We examined the long-term health outcomes associated with being born small for gestational age (SGA) or large for gestational age (LGA). A total of 632 young adults aged ≈20.6 years were recruited from a longitudinal study (Chiang Mai, Thailand) in 2010: 473 born appropriate for gestational age (AGA), 142 SGA, and 17 LGA. The clinical assessments included anthropometry, blood pressure (BP), lipid profile, and an oral glucose tolerance test (OGTT). Young adults born SGA were 1.8 and 3.2 cm shorter than AGA (p = 0.0006) and LGA (p = 0.019) participants, respectively. The incidence of short stature was 8% among SGA compared with 3% in AGA and no cases among LGA participants, with the adjusted relative risk (aRR) of short stature among SGA 2.70 times higher than that of AGA counterparts (p = 0.013). SGA participants also had a 2 h glucose 7% higher than that of the AGA group (105 vs. 99 mg/dL; p = 0.006). Young adults born LGA had a BMI greater by 2.42 kg/m² (p = 0.025) and 2.11 kg/m² (p = 0.040) than those of SGA and AGA, respectively. Thus, the rate of overweight/obesity was 35% in the LGA group compared with 14.2% and 16.6% of SGA and AGA groups, respectively, with corresponding aRR of overweight/obesity of 2.95 (p = 0.011) and 2.50 (p = 0.017), respectively. LGA participants had markedly higher rates of BP abnormalities (prehypertension and/or hypertension) with an aRR of systolic BP abnormalities of 2.30 (p = 0.023) and 2.79 (p = 0.003) compared with SGA and AGA groups, respectively. Thai young adults born SGA had an increased risk of short stature and displayed some impairment in glucose metabolism. In contrast, those born LGA were at an increased risk of overweight/obesity and elevated blood pressure. The long-term follow-up of this cohort is important to ascertain whether these early abnormalities accentuate over time, leading to overt cardiometabolic conditions.

As loyalty cards gain popularity, their role in population health research is expanding. This scoping review examines the use of supermarket loyalty card data for health research, synthesising existing literature to explore: (1) types of loyalty card data analysed, (2) health outcomes studied, and (3) advantages and limitations of loyalty card data. We used a scoping review to address the research questions. Four electronic databases (MEDLINE, EMBASE, PsycINFO, and Scopus) were searched. Studies were included if they used loyalty card data from major supermarkets to investigate health-related outcomes. Two researchers independently screened studies, extracting data on population characteristics, loyalty card and product details, health outcomes, and advantages and limitations. Findings were synthesised using a narrative approach. We identified 44 studies using various loyalty schemes. Most studies focused on grocery products, categorised either by pre-defined criteria (e.g., nutritional value) or product groups (e.g., fruit and vegetables). Health outcomes primarily addressed diet and nutrition, with several studies examining interventions and sociodemographic inequalities. Other studies evaluated responses to government campaigns or tracked medication use. Advantages of loyalty cards included large-scale, objective data collection and individual-, household- and population-level insights. Challenges included missing purchasing activity (e.g., from not using a card), difficulties with linking purchases to nutritional information, and the over-representation of certain demographic groups. Future research should expand data integration (e.g., linking with health records), explore novel dietary research areas, and refine analytical methodologies (e.g., probabilistic product matching) to maximise the use of loyalty card data in health research.

Smoking, unhealthy nutrition, alcohol consumption, and physical inactivity (SNAP risk behaviours) are leading risk factors for multimorbidity and tend to cluster within specific subpopulations. Little is known about how these clusters change with age in older adults and their association with multimorbidity. Repeated measures latent class analysis using data from Waves 4-8 of the English Longitudinal Study of Ageing (n = 4759) identified clusters of respondents with common patterns of SNAP behaviours over time. Disease status (from Wave 9) was used to assess eight body system disorders, multimorbidity, and complex multimorbidity. Logistic regressions examined how clusters were associated with socio-demographic characteristics and disease status. Seven clusters were identified: Low-risk (13.4%), Low-risk yet inactive (16.8%), Low-risk yet heavy drinkers (11·4%), Abstainer yet inactive (20%), Poor diet and inactive (12.9%), Inactive, heavy drinkers (14.5%), and High-risk smokers (10·9%). There was little evidence these clusters changed with age. Clusters characterised by physical inactivity (in combination with other risky behaviours) were associated with lower levels of education and wealth. The heavy drinking clusters were predominantly male. Compared to other clusters, Low-risk and Low-risk yet heavy drinkers had a lower prevalence of all diseases studied. In contrast, the Abstainer but inactive cluster comprised mostly women and had the highest prevalence of multimorbidity, complex multimorbidity, and endocrine disorders. High-risk smokers were most likely to have respiratory disorders. Health-risk behaviours tend to be stable as people age and ought to be addressed early. By identifying seven clusters of older adults with distinct behaviour patterns, socio-demographic characteristics, and disease prevalence, our study provides valuable information for identifying high-risk subpopulations and tailoring interventions to their behaviour patterns and socio-demographic profiles. Key messages: • We identified seven clusters of older adults with distinct behaviour patterns, socio-demographic characteristics and disease prevalence. • Health-risk behaviours tend to be stable as people age and ought to be addressed early.

The growing ubiquity of digital footprint data presents new opportunities for behavioral epidemiology and public health research. Among these, supermarket loyalty card data—passively collected records of consumer purchases—offer objective, high-frequency insights into health-related behaviors at both individual and population levels. This paper explores the potential of loyalty card data to strengthen public health surveillance across 4 key behavioral risk domains: diet, alcohol, tobacco, and over-the-counter medication use. Drawing on recent empirical studies, we outline how these data can complement traditional epidemiological data sources by improving exposure assessment, enabling real-time trend monitoring, and supporting intervention evaluation. We also discuss critical methodological challenges, including issues of representativeness, data integration, and privacy, as well as the need for robust validation strategies. By synthesizing the current evidence base and offering practical recommendations for researchers, this paper highlights how loyalty card data can be responsibly leveraged to advance behavioral risk monitoring and support the adaptation of epidemiological practice to contemporary digital data environments.

This position paper explores the value of supermarket loyalty card data as a novel resource for epidemiological research. Routinely collected at the point of purchase, these data offer high-resolution, longitudinal insights into real-world behaviours. We examine key applications across four core domains—diet, alcohol, tobacco, and over-the-counter medications—and consider how loyalty card data can supplement traditional epidemiological approaches by improving exposure measurement and supporting the evaluation of public health interventions. The paper also addresses key methodological and ethical challenges, including representativeness, data linkage, and privacy. We conclude by outlining future directions for embedding loyalty card data more widely in epidemiological research through linkages with existing health records and longitudinal population studies.

Background: Leading risk factors for chronic disease – smoking, alcohol consumption, poor nutrition and physical inactivity (SNAP behaviours) – cluster together (i.e., appear in specific combinations in distinct subgroups). Longitudinal clustering and its association with health outcomes...