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Applying Epidemiology

Epidemiology is the study of epidemics. More specifically, it is the study of the occurrence and distribution of health problems. Using any of the epidemiological techniques outlined in the chapters from this week’s reading, address the questions for one of the case studies outlined below. 



There are two parts to this assignment: 

Part I: Provide a brief statement of the investigative issue. Describe the epidemiological steps you would take by addressing the questions asked within the case study you select. 

Part II: Address the questions noted at the end of your selected case study. Your paper should be at least four pages in length, but can exceed this depending on how much detail you provide on the epidemiological steps you take for your case. You should use at least one additional scholarly source in addition to the textbook. Format your paper and all citations according to APA style guidelines 

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fri80977_03_c03TrimNoFolio.pdf 

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Chapter 3 Chu Brest-Garo/Phanie/SuperStock Epidemiologic Concepts Applied to Community Health Learning Objectives After reading this chapter, you should be able to: •• Define the term epidemiology. •• Differentiate between descriptive and analytic epidemiology. •• Trace the history of epidemiology. •• Describe the applications of epidemiology to community health research. •• Identify data sources used in community health. •• Define at least three epidemiologic measures. •• Analyze person, place, and time variables in descriptive epidemiological data. •• Distinguish among types of epidemiologic study designs. •• Describe the various levels of disease prevention as it applies to a community. fri80977_03_c03.indd 83 8/30/13 12:47 PM Section 3.2 Definition and Scope of Epidemiology CHAPTER 3 3.1 Introduction This chapter will demonstrate how epidemiology—a fundamental public health discipline that involves the study of incidence, distribution, and control of diseases—contributes to the field of community health. We will demonstrate that epidemiology is important because many circumstances that produce adverse health effects among community residents occur at the population level; thus, it is vital to take a population perspective when examining individual health outcomes in the community. Epidemiology is unique because of its focus on the health of populations, and, in this respect, it differs from clinical medicine’s involvement with individual patients. Furthermore, epidemiology provides a methodological foundation for the entire public health field, which also takes the perSteve McAlister/Getty Images spective of the population when addressing the public’s health. Like detectives, epidemiologists use various methods for Epidemiology embraces a spec- determining the often unknown causes of disease. trum of tools for studying health and illness. These methodologies include natural experiments, descriptive and analytic study designs (e.g., crosssectional, case-control, cohort, and experimental designs), and mapping technologies. Epidemiologic research findings aid in the development of hypotheses for application to the health of the community and the study of potential causal relationships. Epidemiologic research is likened to detective work because the causes of many diseases—especially in their initial presentations—are unknown. There are many examples of dangerous illness outbreaks in the community that began as mysteries. Some examples are Hantavirus in national parks, periodic episodes of foodborne illnesses, West Nile virus, and resurgence of whooping cough (pertussis). Throughout the chapter, we will discuss epidemiologic procedures and methodologies that aid in unraveling the causes of mysterious disease outbreaks and health conditions that can afflict community members. 3.2 Definition and Scope of Epidemiology This section covers basic epidemiologic concepts and describes how epidemiology aids the community health field. Epidemiology is “[t]he study of the occurrence and distribution of health-related states or events in specified populations, including the study of the determinants influencing such states, and the application of this knowledge to control the health problems” (Porta, 2008, p. 81). Often these health-related states or events are called outcome or dependent variables and include diseases, morbidity, injuries, disability, and fri80977_03_c03.indd 84 8/30/13 12:47 PM Section 3.2 Definition and Scope of Epidemiology CHAPTER 3 mortality. Recall from Chapter 1 that the term morbidity refers to a state of disease or symptoms. For example, while the mortality due to tuberculosis is on the decline, morbidity associated with this disease has increased. Denver Post via Getty Images A computer image of a GeneChip™, which holds a DNA sequence on a glass slide. The GeneChip™ and other such microarrays, which have been used in cancer research and disease identification, are just one application of the Human Genome Project’s success. Epidemiology is a discipline that describes, quantifies, and finds possible causes for health phenomena in populations. These postulated causal (etiologic) mechanisms are known as determinants. Etiological factors are those that contribute to a cause of a disease or condition. For example, smoking is a factor in the production of the condition of arteriosclerosis. The results of epidemiologic studies aid public health practitioners in their quest to control health problems like disease outbreaks. Epidemiology is an interdisciplinary enterprise that draws upon diverse and related fields including clinical medicine, biostatistics, toxicology, the social sciences, and genetics. One example of this interdisciplinary approach is the application of genetics and the Human Genome Project to epidemiology in order to conduct research on the genetic bases for breast and ovarian cancer. The Human Genome Project began in 1990 as an international effort to understand the sequence of chemicals in DNA and to identify and map the thousands of genes in the human genome. Researchers delivered a complete map in 2003, propelling further genomic research and exploration into its medical applications. While also concerned with the study and improvement of public health, community health usually focuses on geographical areas rather than individuals with shared characteristics—although “community,” or more specifically, “biological community,” is broadly defined. Epidemiology’s contributions to community health involve the discipline’s concern with populations, use of observational data, development of methodologies for study designs, and application of descriptive and analytic study designs. Concern With Populations As noted, epidemiology is unique with respect to its focus on health outcomes in populations. Accordingly, some public experts call epidemiology “population medicine.” An example of epidemiology’s focus on populations might be a study of the association of air pollution with adverse health effects. A hypothetical epidemiologic study might examine the occurrence of lung cancer mortality across counties or among regional subdivisions known as census tracts—the smallest geographical unit for which population data might be available in most countries. One of the driving hypotheses for the research might be that lung cancer mortality is higher in areas with higher concentrations of “smokestack” fri80977_03_c03.indd 85 8/30/13 12:47 PM Section 3.2 Definition and Scope of Epidemiology CHAPTER 3 industries and associated emissions of air pollution in comparison with areas that have lower levels of air pollution from these sources. The contrasting approach relative to population-based investigations would be a clinician’s diagnosis of an individual’s symptoms (e.g., asthma and respiratory distress) associated with poor air quality. Another example of a common concern with populations comes from total community health studies such as the Framingham Heart Study in Massachusetts, which, as discussed in Chapter 1, was able to identify common factors in cardiovascular disease by following its development in a large population over time. This project, as well as several other noteworthy epidemiologic investigations, studied risk factors for chronic diseases at the community level. Examples of such research are provided later in the chapter. Use of Observational Data As a general rule, ethical issues, such as potential dangers to subjects due to participation in a study, prevent researchers from conducting experiments with human beings. Obviously, it would be unethical for researchers to cause disease or other harm to human subjects. A classic example of failure to adhere to research ethics is the infamous Tuskegee syphilis experiment. Between 1932 and 1972, the United States Public Health Service studied the progression of untreated syphilis in rural Black men, neglecting to inform these men that (1) they had syphilis and (2) a cure—penicillin—had been validated as early as the 1940s. The experiment participants instead thought the federal government was providing free care. Researchers behaved unethically in knowingly withholding both information and appropriate treatment from ill patients. To overcome potential ethical challenges, epidemiologists As scientists cannot ethically initiate disease in humans, often study naturally occurepidemiological researchers observe the effects of naturally ring situations in populations occurring outbreaks such as the 1994 cholera outbreak pictured (e.g., disease outbreaks or other here in Goma, Zaire. adverse health outcomes in the area). Alternatively, they might observe the health effects of community health policies applied to populations, such as mandatory lap belt use in cars, smoking restrictions in workplaces, and fluoridation of water. Epidemiology is primarily an observational science that takes advantage of naturally occurring situations (or, in some cases, policy changes applied to society) in order to study health outcomes. However, this observational approach does not rule out the use of experimental procedures in Tom Stoddart Archive/Getty fri80977_03_c03.indd 86 8/30/13 12:48 PM Section 3.2 Definition and Scope of Epidemiology CHAPTER 3 intervention studies (e.g., randomized controlled trials and community trials). The simplest and most powerful epidemiological study is a randomized controlled trial (RCT), in which researchers allocate people by chance alone to receive one of the clinical interventions. One arm of the study is the standard of care or the control group. The other participants are allocated to the intervention or case group. Researchers then compare the case and control groups in terms of the outcomes measured as a result of the study. Methodology for Study Designs One of the goals of epidemiologic research is to portray the frequency and patterns of disease occurrence in the population and to link them with specific exposures. An exposure (sometimes called an independent variable) is the potential causal factor in an epidemiologic study. Exposures can include “environmental and lifestyle factors, socioeconomic and working conditions, medical treatments, and genetic traits” (Porta, 2008, p. 89). In order to examine the occurrence of disease outbreaks and environmentally caused diseases in the population, the field of epidemiology uses several characteristic study designs. We will discuss study designs in more detail later in the chapter. Descriptive Versus Analytic Epidemiology The two categories of epidemiology are descriptive epidemiology (represented by descriptive studies) and analytic epidemiology (represented by analytic studies). The former category is concerned with describing the occurrence of disease (or other health outcomes) and the latter with identifying the causes of disease. Descriptive epidemiology is defined as the characterization of disease occurrence in populations according to classification by person, place, and time variables. Descriptive epidemiologic studies are the first stage of an epidemiological study and include crosssectional studies (prevalence surveys). Regarded as a fundamental approach by epidemiologists, descriptive studies aim to delineate the patterns and manners in which disease occurs in populations. Descriptive epidemiology examines the effects of