Promoting Health Literacy through Leading Edge Occupational Therapy Education

Synopsis: Health literacy has become a vital focus of U.S. healthcare. By incorporating health literacy knowledge, attitudes, and skills into entry-­‐level education, occupational therapy professionals can enact a leading role in health and human services teams to address our national healthcare goals. Poster presentation at the American Occupational Therapy Association Annual Conference, San Diego, CA, April 2013

Climacteric Lowers Plasma Levels of Platelet-Derived Microparticles: A Pilot Study in Pre-versus Postmenopausal Women

Background: Climacteric increases the risk of thrombotic events by alteration of plasmatic coagulation. Up to now, less is known about changes in platelet-(PMP) and endothelial cell-derived microparticles (EMP). Methods: In this prospective study, plasma levels of microparticles (MP) were compared in 21 premenopausal and 19 postmenopausal women. Results: No altered numbers of total MP or EMP were measured within the study groups. However, the plasma values of CD61-exposing MP from platelets/megakaryocytes were higher in premenopausal women (5,364 x 10(6)/l, range 4,384-17,167) as compared to postmenopausal women (3,808 x 10(6)/l, range 2,009-8,850; p = 0.020). This differentiation was also significant for the subgroup of premenopausal women without hormonal contraceptives (5,364 x 10(6)/l, range 4,223-15,916; p = 0.047; n = 15). Furthermore, in premenopausal women, higher plasma levels of PMP exposing CD62P were also present as compared to postmenopausal women (288 x 10(6)/l, range 139-462, vs. 121 x 10(6)/l, range 74-284; p = 0.024). This difference was also true for CD63+ PMP levels (281 x 10(6)/l, range 182-551, vs. 137 x 10(6)/l, range 64-432; p = 0.015). Conclusion: Climacteric lowers the level of PMP but has no impact on the number of EMP in women. These data suggest that PMP and EMP do not play a significant role in enhancing the risk of thrombotic events in healthy, postmenopausal women. Copyright (C) 2012 S. Karger AG, Base

Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining

[EN] Background: Public health in several countries is characterized by a shortage of professionals and a lack of economic resources. Monitoring and redesigning processes can foster the success of health care institutions, enabling them to provide a quality service while simultaneously reducing costs. Process mining, a discipline that extracts knowledge from information system data to analyze operational processes, affords an opportunity to understand health care processes. Objective: Health care processes are highly flexible and multidisciplinary, and health care professionals are able to coordinate in a variety of different ways to treat a diagnosis. The aim of this work was to understand whether the ways in which professionals coordinate their work affect the clinical outcome of patients. Methods: This paper proposes a method based on the use of process mining to identify patterns of collaboration between physician, nurse, and dietitian in the treatment of patients with type 2 diabetes mellitus and to compare these patterns with the clinical evolution of the patients within the context of primary care. Clustering is used as part of the preprocessing of data to manage the variability, and then process mining is used to identify patterns that may arise. Results: The method is applied in three primary health care centers in Santiago, Chile. A total of seven collaboration patterns were identified, which differed primarily in terms of the number of disciplines present, the participation intensity of each discipline, and the referrals between disciplines. The pattern in which the three disciplines participated in the most equitable and comprehensive manner had a lower proportion of highly decompensated patients compared with those patterns in which the three disciplines participated in an unbalanced manner. Conclusions: By discovering which collaboration patterns lead to improved outcomes, health care centers can promote the most successful patterns among their professionals so as to improve the treatment of patients. Process mining techniques are useful for discovering those collaborations patterns in flexible and unstructured health care processes.This paper was partially funded by the National Commission for Scientific and Technological Research, the Formation of Advanced Human Capital Program and the National Fund for Scientific and Technological Development (CONICYT-PCHA/Doctorado Nacional/2016-21161705 and CONICYT-FONDECYT/1150365; Chile). The authors would like to thank Ancora UC primary health care centers for their help with this research. The founding sponsors had no role in the design of the study in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.Conca, T.; Saint Pierre, C.; Herskovic, V.; Sepulveda, M.; Capurro, D.; Prieto, F.; Fernández Llatas, C. (2018). Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining. JOURNAL OF MEDICAL INTERNET RESEARCH. 20(4). https://doi.org/10.2196/jmir.8884S204Chen, C.-C., Tseng, C.-H., & Cheng, S.-H. (2013). Continuity of Care, Medication Adherence, and Health Care Outcomes Among Patients With Newly Diagnosed Type 2 Diabetes. 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(2012). The influence of power dynamics and trust on multidisciplinary collaboration: a qualitative case study of type 2 diabetes mellitus. BMC Health Services Research, 12(1). doi:10.1186/1472-6963-12-63Gucciardi, E., Espin, S., Morganti, A., & Dorado, L. (2016). Exploring interprofessional collaboration during the integration of diabetes teams into primary care. BMC Family Practice, 17(1). doi:10.1186/s12875-016-0407-1Caron, F., Vanthienen, J., Vanhaecht, K., Limbergen, E. V., De Weerdt, J., & Baesens, B. (2014). Monitoring care processes in the gynecologic oncology department. Computers in Biology and Medicine, 44, 88-96. doi:10.1016/j.compbiomed.2013.10.015Rothman, A. A., & Wagner, E. H. (2003). Chronic Illness Management: What Is the Role of Primary Care? Annals of Internal Medicine, 138(3), 256. doi:10.7326/0003-4819-138-3-200302040-00034Organisation for Economic Co-operation and DevelopmentOECD20162018-03-20OECD Health Policy Overview: Health Policy in Chile http://www.oecd.org/els/health-systems/health-policy-in-your-country.htmRojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224-236. doi:10.1016/j.jbi.2016.04.007Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Mans, R. S., van der Aalst, W. M. P., & Vanwersch, R. J. B. (2015). Process Mining in Healthcare. SpringerBriefs in Business Process Management. doi:10.1007/978-3-319-16071-9Van der Aalst, W. M. P. (2011). Process Mining. doi:10.1007/978-3-642-19345-3Kim, E., Kim, S., Song, M., Kim, S., Yoo, D., Hwang, H., & Yoo, S. (2013). Discovery of Outpatient Care Process of a Tertiary University Hospital Using Process Mining. Healthcare Informatics Research, 19(1), 42. doi:10.4258/hir.2013.19.1.42Harper, P. R., Sayyad, M. G., de Senna, V., Shahani, A. K., Yajnik, C. S., & Shelgikar, K. M. (2003). A systems modelling approach for the prevention and treatment of diabetic retinopathy. European Journal of Operational Research, 150(1), 81-91. doi:10.1016/s0377-2217(02)00787-7Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Ferreira, D., Zacarias, M., Malheiros, M., & Ferreira, P. (2007). Approaching Process Mining with Sequence Clustering: Experiments and Findings. Business Process Management, 360-374. doi:10.1007/978-3-540-75183-0_26Cheong, L. H., Armour, C. L., & Bosnic-Anticevich, S. Z. (2013). Multidisciplinary collaboration in primary care: through the eyes of patients. Australian Journal of Primary Health, 19(3), 190. doi:10.1071/py12019Boyle, E., Saunders, R., & Drury, V. (2016). A qualitative study of patient experiences of Type 2 Diabetes care delivered comparatively by General Practice Nurses and Medical Practitioners. Journal of Clinical Nursing, 25(13-14), 1977-1986. doi:10.1111/jocn.13219UddinSHossainLEffects of Physician Collaboration Network on Hospital Outcomes2012Fifth Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2012)2012Melbourne, AustraliaBorgermans, L., Goderis, G., Van Den Broeke, C., Verbeke, G., Carbonez, A., Ivanova, A., … Grol, R. (2009). Interdisciplinary diabetes care teams operating on the interface between primary and specialty care are associated with improved outcomes of care: findings from the Leuven Diabetes Project. 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Nurse practitioners in Ontario primary healthcare: Referral patterns and collaboration with other healthcare professionals. Journal of Interprofessional Care, 26(3), 232-239. doi:10.3109/13561820.2011.650300Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J., & Tranmer, M. (2015). Social Network Analysis for Ego-Nets. doi:10.4135/9781473911871Ministerio de Salud de Chile2018-03-20Fondo Nacional de Salud https://www.fonasa.cl/sites/fonasa/beneficiariosGoldstein, D. E., Little, R. R., Lorenz, R. A., Malone, J. I., Nathan, D., Peterson, C. M., & Sacks, D. B. (2004). Tests of Glycemia in Diabetes. Diabetes Care, 27(7), 1761-1773. doi:10.2337/diacare.27.7.1761Meduru, P., Helmer, D., Rajan, M., Tseng, C.-L., Pogach, L., & Sambamoorthi, U. (2007). Chronic Illness with Complexity: Implications for Performance Measurement of Optimal Glycemic Control. 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EFSA NDA Panel (EFSA Panel on Dietetic Products, Nutrition and Allergies), 2013 . Scientific opinion on Dietary Reference Values for fluoride

