Helminth infection induces non-functional sensitization to house dust mites

Allergic disease and sensitization disparity in urban and rural China: A EuroPrevall-INCO study

BACKGROUND

Studies in comparison with allergic diseases and sensitization between rural and urban environments in westernized countries might be biased and not adequately reflect countries undergoing rapid transition.

METHODS

A total of 5542 schoolchildren from urban area and 5139 from rural area were recruited for the EuroPrevall-INCO survey. A subsequent case-control sample with 196 children from urban area and 202 from rural area was recruited for a detailed face-to-face questionnaire and assessment of sensitization. Skin prick tests and serum-specific IgE measurements were used to assess sensitizations against food and aeroallergens. Logistic regression analysis was used to determine associations between risk/protective factors, food adverse reactions (FAR), allergic diseases, and sensitizations.

RESULTS

Prevalence of self-reported allergic diseases, including asthma (6.6% vs.2.5%), rhinitis (23.2% vs.5.3%), and eczema (34.1% vs.25.9%), was higher in urban than in rural children. Urban children had a significantly higher prevalence of FAR and related allergic diseases, and lower food/inhalation allergen sensitization rate, than those of rural children. In urban children, frequent changing places of residency (odds ratio 2.85, 95% confidence interval: 1.45-5.81) and antibiotic usage (3.54, 1.77-7.32) in early life were risk factors for sensitization, while sensitization and family history of allergy were risk factors for allergic diseases. In rural children, exposure to rural environments in early life was protective against both allergen sensitizations (0.46, 0.21-0.96) and allergic diseases (0.03, 0.002-0.19).

CONCLUSION

We observed a disparity in rates of allergic diseases and allergen sensitization between rural and urban children. In addition to family history, the development of allergic diseases and allergen sensitization were associated with specific urban/rural environmental exposures in early life.

Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data

Background

Previous epidemiological studies have examined the prevalence and risk factors for a variety of parasitic illnesses, including protozoan and soil-transmitted helminth (STH, e.g., hookworms and roundworms) infections. Despite advancements in machine learning for data analysis, the majority of these studies use traditional logistic regression to identify significant risk factors.

Methods

In this study, we used data from a survey of 54 risk factors for intestinal parasitosis in 954 Ethiopian school children. We investigated whether machine learning approaches can supplement traditional logistic regression in identifying intestinal parasite infection risk factors. We used feature selection methods such as InfoGain (IG), ReliefF (ReF), Joint Mutual Information (JMI), and Minimum Redundancy Maximum Relevance (MRMR). Additionally, we predicted children’s parasitic infection status using classifiers such as Logistic Regression (LR), Support Vector Machines (SVM), Random Forests (RF) and XGBoost (XGB), and compared their accuracy and area under the receiver operating characteristic curve (AUROC) scores. For optimal model training, we performed tenfold cross-validation and tuned the classifier hyperparameters. We balanced our dataset using the Synthetic Minority Oversampling (SMOTE) method. Additionally, we used association rule learning to establish a link between risk factors and parasitic infections.

Key findings

Our study demonstrated that machine learning could be used in conjunction with logistic regression. Using machine learning, we developed models that accurately predicted four parasitic infections: any parasitic infection at 79.9% accuracy, helminth infection at 84.9%, any STH infection at 95.9%, and protozoan infection at 94.2%. The Random Forests (RF) and Support Vector Machines (SVM) classifiers achieved the highest accuracy when top 20 risk factors were considered using Joint Mutual Information (JMI) or all features were used. The best predictors of infection were socioeconomic, demographic, and hematological characteristics.

Conclusions

We demonstrated that feature selection and association rule learning are useful strategies for detecting risk factors for parasite infection. Additionally, we showed that advanced classifiers might be utilized to predict children’s parasitic infection status. When combined with standard logistic regression models, machine learning techniques can identify novel risk factors and predict infection risk.

In developing countries such as Ethiopia, intestinal parasites are a significant public health problem. These parasites are detrimental to the health of schoolchildren. Numerous risk factors for parasitic infections have been identified using uni- and multi-variate logistic regression. However, logistic regression has inherent limitations when applied to data sets with a large number of risk factors. We used machine learning techniques in conjunction with logistic regression models to identify relevant risk factors for parasitic infections in a dataset of 954 Ethiopian schoolchildren with 54 different risk factors for parasitic infections. Additionally, we developed predictive models of parasitic infection. Compared to logistic regression, we discovered that machine learning techniques identified novel risk factors and had higher predictive accuracy. Furthermore, we discovered that infection prediction could be aided by combining socioeconomic, health, and hematological characteristics. As a result, we concluded that advanced machine learning methods should be used in conjunction with logistic regression to study parasitic infections.

Characteristics of allergen profile, sensitization patterns and Allergic Rhinitis in SouthEast Asia

PURPOSE OF REVIEW

To highlight the characteristics of allergic rhinitis, local allergic rhinitis (LAR), and importance of allergens in Southeast Asian countries.

RECENT FINDINGS

The Asia-Pacific region is very diverse with disparities in the epidemiological data between countries as well as in the unmet needs. The prevalence of allergic rhinitis has markedly increased in the past decades, with a high variation between countries, ranging from 4.5--80.3%. In terms of LAR, the reported prevalence in Southeast Asia is similar to that of other Asian countries (3.7-24.9%) but lower than that in western countries. House dust mites, cockroach, pollens, and molds are major allergens that are known triggers for of allergic rhinitis in this region, whereas the association with helminth infection requires further investigation.

SUMMARY

There are gaps and high variation between countries in Southeast Asia regarding the prevalence of allergic rhinitis and LAR. Further studies are needed to fully elucidate the association between allergens and allergic rhinitis in Southeast Asia.