encourage healthy dietary-related behaviours remains underutilised. Sharing the experiences from research groups who have worked with supermarket chains to evaluate strategies that promote healthy eating could improve the efficiency of building such relationships and enhance the design quality of future research studies.

METHODS

A collective case study approach was used to synthesise experiences of engaging and sustaining research collaborations with national supermarket chains to test the effectiveness of health-focused in-store interventions. The collective narrative covers studies conducted in three high-income countries: Australia, the Netherlands and the United Kingdom.

RESULTS

We have distilled our experiences and lessons learned into six recommendations for conducting high quality public health research with commercial supermarket chains. These include: (i) using personal contacts, knowledge of supermarket activities and engaging executive management to establish a partnership and allowing time to build trust; (ii) using scientifically robust study designs with appropriate sample size calculations; (iii) formalising data exchange arrangements and allocating adequate resource for data extraction and re-categorisation; (iv) assessing effects at individual/households level where possible; (v) designing a mixed-methods process evaluation to measure intervention fidelity, dose and unintended consequences; and (vi) ensuring scientific independence through formal contract agreements.

CONCLUSIONS

Our collective experiences of working in non-financial partnerships with national supermarket chains could be useful for other research groups looking to develop and implement supermarket studies in an efficient manner. Further evidence from real-life supermarket interventions is necessary to identify sustainable strategies that can improve population diet and maintain necessary commercial outcomes.

Do we eat what we buy? Relative validity of grocery purchase data as an indicator of food consumption in the LoCard study

The validity of grocery purchase data as an indicator of food consumption is uncertain. This paper investigated (1) the associations between food consumption and grocery purchases using automatically accumulated purchase data and (2) whether the strength of the associations differed in certain sub-populations. The participants filled in a FFQ, and a major Finnish retailer issued us with their loyalty-card holders' grocery purchase data covering the 1- and 12-month periods preceding the FFQ. We used gamma statistics to study the association between thirds/quarters of FFQ and grocery purchase data (frequency/amount) separately for eighteen food groups among the 11 983 participants. Stratified analyses were conducted for subgroups based on sex, family structure, educational level, household income and self-estimated share of purchases from the retailer. We also examined the proportion of participants classified into the same, adjacent, subsequent and opposite categories using the FFQ and purchase data. The gammas ranged from 0·12 (cooked vegetables) to 0·75 (margarines). Single households had stronger gammas than two-adult families, and participants with > 60 % of purchases from the retailer had stronger gammas. For most food groups, the proportion of participants classified into the same or adjacent category was > 70 %. Most discrepancies were observed for fresh/cooked vegetables, berries and vegetable oils. Even though the two methods did not categorise all food groups similarly, we conclude that grocery purchase data are able to describe food consumption in an adult population, and future studies should consider purchase data as a resource-saving and moderately valid measure in large samples.

Fish, Seafood, and Fish Products Purchasing Habits in the Spanish Population during COVID-19 Lockdown

The Mediterranean diet is a healthy dietary pattern in which fish consumption is an important key element. In Spain, fish intake is the second highest in Europe. Dietary guidelines recommend an intake of 1-3 portions a week of fish. However, Spanish fish sales have been decreasing since 2008. The unexpected pandemic spread of the SARS-CoV-2 in 2020 led the Spanish Government to take restrictive measures that had an impact on people's behavior, including food purchases and consumption. The aim of the study was to analyze purchase data of fish, seafood, and fish products during the lockdown in Spain, using data from loyalty card holders (>5,000,000 participants) from a hypermarket chain in Spain. The results show a 45% increase in the purchase of all types of fish, seafood, and fish products, with the highest increment observed in the retiree (+65 years) as compared to the younger populations. Moreover, the retiree, in spite of the digital divide, were also the ones that most increased online shopping. These data should be considered since events like COVID-19 confinement can have a permanent impact on people's dietary habits, a possibility that should be monitored in the future.

