scholarly journals Use of State Sequence Analysis in Pharmacoepidemiology: A Tutorial

2021 ◽  
Vol 18 (24) ◽  
pp. 13398
Author(s):  
Jacopo Vanoli ◽  
Consuelo Rubina Nava ◽  
Chiara Airoldi ◽  
Andrealuna Ucciero ◽  
Virginio Salvi ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Opioid Therapy ◽  
Prescription Data ◽  
Clustering Methods ◽  
Real World Data ◽  
Complexity Measures ◽  
Opioid Prescription ◽  
Inappropriate Use ◽  
State Sequence ◽  
Sequence Patterns

While state sequence analysis (SSA) has been long used in social sciences, its use in pharmacoepidemiology is still in its infancy. Indeed, this technique is relatively easy to use, and its intrinsic visual nature may help investigators to untangle the latent information within prescription data, facilitating the individuation of specific patterns and possible inappropriate use of medications. In this paper, we provide an educational primer of the most important learning concepts and methods of SSA, including measurement of dissimilarities between sequences, the application of clustering methods to identify sequence patterns, the use of complexity measures for sequence patterns, the graphical visualization of sequences, and the use of SSA in predictive models. As a worked example, we present an application of SSA to opioid prescription patterns in patients with non-cancer pain, using real-world data from Italy. We show how SSA allows the identification of patterns in prescriptions in these data that might not be evident using standard statistical approaches and how these patterns are associated with future discontinuation of opioid therapy.

Why are US military personnel prescribed transdermal fentanyl patches?

2021 ◽  
pp. bmjmilitary-2021-001894
Author(s):  
Hailey Murray ◽  
A N Gregoriou ◽  
A Lepore ◽  
G J Booth ◽  
A H Goldman ◽  
...  
Keyword(s):  
Military Personnel ◽  
Clinical Information ◽  
Opioid Therapy ◽  
Active Duty ◽  
Data Repository ◽  
Prescription Data ◽  
Transdermal Fentanyl ◽  
Military Health ◽  
Continuous Release ◽  
Us Military

IntroductionTransdermal fentanyl is a continuous release opioid delivery system intended for use in opioid-tolerant patients requiring around-the-clock opioid therapy. The purpose of this study is to identify the most common indications for transdermal fentanyl prescriptions in active duty US military personnel, and determine whether these prescriptions meet US Food and Drug Administration (FDA) labelling.MethodsActive duty US military personnel initiating transdermal fentanyl therapy with prescriptions filled at Military Health System pharmacies between 2015 and 2019 were identified in the Military Data Repository. Electronic health records were searched for patient demographic information, clinical information and prescription data. A total of 225 patients with complete data were identified.ResultsThe most common reason for transdermal fentanyl initiation was chronic non-cancer musculoskeletal pain. Among patients with non-cancer pain, 36% received their initial prescription from an internal medicine/primary care provider, and 35% did not meet published US FDA criteria for opioid tolerance prior to treatment initiation. There was an 81% decrease in patients initiating therapy between 2015 and 2019.ConclusionsWhile a substantial minority of transdermal fentanyl prescriptions to US military personnel did not meet FDA guidelines on appropriate use, the overall number of prescriptions fell dramatically over the study period. This suggests that automated profile review or additional targeted policies to limit transdermal fentanyl prescribing are unnecessary at this time.

A Novel Imputation Approach for Sharing Protected Public Health Data

2021 ◽  
pp. e1-e9
Author(s):  
Elizabeth A. Erdman ◽  
Leonard D. Young ◽  
Dana L. Bernson ◽  
Cici Bauer ◽  
Kenneth Chui ◽  
...  
Keyword(s):  
Public Health ◽  
Missing Values ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Surveillance Data ◽  
Prescription Data ◽  
Data Set ◽  
Opioid Prescription ◽  
Mean Imputation ◽  
Imputation Approach

Objectives. To develop an imputation method to produce estimates for suppressed values within a shared government administrative data set to facilitate accurate data sharing and statistical and spatial analyses. Methods. We developed an imputation approach that incorporated known features of suppressed Massachusetts surveillance data from 2011 to 2017 to predict missing values more precisely. Our methods for 35 de-identified opioid prescription data sets combined modified previous or next substitution followed by mean imputation and a count adjustment to estimate suppressed values before sharing. We modeled 4 methods and compared the results to baseline mean imputation. Results. We assessed performance by comparing root mean squared error (RMSE), mean absolute error (MAE), and proportional variance between imputed and suppressed values. Our method outperformed mean imputation; we retained 46% of the suppressed value’s proportional variance with better precision (22% lower RMSE and 26% lower MAE) than simple mean imputation. Conclusions. Our easy-to-implement imputation technique largely overcomes the adverse effects of low count value suppression with superior results to simple mean imputation. This novel method is generalizable to researchers sharing protected public health surveillance data. (Am J Public Health. Published online ahead of print September 16, 2021: e1–e9. https://doi.org/10.2105/AJPH.2021.306432 )

OpTrak

2020 ◽  
pp. 103-123
Author(s):  
Peng Zhang ◽  
Breck Stodghill ◽  
Cory Pitt ◽  
Cavin Briody ◽  
Douglas C. Schmidt ◽  
...  
Keyword(s):  
Integrated Approach ◽  
The United States ◽  
Prescription Data ◽  
Opioid Epidemic ◽  
Care Providers ◽  
Opioid Prescription ◽  
National Healthcare ◽  
Blockchain Technology ◽  
Prescription Drug Monitoring Programs ◽  
National Healthcare System

This article describes the structure and functionality of OpTrak, a decentralized app implemented using the Ethereum blockchain that targets the opioid epidemic currently plaguing the United States. Over-prescription and distribution of opioids cost the national healthcare system over $78 billion every year. Problems persist in every stage of the process, from doctors prescribing the medication to the pharmacists fulfilling prescriptions. These problems arise from a combination of factors, including lack of accountability, transparency, and reliability in the current prescription drug monitoring programs. This work provides three key contributions to research on a technical approach to mitigate the opioid epidemic. First, the authors pinpoint key problems in the current opioid prescription system. Second, they propose an integrated approach for addressing the problems by leveraging distributed ledgers, focusing on blockchain technology. Third, the authors describe the structure and functionality of OpTrak that allows a consortium of care providers to exchange patient prescription data securely.

scholarly journals UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model

2019 ◽  
Vol 8 (11) ◽  
pp. 518 ◽  
Author(s):  
Ning Guo ◽  
Shashi Shekhar ◽  
Wei Xiong ◽  
Luo Chen ◽  
Ning Jing
Keyword(s):  
Similarity Measure ◽  
Pattern Mining ◽  
Clustering Methods ◽  
Trajectory Data ◽  
Real World Data ◽  
Asynchronous Sampling ◽  
Ellipse Model ◽  
Spatiotemporal Clustering ◽  
Motion Features ◽  
Trajectory Similarity

Measuring the similarity between a pair of trajectories is the basis of many spatiotemporal clustering methods and has wide applications in trajectory pattern mining. However, most measures of trajectory similarity in the literature are based on precise models that ignore the inherent uncertainty in trajectory data recorded by sensors. Traditional computing or mining approaches that assume the preciseness and exactness of trajectories therefore risk underperforming or returning incorrect results. To address the problem, we propose an amended ellipse model which takes both interpolation error and positioning error into account by making use of motion features of trajectory to compute the ellipse’s shape parameters. A specialized similarity measure method considering uncertainty called UTSM based on the model is also proposed. We validate the approach experimentally on both synthetic and real-world data and show that UTSM is not only more robust to noise and outliers but also more tolerant of different sample frequencies and asynchronous sampling of trajectories.

scholarly journals Use of sequence analysis for classifying individual antidepressant trajectories to monitor population mental health

