mkdir -p /home/daytona/workspace/fmge-cheatsheet && ls /home/daytona/workspace/f…

~/fmge-cheatsheet/generate_cheatsheet.py

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

# ── Color palette ──────────────────────────────────────────────────────────────
C_NAVY    = colors.HexColor('#0A1F44')
C_RED     = colors.HexColor('#C0392B')
C_ORANGE  = colors.HexColor('#E67E22')
C_GREEN   = colors.HexColor('#1E8449')
C_TEAL    = colors.HexColor('#117A65')
C_PURPLE  = colors.HexColor('#6C3483')
C_LBLUE   = colors.HexColor('#D6EAF8')
C_LGREEN  = colors.HexColor('#D5F5E3')
C_LORANGE = colors.HexColor('#FDEBD0')
C_LRED    = colors.HexColor('#FADBD8')
C_LPURPLE = colors.HexColor('#E8DAEF')
C_LGREY   = colors.HexColor('#F2F3F4')
C_WHITE   = colors.white
C_BLACK   = colors.black
C_YELLOW  = colors.HexColor('#FFF9C4')
C_LYELLOW = colors.HexColor('#FFFDE7')

W = A4[0] - 20*mm   # usable width

# ── Document ───────────────────────────────────────────────────────────────────
doc = SimpleDocTemplate(
    '/home/daytona/workspace/fmge-cheatsheet/FMGE_Community_Medicine_Cheatsheet.pdf',
    pagesize=A4,
    leftMargin=10*mm, rightMargin=10*mm,
    topMargin=10*mm, bottomMargin=10*mm,
)

# ── Styles ─────────────────────────────────────────────────────────────────────
base = getSampleStyleSheet()

def S(name, **kw):
    return ParagraphStyle(name, **kw)

TITLE  = S('TITLE',  fontName='Helvetica-Bold',  fontSize=18, textColor=C_WHITE,
            alignment=TA_CENTER, spaceAfter=2)
SUB    = S('SUB',    fontName='Helvetica-Bold',  fontSize=10, textColor=C_WHITE,
            alignment=TA_CENTER, spaceAfter=1)
H1     = S('H1',     fontName='Helvetica-Bold',  fontSize=9,  textColor=C_WHITE,
            alignment=TA_LEFT,   spaceAfter=1,   spaceBefore=0, leading=11)
H2     = S('H2',     fontName='Helvetica-Bold',  fontSize=8,  textColor=C_NAVY,
            alignment=TA_LEFT,   spaceAfter=1,   spaceBefore=2, leading=10)
BODY   = S('BODY',   fontName='Helvetica',       fontSize=7,  textColor=C_BLACK,
            alignment=TA_LEFT,   spaceAfter=0,   spaceBefore=0, leading=9)
BODYSM = S('BODYSM', fontName='Helvetica',       fontSize=6.5,textColor=C_BLACK,
            alignment=TA_LEFT,   spaceAfter=0,   spaceBefore=0, leading=8.5)
BOLD   = S('BOLD',   fontName='Helvetica-Bold',  fontSize=7,  textColor=C_BLACK,
            alignment=TA_LEFT,   spaceAfter=0,   spaceBefore=0, leading=9)
RED    = S('RED',    fontName='Helvetica-Bold',  fontSize=7,  textColor=C_RED,
            alignment=TA_LEFT,   spaceAfter=0,   spaceBefore=0, leading=9)
NOTE   = S('NOTE',   fontName='Helvetica-Oblique',fontSize=6.5,textColor=C_PURPLE,
            alignment=TA_LEFT,   spaceAfter=0,   spaceBefore=0, leading=8)
TH     = S('TH',     fontName='Helvetica-Bold',  fontSize=7,  textColor=C_WHITE,
            alignment=TA_CENTER, spaceAfter=0,   spaceBefore=0, leading=9)
TH_L   = S('TH_L',   fontName='Helvetica-Bold',  fontSize=7,  textColor=C_WHITE,
            alignment=TA_LEFT,   spaceAfter=0,   spaceBefore=0, leading=9)
TD     = S('TD',     fontName='Helvetica',       fontSize=6.8,textColor=C_BLACK,
            alignment=TA_LEFT,   spaceAfter=0,   spaceBefore=0, leading=8.5)
TD_B   = S('TD_B',   fontName='Helvetica-Bold',  fontSize=6.8,textColor=C_BLACK,
            alignment=TA_LEFT,   spaceAfter=0,   spaceBefore=0, leading=8.5)
TD_R   = S('TD_R',   fontName='Helvetica-Bold',  fontSize=6.8,textColor=C_RED,
            alignment=TA_LEFT,   spaceAfter=0,   spaceBefore=0, leading=8.5)
TD_C   = S('TD_C',   fontName='Helvetica',       fontSize=6.8,textColor=C_BLACK,
            alignment=TA_CENTER, spaceAfter=0,   spaceBefore=0, leading=8.5)
TD_BC  = S('TD_BC',  fontName='Helvetica-Bold',  fontSize=6.8,textColor=C_BLACK,
            alignment=TA_CENTER, spaceAfter=0,   spaceBefore=0, leading=8.5)

