Recurrent Event Models Research Articles

Estimating the intensity of use by interacting predators and prey using camera traps.

Understanding how organisms distribute themselves in response to interacting species, ecosystems, climate, human development and time is fundamental to ecological study and practice. A measure to quantify the relationship among organisms and their environments is intensity of use: the rate of use of a specific resource in a defined unit of time. Estimating the intensity of use differs from estimating probabilities of occupancy or selection, which can remain constant even when the intensity of use varies. We describe a method to evaluate the intensity of use across conditions that vary in both space and time. We demonstrate its application on a large mammal community where linear developments and human activity are conjectured to influence the interactions between white-tailed deer (Odocoileus virginianus) and wolves (Canis lupus) with possible consequences on threatened woodland caribou (Rangifer tarandus caribou). We collect and quantify intensity of use data for multiple, interacting species with the goal of assessing management efficacy, including a habitat restoration strategy for linear developments. We test whether blocking linear developments by spreading logs across a 200-m interval can be applied as an immediate mitigation to reduce the intensities of use by humans, predator and prey species in a boreal caribou range. We deployed camera traps on linear developments with and without restoration treatments in a landscape exposed to both timber and oil development. We collected a three-year dataset and employed spatial recurrent event models to analyse intensity of use by an interacting human and large mammal community across a range of environmental and climatic conditions. Spatial recurrent event models revealed that intensity of use by humans influenced the intensity of use by all five large mammal species evaluated, and the intensities of use by wolves and deer were inextricably linked in space and time. Conditions that resist travel on linear developments had a strong negative effect on the intensity of human and large mammal use. Mitigation strategies that resist, or redirect, animal travel on linear developments can reduce the effects of resource development on interacting human and predator-prey interactions. Our approach is easily applied to other continuous time point-based survey methodologies and shows that measuring the intensity of use within animal communities can help scientists monitor, mitigate and understand ecological states and processes.

Multiplicative rates model for recurrent events in case-cohort studies.

In large prospective cohort studies, accumulation of covariate information and follow-up data make up the majority of the cost involved in the study. This might lead to the study being infeasible when there are some expensive variables and/or the event is rare. Prentice (Biometrika 73(1):1-11, 1986) proposed the case-cohort study for time to event data to tackle this problem. There has been extensive research on the analysis of univariate and clustered failure time data, where the clusters are formed among different individuals under case-cohort sampling scheme. However, recurrent event data are quite common in biomedical and public health research. In this paper, we propose case-cohort sampling schemes for recurrent events. We consider a multiplicative rates model for the recurrent events and propose a weighted estimating equations approach for parameter estimation. We show that the estimators are consistent and asymptotically normally distributed. The proposed estimator performed well in finite samples in our simulation studies. For illustration purposes, we examined the association between prior occurrence of measles on acute lower respiratory tract infections (ALRI) among young children in Brazil.

Survival analysis in longitudinal studies for recurrent events: Applications and challenges

Background and objectiveRecurrent event data analysis is most commonly used in biomedical research. However, the researchers dealing with recurrent events in survival analysis have ignored the assumption that the recurrent events are correlated. There are methods available that takes into account dependency between recurrent events. The main objective of this study was to demonstrate the recurrent event models using upper respiratory infection (URI) among Indian infants. MethodsThe frequency of URI among a birth cohort of 210 babies was evaluated monthly with nasopharyngeal swabbing. Data on 11 potential risk factors were noted. The extended Cox models, such as Andersen-Gill counting process (CP), Prentice-Williams-Peterson (PWP-CP), PWP–Gap time model, Marginal Model and Cox frailty model were applied. The better model was assessed based on Loglikehood test statistics. ResultsOf the four models, PWP-CP model had lower log likelihood value. The URI incidence rate was estimated to be 2.27 (95%CI: 1.70–3.03)times significantly higher in the month of July–October and 1.43 (95%CI: 1.19–1.71) times higher in the month of November–February as compared to March–June (p < 0.001). Nasopharyngeal colonization with S. pneumoniae was also another important risk factor (HR = 1.18, 95%CI:1.01–1.39, p = 0.03). ConclusionIn the current study, PWP-CP model was found to be better model as compared to other models. Also biologically appropriate as subsequent events depend on the first event of URI. Hence, the choice of an appropriate method for analyzing the recurrent event data should not be decided only on statistical basis but also based on the research question.

