MBBS Final Part-I Community Medicine - Question Pattern Analysis

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

OUTPUT = "/tmp/workspace/community-medicine-analysis/Community_Medicine_Question_Analysis.pdf"

doc = SimpleDocTemplate(
    OUTPUT, pagesize=A4,
    rightMargin=1.8*cm, leftMargin=1.8*cm,
    topMargin=1.8*cm, bottomMargin=1.8*cm,
    title="MBBS Final Part-I Community Medicine – Question Pattern Analysis",
    author="Orris AI"
)

W, H = A4
styles = getSampleStyleSheet()

BLUE  = colors.HexColor("#1a3a6e")
LBLUE = colors.HexColor("#2563eb")
TEAL  = colors.HexColor("#0e7490")
GOLD  = colors.HexColor("#b45309")
RED   = colors.HexColor("#b91c1c")
GREEN = colors.HexColor("#15803d")
BG1   = colors.HexColor("#eff6ff")
BG2   = colors.HexColor("#f0fdf4")
LGRAY = colors.HexColor("#f1f5f9")
MGRAY = colors.HexColor("#cbd5e1")

def sty(name, **kw):
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    styles.add(s)
    return s

cover_title = sty("CoverTitle", fontSize=24, textColor=colors.white,
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cover_sub   = sty("CoverSub",   fontSize=13, textColor=colors.HexColor("#bfdbfe"),
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ch_head     = sty("ChHead",     fontSize=14, textColor=colors.white,
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sub_head    = sty("SubHead",    fontSize=10, textColor=TEAL, leading=13,
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body        = sty("Body",       fontSize=9,  leading=12, spaceAfter=2)
body_j      = sty("BodyJ",      fontSize=9,  leading=12, spaceAfter=2, alignment=TA_JUSTIFY)
small       = sty("Small",      fontSize=8,  leading=11, textColor=colors.HexColor("#475569"))
bold_body   = sty("BoldBody",   fontSize=9,  leading=12, spaceAfter=2,
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note_s      = sty("NoteS",      fontSize=8.5, leading=11, textColor=GOLD, leftIndent=8,
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story = []

# ─── helpers ──────────────────────────────────────────────────────────────────
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def plain_table(rows_data, col_w, hdr_color=BLUE, alt_bg=LGRAY):
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def bullet_rows(items, cols=2):
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# ═══════════════════════════════════════════════════════════════════════════════
# COVER PAGE
# ═══════════════════════════════════════════════════════════════════════════════
banner = Table([[""]], colWidths=[W-3.6*cm], rowHeights=[3.0*cm])
banner.setStyle(TableStyle([("BACKGROUND",(0,0),(-1,-1),BLUE)]))
story.append(banner)
story.append(Spacer(1,-2.8*cm))
story.append(Paragraph("MBBS Final Part-I", cover_sub))
story.append(Spacer(1,0.1*cm))
story.append(Paragraph("Community Medicine", cover_title))
story.append(Spacer(1,0.1*cm))
story.append(Paragraph("Comprehensive Question Pattern Analysis", cover_sub))
story.append(Spacer(1,3.0*cm))

meta = [
    ["Institutions", "LNCT Vidhyapeeth Univ Indore  |  LNCT Medical College Indore  |  LNCT Univ Bhopal"],
    ["Papers Analysed", "13 papers  (Sessional + Pre-University + University Exams)"],
    ["Period Covered", "2021 – 2026"],
    ["Papers Covered", "Paper-I (Epi & Social Medicine)  |  Paper-II (National Programs & CHC)"],
]
t = Table(meta, colWidths=[3.5*cm, 13.5*cm])
t.setStyle(TableStyle([
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story.append(t)
story.append(Spacer(1,0.5*cm))
story.append(Paragraph("Prepared by Orris AI  |  July 2026", small))
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# CH1 – PAPER INVENTORY
# ═══════════════════════════════════════════════════════════════════════════════
ch_banner("Chapter 1: Exam Papers Inventory", "All papers covered in this analysis")

papers = [
    ["#","Institution","Exam / Paper","Date","Type"],
    ["1","LNCT Vidhyapeeth, Indore","Paper-I MCQs (Q1-10)","Apr 2025","Supplementary"],
    ["2","LNCT Vidhyapeeth, Indore","Paper-I MCQs (Q11-20)","Apr 2025","Supplementary"],
    ["3","LNCT Vidhyapeeth, Indore","Paper-I Theory (LAQ/SAQ/VSQ)","Apr 2025","Supplementary"],
    ["4","LNCT Vidhyapeeth, Indore","Paper-II MCQs (Q1-10)","Apr 2025","Supplementary"],
    ["5","LNCT Vidhyapeeth, Indore","Paper-II MCQs (Q11-20)","Apr 2025","Supplementary"],
    ["6","LNCT Vidhyapeeth, Indore","Paper-II Theory (LAQ/SAQ/VSQ)","Apr 2025","Supplementary"],
    ["7","LNCT Medical College, Indore","Paper-I Theory","09 Jun 2025","1st Sessional"],
    ["8","LNCT Medical College, Indore","Theory (Paper-I & II combined)","18 Sep 2025","2nd Sessional"],
    ["9","LNCT Medical College, Indore","Paper-II Theory","15 Oct 2025","Pre-University"],
    ["10","LNCT Medical College, Indore","Paper-I Theory","14 Oct 2025","Pre-University"],
    ["11","LNCT University, Bhopal","Paper-I Theory","Jul 2026","Pre-University"],
    ["12","LNCT University, Bhopal","Paper-II Theory","Jul 2026","Pre-University"],
    ["13","LNCT Bhopal + LNCT 2021 (PDFs)","University Papers (scanned PDF)","2021 & 2022","University Exam"],
]
story.append(plain_table(papers, [0.7*cm,5.5*cm,5.3*cm,2.5*cm,3.0*cm]))
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# CH2 – HIGH FREQUENCY TOPICS
# ═══════════════════════════════════════════════════════════════════════════════
ch_banner("Chapter 2: High-Frequency Topics", "Topics appearing in 3 or more papers — revise these FIRST", color=TEAL)

story.append(Paragraph(
    "The following topics have appeared consistently across multiple papers in MCQ and theory formats. "
    "Topics in <b>red</b> appeared in 5+ papers and are virtually guaranteed exam content.",
    body_j))
story.append(Spacer(1,0.2*cm))

hf = [
    ["Rank","Topic","Times Appeared","Paper Type","Priority"],
    ["1","Tuberculosis / NTEP","6+","MCQ+LAQ+SAQ","Must Know"],
    ["2","Epidemiology – study designs, RCT, definitions","6+","MCQ+LAQ+SAQ","Must Know"],
    ["3","Biostatistics – diagrams, central tendency, tests","5","MCQ+SAQ","Must Know"],
    ["4","Nutrition – anaemia, IFA, Vit-A, PEM","5","MCQ+SAQ+VSQ","Must Know"],
    ["5","Contraception & Family Planning","5","MCQ+LAQ+SAQ","Must Know"],
    ["6","National Immunization Programme / UIP","4","MCQ+SAQ","Very Important"],
    ["7","Biomedical Waste Management Rules 2016","4","MCQ+SAQ","Very Important"],
    ["8","Diarrhoeal diseases – types, DDCP, ORS, vaccines","4","LAQ+SAQ","Very Important"],
    ["9","PHC & Sub-Centre functions & services","4","SAQ","Very Important"],
    ["10","Fertility Indicators (TFR, CBR, GFR)","4","MCQ+SAQ","Very Important"],
    ["11","UNICEF – functions and services","4","MCQ+VSQ","Important"],
    ["12","Rabies – categories, prophylaxis, prevention","4","MCQ+SAQ+VSQ","Important"],
    ["13","Triage & Disaster Management","4","SAQ","Important"],
    ["14","Water purification / Hardness of water","3","LAQ+SAQ","Important"],
    ["15","Iceberg phenomenon (diagram required)","3","SAQ","Important"],
    ["16","Natural history of disease (diagram required)","3","SAQ","Important"],
    ["17","Levels of Prevention (primary/secondary/tertiary)","3","SAQ+VSQ","Important"],
    ["18","ICDS programme","3","MCQ+SAQ","Important"],
    ["19","Screening vs Diagnostic test","3","LAQ+SAQ","Important"],
    ["20","Haddon Matrix in Road Traffic Accidents","3","SAQ","Important"],
]
rows_hf = []
for r, row in enumerate(hf):
    if r == 0:
        rows_hf.append([Paragraph(c, bold_body) for c in row])
    else:
        # colour priority column
        p_col = row[4]
        p_color = RED if p_col=="Must Know" else (GOLD if p_col=="Very Important" else TEAL)
        rows_hf.append([
            Paragraph(row[0], body),
            Paragraph(row[1], bold_body if r<=5 else body),
            Paragraph(row[2], body),
            Paragraph(row[3], small),
            Paragraph(f"<b>{p_col}</b>",
                      ParagraphStyle("PC", parent=styles["Normal"],
                                     fontSize=8.5, textColor=p_color, leading=12)),
        ])
t_hf = Table(rows_hf, colWidths=[0.8*cm, 7.5*cm, 1.8*cm, 2.2*cm, 2.7*cm])
t_hf.setStyle(TableStyle([
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    ("INNERGRID",(0,0),(-1,-1),0.3,MGRAY),
]))
story.append(t_hf)
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# CH3 – PAPER-I QUESTION BANK
# ═══════════════════════════════════════════════════════════════════════════════
ch_banner("Chapter 3: Paper-I – Complete Question Bank",
          "Epidemiology, Biostatistics & Social Medicine", color=LBLUE)

sec_box("3A. All MCQ Topics – Paper-I", BG1, LBLUE)
mcq1 = [
    ["Q#","Question / Topic","Correct Answer","Source Paper"],
    ["Q1","Branch of social science dealing with human behaviour","Psychology","Apr 2025 Supp"],
    ["Q2","Normal IQ range for a child","90-109","Apr 2025 Supp"],
    ["Q3","Correct models of health education","KAP, IEC and BCC","Apr 2025 Supp"],
    ["Q4","Social Medicine first introduced by","Robert Grotjahn","Apr 2025 Supp"],
    ["Q5","Epidemiology of Cholera in England – classified by","John Snow","Apr 2025 Supp"],
    ["Q6","First country to completely socialize medicine","Russia","Apr 2025 Supp"],
    ["Q7","Free medical service at Govt expense","Comprehensive health care","Apr 2025 Supp"],
    ["Q8","Roman medical teacher","Galen","Apr 2025 Supp"],
    ["Q9","PQLI involves all EXCEPT","Life expectancy at age 1 yr","Apr 2025 Supp"],
    ["Q10","Country called 'Diabetes Capital' of world","India","Apr 2025 Supp"],
    ["Q11","International effort to reduce hypertension by dietary methods – committee","DASH","Apr 2025 Supp"],
    ["Q12","Time between entry of infectious agent & maximum infectivity","Generation time","Apr 2025 Supp"],
    ["Q13","Dengue rapid detection within 3 days of symptoms – antibody detected","IgM","Apr 2025 Supp"],
    ["Q14","Formula of BMI","W / H² (Weight in kg / Height in m²)","Apr 2025 Supp"],
    ["Q15","Environment where infectious agent lives, multiplies & depends for survival","Reservoir of infection","Apr 2025 Supp"],
    ["Q16","Subclinical cases least likely in which disease","Measles","Apr 2025 Supp"],
    ["Q17","Descriptive measure that can take more than one value in a single sample","Mode","Apr 2025 Supp"],
    ["Q18","Appropriate diagram for quantitative variable","Histogram","Apr 2025 Supp"],
    ["Q19","Appropriate diagram for qualitative variable","Pie chart / Bar chart","Apr 2025 Supp"],
    ["Q20","Loss of hand = ?  |  Loss of ability to write = ?","Impairment  |  Disability","Apr 2025 Supp"],
]
story.append(plain_table(mcq1, [0.8*cm, 6.5*cm, 3.4*cm, 2.3*cm], hdr_color=LBLUE, alt_bg=BG1))
story.append(Spacer(1,0.3*cm))

