Supplementary Report of the HIA to the Minister for Health, in accordance with Section 7E(1)(b) of the Health Insurance Acts, 1994-2009 (Redacted Version)

The purpose of this Supplementary Report is to advise on how the budgetary measures impact on the conclusions in relation to tax credits and stamp duty included in the Authority’s November 2011 Report. in doing so, we will assess the direct impacts and we will discuss some scenarios. However, the Authority’s advice in this area relies on projections of the health insurance market and, in light of the above, there is considerable uncertainty surrounding any projections of how claims inflation or the market size may develop, even in the short and medium terms. Supplementary Report of the HIA to the Minister for Health, in accordance with Section 7E(1)(b) of the Health Insurance Acts, 1994-2009 (Redacted Version) Click here to download PDF 3.2mb

Report of the HIA to the Minister for Health, in accordance with Section 7E(1)(b) of the Health Insurance Acts, 1994-2009 (Redacted Version)

The Authority has been asked by the Minister for Health to prepare a Report under Section 7E (1) (b) of the Health Insurance Acts 1994-2009 (“the Health Insurance Acts”). For the purposes of the legislation, the relevant period is 1 July 2010 to 30 June 2011. The basis of the Report is specified in the legislation   Click here to download PDF 7.3mb Click here to download the supplementary document PDF 3.2mb

Report to the Minister for Health from the Health Insurance Authority (Redacted) on an evaluation and analysis of returns for 1 July 2012 to 30 June 2013 including advice on risk equalisation credits

Report to the Minister for Health from the Health Insurance Authority (Redacted) on an evaluation and analysis of returns for 1 July 2012 to 30 June 2013 including advice on risk equalisation credits Click here to download PDF 11MB

Call for Submissions: White Paper on Universal Health Insurance - Supporting Documents

  The Department of Health has published a White Paper on Universal Health Insurance. The White Paper sets out in detail the elements of the proposed Universal Health Insurance model for Ireland. As such, it provides detail on the overall design of the model, the proposed system for deciding on the standard package of services and the financing mechanisms for the system. This is a most fundamental reform of the health system and we recognise the importance of consulting extensively and inclusively with all interested parties.  It is important to seek your views on the policy as it is set out in the White Paper, and we view this as a valuable opportunity for citizens to contribute to the development of policy on the future of their health system.  Therefore, we would like to hear from any individual, group, organisation or other body that wishes to contribute to the consultation on the White Paper. In particular, but not limited to, we would welcome your views on the following issues: A consultation document setting out a number of key questions under each of the above headings has been developed and can be downloaded here. There is an opportunity at the end of the document for views or comments on other aspects of the White Paper to be provided. Alternatively, additional views or comments can be sent as an email or hard copy to the addresses below. It is intended to establish a separate independent Expert Commission to examine the issues around the basket of services to be provided under UHI and within the overall health system. The Minister will announce details of the Commission in the near future. Therefore, it would be useful if the submissions on the White Paper refrained from commenting in detail on the services to be provided under UHI. Views on the basket of services will be sought by the Commission when it commences its consultation process. The White Paper can be downloaded here, and two further supporting documents Background Policy Paper on Designing the Future Health Basket and Background Policy Paper on Raising Resources for Universal Health Insurance, which informed the development of the White Paper are also available for download. Links to other supporting documentation that informed the White Paper are also provided below. Submissions can be submitted: By E-mail to: uhiwhitepaper@health.gov.ie By Post to: UHI White Paper UHI UnitDepartment of HealthRoom 7.26Hawkins HouseHawkins StreetDublin 2 The closing date for submissions is close of business 28th May 2014 and will be strictly adhered to. All submissions received will be subject to the Freedom of Information Acts 1997 & 2003 and may be released in response to a Freedom of Information request. Download the consultation document (MS Word) (From the website of the Health Research Board) Integration of health and wellbeing services with general health services The integration of health and social care services

Report to the Minister for Health and Children on Risk Equalisation in the Irish Private Health Insurance Market

Provision for risk equalisation was first made in the Health Insurance Act, 1994, section 12 of which empowered the Minister to prescribe a scheme for risk equalisation. A Risk Equalsiation Scheme was introduced in 2003. In December 2005, the Minister decided, on the Authorityâ?Ts recommendation, which referred to risks now materialising, to commence risk equalisation payments under the Scheme as from 1 January 2006, but in the event the relevant legislation was overturned by the Courts in 2008. Download document here

Histology-driven data mining of lipid signatures from multiple imaging mass spectrometry analyses: application to human colorectal cancer liver metastasis biopsies.

