Charles Poole

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration.

Editors Note: In order to encourage dissemination of the STROBE Statement, this article is being published simultaneously in Annals of Internal Medicine, Epidemiology, and PLoS Medicine. It is freely accessible on the Annals of Internal Medicine Web site ( www.annals.org ) and will also be published on the Web sites of Epidemiology and PLoS Medicine. The authors jointly hold the copyright of this article. For details on further use, see the STROBE Web site (www.strobe-statement.org). Rational health care practices require knowledge about the etiology and pathogenesis, diagnosis, prognosis, and treatment of diseases. Randomized trials provide valuable evidence about treatments and other interventions. However, much of clinical or public health knowledge comes from observational research (1). About 9 of 10 research papers published in clinical specialty journals describe observational research (2, 3). The STROBE Statement Reporting of observational research is often not detailed and clear enough to assess the strengths and weaknesses of the investigation (4, 5). To improve the reporting of observational research, we developed a checklist of items that should be addressed: the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement (Appendix Table). Items relate to the title, abstract, introduction, methods, results, and discussion sections of articles. The STROBE Statement has recently been published in several journals (6). Our aim is to ensure clear presentation of what was planned, done, and found in an observational study. We stress that the recommendations are not prescriptions for setting up or conducting studies, nor do they dictate methodology or mandate a uniform presentation. Appendix Table. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Checklist of Items That Should Be Addressed in Reports of Observational Studies STROBE provides general reporting recommendations for descriptive observational studies and studies that investigate associations between exposures and health outcomes. STROBE addresses the 3 main types of observational studies: cohort, casecontrol, and cross-sectional studies. Authors use diverse terminology to describe these study designs. For instance, follow-up study and longitudinal study are used as synonyms for cohort study, and prevalence study as a synonym for cross-sectional study. We chose the present terminology because it is in common use. Unfortunately, terminology is often used incorrectly (7) or imprecisely (8). In Box 1, we describe the hallmarks of the 3 study designs. Box 1. Main Study Designs Covered by STROBE The Scope of Observational Research Observational studies serve a wide range of purposes, from reporting a first hint of a potential cause of a disease to verifying the magnitude of previously reported associations. Ideas for studies may arise from clinical observations or from biological insight. Ideas may also arise from informal looks at data that lead to further explorations. Like a clinician who has seen thousands of patients and notes 1 that strikes her attention, the researcher may note something special in the data. Adjusting for multiple looks at the data may not be possible or desirable (9), but further studies to confirm or refute initial observations are often needed (10). Existing data may be used to examine new ideas about potential causal factors, and may be sufficient for rejection or confirmation. In other instances, studies follow that are specifically designed to overcome potential problems with previous reports. The latter studies will gather new data and will be planned for that purpose, in contrast to analyses of existing data. This leads to diverse viewpoints, for example, on the merits of looking at subgroups or the importance of a predetermined sample size. STROBE tries to accommodate these diverse uses of observational researchfrom discovery to refutation or confirmation. Where necessary, we will indicate in what circumstances specific recommendations apply. How to Use this Paper This paper is linked to the shorter STROBE paper that introduced the items of the checklist in several journals (6), and forms an integral part of the STROBE Statement. Our intention is to explain how to report research well, not how research should be done. We offer a detailed explanation for each checklist item. Each explanation is preceded by an example of what we consider transparent reporting. This does not mean that the study from which the example was taken was uniformly well reported or well done; nor does it mean that its findings were reliable, in the sense that they were later confirmed by others: It only means that this particular item was well reported in that study. In addition to explanations and examples, we included boxes with supplementary information. These are intended for readers who want to refresh their memories about some theoretical points or be quickly