factors like age, education, socioeconomic status, availability of health care, race, and gender on outcomes, or dependent variables. Other risk factors may also be included, such as behavioral factors like drug abuse, shift work, diet, and exercise patterns. An example of the results of a descriptive study is the discovery of a correlation between people who work night shifts and high blood pressure. Analytic epidemiology is the process of using data gathered by descriptive experts to study patterns suggesting causes of diseases and other health conditions. Some typical examples of questions analytic epidemiologists ask are: What caused a foodborne disease outbreak? Do toxic chemicals cause cancer? What is the effect of diet on heart disease and diabetes? For the most part, analytic epidemiologic studies employ observational studies (e.g., case-control studies and cohort studies). They also make use of intervention studies. In a case-control study, subjects are enrolled on the basis of whether they have had the disease to determine whether there is an association existing between the disease and exposure. Cohort study subjects are enrolled based on their membership in a controlled subpopulation or category of the population (e.g, racial groupings). fri80977_03_c03.indd 87 8/30/13 12:48 PM CHAPTER 3 Section 3.3 Brief History of Epidemiology 3.3 Brief History of Epidemiology The history of epidemiology extends over many centuries, beginning with the contributions of the ancient Greeks during the classical period. Some of the landmarks in the history of epidemiology overlap the history of public health. Chapter 1 described historically noteworthy and frightening epidemics that threatened the very existence of humanity. These epidemics included the Black Death, which occurred between 1346 and 1352, and the great influenza pandemic that coincided with World War I early in the 20th century. Other significant historical developments (also discussed in Chapter 1) include Edward Jenner’s development of an effective vaccine against smallpox and the work of Robert Koch that led to the identification of microbial agents in human disease. In 1928, Alexander Fleming discovered that certain molds had antibiotic properties. As a result of this research, the antibiotic Science and Society/SuperStock penicillin became available at the end of World War II. Table A major milestone in epidemiology was Alexander Fleming’s 3.1 lists several other impor- contribution of penicillin in 1928. This photograph shows Fleming tant milestones in the history of (right) at St. Mary’s Hospital in Paddington, London, 1945. epidemiology. Table 3.1: Key events in the history of epidemiology 400 BCE Hippocrates publishes On Airs, Waters, and Places Implies that the physical environment is associated with human illness 1662 John Graunt publishes Natural and Political Observations Pioneers work in vital statistics (data that pertain to births and deaths) Mid-1800s John Snow investigates a cholera outbreak in London Uses the method of natural experiment to examine the causes of cholera 1964 The Surgeon General’s report Smoking and Health is published States that smoking is the cause of lung cancer 1974 The Lalonde Report, A New Perspective on the Health of Canadians, is published Initiates large community health studies; community interventions are implemented Some events are further described below. fri80977_03_c03.indd 88 8/30/13 12:48 PM Section 3.3 Brief History of Epidemiology CHAPTER 3 • Hippocrates Links Environment and Illness. In 400 BCE, the Greek physician Hippocrates suggested in his treatise On Airs, Waters, and Places that disease could be linked with a person’s physical environment—marking perhaps the earliest instance that logic, rather than mysticism, was used to explain an illness’s origin (Friis & Sellers, 2009). Hippocrates wrote that the seasons, the influence of the sun, the quality of water, and the elevation at which people live were factors in health. • John Graunt Describes Trends in Vital Statistics. In 1662, John Graunt published Natural and Political Observations Mentioned in a Following Index; and Made Upon the Bills of Mortality. Graunt described various details of birth and death data, including seasonal variations and infant mortality (Glass, 1963). Public health historians regard Graunt’s work as among the first to organize mortality data in tables in order to discern trends in births and deaths from specific causes (Friis & Sellers, 2009). • John Snow Maps Cholera Outbreak. As described in Chapter 1, during the mid-19th century, John Snow investigated a cholera epidemic that occurred in Broad Street, Golden Square, London, eventually linking the epidemic to contaminated water supplies (Snow, 1855). His classic observational study reflects many of the features of modern epidemiologic inquiry: mapping the location of instances of disease and tabulating fatalities. The natural experiment described in Snow on Cholera (Frost, 1936/1965), which includes two papers by John Snow and a biographic memoir, reported the effects of changing to a cleaner water source on reduction in the occurrence of cholera in the Golden Square district of Soho. Despite the absence of knowledge about the nature of microbial agents during his time, Snow made numerous insightful discoveries that could be applied subsequently to the control of epidemics. • Smoking and Lung Cancer Published. The 1964 Surgeon General’s Report Smoking and Health stated that cigarette smoking is a cause of lung cancer in men (United States Department of Health and Human Services). This report caused a global reaction when it first appeared. The report listed five “criteria of judgment” that were used to judge the statistical causal significance of the association between smoking and lung cancer. These criteria were strength of association, time sequence, consistent relationship upon repeated exposure to smoking, specificity of association, and coherence of explanation. The authors of the report said that accumulated research conducted up to the time of the report tended to support these five criteria of judgment in testing causal associations between this health behavior and morbidity due to lung cancer. Contemporary Epidemiology In addition to the ongoing Framingham Heart Study described earlier in the book, during the second half of the 20th century, several renowned large-scale community trials have addressed modifying risk factors for coronary heart disease in order to reduce the impact of this leading cause of mortality. Other trials have been directed toward smoking cessation, prevention of HIV infections, and prevention of chronic disease. One of the most noteworthy community trials was the Stanford Five-City Project (Farquar et al., 1985), a major community trial designed to lower risk of cardiovascular diseases. Both the treatment cities (Monterey and Salinas) and control cities (Modesto and San Luis Obispo) were fri80977_03_c03.indd 89 8/30/13 12:48 PM Section 3.3 Brief History of Epidemiology CHAPTER 3 located in California. The intervention took place over 6 years and used a multifactor risk reduction education program that used multiple communication channels (e.g., releases in the media) and intervention settings. Epidemic Disease and Community Health Many current and historical examples of epidemics exist, including recent epidemics of influenza, West Nile virus (covered later in the chapter), whooping cough, and foodborne illnesses. In order to monitor the presence of epidemic disease, local and state public health departments and the CDC have implemented surveillance systems. These involve the periodic reporting of conditions, known as reportable and notifiable diseases, and compiling statistics on their occurrence. When the incidence of a reportable disease such as influenza exceeds statistical limits, this event suggests that an epidemic is under way. The occurrence of a case of a new disease or of a communicable disease that has been absent from a population for some time (because it was previously brought under control) must be investigated by health officials. The appearance of two cases of such a disease may be considered an epidemic. An example would be doctors discovering new cases of polio, which has been eradicated in the United States. The initial appearance in the early 1980s of a few cases of pneumonia (caused by Pneumocystis carinii) among gay men in Los Angeles signaled the beginning of the AIDS pandemic (Porta, 2008). More recent examples of pandemics are the 2007 avian influenza and the 2009 H1N1 influenza pandemic. Karen Kasmauski/Science Faction/SuperStock An epidemic is endemic when the agents of disease are specific to a certain region. In this photo, a deer mouse—thought to be the vector spreading a hantavirus in California—is captured for further study. Another term used in descriptions of epidemics is endemic, which means that a condition is habitually present in a particular geographic area. An example is whooping cough, an endemic disease in the United States. Often, diseases are endemic because of local environmental conditions such as disease vectors that infest a geographic area. One such disease is endemic plague, which is associated with certain types of ground squirrels and some other rodents that inhabit California. Numerous examples of epidemics and pandemics have occurred during the 20th century and, more recently, during the first decades of the current century. • fri80977_03_c03.indd 90 The 1918 Influenza Pandemic. This pandemic, which also goes by the name “Spanish flu,” occurred from 1918 to 1919 and killed an estimated 50 to 100 million people worldwide. It is estimated that one-third of the world’s population 8/30/13 12:48 PM CHAPTER 3 Section 3.3 Brief History of Epidemiology • became infected and developed symptoms. In contrast with most influenza outbreaks, the victims of the Spanish flu were mainly young healthy adults (aged 20 to 40) who accounted for nearly half of the mortality toll in this pandemic (Friis & Sellers, 2009). Whooping Cough (Pertussis). Since the beginning of the 21st century, whooping cough (pertussis) has been on the rise in the United States, reaching epidemic proportions by 2012. Some experts in infectious disease attribute this growing epidemic to a weakened immune status of immunized people. In other words, immunity waned after a period of time because available vaccines did not induce long-term immunity. Many regions in the United States are enduring the pertussis epidemic, but particularly noteworthy is Washington State. The mid-2011 Washington State pertussis epidemic produced an alarming rise in case numbers—to the highest level reported since 1942 (MMWR, 2012). This prompted the state Secretary of Health to declare an epidemic on April 3, 2012. As of June 16, 2012, the reported caseload had increased 1,300% from 180 to 2,520 cases from the year before; laboratory tests, or epidemiological evidence, confirmed about four-fifths of the 2,520 cases. Figure 3.1 demonstrates the number of confirmed and probable pertussis cases reported by Washington State to the CDC. The figure indicates the number of cases by the week of onset from January 1, 2011, to June 16, 2012 (CDC, 2012a). Figure 3.1: Confirmed and probable pertussis cases in Washington State (January 1, 2011–June 16, 2012) 2011 250 2012 Number of Cases 200 150 100 50 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 Onset Week Source: Adapted from Centers for Disease Control and Prevention. (2012, July 20). Pertussis Epidemic—Washington, 2012. Morbidity and Mortality Weekly Report, 61(28), 517–522. Retrieved October 27, 2012 from http://www.cdc.gov/mmwr/preview/mmwrhtml /mm6128a1.htm Weakened immune status of immunized people is a possible cause for the rise of pertussis cases in the United States. fri80977_03_c03.indd 91 8/30/13 12:48 PM Section 3.4 Applications of Epidemiology for Community Health • CHAPTER 3 Multistate Fungal Meningitis Outbreak. During the fall of 2012, the CDC and the Food and Drug Administration (FDA) investigated cases of fungal meningitis associated with a spinal injection of a steroid medication used to treat joint and back pain. Testing of the medication revealed the presence of noncontagious fungal contamination. The first case was reported in a patient who had received a spinal injection in a Tennessee ambulatory surgical center. Subsequently, the number of cases grew to 137, with 10 deaths. Eventually, investigators found that meningitis was associated with three production batches (lots) of a steroid medication supplied by The New England Compounding Center. The result was that the three lots were voluntarily recalled from the marketplace. 