Suchergebnisse

IntroductionEmergency Medical Services (EMS) systems dispatch paramedics to emergencies in the community. For critically ill patients, paramedic interventions and transport destination decisions may impact outcomes. Research is needed to inform paramedic care, but linking EMS data to health system outcomes is a barrier. Limited research exists on EMS data linkage.
Objectives and ApproachTo optimize linkage of EMS data (fiscal year 2016/17) to the National Ambulatory Care Reporting System/Sunrise Clinical Manager datasets and assess bias. A random sample of EMS records were deterministically linked on provincial health number (PHN), transport destination, and EMS/emergency department arrival/presentation times ≤2hrs. Linked data were manually verified using last name, sex, date of birth, and hospital file number. For patients that remained unlinked (based on the variables listed above), further linkage attempts were made using additional variables. The combination of variables that optimized sensitivity/positive predictive value/f-measure were used to link the fiscal year. Linked/unlinked groups were descriptively compared.
ResultsWhile results are still pending (available April, 2018), we hypothesize that there may be inherent differences in the clinical and encounter characteristics of patients that were linked versus unlinked. Patient identifiers such as PHN and name are important for linkage, but are not always collected on EMS events that require immediate treatment and rapid transport, yet these patients may be the most critically ill.
Conclusion/ImplicationsAs more EMS systems attempt to systematically link their data to health system outcome, these results will be important to mitigate potential bias.

IntroductionRisk prediction models can be used to inform decision-making in clinical settings. With large and detailed electronic medical record data, machine learning may improve predictions. The objective of this work is to determine the feasibility and accuracy of machine learning versus logistic regression to predict unplanned hospital admissions.
Objectives and ApproachData from primary care electronic medical records for community-dwelling adults in Alberta, Canada available from the Canadian Primary Care Sentinel Surveillance Network will be linked to acute care administrative health data held by Alberta Health Services. Two regression methods (forward stepwise logistic, LASSO logistic) will be compared with three machine learning methods (classification tree, random forest, gradient boosted trees). Prior primary and acute care use will be used to predict three outcomes: ≥1 unplanned admission within 1 year, ≥1 unplanned admission within 90 days, and ≥1 unplanned admission within 1 year due to an ambulatory care sensitive condition.
ResultsThe results of this work in progress will be presented at the conference. 41,142 patients will have their primary and acute care data linked. We anticipate that the machine learning methods will improve predictive performance but will be more challenging for clinicians and patients to understand, including why a given patient is predicted to be at higher risk. The primary comparison of machine learning and regression methods will be based on positive predictive values corresponding to the top 5% predicted risk threshold, and estimated via 10-fold cross-validation.
Conclusion/ImplicationsThis project aims to help researchers decide which statistical methods to use for risk prediction models. When considering machine learning methods the best approach may be to try multiple methods, compare their predictive accuracy and interpretability, and then choose a final method.

Abstract

Aims
To examine the effect of alcohol cue exposure on tobacco-related cravings, self-administration and other measures of tobacco-related cue reactivity.


Methods
We searched Medline, PsycINFO, Embase, CINAHL and Scopus from inception to May 2020 for articles reporting on a combination of cue reactivity (and/or cross-cue reactivity), alcohol use and tobacco consumption. A semi-quantitative analysis and study quality assessment were performed for the included articles.


Results
A total of 37 articles met our inclusion criteria and were included in the systematic review. Most studies (60%) reported that alcohol cue exposure increased tobacco cravings, but only 18% of studies reported that alcohol cue exposure resulted in an increase in ad libitum smoking. There was also substantial heterogeneity between studies due to differences in methodology related to alcohol cue exposure, measures of tobacco cravings, as well as variable participant and study characteristics.


Conclusions
Alcohol cue exposure can increase cravings for tobacco. This has important implications for individuals who use both substances but are trying to quit one or both.

IntroductionAntimicrobial resistance (AMR) is an emerging phenomenon where microorganisms develop resistance against treatment antimicrobials, resulting in ineffective clinical interventions. The recent development of AMR surveillance systems at global and national stages highlights the growing importance of this topic from a public health perspective.
Objectives and ApproachThe objective was to link standardized population-based hospital AMR surveillance data with hospitalizationrecords to inform patient safety practices in Alberta, Canada. Incident inpatient cases of Methicillin-Resistant Staphylococcus aureus (MRSA),identified by Alberta Health Services Provincial Infection Prevention and Control(IPC) Surveillance from five acute care facilities in the Calgary zone (April 2011 to March 2016),were deterministically linked to the Discharge Abstract Database using Provincial Healthcare Number and gender. The incident cohort was stratified into hospital-acquired (HA-MRSA) and community-acquired MRSA (CA-MRSA) cases. Descriptive statistics were used to describe the patient outcomes and facility characteristics of these two groups.
ResultsA total of 2550 unique patients, representing 93.5% of the surveillance cohort, were successfully linked to hospitalization records. A total of 1259 patients belonged to HA-MRSA categories and 1291 patients belonged to CA-MRSA categories. Patients with HA-MRSA had longer hospital stays, were older, were more likely to have prior hospitalizations, had higher Charlson Comorbidity Scores, and were more likely to die in hospital when compared to patients with CA-MRSA. HA-MRSA results emphasized the important roles of in-hospital patient safety practices whereas CA-MRSA results alluded to the impact of community public health and primary care services onthe risk of hospitalization, although detected CA-MRSA numbers were likely underestimated due to selection bias within our linked cohort.
Conclusion/ImplicationsThis is first Canadian study describing HA-MRSA and CA-MRSA using linked population databases. It offers a glimpse into the intricate relationship between patient health and our healthcare system. This knowledge represents an important step forwarding building IPC strategies for managing AMR and improving outcomes in Alberta and in Canada.