informed about technical background details. A full understanding of these points may require studying the textbooks or methodological papers that are cited. STROBE recommendations do not specifically address topics, such as genetic linkage studies, infectious disease modeling, or case reports and case series (11, 12). As many of the key elements in STROBE apply to these designs, authors who report such studies may nevertheless find our recommendations useful. For authors of observational studies that specifically address diagnostic tests, tumor markers, and genetic associations, STARD (13), REMARK (14), and STREGA (15) recommendations may be particularly useful. The Items in the STROBE Checklist We now discuss and explain the 22 items in the STROBE checklist (Appendix Table) and give published examples for each item. Some examples have been edited by removing citations or spelling out abbreviations. Eighteen items apply to all 3 study designs, whereas 4 are design-specific. Starred items (for example, item 8) indicate that the information should be given separately for cases and controls in casecontrol studies, or exposed and unexposed groups in cohort and cross-sectional studies. We advise authors to address all items somewhere in their paper, but we do not prescribe a precise location or order. For instance, we discuss the reporting of results under a number of separate items, while recognizing that authors might address several items within a single section of text or in a table. Title and Abstract 1(a) Indicate the studys design with a commonly used term in the title or the abstract. Example Leukaemia incidence among workers in the shoe and boot manufacturing industry: a casecontrol study (18). Explanation Readers should be able to easily identify the design that was used from the title or abstract. An explicit, commonly used term for the study design also helps ensure correct indexing of articles in electronic databases (19, 20). 1(b) Provide in the abstract an informative and balanced summary of what was done and what was found. Example Background: The expected survival of HIV-infected patients is of major public health interest. Objective: To estimate survival time and age-specific mortality rates of an HIV-infected population compared with that of the general population. Design: Population-based cohort study. Setting: All HIV-infected persons receiving care in Denmark from 1995 to 2005. Patients: Each member of the nationwide Danish HIV Cohort Study was matched with as many as 99 persons from the general population according to sex, date of birth, and municipality of residence. Measurements: The authors computed KaplanMeier life tables with age as the time scale to estimate survival from age 25 years. Patients with HIV infection and corresponding persons from the general population were observed from the date of the patients HIV diagnosis until death, emigration, or 1 May 2005. Results: 3990 HIV-infected patients and 379 872 persons from the general population were included in the study, yielding 22 744 (median, 5.8 y/person) and 2 689 287 (median, 8.4 y/person) person-years of observation. Three percent of participants were lost to follow-up. From age 25 years, the median survival was 19.9 years (95% CI, 18.5 to 21.3) among patients with HIV infection and 51.1 years (CI, 50.9 to 51.5) among the general population. For HIV-infected patients, survival increased to 32.5 years (CI, 29.4 to 34.7) during the 2000 to 2005 period. In the subgroup that excluded persons with known hepatitis C coinfection (16%), median survival was 38.9 years (CI, 35.4 to 40.1) during this same period. The relative mortality rates for patients with HIV infection compared with those for the general population decreased with increasing age, whereas the excess mortality rate increased with increasing age. Limitations: The observed mortality rates are assumed to apply beyond the current maximum observation time of 10 years. Conclusions: The estimated median survival is more than 35 years for a young person diagnosed with HIV infection in the late highly active antiretroviral therapy era. However, an ongoing effort is still needed to further reduce mortality rates for these persons compared with the general population (21). Explanation The abstract provides key information that enables readers to understand a study and decide whether to read the article. Typical components include a statement of the research question, a short description of methods and results, and a conclusion (22). Abstracts should summarize key details of studies and should only present information that is provided in the article. We advise presenting key results in a numerical form that includes numbers of participants, estimates of associations, and appropriate measures of variability and uncertainty (for example, odds ratios with confidence intervals). We regard it insufficient to state only that an exposure is or is not significantly associated with an outcom

