Behavioural outcomes of treatment with selective serotonin reuptake inhibitors

Mood and anxiety disorders are some of the biggest contributors to morbidity worldwide, and may be lethal. Appropriate treatment is therefore paramount. Antidepressant medications constitute the primary pharmacological treatment for these disorders, with selective serotonin reuptake inhibitors (SSRIs) as the most common type in several Western countries. While developed to treat disorders that increase the risk of violence and suicide, there is concern that SSRI treatment may in itself increase the risk for these behavioural outcomes, especially among young people. The overarching aim of this thesis is therefore to contribute to the understanding of the risks and benefits of treatment with SSRIs in relation to severe behavioural outcomes in different age groups, including when SSRIs are combined with other central nervous system (CNS) drugs. We also document antidepressant prescription patterns in young individuals – the age group where the balance between benefits and risks of antidepressant treatment is least clear. In study I, we described the prevalence of antidepressant use and polypharmacy of CNS drugs with antidepressants over time in children, adolescents, and young adults living in Sweden. We found that, over time, there was an increasing trend in antidepressant use and an increase in the co-prescription of antidepressants with other CNS drugs. We also found that antidepressant users had higher likelihood than population controls of collecting other CNS drug classes additionally to antidepressants. In Study II, we investigated the hazard of conviction for violent crimes during treatment with SSRIs, including in different time periods since start and end of treatment. In a follow-up of up to 8 years, we found that the hazard of violent crime was statistically significantly elevated throughout treatment periods, and for up to 12 weeks after the end of treatment. This was true in youths as well as older adults, which adds to prior research that has found elevated risk of aggression outcomes during SSRI treatment in young adults but not older individuals. In Study III, we explored the incidence rate of suicide attempts or deaths (suicidal behaviour) in time periods before and after initiation of SSRI treatment. We found that the month immediately prior to SSRI treatment initiation was associated with the greatest incidence rate of suicidal behaviour, that treatment periods up to one year after treatment initiation were associated with lower incidence rate compared to the month immediately before initiation, and that the incidence rate gradually decreased over treatment time. However, all treated periods had higher incidence rates than the month one year before treatment start. These patterns were consistent across age categories, including among children and young adults. In Study IV, we applied a data-driven screening approach to compare the incidence rate of suicidal behaviour in periods after and before initiation of additional CNS drugs during continuous SSRI treatment. We found several drugs that were associated with statistically significantly reduced incidence rate of suicidal behaviour when initiated during SSRI treatment, and only two associated with increased risk of suicidal behaviour. We found no evidence of harmful effects of combining SSRIs with additional CNS drugs. Many of the signals of reduced suicidal behaviour correspond to prior evidence; novel signals could be further investigated to evaluate the use of these drugs concurrently with SSRI treatment. In conclusion, the presented thesis has documented: the increasing prevalence of antidepressant use and polypharmacy of antidepressants with other CNS drugs in young individuals resident in Sweden; the associations between SSRI use and violent crime and suicidal behaviour; and the impact of initiating other CNS drugs during SSRI treatment on the risk for suicidal behaviour. The findings are expected to help guide future research and clinical decision making

IMI – industry guidelines and ethical considerations for myopia control report

PURPOSE. To discuss guidelines and ethical considerations associated with the development and prescription of treatments intended for myopia control (MC). METHODS. Critical review of published papers and guidance documents was undertaken, with a view to carefully considering the ethical standards associated with the investigation, development, registration, marketing, prescription, and use of MC treatments. RESULTS. The roles and responsibilities of regulatory bodies, manufacturers, academics, eye care practitioners, and patients in the use of MC treatments are explored. Particular attention is given to the ethical considerations for deciding whether to implement a MC strategy and how to implement this within a clinical trial or practice setting. Finally, the responsibilities in marketing, support, and education required to transfer required knowledge and skills to eye care practitioners and academics are discussed. CONCLUSIONS. Undertaking MC treatment in minors creates an ethical challenge for a wide variety of stakeholders. Regulatory bodies, manufacturers, academics, and clinicians all share an ethical responsibility to ensure that the products used for MC are safe and efficacious and that patients understand the benefits and potential risks of such products. This International Myopia Institute report highlights these ethical challenges and provides stakeholders with recommendations and guidelines in the development, financial support, prescribing, and advertising of such treatments.</p

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

Approaches to enhance interpretability and meaningful use of big data in population health practice and research

