1. Managerial Epidemiology
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Managerial Epidemiology
Ty Borders, Ph.D.
Assistant Professor
Department of Health Services Research & Management
Texas Tech School of Medicine

2. Learning objectives Define epidemiology
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Define epidemiology
Explain the role of epidemiology in health care management
Calculate major descriptive epidemiologic indicators
Understand what are the more prevalent diseases and disorders in the U.S.
Calculate and interpret Relative Risk
Calculate and interpret Odds Ratio
Understand types and purposes of analytical studies

3. 4/16/2017
What is Epidemiology?
Study of the distribution and determinants of disease
The doctrine of what is among or happening to people
Epi: among
Demos: people
Logos: Doctrine
Note: from Charles Lynch, M.D., Iowa College of Public Health

4. History of epidemiology
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1662, John Graunt
a petty merchandiser in London, publishes a report on births and deaths in London. First to quantify disease patterns.
1839, William Farr
a physician, establishes system for routine compiliation of no. and causes of death in England and Wales
1855, John Snow
a physician, studied whether drinking water in Soutwark and Vauxhall increased risk of cholera

5. Subfields of Epidemiology
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Subfields of Epidemiology
Clinical epidemiology (patients)
Social epidemiology (populations)
Genetic epidemiology (patients/populations)
Health services epidemiology (populations/patients)

6. 4/16/2017
A broader definition
Study of the distribution and determinants of health-related events and states
Utilization of health services
Health-related quality of life
Satisfaction with care

7. Managerial Epidemiology
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Managerial Epidemiology
Epidemiological methods applied to the...
I. Evaluation of community health care needs
II. Study of health services utilization (access)
III. Health outcomes research (study of the impact of health care services on health outcomes)
Effectiveness
Patient satisfaction
Health-related quality of life

8. I. Evaluation of community health care needs
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I. Evaluation of community health care needs
Descriptive morbidity and mortality indicators
Cancer incidence and mortality rates
Infectious disease rates
Infant mortality rate
Descriptive social and demographic indicators
Median income, unemployment rates, etc.
Market research surveys

9. II. Utilization Population Characteristics Environment Behavior
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Population Characteristics
Environment
Behavior
Outcomes
Perceived health status
Personal health practices
Health care system
Evaluated health status
Predisposing
Enabling
Need
Use of health services
External environment
Consumer satisfaction

10. III. Health outcomes research
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Study of the quality of health services
This includes the effectiveness of health services
Results from RCTs may not apply in real world
A number of factors influence who receives a treatment
A treatment may be more/less effective for particular subgroups

11. Descriptive vs. Analytical epi.
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Descriptive epidemiology
Study of the amount and distribution of disease within a population by person, place, and time
Provides info. on patterns of disease occurrence by age, sex, race, marital status, etc.
Analytical epidemiology
Study of the determinants of disease or reasons for relatively high or low frequency in specific groups

12. Biologic Concepts Agent-Host Environment
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Agent-Host Environment
An agent interacts with a host in a particular environment to produce disease (the epidemiologic triangle)
Host
Vector
Agent
Environment

13. Biologic Concepts Almost all diseases have multiple causes
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Almost all diseases have multiple causes
Necessary and sufficient
Without the factor, the disease never develops
Necessary but not sufficient
Requires multiple factors
Sufficient but not necessary
Factor can produce disease, but so can other factors
Neither sufficient nor necessary
Probably represents causal relationships in most chronic diseases

14. Examples of routes of transmission
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Agent Disease
Respiratory Cigarette smoke Lung cancer
Influenza virus Flu
Gastrointestinal Vibrio cholera Cholera
Lead Lead poison.
Sexual transm. Papilloma virus Cervical cancer
Perinatal exposure Rubella virus Cong. Defects
Blood stream exp. Clostridium tetani Tetanus
& skin breakage

15. Incubation or Induction Period
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The period of time between exposure to a causative agent and the appearance of first clinical manifestations
Infection
Incubation/induction/ latent period
Disease
Inapparent
Mild
Moderate
Severe
Fatal
Likely to be seen by doctor
Likely to be hospitalized