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Mark Cherrie ◽  
Sarah Curtis ◽  
Gergő Baranyi ◽  
Stuart McTaggart ◽  
Niall Cunningham ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Sequence Analysis ◽  
Local Level ◽  
Population Level ◽  
The United States ◽  
Individual Risk ◽  
White Female ◽  
Prescription Data ◽  
Scottish Population

Abstract Background Over the past decade, antidepressant prescriptions have increased in European countries and the United States, partly due to an increase in the number of new cases of mental illness. This paper demonstrates an innovative approach to the classification of population level change in mental health status, using administrative data for a large sample of the Scottish population. We aimed to identify groups of individuals with similar patterns of change in pattern of prescribing, validate these groups by comparison with other indicators of mental illness, and characterise the population most at risk of increasing mental ill health. Methods National Health Service (NHS) prescription data were linked to the Scottish Longitudinal Study (SLS), a 5.3% sample of the Scottish population (N = 151,418). Antidepressant prescription status over the previous 6 months was recorded for every month for which data were available (January 2009–December 2014), and sequence dissimilarity was computed by optimal matching. Hierarchical clustering was used to create groups of participants who had similar patterns of change, with multi-level logistic regression used to understand group membership. Results Five distinct prescription pattern groups were observed, indicating: no prescriptions (76%), occasional prescriptions (10%), continuation of prior use of prescriptions (8%), a new course of prescriptions started (4%) or ceased taking prescriptions (3%). Young, white, female participants, of low social grade, residing in socially deprived neighbourhoods, living alone, being separated/divorced or out of the labour force, were more likely to be in the group that started a new course of antidepressant prescriptions. Conclusions The use of sequence analysis for classifying individual antidepressant trajectories offers a novel approach for capturing population-level changes in mental health risk. By classifying individuals into groups based on their anti-depressant medication use we can better identify how over time, mental health is associated with individual risk factors and contextual factors at the local level and the macro political and economic scale.

scholarly journals Opioid prescription practices for patients discharged from the emergency department with acute musculoskeletal fractures

CJEM ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 486-493
Author(s):  
Garrick Mok ◽  
Hailey Newton ◽  
Lisa Thurgur ◽  
Marie-Joe Nemnom ◽  
Ian G. Stiell
Keyword(s):  
Emergency Department ◽  
Tertiary Care ◽  
Multivariable Analysis ◽  
Prescription Data ◽  
Care Hospital ◽  
Total Dosage ◽  
Opioid Prescription ◽  
Prescription Practices ◽  
Ed Visits

ABSTRACTBackgroundOpioid related mortality rate has increased 200% over the past decade. Studies show variable emergency department (ED) opioid prescription practices and a correlation with increased long-term use. ED physicians may be contributing to this problem. Our objective was to analyze ED opioid prescription practices for patients with acute fractures.MethodsWe conducted a review of ED patients seen at two campuses of a tertiary care hospital. We evaluated a consecutive sample of patients with acute fractures (January 2016–April 2016) seen by ED physicians. Patients admitted or discharged by consultant services were excluded. The primary outcome was the proportion of patients discharged with an opioid prescription. Data were collected using screening lists, electronic records, and interobserver agreement. We calculated simple descriptive statistics and a multivariable analysis.ResultsWe enrolled 816 patients, including 441 females (54.0%). Most common fracture was wrist/hand (35.2%). 260 patients (31.8%) were discharged with an opioid; hydromorphone (N = 115, range 1–120 mg) was most common. 35 patients (4.3%) had pain related ED visits <1 month after discharge. Fractures of the lumbar spine (OR 10.78 [95% CI: 3.15–36.90]) and rib(s)/sternum/thoracic spine (OR 5.46 [95% CI: 2.88–10.35)] had a significantly higher likelihood of opioid prescriptions.ConclusionsThe majority of patients presenting to the ED with acute fractures were not discharged with an opioid. Hydromorphone was the most common opioid prescribed, with large variations in total dosage. Overall, there were few return to ED visits. We recommend standardization of ED opioid prescribing, with attention to limiting total dosage.

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