What is the 'problem' that outreach work seeks to address and how might it be tackled? Seeking theory in a primary health prevention programme

<b>Background</b> Preventive approaches to health are disproportionately accessed by the more affluent and recent health improvement policy advocates the use of targeted preventive primary care to reduce risk factors in poorer individuals and communities. Outreach has become part of the health service response. Outreach has a long history of engaging those who do not otherwise access services. It has, however, been described as eclectic in its purpose, clientele and mode of practice; its effectiveness is unproven. Using a primary prevention programme in the UK as a case, this paper addresses two research questions: what are the perceived problems of non-engagement that outreach aims to address; and, what specific mechanisms of outreach are hypothesised to tackle these.<p></p> <b>Methods</b> Drawing on a wider programme evaluation, the study undertook qualitative interviews with strategically selected health-care professionals. The analysis was thematically guided by the concept of 'candidacy' which theorises the dynamic process through which services and individuals negotiate appropriate service use.<p></p> <b>Results</b> The study identified seven types of engagement 'problem' and corresponding solutions. These 'problems' lie on a continuum of complexity in terms of the challenges they present to primary care. Reasons for non-engagement are congruent with the concept of 'candidacy' but point to ways in which it can be expanded.<p></p> <b>Conclusions</b> The paper draws conclusions about the role of outreach in contributing to the implementation of inequalities focused primary prevention and identifies further research needed in the theoretical development of both outreach as an approach and candidacy as a conceptual framework

Genetically-Based Olfactory Signatures Persist Despite Dietary Variation

Individual mice have a unique odor, or odortype, that facilitates individual recognition. Odortypes, like other phenotypes, can be influenced by genetic and environmental variation. The genetic influence derives in part from genes of the major histocompatibility complex (MHC). A major environmental influence is diet, which could obscure the genetic contribution to odortype. Because odortype stability is a prerequisite for individual recognition under normal behavioral conditions, we investigated whether MHC-determined urinary odortypes of inbred mice can be identified in the face of large diet-induced variation. Mice trained to discriminate urines from panels of mice that differed both in diet and MHC type found the diet odor more salient in generalization trials. Nevertheless, when mice were trained to discriminate mice with only MHC differences (but on the same diet), they recognized the MHC difference when tested with urines from mice on a different diet. This indicates that MHC odor profiles remain despite large dietary variation. Chemical analyses of urinary volatile organic compounds (VOCs) extracted by solid phase microextraction (SPME) and analyzed by gas chromatography/mass spectrometry (GC/MS) are consistent with this inference. Although diet influenced VOC variation more than MHC, with algorithmic training (supervised classification) MHC types could be accurately discriminated across different diets. Thus, although there are clear diet effects on urinary volatile profiles, they do not obscure MHC effects

Epidemiology of influenza-associated hospitalization in adults, Toronto, 2007/8

The purpose of this investigation was to identify when diagnostic testing and empirical antiviral therapy should be considered for adult patients requiring hospitalization during influenza seasons. During the 2007/8 influenza season, six acute care hospitals in the Greater Toronto Area participated in active surveillance for laboratory-confirmed influenza requiring hospitalization. Nasopharyngeal (NP) swabs were obtained from patients presenting with acute respiratory or cardiac illness, or with febrile illness without clear non-respiratory etiology. Predictors of influenza were analyzed by multivariable logistic regression analysis and likelihoods of influenza infection in various patient groups were calculated. Two hundred and eighty of 3,917 patients were found to have influenza. Thirty-five percent of patients with influenza presented with a triage temperature ≥38.0°C, 80% had respiratory symptoms in the emergency department, and 76% were ≥65 years old. Multivariable analysis revealed a triage temperature ≥38.0°C (odds ratio [OR] 3.1; 95% confidence interval [CI] 2.3–4.1), the presence of respiratory symptoms (OR 1.7; 95% CI 1.2–2.4), admission diagnosis of respiratory infection (OR 1.8; 95% CI 1.3–2.4), admission diagnosis of exacerbation of chronic obstructive pulmonary disease (COPD)/asthma or respiratory failure (OR 2.3; 95% CI 1.6–3.4), and admission in peak influenza weeks (OR 4.2; 95% CI 3.1–5.7) as independent predictors of influenza. The likelihood of influenza exceeded 15% in patients with respiratory infection or exacerbation of COPD/asthma if the triage temperature was ≥38.0°C or if they were admitted in the peak weeks during the influenza season. During influenza season, diagnostic testing and empiric antiviral therapy should be considered in patients requiring hospitalization if respiratory infection or exacerbation of COPD/asthma are suspected and if either the triage temperature is ≥38.0°C or admission is during the weeks of peak influenza activity

