Identifying Gaps in Mobile Data Collection by Frontline Health Workers in Bangladesh

Background: Mobile health (mHealth) tools are replacing paper-based surveys for frontline health workers, promising speed and cost-effectiveness. Yet, in low- and middle-income settings, there is scope for research on the accuracy of the information captured. Objectives: To assess the quality of socio-demographic and economic data collected through the mHealth platform by BRAC (the largest non-profit in Bangladesh) Shasthya Kormis or SKs (frontline health workers), and to identify reasons for data gaps. Methods: A mixed-methods study (2021) analyzed secondary mHealth records for 388 households drawn via two-stage cluster sampling from the catchment areas of 30 randomly selected SKs working across 61 districts. Descriptive statistics in R quantified missing values and irregular entries in household registration, visits, and member forms. Complementary insights were obtained from 24 in-depth interviews with SKs; transcripts were thematically coded using an iteratively refined codebook. Findings: Core demographic variables were largely complete, but considerable gaps persisted: national ID/birth ID (84% missing), phone numbers (77%), household assets (39-70%), and land-size data. Several explanations were deduced: reluctance of community members to share sensitive information, sometimes to secure social benefits; recall or estimation difficulties for ages and land measurements; and operational barriers: poor connectivity, offline notetaking, and syncing errors that deterred submission in a timely manner. Conclusions: While mHealth simplifies nationwide community data collection in this case, data quality is affected by social hesitancy, recall bias, and technical issues.