#!/usr/bin/env python3
"""
Community Medicine NEET PG High-Yield Questions PDF Generator
Source: Park's Textbook of Preventive and Social Medicine
"""

from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm
from reportlab.lib import colors
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak, KeepTogether
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
from reportlab.platypus import ListFlowable, ListItem

OUTPUT = "/home/daytona/workspace/community-medicine-neet/Community_Medicine_NEET_PG.pdf"

# ─── Styles ────────────────────────────────────────────────────────────────────
styles = getSampleStyleSheet()

TEAL      = colors.HexColor("#1A6B8A")
TEAL_DARK = colors.HexColor("#0E4D65")
ORANGE    = colors.HexColor("#E07B39")
YELLOW    = colors.HexColor("#FFF3CD")
GREEN_BG  = colors.HexColor("#D4EDDA")
RED_BG    = colors.HexColor("#F8D7DA")
BLUE_BG   = colors.HexColor("#D1ECF1")
LIGHT_GREY= colors.HexColor("#F5F5F5")
WHITE     = colors.white

title_style = ParagraphStyle(
    "CustomTitle", parent=styles["Title"],
    fontSize=22, textColor=WHITE, alignment=TA_CENTER,
    spaceAfter=6, spaceBefore=4, leading=28,
    fontName="Helvetica-Bold"
)
sub_title_style = ParagraphStyle(
    "SubTitle", parent=styles["Normal"],
    fontSize=13, textColor=WHITE, alignment=TA_CENTER,
    spaceAfter=4, fontName="Helvetica"
)
chapter_style = ParagraphStyle(
    "Chapter", parent=styles["Heading1"],
    fontSize=15, textColor=WHITE, alignment=TA_CENTER,
    spaceBefore=4, spaceAfter=4, fontName="Helvetica-Bold",
    backColor=TEAL_DARK, borderPad=8
)
section_style = ParagraphStyle(
    "Section", parent=styles["Heading2"],
    fontSize=12, textColor=TEAL_DARK, alignment=TA_LEFT,
    spaceBefore=10, spaceAfter=4, fontName="Helvetica-Bold",
    borderPad=2
)
q_num_style = ParagraphStyle(
    "QNum", parent=styles["Normal"],
    fontSize=10, textColor=TEAL_DARK, fontName="Helvetica-Bold",
    spaceBefore=8, spaceAfter=2
)
question_style = ParagraphStyle(
    "Question", parent=styles["Normal"],
    fontSize=10.5, textColor=colors.HexColor("#1C1C1C"),
    fontName="Helvetica-Bold", spaceBefore=2, spaceAfter=4,
    leading=15
)
option_style = ParagraphStyle(
    "Option", parent=styles["Normal"],
    fontSize=10, textColor=colors.HexColor("#333333"),
    fontName="Helvetica", spaceBefore=1, spaceAfter=1, leftIndent=12
)
correct_ans_style = ParagraphStyle(
    "Correct", parent=styles["Normal"],
    fontSize=10.5, textColor=colors.HexColor("#155724"),
    fontName="Helvetica-Bold", spaceBefore=4, spaceAfter=2
)
explanation_style = ParagraphStyle(
    "Explanation", parent=styles["Normal"],
    fontSize=10, textColor=colors.HexColor("#1C1C1C"),
    fontName="Helvetica", spaceBefore=2, spaceAfter=4,
    leading=15, alignment=TA_JUSTIFY
)
keypoint_style = ParagraphStyle(
    "KeyPoint", parent=styles["Normal"],
    fontSize=9.5, textColor=colors.HexColor("#0C5460"),
    fontName="Helvetica-Oblique", spaceBefore=2, spaceAfter=2,
    leftIndent=10, leading=14
)
note_style = ParagraphStyle(
    "Note", parent=styles["Normal"],
    fontSize=9, textColor=colors.HexColor("#856404"),
    fontName="Helvetica-Oblique", spaceBefore=2, spaceAfter=4,
    leftIndent=10
)
source_style = ParagraphStyle(
    "Source", parent=styles["Normal"],
    fontSize=8.5, textColor=colors.HexColor("#666666"),
    fontName="Helvetica-Oblique", spaceBefore=1, spaceAfter=6
)
toc_style = ParagraphStyle(
    "TOC", parent=styles["Normal"],
    fontSize=11, textColor=TEAL_DARK, fontName="Helvetica",
    spaceBefore=3, spaceAfter=3, leftIndent=10
)
intro_style = ParagraphStyle(
    "Intro", parent=styles["Normal"],
    fontSize=10.5, textColor=colors.HexColor("#333333"),
    fontName="Helvetica", spaceBefore=4, spaceAfter=4,
    leading=16, alignment=TA_JUSTIFY
)

# ─── Helper: coloured box ──────────────────────────────────────────────────────
def color_box(content_flowables, bg=BLUE_BG, border=TEAL):
    data = [[content_flowables]]
    t = Table(data, colWidths=[17*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), bg),
        ("BOX",        (0,0), (-1,-1), 1, border),
        ("TOPPADDING",    (0,0), (-1,-1), 6),
        ("BOTTOMPADDING", (0,0), (-1,-1), 6),
        ("LEFTPADDING",   (0,0), (-1,-1), 10),
        ("RIGHTPADDING",  (0,0), (-1,-1), 10),
    ]))
    return t

def chapter_banner(text):
    data = [[Paragraph(text, chapter_style)]]
    t = Table(data, colWidths=[17*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), TEAL_DARK),
        ("TOPPADDING",    (0,0), (-1,-1), 10),
        ("BOTTOMPADDING", (0,0), (-1,-1), 10),
        ("LEFTPADDING",   (0,0), (-1,-1), 14),
        ("RIGHTPADDING",  (0,0), (-1,-1), 14),
    ]))
    return t

def answer_box(ans_text, explanation_flowables):
    header = Paragraph(ans_text, correct_ans_style)
    inner = [header] + explanation_flowables
    return color_box(inner, bg=GREEN_BG, border=colors.HexColor("#28a745"))

def question_block(num, q_text, options, correct_letter, explanation_paras, keypoints=None, note=None, source=None):
    elems = []
    elems.append(Paragraph(f"Q{num}.", q_num_style))
    elems.append(Paragraph(q_text, question_style))
    for opt in options:
        letter = opt[0]
        txt    = opt[3:]
        marker = "✓ " if letter == correct_letter else "    "
        col    = colors.HexColor("#155724") if letter == correct_letter else colors.HexColor("#333333")
        st = ParagraphStyle(
            f"opt{letter}", parent=option_style,
            textColor=col,
            fontName="Helvetica-Bold" if letter == correct_letter else "Helvetica"
        )
        elems.append(Paragraph(f"{marker}{opt}", st))

    exp_inner = [Paragraph(f"Answer: ({correct_letter})  {[o[3:] for o in options if o[0]==correct_letter][0]}", correct_ans_style)]
    for p in explanation_paras:
        exp_inner.append(Paragraph(p, explanation_style))
    if keypoints:
        for kp in keypoints:
            exp_inner.append(Paragraph(f"► {kp}", keypoint_style))
    if note:
        exp_inner.append(Paragraph(f"Note: {note}", note_style))
    if source:
        exp_inner.append(Paragraph(f"Source: {source}", source_style))

    elems.append(color_box(exp_inner, bg=GREEN_BG, border=colors.HexColor("#28a745")))
    elems.append(Spacer(1, 8))
    return KeepTogether(elems)

# ─── Data ─────────────────────────────────────────────────────────────────────
QUESTIONS = [

# ═══════════════════════════════════════════════════════════════════
# CHAPTER 1: EPIDEMIOLOGY & BIOSTATISTICS
# ═══════════════════════════════════════════════════════════════════

{
"chapter": "CHAPTER 1: EPIDEMIOLOGY & BIOSTATISTICS",
"num": 1,
"q": "Which of the following is the BEST definition of incubation period?",
"opts": [
    "A.  Time from infection to recovery",
    "B.  Time from infection to first sign or symptom of disease",
    "C.  Time from infection to maximum communicability",
    "D.  Time from exposure to source removal"
],
"ans": "B",
"exp": [
    "Incubation period is defined as the time interval between invasion by an infectious agent and the appearance of the first sign or symptom of the disease in question (Park's, 23rd ed).",
    "During the incubation period, the infectious agent undergoes multiplication in the host until sufficient density is reached to disturb the health equilibrium and produce overt disease.",
    "It differs from the latent period, which is used in non-infectious (chronic) diseases and is defined as 'the period from disease initiation to disease detection'."
],
"kp": [
    "Latent period = non-infectious disease equivalent of incubation period",
    "Median incubation period = time for 50% of cases to appear after exposure",
    "Measles, chickenpox, whooping cough and Hepatitis A are communicable during the LATER PART of the incubation period"
],
"note": "Diseases with very SHORT incubation periods: staphylococcal food poisoning (1-6 hrs), cholera (few hrs-5 days), influenza (1-3 days). Diseases with LONG incubation: rabies (weeks-months), leprosy (2-5 years).",
"source": "Park's Textbook of Preventive and Social Medicine, Infectious Disease Epidemiology section"
},

