ArticleBMC medical research methodology2024
Human biomonitoring without in-person interaction: public health engagements during the COVID-19 pandemic and future implications.
Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundPublic health initiatives, including human biomonitoring, have been impacted by unique challenges since the onset of the COVID-19 pandemic, compounding a decades-long trend of declining public participation. To combat low public participation rates, public health professionals often employ extensive engagement approaches including in-person interactions related to enrollment and sampling, success of which is an essential component of a statistically defensible study. The onset of the COVID-19 pandemic challenged public health programs to diversify engagement and sampling approaches, limiting direct interactions for the health and safety of the population. This study explores biomonitoring recruitment strategies through non-contact mechanisms and evaluate the application feasibility for population-based studies.
methodsThe Iowa Biomonitoring Program at the State Hygienic Laboratory developed a human biomonitoring study that utilized a multifaceted, distance-based approach. Traditional techniques, such as mailed recruitment invitations and phone-based discussions, were coupled with internet-based surveys and self-collected, shipped urine and water samples. Participation rates were evaluated by employing different mailing methods, and the demographics of enrolled participants were examined.
resultsThis non-human contact approach achieved a nearly 14% participation rate among a rural population, well above our target rates. Our improved mailing strategy for targeting initially unresponsive participants yielded a significantly increase in the participation rates. The respondents were predominantly individuals with educational attainment of at least high school level. Among all the eligible participants, 83% submitted self-collected samples, a rate comparable to the National Health and Nutrition Examination Survey which involved in-person interviews.
conclusionsThe practice of engaging a rural population during the COVID-19 pandemic by transitioning from face-to-face interactions to a combination of mailing and internet-based approaches resulted in higher-than-expected participant recruitment and sample collection rates. Given the declining trend in the response rates for population-based survey studies, our results suggest conducting human biomonitoring without direct human interaction is feasible, which provides further opportunity to improve response rates and the relevance and reach of public health initiatives.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.