# ── Helper builders ────────────────────────────────────────────────────────────
def section_header(text, bg=C_NAVY, fg=C_WHITE, fs=9):
    style = ParagraphStyle('sh', fontName='Helvetica-Bold', fontSize=fs,
                           textColor=fg, alignment=TA_LEFT, leading=fs+2,
                           spaceAfter=1, spaceBefore=3)
    t = Table([[Paragraph(text, style)]], colWidths=[W])
    t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), bg),
        ('LEFTPADDING', (0,0), (-1,-1), 4),
        ('RIGHTPADDING', (0,0), (-1,-1), 4),
        ('TOPPADDING', (0,0), (-1,-1), 2),
        ('BOTTOMPADDING', (0,0), (-1,-1), 2),
        ('ROUNDEDCORNERS', [3,3,3,3]),
    ]))
    return t

def sub_header(text, bg=C_TEAL):
    return section_header(text, bg=bg, fg=C_WHITE, fs=8)

def make_table(headers, rows, col_widths, header_bg=C_NAVY, alt=True, fontsize=6.8):
    td_s = ParagraphStyle('tds', fontName='Helvetica', fontSize=fontsize,
                          textColor=C_BLACK, leading=fontsize+1.5, spaceAfter=0, spaceBefore=0)
    td_b = ParagraphStyle('tdb', fontName='Helvetica-Bold', fontSize=fontsize,
                          textColor=C_BLACK, leading=fontsize+1.5, spaceAfter=0, spaceBefore=0)
    th_s = ParagraphStyle('ths', fontName='Helvetica-Bold', fontSize=fontsize,
                          textColor=C_WHITE, leading=fontsize+1.5, spaceAfter=0, spaceBefore=0,
                          alignment=TA_CENTER)
    th_l = ParagraphStyle('thl', fontName='Helvetica-Bold', fontSize=fontsize,
                          textColor=C_WHITE, leading=fontsize+1.5, spaceAfter=0, spaceBefore=0)

    data = [[Paragraph(h, th_l) for h in headers]]
    for row in rows:
        data_row = []
        for cell in row:
            if isinstance(cell, str):
                if cell.startswith('**') and cell.endswith('**'):
                    data_row.append(Paragraph(cell[2:-2], td_b))
                else:
                    data_row.append(Paragraph(cell, td_s))
            else:
                data_row.append(cell)
        data.append(data_row)

    t = Table(data, colWidths=col_widths, repeatRows=1)
    style = [
        ('BACKGROUND', (0,0), (-1,0), header_bg),
        ('GRID', (0,0), (-1,-1), 0.3, colors.HexColor('#CCCCCC')),
        ('ROWBACKGROUNDS', (0,1), (-1,-1), [C_WHITE, C_LGREY] if alt else [C_WHITE]),
        ('TOPPADDING', (0,0), (-1,-1), 2),
        ('BOTTOMPADDING', (0,0), (-1,-1), 2),
        ('LEFTPADDING', (0,0), (-1,-1), 3),
        ('RIGHTPADDING', (0,0), (-1,-1), 3),
        ('VALIGN', (0,0), (-1,-1), 'TOP'),
    ]
    t.setStyle(TableStyle(style))
    return t