Public attitudes towards sharing loyalty card data for academic health research: a qualitative study

BACKGROUND

A growing number of studies show the potential of loyalty card data for use in health research. However, research into public perceptions of using this data is limited. This study aimed to investigate public attitudes towards donating loyalty card data for academic health research, and the safeguards the public would want to see implemented. The way in which participant attitudes varied according to whether loyalty card data would be used for either cancer or COVID-19 research was also examined.

METHODS

Participants (N = 40) were recruited via Prolific Academic to take part in semi-structured telephone interviews, with questions focused on data sharing related to either COVID-19 or ovarian/bowel cancer as the proposed health condition to be researched. Content analysis was used to identify sub-themes corresponding to the two a priori themes, attitudes and safeguards.

RESULTS

Participant attitudes were found to fall into two categories, either rational or emotional. Under rational, most participants were in favour of sharing loyalty card data. Support of health research was seen as an important reason to donate such data, with loyalty card logs being considered as already within the public domain. With increased understanding of research purpose, participants expressed higher willingness to donate data. Within the emotional category, participants shared fears about revealing location information and of third parties obtaining their data. With regards to safeguards, participants described the importance of anonymisation and the level of data detail; the control, convenience and choice they desired in sharing data; and the need for transparency and data security. The change in hypothetical purpose of the data sharing, from Covid-19 to cancer research, had no impact on participants' decision to donate, although did affect their understanding of how loyalty card data could be used.

CONCLUSIONS

Based on interviews with the public, this study contributes recommendations for those researchers and the wider policy community seeking to obtain loyalty card data for health research. Whilst participants were largely in favour of donating loyalty card data for academic health research, information, choice and appropriate safeguards are all exposed as prerequisites upon which decisions are made.

Using Shopping Data to Improve the Diagnosis of Ovarian Cancer: Survey Study (Preprint)

BACKGROUND

Shopping data can be analyzed using machine learning techniques to study population health. It is unknown if the use of such methods can successfully investigate prediagnosis purchases linked to self-medication of symptoms of ovarian cancer.

OBJECTIVE

The aims of this study were to gain new domain knowledge from women's experiences, understand how women's shopping behavior relates to their pathway to the diagnosis of ovarian cancer, and inform research on computational analysis of shopping data for population health.

METHODS

A web-based survey on individuals' shopping patterns prior to an ovarian cancer diagnosis was analyzed to identify key knowledge about health care purchases. Logistic regression and random forest models were employed to statistically examine how products linked to potential symptoms related to presentation to health care and timing of diagnosis.

RESULTS

Of the 101 women surveyed with ovarian cancer, 58.4% (59/101) bought nonprescription health care products for up to more than a year prior to diagnosis, including pain relief and abdominal products. General practitioner advice was the primary reason for the purchases (23/59, 39%), with 51% (30/59) occurring due to a participant's doctor believing their health problems were due to a condition other than ovarian cancer. Associations were shown between purchases made because a participant's doctor believing their health problems were due to a condition other than ovarian cancer and the following variables: health problems for longer than a year prior to diagnosis (odds ratio [OR] 7.33, 95% CI 1.58-33.97), buying health care products for more than 6 months to a year (OR 3.82, 95% CI 1.04-13.98) or for more than a year (OR 7.64, 95% CI 1.38-42.33), and the number of health care product types purchased (OR 1.54, 95% CI 1.13-2.11). Purchasing patterns are shown to be potentially predictive of a participant's doctor thinking their health problems were due to some condition other than ovarian cancer, with nested cross-validation of random forest classification models achieving an overall in-sample accuracy score of 89.1% and an out-of-sample score of 70.1%.

CONCLUSIONS

Women in the survey were 7 times more likely to have had a duration of more than a year of health problems prior to a diagnosis of ovarian cancer if they were self-medicating based on advice from a doctor rather than having made the decision to self-medicate independently. Predictive modelling indicates that women in such situations, who are self-medicating because their doctor believes their health problems may be due to a condition other than ovarian cancer, exhibit distinct shopping behaviors that may be identifiable within purchasing data. Through exploratory research combining women sharing their behaviors prior to diagnosis and computational analysis of these data, this study demonstrates that women's shopping data could potentially be useful for early ovarian cancer detection.