While many public health and medical studies use big data, the potential for big data to further population health has yet to be fully realized. Because of the complexities associated with the storage, processing, analysis, and interpretation of these data, few research findings from big data have been translated into practice. Using small area estimation synthetic data and electronic health record (EHR) data, the overall goal of this dissertation research was to characterize health-related exposures with an explicit focus on meaningful data interpretability. In our first aim, we used regression models linked to population microdata to respond to high-priority needs articulated by our community partners in New Bedford, MA. We identified census tracts with an elevated percentage of high-risk subpopulations (e.g., lower rates of exercise, higher rates of diabetes), information our community partners used to prioritize funding opportunities and intervention programs. In our second and third aims, we scrutinized EHR data on children seen at Boston Medical Center (Boston, MA), New England’s largest safety-net hospital, from 2013 through 2017 and uncovered racial/ethnic disparities in asthma severity and residential mobility using logistic regression. We built upon a validated asthma computable phenotype to create a computable phenotype for asthma severity that is based in clinical asthma guidelines. We found that children for whom severity could be ascertained from these EHR data were less likely to be Hispanic and that Black children were less likely to have lung function testing data present. Lastly, we constructed contextualized residential mobility and immobility metrics using EHR address data and the Child Opportunity Index 2.0, identified opportunities and challenges EHR address data present to study this topic, and found significant racial/ethnic disparities in access to neighborhood opportunity. Our findings highlighted the perpetuation of residence in low opportunity areas among non-White children. The main challenge of this dissertation, to work within the limitations inherent to big data to extract meaningful knowledge from these data and by linking to external datasets, turned out to be an opportunity to engage in solutions-oriented research and do work that, to quote Aristotle, “…is greater than the sum of its parts”. Through strategies ranging from engaging with community partners to examining who and what data are captured (and not captured) in EHR health and address data, this dissertation demonstrated potential ways to leverage big data sources to further public health and health equity

PHARMACOGENOMICS IN THE EMIRATI POPULATION: APPLICATIONS IN CARDIOVASCULAR DISEASES AND ONCOLOGY

Pharmacogenetic variations contribute to interindividual differences in drug response. Advances in molecular techniques provided insights into interpopulation pharmacogenomic variations. A limited number of pharmacogenetic studies were conducted in the UAE population. The current study aims to explore the variation landscape in important pharmacogenes in Emiratis. Furthermore, it investigates the association between VKORC1 variants and warfarin dose in cardiovascular patients. Finally, this study explores the applied/needed germline pharmacogenetic tests in oncology in the UAE. In 100 healthy Emiratis, variants and star alleles in 100 relevant pharmacogenes were defined by next-generation sequencing. 63% of detected variants were rare, 30% were novel, and 141 variants were novel and damaging. By clinical annotations, filtering variants resulted in 99 clinically actionable variants, from which 44 are highly significant alleles. Revising the results against the clinical pharmacogenetics implementation consortium guidelines demonstrated that 93% of participants have at least one actionable variant with a dosing recommendation. The effect of VKORC1 on warfarin dose was explored in 90 patients. A model built from two VKORC1 variants, rs9923231 and rs61742245, with age, significantly predicted warfarin dose. High incidence rates of adverse chemotherapy effects were reported from 66 pediatric acute lymphoblastic leukemia patients, which indicates the plausibility of pharmacogenetic research to investigate toxicity biomarkers. Few cases had a clinical pharmacogenetic test of TPMT and NUDT15 before starting oral 6-mercaptopurine. Patients who received pharmacogenetic-guided doses suffered from less adverse effects. Exploring the adverse drug effects in a group of 77 breast cancer patients was faced by deficiencies in adverse effects reporting. The reported adverse events suggested suitable candidates for future pharmacogenetic research. This research highlighted population-specific variants, unexplored adverse drug events, and possible pharmacogenomics applications in the UAE. Various research opportunities were illustrated for the scientific community

Bugs, Drugs and Data: Antibiotic Resistance, Prevalence and Prediction of Bug-Drug Mismatch using Electronic Health Records (EHR) Data

