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Reverse Cold Chain - PSM Exam Notes

Definition

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The Two Contexts (Very Important for MCQs)

1. Specimen Transport (Primary/Classic Use)

2. Vaccine Return

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Why Reverse Cold Chain is Needed - AFP Surveillance

• Collect two samples, 24-48 hours apart
• Both within 14 days of onset of paralysis
• Volume: approximately 8-10 grams each
• Must arrive in the laboratory via reverse cold chain with proper documentation
• Evidence of cold chain maintenance: presence of ice or temperature indicator

• At least 80% of AFP cases must have "adequate" stool specimens
• "Adequate" = two samples of sufficient quantity, collected at least 24 hours apart, within 14 days of onset, arriving in the lab by reverse cold chain with proper documentation
• (Park's Textbook of Preventive and Social Medicine)

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Storage and Transport Conditions

• Do NOT store specimens in the same refrigerator/cold box as vaccines
• If unavoidable, seal specimens in 2-3 layers of plastic bags and separate from vaccines
• Specimens must show no leakage or desiccation

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Specimen Packaging (Standard WHO)

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MCQ High-Yield Points

1. Classic example of reverse cold chain = stool specimens from AFP cases transported to lab for poliovirus identification
2. Also applies to = expired vaccines being returned from PHC to manufacturer/regional store
3. Temperature = 0-8°C (refrigerated) OR -20°C (frozen) if transit is prolonged
4. Specimens must NOT be stored with vaccines in the same cold box (contamination risk)
5. Reverse cold chain is maintained = evidenced by presence of ice or temperature indicator on arrival
6. Park's clearly states: AFP surveillance certification standard requires specimens arriving in the laboratory "by reverse cold chain and with proper documentation"
7. Two specimens at least 24 hours apart, within 14 days of paralysis onset = required for adequate AFP specimen

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Standard Cold Chain vs. Reverse Cold Chain

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Epidemic - Definition and Investigation

Definition of Epidemic

"The occurrence in a community or region of cases of an illness, specific health-related behaviour, or other health-related events clearly in excess of normal expectancy."

• Even a single case of a disease long absent from a population, or first invasion of a new disease, requires immediate reporting and full investigation
• Two cases associated in time and place of such a disease may be sufficient to be considered an epidemic
• The epidemic threshold for common diseases (e.g., influenza) = 2 standard errors above endemic occurrence

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Related Terms (Exam Favorites)

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Types of Epidemics

A. Common-Source Epidemics

• Exposure brief and simultaneous; all cases develop within one incubation period
• Epidemic curve rises and falls rapidly, no secondary waves - "explosive"
• Example: Food poisoning outbreak

• Exposure from same source is prolonged, repeated, or intermittent
• Epidemic extends beyond one incubation period; irregular
• Example: Contaminated water supply, Legionnaire's disease (Philadelphia, 1976)

B. Propagated Epidemics

• Results from person-to-person transmission
• Shows a gradual rise, tails off over a longer period
• Continues until susceptibles are depleted or no longer exposed
• Speed of spread depends on herd immunity, contact opportunities, secondary attack rate
• Example: Hepatitis A, Polio

C. Slow (Modern) Epidemics

• Chronic non-communicable diseases evolving over decades
• Example: Cancer, cardiovascular disease epidemics

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Steps in Investigation of an Epidemic

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Step 1: Verification of Diagnosis

• First and most important step - the reported cases may be spurious or arise from misinterpretation by lay public
• Clinical examination of a sample of cases may suffice (not all cases need examination)
• Laboratory investigations should be done where applicable to confirm diagnosis
• Epidemiological investigation should NOT be delayed until lab results are available

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Step 2: Confirmation of the Existence of an Epidemic

• Compare disease frequencies during the same period of previous years
• Epidemic confirmed when observed frequency exceeds expected frequency for that population based on past experience
• Arbitrary limit: 2 standard errors from endemic occurrence = epidemic threshold (for common diseases like influenza)
• For common-source epidemics (cholera, food poisoning, hepatitis A) - existence is obvious, no comparison needed
• For modern epidemics (cancer, cardiovascular diseases) - existence is not easily recognized

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Step 3: Defining the Population at Risk

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Step 4: Rapid Search for All Cases and Their Characteristics