{
"chapter": "CHAPTER 1: EPIDEMIOLOGY & BIOSTATISTICS",
"num": 2,
"q": "A screening test has the following results: True Positives (TP) = 40, False Positives (FP) = 20, False Negatives (FN) = 10, True Negatives (TN) = 130. What is the SENSITIVITY of this test?",
"opts": [
    "A.  40%",
    "B.  67%",
    "C.  80%",
    "D.  87%"
],
"ans": "C",
"exp": [
    "Sensitivity = TP / (TP + FN) × 100",
    "= 40 / (40 + 10) × 100 = 40/50 × 100 = 80%",
    "Sensitivity measures the ability of the test to correctly identify DISEASED individuals (true positive rate). A highly sensitive test misses few diseased people; if the test is negative, the disease is unlikely (rule OUT disease).",
    "Specificity = TN / (TN + FP) × 100 = 130/150 × 100 = 86.7% ≈ 87%. Specificity identifies NON-DISEASED correctly (rule IN disease when positive)."
],
"kp": [
    "Sensitivity = TP / (TP + FN) — 'SNOUT': SeNsitive test rules OUT disease",
    "Specificity = TN / (TN + FP) — 'SPIN': SPecific test rules IN disease",
    "PPV = TP / (TP + FP); NPV = TN / (TN + FN)",
    "Sensitivity and specificity are INVERSELY related — raising cutoff increases specificity but lowers sensitivity",
    "Predictive values are affected by disease prevalence; sensitivity/specificity are NOT"
],
"note": "Ideal screening test: 100% sensitive + 100% specific. In practice, unachievable.",
"source": "Park's Textbook, Validity of a Screening Test, p.158"
},

{
"chapter": "CHAPTER 1: EPIDEMIOLOGY & BIOSTATISTICS",
"num": 3,
"q": "The secondary attack rate (SAR) is defined as:",
"opts": [
    "A.  Number of new cases in a community per 1000 population per year",
    "B.  Number of exposed persons developing disease within the incubation period after exposure to a primary case",
    "C.  Attack rate in secondary cases divided by attack rate in primary cases",
    "D.  Number of cases from a single source exposure"
],
"ans": "B",
"exp": [
    "Secondary Attack Rate (SAR) = (Number of exposed persons developing disease within incubation period) / (Number of susceptibles exposed to primary case) × 100.",
    "It is particularly used in household/family studies and measures the transmissibility of an infectious agent within a defined exposed group.",
    "SAR is useful in evaluating the effectiveness of quarantine, isolation or prophylaxis measures and in estimating the contagiousness of a disease."
],
"kp": [
    "SAR is measured WITHIN the incubation period following exposure to a primary case",
    "Higher SAR = more transmissible disease",
    "Used to assess vaccine efficacy in household settings",
    "Measles has one of the highest SARs (~90%) of all infectious diseases"
],
"source": "Park's Textbook, Secondary Attack Rate, Epidemiology section"
},

{
"chapter": "CHAPTER 1: EPIDEMIOLOGY & BIOSTATISTICS",
"num": 4,
"q": "Which type of epidemiological study design gives the HIGHEST level of evidence?",
"opts": [
    "A.  Case-control study",
    "B.  Cohort study",
    "C.  Randomized Controlled Trial (RCT)",
    "D.  Cross-sectional study"
],
"ans": "C",
"exp": [
    "The hierarchy of evidence (from highest to lowest) is: Systematic review/Meta-analysis > Randomized Controlled Trial (RCT) > Cohort study > Case-control study > Cross-sectional study > Case reports.",
    "RCTs are the gold standard for evaluating therapeutic interventions because randomization controls for both known and unknown confounders, minimizing bias.",
    "Cohort studies are best for studying INCIDENCE and RISK FACTORS (relative risk can be calculated directly). Case-control studies are best for RARE diseases and calculate ODDS RATIO.",
    "Cross-sectional studies measure PREVALENCE and are good for screening and hypothesis generation."
],
"kp": [
    "RCT = gold standard; allocation by randomization",
    "Cohort study: calculates Relative Risk (RR); prospective or retrospective",
    "Case-control study: calculates Odds Ratio (OR); best for rare diseases",
    "Cross-sectional study: prevalence study; 'snapshot' in time",
    "Ecological study: uses population-level data; susceptible to ecological fallacy"
],
"note": "REMEMBER: 'COHORT goes forward in time, CASE-CONTROL goes backward' — a useful mnemonic.",
"source": "Park's Textbook, Epidemiological Study Designs"
},

{
"chapter": "CHAPTER 1: EPIDEMIOLOGY & BIOSTATISTICS",
"num": 5,
"q": "In a community study, it is found that the crude death rate is higher in country A compared to country B, but when age-standardized, mortality is actually lower in country A. This is best explained by:",
"opts": [
    "A.  Selection bias in data collection",
    "B.  Confounding by age — country A has an older population",
    "C.  Information bias in death registration",
    "D.  Lead-time bias in disease detection"
],
"ans": "B",
"exp": [
    "This is a classic example of the CONFOUNDING effect of age on crude death rates. Country A may have a higher crude death rate because it has a larger proportion of elderly people (who have higher mortality) — not because of poorer health overall.",
    "Age-standardization (direct or indirect) removes this confounding effect by adjusting for the differing age distributions between populations.",
    "Direct standardization: applies age-specific rates of study population to a standard reference population.",
    "Indirect standardization: applies age-specific rates from standard population to study population — produces Standardized Mortality Ratio (SMR).",
    "SMR > 1: More deaths than expected; SMR < 1: Fewer deaths than expected."
],
"kp": [
    "Crude rates are not comparable between populations with different age structures",
    "Direct standardization uses a 'standard population' age distribution",
    "Indirect standardization gives the Standardized Mortality Ratio (SMR)",
    "SMR = Observed deaths / Expected deaths × 100"
],
"source": "Park's Textbook, Rates and Ratios, Standardization"
},

# ═══════════════════════════════════════════════════════════════════
# CHAPTER 2: COMMUNICABLE DISEASES
# ═══════════════════════════════════════════════════════════════════

{
"chapter": "CHAPTER 2: COMMUNICABLE DISEASES & NATIONAL HEALTH PROGRAMMES",
"num": 6,
"q": "The National Immunization Schedule (NIS) of India recommends BCG vaccine at:",
"opts": [
    "A.  Birth (as early as possible, within 1 month)",
    "B.  6 weeks of age",
    "C.  3 months of age",
    "D.  9 months of age"
],
"ans": "A",
"exp": [
    "BCG (Bacillus Calmette-Guerin) vaccine is given at BIRTH, as early as possible and within the first month of life, as per India's National Immunization Schedule under the Universal Immunisation Programme (UIP).",
    "BCG protects primarily against severe forms of childhood TB, including TB meningitis and miliary TB, with efficacy of 60–80% against these severe forms.",
    "BCG is given as a single intradermal injection of 0.05 mL in neonates and 0.1 mL in older children, on the left upper arm (deltoid area).",
    "The Mantoux test (tuberculin skin test) should NOT be done before BCG. After BCG, it becomes positive due to sensitization — this is NOT a contraindication."
],
"kp": [
    "NIS schedule: BCG at birth; OPV + Hepatitis B at birth; Pentavalent (DPT+HepB+Hib) at 6, 10, 14 weeks",
    "Measles/MR vaccine: 9–12 months; booster at 16–24 months",
    "JE vaccine in endemic districts: 9–12 months, booster at 16–24 months",
    "TT (Tetanus Toxoid) for pregnant women: 2 doses in pregnancy",
    "BCG scar appears in 2–6 weeks; no scar is NOT an indication to repeat"
],
"note": "Contraindications to BCG: HIV/AIDS, immunodeficiency states, active TB. NOT contraindicated in premature babies.",
"source": "Park's Textbook, Universal Programme on Immunization (UIP), National Immunization Schedule"
},

