Who Answers the Call? Exploring Response Rates in Men and Women by Age Group Using Paradata from Mobile Phone Surveys Across Nine Low- and Middle-Income Countries
1
CDC Foundation, Atlanta, United States
Popul. Med. 2026;8(Supplement Supplement 1):A900
ABSTRACT
BACKGROUND:
Given the exponential growth in mobile phone usage worldwide (80% of people owned a mobile phone in 2024), accessing hard to reach populations using mobile phone surveys (MPS) has become easier and allows for timely, low-cost data collection. MPS are increasingly used in low- and middle-income countries (LMICs) to collect data on a variety of health issues. However, differential response rates by sex and age are not well understood and can potentially lead to bias and limited utility of data.
METHODS:
This study aims to quantify and analyze differences in MPS response rates among men and women by age group across nine LMICs in South America, Asia, and Africa. The goal is to identify sociodemographic and survey-related factors associated with participation, and to provide evidence-based recommendations to improve participation among underrepresented groups in digital data collection. Data were collected from a series of MPS conducted between 2017-2023 using Surveda, an open-source data collection platform. Surveys were administered via interactive voice response, short message service, mobile web, or mixed modes. Interaction data or paradata, are collected as a separate file alongside primary survey data. These data consist of process indicators generated either by Surveda or through the participant’s interaction with the survey.
RESULTS:
Data from both the primary survey and paradata (interaction data) will be analyzed using chi-square and multivariate analyses to determine the relationship between response rates and variables such as sex, age, education, urban/rural residence, survey mode and day/time of survey.
CONCLUSIONS:
Findings will provide insights into how MPS-related factors can be optimized to address age and sex differences in participation. Understanding how survey timing, mode selection, and demographic factors work together to influence response rates will contribute to a growing body of knowledge on digital health parity and inform best practices for MPS implementation in low-resource environments.
Given the exponential growth in mobile phone usage worldwide (80% of people owned a mobile phone in 2024), accessing hard to reach populations using mobile phone surveys (MPS) has become easier and allows for timely, low-cost data collection. MPS are increasingly used in low- and middle-income countries (LMICs) to collect data on a variety of health issues. However, differential response rates by sex and age are not well understood and can potentially lead to bias and limited utility of data.
METHODS:
This study aims to quantify and analyze differences in MPS response rates among men and women by age group across nine LMICs in South America, Asia, and Africa. The goal is to identify sociodemographic and survey-related factors associated with participation, and to provide evidence-based recommendations to improve participation among underrepresented groups in digital data collection. Data were collected from a series of MPS conducted between 2017-2023 using Surveda, an open-source data collection platform. Surveys were administered via interactive voice response, short message service, mobile web, or mixed modes. Interaction data or paradata, are collected as a separate file alongside primary survey data. These data consist of process indicators generated either by Surveda or through the participant’s interaction with the survey.
RESULTS:
Data from both the primary survey and paradata (interaction data) will be analyzed using chi-square and multivariate analyses to determine the relationship between response rates and variables such as sex, age, education, urban/rural residence, survey mode and day/time of survey.
CONCLUSIONS:
Findings will provide insights into how MPS-related factors can be optimized to address age and sex differences in participation. Understanding how survey timing, mode selection, and demographic factors work together to influence response rates will contribute to a growing body of knowledge on digital health parity and inform best practices for MPS implementation in low-resource environments.
| eISSN: | 2654-1459 |
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