Opioid Use and Safety in United States Nursing Homes

Background: Opioids are often used in nursing homes to manage non-malignant pain, but little is known about their long-term use, initiation, and comparative safety. Methods: We used the Minimum Data Set 3.0 from 2011-2013 merged to Medicare and facility characteristics data to study opioid use and safety among older, long-stay residents. The specific aims were to examine the 1) prevalence of long-term opioid use; 2) geographic variation in the initiation of commonly used opioids (oxycodone, hydrocodone, tramadol); and 3) comparative safety of commonly used opioids and fracture hospitalizations. Results: One in seven long-stay residents were prescribed opioids long-term. There was extensive geographic variation in the initiation of commonly used opioids, with oxycodone (9.4%) initiated less frequently than hydrocodone (56.2%) or tramadol (34.5%) but varying most extensively across the United States, with the majority of variation in prescribing explained by state of residence. Compared to hydrocodone initiators (7.9 fracture hospitalizations per 100-person years), those initiating tramadol had lower rates of fracture hospitalizations (subdistribution hazard ratio [HRSD] = 0.67, 95% Confidence Interval [CI]: 0.56-0.80), whereas oxycodone initiators had similar rates of fracture hospitalizations (HRSD=1.08, 95% CI: 0.79-1.48). Conclusion: The prevalence of long-term opioid use was twice as common in nursing homes as community settings, with initiation patterns varying extensively by region and being strongly driven by state of residence. Although initiating tramadol was associated with lower rates of fractures than hydrocodone, questions on opioid risks and benefits remain and are especially pertinent given the high mortality rates in this population

Doctors, Death, and Drug Money : A Quantitative Analysis of Direct-to-Physician Pharmaceutical Marketing and Mortality

This thesis examines direct-to-physician pharmaceutical marketing in the United States of America. In 2013, about 78 opioid prescriptions were being written for every 100 people, and 17,000 people in the United States died from an opioid overdose. This study asks, what is the relationship, if any, between contemporary direct-to-physician pharmaceutical marketing practices and opioid mortality in the United States? Contained within an expansive piece of U.S. federal legislation, the Patient Protection and Affordable Care Act of 2010 is a provision which mandates pharmaceutical manufacturers to report marketing payments made to physicians, hospitals, and other relevant healthcare providers. By connecting marketing payments to mortality data at several geospatial levels, the study finds that there is a plausible relationship between the direct-to-physician pharmaceutical marketing and mortality

Data Science for Hospital Antibiotic Stewardship

Antibiotics are widely used to treat bacterial infections, but their misuse leads to antibiotic resistance. Antibiotic resistance is one of the biggest threats to global health, food security, and development today. Antibiotic resistance leads to higher medical costs, prolonged hospital stays, and increased mortality. Antimicrobial stewardship is an approach to measure and improve the appropriate use of antibiotics in healthcare settings. Data science has the potential to support these programs by providing insights into antibiotic prescribing patterns, identifying areas for improvement, and predicting patient outcomes. We explored the role of data science in hospital antibiotic stewardship programs, including statistical methods and data visualization techniques. We conducted statistical analysis to identify trends and seasonality in antibiotic usage using autoregressive integrated moving average (ARIMA) models and generalized additive models (GAMs). We developed a pilot interactive dashboard for hospital inpatient antibiotic stewardship using Python. The dashboard visualizes trends in the antibiotic stewardship metric days of therapy (DOT) by various categories, such as indication, therapeutic class, and period. The use of digital dashboards in healthcare is becoming increasingly popular, and our work demonstrates the potential of data visualization tools in hospital antibiotic stewardship

A study assessing the characteristics of big data environments that predict high research impact: application of qualitative and quantitative methods

BACKGROUND: Big data offers new opportunities to enhance healthcare practice. While researchers have shown increasing interest to use them, little is known about what drives research impact. We explored predictors of research impact, across three major sources of healthcare big data derived from the government and the private sector. METHODS: This study was based on a mixed methods approach. Using quantitative analysis, we first clustered peer-reviewed original research that used data from government sources derived through the Veterans Health Administration (VHA), and private sources of data from IBM MarketScan and Optum, using social network analysis. We analyzed a battery of research impact measures as a function of the data sources. Other main predictors were topic clusters and authors’ social influence. Additionally, we conducted key informant interviews (KII) with a purposive sample of high impact researchers who have knowledge of the data. We then compiled findings of KIIs into two case studies to provide a rich understanding of drivers of research impact. RESULTS: Analysis of 1,907 peer-reviewed publications using VHA, IBM MarketScan and Optum found that the overall research enterprise was highly dynamic and growing over time. With less than 4 years of observation, research productivity, use of machine learning (ML), natural language processing (NLP), and the Journal Impact Factor showed substantial growth. Studies that used ML and NLP, however, showed limited visibility. After adjustments, VHA studies had generally higher impact (10% and 27% higher annualized Google citation rates) compared to MarketScan and Optum (p<0.001 for both). Analysis of co-authorship networks showed that no single social actor, either a community of scientists or institutions, was dominating. Other key opportunities to achieve high impact based on KIIs include methodological innovations, under-studied populations and predictive modeling based on rich clinical data. CONCLUSIONS: Big data for purposes of research analytics has grown within the three data sources studied between 2013 and 2016. Despite important challenges, the research community is reacting favorably to the opportunities offered both by big data and advanced analytic methods. Big data may be a logical and cost-efficient choice to emulate research initiatives where RCTs are not possible

