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Background Multi-state models are complex stochastic models which focus on pathways defined by the temporal and sequential occurrence of numerous events of interest. In particular, the so-called illness-death models are especially useful for studying probabilities associated to diseases whose occurrence competes with other possible diseases, health conditions or death. They can be seen as a generalization of the competing risks models, which are widely used to estimate disease-incidences among populations with a high risk of death, such as elderly or cancer patients. The main advantage of the aforementioned illness-death models is that they allow the treatment of scenarios with non-terminal competing events that may occur sequentially, which competing risks models fail to do. Methods We propose an illness-death model using Cox proportional hazards models with Weibull baseline hazard functions, and applied the model to a study of recurrent hip fracture. Data came from the PREV2FO cohort and included 34491 patients aged 65 years and older who were discharged alive after a hospitalization due to an osteoporotic hip fracture between 2008-2015. We used a Bayesian approach to approximate the posterior distribution of each parameter of the model, and thus cumulative incidences and transition probabilities. We also compared these results with a competing risks specification. Results Posterior transition probabilities showed higher probabilities of death for men and increasing with age. Women were more likely to refracture as well as less likely to die after it. Free-event time was shown to reduce the probability of death. Estimations from the illness-death and the competing risks models were identical for those common transitions although the illness-death model provided additional information from the transition from refracture to death. Conclusions We illustrated how multi-state models, in particular illness-death models, may be especially useful when dealing with survival scenarios which include multiple events, with competing diseases or when death is an unavoidable event to consider. Illness-death models via transition probabilities provide additional information of transitions from non-terminal health conditions to absorbing states such as death, what implies a deeper understanding of the real-world problem involved compared to competing risks models.

Introduction: Europe has seen a steady increase in the use of prescription opioids, especially in non-cancer indications. Epidemiological data on the patterns of use of opioids is required to optimize prescription. We aim to describe the patterns of opioid therapy initiation for non-cancer pain and characteristics of patients treated in a region with five million inhabitants in the period 2012 to 2018. Methods: Population-based retrospective cohort study of all adult patients initiating opioid therapy for non-cancer pain in the region of Valencia. We described patient characteristics at baseline and the characteristics of baseline and subsequent treatment initiation. We used multinominal regression models to identify individual factors associated with initiation. Results: A total of 957,080 patients initiated 1,509,488 opioid treatments (957,080 baseline initiations, 552,408 subsequent initiations). For baseline initiations, 738,749 were with tramadol (77.19%), 157,098 with codeine (16.41%) 58,436 (6.11%) with long-acting opioids, 1,518 (0.16%) with short-acting opioids and 1,279 (0.13%) with ultrafast drugs. When compared to tramadol, patients initiating with short-acting, long-acting and ultrafast opioids were more likely to be older and had more comorbidities, whereas initiators with codeine were more prone to be healthier and younger. Treatments lasting less than 7 days accounted for 41.82% of initiations, and 11.89% lasted more than 30 days. 19.55% of initiators with ultrafast fentanyl received more than 120 daily Morphine Milligram Equivalents (MME), and 16.12% of patients initiating with long-acting opioids were prescribed more than 90 daily MME ( p < 0.001). Musculoskeletal indications accounted for 65.05% of opioid use. Overlap with benzodiazepines was observed in 24.73% of initiations, overlap with gabapentinoids was present in 11.04% of initiations with long-acting opioids and 28.39% of initiators with short-acting opioids used antipsychotics concomitantly. In subsequent initiations, 55.48% of treatments included three or more prescriptions (vs. 17.60% in baseline initiations) and risk of overlap was also increased. Conclusion: Opioids are initiated for a vast array of non-oncological indications, and, despite clinical guidelines, short-acting opioids are used marginally, and a significant number of patients is exposed to potentially high-risk patterns of initiation, such as treatments lasting more than 14 days, treatments surpassing 50 daily MMEs, initiating with long-acting opioids, or hazardous overlapping with other therapies.

