U.S. academic libraries: understanding their web presence and their relationship with economic indicators

The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-013-1001-0The main goal of this research is to analyze the web structure and performance of units and services belonging to U.S. academic libraries in order to check their suitability for webometric studies. Our objectives include studying their possible correlation with economic data and assessing their use for complementary evaluation purposes. We conducted a survey of library homepages, institutional repositories, digital collections, and online catalogs (a total of 374 URLs) belonging to the 100 U.S. universities with the highest total expenditures in academic libraries according to data provided by the National Center for Education Statistics. Several data points were taken and analyzed, including web variables (page count, external links, and visits) and economic variables (total expenditures, expenditures on printed and electronic books, and physical visits). The results indicate that the variety of URL syntaxes is wide, diverse and complex, which produces a misrepresentation of academic libraries’ web resources and reduces the accuracy of web analysis. On the other hand, institutional and web data indicators are not highly correlated. Better results are obtained by correlating total library expenditures with URL mentions measured by Google (r = 0.546) and visits measured by Compete (r = 0.573), respectively. Because correlation values obtained are not highly significant, we estimate such correlations will increase if users can avoid linkage problems (due to the complexity of URLs) and gain direct access to log files (for more accurate data about visits).Orduña Malea, E.; Regazzi, JJ. (2014). U.S. academic libraries: understanding their web presence and their relationship with economic indicators. Scientometrics. 98(1):315-336. doi:10.1007/s11192-013-1001-0S315336981Adecannby, J. (2011). Web link analysis of interrelationship between top ten African universities and world universities. Annals of library and information studies, 58(2), 128–138.Aguillo, I. F. (2009). Measuring the institutions’ footprint in the web. Library Hi Tech, 27(4), 540–556.Aguillo, I. F., Ortega, J. L., & Fernández, M. (2008). Webometric Ranking of World Universities: Introduction, methodology, and future developments. Higher education in Europe, 33(2/3), 234–244.Aguillo, I. F., Ortega, J. L., Fernandez, M., & Utrilla, A. M. (2010). Indicators for a webometric ranking of open Access repositories. Scientometrics, 82(3), 477–486.Arakaki, M., & Willet, P. (2009). Webometric analysis of departments of librarianship and information science: A follow-up study. Journal of information science, 35(2), 143–152.Arlitsch, K., & O’Brian, P. S. (2012). Invisible institutional repositories: Addresing the low indexing ratios of IR in Google Scholar. Library Hi Tech, 30(1), 60–81.Bar-Ilan, J. (1999). Search engine results over time—A case study on search engine stability”. Cybermetrics, 2/3. Retrieved February 18, 2013 from http://www.cindoc.csic.es/cybermetrics/articles/v2i1p1.html.Bar-Ilan, J. (2001). Data collection methods on the Web for informetric purposes: A review and analysis. Scientometrics, 50(1), 7–32.Bermejo, F. (2007). The internet audience: Constitution & measurement. New York: Peter Lang Pub Incorporated.Buigues-Garcia, M., & Gimenez-Chornet, V. (2012). Impact of Web 2.0 on national libraries. International Journal of Information Management, 32(1), 3–10.Chu, H., He, S., & Thelwall, M. (2002). Library and information science schools in Canada and USA: A Webometric perspective. Journal of education for Library and Information Science, 43(2), 110–125.Chua, Alton, Y. K., & Goh, D. H. (2010). A study of Web 2.0 applications in library websites. Library and Information Science Research, 32(3), 203–211.Gallego, I., García, I.