16. Study types Descriptive studies Analytical, observational studies
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Descriptive studies
Population level: correlational, ecologic, or aggregate
Individual level: case reports, case series
Analytical, observational studies
Cross sectional survey
Case-control studies
Cohort studies
Analytical, intervention studies

17. Measures of disease occurrence
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Measures of disease occurrence
3 measures used to assess the frequency of disease or other health events
Cummulative incidence (CI), also called Risk
Prevalence
Incidence density, also called incidence rate

18. Types of Incidence and Prevalence Measures
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Rate Type Numer Denom.
Morbidity rate Incidence # new nonfatal Total pop.
cases at risk
Mortality rate Incidence # deaths from Total pop.
a disease(s)
Case-fatality rate Incidence # deaths from # of cases
a disease of that disease
Period Prevalence # existing cases Total pop.
plus new cases
diagnosed during
given time period

19. Risk Sometimes also called cumulative incidence
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Sometimes also called cumulative incidence
Proportion of unaffected individuals who, on average, will contract disease of interest over a specified period of time

20. Calculation of risk R = New cases Persons at risk
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R = New cases
Persons at risk
R = 0 if no new occurrences arise
R = 1 if the entire population becomes infected

21. 4/16/2017
Example
We are interested in the risk of acquiring a nosocomial infection. A study was conducted on 5031 patients.
596 patients developed infection within 48 hours after admission.
R = 596 / 5031 = = 12%

22. Calculation of prevalence
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Calculation of prevalence
Prevalence is a measure of the number of existing cases in a population.
Specifically, the proportion of a population that has a disease at a particular point in time.

23. Prevalence P = Number of cases Number of persons in population
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Prevalence
P = Number of cases
Number of persons in population
Prevalence, like risk, ranges between 0 and 1.

24. Incidence rate Also called incidence density
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Incidence rate
Also called incidence density
Reflects the occurrence of new cases (like risk does)
But, also measures the rapidity with which event occurs

25. Calculation of incidence rate
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Calculation of incidence rate
IR = New cases
Person time

26. Calculation of incidence rate
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Calculation of incidence rate
IR = New cases
Person time

27. 4/16/2017
Example
Patient A develops a disease 2 years after entry into study. Thus, the person-time for Patient A is 2 years.
Patients B,C,D,E an F contribute 2,3,7,2 and 6 years, respectively. Thus, the number of person-years is = 22.
IR = new cases/ PT = 2 / 22

28. Summary Characteristic Risk Prev. IR What is Prob. % of pop. Rapidity
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Characteristic Risk Prev. IR
What is Prob. % of pop. Rapidity
measured of disease with dis. of dis.
Occurrence
Units None None Cases/person-
time
Time of disease Newly Existing Newly
diagnosis diagnosed diagnosed
Synonyms Cumulative Incidence
Incidence Density

29. Survival Probability of remaining alive for a specific length of time
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Probability of remaining alive for a specific length of time
For chronic disease, like cancer, 1-year and 5-year survival are important indicators of prognosis and severity.

30. Calculation of survival
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Survival = A - D A
D = number of deaths observed over a defined period of time
A = number of newly diagnosed patients under observation

31. Calculation of survival
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Survival = A - D A
D = number of deaths observed over a defined period of time
A = number of newly diagnosed patients under observation

32. Types of rates Crude rates Category specific rates
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Crude rates
Rates presented for entire population
e.g. Cancer mortality rate in 1980 (416,481 cancer deaths / midyear U.S. population)
Category specific rates
Rates presented for individuals in specific categories
e.g. Cancer deaths among persons 45-54

33. Adjusted rates
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If we are interested in the magnitude of the health problem, we don’t need adjusted rates
If we are interested in comparing populations, we need to adjust for differences

34. Adjustment methods Take a weighted average of category-specific rates
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Take a weighted average of category-specific rates
Direct method
Indirect method

