The scale of the underlying dataset is one of the study’s most striking features. The team examined primary and secondary care costs for the financial year 2018/19 among 10,119,490 adult patients registered with general practices contributing to the Clinical Practice Research Datalink and linked to Hospital Episode Statistics, a cohort representing roughly 23 percent of the adult population of England. Because the data capture both general practice contacts and hospital activity, the researchers could compute a genuinely comprehensive picture of each patient’s annual cost to the system, valued in 2018 prices using established unit cost references. High-cost patients were defined as those falling in the top one percent of total care costs, a threshold widely used in health economics because spending is so heavily concentrated at the extreme of the distribution.

The numbers illustrate that concentration vividly. High-cost patients had average annual costs of £17,485, with a median of £14,609 and an interquartile range running from £12,028 to £19,633. The rest of the population, by contrast, averaged just £653 per year, with a median of only £103 and an interquartile range of £14 to £352. In other words, the typical high-cost patient cost the system well over a hundred times as much as the typical patient, and even the cheapest quarter of the high-cost group outspent the most expensive quarter of everyone else by a wide margin. Understanding who these patients are, the authors argue, is therefore central to any serious attempt at resource allocation, case management and health system planning.

Methodologically, the study is a careful exercise in unsupervised learning, the branch of machine learning that searches for structure in data without any predefined labels. The researchers clustered patients on the basis of diagnoses, age, sex, deprivation measured by the Index of Multiple Deprivation, and ethnicity. They compared multiple algorithms, including approaches built on multiple correspondence analysis and t-distributed stochastic neighbour embedding for dimensionality reduction, before settling on hierarchical clustering as the optimal method. That choice was not arbitrary: the team evaluated candidate solutions using established validity and stability indices, including the Davies–Bouldin Index, the Calinski–Harabasz index and the Adjusted Rand index, which reward clusters whose members are internally similar, well separated from other clusters, and reproducible under resampling.

The winning solution produced nine clusters with clearly differentiated multimorbidity and sociodemographic profiles. Mean age across the clusters ranged from 56 to 79 years, a reminder that high costs are not confined to the very old. Mean annual costs per cluster ranged between £15,792 and £19,107, with confidence intervals indicating that these differences were estimated precisely. The spread of clinical pictures was equally wide. Several core profiles recurred across different clustering approaches and across subgroup analyses, suggesting they represent genuine and robust archetypes within the high-cost population rather than statistical artefacts of a single algorithm.

Among those recurring archetypes, four stand out. Younger people with liver disease and mental health conditions formed one recognisable group, a combination that clinicians will recognise as associated with complex social circumstances and frequent emergency attendance. Patients with nodal metastases, meaning cancers that have spread to lymph nodes, formed another, as did patients with prostate cancer complicated by nodal metastases. The fourth recurring profile comprised older people with cardiovascular disease and dementia, a group whose costs typically accumulate through repeated admissions, long hospital stays and heavy reliance on both primary and secondary care. That the same profiles emerged repeatedly, including in analyses restricted to in-year decedents and to patients with the highest unplanned care costs, strengthens the case that these are stable, clinically meaningful segments.

The study also stratified its analyses by age, examining age-stratified subgroups alongside the whole population. This matters because the drivers of high cost plausibly differ across the life course: a 58-year-old with alcoholic liver disease and depression uses services very differently from an 80-year-old with heart failure and cognitive decline. By confirming that the core clinical profiles persisted across these stratifications, the researchers provide evidence that segmentation is not simply a proxy for age. The consistency of the clusters across methods and subgroups was assessed formally, and the authors report that the hierarchical solution performed best on the validity indices they applied, giving the nine-group taxonomy a firmer empirical footing than ad hoc clinical categorisations.

Why does this matter for policy? Population health management, the approach increasingly adopted by integrated care systems in England, depends on being able to identify groups of patients whose needs are similar enough to be addressed by a common service model. If the top one percent of patients were homogeneous, a single intensive case management programme might suffice. The new analysis shows the opposite: a younger cohort dominated by liver disease and mental illness needs addiction, psychiatric and social care integration, while an older cohort with dementia and cardiovascular disease needs falls prevention, medication review and coordinated discharge planning. Designing services for a blended average of these groups risks serving none of them well. Cluster-based segmentation, the authors suggest, could inform the targeting of case management programmes, the allocation of budgets and longer-term workforce and capacity planning.