Following a request from the European Commission, the Panel on Dietetic Products, Nutrition and Allergies (NDA) derived Dietary Reference Values (DRVs) for fluoride, which are provided as Adequate Intake (AI) from all sources, including non-dietary sources. Fluoride is not an essential nutrient. Therefore, no Average Requirement for the performance of essential physiological functions can be defined. Nevertheless, the Panel considered that the setting of an AI is appropriate because of the beneficial effects of dietary fluoride on prevention of dental caries. The AI is based on epidemiological studies (performed before the 1970s) showing an inverse relationship between the fluoride concentration of water and caries prevalence. As the basis for defining the AI, estimates of mean fluoride intakes of children via diet and drinking water with fluoride concentrations at which the caries preventive effect approached its maximum whilst the risk of dental fluorosis approached its minimum were chosen. Except for one confirmatory longitudinal study in US children, more recent studies were not taken into account as they did not provide information on total dietary fluoride intake, were potentially confounded by the use of fluoride-containing dental hygiene products, and did not permit a conclusion to be drawn on a dose-response relationship between fluoride intake and caries risk. The AI of fluoride from all sources (including non-dietary sources) is 0.05 mg/kg body weight per day for both children and adults, including pregnant and lactating women. For pregnant and lactating women, the AI is based on the body weight before pregnancy and lactation. Reliable and representative data on the total fluoride intake of the European population are not available

Quantitative Analysis of the Effect of Cancer Invasiveness and Collagen Concentration on 3D Matrix Remodeling

Extracellular matrix (ECM) remodeling is a key component of cell migration and tumor metastasis, and has been associated with cancer progression. Despite the importance of matrix remodeling, systematic and quantitative studies on the process have largely been lacking. Furthermore, it remains unclear if the disrupted tensional homeostasis characteristic of malignancy is due to initially altered ECM and tissue properties, or to the alteration of the tissue by tumor cells. To explore these questions, we studied matrix remodeling by two different prostate cancer cell lines in a three-dimensional collagen system. Over one week, we monitored structural changes in gels of varying collagen content using confocal reflection microscopy and quantitative image analysis, tracking metrics of fibril fraction, pore size, and fiber length and diameter. Gels that were seeded with no cells (control), LNCaP cells, and DU-145 cells were quantitatively compared. Gels with higher collagen content initially had smaller pore sizes and higher fibril fractions, as expected. However, over time, LNCaP- and DU-145-populated matrices showed different structural properties compared both to each other and to the control gels, with LNCaP cells appearing to favor microenvironments with lower collagen fiber fractions and larger pores than DU-145 cells. We posit that the DU-145 cells' preference for denser matrices is due to their higher invasiveness and proteolytic capabilities. Inhibition of matrix proteases resulted in reduced fibril fractions for high concentration gels seeded with either cell type, supporting our hypothesis. Our novel quantitative results probe the dynamics of gel remodeling in three dimensions and suggest that prostate cancer cells remodel their ECM in a synergistic manner that is dependent on both initial matrix properties as well as their invasiveness

Statin-Associated Muscular and Renal Adverse Events: Data Mining of the Public Version of the FDA Adverse Event Reporting System

OBJECTIVE: Adverse event reports (AERs) submitted to the US Food and Drug Administration (FDA) were reviewed to assess the muscular and renal adverse events induced by the administration of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) and to attempt to determine the rank-order of the association. METHODS: After a revision of arbitrary drug names and the deletion of duplicated submissions, AERs involving pravastatin, simvastatin, atorvastatin, or rosuvastatin were analyzed. Authorized pharmacovigilance tools were used for quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Myalgia, rhabdomyolysis and an increase in creatine phosphokinase level were focused on as the muscular adverse events, and acute renal failure, non-acute renal failure, and an increase in blood creatinine level as the renal adverse events. RESULTS: Based on 1,644,220 AERs from 2004 to 2009, signals were detected for 4 statins with respect to myalgia, rhabdomyolysis, and an increase in creatine phosphokinase level, but these signals were stronger for rosuvastatin than pravastatin and atorvastatin. Signals were also detected for acute renal failure, though in the case of atorvastatin, the association was marginal, and furthermore, a signal was not detected for non-acute renal failure or for an increase in blood creatinine level. CONCLUSIONS: Data mining of the FDA's adverse event reporting system, AERS, is useful for examining statin-associated muscular and renal adverse events. The data strongly suggest the necessity of well-organized clinical studies with respect to statin-associated adverse events