-M., & Rodríguez, L. (2009). Universities’ websites: Disclosure practices and the revelation of financial information. The International Journal of Digital Accounting Research, 9(15), 153–192.Gomes, B. & Smith, B. T. (2003). Detecting query-specific duplicate documents. [Patent]. Retrieved February 18, 2013 from http://www.patents.com/Detecting-query-specific-duplicate-documents/US6615209/en-US .Harinarayana, N. S., & Raju, N. V. (2010). Web 2.0 features in university library web sites. Electronic Library, 28(1), 69–88.Lewandowski, D., Wahlig, H., & Meyer-Bautor, G. (2006). The freshness of web search engine databases. Journal of Information Science, 32(2), 131–148.Mahmood, K., & Richardson, J. V, Jr. (2012). Adoption of Web 2.0 in US academic libraries: A survey of ARL library websites. Program, 45(4), 365–375.Orduña-Malea, E., & Ontalba-Ruipérez, J-A. (2012). Selective linking from social platforms to university websites: A case study of the Spanish academic system. Scientometrics. (in press).Ortega, J. L., & Aguillo, I. F. (2009). Mapping World-class universities on the Web. Information Processing and Management, 45(2), 272–279.Ortega, José L. & Aguillo, Isidro F. (2009b). North America Academic Web Space: Multicultural Canada vs. The United States Homogeneity. In: ASIST & ISSI pre-conference symposium on informetrics and scientometrics.Phan, T., Hardesty, L., Sheckells, C., & George, A. (2009). Documentation for the academic libraries survey (ALS) public-use data file: Fiscal year 2008. Washington DC: National Center for Education Statistics. Institute of Education Sciences U.S. Department of Education.Qiu, J., Cheng, J., & Wang, Z. (2004). An analysis of backlinks counts and web impact factors for Chinese university websites. Scientometrics, 60(3), 463–473.Regazzi, J. J. (2012a). Constrained?—An analysis of U.S. Academic Libraries and shifts in spending, staffing and utilization, 1998–2008. College and Research Libraries, 73(5), 449–468.Regazzi, J. J. (2012b). Comparing Academic Library Spending with Public Libraries, Public K-12 Schools, Higher Education Public Institutions, and Public Hospitals Between 1998–2008. Journal of Academic Librarianship, 38(4), 205–216.Rousseau, R. (1999). Daily time series of common single word searches in AltaVista and NorthernLight. Cybermetrics, 2/3. Retrieved February 18, 2013 from http://www.cindoc.csic.es/cybermetrics/articles/v2i1p2.html .Sato, S., & Itsumura, H. (2011). How do people use open access papers in non-academic activities? A link analysis of papers deposited in institutional repositories. Library, Information and Media Studies, 9(1), 51–64.Scholze, F. (2007). Measuring research impact in an open access environment. Liber Quarterly: The Journal of European Research Libraries, 17(1–4), 220–232.Smith, A. G. (2011). Wikipedia and institutional repositories: An academic symbiosis? In: Proceedings of the ISSI 2011 conference. Durban, South Africa, 4–7 July 2011. Retrieved February 18, 2013 from http://www.vuw.ac.nz/staff/alastair_smith/publns/SmithAG2011_ISSI_paper.pdf .Smith, A.G. (2012). Webometric evaluation of institutional repositories. In: Proceedings of the 8th international conference on webometrics informetrics and scientometrics & 13th collnet meeting. Seoul (Korea), 722–729.Smith, A., & Thelwall, M. (2002). Web impact factors for Australasian Universities. Scientometrics, 54(3), 363–380.Tang, R., & Thelwall, M. (2008). A hyperlink analysis of US public and academic libraries’ web sites. Library Quarterly, 78(4), 419–435.Thelwall, M. (2008). Extracting accurate and complete results from search engines: Case study Windows Live. Journal of the American Society for Information Science and Technology, 59(1), 38–50.Thelwall, M. (2009). Introduction to webometrics: Quantitative web research for the social sciences. San Rafael: Morgan & Claypool.Thelwall, M., & Sud, P. (2011). A comparison of methods for collecting web citation data for academic organisations. Journal of the American Society for Information Science and Technology, 62(8), 1488–1497.Thelwall, M., Sud, P., & Wilkinson, D. (2012). Link