Human exposome assessment platform

The Human Exposome Assessment Platform (HEAP) is a research resource for the integrated and efficient management and analysis of human exposome data. The project will provide the complete workflow for obtaining exposome actionable knowledge from population-based cohorts. HEAP is a state-of-the-science service composed of computational resources from partner institutions, accessed through a software framework that provides the world's fastest Hadoop platform for data warehousing and applied artificial intelligence (AI). The software, will provide a decision support system for researchers and policymakers. All the data managed and processed by HEAP, together with the analysis pipelines, will be available for future research. In addition, the platform enables adding new data and analysis pipelines. HEAP's final product can be deployed in multiple instances to create a network of shareable and reusable knowledge on the impact of exposures on public health.

Understanding Barriers to Novel Data Linkages: Topic Modeling of the Results of the LifeInfo Survey

Background

Novel consumer and lifestyle data, such as those collected by supermarket loyalty cards or mobile phone exercise tracking apps, offer numerous benefits for researchers seeking to understand diet- and exercise-related risk factors for diseases. However, limited research has addressed public attitudes toward linking these data with individual health records for research purposes. Data linkage, combining data from multiple sources, provides the opportunity to enhance preexisting data sets to gain new insights.

Objective

The aim of this study is to identify key barriers to data linkage and recommend safeguards and procedures that would encourage individuals to share such data for potential future research.

Methods

The LifeInfo Survey consulted the public on their attitudes toward sharing consumer and lifestyle data for research purposes. Where barriers to data sharing existed, participants provided unstructured survey responses detailing what would make them more likely to share data for linkage with their health records in the future. The topic modeling technique latent Dirichlet allocation was used to analyze these textual responses to uncover common thematic topics within the texts.

Results

Participants provided responses related to sharing their store loyalty card data (n=2338) and health and fitness app data (n=1531). Key barriers to data sharing identified through topic modeling included data safety and security, personal privacy, requirements of further information, fear of data being accessed by others, problems with data accuracy, not understanding the reason for data linkage, and not using services that produce these data. We provide recommendations for addressing these issues to establish the best practice for future researchers interested in using these data.

Conclusions

This study formulates a large-scale consultation of public attitudes toward this kind of data linkage, which is an important first step in understanding and addressing barriers to participation in research using novel consumer and lifestyle data.

Dietary Patterns Derived from UK Supermarket Transaction Data with Nutrient and Socioeconomic Profiles

Poor diet is a leading cause of death in the United Kingdom (UK) and around the world. Methods to collect quality dietary information at scale for population research are time consuming, expensive and biased. Novel data sources offer potential to overcome these challenges and better understand population dietary patterns. In this research we will use 12 months of supermarket sales transaction data, from 2016, for primary shoppers residing in the Yorkshire and Humber region of the UK (n = 299,260), to identify dietary patterns and profile these according to their nutrient composition and the sociodemographic characteristics of the consumer purchasing with these patterns. Results identified seven dietary purchase patterns that we named: Fruity; Meat alternatives; Carnivores; Hydrators; Afternoon tea; Beer and wine lovers; and Sweet tooth. On average the daily energy intake of loyalty card holders -who may buy as an individual or for a household- is less than the adult reference intake, but this varies according to dietary purchase pattern. In general loyalty card holders meet the recommended salt intake, do not purchase enough carbohydrates, and purchase too much fat and protein, but not enough fibre. The dietary purchase pattern containing the highest amount of fibre (as an indicator of healthiness) is bought by the least deprived customers and the pattern with lowest fibre by the most deprived. In conclusion, supermarket sales data offer significant potential for understanding population dietary patterns.

