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Innovative tools and OpenHDS for health and demographic surveillance on Rusinga Island, Kenya.

Homan T, Di Pasquale A, Kiche I, Onoka K, Hiscox A, Mweresa C, Mukabana WR, Takken W, Maire N - BMC Res Notes (2015)

Bottom Line: 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.This novel method of HDSS implementation demonstrates the feasibility of integrating electronic tools in large-scale health interventions.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Entomology, Wageningen University and Research Centre, Wageningen, The Netherlands. tobias.homan@wur.nl.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

Navigating assigned houses: converting the up to date population database into a geodatabase displayed with Google Maps Mobile assists fieldworkers with tracking every house
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Fig4: Navigating assigned houses: converting the up to date population database into a geodatabase displayed with Google Maps Mobile assists fieldworkers with tracking every house

Mentions: On basis of the geographical coordinates of houses and demographic as well as malaria-related data gathered during the census of July 2012, the study design for the sequence of the rollout of the SolarMal intervention was developed and has been described elsewhere (Silkey et al., Personal Communications). Briefly, the island is divided into 81 clusters each containing 50 or 51 households, with nine clusters making up one metacluster. Metaclusters form the geographical basis for the HDSS follow up surveys. The fieldworkers are each assigned one of the metaclusters in which to visit every house and individual once during an interval of 3 months. One fieldworker is deployed to an area conditional on relative progress in the surveillance. For navigational purposes, the demographic database is converted into a geographic database (KML file), allowing us to plot houses to be visited in the Google Earth mobile (Version 7.1.3. 1255) application integrated in the tablet (constructed with ESRI 2011. ArcGIS Desktop: Release 09. Redlands, CA: Environmental Systems Research Institute). Using the GPS function, FWs can track themselves on the map navigating in real time from one house to another (Fig. 4). Furthermore, the geographic database also includes all server data enabling the FWs to select any house on the Google Earth map, consequently displaying the personal information of people living there.Fig. 4


Innovative tools and OpenHDS for health and demographic surveillance on Rusinga Island, Kenya.

Homan T, Di Pasquale A, Kiche I, Onoka K, Hiscox A, Mweresa C, Mukabana WR, Takken W, Maire N - BMC Res Notes (2015)

Navigating assigned houses: converting the up to date population database into a geodatabase displayed with Google Maps Mobile assists fieldworkers with tracking every house
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4556052&req=5

Fig4: Navigating assigned houses: converting the up to date population database into a geodatabase displayed with Google Maps Mobile assists fieldworkers with tracking every house
Mentions: On basis of the geographical coordinates of houses and demographic as well as malaria-related data gathered during the census of July 2012, the study design for the sequence of the rollout of the SolarMal intervention was developed and has been described elsewhere (Silkey et al., Personal Communications). Briefly, the island is divided into 81 clusters each containing 50 or 51 households, with nine clusters making up one metacluster. Metaclusters form the geographical basis for the HDSS follow up surveys. The fieldworkers are each assigned one of the metaclusters in which to visit every house and individual once during an interval of 3 months. One fieldworker is deployed to an area conditional on relative progress in the surveillance. For navigational purposes, the demographic database is converted into a geographic database (KML file), allowing us to plot houses to be visited in the Google Earth mobile (Version 7.1.3. 1255) application integrated in the tablet (constructed with ESRI 2011. ArcGIS Desktop: Release 09. Redlands, CA: Environmental Systems Research Institute). Using the GPS function, FWs can track themselves on the map navigating in real time from one house to another (Fig. 4). Furthermore, the geographic database also includes all server data enabling the FWs to select any house on the Google Earth map, consequently displaying the personal information of people living there.Fig. 4

Bottom Line: 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.This novel method of HDSS implementation demonstrates the feasibility of integrating electronic tools in large-scale health interventions.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Entomology, Wageningen University and Research Centre, Wageningen, The Netherlands. tobias.homan@wur.nl.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus