Incentives to Boost Adult COVID-19 Vaccination in Rural Bangladesh

Project Background

Bangladesh rapidly rolled out free COVID-19 vaccines nationwide, but uptake lags for herd immunity—especially rural/low-income with 33-40%+ hesitancy (higher in older adults, women, poor, Muslims, city corps)—due to rumors, misinformation, efficacy/side-effect fears; contrasts with high childhood vaccination.
ADB/APVAX funds large drives; this RCT identifies cost-effective packages (information, financial incentives like cash/lottery, non-financial access/logistics) for 12,000 adults, yielding lessons for Asia.

Project Scope & Reach

600 locations across districts (rural/urban, agro-climatic/socio-economic zones); 12,000 adults (18+), one per household, nationally representative via Bangladesh Bureau of Statistics (BBS) HIES sampling frame.

Key research partners

Monash University; University of Dhaka; Cornell University; Hong Kong University of Science and Technology; IIT Kanpur (Leads); Global Development and Research Initiative (GDRI, local); Institute of Epidemiology, Disease Control and Research (IEDCR), Govt of Bangladesh.

Funding & Support

Asian Development Bank (ADB) via Asia Pacific Vaccine Access Facility (APVAX) supporting large-scale vaccination drives.

Roles of GDRI

Capacity Development and Intervention Design: Adapted financial (cash BDT 500, 1% lottery BDT 50,000) and non-financial (access support) incentives to public health/community context; designed RCT (BBS frame sampling, stratified randomization to 3 arms + control), survey tools for hesitancy/beliefs/uptake, ethical/IRB procedures, ANZCTR protocol registration.
Field Implementation and Community Engagement: Coordinated with district admins/local leaders for entry/trust/surveys; recruited/trained teams for baseline/endline/follow-up surveys + lab-in-field games (risk/time preferences, pro-sociality); oversaw arms vs info-only control.
Data Management, Cleaning, and Analysis: Handled quantitative data (baselines, attitudes, on-site vaccination card verification); rigorous cleaning for attrition (IPW/Lee bounds), social desirability, multiple testing (Westfall-Young/q-values); collaborated on ITT effects, ML heterogeneity (LASSO/causal forests) for policy findings.

Key stats/details beefed up without bloating. Perfect now?