Modeling marginal features in studies of recurrent events in the presence of a terminal event.

We study models for recurrent events with special emphasis on the situation where a terminal event acts as a competing risk for the recurrent events process and where there may be gaps between periods during which subjects are at risk for the recurrent event. We focus on marginal analysis of the expected number of events and show that an Aalen-Johansen type estimator proposed by Cook and Lawless is applicable in this situation. A motivating example deals with psychiatric hospital admissions where we supplement with analyses of the marginal distribution of time to the competing event and the marginal distribution of the time spent in hospital. Pseudo-observations are used for the latter purpose.

Confidence bands for quantiles as a function of covariates in recurrent event models

AbstractWe investigate the construction of various confidence bands for quantiles of the time between event recurrences when covariates and interventions performed after a recurrence are accounted for via a general Cox‐type model for recurrent events. We propose three types of bands: those based on the asymptotic properties of the properly standardized quantile; those based on a Khmaladze transformation of the original limiting distribution; and those based on bootstrap techniques. Asymptotic properties of the three types of bands are presented and their small and large sample performances and coverage probabilities are assessed via simulation study. The Canadian Journal of Statistics 46: 610–634; 2018 © 2018 Société statistique du Canada

Analyzing Relationship Between Local and Metastasis Relapses with Survival of Patients with Breast Cancer: A Study Using Joint Frailty Model

Background: Individuals with breast cancer may experience multiple types of recurrence and a terminal event during their life. Follow-up may be interrupted for several reasons, including patients lost to follow-up or end of a study. Death is considered as a dependent terminal event. Objectives: The main objective was to model the dependency between (locoregional and metastaticrelapses and death in breast cancer patients. Methods: This retrospective cohort study was conducted in Cancer Research Center, Shahid Beheshti University of Medical Sciences on 342 patients with BC. Patients were followed up to 20 years from January 1996 until February 2015. This study considers the analysis of diseases recurrence and survival for joint modeling of three correlated evens: Local recurrence, metastasis, and death. The aims are to detect the effects of relapses on death and the correlation between local and distant recurrences. We propose a joint frailty model for multiple recurrent events with a terminal event. Results: According to all obtained results of the fitted models, the risk of local and metastatic relapses or death increased for women with positive lymph node (N+) or for women with a grade higher than I. Also, it was found that if the association between these 3 times to events are not taken into account, we may lose a significant association. The variable HR+ was significantly correlated with the hazards of two types of recurrences, and death for both reduced and proposed models (P < 0.05). Conclusions: We concluded that the risk of locoregional recurrences is correlated with the risk of metastatic and the risk of metastatic relapse is also correlated with death. In this special setting, it can be difficult to get an independence between locoregional and distant recurrences. Also, these results should be considered with caution, with the few number of recurrences.

Bayesian estimation of a semiparametric recurrent event model with applications to the penetrance estimation of multiple primary cancers in Li-Fraumeni syndrome

A common phenomenon in cancer syndromes is for an individual to have multiple primary cancers (MPC) at different sites during his/her lifetime. Patients with Li-Fraumeni syndrome (LFS), a rare pediatric cancer syndrome mainly caused by germline TP53 mutations, are known to have a higher probability of developing a second primary cancer than those with other cancer syndromes. In this context, it is desirable to model the development of MPC to enable better clinical management of LFS. Here, we propose a Bayesian recurrent event model based on a non-homogeneous Poisson process in order to obtain penetrance estimates for MPC related to LFS. We employed a familywise likelihood that facilitates using genetic information inherited through the family pedigree and properly adjusted for the ascertainment bias that was inevitable in studies of rare diseases by using an inverse probability weighting scheme. We applied the proposed method to data on LFS, using a family cohort collected through pediatric sarcoma patients at MD Anderson Cancer Center from 1944 to 1982. Both internal and external validation studies showed that the proposed model provides reliable penetrance estimates for MPC in LFS, which, to the best of our knowledge, have not been reported in the LFS literature.