Tennessee\u27s Annual Overdose Report 2019, Understanding and Responding to the Opioid

https://digitalcommons.memphis.edu/govpubs-tn-dept-health-drug-poisonings-in-tennessee/1017/thumbnail.jp

CDSSs for CVD Risk Management: An Integrative Review

Cardiovascular disease (CVD) is a preventable disease affecting almost half of adults in the United States (U.S.) and can have significant negative outcomes such as stroke and myocardial infarction, which can be fatal. Utilizing clinical decision support systems (CDSSs) in the primary care and community health setting can improve primary prevention of CVD by supporting evidence-based decision making at the point of care. This integrative review synthesizes the most up-to-date literature on the use of clinical decision support (CDS) tools to support guideline-based management of CVD risk. Using Whittemore and Knafl’s framework for integrative reviews, a systematic search of CINAHL, Cochrane, and Medline and ancestry search yielded 492 results; 17 articles were included in the final review after applying inclusion and exclusion criteria. Evidence-based CDSSs for CVD prevention improved guideline-based initiation and intensification of pharmacological treatment, increased frequency and accuracy of CVD risk screening, and facilitated shared decision-making discussions with patients about CVD risk; however, they were not effective in promoting smoking cessation and only sometimes effective in improving blood pressure (BP) control. This integrative review supports future evidence-based practice projects implementing CDSSs designed to improve guideline-based primary prevention of CVD as an, albeit partial, solution to improving prevention of CVD in the U.S. and globally

‘Big data’ in mental health research:current status and emerging possibilities

PURPOSE: ‘Big data’ are accumulating in a multitude of domains and offer novel opportunities for research. The role of these resources in mental health investigations remains relatively unexplored, although a number of datasets are in use and supporting a range of projects. We sought to review big data resources and their use in mental health research to characterise applications to date and consider directions for innovation in future. METHODS: A narrative review. RESULTS: Clear disparities were evident in geographic regions covered and in the disorders and interventions receiving most attention. DISCUSSION: We discuss the strengths and weaknesses of the use of different types of data and the challenges of big data in general. Current research output from big data is still predominantly determined by the information and resources available and there is a need to reverse the situation so that big data platforms are more driven by the needs of clinical services and service users

Two Essays on Analytical Capabilities: Antecedents and Consequences

Although organizations are rapidly embracing business analytics (BA) to enhance organizational performance, only a small proportion have managed to build analytical capabilities. While BA continues to draw attention from academics and practitioners, theoretical understanding of antecedents and consequences of analytical capabilities remain limited and lack a systematic view. In order to address the research gap, the two essays investigate: (a) the impact of organization’s core information processing mechanisms and its impact on analytical capabilities, (b) the sequential approach to integration of IT-enabled business processes and its impact on analytical capabilities, and (c) network position and its impact on analytical capabilities. Drawing upon the Information Processing Theory (IPT), the first essay investigates the relationship between organization’s core information processing mechanisms–i.e., electronic health record (EHRs), clinical information standards (CIS), and collaborative information exchange (CIE)–and its impact on analytical capabilities. We use data from two sources (HIMSS Analytics 2013 and AHA IT Survey 2013) to test the theorized relationships in the healthcare context empirically. Using the competitive progression theory, the second essay investigates whether organizations sequential approach to the integration of IT-enabled business processes is associated with increased analytical capabilities. We use data from three sources (HIMSS Analytics 2013, AHA IT Survey 2013, and CMS 2014) to test if sequential integration of EHRs –i.e., reflecting the unique organizational path of integration–has a significant impact on hospital’s analytical capability. Together the two essays advance our understanding of the factors that underlie enabling of firm’s analytical capabilities. We discuss in detail the theoretical and practical implications of the findings and the opportunities for future research

Network approaches and interventions in healthcare settings: a systematic scoping review

Introduction The growing interest in networks of interactions is sustained by the conviction that they can be leveraged to improve the quality and efficiency of healthcare delivery systems. Evidence in support of this conviction, however, is mostly based on descriptive studies. Systematic evaluation of the outcomes of network interventions in healthcare settings is still wanting. Despite the proliferation of studies based on Social Network Analysis (SNA) tools and techniques, we still know little about how intervention programs aimed at altering existing patterns of social interaction among healthcare providers affect the quality of service delivery. We update and extend prior reviews by providing a comprehensive assessment of available evidence. Methods and findings We searched eight databases to identify papers using SNA in healthcare settings published between 1st January 2010 and 1st May 2022. We followed Chambers et al.’s (2012) approach, using a Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. We distinguished between studies relying on SNA as part of an intervention program, and studies using SNA for descriptive purposes only. We further distinguished studies recommending a possible SNA-based intervention. We restricted our focus on SNA performed on networks among healthcare professionals (e.g., doctors, nurses, etc.) in any healthcare setting (e.g., hospitals, primary care, etc.). Our final review included 102 papers. The majority of the papers used SNA for descriptive purposes only. Only four studies adopted SNA as an intervention tool, and measured outcome variables. Conclusions We found little evidence for SNA-based intervention programs in healthcare settings. We discuss the reasons and challenges, and identify the main component elements of a network intervention plan. Future research should seek to evaluate the long-term role of SNA in changing practices, policies and behaviors, and provide evidence of how these changes affect patients and the quality of service delivery

