Jacopo Vanoli

Geographical units are fundamental to epidemiological research, yet their boundaries frequently change over time, creating challenges for long‐term analyses. We propose a simple aggregation‐based framework to harmonize regions with temporally varying boundaries by constructing stable spatial units. The method represents geographic regions from different administrative versions as nodes in a graph and links regions that share underlying postal codes. Connected components of this graph define unified, time‐consistent regions that can be used for downstream analyses without disaggregating outcome data. We demonstrate the approach using the Swiss MedStat Regions (SMR), whose boundaries were substantially revised in 2008, resulting in a many‐to‐many correspondence between old and new definitions. Postal codes, assumed to be largely stable, serve as the bridging key. The resulting harmonized regions (mSMR) were applied to a nationwide time‐series study of mean daily summer temperature and emergency hospital admissions for respiratory diseases among older adults (≥65 years) in Switzerland from 1998 to 2023. The proposed approach is most appropriate when events are sparse at fine scale (e.g., daily counts), exposure can be aggregated to harmonized units, and some loss of spatial resolution is acceptable in exchange for temporal consistency and avoidance of interpolation. Limitations include potential masking of local heterogeneity and susceptibility to the modifiable areal unit problem, particularly for exposures with strong small‐scale spatial gradients. This graph‐based harmonization offers a practical solution for maintaining spatiotemporal consistency in longitudinal epidemiological studies where only aggregate health data are available.

Background: Cardiovascular disease (CVD) prevention is a policy priority because of widening inequalities and increasing preventable deaths. The NHS Health Check (NHS-HC) programme aims to promote early detection and prevention of CVD but has a wider-reaching impact owing to targeting multiple risk factors. Although programme evaluations demonstrate improvements in detection and surrogate markers, research on mortality outcomes remain limited, particularly using representative, real-world data. Aim: To evaluate the effectiveness of NHS-HCs in reducing all-cause mortality. Design and setting: Longitudinal cohort study in an inner-city London borough. Method: Data from electronic primary care records were analysed for 158 645 eligible participants (2009–2023). Survival was modelled using a Cox proportional hazards model with time-varying exposure. All-cause mortality risk was compared between those attending ≥1 NHS-HC ( n = 42 589) and those who did not ( n = 116 056). Sociodemographic, geographic, and behavioural factors were adjusted using propensity score weighting. Results: Of the 158 645 participants, 63 347 (39.9%) were of Black, Asian, mixed, or other ethnicity, and 100 825 (63.6%) lived in the two most deprived quintiles in England. Attendance at ≥1 NHS-HC was associated with lower mortality (adjusted hazard ratio 0.68, 95% confidence interval = 0.63 to 0.74), indicating a 32% reduction in risk. Ten-year absolute mortality risk reduction increased with age, with the largest difference among those aged 70–74 years (18.1% versus 12.9%). Sensitivity analyses supported these findings. Conclusion: This study suggests significant long-term survival benefit associated with the NHS-HC, supporting continued investment in and optimisation of the programme to provide population health benefits. Findings were robust to missing data assumptions and COVID-19, although residual confounding cannot be ruled out.

Background: Cohort studies are instrumental in examining long-term risks associated with environmental exposures but require appropriate control for various confounding effects. In this contribution, we assessed this issue by investigating the relationship between fine particulate matter (PM2.5) exposure and mortality in a UK-based cohort. Methods: We analysed data from half a million adults in the UK Biobank linked with time-varying individual-level exposure data and followed up during the period 2006–21. The assessment focused on confounding related to spatial and temporal patterns as well as due to measurable variables, including both contextual and individual-level factors. We performed an evaluation consisting of descriptive analyses, specification and interpretation of direct acyclic graphs (DAGs), and comparison of results from survival models. Results: We found correlations between PM2.5 exposure and mortality rates across time, geographical areas, and categories of measurable variables. The DAG indicated complex causal pathways and the need to adjust for a wide set of potential confounders. The regression analysis confirmed these patterns: the fully adjusted model estimated a hazard ratio (HR) of 1.25 (95% CI: 1.06–1.49) per 10 μg/m3 increments in PM2.5, but the association reversed to 0.82 (0.76–0.87) when excluding control for recruitment centre, suggesting strong spatial confounding. Calendar time showed stronger confounding effects compared to age. Area-level socio-economic indicators were more important than individual-level counterparts, while lack of control for lifestyle factors led to a noticeable overestimation. Conclusions: This case-study illustration elucidates various confounding mechanisms in cohort studies on environmental risks and offers a critical evaluation of alternative adjustment strategies.

