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SPM SAQs - Complete Exam Answers

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Q1. Add a note on Epidemiological Approach

1. Observation - Observing the pattern of disease in the community (who gets the disease, where, and when)
2. Counting cases - Measuring the frequency of disease using rates and ratios
3. Relating to population at risk - Identifying the denominator (population at risk)
4. Comparison - Comparing disease rates between different groups (exposed vs. unexposed)
5. Formulating hypothesis - Based on data, suggesting a causal relationship
6. Testing hypothesis - Using analytical studies (case-control, cohort) to test the hypothesis
7. Drawing conclusions - Making inferences about causality and recommending control measures

• Agent (biological, chemical, physical)
• Host (age, sex, immunity)
• Environment (physical, biological, social)

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Q2. Write about Tools of Measurement (Rate, Ratio, Proportion) ***

• Expresses the number of events in a given population in a given time period
• Formula: Rate = (Number of events / Population at risk) x 10^n
• Example: Crude Death Rate, Infant Mortality Rate
• Has a time element - most useful tool in epidemiology

• Comparison of two quantities by division (numerator not necessarily included in denominator)
• Formula: Ratio = X / Y
• Example: Sex ratio (number of males per 100 females), Doctor-patient ratio
• No specific time element required

• A ratio in which the numerator is included in the denominator
• Expressed as a percentage
• Formula: Proportion = (a / a+b) x 100
• Example: Proportional mortality ratio - proportion of deaths due to a specific cause
• Shows the relative magnitude of a part compared to the whole

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Q3. Add a note on Mortality Rates ***

• Total deaths in a year per 1000 mid-year population
• Normal value: ~8-10 per 1000

• Deaths in specific age group per 1000 population in that age group

• Deaths due to specific cause per 100,000 population

• Deaths under 1 year per 1000 live births
• Best index of socioeconomic development and health care
• India IMR ~28 (2019)

• Deaths in first 28 days per 1000 live births

• Stillbirths + neonatal deaths in first 7 days per 1000 total births

• Maternal deaths per 100,000 live births due to complications of pregnancy/delivery

• Observed deaths / Expected deaths x 100
• Used to compare mortality between populations with different age structures

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Q4. Describe Incidence and Prevalence ***

• Definition: Number of NEW cases of a disease occurring in a defined population during a specified time period
• Formula: Incidence Rate = (New cases in a period / Population at risk at mid-period) x 1000
• Measures the RISK of getting a disease
• Useful in: acute diseases, studying causation

• Definition: Total number of cases (old + new) of a disease in a population at a given point in time
• Two types:
  • Point prevalence: cases at a single point in time
  • Period prevalence: cases over a period of time
• Formula: Prevalence Rate = (All cases at one point in time / Population at same time) x 1000
• Measures the BURDEN of disease
• Useful in: chronic diseases, planning health services

Prevalence = Incidence x Duration (P = I x D)

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Q5. Strength of Association - Odds Ratio, Relative Risk, Attributable Risk, Population Attributable Risk *****

• Used in Case-Control studies
• Ratio of odds of exposure in cases to odds of exposure in controls
• Formula: OR = (ad) / (bc) using the 2x2 table
• OR >1 = positive association; OR <1 = protective; OR = 1 = no association

• Used in Cohort studies
• Ratio of incidence in exposed group to incidence in unexposed group
• Formula: RR = Incidence in exposed / Incidence in unexposed
• RR >1 = risk factor; RR <1 = protective; RR = 1 = no association

• Also called Risk Difference
• The amount of disease in exposed group that can be attributed to the exposure
• Formula: AR = Incidence in exposed - Incidence in unexposed
• Measures the PUBLIC HEALTH IMPACT of removing the exposure

• Proportion of disease in the TOTAL population that is attributable to the exposure
• Formula: PAR = Incidence in total population - Incidence in unexposed
• Useful for deciding which risk factors to target at the population level

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Q6. Advantages and Disadvantages of Cohort Study ***

Advantages:

1. Can directly calculate incidence rates and relative risk
2. The temporal sequence (cause before effect) is clearly established
3. Can study multiple outcomes from a single exposure
4. Minimal bias in measuring exposure (exposure recorded before disease develops)
5. Best method to study rare exposures (e.g., occupational hazards)
6. Can study the natural history of disease

Disadvantages:

1. Expensive and time-consuming - may take years to decades
2. Requires large sample size
3. Loss to follow-up (attrition) is a major problem - can bias results
4. Not suitable for rare diseases - need very large numbers
5. Changes in diagnostic criteria and methods over long follow-up periods
6. Risk of Neyman bias (late-look bias)

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Q7. Advantages and Disadvantages of Case-Control Studies ***

Advantages:

1. Relatively quick and inexpensive to conduct
2. Suitable for rare diseases - no need to wait for disease to develop
3. Can study multiple exposures/risk factors for a single disease
4. Requires small sample size compared to cohort study
5. No risk of attrition/loss to follow-up
6. Useful for generating and testing causal hypotheses

Disadvantages:

1. Cannot calculate incidence rates or relative risk directly (only Odds Ratio)
2. Selection bias - difficult to find appropriate controls
3. Recall bias - cases may remember exposure better than controls
4. Interviewer bias - investigator may probe cases more
5. Temporal sequence hard to establish (chicken-and-egg problem)
6. Not suitable for rare exposures

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Q8. Differences between Case-Control and Cohort Study ***

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Q9. Types of Bias in Case-Control and Cohort Study

Bias in Case-Control Studies:

• Occurs when cases and controls are not representative of the source population
• Example: Hospital-based controls may not represent community controls
• Types: Berkson's bias (hospital admission bias), Neyman bias

• Cases tend to remember exposure better than controls due to their illness
• Example: Mothers of malformed babies recall drug exposure more than mothers of normal babies

• Investigator probes cases more deeply than controls for exposure history

• A third variable associated with both exposure and disease distorts the true relationship

Bias in Cohort Studies:

• If those lost to follow-up differ systematically from those who remain, results are biased

• Workers appear healthier than general population because severely ill people are not employed

• Knowledge of exposure status influences assessment of outcome

• Exposed workers may migrate out of the study area

Control of Bias:

• Randomization
• Blinding
• Matching
• Standardization

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Q10. Write about Randomization and Blinding

RANDOMIZATION:

1. Simple Randomization - coin toss, random number table
2. Stratified Randomization - randomize within strata (e.g., age, sex)
3. Cluster Randomization - entire clusters (villages, wards) are randomized
4. Systematic Randomization - every nth person is selected

• Controls for both known and unknown confounders
• Basis for valid statistical testing

BLINDING:

1. Single Blind - Only the patient does not know which treatment they receive
2. Double Blind - Neither the patient NOR the investigator knows the treatment allocation
3. Triple Blind - Patient, investigator, AND data analyst are all blinded

• Prevents placebo effect in participants
• Prevents observer bias in outcome assessment
• Prevents ascertainment bias

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Q11. Write about Confounders

• Associated with the exposure
• Independently associated with the disease (risk factor)
• NOT an intermediate step in the causal pathway

• Can cause overestimation or underestimation of the true association
• Can even reverse the direction of association (negative confounding)

How to Control Confounders:

1. Randomization - distributes confounders equally between groups (best method)
2. Restriction - limit study to people who are homogeneous for the confounder
3. Matching - match cases and controls for the confounding variable (e.g., age, sex)

1. Stratification (Mantel-Haenszel method) - analyze data within strata
2. Multivariate analysis - statistical adjustment (logistic regression, etc.)

