Rachel Pinotti, MLIS (She/her/hers)
Director, Library Education & Research Services
When seeking answers to clinical questions, it’s important to consider not only what evidence is exists, but also the quality of the evidence. Evidence grades are designed to give healthcare professionals an idea of the strength or weakness of the evidence available to support a recommendation. Read on for an introduction to evidence grading.
Is evidence grading the same as risk of bias assessment?
Evidence grading is related to, but not synonymous with, risk of bias assessment. Assessing the risk of bias occurs at the individual study level, meaning the assessor is looking at the potential for bias, one or more systematic errors, to impact the results of a single study. In contrast, evidence grades assess the strength of the entire body of evidence on a given topic or question. For instance it might be the case that several studies have been conducted that address the same question. Some of the studies may have utilized observational study design and others may used experimental study design. The available studies likely vary in their sample size and the precision of their estimated effects. Assessing the risk of bias in each of the available studies is a necessary step in order to determine the strength of the body of evidence in order to give a grade. Additionally, while risk of bias assessment makes a determination about the trustworthiness of the results of the study as a whole, evaluators typically assess the evidence and assign grades at the outcome level.
Are evidence grades standardized across information sources?
Unfortunately not. There are a number of different grading schemas, so it is important to familiarize yourself with the grading schema being utilized in the specific resource you’re consulting. For example, the U.S. Preventive Services Task Force (USPSTF) uses a letter grading schema, consisting of five letter grades (A, B, C, D, or I). In the USPSTF schema, a grade of A indicates that, “The USPSTF recommends the service. There is high certainty that the net benefit is substantial.” Check out the Grade Definitions page on the USPSTF website for complete details on their grade definitions and resulting suggestions for practice. To see the USPSTF in action, check out any of their recommendation statements, for example, the USPSTF final recommendation on Pancreatic Cancer Screening, which has a grade D.
Because the existence of different grading schemas can be confusing, the Grading of Recommendations Assessment, Development and Evaluation (GRADE) working group was formed in 2000 with a goal to develop, “a common, sensible and transparent approach to grading quality (or certainty) of evidence and strength of recommendations.” They released the GRADE schema in the early 2000’s and by 2006, the British Medical Journal (BMJ) identified GRADE as the preferred evidence grading system for clinical practice guidelines published in BMJ.1 The GRADE schema consists of a number (1 or 2) to indicate the strength of a recommendation paired with a letter (A, B, or C) to indicate the quality of the evidence available to support the recommendation. While the GRADE schema has been widely recognized and adopted by a number of professional societies, unfortunately adoption is still far from universal.
Where will I see evidence grades?
Evidence grades are available in a number of resources, including the USPSTF as discussed above. Clinical practice guidelines are perhaps the most common setting in which you will find evidence grades. They are also frequently available in clinical evidence summary resources. Here are some helpful examples:
Clinical Practice Guidelines
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American College of Cardiology/American Heart Association (ACC/AHA)
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American Academy of Pediatrics
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Description and explanation: Classifying Recommendations for Clinical Practice Guidelines4
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Example: Clinical Practice Guideline for the Management of Infantile Hemangiomas5
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Clinical Evidence Summary Resources
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UpToDate
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Description: UpToDate Grading Guide
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Example: Evidence grades can be found in the Summary and Recommendations section of most UpToDate entries, e.g. Treatment, prognosis, and follow-up of acute pulmonary embolism in adults
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DynaMed
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Description: Synthesized Recommendation Grading System for DynaMed Content
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Example: Evidence grades can be found in the Overview and Recommendations section of most DynaMed entries, e.g. Pulmonary Embolism Treatment
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Have you ever changed your practice based on the strength or weakness of the evidence available to support it? Let us know on Twitter!
References
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Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, Schünemann HJ. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008 Apr 24;336(7650):924-6.
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Halperin JL, Levine GN, Al-Khatib SM, Birtcher KK, Bozkurt B, Brindis RG, Cigarroa JE, Curtis LH, Fleisher LA, Gentile F, Gidding S. Further evolution of the ACC/AHA clinical practice guideline recommendation classification system: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2016 Apr 5;133(14):1426-8.
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Otto CM, Nishimura RA, Bonow RO, Carabello BA, Erwin III JP, Gentile F, Jneid H, Krieger EV, Mack M, McLeod C, O’Gara PT. 2020 ACC/AHA guideline for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Journal of the American College of Cardiology. 2021 Feb 2;77(4):e25-197.
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Homer CJ, Lannon CM, Harbaugh N, Hodgson ES, Marcuse EK, Shiffman RN, Simpson L. Classifying recommendations for clinical practice guidelines. Pediatrics. 2004 Sep 1;114(3):874-7.
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Krowchuk DP, Frieden IJ, Mancini AJ, Darrow DH, Blei F, Greene AK, Annam A, Baker CN, Frommelt PC, Hodak A, Pate BM. Clinical practice guideline for the management of infantile hemangiomas. Pediatrics. 2019 Jan 1;143(1).
Each month Levy Library showcases the achievements of Mount Sinai faculty and researchers by highlighting an article and its altmetrics. Altmetrics are alternative measures of impact that capture non-traditional data like abstract views, article downloads, and social media activity. Our altmetrics data is provided by the PlumX platform.
This month we highlight Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019. This article was written in part by Philip J. Landrigan, MD.
Background
The association of air pollution with multiple adverse health outcomes is becoming well established, but its negative economic impact is less well appreciated. It is important to elucidate this impact for the states of India.
Methods
We estimated exposure to ambient particulate matter pollution, household air pollution, and ambient ozone pollution, and their attributable deaths and disability-adjusted life-years in every state of India as part of the Global Burden of Disease Study (GBD) 2019. We estimated the economic impact of air pollution as the cost of lost output due to premature deaths and morbidity attributable to air pollution for every state of India, using the cost-of-illness method.
Findings
1·67 million (95% uncertainty interval 1·42–1·92) deaths were attributable to air pollution in India in 2019, accounting for 17·8% (15·8–19·5) of the total deaths in the country. The majority of these deaths were from ambient particulate matter pollution (0·98 million [0·77–1·19]) and household air pollution (0·61 million [0·39–0·86]). The death rate due to household air pollution decreased by 64·2% (52·2–74·2) from 1990 to 2019, while that due to ambient particulate matter pollution increased by 115·3% (28·3–344·4) and that due to ambient ozone pollution increased by 139·2% (96·5–195·8). Lost output from premature deaths and morbidity attributable to air pollution accounted for economic losses of US$28·8 billion (21·4–37·4) and $8·0 billion (5·9–10·3), respectively, in India in 2019. This total loss of $36·8 billion (27·4–47·7) was 1·36% of India's gross domestic product (GDP). The economic loss as a proportion of the state GDP varied 3·2 times between the states, ranging from 0·67% (0·47–0·91) to 2·15% (1·60–2·77), and was highest in the low per-capita GDP states of Uttar Pradesh, Bihar, Rajasthan, Madhya Pradesh, and Chhattisgarh. Delhi had the highest per-capita economic loss due to air pollution, followed by Haryana in 2019, with 5·4 times variation across all states.
Interpretation
The high burden of death and disease due to air pollution and its associated substantial adverse economic impact from loss of output could impede India's aspiration to be a $5 trillion economy by 2024. Successful reduction of air pollution in India through state-specific strategies would lead to substantial benefits for both the health of the population and the economy.
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