• Name, age, sex, occupation, social class
• Travel history, previous exposure
• Time of onset, signs and symptoms
• Personal contacts (home, work, school)
• Special events: parties attended, foods eaten
• Exposure to common vehicles: water, food, milk
• History of injections, blood products, large gatherings

• Ask patients if others in home, family, neighbourhood, school, workplace had illness within the incubation period of the index case
• Check hospitals for admitted cases
• Continue search till area is declared free of epidemic (usually twice the incubation period since the last case)

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Step 5: Data Analysis

• Time relationship with exposure to a suspected source
• Whether common-source or propagated epidemic
• Seasonal or cyclic pattern

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Step 6: Formulation of Hypotheses

• (a) Possible source
• (b) Causative agent
• (c) Possible modes of spread
• (d) Environmental factors enabling it

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Step 7: Testing of Hypotheses

• Consider all reasonable hypotheses
• Compare attack rates in various groups - those exposed vs. not exposed to each suspected factor
• This identifies which hypothesis is consistent with all known facts
• Weigh divergent theories against the subsequent course of events

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Step 8: Evaluation of Ecological Factors

• Sanitary status of eating establishments
• Water and milk supply; breakdown in water supply system
• Movements of the human population
• Atmospheric changes: temperature, humidity, air pollution
• Population dynamics of insects and animal reservoirs
• Study can be conducted in a case-control fashion
• Primary concern: relate disease to environmental factors to identify sources of infection, reservoirs, and modes of transmission

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Step 9: Further Investigation of Population at Risk

• Medical examination
• Screening tests
• Examination of suspected food, faeces, or blood samples
• Biochemical studies
• Assessment of immunity status
• Serological studies may reveal clinically inapparent cases
• Approach may be retrospective or prospective
• Classify all members as: (a) exposed to specific potential vehicles, (b) whether ill or not

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Step 10: Writing the Report

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Important Principle (Frost's Observation)

"An epidemiological investigation is more than the collection of established facts. It includes their orderly arrangement into chains of inference, which extend more or less beyond the bounds of direct observation." - Wade Hampton Frost

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Health Advice to International Travellers / Emporiatrics

Definition

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Why Travellers Face Special Health Risks

• Crowding, long hours of waiting
• Disruption of eating habits
• Change in climate and time zone
• These may cause nausea, indigestion, extreme fatigue, and insomnia - reducing resistance to disease

• Malaria, dengue, filariasis
• Giardiasis, intestinal parasites
• Typhoid and paratyphoid fever
• Viral hepatitis (A, B, E)
• STDs and HIV/AIDS
• Influenza
• Many of these may NOT manifest immediately but appear after the traveller returns home

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Pre-Travel Consultation: Key Elements

1. Trip risk assessment (itinerary, destination, duration, activities, type of accommodation)
2. Immunizations (required, recommended, routine)
3. Malaria chemoprophylaxis (if indicated)
4. Food and water precautions + traveller's diarrhea management
5. Prevention of injuries and other travel-associated conditions

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Health Advice - Detailed Recommendations

1. General Physical Precautions

• Avoid bathing in polluted water - may cause ear, eye, and skin infections
• Avoid excessive heat, humidity, or over-exertion - may lead to exhaustion from water and salt loss
• Carry a medical card noting: blood group, drug sensitivities, chronic diseases

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2. Food and Water (Traveller's Diarrhea)

• Diarrhea affects an estimated 20-50% of all travellers
• Contaminated food and drinks are the most common source
• Appearance of food is NO guide to its safety

• Avoid unpasteurized milk
• Avoid non-bottled drinks
• Avoid uncooked food (except fruits/vegetables that can be peeled or shelled)
• Food should be thoroughly and freshly cooked
• Use boiled water or bottled mineral water
• Travellers should know about oral rehydration salts (ORS) containing salt and glucose for dehydration
• Standby therapy for severe diarrhea: azithromycin or a quinolone + loperamide

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3. Insect Bite Prevention

• Use mosquito repellents (DEET-containing)
• Sleep under insecticide-treated bed nets
• Wear long-sleeved clothing, especially at dusk/dawn
• Avoid areas of high mosquito activity

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4. Malaria

• High risk of acquiring malaria in endemic areas
• Travellers must protect themselves by chemoprophylaxis
• Drug prophylaxis should begin at the latest on the day of arrival in malarious areas
• Continue prophylaxis for 4-6 weeks after leaving the malarious area
• Personal protection against arthropods is also essential