{
"chapter": "CHAPTER 2: COMMUNICABLE DISEASES & NATIONAL HEALTH PROGRAMMES",
"num": 7,
"q": "Which of the following is the MOST common mode of transmission of Hepatitis A?",
"opts": [
    "A.  Sexual contact",
    "B.  Blood transfusion",
    "C.  Feco-oral route (contaminated food and water)",
    "D.  Vertical (mother to child)"
],
"ans": "C",
"exp": [
    "Hepatitis A virus (HAV) is transmitted primarily by the feco-oral route, through ingestion of contaminated water or food (especially raw shellfish, salads, and undercooked food).",
    "Hepatitis A is communicable during the LATER PART of the incubation period (15-50 days; average 28 days) and in the early stages of acute illness, before jaundice appears.",
    "Unlike Hepatitis B and C, HAV does NOT cause chronic liver disease. It is entirely self-limiting.",
    "Hepatitis E (enteric, feco-oral): incubation 15–60 days; particularly dangerous in PREGNANCY (high mortality ~20% in third trimester). No vaccine available for HEV in India."
],
"kp": [
    "Hepatitis A and E: feco-oral; self-limiting; no chronic state",
    "Hepatitis B, C, D: blood-borne/parenteral/sexual; can cause chronic disease",
    "Hepatitis B: incubation 45–180 days; HBsAg = surface antigen (first marker); HBeAg = high infectivity",
    "Hepatitis D (Delta): only co-infection or superinfection with HBV",
    "Hepatitis E in pregnancy: highest mortality among viral hepatitis"
],
"source": "Park's Textbook, Infectious Disease Epidemiology, Hepatitis"
},

{
"chapter": "CHAPTER 2: COMMUNICABLE DISEASES & NATIONAL HEALTH PROGRAMMES",
"num": 8,
"q": "For a disease with Ro (basic reproduction number) > 1, which of the following statements is TRUE?",
"opts": [
    "A.  The epidemic will die out naturally",
    "B.  The epidemic will grow and spread in the population",
    "C.  Herd immunity has already been achieved",
    "D.  The infection is non-transmissible"
],
"ans": "B",
"exp": [
    "The basic reproduction number (R₀) is defined as the average number of secondary infections produced by one infectious case in an entirely susceptible population.",
    "If R₀ > 1: each case produces more than one secondary case → epidemic grows and spreads.",
    "If R₀ = 1: each case produces exactly one secondary case → endemic state (stable level).",
    "If R₀ < 1: epidemic will die out naturally.",
    "Herd immunity threshold = 1 - (1/R₀). For measles (R₀ = 12-18), herd immunity requires 92-95% coverage.",
    "Higher R₀ means more of the population must be immune to stop transmission."
],
"kp": [
    "R₀ > 1: epidemic spreads; R₀ = 1: endemic; R₀ < 1: epidemic dies",
    "Herd immunity threshold = (1 - 1/R₀) × 100%",
    "Measles R₀ ≈ 12–18 → requires 92–95% herd immunity",
    "COVID-19 original strain R₀ ≈ 2.5–3",
    "Smallpox R₀ ≈ 5–7; Polio R₀ ≈ 5–7"
],
"source": "Park's Textbook, Herd Immunity, Epidemiological Concepts"
},

{
"chapter": "CHAPTER 2: COMMUNICABLE DISEASES & NATIONAL HEALTH PROGRAMMES",
"num": 9,
"q": "Under the Revised National TB Control Programme (RNTCP) / National TB Elimination Programme (NTEP), the treatment regimen for new drug-susceptible TB (category I) patients is:",
"opts": [
    "A.  2HRZE / 4HR (2 months intensive + 4 months continuation)",
    "B.  2HRE / 6HR",
    "C.  6HRZE",
    "D.  2HRZE / 4HRE"
],
"ans": "A",
"exp": [
    "Under NTEP (formerly RNTCP), the standard regimen for new drug-susceptible TB is: 2HRZE / 4HR.",
    "Intensive Phase (2 months): Isoniazid (H) + Rifampicin (R) + Pyrazinamide (Z) + Ethambutol (E) — daily.",
    "Continuation Phase (4 months): Isoniazid (H) + Rifampicin (R) — daily.",
    "Total duration: 6 months.",
    "MDR-TB (resistance to at least H and R): requires minimum 18–20 months of second-line drugs (fluoroquinolones, injectable agents).",
    "XDR-TB: resistant to H, R, any fluoroquinolone, and at least one second-line injectable drug."
],
"kp": [
    "NTEP (National TB Elimination Programme) replaced RNTCP in 2020",
    "India aims to ELIMINATE TB by 2025 (5 years ahead of global SDG target of 2030)",
    "DOTS (Directly Observed Treatment Short-course) is the cornerstone of RNTCP/NTEP",
    "New smear positive (NSP) TB = Category I; Treatment failure/relapse = Category II",
    "Nikshay portal: online TB notification and monitoring system in India"
],
"note": "Recent update: NTEP has moved to daily FDC (Fixed Dose Combination) therapy replacing thrice-weekly DOTS.",
"source": "Park's Textbook, Tuberculosis, National TB Elimination Programme"
},

{
"chapter": "CHAPTER 2: COMMUNICABLE DISEASES & NATIONAL HEALTH PROGRAMMES",
"num": 10,
"q": "Which of the following is a characteristic feature of HERD IMMUNITY?",
"opts": [
    "A.  Protection only for immunized individuals",
    "B.  Protection of susceptible individuals through reduced transmission when a sufficient proportion of the population is immune",
    "C.  Complete elimination of a pathogen from a geographic region",
    "D.  Immunity conferred by passive transfer of antibodies"
],
"ans": "B",
"exp": [
    "Herd immunity (or population immunity) occurs when a sufficient proportion of a community becomes immune to an infection (through vaccination or prior infection), thereby reducing the likelihood of infection for individuals who lack immunity.",
    "It protects non-immune members of the population (e.g., immunocompromised, infants, those with contraindications to vaccination) indirectly.",
    "The threshold needed depends on R₀: Higher R₀ → higher threshold needed.",
    "This concept is the basis for achieving disease ERADICATION (e.g., smallpox) or ELIMINATION."
],
"kp": [
    "Herd immunity threshold (HIT) = (1 - 1/R₀) × 100%",
    "Measles: HIT ≈ 95%; Polio: HIT ≈ 80-85%; Smallpox: HIT ≈ 70-80%",
    "Eradication = worldwide reduction to zero; Elimination = reduction to zero in a defined geographic area",
    "Smallpox: only human disease successfully eradicated (1980)",
    "Polio: India declared polio-free in 2014"
],
"source": "Park's Textbook, Herd Immunity and Vaccination Coverage"
},

# ═══════════════════════════════════════════════════════════════════
# CHAPTER 3: NUTRITION
# ═══════════════════════════════════════════════════════════════════

{
"chapter": "CHAPTER 3: NUTRITION & NUTRITIONAL DEFICIENCIES",
"num": 11,
"q": "A 3-year-old child presents with severe wasting, loss of muscle mass, minimal subcutaneous fat, and 'old man' facies. The child appears alert and has good appetite. There is no edema. Which of the following nutritional disorders is MOST likely?",
"opts": [
    "A.  Kwashiorkor",
    "B.  Marasmus",
    "C.  Marasmic-Kwashiorkor",
    "D.  Nutritional rickets"
],
"ans": "B",
"exp": [
    "Marasmus is caused by severe deficiency of BOTH calories AND protein. Key features include: severe wasting of muscle and subcutaneous fat, 'old man' or 'monkey' facies, skin hanging in loose folds, alert child with good appetite, weight < 60% of expected, NO edema.",
    "Kwashiorkor is primarily caused by protein deficiency with relatively adequate caloric intake. Features: edema (pitting), 'flaky-paint' skin rash, sparse reddish hair (flag sign), moon face, fatty liver, child is miserable and irritable, weight 60-80% of expected.",
    "Marasmic-Kwashiorkor combines features of both — wasting with edema.",
    "PEM is most prevalent among children 6 months to 2 years of age in India. According to NFHS-4, 35.7% of children under 5 are underweight, 38.4% stunted, 21% wasted."
],
"kp": [
    "Marasmus: calorie + protein deficiency; no edema; alert; < 60% weight-for-age",
    "Kwashiorkor: protein deficiency; EDEMA; irritable; 60-80% weight-for-age; flag sign",
    "MUAC (Mid-Upper Arm Circumference): < 11.5 cm = severe acute malnutrition (SAM)",
    "Gomez classification: Grade I (75-90%), Grade II (60-74%), Grade III (<60%) of median weight-for-age",
    "Wellcome classification: <60% + edema = Marasmic-Kwashiorkor; 60-80% + edema = Kwashiorkor"
],
"note": "PEM is the MOST COMMON nutritional problem in India and developing countries.",
"source": "Park's Textbook, Protein-Energy Malnutrition, p.449-455"
},

