Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (738 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
Studies designed to examine associations, commonly, hypothesized causal relations. They are usually concerned with identifying or measuring the effects of risk factors or exposures. The common types of analytic study are case-control studies; cohort studies; and cross-sectional studies.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Studies, Epidemiologic; Epidemiologic Study; Study, Epidemiologic; Epidemiological Studies; Epidemiological Study; Studies, Epidemiological; Study, Epidemiological.
Where it fits in the MeSH classification
- Broader topics
- Epidemiologic Study Characteristics
- Narrower topics
- Case-Control Studies; Cohort Studies; Controlled Before-After Studies; Cross-Sectional Studies; Historically Controlled Study; Interrupted Time Series Analysis; Seroepidemiologic Studies
Related concepts
Research landscape
PubMed indexes 3,767,885 records tagged with the MeSH heading “Epidemiologic Studies” (counts retrieved 2026-10-29). 1,252,464 of them (33.2%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 1,352,876 records.
Reviews account for 115,214 records (3.1%) and meta-analyses for 29,227. Randomized controlled trials number 171,734 (4.6%) as a share of all records on the topic.
Show the data
| Period | Records |
|---|---|
| through 1999 | 476,205 |
| 2000-2009 | 686,340 |
| 2010-2019 | 1,352,876 |
| 2020 onward | 1,252,464 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 115,214 | 3.1% |
| Meta-analyses | 29,227 | 0.8% |
| Randomized controlled trials | 171,734 | 4.6% |
Method: PubMed query "Epidemiologic Studies"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in clinical research
Research methods determine how much a result can be trusted. Randomized trials guard against confounding, systematic reviews and meta-analyses combine many studies, and observational designs are valuable but can show only association. Reporting standards, trial registration and pre-specified outcomes reduce bias. When reading about a method, ask what problem it is designed to solve and which problems it does not solve.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Risk of Bias Evaluation of Cross-Sectional Studies: Adaptation of the Newcastle-Ottawa Scale | 2026 | Research article Design not recorded in structured metadata | 167 | cc by PMID 40293188 · DOI · PMC13665615 |
| Framework and baseline examination of the German National Cohort (NAKO) | 2022 | Research article Design not recorded in structured metadata | 164 | cc by PMID 36260190 · DOI · PMC9581448 |
| Measurement of gender as a social determinant of health in epidemiology-A scoping review | 2021 | Research article Design not recorded in structured metadata | 65 | cc by PMID 34731177 · DOI · PMC8565751 |
Risk of Bias Evaluation of Cross-Sectional Studies: Adaptation of the Newcastle-Ottawa Scale [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Cross-sectional studies are widely utilized in medical research to estimate prevalence and examine associations.
- Closing excerpt
- To streamline their application, spreadsheets for both NOS-xs and NOS-x2 are provided.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (236 words)
Cross-sectional studies are widely utilized in medical research to estimate prevalence and examine associations. As such, they can serve as a significant source of data for systematic reviews. However, specific considerations are necessary when evaluating the risk of bias (RoB) of cross-sectional studies, as several potential biases can undermine the validity, reliability, and robustness of their findings. This article introduces a novel, context-specific tool designed to assess the RoB of cross-sectional studies for use in systematic reviews. The proposed tool represents an adaptation of the Newcastle-Ottawa Scale (NOS), originally developed for cohort and case-control studies. Similar to the original NOS, the new tool (named "NOS-xs") features a nine-star rating system to assess six specific items across three main domains: (i) study sample selection, (ii) assessment of exposure(s) and outcome(s), and (iii) confounding factors. Based on the number of awarded stars, studies are categorized as having high (0-3 stars), moderate (4-6 stars), or low (7-9 stars) RoB. The NOS-xs tool maintains consistency with the original NOS tool, facilitating its integration into systematic reviews that also include cohort and/or case-control studies. While the NOS-xs is suited to analytical cross-sectional studies (i.e., association studies), a simplified version ("NOS-xs2") is also introduced for descriptive cross-sectional studies (i.e., prevalence studies). The NOS-xs2 features a four-star rating system to assess three of the six specific items included in the NOS-xs. To streamline their application, spreadsheets for both NOS-xs and NOS-x2 are provided.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 40293188 · DOI · PMC13665615
Framework and baseline examination of the German National Cohort (NAKO) [2]
Abstract reading summary — source excerpts
- Opening excerpt
- The German National Cohort (NAKO) is a multidisciplinary, population-based prospective cohort study that aims to investigate the causes of widespread diseases, identify risk factors and improve early detection and prevention of disease.