Imaging mass spectrometry (IMS) represents an innovative tool in the cancer research pipeline, which is increasingly being used in clinical and pharmaceutical applications. The unique properties of the technique, especially the amount of data generated, make the handling of data from multiple IMS acquisitions challenging. This work presents a histology-driven IMS approach aiming to identify discriminant lipid signatures from the simultaneous mining of IMS data sets from multiple samples. The feasibility of the developed workflow is evaluated on a set of three human colorectal cancer liver metastasis (CRCLM) tissue sections. Lipid IMS on tissue sections was performed using MALDI-TOF/TOF MS in both negative and positive ionization modes after 1,5-diaminonaphthalene matrix deposition by sublimation. The combination of both positive and negative acquisition results was performed during data mining to simplify the process and interrogate a larger lipidome into a single analysis. To reduce the complexity of the IMS data sets, a sub data set was generated by randomly selecting a fixed number of spectra from a histologically defined region of interest, resulting in a 10-fold data reduction. Principal component analysis confirmed that the molecular selectivity of the regions of interest is maintained after data reduction. Partial least-squares and heat map analyses demonstrated a selective signature of the CRCLM, revealing lipids that are significantly up- and down-regulated in the tumor region. This comprehensive approach is thus of interest for defining disease signatures directly from IMS data sets by the use of combinatory data mining, opening novel routes of investigation for addressing the demands of the clinical setting.

Using free data mining software and clustering algorithms to find predictors from student qualifications

In this project a research both in finding predictors via clustering techniques and in reviewing the Data Mining free software is achieved. The research is based in a case of study, from where additionally to the KDD free software used by the scientific community; a new free tool for pre-processing the data is presented. The predictors are intended for the e-learning domain as the data from where these predictors have to be inferred are student qualifications from different e-learning environments. Through our case of study not only clustering algorithms are tested but also additional goals are proposed.

Inferences about the global scenario of human T-cell lymphotropic virus type 1 infection using data mining of viral sequences

Human T-cell lymphotropic virus type 1 (HTLV-1) is mainly associated with two diseases: tropical spastic paraparesis/HTLV-1-associated myelopathy (TSP/HAM) and adult T-cell leukaemia/lymphoma. This retrovirus infects five-10 million individuals throughout the world. Previously, we developed a database that annotates sequence data from GenBank and the present study aimed to describe the clinical, molecular and epidemiological scenarios of HTLV-1 infection through the stored sequences in this database. A total of 2,545 registered complete and partial sequences of HTLV-1 were collected and 1,967 (77.3%) of those sequences represented unique isolates. Among these isolates, 93% contained geographic origin information and only 39% were related to any clinical status. A total of 1,091 sequences contained information about the geographic origin and viral subtype and 93% of these sequences were identified as subtype “a”. Ethnicity data are very scarce. Regarding clinical status data, 29% of the sequences were generated from TSP/HAM and 67.8% from healthy carrier individuals. Although the data mining enabled some inferences about specific aspects of HTLV-1 infection to be made, due to the relative scarcity of data of available sequences, it was not possible to delineate a global scenario of HTLV-1 infection.

Intégration de méthodes de data mining dans le renseignement criminel : analyse par des structures issues de la théorie des graphes dans le profilage des stupéfiants

Data mining can be defined as the extraction of previously unknown and potentially useful information from large datasets. The main principle is to devise computer programs that run through databases and automatically seek deterministic patterns. It is applied in different fields of application, e.g., remote sensing, biometry, speech recognition, but has seldom been applied to forensic case data. The intrinsic difficulty related to the use of such data lies in its heterogeneity, which comes from the many different sources of information. The aim of this study is to highlight potential uses of pattern recognition that would provide relevant results from a criminal intelligence point of view. The role of data mining within a global crime analysis methodology is to detect all types of structures in a dataset. Once filtered and interpreted, those structures can point to previously unseen criminal activities. The interpretation of patterns for intelligence purposes is the final stage of the process. It allows the researcher to validate the whole methodology and to refine each step if necessary. An application to cutting agents found in illicit drug seizures was performed. A combinatorial approach was done, using the presence and the absence of products. Methods coming from the graph theory field were used to extract patterns in data constituted by links between products and place and date of seizure. A data mining process completed using graphing techniques is called ``graph mining''. Patterns were detected that had to be interpreted and compared with preliminary knowledge to establish their relevancy. The illicit drug profiling process is actually an intelligence process that uses preliminary illicit drug classes to classify new samples. Methods proposed in this study could be used \textit{a priori} to compare structures from preliminary and post-detection patterns. This new knowledge of a repeated structure may provide valuable complementary information to profiling and become a source of intelligence.

