524 research outputs found

    Association between Residences in U.S. Northern Latitudes and Rheumatoid Arthritis: A Spatial Analysis of the Nurses’ Health Study

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    Background: The etiology of rheumatoid arthritis (RA) remains largely unknown, although epidemiologic studies suggest genetic and environmental factors may play a role. Geographic variation in incident RA has been observed at the regional level. Objective: Spatial analyses are a useful tool for confirming existing exposure hypotheses or generating new ones. To further explore the association between location and RA risk, we analyzed individual-level data from U.S. women in the Nurses’ Health Study, a nationwide cohort study. Methods: Participants included 461 incident RA cases and 9,220 controls with geocoded addresses; participants were followed from 1988 to 2002. We examined spatial variation using addresses at baseline in 1988 and at the time of case diagnosis or the censoring of controls. Generalized additive models (GAMs) were used to predict a continuous risk surface by smoothing on longitude and latitude while adjusting for known risk factors. Permutation tests were conducted to evaluate the overall importance of location and to identify, within the entire study area, those locations of statistically significant risk. Results: A statistically significant area of increased RA risk was identified in the northeast United States (p-value = 0.034). Risk was generally higher at northern latitudes, and it increased slightly when we used the nurses’ 1988 locations compared with those at the time of diagnosis or censoring. Crude and adjusted models produced similar results. Conclusions: Spatial analyses suggest women living in higher latitudes may be at greater risk for RA. Further, RA risk may be greater for locations that occur earlier in residential histories. These results illustrate the usefulness of GAM methods in generating hypotheses for future investigation and supporting existing hypotheses

    The evolution of mammalian brain size

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    Relative brain size has long been considered a reflection of cognitive capacities and has played a fundamental role in developing core theories in the life sciences. Yet, the notion that relative brain size validly represents selection on brain size relies on the untested assumptions that brain-body allometry is restrained to a stable scaling relationship across species and that any deviation from this slope is due to selection on brain size. Using the largest fossil and extant dataset yet assembled, we find that shifts in allometric slope underpin major transitions in mammalian evolution and are often primarily characterized by marked changes in body size. Our results reveal that the largest-brained mammals achieved large relative brain sizes by highly divergent paths. These findings prompt a reevaluation of the traditional paradigm of relative brain size and open new opportunities to improve our understanding of the genetic and developmental mechanisms that influence brain size