The research also carries implications for how health systems think about equity. By incorporating deprivation and ethnicity into the clustering alongside diagnoses, the analysis acknowledges that cost is shaped by social context, not just by disease burden. Patients in deprived areas often present later, have more multimorbidity at younger ages, and generate higher unplanned care costs, so a segmentation that ignores socioeconomic characteristics would systematically misdescribe the population. The study was approved by the Clinical Practice Research Datalink’s independent scientific advisory committee and used anonymised patient data under ethical approval from the Health Research Authority, and the authors declare no competing interests. Funding came from the Health Foundation and the National Institute for Health and Care Research, with the funders having no role in study design, analysis or the decision to publish.

Limitations and next steps remain. The analysis reflects a single financial year, 2018/19, before the disruptions of the pandemic reshaped waiting lists and care patterns, and cost data capture NHS spending rather than the full social care picture. Unsupervised clustering, however carefully validated, identifies statistical structure rather than causal mechanisms, so the clusters describe who high-cost patients are rather than why their costs are high. Even so, the study offers one of the most comprehensive segmentations of a national high-cost population attempted anywhere, drawing on a cohort of over ten million linked primary and secondary care records. Its central message is likely to travel well beyond England: the costliest patients are a heterogeneous collection of recognisable clinical archetypes, and health systems that learn to see them as distinct groups, rather than as an undifferentiated burden, will be better placed to design care that is both more effective and more humane.

Subject of Research: Machine learning segmentation of high-cost patients in the English NHS

Article Title: Segmentation of high-cost patients in England: insights from a machine learning analysis

Article References: Wang, S., Anselmi, L., Sutton, M., Kontopantelis, E., Beaney, T., & Anderson, M. (2026). Segmentation of high-cost patients in England: insights from a machine learning analysis. BMC Medicine. https://doi.org/10.1186/s12916-026-05233-9

Image Credits: AI Generated

DOI: 10.1186/s12916-026-05233-9

Keywords: high-cost patients, machine learning, hierarchical clustering, multimorbidity, NHS, health economics, population health management, CPRD, Hospital Episode Statistics, patient segmentation, healthcare costs, England

The study, published in BMC Psychiatry, was led by Aman Jat and Hajira Dambha-Miller of the Primary Care Research Centre at the University of Southampton, together with colleagues at the University of Manchester and Newcastle University. The team set out to map the equity-relevant inputs of AI-based mental health diagnostic and triage tools onto routinely collected primary care data, using one of the largest and most representative primary care datasets in the world. Their focus was a group that is often invisible in technology design: adults living with multiple long-term conditions, or MLTC, whose complex health needs and exposure to structural inequalities make them especially vulnerable to biased algorithms.

The scale of the analysis is striking. The researchers drew on linked data from the Clinical Practice Research Datalight — specifically its Gold and Aurum databases — covering 7,186,890 adults aged 18 and over with multiple long-term conditions in England, with records spanning 1987 to 2020. This is not a sample or a convenience cohort; it is a population-based picture of primary care as it is actually delivered. The team then identified five AI-based mental health diagnostic or triage tools through a scoping review and systematically mapped the variables those tools require as inputs onto the sociodemographic, clinical and social-care factors that can be captured in routine primary care records.

To structure that mapping, the researchers turned to two established frameworks for thinking about health inequalities. The first is CORE20PLUS5, NHS England’s approach that identifies the most deprived 20 percent of the population as a priority group alongside five clinical focus areas. The second is PROGRESS-Plus, a widely used framework that enumerates the dimensions along which health opportunities are unequally distributed: place of residence, race, ethnicity, culture and language, occupation, gender and sex, religion, education, socioeconomic status and social capital, plus additional stratifying factors such as disability and social care involvement. Using these lenses, the team built an Equity-Variable Coverage Index — a quantitative measure of the proportion of equity-relevant variables that each tool actually includes among its inputs.

The results reveal a landscape of uneven and often patchy coverage. Across the five tools, equity domain coverage ranged from just 13.0 percent to 56.5 percent — meaning that even the best-performing tool incorporated barely more than half of the equity-relevant characteristics the researchers considered, while the worst captured barely one in eight. Clinical characteristics fared considerably better than sociodemographic or social-care factors. Drug and alcohol misuse was the only equity-relevant variable included in all five tools. By contrast, age, ethnicity, deprivation, disability status and social care involvement were incorporated only inconsistently — a finding with real consequences, because these are precisely the factors that shape who becomes ill, who seeks help, and how their care unfolds.

