Identification of factors associated with diagnostic error in primary care.

BACKGROUND Missed, delayed or incorrect diagnoses are considered to be diagnostic errors. The aim of this paper is to describe the methodology of a study to analyse cognitive aspects of the process by which primary care (PC) physicians diagnose dyspnoea. It examines the possible links between the use of heuristics, suboptimal cognitive acts and diagnostic errors, using Reason's taxonomy of human error (slips, lapses, mistakes and violations). The influence of situational factors (professional experience, perceived overwork and fatigue) is also analysed. METHODS Cohort study of new episodes of dyspnoea in patients receiving care from family physicians and residents at PC centres in Granada (Spain). With an initial expected diagnostic error rate of 20%, and a sampling error of 3%, 384 episodes of dyspnoea are calculated to be required. In addition to filling out the electronic medical record of the patients attended, each physician fills out 2 specially designed questionnaires about the diagnostic process performed in each case of dyspnoea. The first questionnaire includes questions on the physician's initial diagnostic impression, the 3 most likely diagnoses (in order of likelihood), and the diagnosis reached after the initial medical history and physical examination. It also includes items on the physicians' perceived overwork and fatigue during patient care. The second questionnaire records the confirmed diagnosis once it is reached. The complete diagnostic process is peer-reviewed to identify and classify the diagnostic errors. The possible use of heuristics of representativeness, availability, and anchoring and adjustment in each diagnostic process is also analysed. Each audit is reviewed with the physician responsible for the diagnostic process. Finally, logistic regression models are used to determine if there are differences in the diagnostic error variables based on the heuristics identified. DISCUSSION This work sets out a new approach to studying the diagnostic decision-making process in PC, taking advantage of new technologies which allow immediate recording of the decision-making process.

Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study.

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.

Alcohol intake and the risk of coronary heart disease in the Spanish EPIC

Background: The association between alcohol consumption and coronary heart disease (CHD) has been widely studied. Most of these studies have concluded that moderate alcohol intake reduces the risk of CHD. There are numerous discussions regarding whether this association is causal or biased. The objective of this paper is to analyse the association between alcohol intake and CHD risk in the Spanish cohort of the European Prospective Investigation into Cancer (EPIC). Methods: Participants from the EPIC Spanish cohort were included (15 630 men and 25 808 women). The median follow-up period was 10 years. Ethanol intake was calculated using a validated dietary history questionnaire. Participants with a definite CHD event were considered cases. A Cox regression model adjusted for relevant co-variables and stratified by age was produced. Separate models were carried out for men and women. Results: The crude CHD incidence rate was 300.6/100 000 person-years for men and 47.9/100 000 person-years for women. Moderate, high and very high consumption was associated with a reduced risk of CHD in men: hazard ratio 0.90 (95% CI 0.56 to 1.44) for former drinkers, 0.65 (95% CI 0.41 to 1.04) for low, 0.49 (95% CI 0.32 to 0.76) for moderate, 0.46 (95% CI 0.30 to 0.71) for high and 0.50 (95% CI 0.29 to 0.85) for very high consumers. A negative association was found in women, with p values above 0.05 in all categories. Conclusions: Alcohol intake in men aged 29–69 years was associated with a more than 30% lower CHD incidence. This study is based on a large prospective cohort study and is free of the abstainer error.

Testing of Diagnostic Methods for Detection of Influenza Virusfor Optimal Performance in the Context of an Influenza Surveillance Network

Influenza surveillance networks must detect early the viruses that will cause the forthcoming annual epidemics and isolate the strains for further characterization. We obtained the highest sensitivity (95.4%) with a diagnostic tool that combined a shell-vial assay and reverse transcription-PCR on cell culture supernatants at 48 h, and indeed, recovered the strain

Meat consumption and mortality - results from the European Prospective Investigation into Cancer and Nutrition.

