Teleconsultation service to improve healthcare in rural areas: acceptance, organizational impact and appropriateness

Background: Nowadays, new organisational strategies should be indentified to improve primary care and its link with secondary care in terms of efficacy and timeliness of interventions thus preventing unnecessary hospital accesses and costs saving for the health system. The purpose of this study is to assess the effects of the use of teleconsultation by general practitioners in rural areas. Methods: General practitioners were provided with a teleconsultation service from 2006 to 2008 to obtain a second opinion for cardiac, dermatological and diabetic problems. Access, acceptance, organisational impact, effectiveness and economics data were collected. Clinical and access data were systematically entered in a database while acceptance and organisational data were evaluated through ad hoc questionnaires. Results: There were 957 teleconsultation contacts which resulted in access to health care services for 812 symptomatic patients living in 30 rural communities. Through the teleconsultation service, 48 general practitioners improved the appropriateness of primary care and the integration with secondary care. In fact, the level of concordance between intentions and consultations for cardiac problems was equal to 9%, in 86% of the cases the service entailed a saving of resources and in 5% of the cases, it improved the timeliness. 95% of the GPs considered the overall quality positively. For a future routine use of this service, trust in specialists, duration and workload of teleconsultations and reimbursement should be taken into account. Conclusions: Managerial and policy implications emerged mainly related to the support to GPs in the provision of high quality primary care and decision-making processes in promoting similar services

Hot Spot or Not: A Comparison of Spatial Statistical Methods to Predict Prospective Malaria Infections.

Within affected communities, Plasmodium falciparum infections may be skewed in distribution such that single or small clusters of households consistently harbour a disproportionate number of infected individuals throughout the year. Identifying these hotspots of malaria transmission would permit targeting of interventions and a more rapid reduction in malaria burden across the whole community. This study set out to compare different statistical methods of hotspot detection (SaTScan, kernel smoothing, weighted local prevalence) using different indicators (PCR positivity, AMA-1 and MSP-1 antibodies) for prediction of infection the following year. Two full surveys of four villages in Mwanza, Tanzania were completed over consecutive years, 2010-2011. In both surveys, infection was assessed using nested polymerase chain reaction (nPCR). In addition in 2010, serologic markers (AMA-1 and MSP-119 antibodies) of exposure were assessed. Baseline clustering of infection and serological markers were assessed using three geospatial methods: spatial scan statistics, kernel analysis and weighted local prevalence analysis. Methods were compared in their ability to predict infection in the second year of the study using random effects logistic regression models, and comparisons of the area under the receiver operating curve (AUC) for each model. Sensitivity analysis was conducted to explore the effect of varying radius size for the kernel and weighted local prevalence methods and maximum population size for the spatial scan statistic. Guided by AUC values, the kernel method and spatial scan statistics appeared to be more predictive of infection in the following year. Hotspots of PCR-detected infection and seropositivity to AMA-1 were predictive of subsequent infection. For the kernel method, a 1 km window was optimal. Similarly, allowing hotspots to contain up to 50% of the population was a better predictor of infection in the second year using spatial scan statistics than smaller maximum population sizes. Clusters of AMA-1 seroprevalence or parasite prevalence that are predictive of infection a year later can be identified using geospatial models. Kernel smoothing using a 1 km window and spatial scan statistics both provided accurate prediction of future infection

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior

Regulatory Failure in the Subprime Crisis

Banking crisis, Monetary policy, E58, G21,