Antibiotic prophylaxis and clinical outcomes among older adults with recurrent urinary tract infection: cohort study.

clinical guidelines recommend antibiotic prophylaxis for preventing recurrent urinary tract infections (UTIs), but there is little evidence for their effectiveness in older adults. this was a retrospective cohort study of health records from 19,696 adults aged ≥65 with recurrent UTIs. We used prescription records to ascertain ≥3 months' prophylaxis with trimethoprim, cefalexin or nitrofurantoin. We used random effects Cox recurrent event models to estimate hazard ratios (HR) and 95% confidence intervals (CI) for risks of clinical recurrence (primary outcome), acute antibiotic prescribing and hospitalisation. of 4,043 men and 15,653 women aged ≥65 with recurrent UTIs, 508 men (12.6%) and 2,229 women (14.2%) were prescribed antibiotic prophylaxis. In men, prophylaxis was associated with a reduced risk of clinical recurrence (HR, 0.49; 95% CI, 0.45-0.54), acute antibiotic prescribing (HR, 0.54; 95% CI, 0.51-0.57) and UTI-related hospitalisation (HR, 0.78; 95% CI, 0.64-0.94). In women, prophylaxis was also associated with a reduced risk of clinical recurrence (HR, 0.57; 95% CI, 0.55-0.59) and acute antibiotic prescribing (HR, 0.61; 95% CI, 0.59-0.62), but estimates of the risk of UTI-related hospitalisation were inconsistent between our main analysis (HR, 1.16; 95% CI, 1.05-1.28) and sensitivity analysis (HR, 0.82; 95% CI, 0.72-0.94). antibiotic prophylaxis was associated with lower rates of UTI recurrence and acute antibiotic prescribing in older adults. To fully understand the benefits and harms of prophylaxis, further research should determine the frequency of antibiotic-related adverse events and the impact on antimicrobial resistance and quality of life.

Can Heart Failure Events be Prevented by Risk Stratification Based Interventions?

Background We evaluated factors associated with interventions and events in patients (pts) with heart failure (HF) while enrolled in a HF service (BEACON, Medtronic, plc). Methods Certified HF nurses (CHFNs) interpret monthly transmissions from devices with the Medtronic OptiVol™ fluid status monitoring feature. This data, combined with weight, blood pressure and symptoms, are used to stratify a pts risk for a HF event (very high, high, moderate, low) and provide a follow-up recommendation (24 hours, 72 hours, 7 days, routine). The CHFN contacted the pt to assess for intervention (provider outreach, medication change, office visit). A generalized estimating equation model tested whether the probability of intervention varied by age, gender, device type or risk. A recurrent events model assessed the impact of age, gender, occurrence of transmissions in each risk category and occurrence of interventions on the hazard of a HF event. Results This real-world evidence evaluation included 2,931 transmissions from 485 pts (73 ± 10 years age, 67% male) enrolled in the HF service for an average of 177 ± 121 days. Compared to moderate risk, higher risk transmissions resulted in a higher probability of outreach and medication changes (figure). There was no difference in the probability of intervention by age or gender. A total of 24 pts (4.9%) reported 31 HF hospitalizations and 2 ER visits (0.14 HF events/patient-year) while on the HF service. There was no significant association between age, gender and risk of HF events. Each additional higher risk transmission, which may have been followed by an intervention, was associated with a decreasing hazard of HF events (any higher risk: HR = 0.85, p = 0.002; moderate risk: HR = 0.73, p = 0.01; high risk: HR = 0.56, p = 0.001; very high risk: HR = 1.00, p = 0.97). There was a trend towards decreasing risk of HF events with an increasing number of interventions, but the trend was not statistically significant (any intervention: HR = 0.93, p = 0.3; outreach: HR = 0.86, p = 0.25; medication change: HR = 0.71, p = 0.14; office visit: HR = 0.93, p = 0.68). Conclusion Higher risk transmissions have a significantly increased probability for intervention, validating the clinical appropriateness of the HF service risk stratification scheme. An increased number of higher risk transmissions, which leads to increased intervention, may also reduce the hazard of future HF events. Future controlled studies should prospectively evaluate this approach.

A joint model for recurrent events and a semi-competing risk in the presence of multi-level clustering.