IntroductionDespite increased use of electronic health records (EHRs), EHR documentation quality remains poor. Consequently, EHR data quality is also negatively affected. Many services, including disease surveillance and health services research, utilize EHR data. Accordingly, several studies have attempted to improve EHR documentation quality in the inpatient setting using various interventions.
Objectives and ApproachThe purpose of this systematic review was to synthesize the literature, and assess the effectiveness of interventions seeking to improve inpatient EHR documentation quality. To identify relevant experimental, quasi-experimental and observational studies, a search strategy was developed based on elaborate inclusion/exclusion criteria using four main themes: EHR, documentation, interventions, and type of study. Four databases, Cochrane, Medline, EMBASE, and CINAHL, were searched. Study quality assessment and data extraction from selected studies were performed using a Downs and Black and Newcastle-Ottawa Scale hybrid tool, and a REDCap form, respectively. Data was then analyzed and synthesized in a narrative semi-quantitative manner.
ResultsAn in-depth search of the identified databases, grey literature and reference lists, revealed a final 20 studies for inclusion in this systematic review. Due to high heterogeneity in study design, population, interventions, comparators, document types and outcomes, data could not be standardized for a quantitative comparison. However, statistically significant results in interventions and affected outcomes were further presented and discussed. A higher number of studies reported significantly improved EHR documentation when using the interventions: 'Education' and 'Implementing a new EHR Reporting System'. When implementing two or more interventions, more outcome measures were affected. There was no association between study quality or study design and number of interventions used. Only one of the 20 studies found EHR documentation worsened with the interventions used.
Conclusion/ImplicationsInterventions implemented to enhance EHR documentation are highly variable and require standardization. Emphasis should be placed on this novel area of research to improve communication between healthcare providers, enhance continuity of care, reduce the burden in health information management, and to facilitate data sharing between centers, provinces, and countries.

Schizophrenia does not present uniformly among patients and as a result this patient population is characterized by a diversity in the type and amount of healthcare supports needed for daily functioning. Despite this, little work has been completed to understand the heterogeneity that exists among these patients. In this work we used a data-driven approach to identify subgroups of high-cost patients with schizophrenia to identify potentially actionable interventions for the improvement of outcomes and to inform conversations on how to most efficiently allocate resources in an already strained system. Administrative health data was used to conduct a retrospective analysis of "high-cost" adult patients with schizophrenia residing in Alberta, Canada in 2017. Costs were derived from inpatient encounters, outpatient primary care and specialist encounters, emergency department encounters, and drug costs. Latent class analysis was used to group patients based on their unique clinical profiles. Latent class analysis of 1659 patients revealed the following patient groups: (1) young, high-needs males early in their disease course; (2) actively managed middle-aged patients; (3) elderly patients with multiple chronic conditions and polypharmacy; (4) unstably housed males with low treatment rates; (5) unstably housed females with high acute care use and low treatment rates. This taxonomy may be used to inform policy, including the identification of interventions most likely to improve care and reduce health spending for each subgroup.

IntroductionLongitudinal studies ascertain exposure, covariates, and outcomes over time. For estimating treatment effect on mortality, ignoring the time-varying nature of an exposure may lead to immortal time bias. Time-dependent confounding that affects future treatment may bias the estimated effects. Differences in baseline prognosis between treatment groups further complicate this issue.
Objectives and ApproachWe applied sequential Cox modeling to estimate the causal effect of referral for nephrology care on the survival of adults with advanced chronic kidney disease, linking laboratory and administrative data from Alberta, Canada. We created pseudo-data by mimicking successive randomized controlled trials. To address immortal time bias, each "mini-trial" consisted of individuals starting treatment, and those not yet treated, in each 3-month time interval. We incorporated inverse-probability-of-treatment-weights (IPTW) to minimize treatment selection bias for each "mini-trial. " We fit a "mini-trial"-stratified, weighted Cox model to estimate the overall hazard ratio for death by averaging the effect estimates across "mini-trials."
ResultsWe included 9,675 patients who entered the cohort between 2002 and 2013. The mean age was 82 years; 35% were male; and 33% were ultimately referred to a nephrologist after a median wait-period of 6 months. Compared to non-referred patients, those referred were younger and had fewer comorbidities at baseline. Referral was associated with a significant 45% lower hazard for death in an adjusted Cox model. The effect was attenuated in a multivariate Cox model with a time-varying exposure and in a sequential Cox model further controlling for potential time-dependent confounding by measures reflecting kidney-, cardiovascular-, and cerebrovascular-health. After incorporating IPTW for addressing treatment selection bias in the same sequential Cox model, the effect estimate was toward the null and no longer significant.
Conclusion/ImplicationsWe found that applying analytical strategies that addressed immortal time bias, time-dependent confounding, and treatment selection bias, the survival benefit associated with nephrology referral was attenuated. Inverse-probability-of treatment weighted sequential Cox approach may be used to address these important biases and confounding that are common in real-world clinical settings.

