Delirium is defined as an acute disturbance in attention, awareness, and cognition that arises as a direct consequence of a physiological event such as an acute illness or infection. It affects roughly one in four older acute medical inpatients, a figure that has remained stubbornly stable across four decades of research. Far from being a benign and transient confusion, delirium is a red flag of underlying vulnerability. It is associated with increased mortality, longer hospital admissions, long-term cognitive decline, poorer functional status, and higher rates of institutionalisation. Crucially, it is also partially preventable: studies suggest that up to a third of new delirium cases in hospitalised patients could be avoided through multicomponent interventions such as reorientation, sleep protection, early mobilisation, and ensuring patients have their glasses and hearing aids. But prevention depends on knowing who is at risk, and that is where the system frequently breaks down.

The Groningen team, led by geriatric medicine researcher Maaike A. Pouw, set out to compare three distinct approaches to delirium identification in the same cohort of patients. The first was a retrospective extraction of documented prehospital indicators: researchers combed through the free-text referral notes and handovers written by general practitioners and ambulance professionals, marking a case as positive when the notes contained an explicit suspicion of delirium or descriptions consistent with it, such as a patient being progressively confused, disoriented, or incoherent. The second was the VMS delirium risk score, a three-question tool used across Dutch hospitals since 2012 that asks about memory problems, need for help with self-care, and any previous episode of delirium. The third was the 4AT, a widely validated four-item screening instrument that assesses alertness, abbreviated mental test performance, attention, and acute mental status change, and can be administered without special training even to drowsy or agitated patients.

The study drew on Acutelines, a hospital-based data and biobank that enrols patients with acute conditions arriving at the emergency department of the University Medical Center Groningen. From September 2020 to February 2024, the researchers included patients aged 70 and older who were admitted for at least one night after their emergency visit and who had a completed 4AT assessment at admission. In total, 655 patients entered the analysis, with a median age of 76 years and 62 percent male. To establish whether patients actually experienced delirium during hospitalisation, the team performed a careful chart-based review using DSM-5 criteria, supplemented by a check for new antipsychotic prescriptions or elevated scores on the Delirium Observation Screening Scale. Uncertain cases were discussed in consensus meetings, a process that applied to 20 percent of all records.

The headline finding was stark. Delirium occurred in 146 of the 655 patients, or 22 percent, consistent with the well-documented burden of the syndrome in older inpatients. When the researchers calculated positive likelihood ratios, a statistical measure of how strongly a positive result points toward the condition, the documented prehospital indicators scored 5.54, the 4AT scored 2.39, and the VMS risk assessment scored just 1.55. In practical terms, when a general practitioner or ambulance medic had written down a suspicion of delirium, that observation was by far the strongest early predictor that the patient would indeed be delirious during the admission. The positive predictive value of the prehospital documentation was 0.64, meaning nearly two-thirds of flagged patients were confirmed cases, and the 4AT achieved an even higher positive predictive value of 0.83, although it flagged only 22 percent of patients as positive.

Each tool, however, told only part of the story. The documented prehospital indicators had a low sensitivity of 0.34, meaning most patients who went on to develop delirium had no such note in their referral documentation. The researchers caution that this may partly reflect documentation gaps rather than a true absence of symptoms at the time of prehospital contact, since emergency crews understandably prioritise physical stabilisation and may not record or convey observations of confusion during a hectic transfer. Among patients with negative prehospital indicators, 16 percent still developed delirium, a figure similar to the 13 percent false-negative rate of the 4AT. The VMS score behaved differently altogether: as a risk-factor-based tool rather than a feature-based one, it generated many false positives, 231 patients or 38 percent of the cohort, but relatively few false negatives, suggesting it is better suited to casting a wide preventive net than to confirming diagnosis.

The study also revealed troubling inconsistencies in routine screening practice. Although compliance with VMS screening was high at 94 percent, exceeding expectations from prior literature, discrepancies appeared in 12 percent of cases. Some patients had been screened twice at different time points with contradictory answers, and in other instances the screening indicated the patient had never experienced delirium even though a previous episode was clearly documented in their medical history. The authors suggest this may reflect the tool’s reliance on patient self-report, since patients may not recall a previous delirium episode, particularly when it occurred on top of underlying dementia. These reliability concerns matter because the VMS score drives decisions about preventive care across Dutch hospitals.

Another striking observation was the limited overlap between the two feature-based approaches. Both the prehospital documentation and the 4AT aim to capture the actual clinical features of delirium at roughly the same point in the care pathway, yet their positive classifications diverged substantially. The authors attribute this to the inherently fluctuating course of delirium, differences in assessment timing, and the possibility that early symptoms resolve quickly after simple interventions such as intravenous fluids or oxygen. They also note that research settings tend to yield higher detection rates than routine care, and that the 4AT in this study was administered partly by trained researchers rather than solely by clinical staff.

