Dataset Information

Design, Assessment, and Validation of a Questionnaire to Estimate Food-Dependent Exercise-Induced Anaphylaxis Prevalence in Latin American Population.

ABSTRACT: There are no epidemiological data about food-dependent exercise-induced anaphylaxis (FDEIA) in Latin America. Our aim was to design, assess, and validate a questionnaire to identify potential FDEIA cases and/or estimate its prevalence by self-report. Questions were included in the instrument to address the main symptoms of FDEIA, type/intensity of physical activity, and anaphylaxis. The instrument's clarity, comprehension and repeatability were evaluated. These evaluations were carried out by Hispanic people (Argentinians/Colombians/Mexicans/Peruvians), including nine individuals with medical diagnosis of FDEIA, and Brazilians. The Flesch-Kincaid score was calculated using the INFLESZ software. The instrument was translated from Spanish to Brazilian Portuguese following the translation back-translation procedure. The participants rated the two versions of the questionnaire as clear and comprehensible (three-point ordinal scale) and very easy to understand [0.33; average (scale 0-10)]. For these evaluations, the Kendall's W coefficient showed strong agreement among raters (W = 0.80; average). The Flesch-Kincaid score was 63.5 in average (documents considered as readable). The Cohen's Kappa coefficient showed almost perfect agreement in repeatability (0.88; average). The validation process of two versions of an instrument, used to identify potential FDEIA cases, was successfully carried out and it was found applicable to Latin American countries for generating epidemiological data.

SUBMITTER: Gonzalez-Santamaria J

PROVIDER: S-EPMC7712264 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Design, Assessment, and Validation of a Questionnaire to Estimate Food-Dependent Exercise-Induced Anaphylaxis Prevalence in Latin American Population.

González-Santamaría Jhonatan J Arámburo-Gálvez Jesús Gilberto JG Beltrán-Cárdenas Carlos Eduardo CE Mora-Melgem José Antonio JA Figueroa-Salcido Oscar Gerardo OG Ramírez-Torres Giovanni Isaí GI Cárdenas-Torres Feliznando Isidro FI Carvalho Gomes Itallo I Geralda André Tatiane T Macêdo-Callou María Auxiliadora MA Braga Rocha Élida Mara ÉM Ontiveros Noé N Cabrera-Chávez Francisco F

Healthcare (Basel, Switzerland) 20201129 4

There are no epidemiological data about food-dependent exercise-induced anaphylaxis (FDEIA) in Latin America. Our aim was to design, assess, and validate a questionnaire to identify potential FDEIA cases and/or estimate its prevalence by self-report. Questions were included in the instrument to address the main symptoms of FDEIA, type/intensity of physical activity, and anaphylaxis. The instrument's clarity, comprehension and repeatability were evaluated. These evaluations were carried out by Hi ...[more]

PMID: 33260469

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Project description:BackgroundTraditional food allergy assessment of anaphylaxis remains limited in accuracy and accessibility. Current methods of anaphylaxis risk assessment are costly with low predictive accuracy. The Tolerance Induction Program (TIP) for anaphylactic patients undergoing TIP immunotherapy produced large-scale diagnostic data across biosimilar proteins, which was used to develop a machine learning model for patient-specific and allergen-specific anaphylaxis assessment. In explanation of construct, this work describes the algorithm design for assignment of peanut allergen score as a quantitative measure of anaphylaxis risk. Secondarily, it confirms the accuracy of the machine learning model for a specific cohort of food anaphylactic children.Methods and resultsMachine learning model design for allergen score prediction utilized 241 individual allergy assays per patient. Accumulation of data across total IgE subdivision served as the basis of data organization. Two regression based Generalized Linear Models (GLM) were utilized to position allergy assessment on a linear scale. The initial model was further tested with sequential patient data over time. A Bayesian method was then used to improve outcomes by calculating the adaptive weights for the results of the two GLMs of peanut allergy score prediction. A linear combination of both provided the final hybrid machine learning prediction algorithm. Specific analysis of peanut anaphylaxis within one endotype model is estimated to predict the severity of possible anaphylactic reaction to peanut with a recall of 95.2% on a dataset of 530 juvenile patients with various food allergies, including but not limited to peanut allergy. Receiver Operating Characteristic analysis yielded over 99% AUC (area under curve) results within peanut allergy prediction.ConclusionsMachine learning algorithm design established from comprehensive molecular allergy data produces high accuracy and recall in anaphylaxis risk assessment. Subsequent design of additional food protein anaphylaxis algorithms is needed to improve the precision and efficiency of clinical food allergy assessment and immunotherapy treatment.

Project description:INTRODUCTION:Dengue is an escalating public health concern in Latin American Countries with a dramatic increase of cases reported during the past decade. The objectives of this study were to identify and provide insights into current management and attitudes toward dengue and to understand attitudes to vaccination and current behaviors to prevent dengue in Mexico and Colombia. METHODS:This was a community-based, cross-sectional, descriptive study conducted in urban and rural areas in endemic and non-endemic regions. The interviews were conducted face-to-face using a structured questionnaire containing 58 questions. A quota sampling approach was used to obtain a nationally representative sample of the adult population. All data were weighted to correct for differences between the samples surveyed in each country relative to their general population. RESULTS:A total of 1978 participants completed the survey. Two percent and 10% of participants in Mexico and Colombia, respectively, had experienced dengue fever, with just under one-third of adults and almost two-thirds of their children hospitalized as a result of the illness. Awareness of dengue was similar in Colombia (76%) and Mexico (68%), with awareness higher in endemic regions than in non-endemic regions. Colombia had a higher proportion of participants (84%) who considered dengue to be a common disease in their country, compared with Mexico (56%). In Mexico and Colombia, 55% and 54% in endemic areas, and 28% and 46% in non-endemic areas believed that everyone was at risk of contracting dengue. In both countries, the most common action undertaken by participants to prevent dengue infection was removal of standing water. At least 70% of participants believe their government could do more to prevent dengue in their country. CONCLUSIONS:Dengue was identified as a severe and common disease in Mexico and Colombia. Most participants recognized the need to reduce the risk of dengue infection by removal of standing water. Awareness was similar in Colombia and Mexico.

Dataset Information

Design, Assessment, and Validation of a Questionnaire to Estimate Food-Dependent Exercise-Induced Anaphylaxis Prevalence in Latin American Population.

Publications

Design, Assessment, and Validation of a Questionnaire to Estimate Food-Dependent Exercise-Induced Anaphylaxis Prevalence in Latin American Population.

Similar Datasets

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