Top

Archive | Databases

When genomic info is linked to the EHR record

By on May 19, 2017 in Databases, Genomics, News, Pharmacogenomics

We can explore associations between genes and phenotypes

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.

Genomics EHR database

Heatmap of four-digit HLA allelic associations with P < 1 × 10?5.

Data are publicly available www.phewascatalog.org

NHANES – beyond nutrition to prescription meds

By on October 3, 2014 in Databases, Epidemiology, Health Services Research, News, Pharmacoepidemiology, Prescription medications

NHANES prescription medication data hasn’t always been on my radar.

I’m not sure why this was so. NHANES is a well known national survey that began as a nutrition survey and quickly expanded to include a range of health variables, including results of a physician exam and laboratory tests. It is a national probability based sample, which means that one can generalize from the NHANES to the entire United States, and its methodologic standards are of the very highest. Perhaps the reason that I overlooked the prescription medication data is that there is so much data, and also, that NHANES was known primarily for nutrition data. Getting past my own blind spot, I decided to take a closer look at the prescription medication data collected in NHANES.

Here are some key points.

The survey

NHANES began in the 1960s and was conducted in waves, with a NHANES I, NHANES II and NHANES III. We love it so much that it became a permanent fixture. Since 1999 it has been conducted continuously in two-year cycles, and is now called NHANES continuous, or just NHANES. The US population is sampled over a two-year cycle and the data need to be analyzed using the full two-year sample. The sample is representative of the non-institutionalized, U.S. population and for example, does not include residents in nursing homes, or people in prison.

Sample size

Sample size is critical to being able to estimate drug utilization. Unweighted sample sizes by age group are listed in the table below. While the numbers are large (every one of these people was interviewed in their homes), they may not be large enough for many purposes in pharmacoepidemiology. For those of us interested in pediatric medication use there were 4,194 people under 20 included in the sample. When stratified into age groups, the sample might not be large enough to study medications taken by small percentages (fewer than 1%) of children. The table below is taken from the NHANES website and shows unweighted sample sizes.

Table 2. Unweighted sample size and percents by age groups from NHANES 2005-06, 2007-08 and 2009-2010 for examined participants

NHANES prescription medications

NHANES prescription medication information

The medication information is collected during an in person interview in the participant’s home. During the interview, survey participants are asked if they have taken medications in the past 30 days for which they needed a prescription. Those who answer “yes” are asked to show the interviewer the medication containers of all the products used. For each medication reported, the interviewer enters the product’s complete name from the container into a computer. If no container is available, the interviewer asks the participant to verbally report the name of the medication. Participants are also asked how long they had been taking the medication and the main reason for use. This is in contrast to databases that rely on billing or claims data, or electronic health records. Documentation about the 2011-2012 data files containing prescription medication can be found here.

Using NHANES prescription medication data for pharmacoepidemiology

The pros and cons of using NHANES for pharmacoepidemiology are straight forward. On the pro side, NHANES may be the only probability based population sample in the United States with medication information. This alone makes it extremely valuable, and useful in conjunction with other types of data. The second strength, is that unlike health records, claims, or prescription data bases, the NHANES documents the presence of the medication in the patient’s home, demonstrating the the prescription was purchased and brought home. Along the continuum of measures, beginning with prescriptions written and prescriptions filled, documenting the prescription in the patient’s home brings us closer to understanding true exposures and levels of use. Another positive that needs to be explored is the availability of information from the physical exam and laboratory tests for the person using a given prescription.

On the con side, the sample sizes may be too small to provide stable estimates of many medications, especially if one wishes to study use within a sub-group. In terms of bias, my first thought is that this method of estimating use will result in underestimates of use, with people forgetting, omitting or otherwise not reporting their medication use to an interviewer. Misclassification in the other direction might occur when a person has filled a prescription and shows the prescription to the interviewer, but does not take the prescription. This latter source of bias would lead to an over-estimate of use but would also effect each of the other types of measures of prescription medication use (prescriptions written or prescriptions filled also over-estimate the numbers of people actually using the medication.

Recent publications using NHANES prescription medication data

A quick search turns up several publication analyzing prescription medication data in NHANES, but not as many as one might expect. An interesting use of the data is that of Bateman and colleagues (2012) focusing on a group with a risk factor, hypertension, and describing the medication use within that group. This usage may have applications for people working in health economics and outcomes research.

