In using this guide, there are at least 7 (and most likely more) issues affecting data access:
As you view these links to various sources of health data, keep in mind that data does not tell the whole story. There may be a story behind the data. As you will see, data and data gathering are not perfect-- if you see an anomaly or large deviation in the data, find out why. Don't assume.
However, once you look over some of the caveats detailed on this page, realize that there is still comparability amongst the data once you understand the anomalies! And whenever possible, take a look at the technical notes for more insight into the data.
Here are some examples of how data can mislead.
Be certain you understand the rules of data gathering before you try to interpret the data! Below are 3 examples of how the data gathering can mislead. Two of them come from Texas; the third is from Pennsylvania.
Births for Harris County
In this example, there is a fairly substantial jump in the increase of babies born to unmarried mothers in 1994-- over 5,300-- and a corresponding decrease-- over 6,700-- to those born to married mothers. What happened? It turns out that the shift was not in behavior per se, but in what data was gathered by the state of Texas. Prior to 1994, birth certificates did not indicate marital status, but it was assumed when compiling the statistics that the woman was married if the father's name was listed. After 1994, when marital status was specifically asked about, a truer picture emerged. (Thanks go to Dr. Bill Spears for ferreting that out and sharing it with me.)
Death Rate (per 100,000) in Texas
Cause of Death: Diabetes mellitus (ICD 250)
(From Texas VitalWeb)
In this example, we see a fairly substantial jump in the number from 1988 to 1989, then another jump from 1989 to 1990. Were there really that many more deaths from diabetes? Most likely not. If anything, death from diabetes were probably underreported prior to 1989. However, the Texas Death Certificate was changed in 1989 to include an example on the back, with diabetes used in the example. Immediately, the death rate for diabetes increased. Unfortunately for Texas, deaths due to diabetes continue to rise. Nearly one third of the deaths in Texas in 2002 were attributed to Diabetes Mellitus (Texas Health Data Deaths of Texas Residents) although some of the increase could have been a result of the change from ICD-9 to ICD-10. In the case of diabetes, deaths attributed to diabetes rose slightly (less than 1% or a comparability ration of 1.0082). (See below for more discussion on ICD-9 vs. ICD-10.) (Thanks go to Daniel Goldman for ferreting out the death certificate change and sharing it with me.)
In 1994 there were 59 infectious diseases notifiable at the national level. In 2010, there were 100. Not knowing when an infectious disease becomes notifiable can lead to a misinterpretation of the data. Looking at chlamydia, for example, we see the following data for all of Pennsylvania.
Chlamydia in Pennsylvania
|Rate per 100,000
(From: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for HIV, STD and TB Prevention (NCHSTP), Division of STD/HIV Prevention, Sexually Transmitted Disease Morbidity 1984-2014, CDC WONDER Online Database. Accessed at http://wonder.cdc.gov/std-sex.html on Jul 15, 2019 12:57:27 PM )
There is what appears to be a chlamydia epidemic in Chlamydia. The number of chlamydia cases jumped 6-fold between 1991 and 2000. But wait-- when did chlamydia become a notifiable disease? Based on the data, we might guess that it was 1995 as that is when we see a large increase in both count and rate. Prior to 1995, chlamydia was only voluntarily reported; it became a notifiable disease in 1995. Learn more about data reporting for chlamydia.
The CDC lists other concerns when interpreting data:
"Incidence data in the Summary are presented by the date of report to CDC as determined by the MMWR week and year assigned by the state or territorial health department.....Thus, surveillance data reported by other CDC programs may vary from data reported in the Summary because of differences in 1) the date used to aggregate data (e.g., date of report, date of disease occurrence), 2) the timing of reports, 3) the source of the data, 4) surveillance case definitions, and 5) policies regarding case jurisdiction (i.e., which state should report the case to CDC).
The data reported in the Summary are useful for analyzing disease trends and determining relative disease burdens. However, these data must be interpreted in light of reporting practices. Some diseases that cause severe clinical illness (e.g., plague and rabies) are most likely reported accurately if they were diagnosed by a clinician. However, persons who have diseases that are clinically mild and infrequently associated with serious consequences (e.g., salmonellosis) might not seek medical care from a health-care provider. Even if these less severe diseases are diagnosed, they are less likely to be reported.
The degree of completeness of data reporting also is influenced by the diagnostic facilities available; the control measures in effect; public awareness of a specific disease; and interests, resources, and priorities of state and local officials responsible for disease control and public health surveillance. Finally, factors such as changes in the case definitions for public health surveillance, introduction of new diagnostic tests, or discovery of new disease entities can cause changes in disease reporting that are independent of the true incidence of disease."