3.4 Applications of Epidemiology for Community Health The late British epidemiologist Professor Jerry Morris wrote Uses of Epidemiology. Morris published this classic book in 1957, a time in Britain’s history of growing recognition of social and economic factors in health. Morris’s prescient account of seven uses of epidemiology remains apropos to modern community health issues. Refer to Table 3.2 for a list of the seven uses. Table 3.2: The seven uses of epidemiology* 1. Study the history of the health of populations 2. Diagnose the health of the community 3. Study the working of health services 4. Estimate individual risks 5. Identify syndromes 6. Complete the clinical picture 7. Search for causes * Shaded items are discussed in the text. Data from Morris, J. N. (1957). Uses of epidemiology. The following sections highlight four uses of epidemiology that are especially relevant to the community health field. The uses include studying historical trends in health, describing the health of the community, assessing risks to the individual, and examining the causes of disease in the community. Study Historical Trends The historical use of epidemiology refers to the study of time trends in health and illness. One example of the historical use of epidemiology is the study of changes in disease frequency (secular trends) over long time periods (Smith, 2001). In general, chronic conditions have replaced acute infectious diseases as the major causes of morbidity and mortality in contemporary industrialized societies. In Chapters 1 and 2, this change was called the epidemiologic transition. With respect to the health of the community, it is apparent that chronic health conditions (e.g., obesity, nutritional deficiency, and heart disease) fri80977_03_c03.indd 92 8/30/13 12:48 PM Section 3.4 Applications of Epidemiology for Community Health CHAPTER 3 have now become major challenges to the population’s health, although infectious diseases remain important causes of morbidity and mortality. Describe the Health of the Community Describing the health of a community pertains to the identification of demographic, behavioral risks, and environmental factors in a community that impact health-related outcomes. Using epidemiology to describe the health of a community relates to Healthy People 2020’s overarching goals: 1. Attain high-quality, longer lives free of preventable disease, disability, injury, and premature death. 2. Achieve health equity, eliminate disparities, and improve the health of all groups. 3. Create social and physical environments that promote good health for all. 4. Promote quality of life, healthy development, and healthy behaviors across all life stages. (HealthyPeople.gov, 2013) Examples of categories of variables that affect community health include demographic and social composition, community infrastructure, and environmental characteristics. Refer to Spotlight: Variables that Affect the Health of the Community for more detailed information regarding these variables and how they could affect the health of a community. Spotlight: Variables that Affect the Health of the Community •• Demographic and social variables: Those in poorer communities often suffer greater ill health and shorter life expectancies. 1. Age and sex distribution 2. Socioeconomic status 3. Family structure, including marital status and number of single-parent families 4. Racial, ethnic, and religious composition •• Variables related to community infrastructure: Poorer facilities and physical environment directly and indirectly affect health behaviors and subsequently the risk of developing chronic disease. 1. Availability of social and health services including hospitals, emergency rooms, and community clinics 2. Quality of housing stock including presence of lead-based paint and asbestos 3. Social stability (residential mobility), such as community policing and employment opportunities •• Health-related outcome variables: Outcome variables measure dimensions of health status in the community. 1. Homicide and suicide rates 2. Infant mortality rate 3. Mortality from selected conditions 4. Scope of chronic infectious diseases 5. Alcoholism and substance abuse rates 6. Teenage pregnancy rates 7. Occurrence of sexually transmitted diseases 8. Birthrate (continued) fri80977_03_c03.indd 93 8/30/13 12:48 PM Section 3.4 Applications of Epidemiology for Community Health CHAPTER 3 •• Environmental variables: The physical environment can provide protective factors that maintain or improve the health of a population. 1. Air pollution from stationary and mobile sources 2. Access to parks and recreational facilities 3. Availability of clean water 4. Availability of markets that supply healthful groceries 5. Number of liquor stores and fast-food outlets 6. Nutritional quality of foods and beverages vended to schoolchildren 7. Soil levels of radon Source: Adapted from Friis & Sellers. (2009). Epidemiology for public health practice (4th ed.). Sudbury, MA: Jones and Bartlett Publishers. Estimate Individual Risks Epidemiologic methods are linked closely with the field of risk assessment, which seeks to identify potential hazards and analyze potential fallout should a hazard occur. In environmental risk assessment, epidemiological studies can be used to estimate potential health risk factors in the environment and how they are related, or associated. An example of this use of epidemiology is that of projections about the probability of developing lung cancer among smokers versus nonsmokers. Determine the Etiology of Disease Among the most important uses of epidemiology is uncovering the causes (etiology) of disease. When describing the causes of disease, epidemiologists employ exposures and risk factors. Epidemiologic research explores possible associations among exposures and health outcomes through the application of appropriate study designs, measures, and other methodologies. The use of appropriate study designs provides insight Kallista Images/SuperStock into whether an observed association is due to chance or is a The pink in this CT scan represents cancerous growth in causal association. A classic the lungs of a 54-year-old man. In the quest to discover the example of using epidemiology etiology of lung cancer, epidemiological researchers were able to study the etiology of disease to identify smoking as a major risk factor. is research on smoking and lung cancer. Smoking is a risk factor for lung cancer, and understanding the disease etiology will help a clinician prescribe the most effective preventive or therapeutic interventions. fri80977_03_c03.indd 94 8/30/13 12:48 PM CHAPTER 3 Section 3.5 Data Sources Used in Community Health The findings of epidemiologic research aid in policy development by providing methodological skill sets and contributing to the fund of information needed to guide informed decision making. Backed by supportive, evidence-based epidemiologic research, policy actors are able to introduce health-related policies that protect the community’s health. Legislators and government officials are charged with the responsibility of creating policies and enacting and enforcing many laws that have substantial impacts on public health. Following the adoption of desired health policies and programs, public health professionals can apply epidemiologic methods to the evaluation of program effectiveness. An example of health policies that have been implemented in a community comes from the California city of Long Beach, which enacted a no-smoking ordinance, LBMC 8.68 (City of Long Beach, 2013). Within the past decade, the city of Long Beach has created smoke-free indoor public areas, including bars and restaurants, pool halls, office buildings, and elevators and laundry rooms. No-smoking areas include parks; beaches; bus stops; farmer’s markets; and areas within 20 feet of state, county, and city buildings. The rationale for this ordinance stemmed from evidence that linked adverse health effects with secondhand cigarette exposure. In 2011, the state of California passed Senate Bill (SB) 332. This bill “prohibits any person from smoking a cigarette, cigar, or other tobacco-related product, or from disposing of cigarette butts, cigar butts, or any other tobacco-related waste, within a playground” California Legislation Senate Bill 332, 2011, para. 1). Furthermore, SB 332 authorizes “a landlord of a residential dwelling unit to prohibit the smoking of tobacco products on the property, in a dwelling unit, in another interior or exterior area, or on the premises on which the dwelling unit is located” (California Legislation Senate Bill 332, 2011, para. 3). 3.5 Data Sources Used in Community Health This section provides examples of data sources for the description and analysis of the state of a community’s health, as well as measures of morbidity and mortality that are derived from these data sources. Given the quantitative nature of the discipline, the practitioners of epidemiology pay close attention to the quality of data and measures used to assess disease occurrence. Table 3.3 provides examples of data sources for use in epidemiologic and community health research. Table 3.3: Examples of epidemiologic data sources Vital statistics data (e.g., data on births and deaths) Morbidity surveys of the population fri80977_03_c03.indd 95 Notifiable and reportable disease statistics National Health Interview Survey Case registries (e.g., cancer registries) Behavioral Risk Factor Surveillance Survey United States Census (demographic data) The World Health Organization (WHO) 8/30/13 12:48 PM CHAPTER 3 Section 3.5 Data Sources Used in Community Health Vital Statistics Data Types of information included in this category include data collected on vital events (births and deaths) by the vital registration system of the United States. Statistics derived from the vital registration system are computed from the data that are collected routinely on all births and deaths that occur in the United States. Birth certificate data are needed to calculate birth rates. In addition, this information may also contain data about a range of conditions that could affect newborn children, including conditions present during pregnancy, congenital malformations, obstetric procedures, birth weight, length of gestation, and demographic background of mothers. Death certificate data in United States include demographic information about the deceased person and information about the cause of death, including the immediate cause and contributing factors (Friis & Sellers, 2009). Notifiable Diseases and Surveillance Programs By legal statute, physicians and other health care providers must report cases of diseases known as reportable and notifiable diseases. The reporting individuals send the information to local agencies (e.g., health departments), from which it flows to state and federal levels. Notifiable disease reporting at the community level protects the public’s health by ensuring the proper identification and follow-up of cases of disease. These diseases are usually infectious and communicable ones that might endanger a population. See Table 3.4 for examples of these kinds of diseases. Table 3.4: Examples of reportable and notifiable diseases in the United States Anthrax Human Immunodeficiency Virus (HIV) Arboviral diseases Meningococcal disease Botulism Rabies Cholera Salmonella Hepatitis Tuberculosis Reportable and notifiable disease reports contain official statistics in tabular and graphical form. Statistics are collected and compiled from reports sent by state health departments and U.S. territories to the National Notifiable Diseases Surveillance System, which is operated by the CDC in collaboration with the Council of State and Territorial Epidemiologists. Case Registries A registry is a centralized database for collection of information about a particular disease. Registries are used commonly for the compilation of statistical data on cancer, although other types of disease registries exist. An example of a population-based cancer registry is the Surveillance Epidemiology and End Results (SEER) program, which has provided unique and valuable data