{
"chapter": "CHAPTER 3: NUTRITION & NUTRITIONAL DEFICIENCIES",
"num": 12,
"q": "Bitot's spots are a clinical sign of deficiency of which vitamin?",
"opts": [
    "A.  Vitamin C",
    "B.  Vitamin D",
    "C.  Vitamin A",
    "D.  Vitamin B12"
],
"ans": "C",
"exp": [
    "Bitot's spots are triangular, silvery-gray, foamy plaques on the conjunctiva (especially temporal side) composed of desquamated epithelial cells and Corynebacterium xerosis. They are a classical feature of Vitamin A deficiency.",
    "The WHO classification of Vitamin A deficiency (VAD) ocular signs: XN = night blindness; X1A = conjunctival xerosis; X1B = Bitot's spots; X2 = corneal xerosis; X3A = corneal ulceration (<1/3 cornea); X3B = keratomalacia (>1/3 cornea) — medical emergency!",
    "Vitamin A (retinol) is a fat-soluble vitamin essential for: (1) vision (rhodopsin synthesis), (2) epithelial differentiation, (3) immune function, (4) growth.",
    "National Vitamin A prophylaxis programme (India): 9 doses from 9 months to 5 years (9 months, 18 months, then every 6 months till 5 years)."
],
"kp": [
    "Vitamin A deficiency = MOST COMMON cause of PREVENTABLE BLINDNESS in children worldwide",
    "Bitot's spots: Vitamin A deficiency (X1B in WHO classification)",
    "Night blindness (nyctalopia) = earliest symptom of Vitamin A deficiency",
    "Keratomalacia = most severe ocular complication; leads to permanent blindness",
    "Megadose therapy: 2 lakh IU oral Vitamin A given at 9 and 18 months, then 6-monthly"
],
"note": "Other fat-soluble vitamins: Vitamin D (rickets, osteomalacia), Vitamin E (hemolytic anemia in neonates), Vitamin K (bleeding disorders).",
"source": "Park's Textbook, Vitamin A Deficiency, Nutritional Blindness"
},

{
"chapter": "CHAPTER 3: NUTRITION & NUTRITIONAL DEFICIENCIES",
"num": 13,
"q": "The MOST common cause of nutritional anaemia in India is deficiency of:",
"opts": [
    "A.  Vitamin B12",
    "B.  Folic acid",
    "C.  Iron",
    "D.  Vitamin C"
],
"ans": "C",
"exp": [
    "Iron deficiency anemia (IDA) is the MOST COMMON nutritional deficiency and the most prevalent cause of anemia globally and in India.",
    "It is caused by inadequate dietary iron intake, poor bioavailability of iron (non-heme iron from plant sources), increased requirements (pregnancy, adolescence, infancy) and blood loss (hookworm, menstruation).",
    "Laboratory findings: hypochromic, microcytic anemia; low serum iron; low serum ferritin; high TIBC; low transferrin saturation.",
    "National Iron Plus Initiative (India): IFA supplementation for different groups — weekly IFA for children 5-10 yrs and adolescents; daily IFA for pregnant women (180 days) and lactating mothers (180 days post-delivery)."
],
"kp": [
    "Iron deficiency anemia: most common nutritional problem globally",
    "Hypochromic microcytic anemia on peripheral smear",
    "Ferritin is the EARLIEST indicator to fall in iron deficiency",
    "Normal serum ferritin: 12-200 µg/L in adults",
    "WHO criteria for anemia: Hb < 11 g/dL in pregnant women; < 12 g/dL in non-pregnant women; < 13 g/dL in men",
    "Anemia affects 57% of pregnant women in India (NFHS-4)"
],
"source": "Park's Textbook, Nutritional Anaemia, Iron Deficiency"
},

# ═══════════════════════════════════════════════════════════════════
# CHAPTER 4: HEALTH PROGRAMMES & INDICATORS
# ═══════════════════════════════════════════════════════════════════

{
"chapter": "CHAPTER 4: HEALTH INDICATORS & NATIONAL HEALTH PROGRAMMES",
"num": 14,
"q": "Which health indicator BEST reflects the socioeconomic development and quality of health care in a country?",
"opts": [
    "A.  Crude death rate",
    "B.  Infant mortality rate (IMR)",
    "C.  Crude birth rate",
    "D.  Total fertility rate"
],
"ans": "B",
"exp": [
    "Infant Mortality Rate (IMR) is widely considered the BEST single indicator of overall health status, socioeconomic development, and quality of health care in a country.",
    "IMR = (Number of deaths under 1 year of age / Number of live births in the same year) × 1000.",
    "India's IMR has declined from ~110 (1980) to about 28/1000 live births (SRS 2020).",
    "Components of IMR: Neonatal mortality rate (NMR) = deaths in first 28 days; Post-neonatal mortality rate = deaths from 28 days to 1 year.",
    "Perinatal mortality rate = (stillbirths + deaths in first 7 days) / (total births including stillbirths) × 1000."
],
"kp": [
    "IMR = most sensitive indicator of health status of a community",
    "Under-5 Mortality Rate (U5MR) = another key indicator; global < 25/1000 is target",
    "Maternal Mortality Ratio (MMR) = deaths per 100,000 live births",
    "India MMR (SRS 2018-20): 97/100,000 live births",
    "Life Expectancy at Birth: most comprehensive indicator of overall health",
    "Neonatal period = first 28 days; Perinatal period = 28 weeks gestation to 7 days post-birth"
],
"source": "Park's Textbook, Health Indicators, Mortality Indicators, p.14-16"
},

{
"chapter": "CHAPTER 4: HEALTH INDICATORS & NATIONAL HEALTH PROGRAMMES",
"num": 15,
"q": "The Anganwadi Worker (AWW) in the ICDS (Integrated Child Development Services) scheme serves a population of approximately:",
"opts": [
    "A.  500-700",
    "B.  1000",
    "C.  700-800",
    "D.  2000-2500"
],
"ans": "B",
"exp": [
    "The Anganwadi Worker (AWW) is the frontline worker of the Integrated Child Development Services (ICDS) scheme, serving approximately 1000 population (or 40-80 children under 6 years of age).",
    "ICDS was launched on 2nd October 1975. It is the world's largest child development programme.",
    "Services delivered under ICDS (remembered as 'SEHAN' or six services): (1) Supplementary nutrition, (2) Immunization, (3) Health check-up, (4) Referral services, (5) Pre-school non-formal education, (6) Nutrition and health education.",
    "Target beneficiaries: children under 6 years, pregnant women, lactating mothers, and adolescent girls."
],
"kp": [
    "AWW serves ~1000 population; ASHA serves ~1000 population; ANM serves ~5000 population",
    "ICDS launched: 2nd October 1975 (Gandhi Jayanti)",
    "Services: Supplementary nutrition + Immunization + Health checkup + Referral + Pre-school education + Nutrition & health education",
    "Anganwadi Centre (AWC): one per 400-800 population in tribal/rural areas",
    "POSHAN Abhiyaan (National Nutrition Mission): 2018, aims to reduce stunting, undernutrition, anemia"
],
"note": "ICDS is funded by both Central and State governments. The Anganwadi Worker is a honorary volunteer (not a government employee).",
"source": "Park's Textbook, ICDS Programme, p.444-446"
},

{
"chapter": "CHAPTER 4: HEALTH INDICATORS & NATIONAL HEALTH PROGRAMMES",
"num": 16,
"q": "The Primary Health Centre (PHC) in India covers a population of approximately how many people in hilly/tribal/difficult areas?",
"opts": [
    "A.  30,000",
    "B.  20,000",
    "C.  5,000",
    "D.  1,00,000"
],
"ans": "B",
"exp": [
    "As per Indian Public Health Standards (IPHS), a Primary Health Centre (PHC) covers: 30,000 population in PLAINS and 20,000 population in HILLY, TRIBAL, and DIFFICULT AREAS.",
    "A PHC is the first point of contact between the village community and medical officer. It has a minimum of 4-6 beds.",
    "Sub-centre (SC): covers 5,000 (plains) or 3,000 (hilly/tribal) population. Staffed by 1 ANM (Auxiliary Nurse Midwife) + 1 Male Health Worker.",
    "Community Health Centre (CHC): covers 1,20,000 population. Has 30 beds, specialist services (Surgery, Obstetrics, Medicine, Pediatrics — SOMPD).",
    "District Hospital: serves the entire district."
],
"kp": [
    "Sub-Centre: 5000 (plains) / 3000 (hills); staffed by ANM + HW(M)",
    "PHC: 30,000 (plains) / 20,000 (hills); has 1 Medical Officer",
    "CHC: 1,20,000 population; 30 beds; 4 specialists",
    "District Hospital: covers entire district; 75-100+ beds",
    "PHC concept in India: based on Bhore Committee recommendations (1946)"
],
"source": "Park's Textbook, Health Care Infrastructure, Primary Health Care in India"
},