The implications reach beyond the emergency department. The authors argue that it would be beneficial to mandate the documentation of cognitive functioning in referral letters and ambulance reports for older patients conveyed to the emergency department, potentially enabling earlier identification and faster intervention, such as involving family members or placing the patient in a calmer environment. They point to emerging technologies that could help, including targeted training for emergency medical services personnel, clinical decision support systems, and natural language processing tools capable of extracting relevant information from clinical narratives automatically. The Institute for Healthcare Improvement recommends delirium screening at least every 12 hours in acute care, but the Groningen team suggests assessment may need to begin even earlier, at the first point of contact at home or in the ambulance.

The study has limitations that the authors acknowledge candidly. It was a single-centre cohort, the medical records were not independently assessed by two reviewers, and the retrospective design meant outcome parameters were restricted to what could be extracted from electronic records. Verbal observations communicated at handover may never have been written down, and chart-based delirium detection depends on the recognition skills of the initial care provider. The interval between early assessment and the occurrence of delirium during a long hospital stay also complicates interpretation, since an initially negative assessment may have been accurate at the time. Nevertheless, the large sample size, multi-year data collection, and realistic representation of routine emergency practice give the findings weight. As the researchers conclude, all three approaches were associated with delirium during hospitalisation, but the humble prehospital note, often scribbled under pressure in the back of an ambulance, may be an underused and valuable asset in protecting older patients from one of medicine’s most consequential syndromes.

Subject of Research: Comparison of three delirium identification approaches in older emergency department patients

Article Title: Comparing three approaches to delirium identification in older patients presenting to the emergency department

Article References: Pouw, M. A., Haan, E. G., Jansen, C. J., Trzpis, M., & van Munster, B. C. (2026). Comparing three approaches to delirium identification in older patients presenting to the emergency department. European Geriatric Medicine. https://doi.org/10.1007/s41999-026-01612-w

Image Credits: AI Generated

DOI: 10.1007/s41999-026-01612-w

Keywords: delirium, emergency department, older patients, 4AT screening, VMS risk score, prehospital care, geriatric medicine, screening tools, likelihood ratio, hospitalisation, cognitive impairment, ambulance notes

The relationship between eating disorders and substance use disorders has long puzzled clinicians. Epidemiological studies consistently show that these conditions co-occur at rates far higher than chance would predict, yet eating disorder symptoms are rarely measured in substance use research. This gap matters because the two disorders may share underlying mechanisms, particularly difficulties in emotional regulation and impulsive behavior when distressed. If researchers studying youth substance use ignore eating pathology, they may be missing a crucial piece of the puzzle that explains why some young people develop severe substance-related problems while others do not. The new study set out to determine whether a brief screener could close that gap without adding meaningful burden to research participants or clinical staff.

The research team analyzed data from 682 young people between the ages of 15 and 25, assessed across 722 visits through a standardized intake process. Each participant completed the Eating Disorder Screen for Primary Care, known as the ESP, along with a comprehensive battery of psychological measures. These included the Difficulties in Emotion Regulation Scale Short Form for emotional dysregulation, the UPPS-P Revised Child Impulsivity Scale for impulsive personality traits including negative urgency, the Patient Health Questionnaire for depression, the General Anxiety Disorder seven-item questionnaire for anxiety, and validated instruments for problems related to cannabis and electronic cigarette or nicotine use. Participants also reported their substance use over the previous thirty days using timeline follow-back methods, a well-established calendar-based approach for reconstructing recent consumption patterns.

The ESP itself is remarkably simple. It asks respondents to rate, on a scale from zero to two, how often they experience concerns such as feeling terrified of being overweight, avoiding eating when hungry, going on eating binges, vomiting after eating, or exercising excessively to control weight. In primary care, a score of two or higher is traditionally used as the clinical cut-off indicating possible eating disorder. But the researchers wanted to know whether that threshold, developed for general medical patients, would perform well in a population of young people recruited specifically for substance use research, where the presentation of eating pathology may differ from typical clinical samples.

To answer that question, the team applied receiver operating characteristic analysis, a statistical technique that evaluates how well a screening tool discriminates between people who have a condition and those who do not across all possible cut-off scores. Clinical diagnoses were established using the Mini International Neuropsychiatric Interview, a structured diagnostic instrument. The analysis examined sensitivity, the proportion of true cases correctly identified; specificity, the proportion of non-cases correctly cleared; and positive and negative predictive values across a range of possible thresholds from two to five. The results showed that the traditional clinical cut-off of two or higher yielded high sensitivity but poor specificity, meaning it caught most true cases but also flagged many young people who did not meet diagnostic criteria.