Demand for private health insurance: Is there a quality gap?

Quality of care is qualified as a main determinant of the demand forvoluntary private health insurance (PHI) in National Health Systems(NHS). This paper provides new evidence on the influence of the qualitygap between public and private health insurance and other demanddeterminants in the demand for PHI in Catalonia. The demand for PHI ismodelled as a demand for health care quality. Unlike previous studies, the database employed allows for the development of a link between thetheoretical and the empirical model dealing with unobserved heterogeneityand endogeneity issues. Results suggest that a rise in PHI qualityenhances an equivalent influence in the demand for PHI as an equalreduction of NHS quality. Income and price elasticity estimates areconsistent with the observed feature that PHI appears to be a luxurygood and individuals tend to be relatively insensible to tax relief'sand monetary co-payments in insurance contracts.

Social and medical vulnerability factors of emergency department frequent users in a universal health insurance system

Objectifs - Identifier les facteurs de vulnérabilité sociaux et médicaux associés au recours multiple aux consultations des urgences. - Déterminer si les patients à recours multiple sont plus à même de combiner ces facteurs dans un système d'assurance universelle. Méthode Il s'agit d'une étude cas-contrôle rétrospective basée sur l'étude de dossiers médico-administratifs comparant des échantillons randomisés de patients à recours multiple à des patients n'appartenant pas à cette catégorie, au sein des urgences du Centre Hospitalier Universitaire Vaudois et de la Policlinique Médicale Universitaire de Lausanne. Les auteurs ont défini les patients à recours multiple comme comptabilisant au moins quatre consultations aux urgences durant les douze mois précédents. Les patients adultes (>18 ans) ayant consulté les urgences entre avril 2008 et mars 2009 (période d'étude) étaient inclus ; ceux quittant les urgences sans décharge médicale étaient exclus. Pour chaque patient, le premier dossier d'urgence informatisé inclus dans la période d'étude était sélectionné pour l'extraction des données. Outre les variables démographiques de base, les variables d'intérêt comprennent des caractéristiques sociales (emploi, type de résidence) et médicales (diagnostic principal aux urgences). Les facteurs sociaux et médicaux significatifs ont été utilisés dans la construction d'un modèle de régression logistique, afin de déterminer les facteurs associés avec le recours multiple aux urgences. De plus, la combinaison des facteurs sociaux et médicaux a été étudiée. Résultats Au total, 359/Γ591 patients à recours multiple et 360/34'263 contrôles ont été sélectionnés. Les patients à recours multiple représentaient moins d'un vingtième de tous les patients des urgences (4.4%), mais engendraient 12.1% de toutes les consultations (5'813/48'117), avec un record de 73 consultations. Aucune différence en termes d'âge ou de genre n'est apparue, mais davantage de patients à recours multiples étaient d'une nationalité autre que suisse ou européenne (n=117 [32.6%] vs n=83 [23.1%], p=0.003). L'analyse multivariée a montré que les facteurs de vulnérabilité sociaux et médicaux les plus fortement associés au recours multiple aux urgences étaient : être sous tutelle (Odds ratio [OR] ajusté = 15.8; intervalle de confiance [IC] à 95% = 1.7 à 147.3), habiter plus proche des urgences (OR ajusté = 4.6; IC95% = 2.8 à 7.6), être non assuré (OR ajusté = 2.5; IC95% = 1.1 à 5.8), être sans emploi ou dépendant de l'aide sociale (OR ajusté = 2.1; IC95% = 1.3 à 3.4), le nombre d'hospitalisations psychiatriques (OR ajusté = 4.6; IC95% = 1.5 à 14.1), ainsi que le recours à au moins cinq départements cliniques différents durant une période de douze mois (OR ajusté = 4.5; IC95% = 2.5 à 8.1). Le fait de comptabiliser deux sur quatre facteurs sociaux augmente la vraisemblance du recours multiple aux urgences (OR ajusté = 5.4; IC95% = 2.9 à 9.9) ; des résultats similaires ont été trouvés pour les facteurs médicaux (OR ajusté = 7.9; IC95% = 4.6 à 13.4). La combinaison de facteurs sociaux et médicaux est fortement associée au recours multiple aux urgences, puisque les patients à recours multiple étaient dix fois plus à même d'en comptabiliser trois d'entre eux (sur un total de huit facteurs, IC95% = 5.1 à 19.6). Conclusion Les patients à recours multiple aux urgences représentent une proportion modérée des consultations aux urgences du Centre Hospitalier Universitaire Vaudois et de la Policlinique Médicale Universitaire de Lausanne. Les facteurs de vulnérabilité sociaux et médicaux sont associés au recours multiple aux urgences. En outre, les patients à recours multiple sont plus à même de combiner les vulnérabilités sociale et médicale que les autres. Des stratégies basées sur le case management pourraient améliorer la prise en charge des patients à recours multiple avec leurs vulnérabilités afin de prévenir les inégalités dans le système de soins ainsi que les coûts relatifs.