{
"chapter": "CHAPTER 4: HEALTH INDICATORS & NATIONAL HEALTH PROGRAMMES",
"num": 17,
"q": "The Alma-Ata Declaration (1978) emphasized which of the following as a primary approach to achieving 'Health for All'?",
"opts": [
    "A.  Curative medical care at district hospitals",
    "B.  Primary Health Care (PHC) as the key to attaining acceptable health for all",
    "C.  Specialist-based tertiary care",
    "D.  Global eradication of all infectious diseases by 2000"
],
"ans": "B",
"exp": [
    "The Declaration of Alma-Ata (September 1978, Kazakhstan) was a landmark document jointly sponsored by WHO and UNICEF. Its key message: 'Health for All by 2000 AD' is achievable through Primary Health Care (PHC).",
    "PHC was defined as 'Essential health care based on practical, scientifically sound and socially acceptable methods and technology made universally accessible to individuals and families in the community through their full participation and at a cost that the community and country can afford to maintain at every stage of development in the spirit of self-reliance and self-determination'.",
    "The 8 essential components of PHC (remembered as 'CAMELS FIT'): Community education, Appropriate treatment, Maternal and child health, Essential drugs, Locally endemic disease control, Safe food/water, Food supply/nutrition, Immunization, Traditional medicine."
],
"kp": [
    "Alma-Ata 1978: 'Health for All by 2000 AD' through Primary Health Care",
    "Organized by WHO + UNICEF in Alma-Ata, USSR (now Almaty, Kazakhstan)",
    "8 components of PHC: Education, MCH, Nutrition, Safe water, EPI, Endemic disease control, Treatment, Essential drugs",
    "Selective PHC = GOBI: Growth monitoring, ORS, Breastfeeding, Immunization",
    "GOBI-FFF: adds Food supplementation, Female literacy, Family planning"
],
"source": "Park's Textbook, Primary Health Care, Alma-Ata Declaration, p.26-30"
},

# ═══════════════════════════════════════════════════════════════════
# CHAPTER 5: ENVIRONMENT & OCCUPATIONAL HEALTH
# ═══════════════════════════════════════════════════════════════════

{
"chapter": "CHAPTER 5: ENVIRONMENTAL & OCCUPATIONAL HEALTH",
"num": 18,
"q": "The MOST effective method of water purification at the household level to make water microbiologically safe is:",
"opts": [
    "A.  Boiling",
    "B.  Chlorination",
    "C.  Sedimentation",
    "D.  Coagulation"
],
"ans": "A",
"exp": [
    "Boiling is the MOST EFFECTIVE method to render water microbiologically safe at the household level. Sustained boiling for 1 minute (or 3 minutes at altitude) kills all pathogenic bacteria, viruses, and protozoa including Giardia and Cryptosporidium (which are resistant to chlorination).",
    "Chlorination is the most widely used and cost-effective method for large-scale (community) water disinfection. Chlorine is effective against bacteria and most viruses but NOT against Cryptosporidium.",
    "Sedimentation and coagulation are physical methods that REDUCE turbidity and remove suspended particles but do NOT guarantee microbial safety on their own.",
    "Slow sand filtration: removes 99.9% of bacteria; 'Schmutzdecke' (biological layer) is key to its effectiveness."
],
"kp": [
    "Boiling: most effective HH method; kills all pathogens including Cryptosporidium",
    "Chlorination: most common large-scale method; residual chlorine 0.5 ppm at consumer",
    "Chlorine-resistant organisms: Cryptosporidium, Giardia cysts (need boiling or UV)",
    "MPN (Most Probable Number) for E. coli: standard bacteriological water quality test",
    "WHO standard: 0 E. coli/100 mL in drinking water",
    "Water pH: optimal for chlorination = 6.5–8.5"
],
"source": "Park's Textbook, Water Supply and Water Purification"
},

{
"chapter": "CHAPTER 5: ENVIRONMENTAL & OCCUPATIONAL HEALTH",
"num": 19,
"q": "Which disease is associated with CADMIUM toxicity (Itai-Itai disease)?",
"opts": [
    "A.  Neurological damage and 'Mad Hatter' syndrome",
    "B.  Painful osteomalaacia, renal tubular dysfunction, and fragile bones",
    "C.  Hemolytic anemia and renal failure",
    "D.  Peripheral neuropathy and 'glove-stocking' sensory loss"
],
"ans": "B",
"exp": [
    "Itai-Itai disease ('ouch-ouch' disease, named for the cry of pain of patients) is caused by chronic CADMIUM poisoning from contaminated rice and water in Japan (Toyama Prefecture, Jinzu River).",
    "Cadmium accumulates primarily in the kidneys causing proximal renal tubular dysfunction (Fanconi syndrome — glucosuria, aminoaciduria, phosphaturia) → hypophosphatemia → osteomalacia → severe bone pain, fractures.",
    "Mercury (Hg) poisoning: Minamata disease (Japan) — methylmercury from contaminated fish; causes severe neurological damage, 'Mad Hatter' syndrome (inorganic mercury).",
    "Lead (Pb) poisoning: DROPS mnemonic — D: Dragging (wrist/foot drop), R: Red stippling of RBCs (basophilic stippling), O: Opacity of bones (lead lines), P: Peripheral neuropathy, S: Saturnine gout.",
    "Arsenic poisoning: Blackfoot disease (Taiwan); arsenicosis with skin changes (rain-drop pigmentation, keratosis, Mees' lines)."
],
"kp": [
    "Cadmium → Itai-Itai → osteomalacia + renal tubular damage",
    "Mercury → Minamata → neurological damage (methylmercury via fish)",
    "Lead → wrist drop, foot drop, basophilic stippling, Burton's line on gums",
    "Arsenic → Blackfoot disease, rain-drop pigmentation, lung/skin cancer",
    "Fluorosis: dental + skeletal fluorosis; caused by excess fluoride in groundwater (>1.5 ppm)"
],
"source": "Park's Textbook, Environmental Health, Heavy Metal Toxicity"
},

{
"chapter": "CHAPTER 5: ENVIRONMENTAL & OCCUPATIONAL HEALTH",
"num": 20,
"q": "A miner presents with progressive dyspnea and bilateral upper lobe nodular opacities on CXR after 20 years of working in gold mines. The MOST likely occupational lung disease is:",
"opts": [
    "A.  Asbestosis",
    "B.  Berylliosis",
    "C.  Silicosis",
    "D.  Byssinosis"
],
"ans": "C",
"exp": [
    "Silicosis is caused by inhalation of free crystalline silica (quartz) dust. It is the most prevalent occupational lung disease. Occupations at risk: miners (gold, coal, iron), sandblasters, quarry workers, pottery workers.",
    "Pathology: Silica particles are engulfed by macrophages → macrophage death → release of inflammatory mediators → fibrosis with characteristic 'whorled' silicotic nodules in UPPER LOBES.",
    "CXR: Small rounded opacities in upper zones; progressive massive fibrosis (PMF); 'eggshell calcification' of hilar lymph nodes is PATHOGNOMONIC.",
    "Silicosis increases risk of TB (silicotuberculosis — 2-3 fold higher risk).",
    "Asbestosis: caused by asbestos; lower lobe fibrosis; pleural plaques; associated with MESOTHELIOMA and lung cancer.",
    "Byssinosis: caused by cotton dust; 'Monday morning tightness'.",
    "Berylliosis: caused by beryllium; granulomatous disease resembling sarcoidosis."
],
"kp": [
    "Silicosis: silica; upper lobe; eggshell calcification; increased TB risk",
    "Asbestosis: lower lobe; pleural plaques; mesothelioma",
    "Coal workers' pneumoconiosis (CWP): coal dust; upper lobes",
    "Byssinosis: cotton dust; 'Monday fever'",
    "Farmer's lung: Thermophilic actinomycetes in moldy hay; hypersensitivity pneumonitis"
],
"source": "Park's Textbook, Occupational Health, Pneumoconioses"
},

# ═══════════════════════════════════════════════════════════════════
# CHAPTER 6: DEMOGRAPHY & FAMILY PLANNING
# ═══════════════════════════════════════════════════════════════════

{
"chapter": "CHAPTER 6: DEMOGRAPHY & FAMILY PLANNING",
"num": 21,
"q": "As per the Census 2011, the density of population in India is approximately:",
"opts": [
    "A.  234 persons/sq km",
    "B.  382 persons/sq km",
    "C.  275 persons/sq km",
    "D.  500 persons/sq km"
],
"ans": "B",
"exp": [
    "According to Census 2011, India's population density was 382 persons/sq km, up from 325 per sq km in 2001.",
    "India's total population as per Census 2011: 1,210.85 million (121 crore).",
    "Key demographic data (Census 2011): Sex ratio: 943 females/1000 males; Child sex ratio (0-6 years): 918; Literacy rate: 74.04% (males 82.14%, females 65.46%).",
    "India conducts Census every 10 years; decennial census. Started in 1872 (under British); first synchronous census: 1881.",
    "India is 2nd most populous country after China. Population growth rate: 1.64% per year (2001-2011)."
],
"kp": [
    "Census 2011: Population = 1210 million; Density = 382/sq km",
    "Sex ratio = 943 (females per 1000 males)",
    "Child sex ratio (0-6 yrs) = 918 (lowest ever recorded)",
    "Literacy rate = 74.04%; Male = 82.14%; Female = 65.46%",
    "Most populous state: Uttar Pradesh; Highest density: Bihar; Highest literacy: Kerala"
],
"source": "Park's Textbook, Demography and Family Planning, Census Data"
},

