General information about the house construction, composition of household members and the presence and use of bed nets as a malaria preventive tool is collected for every house which is newly added to the database and for existing houses once per year. On basis of the geographical coordinates of houses and demographic as well as malaria-related data gathered during the census of July , the study design for the sequence of the rollout of the SolarMal intervention was developed and has been described elsewhere Silkey et al.
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.
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. Navigating assigned houses: converting the up to date population database into a geodatabase displayed with Google Maps Mobile assists fieldworkers with tracking every house.
Data quality is initially controlled by designing questionnaires which permit answers to fall within an acceptable range. After questionnaires have been entered in the field, the data is transferred to the ODK-Aggregate server.
Unique IDs for individuals, houses and households are automatically generated per FW to ensure that no duplicate values are entered in the system. Questionnaires which were not fully completed are not accepted for upload to the server.
All events entered during field visits are checked for inconsistencies during this step. Faulty records are filtered for further checking, and an error report is sent to the data manager by email.
Births or deaths registered with an event date long in the past, multiple new-borns or separate deaths with the same date of event will be double checked with the FW or with the head of household. In addition, doubtful migrations are double checked, for instance if a child of 3 years old was found to be migrated because of marriage or work.
Once in the OpenHDS server, the data manager has access to information about all individuals who have ever been active in the database, as well as their event history. A range of options to detect residual inconsistencies and perform data cleaning are available. An error often found in HDSSs is that individuals or households were duplicated during the census round under a slightly different name with different unique IDs at geographical border areas of FWs.
An option to merge individuals and their past events provides a practical solution to this problem. In addition to this real time data quality control a web-based monitoring system was introduced that allows the data manager and FWM to extract a weekly snapshot of certain fieldwork related matters in the database [ 21 ]. The web interface displays information on where FWs have been in the past week, as well as which household visits are yet to take place. Subsequently, the geographical database converted to KML files are uploaded to tablets at the beginning of every follow up round.
The tool automatically removes individuals and houses which have already been visited during a given round of surveillance from the visit plan, publishing a file with remaining houses to be visited that can be uploaded to the computer tablets.
Furthermore, the tool can be used to produce graphs of how many individual and houses were visited and how many forms were filled in during the previous week, allowing the performance of fieldworkers to be tracked. The tool gives the opportunity to see where FWs have been, how long they have taken to conduct the work delivered, as well as which forms have been filled in and how often. Additionally, on a weekly basis the tool generates 20 houses on basis of the houses already visited, to be revisited by the FWM.
During re-visits, the usual procedure of demographic questionnaires is conducted and discrepancies between the results obtained by the FWM and FW are discussed with the FW in question.
Finally, all data of the HDSS, as well as entomological, parasitological, geographical and sociological data are fed into a MySQL relational database ready to be analysed. All data are linked through the unique individual, house or household IDs, making extraction of spatial and temporal data a mere case of entering the desired query into MySQL.
Nightly backups of the databases are automatically copied to a network-attached storage system The local server is a highly secured drive located at the field station icipe. All participants are provided with information regarding the project outline, the ongoing HDSS procedures, the implementation of the intervention, and the collection and use of blood samples.
Adults, mature minors and caregivers of children provided written informed consent in the local language agreeing to participation in the SolarMal project. We describe a data collection and management platform which advances the electronic systems employed in HDSSs in developing countries a step further mainly by integrating mobile-device based data collection with a centralized real-time data system.
This integration is one of the important improved aspects within the described HDSS, resulting in organizational and scientific advantages. HDSS sites often rely on paper-based conducting of questionnaires before the data is entered into a digital database [ 7 , 9 , 10 , 22 , 23 ].
The Android operating system is used on powerful tablet computers, allowing us to develop or deploy the desired software. In combination with the freely available mobile data collection software, ODK-Collect and OpenHDS mobile, collecting data on paper is set to become obsolete. This not only saves time because data can be entered by merely navigating through the digitalized form, and the process of double-entry of paper questionnaires into a digital format is no longer necessary.