There is an important technical caveat here, and the authors are careful to make it. Many of the symptom-level psychiatric inputs that are central to the original AI models — the granular clinical features that machine learning systems often rely on for mental health diagnosis — could not be captured in routine primary care data and were therefore excluded from the mapping exercise. The Equity-Variable Coverage Index, the researchers stress, measures input coverage rather than demonstrated fairness. A tool that includes more equity-relevant variables is not automatically a fairer tool; but a tool that is blind to deprivation, ethnicity or disability cannot even be assessed for fairness across those dimensions, let alone corrected.

To demonstrate why these variables matter empirically, the team fitted multivariable logistic regression models for hospital admission — used as a proxy outcome influenced by both system-level and clinical factors — and then systematically removed each equity-related variable in turn, a leave-one-out design that reveals how much explanatory work each variable performs. The results were unambiguous. Removing ethnicity and age produced the largest reductions in the model’s explanatory performance, followed by outpatient attendance, Accident and Emergency attendance, and drug and alcohol misuse. In other words, several of the very variables that AI mental health tools most often omit are among the strongest statistical signals associated with hospital admission among adults with multiple long-term conditions.

The implications extend well beyond the United Kingdom. Multimorbidity is one of the fastest-growing challenges facing health systems worldwide, and people with multiple long-term conditions are disproportionately likely to experience mental ill health, poverty and fragmented care. If AI triage tools are deployed in such populations without adequate representation of deprivation, ethnicity, disability or social care involvement, they risk systematically underestimating risk in the groups who need the most support — quietly amplifying the very inequalities that health policy aims to reduce. An algorithm that performs well on average can still fail badly for specific subgroups, and input coverage is the first, necessary condition for detecting such failures.

The study also highlights a structural tension in health AI development. Research-grade models are often trained on rich, purpose-collected datasets containing detailed symptom assessments, validated questionnaires and self-reported social information. Routine primary care records, by contrast, are collected for clinical and administrative purposes, and their coverage of social determinants — housing, employment, social support, care involvement — is notoriously incomplete. When such models are operationalised in real health systems, the gap between what they were designed to consume and what the data infrastructure can supply becomes a source of silent degradation. The authors argue that embedding equity considerations into tool design from the outset, and routinely evaluating both model inputs and performance using population-scale data, is essential for fair and responsible deployment.

Funded by the National Institute for Health and Care Research’s cross-NIHR collaboration on multiple long-term conditions, the study offers a practical template that other health systems could adopt: identify the equity-relevant variables available in routine data, quantify how much of that landscape each AI tool actually sees, and test empirically how much predictive information those variables carry. As mental health AI moves from pilot projects to frontline deployment, this research delivers a clear message to developers, regulators and clinicians alike. Fairness in medical AI does not begin with clever debiasing algorithms; it begins with making sure the model knows who the patient is. On that measure, the current generation of AI mental health tools still has a long way to go.

Subject of Research: Equity-relevant input coverage of AI mental health tools mapped to routine primary care data in adults with multiple long-term conditions

Article Title: Mapping equity-relevant inputs of AI mental health tools to routine primary care data: a population-based study of 7.19 million adults with multiple long-term conditions in England

Article References: Jat, A., Zghebi, S. S., Suklan, J., Smith, L., & Dambha-Miller, H. (2026). Mapping equity-relevant inputs of AI mental health tools to routine primary care data: a population-based study of 7.19 million adults with multiple long-term conditions in England. BMC Psychiatry. https://doi.org/10.1186/s12888-026-08723-5

Image Credits: AI Generated

DOI: 10.1186/s12888-026-08723-5

Keywords: artificial intelligence, mental health, health equity, multimorbidity, electronic health records, primary care, CPRD, health inequalities, machine learning, clinical decision support, social determinants of health, NHS

The research team, led by Xiaochen Yang and Min Gao of the Nuffield Department of Primary Care Health Sciences, drew on the Clinical Practice Research Datalink Aurum, a vast database of anonymised electronic health records from British general practices. Because almost the entire UK population is registered with a GP, these records offer an unusually complete picture of what happens to patients long after they leave the operating theatre. The researchers identified adults aged 18 or older with severe obesity, defined as a body mass index of 35 kilograms per square metre or higher, who had never previously been diagnosed with depression or anxiety. Crucially, by excluding people with a pre-existing psychiatric history, the study could focus on new-onset, or incident, mental illness rather than the continuation of old conditions.

