descriptive epidemiology principles of epidemiology lecture 6 dona schneider, phd, mph, face

Descriptive Epidemiology

Principles of Epidemiology

Lecture 6

Dona Schneider, PhD, MPH, FACE

Epidemiology (Schneider)

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

Epidemiology (Schneider)

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

41 65 85118

158211

268311

382443

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0

100

200

300

400

500

600

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Years

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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).

Epidemiology (Schneider)

Possible Reasons for Changes in Trends Artifactual

Errors in numerator due toChanges 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

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.)

Epidemiology (Schneider)

ICD-10 International Classification of Disease (ICD) 10th

RevisionICD-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!

Epidemiology (Schneider)

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

Epidemiology (Schneider)

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

Epidemiology (Schneider)

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

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.

Infant Mortality Rates by Race

Epidemiology (Schneider)

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 StudiesCross 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 PrevalenceIncreased 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)

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

Prevalence of Congenital Malformations Across Maternal Age

Prevalence of Cigarette Smoking Among Successive Birth Cohorts

Comparing Cross-Sectional and Longitudinal Data

How you organize your data depends on your research question.

45 A B C D E

40 B C D E F

35 C D E F G

30 D E F G H

25 E F G H I

1955 1960 1965 1970 1975

Year of Examination

Cohort or

Longitudinal

Data

30 Year Olds in Successive

Years

Cross Sectional Data

Epidemiology (Schneider)

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

ADVANTAGES 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

Epidemiology (Schneider)

Correlational Studies (cont.) LIMITATIONS

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

Breast Cancer Mortality and Dietary Fat Intake