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Please write a descriptive epidemiological analysis of a disease or health condition of your choice. Describe the basic patterns of this health condition by person, place, and time. Use the online library or the internet to research your health conditio

Please write a descriptive epidemiological analysis of a disease or health condition of your choice.  Describe the basic patterns of this health condition by person, place, and time.  Use the online library or the internet to research your health condition. Use the Descriptive Epidemiology Information in the Module home page.

 

Assignment Expectations, in order to earn full credit

Please write your paper in your own words. That is the only way I can evaluate your level of understanding. Quotes are rarely needed; if necessary, they should comprise less than 10% of a paper and must be properly cited.

Even though the papers must be written in your own words, you are required to cite sources for any statement of fact or idea that is not common knowledge. You must cite the sources within the body of the paper and include a reference list at the end of the paper.

Note Wikipedia is not an acceptable source of information. Use credible, professional, and scholarly sources such as journal articles from ProQuest or EBSCO, and government, university, or nonprofit organizations’ Web sites.

You must clearly show that you have read the module homepage and the required background materials. You are welcome to do research in addition to — but not instead of — the required readings.

Your papers will be evaluated on the following factors

  • References – citations are used within the body of the paper any time you state a fact or idea that is not common knowledge. A reference list is included at the end of the paper.
  • Precision – you follow all instructions and you answer each part of the assignment.
  • Breadth – you show broad knowledge of the module’s topic.
  • Depth – you go into detail to show more critical thought about the specific tasks or questions in the assignment.
  • Clarity – the extent to which you elaborate and include discussion or examples as asked.
  • Application – the extent to which you apply the information to a real-life situation related to the assignment, if asked.

Before you begin, please review this information about When to Cite Sources http//www.princeton.edu/pr/pub/integrity/pages/cite/

Other resources are available at http//owl.english.purdue.edu/owl/resource/560/02/

 

 

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Modular Learning Objectives

By the end of this module, the student shall be able to satisfy the following outcomes expectations

  • Case
    • Use descriptive epidemiology to study a health condition.
  • SLP
    • Summarize data using descriptive statistics.
    • Interpret an epidemic curve.
  • TD
    • Describe disease surveillance systems.

Epidemiology is defined as the study of the amount and distribution of disease within a population by person, place, and time.  Descriptive epidemiology answers the following questions Who is affected?  Where and when do cases occur?  It describes cases by person, place, and time.  

In epidemiologic studies, there are many characteristics that may be used to describe the affected population.  These characteristics include age, sex, ethnic group or race, social class, occupation, marital status, family variables, blood type, environmental exposures, and personality traits.  Among these characteristics, the most commonly used are age, sex, and ethnic group or race.  Age is the most important variable among the personal variables because of its effect on morbidity and mortality rates.  In general, chronic conditions tend to increase with age, while the relation of age to acute infectious diseases is less consistent.  Age is also related to the severity of infectious disease.  For example, certain organisms such as Salmonella tend to produce severe disease in the very young, the very old, and the debilitated.  

The analysis of disease rates by sex reveals a marked contrast between morbidity and mortality rates.  Death rates are higher for males than for females, but morbidity rates are generally higher in females.  Possible explanations for this disparity are that women seek medical care more frequently and perhaps earlier in the stage of disease and that the same disease tends to have a less lethal course in women than in men.

Although the classification of disease by race or ethnicity has been controversial, it has been  used traditionally in health statistics since many diseases differ considerably in frequency and severity for different racial groups.  For example, Whites have higher rates of death from arteriosclerotic heart disease, suicide, and leukemia, while African Americans have higher rates of deaths caused by hypertensive heart disease, cerebrovascular accidents, tuberculosis, homicide, and accidental death.

Social class is a commonly used concept for ranking a population into subgroups that differ in prestige, wealth and power.  Epidemiologic data indicates an inverse relationship between mortality and social class.  Because of practical considerations, occupation is often used alone as a measure of overall socioeconomic status.  The poor health status of individuals in lower socioeconomic groups may be largely due to poverty.  Because of limited financial resources and restricted access to medical care, the poor tend to underutilize preventive services.

Occupation can have a significant effect on morbidity and mortality rates since individuals spend a substantial part of their lives working in diverse conditions.  These conditions may include unfavorable physical conditions (e.g. heat and cold), chemicals, noise, and occupationally induced stress.  For example, air traffic controllers (who have unusually stressful working conditions because of the potentially disastrous effects of errors in judgment) have higher rates of hypertension and peptic ulcer.  The rates of disease among occupational groups may also differ because of selective factors (differences that caused individuals to choose the occupation, rather than the work conditions).

Marital status has been related to the level of mortality for both sexes.  Death rates have generally been lowest among the married and highest among the divorced.  The lower mortality rates among the married may be attributed to psychological and physical support provided by the spouse, selective factors, and health differences in pregnancy and childbearing (which have been inversely associated with cervical and breast cancer).