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Q12. Bradford Hill Criteria for Judging Causality

• A strong association (high RR or OR) is more likely to be causal
• Example: RR of lung cancer in smokers vs. non-smokers = 9-10

• Association has been repeatedly observed by different investigators, in different places, at different times using different methods

• One specific cause leads to one specific effect
• Example: Mesothelioma is specifically caused by asbestos

• Cause MUST precede the effect
• The only absolute criterion for causality

• Greater exposure leads to greater frequency of disease
• Example: More cigarettes smoked = higher lung cancer risk

• The association is biologically plausible and consistent with known facts

• The association should not conflict with the known natural history and biology of the disease

• Prevention of disease by removing the causal factor provides strong evidence

• Similar cause-effect relationships are known for similar exposures/diseases

Mnemonic: Strong Consistent Specific Temporal Biological gradient Plausible Coherent Experimental Analogy = SC ST BP CEA

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Q13. Uses of Epidemiology ****

• Study trends of disease over time to project future health problems
• Example: Epidemiology identified coronary heart disease as an "epidemic"

• Identify and quantify health problems in a community
• Quantify morbidity and mortality rates to define priorities
• Epidemiology is called the "diagnostic tool" of community medicine

• Epidemiological data forms the basis for rational planning of health services
• Evaluate effectiveness of control programs (e.g., vaccine effectiveness)

• Calculate relative risk and attributable risk for specific factors
• Example: Risk of Down syndrome increases with maternal age

• Epidemiological methods help define and refine disease syndromes
• Example: Differentiation of gastric and duodenal ulcers; identification of AIDS

• Studying disease in the community reveals the full spectrum (iceberg phenomenon)
• Example: One-third to two-thirds of IHD deaths are sudden - discovered by epidemiology

• Identifying risk factors and causative agents of disease
• Example: Doll and Hill's work linking smoking to lung cancer

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Q14. Types of Epidemic and Epidemic Curve ***

TYPES OF EPIDEMIC:

• All cases trace back to a SINGLE source of infection
• a) Point Source: All exposed at same time and place (e.g., food poisoning at a party)
  • Epidemic curve shows a sharp rise and rapid fall
  • All cases occur within ONE incubation period
• b) Continuous/Propagated Source: Exposure continues over time (e.g., contaminated water supply)
  • Cases continue as long as exposure continues

• Disease spreads from person to person
• Multiple waves - each wave separated by one incubation period
• Example: Measles, chickenpox, COVID-19
• Epidemic curve shows multiple peaks

• Starts as common source, then spreads person-to-person
• Example: Initial food poisoning from a party, then spread by contact

EPIDEMIC CURVE:

• A histogram plotting number of cases (Y-axis) against time of onset (X-axis)

1. Helps identify the type of epidemic (point source vs. propagated)
2. Identifies the probable time of exposure by back-calculating from the peak (using incubation period)
3. Helps assess whether epidemic is over
4. Provides clues about mode of transmission

• Point source: Steep rise, rapid fall, all within one incubation period
• Propagated: Multiple waves/peaks, each wave = one incubation period apart

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Q15. Live vs Killed Vaccines *****

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Q16a. Cold Chain ********

• Most vaccines: +2°C to +8°C (refrigerator temperature)
• OPV, Varicella: -15°C to -25°C (freezer)
• Note: Some vaccines (OPV) can be stored frozen; others (DPT, Hep B) must NOT be frozen

1. Cold rooms/Walk-in coolers (at state/national level) - 4°C
2. Deep Freezers (at district level) - -20°C
3. Ice-lined refrigerators (ILR) - at PHC/CHC level
4. Cold boxes (for transport)
5. Vaccine carriers (for field use)
6. Ice packs

• Thermometers - daily monitoring
• Vaccine Vial Monitors (VVM)
• Freeze watch / Freeze indicator - detect accidental freezing
• Cold chain log books

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Q16b. Vaccine Vial Monitors (VVM) ***

• Contains a chemical square that darkens (changes color) with cumulative heat exposure
• If the inner square becomes DARKER than the outer circle = vaccine has been heat-damaged = DO NOT USE

1. Simple and easy to read by field workers
2. Continuous monitoring - works even during storage and transport
3. Reduces vaccine wastage by allowing use of vaccines that have left the cold chain briefly but are still potent
4. Works at field level without electricity

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Q17. Steps in Investigation of an Epidemic

• Confirm the diagnosis clinically and/or by laboratory tests
• A sample of cases may be examined; lab results should not delay investigation

• Compare current case numbers with expected frequency (based on past records)
• Epidemic exists when observed cases exceed expected by more than 2 standard deviations

• Establish clear criteria for what constitutes a case (clinical, lab, epidemiological)

• Active case search in the community
• Line listing of all cases with details: name, age, sex, address, date of onset, exposure

• Person: Who is affected (age, sex, occupation, habits)?
• Place: Where are the cases (spot map, clustering)?
• Time: When did cases occur (epidemic curve)?

• Based on descriptive data, form hypothesis about source, agent, and mode of transmission

• Use analytical methods (case-control or cohort study) to test the hypothesis

• Do not wait for investigation to be complete
• Remove the source; treat cases; protect susceptibles (vaccination, prophylaxis)

• Written report with findings, conclusions, and recommendations to prevent recurrence

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Q18. Adverse Events Following Immunization (AEFI) ***

Classification of AEFI:

• Due to the inherent properties of the vaccine
• Example: Local swelling at injection site from DPT; fever after measles vaccine

• Due to manufacturing error
• Example: Vaccine not properly attenuated

• Due to errors in handling/administration
• Example: Wrong dose, wrong site, wrong diluent, non-sterile technique (abscesses)

• Due to anxiety about immunization, not the vaccine itself
• Example: Fainting (vasovagal syncope), hyperventilation

• Occur after immunization but NOT caused by it
• Example: Fever from concurrent infection

Common AEFIs by Vaccine:

• BCG: Local ulcer, BCG adenitis
• DPT: Fever, local swelling, febrile convulsions (rare), hypotonic-hyporesponsive episode
• OPV: Vaccine-Associated Paralytic Poliomyelitis (VAPP) - 1 in 2.5 million doses
• Measles: Fever (day 5-12), mild rash
• MMR: Parotitis, thrombocytopenia (rare)

Management of AEFI:

• Treat the reaction appropriately
• Report to national AEFI surveillance system
• Investigate to identify causality
• Do NOT stop immunization program

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Q19. National Immunization Schedule ******

• UIP was launched in 1985
• BCG given at birth (up to 1 year if missed)
• Pentavalent replaced DPT + Hep B + Hib in NIS

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Q11 (Second). Outline the Steps Involved in Cohort Study and Case-Control Study

Steps in a COHORT STUDY:

1. Identify the study population (a group without the disease)
2. Classify the population into exposed and unexposed groups based on the risk factor
3. Match the two groups for confounders (age, sex, etc.)
4. Follow up both groups over a defined period of time
5. Observe and record the occurrence of the disease in both groups
6. Compare incidence rates between exposed and unexposed
7. Calculate Relative Risk (RR) and Attributable Risk
8. Draw conclusions about the association

Steps in a CASE-CONTROL STUDY:

1. Define and select CASES - persons with the disease (incident cases preferred over prevalent)
2. Select CONTROLS - persons without the disease, matched for age, sex, and other confounders
3. Determine exposure status in both groups by interview/records (looking BACKWARD)
4. Construct a 2x2 table with the data
5. Calculate Odds Ratio (OR) to measure association
6. Apply statistical tests (Chi-square, p-value, confidence intervals)
7. Draw conclusions about causality

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SPM SAQs - Full 2-Page Exam Answers

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Q1. Add a Note on Epidemiological Approach

Definition

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Steps in the Epidemiological Approach

┌─────────────────────────────────────────────────────────┐
│           EPIDEMIOLOGICAL APPROACH - FLOWCHART          │
└─────────────────────────────────────────────────────────┘

        ┌──────────────────────┐
        │  1. OBSERVATION      │  ← Observe disease pattern
        │  (Who? Where? When?) │    in community
        └──────────┬───────────┘
                   │
        ┌──────────▼───────────┐
        │  2. COUNTING CASES   │  ← Measure frequency
        │  (Rates & Ratios)    │    using rates
        └──────────┬───────────┘
                   │
        ┌──────────▼───────────┐
        │  3. RELATE TO        │  ← Define population
        │  POPULATION AT RISK  │    at risk (denominator)
        └──────────┬───────────┘
                   │
        ┌──────────▼───────────┐
        │  4. COMPARISON       │  ← Compare exposed vs
        │  of groups           │    unexposed groups
        └──────────┬───────────┘
                   │
        ┌──────────▼───────────┐
        │  5. HYPOTHESIS       │  ← Formulate causal
        │  FORMULATION         │    hypothesis
        └──────────┬───────────┘
                   │
        ┌──────────▼───────────┐
        │  6. HYPOTHESIS       │  ← Analytical studies
        │  TESTING             │    (case-control/cohort)
        └──────────┬───────────┘
                   │
        ┌──────────▼───────────┐
        │  7. CONCLUSIONS &    │  ← Recommend control
        │  RECOMMENDATIONS     │    measures
        └──────────────────────┘

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Types of Epidemiological Studies

┌──────────────────────────────────────────────────────────┐
│                 EPIDEMIOLOGICAL STUDIES                  │
├──────────────────┬───────────────────────────────────────┤
│  OBSERVATIONAL   │           EXPERIMENTAL                │
├──────────────────┼───────────────────────────────────────┤
│  DESCRIPTIVE     │  Randomized Controlled Trial (RCT)    │
│  - Case reports  │  Field Trial                          │
│  - Case series   │  Community Trial                      │
│  - Cross-sect.   │                                       │
├──────────────────┤                                       │
│  ANALYTICAL      │                                       │
│  - Case-control  │                                       │
│  - Cohort study  │                                       │
└──────────────────┴───────────────────────────────────────┘