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5. Vaccines for Travellers

Disease-Specific Vaccine Advice:

• Vaccination certificate for yellow fever is the ONLY certificate required for international travel (IHR requirement)
• Recommended for travellers to countries in the yellow fever endemic zone
• Live attenuated virus vaccine; must be administered at a WHO-approved yellow fever vaccination centre
• Should be given at least 10 days before travel
• Certificate issued = International Certificate of Vaccination or Prophylaxis (ICVP) - "yellow card"

• Normal human immunoglobulin: 0.02-0.05 mg/kg body weight, every 4 months
• Ideally, immunoglobulin should NOT be given within 3 weeks before or until 2 weeks after a live vaccine
• Inactivated HAV vaccine also available (safe and effective)

• Safe vaccines available
• 3-dose schedule: first two doses 1 month apart; third dose ~6 months later

• No vaccine against Hepatitis E
• Immunoglobulin prepared in Europe/USA does NOT give much protection
• Only effective measure: avoidance of contaminated food and water

• Wise precaution: booster dose of tetanus toxoid if 10 or more years have elapsed since the last complete course or booster

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6. STDs and HIV

• Same precautions apply whether travelling abroad or not:
  • Avoidance of sex altogether, OR
  • Limit to a single faithful, uninfected partner
• Use of condom is an important preventive measure
• To reduce risk of HIV/hepatitis B from syringes: avoid injectable drugs
• If injection is essential - ensure needle and syringe come from a sterile pack

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7. Jet Lag

• Follows eastward or westward travel crossing several time zones (not north-south travel)
• Symptoms: difficulty maintaining sleep, frequent arousals, excessive daytime sleepiness
• Usually resolves within a few days to 2 weeks
• Management: exposure to daylight at destination, melatonin (short-term), sleep hygiene

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8. Motion Sickness

• Most effective agents: scopolamine (transdermal patch, lasts up to 3 days) and first-generation H1 antihistamines (diphenhydramine, promethazine, dimenhydrinate)
• Much more effective as prophylaxis than for treatment once motion sickness begins

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9. Altitude Illness

• Discuss and prescribe preventive measures for travellers going to high-altitude destinations
• Consider acetazolamide for prophylaxis

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Medical Kit for Travellers

• Disinfectant and dressings
• Sun cream
• Mosquito repellent
• Oral rehydration salts (ORS)
• First-aid articles
• Patients with chronic diseases (diabetes, cardiac conditions) should carry sufficient medications to avoid any break in treatment

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Special Populations

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WHO Resource

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Complete Biostatistics for PSM Exam

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CHAPTER OUTLINE

1. Definition and Scope
2. Types of Data
3. Presentation of Data
4. Statistical Averages (Measures of Central Tendency)
5. Measures of Dispersion
6. Normal Distribution
7. Sampling
8. Tests of Significance
9. Correlation and Regression
10. Key Formulas at a Glance

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1. DEFINITION AND SCOPE

• Collection of data
• Organization and presentation of data
• Analysis and interpretation of data
• Drawing conclusions

• To plan and evaluate health programmes
• To measure health status of populations
• To compare disease occurrence over time and place
• To test whether an observed difference is real or due to chance

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2. TYPES OF DATA

A. Qualitative (Categorical) Data

• Nominal (no order): Blood group (A, B, O, AB), sex, religion
• Ordinal (ordered categories): Severity (mild/moderate/severe), socioeconomic status (low/middle/high)
• Dichotomous/Binary: Only two categories - diseased/not diseased, alive/dead

B. Quantitative (Numerical) Data

• Discrete: Only whole number values possible - number of children, number of hospital admissions
• Continuous: Can take any value within a range - height, weight, blood pressure, temperature

Key Distinction for Tests:

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3. PRESENTATION OF DATA

A. Tabular Presentation

• Simple/frequency tables
• Contingency tables (cross-tabulation, used in chi-square)

B. Diagrams and Charts

• In a histogram, bars touch each other (continuous data)
• In a bar chart, bars are separated (discrete/categorical data)

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4. STATISTICAL AVERAGES (MEASURES OF CENTRAL TENDENCY)

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(a) Arithmetic Mean (x̄)

x̄ = Σx / n

• Total = 810
• Mean = 810/10 = 81

• Easy to calculate and understand
• Uses all values in the data
• Best measure for normally distributed data