sec_box("3B. Long Answer Questions – Paper-I (10 marks each)", BG1, LBLUE)
laq1 = [
    ["Topic","Question Details","Appeared In"],
    ["TB Clinical Scenario",
     "25-yr female: cough 3 months, night sweats, fever 38.5C, cavitary lesion RUL.\n"
     "Q1: Most likely diagnosis  Q2: First-line treatment regimen\n"
     "Q3: Purpose of ethambutol  Q4: Rifampicin side effects\n"
     "Q5: Duration of treatment",
     "Apr 2025 Supp\nBhopal Jul 2026\n1st Sessional 2025"],
    ["Epidemiology (Full)",
     "Define Epidemiology. Classify epidemiological studies.\n"
     "Discuss steps of RCT in brief with types of blinding.",
     "Apr 2025 Supp"],
    ["Epidemic Investigation",
     "Medical officer at PHC – Sarpanch reports vomiting & diarrhoea outbreak.\n"
     "Describe steps of epidemic investigation.",
     "Pre-Univ Oct 2025"],
    ["Screening vs Diagnostic Test",
     "Define screening. Difference from diagnostic test.\n"
     "Criteria of screening (Wilson-Jungner).",
     "Pre-Univ Oct 2025"],
    ["Diabetes Mellitus",
     "(i) Multi-causation in DM  (ii) Prevention & control of Type-2 DM\n"
     "(iii) Write in brief NP-NCD",
     "Bhopal Jul 2026"],
    ["RCH Programme",
     "Describe Reproductive & Child Health programme in detail.",
     "1st Sessional Jun 2025"],
    ["Case-Control Study",
     "Define Epidemiology. Describe steps of Case-Control Study.",
     "2nd Sessional Sep 2025"],
]
story.append(plain_table(laq1, [3.2*cm, 8.4*cm, 3.4*cm], hdr_color=BLUE))
story.append(Spacer(1,0.3*cm))

sec_box("3C. Short Answer Questions – Paper-I (5 marks each)", BG1, LBLUE)
saq1 = [
    "Doctor-Patient Relationship (AETCOM)","Normal distribution curve with diagram",
    "Difference between Screening & Diagnostic test","Haddon Matrix in Road Traffic Accidents",
    "IDSP – Integrated Disease Surveillance Programme","Natural history of disease with diagram",
    "National Immunization Schedule (current NIS)","Iceberg phenomenon with diagram",
    "Modes of intervention with examples","Triage in disaster management",
    "Sampling – types (probability & non-probability)","DALY – Disability Adjusted Life Years",
    "Case-Control vs Cohort study – differences","Types of RCT (parallel, crossover, factorial)",
    "Vision 2020 – blindness control programme","Levels of prevention with examples",
    "Epidemiology of Hypertension","Disaster management cycle",
    "Road traffic accidents – public health perspective","SDGs – Sustainable Development Goals",
    "Epidemiological determinants of Hepatitis-B","Non-probability sampling techniques",
    "HPV vaccine & cervical cancer","Sentinel surveillance",
    "MTCT risk factors for HIV","Epidemiological transition",
    "Barriers of communication","Warning signals of poor mental health",
    "Network analysis","Juvenile Delinquency",
    "Types of communication","Steps of newborn examination",
    "Uses of epidemiology","Statistical diagrams – enumerate",
]
bullet_rows(saq1, 2)
story.append(Spacer(1,0.3*cm))

sec_box("3D. Very Short Answer Questions – Paper-I (2 marks each)", BG1, LBLUE)
vsq1 = [
    "Define Epidemiology","ASHA & USHA – roles",
    "Freeze-dried vaccine – storage","Modes of transmission of HIV/AIDS",
    "Define Chi-Square test","Define Epidemic with example",
    "Levels of prevention","Define Health (WHO definition)",
    "Iodine deficiency disorders","Diseases under NVBDCP",
]
bullet_rows(vsq1, 3)
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# CH4 – PAPER-II QUESTION BANK
# ═══════════════════════════════════════════════════════════════════════════════
ch_banner("Chapter 4: Paper-II – Complete Question Bank",
          "National Programs, MCH, Environment & Occupational Health", color=GREEN)

sec_box("4A. All MCQ Topics – Paper-II", BG2, GREEN)
mcq2 = [
    ["Q#","Question / Topic","Correct Answer","Source Paper"],
    ["Q1","School health seeds/manure program done by","CARE","Apr 2025 Supp"],
    ["Q2","WHO step-wise approach – includes all EXCEPT","Therapeutic","Apr 2025 Supp"],
    ["Q3","WHO headquarters located in","Geneva","Apr 2025 Supp"],
    ["Q4","Cannot be used as post-coital contraceptive","CU-T 200","Apr 2025 Supp"],
    ["Q5","Maximum work hours incl. overtime – Factory Act 1948","60 hours/week","Apr 2025 Supp"],
    ["Q6","Most logical & cost-effective rabies control in urban population","Remove stray dogs & vaccinate dog population","Apr 2025 Supp"],
    ["Q7","Instrument for low wind velocity & cooling power","Kata Thermometer","Apr 2025 Supp"],
    ["Q8","Pasteurization temperature","63 deg C for 30 min (Holder method)","Apr 2025 Supp"],
    ["Q9","UNICEF provides all EXCEPT","Family Planning","Apr 2025 Supp"],
    ["Q10","IFA frequency – pregnant mothers under Anaemia Mukt Bharat","Daily","Apr 2025 Supp"],
    ["Q11","Stage showing maximum demographic gap","Stage 3","Apr 2025 Supp"],
    ["Q12","Most reliable indicator of fertility in a country","Total Fertility Rate (TFR)","Apr 2025 Supp"],
    ["Q13","Presbyopia occurs in elderly due to","Insufficiency of power accommodation","Apr 2025 Supp"],
    ["Q14","Commonest cause of maternal death","PPH (Postpartum Haemorrhage)","Apr 2025 Supp"],
    ["Q15","Hardy-Weinberg Law is related to","Population Genetics","Apr 2025 Supp"],
    ["Q16","ICDS is run by","Ministry of Women & Child Development","Apr 2025 Supp"],
    ["Q17","Severe mental retardation – IQ range","20-34","Apr 2025 Supp"],
    ["Q18","Mesothelioma is caused by","Asbestos","Apr 2025 Supp"],
    ["Q19","Most important test for diagnosing acute Hepatitis B","IgM Anti-HBc","Apr 2025 Supp"],
    ["Q20","Census conducted every ___ years in India","10 years","Apr 2025 Supp"],
]
story.append(plain_table(mcq2, [0.8*cm, 6.5*cm, 3.4*cm, 2.3*cm], hdr_color=GREEN, alt_bg=BG2))
story.append(Spacer(1,0.3*cm))

sec_box("4B. Long Answer Questions – Paper-II (10 marks each)", BG2, GREEN)
laq2 = [
    ["Topic","Question Details","Appeared In"],
    ["LBW / Premature Baby",
     "25-yr female delivers at 7 months GA, birth wt 1.8 kg at community health centre.\n"
     "(a) Medical condition of child  (b) Types of care\n"
     "(c) Predicted complications  (d) Prevention of complications",
     "Apr 2025 Supp"],
    ["NTEP – Nat TB Elimination",
     "Write salient features & recent updates in National Tuberculosis Elimination Programme (NTEP).",
     "Apr 2025 Supp"],
    ["Silicosis",
     "Many cases of silicosis from pottery/ceramic industry.\n"
     "As Industry Health Officer: write epidemiology & recommend prevention & control.",
     "Bhopal Jul 2026\nBhopal Paper-II"],
    ["Water Hardness",
     "Water sample: hardness 5 meq/L + bicarbonates of Ca & Mg.\n"
     "(a) Define & classify hardness  (b) Type of hardness present\n"
     "(c) Methods to remove this hardness",
     "Pre-Univ Oct 2025"],
    ["Contraception (Full)",
     "28-yr mother (children 4yr & 1yr) seeks family planning advice.\n"
     "Describe all contraceptive methods with merits & demerits.",
     "Pre-Univ Oct 2025"],
    ["Night Blindness / Vit-A",
     "18-month child: night blindness.\n"
     "Treatment and National Prophylaxis Programme for Vitamin-A.",
     "Bhopal Paper-II"],
    ["Diabetes Mellitus (Reasoning)",
     "44-yr female RBS 166 mg/dl. Confirm diagnosis.\n"
     "Enumerate modifiable risk factors for DM.",
     "Bhopal Paper-II"],
    ["Anaemia in Pregnancy",
     "26-yr ANC patient Hb 9.6 g/dl.\n"
     "When to start iron supplementation? Salient features of Anaemia Mukt Bharat.",
     "Bhopal Paper-II"],
    ["Diarrhoea Outbreak",
     "Block reports diarrhoea for one month.\n"
     "(a) Types of diarrhoeal diseases  (b) Clinical management\n"
     "(c) Vaccine name, dose, route, schedule  (d) Preventive strategies",
     "2nd Sessional Sep 2025"],
]
story.append(plain_table(laq2, [3.2*cm, 8.4*cm, 3.4*cm], hdr_color=GREEN))
story.append(Spacer(1,0.3*cm))

sec_box("4C. Short Answer Questions – Paper-II (5 marks each)", BG2, GREEN)
saq2 = [
    "Functions of PHC + services at Subcentre","Methods of contraception (all types)",
    "Water purification at large scale","Triage – types, START, SALT, colour codes",
    "Geriatric health problems & preventive measures","Pasteurization of milk (methods & temps)",
    "IMNCI / IMNCI Plus programme","BMW Rules 2016 – colour coding (4 bins)",
    "Fertility indicators – enumerate all","Pneumoconiosis",
    "Antenatal examination – all points","Primary Health Care – principles & examples",
    "Occupational health – definition & hazards","Nutritional assessment in children <5 yrs",
    "Genetic Counselling","Prevention of Drug Dependence",
    "Sources of Health Information","Adolescent health – RKSK programme",
    "Air Pollution – AQI & preventive measures","Disaster Management – public health response",
    "Outbreak Investigation – steps (food poisoning)","Home Based Young Care programme",
    "Measures to improve sex ratio","ARSH programme – key features",
    "Focus group discussion","PERT analysis",
    "SPIKES model of breaking bad news","Haddon's Matrix in RTA",
    "NP-NCD programme","UIP – cold chain maintenance",
    "Family Planning counselling for eligible couple","Ebola viral disease",
    "Sentinel surveillance","Epidemiological transition",
    "Non-probability sampling techniques","HPV vaccine & cervical cancer prevention",
]
bullet_rows(saq2, 2)
story.append(Spacer(1,0.3*cm))

sec_box("4D. Very Short Answer Questions – Paper-II (2 marks each)", BG2, GREEN)
vsq2 = [
    "UNICEF functions","Sanitation barriers","Eugenics & Euthenics",
    "Vitamin-A deficiency diseases","Category of pre-exposure rabies prophylaxis",
    "NITI Aayog","Street children","Anthropology",
    "Three-generation family","Acculturation",
    "Battered baby syndrome","Panel discussion",
    "Role of ASHA","Stages of Lathyrism","Define Quarantine",
]
bullet_rows(vsq2, 3)
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# CH5 – TOPIC CLUSTER ANALYSIS
# ═══════════════════════════════════════════════════════════════════════════════
ch_banner("Chapter 5: Topic Cluster Analysis",
          "Key facts and points for each major topic cluster", color=GOLD)