Take a look at a list of notifiable diseases as well as statistics from the MMWR Summary of Notifiable Diseases for 1993 through 2015 and the NNDSS Data and Statistics System from 2016 forward. The most current list is available from through the National Notifiable Diseases Surveillance System.
Age adjustments are used to compare two populations during the same time period or the same population during different time periods. They are used to eliminate observed differences in the population that are age-related. There are four common standards, the most current being the 2000 standard. Other standards include: the 1980 standard (not as common), the 1970 standard (common), and the 1940 standard (common). In order to get a viable comparison, you must use the same standard. The TX Dept of State Health Services has a nice description and example of age adjustment.
ICD-10 is in current use to classify mortality data; ICD-9 was in use prior to 2010. This international classification provides for a means of comparison between the US and other countries. ICD-10 is more detailed than ICD-9 and utilizes an alpha-numeric system; ICD-9 was a numeric only system. For the purpose of comparison, see Anderson, RN, et al. (2001). Comparability of Cause of Death Between ICD-9 and ICD-10: Preliminary Estimates. National Vital Statistics Reports, 49(2). The World Health Organization has posted ICD-10 codes online (2019 edition).
Some Healthy People data (specifically diabetes) reports using multiple causes; mortality data via many of the sources on these pages show underlying cause only. Be certain you know which you are looking at so you aren't misled by conflicting data.
As you look at the data, be sure you understand what the actual unit of measure is. Are you looking at a count or a rate? Is the rate age-adjusted? Which standard was used? If you aren't certain what that means, take a look at the Rates and Formulas page for additional information.
Be sure you understand the unit of measure so that you can compare apples to apples. You cannot compare a non-adjusted rate with an age-adjusted rate. And quite honestly, you probably do not want to compare crude rates if there are several years separating them (i.e. a decade), especially in areas that are rapidly changing. It is possible to calculate an age-adjusted rate fairly easily. A general epidemiology book will explain how.
Medicine has made great strides in keeping people alive when twenty years ago, or even ten years ago, they would have died. Just think about AIDS, heart attacks, strokes, and cancer. Mortality data is not always the most accurate reflection of the health of a people.
Another example is infant mortality. Rates increased in the United States in 2002, from 6.8 deaths per 1,000 births in 2001 to 7.0 deaths in 2002. What has happened? The causes are not fully known yet, but the CDC has some thoughts on the reasons why. Take a look at the "Supplemental Analyses of Recent Trends in Infant Mortality." Again, medical technology could have influenced infant mortality. What would have once been a miscarriage is now a preterm delivery.
Has the population aged? If so, we may see a sharp increase in cancer and cardiovascular diseases. Is it a younger population? Then there may be an increase in the number of pregnancies and STDs. Be sure to look at the demographics of the population when examining the number of occurrences of an event.
Data for self-reported behaviors cannot expect to be accurate. After all, a survey participant may be asked about behaviors that are embarrassing or even illegal. Consequently, when questioned, participants may under-report certain behaviors (drinking while pregnant) and over-report others (exercise).
Be careful when working with diseases in which there are not a large number of deaths or the population is fairly small. While the mortality rate may be expressed as the number per 100,000 (i.e. a point estimate), you should also take into consideration the confidence interval which indicates how large or small the spread of uncertainty is. What is a confidence interval? "A confidence interval is a range around a measurement that conveys how precise the measurement is. For most chronic disease and injury programs, the measurement in question is a proportion or a rate (the percent of New Yorkers who exercise regularly or the lung cancer incidence rate).
The confidence interval tells you more than just the possible range around the estimate. It also tells you about how stable the estimate is. A stable estimate is one that would be close to the same value if the survey were repeated. An unstable estimate is one that would vary from one sample to another. Wider confidence intervals in relation to the estimate itself indicate instability. For example, if 5 percent of voters are undecided, but the margin of error of your survey is plus or minus 3.5 percent, then the estimate is relatively unstable. In one sample of voters, you might have 2 percent say they are undecided, and in the next sample, 8 percent are undecided. This is four times more undecided voters, but both values are still within the margin of error of the initial survey sample.
On the other hand, narrow confidence intervals in relation to the point estimate tell you that the estimated value is relatively stable; that repeated polls would give approximately the same results." (From the NY State Department of Health: Confidence Intervals - Statistics Teaching Tools)
For example, CDC Wonder Underlying Cause of Death dataset was queried. The table was set up to show the crude rate of mortality from brain cancer by gender for each county in Pennsylvania along with a 95% confidence interval. The 3 years of data were aggregated. A point estimate rate was calculated for most of the counties but 24 counties in Pennsylvania were flagged as the numerator was less than 20; the crude rate was not calculated although the confidence interval was. And in one case, the confidence interval ran from 9.0 to 34.5. The "true" crude rate would be somewhere in between.