000056178 001__ 56178
000056178 005__ 20200221144252.0
000056178 0247_ $$2doi$$a10.2147/CEOR.S97548
000056178 0248_ $$2sideral$$a95392
000056178 037__ $$aART-2016-95392
000056178 041__ $$aeng
000056178 100__ $$aMenditto, E.
000056178 245__ $$aScaling up health knowledge at European level requires sharing integrated data: An approach for collection of database specification
000056178 260__ $$c2016
000056178 5060_ $$aAccess copy available to the general public$$fUnrestricted
000056178 5203_ $$aComputerized health care databases have been widely described as an excellent opportunity for research. The availability of “big data” has brought about a wave of innovation in projects when conducting health services research. Most of the available secondary data sources are restricted to the geographical scope of a given country and present heterogeneous structure and content. Under the umbrella of the European Innovation Partnership on Active and Healthy Ageing, collaborative work conducted by the partners of the group on “adherence to prescription and medical plans” identified the use of observational and large-population databases to monitor medication-taking behavior in the elderly. This article describes the methodology used to gather the information from available databases among the Adherence Action Group partners with the aim of improving data sharing on a European level. A total of six databases belonging to three different European countries (Spain, Republic of Ireland, and Italy) were included in the analysis. Preliminary results suggest that there are some similarities. However, these results should be applied in different contexts and European countries, supporting the idea that large European studies should be designed in order to get the most of already available databases.
000056178 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttp://creativecommons.org/licenses/by-nc/3.0/es/
000056178 592__ $$a0.885$$b2016
000056178 593__ $$aHealth Policy$$c2016$$dQ1
000056178 593__ $$aEconomics, Econometrics and Finance (miscellaneous)$$c2016$$dQ1
000056178 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000056178 700__ $$aDe Gea, A.B.
000056178 700__ $$aCahir, C.
000056178 700__ $$aMarengoni, A.
000056178 700__ $$aRiegler, S.
000056178 700__ $$aFico, G.
000056178 700__ $$aCosta, E.
000056178 700__ $$aMonaco, A.
000056178 700__ $$aPecorelli, S.
000056178 700__ $$aPani, L.
000056178 700__ $$0(orcid)0000-0002-5704-6056$$aPrados-Torres, A.$$uUniversidad de Zaragoza
000056178 7102_ $$11008$$2615$$aUniversidad de Zaragoza$$bDpto. Microb.Med.Pr.,Sal.Públ.$$cÁrea Medic.Prevent.Salud Públ.
000056178 773__ $$g8 (2016), 253-265$$pClinicoEcon. outcomes res.$$tClinicoEconomics and Outcomes Research$$x1178-6981
000056178 8564_ $$s574516$$uhttps://zaguan.unizar.es/record/56178/files/texto_completo.pdf$$yVersión publicada
000056178 8564_ $$s115588$$uhttps://zaguan.unizar.es/record/56178/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000056178 909CO $$ooai:zaguan.unizar.es:56178$$particulos$$pdriver
000056178 951__ $$a2020-02-21-13:28:15
000056178 980__ $$aARTICLE