JBI Critical Appraisal Tool for Analytical Cross-Sectional Studies

What it is

The JBI Critical Appraisal Tool for Analytical Cross-Sectional Studies is a risk-of-bias / critical-appraisal instrument maintained by JBI (formerly the Joanna Briggs Institute) as part of the JBI Manual for Evidence Synthesis. It is one of a family of design-specific JBI tools (cohort, case-control, case series, prevalence, qualitative, RCT, quasi-experimental). Its purpose is to let two independent appraisers judge, item by item, whether the internal validity of a single analytical cross-sectional study can be trusted before that study is included in or weighted within a systematic review, meta-analysis, or HTA evidence base. The original eight-item tool was operationalised by Moola and colleagues (the JBI etiology/association approach); JBI released a revised tool in 2025/2026 (Barker et al.) that reframes the items explicitly as risk-of-bias domains with response options Yes / No / Unclear / Not applicable and per-item guidance. It is a judgment instrument — it tells you how much to believe a study, not how to write one up.

When to use

Reach for this tool when you are appraising (not authoring) an analytical cross-sectional study — one that examines an association between an exposure and an outcome measured at the same point in time (e.g., a prevalence-of-disease study that also estimates exposure-outcome odds ratios). The typical decision contexts are: a systematic review or meta-analysis of observational evidence where each included study needs a documented risk-of-bias assessment; an HTA/payer evidence synthesis that must grade the certainty of the included real-world studies (often feeding a GRADE assessment); and peer-reviewed methods reporting where reviewers expect a transparent appraisal table. Decision rules for picking the right member of the family: if the included study merely describes a prevalence or proportion with no exposure-outcome contrast, use the JBI Prevalence tool instead; if subjects are followed over time, use JBI Cohort; if cases and controls are sampled on outcome, use JBI Case-Control. And critically: if your task is to report your own cross-sectional study, this is the wrong document — use the reporting guideline STROBE (cross-sectional) instead.

What it requires

The tool enforces appraisal across eight substantive domains, each answered with evidence from the paper:

1. clearly defined inclusion/eligibility criteria for the sample;
2. detailed description of study subjects and setting (the source population and sampling frame);
3. valid and reliable measurement of the exposure — in RWD terms, the phenotype/algorithm used to define exposure;
4. objective, standardised criteria for measuring the condition/outcome — the outcome algorithm and its validation;
5. explicit identification of confounding factors;
6. explicit strategies to deal with confounding (restriction, matching, adjustment, weighting);
7. valid and reliable measurement of outcomes; and
8. appropriate statistical analysis. For real-world data the load-bearing items are 3, 4, 5, 6 and 7: misclassification of exposure or outcome from imperfect claims/EHR algorithms, and uncontrolled confounding, are exactly where cross-sectional RWD studies fail. The revised (Barker) tool pushes appraisers to reason about the direction and magnitude of bias each domain introduces rather than ticking a box.

When NOT to use — limitations and common misapplications

• It is a risk-of-bias instrument, not a reporting checklist. Using it to structure how you write a cross-sectional study is a category error; STROBE-CSS is the reporting tool. Conversely, completing STROBE does not appraise a study — it only documents it.
• Do not convert the items into a numeric quality score and threshold (e.g., "include if ≥6/8"). JBI explicitly advises against summing items into a cut-off; doing so masks which specific domain is fatally biased and gives spurious precision. Report each domain's judgment.
• Wrong tool in the family. Routing a descriptive prevalence study, a cohort, or a case-control study through the analytical cross-sectional tool (or vice versa) is a frequent reviewer error.
• Passing the checklist does not make the study causal. A cross-sectional design measures exposure and outcome simultaneously, so temporality is unestablished — reverse causation and prevalence-incidence (Neyman) bias survive a clean appraisal. A "low risk of bias" rating is a statement about execution, not about whether the design can support a causal claim.
• Appraisal-as-theater. A single appraiser filling boxes without dual independent review, adjudication of disagreements, and a narrative on bias direction defeats the purpose.

How it maps to this catalog

This guideline tells a reviewer what to interrogate; the following concepts tell them how each requirement is operationalised in RWD:

• Item 1–2 (eligibility, sampling frame) → `cross-sectional`, `descriptive-epidemiology-rwe`, `selection-bias-sensitivity-analysis-rwe`.
• Item 3 (exposure measurement validity) → `diagnosis-phenotype-algorithm-1ip-2op-time-window-rwe`, `claims-analysis`, `ehr-study`.
• Items 4 & 7 (outcome/condition measurement validity) → `claims-outcome-algorithm-ppv-sensitivity-rwe`, `misclassification-bias-correction-rwe`.
• Items 5–6 (identifying and handling confounding) → `dags-backdoor-criterion-drug-studies`, `quantitative-bias-analysis-toolkit-rwe`.
• Item 8 (appropriate analysis of a prevalence/association estimate) → `prevalence-point-period-annual-rwe`.

Applied note (claims/EHR/registry RWE)

For a claims- or EHR-based analytical cross-sectional study, the appraisal must look behind every "valid measurement" claim: an exposure defined by a single diagnosis code has low positive predictive value (weakening item 3); an outcome captured only when a patient happens to have an encounter introduces differential ascertainment (item 4/7); and because exposure and outcome are read from the same cross-section of the data, you cannot tell which came first — so even a tool-clean study should be downgraded for temporality and for unmeasured confounding when grading certainty for an HTA dossier.

Citations

1. [1]Moola S, Munn Z, Sears K, et al. Conducting systematic reviews of association (etiology): The Joanna Briggs Institute's approach. International Journal of Evidence-Based Healthcare. 2015;13(3):163-169.
2. [2]Barker TH, Hasanoff S, Aromataris E, et al. The revised JBI critical appraisal tool for the assessment of risk of bias for analytical cross-sectional studies. JBI Evidence Synthesis. 2025;24(3):401-408.

1. [3]Moola S, Munn Z, Tufanaru C, et al. Chapter 7: Systematic reviews of etiology and risk. In: Aromataris E, Munn Z (eds). JBI Manual for Evidence Synthesis. JBI; 2020.