BACKGROUND Recently, some US cohorts have shown a moderate association between red and processed meat consumption and mortality supporting the results of previous studies among vegetarians. The aim of this study was to examine the association of red meat, processed meat, and poultry consumption with the risk of early death in the European Prospective Investigation into Cancer and Nutrition (EPIC). METHODS Included in the analysis were 448,568 men and women without prevalent cancer, stroke, or myocardial infarction, and with complete information on diet, smoking, physical activity and body mass index, who were between 35 and 69 years old at baseline. Cox proportional hazards regression was used to examine the association of meat consumption with all-cause and cause-specific mortality. RESULTS As of June 2009, 26,344 deaths were observed. After multivariate adjustment, a high consumption of red meat was related to higher all-cause mortality (hazard ratio (HR) = 1.14, 95% confidence interval (CI) 1.01 to 1.28, 160+ versus 10 to 19.9 g/day), and the association was stronger for processed meat (HR = 1.44, 95% CI 1.24 to 1.66, 160+ versus 10 to 19.9 g/day). After correction for measurement error, higher all-cause mortality remained significant only for processed meat (HR = 1.18, 95% CI 1.11 to 1.25, per 50 g/d). We estimated that 3.3% (95% CI 1.5% to 5.0%) of deaths could be prevented if all participants had a processed meat consumption of less than 20 g/day. Significant associations with processed meat intake were observed for cardiovascular diseases, cancer, and 'other causes of death'. The consumption of poultry was not related to all-cause mortality. CONCLUSIONS The results of our analysis support a moderate positive association between processed meat consumption and mortality, in particular due to cardiovascular diseases, but also to cancer.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).

In vitro methods for diagnosing nonimmediate hypersensitivity reactions to drugs.

Nonimmediate drug hypersensitivity reactions (DHRs) are difficult to manage in daily clinical practice, mainly owing to their heterogeneous clinical manifestations and the lack of selective biological markers. In vitro methods are necessaryto establish a diagnosis, especially given the low sensitivity of skin tests and the inherent risks of drug provocation testing. In vitro evaluation of nonimmediate DHRs must include approaches that can be applied during the different phases of the reaction. During the acute phase, monitoring markers in both skin and peripheral blood helps to discriminate between immediate and nonimmediate DHRs with cutaneous responses and to distinguish between reactions that, although they present similar clinical symptoms, are produced by different immunological mechanisms and therefore have a different treatment and prognosis. During the resolution phase, in vitro testing is used to detect the response of T cells to drug stimulation; however, this approach has certain limitations, such as the lack of validated studies assessing sensitivity. Moreover, in vitro tests indicate an immune response that is not always related to a DHR. In this review, members of the Immunology and Drug Allergy Committee of the Spanish Society of Allergy and Clinical Immunology (SEAIC) provide an overview of the most widely used in vitro tests for evaluating nonimmediate DHRs.

Performing masculinity, influencing health: a qualitative mixed-methods study of young Spanish men

Background: The literature shows how gender mandates contribute to differences in exposure and vulnerability to certain health risk factors. This paper presents the results of a study developed in the south of Spain, where research aimed at understanding men from a gender perspective is still limited. Objective: The aim of this paper is to explore the lay perceptions and meanings ascribed to the idea of masculinity, identifying ways in which gender displays are related to health. Design: The study is based on a mixed-methods data collection strategy typical of qualitative research. We performed a qualitative content analysis focused on manifest and latent content. Results: Our analysis showed that the relationship between masculinity and health was mainly defined with regard to behavioural explanations with an evident performative meaning. With regard to issues such as driving, the use of recreational drugs, aggressive behaviour, sexuality, and body image, important connections were established between manhood acts and health outcomes. Different ways of understanding and performing the male identity also emerged from the results. The findings revealed the implications of these aspects in the processes of change in the identity codes of men and women. Conclusions: The study provides insights into how the category ‘man’ is highly dependent on collective practices and performative acts. Consideration of how males perform manhood acts might be required in guidance on the development of programmes and policies aimed at addressing gender inequalities in health in a particular local context.

Evaluation of quality improvement for cesarean sections caesarean section programmes through mixed methods.

