Commentary

Introduction
Public health outcomes are shaped by determinants operating simultaneously at multiple levels, including the individual, family, community, and national policy levels [1]. Researchers often analyze data from complex surveys, such as Demographic and Health Surveys (DHS) or WHO STEPS surveillance, which are inherently hierarchical. Traditional regression methods, such as ordinary least squares or logistic regression, assume that observations are independent. Individuals within the same group commonly share exposures and environments, producing correlated errors; ignoring this correlation can inflate Type I errors and produce unreliable findings [2]. It is therefore important that analytic choices reflect data structure and the scientific question.

Terminology requires clarification. “Multilevel models” and “mixed‑effects models” share the same mathematical core (fixed and random effects), though the terms arise from different research traditions and are sometimes used with subtle differences [3]. In public health, researchers use “fixed effects” to refer to population‑level parameters and “random effects” to represent cluster‑specific deviations that permit generalization beyond sampled clusters [4]. Importantly, model choice should not be equated with evidence quality: sound study design, measurement validity, bias control, and causal reasoning remain central to generating high‑quality public health evidence [5].

Multilevel models and variance decomposition
Multilevel models explicitly account for nested data structures. A basic two‑level model includes random intercepts for groups, separating group‑level variance from individual‑level variance [6]. The intraclass correlation coefficient (ICC) quantifies the proportion of total variance attributable to group membership and thus provides a practical metric for assessing the importance of clustering. Variance decomposition is a critical contribution of multilevel models because it reveals where variation lies and therefore helps guide policy priorities [7].

Example: If a study shows that most variability in childhood BMI is attributable to neighbourhood-level factors rather than individual behaviours, policy interventions should prioritize community design over interventions targeting individual behaviour change. Conversely, when between‑group variance is negligible (very low ICC), simpler marginal methods may be sufficient [8, 9].

Mixed‑effects models for longitudinal data
Mixed‑effects models extend multilevel frameworks to repeated measures by incorporating random intercepts for baseline differences and random slopes for individual trajectories over time [10]. This flexibility exceeds repeated‑measures ANOVA, which assumes balanced designs and equal measurement intervals. Mixed‑effects models also make efficient use of unbalanced data and can include all available observations under the missing‑at‑random (MAR) assumptions [11]. Literature emphasises, however, that MAR is a strong and fundamentally untestable assumption; researchers should assess patterns of missingness and, where appropriate, conduct sensitivity analyses for missing‑not‑at‑random (MNAR) scenarios [12]. Practical checks include comparing baseline characteristics of completers and non‑completers, documenting reasons for dropout, and using pattern‑mixture or selection models as sensitivity analyses when dropout is plausibly related to outcomes [13].

Cross‑level interactions and contextual effects
Cross‑level interactions test whether group‑level moderators modify individual‑level associations and are readily estimated in multilevel frameworks [14]. For example, while individual income may predict physical activity, this association could weaken in neighborhoods with high walkability; detecting such moderation informs whether structural policies can compensate for individual‑level disadvantages. Without multilevel modelling, contextual modifications like these can remain hidden [15]. These models therefore permit testing whether an intervention’s effect is uniform or environment‑dependent, which is essential when the substantive question is contextual rather than purely individual [16].

Underuse and methodological alternatives
Despite their advantages, multilevel models remain underutilised across public health subfields [17]. That said, multilevel models are not always the optimal choice. When the ICC is very small or when the research aim is marginal (population‑averaged) inference, generalized estimating equations (GEE) or cluster‑robust standard errors provide well‑established alternatives [18]. Fixed‑effects approaches are preferable when the objective is to control for unobserved, time‑invariant cluster characteristics. GEE is particularly useful for marginal effects and robustness to certain misspecifications. Whereas cluster-robust standard errors are a straightforward way to guard against within‑cluster dependence without modelling random effects explicitly [19]. Literature therefore recommends that authors select analytic approaches according to the inferential target: partitioning variance and estimating random effects (multilevel), marginal population averages (GEE), or protection against cluster dependence (cluster‑robust SEs or fixed effects) and justify these choices in the manuscript [20].

Bayesian multilevel models
Bayesian multilevel models provide advantages in contexts with small numbers of groups or complex random‑effects structures because priors can stabilize estimates and posterior distributions yield direct probability statements about parameters [21]. Bayesian methods are especially helpful when fewer than ~30 groups are available or when substantive prior information exists [22]. Nonetheless, researchers have stressed that Bayesian estimation should be selected because it aligns with the inferential problem rather than because it is inherently superior; practical considerations (computational cost, required expertise) and transparency about prior choices should be addressed when adopting Bayesian approaches [23].

Limitations and methodological cautions
Multilevel models assume normally distributed random effects; severe departures from this assumption can bias variance estimates [24]. Sample size considerations are important: many practical recommendations suggest at least ~30 groups for stable random‑intercept estimation, with larger numbers required for reliable random‑slope estimation [25]. Convergence problems often indicate insufficient data for the model’s complexity; when these arise, simplify the model or consider Bayesian shrinkage methods [26]. Crucially, multilevel modelling does not eliminate confounding or selection bias; these remain challenges for causal inference that require robust design or analytic strategies (e.g., randomized trials, instrumental variables, difference‑in‑differences). Therefore, it is important to treat multilevel models as analytic tools within a broader causal and design framework [27].