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

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    [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. Medical Care, 51(3), 231-237. doi:10.1097/mlr.0b013e31827da5b9International Diabetes FederationIDF20152018-03-19IDF Diabetes Atlas 7th Edition (2015) https://www.idf.org/e-library/epidemiology-research/diabetes-atlas/13-diabetes-atlas-seventh-edition.htmlMinisterio de Salud de Chileminsal.cl20102018-03-23Encuesta Nacional de Salud ENS Chile 2009-2010 http://www.minsal.cl/estudios_encuestas_salud/Ministerio de Salud de Chileminsal.cl20102018-03-20Guía Clinica Diabetes Mellitus Tipo 2 http://www.minsal.cl/portal/url/item/72213ed52c3e23d1e04001011f011398.pdfSapunar Z., J. (2016). EPIDEMIOLOGÍA DE LA DIABETES MELLITUS EN CHILE. Revista Médica Clínica Las Condes, 27(2), 146-151. doi:10.1016/j.rmclc.2016.04.003World Health Organizationwho.int2018-03-20Global Report on Diabetes http://www.who.int/diabetes/global-report/en/Poblete, F., Glasinovic, A., Sapag, J., Barticevic, N., Arenas, A., & Padilla, O. (2015). Apoyo social y salud cardiovascular: adaptación de una escala de apoyo social en pacientes hipertensos y diabéticos en la atención primaria chilena. Atención Primaria, 47(8), 523-531. doi:10.1016/j.aprim.2014.10.010Tuligenga, R. H., Dugravot, A., Tabák, A. G., Elbaz, A., Brunner, E. J., Kivimäki, M., & Singh-Manoux, A. (2014). Midlife type 2 diabetes and poor glycaemic control as risk factors for cognitive decline in early old age: a post-hoc analysis of the Whitehall II cohort study. The Lancet Diabetes & Endocrinology, 2(3), 228-235. doi:10.1016/s2213-8587(13)70192-xGamiochipi, M., Cruz, M., Kumate, J., & Wacher, N. H. (2016). Effect of an intensive metabolic control lifestyle intervention in type-2 diabetes patients. Patient Education and Counseling, 99(7), 1184-1189. doi:10.1016/j.pec.2016.01.017Wagner, E. H. (2001). Effect of Improved Glycemic Control on Health Care Costs and Utilization. JAMA, 285(2), 182. doi:10.1001/jama.285.2.182McDonald, J., Jayasuriya, R., & Harris, M. F. (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. BMC Health Services Research, 9(1). doi:10.1186/1472-6963-9-179Bosch, M., Dijkstra, R., Wensing, M., van der Weijden, T., & Grol, R. (2008). Organizational culture, team climate and diabetes care in small office-based practices. BMC Health Services Research, 8(1). doi:10.1186/1472-6963-8-180Counsell, S. R., Callahan, C. M., Clark, D. O., Tu, W., Buttar, A. B., Stump, T. E., & Ricketts, G. D. (2007). Geriatric Care Management for Low-Income Seniors. JAMA, 298(22), 2623. doi:10.1001/jama.298.22.2623Anderson, J. G. (2002). Evaluation in health informatics: social network analysis. Computers in Biology and Medicine, 32(3), 179-193. doi:10.1016/s0010-4825(02)00014-8Gray, J. E., Davis, D. A., Pursley, D. M., Smallcomb, J. E., Geva, A., & Chawla, N. V. (2010). Network Analysis of Team Structure in the Neonatal Intensive Care Unit. PEDIATRICS, 125(6), e1460-e1467. doi:10.1542/peds.2009-2621Mian, O., Koren, I., & Rukholm, E. (2012). 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. Journal of General Internal Medicine, 22(S3), 408-418. doi:10.1007/s11606-007-0310-5Vermeire, E., Hearnshaw, H., Van Royen, P., & Denekens, J. (2001). Patient adherence to treatment: three decades of research. A comprehensive review. Journal of Clinical Pharmacy and Therapeutics, 26(5), 331-342. doi:10.1046/j.1365-2710.2001.00363.xKarter, A. J., Parker, M. M., Moffet, H. H., Ahmed, A. T., Ferrara, A., Liu, J. Y., & Selby, J. V. (2004). Missed Appointments and Poor Glycemic Control. Medical Care, 42(2), 110-115. doi:10.1097/01.mlr.0000109023.64650.73World Health Organization20032018-03-20Adherence to long-term therapies: evidence for action http://www.who.int/chp/knowledge/publications/adherence_report/en/Toth, E. L., Majumdar, S. R., Guirguis, L. M., Lewanczuk, R. Z., Lee, T. K., & Johnson, J. A. (2003). Compliance with Clinical Practice Guidelines for Type 2 Diabetes in Rural Patients: Treatment Gaps and Opportunities for Improvement. Pharmacotherapy, 23(5), 659-665. doi:10.1592/phco.23.5.659.32203Melnikow, J., & Kiefe, C. (1994). Patient compliance and medical research. Journal of General Internal Medicine, 9(2), 96-105. doi:10.1007/bf02600211Fernandez-Llatas, C., Valdivieso, B., Traver, V., & Benedi, J. M. (2014). Using Process Mining for Automatic Support of Clinical Pathways Design. Data Mining in Clinical Medicine, 79-88. doi:10.1007/978-1-4939-1985-7_5Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Wishah, R. A., Al-Khawaldeh, O. A., & Albsoul, A. M. (2015). Impact of pharmaceutical care interventions on glycemic control and other health-related clinical outcomes in patients with type 2 diabetes: Randomized controlled trial. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 9(4), 271-276. doi:10.1016/j.dsx.2014.09.00

    A Pilot Randomized Controlled Trial of Omega-3 Fatty Acids for Autism Spectrum Disorder