From this population, the team assembled two carefully matched groups. Between January 2000 and January 2022, 7,814 patients underwent bariatric surgery, whether gastric bypass, sleeve gastrectomy, or adjustable gastric banding. Each surgical patient was matched to five patients with severe obesity who received behavioural weight management, such as structured diet and lifestyle programmes, producing a comparison group of 36,941 people. Matching was performed on age, sex, body mass index, and calendar year, so that the two groups started from broadly similar demographic and clinical positions. In total, 44,755 participants were followed for a median of 4.8 years, with some tracked for as long as 15 years, generating an unusually robust long-term dataset for this kind of question.

The results were striking. Over the follow-up period, 23.1 percent of surgical patients developed depression, compared with 17.4 percent of those in the behavioural weight management group. Anxiety followed a similar pattern, with new diagnoses in 9.0 percent of the surgery group versus 7.1 percent of the comparison group. When the researchers applied multivariable Cox proportional hazards models, statistical tools that estimate the risk of an event over time while adjusting for other factors, surgery was associated with a 36 percent higher hazard of incident depression and a 31 percent higher hazard of incident anxiety. In epidemiological terms, the hazard ratios of 1.36 and 1.31, with confidence intervals that excluded the null value of one, indicate associations that are unlikely to be explained by chance.

The consistency of the signal across subgroups strengthens the finding. The elevated risk of depression appeared regardless of whether patients started with a body mass index of 35 or well above it, and both depression and anxiety risks were raised across all three procedure types, from the restrictive gastric band to the more invasive gastric bypass. The researchers also tested whether the effect differed by age or sex and found no evidence of interaction, meaning the association held for men and women, and for younger and older patients alike. Sensitivity analyses, in which the team varied definitions and model assumptions, left the conclusions intact, suggesting the result is not an artefact of a particular analytical choice.

Why might an operation that so often improves lives also raise the risk of psychiatric illness? The study was observational, so it cannot establish cause and effect, but several biological and psychological mechanisms are plausible. Rapid and substantial weight loss after surgery dramatically alters gut hormone signalling, including changes in ghrelin, GLP-1, and peptide YY, hormones that communicate between the gut and the brain and are increasingly implicated in mood regulation. Nutrient malabsorption, particularly after gastric bypass, can lead to deficiencies in thiamine, vitamin B12, folate, and iron, all of which have documented links to depressive symptoms when left uncorrected. Alcohol absorption also changes after bypass procedures, and disordered drinking is a recognised psychiatric complication in some surgical populations.

Psychological factors may be equally important. Surgery reshapes a person’s relationship with food at extraordinary speed, and patients who relied on eating as a coping strategy can find themselves without that mechanism while still facing the life stressors that contributed to their obesity in the first place. Body image does not always adjust in step with the scales, and some patients report a difficult transition period in which loose skin, changed social dynamics, and unmet expectations take a toll. There is also the possibility of reverse causation in a subtler form: patients who undergo surgery may simply interact with the health system more frequently, increasing the chances that emerging depression or anxiety is detected and recorded, whereas similar symptoms in the behavioural group might go unrecognised in primary care records.

The authors are careful to frame their findings as a call for vigilance rather than a warning against surgery. Bariatric procedures remain among the most effective treatments available for severe obesity and its metabolic complications, and the absolute increase in psychiatric diagnoses, while statistically robust, translates into a modest difference for any individual patient. The practical implication is that mental health should be monitored as systematically as nutritional status in the years after an operation. Structured screening for depression and anxiety at routine follow-up appointments, clear pathways to psychological support, and education for patients and clinicians about early warning signs could all help ensure that problems are caught before they become disabling.

The study also carries a broader message for the field of obesity medicine. As newer pharmacological therapies, including GLP-1 receptor agonists, reshape the treatment landscape, questions about the psychological consequences of dramatic weight loss are no longer confined to the surgical clinic. Weight loss of any kind, achieved by scalpel or by injection, produces profound physiological and emotional change, and the Oxford findings suggest that clinicians should not assume mental wellbeing improves automatically alongside physical health. The research was funded by the National Institute for Health and Care Research through the Oxford Health Biomedical Research Centre and the Oxford and Thames Valley Applied Research Collaboration, and the authors declare no competing interests.

