LEARNING OBJECTIVES
By the end of this chapter the reader will be able to:
■ Describe and provide examples of the three main elements of descriptive epidemiology: person, place, and time.
■ Define the terms disease cluster, outbreak, and epidemic.
■ Describe the steps involved in investigating a disease outbreak.
■ Describe the Ebola outbreaks and their investigation in Africa.
■ Discuss the scientific and administrative uses of descriptive epidemiology.
■ Describe the demographic characteristics of the U.S. population and its pattern of mortality by age.
■ List the strengths and limitations of mortality data.
■ Discuss the descriptive epidemiology of childhood lead poisoning, human immunodeficiency virus (HIV) infection, and breast cancer in the United States.
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▸ Person
Personal characteristics that are usually available for descriptive epi- demiology include age, gender, race and ethnic group, socioeconomic status, occupation, religion, and marital status. These attributes can be associated with major variations in disease occurrence. Age is probably the most important among them because it is associated with striking changes in disease rates. The frequency of most diseases increases with age. For example, the prevalence of hypertension among adults in the period from 2011 to 2014 increased steadily with age from 7.3% of 18 to 39 year olds to 64.9% of those age 60 and older.3 On the other hand, the incidence of some diseases declines with age. For example, pertussis (also known as whooping cough) occurs predominantly in childhood, particularly among young children.4(pp449-454)
Why does disease occurrence vary dramatically with age? The answer is complicated because an individual’s numerical age reflects both the aging process and that person’s experiences. The latter includes the accu- mulation of harmful exposures as well as protective factors. For example, the prevalence of habits such as alcohol consumption increases with age (at least from 12 through 34 years of age)5(p213) as does the prevalence of protective characteristics such as immunity to infectious diseases.
Sex is another personal characteristic associated with variations in disease occurrence. Certain diseases are more common among men and others are more prevalent among women. A striking example of this type of variation is breast cancer, a disease for which less than 1% of cases occur among men and more than 99% occur among women.6 The opposite is seen with HIV infection in the United States, for which women accounted for only 19% of HIV diagnoses in 2015.7 Possible reasons for variations in disease rates between sexes include differences in (1) hormone levels (e.g., female hormones may protect women against heart disease); (2) habits, such as the use of tobacco, alcohol, and drugs (which are more common among men); (3) sexual practices (e.g., anal intercourse, a risk factor for HIV transmission, is most commonly practiced among men who have sex with men); and (4) occupational exposures (e.g., men are more likely than women to hold jobs involving exposure to toxic exposures).8
Race and ethnicity also have a profound influence on disease patterns and can be particularly difficult to measure. The U.S. Census currently distinguishes between more than 12 racial groups, including White, Black, American Indian or Alaskan Native, Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, Native Hawaiian, Samoan, and Gaumanian or Chamorro.9 The Census also identifies persons of Hispanic, Latino, or Spanish origin independent of racial category. In the 2010 U.S. Census,
“Some Other Race” represented the third largest race group primarily because almost half of Hispanic or Latino respondents do not identify within any of the racial categories as defined in the U.S. Census.9,10
Rates for many diseases in the United States are higher among minority groups, particularly Black people.For example, diabetes is nearly twice as high among Black and Mexican people as among Whites.
In addition, infants born to Black women are more than two times as likely to die before their first birthdays compared to those born to White women.11 These racial health disparities stem from complex histories of racial discrimination and dispossession coupled with differences in socioeconomic status, health practices, psychosocial stress and resources, environmental exposures, and access to health care. Because many of these factors are highly correlated, it is often difficult for epidemiologists to tease apart their contributions.
Socioeconomic status is also a prominent characteristic by which diseases vary. Commonly used measures of socioeconomic status include educational level, income, and occupation. The sinking of the Titanic is a historic example of health disparities between the poor and wealthy.
Death rates among passengers of low socioeconomic status were twice as high as those among passengers of high socioeconomic status because the small supply of life jackets was preferentially given to wealthy passen- gers, particularly wealthy women and children.12
Today, large disparities for almost all measures of health exist between people from low and high socioeconomic groups. For example, life expectancy is strongly related to income levels. A recent analysis found that, at the age of 40, the gap in life expectancy between individ- uals in the top and bottom 1% of the income distribution in the United States is 15 years for men and 10 years for women.13 The relationship between income and health is complex because income is related to race, nutrition, risk factors such as smoking and alcohol use, environmental and occupational exposures, and access to and use of healthcare services.
