Applications of Epidemiology in Rural and Urban Communities

Applications of Epidemiology in Rural and Urban Communities

NURS 633

Kimberly Carter PhD, RN

Fall 2000

Epidemiology

The study of the distribution of health and of the determinants of deviations from health in populations

Purpose of Epidemiology

To obtain necessary data to prevent and control disease through Community Health Intervention

Epi - “on or upon”

Demos - “the people”

Logos - “Knowledge”

Uses of epidemiology

study effects of disease states in populations over time and predict future health needs

diagnose the health of the communityevaluate health servicesestimate individual risk from group

experience

Uses of epidemiology (con’t)

Identify syndromescomplete the clinical picture so that

prevention can be accomplished before disease is irreversible

search for cause

Epidemiologic investigations focus on:

Infectious diseaseNon-infectious chronic conditionsAcute eventsEmotional/Mental Health conditionsNormal characteristics of populations

2 Models to describe the factors necessary to make an epidemiologic event happen

Web of CausationEcological (or

epidemiologic) Model

Example of a Web of Causation

Susceptible Host Infection Tuberculosis

Vaccination Genetic

Overcrowding Malnutrition

Tissue Invasion and Reaction

Exposure to Mycobacterium

Agent:Amount, infectivity, pathogenicity, virulence, chemical composition,

cell reproduction

Environment:Physical, biological, social

Host:Intrinsic factors, physical factors, psychological factors, immunity

Health

or

Illness

?

Examples of Agents of Disease

Nutritive excesses or deficiencies (Cholesterol, vitamins, proteins)

Chemical agents (carbon monoxide, drugs, ragweed, medications)

Physical agents (Ionizing radiationInfectious agents (hookworm, amoebae,

malaria, tuberculosis, syphilis, histoplasmosis, polio, rabies, mumps)

Examples of Host Factors

Genetic (Sickle cell disease)AgeGenderEthnic groupPhysiologic state (fatigue, pregnancy, puberty)Prior immunologic experience (maternal antibodies,

immunization, prior infection) Intercurrent or preexisting diseaseHuman behavior (Food handling, diet, hygient,

recreation, use of resources)

Examples of Environmental Factors

Physical environment (geology, climateBiologic environment (population density,

sources of food, influence of vertebrates and arthropods

Socioeconomic (exposure to chemical agents, urban crowding, tensions/ pressures, cooperative efforts in health education, wars, floods

Epidemiology within the U.S. Health Care System

Individual Aggregate

Curative

Preventive

Basic Nursing Community Health Nursing/Public Health

Medicine Epidemiology

Levels of Prevention

Primary: Activities to decrease the probability of specific illnesses or dysfunctions No Disease Present

Secondary: Early Diagnosis and prompt intervention allowing early return to ADLs. Disease has occurred

Tertiary: A defect or disability is fixed, stabilized or irreversible. Rehabilitation. Disease has advanced

Natural History of Disease

The process by which diseases occur and progress in humans

Natural History of Disease

Primary Prevention

Secondary Prevention

Tertiary Prevention

Pre-exposure Stage:

Factors present leading to problem development

Preclinical Stage:

Exposure to causative agent: no symptoms present

Clinical Stage:

Symptoms present

Resolution Stage:

Problem resolved. Returned to health or chronic state or death

Exposure to AgentSymptom Development

Epidemiologic Control Measures

Rapid identification of isolated disease outbreaks

Notification to local health authority Local District or state National WHO

Role of CDCP

Publish MMWRTrack diseases required by Federal lawSurveillance monitoringTrack noncommunicable disease

Relative Risk (Risk Ratio)

The ratio of the risk of death among those exposed to a factor to the risk among those not exposed.