35. Pros/cons of crude, specific, and adjusted rates
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Type Strengths Limitations
Crude Actual summary Difficult to interpret
rates b/c populations may
vary in composition
Specific Homogeneous Cumbersome to compare
subgroups many subgroups of 2
or more populations
Adjusted Summary statistics Fictional rates
Differences in Absolute magnitude
composition removed depends on standard
population chosen

36. Standardized mortality rate (SMR)
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SMR = observed deaths / expected deaths
= indirect adjusted rate / crude rate of standard pop.
Usually expressed as a percent

37. Percentage Uninsured
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38. 4/16/2017
Person-years of life lost (in 1,000s) from leading causes of cancer, 1991 (from Greenberg, 1996)

39. 4/16/2017
Years of Potential Life Lost before age 65 by cause of death (per 100,000 person years) (from Greenberg, 1996)

40. Leading causes of death, 1996
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Cause Frequency
Heart disease %
Cancer %
Stroke %
Chronic lung disease %
Accidents %
Pneumonia/influenza %
Diabetes mellitus %
HIV/AIDS %

41. Observational Studies
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Cross - sectional
Provides estimate of the strength of association between a factor and outcomes or event
Can not determine timing of exposure
e.g. A telephone survey of rural residents conducted at one point in time
Case - control study
Compare the prevalence of exposure between 2 or more groups (i.e. cases and controls)

42. Observational Studies (cont.)
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Prospective cohort studies
Retrospective (historical) cohort studies

43. Cohort Studies Onset of study Time Eligible subjects Disease Exposed
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Onset of study
Time
Eligible subjects
Disease
Exposed
No Disease
Disease
Unexposed
No Disease
Direction of inquiry

44. Comparison of prospective and retrospective studies
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Attribute Retrospective Prospective
Information Less complete More complete
Discontinued
exposures Useful Not useful
Emerging, new
exposures Not useful Useful
Expense Less costly More costly
Completion
time Shorter Longer
from Greenberg et al.

45. Adv./Disadv. of cohort studies
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Advantages Disadvantages
Direct calculation Time consuming
of relative risk
May yield info. on incidence Require large sample sizes
Clear temporal relationship Expensive
Can yield info. on multiple Not efficient for study of
exposures rare events
Minimizes bias Losses to follow-up
Strongest observational design
for establishing cause-effect
from Greenberg et al.

46. Relative Risk Relative risk (or risk ratio) = ratio of two rates
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Relative risk (or risk ratio) = ratio of two rates
RR = incidence rate among exposed
incidence rate among unexposed

47. Relative Risk Exposure Yes No Outcome Death a b a+b No death c d c+d
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Exposure
Yes No
Outcome Death a b a+b
No death c d c+d
a+c b+d
RR = a/ (a+c)
b/ (b+d)

48. Example of Relative Risk
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Apgar score
Outcome Death No death
Risk among exposed = 42 / 122 = 34.4%
Risk among unexposed = 43 / 345 = 12.5%
RR = 34.4 / 12.5 = 2.8

49. Observational Studies (cont.)
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Case - control study
Compare the prevalence of exposure between 2 or more groups (i.e. cases and controls)
Pairwise matching

50. Case-Control Studies Study Onset Time Onset of study Exposed Cases
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Study Onset
Time
Onset of study
Exposed
Cases
Unexposed
Exposed
Controls
Unexposed
Direction of Inquiry

51. Odds Ratio Often, we do not have info. about risk
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Often, we do not have info. about risk
Therefore, we calculate the OR
Exposure
Yes No
Outcome Yes a b
No c d
Odds of case exposure = (a/a+b) / (b/a+b) = a / b
Odds of control exposure = c / d

52. Example of Odds Ratio Exposure Yes No Cases 50 15 Controls 30 20
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Exposure
Yes No
Cases
Controls
OR = (a/b) / (c/d)
= ad / bc = 50*20 / 30*15 = 2.22

53. Experimental Studies Experimental Randomization to an intervention
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Experimental
Randomization to an intervention
Voluntary participation
Experimental control