4.1 Summary of Findings

In this population-based cohort of 123,662 patients with polypharmacy, 2.6% experienced an implied ADR-admission within 1 year. This compares with 5.7% with AE-admissions (approach 1), 0.4% with explicit ADR-admissions (approach 2) and 19.0% with all-cause admissions (i.e. any emergency admission). Among those hospitalised for GI bleeding specifically, preventable ADR-admissions (approach 3) were observed in 3.1% of cases compared with 61.8% under the implied ADR-admission approach. These findings highlight the implied ADR-admission approach as a method that captures more drug-related cases than coding- or preventability-based definitions, while remaining more specific than AE-based definitions. The implied ADR-admissions accounted for 13.7% of all-cause admissions—consistent with previous estimates from studies using expert review as a gold standard [3].

Fall or fall injury had the highest incidence of both AE- and implied ADR-admissions, accounting for about one third of cases in each group when measured by incidence rates. However, the distribution of other event types differed notably. Acute kidney injury and heart failure were less common in implied ADR-admissions, suggesting these may occur without a high-risk medication trigger. In contrast, bleeding events were more prominent among implied ADR-admissions, often linked to antithrombotic medications. Two drug-event combinations— fall or fall injury with anticholinergic drugs and/or benzodiazepines and bleeding outside the GIT with antithrombotic medications—accounted for nearly half of all implied ADR-admission incidence. Findings based on patients’ first admissions closely reflected those from analyses including all admissions, underscoring the robustness of the results across different analytical perspectives.

In a secondary analysis, we examined baseline predictors for all-cause, AE-admissions and implied ADR-admissions to explore distinct risk profiles. Older age, polypharmacy, multimorbidity and prior healthcare use were common predictors across all admission types, highlighting the vulnerability of high-risk patients. Some differences emerged: sex was associated only with all-cause admissions, and prior heart failure-related hospitalisations predicted AE-admissions but not implied ADR-admissions. Notably, exposure to PIMs was uniquely associated with implied ADR-admissions, suggesting it as a key marker of drug-related harm. Prior hypokalaemia-related hospitalisation (OR > 6) and elevated liver enzymes (gamma-glutamyl transferase) were also strongly linked to implied ADR-admissions, pointing to risks related to diuretic and alcohol use. These findings indicate that, while implied ADR-admissions share general risk factors with other admissions, certain characteristics—especially PIM exposure and specific prior AEs—disproportionately increase ADR risk. This supports the utility of the implied ADR-admission approach in identifying high-risk patients and guiding preventive strategies.

4.2 Comparison to Literature4.2.1 Incidence of Drug-Related Admissions

Our study introduces the concept of implied ADR-admissions, defined as hospitalisations for specific AEs in patients recently exposed to medications known to cause those events, without requiring explicit ADR codes or pre-specified medication errors. Using this approach, we found that roughly one in seven all-cause admissions in polypharmacy patients were drug-related. This compares to two major bodies of literature: studies using explicit ADR codes and those using primary data collection (e.g. chart review or prospective observation).

Studies relying on explicit ADR coding have consistently reported very low rates of drug-related admissions because of under-reporting, with figures as low as 0.7% in a German study [11, 12]. Even when broader algorithms are applied (e.g. including “likely” or “possible” ADRs), the incidence only rises slightly. Our findings (the explicit ADR-admission incidence was 2.3% of that of all-cause admissions) are consistent with these low estimates. Even the preventable ADR-admission approach (i.e. identifying cases linked to prior medication errors) captures drug-related hospitalisations only to a small extent, as we demonstrated using GI bleeding as an example. In contrast, studies using primary data collection have reported up to 14% of hospital admissions as drug-related [3, 4, 6, 8]. Our implied ADR-admission rate (approximately 13.7% of all-cause admissions) falls within this range, far exceeding the rates captured by reliance on ADR coding alone or prior medication errors. This suggests that the implied ADR-admission approach markedly improves sensitivity in identifying drug-related hospitalisations, capturing cases that may otherwise remain undetected. In sum, the observed incidence of implied ADR-admissions confirms that a substantial portion of acute hospitalisations in polypharmacy patients is likely drug-related, and that our administrative data-based approach can effectively capture this burden with reasonable fidelity to the “true” rates found in observational studies with primary data collection and expert judgement.