Religious affiliation also influences disease rates. Like most of the personal characteristics described thus far, religion represents a mixture of factors, including genetic, environmental, cultural, and behavioral fac- tors. For example, Tay-Sachs disease, a degenerative disease of the brain and nervous system, is associated with a genetic mutation that is pres- ent mainly among Jewish people of Eastern European decent.14(pp347-350)
On the other hand, the likely reason for the 2.9% fewer cases of cancer among male Mormons and the 7.9% fewer cases among female Mormons is their prohibition against cigarette smoking and alcohol consumption as well as different sexual and reproductive patterns.15
It is not surprising that occupation influences disease patterns because potent and sustained exposures to harmful substances can occur on some jobs.16(pp94-98) One of the earliest associations between an occu- pation and disease was observed almost 200 years ago by Dr. Percivall Pott, who noted that London chimney sweeps had a high rate of scrotal cancer. It was only many years later that the constituents of soot, called polycyclic aromatic hydrocarbons, were found to cause cancer in labo- ratory animals. Today, we know that the patterns of numerous diseases
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vary by occupation. For example, people with jobs in aluminum produc- tion, boot and shoe manufacturing, coal gasification, furniture making, iron and steel founding, rubber manufacturing, and nickel refining are known to have higher rates of cancer than the general population.
Finally, marital status is known to have an important effect on the patterns of disease and death. For example, death rates are higher among people who are unmarried than for those who are married and living with their spouses.17 The increased rates of death are greatest among those who never married, particularly never-married men. Data such as these suggest that the psychological and economic support associated with marriage exerts a protective effect against certain adverse health events, especially for men.18,19 Alternatively, it is possible that the characteristics that lead a person to marry may be responsible for this protection.20
▸ Place
Place can be defined in terms of geopolitical units, such as countries or states, or in terms of natural geographic features, such as mountains or riv- ers. The characteristics of place encompass numerous aspects of the envi- ronment, including the physical environment (such as climate, water, and air), biological environment (such as flora and fauna), and social environ- ment (such as cultural traditions). For example, malaria occurs in parts of the world where all these facets of the environment are conducive to the life cycle of the Anopheles mosquito, the vector that carries disease from one host to another.21 Physical conditions that are necessary for the devel- opment and survival of the mosquito include a favorable temperature (20°C to 30°C is optimal), adequate humidity, moderate rainfall, and the presence of standing or gently flowing water. Biological factors beneficial to the mosquito include plants that can collect small pools of water. Social factors that encourage transmission of the disease include the proximity of homes to mosquito breeding sites, housing construction that facilitates mosquito entry, and certain occupations that increase a person’s exposure to mosquitos, such as those involving outdoor work at night.
The scale of geographic comparisons can range from a global scale, in which rates are compared between continents and countries; to a regional scale, in which regions, states, and cities are compared; and to a local scale, in which neighborhoods are examined. Regardless of the scale that is used, striking geographic patterns of infectious and noninfectious diseases are often observed. For example, almost all cases of malaria are limited to Africa south of the Sahara Desert, central and southeast Asia, eastern Asia, and Central and South America (see FIGURE 5-1).
Rates of chronic diseases, such as cancer, also show tremendous worldwide variation (see TABLE 5-1). For example, rates of liver cancer among males are 9 times higher in Eastern Asia than rates in South- Central Asia.22 Epidemiologists hypothesize that higher rates of hepatitis infection in Eastern Asia account for this particular difference.23
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TABLE 5-1 International Range of Cancer Incidence
Cancer site Area with high
rate Rate* Area with low
rate Rate* Ratio
(high to low) Males
Liver Eastern Asia 31.9 South-Central Asia 3.7 8.6
Stomach Eastern Asia 35.4 Western Africa 3.3 10.7
Bladder Southern Europe 21.8 Western Africa 2.1 10.4
Females
Cervix Eastern Africa 42.7 Western Asia 4.4 9.7
Lung Northern America 33.8 Middle Africa 0.8 42.3
Breast Western Europe 96.0 Middle Africa 26.8 3.6
*Rate per 100,000 population. The rates were age adjusted to eliminate the differences in rates caused by differences in the age composition of the underlying population.
