Enhancing Estimation Precision in Sensitive Public Health Data: Implications for Evidence-Based Decision-Making

Reliable estimation of sensitive public health outcomes, such as underreported COVID-19 cases and vaccine hesitancy, is frequently hindered by social stigma and nonresponse bias, particularly in culturally sensitive contexts like Pakistan. Traditional surveys often underestimate prevalence, limiting the effectiveness of public health interventions.

Multi-stage stratified paired response frameworks (MSPRFs) incorporating two and three-stage randomization with dual scrambling mechanisms under stratified simple random sampling were applied. A cross-sectional survey of 1,200 participants (600 urban, 600 rural) from Faisalabad, Lahore, Multan, and Rawalpindi (June-August 2021) was conducted using anonymous questionnaires to collect data on outbreak cases and vaccine hesitancy.

The MSPRFs demonstrated substantially enhanced estimation efficiency (PRE 290-399%) relative to conventional methods. MSPRF-I estimated COVID-19 prevalence at 16.8% (urban) and 20.8% (rural), compared with reported rates of 12.1% and 13.6%; MSPRF-II estimated 16.8% and 19.2% versus reported 12.6% and 14.6%. Vaccine hesitancy was estimated at 33.8% (MSPRF-I) and 29.5% (MSPRF-II), relative to 24.3% reported. Urban respondents exhibited higher reporting sensitivity ([Formula: see text]) than rural respondents ([Formula: see text]), indicating geographic disparities in disclosure behavior.

MSPRFs provide a robust, privacy-preserving methodology for accurately estimating underreported COVID-19 cases and vaccine hesitancy, supporting evidence-based public health decision-making. MSPRF-II offers marginally higher adjustment efficiency due to inclusion of a truthful-response component. These frameworks are appropriate for sensitive epidemiological surveys in contexts where conventional self-reporting underestimates prevalence.