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The Epidemiological Triad (For Infectious Disease)

                    AGENT
                   /     \
                  /       \
            HOST ─────────── ENVIRONMENT

• Agent: Biological (virus, bacteria), chemical, physical, nutritional
• Host: Age, sex, genetic makeup, immunity, occupation
• Environment: Physical (climate), biological (vectors), social (overcrowding, poverty)

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Descriptive Epidemiology - Person, Place, Time

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Analytical Epidemiology

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Uses of the Epidemiological Approach

1. Identifies causes of disease
2. Measures the burden of disease in the community
3. Guides planning of health services
4. Evaluates effectiveness of interventions
5. Studies natural history of disease
6. Helps in community diagnosis

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Levels of Prevention (Application of Epidemiology)

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Q2. Tools of Measurement - Rate, Ratio, Proportion ***

Introduction

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1. RATE

Definition

Formula

           Number of events in a given period
Rate =  ─────────────────────────────────────── × 10ⁿ
         Population at risk during same period

Components of a Rate

┌─────────────────────────────────────────┐
│           COMPONENTS OF A RATE         │
├─────────────────┬───────────────────────┤
│  NUMERATOR      │ Events (deaths, cases)│
│  DENOMINATOR    │ Population at risk    │
│  TIME PERIOD    │ Per year (usually)    │
│  MULTIPLIER     │ 10ⁿ (1000, 10,000)   │
└─────────────────┴───────────────────────┘

Types of Rates

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2. RATIO

Definition

Formula

Ratio = X / Y    (where X and Y are independent quantities)

Examples of Ratios

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3. PROPORTION

Definition

Formula

              a
Proportion = ─────  × 100
             a + b

where 'a' is the subgroup and 'a+b' is the total group

Examples of Proportions

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Comparison - Rate vs Ratio vs Proportion

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Specific Rates vs Crude Rates

┌──────────────────────────────────────┐
│ CRUDE RATE: Applied to entire        │
│ population without subdivision       │
│ → Simple but affected by population  │
│   composition (age, sex)             │
├──────────────────────────────────────┤
│ SPECIFIC RATE: Applied to a specific │
│ subgroup (age-specific, sex-specific)│
│ → More meaningful for comparison     │
├──────────────────────────────────────┤
│ STANDARDIZED RATE: Adjusted for      │
│ population composition               │
│ → Best for comparing two populations │
└──────────────────────────────────────┘

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Importance in Epidemiology

• Rates allow comparison between different populations and time periods
• They form the basis of all epidemiological measurements
• Help in identifying high-risk groups
• Essential for planning and evaluating health services

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Q3. Add a Note on Mortality Rates ***

Definition

                     Number of deaths (in defined category)
Mortality Rate =  ──────────────────────────────────────────  × 10ⁿ
                        Mid-year population

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Classification of Mortality Rates

┌────────────────────────────────────────────────────┐
│                  MORTALITY RATES                   │
├──────────────────────┬─────────────────────────────┤
│   GENERAL RATES      │     SPECIFIC RATES          │
├──────────────────────┼─────────────────────────────┤
│ Crude Death Rate     │ Age-Specific Death Rate      │
│                      │ Sex-Specific Death Rate      │
│                      │ Cause-Specific Death Rate    │
│                      │ Proportional Mortality Rate  │
├──────────────────────┴─────────────────────────────┤
│              SPECIAL MORTALITY RATES               │
│ Infant Mortality Rate (IMR)                        │
│ Neonatal Mortality Rate (NMR)                      │
│ Perinatal Mortality Rate                           │
│ Post-Neonatal Mortality Rate                       │
│ Under-5 Mortality Rate (U5MR)                      │
│ Maternal Mortality Rate (MMR)                      │
└────────────────────────────────────────────────────┘

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Detailed Description of Each Rate

1. Crude Death Rate (CDR)

• Total deaths in a year per 1000 mid-year population
• Formula: CDR = (Total deaths / Mid-year population) × 1000
• India CDR: ~6.0 per 1000 (2020)
• Limitation: Does not account for age composition; affected by population structure

2. Age-Specific Death Rate (ASDR)

• Deaths in a specific age group per 1000 mid-year population of that age group
• More meaningful than CDR for comparisons

3. Cause-Specific Death Rate

• Deaths due to specific cause per 100,000 population
• Example: Cancer mortality rate, CVD mortality rate

4. Proportional Mortality Rate (PMR)

• Deaths from specific cause / Total deaths × 100
• Shows relative importance of a cause of death
• Does NOT measure risk; cannot compare across populations

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Special Mortality Rates (Most Important for Exams)

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Infant Mortality Rate (IMR) - Special Importance

┌────────────────────────────────────────────────────────┐
│    COMPONENTS OF INFANT MORTALITY RATE (IMR)           │
├────────────────────────────────────────────────────────┤
│  IMR (deaths under 1 year / 1000 live births)          │
│                    │                                   │
│         ┌──────────┴───────────┐                       │
│    NEONATAL MR              POST-NEONATAL MR            │
│    (0-28 days)              (28 days - 1 year)          │
│    Reflects:                Reflects:                   │
│    - Birth asphyxia         - Diarrheal diseases        │
│    - LBW                    - Respiratory infections    │
│    - Congenital anomalies   - Malnutrition              │
│    - Tetanus                - Poor sanitation           │
└────────────────────────────────────────────────────────┘

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Maternal Mortality Rate (MMR)

• Deaths from causes related to pregnancy/childbirth per 100,000 live births
• 3 Delays Model (causes of maternal death):
  • Delay 1: Decision to seek care
  • Delay 2: Reaching care
  • Delay 3: Receiving care at facility

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Standardized Mortality Ratio (SMR)

SMR = (Observed deaths / Expected deaths) × 100

SMR >100 = Higher mortality than expected
SMR <100 = Lower mortality than expected

• Used to compare mortality between occupational groups or regions with different age structures

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Importance of Mortality Rates

1. Measure the health status of a community
2. Identify high-risk groups needing intervention
3. IMR is considered the single best indicator of overall health and socioeconomic development
4. Form the basis for health planning and priority setting
5. Allow international and historical comparisons

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Q4. Describe Incidence and Prevalence ***

Introduction

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INCIDENCE

Definition

Formula

                    New cases of disease during given period
Incidence Rate =  ──────────────────────────────────────────  × 10ⁿ
                    Population at risk at mid-period

Types of Incidence

• Takes into account the time each person is at risk
• Denominator: person-years at risk
• Used in cohort studies

• Proportion of a fixed population that develops disease over a period
• Used in outbreak investigation

             New cases in specified period
Attack Rate = ────────────────────────── × 100
              Initial population at risk

• Number of cases among contacts of primary cases / Total susceptible contacts × 100
• Measures communicability of a disease

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PREVALENCE

Definition

Types of Prevalence

• All cases at a SINGLE point in time

                  All cases at one point in time
Point Prevalence = ────────────────────────────── × 100
                    Total population at same time

• All cases during a DEFINED PERIOD (includes new + existing at start)

               All cases during defined period
Period Prevalence = ──────────────────────────────── × 100
                     Mid-period population

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The Relationship Between Incidence and Prevalence

                DISEASE FREE
                POPULATION
                     │
              ┌──────▼──────┐
              │   NEW CASES │ ← INCIDENCE (inflow)
              │   DEVELOP   │
              └──────┬──────┘
                     │
              ┌──────▼──────┐
              │  PREVALENCE │ = Pool of existing cases
              │  POOL       │
              └──────┬──────┘
                     │
           ┌─────────┴─────────┐
           │                   │
    ┌──────▼──────┐    ┌───────▼──────┐
    │  RECOVERY   │    │   DEATHS/    │
    │  (outflow)  │    │   EMIGRATION │
    └─────────────┘    └──────────────┘

  PREVALENCE = INCIDENCE × DURATION (P = I × D)

• Prevalence increases when incidence increases OR disease duration increases
• Prevalence decreases when incidence decreases OR effective treatment shortens disease duration

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Comparison - Incidence vs Prevalence

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Factors Affecting Prevalence

1. Duration of disease (longer = higher prevalence)
2. Incidence rate
3. In-migration of cases
4. Out-migration of susceptibles
5. Improved case survival

Factors Affecting Incidence

1. Changes in exposure to risk factors
2. Changes in susceptibility (vaccination)
3. Natural immunity in population
4. Environmental changes

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Practical Significance

• Incidence is used in aetiological research - to study CAUSES
• Prevalence is used in health service planning - to measure BURDEN
• Both together describe the epidemiology of a disease completely

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Q5. Strength of Association - Odds Ratio, Relative Risk, Attributable Risk, Population Attributable Risk *****