on cancer survival, incidents, and treatment (Friis & Sellers, 2009). fri80977_03_c03.indd 96 8/30/13 12:48 PM Section 3.5 Data Sources Used in Community Health CHAPTER 3 The United States Census Bureau The Census Bureau is an invaluable resource for characteristics of the United States population. Census data provide information about the denominators used in the calculation of measures of morbidity and mortality. (A denominator is the number of parts to which the whole is divided.) The decennial (10-year) census counts every resident in the country. One constitutionally mandated use of data from the decennial census is to determine the number of seats each state has in the House of Representatives. Census 2010, the most recent census to date, provides detailed information about the country’s entire population, including the variables of age, sex, race, and ethnicity, as well as housing characteristics and household information (United States Census, 2010). Researchers can extract this information from census data sets in order describe the sociodemographic and other characteristics of a specific community. Morbidity Surveys of the Population Morbidity surveys are procedures for collecting information on the health status of a population group by using self-administered questionnaires, interviews, and direct examinations of participants (Friis & Sellers, 2009). Some of the purposes of morbidity surveys are to determine the frequency of chronic and acute diseases and disability, to obtain measurements of bodily characteristics, to conduct physical examinations and laboratory tests, and to probe other health-related characteristics of special concern to those who sponsor the survey. The National Health Interview Survey An example of a morbidity survey is the National Health Interview Survey (NHIS). Conducted by the National Center for Health Statistics, the NHIS is a household health interview survey that has been collecting data on a broad range of health topics since 1957 (Centers for Disease Control, 2012a). Behavioral Risk Factor Surveillance Survey The Behavioral Risk Factor Surveillance System (BRFSS) is the world’s largest, ongoing telephone health survey system. BRFSS began collecting information on risk behaviors and tracking health conditions in the United States in 1984. Results from the BRFSS have provided data to track a community’s health status, access to health care, and progress toward achieving state and national health objectives. Data From the Centers for Disease Control and Prevention On July 1, 1946, the Communicable Disease Center (now known as the Centers for Disease Control and Prevention) was established to save lives and protect the population of the United States. Since its inception, the CDC has been instrumental in responding to natural fri80977_03_c03.indd 97 8/30/13 12:48 PM CHAPTER 3 Section 3.6 Measures of Disease Occurrence disasters, identifying the H1N1 influenza virus, investigating massive foodborne outbreaks, providing support for laws related to public health, and recommending guidelines for public health purposes (CDC, 2012b). As part of its ongoing functions, the CDC provides a wealth of epidemiologic data relevant to community health. Some of these data are available online from the CDC website (www.CDC.gov) and from CDC-sponsored publications: • • • Morbidity and Mortality Weekly Report (MMWR) National Center for Health Statistics Data Briefs National Vital Statistics Reports (NVSS) World Health Organization The World Health Organization (WHO) was established on April 7, 1948, within the United Nations system. The WHO aims to provide leadership on global health matters, shape the health research agenda, set norms and standards, articulate evidence-based policy options, provide technical support to countries, and monitor and assess health trends (World Health Organization, 2012). A wealth of international health-related data from WHO is available by accessing the organization’s website at www.who.int. 3.6 Measures of Disease Occurrence Community health researchers use measures of disease occurrence for a variety of purposes. These include identifying epidemics and other health problems, assessing the effectiveness of prevention programs, finding health disparities, and showing associations between exposures and health outcomes. Fundamental measures discussed in this section are counts, ratios, and proportions. Two examples of more complex epidemiologic measures are prevalence and incidence—two terms that often are used incorrectly in the professional literature. Other more complex measures are rates such as incidence rates and mortality rates. Refer to Table 3.5. Table 3.5: E xamples of common epidemiologic measures encountered in the medical literature Count Case fatality rate Ratio Incidence Proportion Rate Prevalence Incidence rate Point prevalence Mortality rate Fundamental Measures The simplest and most commonly used quantitative measure in epidemiology is a count. A count refers to the total number of cases of the disease or other health phenomenon being studied. One example could be the number of falls in a nursing home during a 1-month period. fri80977_03_c03.indd 98 8/30/13 12:48 PM Section 3.6 Measures of Disease Occurrence CHAPTER 3 A ratio shows the relationship between two comparable amounts. For example, a sex ratio would compare the number of male cases to female cases. A ratio can also be expressed as a value, when one divides the first quantity by the second. Using the earlier example, the sex ratio of births in the United States is greater than one, indicating that more boys than girls are born. A proportion is a type of ratio and may be expressed as a percentage, indicating the part of a whole. For example, only a small proportion of graduates fail to find employment. Prevalence and Incidence The term prevalence is a measure of disease occurrence meaning the total number of cases of disease. Point prevalence is defined as the frequency of disease or other condition at a given point in time. For example, the prevalence of influenza or pertussis is the number of cases of that disease in a population at some designated time, such as on a particular date. Point prevalence is a proportion. The term incidence refers to a count of new cases of disease among a group within a time period. An example of incidence is the number of new cases of influenza that occur within a time interval such as a 2-week period. Rates A rate is a measure that includes time as a part of the denominator. As an example, an incidence rate describes a disease’s rate of development within a group during a specified period. Another commonly used rate is a mortality rate. A rate consists of a numerator (the frequency of new cases of disease during a time period) and a denominator, which is a unit size of population. The population could be of a community, a state, or the entire country. To calculate a rate, one must consider two points in time: the beginning of the period and the end of the period during which the new cases occur. Rates are expressed as numbers per unit size of population (e.g., 10 per 1,000 persons in a population or some other number per unit size of population). Incidence can be expressed as a rate, but prevalence is never a rate. An incidence rate uses only the frequency of new cases that occur during a time period in the numerator. Consequently, individuals who already have the disease are not included in the numerator. The denominator for incidence rates is the population at risk: those who are capable of developing the disease either because they are not immune or for some other reason. An incidence rate includes (1) a numerator—the number of new cases; (2) a denominator—the population at risk; and (3) time—the period during which the cases amass. The mortality rate refers to the number of deaths during a given year divided by the size of a reference population (or denominator) during the middle of the year in question. Another measure of mortality is the case fatality rate, which is expressed as a percentage. The case fatality rate refers to the number of deaths caused by disease among those who have the disease during a time period. To better understand this concept, consider this example: If 50 people became infected with Hantavirus and 8 of them died, the case fatality rate would be (8 ÷ 50) × 100 = 16%. fri80977_03_c03.indd 99 8/30/13 12:48 PM CHAPTER 3 Section 3.7 Descriptive Epidemiology 3.7 Descriptive Epidemiology Descriptive epidemiology is one of the most useful applications of epidemiology. The three broad objectives of descriptive epidemiology are to (1) permit evaluation and comparisons of trends in health and disease among populations or subpopulations (these can include countries and subgroups within countries); (2) provide a basis for planning, provision, and evaluation of health services in order to facilitate the efficient allocation of resources; and (3) identify problems to be studied by analytic methods and to suggest areas that may be fruitful for investigation. The work of health care researchers with respect to the acquired immune deficiency syndrome (AIDS) epidemic in Long Beach, California, illustrates how these objectives could be applied to a community. Refer to Case Study: The AIDS Epidemic in Long Beach, California for more on this issue. Case Study: The AIDS Epidemic in Long Beach, California Photononstop/SuperStock The city of Long Beach might have implemented an education session such as this as one on its HIV/AIDS prevention strategies. The first objective in descriptive epidemiology is to evaluate trends. In 2008, the city of Long Beach, California, released a report describing the HIV/AIDS epidemic between 1981 and 2008. The report identified a cumulative incidence rate of 1259.74 AIDS cases per 100,000 residents during that period—more than double the rate for the entire state (449.69 cases per 100,000) (City of Long Beach Department of Health and Human Services, 2008). By identifying this trend and comparing it to other populations, the city was able to demonstrate that AIDS remains a significant public health issue in the Long Beach community. The city proceeded to the second objective by assessing community HIV/AIDS prevention and care needs in order to enhance its local HIV/ AIDS services. Information gathered from the assessment, which used client and provider surveys and client focus groups, aided in planning and implementing HIV/AIDS education and prevention services. As for the third objective, findings from this assessment assisted program planners with identifying areas for further consideration such as which populations are target populations and resource allocation needs (Friis & Sellers, 2009). 1 City of Long Beach HIV/AIDS Monitoring Report, December 2008. Available at: http://www.longbeach.gov/civica/filebank/ blobdload.asp?BlobID=22225. Accessed October 18, 2012. 