IntroductionAdministrative data are commonly used for a variety of secondary purposes. Although they lack clinical detail and risk factor information, linkage to primary care electronic medical records (EMR) could fill this gap. Primary care EMRs are a relatively new data source available in Alberta and thus, EMR-administrative linkages are novel.
Objectives and ApproachTo describe the process undertaken for linking de-identified primary care EMR data from two regional Alberta networks of the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) with administrative data (hospital admissions, emergency department visits, pharmacy information) from Alberta Health Services Analytics, specifically as it relates to a study on patients with complex, chronic diseases. As this linkage process is new in Alberta, we will describe the challenges encountered and possible solutions to inform future data linkage for research studies.
ResultsLinkage steps: 1) approval from research ethics board and individual CPCSSN providers as data custodians; 2) notify Privacy Commissioner on behalf of custodian; 3) send linking key (CPCSSN patient ID, EMR ID) from regional database to Analytics; 4) send linking files (patient personal health number [PHN], EMR ID) from custodian's EMR system to Analytics; 5) match unique EMR ID from linking key and clinic linking files; 6) PHN from clinic linking file mapped to administrative data; 7) data de-identified before transferring to secure repository; administrative data matched to EMR data using CPCSSN ID.
Challenges: obtaining individual provider consent for each study; sampling bias; delays/issues generating clinic linkage file; mismatch between patients in clinic \& regional linking files.
Current and potential solutions will be discussed during the presentation.
Conclusion/ImplicationsAs primary care EMR and administrative data become more routinely linked and accepted, the process will become more efficient and streamlined. These data will contribute to a better understanding of patients and their care in Alberta.

Objectives
Prevalence, and associated morbidity and mortality of chronic sleep disorders have been limited to small cohort studies, however, administrative data may be used to provide representation of larger population estimates of disease. With no guidelines to inform the identification of cases of sleep disorders in administrative data, the objective of this study was to develop and validate a set of ICD-codes used to define sleep disorders including narcolepsy, insomnia, and obstructive sleep apnea (OSA) in administrative data.
Methods
A cohort of adult patients, with medical records reviewed by two independent board-certified sleep physicians from a sleep clinic in Calgary, Alberta between January 1, 2009 and December 31, 2011, was used as the reference standard. We developed a general ICD-coded case definition for sleep disorders which included conditions of narcolepsy, insomnia, and OSA using: 1) physician claims data, 2) inpatient visit data, 3) emergency department (ED) and ambulatory care data. We linked the reference standard data and administrative data to examine the validity of different case definitions, calculating estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Results
From a total of 1186 patients from the sleep clinic, 1045 (88.1%) were classified as sleep disorder positive, with 606 (51.1%) diagnosed with OSA, 407 (34.4%) with insomnia, and 59 (5.0%) with narcolepsy. The most frequently used ICD-9 codes were general codes of 307.4 (Nonorganic sleep disorder, unspecified), 780.5 (unspecified sleep disturbance) and ICD-10 codes of G47.8 (other sleep disorders), G47.9 (sleep disorder, unspecified). The best definition for identifying a sleep disorder was an ICD code (from physician claims) 2 years prior and 1 year post sleep clinic visit: sensitivity 79.2%, specificity 28.4%, PPV 89.1%, and NPV 15.6%. ICD codes from ED/ambulatory care data provided similar diagnostic performance when at least 2 codes appeared in a time period of 2 years prior and 1 year post sleep clinic visit: sensitivity 71.9%, specificity 54.6%, PPV 92.1%, and NPV 20.8%. The inpatient data yielded poor results in all tested ICD code combinations.
Conclusion
Sleep disorders in administrative data can be identified mainly through physician claims data and with some being determined through outpatient/ambulatory care data ICD codes, however these are poorly coded within inpatient data sources. This may be a function of how sleep disorders are diagnosed and/or reported by physicians in inpatient and outpatient settings within medical records. Future work to optimize administrative data case definitions through data linkage are needed.