PARENTS/CAREGIVERS OF CHILDREN’S AGED UNDER FIVE KNOWLEDGE, ATTITUDES AND PRACTICES TOWARDS SOIL TRANSMITTED HELMINTHS IN TARABA STATE, NIGERIA
DOI:
https://doi.org/10.33003/fjs-2025-0901-3172Keywords:
Prevalence, Under-5 children, Parents/caregivers, Soil Transmitted Helminths, Taraba StateAbstract
Soil-transmitted helminths (STHs) are included in the listof the world's neglected tropical diseases. The STHs include the roundworm Ascaris lumbricoides, the whipworm Trichuris trichiura, the hookworms, Ancylostoma duodenale and Necator americanus, and Strongyloides stercoralis. The study was aimed to determine parents/car-givers of under-five children’s knowledge, attitudes and practices towards STHs in Taraba State, Nigeria. STHs infection is among the most chronic diseases globally. Because of its impact on human health, the WHO recommended the carrying out of robust approaches targeted at controlling or eliminating disease. The execution of this approach depends on the vivid understanding of the parent’s/caregivers’ knowledge, attitudes and practices in relation to this infection. A cross-sectional survey was conducted and data were assembled with the aid of a standardized questionnaires from 2,283 caregivers of under-five children. Extensive focus group discussions were carried out among parents/caregivers and the collected data were analysed thematically. Out of the six selected LGAs in Taraba State, highest albendazole coverage of 193(50.79%) was recorded in Bali LGA and least (47.89%) was recorded in Jalingo LGA. More so, a total STH prevalence of 3.29% was recorded among under-5 children who were dewormed six months ago before the survey period and 12.52% was also recorded among under-5 children who were not dewormed. Findings from this study also reflected adequate knowledge and attitudes with bad practices in connection to STHs among parents/caregivers while recognition of soil-transmitted helminths was high (94.2%). These reports are important in planning behavioural change approaches towards improving health results across community-based involvement...
References
Alzubaidi, Laith., Zhang, Jinglan., Humaidi, A. J., Al-Dujaili, Ayad., Duan, Ye., Al-Shamma, Omran., Santamaría, J., Fadhel, M. A., Al-Amidie, Muthana., & Farhan, Laith. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00444-8
Bani-Salameh, H., Alkhatib, S. M., Abdalla, M., Al-Hami, M., Banat, R., Zyod, H., & Alkhatib, A. J. (2021). Prediction of diabetes and hypertension using multi-layer perceptron neural networks. Https://Doi.Org/10.1142/S1793962321500124, 12(2). https://doi.org/10.1142/S1793962321500124
Chowdhury, M. H., Shuzan, M. N. I., Chowdhury, M. E. H., Mahbub, Z. B., Monir Uddin, M., Khandakar, A., & Reaz, M. B. I. (2020). Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques. Sensors 2020, Vol. 20, Page 3127, 20(11), 3127. https://doi.org/10.3390/S20113127
Ciampiconi, L., Elwood, A., Leonardi, M., Mohamed, A., & Rozza, A. (2023). A survey and taxonomy of loss functions in machine learning; A survey and taxonomy of loss functions in machine learning.
Dash, T., Chitlangia, S., Ahuja, A., & Srinivasan, A. (2022). A review of some techniques for inclusion of domain-knowledge into deep neural networks. Scientific Reports 2022 12:1, 12(1), 1–15. https://doi.org/10.1038/s41598-021-04590-0
Datta, S., Morassi Sasso, A., Kiwit, N., Bose, S., Nadkarni, G., Miotto, R., & Böttinger, E. P. (2022). Predicting hypertension onset from longitudinal electronic health records with deep learning. JAMIA Open, 5(4), 1–10. https://doi.org/10.1093/JAMIAOPEN/OOAC097
DeGuire, J., Clarke, J., Rouleau, K., Roy, J., & Bushnik, T. (2019). Blood pressure and hypertension. Health Reports, 30(2), 14–21. https://doi.org/10.25318/82-003-x201900200002
Estiko, R. I., Rijanto, E., Juwana, Y. B., Juzar, D. A., & Widyantoro, B. (2024). 73. Hypertension Prediction Models Using Machine Learning with Easy-to-Collect Risk Factors: A Systematic Review. Journal of Hypertension, 42(Suppl 2), e19. https://doi.org/10.1097/01.HJH.0001027072.19895.81
Farahani, A., Voghoei, S., Rasheed, K., & Arabnia, H. R. (2021). A Brief Review of Domain Adaptation. 877–894. https://doi.org/10.1007/978-3-030-71704-9_65/COVER
Feature Importance Explained. What is Feature importance ? | by akhil anand | Analytics Vidhya | Medium. (202 C.E.). https://medium.com/analytics-vidhya/feature-importance-explained-bfc8d874bcf
Goyal, A., Hossain, G., Chatrati, S. P., Bhattacharya, S., Bhan, A., Gaurav, D., & Tiwari, S. M. (2020). Smart Home Health Monitoring System for Predicting Type 2 Diabetes and Hypertension. J. King Saud Univ. Comput. Inf. Sci.
G. Zhang, Z. M. Y. Z. X. M. B. L. D. C. and Y. Z. (2020). A noninvasive blood glucose monitoring system based on smartphone ppg signal processing and machine learning. IEEE Transactions on Industrial Informatics, 7209–7218.