{
"chapter": "CHAPTER 6: DEMOGRAPHY & FAMILY PLANNING",
"num": 22,
"q": "Which contraceptive method has the HIGHEST theoretical effectiveness (lowest Pearl Index)?",
"opts": [
    "A.  Condom",
    "B.  Combined oral contraceptive pills (COCPs)",
    "C.  Copper-T (IUCD)",
    "D.  Female sterilization (tubectomy)"
],
"ans": "D",
"exp": [
    "The Pearl Index measures contraceptive failure rate = number of pregnancies per 100 woman-years of use.",
    "Lower Pearl Index = more effective contraception.",
    "Approximate Pearl Index values: Female sterilization (tubectomy) ≈ 0.1-0.5; Vasectomy ≈ 0.1; Copper-T IUD ≈ 0.6-0.8; COCPs ≈ 0.1-0.3 (perfect use) / 3-8 (typical use); Condom ≈ 2 (perfect use) / 15 (typical use); Rhythm method ≈ 9-25.",
    "Female sterilization has the highest overall effectiveness because it is permanent, irreversible, and does not depend on user compliance.",
    "Emergency contraception (ECP): Levonorgestrel 1.5 mg within 72 hours of unprotected intercourse; or copper-T IUD within 5 days."
],
"kp": [
    "Pearl Index: pregnancies/100 woman-years; LOWER = more effective",
    "Sterilization (tubal ligation/vasectomy): most effective permanent methods",
    "Copper-T IUCD: effective, long-acting, reversible; also used as emergency contraception",
    "COCPs: very effective with perfect use; main side effects: DVT, MI (especially in smokers >35 yrs)",
    "Barrier methods (condom): also protect against STIs/HIV"
],
"source": "Park's Textbook, Family Planning Methods, Contraception"
},

# ═══════════════════════════════════════════════════════════════════
# CHAPTER 7: MATERNAL & CHILD HEALTH
# ═══════════════════════════════════════════════════════════════════

{
"chapter": "CHAPTER 7: MATERNAL & CHILD HEALTH",
"num": 23,
"q": "The Integrated Management of Neonatal and Childhood Illness (IMNCI) strategy focuses primarily on which age group?",
"opts": [
    "A.  Children 5-12 years",
    "B.  Neonates (0-28 days) and children under 5 years",
    "C.  Adolescents 10-19 years",
    "D.  Pregnant women in third trimester"
],
"ans": "B",
"exp": [
    "IMNCI (Integrated Management of Neonatal and Childhood Illness) is adapted from IMCI (Integrated Management of Childhood Illness, WHO/UNICEF) with an added neonatal component.",
    "It targets neonates (0-28 days) and children 2 months to 5 years, addressing the FIVE major killers of under-5 children: Pneumonia, Diarrhea, Malaria, Measles, and Malnutrition (remembered as PDMMM or 'P-D-M-M-M').",
    "IMNCI trains health workers to: (1) assess and classify illness, (2) identify treatment, (3) treat and/or refer, (4) counsel caretakers, (5) provide follow-up care.",
    "IMNCI is incorporated into India's RCH (Reproductive and Child Health) programme."
],
"kp": [
    "IMNCI targets: neonates + children < 5 years",
    "5 major childhood killers: Pneumonia, Diarrhea, Malaria, Measles, Malnutrition",
    "IMNCI approach: assess → classify → treat/refer → counsel → follow-up",
    "Pneumonia is the single LARGEST cause of under-5 death globally",
    "ORS + zinc for diarrhea; amoxicillin for non-severe pneumonia (WHO guidelines)"
],
"source": "Park's Textbook, Maternal and Child Health, IMNCI"
},

{
"chapter": "CHAPTER 7: MATERNAL & CHILD HEALTH",
"num": 24,
"q": "The Maternal Mortality Ratio (MMR) in India as per SRS (2018-20) was approximately:",
"opts": [
    "A.  54 per 100,000 live births",
    "B.  97 per 100,000 live births",
    "C.  210 per 100,000 live births",
    "D.  160 per 100,000 live births"
],
"ans": "B",
"exp": [
    "India's Maternal Mortality Ratio (MMR) as per the Sample Registration System (SRS) 2018-20 was 97 per 100,000 live births, a significant decline from 254 in 2004-06.",
    "MMR is defined as: number of maternal deaths per 100,000 live births. A 'maternal death' is death of a woman while pregnant or within 42 days of termination of pregnancy, from any cause related to or aggravated by the pregnancy.",
    "SDG target: Reduce global MMR to < 70 per 100,000 live births by 2030.",
    "India's National Health Policy 2017 target: MMR < 100 by 2020 (ACHIEVED).",
    "Major causes of maternal mortality in India: Hemorrhage (25-30%), hypertensive disorders of pregnancy (20%), sepsis (15%), obstructed labor, unsafe abortion."
],
"kp": [
    "MMR India (SRS 2018-20): 97 per 100,000 live births",
    "Leading state: Assam (highest MMR ~195); Kerala (lowest MMR ~19)",
    "SDG target: MMR < 70 by 2030",
    "Obstetric hemorrhage: most common cause of maternal death",
    "JSY (Janani Suraksha Yojana): cash incentive for institutional delivery to reduce MMR"
],
"source": "Park's Textbook, Maternal Mortality, MCH Indicators"
},

# ═══════════════════════════════════════════════════════════════════
# CHAPTER 8: MISCELLANEOUS HIGH-YIELD
# ═══════════════════════════════════════════════════════════════════

{
"chapter": "CHAPTER 8: MISCELLANEOUS HIGH-YIELD TOPICS",
"num": 25,
"q": "The Sustainable Development Goals (SDGs) target for Under-5 Mortality Rate (U5MR) by 2030 is:",
"opts": [
    "A.  Less than 10 per 1000 live births",
    "B.  Less than 25 per 1000 live births",
    "C.  Less than 50 per 1000 live births",
    "D.  Zero deaths"
],
"ans": "B",
"exp": [
    "SDG Goal 3.2 targets: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce NMR to at least 12/1000 live births and U5MR to at least 25/1000 live births.",
    "The Millennium Development Goals (MDGs 1990-2015) aimed to reduce U5MR by two-thirds by 2015.",
    "India's U5MR (SRS 2020): approximately 32 per 1000 live births.",
    "India's NMR (SRS 2020): approximately 20 per 1000 live births.",
    "Key strategies to reduce U5MR: IMNCI, immunization (UIP), ICDS, ORS, promotion of breastfeeding, safe drinking water, sanitation (Swachh Bharat Mission)."
],
"kp": [
    "SDG U5MR target: < 25 per 1000 live births by 2030",
    "SDG NMR target: < 12 per 1000 live births by 2030",
    "India U5MR 2020: ~32/1000; NMR ~20/1000",
    "SDG 3 covers health goals: ends epidemics (AIDS, TB, malaria), reduces MMR/U5MR, universal health coverage",
    "NMR = deaths in first 28 days / 1000 live births"
],
"source": "Park's Textbook, MDGs and SDGs, Health Indicators"
},

{
"chapter": "CHAPTER 8: MISCELLANEOUS HIGH-YIELD TOPICS",
"num": 26,
"q": "Which of the following is the recommended first-line drug for mass drug administration (MDA) in the National Programme for Elimination of Lymphatic Filariasis (NPELF)?",
"opts": [
    "A.  Ivermectin + Albendazole",
    "B.  Diethylcarbamazine (DEC) + Albendazole",
    "C.  Albendazole alone",
    "D.  Doxycycline + DEC"
],
"ans": "B",
"exp": [
    "India's National Programme for Elimination of Lymphatic Filariasis (NPELF) uses annual Mass Drug Administration (MDA) with Diethylcarbamazine (DEC) + Albendazole for all eligible persons in endemic areas.",
    "Target: Reduce microfilaremia prevalence to < 1% in all endemic districts.",
    "India aims to ELIMINATE lymphatic filariasis by 2027 (revised from 2021).",
    "Recently (2019 onwards), India is implementing triple drug therapy in some states: IDA (Ivermectin + DEC + Albendazole) for accelerated elimination.",
    "Filariasis is transmitted by Culex quinquefasciatus mosquito (night-biting). Causative agents: Wuchereria bancrofti (most common in India), Brugia malayi."
],
"kp": [
    "MDA for filariasis: DEC + Albendazole (standard) or IDA triple therapy (accelerated)",
    "Vector: Culex quinquefasciatus",
    "Causative agent: Wuchereria bancrofti (most common in India)",
    "Elimination target: microfilaremia < 1% in endemic areas",
    "Night blood sample: optimal for detecting microfilaria (nocturnal periodicity)"
],
"source": "Park's Textbook, Lymphatic Filariasis, NPELF"
},

