We are fortunate that coronavirus resources are increasing daily. I always recommend starting with the most authoritative resources. Some good places to start are listed below, however there are many more resources, assembled by key journals and leading professional and scientific organizations.
US Centers for Disease Control and Prevention
US Food and Drug Administration
The New England Journal of Medicine
Institute for Health Metrics and Evaluation
The techies among us were the first to play with color, line and movement to make data more intriguing, entertaining, and attractive, and data visualizations have progressed to the point of becoming reference resources rich in data about a given topic.
The Institute for Health Metrics and Evaluation has produced data visualizations on a range of topics. Particularly timely in the US, is this visualization showing personal health care spending by disease, type of care (ambulatory, inpatient, prescribed pharmaceutical, nursing facility, dental, and emergency), year, gender, and age group. We can see that in 2013 $18 billion was spent for pharmaceuticals for those under 20, compared to $112 billion spent for those 65 and over. In the group under 20, the greatest amounts were spent on mental and substance use disorders, followed by chronic respiratory diseases. In the group 65 and over, diabetes, treatment of risk factors, and cardiovascular diseases led the way.
And publications are pouring out of these data visualization tools. A good example is the article by Roth et al, “Trends and patterns of geographic variation in cardiovascular mortality among US counties, 1980–2014” published in JAMA this past May. They estimate age-standardized mortality rates by county from cardiovascular diseases (CVD) for the United States.
US County-Level Mortality From Cardiovascular Diseases A, Age-standardized mortality rate for both sexes combined in 2014. B, Percent change in the age-standardized mortality rate for both sexes combined between 1980 and 2014
The National Academies of Science held a workshop last October, “Real-World Evidence Generation and Evaluation of Therapeutics—A Workshop”, bringing together key thinkers to examine opportunities and challenges for incorporating real-world evidence into evaluation of medical products. Regulatory experts and FDA scientists followed-up with a cogent discussion of RWE and its use in the regulatory setting published in the New England Journal of Medicine, “Real-World Evidence — What Is It and What Can It Tell Us?” They reported that “the FDA is developing guidance on the use of ‘real-world evidence’ — health care information from atypical sources, including electronic health records, billing databases, and product and disease registries — to assess the safety and effectiveness of drugs and devices.”
NAS workshop reviews RWE
Real-world evidence is of special importance to children because we can’t always conduct randomized clinical trials in pediatric populations. Real-world evidence, from large databases, electronic health records, and similar sources, may be the only evidence we have about medications or devices used by children. All the more reason to make sure that real-world evidence meets the unique needs we have when studying children: adequate sample sizes for all age sub-groups, critical data such as birth date, birth weight, and gestational age, ability to link to parental (and even sibling) data, and linkage with school performance metrics.
As efforts increase to address the challenge of developing high-quality RWE, we must consider and address the special considerations and challenges in using RWE to answer questions about the effects of medications in children. Asking these questions now will ensure that advances in the use of RWE will benefit children as well as other sectors of the population.
See Tamar Lasky’s editorial in the journal Drugs – Real World Outcomes, “In the Real-World, Kids Use Medications and Devices”.
Genomic information plus clinical information in a large database allows us to identify genes associated with disease variants and truly begin precision medicine. In pharmacoepidemiology this will mean that we can identify patients who benefit most or are at greatest risk of an adverse event from a specific treatment.
The power of linking genomic information with clinical information is demonstrated in a recent study by Karnes et al 2017. They examined variations in major histocompatibility complex (MHC) and human leukocyte antigen (HLA) genes by linking information from DNA biobanks databases with EHR records for almost 30,000 people. Most importantly, they discovered previously unidentified associations between gene variation and disease severity for several conditions. They identified associations with type 1 diabetes and ankylosing spondylitis, among other conditions. Most importantly, they discovered several previously unidentified associations with MS and cervical cancer, as well as associations between gene variation and disease severity for several conditions.
The heatmap crosstabulates HLA alleles with a range of disease diagnoses with darker reds being stronger positive associations (the allele increases the probability of having the disease or phenotype) and darker blues being stronger negative associations (the allele is associated with a protective effect against the disease or phenotype).
The authors note that this is one of the first systematic studies of clinical phenotypes with HLA variation in a single population.
Heatmap of four-digit HLA allelic associations with P < 1 × 10?5.
Data are publicly available www.phewascatalog.org
The recently published text, Drug Utilization Research: Methods and Applications, represents a formidable accomplishment, pulling together a comprehensive range of information on all topics related to this key area. With 48 chapters, it addresses study design, data collection, secondary data sources, classification systems, measurement units, drug expenditures, statistical methods, data visualization, and much more. I was happy to see a chapter addressing issues around pediatric drug utilization, and chapters on drug utilization of pregnant women and older populations, as well.
Drug Utilization Research (DUR) is an eclectic scientific discipline, integrating descriptive and analytical methods for the quantification, understanding and evaluation of the processes of prescribing, dispensing and consumption of medicines and for the testing of interventions to enhance the quality of these processes. The discipline is closely related and linked mainly to the broader field of pharmacoepidemiology, but also to health outcomes research, pharmacovigilance and health economics.
Drug Utilization Research is a unique, practical guide to the assessment and evaluation of prescribing practices and to interventions to improve the use of medicines in populations. Edited by an international expert team from the International Society for Pharmacoepidemiology (ISPE), DUR is the only title to cover both the methodology and applications of drug utilization research and covers areas such as health policy, specific populations, therapeutics and adherence.
The only way to assemble such a book is with a strong group of editors, Monique Elseviers, Björn Wettermark, Anna Birna Almarsdóttir, Morten Andersen, Ria Benko, Marion Bennie, Irene Eriksson, Brian Godman, Janet Krska, Elisabetta Poluzzi, Katja Taxis, Vera Vlahovi?-Pal?evski, Robert Vander Stichele are all accomplished researchers in this field.