clusters = [
    ("Epidemiology & Biostatistics", BLUE, [
        "Epidemic definition: occurrence in excess of expected frequency in defined community & period",
        "Generation time = time between entry of agent and peak infectivity (not incubation period)",
        "Study designs in order of evidence: Systematic review > RCT > Cohort > Case-control > Cross-sectional",
        "RCT randomisation removes CONFOUNDING (not random error, not external validity)",
        "Blinding types: single (subject), double (subject + observer), triple (+ data analyst)",
        "BMI = Weight (kg) / Height (m)² – Normal range 18.5-24.9",
        "Histogram = quantitative continuous variable  |  Bar chart = qualitative/discrete",
        "Pie chart = qualitative/proportional  |  Scatter plot = correlation between 2 variables",
        "Mode is the only measure of central tendency that can have multiple values",
        "Iceberg: clinical cases = tip; subclinical/latent = submerged majority",
        "Natural history: susceptibility -> pre-pathogenesis -> pathogenesis -> resolution/death",
        "DALY = YLL (years of life lost) + YLD (years lived with disability)",
        "Chi-square test: compares observed vs expected frequencies in categorical data",
        "Screening criteria (Wilson-Jungner): important problem, natural history understood, latent stage, test acceptable, treatment available, cost-effective",
        "Haddon Matrix: rows = pre-event/event/post-event; columns = host/agent/environment",
        "IDSP reporting: P-form (presumptive), L-form (lab confirmed), S-form (syndromic)",
    ]),
    ("Communicable Diseases & Immunization", TEAL, [
        "NTEP regimen: 2 months HRZE (Intensive) + 4 months HR (Continuation) = 6 months",
        "Rifampicin SE: hepatotoxicity, orange-red urine, flu-like syndrome, drug interactions",
        "Ethambutol purpose: prevents resistance; SE = optic neuritis (test visual acuity regularly)",
        "DOTS = Directly Observed Treatment Short-course (still backbone of NTEP)",
        "NTEP key updates: universal DST, bedaquiline & delamanid for DR-TB, Ni-kshay portal",
        "IgM Anti-HBc = best marker for ACUTE Hepatitis B infection",
        "Dengue: IgM detectable from day 5 onwards; NS1 antigen in first 3 days",
        "Rabies categories: Cat-I (touching) = wash; Cat-II (scratch) = wash + vaccine; Cat-III (bite/scratch with bleeding) = wash + vaccine + RIG",
        "Cold chain: OPV at -20 deg C; other vaccines 2-8 deg C; never freeze Hep-B, DPT, TT",
        "NVBDCP diseases: malaria, dengue, chikungunya, filaria, kala-azar, Japanese encephalitis",
        "National Immunization Schedule: BCG at birth; OPV 0+6+10+14wks+16-24mo; Penta at 6+10+14wks; MR at 9-12mo & 16-24mo; JE in endemic areas",
        "IDSP: reports via PH-form (P), Lab (L), Syndromic (S) at district level weekly",
    ]),
    ("Maternal, Child & Adolescent Health", GREEN, [
        "Anaemia Mukt Bharat: IFA daily for pregnant/lactating; weekly for adolescents (WIFS)",
        "Hb cutoff: anaemia in pregnancy = <11 g/dL (first & third trimester), <10.5 g/dL (second)",
        "Start iron supplementation: Hb <11 g/dL (not 9.6 g/dL specifically – continue regardless)",
        "LBW = birth weight <2500 g; VLBW <1500 g; ELBW <1000 g",
        "KMC (Kangaroo Mother Care): skin-to-skin contact, promotes thermoregulation in LBW",
        "IMNCI classification: General danger signs -> assess cough/diarrhoea/fever/ear/nutrition",
        "Post-coital contraceptives: RU-486 (mifepristone), high-dose estrogen, levonorgestrel, Yuzpe method, Cu-IUCD (not CU-T 200 for post-coital)",
        "Vitamin A prophylaxis: 1 lakh IU at 9 months with measles vaccine; 2 lakh IU every 6 months from 18 months",
        "RKSK (Rashtriya Kishor Swasthya Karyakram): replaces ARSH; covers 10-19 yrs; 6 domains",
        "JSSK: Janani Shishu Suraksha Karyakram – free drugs, diagnostics, diet, transport for pregnant women",
    ]),
    ("Environment & Occupational Health", GOLD, [
        "Water hardness: TEMPORARY = due to bicarbonates of Ca/Mg (removed by boiling)",
        "Water hardness: PERMANENT = due to sulphates & chlorides of Ca/Mg (removed by chemicals)",
        "Lime-soda process removes permanent hardness; Clark's process for temporary hardness",
        "Pasteurization: Holder = 63 deg C/30 min; HTST = 72 deg C/15 sec; UHT = 135 deg C/2 sec",
        "Kata thermometer: measures BOTH low wind velocity AND cooling power (not temperature)",
        "BMW Rules 2016: YELLOW bag = anatomical/infectious solid; RED = contaminated recyclables; WHITE/TRANSLUCENT = sharps; BLUE = glass/metallic implants",
        "Foetal/dead baby disposal: yellow bag -> autoclaving -> municipal landfill (not burial/incineration without autoclaving)",
        "Silicosis: quartz dust inhalation; pottery, mining, sandblasting workers; no cure; nodular fibrosis on X-ray",
        "Mesothelioma: asbestos exposure; pleural malignancy; blue asbestos (crocidolite) most dangerous",
        "Byssinosis: cotton/jute/hemp dust; Pneumoconiosis = silica; Anthracosis = coal",
        "Factory Act 1948: max 48 hrs/week regular + 12 hrs overtime = 60 hrs max; 30 min break after 5 hrs",
        "Air Quality Index (AQI): 0-50 = good; 51-100 = satisfactory; >400 = severe; PM2.5 most harmful",
    ]),
    ("Social Medicine, Demographics & National Programs", RED, [
        "Demographic transition Stage 3: birth rate falling + death rate low = maximum demographic gap",
        "TFR = most reliable fertility indicator; CBR = simplest/most widely used",
        "Census India: every 10 years; last 2011; 2021 delayed due to COVID; age-sex pyramid",
        "PQLI = Physical Quality of Life Index = Literacy + IMR + Life expectancy at age 1",
        "Galen: Roman medical teacher; Hippocrates: Greek (Father of Medicine)",
        "John Snow: Cholera Broad Street pump epidemic, London; Father of Epidemiology",
        "Robert Grotjahn: introduced Social Medicine concept (1898)",
        "IQ classification: Borderline 70-79; Mild retardation 50-69; Moderate 35-49; Severe 20-34; Profound <20",
        "ICDS: Ministry of Women & Child Development; beneficiaries = 0-6 yr children + pregnant/lactating mothers",
        "UNICEF role: child health, nutrition, immunization, education, protection; NOT family planning",
        "NP-NCD: screening at Health & Wellness Centres; hypertension + diabetes + cancer + COPD",
        "India = Diabetes Capital: highest number of diabetics in world (77 million+)",
        "NITI Aayog: replaced Planning Commission in 2015; policy think tank; SDG monitoring",
    ]),
]

for cname, color, pts in clusters:
    hdr = Table([[Paragraph(cname, ch_head)]], colWidths=[W-3.6*cm], rowHeights=[0.75*cm])
    hdr.setStyle(TableStyle([("BACKGROUND",(0,0),(-1,-1),color),
                              ("VALIGN",(0,0),(-1,-1),"MIDDLE")]))
    story.append(KeepTogether([hdr, Spacer(1,0.1*cm)]))
    for p in pts:
        story.append(Paragraph(f"◆  {p}", bullet_s))
    story.append(Spacer(1,0.25*cm))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# CH6 – EXAM STRATEGY
# ═══════════════════════════════════════════════════════════════════════════════
ch_banner("Chapter 6: Exam Strategy & Revision Planner",
          "Evidence-based priority plan from pattern analysis", color=RED)

story.append(Paragraph("<b>Critical Observations from Pattern Analysis:</b>", sub_head))
story.append(Spacer(1,0.1*cm))

obs_data = [
    (RED, "TB Scenario is virtually guaranteed",
     "The TB clinical case (25-yr female, cavitary lesion, cough/fever/weight loss) appeared verbatim "
     "in 3 separate papers. Know the 2HRZE/4HR regimen, ethambutol role (prevents resistance, optic "
     "neuritis SE), rifampicin SEs (hepatitis, orange urine, flu-like), and NTEP updates thoroughly."),
    (LBLUE, "Paper-I MCQs = Epi + Biostat + Social Medicine",
     "All 20 MCQs in Paper-I are from Epidemiology, Biostatistics, and Social/Preventive Medicine. "
     "Memorise: BMI formula, diagram types (histogram vs pie chart), generation time vs incubation, "
     "John Snow, Grotjahn, PQLI components, Russia as first socialist medicine country."),
    (GREEN, "Paper-II MCQs = National Programs + Environment",
     "Paper-II MCQs test ICDS ministry (Women & Child Dev), Anaemia Mukt Bharat (IFA daily), "
     "demographics (Stage 3 = max gap, census = 10 yrs), occupational health (Kata thermometer, "
     "asbestos = Mesothelioma), and pasteurization (63 deg C/30 min = Holder method)."),
    (TEAL, "AETCOM is mandatory in every theory paper",
     "Doctor-Patient Relationship appears as 5 marks in every Paper-I theory section. Prepare: "
     "types of relationships (paternalistic, informative, interpretive, deliberative), SPIKES protocol "
     "for breaking bad news, fiduciary duty, informed consent, confidentiality."),
    (GOLD, "Diagrams are specifically asked by name",
     "Normal distribution curve, Natural history of disease, Iceberg phenomenon, and Haddon Matrix "
     "are specifically asked to be drawn. Practise neat, labelled diagrams for all four. "
     "Mark the axes, label components, and include a title."),
    (RED, "Recent 2025-26 programs are being tested",
     "Newer papers test: NITI Aayog, Anaemia Mukt Bharat, ARSH/RKSK, Home Based Young Care, "
     "SPIKES model, AQI levels, NP-NCD, Health & Wellness Centres, IMNCI Plus. Do not skip these."),
]

for color, title, text in obs_data:
    hdr = Table([[Paragraph(f"  {title}", ParagraphStyle("OT", parent=styles["Normal"],
                  fontSize=9.5, textColor=colors.white, fontName="Helvetica-Bold", leading=13))]],
                colWidths=[W-3.6*cm], rowHeights=[0.55*cm],
                style=TableStyle([("BACKGROUND",(0,0),(-1,-1),color),
                                   ("VALIGN",(0,0),(-1,-1),"MIDDLE")]))
    story.append(KeepTogether([hdr, Spacer(1,0.05*cm),
                                Paragraph(text, body_j), Spacer(1,0.25*cm)]))

story.append(HRFlowable(width="100%", thickness=1, color=MGRAY))
story.append(Spacer(1,0.2*cm))
story.append(Paragraph("<b>Suggested 4-Week Revision Priority Order:</b>", sub_head))

pri = [
    ["Week","Priority Topics","Hours"],
    ["Week 1\nCritical",
     "Tuberculosis (NTEP complete) + Epidemiology study designs + RCT\n"
     "Biostatistics (diagram types, measures, normal distribution curve)",
     "TB: 3 hrs\nEpi: 4 hrs\nBiostat: 3 hrs"],
    ["Week 2\nHigh",
     "National Immunization Schedule + Diarrhoeal disease + IMNCI\n"
     "Contraception (all methods) + PHC/Subcentre functions + BMW Rules 2016",
     "2 hrs per topic"],
    ["Week 3\nImportant",
     "Water purification & hardness + Nutrition (Vit-A, PEM, Anaemia)\n"
     "Disaster management + Triage + Geriatric health + Occupational diseases",
     "1.5 hrs per topic"],
    ["Week 4\nConsolidate",
     "Social medicine history + Demographics + MCQ drill (all 40 MCQs)\n"
     "Doctor-Patient Rel. + Recent programs (ARSH, RKSK, SPIKES, AQI, NITI Aayog)",
     "MCQ drills + revision of notes"],
]
t_pri = plain_table(pri, [2.0*cm, 10.0*cm, 3.0*cm], hdr_color=RED)
story.append(t_pri)
story.append(Spacer(1,0.4*cm))

story.append(HRFlowable(width="100%", thickness=1, color=MGRAY))
story.append(Spacer(1,0.15*cm))
story.append(Paragraph(
    "This analysis covers 13 papers from LNCT Vidhyapeeth University Indore, LNCT Medical College "
    "Indore, and LNCT University Bhopal spanning 2021-2026 (Supplementary, Sessional, Pre-University, "
    "and University examinations for MBBS Final Part-I Community Medicine, Paper-I & Paper-II). "
    "Generated by Orris AI | July 2026.",
    small))

# ── Build PDF ──────────────────────────────────────────────────────────────────
doc.build(story)
print("SUCCESS:", OUTPUT)

"""
Community Medicine Quick Reference Cheat Sheet
Dense, colour-coded, 2-column layout with formulas, diagrams, mnemonics
"""
from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm, mm
from reportlab.platypus import (SimpleDocTemplate, Paragraph, Spacer, Table,
                                 TableStyle, HRFlowable, PageBreak, KeepTogether,
                                 Frame, BaseDocTemplate, PageTemplate)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_RIGHT
from reportlab.pdfbase import pdfmetrics
from reportlab.graphics.shapes import Drawing, Rect, String, Line, Polygon, Circle, PolyLine
from reportlab.graphics import renderPDF
import math

OUTPUT = "/tmp/workspace/community-medicine-analysis/Community_Medicine_CheatSheet.pdf"

W, H = A4
L_MAR = R_MAR = 1.0*cm
T_MAR = B_MAR = 1.0*cm
COL_GAP = 0.4*cm
COL_W = (W - L_MAR - R_MAR - COL_GAP) / 2   # two-column layout

doc = SimpleDocTemplate(
    OUTPUT, pagesize=A4,
    leftMargin=L_MAR, rightMargin=R_MAR,
    topMargin=T_MAR, bottomMargin=B_MAR,
    title="Community Medicine Cheat Sheet",
    author="Orris AI"
)

styles = getSampleStyleSheet()

# ── palette ──
NAVY   = colors.HexColor("#0f2d6b")
BLUE   = colors.HexColor("#1d4ed8")
LTBLUE = colors.HexColor("#dbeafe")
TEAL   = colors.HexColor("#0f766e")
LTTEAL = colors.HexColor("#ccfbf1")
GREEN  = colors.HexColor("#15803d")
LTGRN  = colors.HexColor("#dcfce7")
GOLD   = colors.HexColor("#92400e")
LTGOLD = colors.HexColor("#fef3c7")
RED    = colors.HexColor("#991b1b")
LTRED  = colors.HexColor("#fee2e2")
PURPLE = colors.HexColor("#6b21a8")
LTPUR  = colors.HexColor("#f3e8ff")
PINK   = colors.HexColor("#9d174d")
LTPINK = colors.HexColor("#fce7f3")
MGRAY  = colors.HexColor("#94a3b8")
LGRAY  = colors.HexColor("#f1f5f9")
DGRAY  = colors.HexColor("#334155")
WHITE  = colors.white

def sty(name, **kw):
    s = ParagraphStyle(name, parent=styles["Normal"], **kw)
    styles.add(s)
    return s

