(Re)search Filter Bubble Effect—An Issue Still Unfairly Neglected

Dear Editor:

In their recent article, Biswas et al. (1) systematically assessed the double burden of malnutrition (coexistence of underweight and overweight individuals) in South and Southeast Asian women. In their study, a search of scientific literature databases was performed (such as PubMed, EMBASE, CINAHL, and Google Scholar), alongside gray literature sources using the Google search engine. The authors in this systematic review and meta-analysis applied strict inclusion and exclusion criteria, endeavouring to maintain robust data extraction and quality assessment. However, the results may have been influenced by the (re)search bubble effect (2–4). In other words, using the Google search engine may have influenced study results due to underlying, highly personalized algorithms that are an integral part of its search strategy, common to all “nonscientific” Internet search engines (2–5). This may be even of greater importance here as data targeted by the authors are related to a specific geographic area and due to the extremely heterogeneous nature of identified studies, in study design, data-collection processes, and outcomes measured and reported (1, 2, 6).

Generally, there is a growing need for reliable and robust evidence synthesis corresponding to the growing expansion of scientific literature (7). Among many different types of evidence synthesis research, the systematic review family (including systematic reviews and meta-analyses) hold a prominent place in the hierarchy of evidence. These have a high impact in ideal evidence-based decision making (6, 7). Consequently, any bias introduced at this level may have a substantial impact on scientific inquiry and decision making (2, 7). All review studies follow some system of inquiry, but systematic reviews apply structured, comprehensive procedures that are designed to minimize bias (6, 8). Such reviews of scientific literature should use rigorous, transparent, and reproducible methods for identifying, screening, critically appraising, summarizing, and synthetizing all relevant, available evidence (6–8). However, such reviews are not well suited for a broad field of inquiry with heterogeneous sources of evidence (6). Gray literature, even though a valuable source of evidence, still offers substantial ambiguity regarding filtering search results in a rigorous and reliable way, while lacking widely recognized standards and guidelines (2, 6, 9, 10). As previously argued, even though Internet search engines are and could be useful in evidence synthesis research, there is a great need to actively address and tackle biases that follow their application in systematic reviews and meta-analyses (2, 3, 5, 10). One place to start is to respect rigorous reporting and the principle of transparency (2).

Notes

Both authors read, edited, and approved the final manuscript.

The authors did not receive any funding related to preparation of this manuscript.

Author disclosures: MĆ has received lecture honoraria from Lundbeck, Sandoz, Janssen, and Alkaloid. AK reports no conflicts of interest.