and co-inlink network diagrams with URL citations or title mentions. Journal of the American Society for Information Science and Technology, 63(10), 1960–1972.Thelwall, M., & Zuccala, A. (2008). A University-centred European Union link analysis. Scientometrics, 75(3), 407–442.Uyar, A. (2009a). Google stemming mechanisms. Journal of Information Science, 35(5), 499–514.Uyar, A. (2009b). Investigation of the accuracy of search engine hit counts. Journal of Information Science, 35(4), 469–480.Zuccala, A., Thelwall, M., Oppenheim, C., & Dhiensa, R. (2007). Web intelligence analyses of digital libraries: A case study of the National Electronic Library for Health (NeLH). Journal of Documentation, 63(4), 558–589

Pharmaceutical Cost Management in an Ambulatory Setting Using a Risk Adjustment Tool

© 2014 Vivas-Consuelo et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Background Pharmaceutical expenditure is undergoing very high growth, and accounts for 30% of overall healthcare expenditure in Spain. In this paper we present a prediction model for primary health care pharmaceutical expenditure based on Clinical Risk Groups (CRG), a system that classifies individuals into mutually exclusive categories and assigns each person to a severity level if s/he has a chronic health condition. This model may be used to draw up budgets and control health spending. Methods Descriptive study, cross-sectional. The study used a database of 4,700,000 population, with the following information: age, gender, assigned CRG group, chronic conditions and pharmaceutical expenditure. The predictive model for pharmaceutical expenditure was developed using CRG with 9 core groups and estimated by means of ordinary least squares (OLS). The weights obtained in the regression model were used to establish a case mix system to assign a prospective budget to health districts. Results The risk adjustment tool proved to have an acceptable level of prediction (R2 0.55) to explain pharmaceutical expenditure. Significant differences were observed between the predictive budget using the model developed and real spending in some health districts. For evaluation of pharmaceutical spending of pediatricians, other models have to be established. Conclusion The model is a valid tool to implement rational measures of cost containment in pharmaceutical expenditure, though it requires specific weights to adjust and forecast budgets.This study was financed by a grant from the Fondo de Investigaciones de la Seguridad Social Instituto de Salud Carlos III, the Spanish Ministry of Health (FIS PI12/0037). The authors would like to thank members (Juan Bru and Inma Saurf) of the Pharmacoeconomics Office of the Valencian Health Department. The opinions expressed in this paper are those of the authors and do not necessary reflect those of the afore-named. Any errors are the authors' responsibility. We would also like to thank John Wright for the English editing.Vivas Consuelo, DJJ.; Usó Talamantes, R.; Guadalajara Olmeda, MN.; Trillo Mata, JL.; Sancho Mestre, C.; Buigues Pastor, L. (2014). Pharmaceutical Cost Management in an Ambulatory Setting Using a Risk Adjustment Tool. BMC Health Services Research. 14:462-472. https://doi.org/10.1186/1472-6963-14-462S46247214Hux JE, Naylor CD: Drug prices and third party payment: do they influence medication selection?. Pharmacoecon. 1994, 5 (4): 343-350. 10.2165/00019053-199405040-00008.Sicras-Mainar A, Serrat-Tarres J, Navarro-Artieda R, Llopart-Lopez J: [Prospects of adjusted clinical groups (ACG’s) in capitated payment risk adjustment]. Rev Esp Salud Publica. 2006, 80 (1): 55-65. 