Using Supermarket Loyalty Card Data to Provide Personalised Advice to Help Reduce Saturated Fat Intake among Patients with Hypercholesterolemia: A Qualitative Study of Participants' Experiences

Background: The ‘Primary Care SHOPping Intervention for Cardiovascular Disease Prevention’ (PCSHOP) trial tested the effectiveness and feasibility of a behavioural intervention to reduce saturated fat in food purchases. The intervention offered feedback from data collected through a supermarket loyalty card to supplement brief advice from a nurse. This qualitative study aimed to describe participants’ experiences of receiving this intervention. Methods: We conducted semi-structured, one-to-one, telephone interviews with participants from the PCSHOP trial. Interviews were audio-recorded and transcribed verbatim. We employed the one sheet of paper technique and a thematic analysis to develop high-level themes in NVivo software. Results: Twenty-four participants were interviewed (mean age: 63 years (SD 12)). They reported that the brief advice did not provide any new information but they welcomed the sense of accountability the nurse provided. The personalised shopping feedback and healthier swap suggestions provided novel information that challenged previously held beliefs about the saturated fat content of food purchases and encouraged some positive dietary changes. However, the taste preferences of the participant or other household members were a barrier to changing food shopping behaviours. Conclusion: Harnessing loyalty card data is a novel and acceptable method to offering personalised dietary feedback. Yet, issues on the suitability of the healthier swap suggestions limited the extent of dietary change. Trial registration: ISRCTN14279335. Registered 1 September 2017.

Characterization and Correction of Bias Due to Nonparticipation and the Degree of Loyalty in Large-Scale Finnish Loyalty Card Data on Grocery Purchases: Cohort Study

Background

To date, the evaluation of diet has mostly been based on questionnaires and diaries that have their limitations in terms of being time and resource intensive, and a tendency toward social desirability. Loyalty card data obtained in retailing provides timely and objective information on diet-related behaviors. In Finland, the market is highly concentrated, which provides a unique opportunity to investigate diet through grocery purchases.

Objective

The aims of this study were as follows: (1) to investigate and quantify the selection bias in large-scale (n=47,066) loyalty card (LoCard) data and correct the bias by developing weighting schemes and (2) to investigate how the degree of loyalty relates to food purchases.

Methods

Members of a loyalty card program from a large retailer in Finland were contacted via email and invited to take part in the study, which involved consenting to the release of their grocery purchase data for research purposes. Participants’ sociodemographic background was obtained through a web-based questionnaire and was compared to that of the general Finnish adult population obtained via Statistics Finland. To match the distributions of sociodemographic variables, poststratification weights were constructed by using the raking method. The degree of loyalty was self-estimated on a 5-point rating scale.

Results

On comparing our study sample with the general Finnish adult population, in our sample, there were more women (65.25%, 30,696/47,045 vs 51.12%, 2,273,139/4,446,869), individuals with higher education (56.91%, 20,684/36,348 vs 32.21%, 1,432,276/4,446,869), and employed individuals (60.53%, 22,086/36,487 vs 52.35%, 2,327,730/4,446,869). Additionally, in our sample, there was underrepresentation of individuals aged under 30 years (14.44%, 6,791/47,045 vs 18.04%, 802,295/4,446,869) and over 70 years (7.94%, 3,735/47,045 vs 18.20%, 809,317/4,446,869), as well as retired individuals (23.51%, 8,578/36,487 vs 31.82%, 1,414,785/4,446,869). Food purchases differed by the degree of loyalty, with higher shares of vegetable, red meat & processed meat, and fat spread purchases in the higher loyalty groups.

Conclusions

Individuals who consented to the use of their loyalty card data for research purposes tended to diverge from the general Finnish adult population. However, the high volume of data enabled the inclusion of sociodemographically diverse subgroups and successful correction of the differences found in the distributions of sociodemographic variables. In addition, it seems that food purchases differ according to the degree of loyalty, which should be taken into account when researching loyalty card data. Despite the limitations, loyalty card data provide a cost-effective approach to reach large groups of people, including hard-to-reach population subgroups.