Clinical trial designs often include multiple levels of clustering in which patients are nested within clinical sites and recurrent outcomes are nested within patients who may also experience a semi-competing risk. Traditional survival methods that analyze these processes separately may lead to erroneous inferences as they ignore possible dependencies. To account for the association between recurrent events and a semi-competing risk in the presence of two levels of clustering, we developed a semi-parametric joint model. The Gaussian quadrature with a piecewise constant baseline hazard was used to estimate the unspecified baseline hazards and the likelihood. Simulations showed that the proposed joint model has good statistical properties (i.e. <5% bias and 95% coverage) compared to the shared frailty and joint frailty models when informative censoring and multiple levels of clustering were present. The proposed method was applied to data from an AIDS clinical trial to investigate the impact of antiretroviral treatment on recurrent AIDS-defining events in the presence of a semi-competing risk of death and multi-level clustering and showed a significant dependency between AIDS-defining events and death at the patient level but not at the clinic level.

Survival Analysis and Interpretation of Time-to-Event Data: The Tortoise and the Hare.

Survival analysis, or more generally, time-to-event analysis, refers to a set of methods for analyzing the length of time until the occurrence of a well-defined end point of interest. A unique feature of survival data is that typically not all patients experience the event (eg, death) by the end of the observation period, so the actual survival times for some patients are unknown. This phenomenon, referred to as censoring, must be accounted for in the analysis to allow for valid inferences. Moreover, survival times are usually skewed, limiting the usefulness of analysis methods that assume a normal data distribution. As part of the ongoing series in Anesthesia & Analgesia, this tutorial reviews statistical methods for the appropriate analysis of time-to-event data, including nonparametric and semiparametric methods—specifically the Kaplan-Meier estimator, log-rank test, and Cox proportional hazards model. These methods are by far the most commonly used techniques for such data in medical literature. Illustrative examples from studies published in Anesthesia & Analgesia demonstrate how these techniques are used in practice. Full parametric models and models to deal with special circumstances, such as recurrent events models, competing risks models, and frailty models, are briefly discussed.

Abstract 2204: Influence of chronic comorbidities on colorectal cancer screening adherence in a population-based Canadian cohort

Abstract Background: Regular screening for colorectal cancer (CRC) is associated with a lower stage at diagnosis and improved survival. National guidelines recommend periodic screening for adults aged 50-74 years. Chronic diseases may distract from regular screening and increase CRC burden. We sought to determine whether having certain chronic conditions is associated with a lower rate of CRC screening adherence. Methods: Using linked healthcare databases from Ontario, Canada, we assembled a population-based cohort of adults aged 50-74 years, who were eligible and overdue for routine CRC screening (fecal occult blood testing &amp;gt; 2 years, sigmoidoscopy &amp;gt; 5 years, or colonoscopy &amp;gt; 10 years) at any point between April 1, 2004 and March 31, 2016. We implemented Andersen-Gill extended Cox models for recurrent events using an age time scale to examine the impact of chronic conditions on the relative rate (RR, 95% confidence interval, CI) of becoming adherent with periodic CRC tests, adjusted for the relevant time-varying covariates. Results: During the 12-year observation window, included individuals contributed a total of 19,818,050 person-years spent overdue for CRC tests. Over 70% of all CRC tests performed during the study period were fecal occult blood tests. At baseline, of the 4,642,422 individuals that met the inclusion criteria, 31% had hypertension, 12% had diabetes, 9% had chronic obstructive pulmonary disease (COPD), 7% had mental illness, 5% had cardiovascular disease (CVD), 1.6% had congestive heart failure (CHF), and 0.1% had end-stage renal disease (ESRD). After adjustment for sociodemographic and health system factors, the highest rates of nonadherence were observed for ESRD on dialysis (RR 0.66, CI 0.63-0.68), CHF (RR 0.75, CI 0.75-0.76), COPD (RR 0.84, CI 0.83-0.84), CVD (RR 0.85, CI 0.84-0.85), diabetes (RR 0.86, CI 0.86-0.87), and mental illness (RR 0.88, CI 0.87-0.88). Hypertension (RR 0.98, CI 0.97-0.98) and ESRD with kidney transplant (RR 1.00, CI 0.84-1.19) were associated with modest to no risk. Conclusion: The presence of a major chronic condition is associated with lower adherence to CRC screening. Future studies should explore reasons for lower CRC screening adherence in individuals with chronic conditions and the appropriateness of secondary cancer prevention and early diagnosis in this population. Citation Format: Dominika Bhatia, Rinku Sutradhar, Jill Tinmouth, Simron Singh, Lorraine Lipscombe. Influence of chronic comorbidities on colorectal cancer screening adherence in a population-based Canadian cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2204.

