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Frequently Asked Questions - Heart Attack Hospitalization

What is heart attack hospitalization tracking?

Heart attack hospitalization tracking, or surveillance, is the ongoing collection, analysis, and interpretation of hospitalization data for heart attacks or acute myocardial infarctions (MI).  The Massachusetts Environmental Public Health Tracking (MA EPHT) Program uses data on hospital admissions due to heart attacks to reflect the public health burden of the disease.

Why is the MA EPHT Program tracking heart attack hospitalization?

In 2002, Massachusetts was one of seven states across the U.S. to be awarded funds from the U.S. Centers for Disease Control and Prevention (CDC) to track health conditions thought to be impacted by the environment.  A large number of epidemiologic studies have reported associations between air pollution exposure and heart attacks.  Each year, about 1.2 million Americans are expected to have a new or recurrent heart attack. 

What information can be obtained from tracking heart attack hospitalizations?

There is currently no single heart attack surveillance system in place in the U.S., nor does this exist for coronary heart disease in general.  Tracking heart attack hospital admissions will help to:

  • Examine time trends in heart attack hospital admissions
  • Identify seasonal patterns
  • Assess geographic differences in hospital admissions
  • Evaluate differences in heart attack hospital admissions by age, gender, and race/ethnicity
  • Identify populations in need of targeted interventions

In the future, heart attack hospitalization data will be linked to air pollution data to provide more in-depth examination of linkages between environmental and health data to assess potential public health impacts at the community level, and guide disease prevention and intervention programs.

What is the relationship between heart attacks and air pollution?

Exposure to air pollution, including particulate matter and ozone, has been linked to a wide range of cardiovascular and respiratory health effects (e.g., decreases in lung function, heart attacks).  Sensitive groups such as the elderly, patients with pre-existing heart disease, those who are survivors of heart attack, or those with chronic obstructive pulmonary disease (COPD) are particularly vulnerable. The outdoor air pollutant most commonly linked to heart attacks is particulate matter.  The US EPA Air Quality Index (AQI) is an index for reporting daily air quality.  The AQI tells you how clean or polluted your air is, and what associated health effects might be a concern for you.

How do I interpret a rate, and what is the difference between age-specific, crude, and age-adjusted rates?

A rate tells us how frequently a disease or disease-related event (in this case heat stress hospitalization) is occurring in a population. An age-specific rate for heart attacks is calculated for each age group to show how the incidence of heart attack changes with age.  A crude rate for heart attack is the number of heart attack admissions over a specified period of time, divided by the total population. An age-adjusted rate enables comparisons to be made between populations which have different age structures.

What is a confidence interval?

To determine if prevalence is significantly different from the state rate or if the difference may be due solely to chance, a 95% confidence interval (CI) was calculated for each rate.  A 95% CI assesses the magnitude and stability of a rate.  Specifically, a 95% CI is the range of estimated prevalence values that has a 95% probability of including the true prevalence for the population. 

One method for determining if one prevalence estimate is statistically significantly different from another is by comparing the confidence intervals.  If the 95% CI for one community or population does not overlap the CI of another, then it can be concluded that the two populations are statistically significantly different from each other.  If they do overlap, then it can be concluded that the two populations are likely not statistically significantly different.  “Statistically significantly different” means that the difference observed between the rates will occur by chance less than 5 percent of the time.  For example, if the prevalence for community A is 5.6 with a 95% confidence interval of 4.8-6.4 and the state prevalence is 10.2 with a confidence interval of 10.0-10.4, when the two intervals are compared, the interval for community A does not fall within the range of the state confidence interval.  Therefore, it is concluded that community A’s prevalence rate of 5.6 is statistically significantly different than the state estimate of 10.2.  And because community A’s is lower than the state’s, it can be concluded that the community’s rate is statistically significantly lower than the state rate.   

What are limitations of the data?

  • Data may only be presented to the public if confidentiality guidelines of the MDPH and DHCFP are followed through data aggregation and/or suppression in order to protect privacy. Access to restricted data must follow the application procedure specified on the MDPH website.

  • Hospitalization data, by definition, does not include individuals who do not receive medical care or who are not hospitalized, including those who die in emergency rooms, in nursing homes, or at home without being admitted to a hospital, and those treated in outpatient settings. 

  • Data may exclude admissions from specialty hospitals (e.g. psychiatric), long-term care facilities, and federal hospitals which are exempt from state reporting requirements.

  • Transfers from one hospital to another are not excluded from the data.

  • Reporting rates at the state and/or county level will not show the true disease burden at a more local level (i.e., neighborhood).

  • Reporting rates at the state and/or county level will not be geographically resolved enough to be linked with many types of environmental data.

  • When comparing rates across geographic areas, a variety of non-environmental factors, such as access to medical care and diet, can impact the likelihood of persons hospitalized for heart attack.

  • When looking at small geographic levels (e.g., zip code), users must take into consideration appropriate cell suppression rules imposed by the data providers or individual state programs.

  • Differences in rates by time or area may reflect differences or changes in diagnostic techniques and criteria and in the coding of a heart attack

  • Prevalence is based on the residential location of the cases and not necessarily the location of a source of exposure.

  • Numbers and rates may differ slightly from those contained in other publications. These differences may be due to file updates, differences in calculating rates and updates in population estimates.
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