4.2.2 Causes of Drug-Related Admissions

Our study’s findings on implied ADR-admissions align with key patterns from studies with primary data collection, such as the frequent involvement of antithrombotic medications (bleeding complications) and diuretics (electrolyte disturbances) [3, 4]. However, our results also highlight a prominent role of falls and fall-related injuries, particularly associated with anticholinergic drugs and benzodiazepines—a key finding not typically emphasised in such studies [3, 4]. The likely explanation is that many of these prior studies have focused on internal medicine wards (presumably because of resource constraints) while under-representing patients in surgical or orthopaedic wards, where fall injuries are predominantly treated [3]. In contrast, our scalable administrative data-based methodology captures ADRs across all emergency admissions (irrespective of the ward patients were admitted to), offering a broader view of drug-related harm. Our findings thus show that when a broad range of events is considered across all hospital wards—including those often seen in surgical contexts—falls (often medication-induced) emerge as equally or more important than those highlighted by studies with primary data collection, such as bleeding or renal events, which aligns with the central focus of falls prevention in geriatric pharmacotherapy [22].

In contrast to our findings here and those of investigations with primary data collection, studies applying the explicit ADR-admission approach [11], like the German claims data analysis, have identified Clostridium difficile colitis linked to antibiotics as a key driver of drug-related hospital admissions. This discrepancy partly reflects that our predefined set of AEs did not include infection-related complications, but also highlights the documentation bias inherent in the explicit ADR-admission approach. Clostridium difficile colitis may be more readily captured by specific drug-related ICD codes in administrative data, whereas other AEs with a broader range of possible causes (e.g. fall injuries or GI bleeding), may be under-reported. In contrast, our implied ADR-admission approach avoids this documentation bias, providing a more comprehensive detection of drug-related hospitalisations.

4.2.3 Predictors for Drug-Related Admissions

Several studies have identified various predictors for drug-related hospitalisations, including sociodemographic factors, medication use, behavioural characteristics, healthcare utilisation patterns and comorbidities [6, 7, 23,24,25,26]. Research using explicit ADR coding has highlighted predictors such as older age, male sex, residence in long-term care facilities, polypharmacy, newly prescribed medications, multiple pharmacies, recent hospitalisations and a higher comorbidity burden [12]. In contrast, studies employing primary data collection methods—like chart review or prospective observation—have identified additional predictors not consistently captured by the explicit ADR-admission approach, including impaired cognition [6, 24], renal impairment [6, 24], medication non-adherence [6], alcohol use [23] and specific drug classes, including antihypertensive drugs and anticholinergic drugs [24]. Administrative data-based studies have captured a subset of these predictors, notably comorbidity burden and male sex, and have also identified the use of PIMs as a predictor of drug-related hospitalisations [25, 26].

Our study, using the novel implied ADR-admission approach, identified a set of predictors that largely aligns with findings from both primary data collection and administrative data studies. These include prior hospitalisations, multimorbidity, renal impairment, polypharmacy, PIM use and anticholinergic exposure. Older age emerged as a significant predictor in our analysis, consistent with studies using explicit ADR coding, though not with those based on primary data collection. In contrast to some previous findings, we did not observe a significant association with sex. The observed association between elevated liver enzymes (gamma-glutamyl transferase) and implied ADR-admissions may reflect alcohol use as a known risk factor [23], while the strong association with prior hypokalaemia-related admissions likely points to complications associated with diuretic therapy or the underlying conditions they treat. Our findings therefore demonstrate that the implied ADR-admission approach effectively identifies key predictors of drug-related hospitalisations, aligning closely with results from both primary data collection studies and administrative data analyses. The consistency of these findings validates our approach and highlights its robustness and potential for broader application in pharmacovigilance research.

4.3 Strengths and Limitations

Key strengths of the study are its population-based design and large sample size, as well as the availability of laboratory data to measure renal impairment and elevated liver enzymes as predictors, and acute kidney injury and electrolyte disturbances as endpoints. A further methodological strength is the use of group lasso regression for variable selection prior to multivariate logistic regression to prevent overfitting in the presence of multicollinearity of predictors or high-dimensional data.