ObjectiveTo identify individual and initial prescription-related factors associated with an increased risk for opioid-related misuse, poisoning and dependence (MPD) in patients with non-cancer pain.MethodsCohort study linking several databases covering 5 million inhabitants of the region of Valencia, Spain, including all adults initiating prescription opioids in the period 2012–2018. To ascertain the association between the characteristics of the initial prescription choice and the risk of opioid MPD, we used shared frailty Cox regression models. We additionally considered death as a competing risk in sensitivity analyses.Results958 019 patients initiated opioid prescription from 2012 to 2018, of which 0.13% experienced MPD. Most patients were prescribed tramadol as initial opioid (76.7%) followed by codeine (16.3%), long-acting opioids (6.7%), short-acting opioids (0.2%) and ultrafast opioids (0.1%). Initiation with ultrafast (HR 7.2; 95% CI 4.1 to 12.6), short-acting (HR 4.8; 95% CI 2.3 to 10.2) and long-acting opioids (HR 1.5; 95% CI 1.2 to 1.9) were associated with a higher risk of MPD when compared with tramadol. Initial prescriptions covering 4–7 days (HR 1.3; 95% CI 1.0 to 1.8), 8–14 days (HR 1.4; 95% CI 1.0 to 1.9), 15–30 days (HR 1.7; 95% CI 1.2 to 2.3) and more than one a month (HR 1.8; 95% CI 1.3 to 2.5) were associated with more MPD risk than initial prescriptions for 1–3 days. Treatments with >120 daily morphine milligram equivalents (MME) increased MPD risk (vs <50 MME, HR 1.6; 95% CI 1.1 to 2.2). Main individual factors associated with increased risk of MPD risk were male sex (HR 2.4; 95% CI 2.1 to 2.7), younger age (when compared with patients aged 18–44 years, patients aged 45–64 years, HR 0.4; 95% CI 0.4 to 0.5; patients aged 65–74 years, HR 0.4; 95% CI 0.3 to 0.5 and patients aged 75 years old and over, HR 0.7; 95% CI 0.6 to 0.8), lack of economic resources (2.1; 95% CI 1.8 to 2.5) and registered misuse of alcohol (2.9; 95% CI 2.4 to 3.5). Sensitivity analyses yielded overall comparable results.ConclusionsOur study identifies riskier patterns of opioid prescription initiation for non-cancer indications, as well as patient subgroups with higher risk of misuse, poisoning and dependence.

Illness-death models are a class of stochastic models inside the multi-state framework. In those models, individuals are allowed to move over time between different states related to illness and death. They are of special interest when working with non-terminal diseases, as they not only consider the competing risk of death but also allow to study progression from illness to death. The intensity of each transition can be modelled including both fixed and random effects of covariates. In particular, spatially structured random effects or their multivariate versions can be used to assess spatial differences between regions and among transitions. We propose a Bayesian methodological framework based on an illness-death model with a multivariate Leroux prior for the random effects. We apply this model to a cohort study regarding progression after osteoporotic hip fracture in elderly patients. From this spatial illness-death model we assess the geographical variation in risks, cumulative incidences, and transition probabilities related to recurrent hip fracture and death. Bayesian inference is done via the integrated nested Laplace approximation (INLA).

Survival competing risks models are very useful for studying the incidence of diseases whose occurrence competes with other possible diseases or health conditions. These models perform properly when working with terminal events, such as death, that imply the conclusion of the corresponding study. But they do not allow the treatment of scenarios with non-terminal competing events that may occur sequentially. Multi-state models are complex survival models. They focus on pathways defined by the temporal and sequential occurrence of numerous events of interest and thus they are suitable for connecting competing non-terminal events as well as to manage other survival scenarios with higher complexity. We discuss competing risks within the framework of multi-state models and clarify the usefulness of both models for analysing epidemiological data. We highlight the power of multi-state models through a real-world study of recurrent hip fracture from Bayesian inferential methodology.