title: A comparison of smartphones to paper-based questionnaires for routine influenza sentinel surveillance, Kenya, 2011-2012.
abstract: BACKGROUND
  For disease surveillance, manual data collection using paper-based questionnaires can be time consuming and prone to errors. We introduced smartphone data collection to replace paper-based data collection for an influenza sentinel surveillance system in four hospitals in Kenya. We compared the quality, cost and timeliness of data collection between the smartphone data collection system and the paper-based system.
  
  
  METHODS
  Since 2006, the Kenya Ministry of Health (MoH) with technical support from the Kenya Medical Research Institute/Centers for Disease Control and Prevention (KEMRI/CDC) conducted hospital-based sentinel surveillance for influenza in Kenya. In May 2011, the MOH replaced paper-based collection with an electronic data collection system using Field Adapted Survey Toolkit (FAST) on HTC Touch Pro2 smartphones at four sentinel sites. We compared 880 paper-based questionnaires dated Jan 2010-Jun 2011 and 880 smartphone questionnaires dated May 2011-Jun 2012 from the four surveillance sites. For each site, we compared the quality, cost and timeliness of each data collection system.
  
  
  RESULTS
  Incomplete records were more likely seen in data collected using pen-and-paper compared to data collected using smartphones (adjusted incidence rate ratio (aIRR) 7, 95% CI: 4.4-10.3). Errors and inconsistent answers were also more likely to be seen in data collected using pen-and-paper compared to data collected using smartphones (aIRR: 25, 95% CI: 12.5-51.8). Smartphone data was uploaded into the database in a median time of 7 days while paper-based data took a median of 21 days to be entered (p < 0.01). It cost USD 1,501 (9.4%) more to establish the smartphone data collection system ($17,500) than the pen-and-paper system (USD $15,999). During two years, however, the smartphone data collection system was $3,801 (7%) less expensive to operate ($50,200) when compared to pen-and-paper system ($54,001).
  
  
  CONCLUSIONS
  Compared to paper-based data collection, an electronic data collection system produced fewer incomplete data, fewer errors and inconsistent responses and delivered data faster. Although start-up costs were higher, the overall costs of establishing and running the electronic data collection system were lower compared to paper-based data collection system. Electronic data collection using smartphones has potential to improve timeliness, data integrity and reduce costs.
authors.affiliation: Influenza Program, Centers for Disease Control and Prevention-Kenya, P.O. Box 606, 00621, Village Market, Nairobi, Kenya. vkc7@cdc.gov.
authors.forename: Henry N
authors.initials: HN
authors.lastname: Njuguna
authors.affiliation: Influenza Program, Centers for Disease Control and Prevention-Kenya, P.O. Box 606, 00621, Village Market, Nairobi, Kenya. dcaselton@gmail.com.
authors.forename: Deborah L
authors.initials: DL
authors.lastname: Caselton
authors.affiliation: Kenya Medical Research Institute (KEMRI), Nairobi, Kenya. garunga@kemricdc.org.
authors.forename: Geoffrey O
authors.initials: GO
authors.lastname: Arunga
authors.affiliation: Influenza Program, Centers for Disease Control and Prevention-Kenya, P.O. Box 606, 00621, Village Market, Nairobi, Kenya. uyr9@cdc.gov.
authors.forename: Gideon O
authors.initials: GO
authors.lastname: Emukule
authors.affiliation: Kenya Medical Research Institute (KEMRI), Nairobi, Kenya. dkinyanjui@kemricdc.org.
authors.forename: Dennis K
authors.initials: DK
authors.lastname: Kinyanjui
authors.affiliation: Department of Disease Surveillance and Response (DDSR) Ministry of Health, Nairobi, Kenya. rosaliakalani@yahoo.co.uk.
authors.forename: Rosalia M
authors.initials: RM
authors.lastname: Kalani
authors.affiliation: Center for Surveillance, Epidemiology, and Laboratory Services, Centers for Disease Control, Atlanta, Georgia, USA. mke5@cdc.gov.
authors.forename: Carl
authors.initials: C
authors.lastname: Kinkade
authors.affiliation: Department of Disease Surveillance and Response (DDSR) Ministry of Health, Nairobi, Kenya. epmmuthoka@gmail.com.
authors.forename: Phillip M
authors.initials: PM
authors.lastname: Muthoka
authors.affiliation: Influenza Program, Centers for Disease Control and Prevention-Kenya, P.O. Box 606, 00621, Village Market, Nairobi, Kenya. markakatz@gmail.com.
authors.forename: Mark A
authors.initials: MA
authors.lastname: Katz
authors.affiliation: Influenza Program, Centers for Disease Control and Prevention-Kenya, P.O. Box 606, 00621, Village Market, Nairobi, Kenya. zud9@cdc.gov.
authors.forename: Joshua A
authors.initials: JA
authors.lastname: Mott
mesh_terms: D015982:Bias; D040421:Cell Phone; D002675:Child, Preschool; D003365:Costs and Cost Analysis; D003625:Data Collection / Q000191:economics / Q000379:methods* / Q000592:standards; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007251:Influenza, Human / Q000453:epidemiology*; D007630:Kenya / Q000453:epidemiology; D008297:Male; D018571:Sentinel Surveillance; D045169:Severe Acute Respiratory Syndrome / Q000453:epidemiology*; D011795:Surveys and Questionnaires; D013997:Time Factors; D014956:Writing