Evidence from big data in obesity research: international case studies

BACKGROUND/OBJECTIVE

Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of 'big data' presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital, has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). 'Additional computing power' introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered.

METHODS AND RESULTS

Three case studies are presented. The first investigated the influence of the built environment on physical activity. It used spatial data on green spaces and exercise facilities alongside individual-level data on physical activity and swipe card entry to leisure centres, collected as part of a local authority exercise class initiative. The second used a variety of linked electronic health datasets to investigate associations between obesity surgery and the risk of developing cancer. The third used data on tax parcel values alongside data from the Seattle Obesity Study to investigate sociodemographic determinants of obesity in Seattle.

CONCLUSIONS

The case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation to size, coverage, temporality, and objectivity of measures. However, the case studies also encountered challenges or limitations; particularly in relation to hidden/unforeseen biases and lack of contextual information. Overall, despite challenges, big data presents a relatively untapped resource that shows promise in helping to understand drivers of obesity.

Examining retail purchases of cigarettes and nicotine replacement therapy in Finland

INTRODUCTION

Finland's success in achieving the goal of its tobacco endgame largely depends on rectifying deficiencies in the delivery of smoking cessation services. One such weakness, which has not been documented with empirical data, is misuse of nicotine replacement therapy (NRT). This study's objective was to examine purchase patterns of NRT for estimating improper use of the medication. The study was based on the assumption that duration of a purchase episode is indicative of either proper use or misuse of NRT.

METHODS

The participants (n=728), who purchased at least one NRT product in 2016 (mostly gum/lozenge), were selected through enrollment in a large customer loyalty program in Finland (LoCard). Participants were categorized into one of five groups according to their longest purchase episode of NRT, defined by purchases made in consecutive, 4-week intervals.

RESULTS

Most participants, who did not adhere to NRT guidelines, either purchased the medication for too short (≤4 weeks, 63.5%) or too long (>24 weeks, 13.2%) of a purchase episode. Median purchases of NRT in the first month of use were one and four in the former and latter, respectively. In contrast to other groups, persistent users (>24 weeks) did not curtail purchases of NRT across several 4-week intervals, suggesting potential for dependence on NRT.

CONCLUSIONS

The observation that most purchase episodes were terminated prematurely is consistent with surveys reporting widespread NRT misuse. Given uncertainty of greater regulation of NRT sales through legislation, it would be prudent for Finnish retailers to promote proper use of the therapy.

Alcohol expenditure in grocery stores and their associations with tobacco and food expenditures

Background

Alcohol consumption is a significant cause of disease, death and social harm, and it clusters with smoking tobacco and an unhealthy diet. Using automatically registered retail data for research purposes is a novel approach, which is not subject to underreporting bias. Based on loyalty card data (LoCard) obtained by a major Finnish retailer holding a market share of 47%, we examined alcohol expenditure and their associations with food and tobacco expenditures.

Methods

The data consisted of 1,527,217 shopping events in 2016 among 13,274 loyalty card holders from southern Finland. A K-means cluster analysis was applied to group the shopping baskets according to their content of alcoholic beverages. The differences in the absolute and relative means of food and tobacco between the clusters were tested by linear mixed models with the loyalty card holder as the random factor.

Results

By far, the most common basket type contained no alcoholic beverages, followed by baskets containing a small number of beers or ciders. The expenditure on food increased along with the expenditure on alcoholic beverages. The foods most consistently associated with alcohol purchases were sausages, soft drinks and snacks. The expenditure on cigarettes relative to total basket price peaked in the mid-price alcohol baskets.

Conclusion

Clustering of unhealthy choices occurred on the level of individual shopping events. People who bought many alcoholic beverages did not trim their food budget. Automatically registered purchase data provide valuable insight into the health behaviours of individuals and the population.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-7096-3) contains supplementary material, which is available to authorized users.