HSCI 488 - Study Guide #2 Flashcards

Public Health Surveillance

• Definition: Ongoing, systematic collection, analysis, and interpretation of health data.
• Purpose:
  • Plan, implement, and evaluate public health practices.
  • Plan how and when to disseminate data.
• Applications:
  • Developing public health policy.
  • Implementing disease prevention programs.
  • Estimating the burden of diseases.
  • Detecting outbreaks.

Notifiable Diseases

• Definition: Diseases important to public health, typically infectious diseases that pose a risk to the population's health.
• Examples: STIs, vaccine-preventable diseases, diseases associated with outbreaks.

Passive Surveillance

• Definition: Medical practitioners and diagnostic labs report notifiable diseases on a case-by-case basis to state/local health agencies.
• Application: Non-communicable diseases reported to disease registries, collecting incidence/prevalence/survival data in centralized databases for chronic diseases.
• Benefits:
  • Monitor trends over time.
  • Find patterns.
  • Plan and evaluate programs.
  • Prioritize health needs.
  • Conduct research.
• Issues:
  • Incomplete data.
  • Lagged behind real-time diagnosis due to underreporting.
  • Limited to people seeking treatment.

Active Surveillance

• Definition: Public health agency staff actively contact healthcare providers, labs, and health clinics to identify potential cases.
• Example: Health officials in the 80s contacting clinics treating gay men/IV drug users to identify deaths from specific diseases.
• Purpose: To protect public health, especially during infectious outbreaks in healthcare settings.
• Benefits: Identification of all cases of a specific disease (outbreak or research).
• Issues: Time-consuming and labor-intensive.

Syndromic Surveillance

• Definition: Uses symptom information to alert public health officials to a potential problem.
• Application: Using pharmaceutical sales data to detect increases in antidiarrheal drug purchases.
• Benefits:
  • Allows for timely prevention to reduce morbidity/mortality.
  • Can indicate a bioterrorism attack.
  • Early detection of foodborne illness/contamination (pre-diagnosis).
• Issues: Labor-intensive and time-consuming.

Primary Prevention

• Definition: Aims to stop disease before it occurs using personal and community efforts.
• Rationale: Prevents the disease from becoming an issue.
• Why it is the "best" type: Stops the disease before it starts.
• Noticeability: Results are often unnoticed because nothing appears to happen.
• Examples: Health education, improved nutrition, immunizations, sanitation, and infection control.

Secondary Prevention

• Definition: Aims to reduce the progress of disease through early detection/action.
• Screening:
  • Method to identify unrecognized diseases/conditions.
  • Crucial when symptoms are not present.
  • Does not diagnose; identifies diseases before symptoms appear.
  • Examples: Mammography, blood work, prostate exams.
• Benefits:
  • Early disease detection can lead to better patient outcomes.
  • Allows for lifestyle changes (diet, exercise, medication) to prevent negative health outcomes.
• Diagnostic Tests: Used to diagnose disease when symptoms are present.

Tertiary Prevention

• Definition: Focuses on reducing impairment/harm and helping people manage long-term health problems/chronic injuries.
• Purpose:
  • Improve quality of life.
  • Improve ability to function.
  • Potentially increase life expectancy.
• Examples: Physical, occupational, speech, and audiological therapy, cardiac rehabilitation, diabetes management programs, support groups.
• Providers: Physical therapists, occupational therapists, mental health professionals, speech therapists, audiologists.

Screening Tests

• Requirements:
  • Address a major health problem with a known natural history.
  • Acceptable form of treatment must exist.
  • Recognized latent stage of disease.
  • Early treatment must improve the outcome.

Evaluating Screening Tests

• Sensitivity: Ability of a test to correctly identify cases (true positive rate).
  • Sensitivity = \frac{True Positive}{True Positive + False Negative} * 100
• Specificity: Ability of a test to correctly identify non-cases (true negative rate).
  • Specificity = \frac{True Negative}{True Negative + False Positive} * 100

Prostate Cancer Screening Example

• Data Table:

  	PROSTATE CANCER +	NO PROSTATE CANCER +	TOTAL
  PSA+ (Tested +)	175 (a)	50 (b)	225
  PSA- (Tested -)	80 (c)	415 (d)	495
  TOTAL	255	465	720

• Calculations:

  • Sensitivity = 175 / 255 = 69%
    • Interpretation: Of those who truly had prostate cancer, 69% screened positive.
  • Specificity = 415 / 465 = 89%
    • Interpretation: Of those who truly did not have prostate cancer, 89% screened negative.