Group sequential designs with robust semiparametric recurrent event models.

Robust semiparametric models for recurrent events have received increasing attention in the analysis of clinical trials in a variety of diseases including chronic heart failure. In comparison to parametric recurrent event models, robust semiparametric models are more flexible in that neither the baseline event rate nor the process inducing between-patient heterogeneity needs to be specified in terms of a specific parametric statistical model. However, implementing group sequential designs in the robust semiparametric model is complicated by the fact that the sequence of Wald statistics does not follow asymptotically the canonical joint distribution. In this manuscript, we propose two types of group sequential procedures for a robust semiparametric analysis of recurrent events. The first group sequential procedure is based on the asymptotic covariance of the sequence of Wald statistics and it guarantees asymptotic control of the type I error rate. The second procedure is based on the canonical joint distribution and does not guarantee asymptotic type I error rate control but is easy to implement and corresponds to the well-known standard approach for group sequential designs. Moreover, we describe how to determine the maximum information when planning a clinical trial with a group sequential design and a robust semiparametric analysis of recurrent events. We contrast the operating characteristics of the proposed group sequential procedures in a simulation study motivated by the ongoing phase 3 PARAGON-HF trial (ClinicalTrials.gov identifier: NCT01920711) in more than 4600 patients with chronic heart failure and a preserved ejection fraction. We found that both group sequential procedures have similar operating characteristics and that for some practically relevant scenarios, the group sequential procedure based on the canonical joint distribution has advantages with respect to the control of the type I error rate. The proposed method for calculating the maximum information results in appropriately powered trials for both procedures.

A semiparametric extension of the stochastic block model for longitudinal networks

We propose an extension of the stochastic block model for recurrent interaction events in continuous time, where every individual belongs to a latent group and conditional interactions between two individuals follow an inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. We show that the model is identifiable and estimate it with a semiparametric variational expectation-maximization algorithm. We develop two versions of the method, one using a nonparametric histogram approach with an adaptive choice of the partition size, and the other using kernel intensity estimators. We select the number of latent groups by an integrated classification likelihood criterion. We demonstrate the performance of our procedure on synthetic experiments, analyse two datasets to illustrate the utility of our approach, and comment on competing methods.

Alternative Design and Analytical Techniques for Longitudinal Rheumatology Studies: Improved Understanding of Outcomes.

Longitudinal cohort designs (with three or more measurement occasions) are invaluable to investigate between- and within-individual variation in outcomes. However, traditional longitudinal designs require a lengthy implementation and data collection period and impose a substantial burden on participants and investigators. We discuss alternative longitudinal designs, including planned missing data designs and retrospective cohort studies with secondary data, which require a shorter period for data accrual and reduce participant burden while maintaining statistical power. We also discuss analysis strategies to maximize data use and produce unbiased estimates of treatment effectiveness, including models for recurrent or multistate events and time-varying covariates.

A systematic comparison of recurrent event models for application to composite endpoints

BackgroundMany clinical trials focus on the comparison of the treatment effect between two or more groups concerning a rarely occurring event. In this situation, showing a relevant effect with an acceptable power requires the observation of a large number of patients over a long period of time. For feasibility issues, it is therefore often considered to include several event types of interest, non-fatal or fatal, and to combine them within a composite endpoint. Commonly, a composite endpoint is analyzed with standard survival analysis techniques by assessing the time to the first occurring event. This approach neglects that an individual may experience more than one event which leads to a loss of information. As an alternative, composite endpoints could be analyzed by models for recurrent events. There exists a number of such models, e.g. regression models based on count data or Cox-based models such as the approaches of Andersen and Gill, Prentice, Williams and Peterson or, Wei, Lin and Weissfeld. Although some of the methods were already compared within the literature there exists no systematic investigation for the special requirements regarding composite endpoints.MethodsWithin this work a simulation-based comparison of recurrent event models applied to composite endpoints is provided for different realistic clinical trial scenarios.ResultsWe demonstrate that the Andersen-Gill model and the Prentice- Williams-Petersen models show similar results under various data scenarios whereas the Wei-Lin-Weissfeld model delivers effect estimators which can considerably deviate under commonly met data scenarios.ConclusionBased on the conducted simulation study, this paper helps to understand the pros and cons of the investigated methods in the context of composite endpoints and provides therefore recommendations for an adequate statistical analysis strategy and a meaningful interpretation of results.