Introduction

1. Odds Ratio (OR) - case-control studies
2. Relative Risk (RR) - cohort studies
3. Attributable Risk (AR)
4. Population Attributable Risk (PAR)

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The 2×2 Contingency Table (Foundation of All Calculations)

┌─────────────────────┬──────────────┬──────────────┬────────┐
│                     │  DISEASE (+) │  DISEASE (-) │ TOTAL  │
├─────────────────────┼──────────────┼──────────────┼────────┤
│  EXPOSED (+)        │      a       │      b       │  a+b   │
│  NOT EXPOSED (-)    │      c       │      d       │  c+d   │
├─────────────────────┼──────────────┼──────────────┼────────┤
│  TOTAL              │     a+c      │     b+d      │   N    │
└─────────────────────┴──────────────┴──────────────┴────────┘

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1. ODDS RATIO (OR)

Used in: Case-Control Studies

          Odds of exposure in CASES
OR =  ────────────────────────────────
         Odds of exposure in CONTROLS

       a/c       a × d
OR =  ───── =  ─────────
       b/d       b × c

       ∴ OR = ad/bc

Interpretation

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2. RELATIVE RISK (RR)

Used in: Cohort Studies

         Incidence in EXPOSED group
RR =  ──────────────────────────────────
         Incidence in NON-EXPOSED group

       a/(a+b)
RR =  ─────────
       c/(c+d)

Interpretation

---

3. ATTRIBUTABLE RISK (AR)

Also called: Risk Difference / Excess Risk

AR = Incidence in Exposed - Incidence in Unexposed

AR = [a/(a+b)] - [c/(c+d)]

• Measures how much of the disease in exposed persons is due to the exposure
• Represents the AMOUNT OF DISEASE that could be PREVENTED by eliminating the exposure

Attributable Risk Percent (AR%)

AR% = [(RR-1)/RR] × 100

• Proportion of disease in exposed persons that can be attributed to the exposure

---

4. POPULATION ATTRIBUTABLE RISK (PAR)

Also called: Attributable Risk in the Population

PAR = Incidence in TOTAL population - Incidence in UNEXPOSED

PAR% = [PAR / Incidence in total population] × 100

• Measures the proportion of disease in the ENTIRE POPULATION that could be prevented if the exposure were removed
• Depends on BOTH the strength of association AND the prevalence of exposure in the population

---

Comparison of All Four Measures

---

Summary Diagram

┌──────────────────────────────────────────────────────────────┐
│               MEASURES OF ASSOCIATION                        │
│                                                              │
│  STRENGTH OF    ─────→  ODDS RATIO (case-control)           │
│  ASSOCIATION    ─────→  RELATIVE RISK (cohort)              │
│                                                              │
│  IMPACT OF      ─────→  ATTRIBUTABLE RISK (in exposed)      │
│  EXPOSURE       ─────→  POPULATION ATTR. RISK (in all)      │
└──────────────────────────────────────────────────────────────┘

---

Q6. Advantages and Disadvantages of Cohort Study ***

Definition

Key Feature: Direction is from CAUSE → EFFECT

---

Framework of a Cohort Study

                         FOLLOW-UP PERIOD
                         ─────────────────►
┌─────────────────────┐                  ┌──────────────────┐
│                     │ ─── DISEASE? ──► │  a = Disease +   │
│  EXPOSED GROUP      │                  │  b = Disease -   │
│  (a + b)            │                  │                  │
│                     │                  │  Incidence = a/  │
│                     │                  │  (a+b)           │
└─────────────────────┘                  └──────────────────┘

└─────────────────────┐                  ┌──────────────────┐
│  NON-EXPOSED GROUP  │ ─── DISEASE? ──► │  c = Disease +   │
│  (c + d)            │                  │  d = Disease -   │
│                     │                  │                  │
│                     │                  │  Incidence = c/  │
└─────────────────────┘                  │  (c+d)           │
                                         └──────────────────┘
                                                 │
                                         RR = [a/(a+b)] / [c/(c+d)]

---

Steps in a Cohort Study

STEP 1: Select the study population (all disease-free at start)
    ↓
STEP 2: Classify into EXPOSED and NON-EXPOSED groups
    ↓
STEP 3: Ensure both groups are comparable (matching)
    ↓
STEP 4: Follow up both groups over time
    ↓
STEP 5: Record new cases as they develop
    ↓
STEP 6: Calculate incidence in both groups
    ↓
STEP 7: Calculate Relative Risk and Attributable Risk
    ↓
STEP 8: Draw conclusions

---

Types of Cohort Studies

---

ADVANTAGES of Cohort Study

---

DISADVANTAGES of Cohort Study

---

Classic Examples

• Framingham Heart Study - CVD risk factors (ongoing since 1948)
• Doll and Hill's study - Smoking and lung cancer in British doctors (1951)
• British Doctors Study - Smoking and mortality

---

Q7. Advantages and Disadvantages of Case-Control Studies ***

Definition

Key Feature: Direction is from EFFECT → CAUSE (Retrospective)

---

Framework of a Case-Control Study

                         ← LOOK BACK IN TIME
                         ─────────────────────
┌──────────────┐         ┌─────────────────────────────────┐
│              │ ──────► │  a = Exposed + Disease          │
│   CASES      │         │  c = Not Exposed + Disease      │
│  (Disease +) │         └─────────────────────────────────┘
└──────────────┘
                                    PRESENT TIME
└──────────────┐         ┌─────────────────────────────────┐
│              │ ──────► │  b = Exposed + No Disease       │
│   CONTROLS   │         │  d = Not Exposed + No Disease   │
│  (Disease -) │         └─────────────────────────────────┘
└──────────────┘

                    ODDS RATIO = ad/bc

---

Selection of Cases and Controls

• Must have the disease under study
• Incident (new) cases preferred over prevalent cases
• Clear case definition needed (diagnostic criteria + eligibility criteria)
• Sources: hospital, community, disease registries

• Must NOT have the disease
• Should be from same source population as cases
• Matched to cases for confounders (age, sex, residence)
• Sources: hospital controls, community controls, neighborhood controls

---

The 2×2 Table in Case-Control

┌──────────────┬──────────────┬──────────────┐
│              │ CASES (+)    │ CONTROLS (-) │
├──────────────┼──────────────┼──────────────┤
│ EXPOSED (+)  │      a       │      b       │
│ NOT EXPOSED  │      c       │      d       │
└──────────────┴──────────────┴──────────────┘

Odds Ratio (OR) = ad/bc

---

ADVANTAGES of Case-Control Studies

---

DISADVANTAGES of Case-Control Studies

---

Types of Bias in Case-Control Studies

┌───────────────────────────────────────────────────────┐
│          BIAS IN CASE-CONTROL STUDIES                 │
├───────────────────────────┬───────────────────────────┤
│      SELECTION BIAS       │     INFORMATION BIAS      │
├───────────────────────────┼───────────────────────────┤
│ • Berkson's bias          │ • Recall bias             │
│ • Neyman bias             │ • Interviewer bias        │
│ • Volunteer bias          │ • Misclassification       │
│ • Unrepresentative        │ • Observer bias           │
│   controls                │                           │
└───────────────────────────┴───────────────────────────┘

---

Q8. Differences Between Case-Control and Cohort Study ***

Introduction

---

Schematic Diagram Showing the Difference

COHORT STUDY (Forward - Cause to Effect)
─────────────────────────────────────────►  TIME

PAST                 PRESENT                  FUTURE
│                       │                        │
│    Select by          │                        │
│    EXPOSURE  ─────────►─────────────────────► OUTCOME
│                       │                        │
│  Exposed  ────────────────────────────►  Disease +/-
│  Not Exposed ─────────────────────────►  Disease +/-
│                                              ↓
│                                         Measure: RR


CASE-CONTROL STUDY (Backward - Effect to Cause)
◄─────────────────────────────────────────  TIME

PAST                 PRESENT                  FUTURE
│                       │
│         Look Back     │  Select by
│  ◄────────────────────│  DISEASE
│                       │
│  Exposure? ──────────← Cases (Disease +)
│  Exposure? ──────────← Controls (Disease -)
│                            ↓
│                         Measure: OR

---

Detailed Comparison Table

---

When to Use Which Study

┌─────────────────────────────────────────────────────┐
│              CHOOSE CASE-CONTROL WHEN:              │
│  • Disease is RARE                                  │
│  • Multiple exposures to be studied                 │
│  • Quick answer needed                              │
│  • Limited funds                                    │
│  • Long latency period                              │
└─────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────┐
│                CHOOSE COHORT WHEN:                  │
│  • Exposure is RARE                                 │
│  • Multiple outcomes to be studied                  │
│  • Incidence rate needed                            │
│  • Temporal sequence must be clear                  │
│  • Ample funds and time available                   │
└─────────────────────────────────────────────────────┘