Screening Test Results

• True Positive (a): Person with the disease screens positive.
• False Positive (b): Person without the disease screens positive.
• False Negative (c): Person with the disease screens negative.
• True Negative (d): Person without the disease screens negative.

Predictive Values

• Positive Predictive Value (PPV): Proportion of those with a positive screening test who actually have the disease.
  • PPV = \frac{True Positive}{True Positive + False Positive}
• Negative Predictive Value (NPV): Proportion of those with a negative screening test who do not have the disease.
  • NPV = \frac{True Negative}{True Negative + False Negative}
• Prostate Cancer Example:
  • PPV = 175 / 225 = 78%
    • Interpretation: Of those who SCREENED positive, 78% actually have prostate cancer.
  • NPV = 415 / 495 = 84%
    • Interpretation: Of those who SCREENED negative, 84% actually do not have prostate cancer.

Screening Test Cut-off Points

• Raising the cut-off point:
  • Increased specificity (fewer false positives).
  • Decreased sensitivity (more false negatives).
  • Some people with the disease will screen negative and think they are healthy.
• Lowering the cut-off point:
  • Increased sensitivity (fewer false negatives).
  • Decreased specificity (more false positives).
  • Some healthy people will screen positive and think they have the disease.

Issues with Poor Screening Tests

• False Positive Tests:
  • Overdiagnosis: Detecting a disease that is unlikely to worsen or may resolve on its own.
  • Overtreatment: Unneeded treatments resulting from overdiagnosis.
• False Negatives:
  • Delaying treatment.
• Harms:
  • Stress/anxiety related to undergoing tests, regardless of results.
  • Cost/inconvenience.
  • Physical harm: complications, invasive tests.
  • Psychological harm: anxiety/distress.
  • Financial harm: costs related to treatment, loss of work time.

Biases Associated with Screeners

• Lead Time Bias: Overestimation of survival time because screening detects the disease earlier.
  • Increases the time from diagnosis to death, but not necessarily the length of life.
• Length Bias: Overestimation of survival duration due to the excess of slowly progressing cases detected by screening.
  • People with less aggressive diseases are overrepresented in screening and have better survival.
• Healthy Volunteer Bias: Healthier, health-conscious, or insured individuals are more likely to be screened.
  • Screening test volunteers are not a random sample of the population.
• Surveillance Bias: Disease ascertainment is better in a monitored population because practitioners are actively looking for the disease.

Outbreak vs. Cluster

• Outbreak: A much higher than expected number of disease cases in a place and time.
• Cluster: Aggregation of uncommon events in an area that is perceived to be greater than expected by chance.
• Determining an Outbreak: Compare the actual incidence of disease (observed) to the expected incidence using statistical software.
  • A significant p-value (p < 0.05) indicates an outbreak.

Infectious Disease Spread

• Common Vehicle Spread: Disease spread through a shared source (air, water, food, or drugs).
  • Control measures include removing contaminated sources or proper food preparation.
• Serial Transfer Transmission: Disease transmission from human to human, human to animal, or human environment to human in a sequence.
  • Examples: Measles, chickenpox, syphilis.
• Epidemiological Curve: Graph showing the distribution of cases of disease by the time of onset.

Common Source Epidemics

• Clustering of cases within a short time due to exposure to a shared or common source of infection (food or water).
• Point Source Epidemic:
• The exposed develop the disease very quickly, often over 1 incubation period.
• Ex: food borne illnesses suddenly everyone is sick
  • Continuous Source Epidemic: Prolonged exposure to a source over an extended period.
    • Example: A community continuing to drink from a contaminated water supply.
• Propagated Epidemics: Infections are transmitted from one infected person to another through direct or indirect routes.
  • Examples: COVID-19, influenza, measles. Short time of initial infection.