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Flashcards in Epi Class 13 Deck (23):
1

Surveillance

Ongoing systematic collection of health data

– Monitoring health events: detect sudden changes & follow secular (long-term) trends

– Priority setting, planning, implementing, and evaluating disease (investigation,
control, and prevention)

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Biosurveillance

term used more commonly (often with anti-terrorism) as it is less frightening to the public that "surveillance"

“the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels"

3

Requirements for Biosurveillance

1. Case detection (find one patient—requires a reporting mechanism)

2. Cluster detection (detect unusual patterns—requires baseline epidemiological
data)

3. Signal validation (confirm need for a public health response—requires data
integration)

4. Event characterization (outbreak investigation—requires data collection and laboratory analysis)

5. Notification and communication (such as notifiable disease reporting from clinicians
and labs)

6. Quality control and improvement (such as ensuring confidentiality of data and
monitoring performance)

4

Types of Epidemiological Surveillance

Active surveillance

Passive surveillance

Sentinel surveillance

Syndromic surveillance

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Active surveillance

public health agency reaches out to local healthcare providers to request information

6

Passive surveillance

local healthcare providers provide reports to a public health organization (either voluntary reports or notifications mandated by law)

7

Sentinel surveillance

a public health organization selects a sample of healthcare providers and receives regular reports from them; active surveillance of a larger number of providers can be initiated if an outbreak appears to be occurring

8

Syndromic surveillance

asking for a report to be submitted when a patient has a particular set of symptoms, rather than requiring a formal laboratory diagnosis

9

Emerging Surveillance Methods

Community-based surveillance

Crowd-sourced surveillance

10

Community-based surveillance

community health volunteers (CHVs) assist with data collection and reporting of a limited number of syndromes

11

Crowd-sourced surveillance

an emerging technique that scans reports from Twitter and other social media to detect outbreaks early

12

Common Surveillance Problems

• Reports often are made only for patients who have sought care from formal healthcare providers

• Healthcare workers have little incentive to report cases

• Case definitions may be unclear

• Incidence rates (# new cases / # population) can be impossible to calculate if the denominator is unknown

13

Surveillance Biases

• Attendance patterns (media storms!)

• Diagnostic methods (more testing = more diagnoses, even of subclinical infections)

• Screening (“seek and ye shall find”)

• Reporting propensity (do people really report all notifiable diseases?)
– Notification delays
– Failure of the agency to report back

14

Research Approaches

• Implementation (process) research: What policies would improve health services?

• Evaluation (outcomes) research: Do implemented policies work? Do they improve
patient outcomes and reduce costs?

15

What does implementation (process) research ask?

What policies would improve health services?

16

What does evaluation (outcomes) research ask?

Do implemented policies work? Do they improve patient outcomes and reduce costs?

17

Outcomes Research

• Efficacy: Does the program work in “laboratory” (ideal, controlled) conditions?

• Effectiveness: Does the program work in “real life” conditions?

• Efficiency: Is the cost-benefit ratio favorable?

• Communicating with Policymakers

18

KAP

(knowledge, attitudes, practices)

– Raise awareness (knowledge) and health literacy
– Influence attitudes/perceptions
– Promote changes in behavior/practices

19

Type 1 error

"alpha"

finding an association when there is really NO association
- this occurs because by chance ~5% of samples drawn from a source population will be “extreme” (like being unusually younger or unusually old)

20

CI (confidence interval) formula

1 - alpha

21

Type 2 error

"beta"

finding NO association when there really is an association
- this is usually due to having too small a sample size

22

power formula

1 - beta

23

What are generally acceptable levels for alpha and power?

alpha = 5%
power = 80%