Garbage in, garbage out: Essential steps in the development of phone surveys necessary to avoid poor quality data

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Description of Session
Near ubiquitous access to mobile phones globally has catalyzed discourse on the potential of phone surveys for use in the monitoring of population health. In contrast to resource and time intensive face to face surveys, phone surveys offer respondents the option of being interviewed over a personal or shared mobile phone in the privacy of their own home. Their increasing use, particularly in low and middle income countries (LMICs) where disease burden is highest, has allowed for the rapid, routine, and low cost measurement of population based health outcomes as well as key outcomes among health care providers, including knowledge. Despite their promise, these surveys are often not developed using rigorous pre-testing activities essential for ensuring quality data, including cognitive, reliability, and validity testing. In the absence of these activities, emerging data could over- or under-estimate the burden of disease and/or health care practices under assessment. Further, to improve the standardization of phone survey assessments, research must be undertaken to systematically test the effects of alternative survey modalities on factors influencing cost and key survey metrics including contact, response, completion and refusal rates as well as demographic representativeness. In this panel, we present findings from India on the essential steps required to develop valid and reliable phone surveys for use in measuring a range of health outcomes among women and frontline health workers in India. We start by reviewing methods on cognitive testing to enhance structured surveys, reflecting on the use of scales as response options. We next review efforts to assess the reliability of phone surveys for measuring outcomes among pregnant and postpartum women including knowledge and women’s experiences during pregnancy and childbirth. Finally, we present data on factors influencing response, and completion rates.
Abstract ID :
GDHF42377
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Johns Hopkins School of Public Health
Associate Professor
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University of Cape Town
Assistant Scientist
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Johns Hopkins School of Public Health
Associate
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Johns Hopkins School of Public Health

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