10.1590/S1135-57272006000100006.Mossey JM, Roos LL: Using insurance claims to measure health-status - the illness scale. J Chronic Dis. 1987, 40: S41-S50.Newhouse JP, Manning WG, Keeler EB, Sloss EM: Adjusting capitation rates using objective health measures and prior utilization. Health Care Financ Rev. 1989, 10 (3): 41-54.Ash A, Porell F, Gruenberg L, Sawitz E, Beiser A: Adjusting Medicare capitation payments using prior hospitalization data. Health Care Financ Rev. 1989, 10 (4): 17-29.Ellis RP, Pope GC, Iezzoni L, Ayanian JZ, Bates DW, Burstin H, Ash AS: Diagnosis-based risk adjustment for Medicare capitation payments. Health Care Financ Rev. 1996, 17 (3): 101-128.Pope GC, Kautter J, Ellis RP, Ash AS, Ayanian JZ, Lezzoni LI, Ingber MJ, Levy JM, Robst J: Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financ Rev. 2004, 25 (4): 119-141.Starfield B, Weiner J, Mumford L, Steinwachs D: Ambulatory care groups: a categorization of diagnoses for research and management. Health Serv Res. 1991, 26 (1): 53-74.Weiner JP, Starfield BH, Steinwachs DM, Mumford LM: Development and application of a population-oriented measure of ambulatory care case-mix. Med Care. 1991, 29 (5): 452-472. 10.1097/00005650-199105000-00006.Hughes JS, Averill RF, Eisenhandler J, Goldfield NI, Muldoon J, Neff JM, Gay JC: Clinical Risk Groups (CRGs): a classification system for risk-adjusted capitation-based payment and health care management. Med Care. 2004, 42 (1): 81-90. 10.1097/01.mlr.0000102367.93252.70.Berlinguet M, Preyra C, Dean S: Comparing the Value of Three Main Diagnostic-Based Risk-Adjustment Systems (DBRAS). 2005, Ottawa, Ontario: Edited by Foundation CHSRVon Korff M, Wagner EH, Saunders K: A chronic disease score from automated pharmacy data. J Clin Epidemiol. 1992, 45 (2): 197-203. 10.1016/0895-4356(92)90016-G.Malone DC, Billups SJ, Valuck RJ, Carter BL: Development of a chronic disease indicator score using a Veterans Affairs Medical Center medication database. IMPROVE Investigators. J Clin Epidemiol. 1999, 52 (6): 551-557. 10.1016/S0895-4356(99)00029-3.Clark DO, Von Korff M, Saunders K, Baluch WM, Simon GE: A chronic disease score with empirically derived weights. Med Care. 1995, 33 (8): 783-795. 10.1097/00005650-199508000-00004.Lamers LM: Pharmacy costs groups: a risk-adjuster for capitation payments based on the use of prescribed drugs. Med Care. 1999, 37 (8): 824-830. 10.1097/00005650-199908000-00012.Lamers LM: Health-based risk adjustment: is inpatient and outpatient diagnostic information sufficient?. Inquiry. 2001, 38 (4): 423-431.Lamers LM, van Vliet RC: The Pharmacy-based Cost Group model: validating and adjusting the classification of medications for chronic conditions to the Dutch situation. Health Policy. 2004, 68 (1): 113-121. 10.1016/j.healthpol.2003.09.001.Lamers LM, Vliet RC: Health-based risk adjustment Improving the pharmacy-based cost group model to reduce gaming possibilities. Eur J Health Econ. 2003, 4 (2): 107-114. 10.1007/s10198-002-0159-9.Johnson RE, Hornbrook MC, Nichols GA: Replicating the chronic disease score (CDS) from automated pharmacy data. J Clin Epidemiol. 1994, 47 (10): 1191-1199. 10.1016/0895-4356(94)90106-6.Zhao Y, Ellis RP, Ash AS, Calabrese D, Ayanian JZ, Slaughter JP, Weyuker L, Bowen B: Measuring population health risks using inpatient diagnoses and outpatient pharmacy data. Health Serv Res. 2001, 36 (6 Pt 2): 180-193.Stam PJ, van Vliet RC, van de Ven WP: Diagnostic, pharmacy-based, and self-reported health measures in risk equalization models. Med Care. 2010, 48 (5): 448-457. 10.1097/MLR.0b013e3181d559b4.Hanley GE, Morgan S, Reid RJ: Explaining prescription drug use and expenditures using the adjusted clinical groups case-mix system in the population of British Columbia, Canada. Can Med Care. 2010, 48 (5): 402-408. 10.1097/MLR.0b013e3181ca3d5d.Aguado A, Guino E, Mukherjee B, Sicras A, Serrat J, Acedo M, Ferro JJ, Moreno V: Variability in prescription drug expenditures explained by adjusted clinical groups (ACG) case-mix: a cross-sectional study of patient electronic records in primary care. BMC Health Serv Res. 2008, 8 (4): 11.Garcia-Goni M, Ibern P: Predictability of drug expenditures: An application using morbidity data. Health Econ. 2008, 17 (1): 119-126. 10.1002/hec.1238.Garcia-Goni M, Ibern P, Inoriza JM: Hybrid risk adjustment for pharmaceutical benefits. Eur J Health Econ. 2009, 10 (3): 299-308. 