---

Q9. Types of Bias in Case-Control and Cohort Study

Definition

---

Classification of Bias

┌─────────────────────────────────────────────────────────┐
│                    TYPES OF BIAS                        │
├─────────────────┬───────────────────┬───────────────────┤
│  SELECTION BIAS │ INFORMATION BIAS  │  CONFOUNDING      │
├─────────────────┼───────────────────┼───────────────────┤
│• Berkson's bias │• Recall bias      │• Age              │
│• Neyman bias    │• Interviewer bias │• Sex              │
│• Volunteer bias │• Misclassification│• Occupation       │
│• Admission bias │• Observer bias    │• Social class     │
└─────────────────┴───────────────────┴───────────────────┘

---

BIAS IN CASE-CONTROL STUDIES

1. Selection Bias

• Berkson's Bias (Hospital Admission Bias): When hospital cases and hospital controls are used; since hospital admission rates differ for different diseases, the exposure-disease relationship may be distorted
• Volunteer Bias: Volunteers tend to be healthier and more health-conscious than the general population
• Neyman Bias (Late-Look Bias): Only survivors are studied; those who died early are missed

2. Recall Bias

• Cases (sick people) are more likely to remember and report past exposure than controls (healthy people)
• Called "differential recall"
• Example: A mother of a baby with birth defects is more likely to recall any drug she took during pregnancy than a mother of a normal baby
• This leads to OVERESTIMATION of the association

3. Interviewer Bias

• The interviewer, knowing who is a case and who is a control, may probe cases more intensively about exposure
• Also called observer bias
• Control: Double-blinding (interviewer should not know case/control status)

4. Misclassification Bias

• Cases are classified as controls or vice versa
• Non-differential: Both groups misclassified equally → underestimates association
• Differential: Groups misclassified unequally → can over or underestimate

---

BIAS IN COHORT STUDIES

1. Loss to Follow-Up Bias (Attrition Bias)

• Most important bias in cohort studies
• If those lost to follow-up differ from those who remain (in terms of disease development), the results are biased
• Control: Minimize loss to follow-up; compare characteristics of those lost vs. those remaining

2. Healthy Worker Effect

• Workers as a study group appear healthier than the general population
• Because severely ill people are not employed
• This leads to underestimation of occupational hazard
• Example: Factory workers show lower mortality than general population, NOT because the factory is safe, but because very sick people are not hired

3. Observation Bias (Hawthorne Effect)

• Knowledge that one is being observed changes behavior
• Can affect both exposed and unexposed groups

4. Migratory Bias

• Exposed workers may leave the area, making follow-up difficult
• Remaining workers may not be representative

5. Contamination Bias

• Some members of the unexposed group may develop exposure during follow-up

---

Comparison of Bias by Study Type

---

Control of Bias

┌──────────────────────────────────────────────────────┐
│              CONTROL OF BIAS                         │
├──────────────────────────────────────────────────────┤
│  AT DESIGN STAGE:                                    │
│  • Randomization                                     │
│  • Blinding (single/double)                          │
│  • Matching cases and controls                       │
│  • Clear case definition                             │
│  • Standardized data collection                      │
├──────────────────────────────────────────────────────┤
│  AT ANALYSIS STAGE:                                  │
│  • Stratification (Mantel-Haenszel)                  │
│  • Multivariate analysis (logistic regression)       │
│  • Sensitivity analysis                              │
└──────────────────────────────────────────────────────┘

---

Q10. Write about Randomization and Blinding

Introduction

---

RANDOMIZATION

Definition

Why Randomization is Needed

• Ensures comparability of groups at baseline
• Controls for both known and unknown confounders
• Provides the basis for valid statistical inference
• Eliminates selection bias

---

Types of Randomization

┌─────────────────────────────────────────────────────────────┐
│                   TYPES OF RANDOMIZATION                    │
├────────────────────────┬────────────────────────────────────┤
│  TYPE                  │  DESCRIPTION                       │
├────────────────────────┼────────────────────────────────────┤
│ Simple Randomization   │ Coin toss, random number table,    │
│                        │ lottery method                     │
├────────────────────────┼────────────────────────────────────┤
│ Systematic Random.     │ Every nth person selected          │
│                        │ (e.g., every 5th patient)          │
├────────────────────────┼────────────────────────────────────┤
│ Stratified Random.     │ Population divided into strata     │
│                        │ (age, sex), then randomized within │
│                        │ each stratum                       │
├────────────────────────┼────────────────────────────────────┤
│ Block Randomization    │ Blocks of equal size (e.g., 4)    │
│                        │ ensure equal numbers at all times  │
├────────────────────────┼────────────────────────────────────┤
│ Cluster Randomization  │ Entire groups (villages, wards)   │
│                        │ are randomized as a unit           │
└────────────────────────┴────────────────────────────────────┘

Random Allocation Process

    ALL ELIGIBLE SUBJECTS
           │
    ┌──────┴──────┐
    │  RANDOM     │
    │  ALLOCATION │
    └──────┬──────┘
           │
    ┌──────┴──────┐
    │             │
    ▼             ▼
TREATMENT     CONTROL
  GROUP        GROUP
(Drug A)     (Placebo)
    │             │
    └──────┬──────┘
           │
    COMPARE OUTCOMES

---

BLINDING

Definition

Types of Blinding

---

Why Blinding is Necessary

• A patient who KNOWS they are receiving treatment may feel better just due to the belief in treatment
• Blinding prevents this

• An investigator who knows a patient is on the active drug may assess outcomes more favorably
• Double-blinding prevents this

• Knowing treatment allocation can affect how rigorously outcomes are measured

---

Placebo

• An inert substance (e.g., sugar tablet, saline injection) that looks identical to the real treatment
• Given to the control group to maintain blinding
• Must be identical in appearance, taste, smell, and route of administration

┌──────────────────────────────────────────────────┐
│                 BLINDING CONCEPT                 │
├──────────────────────────────────────────────────┤
│  Treatment Group  ──► Active Drug                │
│       ↕   IDENTICAL APPEARANCE                  │
│  Control Group    ──► PLACEBO (inert)            │
│                                                  │
│  Neither group knows which they received         │
│  (Double Blind)                                  │
└──────────────────────────────────────────────────┘

---

Allocation Concealment

• Different from blinding
• Ensures that the person enrolling patients does not know the upcoming assignment
• Prevents selection bias BEFORE randomization
• Methods: sealed opaque envelopes, central telephone randomization

---

Combined Effect of Randomization + Blinding in RCT

ELIGIBLE POPULATION
       │
       ▼
RANDOMIZATION ──────────────────────► Controls selection bias
       │
       ▼
ALLOCATION CONCEALMENT ─────────────► Controls pre-randomization bias
       │
       ▼
BLINDING ───────────────────────────► Controls performance & detection bias
       │
       ▼
OUTCOME ASSESSMENT
       │
       ▼
ANALYSIS (Intention-to-Treat) ──────► Controls attrition bias

---

Q11. Write about Confounders

Definition

1. Is associated with the exposure under study
2. Is an independent risk factor for the disease/outcome
3. Is NOT an intermediate step in the causal pathway between exposure and disease

---

Classic Example of Confounding

                    AGE (Confounder)
                   /              \
                  /                \
         GREY HAIR ──────────────► HEART DISEASE
         (Exposure)  ← spurious    (Outcome)
                       association

Grey hair does NOT cause heart disease.
Age causes BOTH grey hair AND heart disease.
∴ Age is a CONFOUNDER.

• Coffee drinkers also tend to smoke
• Smoking is a risk factor for MI
• Smoking is a CONFOUNDER here (not coffee)

---

Criteria for a Variable to be a Confounder

┌────────────────────────────────────────────────────────┐
│          FOR A VARIABLE TO BE A CONFOUNDER:            │
│                                                        │
│  1. It must be associated with the EXPOSURE            │
│  2. It must be a risk factor for the DISEASE           │
│  3. It must NOT be on the causal pathway               │
│     (not an intermediate step)                         │
└────────────────────────────────────────────────────────┘

---

Effect of Confounding

TRUE ASSOCIATION:  RR = 2.0

After controlling for confounder:
  → RR = 2.0 (no change)          = Not a confounder
  → RR = 1.0 (association gone)   = Positive confounding (overestimated)
  → RR = 4.0 (association ↑)      = Negative confounding (underestimated)
  → RR reversal (positive→neg)    = Negative confounding (Simpson's paradox)

---

Confounder vs Effect Modifier

---

Methods to Control Confounding

At Study Design Stage

At Analysis Stage

---

Residual Confounding

• Confounding that remains even after controlling for known confounders
• Due to imprecise measurement of confounders or unmeasured confounders
• Cannot be eliminated in observational studies

---

Practical Note

Confounding is a major reason why observational studies may give misleading results. It is the biggest threat to internal validity in observational epidemiology. Only randomization can control ALL confounders (known and unknown).