2 City of Long Beach, HIV/AIDS Prevention and Care Needs Assessment, 2006. Available at: http://www.longbeach.gov/civica/ filebank/blobdload.asp?BlobID=3096. Accessed October 18, 2012. Descriptive epidemiology refers to the classification of diseases and other health characteristics according to person, place, and time. Examples of person variables are demographic characteristics such as sex, age, race, and ethnicity. Place variables denote geographic fri80977_03_c03.indd 100 8/30/13 12:48 PM Section 3.7 Descriptive Epidemiology CHAPTER 3 locations including countries or a specific country, regions within countries, and places where localized patterns of disease may occur. Time variables include occurrence of health outcomes by decades, years, months, weeks, or days. Examples of descriptive epidemiologic data for the variables of person, place, and time are given in the following sections. Person Variables This discussion will focus in depth on age, sex, race and ethnicity, and socioeconomic status, which are among the most important descriptive epidemiologic variables. However, we will briefly discuss marital status, nativity and migration, and religious background, as these also can influence health. Marital status—whether one is single or nonmarried, married, or unmarried but living with a partner—has been found to be a protective factor in health. Rates of morbidity and mortality vary according to marital status, and evidence suggests that married adults tend to be healthier overall than nonmarried adults. Migration is related to patterns of chronic disease and life expectancy. When persons immigrate to a new country, often they acquire the health-related characteristics of the inhabitants of the host country, perhaps as a result of changes in diet and lifestyle. The process of adopting the cultural practices of the host country is known as acculturation. For example, Japanese immigrants (a low-risk group for coronary heart disease in their home country) who relocated to the United States and became acculturated had a higher incidence of coronary heart disease than immigrants who were less acculturated. The healthrelated aspects of immigration are significant for the many American communities that have large immigrant populations. Religious background affects personal lifestyle characteristics, and consequently is associated with variations in morbidity and mortality. For example, the adherents to some religious groups (e.g., Seventh-Day Adventists who practice vegetarianism) have lower rates of cancer and chronic disease mortality in comparison with the general population. Age Age is one of the most important descriptive epidemiologic variables for showing the distribution of health outcomes. Some ways to classify age are according to 10-year intervals; larger age categories are used as well (e.g., children and teenagers, young adults, older adults, and the elderly). Age is related to many health outcomes, including morbidity and mortality from infectious diseases, chronic diseases, unintentional injuries, and disabilities. For example, the chronic diseases (cancer, heart disease, and diabetes) affect the elderly more frequently than the young. Another example of age effects is the tendency for cancer mortality to increase linearly with age. In comparison, the leading cause of mortality among younger individuals is unintentional injuries. Still other examples of age effects are variations in both suicide and homicide mortality among different age groups. According to 2009 data from the CDC, those between 18 and 24 years old had the highest rate of homicide, while those between 45 and 54 years old had the highest rate of suicide. The suicide rate was higher than the homicide rate among those 25 years old and older; this difference increased with age: While the rate of suicide for those between 25 and 44 fri80977_03_c03.indd 101 8/30/13 12:48 PM CHAPTER 3 Section 3.7 Descriptive Epidemiology was nearly twice the rate of homicide, the suicide rate was nearly seven times the homicide rate for those 65 and older (Centers for Disease Control, 2012c; see Figure 3.2). Figure 3.2: Suicide and homicide mortality among U.S. age groups Suicide Rate per 100,000 Population 25 Homicide 20 15 10 5 0 0–9 10–17 18–24 25–44 45–54 55–64 ³65 Age Source: Adapted from Centers for Disease Control and Prevention. (2012c, July 20). QuickStats: Suicide and homicide rates, by age group—United States, 2009. Morbidity and Mortality Weekly Report, 61(28), 543. Retrieved October 26, 2012 from http://www.cdc.gov/ mmwr/preview/mmwrhtml/mm6128a8.htm?s_cid=mm6128a8_w Suicide and homicide rates are examples of age effects on health outcomes. Sex Many outcomes in morbidity and mortality reflect sex or gender differences. One example of sex differences is in overall mortality, which is higher for males than females for all causes. Men also have higher mortality rates than women for cancer of the lung and bronchus. Although men have higher rates of mortality, the reverse is true for morbidity rates, which for many acute and chronic conditions are higher for women than for men. An example of sex differences in health outcomes is the occurrence of heat-related deaths by sex in the United States during the period 1999–2010 (MMWR, 2012). See Figure 3.3. The CDC noted that [f]rom 1999 to 2010, a total of 7,415 deaths in the United States, an average of 618 per year, were associated with exposure to excessive natural heat. The highest yearly total of heat-related deaths (1,050) was in 1999 and the lowest (295) in 2004. Approximately 68% of heat-related deaths were among males. (Centers for Disease Control, 2012d) fri80977_03_c03.indd 102 8/30/13 12:48 PM CHAPTER 3 Section 3.7 Descriptive Epidemiology Number of Deaths Figure 3.3: Heat-related deaths by sex 800 Male 700 Female 600 500 400 300 200 100 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 0 Year Source: Adapted from Centers for Disease Control and Prevention. (2012d, September 14). QuickStats: Number of heat-related deaths, by sex—National Vital Statistics System, United States, 1999–2010. Morbidity and Mortality Weekly Report, 61(36), 729. Retrieved from http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6136a6.htm?s_cid=mm6136a6_w One of the many ways in which morbidity and mortality outcomes differ is by sex. Different health and environmental circumstances affect males and females differently. For example, men tend to suffer from heat-related deaths at a higher rate than women, as is shown in this graph. Race One of the well-developed systems for classifying race has been implemented by the United States Census Bureau (2011). This system uses five categories plus a “some other race” category. Respondents to the 2010 Census questionnaire could self-identify their racial group membership and also select more than one racial group. The racial categories used in the 2010 questionnaire were White, Black or African American, American Indian or Alaska native, Asian, and native Hawaiian or other Pacific Islander. Respondents were also requested to indicate whether they were of Hispanic origin; persons of Hispanic origin could be of any race. For example, Hispanics can also be classified as Black, White, or Asian depending on the race with which they identify themselves. The United States is becoming increasingly diverse with respect to race and ethnicity. Racial characteristics are associated with substantial variations in several indices of morbidity and mortality, such as birth rates. One application of data on racial differences in health outcomes is to identify health disparities and develop community programs for groups at greatest risk of adverse health outcomes. We will now discuss a study of racial disparities in teenage birth rates. fri80977_03_c03.indd 103 8/30/13 12:48 PM CHAPTER 3 Section 3.7 Descriptive Epidemiology Figure 3.4 compares teenage birth rates for racial and ethnic groups in the United States. Although all of these groups have reflected declining trends in teenage birth rates, significant disparities in teenage birth rates exist in the country. The CDC reported that the birth rate in 2011 for females aged 15 to 19 years was the lowest rate ever recorded in the United States. The rate declined by 25% between 2007 and 2011 from 41.5 to 31.3 births per 1,000 women in this age group. Also showing declines were non-Hispanic White, nonHispanic Black, American Indian or Alaska Native, and Asian or Pacific Islander teenagers. Hispanic teenage girls had the greatest decline in teenage birth rates. Nevertheless, researchers pointed out that the teenage birth rate was higher than that found in many other affluent countries (Centers for Disease Control and Prevention, 2012e). Figure 3.4: Birth rates for females aged 15–19 years by race and ethnicity 100 2007 90 2011 Births per 1,000 80 70 60 50 40 30 20 10 0 All Races NonHispanic White NonHispanic Black Hispanic American Asian/ Indian or Pacific Alaska Native Islander Race/Ethnicity Source: Centers for Disease Control and Prevention. (2012e, October 26). QuickStats: Birth rates for females aged 15-19 years, by race/ ethnicity–National Vital Statistics System, United States, 2007 and 2011. Morbidity and Mortality Weekly Report, 61(42), 865. Retrieved from http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6142a8.htm Tracking teenage birth rates is one way to identify health disparities based on racial and ethnic differences. The graph indicates the rate of teenage births by race and ethnicity, and then compares these rates across the six race and ethnicity categories established by the United States Census Bureau. fri80977_03_c03.indd 104 8/30/13 12:48 PM Section 3.7 Descriptive Epidemiology CHAPTER 3 Socioeconomic Status The variable of socioeconomic status is defined by one’s type of occupation, income level, and amount of formal education. By definition, individuals who have higher income levels, higher education levels, and are employed in occupations such as the learned professions have higher socioeconomic statuses than those who are lower with respect to these three characteristics. Low socioeconomic status is associated with higher rates of morbidity from infectious and chronic conditions, and generally a greater frequency of adverse health outcomes in comparison with high socioeconomic status. We discussed the association between low socioeconomic status and health disparities in Chapters 1 and 2. Place Variables The variable of place includes international, within-country, regional, and local patterns in health outcomes. Within-country variations can be subdivided further into urban and rural comparisons. The United States Census Bureau defines urbanization by using geographic locations called Metropolitan Statistical Areas (MSAs), which are urbanized areas based on the number of inhabitants in a particular area. An MSA is a geographical region with a relatively high population density at its core and close economic ties throughout the area. It is typically centered around a single city that wields substantial influence on the region (e.g., Chicago). International Numerous examples of international variations in health outcomes can be cited, especially with respect to infectious diseases (e.g., cholera, malaria, and vaccine-preventable conditions). An example of international variations is the occurrence of measles, which has been eradicated in the United States. In 2010, the World Health Assembly identified goals (e.g., increasing the first dose coverage with measles vaccine to 90% or more within a country) to be accomplished by 2015 as benchmarks toward the eradication of measles worldwide. The CDC noted that Ethiopia, Kenya, and Somalia are three countries in the Horn of Africa where measles remains endemic (CDC, 2012f). Outbreaks occurred during 2010 through 2011, primarily among unvaccinated persons. The respective numbers of cases were 9,756, 2,566, and 16,135. The cases in Ethiopia and Kenya had wide age distributions, whereas the cases in Somalia occurred mainly among children younger than 5 years of age. Figure 3.5 presents annual reported measles incidence, by administrative area, in the Horn of Africa during 2010–2011. fri80977_03_c03.indd 105 8/30/13 12:48 PM CHAPTER 3 Section 3.7 Descriptive Epidemiology Figure 3.5: Measles in Africa January–June 2010 July–December 2010 Ethiopia Ethiopia Somalia Somalia Kenya Kenya 0 250 500 750 1,000 km January–June 2011 Ethiopia July–December 2011 Ethiopia Somalia Kenya Somalia Kenya Measles Incidence per 1 Million Integrated Food Security Classification (IPC) 5000 1–4 4 Humanitarian emergency 1,000–4,999 0 5 Famine/Humanitarian catastrophe 50–999 No data Dadaab refugee camp 5–49 Dollo Ado refugee camp Source: Adapted from Centers for Disease Control and Prevention. (2012f, August 31). Measles – Horn of Africa, 2010–2011. Morbidity and Mortality Weekly Report, 61(34), 678–684. Retrieved from http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6134a4.htm?s_ cid=mm6134a4_w How can comparing international variations in health outcomes help researchers identify and develop potential solutions to health concerns, such as measles outbreaks in Ethiopia, Kenya, and Somalia? fri80977_03_c03.indd 106 9/18/13 2:05 PM Section 3.7 Descriptive Epidemiology CHAPTER 3 Regional Variations Within the United States Conditions that show regional variations are Lyme disease, obesity, HIV infections, and many others. Among the causes of these variations are differences in climate, local environmental conditions, socioeconomic status, lifestyle and cultural factors, and availability of health care services. As an illustration, local physical environmental conditions may be conducive to the survival of microbes and disease vectors. A specific example of a health-related phenomenon that varies within the United States is underage drinking and driving. Every state in the United States prohibits those under the age of 21 years from driving with any measurable