ArticleBMC medical research methodology2025
Utilizing stratified double response techniques in public health investigations to optimize privacy and efficiency via sensitive data estimation.
Article in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Enhancing Estimation Precision in Sensitive Public Health Data: Implications for Evidence-Based Decision-Making.Journal of epidemiology and global health · 2026Article
- Leveraging randomized response frameworks to enhance sensitive data acquisition: implications for data regarding public health.Scientific reports · 2026Article
- Breaking barriers in health surveillance: privacy-preserving techniques for tracking vaccine hesitancy and disease outbreaks in Pakistan.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
This study examines the complex issue of specifically quantifying sensitive quantitative variables while ensuring respondents' privacy and maintaining data integrity. The present study presents two novel optional stratified double response models (OSDRMs) that integrate simultaneously additive and subtractive frantically mechanisms to improve privacy protection while maintaining efficiency. The models utilize two scrambling variables for each response and employ stratified random sampling (SRS) to divide the population into similar subgroups (strata), ensuring that each subgroup is representative. In every stratum, simple random sampling with replacement (SRSWR) is employed, resulting in more precise estimates. The OSDRMs develop unbiased and viable estimates by assessing the mean as well as the sensitivity level for highly sensitive information, thereby reducing the likelihood of response bias and social desirability effects. The mathematical derivations and comprehensive simulation studies indicate notable enhancements in relative efficiency when contrasted with conventional randomized response methods. We investigated the suggested models in actual scenarios by conducting a cross-sectional survey in the Lahore, Faisalabad, and Sargodha districts of Punjab, Pakistan. The results demonstrate that OSDRMs are an effective, privacy-protecting technique for collecting sensitive data, resulting in improved precision and dependability of data. This strategy is significant for public health management because it promotes ethical data gathering while also improving monitoring and policy formulation in circumstances involving sensitive data.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.