---

Q12. Bradford Hill Criteria for Judging Causality

Introduction

---

Background

• Bradford Hill developed these criteria after studying the association between smoking and lung cancer
• His landmark paper: "The Environment and Disease: Association or Causation?" (1965)
• These criteria are guidelines, not absolute rules - no single criterion is either necessary or sufficient (except temporality)

---

The 9 Criteria in Detail

1. STRENGTH OF ASSOCIATION

┌──────────────────────────────────────────────────────┐
│ The stronger the association, the more likely it     │
│ is to be causal.                                     │
│                                                      │
│ Measured by: RR or OR                                │
│ Example: RR of lung cancer in heavy smokers = 20-25  │
│          → Much more likely causal than RR = 1.2     │
└──────────────────────────────────────────────────────┘

2. CONSISTENCY

• The association has been repeatedly observed by different investigators, in different places, at different times, using different methods
• Example: Smoking-lung cancer association observed in USA, UK, Japan consistently

3. SPECIFICITY

• One specific exposure causes one specific disease
• Example: Mesothelioma is specifically caused by asbestos exposure
• Limitation: Many diseases have multiple causes, so this criterion is weakest

4. TEMPORALITY (THE ONLY ABSOLUTE CRITERION)

┌──────────────────────────────────────────────────────────┐
│  EXPOSURE MUST PRECEDE THE DISEASE                       │
│                                                          │
│  CAUSE ──────────────────► EFFECT                        │
│  (Exposure first)            (Disease later)             │
│                                                          │
│  This is the ONLY criterion that is MANDATORY            │
└──────────────────────────────────────────────────────────┘

5. BIOLOGICAL GRADIENT (Dose-Response Relationship)

• As the amount/duration of exposure increases, the frequency of disease increases

  Number      │                            ●
  of cases    │                       ●
              │                  ●
              │              ●
              │          ●
              │      ●
              └─────────────────────────────►
                   Increasing dose/exposure

• Example: More cigarettes/day → higher lung cancer risk
• Supports causation strongly

6. BIOLOGICAL PLAUSIBILITY

• The association must be biologically plausible based on current knowledge
• The mechanism should make biological sense
• Example: Smoking → carcinogens in tobacco → DNA damage → cancer (plausible)
• Limitation: Depends on the state of current biological knowledge

7. COHERENCE

• The association should not contradict known facts about the natural history and biology of the disease
• "The association should be coherent with generally known facts"
• Example: Lung cancer mortality trends match cigarette sales trends

8. EXPERIMENTAL EVIDENCE

• If removing the exposure prevents the disease, this strongly supports causality
• Strongest form of evidence
• Example: Reducing smoking rates → decreased lung cancer incidence
• Also includes animal experiments showing the causal mechanism

9. ANALOGY

• Similar cause-effect relationships are already known for similar exposures
• Example: If thalidomide causes birth defects, another drug in pregnancy may also do so
• Weakest criterion

---

Summary Table

Mnemonic: SC ST BP CEA (SC ST - BP - CEA)

---

Important Note

• None of the criteria are absolute (except temporality)
• The more criteria satisfied, the stronger the evidence for causation
• These are probabilistic guidelines - not a checklist

---

Q13. Uses of Epidemiology ****

Introduction

---

Definition Revisited

---

The 7 Uses of Epidemiology (Morris, 1957)

┌───────────────────────────────────────────────────────────────┐
│                    7 USES OF EPIDEMIOLOGY                     │
├───────────────────────────────────────────────────────────────┤
│  1. Historical study of rise and fall of disease              │
│  2. Community diagnosis                                       │
│  3. Planning and evaluation of health services                │
│  4. Evaluation of individual risks and chances                │
│  5. Syndrome identification                                   │
│  6. Completing natural history of disease                     │
│  7. Searching for causes (causation)                          │
└───────────────────────────────────────────────────────────────┘

---

1. Study of Historical Rise and Fall of Disease

• Epidemiology provides a means to study disease trends over time in human populations
• Helps understand why certain diseases disappeared (smallpox) and new ones emerged (AIDS, COVID-19)
• "The farther back you look, the farther forward you can see" - Winston Churchill
• Application: Trend analysis, time-series data, secular trends
• Example: Epidemiology identified coronary heart disease as a modern "epidemic"

2. Community Diagnosis

• Identification and quantification of health problems in a community in terms of morbidity and mortality rates
• Identifies groups at risk and those in need of health care
• Epidemiology is described as the "diagnostic tool of community medicine"
• Three purposes:
  • Lay down priorities in disease control
  • Provide benchmark for evaluating health services
  • Source of new knowledge about disease distribution

3. Planning and Evaluation of Health Services

• Provides fundamental basis for rational planning of health services
• Planning includes:
  • Number of hospital beds needed
  • Health manpower planning
  • Screening programs
  • Immunization campaigns
• Evaluation of effectiveness of control measures (e.g., vaccine effectiveness, treatment outcomes)
• Known as the "new epidemiology" - application of epidemiology to health care

4. Evaluation of Individual Risks and Chances

• Calculation of relative risk and attributable risk for individual patients
• Allows clinicians to counsel patients about their personal risk
• Examples:
  • Risk of Down syndrome with increasing maternal age
  • Risk of MI in smokers vs. non-smokers
  • Cancer risk in those with family history

5. Syndrome Identification

• Epidemiological studies can define and refine disease syndromes
• Can correct misconceptions about disease associations
• Examples:
  • Differentiation of gastric and duodenal ulcers (gradient by social class)
  • Patterson-Kelly syndrome (dysphagia + iron deficiency) - epidemiological testing showed it was less common than thought
  • Identification of AIDS as a new syndrome in 1981

6. Completing the Natural History of Disease

• Hospital-based picture of disease is incomplete and biased
• Epidemiologist studies the entire spectrum of disease in the community (iceberg phenomenon)
• Fills in gaps that clinicians cannot see
• Example: Epidemiology showed that 1/3 to 2/3 of all IHD deaths are sudden (occur within 1 hour) - something hospital studies could NEVER have revealed

7. Searching for Causes (Causation)

• The classic use of epidemiology
• Identifying risk factors, causative agents, and mechanisms of disease
• Examples:
  • Doll and Hill: smoking → lung cancer
  • Snow: contaminated water → cholera
  • Semmelweis: handwashing → reduction in puerperal fever
• Bradford Hill's criteria used to judge causality

---

Additional Uses (Beyond Morris's 7)

---

Summary Diagram

                    USES OF EPIDEMIOLOGY
                           │
          ┌────────────────┼─────────────────┐
          │                │                 │
    DESCRIPTIVE       ANALYTICAL       EVALUATIVE
          │                │                 │
    Community         Finding          Planning &
    diagnosis         causes           evaluation
    Natural           Syndrome         Risk
    history           identification   assessment

---

Q14. Types of Epidemic and Epidemic Curve ***

Definition of Epidemic

---

Epidemic Threshold

• The level of disease activity beyond which an epidemic is considered to exist
• Usually defined as 2 standard deviations above the endemic level
• When cases exceed this threshold = EPIDEMIC

---

Types of Epidemic

┌────────────────────────────────────────────────────────────────┐
│                     TYPES OF EPIDEMIC                         │
├──────────────────────────┬─────────────────────────────────────┤
│   COMMON SOURCE          │      PROPAGATED (HOST-TO-HOST)      │
├──────────────────────────┼─────────────────────────────────────┤
│  Point Source            │  Person-to-person spread            │
│  Continuous Source       │                                     │
│  Intermittent Source     │                                     │
└──────────────────────────┴─────────────────────────────────────┘
                    Also: MIXED EPIDEMIC

1. POINT SOURCE EPIDEMIC (Common Source, Single Exposure)

• All cases exposed to the same source at the same time
• All cases develop within ONE incubation period
• Epidemic curve: Sharp rise, rapid fall (like a cliff or bell shape)
• Example: Food poisoning at a party, cholera from a single contaminated well

2. CONTINUOUS SOURCE EPIDEMIC

• Cases exposed to the same source but over a prolonged period
• Cases continue as long as source continues
• Epidemic curve: Plateau pattern - sustained high number of cases
• Example: Contaminated water supply running for weeks

3. INTERMITTENT SOURCE EPIDEMIC

• Exposure to the source is intermittent (on and off)
• Epidemic curve: Multiple peaks corresponding to each exposure event