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration

Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalisability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies. The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated Web site (http://www.strobe-statement.org/) should be helpful resources to improve reporting of observational research.

Strengthening the Reporting of Observational Studies in Epidemiology (strobe): Explanation and Elaboration

Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalizability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies. The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated web site (http://www.strobe-statement.org) should be helpful resources to improve reporting of observational research.

A pooled analysis of magnetic fields, wire codes, and childhood leukemia

We obtained original individual data from 15 studies of magnetic fields or wire codes and childhood leukemia, and we estimated magnetic field exposure for subjects with sufficient data to do so. Summary estimates from 12 studies that supplied magnetic field measures exhibited little or no association of magnetic fields with leukemia when comparing 0.1–0.2 and 0.2–0.3 microtesla (&mgr;T) categories with the 0–0.1 &mgr;T category, but the Mantel-Haenszel summary odds ratio comparing >0.3 &mgr;T to 0–0.1 &mgr;T was 1.7 (95% confidence limits = 1.2, 2.3). Similar results were obtained using covariate adjustment and spline regression. The study-specific relations appeared consistent despite the numerous methodologic differences among the studies. The association of wire codes with leukemia varied considerably across studies, with odds ratio estimates for very high current vs low current configurations ranging from 0.7 to 3.0 (homogeneity P = 0.005). Based on a survey of household magnetic fields, an estimate of the U.S. population attributable fraction of childhood leukemia associated with residential exposure is 3% (95% confidence limits = –2%, 8%). Our results contradict the idea that the magnetic field association with leukemia is less consistent than the wire code association with leukemia, although analysis of the four studies with both measures indicates that the wire code association is not explained by measured fields. The results also suggest that appreciable magnetic field effects, if any, may be concentrated among relatively high and uncommon exposures, and that studies of highly exposed populations would be needed to clarify the relation of magnetic fields to childhood leukemia.

Bacterial vaginosis and HIV acquisition: A meta-analysis of published studies

Objectives:To assess and summarize the published literature on the extent to which bacterial vaginosis may increase the risk of HIV acquisition. Design:Meta-analysis of published studies. Methods:Medline and other electronic databases were systematically searched for eligible publications. The association between bacterial vaginosis and incident HIV was separately analyzed from that between bacterial vaginosis and prevalent HIV. The latter was further analyzed, stratified by bacterial vaginosis diagnostic method, HIV risk profile of the study population, and whether or not adjusted estimates were presented. Results:Twenty-three eligible publications were identified, including a total of 30 739 women. Bacterial vaginosis was associated with an increased risk of HIV acquisition in HIV-incidence studies (relative risk = 1.6, 95% confidence interval: 1.2, 2.1). All but one of 21 HIV-prevalence studies reported estimates above the null. The latter results were heterogeneous and showed some evidence of funnel plot asymmetry, precluding the estimation of a single summary measure. The association between bacterial vaginosis and HIV in prevalence studies appeared stronger for women without high-risk sexual behavior. Conclusion:Bacterial vaginosis was consistently associated with an increased risk of HIV infection. High bacterial vaginosis prevalence may result in a high number of HIV infections being attributable to bacterial vaginosis. More prospective studies are needed to accurately evaluate the role of bacterial vaginosis in HIV acquisition in low-risk versus high-risk women. Furthermore, randomized clinical trials may be worth considering to determine the effect of bacterial vaginosis control measures on HIV acquisition.

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)

Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalisability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies. The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated Web site (http://www.strobe-statement.org/) should be helpful resources to improve reporting of observational research.

A meta-analysis of the incidence of non-AIDS cancers in HIV-infected individuals.

Objective:To estimate summary standardized incidence ratios (SIRs) of non-AIDS cancers among HIV-infected individuals compared with general population rates overall and stratified by gender, AIDS, and highly active antiretroviral therapy (HAART) era. Design:A meta-analysis using SIRs from 18 studies of non-AIDS cancer in HIV-infected individuals. Methods:SIRs for non-AIDS cancers in HIV-infected individuals and 95% confidence limits (CLs) were abstracted from each study. Random effects meta-analyses were used to estimate summary SIRs. Modifications by gender, AIDS, and HAART era were estimated with meta-regression. Results:Four thousand seven hundred ninety-seven non-AIDS cancers occurred among 625,716 HIV-infected individuals. SIRs for several cancers were elevated. In particular, cancers associated with infections, such as anal (SIR = 28; 95% CL 21 to 35), liver (SIR = 5.6; 95% CL 4.0 to 7.7), and Hodgkin lymphoma (SIR = 11; 95% CL 8.8 to 15) and smoking, such as lung (SIR = 2.6; 95% CL 2.1 to 3.1), kidney (SIR = 1.7; 95% CL 1.3 to 2.2), and laryngeal (SIR = 1.5; 95% CL 1.1 to 2.0). AIDS was associated with greater SIRs for Hodgkin lymphoma, leukemia, lung, brain, and all non-AIDS cancers combined. Conclusions:HIV-infected individuals may be at an increased risk of developing non-AIDS cancers, particularly those associated with infections and smoking. An association with advanced immune suppression was suggested for certain cancers.

