http://http://dspace.icddrb.org:80/jspui/handle/123456789/5922 Tue, 14 Apr 2026 03:22:14 GMT 2026-04-14T03:22:14Z http://http://dspace.icddrb.org:80/jspui/handle/123456789/11884 Title: Analysis of Health Needs and Health System Response in the Coastal Districts of Bangladesh Authors: Fauzia Akhter, Huda; Hassan Rushekh, Mahmood; Aniqa Tasnim, Hossain; Jasmin, Khan; Omar, Faruk; Zahed Shafiqur, Razzak; Kazi Tamara Binta, Kamal; Shams El, Arifeen Abstract: This research project was supported by South Asia Research Hub, Department for International Development, Government of UK. icddr,b acknowledges with gratitude the commitment of DFID to its research efforts. icddr,b is also grateful to the Governments of Bangladesh, Canada, Sweden and the UK for providing unrestricted support Mon, 29 Sep 2025 00:00:00 GMT http://http://dspace.icddrb.org:80/jspui/handle/123456789/11884 2025-09-29T00:00:00Z http://http://dspace.icddrb.org:80/jspui/handle/123456789/6197 Title: Using and comparing different decision tree classification techniques for mining ICDDR,B Hospital Surveillance data Authors: Rahman, Rashedur M.; Hasan, Fazle Rabbi Md. Abstract: In this research we have used decision tree induction algorithm on Hospital Surveillance data to classify admitted patients according to their critical condition. Three class labels, low, medium and high, are used to distinguish the criticality of the admitted patients. Several decision tree models are developed, evaluated, and compared with different performance metrics. Finally an efficient classifier is developed to classify records and make decision/predictions on some input parameters. The models developed in this research could be helpful during epidemic when huge number of patients arrive daily. Due to rush of duty doctors and scarcity of required number of physicians, it is hard to diagnose every patient. Any computer application could be helpful to diagnose and measure the criticality of the newly arrived patient with the help of the historical data kept in the surveillance database. The application would ask few questions on physical condition and on history of disease of the patient and accordingly determines the critical condition of the patient as low, medium or high. Sat, 01 Jan 2011 00:00:00 GMT http://http://dspace.icddrb.org:80/jspui/handle/123456789/6197 2011-01-01T00:00:00Z http://http://dspace.icddrb.org:80/jspui/handle/123456789/6196 Title: Vulnerability of newborns to environmental factors : findings from community based surveillance data in Bangladesh Authors: Mannan, Ishtiaq; Choi, Yoonjoung; Coutinho, Anastasia J.; Chowdhury, Atique I.; Rahman, Syed Moshfiqur; Seraji, Habib R.; Bari, Sanwarul; Shah, Rasheduzzaman; Winch, Peter J.; Arifeen, Shams El; Darmstadt, Gary L.; Baqui, Abdullah H. Abstract: Abstract: Infection is the major cause of neonatal deaths. Home born newborns in rural Bangladeshi communities are exposed to environmental factors increasing their vulnerability to a number of disease agents that may compromise their health. The current analysis was conducted to assess the association of very severe disease (VSD) in newborns in rural communities with temperature, rainfall, and humidity. A total of 12,836 newborns from rural Sylhet and Mirzapur communities were assessed by trained community health workers using a sign based algorithm. Records of temperature, humidity, and rainfall were collected from the nearest meteorological stations. Associations between VSD and environmental factors were estimated. Incidence of VSD was found to be associated with higher temperatures (odds ratios: 1.14, 95% CI: 1.08 to 1.21 in Sylhet and 1.06, 95% CI: 1.04 to 1.07 in Mirzapur) and heat humidity index (odds ratios: 1.06, 95% CI: 1.04 to 1.08 in Sylhet and, 1.03, 95% CI: 1.01 to 1.04 in Mirzapur). Four months (June-September) in Sylhet, and six months in Mirzapur (April-September) had higher odds ratios of incidence of VSD as compared to the remainder of the year (odds ratios: 1.72, 95% CI: 1.32 to 2.23 in Sylhet and, 1.62, 95% CI: 1.33 to1.96 in Mirzapur). Prevention of VSD in neonates can be enhanced if these interactions are considered in health intervention strategies. Sat, 01 Jan 2011 00:00:00 GMT http://http://dspace.icddrb.org:80/jspui/handle/123456789/6196 2011-01-01T00:00:00Z http://http://dspace.icddrb.org:80/jspui/handle/123456789/6195 Title: The role of age, ethnicity and environmental factors in modulating malaria risk in Rajasthali, Bangladesh Authors: Haque, Ubydul; Magalhães, Ricardo J Soares; Mitra, Dipak; Kolivras, Korine N; Schmidt, Wolf-Peter; Haque, Rashidul; Glass, Gregory E Abstract: Background: Malaria is endemic in the Rajasthali region of the Chittagong Hill Tracts in Bangladesh and the Rajasthali region is the most endemic area of Bangladesh. Quantifying the role of environmental and socioeconomic factors in the local spatial patterns of malaria endemicity can contribute to successful malaria control and elimination. This study aimed to investigate the role of environmental factors on malaria risk in Rajasthali and to quantify the geographical clustering in malaria risk unaccounted by these factors. Method: A total of 4,200 (78.9%; N = 5,322) households were targeted in Rajasthali in July, 2009, and 1,400 individuals were screened using a rapid diagnostic test (Falci-vax). These data were linked to environmental and socio-economic data in a geographical information system. To describe the association between environmental factors and malaria risk, a generalized linear mixed model approach was utilized. The study investigated the role of environmental factors on malaria risk by calculating their population-attributable fractions (PAF), and used residual semivariograms to quantify the geographical clustering in malaria risk unaccounted by these factors. Results: Overall malaria prevalence was 11.7%. Out of 5,322 households, 44.12% households were living in areas with malaria prevalence of ≥ 10%. The results from statistical analysis showed that age, ethnicity, proximity to forest, household density, and elevation were significantly and positively correlated with the malaria risk and PAF estimation. The highest PAF of malaria prevalence was 47.7% for third tertile (n = 467) of forest cover, 17.6% for second tertile (n = 467) of forest cover and 19.9% for household density >1,000. Conclusion: Targeting of malaria health interventions at small spatial scales in Bangladesh should consider the social and socio-economic risk factors identified as well as alternative methods for improving equity of access to interventions across whole communities. Sat, 01 Jan 2011 00:00:00 GMT http://http://dspace.icddrb.org:80/jspui/handle/123456789/6195 2011-01-01T00:00:00Z