- Closing excerpt
- NAKO is a major resource for population-based epidemiology to identify new and tailored strategies for early detection, prediction, prevention and treatment of major diseases for the next 30 years.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (249 words)
The German National Cohort (NAKO) is a multidisciplinary, population-based prospective cohort study that aims to investigate the causes of widespread diseases, identify risk factors and improve early detection and prevention of disease. Specifically, NAKO is designed to identify novel and better characterize established risk and protection factors for the development of cardiovascular diseases, cancer, diabetes, neurodegenerative and psychiatric diseases, musculoskeletal diseases, respiratory and infectious diseases in a random sample of the general population. Between 2014 and 2019, a total of 205,415 men and women aged 19-74 years were recruited and examined in 18 study centres in Germany. The baseline assessment included a face-to-face interview, self-administered questionnaires and a wide range of biomedical examinations. Biomaterials were collected from all participants including serum, EDTA plasma, buffy coats, RNA and erythrocytes, urine, saliva, nasal swabs and stool. In 56,971 participants, an intensified examination programme was implemented. Whole-body 3T magnetic resonance imaging was performed in 30,861 participants on dedicated scanners. NAKO collects follow-up information on incident diseases through a combination of active follow-up using self-report via written questionnaires at 2-3 year intervals and passive follow-up via record linkages. All study participants are invited for re-examinations at the study centres in 4-5 year intervals. Thereby, longitudinal information on changes in risk factor profiles and in vascular, cardiac, metabolic, neurocognitive, pulmonary and sensory function is collected. NAKO is a major resource for population-based epidemiology to identify new and tailored strategies for early detection, prediction, prevention and treatment of major diseases for the next 30 years.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 36260190 · DOI · PMC9581448
Measurement of gender as a social determinant of health in epidemiology-A scoping review [3]
Abstract reading summary — source excerpts
- Opening excerpt
- BackgroundThe relevance of gender as a social determinant of health and its role in the production of health inequalities is now broadly acknowledged.
- Closing excerpt
- With our theory-informed, practice-driven scoping review, we highlighted strengths and limitations of such measures and provided analytical tools for researchers interested in conducting intersectional, gender-sensitive analyses.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (253 words)
BackgroundThe relevance of gender as a social determinant of health and its role in the production of health inequalities is now broadly acknowledged. However, the plethora of existing approaches to capture gender, which often stem from disciplines outside of epidemiology, makes it difficult to assess their practicality and relevance for a given research purpose. We conducted a scoping review to 1) map the evidence of how gender can be operationalised in quantitative epidemiology and 2) design a tool to critically evaluate the measures identified.MethodsWe identified peer-reviewed articles in electronic databases (PubMed, Embase and PsycINFO). Eligible sources described the quantitative operationalisation of the social dimension of gender. With the help of a newly developed checklist, we assessed their relevance from an analytical perspective (e.g. intersectionality) and their potential for implementation in epidemiology.ResultsGender measures principally assessed gender roles and norms, gender-based discrimination and violence, and structural gender (in)equality. Of the 344 measures included in this review, the majority lacked theoretical foundation, and tended to reinforce the binary understanding of gender through stereotypes of femininity and masculinity. Only few measures allowed for an intersectional approach and a multilevel understanding of gender mechanisms. From a practical point of view, gender measures demonstrated potential for use in varied populations and contexts.ConclusionsA range of gender measures are readily available for epidemiological research, addressing different levels and dimensions of gender as a social construct. With our theory-informed, practice-driven scoping review, we highlighted strengths and limitations of such measures and provided analytical tools for researchers interested in conducting intersectional, gender-sensitive analyses.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 34731177 · DOI · PMC8565751
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article The Danish Civil Registration System as a tool in epidemiology [4]
- Research article Neuropathic pain in the general population: a systematic review of epidemiological studies [5]
- Research article Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations [6]
- Research article IMI - Defining and Classifying Myopia: A Proposed Set of Standards for Clinical and Epidemiologic Studies [7]
- Research article Use of interrupted time series analysis in evaluating health care quality improvements [8]
- Research article Competing risks in epidemiology: possibilities and pitfalls [9]
References
- Carra MC, Romandini P, Romandini M. Risk of Bias Evaluation of Cross-Sectional Studies: Adaptation of the Newcastle-Ottawa Scale. . 2026. PMID 40293188 · DOI · PMC13665615
- Peters A, German National Cohort (NAKO) Consortium, Peters A, Greiser KH, Göttlicher S, Ahrens W, et al.. Framework and baseline examination of the German National Cohort (NAKO). . 2022. PMID 36260190 · DOI · PMC9581448
- Miani C, Wandschneider L, Niemann J, Batram-Zantvoort S, Razum O. Measurement of gender as a social determinant of health in epidemiology-A scoping review. . 2021. PMID 34731177 · DOI · PMC8565751
- Schmidt M, Pedersen L, Sørensen HT. The Danish Civil Registration System as a tool in epidemiology. Eur J Epidemiol. 2014. PMID 24965263 · DOI
- van Hecke O, Austin SK, Khan RA, Smith BH, Torrance N. Neuropathic pain in the general population: a systematic review of epidemiological studies. Pain. 2014. PMID 24291734 · DOI
- Wang X, Cheng Z. Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. Chest. 2020. PMID 32658654 · DOI
- Flitcroft DI, He M, Jonas JB, Jong M, Naidoo K, Ohno-Matsui K, et al.. IMI - Defining and Classifying Myopia: A Proposed Set of Standards for Clinical and Epidemiologic Studies. Invest Ophthalmol Vis Sci. 2019. PMID 30817826 · DOI · PMC6735818
- Penfold RB, Zhang F. Use of interrupted time series analysis in evaluating health care quality improvements. Acad Pediatr. 2013. PMID 24268083 · DOI
- Andersen PK, Geskus RB, de Witte T, Putter H. Competing risks in epidemiology: possibilities and pitfalls. Int J Epidemiol. 2012. PMID 22253319 · DOI · PMC3396320
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.