The receiver operating characteristic curves pointed to a better balance at a score of three or higher. At this revised threshold, the ESP achieved acceptable diagnostic accuracy for detecting eating disorder diagnoses within this particular population, trading a small loss in sensitivity for a meaningful gain in specificity. The finding carries practical significance: screening tools embedded in substance use research protocols and clinical intakes can be tuned to the population being assessed rather than relying on thresholds imported from different settings. For researchers, the tailored cut-off means that a two-minute questionnaire can now yield interpretable eating disorder risk data in studies that were never designed with eating pathology in mind.

Having established the optimal threshold, the researchers then compared young people who screened at risk with those who did not, using Wilcoxon rank sum tests to account for the non-normal distribution of the psychological and substance use measures. The differences were substantial and consistent. Youth who endorsed eating disorder risk reported significantly higher emotional dysregulation, meaning greater difficulty identifying, tolerating, and modulating their emotional states. They also scored higher on negative urgency, a specific facet of impulsivity that describes the tendency to act rashly when experiencing intense negative emotions. This trait has been implicated in both binge eating and substance use, and the new findings suggest it may be a common pathway connecting the two classes of disorders.

The psychiatric burden among those at risk extended to mood and anxiety. Participants flagged by the revised ESP cut-off reported significantly more depressive symptoms on the PHQ and more anxiety symptoms on the GAD-7 than their non-risk peers. Their substance use profiles were equally distinctive. Although they reported lower quantity and frequency of alcohol consumption, they used cannabis more frequently and reported greater problems related to both cannabis, measured with the Marijuana Adolescent Problem Inventory, and nicotine, measured with the Penn State Electronic Cigarette Dependence Index. This pattern suggests that eating disorder risk in youth does not simply generalize to all substance use; instead, it clusters with specific substances, particularly cannabis and nicotine, while alcohol use may actually be reduced.

The authors argue that these findings point to emotional dysregulation as a central risk factor for the co-occurrence of eating disorder symptoms and substance use in young people. When the capacity to manage distressing emotions is compromised, multiple maladaptive behaviors, from restrictive eating and bingeing to cannabis and nicotine use, may emerge as alternative strategies for emotional relief. This transdiagnostic perspective has implications for treatment: interventions that build emotion regulation skills, such as dialectical behavior therapy approaches, could potentially address both conditions simultaneously rather than treating them in isolation. It also suggests that substance use treatment programs should consider screening for eating pathology, since young people with co-occurring eating disorder risk may require tailored support to achieve better outcomes.

For clinicians and researchers alike, the study offers a pragmatic message. Brief, low-effort screening tools like the ESP can be incorporated into substance use research and clinical intake processes with minimal burden, yet they capture clinically meaningful information that would otherwise be lost. The tailored cut-off of three or higher demonstrated acceptable diagnostic accuracy in this youth sample, and the psychological profile it identifies, marked by emotional dysregulation, negative urgency, depression, anxiety, and heavier cannabis and nicotine involvement, provides researchers with concrete targets for understanding why eating disorders and substance use disorders so often travel together. As youth mental health services grapple with rising rates of both conditions, a one-minute questionnaire may prove to be an unexpectedly powerful window into the shared roots of these intertwined disorders.

Subject of Research: Eating disorder risk screening and its mental health and substance use correlates in adolescents and young adults

Article Title: Distinct mental health and substance use patterns in youth who endorse eating disorder risk using the eating disorder screen for primary care

Article References: Giddens, E., Kirkland, A. E., Mahlberg, J., Ferguson, P. L., Steward, T., Verdejo-Garcia, A., Tomko, R. L., & Squeglia, L. M. (2026). Distinct mental health and substance use patterns in youth who endorse eating disorder risk using the eating disorder screen for primary care. Journal of Eating Disorders. https://doi.org/10.1186/s40337-026-01792-7

Image Credits: AI Generated

DOI: 10.1186/s40337-026-01792-7

Keywords: eating disorders, substance use, screening tools, youth, emotional dysregulation, impulsivity, cannabis, nicotine, depression, anxiety, primary care, diagnostic accuracy

Screening tools such as the federal Climate and Economic Justice Screening Tool and various state-level equivalents are designed to synthesize large volumes of environmental, demographic, and socioeconomic data into a single judgment about whether a community qualifies as disadvantaged. On the surface, this seems like a straightforward task of measurement. In practice, the researchers show, it involves a cascade of decisions, each of which can shift outcomes substantially. Analysts must decide which indicators to include, how to combine them, whether to compare communities at the national or state level, which geographic units to use as the basis of analysis, and what thresholds separate the designated from the undesignated. None of these decisions is dictated by data alone; each reflects a policy judgment about what disadvantage means and how it should be recognized.