{
"chapter": "CHAPTER 8: MISCELLANEOUS HIGH-YIELD TOPICS",
"num": 27,
"q": "The Leprosy elimination programme in India aims to achieve elimination defined as prevalence of less than:",
"opts": [
    "A.  1 case per 10,000 population",
    "B.  1 case per 1,000 population",
    "C.  1 case per 1,00,000 population",
    "D.  Zero cases"
],
"ans": "A",
"exp": [
    "Leprosy elimination is defined as reducing prevalence to LESS THAN 1 case per 10,000 population.",
    "India achieved national-level leprosy elimination in December 2005.",
    "However, some high-endemic districts/states (Chhattisgarh, UP, Bihar, Odisha) still have prevalence >1/10,000.",
    "NLEP (National Leprosy Eradication Programme): Multidrug Therapy (MDT) is the mainstay. MDT was introduced in India in 1982.",
    "MDT regimens: PB (Paucibacillary): Dapsone + Rifampicin for 6 months. MB (Multibacillary): Dapsone + Rifampicin + Clofazimine for 12 months.",
    "Leprosy is caused by Mycobacterium leprae; diagnosis by slit-skin smear and biopsy."
],
"kp": [
    "Elimination = < 1 case per 10,000 population",
    "India achieved national leprosy elimination in 2005",
    "PB MDT: Rifampicin + Dapsone × 6 months",
    "MB MDT: Rifampicin + Dapsone + Clofazimine × 12 months",
    "Leprosy: chronic granulomatous disease; affects skin and peripheral nerves; spread by droplets"
],
"source": "Park's Textbook, Leprosy, NLEP"
},

{
"chapter": "CHAPTER 8: MISCELLANEOUS HIGH-YIELD TOPICS",
"num": 28,
"q": "The 'cold chain' in the National Immunization Programme refers to:",
"opts": [
    "A.  Supply chain for cold beverages in hospitals",
    "B.  A system of storing and distributing vaccines within a required temperature range from the point of manufacture to the point of use",
    "C.  A method of freezing blood products",
    "D.  Refrigerated transport of human organs"
],
"ans": "B",
"exp": [
    "The 'cold chain' is the system of storing and transporting vaccines in a safe temperature range (usually +2°C to +8°C for most vaccines, or colder for certain vaccines) from the manufacturer to the final user, to maintain vaccine potency.",
    "Components of cold chain: (1) Cold chain equipment (ICE-LINED REFRIGERATORS/ILRs, Deep Freeze units, Cold boxes, Vaccine carriers), (2) Trained personnel, (3) Monitoring devices (VVM - Vaccine Vial Monitor).",
    "VVM (Vaccine Vial Monitor): A heat-sensitive label that changes color to indicate if vaccine has been exposed to excessive heat — if square turns darker than circle, vaccine should NOT be used.",
    "Freeze-sensitive vaccines (must not be frozen): OPV is the exception — stored at -15°C to -25°C. Most other vaccines: +2°C to +8°C.",
    "Walk-in cooler (WIC) at national level; ILR at PHC level; vaccine carriers at ANM level."
],
"kp": [
    "Cold chain: +2°C to +8°C for most vaccines; -15°C to -25°C for OPV",
    "VVM: outer square darker than inner circle = discard vaccine",
    "ILR (Ice Lined Refrigerator): at PHC and CHC level",
    "Deep freeze: at district and subdivisional level; for OPV storage",
    "Never freeze: DPT, TT, Hepatitis B, BCG — will lose potency if frozen"
],
"source": "Park's Textbook, Universal Immunization Programme, Cold Chain"
},

{
"chapter": "CHAPTER 8: MISCELLANEOUS HIGH-YIELD TOPICS",
"num": 29,
"q": "The key difference between SURVEILLANCE and MONITORING in public health is:",
"opts": [
    "A.  Surveillance is passive; monitoring is active",
    "B.  Surveillance is the ongoing systematic collection and analysis of health data for action; monitoring tracks the progress of a specific programme toward its objectives",
    "C.  Surveillance applies only to infectious diseases; monitoring applies to chronic diseases",
    "D.  There is no difference; the terms are interchangeable"
],
"ans": "B",
"exp": [
    "Epidemiological SURVEILLANCE: The ongoing, systematic collection, analysis, interpretation, and dissemination of health data for public health action. It includes disease reporting, sentinel surveillance, and population-based surveys.",
    "MONITORING: The periodic or continuous tracking of the PROCESS and OUTPUT indicators of a specific health programme, to check whether it is proceeding as planned (e.g., vaccine coverage monitoring, ANC attendance).",
    "EVALUATION: Assessment of the OUTCOME and IMPACT of the programme — did it achieve its objectives?",
    "Active surveillance: health workers actively seek out cases. Passive surveillance: health workers passively receive reports. Sentinel surveillance: selected sites report for specific diseases."
],
"kp": [
    "Surveillance: ongoing data collection → action (disease-oriented)",
    "Monitoring: tracks programme PROCESS and OUTPUTS",
    "Evaluation: assesses programme OUTCOMES and IMPACT",
    "Active surveillance: more complete but costly (e.g., AFP surveillance for polio)",
    "Sentinel surveillance: selected sites; used for HIV, influenza"
],
"source": "Park's Textbook, Epidemiological Surveillance and Monitoring"
},

{
"chapter": "CHAPTER 8: MISCELLANEOUS HIGH-YIELD TOPICS",
"num": 30,
"q": "The AYUSH programmes in India include Ayurveda, Yoga and Naturopathy, Unani, Siddha, and:",
"opts": [
    "A.  Homoeopathy",
    "B.  Allopathy",
    "C.  Chiropractic",
    "D.  Acupuncture"
],
"ans": "A",
"exp": [
    "AYUSH stands for: Ayurveda, Yoga & Naturopathy, Unani, Siddha, and Homeopathy.",
    "Ministry of AYUSH was established in November 2014 (earlier known as Department of AYUSH under Ministry of Health and Family Welfare).",
    "The AYUSH system is recognized as an important component of India's healthcare delivery system, especially in rural areas where conventional medical care may be limited.",
    "Sowa-Rigpa (traditional Tibetan medicine) was added to AYUSH in 2020.",
    "AYUSH practitioners serve as a significant workforce in India's primary health care system."
],
"kp": [
    "AYUSH: Ayurveda + Yoga/Naturopathy + Unani + Siddha + Homoeopathy",
    "Ministry of AYUSH established: 9 November 2014",
    "Sowa-Rigpa added to AYUSH: 2020",
    "CCIM (Central Council of Indian Medicine): regulatory body for Ayurveda, Unani, Siddha",
    "CCH (Central Council of Homoeopathy): regulatory body for Homoeopathy"
],
"source": "Park's Textbook, National Health System, AYUSH"
},
]

# ─── Build PDF ────────────────────────────────────────────────────────────────
def build():
    doc = SimpleDocTemplate(
        OUTPUT,
        pagesize=A4,
        leftMargin=2*cm,
        rightMargin=2*cm,
        topMargin=2.2*cm,
        bottomMargin=2*cm,
        title="Community Medicine NEET PG High-Yield Questions",
        author="Orris Medical AI | Based on Park's Textbook",
        subject="NEET PG Community Medicine Exam Preparation"
    )

    story = []

    # ── Cover page ──────────────────────────────────────────────────────────
    cover_data = [[
        Paragraph("COMMUNITY MEDICINE", title_style),
        Paragraph("NEET PG High-Yield Questions & Explanations", sub_title_style),
        Paragraph("Based on Park's Textbook of Preventive & Social Medicine", sub_title_style),
        Paragraph("30 Previous Year Pattern Questions with Comprehensive Explanations", sub_title_style),
    ]]
    cover = Table(cover_data, colWidths=[17*cm])
    cover.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), TEAL),
        ("TOPPADDING",    (0,0), (-1,-1), 30),
        ("BOTTOMPADDING", (0,0), (-1,-1), 30),
        ("LEFTPADDING",   (0,0), (-1,-1), 20),
        ("RIGHTPADDING",  (0,0), (-1,-1), 20),
    ]))
    story.append(cover)
    story.append(Spacer(1, 20))

