Ranking causes of death is a popular method of presenting mortality statistics. This method has been used for over 50 years to show the most frequently occurring causes of death and their relative impact. All states use a standard method to classify causes of death, and periodically, the cause of death list is updated based on the International Classification of Diseases (ICD). The custom is to rank causes by the total number of events in a year for the 50 leading, rankable causes of death. This atlas differs in that it combines three years of data to present leading causes of death in a manner that smooths differences due to small numbers, particuarly at the county level. It concentrates on the most prevalent causes of death in Florida over that period: cancer (malignant neoplasm), heart disease, chronic lower respiratory disease (CLRD), unintentional injury, stroke (cerebrovascular disease), diabetes mellitus, Alzheimer’s disease, kidney disease (nephritis), suicide, chronic liver disease and cirrhosis (CLDC), influenza and pneumonia, septicemia, hypertension, Parkinson’s disease, benign neoplasms, homicide, Pneumonitis Due to Solids & Liquids and Human Immunodeficiency Virus (HIV).

The International Classification of Diseases (ICD) is the system used to code and classify mortality data from death certificates. The ICD is designed to promote international comparability in the collection, processing, classification, and presentation of mortality statistics. This includes providing a standardized format for reporting causes of death on death certificates. The reported conditions are translated into medical codes through use of this classification system, which is published by the World Health Organization (WHO). In order to keep abreast of changes in medical knowledge, the ICD is revised approximately every ten to twenty years.

Due to these revisions, some of which involve major changes, year-to-year comparisons of deaths by cause can be misleading unless such comparisons span a period of years in which only one revision was used or in which the changes from one revision to another were minor.

In this Atlas, the International Classification of Diseases Eighth Revision (ICD-8) was used for the coding of 1970 through 1974 underlying causes of death, the Ninth Revision (ICD-9) for was used from 1979-1989, and the Tenth Revision (ICD-10) was used for coding the 1999 through present day underlying causes of death. Two causes of death, Alzheimer’s disease and HIV, were not yet classified at the time the ICD-8 was issued. Changes from the ICD-8 to ICD-9 were minor but differences between the ninth and tenth revisions are more apparent. ICD-10 contains major changes, so that a greater or fewer number of deaths are now assigned to certain causes than under ICD-9 rules. Causes that changed the most include Alzheimer’s disease and influenza/pneumonia.

The maps in the Florida Mortality Atlas are colored using a quartile method. In this method, data (age-adjusted death rates) are calculated and then ranked from lowest to highest for all 67 counties. Next, the counties are divided into four groups. Each group is assigned a number from 1 to 4. The counties with the lowest ranking rates are assigned to the first quartile (1) and are shaded with the lightest color, while the counties with the highest-ranking rates are assigned to the fourth quartile (4) and are shaded with the darkest color. Because quartiles are calculated using data from all 67 counties, the color-coded map provides a relative ranking among counties.

Mortality varies by county, consequently the quartile limits are different for each map, and the range of values represented by a given quartile varies from map to map. Therefore, comparisons of the spatial patterns of mortality across maps should be limited to comparing relative differences between different groups (e.g. white and black). To determine whether the mortality rates were absolutely higher or lower for one group than for another, the reader must study the relevant legends and compare the quartile limits.

Much of community health assessment involves describing the health status of a defined community by looking at changes in the community over time or by comparing health events in that community to events occurring in other communities or the state as a whole. In making these comparisons, we need to account for the fact that the number of health events depend in part on the number of people in the community. To account for growth in a community or to compare communities of different sizes, we usually develop rates to provide the number of events per population unit.

A rate consists of a numerator and a denominator. The two numbers are divided, then multiplied by a constant (such as 100,000) to provide the number per 100,000 population.

The numerator is the number of health events. This is often the same as the number of people who experience an event, but for some health conditions, one person may experience the event more than once. For example, one individual may have multiple hospitalizations for the same condition in a given year.

To measure incidence or prevalence of the condition, you usually want to count people. To measure the public health burden, you may want to count events. Actions based on the data may be different depending on whether the rate represents many individuals with only one event or a smaller number of individuals who have had many events. It is customary to count only events that occur among the population at risk.

The denominator is also known as the population at risk. Everyone in the population at risk must be eligible to be counted in the numerator if they have the event of interest. For example, in looking at female cervical cancer, we cannot include men in the population at risk. Once the numerator and denominator are established, a decision must be made as to the appropriate rate to use.

