ArticleJournal of acquired immune deficiency syndromes (1999)2014
Using information and communications technology in a national population-based survey: the Kenya AIDS Indicator Survey 2012.
Article in Journal of acquired immune deficiency syndromes (1999), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data Quality and Cost-effectiveness Analyses of Electronic and Paper-Based Interviewer-Administered Public Health Surveys: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Data Architecture to Support Real-Time Data Analytics for the Population-Based HIV Impact Assessments.Journal of acquired immune deficiency syndromes (1999) · 2021Article
- Improving Information and Communications Technology (ICT) Knowledge and Skills to Develop Health Research Capacity in Kenya.Online journal of public health informatics · 2019Article
- Finding Hidden HIV Clusters to Support Geographic-Oriented HIV Interventions in Kenya.Journal of acquired immune deficiency syndromes (1999) · 2018Article
- Mobile Device-Based Electronic Data Capture System Used in a Clinical Randomized Controlled Trial: Advantages and Challenges.Journal of medical Internet research · 2017Article
- Review of Developments in Electronic, Clinical Data Collection, and Documentation Systems over the Last Decade - Are We Ready for Big Data in Routine Health Care?Frontiers in oncology · 2016Review
- Kenya AIDS Indicator Surveys 2007 and 2012: implications for public health policies for HIV prevention and treatment.Journal of acquired immune deficiency syndromes (1999) · 2014Review
- Status of voluntary medical male circumcision in Kenya: findings from 2 nationally representative surveys in Kenya, 2007 and 2012.Journal of acquired immune deficiency syndromes (1999) · 2014Article
- The Kenya AIDS Indicator Survey 2012: rationale, methods, description of participants, and response rates.Journal of acquired immune deficiency syndromes (1999) · 2014Article
- Use of Computer-Assisted Personal Interviewing and Information Management System in a Survey among HIV High-Risk Groups in India: Strengths, Weaknesses, Opportunities, and Threats Analysis.Indian journal of community medicine : official publication of Indian Association of Preventive & Social MedicineArticle
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
backgroundWith improvements in technology, electronic data capture (EDC) for large surveys is feasible. EDC offers benefits over traditional paper-based data collection, including more accurate data, greater completeness of data, and decreased data cleaning burden.
methodsThe second Kenya AIDS Indicator Survey (KAIS 2012) was a population-based survey of persons aged 18 months to 64 years. A software application was designed to capture the interview, specimen collection, and home-based testing and counseling data. The application included: interview translations for local languages; options for single, multiple, and fill-in responses; and automated participant eligibility determination. Data quality checks were programmed to automate skip patterns and prohibit outlier responses. A data sharing architecture was developed to transmit the data in real-time from the field to a central server over a virtual private network.
resultsKAIS 2012 was conducted between October 2012 and February 2013. Overall, 68,202 records for the interviews, specimen collection, and home-based testing and counseling were entered into the application. Challenges arose during implementation, including poor connectivity and a systems malfunction that created duplicate records, which prevented timely data transmission to the central server. Data cleaning was minimal given the data quality control measures.
conclusionsKAIS 2012 demonstrated the feasibility of using EDC in a population-based survey. The benefits of EDC were apparent in data quality and minimal time needed for data cleaning. Several important lessons were learned, such as the time and monetary investment required before survey implementation, the importance of continuous application testing, and contingency plans for data transmission due to connectivity challenges.
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