Title from PDF of title page viewed December 14, 2021Dissertation advisor: An-Lin ChengVitaIncludes bibliographical references (pages 92-130)Thesis (Ph.D.)--School of Medicine, School of Computing and Engineering, and School of Biological and Chemical Sciences. University of Missouri--Kansas City, 2021Bug-Drug Mismatch (BDM) occurrences are an important and modifiable category of inappropriate antibiotic therapy (IAAT) that increases adverse outcomes for patients and drives overall antibiotic resistance (AR). Surveillance of baseline AR, emerging trends in resistance among priority bacterial pathogens and prevalence of BDM with respect to the age of the patients and the type of health care-setting are required due to differences in antimicrobial need and use in these populations. Additionally, very little is known about the risk factors associated with BDM occurrence. We performed a retrospective study using de-identified, electronic health record (EHR) data in the Cerner Health Facts™ data warehouse. We assessed antibiotic susceptibility data between the years 2012 to 2017 and visualized the slope coefficient from linear regression to compare changes in resistance over time. We examined the prevalence of BDM for critically important antibiotics and clinically relevant pathogens between the year 2009 to 2017 in four groups of patients: adults; children; children treated in freestanding pediatric facilities and children treated in blended facilities (adults and children). We implemented multiple logistic regression as a reference model to identify risk factors for BDM occurrences and compared the predictive performance measure with 4 machine learning models (logistic regression with lasso regularization, random forest, gradient boosted decision tree and deep neural network). The trends in resistance rates to clinically relevant antibiotics were influenced by age and care setting. BDM prevalence for several critically important antibiotics differed between children and adults as well as within pediatric and blended facilities. Risk factors such as age of the patient, patient comorbidities and size of the facility were significantly associated with BDM occurrence. Additionally, the machine learning models developed in our study has a high predictive ability (C-statistic), higher sensitivity, specificity, positive predictive value and positive likelihood ratio to identify BDM occurrence than the reference model. This study describes the utility of data visualization to interpret large scale EHR data on the trends of AR, prevalence and risk factors of BDM which are critical in tailoring antibiotic stewardship efforts to improving appropriate antibiotic prescribing and ultimately reduce AR.Introduction -- Background -- Variation in antibiotic resistance patterns for children and adults treated at 166 non-affiliated facilities -- Differences in the prevalence of definitive bug-drug mismatch (BDM) therapy between adults and children by care setting -- Predicting bug-drug mismatch (BDM) occurrence in EHR data using machine Learning models -- Conclusio

Enhancing the understanding of palatability assessment used in the development of paediatric medicines

Children are averse to unpalatable medicines. A medicine will only elicit its desired effect if it is taken by the patient, therefore unpalatable medicines threaten the effective treatment of paediatric indications. Regulators thus now require all new medicines to have associated plans for paediatric formulation development; key to which is palatability testing. Therefore, there is a real need to enhance our understanding of the nascent area of pharmaceutical palatability testing. Much of this research has focused on the rat brief access taste aversion (BATA) model, which uses water-deprived rats to evaluate aversiveness of a given sample by counting the number of rat licks relative to water and has the distinct advantage of being used preclinically due to the absence of human participants. The overall aims of this research were to: explore the methodological limitations of promising palatability assessment methodologies; expand the formulation repertoire and push the limits of the BATA model; and leverage the data from the BATA model to minimise animal use. Our understanding of pharmaceutical palatability testing has been enhanced. Key questions such as the number of participants necessary for a human pharmaceutical taste panel are now known. The limits of the BATA model have been explored, and we now know that it can provide information on mouthfeel as well as taste, enabling assessment of more complex liquid oral dosage forms such as suspensions. Furthermore, by leveraging the data from the BATA model, a methodology for assessing solid oral dosage forms and an in silico model for prediction of palatability were developed. This work has both answered and yielded questions and more work is need to improve pharmaceutical palatability assessment and thus children’s medicines. However, it is clear we are on a path towards more palatable children’s medicines and thus more effective treatment of paediatric diseases

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

Relationship Between Hospital Performance Measures and 30-Day Readmission Rates

Medical errors occur at the prescription step due to lack adequate knowledge of medications by the physician, failure to adhere to policies and procedures, memory lapses, confusion in nomenclature, and illegible handwriting. Unfortunately, these errors can lead to patient readmission within 30 days of dismissal. Hospital leaders lose 0.25% to 1% of Medicare’s annual reimbursement for a patient readmitted within 30 days for the same illness. United States, lawmakers posited the use of health information technology, such as computerized physician order entry scores systems (CPOES), reduced hospital readmission, improved the quality of service, and reduced the cost of healthcare. Grounded in systems theory, the purpose of this correlational study was to examine the relationship between computerized physician order entry scores, medication reconciliation scores, and 30-day readmission rates. Archival data were collected from 117 hospitals in the southeastern region of the United States. Using multiple linear regression to analyze the data, the model as a whole did not significantly predict 30-day hospital readmission rate, F (2, 114) = 1.928, p = .150, R2 = .033. However, medical reconciliation scores provided a slightly higher contribution to the model (β = .173) than CPOES (β = .059. The implications for positive social change included the potential to provide hospital administrators with a better understanding of factors that may relate to 30-day readmission rates. Patients stand to benefit from improved service, decreased cost, and quality of healthcare