Fewer field workers and staff are required to perform the same job as before. Besides the cost-effectiveness on the basis of reduced staffing, the use of stationery is reduced to a minimum amount. Fieldworkers are provided with computer tablets, tablet protection covers and a paper notebook for occasional notes. Stationary in the office is reduced to a flip board to manage discussions, and some paper notebooks and pencils.
All data collection and management is fully digital. Thus where traditional paper based HDSSs would approximately use one A4 for updates on household information and one A4 for individual health information, a digitalized data collection with 25, people and houses would save over 30, A4 papers per survey.
In the last 5 years there are sites where HDSSs have migrated from paper-based to some sort of digitalized entering system [ 8 , 24 — 27 ]. However, none of these sites have linked data collection software in the field directly to a real-time database. At the moment of writing, there is at least one other collection system using computer technology to integrate collection, management and database utilities; the LINKS system is in some ways similar to the system described in this paper [ 28 ].
It is an easy implementable, cost reducing and efficient platform, however, the concept of a near real time database and its advantages seems not to be exploited. Furthermore, there are examples of health data collection systems where PDAs and telephones are used, which is considerably more efficient than the paper based surveillances.
However, they show major limitations in terms of user-friendliness and scalability [ 29 , 30 ]. This is mostly caused by the obsolescence and limited compatibility of software and hardware used. Making use of the latest openly available technology, data collection in the field enables researchers and field workers to be time efficient, resulting in cost reductions and organizational efficacy.
There are also examples of HDSS sites where a different data management system is developed relying on paper or non-paper based data collection [ 7 , 9 , 24 ]. The data collection system described in this paper has several advantages compared to the HRS in terms of organizational efficiency [ 31 ]: Firstly, traditional cleaning of data accumulating to an entity like an individual or household is largely removed.
As the OpenHDS mobile application is a copy of the aggregated longitudinal database, in the application interface, adding data is only possible after selecting an existing entity.
The constant uploading of collected data to the OpenHDS server and the synchronization of the database to the tablets makes reliable continuity of the data achievable. Secondly, the entire process of creating an electronic questionnaire, up to viewing the collected data in a server, is a manageable, time efficient task for any scientist once basic training has been provided. The XLS-Form authoring tool allows also non-computer scientists to create a questionnaire with the option to apply the preferred constraints.
Concepts in questionnaires such as skip logic, input constraints, structured data model and an entry concept from the start, which the HRSs lack [ 31 ], have in our project let to only few forms of mistakes and errors that were relatively easy to detect. In a sample of our data we detected some incorrectly entered dates of birth and names, however in the following visit this personal data is always checked and corrected appropriately.
The number of corrected mistakes in demographic data after one data collection round was never more than one percent. All questionnaires related to the core demographic data collection are standardized and configured to OpenHDS mobile.
Thirdly, translating the real time database into a geographical database is a convenient way to assist FWs in real-time navigating their area of data collection. Demographic or disease-related data can be linked to a house location with its coordinate using the free Google Earth software. Tapping a house location on the device shows all the available household information.
This combination of real time GPS navigation and fixed visiting points in space enables the FW to invest a minimal amount of effort in locating households at the study site. In this way fieldworkers of the HDSS manage to visit an average of approximately 15 houses and 40 people per day. The visiting of houses without a digital navigation platform can leave room for suboptimal walking routes. Finally, after data collection has finished and data content has been cleaned, records can immediately be used to guide other parts of the project that rely on data collection structure of OpenHDS.
Also, where the analysis of data in current HDSSs can only commence after it is manually entered and cleaned, this system allows one to have a dataset ready for analysis shortly after collection.
Data cleaning is performed on a daily basis and, with roughly data entries per day the data manager usually finishes routine cleaning in less than 2 h. Manually entering great amounts of questionnaires and post hoc cleaning of entered data can take many more hours even if every single questionnaire is digitally entered and cleaned in 1 min.
One aspect of this particular HDSS is the facilitation of healthy team cohesion. Fig 4 of the L doc from ST says it can get to within 0. A 6V output into ohm is 0. Bug Report - Free download as Text File. This drive comes in an in-built fingerprint reader, letting you secure its data with your fingerprints. Note To upgrade DirectX further, you will need to upgrade your operating system.
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