Herrera-Huisa, L., Arias-Meza, N., & Cabanillas-Carbonell, M. (2021). Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature. 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 769–775. https://doi.org/10.1109/ISPA-BDCLOUD-SOCIALCOM-SUSTAINCOM52081.2021.00110
Hosna, A., Merry, E., Gyalmo, J., Alom, Z., Aung, Z., & Abdul Azim, M. (2021). Transfer learning: a friendly introduction. https://doi.org/10.1186/s40537-022-00652-w
Hypertension. (n.d.). Retrieved September 24, 2024, from https://www.who.int/news-room/fact-sheets/detail/hypertension
Kario, K., Okura, A., Hoshide, S., & Mogi, M. (2024). The WHO Global report 2023 on hypertension warning the emerging hypertension burden in globe and its treatment strategy. Hypertension Research 2024 47:5, 47(5), 1099–1102. https://doi.org/10.1038/s41440-024-01622-w
Koshimizu, H., Kojima, R., Kario, K., & Okuno, Y. (2020). Prediction of blood pressure variability using deep neural networks. International Journal of Medical Informatics, 136, 104067. https://doi.org/10.1016/J.IJMEDINF.2019.104067
Layton, A. T. (2024). AI, Machine Learning, and ChatGPT in Hypertension. Hypertension, 81(4), 709–716. https://doi.org/10.1161/HYPERTENSIONAHA.124.19468/ASSET/21BAE21B-71F2-4AE0-BD29-E8F8452B742E/ASSETS/GRAPHIC/HYPERTENSIONAHA.124.19468.FIG03.JPG
Martinez-Ríos, E., Montesinos, L., Alfaro-Ponce, M., & Pecchia, L. (2021). A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data. Biomedical Signal Processing and Control, 68, 102813. https://doi.org/10.1016/J.BSPC.2021.102813
Mishra, R. K., Reddy, G. Y. S., & Pathak, H. (2021). The Understanding of Deep Learning: A Comprehensive Review. In Mathematical Problems in Engineering (Vol. 2021). Hindawi Limited. https://doi.org/10.1155/2021/5548884
Nematollahi, M. A., Jahangiri, S., Asadollahi, A., Salimi, M., Dehghan, A., Mashayekh, M., Roshanzamir, M., Gholamabbas, G., Alizadehsani, R., Bazrafshan, M., Bazrafshan, H., Bazrafshan drissi, H., & Shariful Islam, S. M. (2023). Body composition predicts hypertension using machine learning methods: a cohort study. Scientific Reports 2023 13:1, 13(1), 1–11. https://doi.org/10.1038/s41598-023-34127-6
Nour, M., & Polat, K. (2020). Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms. https://doi.org/10.1155/2020/2742781
Pan, S. J., & Yang, Q. (2020). A survey on transfer learning. In IEEE Transactions on Knowledge and Data Engineering (Vol. 22, Issue 10, pp. 1345–1359). https://doi.org/10.1109/TKDE.2009.191
PPG-BP. (n.d.). Retrieved November 15, 2024, from https://www.kaggle.com/datasets/phamminhhiu/ppgbp
PPG-BP Database. (2021). https://figshare.com/articles/dataset/PPG-BP_Database_zip/5459299
Sablons De Gélis, R. (2021). Transfer learning techniques in time series analysis KTH Master Thesis Report. In DEGREE PROJECT IN TECHNOLOGY.
Shrivastava, A., Chakkaravarthy, M., & Shah, M. A. (2023). A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. Healthcare Analytics, 4, 100219. https://doi.org/10.1016/J.HEALTH.2023.100219
Transfer Learning Guide: A Practical Tutorial With Examples for Images and Text in Keras. (2023). Neptune. https://neptune.ai/blog/transfer-learning-guide-examples-for-images-and-text-in-keras
Understanding Transfer Learning for Deep Learning. (2021). https://www.analyticsvidhya.com/blog/2021/10/understanding-transfer-learning-for-deep-learning/
Unger, T., Borghi, C., Charchar, F., Khan, N. A., Poulter, N. R., Prabhakaran, D., Ramirez, A., Schlaich, M., Stergiou, G. S., Tomaszewski, M., Wainford, R. D., Williams, B., & Schutte, A. E. (2020). 2020 International Society of Hypertension Global Hypertension Practice Guidelines. Hypertension, 75(6), 1334–1357. https://doi.org/10.1161/hypertensionaha.120.15026
Weber-Boisvert, G., Gosselin, B., & Sandberg, F. (2023). Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied. Frontiers in Physiology, 14. https://doi.org/10.3389/FPHYS.2023.1126957
Wu, L., Gao, J., Zhuang, J., Wu, M., Chen, S., Wang, G., Hong, L., Wu, S., & Hong, J. (2023). Hypertension combined with atherosclerosis increases the risk of heart failure in patients with diabetes. Hypertension Research 2023, 1–13. https://doi.org/10.1038/s41440-023-01529-y
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Member, S., Xiong, H., & He, Q. (2020). A Comprehensive Survey on Transfer Learning.
Published
How to Cite
Issue
Section
FUDMA Journal of Sciences