amount of alcohol in their blood; however, many young individuals drink and drive. The Youth Risk Behavior Survey (YRBS) collects information on the prevalence of drinking and driving among high school students. See Figure 3.6 for 2011 data on the percentage of high school students aged 16 years and over who had been drinking and driving. The CDC found that [a]mong the 41 states with available YRBS results in 2011, prevalence of drinking and driving varied threefold, from 4.6% in Utah to 14.5% in North Dakota (median: 10.1%). States in the highest tertile included much of the upper Midwest; the western states of Montana, Wyoming, and New Mexico; South Carolina; and states along the Gulf Coast, except for Florida. (CDC, 2012g, para. 12) fri80977_03_c03.indd 107 8/30/13 12:48 PM CHAPTER 3 Section 3.7 Descriptive Epidemiology Figure 3.6: Percentage of high school students age 16 and older who had been drinking and driving, 2011 WA MT OR VT ND ME MN ID WY UT CA AZ CO PA IL KS OK NM MI IA NE NV OH IN WV MO VA KY NC TN AR SC MS TX NH MA RI CT NJ DE MD NY WI SD AL GA LA AK FL HI Percentage 11.3–14.5 11.3–14.5 11.3–14.5 11.3–14.5 Source: Adapted from Centers for Disease Control and Prevention. (2012g, October 5). Vital signs: Drinking and driving among high school students aged ≥16 years—United States, 1991–2011. Morbidity and Mortality Weekly Report, 61(39), 796–800. Retrieved from http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6139a5.htm?s_cid=mm6139a5_w#fig1 What factors might have caused variations in the rates of drinking and driving between states? Another illustration of regional variation is differential occurrence of motor vehicle crashes (MVCs). Although death rates from MVCs have shown an improving trend in recent years, they continue to be one of the dominant causes of injury and death in the United States (CDC, 2012h). In general, MVC death rates for MSAs tend to be higher in southern states, with the highest rates concentrated in the southeastern part of the United States. See the map shown in Figure 3.7 for death rates during 2009 from MVCs for the most highly populated MSAs. Of the 50 MSAs, the overall MVC death rate for the 63 major cities combined was 7.9 per 100,000 residents. The CDC reported that In 2009, MVC death rates for the 50 most populous U.S. metropolitan statistical areas (MSAs) combined were lower than for the nation overall, for persons of all ages and for those aged 15–24 years. However, MVC death rates varied widely among MSAs, ranging from 4.4 to 17.8 per 100,000 residents of all ages (age-adjusted) and from 7.3 to 25.8 per 100,000 residents aged 15–24 years. (2012h, para. 16) fri80977_03_c03.indd 108 9/18/13 2:04 PM CHAPTER 3 Section 3.7 Descriptive Epidemiology Figure 3.7: Motor vehicle crash death rates WA MT OR VT ND ME MN ID WI SD WY NE NV UT CO IL KS CA MI IA PA IN OH WV MO KY TN AZ NM OK AR LA VA NC SC MS TX NH MA RI CT NJ DE MD NY AL GA AK FL HI Motor Vehicle Crash Deaths* in Metropolitan Areas — United States, 2009 11.1–17.8 8.6–10.7 6.9–8.4 4.4–6.8 *Per 100,000 population, age-adjusted. Source: Adapted from Centers for Disease Control and Prevention. (2012h, July 20). Motor vehicle crash deaths in metropolitan areas— United States, 2009. Morbidity and Mortality Weekly Report, 61(28), 523–528. Retrieved from http://www.cdc.gov/mmwr/preview/ mmwrhtml/mm6128a2.htm?s_cid=mm6128a2_w Comparing regional variations could uncover information about why certain regions have a higher incidence of certain outcomes. What might researchers find if they were to compare the differential occurrence of motor vehicle crashes across the country? Time (Temporal) Variables The epidemiologic variable of time includes the following time trends: secular (longterm), seasonal, clustering, and epidemic. Investigations of temporal variation help to identify changing patterns in morbidity and mortality, demonstrate the success of preventive efforts for chronic and other diseases, and signal the occurrence of epidemics. fri80977_03_c03.indd 109 8/30/13 12:48 PM CHAPTER 3 Section 3.7 Descriptive Epidemiology Secular Trends The term secular trend refers to the gradual changes in disease frequency over long time periods. For example, mortality due to coronary heart disease in the United States for both females and males has shown a decreasing secular trend during the past few decades. Figure 3.8 illustrates secular trends in coronary heart disease mortality by sex in the United States. Between 1999 and 2006, mortality rates per 100,000 persons declined for both males and females. Rate per 100,000 Figure 3.8: U.S. secular trends in coronary heart disease mortality by sex 344.6 Male 299.0 Female 253.4 207.8 162.2 116.6 2006 2005 2004 2003 2002 2001 2000 1999 71.0 Year Source: Adapted from Division for Heart Disease and Stroke Prevention: Data Trends & Maps. (2010). Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention (CDC), National Center for Chronic Disease Prevention and Health Promotion. Retrieved from http://www.cdc.gov/dhdsp/ Secular trends, such as the rate of coronary disease mortality by sex shown in this graph, illustrate the changes in disease frequency over long periods. Cyclic Fluctuations The phenomenon of cyclic fluctuations reflects increases or decreases in the frequency of diseases or other health conditions during a year or over a period of several years. Often, cyclical fluctuations reflect seasonal patterns in the occurrence of disease; these fluctuations reflect the influences of weather, temperature, rainfall, and changes in the characteristics of disease agents and host susceptibility. Seasonal influenza is an example of a condition that shows cyclic fluctuations, peaking during the winter and declining during the summer. fri80977_03_c03.indd 110 8/30/13 12:48 PM CHAPTER 3 Section 3.7 Descriptive Epidemiology Epidemic Trends One type of epidemic trend is a point epidemic, which is the response of a group of people to a common exposure that happened to all people in the group at about the same time. An epidemic curve is “[a] graphic plotting of the distribution of cases by time of onset” (Porta, 2008, p. 80). Figure 3.9 demonstrates an epidemic curve for an outbreak of Salmonella that was associated with live poultry in backyard flocks. Ill persons reported having had contact with live baby poultry—for example, chickens, ducks, and turkeys. Individuals who were made ill by Salmonella bacteria had purchased the poultry from a mail order company; they had intended to use the poultry for eggs and meat or as pets. A total of 93 persons in 23 states were afflicted; there were 21 hospitalizations and one death. Figure 3.9: Persons infected with the outbreak strain of Salmonella Montevideo, by date of illness onset Number of Persons 5 4 3 2 1 26 p- 12 Se p- 9 Se -1 -2 Au g 18 Au g Ju l- 0 Ju l-4 -6 3 -2 Ju n Ju n ay -2 M 5 ay -9 M r-2 Ap 28 r-1 1 Ap ar- M ar14 9 M -2 Fe b Fe b- 15 0 Date of Illness Onset, 2012 Source: Adapted from Centers for Disease Control and Prevention. (2012). Salmonella. Multistate outbreak of human Salmonella Montevideo infections linked to live poultry in backyard flocks (final update). Retrieved from http://www.cdc.gov/salmonella/ montevideo-06-12/epi.html The epidemic curve shown in the graph represents the distribution of onset of Salmonella within a specific group and time period. What factors may have caused the increased rate of onset between April 12 and May 6? fri80977_03_c03.indd 111 8/30/13 12:48 PM Section 3.7 Descriptive Epidemiology CHAPTER 3 Clustering The phenomenon of clustering refers to [a] closely grouped series of events or cases of a disease or other healthrelated phenomena with well-defined distribution patterns in relation to time or place or both. The term is normally used to describe aggregation of relatively uncommon events or diseases (e.g., leukemia, multiple sclerosis). (Porta, 2008, p. 43) Case clustering denotes an unusual aggregation of health events grouped together in time (temporal clustering) or space (spatial clustering). One illustration of temporal clustering is postvaccination reactions following immunizations; these reactions usually occur within a number of hours after a vaccination. An example of spatial clustering occurred in New York City in 1999, when the city experienced an outbreak of the West Nile virus, a type of virus called a flavivirus; this virus can infect humans, birds, mosquitos, horses, and some other mammals. West Nile virus (WNV) is a potentially serious neurological illness. It can infect wild birds following mosquito bites. Researchers devised an early warning system for WNV outbreaks that used small-area clusters of dead birds in New York City (Mostashari et al., 2003). They developed a method for detecting dead bird clusters that could differentiate between normal levels of bird mortality and increases in bird mortality above expected levels. This information suggested when mortality from WNV was on the increase and served as a possible harbinger of WNV-carrying mosquitos that could bite humans. Figure 3.10 shows concentrations of dead bird clusters, WNV-positive dead birds, human cases, and mosquito traps in New York City in 2001. Shading in the figure reveals the cumulative frequency of dead bird clusters in census tracts. fri80977_03_c03.indd 112 8/30/13 12:48 PM CHAPTER 3 Section 3.7 Descriptive Epidemiology Figure 3.10: Concentrations of dead bird clusters A B June 22 July 22 Bronx Manhattan Queens Retrospective Brooklyn Staten Island 50 0 50 Miles C D September 20 August 21 E October 31 Census Tract Clusters, 2001 0 Positive human cases 1–2 Positive mosquito traps 3–5 6–9 Positive dead birds 10–14 15+ Source: Adapted from Mostashari, F., Kulldorff, M., Hartman, J. J., Miller, J. R., & Kulasekera, V. (2003, June). Dead bird clusters as an early warning system for West Nile virus activity. Emerging Infectious Diseases [serial online]. Retrieved from http://wwwnc.cdc.gov/eid/ article/9/6/02-0794-f3.htm How can clustering help researchers develop early warning systems to alert populations to potential outbreaks of serious illnesses? fri80977_03_c03.indd 113 8/30/13 12:48 PM Section 3.8 Epidemiologic Study Designs Relevant to Community Health CHAPTER 3 3.8 Epidemiologic Study Designs Relevant to Community Health Among the major types of study designs of importance to community health are crosssectional studies, cohort studies, and intervention studies. A number of considerations influence the choice of a study design (e.g., the purpose of the epidemiologic research [descriptive or analytic]; financial and other resource constraints; speed with which the results are needed; and frequency of occurrence [prevalence] of particular health outcomes and of exposures in the population). Cross-Sectional Studies A cross-sectional study is a type of study design referring to a single period of time in which exposure and outcome information are collected at the same time. An example of a cross-sectional study is a community prevalence survey that seeks information about the frequency of a health issue (say, cigarette smoking), and factors associated with the health issue. Possible factors associated with tobacco use could be demographic characteristics and cultural factors stemming from ethnic group membership. Often, demographic factors affect variations in a range of health outcomes as noted in the previous discussion of descriptive epidemiology. Case-Control Studies Case-control studies consider two groups: one in which every member has the disease of interest (cases) and a comparison group in which every participant is free from the disease (controls). A case-control study attempts to identify possible causes of the disease by finding out how the two groups differ with respect to a past exposure. To illustrate, a casecontrol study of smoking could compare the smoking histories of lung cancer patients (the cases) with the corresponding histories of noncases (controls). A case-control study is a type of retrospective study because information is collected about exposures that have already occurred. Case-control studies are used extensively in epidemiologic research for studies of cancer, heart disease, infectious diseases, and many other conditions. An example of a case-control study is one that examined the effects of pregnant women’s use of diethylstilbestrol (DES) upon their female fetuses. Between 1938 and 1971, U.S. doctors prescribed DES to prevent miscarriages