    # Disclaimer
    disclaimer_inner = [
        Paragraph("EXAM PREPARATION RESOURCE", ParagraphStyle("DH", parent=section_style, fontSize=11, textColor=ORANGE)),
        Paragraph(
            "This document is a high-yield NEET PG preparation resource compiled from Park's Textbook of Preventive "
            "and Social Medicine. Questions follow the pattern of previous year NEET PG, AIIMS PG, and USMLE Step 1 "
            "style MCQs. All explanations are sourced from standard textbooks. Always cross-reference with the latest "
            "edition of Park's for updated statistics and programme details.",
            intro_style
        ),
        Paragraph("Compiled by: Orris Medical AI  |  Date: June 2026  |  Source: Park's Textbook (23rd ed.)", source_style),
    ]
    story.append(color_box(disclaimer_inner, bg=YELLOW, border=ORANGE))
    story.append(Spacer(1, 16))

    # ── Table of Contents ────────────────────────────────────────────────────
    story.append(Paragraph("TABLE OF CONTENTS", section_style))
    story.append(HRFlowable(width="100%", thickness=1, color=TEAL))
    story.append(Spacer(1, 8))
    chapters = [
        ("Chapter 1", "Epidemiology & Biostatistics", "Q1–Q5"),
        ("Chapter 2", "Communicable Diseases & National Health Programmes", "Q6–Q10"),
        ("Chapter 3", "Nutrition & Nutritional Deficiencies", "Q11–Q13"),
        ("Chapter 4", "Health Indicators & National Health Programmes", "Q14–Q17"),
        ("Chapter 5", "Environmental & Occupational Health", "Q18–Q20"),
        ("Chapter 6", "Demography & Family Planning", "Q21–Q22"),
        ("Chapter 7", "Maternal & Child Health", "Q23–Q24"),
        ("Chapter 8", "Miscellaneous High-Yield Topics", "Q25–Q30"),
    ]
    toc_data = [[
        Paragraph(f"<b>{ch}</b>", toc_style),
        Paragraph(title, toc_style),
        Paragraph(qs, toc_style)
    ] for ch, title, qs in chapters]
    toc_table = Table(toc_data, colWidths=[3.5*cm, 10.5*cm, 3*cm])
    toc_table.setStyle(TableStyle([
        ("GRID",         (0,0), (-1,-1), 0.5, colors.HexColor("#CCCCCC")),
        ("BACKGROUND",   (0,0), (-1,-1), LIGHT_GREY),
        ("BACKGROUND",   (0,0), (0,-1), colors.HexColor("#E8F4F8")),
        ("TOPPADDING",   (0,0), (-1,-1), 5),
        ("BOTTOMPADDING",(0,0), (-1,-1), 5),
        ("LEFTPADDING",  (0,0), (-1,-1), 8),
    ]))
    story.append(toc_table)
    story.append(PageBreak())

    # ── Quick Reference Table ─────────────────────────────────────────────────
    story.append(Paragraph("QUICK REFERENCE: HIGH-YIELD FACTS", section_style))
    story.append(HRFlowable(width="100%", thickness=1, color=TEAL))
    story.append(Spacer(1, 8))
    ref_data = [
        [Paragraph("<b>Topic</b>", q_num_style), Paragraph("<b>Key Fact</b>", q_num_style)],
        ["Incubation period", "Time from infection to 1st sign/symptom"],
        ["Sensitivity", "TP/(TP+FN) × 100 — rules OUT disease when negative (SNOUT)"],
        ["Specificity", "TN/(TN+FP) × 100 — rules IN disease when positive (SPIN)"],
        ["Herd immunity threshold", "(1 - 1/R₀) × 100%; Measles ≈ 95%"],
        ["IMR", "Best single indicator of health status; India IMR 2020 ≈ 28/1000"],
        ["MMR India (SRS 2018-20)", "97 per 100,000 live births"],
        ["BCG vaccine", "Given at birth (within 1 month); intradermal, left deltoid"],
        ["ICDS launched", "2nd October 1975; AWW serves ~1000 population"],
        ["PHC coverage", "30,000 (plains) / 20,000 (hills)"],
        ["Alma-Ata 1978", "PHC as key to 'Health for All by 2000'"],
        ["Leprosy elimination", "< 1/10,000; India achieved national level 2005"],
        ["Marasmus vs Kwashiorkor", "Marasmus: no edema, wasting; Kwashiorkor: EDEMA, protein deficiency"],
        ["Iron deficiency", "Most common nutritional deficiency globally; hypochromic microcytic anemia"],
        ["Bitot's spots", "Vitamin A deficiency; earliest = night blindness; worst = keratomalacia"],
        ["Silicosis", "Silica dust; upper lobes; eggshell calcification; increased TB risk"],
        ["Itai-Itai disease", "Cadmium toxicity; osteomalacia + renal tubular dysfunction"],
        ["AYUSH", "Ayurveda, Yoga/Naturopathy, Unani, Siddha, Homoeopathy"],
        ["Cold chain for vaccines", "+2 to +8°C; OPV: -15 to -25°C; VVM monitors heat exposure"],
        ["NTEP/RNTCP regimen", "2HRZE/4HR (6 months total) for new DS-TB"],
        ["SDG U5MR target", "< 25/1000 live births by 2030"],
    ]
    ref_table = Table(ref_data, colWidths=[6.5*cm, 10.5*cm])
    ref_table.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (-1,0), TEAL),
        ("TEXTCOLOR",    (0,0), (-1,0), WHITE),
        ("FONTNAME",     (0,0), (-1,0), "Helvetica-Bold"),
        ("GRID",         (0,0), (-1,-1), 0.5, colors.HexColor("#AAAAAA")),
        ("ROWBACKGROUNDS", (0,1), (-1,-1), [LIGHT_GREY, WHITE]),
        ("FONTSIZE",     (0,0), (-1,-1), 9),
        ("TOPPADDING",   (0,0), (-1,-1), 4),
        ("BOTTOMPADDING",(0,0), (-1,-1), 4),
        ("LEFTPADDING",  (0,0), (-1,-1), 7),
        ("VALIGN",       (0,0), (-1,-1), "MIDDLE"),
    ]))
    story.append(ref_table)
    story.append(PageBreak())

    # ── Questions by chapter ─────────────────────────────────────────────────
    current_chapter = None
    for q in QUESTIONS:
        if q["chapter"] != current_chapter:
            if current_chapter is not None:
                story.append(Spacer(1, 10))
            current_chapter = q["chapter"]
            story.append(chapter_banner(current_chapter))
            story.append(Spacer(1, 10))

        story.append(question_block(
            num=q["num"],
            q_text=q["q"],
            options=q["opts"],
            correct_letter=q["ans"],
            explanation_paras=q["exp"],
            keypoints=q.get("kp"),
            note=q.get("note"),
            source=q.get("source")
        ))

    # ── Final page ────────────────────────────────────────────────────────────
    story.append(PageBreak())
    final_data = [[
        Paragraph("END OF DOCUMENT", title_style),
        Paragraph("Good luck with your NEET PG preparation!", sub_title_style),
        Paragraph("Remember: Understanding > Memorization", sub_title_style),
        Spacer(1, 10),
        Paragraph(
            "This document covers 30 high-yield MCQs across all major community medicine topics tested in NEET PG. "
            "For a complete preparation, also study: (1) Park's Textbook (latest edition), (2) Mahajan & Gupta — "
            "Textbook of Preventive and Social Medicine, (3) Previous 10 years NEET PG question papers.",
            ParagraphStyle("FI", parent=intro_style, textColor=WHITE, alignment=TA_CENTER)
        ),
    ]]
    final_cover = Table(final_data, colWidths=[17*cm])
    final_cover.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), TEAL),
        ("TOPPADDING",    (0,0), (-1,-1), 30),
        ("BOTTOMPADDING", (0,0), (-1,-1), 30),
        ("LEFTPADDING",   (0,0), (-1,-1), 20),
        ("RIGHTPADDING",  (0,0), (-1,-1), 20),
    ]))
    story.append(final_cover)

    # ── Build ─────────────────────────────────────────────────────────────────
    def footer(canvas, doc):
        canvas.saveState()
        canvas.setFont("Helvetica", 8)
        canvas.setFillColor(colors.HexColor("#888888"))
        canvas.drawString(2*cm, 1.2*cm,
            "Community Medicine NEET PG | Park's Textbook of Preventive & Social Medicine | Orris Medical AI")
        canvas.drawRightString(A4[0]-2*cm, 1.2*cm, f"Page {doc.page}")
        canvas.setStrokeColor(TEAL)
        canvas.setLineWidth(0.5)
        canvas.line(2*cm, 1.5*cm, A4[0]-2*cm, 1.5*cm)
        canvas.restoreState()

    doc.build(story, onFirstPage=footer, onLaterPages=footer)
    print(f"PDF created: {OUTPUT}")

build()