BACKGROUND The rate of avoidable caesarean sections (CS) could be reduced through multifaceted strategies focusing on the involvement of health professionals and compliance with clinical practice guidelines (CPGs). Quality improvements for CS (QICS) programmes (QICS) based on this approach, have been implemented in Canada and Spain. OBJECTIVES Their objectives are as follows: 1) Toto identify clusters in each setting with similar results in terms of cost-consequences, 2) Toto investigate whether demographic, clinical or context characteristics can distinguish these clusters, and 3) Toto explore the implementation of QICS in the 2 regions, in order to identify factors that have been facilitators in changing practices and reducing the use of obstetric intervention, as well as the challenges faced by hospitals in implementing the recommendations. METHODS Descriptive study with a quantitative and qualitative approach. 1) Cluster analysis at patient level with data from 16 hospitals in Quebec (Canada) (n = 105,348) and 15 hospitals in Andalusia (Spain) (n = 64,760). The outcome measures are CS and costs. For the cost, we will consider the intervention, delivery and complications in mother and baby, from the hospital perspective. Cluster analysis will be used to identify participants with similar patterns of CS and costs based, and t tests will be used to evaluate if the clusters differed in terms of characteristics: Hospital level (academic status of hospital, level of care, supply and demand factors), patient level (mother age, parity, gestational age, previous CS, previous pathology, presentation of the baby, baby birth weight). 2) Analysis of in-depth interviews with obstetricians and midwives in hospitals where the QICS were implemented, to explore the differences in delivery-related practices, and the importance of the different constructs for positive or negative adherence to CPGs. Dimensions: political/management level, hospital level, health professionals, mothers and their birth partner. DISCUSSION This work sets out a new approach for programme evaluation, using different techniques to make it possible to take into account the specific context where the programmes were implemented.

Epigenetic mechanisms in non-alcoholic fatty liver disease: An emerging field.

Non-alcoholic fatty liver disease (NAFLD) is an emerging health concern in both developed and non-developed world, encompassing from simple steatosis to non-alcoholic steatohepatitis (NASH), cirrhosis and liver cancer. Incidence and prevalence of this disease are increasing due to the socioeconomic transition and change to harmful diet. Currently, gold standard method in NAFLD diagnosis is liver biopsy, despite complications and lack of accuracy due to sampling error. Further, pathogenesis of NAFLD is not fully understood, but is well-known that obesity, diabetes and metabolic derangements played a major role in disease development and progression. Besides, gut microbioma and host genetic and epigenetic background could explain considerable interindividual variability. Knowledge that epigenetics, heritable events not caused by changes in DNA sequence, contribute to development of diseases has been a revolution in the last few years. Recently, evidences are accumulating revealing the important role of epigenetics in NAFLD pathogenesis and in NASH genesis. Histone modifications, changes in DNA methylation and aberrant profiles or microRNAs could boost development of NAFLD and transition into clinical relevant status. PNPLA3 genotype GG has been associated with a more progressive disease and epigenetics could modulate this effect. The impact of epigenetic on NAFLD progression could deserve further applications on therapeutic targets together with future non-invasive methods useful for the diagnosis and staging of NAFLD.

Endometriosis: alternative methods of medical treatment.

Endometriosis is an inflammatory estrogen-dependent disease defined by the presence of endometrial glands and stroma at extrauterine sites. The main purpose of endometriosis management is alleviating pain associated to the disease. This can be achieved surgically or medically, although in most women a combination of both treatments is required. Long-term medical treatment is usually needed in most women. Unfortunately, in most cases, pain symptoms recur between 6 months and 12 months once treatment is stopped. The authors conducted a literature search for English original articles, related to new medical treatments of endometriosis in humans, including articles published in PubMed, Medline, and the Cochrane Library. Keywords included "endometriosis" matched with "medical treatment", "new treatment", "GnRH antagonists", "Aromatase inhibitors", "selective progesterone receptor modulators", "anti-TNF α", and "anti-angiogenic factors". Hormonal treatments currently available are effective in the relief of pain associated to endometriosis. Among new hormonal drugs, association to aromatase inhibitors could be effective in the treatment of women who do not respond to conventional therapies. GnRH antagonists are expected to be as effective as GnRH agonists, but with easier administration (oral). There is a need to find effective treatments that do not block the ovarian function. For this purpose, antiangiogenic factors could be important components of endometriosis therapy in the future. Upcoming researches and controlled clinical trials should focus on these drugs.