  • Farina EK, Austin KG, Lieberman HR, “Concomitant Dietary Supplement and Prescription Medication Use Is Prevalent among US Adults with Doctor-Informed Medical Conditions” J Acad Nutr Diet 2014 Apr 4 S2212-2672(14)
  • Bertisch SM, Herzig SJ, Winkelman JW, Buettner C, “National use of prescription medications for insomnia: NHANES 1999-2010” Sleep. 2014 Feb 1;37(2):343-9
  • Chong Y, Fryer CD, Gu Q, “Prescription sleep aid use among adults: United States, 2005-2010” NCHS Data Brief. 2013 Aug;(127):1-8
  • Gu Q, Burt VL, Dillon CF, Yoon S, “Trends in antihypertensive medication use and blood pressure control among United States adults with hypertension: the National Health And Nutrition Examination Survey, 2001 to 2010” Circulation. 2013 Jun 18;127(24)
  • Bateman BT, Shaw KM, Kuklina EV, Callaghan WM, Seely EW, Hernandez-Diaz S, “Hypertension in women of reproductive age in the United States: NHANES 1999-2008” PLoS One. 2012;7(4):e36171
  • Kinjo M, Setoguchi S, Solomon DH, “Antihistamine therapy and bone mineral density: analysis in a population-based US sample” Am J Med. 2008 Dec;121(12):1085-91

Variation in Vancomycin Use in Pediatric Hospitalizations in the 2008 Premier Database

By on August 21, 2012 in Antibiotic use, Databases, Epidemiology, News, Pediatric Medication Use

How much variation in use is too much?

Vancomycin is indicated for the treatment of serious or severe infections caused by susceptible strains of methicillin-resistant (beta-lactam-resistant) staphylococci.  Because of concerns about the development of drug-resistant bacteria, recommendations to prevent the spread of vancomycin resistance have been in place since 1995 and include guidelines for inpatient pediatric use of vancomycin.  With such guidelines in place, it is of special interest to compare inpatient pediatric vancomycin administration across hospitals.

Our recent publication, “Pediatric Vancomycin Use in 421 Hospitals in the United States 2008” published in PLOS ONE on 8/16/2012 (Lasky T, Greenspan J, Ernst FR, and Gonzalez L), compares vancomycin use in all pediatric hospitalizations (hospitalizations of children under age 18) in 421 hospitals in the Premier database.

Key Findings

  • Vancomycin was administered to children at 374 hospitals in the Premier hospital database.
  • Another 47 hospitals with 17,271 pediatric hospitalizations (13,233 under age 2) reported no vancomycin use during 2008.
  • The number of pediatric hospitalizations with vancomycin use ranged from 0 to 1225 at individual hospitals.
  • Most hospitals (221) had fewer than 10 pediatric hospitalizations with vancomycin use in the study period.
  • 21 hospitals (5.6% of hospitals) each had over 200 hospitalizations with vancomycin use, and together, accounted for more than 50% of the pediatric hospitalizations with vancomycin use.
  • Percentage of hospitalizations with vancomycin use ranged up to 33.3% when hospitals with few pediatric hospitalizations were kept in the sample, the high percenetages being an artifact of the small number of hospitalizations in the denominator. For this reason, percentage, by itself, may not be a useful indicator in small hospitals.
  • In hospitals with more than 100 pediatric hospitalizations with vancomycin use, the percentage with vancomycin use ranged from 1.26 to 12.90, a 10 fold range in the prevalence of vancomycin use.
  • Our stratified analyses and logistic modeling showed variation in vancomycin use by individual hospital that was not explained by hospital or patient characteristics including: bed size, teaching status, region of the country, rural or urban geography, and patient sex, race, APR-DRG risk of mortality and APR-DRG severity of illness.

For Discussion and Further Investigation

Until recently, few studies have compared pediatric antibiotic use across large numbers of hospitals or geography, and it was not possible to assess variation in use across institutions. Hospital variation in care of adults has been studied for several decades, much of it made possible by large Medicare claims databases. With the availability of aggregated data for pediatric hospitalizations we can begin describing and attempting to understand variation in pediatric practice. This first study of hospital variation in pediatric vancomycin use raises questions for further research.

Morphine Use in Pediatric Inpatients

By on March 19, 2012 in Databases, Health Services Research, News, Pain medications, Pediatric Medication Use, Pharmacoepidemiology

Pediatric morphine use in the hospital

As with so many medications used widely to treat children, morphine is not labeled for pediatric use. Describing patterns of use helps us understand how many children are receiving a drug that is not approved for pediatric use by the FDA.

A statistical analysis of 877,201 pediatric hospitalizations in the United States in 2008 estimated that morphine was used in 54,613 (6.2%) hospitalizations in the database. If this percentage is applied to the total number of children’s hospitalizations in the US in 2008, as many as 476,205 children will have received morphine during their hospital stay that year. Fractures and appendicitis were two of the diagnoses most frequently listed for children receiving morphine.

While morphine can be used safely for pain management during hospital procedures, and has been used for this purpose for several decades, the lack of pediatric labeling is undesirable. In a discussion about whether the off-label use of a drug constitutes experimentation and research, the American Academy of Pediatrics Committee on Drugs noted that “discussion about the off-label status of a drug may, as a matter of professional judgment, be part of the information provided to the patient or parents.”

The article reporting statistical analysis on morphine use in pediatric inpatients can be found here:”Morphine Use in Hospitalized Children in the United States: A Descriptive Analysis of Data From Pediatric Hospitalizations in 2008″Lasky T, Greenspan J, Ernst FR, and Gonzalez L Clinical Therapeutics 2012, 34(3): pp.720-727.

The American Academy of Pediatrics discussion on “Uses of drugs not described in the package insert (off-label uses)” can be found here. Pediatrics. 2002;110: 181–183.