= Incidence of disease in exposed group

--------------------------------------------------

Incidence of disease in nonexposed group

Calculating Relative Risk

Daily avg # drinks Cirrhosis Cases (per 1,000)

Relative Risk

0 7

1 26

2-3 48

4+ 40

Attributable Risk

Attributable Risk: Rate of a disease among exposed individuals that can be attributed to the exposure and not to other causes

rate of outcome (incidence or mortality) among exposed - rate among the unexposed per K

Calculating Attributable Risk

Exposed Rate-Unexposed Rate

=

Basic Definitions

MorbidityMortalityEpidemicEndemicPandemic

RatesAllows comparison

between populationsFrequency in numeratorComparison population

in denominatorX/Y x KUsually per 1,000 or

100,000 (NOT %)

Incidence

# of new cases of disease in a place

from Time 1 to Time 2

___________________________________ x K

# of persons in a place at midpoint

of time period

Prevalence

# of existing cases in a place at a given time

_________________________________ x K

# of persons in a place at midpoint of year

Crude Mortality Rate

# of deaths during a year

___________________________

Average (midyear) population

/per 100,000 population

Cause-Specific Mortality Rate

# of deaths from a stated cause in a year

_______________________________

Average (midyear) population

/per 100,000 population

Age-specific mortality rate

# of deaths of a given age group in a year

____________________________________

Average (midyear) population of same group

/per 100,000

Standardized Mortality Rates

Adjusts for differences in populations so that comparisons are interpretable.

Age-adjustedRace-adjustedGender-adjusted

Maternal Mortality Rate

# of maternal deaths during a year

__________________________________

# of live births in same year

per 100,000 live births

Infant Mortality Rate

# of deaths of children < 1 year during a year

__________________________________

# of live births in same year

per 100,000 live births

Crude Birth Rate

# of live births during year

_____________________________

Total midyear population

per 100,000

Case Fatality Percentage

# of deaths from specific disease

___________________________

# of cases

TIMES 100%

Interpreting Epidemiological Information

Indices of population change are fertility, mortality, and migration

Indices of overall health status are IMR and MMR

To plan for future health needs, look at age distribution, “at-risk” groups, screening protocols, treatment modalities, and referral mechanisms

Screening Validity

SensitivitySpecificityPositive and Negative Predictive Values

The Ideal Screening TestNormal Diabetic

Blood Glucose

Sensitivity

Test’s ability to identify correctly those who do have disease

= True positives/All those with the disease= TP /TP + FN

Specificity

Test’s ability to identify correctly those who do not have disease

= True negatives/All without the disease=TN/ TN + FP

Indices to evaluate accuracy of Screen or diagnostic test

Test Disease Present Disease Absent

Positive Result AA(True positives)

B B (False positives)

Negative Result CC(False Negatives)

DD(True negatives)

Totals A+CA+C B+DB+D

Application of Sensitivity & SpecificityBlood Glucose Level

(mg/100 ml) using screening test

Actual diabetics; n=70) Actual nondiabetics; n=510)

80

90

100

110

100.0 (n=1)

98.6 (n=1)

97.1 (n=3)

92.9 (n = 3)

1.2 (n=6)

7.3 (n=31)

25.3 (n=91)

48.4 (n=116)

120

130

140

150

160

170

180

190

200

88.6 (n=4)

81.4 (n= 5)

74.3 (n=7)

64.3 (n= 6)

55.7 (n= 3)

52.9 (n=2)

50.0 (n=4)

44.3 (n=5)

37.1 (n=26)

68.2 (n=101)

82.4 (n=71)

91.2 (n=45)

96.1 (n=25)

98.6 (n=12)

99.6 (n=5)

99.8 (n=6)

99.8 (n=0)

100.0 (n=1)

Application (Continued)

Blood Glucose Level (mg/100 ml)

True Diabetics (%) True Nondiabetics (%)

All over 110mg/100 ml are classified as

diabetics (True positives)(False positives)

All under 110mg/100 ml are classified as

non-diabetics (False Negatives) (True negatives)

Totals 100.0 100.0

Application (Continued)

Sensitivity =

Specificity =

Predictive Values

Determines relationship between sensitivity, specificity, and prevalence

When prevalence is low, even a highly specific test will give a relatively large number of false positives because of the many nondiseased persons being tested.

Positive Predictive Value

Likelihood that an individual with a positive test has the disease

TP/TP+FP

Negative Predictive Value

Likelihood that an individual with a negative test does not have the disease

TN / TN + FN

Predictive Value of Diabetes Application

Positive Predictive Value =

Negative Predictive Value =

Considerations for Selection of Screens

PrevalenceFinancialAvailability/

Feasibility of Treatment

Relative costs of classifying persons as FN and FP