10.1007/s10198-008-0133-2.Vivas-Consuelo D, Uso-Talamantes R, Trillo-Mata JL, Caballer-Tarazona M, Barrachina-Martinez I, Buigues-Pastor L: Predictability of pharmaceutical spending in primary health services using Clinical Risk Groups. Health Policy. 2014, 116 (2–3): 188-195.Robst J, Levy JM, Ingber MJ: Diagnosis-based risk adjustment for medicare prescription drug plan payments. Health Care Financ Rev. 2007, 28 (4): 15-30.Zhao Y, Ash AS, Ellis RP, Ayanian JZ, Pope GC, Bowen B, Weyuker L: Predicting pharmacy costs and other medical costs using diagnoses and drug claims. Med Care. 2005, 43 (1): 34-43.Buchner F, Goepffarth D, Wasem J: The new risk adjustment formula in Germany: implementation and first experiences. Health Policy. 2013, 109 (3): 253-262. 10.1016/j.healthpol.2012.12.001.Inoriza JM, Coderch J, Carreras M, Vall-Llosera L, Garcia-Goni M, Lisbona JM, Ibern P: [Measurement of morbidity attended in an integrated health care organization]. Gac Sanit. 2009, 23 (1): 29-37. 10.1016/j.gaceta.2008.02.003.Orueta JF, Mateos Del Pino M, Barrio Beraza I, Nuno Solinis R, Cuadrado Zubizarreta M, Sola Sarabia C: [Stratification of the population in the Basque Country: results in the first year of implementation.]. Aten Primaria. 2012, 45 (1): 54-60.Sicras-Mainar A, Navarro-Artieda R: [Validating the Adjusted Clinical Groups [ACG] case-mix system in a Spanish population setting: a multicenter study]. Gac Sanit. 2009, 23 (3): 228-231. 10.1016/j.gaceta.2008.04.005.Omar RZ, O’Sullivan C, Petersen I, Islam A, Majeed A: A model based on age, sex, and morbidity to explain variation in UK general practice prescribing: cohort study. BMJ. 2008, 337: a238-10.1136/bmj.a238.Caballer-Tarazona M, Buigues-Pastor L, Saurí- Ferrer I, Uso-Talamantes R, Trillo-Mata JL: [A standardized amount indicator by equivalent patient to control outpatient pharmaceutical expenditure, Spain]. Rev Esp Salud Publica. 2011, 86: 371-380.De la Poza-Plaza E, Barrachina I, Trillo-Mata J, Uso-Talamantes R: Sistema de Prescripción y dispensación electrónica en la Agencia Valenciana de Salud. El Prof de la Inf. 2011, 20: 9.Vivas D, Guadalajara N, Barrachina I, Trillo JL, Uso R, De-la-Poza E: Explaining primary healthcare pharmacy expenditure using classification of medications for chronic conditions. Health Policy. 2011, 103 (1): 9-15. 10.1016/j.healthpol.2011.08.014.Buntin MB, Zaslavsky AM: Too much ado about two-part models and transformation? Comparing methods of modeling Medicare expenditures. J Health Econ. 2004, 23 (3): 525-542. 10.1016/j.jhealeco.2003.10.005.Duan N: Smearing estimate - a nonparametric retransformation method. J Am Stat Assoc. 1983, 78 (383): 605-610. 10.1080/01621459.1983.10478017.Calderon-Larranaga A, Abrams C, Poblador-Plou B, Weiner JP, Prados-Torres A: Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: the impact of a local calibration. BMC Health Serv Res. 2010, 10: 22-10.1186/1472-6963-10-22

Adapting AC Lines to DC Grids for Large-Scale Renewable Power Transmission

All over the world, governments of different countries are nowadays promoting the use of clean energies in order to achieve sustainable energy systems. In this scenario, since the installed capacity is continuously increasing, renewable sources can play an important role. Notwithstanding that, some important problems may appear when connecting these sources to the grid, being the overload of distribution lines one of the most relevant. In fact, renewable generation is usually connected to the nearest AC grid, although this HV system may not have been designed considering distributed generation. In the particular case of large wind farms, the electrical grid has to transmit all the power generated by wind energy and, as a consequence, the AC system may get overloaded. It is therefore necessary to determine the impact of wind power transmission so that appropriate measures can be taken. Not only are these measures influenced by the amount of power transmitted, but also by the