and other problems in pregnancy, exposing an estimated 5–10 million pregnant women and children to DES. In 1953, research showed that DES did not prevent miscarriages and premature births. Further, in 1971, the FDA issued a bulletin advising doctors that DES was associated with a rare vaginal cancer in girls and young women exposed to DES in the womb. fri80977_03_c03.indd 114 8/30/13 12:48 PM Section 3.8 Epidemiologic Study Designs Relevant to Community Health CHAPTER 3 Cohort Studies Cohort studies are types of longitudinal (often prospective) studies that trace adverse health effects associated with specific exposures. A cohort is defined as a “population group, or subset thereof (distinguished by a common characteristic [such as a specific exposure]), that is followed over a period of time” (Friis & Sellers, 2009, p. 284). Because they are able to preserve the temporal ordering between Flirt/SuperStock cause and effect, cohort studies are among the strongest epide- As smartphones become smaller and more useful, they play a miologic designs for assessing greater role in everyday activities. In a cohort study involving causal associations between participants aged 20–24, increased email/chat use was tied to exposures and health outcomes. an increase in difficulty sleeping. Temporal ordering of cause and effect means that the occurrence of the cause is observed before the occurrence of the effect. This arrangement is not possible with cross-sectional and case-control studies. Cohort studies begin with groups of participants who lack a positive history of an outcome of interest (e.g., a disease) and who have differing levels of exposures to risk factors for the disease at the initiation of the study. Possible outcome measures include the incidence of disease and mortality, which are examined for changes that occur as a result of exposure to a risk factor. Examples of risk factors include smoking, excessive intake of high caloric foods, insufficient levels of daily physical activity, and inadequate applications of sunscreen. An example of a published cohort study is one performed by Thomee, Harenstam, and Hagberg (2012). These researchers examined the association of high computer use (the exposure or risk-factor variable) with the development of mental health symptoms (outcome variable) among a cohort of 20- to 24-year-old participants. Exposure variables included the amount of time spent on the computer (i.e., email/chat, computer gaming, general use, use of the computer without a break, and use of a computer during the night time causing sleep loss). Outcome variables encompassed mental health states (e.g., perceived stress, sleep disturbances, symptoms of depression, and tiredness; Thomee, Harenstam, & Hagberg, 2012). One of the findings of the study was that high email/chat use was a factor in sleep disturbance among male participants. fri80977_03_c03.indd 115 8/30/13 12:49 PM Section 3.9 Preventing Disease in the Community CHAPTER 3 Intervention Studies In a broad sense, there are two types of intervention designs: controlled clinical trials, which focus on the individual, and community interventions, which focus on the group or community. A clinical trial is a carefully designed and executed investigation of the effects of a treatment or technology that uses randomization of participants to study conditions, blinding a subject to study conditions, and manipulation of the study factor. Randomization of subjects involves the use of a computer or similar means to assign subjects at random in order to prevent study biases. Blinding (also called masking) is a means of controlling study bias by preventing the subjects (as well as researchers) from knowing whether the subjects are under the experimental or control conditions of the experiment. Double-blind studies are experimental procedures in which neither the subjects of the experiment nor the persons administering the treatment know the critical aspects of the experiment, in order to control for biases in reporting outcomes or treatment protocols. Clinical trials are used to test the efficacy of preventive or therapeutic measures such as vaccines and new medicines. A community intervention is a preventative effort that aims to change a population’s education and behavior. An example of a community intervention occurred in Colorado. With funding from the CDC, the Colorado Department of Public Health & Environment’s Oral Health Unit expanded its community intervention Be Smart & Seal Them! program to provide sealants to all Colorado children at greatest risk for tooth decay. School-based programs offered children in rural and urban areas dental sealants, as well as other modalities for improved dental health including oral health screenings and treatment plans (CDC, 2008). Prior to this intervention, many of the participating children had never seen a dental provider. Another example of a community intervention is the Community Trials Intervention to Reduce High-Risk Drinking (SAMHSA’s National Registry of Evidence-Based Programs and Practices, 2013). This intervention is a multicomponent, community-based program that targets alcohol abuse and associated issues among all age groups. The program uses various environmental interventions such as zoning and municipal regulations that restrict alcohol access, educational programs for beverage servers and retailers, increased law enforcement of sobriety rules, reductions in youth access to alcohol, and formation of community prevention coalitions. 3.9 Preventing Disease in the Community Once the causes of disease or other adverse health outcomes have been identified, the next major goal of epidemiology with respect to the health of the community is prevention of these conditions. Community disease prevention programs encompass the three main levels described below. One of the components related to prevention is screening for disease in the community. fri80977_03_c03.indd 116 8/30/13 12:49 PM Section 3.9 Preventing Disease in the Community CHAPTER 3 Prevention of Disease Earlier in the chapter, we learned that epidemiologic studies lead to practical steps to control diseases in the community. Based on identification of risk factors for diseases, individuals and public health departments can introduce preventive measures. One of the helpful public health/epidemiologic models for prevention of disease proposes three levels of prevention: primary, secondary, and tertiary. Each of the three levels of prevention is related to phases of the pathogenesis of disease. Pathogenesis ranges from the periods of prepathogenesis (before a diseasecausing agent interacts with a host), early pathogenesis (early phases of the disease), and late pathogenesis (when the patient recovers, is permanently disabled, or passes away). Primary prevention attempts to reduce the occurrence of disease by preventing disease agents from interacting with the host. It is directed to the period of pre-pathogenesis (CDC, 2007). Examples of primary prevenB. BOISSONNET/BSIP/BSIP/SuperStock tion of disease are providing clean water to the community, Taking vitamins is an example of a primary prevention strategy. creating a healthful living environment, and making foods safe for human consumption. Other examples of primary prevention are general health promotion and risk reduction practices, such as wearing protective devices to prevent occupational injuries, vitamin consumption, immunizations against infectious diseases, and educational programs for bicycle riders about helmet safety. Secondary prevention (CDC, 2007) aims to reduce the progression of disease once it has occurred and is directed to the period of early pathogenesis. The primary goal of secondary prevention is to arrest diseases in their earliest stages once they have infected a human host and to limit disability from them. Almost all screening programs are forms of secondary prevention. Examples are cancer screening programs (e.g., breast and prostate cancer screening), diabetes screening, and cardiovascular disease screening. Tertiary prevention includes efforts to rehabilitate and restore an individual to an optimal functional level. Examples are physical therapy for stroke victims, fitness programs for heart attack patients, and transitional living arrangements for persons released from a drug treatment center. Tertiary prevention is directed toward the late stages in pathogenesis of disease. fri80977_03_c03.indd 117 8/30/13 12:49 PM CHAPTER 3 Summary Screening Programs The process of screening aims to quickly identify unrecognized disease or defects through rapidly applied tests, examinations, or other procedures. The purpose of screening is to prevent disease or to detect disease early by identifying persons at a point in the natural history of disease when the disease process can be influenced. Examples of screening tests applied to community settings are mammography screening for breast cancer, cervical cancer screening, hypertension screening, and tests for diabetes. B BOISSONNET/BSIP/SuperStock Screening for diabetes is an example of a secondary prevention strategy. A number of considerations determine whether a health condition is suitable for screening programs (Wilson & Jungner, 1968). The considerations about whether to screen for “disease X” are social, scientific, and ethical. The social aspect of screening means that the health condition must be important for the individual and the community. The scientific consideration means that the natural history of the condition being screened is understood sufficiently so that early identification of the condition is possible. The ethical dimension of screening means that it is feasible to alter the natural history of the condition among those screened. In some instances, the process of screening for diseases has become controversial. One of the downsides of screening tests is that they may produce false positive results. As an example, screening for prostate cancer by measuring PSA (prostate-specific antigen) has become controversial because positive test results could lead to invasive follow-up procedures and surgery that result in unnecessary complications. If a cancerous lesion is detected, slow-growing prostate cancers may be less dangerous than available surgical and other forms of treatment. Similarly, screening for breast cancer is also controversial. One issue concerns who should be screened and the age at which screening for breast cancer should begin. For example, should screening first be applied to asymptomatic women aged 40 through 49 years, or should screening be started later in life? Summary Many public health experts regard epidemiology as the fundamental discipline of both public and community health. This chapter has provided an overview of key concepts in epidemiology and how they relate to the health of the community. We have seen that epidemiology is unique with respect to its concern with the health of a total community and fri80977_03_c03.indd 118 8/30/13 12:49 PM Study Questions and Exercises CHAPTER 3 not merely the health of individuals. The two types of epidemiology are descriptive epidemiology (describing the occurrence of disease) and analytic epidemiology (determining the causes of disease). We have traced the history of epidemiology from its beginnings in classical history to contemporary epidemiologic research. We reviewed four of the most important uses (e.g., studying disease causality) of epidemiology for analyzing community health, and the remainder of the chapter covered data sources and measures used in community health research, descriptive epidemiology and other epidemiologic study designs, and how epidemiology contributes to the prevention of disease in the community. Study Questions and Exercises 1. Define the term “epidemiology.” Provide one example of how epidemiology has been used to study a health problem in a community. 2. Explain why epidemiology is important for the control and prevention of health problems in communities. 3. In 2009, the leading cause of death in the United States was heart disease. Compare and contrast how an epidemiologist and a clinician would focus on this problem. 4. In your own words, explain the difference between descriptive and analytic epidemiology. 5. The definition of descriptive epidemiology includes the use of “person,” “place,” and “time.” Provide one example for each variable in relationship to a health outcome. 