For the hundreds of thousands of people worldwide who undergo bariatric surgery each year, the message is not that the operation is dangerous to the mind, but that recovery is a whole-person process. A procedure that successfully reverses diabetes and extends life expectancy can still leave a patient struggling in ways that never appear on a surgical chart. This study, one of the largest population-based comparisons of its kind, gives clinicians the evidence they need to make psychological follow-up a standard of care rather than an afterthought, and it gives patients permission to raise low mood or anxiety as legitimate medical concerns in the years after their operation, deserving of the same attention as any wound, deficiency, or metabolic change.

Subject of Research: Long-term risk of depression and anxiety after bariatric surgery compared with behavioural weight management

Article Title: Long-term risk of clinical depression and anxiety after bariatric surgery: a population-based matched cohort study

Article References: Yang, X., Dong, X., Jebb, S. A., Aveyard, P., & Gao, M. (2026). Long-term risk of clinical depression and anxiety after bariatric surgery: a population-based matched cohort study. BMC Medicine. https://doi.org/10.1186/s12916-026-04922-9

Image Credits: AI Generated

DOI: 10.1186/s12916-026-04922-9

Keywords: bariatric surgery, depression, anxiety, obesity, mental health, cohort study, CPRD, gastric bypass, weight management, epidemiology, psychiatry, long-term outcomes

The research, published in eClinicalMedicine, drew on the Clinical Practice Research Datalink Aurum database, an anonymized repository of medical records covering more than 50 million patients registered with over 1800 general practices across England. The investigators linked these primary care records to Hospital Episode Statistics, which capture every admission to publicly funded hospitals in the country, allowing them to track both routine diagnoses and serious exacerbations of lung disease. From this vast data resource, they assembled a cohort of 444,668 adults aged 50 and over with a physician diagnosis of COPD, who together contributed nearly 2.3 million person-years of follow-up. Within this group, 37,645 people, or 8.5 percent, were newly diagnosed with atrial fibrillation during the study window.

The scale of the comorbidity problem is difficult to overstate. COPD affects more than 390 million people worldwide and is the third leading cause of death globally, while atrial fibrillation touches over 52 million lives. Previous meta-analyses have estimated that patients with COPD face roughly twice the risk of developing atrial fibrillation compared with those without the lung disease, and that risk climbs as lung function deteriorates. The new study reinforces this dose-response relationship with unusual granularity. Incidence rates rose consistently with age, were higher in men than women across every age band, and were markedly elevated among patients with more severe COPD, whether severity was measured by forced expiratory volume in one second, breathlessness scores, or recent exacerbations requiring hospitalization. Patients hospitalized for a severe COPD flare in the previous year showed incidence rates above 55 per 1000 person-years by 2022, several times higher than those with well-preserved lung function.

The temporal pattern also tells a story about modern healthcare. After a gradual year-on-year climb, incidence plateaued between 2016 and 2019, then dropped 22 percent in 2020 compared with 2019, a dip the authors attribute to reduced access to primary care during the COVID-19 pandemic. The rebound afterward suggests the underlying trend was never interrupted, merely masked. The researchers note that the rising trajectory mirrors patterns seen in the general UK population, implying that improved detection and better survival among people with atrial fibrillation risk factors, rather than something unique to COPD, may be driving much of the increase. Still, the consistency of the elevation across every marker of COPD severity points to a genuine biological contribution, likely involving chronic inflammation, hypoxia, and the structural cardiac changes that accompany long-standing lung disease.

Why does this matter so much? Because atrial fibrillation in a COPD patient is more dangerous than atrial fibrillation alone. Recent cohort evidence indicates that people with both conditions face approximately 1.6 times the risk of ischemic stroke compared with those who have atrial fibrillation without COPD. Oral anticoagulants are the cornerstone of stroke prevention in atrial fibrillation, and current guidelines recommend that treatment decisions be made independently of COPD status. Yet the real-world evidence on whether this actually happens has been sparse and contradictory, with some studies suggesting higher anticoagulation rates in COPD patients and others suggesting lower or equivalent rates.

The new findings go further than any previous work by tracking prescribing trends over thirteen years. Among the 34,851 patients newly diagnosed with atrial fibrillation during follow-up, 66.3 percent received an oral anticoagulant within a year of diagnosis. The rate of initiation nearly tripled over the study period, from 10.3 prescriptions per 100 person-months in 2010 to 30.1 per 100 person-months in 2022, a 2.9-fold increase. The most dramatic rise occurred among patients aged 90 and older, whose initiation rate was more than nine times higher in 2022 than in 2010, albeit from a very low baseline. This transformation was driven almost entirely by the direct oral anticoagulants, or DOACs, which received European approval for stroke prevention in 2011. Warfarin and other vitamin K antagonists, once the only option, collapsed to a 96 percent lower initiation rate by 2022, while DOAC use surged 64-fold between 2012 and 2022.

