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Descriptive Epidemiology. Principles of Epidemiology Lecture 6 Dona Schneider, PhD, MPH, FACE. Objectives of Descriptive Epidemiology. To evaluate trends in health and disease and allow comparisons among countries and subgroups within countries

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descriptive epidemiology

Descriptive Epidemiology

Principles of Epidemiology

Lecture 6

Dona Schneider, PhD, MPH, FACE

objectives of descriptive epidemiology
Objectives of Descriptive Epidemiology
  • To evaluate trends in health and disease and allow comparisons among countries and subgroups within countries
  • To provide a basis for planning, provision and evaluation of services
  • To identify problems to be studied by analytic methods and to test hypotheses related to those problems
descriptive studies
Descriptive Studies
  • Relatively inexpensive and less time-consuming than analytic studies, they describe
    • Who gets sick and/or who does not
    • Where rates are highest and lowest
    • Temporal patterns of disease
      • Seasonality
      • Secular trends which are affected by
        • Changes in diagnostic techniques
        • Changes in the accuracy of the denominator data
        • Changes in the age distribution of the population
        • Changes in survival from improved treatment or disease mutation
        • Changes in actual disease incidence
forecast of cancer deaths
Forecast of Cancer Deaths

Forecast of cancer deaths if present trends continue (Data from the American Cancer Society)


Cancer Death Rates by Site, United States, 1930-87

Figure 5-1. Cancer death rates by site, United States, 1930-1987. Source: American Cancer Society (1991).

possible reasons for changes in trends
Possible Reasons for Changes in Trends
  • Artifactual
    • Errors in numerator due to
      • Changes in the recognition of disease
      • Changes in the rules and procedures for classification of causes of death
      • Changes in the classification code of causes of death
      • Changes in accuracy of reporting age at death
      • Errors in the denominator due to error in the enumeration of the population
death rates for individuals with diabetes
Death Rates for Individuals with Diabetes

Figure 3-27. Drop in death rates for diabetes among 55 to 64 year old men and women, United States, 1930-1960, due to changes in ICD coding. (From US Public Health Service publication no. 1000, series 3, No. 1. Washington, DC, US Government Printing Office, 1964.)

icd 10
  • International Classification of Disease (ICD) 10th Revision
    • ICD-10 has 8,000 categories vs. only 4,000 for ICD-9
    • ICD-10 uses 4 digit alphanumeric system where ICD-9 uses 4 digit numeric system only (much more detail available with ICD-10)
    • Rules for coding simplified
    • Will create discontinuities!
icd 10 cont
ICD-10 (cont.)
  • Notable improvements in the content and format of ICD-10 include:
    • The addition of information relevant to ambulatory and managed care encounters
    • Expanded injury codes
    • The creation of combination diagnosis/symptoms codes to reduce the number of codes needed to fully describe a condition
    • Greater specificity in code assignment
icd 10 cont1
ICD-10 (cont.)
  • At present ICD-10 is widely used in Europe
  • In the US, however, migration to ICD-10 is complicated by the fact that ICD-9-CM is embedded in hospital billing systems
    • NCHS developed a timeline to have ICD-10-CM in use for morbidity diagnoses by 2001
possible reasons for changes in trends cont
Possible Reasons for Changes in Trends (cont.)
  • Real
    • Changes in age distribution of the population
    • Changes in survivorship
    • Changes in incidence of disease resulting from
      • Genetic factors
      • Environmental factors

Infant Mortality Rates by Race

Figure 3-3 Infant mortality rates by race: United States, 1950-1991. Source: Reprinted from National Center for Health Statistics, Advance Report of Final Mortality Statistics, 1991, Monthly Vital Statistics Report, Vol. 42, No. 2, p. 11, 1993.

case reports
Case Reports
  • Case reports (case series) – report of a single individual or a group of individuals with the same diagnosis
  • Advantages
      • You can aggregate cases from disparate sources to generate hypotheses and describe new syndromes
      • Example: hepatitis, AIDS, “pool fingers”
  • Limitations
      • You cannot test for statistical association because there is no relevant comparison group
cross sectional studies
Cross-Sectional Studies

Cross sectional studies or prevalence studies measure disease and exposure simultaneously in a well-defined population

  • Advantages
    • Prevalence studies cut across the general population, not simply those seeking medical care
    • They are good for identifying the prevalence of common outcomes, such as arthritis, blood pressure or allergies
  • Limitations
    • You cannot determine whether exposure preceded disease
    • Since you determine prevalent rather than incident cases, results will be influenced by survival factors

Remember: P = I x D

factors influencing prevalence

Decreased by:

Shorter duration of disease

High case-fatality rate from disease

Decrease in new cases (decrease in incidence)

In-migration of healthy people

Out-migration of cases

Improved cure rate of cases

Increased by:

Longer duration of the disease

Prolongation of life of patients without cure

Increase in new cases

(increase in incidence)

In-migration of cases

Out-migration of healthy people

In-migration of susceptible people

Improved diagnostic facilities

(better reporting)

Factors Influencing Prevalence
comparing cross sectional and longitudinal data
Comparing Cross-Sectional and Longitudinal Data

How you organize your data depends on your research question.

Cross Sectional Data

Cohort or



30 Year Olds in Successive Years

correlational studies
Correlational Studies
  • Correlational studies (ecological studies) use measures that represent characteristics of entire populations (areal aggregates) to describe outcomes in relation to some factor of interest such as age, time, utilization of services, or exposures
    • You can generate hypotheses for case-control studies and environmental studies
    • You can target high-risk populations, time-periods, or geographic regions for future studies
correlational studies cont
Correlational Studies (cont.)
    • Because data are for groups, you cannot link disease and exposure in individuals
      • Example: Percentage of teenagers taking drivers education and fatal teenage car accidents study done by National Safety Council
    • You cannot control for potential confounders
    • Data represent average exposures rather than individual exposures, so you cannot determine a dose-response relationship
    • Caution must be taken to avoid drawing inappropriate conclusions, or ecological fallacy