Could confounding factors like healthcare-seeking behavior explain the study's results?
Executive summary
Confounding by healthcare‑seeking behavior is a plausible alternative explanation for an observed association between online health information seeking (OHIS) and higher clinic visit frequency: the JMIR cross‑sectional study found OHIS significantly increases clinic visit frequency after controlling for some variables [1]. Multiple reviews and meta‑analyses identify trust, access, sociodemographic factors and unobserved heterogeneity as strong determinants of healthcare‑seeking, any of which can confound estimates if not fully adjusted for [2] [3] [4] [5].
1. Why healthcare‑seeking behavior is a textbook confounder
Healthcare‑seeking behavior is both a driver of online information use and of service utilization: people more inclined to seek care look up health information online and also visit clinics more often, so an observed OHIS–visit link can reflect this common cause rather than a causal effect (frameworks identifying determinants of healthcare‑seeking list information access, trust, and sociodemographics as antecedents) [3] [2]. The JMIR paper acknowledges complex pathways — OHIS affects utilization directly and indirectly through online information perception — but a cross‑sectional design cannot fully separate preexisting propensity to seek care from OHIS effects [1].
2. What the JMIR study did and what it left vulnerable to confounding
The JMIR cross‑sectional analysis reports that OHIS “significantly increases the frequency of clinic visits after controlling for other variables,” but it is based on observational, cross‑sectional data where residual confounding and reverse causation remain risks [1]. The paper controls for some covariates and models mediation via online information perception, yet available sources do not detail whether it adjusted for deeper determinants identified in meta‑analyses — such as institutional trust, social capital, prior healthcare use patterns, or unobserved heterogeneity — which are documented drivers of care seeking [1] [2] [3].
3. Evidence that unobserved traits matter: trust and heterogeneity
Research shows trust in institutions and interpersonal networks shapes whether people use formal medical systems, and trust varies by age, politics and social context — factors that also predict online behavior and clinic visits [6] [4]. Studies that explicitly model unobserved heterogeneity find online health seeking’s relationship with healthcare access can change once latent traits are accounted for, underscoring confounding risk in simpler regressions [5].
4. Competing findings in the literature — direction matters
Not all studies point the same way: while JMIR finds OHIS linked to more visits after adjustment, other analyses report that increased mobile internet use reduced inclination to seek primary care in a Chinese national analysis cited within the JMIR article [1]. Systematic reviews also document mixed outcomes of online information on care seeking across populations and settings [3]. Those divergent findings signal either heterogeneity of causal effects or differing degrees of confounding across studies.
5. Practical markers that indicate residual confounding in a study
Look for the following red flags in the original study: cross‑sectional design vs longitudinal or instrumental approaches; incomplete adjustment for prior utilization, trust, social capital, and sociodemographic predictors; failure to model unobserved heterogeneity; and inconsistent subgroup effects. Meta‑analytic work on determinants of healthcare‑seeking emphasizes these variables as important modifiers and confounders [2] [3].
6. How stronger designs address the problem
Analyses that reduce confounding use longitudinal data, instrumental variables, recursive models for unobserved heterogeneity, or randomized interventions altering information exposure. The SHARE‑based study modeled e‑HISB, healthcare access and health with recursive methods to account for unobserved heterogeneity as an example of a more robust approach [5]. The JMIR cross‑sectional paper does not employ these stronger causal tools in the sources provided [1].
7. Bottom line for interpreting results and policymaking
Available sources show OHIS is associated with clinic use, but competing evidence and well‑documented drivers of care seeking mean confounding by underlying healthcare‑seeking propensity is a credible alternative explanation [1] [2] [3] [4] [5]. Policymakers and readers should treat cross‑sectional associations as hypothesis‑generating, demand analyses that adjust for trust, prior use and unobserved heterogeneity, and prioritize longitudinal or quasi‑experimental evidence before inferring that promoting OHIS will necessarily raise clinic visits [1] [5].