An empirical model for probabilistic decadal prediction: global attribution and regional hindcasts

Empirical models, designed to predict surface variables over seasons to decades ahead, provide useful benchmarks for comparison against the performance of dynamical forecast systems; they may also be employable as predictive tools for use by climate services in their own right. A new global empirical decadal prediction system is presented, based on a multiple linear regression approach designed to produce probabilistic output for comparison against dynamical models. A global attribution is performed initially to identify the important forcing and predictor components of the model . Ensemble hindcasts of surface air temperature anomaly fields are then generated, based on the forcings and predictors identified as important, under a series of different prediction ‘modes’ and their performance is evaluated. The modes include a real-time setting, a scenario in which future volcanic forcings are prescribed during the hindcasts, and an approach which exploits knowledge of the forced trend. A two-tier prediction system, which uses knowledge of future sea surface temperatures in the Pacific and Atlantic Oceans, is also tested, but within a perfect knowledge framework. Each mode is designed to identify sources of predictability and uncertainty, as well as investigate different approaches to the design of decadal prediction systems for operational use. It is found that the empirical model shows skill above that of persistence hindcasts for annual means at lead times of up to 10 years ahead in all of the prediction modes investigated. It is suggested that hindcasts which exploit full knowledge of the forced trend due to increasing greenhouse gases throughout the hindcast period can provide more robust estimates of model bias for the calibration of the empirical model in an operational setting. The two-tier system shows potential for improved real-time prediction, given the assumption that skilful predictions of large-scale modes of variability are available. The empirical model framework has been designed with enough flexibility to facilitate further developments, including the prediction of other surface variables and the ability to incorporate additional predictors within the model that are shown to contribute significantly to variability at the local scale. It is also semi-operational in the sense that forecasts have been produced for the coming decade and can be updated when additional data becomes available

Probabilistic skill in ensemble seasonal forecasts

Simulation models are widely employed to make probability forecasts of future conditions on seasonal to annual lead times. Added value in such forecasts is reflected in the information they add, either to purely empirical statistical models or to simpler simulation models. An evaluation of seasonal probability forecasts from the Development of a European Multimodel Ensemble system for seasonal to inTERannual prediction (DEMETER) and ENSEMBLES multi‐model ensemble experiments is presented. Two particular regions are considered: Nino3.4 in the Pacific and the Main Development Region in the Atlantic; these regions were chosen before any spatial distribution of skill was examined. The ENSEMBLES models are found to have skill against the climatological distribution on seasonal time‐scales. For models in ENSEMBLES that have a clearly defined predecessor model in DEMETER, the improvement from DEMETER to ENSEMBLES is discussed. Due to the long lead times of the forecasts and the evolution of observation technology, the forecast‐outcome archive for seasonal forecast evaluation is small; arguably, evaluation data for seasonal forecasting will always be precious. Issues of information contamination from in‐sample evaluation are discussed and impacts (both positive and negative) of variations in cross‐validation protocol are demonstrated. Other difficulties due to the small forecast‐outcome archive are identified. The claim that the multi‐model ensemble provides a ‘better’ probability forecast than the best single model is examined and challenged. Significant forecast information beyond the climatological distribution is also demonstrated in a persistence probability forecast. The ENSEMBLES probability forecasts add significantly more information to empirical probability forecasts on seasonal time‐scales than on decadal scales. Current operational forecasts might be enhanced by melding information from both simulation models and empirical models. Simulation models based on physical principles are sometimes expected, in principle, to outperform empirical models; direct comparison of their forecast skill provides information on progress toward that goal