Archive | Geographic variation

Data visualization tools become mainstream

We’ve come a long way from the hand drawn and photographed black and white graphs that were used to illustrate a key point in the days before computer graphics.

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.

Data visualizations keep coming

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.

Data visualizations help us understand causes of mortality.

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

Pediatric hospitalizations for mood disorders

Children get hospitalized for depression and bipolar disorders.

I wasn’t aware of how frequently this happens until I got my hands on some BIG DATA – the HCUP KID database of children’s hospitalizations. My colleagues and I analyzed hospitalizations in 2000, 2003 and 2006 and published our results in the journal, Child and Adolescent Psychiatry and Mental Health in 2011. For each of these years, we were able to look at records for over 2 million hospitalizations, and able to project these to the entire number of pediatric hospitalizations in the US in those years.

Some take home points

Percentages of hospitalizations where the principal diagnosis was a mental health diagnosis

  • In children age 15-17, 13.7 to 15.2% of hospitalizations had a mental health principal diagnosis
  • In children age 10-14, 15.0 to 15.6% of hospitalizations had a mental health principal diagnosis
  • In children age 5-9, 4.4 to 4.8% of hospitalizations had a mental health principal diagnosis accounted

The incidence of hospitalizations with mood disorders as the principal diagnosis compared to the entire population of children

12.1-13.0 out of every 10,000 children were hospitalized with mood disorders as the principal diagnosis in 2000-2006.


The incidence of hospitalizations for children with mood disorders increased with age – this figure uses data from 2006 to show the trend.

Children mood disorders by age

Region of the country

A surprising finding was the big differences between regions of the country. As an example, in 2006, the Western region experienced the lowest rates (10.2/10,000) while the Midwest had the highest rates (25.4/10,000). This figure shows the rates for 2000, 2003 and 2006.

children mood disorders by region of the US

Did you know?

Mood disorders including depression and bipolar disorders are a major cause of morbidity in childhood and adolescence, and hospitalizations for mood disorders are the leading diagnosis for all hospitalizations in general hospitals for children age 13 to 17.

Between 2000 and 2006, inflation-adjusted hospital charges increased from $10,600 to $16,300.

Geographic Variation in Prescribing Practices

The authors used HEDIS quality measures, and mapped them by hospital referral region.

The map above shows variation in quality of prescribing high-risk drugs (medications considered to be high-risk for the elderly), and the map below shows variation in prescribing drugs with potentially harmful drug-disease interactions.

NEJM November 2010

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