def highlight_box(text, bg=C_LYELLOW, border=C_ORANGE):
    p = Paragraph(text, ParagraphStyle('hb', fontName='Helvetica-Bold', fontSize=7,
                  textColor=C_BLACK, leading=9, spaceAfter=0, spaceBefore=0))
    t = Table([[p]], colWidths=[W])
    t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), bg),
        ('BOX', (0,0), (-1,-1), 1, border),
        ('LEFTPADDING', (0,0), (-1,-1), 5),
        ('RIGHTPADDING', (0,0), (-1,-1), 5),
        ('TOPPADDING', (0,0), (-1,-1), 3),
        ('BOTTOMPADDING', (0,0), (-1,-1), 3),
    ]))
    return t

def two_col(left_content, right_content, lw=None, rw=None):
    lw = lw or W*0.5 - 2*mm
    rw = rw or W*0.5 - 2*mm
    t = Table([[left_content, right_content]], colWidths=[lw, rw])
    t.setStyle(TableStyle([
        ('VALIGN', (0,0), (-1,-1), 'TOP'),
        ('LEFTPADDING', (0,0), (-1,-1), 0),
        ('RIGHTPADDING', (0,0), (-1,-1), 2),
        ('TOPPADDING', (0,0), (-1,-1), 0),
        ('BOTTOMPADDING', (0,0), (-1,-1), 0),
    ]))
    return t

def sp(h=2): return Spacer(1, h)

# ══════════════════════════════════════════════════════════════════════════════
# BUILD CONTENT
# ══════════════════════════════════════════════════════════════════════════════
story = []

# ─────────────────────────── COVER / PAGE 1 ────────────────────────────────
cover = Table([[
    Paragraph('FMGE COMMUNITY MEDICINE', TITLE),
    Paragraph('Quick Reference Cheat Sheet  •  Exam Day Edition  •  Park\'s Textbook Based', SUB),
    Paragraph('Epidemiology • Biostatistics • Preventive Medicine • Health Programs • Environment', SUB),
]], colWidths=[W])
cover.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,-1), C_NAVY),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
    ('RIGHTPADDING', (0,0), (-1,-1), 6),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('ROUNDEDCORNERS', [4,4,4,4]),
]))
story.append(cover)
story.append(sp(3))

# ─────────────────────── PAGE 1: EPIDEMIOLOGY CORE ─────────────────────────
story.append(section_header('📊  PAGE 1 — EPIDEMIOLOGY: STUDY DESIGNS & MEASURES', C_NAVY))
story.append(sp(2))

# Study designs table
story.append(sub_header('STUDY DESIGNS AT A GLANCE', C_TEAL))
story.append(sp(1))
story.append(make_table(
    ['Study', 'Direction', 'Unit', 'Measures', 'Best For', 'Key Flaw'],
    [
        ['**RCT**', 'Prospective', 'Individual', 'RR, NNT', '**Gold std causation**', 'Expensive, ethical issues'],
        ['**Cohort**', 'Prospective', 'Individual', '**RR, AR, Incidence**', 'Common exposure, multiple outcomes', 'Loss to follow-up, costly'],
        ['**Case-Control**', 'Retrospective', 'Individual', '**Odds Ratio (OR)**', '**Rare disease**', 'Recall bias, no incidence'],
        ['**Cross-Sectional**', 'Snapshot', 'Individual', '**Prevalence**', 'Planning, prevalence surveys', 'Cannot prove causality'],
        ['**Ecological**', 'Any', 'Group', 'Correlation', 'Hypothesis generation', '**Ecological fallacy**'],
        ['**Case Series/Report**', 'Retrospective', 'Individual', 'Descriptive', 'Rare/new disease description', 'No control group'],
    ],
    [22*mm, 22*mm, 22*mm, 32*mm, 38*mm, 38*mm],
    header_bg=C_TEAL
))
story.append(sp(2))