6. Define “prevalence” and “incidence.” Explain the difference between these two terms. 7. Which descriptive study design is useful for studying the prevalence of a health problem in a community? 8. Describe the key features of the following study designs: case-control and cohort. 9. An important feature of clinical trials involves randomization. What is randomization and why is it an important aspect of clinical trials? 10. The three levels of prevention are primary, secondary, and tertiary. For each description listed below, identify the level of prevention. a. A community mental health nurse conducting a workshop on suicide to the local Parent–Teacher Association b. A doctor checking a patient for suspicious skin growths c. A community-wide educational campaign about the importance of not texting while driving d. A father initiating dietary changes to reduce his blood cholesterol levels e. A nurse teaching a recently diagnosed diabetic how to identify and prevent complications f. A health educator teaching college students the correct way to use a condom g. A doctor referring a stroke patient to a physical therapy program h. A local health department offering free seasonal influenza vaccines to the community i. A young woman visiting the local clinic for an annual pelvic exam and Pap smear fri80977_03_c03.indd 119 8/30/13 12:49 PM Key Terms CHAPTER 3 Key Terms analytic epidemiology The process of using data (including descriptive epidemiological information) to study patterns that suggest potential causes of or associations between diseases and other health conditions. blinding A means of controlling study bias by preventing researchers and subjects from knowing whether the subjects are part of the experimental or control conditions; also known as masking. case fatality rate The number of deaths caused by disease among those who have the disease during a specific time period. clinical trial A carefully designed and executed investigation of the effects of a treatment or technology that uses randomization of participants to study conditions. clustering The grouping of health-related events or diseases that are related both in proximity and temporally. cohort A population, group, or subset of a population/group that is studied over a period of time. count The total number of cases of a disease or other health phenomenon. cyclic fluctuations Increases or decreases in the frequency of diseases or other health conditions during a year or over a period of several years. descriptive epidemiology The characterization of disease occurrence in populations according to classification by person, place, and time (includes factors such as age, race, gender, and educational level). epidemic curve A graphic plot of the distribution of disease cases by onset time. fri80977_03_c03.indd 120 epidemiology The study of incidence, distribution, and control of diseases. exposure The potential causal factor in an epidemiological study. incidence The number of new cases of a disease or condition within a specific time period. incidence rate Describes the rate of development of a disease within a group during a specific time period. mortality rate Death rate; the number of deaths during a given year divided by the size of the population during the midpoint of the year. point epidemic The response of a group of people to a common exposure that happened to all people in the group at about the same time. point prevalence The frequency of disease or other condition at a given point in time. prevalence A measure of disease occurrence (i.e. the total number of cases). primary prevention A means of preventing a disease or condition from occurring. proportion A type of ratio in which the numerator is part of the denominator; indicates a part of a whole, such as a percentage. rate A measure that includes time as part of the denominator; in public health, the numerator is typically the frequency of disease occurrence, and the denominator is typically the unit size of the population. ratio A value obtained by dividing one quantity by another; examples include rate, proportion, and percentage. 9/16/13 4:18 PM Key Terms registry A centralized database for collection of information about a particular disease. screening The presumptive identification of unrecognized disease or defects by the application of tests, examinations, or other procedures that can be applied rapidly. CHAPTER 3 secular trends Changes in disease frequency. tertiary prevention A means of rehabilitating or restoring an individual to optimal functioning; it is directed toward the late stages in pathogenesis of disease. secondary prevention A means of reducing the progression of a disease or condition once it has occurred. fri80977_03_c03.indd 121 8/30/13 12:49 PM fri80977_03_c03.indd 122 8/30/13 12:49 PM Chapter 4 Exactostock/SuperStock Epidemiology of Infectious Diseases Learning Objectives After reading this chapter, you should be able to: •• Define the term infectious disease. •• Explain the significance of infectious diseases for community health. •• Describe modes of transmission of infectious diseases. •• Apply the epidemiologic triangle to at least three infectious diseases. •• State two methods for the prevention of infectious disease outbreaks. fri80977_04_c04.indd 123 8/30/13 1:08 PM CHAPTER 4 Section 4.1 Introduction 4.1 Introduction Infectious diseases are a major focus of epidemiology and community health. Globally, infectious and parasitic diseases account for a significant proportion of deaths. In 2008, the World Health Organization estimated that about 15% (8.5 million) of a total of 56.9 million deaths globally were associated with infectious and parasitic conditions. The leading infectious killers and their respective numbers of deaths in millions were tuberculosis (1.3), human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS) (1.8), and malaria (0.8). Moreover, childhood infectious diseases such as whooping cough and measles were among the leading causes of death among the world’s children (WHO, 2011). Great strides have been made in the control of infectious diseases. By the mid-20th century, some public health experts believed that the tide of mortality and morbidity from infectious diseases, especially those caused by bacteria, had been reduced and that infectious diseases might even be eradicated by newly introduced antibiotics and other “magic bullets.” Today, few public health experts hold this opinion, as microbes have developed resistance to antibiotics, causing the reemergence of once-controlled infectious diseases. Conditions known as “emerging infections” have gained a foothold worldwide and continue to endanger the population. In the United States, infectious diseases continue to be major causes of mortality, although chronic noncommunicable diseases such as heart disease, cancer, and chronic respiratory disease form the top tier of leading causes of death. The category of pneumonia and influenza was the eighth leading cause in 2009 (CDC, 2013a). The advent of an increasingly international world is one of the influences that could lead to epidemics of infectious diseases. Modern jet travel means that people can travel rapidly from one distant area to another. Nonendemic diseases, such as malaria, may be imported into the United States by travelers returning from areas in which these conditions are endemic. A dangerous communicable disease that has broken out in a remote corner of the globe can be introduced within a 24-hour period to a major city in Asia or Europe. From these continents, the disease can spread to the United States. As an extreme fictional example, the 2011 medical disaster film Contagion portrayed the epidemic spread of a lethal virus transmitted by fomites (inanimate objects such as skin or hair), attempts by epidemiologists and public health officials to identify and contain diseases, the loss of a social order due to the pandemic, and the introduction of a vaccine to halt the disease’s spread. Along with the trend of rapid international travel, major growth in international trade of food and medicines during the current century can also contribute to the spread of diseases. Improperly processed foods and medicines can bypass normal fri80977_04_c04.indd 124 Exactostock/SuperStock Modern technological advances such as air travel could contribute to the quick, international spread of infectious disease. 8/30/13 1:08 PM CHAPTER 4 Section 4.2 Microbes in History inspection and safety procedures, especially when the volume of such products exceeds the capacity of health authorities. An additional possible factor linked with the spread of infectious diseases is deforestation in many parts of the world. Deforestation causes the destruction of natural ecosystems that are important for human survival. Among the consequences of ecosystem loss is the potential for increases in runoff of pollutants into human water supplies and changes in the distribution of disease-causing vectors. Growing urbanization and pressures from the human population explosion cause people to move into previously forested areas, where they may come into contact with new vectors and infectious disease agents. This chapter provides an overview of infectious diseases and their impact on the health of communities. Topics covered include the discovery of disease-causing microbes, the epidemiologic triangle (agent, host, and environment), examples of significant infectious diseases, and methods for the prevention of communicable diseases. 4.2 Microbes in History Chapter 1 described historical accounts of dramatic epidemics that caused devastating effects. Examples of historically significant epidemics were the Black Death (caused by Yersenia pestis), recurring smallpox epidemics, and pandemic influenza. The Black Death ravaged the populations of Europe, northern Africa, and the Middle East from 1346 to 1352 (McEvedy, 1988). In 1346, the Black Death killed 20 million people in Europe. The 1918–1919 Spanish flu pandemic was responsible for 50 million deaths worldwide (Taubenberger & Morens, 2006). Before the late 19th century, the causes of infectious diseases were largely unknown. Gradually, during the mid-19th century, disease detectives became aware of microbes as causes of human diseases and developed the germ theory of disease. The germ theory of disease arose from the discovery that certain infectious diseases were caused by microorganisms that invade the body. Microorganisms are small organisms that cannot be seen without magnification. This germ theory of disease contributed to an understanding of the function that microbial agents perform in causing the spread of infectious disease. Eventually these new insights gained the attention of the public health community and led to the development of disease prevention efforts. Among the scientists who stood out in history as instrumental in identifying causative microbes for infectious diseases were Ignaz Semmelweiss in 1840 and Robert Koch in 1847. Other breakthroughs in the control of infectious diseases included Edward Jenner’s development of a vaccine against smallpox and Jonas Salk’s formulation of a polio immunization in 1953. These innovations lead to the eradication of many formerly common epidemic diseases worldwide. Smallpox, the ancient scourge of humanity, was declared eradicated in 1979. Polio has a very limited presence in the United States, and does not exist in epidemic form in most parts of the globe. fri80977_04_c04.indd 125 8/30/13 1:08 PM CHAPTER 4 Section 4.2 Microbes in History Ignaz Semmelweiss Ignaz Semmelweiss was a clinical assistant in obstetrics and gynecology at a Vienna hospital in the mid-19th century. According to Friis and Sellers (2009, p. 35), Silvio Fiore/SuperStock Ignaz Semmelweiss (1745–1822) contributed to the germ theory of disease in 1840 with his theory that hand contamination might be responsible for the spread of disease. Semmelweiss’s hypothesis led to hand washing and, therefore, a decreased spread of infection in Viennese hospitals. In 1840, he noticed a much higher mortality rate of puerperal fever (childbirth fever) among the women who were located in the teaching quarters for medical students and physicians than in the teaching areas for midwives. He hypothesized that medical students and physicians had contaminated their hands during autopsies, and consequently transmitted in...
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