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dc.contributor.authorHoman, Tobias
dc.contributor.authorDi Pasquale, Aurelio
dc.contributor.authorKiche, Ibrahim
dc.contributor.authorOnoka, Kelvin
dc.contributor.authorHiscox, Alexandra
dc.contributor.authorMweresa, Collins
dc.contributor.authorMukabana, Wolfgang R
dc.contributor.authorTakken, Willem
dc.contributor.authorMaire, Nicolas
dc.date.accessioned2015-08-31T16:02:59Z
dc.date.available2015-08-31T16:02:59Z
dc.date.issued2015-09-01
dc.identifier.citationBMC Research Notes. 2015 Sep 01;8(1):397
dc.identifier.urihttp://dx.doi.org/10.1186/s13104-015-1373-8
dc.identifier.urihttp://hdl.handle.net/11295/90285
dc.description.abstractAbstract Background Health in low and middle income countries is on one hand characterized by a high burden associated with preventable communicable diseases and on the other hand considered to be under-documented due to improper basic health and demographic record-keeping. health and demographic surveillance systems (HDSSs) have provided researchers, policy makers and governments with data about local population dynamics and health related information. In order for an HDSS to deliver high quality data, effective organization of data collection and management are vital. HDSSs impose a challenging logistical process typically characterized by door to door visits, poor navigational guidance, conducting interviews recorded on paper, error prone data entry, an extensive staff and marginal data quality management possibilities. Methods A large trial investigating the effect of odour-baited mosquito traps on malaria vector populations and malaria transmission on Rusinga Island, western Kenya, has deployed an HDSS. By means of computer tablets in combination with Open Data Kit and OpenHDS data collection and management software experiences with time efficiency, cost effectiveness and high data quality are illustrate. Step by step, a complete organization of the data management infrastructure is described, ranging from routine work in the field to the organization of the centralized data server. Results and discussion Adopting innovative technological advancements has enabled the collection of demographic and malaria data quickly and effectively, with minimal margin for errors. Real-time data quality controls integrated within the system can lead to financial savings and a time efficient work flow. Conclusion This novel method of HDSS implementation demonstrates the feasibility of integrating electronic tools in large-scale health interventions.
dc.titleInnovative tools and OpenHDS for health and demographic surveillance on Rusinga Island, Kenya
dc.typeJournal Article
dc.date.updated2015-08-31T16:03:00Z
dc.language.rfc3066en
dc.rights.holderHoman et al.


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