A contingent valuation study to estimate the parental willingness-to-pay for childhood diarrhoea and gender bias among rural households in India

We used contingent valuation technique to estimate the parental willingness to pay for an episode of diarrhoea among 324 children of both sexes aged between five and seven years in two rural villages of Chennai in India. The aim was to examine if there was any gender bias in the parental willingness to treat children for a diarrhoeal episode, and if so to what extent. The willingness to pay was specified as a hedonic function of the duration and severity of an episode, and of parents' socioeconomic characteristics. The findings suggest that parents were willing to pay more to protect their male child compared to the female child suffering from a diarrhoeal episode. The median willingness to pay to avoid an episode for male and female children were calculated at Rs. 33.7 (approx. US$0.72) and Rs. 25.2 (approx. US$ 0.54) respectively – a difference of around 34%. After adjusting for the greater duration and severity of the illness, it was found that the difference between the two medians increased to 51%

A contingent valuation study to estimate the parental willingness-to-pay for childhood diarrhoea and gender bias among rural households in India

Abstract We used contingent valuation technique to estimate the parental willingness to pay for an episode of diarrhoea among 324 children of both sexes aged between five and seven years in two rural villages of Chennai in India. The aim was to examine if there was any gender bias in the parental willingness to treat children for a diarrhoeal episode, and if so to what extent. The willingness to pay was specified as a hedonic function of the duration and severity of an episode, and of parents' socioeconomic characteristics. The findings suggest that parents were willing to pay more to protect their male child compared to the female child suffering from a diarrhoeal episode. The median willingness to pay to avoid an episode for male and female children were calculated at Rs. 33.7 (approx. US$0.72) and Rs. 25.2 (approx. US$ 0.54) respectively – a difference of around 34%. After adjusting for the greater duration and severity of the illness, it was found that the difference between the two medians increased to 51%.</p

Isolation, Characterization and Determination of Antimicrobial Properties of Lactic Acid Bacteria from Human Milk

Abstract Breast milk has a distinctive combination of proteins, carbohydrates, minerals, lipids and various vitamins that promote the proper growth, development and immunity of the infants. That&apos;s why it is considered to be a complete food for new born babies. Moreover, it is also rich in various bioactive compounds which promote the maturation of immune system as well as develop body&apos;s defense against infections. Among these bioactive agents, probiotic bacteria were isolated from human milk in this research work using selective MRS media. Two Lactobacillus spp. were isolated from each of the two breast milk samples, were observed as potential probiotics, and identified using morphological and biochemical tests. These bacteria were facultative anaerobic, gram positive, catalase negative and non-endospore forming. They showed tolerance against 0.3% bile concentration and 1-10% NaCl. Sugar fermentation patterns of both isolated bacteria also greatly varied. Isolate-1 from both sample 1 and 2 showed antimicrobial activity against Shigella flexneri, Shigella dysenteriae, Vibrio cholerae and Salmonella typhi. Isolate-2 from sample 1 and 2 showed antimicrobial activity against Shigella flexneri, Shigella dysenteriae, Vibrio cholerae, Salmonella typhi, Staphylococcus epidermidis and Pseudomonas spp. The addition of breast milk probiotics to infant formulas could be a new alternative to mimic some of the functional effects of human milk in children who are not breastfe

An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP

A healthcare monitoring system needs the support of recent technologies such as artificial intelligence (AI), machine learning (ML), and big data, especially during the COVID-19 pandemic. This global pandemic has already taken millions of lives. Both infected and uninfected people have generated big data where AI and ML can use to combat and detect COVID-19 at an early stage. Motivated by this, an improved ML framework for the early detection of this disease is proposed in this paper. The state-of-the-art Harris hawks optimization (HHO) algorithm with an improved objective function is proposed and applied to optimize the hyperparameters of the ML algorithms, namely HHO-based eXtreme gradient boosting (HHOXGB), light gradient boosting (HHOLGB), categorical boosting (HHOCAT), random forest (HHORF) and support vector classifier (HHOSVC). An ensemble technique was applied to these optimized ML models to improve the prediction performance. Our proposed method was applied to publicly available big COVID-19 data and yielded a prediction accuracy of 92.38% using the ensemble model. In contrast, HHOXGB provided the highest accuracy of 92.23% as a single optimized model. The performance of the proposed method was compared with the traditional algorithms and other ML-based methods. In both cases, our proposed method performed better. Furthermore, not only the classification improvement, but also the features are analyzed in terms of feature importance calculated by SHapely adaptive exPlanations (SHAP) values. A graphical user interface is also discussed as a potential tool for nonspecialist users such as clinical staff and nurses. The processed data, trained model, and codes related to this study are available at GitHub