The fine-grained prescribing data reveal how completely the therapeutic landscape has changed. Apixaban emerged as the dominant agent, peaking at 208.1 prescriptions per 1000 person-months in 2021 and accounting for 61.6 percent of all oral anticoagulants started between 2020 and 2022. Edoxaban, introduced in 2016, overtook rivaroxaban by 2020 and then jumped from 49.4 to 95.4 prescriptions per 1000 person-months between 2021 and 2022. Rivaroxaban, an early favorite, peaked in 2016 and declined steadily thereafter. Dabigatran, the first DOAC approved, never gained significant traction. Notably, these patterns closely match prescribing trends reported in atrial fibrillation patients generally, suggesting that the presence of COPD did not materially alter which drug clinicians chose, only, in some cases, whether they prescribed one at all.

And that is where the treatment gap emerges. Despite the overall tripling of anticoagulation, several high-risk groups were persistently less likely to receive treatment. Patients aged 90 and older had the lowest initiation rates of any age group throughout the study, even though stroke risk climbs steeply with age and guidelines recommend anticoagulation for most patients with atrial fibrillation over 75. Non-White patients were prescribed anticoagulants less often than White patients, echoing documented disparities in atrial fibrillation care more broadly. Most strikingly, people with the most severe COPD, identified by low percent-predicted lung function, severe breathlessness, or recent hospitalization for an exacerbation, consistently received fewer prescriptions than those with milder disease, a pattern that held across all years, ages, and sexes. Patients with chronic kidney disease, liver disease, a history of bleeding, cancer, or dementia were also less likely to be treated.

The authors caution that these patterns may not reflect inappropriate care alone. Contraindications, frailty, polypharmacy, and patient preferences all legitimately shape prescribing decisions, and clinicians may reasonably hesitate before adding a blood thinner to a regimen already burdened by multiple respiratory medications. Yet the concern about physician hesitancy is hard to dismiss, because evidence on the safety and efficacy of anticoagulants specifically in patients with coexisting COPD and atrial fibrillation remains thin. Without dedicated trials or robust observational outcome data in this population, uncertainty itself becomes a barrier to treatment, and the patients facing the highest thromboembolic risk are left in a zone of clinical ambiguity.

The study has limitations worth noting. The database does not capture prescriptions issued by specialists or during hospitalizations, which may lead to modest underestimation of prescribing rates, though the central role of general practitioners in the UK system likely mitigates this. Restricting the cohort to adults over 50 reduced but did not eliminate the possibility of misclassifying some asthma patients as having COPD. The ethnicity-stratified analyses aggregated small numbers of non-White patients into a single category, limiting insight into specific minority subgroups, and the cohort included only individuals with COPD, so comparisons with the general population are indirect. Even so, the sheer size of the cohort, the representativeness of the data, and the consistency of results across sensitivity analyses using multiple imputation lend considerable weight to the conclusions. The message for clinicians and health systems is clear: as atrial fibrillation becomes increasingly common among people with COPD, stroke prevention must keep pace, and the oldest, sickest, and most ethnically marginalized patients, precisely those at greatest risk, should not be left behind. Integrated care pathways and routine bidirectional screening for both conditions, identified as research priorities by the American Thoracic Society, may offer a route forward, but the first step is recognizing that a growing treatment gap exists at all.

Subject of Research: Temporal trends in atrial fibrillation incidence and oral anticoagulant prescribing among adults with COPD in UK primary care

Article Title: Trends in the incidence of atrial fibrillation and the prescription of oral anticoagulants in adults with chronic obstructive pulmonary disease in UK primary care (2010–2022): a cohort study

Article References: Murray, B., Beradid, S., Ernst, P., Suissa, S., & Renoux, C. (2026). Trends in the incidence of atrial fibrillation and the prescription of oral anticoagulants in adults with chronic obstructive pulmonary disease in UK primary care (2010–2022): a cohort study. eClinicalMedicine, 100, Article 104245. https://doi.org/10.1016/j.eclinm.2026.104245

Image Credits: AI Generated

DOI: 10.1016/j.eclinm.2026.104245

Keywords: atrial fibrillation, COPD, oral anticoagulants, DOACs, stroke prevention, UK primary care, CPRD, cohort study, health disparities, warfarin, apixaban, epidemiology