STROBE for Cross-Sectional Studies

What it is

— STROBE for Cross-Sectional Studies (STROBE-CS) is not a separate statement but the cross-sectional design variant of the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) statement (von Elm, Vandenbroucke et al., 2007). The single STROBE statement covers the three core analytical observational designs — cohort, case-control, and cross-sectional — in 22 items spanning title/abstract, introduction, methods, results, and discussion; the STROBE Initiative and the EQUATOR Network then publish design-specific checklists so that the items whose wording differs by design (notably item 6 on participant eligibility/selection, item 12 on statistical methods, and item 13 on the participant flow) are stated in cross-sectional terms. STROBE-CS is therefore the checklist you hand to authors, peer reviewers, and editors when the study estimates prevalence, associations, or diagnostic/screening quantities from data captured at a single point in time rather than followed forward (cohort) or sampled on outcome status (case-control). Its purpose is reporting transparency — to make the design, setting, eligibility, variables, statistical handling, and limitations legible enough that a reader can judge validity and a methodologist could in principle reproduce the analysis. It is maintained as a public, freely downloadable checklist within EQUATOR.

When to use

— Use STROBE-CS whenever you write, review, or register the report of a cross-sectional observational study: a prevalence survey, a single-timepoint association study, a serosurvey, a cross-sectional analysis of a registry or claims snapshot, or a cross-sectional baseline analysis carved out of a larger cohort. It is the right checklist for a peer-reviewed journal submission, for the descriptive or prevalence component of an HTA/payer dossier, and as the reporting backbone of a cross-sectional PASS or non-interventional study report. Decision rule for choosing the correct family member: use the cohort STROBE checklist when participants are followed forward from an exposure/time-zero to an incident outcome; the case-control checklist when sampling is conditioned on outcome status; and STROBE-CS when exposure and outcome are ascertained together at one point or window with no forward follow-up. Critically, if the cross-sectional study is built on routinely collected health data (claims, EHR, disease/device registries), STROBE-CS alone is not enough — add RECORD (RECORD-PE for pharmacoepidemiology), which extends STROBE with the database-specific items (code lists, data-cleaning, linkage, validation) that real-world data demand. For a prospective protocol of an RWD study use HARPER or the ENCePP checklist, not a reporting checklist. STROBE-CS governs the report of a completed cross-sectional study; it is not a planning instrument.

What it requires

— STROBE-CS enforces the substantive reporting domains that make a cross-sectional analysis interpretable: an informative, design-labeled title/abstract (item 1 — "cross-sectional" stated explicitly); rationale and pre-specified objectives/hypotheses (items 2–3); a transparent design, setting, and recruitment window (items 4–5); eligibility criteria and the sampling/selection method with the source population defined (item 6 — the cross-sectional phrasing emphasizes how participants were selected at the single timepoint); precise operational definitions of every exposure, outcome, predictor, confounder, and effect modifier, with data sources and measurement methods and comparability across groups (items 7–8); explicit handling of bias, study size, and quantitative variables (items 9–11); a complete statistical-methods section covering confounding control, subgroups/interactions, missing-data handling, sampling-strategy accounting, and sensitivity analyses (item 12); a participant flow with numbers at each stage and reasons for non-participation (item 13); descriptive, outcome, and main results with adjusted estimates, confidence intervals, and the analytic denominator (items 14–16); a key-results summary, limitations (direction and magnitude of potential bias), generalizability, and funding (items 18–22). For real-world cross-sectional data these generic items carry specific weight: data-source fitness-for-use and the snapshot's coverage window must be reported under design/setting; phenotype/algorithm definitions for prevalent conditions (e.g., a 1-inpatient/2-outpatient claims rule) and any validation belong under variables/measurement; and because there is no forward follow-up, the report must be explicit that the design supports prevalence and association, not incidence or causal temporality, and must surface missingness and selection at the snapshot.

When NOT to use — limitations and common misapplications

— STROBE-CS is a reporting checklist, not a risk-of-bias instrument, not a quality score, and not a study-validity certificate. Concrete failure modes: (1) Treating completion as appraisal — to grade the internal validity of a cross-sectional/RWE study use a risk-of-bias tool (ROBINS-I, ROBINS-E) or a design appraisal checklist (e.g., JBI for cross-sectional studies, NOS); STROBE tells you what was reported, not whether it was done well. (2) Checklist-as-theater — citing "reported per STROBE" while leaving the source population, the algorithm definitions, the missing-data approach, or the sensitivity analyses vague defeats the purpose; the value is the substance behind each item, not the ticked box or page reference. (3) Wrong design variant — using the cross-sectional checklist to report a cohort (forward follow-up) or case-control (outcome-conditioned sampling) study mislabels the design and invites a temporality claim the data cannot support. (4) Using plain STROBE-CS where RECORD/RECORD-PE is required — a claims- or EHR-based cross-sectional study reported without RECORD's database items (code lists, linkage, data cleaning, validation) is under-reported by current RWE standards. (5) Inferring causation or incidence — completing STROBE-CS does not convert a single snapshot into evidence of effect or of temporal sequence; reverse causation and prevalent-case (length-time) bias remain, and the limitations item exists precisely to state this. (6) Wrong specialty/topic extension — surgical observational studies use STROCSS, nutritional epidemiology uses STROBE-nut, molecular epidemiology STROME-ID, genetic association STREGA; do not substitute the generic cross-sectional checklist where a topic extension governs.

How it maps to this catalog

— STROBE-CS's reporting items are operationalized by the concept entries a reviewer can check the report against: - The data substrate and its fitness-for-use (design/setting/source items): claims-analysis and medicare-ffs-ma-commercial-claims-differences-rwe describe the coverage, denominators, and snapshot limitations that the setting and limitations items must surface. - Variable/measurement definitions (items 7–8): diagnosis-phenotype-algorithm-1ip-2op-time-window-rwe and claims-outcome-algorithm-ppv-sensitivity-rwe implement the prevalent-condition phenotyping and the PPV/sensitivity validation that the report must disclose; procedure-identification-and-measurement-in-claims-ehr does the same for procedure-defined variables. - Participant flow and selection (items 6, 13): database-feasibility-attrition-funnel-rwe and attrition-and-loss-to-follow-up-rwe supply the enumerated source-to-analytic funnel and missingness accounting the flow item demands. - Statistical methods, estimand, and confounding (items 9, 12, 16): estimands-ate-att-intercurrent-events-rwe and estimand-analysis-traceability-rwe make the target quantity and its analytic chain explicit; high-dimensional-propensity-score-hdps-rwe and active-comparator-new-user are the confounding-control machinery to report when a cross-sectional design is pushed toward an association/comparative estimate (with the standing caveat that cross-sectional data cannot establish temporality the way target-trial-emulation can for longitudinal designs). These concepts implement what to report; STROBE-CS specifies that you must report it, and where.

Applied note (claims/EHR/registry RWE)

For a cross-sectional prevalence or association study on a claims/EHR snapshot, satisfy STROBE-CS by: stating the exact data source, version, and snapshot/coverage window (design/setting); defining every condition, exposure, and covariate by an explicit, citable algorithm and reporting any PPV/sensitivity validation (variables/measurement); presenting the source-population-to-analytic-sample funnel with reasons for exclusion (participant flow); pre-specifying the estimand and confounding-control strategy and reporting missing-data and sensitivity analyses (statistical methods); and stating plainly in the limitations that a single snapshot supports prevalence and association — not incidence or causal temporality — with prevalent-case and selection bias named. Because this is RWD, layer RECORD/RECORD-PE on top of STROBE-CS rather than relying on STROBE alone.