However, a key limitation of this study is the lack of direct validation of the implied ADR-admission approach against clinical hospital records. While our method uses linked administrative data to infer likely drug-related admissions, we were unable to validate its classification through detailed case note reviews, which would have allowed for a formal assessment of its sensitivity and specificity. As such, although the method shows promise as a tool for identifying drug-related hospitalisations at scale, its diagnostic performance remains uncertain. Any incidence estimates presented should therefore be interpreted cautiously until further validation studies—ideally involving expert adjudication—are conducted. Moreover, our approach of measuring implied ADR-admissions using administrative data relies on pre-specified drug-event combinations, without assessment of causality for individual cases. Instead, it focuses on hospitalisations where the drug exposure could have plausibly contributed to the ADR known to be associated with the drug. Not all such admissions will therefore actually be caused by the drug exposure alone. As such, commonly used causality assessment algorithms [9, 10] would classify these admissions as “possibly” drug-related. Nevertheless, it is worth noting that “possible” causality is also the most frequent classification in studies with primary data collection. For example, a multi-centre prospective observational study in Germany found that 87.6% of drug-related emergency admissions were rated as “possibly” drug-related, compared with 10.7% as “probably” and 1.7% as “certainly” drug-related [27]. In addition, even with access to more detailed health records, distinguishing between drug-related and alternative causes for hospitalisation therefore often remains challenging, as data typically requested by causality assessment algorithms [9, 10, 28] (e.g. drug concentrations, and de-challenge/rechallenge) are rarely available. The limitations of our implied ADR-admission approach are therefore largely shared by the current reference standard of an expert-based causality assessment. Another limitation of the study is that, although drug selection for each AE was based on a structured literature review and internal consensus within the research team, the inclusion of a larger expert panel might have improved face validity. Nonetheless, further refinement of drug-event linkages is likely best guided by empirical validation studies using hospital records. Additional limitations of our approach include reliance on ICD-10 codes for detecting most AEs, which may result in some AEs being missed. Furthermore, the absence of data on over-the-counter drug use and the use of a 90-day exposure window for dispensed prescriptions could, in some cases, lead to misclassification of drug exposure at the time of admission. The absence of data on certain predictors, such as nursing home residence and cognitive impairment, may have contributed to the relatively low proportion of variance explained by the multivariate models (10.6%, 11.1% and 10.5% for all-cause, AE-admissions and implied ADR-admissions, respectively).

4.4 Implications for Clinical Practice and Research

Our findings have important implications for clinical practice and future research. First, the implied ADR-admission approach offers a useful balance of specificity and sensitivity for identifying drug-related hospitalisations. By linking drug exposure to outcomes, it improves specificity compared to all-cause or AE-admissions, which may include non-drug-related events. We found that the incidence of implied ADR-admissions was about half of that of AE-admissions, suggesting our approach filters out admissions unlikely to be drug-related, while improving sensitivity over methods relying solely on ADR codes or prior medication errors. The proportion of admissions flagged by our method (roughly one in seven all-cause admissions in polypharmacy patients) aligns with observational studies with primary data collection, showing that many drug-related hospitalisations are missed in routine coding.

Second, the implied ADR-admission approach can immediately guide medication safety interventions, offering an automated, clinically relevant endpoint that tracks drug-related hospitalisations more accurately than all-cause admissions. It is a promising candidate for integration into healthcare analytics and pharmacovigilance systems, helping track drug-related harm and evaluate policy impacts. Our findings reinforce current safety efforts around antithrombotic medications, diuretics and psychotropic medications, emphasising the need for careful prescribing and monitoring of these drug classes, especially to minimise fall risks associated with central nervous system-active medications.

Finally, this study opens avenues for further ADR detection and prevention research. Future studies should validate the implied ADR detection algorithm against expert case reviews to quantify its accuracy. Our findings indicate that predicting the combined endpoint of implied ADR-admissions using administrative data sources is challenging, as demonstrated by the relatively low proportion of variance explained by the multivariate models (10.6%, 11.1% and 10.5% for all-cause, AE-admissions and implied ADR-admissions, respectively). Incorporating additional patient factor data may enhance prediction to some degree. However, because of the heterogeneity of ADRs within the combined implied ADR-admission endpoint, focusing on predicting individual ADRs or clusters of ADRs with shared predictors may represent a more effective strategy. Further research could explore machine learning approaches to improve risk prediction and expand the list of drug-event combinations. We also see potential in applying this approach to other populations and healthcare settings to compare ADR patterns internationally.