Measurement of discontinuous drug exposure in large healthcare databases

Le contexte international de la pharmacoépidémiologie, marqué par la mise en œuvre d'un nombre croissant d'études multi-sources, a fait émerger un certain nombre de questionnements autour de la gestion de données conflictuelles ou de l'impact des choix méthodologiques sur les résultats. Accroître la confiance dans ces études observationnelles et renforcer leur crédibilité face aux données issues des essais cliniques représente un enjeu majeur, qui dépend étroitement de la robustesse des conclusions produites. Dans ce domaine, la mesure de l'exposition médicamenteuse revêt donc une importance toute particulière, tant pour des études portant sur l'estimation d'un risque ou d'un critère d'efficacité, que lors de la description des modalités d'utilisation en vie réelle. L'exposition médicamenteuse reste un phénomène complexe qui se caractérise la plupart du temps par des cycles discontinus, marqués par des évolutions de doses et la présence de médicaments concomitants. Compte tenu des caractéristiques pharmacodynamiques et pharmacocinétiques propres à chaque médicament, cette mesure d'exposition revêt un caractère majeur. Cependant, la façon d'appréhender les cycles d'exposition au sein des bases de données-médico-administratives peut varier selon les études. Or, on connaît peu l'impact de ces méthodes de mesure sur les estimations de risque obtenues. De plus, elles sont parfois peu adaptées à la prise en compte d'expositions concomitantes multiples, d'où la nécessité de développer de nouvelles approches. Après avoir réalisé une revue des données sur le médicament contenues dans les bases de données de l'assurance maladie française, en insistant plus particulièrement sur les ruptures dans la disponibilité des données, des études de cas ont été menées afin d'explorer ces questions dans différents contextes. Dans un premier temps, un modèle générique a été employé comme prototype d'une exposition discontinue, celui de la population générale utilisatrice de benzodiazépines anxiolytiques et hypnotiques, médicaments très répandus. Cette étude explorant la mortalité associée aux benzodiazépines a également été utilisée pour évaluer l'impact des périodes d'exposition inobservables lors des hospitalisations. Dans un second temps, des travaux ont été menés dans le champ de l'onco-hématologie, en prenant comme modèle d'exposition complexe, à la fois discontinue et multiple, les protocoles de chimiothérapie du myélome multiple. Enfin, un dernier projet a étudié l'apport potentiel des méthodes de visualisation de données pour améliorer la description de l'exposition longitudinale au médicament et des situations de concomitance, et rendre plus pertinente leur modélisation. Ces travaux méthodologiques ont ainsi cherché à améliorer la validité et la robustesse de la mesure de l'exposition médicamenteuse dans des contextes d'expositions multiples et discontinues.The multinational context of pharmacoepidemiology, and the resulting increased number of multi-sources studies have generated concerns in relation with conflicting results and the question of the impact of methodological choices on study results. Increasing the confidence in the conclusions derived from these observational studies is a crucial issue, which is closely related to the robustness of the evidence produced. In this area, impact of drug exposure measurement and risk window might be crucial. Drug exposure is mostly characterized by discontinuous episodes, marked by changes in doses and presence of concomitant medications. Considering the pharmacokinetic and pharmacodynamics characteristics specific to each individual drug, the way in which the drug exposure is presented is of great importance. However, methods used for handling drug exposure episodes in electronic healthcare databases are varying widely according studies. However, the impact of these methods on risk estimates has been insufficiently investigated. In addition, these methods are sometimes poorly adapted to the context multiple concomitant exposures, suggesting that new approaches might be necessary. After making an overview of drug data contained in the French health insurance databases, with a particular emphasis on gaps in longitudinal data availability, case studies were carried out to investigate these issues in different contexts. First, a generic model of discontinuous exposure was used: the general population exposed to the widespread drugs anxiolytic and hypnotic benzodiazepines. This case study on the risk of death associated with benzodiazepines was also used to assess the impact of immeasurable exposure periods during hospitalizations. In a second part, two studies have been conducted in the context of onco-hematology, using multiple myeloma chemotherapy regimens as a model of complex, both discontinuous and multiple, exposure. A final project examined the potential contribution of data visualization for increasing the knowledge of longitudinal and concomitant drug exposure patterns and for making their modelling more relevant. In final, these methodological projects are intended to improve the validity and the robustness of drug exposure measurement in medico-administrative databases in the context of longitudinal and multiple concomitant exposures