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    We conducted a pilot randomized controlled trial to determine the feasibility and initial safety and efficacy of omega-3 fatty acids (1.3 g/day) for the treatment of hyperactivity in 27 children ages 3–8 with autism spectrum disorder (ASD). After 12 weeks, hyperactivity, as measured by the Aberrant Behavior Checklist, improved 2.7 (±4.8) points in the omega-3 group compared to 0.3 (±7.2) points in the placebo group (p = 0.40; effect size = 0.38). Correlations were found between decreases in five fatty acid levels and decreases in hyperactivity, and the treatment was well tolerated. Although this pilot study did not find a statistically significant benefit from omega-3 fatty acids, the small sample size does not rule out small to moderate beneficial effects

    Patient characteristics associated with differences in patients' evaluation of their general practitioner

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    <p>Abstract</p> <p>Background</p> <p>Knowledge of the extent to which patient characteristics are systematically associated with variation in patient evaluations will enable us to adjust for differences between practice populations and thereby compare GPs. Whether this is appropriate depends on the purpose for which the patient evaluation was conducted. Associations between evaluations and patient characteristics may reflect gaps in the quality of care or may be due to inherent characteristics of the patients. This study aimed to determine such associations in a setting with a comprehensive list system and gate-keeping.</p> <p>Methods</p> <p>A nationwide Danish patient evaluation survey among voluntarily participating GPs using the EUROPEP questionnaire, which produced 28,260 patient evaluations (response rate 77.3%) of 365 GPs. In our analyses we compared the prevalence of positive evaluations in groups of patients.</p> <p>Results</p> <p>We found a positive GP assessment to be strongly associated with increasing patient age and increasing frequency of attendance. Patients reporting a chronic condition were more positive, whereas a low self-rated health was strongly associated with less positive scores also after adjustment. The association between patient gender and assessment was weak and inconsistent and depended on the focus. We found no association either with the patients' educational level or with the duration of listing with the GP even after adjusting for patient characteristics.</p> <p>Conclusion</p> <p>Adjustment for patient differences may produce a more fair comparison between GPs, but may also blur the assessment of GPs' ability to meet the needs of the populations actually served. On the other hand, adjusted results will enable us to describe the significance of specific patient characteristics to patients' experience of care.</p

    Rapid HIV testing program implementation: lessons from the emergency department

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    Background: The US Centers for Disease Control and Prevention (CDC) guidelines and the World Health Organization (WHO) both recommend HIV testing in health-care settings. However, neither organization provides prescriptive details regarding how these recommendations should be adapted into clinical practice in an emergency department. Methods: We have implemented an HIV-testing program in the ED of a major academic medical center within the scope of the Universal Screening for HIV Infection in the Emergency Room (USHER) Trial—a randomized clinical trial evaluating the feasibility and cost-effectiveness of HIV screening in this setting. Results and conclusion: Drawing on our collective experiences in establishing programs domestically and internationally, we offer a practical framework of lessons learned so that others poised to embark on such HIV testing programs may benefit from our experiences

    Advantages of the nested case-control design in diagnostic research

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    Abstract Background Despite its benefits, it is uncommon to apply the nested case-control design in diagnostic research. We aim to show advantages of this design for diagnostic accuracy studies. Methods We used data from a full cross-sectional diagnostic study comprising a cohort of 1295 consecutive patients who were selected on their suspicion of having deep vein thrombosis (DVT). We draw nested case-control samples from the full study population with case:control ratios of 1:1, 1:2, 1:3 and 1:4 (per ratio 100 samples were taken). We calculated diagnostic accuracy estimates for two tests that are used to detect DVT in clinical practice. Results Estimates of diagnostic accuracy in the nested case-control samples were very similar to those in the full study population. For example, for each case:control ratio, the positive predictive value of the D-dimer test was 0.30 in the full study population and 0.30 in the nested case-control samples (median of the 100 samples). As expected, variability of the estimates decreased with increasing sample size. Conclusion Our findings support the view that the nested case-control study is a valid and efficient design for diagnostic studies and should also be (re)appraised in current guidelines on diagnostic accuracy research.</p
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