# 2x2 table + OR/RR
left_col = []
left_col.append(sub_header('2×2 CONTINGENCY TABLE', C_RED))
left_col.append(sp(1))
left_col.append(make_table(
    ['', 'Disease +', 'Disease −', 'Total'],
    [
        ['**Exposed**', 'a', 'b', 'a+b'],
        ['**Not Exposed**', 'c', 'd', 'c+d'],
        ['**Total**', 'a+c', 'b+d', 'N'],
    ],
    [22*mm, 20*mm, 20*mm, 16*mm], header_bg=C_RED
))
left_col.append(sp(2))
left_col.append(make_table(
    ['Measure', 'Formula', 'Study'],
    [
        ['**OR**', 'ad / bc', 'Case-control'],
        ['**RR**', '[a/(a+b)] ÷ [c/(c+d)]', 'Cohort/RCT'],
        ['**AR**', 'Inc(exp) − Inc(unexp)', 'Cohort'],
        ['**AR%**', 'AR / Inc(exp) × 100', 'Cohort'],
        ['**PAR**', 'Inc(total) − Inc(unexp)', 'Cohort'],
        ['**NNT**', '1 / ARR', 'RCT'],
    ],
    [20*mm, 35*mm, 22*mm], header_bg=C_RED
))

right_col = []
right_col.append(sub_header('INTERPRETATION', C_GREEN))
right_col.append(sp(1))
right_col.append(make_table(
    ['Value', 'Meaning'],
    [
        ['RR/OR = 1', 'No association'],
        ['RR/OR > 1', 'Risk factor (positive assoc.)'],
        ['RR/OR < 1', 'Protective factor'],
        ['p < 0.05', '**Statistically significant**'],
        ['95% CI includes 1', 'NOT significant'],
        ['95% CI excludes 1', 'Significant'],
    ],
    [18*mm, 52*mm], header_bg=C_GREEN
))
right_col.append(sp(2))
right_col.append(highlight_box('⚠ OR ≈ RR only when disease prevalence < 10% (rare disease assumption)', C_LYELLOW, C_ORANGE))
right_col.append(sp(1))
right_col.append(make_table(
    ['Error', 'Type', 'α/β'],
    [
        ['False Positive (reject true H₀)', '**Type I (α)**', 'α = 0.05'],
        ['False Negative (accept false H₀)', '**Type II (β)**', 'β = 0.20'],
        ['Power = 1 − β', '**0.80 (80%)**', ''],
    ],
    [42*mm, 22*mm, 13*mm], header_bg=C_PURPLE
))

lw = W*0.51
rw = W - lw - 2*mm
story.append(two_col(
    Table([[f] for f in left_col], colWidths=[lw]),
    Table([[f] for f in right_col], colWidths=[rw]),
    lw=lw, rw=rw
))

story.append(PageBreak())

# ──────────────────── PAGE 2: BIOSTATISTICS ─────────────────────────────────
story.append(section_header('🔢  PAGE 2 — BIOSTATISTICS: TESTS, DISTRIBUTIONS & SAMPLING', C_NAVY))
story.append(sp(2))

b_left = []
b_left.append(sub_header('STATISTICAL TESTS', C_TEAL))
b_left.append(sp(1))
b_left.append(make_table(
    ['Situation', 'Test'],
    [
        ['2 groups, continuous, normal', '**Independent t-test**'],
        ['2 groups, continuous, non-normal', '**Mann-Whitney U**'],
        ['Paired (before/after) normal', '**Paired t-test**'],
        ['Paired, non-normal', '**Wilcoxon signed-rank**'],
        ['≥3 groups, normal', '**ANOVA (F-test)**'],
        ['≥3 groups, non-normal', '**Kruskal-Wallis**'],
        ['2 categorical variables', '**Chi-square (χ²)**'],
        ['Expected cell < 5', '**Fisher\'s Exact**'],
        ['Correlation, normal', '**Pearson\'s r**'],
        ['Correlation, non-normal/ordinal', '**Spearman\'s rank**'],
        ['Binary outcome, multiple predictors', '**Logistic regression**'],
        ['Continuous outcome, multiple predictors', '**Multiple linear regression**'],
    ],
    [52*mm, 36*mm], header_bg=C_TEAL
))