The global prevalence of glucose-6-phosphate dehydrogenase deficiency: a systematic review and meta-analysis.

Glucose-6-phosphate deficiency is the most prevalent enzyme deficiency, with an estimated 400 million people affected worldwide. This inherited deficiency causes neonatal hyperbilirubinemia and chronic hemolytic anemia. Although most affected individuals are asymptomatic, exposure to oxidative stressors such as certain drugs or infection, can elicit acute hemolysis. To characterize the global prevalence of G6PD deficiency, we conducted a systematic review of the G6PD deficiency literature, drawing studies from various databases, including MEDLINE/Pubmed and Biosis. Selected studies included cross-sectional and longitudinal studies published between 1960 and 2008. Additionally, meta-analytic procedures were employed to assess the degree of heterogeneity amongst prevalence estimates and, where appropriate, pool them. The searches yielded a total of 280 prevalence estimates, corresponding to 88 countries. The highest prevalence rates were reported among Sub-Saharan African countries, even after adjusting for assessment method. Meta-analysis revealed a high degree of heterogeneity for regional and global prevalence estimates. This heterogeneity in reported estimates appeared to be due to differences in G6PD deficiency assessment and diagnostic procedures. The magnitude and variation in global, regional, and country-level prevalence rates of G6PD deficiency are of public health import, particularly in planning programs to improve neonatal health and in the distribution of various medications, especially antimalarial drugs, as G6PD deficiency is most prevalent in malaria-endemic areas.

Illustrating bias due to conditioning on a collider

That conditioning on a common effect of exposure and outcome may cause selection, or collider-stratification, bias is not intuitive. We provide two hypothetical examples to convey concepts underlying bias due to conditioning on a collider. In the first example, fever is a common effect of influenza and consumption of a tainted egg-salad sandwich. In the second example, case-status is a common effect of a genotype and an environmental factor. In both examples, conditioning on the common effect imparts an association between two otherwise independent variables; we call this selection bias.

Rethinking the heterosexual infectivity of HIV-1: a systematic review and meta-analysis

Studies of cumulative HIV incidence suggest that cofactors such as genital ulcer disease, HIV disease stage, and male circumcision influence HIV transmission; however, the heterosexual infectivity of HIV-1 is commonly cited as a fixed value (approximately 0.001, or one transmission per 1000 contacts). We sought to estimate transmission cofactor effects on the heterosexual infectivity of HIV-1 and to quantify the extent to which study methods have affected infectivity estimates. We undertook a systematic search (up to April 27, 2008) of PubMed, Web of Science, and relevant bibliographies to identify articles estimating the heterosexual infectivity of HIV-1. We used meta-regression and stratified random-effects meta-analysis to assess differences in infectivity associated with cofactors and study methods. Infectivity estimates were very heterogeneous, ranging from zero transmissions after more than 100 penile-vaginal contacts in some serodiscordant couples to one transmission for every 3.1 episodes of heterosexual anal intercourse. Estimates were only weakly associated with study methods. Infectivity differences, expressed as number of transmissions per 1000 contacts, were 8.1 (95 % CI 0.4-15.8) when comparing uncircumcised to circumcised susceptible men, 6.0 (3.3-8.8) comparing susceptible individuals with and without genital ulcer disease, 1.9 (0.9-2.8) comparing late-stage to mid-stage index cases, and 2.5 (0.2-4.9) comparing early-stage to mid-stage index cases. A single value for the heterosexual infectivity of HIV-1 fails to reflect the variation associated with important cofactors. The commonly cited value of 0.001 was estimated among stable couples with low prevalences of high-risk cofactors, and represents a lower bound. Cofactor effects are important to include in epidemic models, policy considerations, and prevention messages.