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Last Edited: 6/24/2014

Heart Attack: Measurement and Limitations

To understand who is at risk for heart attacks in California, it is important to look at who has had a heart attack (prevalence), who has been hospitalized or visited an emergency department for heart attacks (morbidity), who has died from heart attacks (mortality), and who has certain risk factors that increase their chances of experiencing a heart attack.

Currently, the data are not readily available to look at all these phenomena as they relate to heart attacks.  Often times what is available is mortality and morbidity data due to cardiovascular disease (CVD) and heart disease.  Both of these include events involving heart attacks, as well as other conditions and diseases.  The category of CVD includes heart disease, stroke, and heart failure; and the category of “heart disease” includes heart attacks and other heart conditions.

Here, we provide data and information specific to heart attack-related morbidity, which is measured by looking at how many hospitalizations or emergency department visits are due to heart attacks. More specifically, cases of heart attack-related hospitalizations and ED visits are those that have an International Classification of Disease (ICD-9) diagnosis code of 410.

Learn more about heart attack data sources and limitations

This section contains the following topics:


Data Sources

Hospitalization and Emergency Department (ED) Data

Since 1986, the Office of Statewide Health Planning and Development (OSHPD) has been responsible for routinely collecting data on hospital discharges from every licensed acute care hospital in California, excluding federal hospitals. Since 2005, OSHPD has been responsible for collecting data on ED visits from California hospitals.  Each year, OSHPD compiles the data from all hospitals to create the Patient Discharge Database (PDD) and Emergency Department (ED) database.

These datasets include information like age, gender, race/ethnicity, and diagnosis. A case due to a heart attack is identified by looking at the principal diagnosis based on the International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM). A principal diagnostic ICD-9-CM code of 410 indicates a patient was admitted to a hospital or visited an emergency department because of a heart attack (acute myocardial infarction).

PDD and ED data are available for hospitals to assess their level of performance. The data are also available to some California State and Federal government programs and some academic institutions to be used for research and other public health purposes.

To find out more about what data is available from OSHPD, go to their website:
To request public use data from OSHPD, go here:

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Population Data

To calculate rates within a certain population and timeframe, one uses population data (i.e. denominator data) that represent that population during the specified period of time.  The U.S. Census is the only comprehensive source of population data and is collected every ten years.  The last census was collected in 2010 and the next collection will be in 2020.  To estimate the population during the interim (2011-2020), demographers use various algorithms to project how the population has changed.


Hospitalization and ED rates presented by other data providers (e.g. CDC) may differ slightly because they may use population projections calculated by other methods.

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Statistical Concepts


The number of hospitalizations or ED visits among California residents is calculated by summing the total number of events (i.e., hospital discharges for heart attacks) for a given time period (e.g. year), geography (e.g. county), and demographic group (e.g. Hispanics). The event is based on the date of discharge. The events are per discharge or visit to the ED and not per person, since some people can be admitted to the hospital or visit the ED more than once in a given year.

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  • Crude rates

Crude rates (i.e. unadjusted) are calculated by taking the total number of events for a given time period (e.g. year), geography (e.g. county), and demographic group (e.g. Hispanics), and dividing by the total underlying population for the same time period, geography, and demographic group. The rates are then multiplied by 10,000 and expressed as X hospitalizations per 10,000 California residents.

  • Age-adjusted rates

Age-adjusted rates take into account the age-distribution of a population and are calculated to allow for direct comparisons between two or more populations at one point in time or between a single population at two or more points in time. Crude rates measure the true risk for a population, while age-adjusted rates are useful as a relative index of risk.

Using the direct method of age-adjustment, crude rates are weighted to be comparable to a standard population. For the age-adjusted hospitalization rates presented here, we use the U.S. Census 2000 population as the standard population (

Below are the steps taken to calculate the age-adjusted rates for adults over the age of 35:

1. Group the numerator (i.e. hospitalizations, a) and denominator (i.e. population, b) data into 5-year age group strata (35-39 years old, 40-44 years old, …, 75-79 years old, 85 years old and over).