• Unduly influenced by extreme (outlier) values
• May give an unrealistic value (e.g., 4.76 children)

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(b) Median

• Odd number of observations → middle value
• Even number of observations → average of the two middle values

• Not affected by extreme values
• Best for skewed distributions

• When one extreme value distorts the mean
• Example: Income of 7 people: 5, 5, 5, 7, 10, 20, 102
  • Mean = 154/7 = 22 (distorted by 102)
  • Median = 7 (far more representative)

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(c) Mode

• Mode = 75 (appears most frequently)

• Easy to understand
• Not affected by extreme values

• Exact location is often uncertain
• Not often used in biological statistics

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Relationship Between Mean, Median, and Mode:

Tip: In a skewed distribution, the median is always between the mean and mode.

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5. MEASURES OF DISPERSION

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(a) Range

• Highest = 95, Lowest = 71
• Range = 71 to 95 (or difference = 24)

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(b) Mean Deviation (MD)

MD = Σ|x - x̄| / n

1. Calculate the mean
2. Find the deviation of each value from the mean
3. Ignore the sign (take absolute values)
4. Average those deviations

• Sum of deviations = 2+6+0+2+10+14+6+4+3+9 = 56 (ignoring signs)
• MD = 56/10 = 5.6

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(c) Standard Deviation (SD or σ)

• For large samples (n > 30): SD = √(Σ(x - x̄)² / n)
• For small samples (n < 30): SD = √(Σ(x - x̄)² / (n-1)) ← use (n-1) to avoid underestimation

1. Find the mean (x̄)
2. Find deviation of each value from mean (x - x̄)
3. Square each deviation: (x - x̄)²
4. Sum the squared deviations: Σ(x - x̄)²
5. Divide by n (or n-1 for small samples)
6. Take the square root

• Population parameters: Greek letters → mean = μ, SD = σ
• Sample statistics: Roman letters → mean = x̄, SD = s

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(d) Variance

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(e) Coefficient of Variation (CV)

CV = (SD / Mean) × 100

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6. NORMAL DISTRIBUTION

The Normal Curve (Gaussian Distribution)

• Bell-shaped, perfectly symmetrical curve
• Mean = Median = Mode (all coincide at the centre)
• Total area under the curve = 1 (or 100%)
• Based on an infinitely large number of observations

Properties - The 68-95-99.7 Rule (Critical for Exams!)

Standard Normal Curve

• Mean = 0
• SD = 1
• Total area = 1

Standard Normal Deviate (Z-score)

Z = (x - x̄) / σ

• Z = (80 - 72) / 2 = 4
• Area corresponding to Z=4 = 0.49997 (from table)
• Area beyond Z=4 = 0.5 - 0.49997 = 0.00003
• Probability = only 3 in 100,000 individuals would have pulse ≥ 80

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7. SAMPLING

Why Sample?

Sampling Frame

Types of Sampling

Sampling Errors

• Larger sample size → smaller sampling error
• Greater variability in population → larger sampling error

Non-Sampling Errors

• Poorly calibrated instruments
• Observer variation
• Incomplete coverage of selected subjects
• Conceptual errors
• Often MORE important than sampling errors

Standard Error (SE)

SE of Mean = σ / √n (or S / √n from sample)

• 95% of sample means lie within μ ± 2 SE of the population mean
• SE decreases as sample size (n) increases
• SE is the key measure used in tests of significance

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8. TESTS OF SIGNIFICANCE (HYPOTHESIS TESTING)

The Null Hypothesis (H₀)

P-value (Probability Value)

• P = probability that the observed difference is due to chance alone
• P < 0.05 → difference is statistically significant (1 in 20 chance it's due to chance)
• P < 0.01 → difference is highly significant (1 in 100 chance)
• P > 0.05 → difference is NOT significant (likely due to chance)

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(a) Standard Error of the Mean

• 95% CI = x̄ ± 2 SE (limits within which the true population mean lies in 95% of samples)
• 99% CI = x̄ ± 2.58 SE

• SE = 0.6/√25 = 0.12
• 95% CI = 98.14 ± 2(0.12) = 97.90 to 98.38°F
• Probability only 1 in 20 (P=0.05) that population mean is outside these limits

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(b) Standard Error of Proportion

SE of Proportion = √(pq/n)

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(c) Standard Error of Difference Between Two Means