Bayesian regression model for recurrent event data with event-varying covariate effects and event effect

ABSTRACTIn the course of hypertension, cardiovascular disease events (e.g. stroke, heart failure) occur frequently and recurrently. The scientific interest in such study may lie in the estimation of treatment effect while accounting for the correlation among event times. The correlation among recurrent event times comes from two sources: subject-specific heterogeneity (e.g. varied lifestyles, genetic variations, and other unmeasurable effects) and event dependence (i.e. event incidences may change the risk of future recurrent events). Moreover, event incidences may change the disease progression so that there may exist event-varying covariate effects (the covariate effects may change after each event) and event effect (the effect of prior events on the future events). In this article, we propose a Bayesian regression model that not only accommodates correlation among recurrent events from both sources, but also explicitly characterizes the event-varying covariate effects and event effect. This model is especially useful in quantifying how the incidences of events change the effects of covariates and risk of future events. We compare the proposed model with several commonly used recurrent event models and apply our model to the motivating lipid-lowering trial (LLT) component of the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) (ALLHAT-LLT).

Predicted impact of lipid lowering therapy on cardiovascular and economic outcomes of Swedish atherosclerotic cardiovascular disease guideline

BackgroundThe effects on cardiovascular disease (CVD) by treatment recommendations on prevention of atherosclerotic CVD remain to be evaluated. The objectives were to assess treatment gap for low density lipoprotein cholesterol (LDL-C) according to guidelines, potential impact on CVD outcomes, and possible avoided economic costs, in post myocardial infarction (MI) patients, if target LDL-C levels of ≤1.8 mmol/L would be achieved.MethodsAll patients registered in the Swedish Secondary Prevention after Heart Intensive care Admission register, with one-year post-MI follow-up during 2013 were selected. The REACH risk prediction and a calibrated model for recurrent cardiovascular events and death were used to estimate unadjusted risk prediction based on the REACH equation henceforth called base case, and calibrated CVD outcomes based on gender-specific risk factors. The predicted impact of the LDL-C reduction on the risk of CVD was based on the Cholesterol Treatment Trialists´ Collaboration findings.ResultsA sample of n = 5904 patients (74% men) with a mean age of 64 years were included. Around 70% did not reach LDL-C target ≤1.8 mmol/L. Over a 10-year period, 820–2262 events were predicted to occur in those who did not reach target corresponding to 20% – 55% risk of CVD events. To achieve LDL-C target, the mean LDL-C had to be reduced by 0.73 mmol/L (29%). If this LDL-C reduction was achieved, 195–544 life years, 132–343 CVD events, and 7.9–20.9 million Swedish crowns (MSEK) of direct costs, and 19.3−51.0 MSEK of total costs would be avoided.ConclusionLowering of LDL cholesterol to achieve target levels according to guidelines for post-MI patients may lead to fewer cardiovascular events and avoidance of event costs.