# styles ─────────────────────────────────────────────────────────────────────
HDR   = sty("HDR",  fontSize=7.5, textColor=WHITE, fontName="Helvetica-Bold",
            leading=9, alignment=TA_CENTER, spaceAfter=0)
SHDR  = sty("SHDR", fontSize=7,   textColor=WHITE, fontName="Helvetica-Bold",
            leading=9, alignment=TA_CENTER, spaceAfter=0)
BODY  = sty("BODY", fontSize=7,   leading=9.5, spaceAfter=1)
BODYB = sty("BODYB",fontSize=7,   leading=9.5, spaceAfter=1, fontName="Helvetica-Bold")
BODYI = sty("BODYI",fontSize=7,   leading=9.5, spaceAfter=1, textColor=BLUE)
FORM  = sty("FORM", fontSize=8,   leading=11,  spaceAfter=2, fontName="Helvetica-Bold",
            textColor=NAVY, alignment=TA_CENTER)
MNEM  = sty("MNEM", fontSize=7.5, leading=10,  spaceAfter=2, fontName="Helvetica-Bold",
            textColor=PURPLE)
ANS   = sty("ANS",  fontSize=7,   leading=9.5, spaceAfter=1, textColor=GREEN,
            fontName="Helvetica-Bold")
TINY  = sty("TINY", fontSize=6.5, leading=8.5, spaceAfter=1, textColor=DGRAY)
BOX_T = sty("BOXT", fontSize=7,   leading=9.5, spaceAfter=1, leftIndent=4)
MONO  = sty("MONO", fontSize=6.5, leading=9,   fontName="Courier",
            textColor=DGRAY, spaceAfter=1)

# ── helpers ──────────────────────────────────────────────────────────────────
def section_hdr(text, color=NAVY, fg=WHITE, width=COL_W):
    t = Table([[Paragraph(text, HDR)]], colWidths=[width], rowHeights=[0.42*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,-1),color),
        ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
        ("LEFTPADDING",(0,0),(-1,-1),4),
        ("RIGHTPADDING",(0,0),(-1,-1),4),
    ]))
    return t

def sub_hdr(text, color=BLUE, width=COL_W):
    t = Table([[Paragraph(text, SHDR)]], colWidths=[width], rowHeights=[0.35*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,-1),color),
        ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
        ("LEFTPADDING",(0,0),(-1,-1),4),
    ]))
    return t

def formula_box(formula, note="", color=LTBLUE, border=BLUE, width=COL_W):
    inner = [Paragraph(formula, FORM)]
    if note:
        inner.append(Paragraph(note, TINY))
    t = Table([inner], colWidths=[width])
    t.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,-1),color),
        ("BOX",(0,0),(-1,-1),1,border),
        ("TOPPADDING",(0,0),(-1,-1),4),
        ("BOTTOMPADDING",(0,0),(-1,-1),4),
        ("LEFTPADDING",(0,0),(-1,-1),5),
    ]))
    return t

def mnemonic_box(mnem, expansion, color=LTPUR, border=PURPLE, width=COL_W):
    rows = [[Paragraph(mnem, MNEM)]]
    for line in expansion:
        rows.append([Paragraph(line, BOX_T)])
    t = Table(rows, colWidths=[width])
    t.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,-1),color),
        ("BOX",(0,0),(-1,-1),1,border),
        ("LEFTPADDING",(0,0),(-1,-1),5),
        ("TOPPADDING",(0,0),(-1,-1),3),
        ("BOTTOMPADDING",(0,0),(-1,-1),3),
        ("LINEBELOW",(0,0),(0,0),0.5,border),
    ]))
    return t

def kv_table(rows_data, col1=3.2*cm, col2=None, hdr_color=BLUE):
    col2 = col2 or (COL_W - col1)
    rows = []
    for r, row in enumerate(rows_data):
        if r == 0:
            rows.append([Paragraph(c, SHDR) for c in row])
        else:
            rows.append([Paragraph(str(c), BODYB if i==0 else BODY)
                         for i,c in enumerate(row)])
    t = Table(rows, colWidths=[col1, col2])
    t.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,0),hdr_color),
        ("FONTSIZE",(0,0),(-1,-1),7),
        ("ROWPADDING",(0,0),(-1,-1),3),
        ("VALIGN",(0,0),(-1,-1),"TOP"),
        ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE, LGRAY]),
        ("BOX",(0,0),(-1,-1),0.4,MGRAY),
        ("INNERGRID",(0,0),(-1,-1),0.2,MGRAY),
    ]))
    return t

def three_col_table(rows_data, c1=2.0*cm, c2=2.5*cm, c3=None, hdr_color=TEAL):
    c3 = c3 or (COL_W - c1 - c2)
    rows = []
    for r, row in enumerate(rows_data):
        rows.append([Paragraph(str(c), SHDR if r==0 else BODY) for c in row])
    t = Table(rows, colWidths=[c1,c2,c3])
    t.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,0),hdr_color),
        ("FONTSIZE",(0,0),(-1,-1),6.5),
        ("ROWPADDING",(0,0),(-1,-1),3),
        ("VALIGN",(0,0),(-1,-1),"TOP"),
        ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE,LGRAY]),
        ("BOX",(0,0),(-1,-1),0.4,MGRAY),
        ("INNERGRID",(0,0),(-1,-1),0.2,MGRAY),
    ]))
    return t

def sp(h=0.1):
    return Spacer(1, h*cm)

# ─── ASCII art diagrams using Table ─────────────────────────────────────────
def iceberg_diagram(width=COL_W):
    lines = [
        " ▲  CLINICAL CASES (tip visible)  ▲ ",
        "   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━   ",
        "   ~ ~ ~ ~ WATERLINE ~ ~ ~ ~ ~ ~  ",
        "  SUBCLINICAL / LATENT (submerged) ",
        "  (majority of cases – not seen)   ",
    ]
    bgs = [LTBLUE, LTBLUE, colors.HexColor("#bfdbfe"), LTGOLD, LTGOLD]
    rows = [[Paragraph(l, ParagraphStyle("IC",parent=styles["Normal"],
                        fontSize=6.5,leading=9,alignment=TA_CENTER,
                        fontName="Helvetica-Bold" if i<2 else "Helvetica"))]
            for i,l in enumerate(lines)]
    t = Table(rows, colWidths=[width])
    ts = [("VALIGN",(0,0),(-1,-1),"MIDDLE"),
          ("TOPPADDING",(0,0),(-1,-1),2),
          ("BOTTOMPADDING",(0,0),(-1,-1),2),
          ("BOX",(0,0),(-1,-1),1,BLUE)]
    for i,bg in enumerate(bgs):
        ts.append(("BACKGROUND",(0,i),(0,i),bg))
    t.setStyle(TableStyle(ts))
    return t

def demographic_transition_diagram(width=COL_W):
    stages = [
        ["Stage","BR","DR","NI","Pop","Example"],
        ["1","High","High","Low","Static","Pre-industrial"],
        ["2","High","Falling","HIGH","Rapid rise","Developing 1"],
        ["3*","Falling","Low","MAX GAP","Rapid rise","Developing 2"],
        ["4","Low","Low","Low","Static","Developed"],
    ]
    rows = []
    for r,row in enumerate(stages):
        rows.append([Paragraph(c, SHDR if r==0 else
                    (ParagraphStyle("S3", parent=styles["Normal"], fontSize=6.5,
                                    leading=9, fontName="Helvetica-Bold",
                                    textColor=RED) if (r==3 and i==0) else BODY))
                    for i,c in enumerate(row)])
    cws = [1.0*cm, 0.7*cm, 0.8*cm, 1.0*cm, 1.2*cm, None]
    last = width - sum(x for x in cws if x)
    cws[-1] = last
    t = Table(rows, colWidths=cws)
    t.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,0),NAVY),
        ("BACKGROUND",(0,3),(-1,3),LTRED),  # Stage 3 highlighted
        ("FONTSIZE",(0,0),(-1,-1),6.5),
        ("ROWPADDING",(0,0),(-1,-1),3),
        ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE,LGRAY,WHITE,LGRAY]),
        ("BOX",(0,0),(-1,-1),0.5,MGRAY),
        ("INNERGRID",(0,0),(-1,-1),0.2,MGRAY),
        ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
    ]))
    return t

def haddon_matrix_diagram(width=COL_W):
    data = [
        ["Phase","Host","Agent/Vehicle","Environment"],
        ["Pre-event","Alcohol, fatigue","Brakes, speed","Road design, lighting"],
        ["Event","Seatbelt use","Airbag, padding","Guardrails, trees"],
        ["Post-event","Health status","Fuel leak","Rescue services, hospitals"],
    ]
    c1 = 1.4*cm
    c_rest = (width - c1) / 3
    rows = [[Paragraph(c, SHDR if r==0 else (BODYB if i==0 else BODY))
             for i,c in enumerate(row)] for r,row in enumerate(data)]
    t = Table(rows, colWidths=[c1, c_rest, c_rest, c_rest])
    t.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,0),GOLD),
        ("BACKGROUND",(0,1),(-1,1),LTGOLD),
        ("BACKGROUND",(0,2),(-1,2),colors.HexColor("#fef9c3")),
        ("BACKGROUND",(0,3),(-1,3),LTGRN),
        ("FONTSIZE",(0,0),(-1,-1),6.5),
        ("ROWPADDING",(0,0),(-1,-1),3),
        ("VALIGN",(0,0),(-1,-1),"TOP"),
        ("BOX",(0,0),(-1,-1),0.5,MGRAY),
        ("INNERGRID",(0,0),(-1,-1),0.2,MGRAY),
    ]))
    return t

def bmw_colour_table(width=COL_W):
    data = [
        ["Colour","Waste Type","Treatment"],
        ["YELLOW","Anatomical/infectious solid waste, cytotoxic, discarded medicines","Incineration/deep burial"],
        ["RED","Contaminated recyclable: tubing, bottles, IV sets","Autoclaving then shredding"],
        ["WHITE\n(Translucent)","Sharps: needles, syringes, scalpels, blades","Autoclaving then shredding"],
        ["BLUE","Glass, metallic implants, glassware","Autoclaving/disinfect then recycling"],
    ]
    bg = [NAVY, colors.HexColor("#fef08a"), colors.HexColor("#fca5a5"),
          colors.HexColor("#e2e8f0"), colors.HexColor("#bfdbfe")]
    fg = [WHITE, DGRAY, RED, DGRAY, BLUE]
    c1 = 1.6*cm; c2 = 3.5*cm; c3 = width - c1 - c2
    rows = []
    for r,row in enumerate(data):
        rows.append([Paragraph(c, SHDR if r==0 else
                    ParagraphStyle(f"BMW{r}", parent=styles["Normal"], fontSize=6.5,
                                   leading=9, fontName="Helvetica-Bold" if r==0 else "Helvetica",
                                   textColor=fg[r]))
                     for c in row])
    t = Table(rows, colWidths=[c1,c2,c3])
    ts = [("FONTSIZE",(0,0),(-1,-1),6.5),
          ("ROWPADDING",(0,0),(-1,-1),3),
          ("VALIGN",(0,0),(-1,-1),"TOP"),
          ("BOX",(0,0),(-1,-1),0.5,MGRAY),
          ("INNERGRID",(0,0),(-1,-1),0.2,MGRAY),
          ("BACKGROUND",(0,0),(-1,0),NAVY)]
    for i,b in enumerate(bg[1:], 1):
        ts.append(("BACKGROUND",(0,i),(-1,i),b))
    t.setStyle(TableStyle(ts))
    return t

def nis_table(width=COL_W):
    data = [
        ["Age","Vaccines"],
        ["Birth","BCG, OPV-0, Hep-B-0"],
        ["6 weeks","OPV-1, Penta-1 (DPT+HepB+Hib), IPV-1, RV-1, PCV-1"],
        ["10 weeks","OPV-2, Penta-2, RV-2, PCV-2"],
        ["14 weeks","OPV-3, Penta-3, IPV-2, RV-3, PCV-3"],
        ["9-12 mo","MR-1, JE-1 (endemic), Vit-A-1 (1 lakh IU)"],
        ["16-24 mo","DPT-B1, OPV-B1, MR-2, JE-2, Vit-A-2 (2 lakh IU)"],
        ["5-6 yrs","DPT-B2"],
        ["10 yrs","TT"],
        ["16 yrs","TT"],
        ["Pregnant","TT-1, TT-2 (or TT booster)"],
    ]
    c1 = 1.8*cm; c2 = width - c1
    rows = [[Paragraph(c, SHDR if r==0 else (BODYB if i==0 else BODY))
             for i,c in enumerate(row)] for r,row in enumerate(data)]
    t = Table(rows, colWidths=[c1,c2])
    t.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,0),GREEN),
        ("FONTSIZE",(0,0),(-1,-1),6.5),
        ("ROWPADDING",(0,0),(-1,-1),3),
        ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE,LTGRN]),
        ("VALIGN",(0,0),(-1,-1),"TOP"),
        ("BOX",(0,0),(-1,-1),0.4,MGRAY),
        ("INNERGRID",(0,0),(-1,-1),0.2,MGRAY),
    ]))
    return t