b_right = []
b_right.append(sub_header('NORMAL DISTRIBUTION', C_NAVY))
b_right.append(sp(1))
b_right.append(make_table(
    ['Range', '% Population'],
    [
        ['Mean ± 1 SD', '**68.27%**'],
        ['Mean ± 1.96 SD', '**95%** (reference range)'],
        ['Mean ± 2 SD', '**95.45%**'],
        ['Mean ± 2.58 SD', '**99%**'],
        ['Mean ± 3 SD', '**99.73%**'],
    ],
    [34*mm, 30*mm], header_bg=C_NAVY
))
b_right.append(sp(2))
b_right.append(sub_header('CONFIDENCE INTERVALS', C_PURPLE))
b_right.append(sp(1))
b_right.append(make_table(
    ['CI Level', 'Formula', 'Z'],
    [
        ['95% CI', 'Mean ± 1.96 × SE', '1.96'],
        ['99% CI', 'Mean ± 2.58 × SE', '2.58'],
        ['SE of mean', 'SD / √n', '—'],
    ],
    [22*mm, 32*mm, 10*mm], header_bg=C_PURPLE
))
b_right.append(sp(2))
b_right.append(sub_header('SAMPLING METHODS', C_ORANGE))
b_right.append(sp(1))
b_right.append(make_table(
    ['Method', 'Key Feature'],
    [
        ['Simple Random', 'Equal probability; random number table'],
        ['Systematic', 'Every nth; risk of periodicity bias'],
        ['Stratified', 'Subgroups proportionally represented'],
        ['**Cluster**', 'Natural groups; used in **national surveys**; highest sampling error'],
        ['Multistage', 'Stages: state→district→village→HH'],
    ],
    [26*mm, 46*mm], header_bg=C_ORANGE
))

lw2 = W*0.53
rw2 = W - lw2 - 2*mm
story.append(two_col(
    Table([[f] for f in b_left], colWidths=[lw2]),
    Table([[f] for f in b_right], colWidths=[rw2]),
    lw=lw2, rw=rw2
))
story.append(sp(2))

# Measures of central tendency
story.append(sub_header('MEASURES OF CENTRAL TENDENCY & DISPERSION', C_GREEN))
story.append(sp(1))
story.append(make_table(
    ['Measure', 'Formula / Definition', 'Use When'],
    [
        ['**Mean**', 'Sum / n', 'Normal distribution'],
        ['**Median**', 'Middle value (n/2 rank)', 'Skewed data, ordinal'],
        ['**Mode**', 'Most frequent value', 'Nominal data, bimodal'],
        ['**SD**', '√[Σ(x−x̄)² / (n−1)]', 'Spread around mean'],
        ['**SE**', 'SD / √n', 'Precision of sample mean (↑n → ↓SE)'],
        ['**CV**', 'SD / Mean × 100', 'Compare variability between different units'],
        ['**IQR**', 'Q3 − Q1', 'Non-normal data spread'],
    ],
    [22*mm, 50*mm, 55*mm], header_bg=C_GREEN
))

story.append(PageBreak())

# ──────────────────── PAGE 3: SCREENING & VALIDITY ──────────────────────────
story.append(section_header('🔍  PAGE 3 — SCREENING, VALIDITY & RELIABILITY', C_NAVY))
story.append(sp(2))

sc_left = []
sc_left.append(sub_header('SENSITIVITY & SPECIFICITY', C_RED))
sc_left.append(sp(1))
sc_left.append(make_table(
    ['', 'Disease +', 'Disease −'],
    [
        ['**Test +**', '**TP**', '**FP** (Type I)'],
        ['**Test −**', '**FN** (Type II)', '**TN**'],
    ],
    [22*mm, 28*mm, 28*mm], header_bg=C_RED
))
sc_left.append(sp(1))
sc_left.append(make_table(
    ['Measure', 'Formula', 'Mnemonic'],
    [
        ['**Sensitivity**', 'TP / (TP+FN)', '**SnNout**: High Sn → −ve rules OUT'],
        ['**Specificity**', 'TN / (TN+FP)', '**SpPin**: High Sp → +ve rules IN'],
        ['**PPV**', 'TP / (TP+FP)', '↑ with ↑ prevalence'],
        ['**NPV**', 'TN / (TN+FN)', '↑ with ↓ prevalence'],
        ['**LR+**', 'Sensitivity / (1−Specificity)', '>10 = very useful'],
        ['**LR−**', '(1−Sensitivity) / Specificity', '<0.1 = very useful'],
        ['**Accuracy**', '(TP+TN) / Total', '—'],
    ],
    [22*mm, 34*mm, 40*mm], header_bg=C_RED
))
sc_left.append(sp(1))
sc_left.append(highlight_box('KEY: Sensitivity & Specificity are properties of the TEST (unchanged by prevalence). PPV & NPV depend on PREVALENCE of the disease.', C_LRED, C_RED))