Builiding Methods for a Proactive Prescription Drug Surveillance System

ObjectiveThis study aims to show the application of longitudinal statisticaland epidemiological methods for building a proactive prescriptiondrug surveillance system for public health.IntroductionPrescription Drug Monitoring Programs (PDMPs) are operating in49 states and several U.S. territories. Current methods for surveillanceof prescription drug related behaviors, include the mean daily dosageof morphine milligram equivalent (MME) per patient, annualpercentage of days with overlapping prescriptions per patient, andannual multiple provider episodes for multiple controlled substanceprescription drugs per patient that are described elsewhere.1,2Thiswork builds on these efforts by extending longitudinal methodsto prescription drug behavior surveillance in order to predict risksassociated with prescription drug use.MethodsSchedule II prescription opioids from January 1, 2014 to February29, 2016 from the Kansas Tracking and Reporting of ControlledSubstances (KTRACS) was used for this analysis. Prescription opioidswere linked to the 2016 version of the morphine milligram equivalentconversion table from the National Center for Injury Preventionand Control.3Population estimates were based on the 2015 CountyVintage single-year of age bridged-race estimates from the NationalCenter for Health Statistics and used to calculate age-adjusted rates. Adaily high dose opioid prescription was defined as having greater thanor equal to 90 morphine milligram equivalent. Since this is a unit-daymeasure with patients experiencing multiple daily high dose opioiddays, the Prentice, William, and Peterson (PWP) recurrent eventmodel was used to estimate the number of high-dose opioid days forKansas patients by gender and age groups.4,5Start time was the firstprescription date with a high-dose opioid and stop time was the nexthigh-dose opioid date during a study period from January 1, 2014to Feb 29, 2016. The PWP model is a statistical model that allowsfor the estimation of covariates on an event history (i.e. total timewith prescription opioids, specifically high-dose opioids). Analysiswas completed with a stratified Cox-proportional hazard model,sandwich covariance for dependent observations, and statisticalsignificance was assessed with a Wald Chi-square. PROC PHREGin SAS/STAT(R) 14.1 was used since it has a new FAST option forfitting large proportional counting process hazard model.ResultsThe age-adjusted rate of daily high-dose opioid patients was3.2 patients per 100 Kansas population-year (95% CI: 3.1 – 3.2).Kansas patients aged 85 and older had the highest age-specific rateof 11.7 (95% CI: 11.5 –11.9). Preliminary recurrent event analysisshows on average nearly a quarter of approximately 50 millionSchedule II opioid patient days were high-dose opioid patient daysamong 785,514 Kansan patients with any prescribed opioid history.In an initial result stratified by the number of high-dose opioid daysand adjusting only for age, males on average had approximately 7%higher hazard of recurrent Schedule II high-dose opioid prescriptiondays than females (β: 0.07, S.E: 0.002, p&lt;0.0001). Kansas patientsaged 45 to 54 compared to Kansas patients 85 and older on averagehad approximately 14% higher hazard of recurrent Schedule II high-dose opioid prescription days (β: 0.14, S.E: 0.007, p&lt;0.0001).ConclusionsThis work demonstrates the application of survival analysistechniques to estimate the population at risk for high-dose opioids,which varies by the length of the total opioid prescription history. Earlyresults from the recurrent event analysis showed that Kansas maleand patients aged 45 to 54 years had the longest history of high-doseopioids. Annual cross-sectional population estimates may incorrectlyestimate the estimated risk of high-dose prescription opioids sinceit assumes all patients have the same prescription history. PDMPsare longitudinal databases. Survival analysis methods like recurrentevent models can leverage the longitudinal structure to more preciselyestimate risk statistics. Future work includes incorporation of healthoutcomes data and further prescription covariates to assess the timingand intensity of opioid potency escalation.

Body Mass Index and Cardiorespiratory Fitness in Mid-Life and Risk of Heart Failure Hospitalization in Older Age: Findings From the Cooper Center Longitudinal Study

ObjectivesThis study evaluated the contributions of obesity and changes in body mass index (BMI) in mid-life to long-term heart failure (HF) risk independent of cardiorespiratory fitness (CRF) levels. BackgroundObesity and low CRF are well-established risk factors for HF. However, given the inverse association between CRF and obesity, the independent contributions of BMI toward HF risk are not fully understood. MethodsWe included 19,485 participants from the Cooper Center Longitudinal Study who survived to receive Medicare coverage, from 1999 to 2009. CRF was estimated in metabolic equivalents (METS) according to Balke treadmill time. Associations of BMI and BMI change with HF hospitalization after age 65 were assessed by applying a proportional hazards recurrent events model to the failure time data. ResultsAfter 127,110 person-years of follow-up, we observed 1,038 HF hospitalization events. Higher mid-life BMI was significantly associated with greater risk of HF hospitalization after adjusting for established HF risk factors (hazard ratio [HR]: 1.19; 95% confidence interval [CI]: 1.12 to 1.26 per 3 kg/m2 higher BMI). This association was attenuated after adjusting for CRF (HR: 1.10; 95% CI: 1.03 to 1.17 per 3 kg/m2 higher BMI). CRF accounted for 47% of the HF risk associated with BMI. BMI change was not significantly associated with risk of HF in older age after adjustment for CRF change. ConclusionsHigher BMI-associated risk of HF is explained largely by differences in CRF levels. Furthermore, BMI change is not significantly associated with HF risk after adjusting for CRF changes. These findings highlight the importance of CRF in mediating BMI-associated HF risk.