Community Policing in Switzerland's Major Urban Areas: An Observational Study of the Implementation and Impact Using Geospatial Data Mining

La présente étude est à la fois une évaluation du processus de la mise en oeuvre et des impacts de la police de proximité dans les cinq plus grandes zones urbaines de Suisse - Bâle, Berne, Genève, Lausanne et Zurich. La police de proximité (community policing) est à la fois une philosophie et une stratégie organisationnelle qui favorise un partenariat renouvelé entre la police et les communautés locales dans le but de résoudre les problèmes relatifs à la sécurité et à l'ordre public. L'évaluation de processus a analysé des données relatives aux réformes internes de la police qui ont été obtenues par l'intermédiaire d'entretiens semi-structurés avec des administrateurs clés des cinq départements de police, ainsi que dans des documents écrits de la police et d'autres sources publiques. L'évaluation des impacts, quant à elle, s'est basée sur des variables contextuelles telles que des statistiques policières et des données de recensement, ainsi que sur des indicateurs d'impacts construit à partir des données du Swiss Crime Survey (SCS) relatives au sentiment d'insécurité, à la perception du désordre public et à la satisfaction de la population à l'égard de la police. Le SCS est un sondage régulier qui a permis d'interroger des habitants des cinq grandes zones urbaines à plusieurs reprises depuis le milieu des années 1980. L'évaluation de processus a abouti à un « Calendrier des activités » visant à créer des données de panel permettant de mesurer les progrès réalisés dans la mise en oeuvre de la police de proximité à l'aide d'une grille d'évaluation à six dimensions à des intervalles de cinq ans entre 1990 et 2010. L'évaluation des impacts, effectuée ex post facto, a utilisé un concept de recherche non-expérimental (observational design) dans le but d'analyser les impacts de différents modèles de police de proximité dans des zones comparables à travers les cinq villes étudiées. Les quartiers urbains, délimités par zone de code postal, ont ainsi été regroupés par l'intermédiaire d'une typologie réalisée à l'aide d'algorithmes d'apprentissage automatique (machine learning). Des algorithmes supervisés et non supervisés ont été utilisés sur les données à haute dimensionnalité relatives à la criminalité, à la structure socio-économique et démographique et au cadre bâti dans le but de regrouper les quartiers urbains les plus similaires dans des clusters. D'abord, les cartes auto-organisatrices (self-organizing maps) ont été utilisées dans le but de réduire la variance intra-cluster des variables contextuelles et de maximiser simultanément la variance inter-cluster des réponses au sondage. Ensuite, l'algorithme des forêts d'arbres décisionnels (random forests) a permis à la fois d'évaluer la pertinence de la typologie de quartier élaborée et de sélectionner les variables contextuelles clés afin de construire un modèle parcimonieux faisant un minimum d'erreurs de classification. Enfin, pour l'analyse des impacts, la méthode des appariements des coefficients de propension (propensity score matching) a été utilisée pour équilibrer les échantillons prétest-posttest en termes d'âge, de sexe et de niveau d'éducation des répondants au sein de chaque type de quartier ainsi identifié dans chacune des villes, avant d'effectuer un test statistique de la différence observée dans les indicateurs d'impacts. De plus, tous les résultats statistiquement significatifs ont été soumis à une analyse de sensibilité (sensitivity analysis) afin d'évaluer leur robustesse face à un biais potentiel dû à des covariables non observées. L'étude relève qu'au cours des quinze dernières années, les cinq services de police ont entamé des réformes majeures de leur organisation ainsi que de leurs stratégies opérationnelles et qu'ils ont noué des partenariats stratégiques afin de mettre en oeuvre la police de proximité. La typologie de quartier développée a abouti à une réduction de la variance intra-cluster des variables contextuelles et permet d'expliquer une partie significative de la variance inter-cluster des indicateurs d'impacts avant la mise en oeuvre du traitement. Ceci semble suggérer que les méthodes de géocomputation aident à équilibrer les covariables observées et donc à réduire les menaces relatives à la validité interne d'un concept de recherche non-expérimental. Enfin, l'analyse des impacts a révélé que le sentiment d'insécurité a diminué de manière significative pendant la période 2000-2005 dans les quartiers se trouvant à l'intérieur et autour des centres-villes de Berne et de Zurich. Ces améliorations sont assez robustes face à des biais dus à des covariables inobservées et covarient dans le temps et l'espace avec la mise en oeuvre de la police de proximité. L'hypothèse alternative envisageant que les diminutions observées dans le sentiment d'insécurité soient, partiellement, un résultat des interventions policières de proximité semble donc être aussi plausible que l'hypothèse nulle considérant l'absence absolue d'effet. Ceci, même si le concept de recherche non-expérimental mis en oeuvre ne peut pas complètement exclure la sélection et la régression à la moyenne comme explications alternatives. The current research project