2. For each stratum, divide the numerator by the denominator (a/b).

3. For each stratum, multiply a/b by the stratum-specific weight of the standard population (a/b*w). The weight is calculated by taking the total number of people in each stratum of the standard population and dividing by the total number of people in the entire standard population.

4. Sum a/b*w across all strata and multiply by 10,000.The total is the age-adjusted rate per 10,000 California residents.

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Confidence Intervals

Given the data at hand, to understand the range of possible values for the true rate, we calculate the 95% confidence interval for each rate. Statisticians have developed a large number of methods for calculating these confidence intervals. Usually the result is the same no matter which method is used, although when numbers of events are small, the results may differ.

The method developed by Tiwari, Clegg, and Zou (Methods in Medical Research, 2006; 15:547-569), applies specifically to the situation of age-adjusted health outcomes such as hospitalizations due to asthma. We have chosen to use this method for calculating confidence intervals for the CEHTP Portal, but it is important that users understand that others in the field may not be using the same method.

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Common limitations with the data

Purpose of data collection differs from EPHT

Per legislation, hospitals are required to report patient data to the California State Office of Statewide Health Planning and Development (OSHPD). The data are collected for purposes of assessing healthcare quality, services, and insurance coverage and not reported specifically for environmental public health tracking. Although the data are being used for public health purposes, the limitations associated with how the data is collected must be kept in mind and when possible, accounted for.

An example of such a limitation has to do with how a patient’s principal diagnosis gets coded. Since the diagnosis codes are recorded by hospitals for reimbursement purposes, the code might have been different if the purpose for recording was for primarily public health surveillance. Since we identify events based solely on the principal diagnosis code, we must keep this limitation in mind.

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Low-level of resolution

At this time, hospitals are not mandated to report patient addresses.  Thus the level of geographic resolution of the data is limited to state, county, and zip code.  Currently, the CEHTP web portal only displays county-level counts and rates, but more local-level or neighborhood-level patterns of disease currently cannot be examined.  Thus, the data are not as useful to users who might like to examine the rates of hospitalization in their immediate neighborhoods.

 Zip code-level data have a higher geographic resolution than county-level data and are of more value to some users.  However, at this time, there is not a reliable zip code-level population data source available, so rates are difficult to calculate.

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Potential for race/ethnicity misclassification

The concepts of race and ethnicity are difficult to define. Although hospitalization data include information on individual-level race and ethnicity, we know that these data are not necessarily recorded consistently and may not reflect peoples' self-identification of their race/ethnicity, much less capture their experiences with respect to discrimination, acculturation, or vulnerability to health problems. When using race/ethnicity information from these data sources, these limitations should be kept in mind.

In hospitalization data, only one race/ethnicity is recorded for each patient, which does not allow for the identification of multiracial patients. Hispanics are classified based on the national origin of their family, even if they were born in the United States. If an individual is reported as “Hispanic”, regardless of the race reported, they are classified as “Hispanic” in the data. Thus, these are the broad race/ethnicity categories used with hospitalization data:

• Hispanic, which includes a person having origins in or who identifies with peoples of Mexico, Puerto Rico, Cuba, Central or South America or other Spanish-culture country

• Non-Hispanic Asian/Pacific Islander, which includes a person having origins in or who identifies with peoples of Hawaii, Laos, Vietnam, Cambodia, Hong Kong, Japan, China, India, Taiwan, the Philippines, and Samoa

• Non-Hispanic Black, which includes a person who identifies as an African American and/or who has origins in any of the black racial groups of Africa

• Non-Hispanic White, which includes a person who identifies as a Caucasian and/or who has origins from Europe, North Africa, and the Middle East

• Other, which includes any possible option not covered by the above categories including Native American, Eskimo, Aleut, and Unknown

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