Avian influenza (bird flu) occurs sporadically in American poultry flocks, decimating these flocks and causing substantial economic losses. Avian influenza also impacts the beliefs of food handlers and preparers in the home (home cooks). Although those who properly handle and prepare processed poultry products cannot succumb to avian influenza, there is a widespread belief that one can contract the bird flu from these foods. Beliefs about getting avian influenza from poultry products and intentions to avoid consuming poultry products are the focus of this study of 285 home cooks. False beliefs about getting avian influenza from handling, preparing, and consuming poultry products are apparent in this sample. Correlational analysis also shows that those holding the false beliefs intend to act upon those beliefs by planning not to consume poultry products. Moreover, the false beliefs about contracting avian influenza from poultry products are correlated with a bias to see oneself as less likely to produce food that contains foodborne diseases. These findings are consistent with, and contribute to, the research literatures on belief formation and change, behavioral intentions, and with research showing how guilt by association thought processes underlie false beliefs related to food safety. This research has important implications for poultry and other food processing industries, and for campaigns to persuade the public about real and imaginary risks associated with particular food products.Full article

Even patients from developed nations have trouble staying on top of their drug compliance. When it comes to improper medicine use, there is an odd parallel between underdeveloped, emerging nations and the so-called developed world in the West. The key factor influencing whether patients stick to their treatment plan is their understanding and perception of the disease.Full article

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Background: Family planning (FP) methods have been found as an efficient approach of reducing fertility and are therefore widely supported in order to decrease population growth, particularly in poor nations. Promoting contraception availability among women (15 – 49) age has also been shown to be an efficient public health strategy for improving maternal and newborn health outcomes. This paper aimed at exploring the pooled prevalence of contraceptive uptake and its contextual determinants among women of childbearing age in The Gambia. Methods: The Gambia Demographic and Health Survey (GDHS) in both 2013 and 2019-20 was used for this study. Data were obtained from a pooled 22,098 women aged 15-49 (10,233 for 2013 and 11,865 for 2019-20) through a stratified two-stage cluster sampling approach. Percentages and chi-square tests were used and variables with p-value <0.05 were included into the model. A multivariable logistic regression model was used to assess the predictors of contraceptive usage at 95% confidence interval (CIs) with computed adjusted odds ratios (aORs). All the study data were analyzed using Stata version 15. Results: The weighted pooled prevalence of modern contraceptive utilization in The Gambia was 10.1%. Younger age, compared with women aged 25-29; 30-34; 35-39; 40-44; primary education (aOR=1.25, 95% CI=1.05-1.49); secondary education (aOR=1.57, 95% CI= 1.32-1.85); Higher education (aOR=1.90, 95% CI=1.34-12.69); living in urban areas (aOR=1.49, 95% CI= 1.25-1.79); parity 2-4 (aOR=1.21, 95% CI= 1.01-1.47); told about FP at health facility (aOR=2.97, 95% CI= 2.61-3.38), and no desire for many children (aOR=1.96, 95% CI= 1.62-2.37) were more like to use modern contraceptives among Gambian women. Conclusion: The programme certainly needs to consider improvements in the quality of care being offered to acceptors. Government agencies should target these programs and campaigns on regional FP demands and provide suitable culturally sensitive and regionally adaptive services to the communities' contexts. The programme should intensify its efforts in rural and urban settings to improve accessibility to and availability of FP services.Full article

Advances in web-centric cloud computing have facilitated the establishment of an integrated cloud environment connecting a wide variety of clinical trial stakeholders. A web-centric cloud framework is proposed for real-time monitoring and risk prediction during clinical trials. The framework focuses on identifying relevant datasets, developing a data-management interface, and implementing machine-learning algorithms for data analysis. Detailed descriptions of the data-management interface and the machine-learning processes are provided, targeting active clinical trials with therapeutic uses in cancer. Demonstrations utilize publicly available clinical-trial data from the ClinicalTrials.gov repository. The real-time monitoring and risk prediction systems were assessed by developing five supervised-classification-machine-learning models for trial-status prediction and six unsupervised models for patient-safety-profile assessment, each representing a different phase of the clinical-trial process. All supervised models yielded high accuracy and area-under-the-curve values at the testing stage, while the unsupervised models demonstrated practical applicability. The results underscore the advantages of using the trial-status algorithm, the patient-safety-profile model, and the proposed framework for performing real-time monitoring and risk prediction of clinical trials.Full article