is both a process and impact evaluation of community policing in Switzerland's five major urban areas - Basel, Bern, Geneva, Lausanne, and Zurich. Community policing is both a philosophy and an organizational strategy that promotes a renewed partnership between the police and the community to solve problems of crime and disorder. The process evaluation data on police internal reforms were obtained through semi-structured interviews with key administrators from the five police departments as well as from police internal documents and additional public sources. The impact evaluation uses official crime records and census statistics as contextual variables as well as Swiss Crime Survey (SCS) data on fear of crime, perceptions of disorder, and public attitudes towards the police as outcome measures. The SCS is a standing survey instrument that has polled residents of the five urban areas repeatedly since the mid-1980s. The process evaluation produced a "Calendar of Action" to create panel data to measure community policing implementation progress over six evaluative dimensions in intervals of five years between 1990 and 2010. The impact evaluation, carried out ex post facto, uses an observational design that analyzes the impact of the different community policing models between matched comparison areas across the five cities. Using ZIP code districts as proxies for urban neighborhoods, geospatial data mining algorithms serve to develop a neighborhood typology in order to match the comparison areas. To this end, both unsupervised and supervised algorithms are used to analyze high-dimensional data on crime, the socio-economic and demographic structure, and the built environment in order to classify urban neighborhoods into clusters of similar type. In a first step, self-organizing maps serve as tools to develop a clustering algorithm that reduces the within-cluster variance in the contextual variables and simultaneously maximizes the between-cluster variance in survey responses. The random forests algorithm then serves to assess the appropriateness of the resulting neighborhood typology and to select the key contextual variables in order to build a parsimonious model that makes a minimum of classification errors. Finally, for the impact analysis, propensity score matching methods are used to match the survey respondents of the pretest and posttest samples on age, gender, and their level of education for each neighborhood type identified within each city, before conducting a statistical test of the observed difference in the outcome measures. Moreover, all significant results were subjected to a sensitivity analysis to assess the robustness of these findings in the face of potential bias due to some unobserved covariates. The study finds that over the last fifteen years, all five police departments have undertaken major reforms of their internal organization and operating strategies and forged strategic partnerships in order to implement community policing. The resulting neighborhood typology reduced the within-cluster variance of the contextual variables and accounted for a significant share of the between-cluster variance in the outcome measures prior to treatment, suggesting that geocomputational methods help to balance the observed covariates and hence to reduce threats to the internal validity of an observational design. Finally, the impact analysis revealed that fear of crime dropped significantly over the 2000-2005 period in the neighborhoods in and around the urban centers of Bern and Zurich. These improvements are fairly robust in the face of bias due to some unobserved covariate and covary temporally and spatially with the implementation of community policing. The alternative hypothesis that the observed reductions in fear of crime were at least in part a result of community policing interventions thus appears at least as plausible as the null hypothesis of absolutely no effect, even if the observational design cannot completely rule out selection and regression to the mean as alternative explanations.

Traumatic Brain Injury in Iowa: Iowa Brain Injury Resource Network Outcome Evaluation 2007-2009, August 2011

Termed the “silent epidemic,” traumatic brain injury (TBI) is the most debilitating outcome of injury, and is characterized by the irreversibility of its damages, long-term effects on quality of life and healthcare costs. The latest data available from the CDC estimate that nationally, 52,000 people die each year from TBI2. In Iowa, TBI is a major public health problem. The numbers and rates of hospitalizations and emergency department (ED) visits due to TBIs are steadily increasing. From 2006 to 2008, there were on average 545 injury deaths per year. Among the injured Iowans, TBI constituted nearly 30 percent (545) of all injury deaths, ten percent (1,591) of people hospitalized and seven percent (17,696) of ED visitors. 3 The state of Iowa has been supporting secondary prevention services to TBI survivors for several years. An Iowa organization that has made a significant effort in assisting TBI survivors is the Brain Injury Association of Iowa (BIAIA). The BIAIA administers the IBIRN program in cooperation with the Iowa Department of Public Health (IDPH) through HRSA TBI Implementation grant funding and state appropriations.