4. PROPAGATED EPIDEMIC (Person-to-Person)

• Disease spreads from person to person
• Each generation of cases infects the next
• Epidemic curve: Multiple successive waves, each wave separated by ONE incubation period
• Each wave is larger than the previous (until herd immunity develops)
• Example: Measles, COVID-19, chickenpox

5. MIXED EPIDEMIC

• Begins as a common source epidemic, then continues as a propagated epidemic
• Example: Typhoid - initial contaminated water source, then spread by contact

---

Epidemic Curve (Epi Curve)

Definition

---

Epidemic Curve Patterns

POINT SOURCE EPIDEMIC CURVE:
                    ████
                   ██████
                  ████████
                 ██████████
                ████████████
              ██████████████
─────────────────────────────────►
         Time (1 incubation period)

Sharp rise → peak → rapid fall
All within ONE incubation period


PROPAGATED EPIDEMIC CURVE:
              ██                      ██
             ████                    ████
            ██████              ████████
           ████████         ██████████
          ████████████    ████████████
─────────────────────────────────────────►
     1st wave      2nd wave      3rd wave
          ←  IP  →← IP  →← IP  →
     Each wave = 1 incubation period apart
     (IP = incubation period)


CONTINUOUS SOURCE EPIDEMIC CURVE:
              ██████████████████████████
             ████████████████████████████
            ██████████████████████████████
─────────────────────────────────────────────►
         Sustained plateau while source continues

---

Uses of Epidemic Curve

1. Identify type of epidemic (point source vs. propagated)
2. Estimate time of exposure - by counting back from the peak using the incubation period
3. Predict the future course of the epidemic
4. Assess whether epidemic is declining
5. Guide control measures - e.g., if point source, find and eliminate the source

---

Investigation of Epidemic Curve

• Use incubation period to back-calculate the probable time of exposure

Probable exposure time = Peak of curve − Median incubation period

Example: Peak on Day 10, incubation period = 2-4 days
∴ Exposure probably occurred on Day 6-8

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Q15. Live Vs Killed Vaccines *****

Introduction

• Live attenuated vaccines
• Killed (inactivated) vaccines
• Also: Toxoids, Subunit vaccines, Recombinant vaccines

---

Live Attenuated Vaccines

Definition

How Attenuation is Done

• Repeated passage in tissue culture or embryonated eggs
• Growth at suboptimal temperatures
• Chemical treatment

Examples of Live Vaccines

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Killed (Inactivated) Vaccines

Definition

Types of Killed Vaccines

1. Whole killed organisms: DPT, Salk (IPV), Rabies
2. Subunit vaccines: Hepatitis B (surface antigen), Pertussis (acellular)
3. Toxoids: DT, TT (toxins treated with formaldehyde)
4. Conjugate vaccines: Hib conjugate, Pneumococcal conjugate

Examples of Killed Vaccines

---

Comprehensive Comparison Table

---

Summary Diagram

                       VACCINES
                          │
          ┌───────────────┴────────────────┐
          │                                │
    LIVE ATTENUATED                 KILLED/INACTIVATED
          │                                │
  BCG, OPV, MMR,              IPV, DPT, Hep B,
  Yellow Fever, Varicella      Typhoid Vi, Rabies
          │                                │
  Strong, long immunity        Weaker, needs boosters
  Single dose usually          Multiple doses needed
  Can cause disease            Cannot cause disease
  (rare reversal)              (safer)

---

Special Note on OPV vs IPV

---

Q16a. Add a Note on Cold Chain ********

Definition

---

Why Cold Chain is Important

• Vaccines are biological products that can be destroyed by:
  • Heat (most vaccines)
  • Freezing (DPT, Hep B, TT - these MUST NOT be frozen)
  • Light (BCG, MMR, OPV)
• Once vaccine potency is lost, it cannot be regained
• Vaccine failure can cause disease in a "well-immunized" population

---

The 6 Rights of Cold Chain (Supply Chain Principle)

Right VACCINE + Right QUANTITY + Right PLACE +
Right TIME + Right CONDITION + Right COST

---

Temperature Requirements for Vaccines

---

Cold Chain from National to Peripheral Level

┌─────────────────────────────────────────────────────────────┐
│               COLD CHAIN HIERARCHY                          │
├─────────────────┬───────────────────────────────────────────┤
│ NATIONAL LEVEL  │ Walk-in coolers/freezers (large capacity) │
│                 │ Refrigerated trucks for transport         │
├─────────────────┼───────────────────────────────────────────┤
│ STATE LEVEL     │ Walk-in coolers (4°C), Walk-in freezers   │
│                 │ Deep Freezers, Ice-lined Refrigerators    │
├─────────────────┼───────────────────────────────────────────┤
│ DISTRICT LEVEL  │ Deep Freezers (-20°C)                     │
│                 │ Ice-lined refrigerators (ILR)             │
├─────────────────┼───────────────────────────────────────────┤
│ PHC/CHC LEVEL   │ Ice-lined refrigerators (ILR)             │
│                 │ Deep Freezers (for OPV)                   │
├─────────────────┼───────────────────────────────────────────┤
│ FIELD LEVEL     │ Vaccine carriers (4-8 hours)              │
│                 │ Cold boxes with ice packs                 │
└─────────────────┴───────────────────────────────────────────┘

---

Cold Chain Equipment in Detail

---

Freeze Damage (Very Important)

• DPT, DT, TT (tetanus toxoid)
• Hepatitis B
• IPV
• Liquid Hib
• Pentavalent vaccine

Shake Test (to detect freeze damage)

SHAKE TEST:
1. Take a vial that was possibly frozen
2. Take a control vial (known to be unaffected)
3. Shake both vigorously for 10-15 seconds
4. Allow to rest for 30 minutes
5. COMPARE sedimentation

If test vial sediments FASTER = Freeze damaged = DISCARD
If test vial looks SAME as control = NOT freeze damaged = USE

---

Cold Chain Monitoring Tools

---

Common Causes of Cold Chain Failure

1. Power failures
2. Improper temperature settings on refrigerators
3. Overcrowding of vaccines in refrigerator
4. Storing vaccines in door (fluctuating temperature)
5. Storing food items with vaccines (forbidden!)
6. Improper transport conditions

---

Q16b. Vaccine Vial Monitors (VVM) ***

Definition

---

Principle

• Contains a chemical square inside a circle printed on the label
• The chemical undergoes color change (darkens) with cumulative heat exposure
• Higher temperature = faster color change

---

Reading a VVM

┌──────────────────────────────────────────────────────────────┐
│                    VVM READING GUIDE                         │
├───────────┬────────────────────────────┬─────────────────────┤
│  STAGE    │    APPEARANCE              │     ACTION          │
├───────────┼────────────────────────────┼─────────────────────┤
│  VVM 1    │  Inner square LIGHTER than │  USE - vaccine      │
│  (OK)     │  outer circle              │  is good            │
├───────────┼────────────────────────────┼─────────────────────┤
│  VVM 2    │  Inner square SAME shade   │  USE - but use      │
│  (Near    │  as outer circle           │  first, order new   │
│  discard) │                            │  stock soon         │
├───────────┼────────────────────────────┼─────────────────────┤
│  VVM 3    │  Inner square DARKER than  │  DO NOT USE         │
│  (Discard)│  outer circle              │  DISCARD            │
├───────────┼────────────────────────────┼─────────────────────┤
│  VVM 4    │  Inner square VERY DARK    │  DO NOT USE         │
│  (Discard)│  (approaching black)       │  DISCARD            │
└───────────┴────────────────────────────┴─────────────────────┘

---

Diagram of VVM

        VVM Stage 1 (USE):          VVM Stage 3 (DISCARD):
        ┌──────────────┐            ┌──────────────┐
        │  ╔════════╗  │            │  ╔████████╗  │
        │  ║  LIGHT ║  │            │  ║  DARK  ║  │
        │  ║INNER SQ║  │            │  ║INNER SQ║  │
        │  ╚════════╝  │            │  ╚████████╝  │
        │  OUTER CIRCLE│            │  OUTER CIRCLE│
        └──────────────┘            └──────────────┘
        Inner lighter = OK          Inner darker = DISCARD

---

Types of VVM Based on Heat Exposure Threshold

---

Advantages of VVM

1. Simple to read - even by non-literate field workers
2. Cumulative indicator - integrates total heat exposure over entire storage/transport period
3. Works at all times - even when there is no electricity
4. Reduces vaccine wastage - allows use of vaccines that temporarily left cold chain but are still potent
5. Increases confidence - health workers can be sure vaccine is potent
6. No special equipment needed to read it

---

Open Vial Policy and VVM

• VVM has not reached the discard point
• Expiry date has not passed
• Vaccines stored at proper temperature
• Vial septum has not been submerged in water
• Aseptic technique has been followed