Reproduced from the World Health Organization. Malaria: number of reported cases (confirmed by slide examination or rapid diagnostic test): 2014.
http://gamapserver.who.int/gho/interactive_charts/malaria/cases/atlas.html. Accessed September 2017.
FIGURE 5-1 Number of malaria reported confirmed cases, 2014.
<10,000
>1,000,000 Not applicable No data
Non-malaria endemic
10,000–49,999 50,000–199,999 200,000–499,999 500,000–1,000,000
Reproduced from the World Health Organization. Malaria: number of reported cases (confirmed by slide examination or rapid diagnostic test): 2014. http://gamapserver.who.int/gho/interactive_charts/malaria /cases/atlas.html. Accessed September 2017.
An example of disease variation on a regional scale is the apparent east-to-west gradient in semen quality across the Nordic-Baltic area of Europe. The adjusted total sperm counts among Finnish, Estonian, Danish, and Norwegian men are 185, 174, 144, and 133 million, respec- tively.24 A common protocol was used to examine the men who were considered “representative of the normal population of young men,” and therefore the researchers concluded that the gradient was real.
An example of neighborhood variation in disease occurrence is the distribution of childhood lead poisoning within the city of Boston. The prevalence of childhood lead poisoning was been highest in certain areas of the city. Historically, the residences in these areas contained lead-based paint, and the surrounding soil had high levels of lead contamination.
Migrant studies are one of the ways that epidemiologists investigate the effect of place on disease occurrence. These studies compare the rates of disease among natives of a homeland to rates among immigrants (and their offspring) and among natives of the adopted country. For example, migrant studies have found that the rate of prostate cancer is low among Japanese in Japan, intermediate among Japanese immigrants to Hawaii, and high among Hawaiian Whites.25(pp185-187) Recent data comparing breast cancer incidence rates among Japanese women living in the United States to Japanese women living in Japan show a similar increase toward rates approaching that of White women in the United States.26 If the rate of disease among migrants approaches that of the host country, epidemi- ologists hypothesize that environmental factors may cause the disease.
In the case of prostate cancer, those environmental factors may include the adoption of the dietary patterns of the host country, such as higher consumption of animal fat. For breast cancer, it may include changes in reproductive factors, such as age at first birth and age at menopause.
▸ Time
Analysis of the changes in disease and death rates over calendar time provides epidemiologists with useful information for causal research and public health planning and evaluation. The scale of time that is exam- ined depends on the disease and can range from decades or years to months, weeks, days, or hours. For example, the age-adjusted death rate from Alzheimer’s disease has increased 25% among women from 2005 to 2015.5 Over the same period, there has been a dramatic decline in deaths from stroke. Both of these are examples of long-term trends.
Short-term trends are commonly examined for infectious diseases.
For example, the famous 1976 outbreak of Legionnaires’ disease at a Philadelphia convention occurred over a 1-month period.27 Short-term trends are also relevant for noninfectious diseases that follow climatic changes, such as heat waves, hurricanes, and pollution episodes. For example, the 4-day 1952 smog disaster in London was associated with an increase in cardiovascular and respiratory deaths, particularly among
the elderly.28 More recently, temporal elevations in air pollution levels in Philadelphia were associated with concomitant increases in death rates from chronic obstructive pulmonary disease, pneumonia, heart disease, and stroke.28,29
Other types of temporal changes include periodic or regular fluc- tuations occurring on an annual, weekly, or even daily basis. Seasonal variations in disease frequency are the most common type of peri- odic fluctuations. For example, influenza peaks every winter season, and Lyme disease crests in late spring and summer.4(pp363-367) Regarding weekly and diurnal variations, studies have found that heart attacks occur most frequently on weekends and Mondays and in the morning and afternoon.30-32
What can we infer from time trends? First, they may result from concomitant changes in exposure to causal agents and/or susceptibility to the disease. However, it is also possible that temporal changes in disease and death rates result from parallel fluctuations in diagnostic capabilities, disease definition or reporting methods, the accuracy of the enumeration of the denominator population, or age distribution of the population. For example, the increased prevalence of birth defects of the heart in the late 1990s stemmed in part from the use of sophisticated ultrasounds that could detect theretofore undiagnosed cases.33 In addition, when exam- ining mortality rates over time, epidemiologists must consider the influ- ence of improvements in treatment that increase survival. How do we know which factor or factors are responsible for a particular time trend?
Information gathering and detailed analysis of all possible explanations provide useful clues, but in many cases the answers are never learned.