SE of difference = √(SD₁²/n₁ + SD₂²/n₂)

• Compare with t-table at appropriate degrees of freedom
• If t > critical value → significant difference

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(d) Chi-Square Test (χ²)

χ² = Σ(O - E)² / E

1. State the null hypothesis
2. Calculate expected values: E = (row total × column total) / grand total
3. Calculate χ² = Σ(O-E)²/E
4. Find degrees of freedom (df) = (rows-1)(columns-1)
   • For a 2×2 table: df = (2-1)(2-1) = 1
5. Compare calculated χ² with critical value from chi-square table
6. For df=1: χ² critical value at P=0.05 is 3.84
   • If calculated χ² < 3.84 → NOT significant (accept H₀)
   • If calculated χ² > 3.84 → Significant (reject H₀)

• Testing vaccine efficacy (attacked vs. not attacked in two vaccine groups)
• Testing association between two categorical variables
• Testing goodness of fit

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Summary: Which Test to Use?

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9. CORRELATION AND REGRESSION

Correlation

• Represented by the symbol r (Pearson's correlation coefficient)
• Ranges from -1 to +1

r = Σ(x - x̄)(y - ȳ) / √[Σ(x - x̄)² × Σ(y - ȳ)²]

• Used to visually assess correlation before calculating r
• Dots clustering around a straight line = linear relationship
• No clustering pattern = no relationship

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Regression

Y = a + bX

• Y = dependent variable (outcome)
• X = independent variable (predictor)
• a = Y-intercept (value of Y when X = 0)
• b = regression coefficient (slope - how much Y changes for each unit change in X)

• Correlation: measures the strength of association between two variables
• Regression: measures the direction and magnitude of the relationship; allows prediction

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10. KEY FORMULAS AT A GLANCE

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11. QUICK REVISION TABLE - HIGH-YIELD EXAM POINTS

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BIOSTATISTICS
│
├── TYPES OF DATA
│   ├── Qualitative (nominal, ordinal, binary)
│   └── Quantitative (discrete, continuous)
│
├── PRESENTATION
│   ├── Tables, Bar charts, Histogram, Pie chart, Scatter diagram
│   └── Histogram (bars touch) vs Bar chart (bars separate)
│
├── CENTRAL TENDENCY
│   ├── Mean → use for normal data; affected by outliers
│   ├── Median → use for skewed data; NOT affected by outliers
│   └── Mode → most frequent value
│
├── DISPERSION
│   ├── Range → simplest; only uses extremes
│   ├── Mean Deviation → average of absolute deviations
│   └── Standard Deviation → most useful; "root-mean-square deviation"
│
├── NORMAL DISTRIBUTION
│   ├── Mean = Median = Mode
│   ├── ±1 SD = 68.3%
│   ├── ±2 SD = 95.4%
│   └── Z-score = (x - mean)/SD
│
├── SAMPLING
│   ├── Simple random, Systematic, Stratified, Cluster, Multistage
│   └── SE = SD/√n (decreases as n increases)
│
├── TESTS OF SIGNIFICANCE
│   ├── P < 0.05 = significant
│   ├── Z-test / t-test → for means (quantitative data)
│   └── Chi-square → for proportions (categorical data); df=(r-1)(c-1)
│
└── CORRELATION & REGRESSION
    ├── r = -1 to +1; r=0 means no correlation
    └── Regression: Y = a + bX; used for prediction

1. Definition and Scope - what biostatistics is and why it matters in PSM
2. Types of Data - qualitative (nominal, ordinal, binary) vs quantitative (discrete, continuous)
3. Presentation of Data - bar charts, histograms, pie charts, scatter diagrams, frequency polygons
4. Measures of Central Tendency - Mean, Median, Mode with formulas and examples
5. Measures of Dispersion - Range, Mean Deviation, Standard Deviation, Variance with formulas
6. Normal Distribution - the 68-95-99.7 rule, Z-score, standard normal curve
7. Sampling - simple random, systematic, stratified, cluster, multistage, standard error
8. Tests of Significance - Null hypothesis, P-value, Z-test, t-test, Chi-square (with full worked example)
9. Correlation and Regression - Pearson's r, scatter diagram, regression equation Y = a + bX
10. Key Formulas at a Glance - all important formulas in one table
11. High-Yield Exam Points - a revision table of the most tested facts