quality of the transmitted power, due to the output voltage fluctuation caused by the highly variable nature of wind. When designing a power grid, although AC systems are usually the most economical solution because of its highly proven technology, HVDC may arise in some cases (e.g. offshore wind farms) as an interesting alternative, offering some added values such as lower losses and better controllability. This way, HVDC technology can solve most of the aforementioned problems and has a good potential for future use. Additionally, the fast development of power electronics based on new and powerful semiconductor devices allow the spread of innovative technologies, such as VSC-HVDC, which can be applied to create DC grids. This paper focuses on the main aspects involved in adapting the existing overhead AC lines to DC grids, with the objective of improving the transmission of distributed renewable energy to the centers of consumption

Quaternary atoll development : new insights from the two-dimensional stratigraphic forward modelling of Mururoa island (central Pacific ocean).

International audienceKnowledge about the Quaternary evolution of mid-ocean atolls comes mainly from drilling and field observations carried out on a number of Pacific carbonate islands. However, little is known about the early to mid Pleistocene atoll development history, especially at margin and foreslope settings. Using previous field and subsurface data from Mururoa Atoll and a process-based modelling software (DIONISOS), a two-dimensional forward stratigraphic model of atoll development is proposed for the past 1·8 million years (Myr). Observational data from vertical to inclined coring, seismic and bathymetric surveys indicate that, from approximately 0·45 to 0·40 million years before present (Ma), carbonate deposition at Mururoa Atoll resulted in a series of mostly prograding reef units. The model is first constrained at the base by the shape and topography of the pre-Quaternary basement. A number of sensitivity tests were performed to define the respective influence of variant parameters. The best-fit development scenario that accounts for the overall geometry and stratigraphic architecture of the Quaternary sediment packages is obtained by using the sea-level curve by Miller et al. (2005), uniform subsidence rate of 105 m Myr−1, and carbonate production rates gradually increasing from 0·50 to 8 mm yr−1 between 1·80 Ma and the present. Additional controlling parameters include subaerial erosion (at a constant rate of 0·25 m/kyr), wave-energy and sediment-transport processes. The stratigraphic forward model predicts a succession of three distinct types of carbonate systems that have developed since the earliest Pleistocene: toe of slope systems from 1·80 Ma to about 0·80 Ma, open-platform systems from 0·80 Ma to 0·50 Ma, and framework-reef systems from about 0·50 Ma to the present. The development of these different systems is most likely to be controlled by climate and changes in sea-level cycles. During the low-amplitude 41 kyr cycle periods of the earliest Pleistocene, ambient conditions were not conducive to framework-reef growth; shallow-water carbonate sedimentation was dominantly gravity-driven, operating along the platform foreslopes only. During the Mid-Pleistocene Climate Transition, narrow, open-platform units have developed at the upper parts of the pre-Quaternary basement flanks. With the onset of the high-amplitude 100 kyr sea-level modes and climate restoration, reef frameworks started to be generated. These models from Mururoa agree with a number of previous studies suggesting that most of the true framework reefs were not initiated prior to 0·50 Ma. Mururoa Atoll is demonstrated to be a robust analogue for providing more realistic interpretations of the development history of Pacific atolls. Further modelling with three-dimensional DIONISOS could generate better predictions by taking into account hydrodynamic and transport parameters more accurately