def nat_history_diagram(width=COL_W):
    phases = [
        ("SUSCEPTIBILITY", LGRAY, DGRAY),
        ("PRE-PATHOGENESIS", LTGOLD, GOLD),
        ("PATHOGENESIS\n(Pathological changes)", LTRED, RED),
        ("RESOLUTION/\nCHRONICITY/DEATH", LTGRN, GREEN),
    ]
    rows = []
    for label, bg, fg in phases:
        rows.append([Paragraph(label, ParagraphStyle("NH", parent=styles["Normal"],
                     fontSize=6, leading=8, alignment=TA_CENTER,
                     textColor=fg, fontName="Helvetica-Bold"))])
    arrow_row = [Paragraph("──────────────────────────────►  Time", TINY)]
    col_w = width / 4
    phase_data = [[Paragraph(p[0], ParagraphStyle("NH2", parent=styles["Normal"],
                              fontSize=6, leading=8, alignment=TA_CENTER,
                              textColor=p[2], fontName="Helvetica-Bold"))
                   for p in phases]]
    t = Table(phase_data, colWidths=[col_w]*4)
    ts_list = [("VALIGN",(0,0),(-1,-1),"MIDDLE"),
               ("TOPPADDING",(0,0),(-1,-1),4),
               ("BOTTOMPADDING",(0,0),(-1,-1),4),
               ("BOX",(0,0),(-1,-1),0.5,MGRAY),
               ("INNERGRID",(0,0),(-1,-1),0.3,MGRAY)]
    for i, p in enumerate(phases):
        ts_list.append(("BACKGROUND",(i,0),(i,0),p[1]))
    t.setStyle(TableStyle(ts_list))
    # prevention levels overlay
    prev_data = [["1° Prevention\n(Pre-pathogenesis)",
                  "2° Prevention\n(Early diagnosis)",
                  "3° Prevention\n(Limitation & Rehab)"]]
    tp = Table(prev_data, colWidths=[col_w, col_w*2, col_w])
    tp.setStyle(TableStyle([
        ("FONTSIZE",(0,0),(-1,-1),6),
        ("LEADING",(0,0),(-1,-1),8),
        ("ALIGN",(0,0),(-1,-1),"CENTER"),
        ("TEXTCOLOR",(0,0),(0,0),BLUE),
        ("TEXTCOLOR",(0,0),(1,0),TEAL),
        ("TEXTCOLOR",(2,0),(2,0),RED),
        ("FONTNAME",(0,0),(-1,-1),"Helvetica-Bold"),
        ("TOPPADDING",(0,0),(-1,-1),2),
        ("BOX",(0,0),(-1,-1),0,WHITE),
    ]))
    wrapper = Table([[t],[Paragraph("── Prevention Levels ──", TINY)],[tp]],
                    colWidths=[width])
    wrapper.setStyle(TableStyle([("TOPPADDING",(0,0),(-1,-1),2),
                                  ("BOTTOMPADDING",(0,0),(-1,-1),2)]))
    return wrapper

def normal_dist_diagram(width=COL_W):
    lines = [
        " ___________Bell-Shaped Curve___________ ",
        "       /  \\      Symmetrical              ",
        "      /    \\     Mean = Median = Mode      ",
        "     /      \\    68% within ±1 SD         ",
        "    /        \\   95% within ±2 SD         ",
        "___/          \\__99.7% within ±3 SD_______",
        "  -3  -2  -1  0  +1  +2  +3  (SD units)  ",
    ]
    bgs = [LTBLUE]*5 + [colors.HexColor("#bfdbfe")]*2
    rows = [[Paragraph(l, MONO)] for l in lines]
    t = Table(rows, colWidths=[width])
    ts = [("BOX",(0,0),(-1,-1),1,BLUE),
          ("TOPPADDING",(0,0),(-1,-1),1),
          ("BOTTOMPADDING",(0,0),(-1,-1),1),
          ("LEFTPADDING",(0,0),(-1,-1),3)]
    for i,b in enumerate(bgs):
        ts.append(("BACKGROUND",(0,i),(0,i),b))
    t.setStyle(TableStyle(ts))
    return t

# ════════════════════════════════════════════════════════════════════════════
# BUILD CONTENT COLUMNS
# Each column = list of flowables. We place them side-by-side with Table.
# ════════════════════════════════════════════════════════════════════════════

def col_block(*items):
    """Wrap a list of flowables into a single-cell Table column."""
    return items  # just return the items; we'll assemble into 2-col table

# ─────────────────────────────────────────────────────────────
# Page 1: Formulas + Key Facts
# ─────────────────────────────────────────────────────────────
story = []

# ── HEADER ──
title_row = Table(
    [[Paragraph("Community Medicine  —  QUICK REFERENCE CHEAT SHEET",
                ParagraphStyle("Title", parent=styles["Normal"], fontSize=12,
                               textColor=WHITE, fontName="Helvetica-Bold",
                               leading=14, alignment=TA_CENTER)),
      Paragraph("MBBS Final Part-I  |  LNCT  |  Orris AI 2026",
                ParagraphStyle("Sub", parent=styles["Normal"], fontSize=8,
                               textColor=colors.HexColor("#bfdbfe"),
                               alignment=TA_RIGHT, leading=10))]],
    colWidths=[12*cm, 5.7*cm], rowHeights=[0.8*cm]
)
title_row.setStyle(TableStyle([("BACKGROUND",(0,0),(-1,-1),NAVY),
                                ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
                                ("LEFTPADDING",(0,0),(0,0),8),
                                ("RIGHTPADDING",(-1,0),(-1,0),8)]))
story.append(title_row)
story.append(sp(0.15))

# ════════ LEFT COLUMN – PAGE 1 ════════════════════════════════════
L1 = []

L1.append(section_hdr("KEY FORMULAS & DEFINITIONS", NAVY))
L1.append(sp(0.06))

formulas = [
    ("BMI", "Weight (kg) / Height (m)²", "Normal: 18.5-24.9 | Obese: ≥30"),
    ("Crude Birth Rate", "(Live births / Midyear pop) × 1000", "per 1000 population"),
    ("Crude Death Rate", "(Deaths / Midyear pop) × 1000", "per 1000 population"),
    ("Infant Mortality Rate", "(Deaths <1yr / Live births) × 1000", "per 1000 live births"),
    ("Total Fertility Rate", "Sum of Age-Specific Fertility Rates × 5", "Most reliable fertility indicator"),
    ("Maternal Mortality Ratio", "(Maternal deaths / Live births) × 100,000", "per 100,000 live births"),
    ("Neonatal Mortality Rate", "(Deaths <28 days / Live births) × 1000", "per 1000 live births"),
    ("Attack Rate", "(Cases / Susceptible pop exposed) × 100", "used in outbreaks"),
    ("Incidence Rate", "(New cases / At-risk pop) × 1000", "over specified time period"),
    ("Prevalence Rate", "(All cases / Total pop) × 1000", "point or period prevalence"),
    ("Sensitivity", "TP / (TP + FN) × 100", "ability to detect TRUE positives"),
    ("Specificity", "TN / (TN + FP) × 100", "ability to detect TRUE negatives"),
    ("PPV", "TP / (TP + FP) × 100", "positive predictive value"),
    ("NPV", "TN / (TN + FN) × 100", "negative predictive value"),
    ("Relative Risk (RR)", "Incidence in exposed / Incidence in unexposed", "cohort studies"),
    ("Odds Ratio (OR)", "(a/c) / (b/d)  =  ad / bc", "case-control studies"),
    ("Attributable Risk", "Incidence(exposed) - Incidence(unexposed)", "excess risk due to exposure"),
    ("NMR", "(Neonatal deaths / Live births) × 1000", "<28 days"),
    ("PQLI", "Literacy + IMR + Life expectancy at age 1", "max score = 100"),
    ("DALY", "YLL (years life lost) + YLD (years lived with disability)", "burden of disease"),
]
for name, formula, note in formulas:
    L1.append(Table([
        [Paragraph(name, BODYB), Paragraph(formula, ParagraphStyle("FRM", parent=styles["Normal"],
            fontSize=7, leading=9, textColor=NAVY, fontName="Helvetica-Bold")),
         Paragraph(note, TINY)]
    ], colWidths=[2.5*cm, 3.8*cm, COL_W-6.3*cm]))
    L1[-1].setStyle(TableStyle([
        ("ROWBACKGROUNDS",(0,0),(-1,-1),[WHITE, LGRAY]),
        ("FONTSIZE",(0,0),(-1,-1),6.5),
        ("ROWPADDING",(0,0),(-1,-1),2),
        ("BOX",(0,0),(-1,-1),0.3,MGRAY),
        ("INNERGRID",(0,0),(-1,-1),0.2,MGRAY),
        ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
    ]))
L1.append(sp(0.15))

L1.append(section_hdr("PASTURIZATION – QUICK RECALL", TEAL))
L1.append(sp(0.06))
past = [
    ["Method","Temp","Time","Status"],
    ["Holder (LTLT)","63°C","30 min","Batch"],
    ["HTST","72°C","15 sec","Continuous"],
    ["UHT","135°C","2 sec","Sterilization"],
    ["Boiling","100°C","Till cool","Home use"],
]
L1.append(three_col_table(past, c1=2.8*cm, c2=1.4*cm, c3=None, hdr_color=TEAL))
L1.append(sp(0.1))

L1.append(section_hdr("IQ CLASSIFICATION", PURPLE))
L1.append(sp(0.06))
iq = [
    ["Category","IQ Range"],
    ["Very Superior / Gifted","120+"],
    ["Normal / Average","90 – 109"],
    ["Borderline","70 – 79"],
    ["Mild Retardation","50 – 69"],
    ["Moderate Retardation","35 – 49"],
    ["Severe Retardation","20 – 34"],
    ["Profound Retardation","< 20"],
]
L1.append(kv_table(iq, col1=3.5*cm, hdr_color=PURPLE))
L1.append(sp(0.1))

L1.append(section_hdr("WATER HARDNESS", BLUE))
L1.append(sp(0.06))
wh = [
    ["Type","Cause","Removal Method"],
    ["Temporary","Bicarbonates of Ca/Mg","Boiling; Clark's process (lime)"],
    ["Permanent","Sulphates & Chlorides of Ca/Mg","Lime-soda process; Zeolite; Reverse osmosis"],
]
L1.append(three_col_table(wh, c1=1.5*cm, c2=2.5*cm, hdr_color=BLUE))

# ════════ RIGHT COLUMN – PAGE 1 ════════════════════════════════════
R1 = []

R1.append(section_hdr("MNEMONICS", PURPLE))
R1.append(sp(0.06))

R1.append(mnemonic_box("NTEP Drugs: RIPE (2 months) then RI (4 months)",
    ["R = Rifampicin (orange urine, hepatotoxic, induces CYP450)",
     "I = Isoniazid (peripheral neuropathy – give pyridoxine B6)",
     "P = Pyrazinamide (hyperuricaemia, hepatotoxic)",
     "E = Ethambutol (optic neuritis – test visual acuity)"],
    color=LTPUR, border=PURPLE))
R1.append(sp(0.08))

R1.append(mnemonic_box("BMW 2016 Colour Coding: 'Yellow Rubber Wins Blue'",
    ["Y = Yellow bag   → Anatomical / infectious waste → Incinerate",
     "R = Red bag      → Contaminated recyclables → Autoclave+shred",
     "W = White/Transl → Sharps (needles) → Autoclave+shred",
     "B = Blue bag     → Glass/metallic implants → Autoclave+recycle"],
    color=LTGOLD, border=GOLD))
R1.append(sp(0.08))

R1.append(mnemonic_box("Wilson-Jungner Screening Criteria: 'SIMPLE TEST'",
    ["S = Significant health problem",
     "I = Intervention (effective treatment) available",
     "M = Machine/test acceptable to population",
     "P = Pre-clinical / latent stage detectable",
     "L = Latent period understood (natural history known)",
     "E = Equipment/facilities available",
     "T = Test valid, reliable, cheap",
     "E = Early diagnosis improves prognosis",
     "S = System to follow-up positives",
     "T = Total cost justified"],
    color=LTPINK, border=PINK))
R1.append(sp(0.08))

