Last week I turned 69! Can you believe that??? This is so amazing to me—how could I be THAT OLD?? Two years ago (when I was just 67!), I shared that…
So now that I am 69, I still believe all those things are true—particularly the wisdom part. I am wiser about the speed of change, the value of tempering my vision with a dose of realism, and the importance of understanding people clearly. I still feel youthful, look pretty good for a woman my age, and remain proud of my career.
But suppose I want to pick the number that really represents my age. Age is a very important descriptor of patients and research participants. Across all types of clinical research, one of the most common variables collected is a participant’s age. Age is an important indicator of many things human, from physical capabilities that determine their likely response to a treatment, to potential behavioral or mental health challenges. Knowing participants’ ages helps guide the interpretation of research results, allowing scientists and clinicians to determine the relevance of those results to specific groups of people or to better understand the clinical manifestation of a disease. And knowing the age of a participant provides evidence that our NIH studies appropriately engage people across their lifespan.
You might be surprised to know that there are many ways to represent age. For most of us, age is estimated by counting the number of years since our birth. However, for babies, it may be more important to know the number of days, weeks, or months since birth. Some studies compute age as the difference between the date of birth and the date that the data are collected. In fact, in the PhenX Toolkit, a web-based catalogue of expert-provided recommended measurement protocols, there are almost 200 different ways to measure age in a research study. Sometimes information about age is acquired through self-report of the participant, and other times the information is obtained from some existing document like a patient’s clinical record. The PhenX Toolkit is an enumeration of a wide range of measurement approaches and allows for broad coverage in a way that lets a researcher pick the measure that best represents the phenomena of interest to their study.
Over the past decade, NLM has supported the creation, identification, and distribution of Common Data Elements (CDEs). CDEs are specialized ways to measure concepts common to two or more research projects in a manner that is consistent across studies. Using a similar approach to measures similar concepts sounds like a no-brainer, right? It improves the rigor and reproducibility of research and allows data collected in different studies to be grouped together, adding power to the interpretation of research efforts. The COVID-19 pandemic illustrated the value of the common approaches to measuring research concepts by allowing us to track this deadly virus and its manifestations across time and people.
NLM established the NIH CDE Repository to serve as a one-stop location for research programs and for NIH Institutes and Centers to house CDEs and make them available to other researchers. Each record includes the definition of the variable as an indicator of the concept, a way to measure the variable (usually a question-and-answer pair with acceptable responses), and machine-readable codes where possible. Recently, the NIH CDE Repository began supporting an NIH governance process that indicates which of the proposed CDEs that have been received are described with sufficient rigor to be designated as NIH-endorsed. This endorsement helps potential users who are seeking good ways to measure complex concepts. NIH-endorsed CDEs support FAIR (findable, accessible, interoperable, and reusable) data sharing. Adherence to FAIR principles provides high-quality, “computation-ready” data with standardized vocabularies and readable metadata retrievable by identifiers that modernize the NIH data ecosystem. When data are collected consistently across studies using CDEs, it’s possible to integrate data from multiple studies, which can make it easier to get meaningful results. CDEs can also make it easier to reuse data for future research by improving the data quality.
So if I wanted to be “counted” according to the years-alive mode of assessing age, I guess I am 69. But if you really want to know something else, like how happy I am in my career or how I’m feeling, don’t be surprised if I give a different number!
Dr. Brennan is the Director of the NIH National Library of Medicine, a leader in biomedical informatics and computational health data science research and the world’s largest biomedical library. Under her leadership, NLM has grown its intramural and extramural research enterprise, extended stakeholders’ access to credible and reliable health information, and acquired and preserved biomedical literature using cutting-edge digital research and outreach. Read more about Dr. Brennan.