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Background: Survey data are traditionally collected using pen-and-paper, with double data entry, comparison of entries and reconciliation of discrepancies before data cleaning can commence. We used Personal Digital Assistants (PDAs) for data entry at the point of collection, to save time and enhance the quality of data in a survey of over 21,000 scattered rural households in southern Tanzania.

Methods: Pendragon Forms 4.0 software was used to develop a modular questionnaire designed to record information on household residents, birth histories, child health and health-seeking behaviour. The questionnaire was loaded onto Palm m130 PDAs with 8 Mb RAM. One hundred and twenty interviewers, the vast majority with no more than four years of secondary education and very few with any prior computer experience, were trained to interview using the PDAs. Logical checks were performed and skip patterns taken care of at the time of data entry. Data records could not be edited after leaving each household, to ensure the integrity of the data from each interview. A small group of interviewees from the community, as well as supervisors and interviewers, were asked about their attitudes to the use of PDAs.

Results: Following two weeks of training and piloting, data were collected from 21,600 households (83,346 individuals) over a seven-week period in July-August 2004. No PDA-related problems or data loss were encountered. Fieldwork ended on 26 August 2004, the full dataset was available on a CD within 24 hours and the results of initial analyses were presented to district authorities on 28 August. Data completeness was over 99%. The PDAs were well accepted by both interviewees and interviewers.

Conclusion: The use of PDAs eliminated the usual time-consuming and error-prone process of data entry and validation. PDAs are a promising tool for field research in Africa.

Epi Data and Epi Info are often used together by public health agencies around the world, particularly in developing countries, to meet their needs of low-cost public health data management; however, the current open source data management technology lacks a mobile component to meet the needs of mobile public health data collectors. The goal of this project is to explore the opportunity of filling this gap through developing and trial of a personal digital assistant (PDA) based data collection/entry system. It evaluated whether such a system could increase efficiency and reduce data transcription errors for public surveillance data collection in developing countries represented by Fiji.

120 participants were recruited from the Fiji School of Medicine were randomly assigned to be interviewed by one of six interviewers in one of the two ways: (1) paper-based survey followed by PDA survey and (2) PDA survey followed by paper-based survey. Data quality was measured by error rates (logical range errors/inconsistencies, skip errors, missing values, date or time field errors and incorrect data type). Work flow and cost were evaluated in three stages of the survey process: (1) preparation of data collection instrument, (2) data collection and (3) data entry, validation and cleaning. User acceptance was also evaluated in the two groups of participants: (1) data collectors and (2) survey participants.

None of the errors presented in 20.8% of the paper questionnaires was found in the data set collected using PDA. Sixty-two percent of the participants perceived that the PDA-based questionnaire took less time to complete. Data entry, validation and cleaning for the PDA-based data collection from 120 participants took a total of 1.5 h, a 93.26% reduction of time from 20.5h required using paper and pen. The cost is also significantly reduced with PDA-based protocol. Both data collectors and participants prefer to use PDA instead of paper for data collection. The trial results prove that eSTEPS is a feasible solution for public health surveillance data collection in the field. Several deficiencies of the softwarewere also identified and would be addressed in the next version.

eSTEPS offers the potential to meet the need for an effective mobile public health data collection tool for use in the field. The eSTEPS field trial proves that PDA was more efficient than paper for public health survey data collection. It also significantly reduced errors in data entry. The later benefit was derived from the software providing its users with the flexibility of building their own constraints to control the data type, range and logic of data entry.