---

Q17. Steps in Investigation of an Epidemic

Introduction

1. Defining the magnitude of the epidemic
2. Identifying the cause, source, and mode of transmission
3. Implementing control measures
4. Preventing recurrence

---

Step-by-Step Flowchart of Epidemic Investigation

┌─────────────────────────────────────────────────────────────────┐
│            STEPS IN INVESTIGATION OF AN EPIDEMIC                │
└─────────────────────────────────────────────────────────────────┘

  STEP 1: VERIFY THE DIAGNOSIS
          │
          ▼
  STEP 2: CONFIRM EXISTENCE OF EPIDEMIC
          │
          ▼
  STEP 3: DEFINE THE POPULATION AT RISK
          │
          ▼
  STEP 4: RAPID SEARCH FOR ALL CASES
          (Case finding + Line listing)
          │
          ▼
  STEP 5: DESCRIPTIVE EPIDEMIOLOGY
          (Person, Place, Time)
          │
          ▼
  STEP 6: FORMULATE HYPOTHESIS
          │
          ▼
  STEP 7: TEST THE HYPOTHESIS
          (Analytical study: case-control/cohort)
          │
          ▼
  STEP 8: INSTITUTE CONTROL MEASURES
          (DO NOT wait for investigation to complete)
          │
          ▼
  STEP 9: PREPARE EPIDEMIC REPORT

---

Detailed Description of Each Step

Step 1: Verification of Diagnosis

• Do not trust the initial report blindly
• Conduct clinical examination of a sample of cases
• Confirm with laboratory investigations (where applicable)
• Important: do NOT delay field investigation waiting for lab results
• Purpose: confirm the disease is what it is reported to be

Step 2: Confirmation of Existence of Epidemic

• Compare current case frequency with expected frequency (based on past records)
• Epidemic exists when: Observed cases > Expected cases (by >2 SD)
• Some epidemics are obvious (cholera, food poisoning)
• Modern epidemics (cancer, CVD) may need careful comparison

Step 3: Defining the Population at Risk

• Obtain a detailed map of the affected area
• Conduct a census of the population (by age, sex)
• Required to calculate attack rates (denominator)
• Without the denominator, no meaningful rates can be calculated

Step 4: Rapid Case Finding and Line Listing

• House-to-house survey
• Contact tracing
• Medical survey in the area

• Name, age, sex, address, occupation
• Date and time of onset
• Food history, water source
• Contacts, travel history
• Signs and symptoms

Step 5: Descriptive Epidemiology (Person, Place, Time)

• Who is affected? (age, sex, occupation, social class)
• Attack rate by subgroups

• Where are cases occurring?
• Spot map (plot each case on a map)
• Clustering suggests common source

• Construct the epidemic curve (histogram of cases by date of onset)
• Helps identify type of epidemic and time of exposure

Step 6: Formulation of Hypothesis

• Based on descriptive data, generate a testable hypothesis about:
  • Source of infection
  • Agent responsible
  • Mode of transmission
• Example: "The epidemic is a foodborne outbreak caused by Salmonella from contaminated chicken served at the party on Date X"

Step 7: Test the Hypothesis

• Use analytical epidemiology to test the hypothesis
• Usually a case-control study (quick, feasible during outbreak)
• Sometimes a cohort study if all exposed are known
• Calculate OR or RR, p-value, confidence intervals
• Calculate food-specific attack rates in foodborne outbreak

Step 8: Institute Control Measures

• Do NOT wait for all steps to be complete
• Act as soon as the source or mode is suspected

Step 9: Prepare an Epidemic Report

1. Background and objectives
2. Methods of investigation
3. Case definition used
4. Descriptive analysis (person, place, time)
5. Analytical findings (OR/RR)
6. Conclusions (cause, source, mode)
7. Recommendations to prevent recurrence
8. Lessons learned

---

Q18. Adverse Events Following Immunization (AEFI) ***

Definition

• A truly vaccine-caused reaction
• An event that occurred by coincidence after vaccination
• An event due to error in immunization technique

---

WHO Classification of AEFI (2013 Revised Classification)

┌──────────────────────────────────────────────────────────────┐
│                    WHO CLASSIFICATION OF AEFI                │
├──────────────────────────┬───────────────────────────────────┤
│  VACCINE-RELATED CAUSES  │  NON-VACCINE-RELATED CAUSES       │
├──────────────────────────┼───────────────────────────────────┤
│ 1. Vaccine Product-      │ 4. Immunization Anxiety-          │
│    Related Reaction      │    Related Reaction               │
│                          │                                   │
│ 2. Vaccine Quality       │ 5. Coincidental Event             │
│    Defect Reaction        │                                   │
│                          │                                   │
│ 3. Immunization Error-   │                                   │
│    Related Reaction      │                                   │
└──────────────────────────┴───────────────────────────────────┘

---

Detailed Description of Each Category

• Due to inherent properties of the vaccine (even properly prepared and administered)
• Unavoidable adverse effects
• Examples:
  • Local swelling and pain at DPT injection site
  • Mild fever after measles vaccine (day 5-12)
  • BCG adenitis
  • VAPP (vaccine-associated paralytic poliomyelitis) - OPV

• Due to manufacturing defect (not meeting quality standards)
• Examples:
  • Inadequate inactivation of poliovirus → disease caused by IPV
  • Contaminated vaccine causing sepsis
• Prevention: Strict quality control by manufacturers

• Due to errors in vaccine preparation, handling, or administration
• Most preventable type of AEFI
• Examples:

• Due to anxiety about the injection (not the vaccine itself)
• Examples:
  • Vasovagal syncope (fainting)
  • Hyperventilation
  • Panic attacks
  • Psychogenic mass reactions (one person faints → others follow)
• Prevention: Relaxed environment, seated position for vaccination

• Occurs after vaccination by coincidence - not causally related
• Example: Child develops fever due to malaria after vaccination
• Most difficult category - requires epidemiological investigation to establish

---

Specific AEFIs by Vaccine

---

AEFI Surveillance and Reporting

┌─────────────────────────────────────────────────────────┐
│              AEFI SURVEILLANCE FLOWCHART                │
│                                                         │
│  AEFI OCCURS                                            │
│       │                                                 │
│       ▼                                                 │
│  REPORT to ANM/Health worker                            │
│       │                                                 │
│       ▼                                                 │
│  REPORT to Medical Officer/PHC (within 24-48 hrs)       │
│       │                                                 │
│       ▼                                                 │
│  INVESTIGATION by District Immunization Officer         │
│       │                                                 │
│       ▼                                                 │
│  CAUSALITY ASSESSMENT (WHO-UMC criteria)                │
│       │                                                 │
│       ▼                                                 │
│  REPORT to State/National level                         │
│       │                                                 │
│       ▼                                                 │
│  CORRECTIVE ACTION if programme error identified        │
└─────────────────────────────────────────────────────────┘

---

Causality Assessment (WHO-UMC Classification)

---

Important Points

• Never dismiss an AEFI as unimportant - investigate ALL serious AEFIs
• Reporting an AEFI does NOT mean blaming the vaccine
• AEFI surveillance helps maintain public trust in immunization programs
• Do NOT stop immunization program because of a single AEFI

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Q19. National Immunization Schedule ******

Introduction

---

National Immunization Schedule (Current - India)

---

Vitamin A Supplementation Schedule

---

Routes of Administration

┌──────────────────────────────────────────────────────┐
│                ROUTES OF IMMUNIZATION               │
├──────────────┬───────────────────────────────────────┤
│  ROUTE       │  VACCINES                             │
├──────────────┼───────────────────────────────────────┤
│  Oral        │  OPV, Rotavirus, Typhoid (oral)       │
│  Intradermal │  BCG (left deltoid)                   │
│  Subcutaneous│  Measles, MMR, Yellow Fever           │
│  Intramuscular│ DPT, Hepatitis B, IPV, Pentavalent   │
└──────────────┴───────────────────────────────────────┘

---

Key Points About NIS

1. BCG can be given anytime from birth up to 1 year if missed at birth
2. Pentavalent vaccine has replaced separate DPT + Hep B + Hib vaccines
3. IPV (injectable polio vaccine) added to NIS in 2015 alongside OPV (Switch strategy)
4. MR vaccine replaced standalone measles vaccine in 2017
5. Pulse Polio Immunization (PPI): Additional OPV given on National Immunization Days (NIDs) - usually January/February
6. Mission Indradhanush: Intensified immunization drive to reach unimmunized/under-immunized children
7. Vaccines given under UIP are FREE of cost at government facilities

---

Vaccines Recently Added to UIP (India)

---

Special Situations

• Do NOT restart from beginning
• Continue from where schedule was left off (catch-up vaccination)

---