Geographically skewed recruitment and COVID-19 seroprevalence estimates: a cross-sectional serosurveillance study and mathematical modelling analysis

Abstract

ObjectivesConvenience sampling is an imperfect but important tool for seroprevalence studies. For COVID-19, local geographic variation in cases or vaccination can confound studies that rely on the geographically skewed recruitment inherent to convenience sampling. The objectives of this study were: (1) quantifying how geographically skewed recruitment influences SARS-CoV-2 seroprevalence estimates obtained via convenience sampling and (2) developing new methods that employ Global Positioning System (GPS)-derived foot traffic data to measure and minimise bias and uncertainty due to geographically skewed recruitment.DesignWe used data from a local convenience-sampled seroprevalence study to map the geographic distribution of study participants’ reported home locations and compared this to the geographic distribution of reported COVID-19 cases across the study catchment area. Using a numerical simulation, we quantified bias and uncertainty in SARS-CoV-2 seroprevalence estimates obtained using different geographically skewed recruitment scenarios. We employed GPS-derived foot traffic data to estimate the geographic distribution of participants for different recruitment locations and used this data to identify recruitment locations that minimise bias and uncertainty in resulting seroprevalence estimates.ResultsThe geographic distribution of participants in convenience-sampled seroprevalence surveys can be strongly skewed towards individuals living near the study recruitment location. Uncertainty in seroprevalence estimates increased when neighbourhoods with higher disease burden or larger populations were undersampled. Failure to account for undersampling or oversampling across neighbourhoods also resulted in biased seroprevalence estimates. GPS-derived foot traffic data correlated with the geographic distribution of serosurveillance study participants.ConclusionsLocal geographic variation in seropositivity is an important concern in SARS-CoV-2 serosurveillance studies that rely on geographically skewed recruitment strategies. Using GPS-derived foot traffic data to select recruitment sites and recording participants’ home locations can improve study design and interpretation.

Who is missed in a community-based survey: Assessment and implications of biases due to incomplete sampling frame in a community-based serosurvey, Choma and Ndola Districts, Zambia, 2022

“…However, the extent to which these different convenience sampling approaches result in biased estimates of seroprevalence still needs to be determined, requiring careful study design and characterization [ 8 ]. Immunity estimates derived from convenience samples can diverge from those derived from population-based surveys due to differences in disease severity or exposure levels [ 14 , 15 ], participation rates [ 16 , 17 ], location of residence [ 18 ], and sociodemographic characteristics [ 19 ]. On the contrary, some studies have found little variation in estimates between convenience and population-representative samples [ 20 , 21 ], or that the amount of bias introduced due to sampling bias can vary by outcome of interest [ 22 , 23 ].…”