Anthracyclines, proteasome activity and multi-drug-resistance

BACKGROUND: P-glycoprotein is responsible for the ATP-dependent export of certain structurally unrelated compounds including many chemotherapeutic drugs. Amplification of P-glycoprotein activity can result in multi-drug resistance and is a common cause of chemotherapy treatment failure. Therefore, there is an ongoing search for inhibitors of P-glycoprotein. Observations that cyclosporin A, and certain other substances, inhibit both the proteasome and P-glycoprotein led us to investigate whether anthracyclines, well known substrates of P-gp, also inhibit the function of the proteasome. METHODS: Proteasome function was measured in cell lysates from ECV304 cells incubated with different doses of verapamil, doxorubicin, daunorubicin, idarubicin, epirubicin, topotecan, mitomycin C, and gemcitabine using a fluorogenic peptide assay. Proteasome function in living cells was monitored using ECV304 cells stably transfected with the gene for an ubiquitin/green fluorescent protein fusion protein. The ability of the proteasome inhibitor MG-132 to affect P-glycoprotein function was monitored by fluorescence due to accumulation of daunorubicin in P-glycoprotein overexpressing KB 8-5 cells. RESULTS: Verapamil, daunorubicin, doxorubicin, idarubicin, and epirubicin inhibited 26S chymotrypsin-like function in ECV304 extracts in a dose-dependent fashion. With the exception of daunorubicin, 20S proteasome function was also suppressed. The proteasome inhibitor MG-132 caused a dose-dependent accumulation of daunorubicin in KB 8-5 cells that overexpress P-glycoprotein, suggesting that it blocked P-glycoprotein function. CONCLUSION: Our data indicate that anthracyclines inhibit the 26S proteasome as well as P-glycoprotein. Use of inhibitors of either pathway in cancer therapy should take this into consideration and perhaps use it to advantage, for example during chemosensitization by proteasome inhibitors

Search for direct pair production of the top squark in all-hadronic final states in proton-proton collisions at s√=8 TeV with the ATLAS detector

The results of a search for direct pair production of the scalar partner to the top quark using an integrated luminosity of 20.1fb−1 of proton–proton collision data at √s = 8 TeV recorded with the ATLAS detector at the LHC are reported. The top squark is assumed to decay via t˜→tχ˜01 or t˜→ bχ˜±1 →bW(∗)χ˜01 , where χ˜01 (χ˜±1 ) denotes the lightest neutralino (chargino) in supersymmetric models. The search targets a fully-hadronic final state in events with four or more jets and large missing transverse momentum. No significant excess over the Standard Model background prediction is observed, and exclusion limits are reported in terms of the top squark and neutralino masses and as a function of the branching fraction of t˜ → tχ˜01 . For a branching fraction of 100%, top squark masses in the range 270–645 GeV are excluded for χ˜01 masses below 30 GeV. For a branching fraction of 50% to either t˜ → tχ˜01 or t˜ → bχ˜±1 , and assuming the χ˜±1 mass to be twice the χ˜01 mass, top squark masses in the range 250–550 GeV are excluded for χ˜01 masses below 60 GeV

Search for pair-produced long-lived neutral particles decaying to jets in the ATLAS hadronic calorimeter in ppcollisions at √s=8TeV

The ATLAS detector at the Large Hadron Collider at CERN is used to search for the decay of a scalar boson to a pair of long-lived particles, neutral under the Standard Model gauge group, in 20.3fb−1of data collected in proton–proton collisions at √s=8TeV. This search is sensitive to long-lived particles that decay to Standard Model particles producing jets at the outer edge of the ATLAS electromagnetic calorimeter or inside the hadronic calorimeter. No significant excess of events is observed. Limits are reported on the product of the scalar boson production cross section times branching ratio into long-lived neutral particles as a function of the proper lifetime of the particles. Limits are reported for boson masses from 100 GeVto 900 GeV, and a long-lived neutral particle mass from 10 GeVto 150 GeV