R1.append(mnemonic_box("Levels of Prevention: 'PAS' ",
    ["P = Primary  → Health promotion + specific protection (before disease)",
     "A = Secondary → Early diagnosis + prompt treatment (disease present)",
     "S = Specific tertiary → Disability limitation + Rehabilitation"],
    color=LTGRN, border=GREEN))
R1.append(sp(0.08))

R1.append(mnemonic_box("Rabies Exposure Categories: '1-2-3'",
    ["Cat I   → Touching/feeding animal, licks on intact skin → WASH only",
     "Cat II  → Nibbling/scratching, minor scratches → WASH + VACCINE",
     "Cat III → Bite through skin, licks on broken skin/mucosa → WASH + VACCINE + RIG"],
    color=LTRED, border=RED))
R1.append(sp(0.08))

R1.append(mnemonic_box("PQLI = 'LIE' (all out of 100)",
    ["L = Literacy rate",
     "I = Infant Mortality Rate (inverted)",
     "E = Life Expectancy at age 1 year",
     "Note: Does NOT include per capita income (unlike HDI)"],
    color=LTBLUE, border=BLUE))
R1.append(sp(0.08))

R1.append(mnemonic_box("ICEBERG Principle",
    ["Tip (visible)    = Clinical cases (signs/symptoms present)",
     "Submerged (bulk) = Subclinical + Latent + Carrier cases",
     "Least subclinical: Measles, Rabies, Smallpox",
     "Most subclinical: Polio (1 clinical : 200 subclinical)"],
    color=LTBLUE, border=BLUE))
R1.append(sp(0.08))

R1.append(mnemonic_box("Epidemiology Terminology Quick-Fire",
    ["Incubation period = agent entry → clinical symptoms",
     "Generation time   = agent entry → maximum INFECTIVITY",
     "Serial interval   = onset in primary → onset in secondary case",
     "Quarantine period = longest incubation of disease",
     "Herd immunity threshold = 1 - (1/R0)"],
    color=LTGOLD, border=GOLD))
R1.append(sp(0.1))

R1.append(section_hdr("SENSITIVITY vs SPECIFICITY (2×2 TABLE)", TEAL))
R1.append(sp(0.06))
two_by_two = [
    ["","Disease +","Disease -",""],
    ["Test +","TP","FP","PPV = TP/(TP+FP)"],
    ["Test -","FN","TN","NPV = TN/(TN+FN)"],
    ["","Sens=TP/\n(TP+FN)","Spec=TN/\n(TN+FP)",""],
]
t22 = Table([[Paragraph(c, SHDR if r==0 or (r>0 and i==0) else
                        (ANS if (i==3 and r<3) else BODY))
              for i,c in enumerate(row)] for r,row in enumerate(two_by_two)],
            colWidths=[1.3*cm, 1.4*cm, 1.4*cm, COL_W-4.1*cm])
t22.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(-1,0),TEAL),
    ("BACKGROUND",(0,1),(0,-1),TEAL),
    ("BACKGROUND",(1,1),(2,2),LGRAY),
    ("FONTSIZE",(0,0),(-1,-1),6.5),
    ("ROWPADDING",(0,0),(-1,-1),3),
    ("ALIGN",(0,0),(-1,-1),"CENTER"),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
    ("BOX",(0,0),(-1,-1),0.5,MGRAY),
    ("INNERGRID",(0,0),(-1,-1),0.3,MGRAY),
    ("BACKGROUND",(1,1),(1,1),colors.HexColor("#bbf7d0")),  # TP green
    ("BACKGROUND",(2,2),(2,2),colors.HexColor("#bbf7d0")),  # TN green
    ("BACKGROUND",(2,1),(2,1),colors.HexColor("#fecaca")),  # FP red
    ("BACKGROUND",(1,2),(1,2),colors.HexColor("#fecaca")),  # FN red
]))
R1.append(t22)

# ── assemble page 1 as 2-col ──
max_len = max(len(L1), len(R1))
page1 = Table([[L1, R1]], colWidths=[COL_W, COL_W],
              colSep=COL_GAP)
# We can't directly put lists; use nested tables
def wrap_col(items, w):
    rows = [[item] for item in items]
    t = Table(rows, colWidths=[w])
    t.setStyle(TableStyle([("TOPPADDING",(0,0),(-1,-1),0),
                            ("BOTTOMPADDING",(0,0),(-1,-1),0),
                            ("LEFTPADDING",(0,0),(-1,-1),0),
                            ("RIGHTPADDING",(0,0),(-1,-1),0)]))
    return t

p1_tbl = Table([[wrap_col(L1, COL_W), wrap_col(R1, COL_W)]],
               colWidths=[COL_W, COL_W],
               style=TableStyle([
                   ("VALIGN",(0,0),(-1,-1),"TOP"),
                   ("TOPPADDING",(0,0),(-1,-1),0),
                   ("BOTTOMPADDING",(0,0),(-1,-1),0),
                   ("LEFTPADDING",(0,0),(-1,-1),0),
                   ("RIGHTPADDING",(0,0),(-1,-1),COL_GAP),
               ]))
story.append(p1_tbl)
story.append(PageBreak())

# ─────────────────────────────────────────────────────────────
# Page 2: Diagrams + More Mnemonics
# ─────────────────────────────────────────────────────────────
story.append(title_row)
story.append(sp(0.15))

L2 = []
R2 = []

# ── LEFT col page 2 ─────────────────────────────────────────
L2.append(section_hdr("ICEBERG PHENOMENON", BLUE))
L2.append(sp(0.06))
L2.append(iceberg_diagram())
L2.append(sp(0.1))

L2.append(section_hdr("NATURAL HISTORY OF DISEASE", GOLD))
L2.append(sp(0.06))
L2.append(nat_history_diagram())
L2.append(sp(0.06))
L2.append(Paragraph("<b>Leavell & Clark</b>: 5 levels of prevention aligned with stages", TINY))
L2.append(sp(0.1))

L2.append(section_hdr("NORMAL DISTRIBUTION CURVE", BLUE))
L2.append(sp(0.06))
L2.append(normal_dist_diagram())
L2.append(sp(0.06))
L2.append(Table([
    [Paragraph("68% → ±1 SD", ANS), Paragraph("95% → ±2 SD", ANS), Paragraph("99.7% → ±3 SD", ANS)]
], colWidths=[COL_W/3]*3, style=TableStyle([
    ("BACKGROUND",(0,0),(-1,-1),LTBLUE), ("ALIGN",(0,0),(-1,-1),"CENTER"),
    ("FONTSIZE",(0,0),(-1,-1),7), ("FONTNAME",(0,0),(-1,-1),"Helvetica-Bold"),
    ("ROWPADDING",(0,0),(-1,-1),3),
])))
L2.append(sp(0.1))

L2.append(section_hdr("DEMOGRAPHIC TRANSITION (*Stage 3 = MAX DEMO GAP)", NAVY))
L2.append(sp(0.06))
L2.append(demographic_transition_diagram())
L2.append(sp(0.06))
L2.append(Paragraph("BR = Birth Rate  |  DR = Death Rate  |  NI = Natural Increase  |  *RED = Exam favourite", TINY))
L2.append(sp(0.1))

L2.append(section_hdr("HADDON MATRIX (Road Traffic Accident)", GOLD))
L2.append(sp(0.06))
L2.append(haddon_matrix_diagram())
L2.append(sp(0.06))
L2.append(Paragraph("Rows = phases of injury event  |  Columns = factors to intervene", TINY))
L2.append(sp(0.1))

L2.append(section_hdr("BIOMEDICAL WASTE – COLOUR CODING (BMW Rules 2016)", RED))
L2.append(sp(0.06))
L2.append(bmw_colour_table())
L2.append(sp(0.06))
L2.append(Paragraph("FOETAL DISPOSAL: Yellow bag → autoclave → deep burial / municipal landfill", TINY))
L2.append(sp(0.1))

L2.append(section_hdr("DIAGRAM TYPE SELECTOR", TEAL))
L2.append(sp(0.06))
diag = [
    ["Variable Type","Best Diagram","Also Acceptable"],
    ["Quantitative / Continuous","Histogram","Box-whisker, Frequency polygon"],
    ["Qualitative / Categorical","Bar chart / Pie chart","Pictogram"],
    ["Two continuous variables","Scatter plot","—"],
    ["Time series","Line graph","—"],
    ["Frequency distribution","Histogram","Stem-and-leaf"],
]
L2.append(three_col_table(diag, c1=2.5*cm, c2=2.5*cm, hdr_color=TEAL))

# ── RIGHT col page 2 ────────────────────────────────────────
R2.append(section_hdr("NATIONAL IMMUNIZATION SCHEDULE (NIS 2024)", GREEN))
R2.append(sp(0.06))
R2.append(nis_table())
R2.append(sp(0.06))
R2.append(Paragraph("Cold chain: OPV at -20°C | All others 2-8°C | Never freeze DPT/Hep-B/TT", TINY))
R2.append(sp(0.1))

R2.append(section_hdr("FERTILITY INDICATORS (ranked by reliability)", BLUE))
R2.append(sp(0.06))
fert = [
    ["Indicator","Formula","Notes"],
    ["TFR (BEST)","Sum ASFR × 5","Live births per woman over lifetime"],
    ["GFR","(Live births/Women 15-44) × 1000","Per 1000 women 15-44 yrs"],
    ["ASFR","(Births in age group / Women in age group) × 1000","Age-specific"],
    ["CBR","(Live births / Midyr pop) × 1000","Simplest; crude"],
    ["NRR","Replacement level indicator","NRR=1 = replacement; <1 = declining"],
]
R2.append(three_col_table(fert, c1=2.0*cm, c2=3.2*cm, hdr_color=BLUE))
R2.append(sp(0.1))

R2.append(section_hdr("SPIKES PROTOCOL (Breaking Bad News)", PURPLE))
R2.append(sp(0.06))
R2.append(mnemonic_box("S-P-I-K-E-S",
    ["S = Setting & Listening skills (private, no interruptions)",
     "P = Perception (ask patient's understanding: 'What do you know?')",
     "I = Invitation (ask permission: 'How much do you want to know?')",
     "K = Knowledge (warn, give info in small chunks, plain language)",
     "E = Emotions (acknowledge, Empathise with patient response)",
     "S = Summary & Strategy (treatment plan, hope, follow-up)"],
    color=LTPUR, border=PURPLE))
R2.append(sp(0.1))

R2.append(section_hdr("DOCTOR-PATIENT RELATIONSHIP MODELS (AETCOM)", TEAL))
R2.append(sp(0.06))
dpr = [
    ["Model","Description","Autonomy"],
    ["Paternalistic","Doctor decides; patient follows","Low – doctor high"],
    ["Informative","Doctor gives info; patient decides","High – patient decides"],
    ["Interpretive","Doctor helps clarify patient values","Moderate"],
    ["Deliberative","Shared decision-making; moral dialogue","Highest"],
]
R2.append(three_col_table(dpr, c1=1.9*cm, c2=3.5*cm, hdr_color=TEAL))
R2.append(sp(0.1))

R2.append(section_hdr("CONTRACEPTION QUICK REFERENCE", RED))
R2.append(sp(0.06))
contra = [
    ["Method","Type","Key Points"],
    ["CU-T 380A","IUCD","Most effective IUCD; lasts 10 yrs; post-coital within 5 days"],
    ["RU-486 (Mifepristone)","Emergency","Anti-progesterone; within 72 hrs"],
    ["OCP","Hormonal","Combined E+P; taken day 1-21; skip 7 days"],
    ["Minipill","POP","Progestin-only; for lactating mothers"],
    ["Condom","Barrier","Only method protecting against STI"],
    ["Vasectomy","Permanent","No-scalpel; failure rate <0.1%"],
    ["Tubectomy","Permanent","Pomeroy's method most common in India"],
    ["Implants","Long-acting","Norplant; 3-5 yrs; subdermal"],
    ["CU-T 200","IUCD","NOT for post-coital (only Cu-T 380A/Lippes loop)"],
]
R2.append(three_col_table(contra, c1=2.3*cm, c2=1.8*cm, hdr_color=RED))
R2.append(sp(0.1))

R2.append(section_hdr("EPIDEMIOLOGICAL STUDY DESIGNS (Evidence Hierarchy)", NAVY))
R2.append(sp(0.06))
evid = [
    ["Design","Direction","Use","Measure"],
    ["Systematic Review","—","Pooling evidence","—"],
    ["RCT","Prospective","Gold std for treatment","RR, ARR, NNT"],
    ["Cohort","Prospective","Incidence, causation","RR, AR"],
    ["Case-Control","Retrospective","Rare diseases","OR"],
    ["Cross-sectional","Present","Prevalence","PR"],
    ["Ecological","Group level","Hypothesis generation","—"],
]
R2.append(three_col_table(evid, c1=2.4*cm, c2=1.6*cm, hdr_color=NAVY))
R2.append(sp(0.06))
R2.append(mnemonic_box("RCT Blinding: 'Single Double Triple'",
    ["Single blind  = only SUBJECT unaware of allocation",
     "Double blind  = SUBJECT + OBSERVER unaware",
     "Triple blind  = SUBJECT + OBSERVER + DATA ANALYST unaware",
     "Randomisation: removes CONFOUNDING (not random error or measurement bias)"],
    color=LTBLUE, border=BLUE))

# ── assemble page 2 as 2-col ──
p2_tbl = Table([[wrap_col(L2, COL_W), wrap_col(R2, COL_W)]],
               colWidths=[COL_W, COL_W],
               style=TableStyle([
                   ("VALIGN",(0,0),(-1,-1),"TOP"),
                   ("TOPPADDING",(0,0),(-1,-1),0),
                   ("BOTTOMPADDING",(0,0),(-1,-1),0),
                   ("LEFTPADDING",(0,0),(-1,-1),0),
                   ("RIGHTPADDING",(0,0),(-1,-1),COL_GAP),
               ]))
story.append(p2_tbl)
story.append(PageBreak())

# ─────────────────────────────────────────────────────────────
# Page 3: TB, Programs, MCQ hotspots
# ─────────────────────────────────────────────────────────────
story.append(title_row)
story.append(sp(0.15))

L3 = []
R3 = []

# ── LEFT ──
L3.append(section_hdr("TUBERCULOSIS / NTEP – MASTER REFERENCE", RED))
L3.append(sp(0.06))
L3.append(mnemonic_box("RIPE Regimen: 2HRZE + 4HR",
    ["Intensive phase (2 months): H+R+Z+E daily",
     "Continuation phase (4 months): H+R daily",
     "All new TB patients: 6 months total",
     "DR-TB: Bedaquiline-based regimen (BPaL/BPaLM)"],
    color=LTRED, border=RED))
L3.append(sp(0.06))
tb_se = [
    ["Drug","Major Side Effects","Monitoring"],
    ["Rifampicin (R)","Hepatotoxicity, orange urine, flu-like, CYP450 inducer","LFTs, drug interactions"],
    ["Isoniazid (H)","Peripheral neuropathy, hepatotoxic","Give Pyridoxine (B6) prophylactically"],
    ["Pyrazinamide (Z)","Hyperuricaemia, hepatotoxic, arthralgia","Uric acid, LFTs"],
    ["Ethambutol (E)","Optic neuritis (dose-dependent)","Visual acuity, colour vision monthly"],
]
L3.append(three_col_table(tb_se, c1=2.0*cm, c2=3.5*cm, hdr_color=RED))
L3.append(sp(0.06))
L3.append(Table([[Paragraph("TB Clinical Scenario ANSWER TEMPLATE", BODYB)]],
                colWidths=[COL_W], style=TableStyle([("BACKGROUND",(0,0),(-1,-1),LTRED),
                ("ROWPADDING",(0,0),(-1,-1),3)])))
tb_ans = [
    "Diagnosis: Pulmonary Tuberculosis (based on cough >2 wks, weight loss, night sweats, cavitary lesion on CXR)",
    "Regimen: 2HRZE / 4HR (NTEP guidelines 2021)",
    "Ethambutol purpose: prevents emergence of drug resistance",
    "Rifampicin SE to warn: orange urine (harmless), avoid alcohol, potential hepatitis",
    "Duration: 6 months (2 months intensive + 4 months continuation)",
]
for a in tb_ans:
    L3.append(Paragraph(f"  ► {a}", BOX_T))
L3.append(sp(0.1))

L3.append(section_hdr("DIARRHOEA – DDCP & ORS", TEAL))
L3.append(sp(0.06))
L3.append(mnemonic_box("ORS Composition (WHO 2003 Low-Osmolarity)",
    ["NaCl: 2.6 g/L   |  Glucose: 13.5 g/L",
     "KCl: 1.5 g/L    |  Na-Citrate: 2.9 g/L",
     "Osmolarity: 245 mOsm/L  |  Na: 75, K: 20, Cl: 65",
     "Home remedy: 1 pinch salt + 1 tsp sugar in 1 glass water"],
    color=LTTEAL, border=TEAL))
L3.append(sp(0.06))
dehy = [
    ["Sign","No Dehydration","Some Dehydration","Severe Dehydration"],
    ["Eyes","Normal","Sunken","Very sunken"],
    ["Skin pinch","<2 sec","2-3 sec",">3 sec (very slow)"],
    ["Consciousness","Alert","Irritable","Lethargic/unconscious"],
    ["Thirst","Normal","Thirsty","Drinks poorly/unable"],
    ["Treatment","ORS at home (Plan A)","ORS in facility (Plan B)","IV fluids (Plan C)"],
]
L3.append(Table([[Paragraph(c, SHDR if r==0 else (BODYB if i==0 else BODY))
                  for i,c in enumerate(row)] for r,row in enumerate(dehy)],
                colWidths=[1.6*cm, 1.5*cm, 1.8*cm, COL_W-4.9*cm],
                style=TableStyle([
                    ("BACKGROUND",(0,0),(-1,0),TEAL),
                    ("FONTSIZE",(0,0),(-1,-1),6.5),
                    ("ROWPADDING",(0,0),(-1,-1),3),
                    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE,LGRAY]),
                    ("BOX",(0,0),(-1,-1),0.4,MGRAY),
                    ("INNERGRID",(0,0),(-1,-1),0.2,MGRAY),
                    ("VALIGN",(0,0),(-1,-1),"TOP"),
                ])))
L3.append(sp(0.1))

L3.append(section_hdr("PHC & SUB-CENTRE FUNCTIONS", GREEN))
L3.append(sp(0.06))
L3.append(mnemonic_box("PHC Functions: 'MINCE'",
    ["M = Maternal & child health including family planning",
     "I = Immunization & communicable disease control",
     "N = Nutrition & health education",
     "C = Curative care (OPD, minor surgery, first referral)",
     "E = Environmental sanitation & safe water supply"],
    color=LTGRN, border=GREEN))
L3.append(sp(0.06))
phc = [
    ["Item","PHC","Sub-Centre"],
    ["Population covered","30,000 (plain) / 20,000 (tribal/hilly)","5,000 / 3,000 (tribal)"],
    ["Staff","MO + 14 paramedicals","1 ANM + 1 Male MPW"],
    ["Beds","4-6 beds","None"],
    ["Drug supply","Essential drug list","Basic kit"],
    ["Services","OPD + MCH + FP + Lab","Antenatal, delivery, immunization"],
]
L3.append(three_col_table(phc, c1=2.0*cm, c2=2.8*cm, hdr_color=GREEN))
L3.append(sp(0.1))

L3.append(section_hdr("TRIAGE – COLOUR CODE", RED))
L3.append(sp(0.06))
triage_data = [
    ["Colour","Priority","Condition","Action"],
    ["RED","1st","Immediate – life-threatening, salvageable","Treat NOW"],
    ["YELLOW","2nd","Delayed – serious but can wait","Treat SOON"],
    ["GREEN","3rd","Minor – walking wounded","Treat LAST"],
    ["BLACK","0","Expectant – dead or unsurvivable","Comfort only"],
]
t_tri = Table([[Paragraph(c, SHDR if r==0 else BODY) for c in row]
               for r, row in enumerate(triage_data)],
              colWidths=[1.2*cm, 1.0*cm, 3.0*cm, COL_W-5.2*cm])
bgs = [None, colors.HexColor("#fca5a5"), colors.HexColor("#fde68a"),
       colors.HexColor("#86efac"), colors.HexColor("#334155")]
fgs = [None, RED, GOLD, GREEN, WHITE]
t_tri.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(-1,0),RED),
    ("FONTSIZE",(0,0),(-1,-1),6.5),
    ("ROWPADDING",(0,0),(-1,-1),3),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
    ("BOX",(0,0),(-1,-1),0.4,MGRAY),
    ("INNERGRID",(0,0),(-1,-1),0.2,MGRAY),
] + [("BACKGROUND",(0,i),(-1,i),bgs[i]) for i in range(1,5) if bgs[i]]
  + [("TEXTCOLOR",(0,4),(-1,4),WHITE)]))
L3.append(t_tri)

# ── RIGHT col page 3 ──
R3.append(section_hdr("HIGH-YIELD MCQ FACTS – PAPER-I", BLUE))
R3.append(sp(0.06))
mcqf1 = [
    ["Question Stem","Answer"],
    ["Social Medicine first introduced by","Robert Grotjahn (1898)"],
    ["Cholera epidemiology England classified by","John Snow (Broad St pump)"],
    ["Roman medical teacher","Galen"],
    ["Branch dealing with human behaviour","Psychology"],
    ["Normal IQ range for a child","90 – 109"],
    ["PQLI includes all EXCEPT","Per capita income"],
    ["Diabetes Capital of world","India (77 million+)"],
    ["First country to socialize medicine","Russia"],
    ["Comprehensive health care =","Free medical service at Govt expense"],
    ["BMI formula","Weight(kg) / Height(m)²"],
    ["Generation time =","Entry of agent to MAXIMUM INFECTIVITY"],
    ["Dengue IgM detectable from","Day 5 (NS1 in first 3 days)"],
    ["Subclinical cases LEAST likely in","Measles"],
    ["Mode in a sample can take","More than one value"],
    ["Histogram represents","Quantitative / Continuous variable"],
    ["Pie chart represents","Qualitative / Proportional data"],
    ["Loss of hand =","Impairment"],
    ["Loss of ability to write =","Disability"],
    ["DASH committee: hypertension diet","Dietary Approaches to Stop Hypertension"],
    ["RCT randomisation removes","CONFOUNDING (not random error)"],
]
R3.append(kv_table(mcqf1, col1=4.0*cm, hdr_color=BLUE))
R3.append(sp(0.1))

R3.append(section_hdr("HIGH-YIELD MCQ FACTS – PAPER-II", GREEN))
R3.append(sp(0.06))
mcqf2 = [
    ["Question Stem","Answer"],
    ["Max demographic gap at stage","Stage 3 of demographic transition"],
    ["Most reliable fertility indicator","Total Fertility Rate (TFR)"],
    ["Census interval in India","Every 10 years"],
    ["Commonest cause of maternal death","PPH (Postpartum Haemorrhage)"],
    ["ICDS run by ministry","Women & Child Development"],
    ["IFA frequency – Anaemia Mukt Bharat","Daily (pregnant/lactating)"],
    ["Severe mental retardation IQ","20 – 34"],
    ["Mesothelioma caused by","Asbestos (especially crocidolite)"],
    ["Acute Hepatitis B best marker","IgM Anti-HBc"],
    ["Kata thermometer measures","Low wind velocity + cooling power"],
    ["Pasteurization – Holder method","63°C for 30 minutes"],
    ["UNICEF provides all EXCEPT","Family Planning"],
    ["Post-coital contraceptive NOT used","CU-T 200"],
    ["WHO HQ located in","Geneva, Switzerland"],
    ["WHO step-wise approach – EXCEPT","Therapeutic"],
    ["Hardy-Weinberg Law related to","Population Genetics"],
    ["School health seeds/manure by","CARE (Cooperative for Assistance)"],
    ["Factory Act 1948 – max hours","60 hours/week (48 regular + 12 OT)"],
    ["Presbyopia in elderly due to","Insufficiency of power accommodation"],
    ["Rabies control – most cost-effective","Remove stray dogs + vaccinate dogs"],
]
R3.append(kv_table(mcqf2, col1=4.0*cm, hdr_color=GREEN))
R3.append(sp(0.1))

R3.append(section_hdr("ANAEMIA MUKT BHARAT – KEY FACTS", RED))
R3.append(sp(0.06))
amb = [
    ["Beneficiary","IFA Dose","Frequency"],
    ["Pregnant women (all)","100 mg elemental Fe + 500 µg FA","Daily"],
    ["Lactating mothers","100 mg Fe + 500 µg FA","Daily (3 months post-partum)"],
    ["Adolescents 10-19 yrs (WIFS)","Weekly Iron Folic Acid (WIFS)","Weekly"],
    ["Children 6-59 months","20 mg Fe + 100 µg FA syrup","Weekly"],
    ["Children 5-10 yrs","30 mg Fe tablet","Weekly"],
]
R3.append(three_col_table(amb, c1=2.4*cm, c2=2.8*cm, hdr_color=RED))
R3.append(sp(0.06))

R3.append(section_hdr("VITAMIN A SUPPLEMENTATION (National Programme)", GOLD))
R3.append(sp(0.06))
vit_a = [
    ["Age","Dose","Route","Notes"],
    ["9 months","1 lakh IU","Oral","Given with measles vaccine"],
    ["18 months","2 lakh IU","Oral","Every 6 months thereafter"],
    ["24 months","2 lakh IU","Oral","Up to 5 years of age"],
    ["Treatment (deficiency)","2 lakh IU","Oral","Days 1, 2, and 14"],
]
R3.append(Table([[Paragraph(c, SHDR if r==0 else BODY) for c in row]
                 for r,row in enumerate(vit_a)],
                colWidths=[1.5*cm, 1.5*cm, 1.0*cm, COL_W-4.0*cm],
                style=TableStyle([
                    ("BACKGROUND",(0,0),(-1,0),GOLD),
                    ("FONTSIZE",(0,0),(-1,-1),6.5),
                    ("ROWPADDING",(0,0),(-1,-1),3),
                    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE,LTGOLD]),
                    ("BOX",(0,0),(-1,-1),0.4,MGRAY),
                    ("INNERGRID",(0,0),(-1,-1),0.2,MGRAY),
                    ("VALIGN",(0,0),(-1,-1),"TOP"),
                ])))

# ── assemble page 3 ──
p3_tbl = Table([[wrap_col(L3, COL_W), wrap_col(R3, COL_W)]],
               colWidths=[COL_W, COL_W],
               style=TableStyle([
                   ("VALIGN",(0,0),(-1,-1),"TOP"),
                   ("TOPPADDING",(0,0),(-1,-1),0),
                   ("BOTTOMPADDING",(0,0),(-1,-1),0),
                   ("LEFTPADDING",(0,0),(-1,-1),0),
                   ("RIGHTPADDING",(0,0),(-1,-1),COL_GAP),
               ]))
story.append(p3_tbl)
story.append(PageBreak())

# ─────────────────────────────────────────────────────────────
# Page 4: Occupational, Environment, Social Medicine, Last-minute facts
# ─────────────────────────────────────────────────────────────
story.append(title_row)
story.append(sp(0.15))

L4 = []
R4 = []

L4.append(section_hdr("OCCUPATIONAL DISEASES – QUICK RECALL", GOLD))
L4.append(sp(0.06))
occ = [
    ["Disease","Causative Agent","Occupation","Key Fact"],
    ["Silicosis","Silica (SiO2)","Mining, pottery, sandblasting","Nodular fibrosis; no cure; legal dust disease"],
    ["Asbestosis","Asbestos fibres","Insulation, shipbuilding","Ferruginous bodies on histology"],
    ["Mesothelioma","Asbestos (crocidolite)","Insulation workers","Pleural malignancy; long latency"],
    ["Byssinosis","Cotton/jute/hemp dust","Textile workers","Monday fever; reversible early"],
    ["Anthracosis","Coal dust","Coal miners","Usually benign; simple black lung"],
    ["Siderosis","Iron oxide dust","Welders","Benign; no fibrosis"],
    ["Berylliosis","Beryllium","Electronics, aerospace","Granulomatous lung disease"],
    ["Lead poisoning","Lead","Painting, battery, printing","Anaemia, encephalopathy, Burton's line"],
    ["Mercury poisoning","Mercury","Chloralkali, dentistry","Minamata disease, tremors"],
    ["Arsenicosis","Arsenic","Pesticides, mining","Keratosis, Mees' lines"],
]
L4.append(Table([[Paragraph(c, SHDR if r==0 else BODY) for c in row]
                 for r,row in enumerate(occ)],
                colWidths=[1.5*cm, 1.6*cm, 2.0*cm, COL_W-5.1*cm],
                style=TableStyle([
                    ("BACKGROUND",(0,0),(-1,0),GOLD),
                    ("FONTSIZE",(0,0),(-1,-1),6.5),
                    ("ROWPADDING",(0,0),(-1,-1),3),
                    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE,LTGOLD]),
                    ("BOX",(0,0),(-1,-1),0.4,MGRAY),
                    ("INNERGRID",(0,0),(-1,-1),0.2,MGRAY),
                    ("VALIGN",(0,0),(-1,-1),"TOP"),
                ])))
L4.append(sp(0.1))

L4.append(section_hdr("WATER QUALITY STANDARDS & PURIFICATION", BLUE))
L4.append(sp(0.06))
wq = [
    ["Parameter","Safe Limit","Notes"],
    ["pH","6.5 – 8.5","—"],
    ["Turbidity","< 5 NTU (ideal 1)","NTU = Nephelometric Turbidity Units"],
    ["Dissolved oxygen","> 4 mg/L (ideal 6+)","Indicates unpolluted water"],
    ["BOD","< 5 mg/L","Biological Oxygen Demand"],
    ["Coliform bacteria","0 per 100 mL","Zero tolerance for faecal coliform"],
    ["Fluoride","0.5 – 1.5 mg/L","Dental caries < 0.5; fluorosis > 1.5"],
    ["Nitrates","< 45 mg/L","Methaemoglobinaemia (blue baby) if high"],
    ["Residual chlorine","0.2 – 0.5 mg/L","After 30 min contact time"],
]
L4.append(three_col_table(wq, c1=2.0*cm, c2=1.6*cm, hdr_color=BLUE))
L4.append(sp(0.06))
L4.append(mnemonic_box("Large Scale Water Purification Steps: 'SCFC'",
    ["S = Sedimentation (plain – gravity; coagulation – alum added)",
     "C = Coagulation-Flocculation (alum = aluminium sulphate)",
     "F = Filtration (rapid sand filter most common in municipal supply)",
     "C = Chlorination (0.5 ppm chlorine; 30 min contact; check residual 0.2 ppm)"],
    color=LTBLUE, border=BLUE))
L4.append(sp(0.1))

L4.append(section_hdr("EPIDEMIOLOGY OF HEPATITIS – QUICK TABLE", TEAL))
L4.append(sp(0.06))
hep = [
    ["Feature","Hep A","Hep B","Hep C","Hep E"],
    ["Route","Faeco-oral","Parenteral/sexual","Parenteral","Faeco-oral"],
    ["Acute marker","Anti-HAV IgM","IgM Anti-HBc","Anti-HCV IgM","Anti-HEV IgM"],
    ["Carrier state","No","Yes","Yes","No"],
    ["Vaccine","Yes (killed)","Yes (recombinant)","No","No (India)"],
    ["Chronicity","No","5-10% adults","50-80%","No"],
    ["Worst in pregnancy","No","No","No","Yes (20-25% mortality)"],
]
L4.append(Table([[Paragraph(c, SHDR if r==0 else (BODYB if i==0 else BODY))
                  for i,c in enumerate(row)] for r,row in enumerate(hep)],
                colWidths=[1.8*cm, 1.0*cm, 1.1*cm, 1.0*cm, COL_W-4.9*cm],
                style=TableStyle([
                    ("BACKGROUND",(0,0),(-1,0),TEAL),
                    ("FONTSIZE",(0,0),(-1,-1),6.5),
                    ("ROWPADDING",(0,0),(-1,-1),3),
                    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE,LTTEAL]),
                    ("BOX",(0,0),(-1,-1),0.4,MGRAY),
                    ("INNERGRID",(0,0),(-1,-1),0.2,MGRAY),
                    ("VALIGN",(0,0),(-1,-1),"TOP"),
                ])))

# ── RIGHT col page 4 ──
R4.append(section_hdr("SOCIAL MEDICINE – KEY DATES & PEOPLE", NAVY))
R4.append(sp(0.06))
social = [
    ["Person/Event","Contribution / Date"],
    ["Hippocrates (460 BC)","Father of Medicine; Corpus Hippocraticum"],
    ["Galen (130-200 AD)","Roman medical teacher; humoral theory"],
    ["John Snow (1854)","Cholera Broad Street pump; Father of Epidemiology"],
    ["Robert Grotjahn (1898)","Introduced concept of 'Social Medicine'"],
    ["William Farr","Vital statistics; standardized mortality"],
    ["Edward Jenner (1796)","Smallpox vaccine (variolation to vaccination)"],
    ["Louis Pasteur","Germ theory; rabies vaccine; pasteurization"],
    ["Joseph Lister","Antiseptic surgery; carbolic acid"],
    ["WHO founded","1948; HQ Geneva; 194 member states"],
    ["UNICEF founded","1946; HQ New York"],
    ["India independence","1947; DGHS established 1947; NHP 1983, 2002, 2017"],
    ["Russia – socialized medicine","First country; Semashko model"],
    ["Bhore Committee (1946)","Foundation of PHC in India"],
    ["Mudaliar Committee (1962)","Strengthening PHC"],
    ["Shrivastav Committee (1975)","Community health workers (CHW)"],
    ["Alma Ata Declaration (1978)","Health for All by 2000; primary health care"],
    ["MDGs (2000-2015)","8 goals; replaced by SDGs"],
    ["SDGs (2015-2030)","17 goals; Goal 3 = Good Health & Well-being"],
]
R4.append(kv_table(social, col1=3.5*cm, hdr_color=NAVY))
R4.append(sp(0.1))

R4.append(section_hdr("NATIONAL PROGRAMS – QUICK REFERENCE", GREEN))
R4.append(sp(0.06))
nprog = [
    ["Program","Ministry","Key Features"],
    ["NTEP","Health & FW","Replaces RNTCP; universal DST; bedaquiline for DR-TB; Ni-kshay portal"],
    ["ICDS","Women & Child Dev","0-6 yrs + pregnant/lactating; 6 services; run by Anganwadi worker"],
    ["RMNCH+A","Health & FW","Reproductive + maternal + neonatal + child + adolescent health"],
    ["RKSK / ARSH","Health & FW","10-19 yrs; 6 domains; replaces ARSH; WIFS programme"],
    ["IDSP","Health & FW","District-level surveillance; P/L/S forms; rapid response team"],
    ["NHM","Health & FW","Rural (NRHM) + Urban (NUHM); ASHAs; strengthens PHC"],
    ["JSSK","Health & FW","Free drugs/diagnostics/diet/transport for pregnant women"],
    ["PM-JAY (Ayushman Bharat)","Health & FW","5 lakh health cover per family; poorest 40%"],
    ["Anaemia Mukt Bharat","Health & FW","IFA supplementation; target <20% anaemia by 2022"],
    ["NVBDCP","Health & FW","Malaria, dengue, filaria, kala-azar, JE, chikungunya"],
    ["NP-NCD","Health & FW","Screening at HWC; HT, DM, cancer, COPD, stroke"],
    ["WIFS","Health & FW","Weekly Iron Folic Acid for adolescents 10-19 yrs in schools"],
    ["UIP","Health & FW","Universal Immunization Programme; NIS; cold chain"],
    ["HWC","Health & FW","Health & Wellness Centres; upgraded SC/PHC; comprehensive PHC"],
]
R4.append(three_col_table(nprog, c1=1.8*cm, c2=2.0*cm, hdr_color=GREEN))
R4.append(sp(0.1))

R4.append(section_hdr("LAST-MINUTE EXAMINER FAVOURITES", RED))
R4.append(sp(0.06))
last = [
    ["Fact","Answer"],
    ["Demographic transition max gap","Stage 3 (birth rate falling, death rate low)"],
    ["Census frequency India","Every 10 years"],
    ["PQLI does NOT include","Per capita income"],
    ["IQ normal range","90-109"],
    ["Severe mental retardation","IQ 20-34"],
    ["Factory Act 1948 max work hours","60 hours/week"],
    ["Pasteurization Holder method","63°C for 30 minutes"],
    ["Kata thermometer measures","Wind velocity AND cooling power"],
    ["Mesothelioma causative agent","Asbestos (crocidolite)"],
    ["Hardy-Weinberg Law relates to","Population genetics"],
    ["ICDS ministry","Ministry of Women & Child Development"],
    ["UNICEF does NOT provide","Family Planning services"],
    ["Rabies Cat III management","Wash + Vaccine + Rabies Immunoglobulin (RIG)"],
    ["Post-coital NOT used","CU-T 200 (only 380A or Lippes loop)"],
    ["Dengue rapid detection <3 days","NS1 antigen (NOT IgM)"],
    ["IgM Anti-HBc indicates","Acute Hepatitis B infection"],
    ["BMI formula","W(kg) / H(m)²"],
    ["Mode can take","More than 1 value in a sample"],
    ["Randomisation removes","Confounding"],
    ["Subclinical least likely","Measles, Rabies"],
    ["Iceberg most subclinical","Poliomyelitis (1:200 ratio)"],
]
R4.append(kv_table(last, col1=3.8*cm, hdr_color=RED))

# ── assemble page 4 ──
p4_tbl = Table([[wrap_col(L4, COL_W), wrap_col(R4, COL_W)]],
               colWidths=[COL_W, COL_W],
               style=TableStyle([
                   ("VALIGN",(0,0),(-1,-1),"TOP"),
                   ("TOPPADDING",(0,0),(-1,-1),0),
                   ("BOTTOMPADDING",(0,0),(-1,-1),0),
                   ("LEFTPADDING",(0,0),(-1,-1),0),
                   ("RIGHTPADDING",(0,0),(-1,-1),COL_GAP),
               ]))
story.append(p4_tbl)

# ── Build ──────────────────────────────────────────────────────────────────────
doc.build(story)
print("SUCCESS:", OUTPUT)