sc_right = []
sc_right.append(sub_header('SCREENING CRITERIA (Wilson & Jungner)', C_GREEN))
sc_right.append(sp(1))
sc_right.append(make_table(
    ['#', 'Criterion'],
    [
        ['1', 'Important health problem'],
        ['2', 'Recognizable latent / early stage'],
        ['3', 'Accepted treatment available'],
        ['4', 'Suitable screening test exists'],
        ['5', 'Natural history understood'],
        ['6', 'Cost-effective'],
        ['7', 'Facilities for diagnosis & treatment available'],
    ],
    [8*mm, 60*mm], header_bg=C_GREEN
))
sc_right.append(sp(2))
sc_right.append(sub_header('VALIDITY vs RELIABILITY', C_PURPLE))
sc_right.append(sp(1))
sc_right.append(make_table(
    ['Term', 'Definition'],
    [
        ['**Validity**', 'Measures what it should (Sn + Sp)'],
        ['**Reliability**', 'Reproducibility / Consistency'],
        ['**Accuracy**', 'Closeness to true value'],
        ['**Precision**', 'Repeatability (low random error)'],
        ['**Kappa (κ)**', 'Inter-rater agreement beyond chance: >0.75 excellent, 0.4–0.75 good, <0.4 poor'],
    ],
    [22*mm, 46*mm], header_bg=C_PURPLE
))
sc_right.append(sp(2))
sc_right.append(sub_header('LEAD TIME & LENGTH BIAS', C_ORANGE))
sc_right.append(sp(1))
sc_right.append(make_table(
    ['Bias', 'Description'],
    [
        ['**Lead time**', 'Early detection falsely appears to prolong survival'],
        ['**Length**', 'Slowly progressing disease over-represented in screening'],
        ['**Selection (Berkson\'s)**', 'Hospital controls differ from general population'],
        ['**Neyman**', 'Prevalence ≠ incidence cases (fatal cases missed)'],
        ['**Recall**', 'Cases recall exposure better than controls'],
    ],
    [26*mm, 42*mm], header_bg=C_ORANGE
))

lw3 = W*0.51
rw3 = W - lw3 - 2*mm
story.append(two_col(
    Table([[f] for f in sc_left], colWidths=[lw3]),
    Table([[f] for f in sc_right], colWidths=[rw3]),
    lw=lw3, rw=rw3
))

story.append(PageBreak())

# ──────────────────── PAGE 4: MORTALITY & MORBIDITY RATES ───────────────────
story.append(section_header('📈  PAGE 4 — MORTALITY, MORBIDITY & DEMOGRAPHIC RATES', C_NAVY))
story.append(sp(2))

story.append(sub_header('KEY MORTALITY RATES & RATIOS', C_RED))
story.append(sp(1))
story.append(make_table(
    ['Rate / Ratio', 'Numerator', 'Denominator', 'Multiplier', 'Normal (India)'],
    [
        ['**Crude Death Rate**', 'Total deaths in year', 'Midyear population', '×1000', '~6-7/1000'],
        ['**Infant Mortality Rate (IMR)**', 'Deaths <1 yr', '**Live births**', '×1000', '~27-28 (India 2020)'],
        ['**Neonatal MR**', 'Deaths <28 days', 'Live births', '×1000', '~20 (India)'],
        ['**Post-neonatal MR**', 'Deaths 28d−<1yr', 'Live births', '×1000', '—'],
        ['**Perinatal MR**', 'Stillbirths + Deaths <7days', '**Stillbirths + Live births**', '×1000', '~30'],
        ['**Under-5 Mortality Rate**', 'