DESCRIPTIVE EPIDEMIOLOGYDESCRIPTIVE EPIDEMIOLOGYfor Public Health Professionals for Public Health Professionals
Part 1Part 1
Ian R.H. Rockett, PhD, MPHDepartment of Community Medicine
West Virginia University School of Medicine
Prepared under the auspices of the Prepared under the auspices of the Southeast Public Health Training Southeast Public Health Training Center, University of North Carolina, Center, University of North Carolina, Chapel Hill, 2005.Chapel Hill, 2005.
Learning ObjectivesLearning Objectives
1.1. To introduce some key historical contributors To introduce some key historical contributors to the evolution of epidemiologyto the evolution of epidemiology
2.2. To present basic models of disease and injuryTo present basic models of disease and injury
3.3. To address data sources, classification, and To address data sources, classification, and measurementmeasurement
4.4. To build a bridge between descriptive and To build a bridge between descriptive and analytic epidemiologyanalytic epidemiology
Performance ObjectivesPerformance Objectives
1.1. To be sensitive to the history of epidemiology To be sensitive to the history of epidemiology against the background of broad population against the background of broad population changechange
2.2. To identify mortality and morbidity data sourcesTo identify mortality and morbidity data sources
3.3. To calculate basic measuresTo calculate basic measures
4.4. To generate hypotheses from descriptive dataTo generate hypotheses from descriptive data
POPULATION TRANSITIONS POPULATION TRANSITIONS
and and HISTORYHISTORY
The Big Population Picture
Source: Joseph A. McFalls, Jr. Population: A Lively Introduction. Third edition. Population Bulletin 53(3); 1998: 38.
The Demographic Transition
The demographic transition framework illustrates population growth in terms of discrepancies and changes in two crude vital rates – mortality and fertility (ignores the third component of growth, migration)
Source: Joseph A. McFalls, Jr. Population: A Lively Introduction. Third edition. Population Bulletin 53(3); 1998: 39.
Top 10 Causes of Death in the U.S. , 1900Rank Cause of Death Deaths per
100,000 Percent of all Deaths
1 Pneumonia 202 12 2 Tuberculosis 194 11 3 Diarrhea and enteritis 140 8 4 Heart disease 137 8 5 Chronic nephritis
(Bright’s disease) 81 5
6 Unintentional injury (accidents)
76 4
7 Stroke 73 4 8 Diseases of early
infancy 72 4
9 Cancer 64 4 10 Diptheria 40 2
Top 10 Causes of Death in the U.S. , 2000Rank Cause of Death Deaths per
100,000 Percent of all Deaths
1 Heart Disease 258 30 2 Cancer 200 23 3 Stroke 60 7 4 Lung Diseases 45 5 5 Unintentional injury
(accidents) 34 4
6 Diabetes 25 3 7 Pneumonia and
influenza 24 3
8 Alzheimer’s Disease 18 2 9 Nephritis, Kidney
disease 14 2
10 Septicemia 12 1
Source: Ian R.H. Rockett. Population and Health: An Introduction to Epidemiology. Second edition. Population Bulletin 54(4); 1999: 9.
EPIDEMIOLOGYepi – upon
demos – people
logos – study
The scientific study of the distribution and determinants of health-related states or events in specified populations, and the application of resulting knowledge to the prevention and control of health problems
Epidemiology as a Liberal Art
An accessible low-technology science, which incorporates the “scientific method, analogic thinking, deductive reasoning, problem solving within constraints, and concern for aesthetic values”
David Fraser, New England Journal of Medicine, 316(6); 1987:309-314.
Some Epidemiologic History
HippocratesHippocrates
On Airs, On Airs, Waters, and Waters, and Places (5Places (5thth century BCEcentury BCE)
Hippocrates spearheaded a move away from looking to blame demons for disease and injury
FAST FORWARDFAST FORWARD
Enter John Graunt (1629-1674)
vocation – haberdasher (seller of men’s accessories)
avocation – father/founder of demography and epidemiology
Graunt counted rather than considered (Major Greenwood)
Among his observations, he noted:
regularity of biological phenomena in the mass
that more males are born than females and more males die
than females (annually)
Partial Translation
Ague = Malaria
Purples & Spotted Feaver = Meningococcal Meningitis
King’s Evil = Tuberculosis of the lymph glands of the neck
Age
17th century
2002
London, England
United States
0 100 100 6 64 99 16 40 99 26 25 98 36 16 97 46 10 95
56 6 91 66 3 81 76 1 63
Population Survivorship: Two Populations
Miasmatists Vs Contagionists
miasm – pathogenic emanation dispersed in the atmosphere
(malaria – bad ‘air’)
contagion – vehicle of person-to- person disease transmission (forerunner of germ theory)
EnterEnter JohnJohn Snow (1813-1858)Snow (1813-1858)
Spot Map of Fatal Cholera Cases in London, 1854
Source: Ian R.H. Rockett. Population and Health: An Introduction to Epidemiology. Second edition. Population Bulletin 54(4); 1999: 6.
Filippo Pacini, 1812-1883Filippo Pacini, 1812-1883