We are writing to bring to your attention certain concerns regarding the article titled ‘Co-exposure to multiple air pollutants, genetic susceptibility, and the risk of myocardial infarction onset: a cohort analysis of the UK Biobank participants’ authored by Jiang et al.¹ We believe this represents an important publication that enriches the epidemiological literature. However, after careful examination of the methodology and data presented in the aforementioned paper, it has come to our attention that there seems to be a significant flaw in the design of the study, specifically related to the exposure assignment, which could compromise the validity of the research findings. The authors state in the Methods section that ‘We calculated the average concentration level of air pollution for each participant during the period from the four years preceding the enrollment to the dates when MI onset, death, or the end of follow-up, whichever came first’. In this sentence, it is implied that the exposure average assigned depends on the date of exit from the follow-up for a participant, either as a result of the event occurrence, death, or loss to follow-up.

Recent developments in linkage procedures and exposure modelling offer great prospects for cohort analyses on the health risks of environmental factors. However, assigning individual-level exposures to large population-based cohorts poses methodological and practical problems. In this contribution, we illustrate a linkage framework to reconstruct environmental exposures for individual-level epidemiological analyses, discussing methodological and practical issues such as residential mobility and privacy concerns. The framework outlined here requires the availability of individual residential histories with related time periods, as well as high-resolution spatio-temporal maps of environmental exposures. The linkage process is carried out in three steps: (1) spatial alignment of the exposure maps and residential locations to extract address-specific exposure series; (2) reconstruction of individual-level exposure histories accounting for residential changes during the follow-up; (3) flexible definition of exposure summaries consistent with alternative research questions and epidemiological designs. The procedure is exemplified by the linkage and processing of daily averages of air pollution for the UK Biobank cohort using gridded spatio-temporal maps across Great Britain. This results in the extraction of exposure summaries suitable for epidemiological analyses of both short and long-term risk associations and, in general, for the investigation of temporal dependencies. The linkage framework presented here is generally applicable to multiple environmental stressors and can be extended beyond the reconstruction of residential exposures. Impact: This contribution describes a linkage framework to assign individual-level environmental exposures to population-based cohorts using high-resolution spatio-temporal exposure. The framework can be used to address current limitations of exposure assessment for the analysis of health risks associated with environmental stressors. The linkage of detailed exposure information at the individual level offers the opportunity to define flexible exposure summaries tailored to specific study designs and research questions. The application of the framework is exemplified by the linkage of fine particulate matter (PM_2.5) exposures to the UK Biobank cohort.

While state sequence analysis (SSA) has been long used in social sciences, its use in pharmacoepidemiology is still in its infancy. Indeed, this technique is relatively easy to use, and its intrinsic visual nature may help investigators to untangle the latent information within prescription data, facilitating the individuation of specific patterns and possible inappropriate use of medications. In this paper, we provide an educational primer of the most important learning concepts and methods of SSA, including measurement of dissimilarities between sequences, the application of clustering methods to identify sequence patterns, the use of complexity measures for sequence patterns, the graphical visualization of sequences, and the use of SSA in predictive models. As a worked example, we present an application of SSA to opioid prescription patterns in patients with non-cancer pain, using real-world data from Italy. We show how SSA allows the identification of patterns in prescriptions in these data that might not be evident using standard statistical approaches and how these patterns are associated with future discontinuation of opioid therapy.