Family variables that may be associated with mortality and morbidity rates include family size, birth order, maternal age, and parental deprivation.  Larger families tend to be more common among the poor.  Therefore, children may be at a disadvantage, especially since many persons must share a family’s limited resources.  A variety of findings have indicated that first-borns, who tend to be more educated, have higher rates of asthma, peptic ulcer, and schizophrenia.  Birth order may play a role in a person’s life experiences because first-borns tend to receive more attention than his younger siblings.  The maternal age (i.e., age of a mother at the time of childbirth) is associated with birth defects, which in turn, affects morbidity and mortality rates in their children.  For example, the incidence rate of Down’s syndrome increases with maternal age.  Parental deprivation (due to death, divorce, or separation) has been found to be high among individuals with psychiatric and psychosomatic disorders, individuals with tuberculosis, and those who have attempted suicide.

Blood type has been associated with several diseases.  Individuals with type A blood have an increased risk of stomach cancer, while individuals with type O blood are more likely to develop duodenal ulcer.  Environmental exposures may also affect the risk of disease.  For example, specific immunity (defined in module 1) decreases the risk of acquiring immunizable diseases, such as measles.  Cigarette smoking and exposure to asbestos increases the risk of certain cancers.  Personality traits may influence the course of illness because of differences in tendency to seek medical care and comply with medical advice.  Also, the association between coronary heart disease and personality type has been well-documented in the literature.  Type A individuals, characterized by ambition, competitiveness, and a sense of time urgency, had higher rates of heart disease than Type B individuals, who did not exhibit these characteristics.

Frequency of disease can also be described by place of occurrence, according to natural boundaries or political boundaries.  An area defined by natural boundaries may have a high or low frequency of a disease because it is characterized by environmental or climatic conditions, such as temperature, humidity, rainfall, and altitude.  Some examples include goiter (which is more common in iodine-deficient inland regions) and Lyme Disease (which is transmitted by a tick which favors humid regions, such as the Northeastern region of the U.S.).  Although natural boundaries are more useful in understanding the etiology or cause of a disease, political boundaries are more readily available.

To examine the distribution of disease even more specifically, it is common practice to plot individual cases by location (spot map).  Cases may be plotted by natural boundaries, political boundaries, census tract, facility location, etc.  A superimposed representation of environmental factors (such as water supply, milk routes, direction of prevailing winds) may also be included on the map to provide a clue about the mode of spread.

John Snow plotted one of the first spot maps to support his findings that the Broad Street pump was the source of transmission in the 1854 cholera outbreak. 

The frequency of disease occurrence is also described with respect to time.  Occurrence is often expressed on a monthly or annual basis.  Three major changes with time may be identified secular trends, cyclic changes, and short-term fluctuations.  Secular trends refer to changes over a long period of time (i.e. years or decades).  Cyclic changes refer to periodic fluctuations in the frequency of disease.  Cycles may be seasonal (annual) or have some other periodicity.  For example, measles epidemics used to occur every two or three years.  Short-term fluctuations refer to outbreaks of disease that do not occur in cycles. 

Three statistics that are frequently used to describe epidemiology data include the mean, medium, and range.  The mean and median estimate an average value in a data set.  The background reading contains more detailed information on means and medians.  

The range is the interval between the minimum and the maximum values in a data set.  For example, if the incubation period for four individuals was 8 hours, 12 hours, 10 hours, and 5 hours, then the range would be between 5 and 12 hours. 

Epidemic curves are used to describe an outbreak by time.  To construct an epidemic curve, cases of disease are plotted by time of onset.  An epidemic curve reveals important information about whether an outbreak has a common source or is spread from one susceptible host to another.  Common source epidemics are outbreaks caused by an exposure of a group of persons to a common source (e.g. the Broad Street pump in the cholera outbreak investigated by John Snow).  In a common source epidemic, all of the cases develop illness within one incubation period (time period between exposure and onset of illness).  A common source epidemic is characterized by a rapid rise and fall of the epidemic curve.  Propagated epidemics do not have common sources; they are spread from one susceptible host to another (e.g. flu outbreak at a school).  In contrast to a common source epidemic, the propagated epidemic extends over a number of incubation periods.  

The shape of the curve typically contains a series of progressively larger peaks, reflective of the increasing number of cases caused by person-to-person contact, until control of the outbreak is achieved.

Sources

Brody H. Map-making and myth-making in Broad Street
the London cholera epidemic, 1854 The Lancet 356 64 – 68, 2000.
Retrieved on Septemer 2, 2011 from http//www.ph.ucla.edu/EPI/snow/mapmyth/mapmyth.html (Click on the map to the left to view and read about Snow’s spot map.)

Jacco Wallinga and Peter Teunis Different Epidemic Curves for Severe Acute Respiratory Syndrome RevealSimilar Impacts of Control Measures. AJE Vol 1606 Sept 15, 2004 Retrieved September 2, 2011 from http//octavia.zoology.washington.edu/publications/others/WallingaAndTeunis04a.pdf

Centre for Innovation in Mathematics Teaching MEP Book 8 Unit 5 Section 2 Mean, Median, Mode and Range. Retrieved on September 2, 2011 from http//www.cimt.plymouth.ac.uk/projects/mepres/book8/bk8i5/bk8_5i2.htm

 

 

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