A crude rate is calculated by dividing the total number of events in a specified time period by the total number of individuals in the population who are at risk for these events and multiplying by a constant, such as 1,000 or 100,000 [e.g., (numerator/denominator) × constant].

Example: The total crude death rate in Orange County for 2002 is the number of total deaths in Orange County (numerator) divided by the population of Orange County in 2002 (denominator). The result of this calculation is multiplied by 100,000 (constant) to arrive at the 2002 crude death rate per 100,000 population for Orange County.

(6,469 (total deaths) / 962,531 (total population)) × 100,000 = 672.1 deaths per 100,000 population

Although useful for certain purposes, the crude death rate as a comparative measure has a major shortcoming: it is a function of the age distribution of the population at risk. For example, the population at risk in one county may be primarily elderly persons ages 65 and older while the population at risk in another county may be primarily of persons ages 40 to 50. Crude rates are recommended when a summary measure is needed and it is not necessary or desirable to adjust for other factors. For example, rates of infectious diseases, such as tuberculosis and hepatitis, are usually not age adjusted, because public health officials are interested in the overall burden of disease in the total population irrespective of age.

The frequency with which health events occur is almost always related to age. In fact, the relationship of age to risk often dwarfs other important risk factors. For example, acute respiratory infections are more common in children of school age because of their immunologic susceptibility and exposure to other children in schools. Chronic conditions, such as arthritis and atherosclerosis, occur more frequently in older adults because of a variety of physiologic consequences of aging. Mortality rates tend to increase after the age of 40.

Because the occurrence of many health conditions is related to age, the most common adjustment for public health data is age adjustment. The age-adjustment process removes differences in the age composition of two or more populations to allow comparisons between these populations independent of their age structure.

The age-adjusted death rate is a summary measure that eliminates the effect of the underlying age distribution of the population. The result is a figure that represents the theoretical risk of mortality for a population, if the population had an age distribution identical to that of a standard population. For example, a county’s age-adjusted death rate is the weighted average of the age-specific death rates observed in that county, with the weights derived from the age distribution in an external population standard, such as the U.S. population.

In the past, the National Center for Health Statistics (NCHS) age-adjusted rates using the US 1940 standard population. Other agencies used the US 1970 Standard. Beginning with 1999 data, federal agencies began age-adjusting to the US 2000 Standard Million Population.

Example: To calculate the Age-Adjusted Death Rate, follow these steps:

1. Calculate death rates per 100,000 for each age group.

2. Multiply this rate by the 2000 US population proportion. This is the standard 2000 US population proportion, which FLHealthCHARTS.com uses to calculate age-adjusted death rates.

3. Sum values for all age groups to arrive at the Age-Adjusted Death Rate.

Age-adjusted death rates enable health professionals to measure health conditions versus the distribution of persons by age. Age-adjusted death rates are more useful than crude death rates when comparing death trends from different populations. For instance, crude death rates may show a disease to be low in County A when compared to County B. But, is this the true picture of what is occurring in these counties? Since crude death rates are sensitive to the distribution of persons in the population, it could be that County A’s rate is low because fewer people at-risk of dying live in County A than in County B. Age-adjusted death rates can also help to study death trends in a single county over time. Age-specific death rates within the county may remain stationary over time, but with an aging population the crude death rate may increase from the higher number of persons at greater risk of dying.

Age-adjusted rates are utilized throughout the Florida Mortality Atlas. The availability of official Hispanic population data from the Office of Economic and Demographic Research began in 2004. Therefore Hispanic rates are available beginning with this year.

• Age-adjusted rates answer the question: “How does the rate in my county compare to the rate in another even though the distribution of persons by age may vary?”
• Age-adjusted rates are specialized measurements and therefore should not be compared with other types of rates or be used to calculate the actual number of events.
• Age-adjusted rates can illuminate important trends by removing age-related differences.
• Age-adjusted rates using the same standard US populations (1940, 1970, or 2000) may be compared. Because of shifts in the distribution of persons by age in each year, rates calculated using the 1940 standard population, for example, should not be compared to rates calculated using the 2000 standard population.

Rates based on small numbers of events can fluctuate widely from year to year for reasons other than a true change in the underlying frequency of occurrence of the event. This is especially true in counties with small populations. To alleviate this problem, a multi-year has been used instead of a single-year rate.

A multi-year rate combines several years of data into one rate. The Florida Health Atlas uses age-adjusted rates from three consecutive years to calculate multi-year rates by using the total number of deaths for the three-year period and the sum of the population at risk for each of the three years to calculate a single rate per 100,000 population.