The study systematically varies these methodological choices and measures how sensitive the resulting maps of disadvantage are to each one. The results are striking. Depending on the combination of choices made, the same underlying data can produce markedly different sets of designated communities, with some methodological configurations flagging far larger populations than others. The geographic unit of analysis emerges as one of the most powerful levers. Tools that operate on census tracts can identify pockets of disadvantage that are invisible when counties serve as the unit, while county-level aggregation can dilute concentrated hardship within larger, more heterogeneous areas. Conversely, very fine-grained units can be sensitive to boundary artifacts and small-sample noise in survey data.

Threshold selection proves equally consequential. Many screening tools designate a community as disadvantaged if it exceeds a percentile cutoff on one or more indicators, for example falling within the top quarter of all communities for pollution burden or the bottom quarter for income. Moving a cutoff even modestly can add or remove thousands of communities from the designated list. The researchers demonstrate that these threshold effects are not uniform across the country: in densely populated urban regions, small changes in cutoffs translate into large swings in the number of affected residents, while in rural areas the same changes may alter designations for vast land areas but relatively few people. The practical stakes of a seemingly technical parameter therefore differ enormously depending on where a community sits.

The choice of indicators themselves introduces another layer of variability. Some tools emphasize environmental exposure measures such as air toxics concentrations, particulate matter levels, and proximity to hazardous facilities. Others weight socioeconomic vulnerability more heavily, incorporating poverty rates, educational attainment, housing costs, linguistic isolation, and health prevalence data. Because these dimensions of disadvantage only partially overlap, a community that scores as severely burdened on an exposure-centered tool may fail to qualify under a vulnerability-centered one, and vice versa. The study shows that the correlation between designations produced by different indicator sets is far from perfect, meaning that communities can be treated inconsistently across programs even within the same jurisdiction.

Aggregation methods add further complexity. When multiple indicators must be combined into a composite score, analysts must choose between approaches such as averaging, summing binary flags, or requiring that a community exceed thresholds on multiple categories simultaneously. These choices encode different assumptions about whether disadvantages are interchangeable, whether they compound, or whether certain burdens are non-negotiable. A tool that designates a community when any single indicator crosses a threshold will cast a far wider net than one that requires burdens across several categories at once. The researchers find that this single design decision can be as influential as the choice of data sources, reshaping the designated population by substantial margins.

These methodological variations matter because designation carries real consequences. Disadvantaged community status increasingly serves as a gatekeeper for funding under major climate and infrastructure legislation, influencing where investments in clean energy, transit, water systems, and resilience projects flow. Communities that fall just outside a designation may be excluded from programs despite facing conditions nearly identical to those just inside the line. The study highlights how such boundary effects can produce sharp discontinuities in eligibility between neighboring areas, raising questions about fairness and administrative coherence. When two adjacent neighborhoods with similar pollution burdens and incomes receive different treatment because of where a percentile cutoff happens to fall, the legitimacy of the screening exercise is called into question.

The authors argue that the solution is not to search for a single objectively correct methodology, since every design choice involves legitimate value judgments, but to make those judgments transparent, deliberate, and accountable. They recommend that agencies document the rationale behind indicator selection, threshold placement, and geographic choices, and that they test the sensitivity of their designations to reasonable alternative configurations. Publishing designation maps alongside uncertainty or sensitivity analyses would allow policymakers, advocates, and residents to understand how robust a given designation is, and would help identify communities that hover near eligibility boundaries and may warrant case-by-case review. The study also suggests that tools could be designed with explicit equity objectives in mind, choosing methodological configurations that align with the distributive goals of the programs they serve rather than treating technical defaults as neutral.

For the growing community of researchers and practitioners working at the intersection of data science and environmental policy, the study offers a caution about algorithmic governance more broadly. Screening tools compress complex, multidimensional social and environmental conditions into binary categories, and that compression inevitably involves choices that shape outcomes. The lesson is not that such tools should be abandoned, since they bring consistency, scale, and defensibility to decisions that would otherwise be made ad hoc, but that their architecture deserves the same scrutiny as the policies they implement. As more governments at every level adopt screening tools to operationalize justice commitments, the methodological details examined in this research will increasingly determine whether those commitments reach the communities they were intended to serve, or whether they dissolve into the fine print of percentile cutoffs and geographic units chosen without deliberation.

Subject of Research: How methodological variations in environmental justice screening tools affect disadvantaged community designations

Article Title: Methodological Variations in Environmental Justice Screening Tools and Their Impact on Disadvantaged Community Designations

Article References: Robbins, T., Li, Q., Bird, S., & Powers, S. E. (2026). Methodological Variations in Environmental Justice Screening Tools and Their Impact on Disadvantaged Community Designations. Nature Communications. https://doi.org/10.1038/s41467-026-77824-2

Image Credits: AI Generated

DOI: 10.1038/s41467-026-77824-2

Keywords: environmental justice, screening tools, disadvantaged communities, Climate and Economic Justice Screening Tool, policy designations, census tracts, threshold sensitivity, indicator selection, algorithmic governance, environmental policy, funding allocation, geographic units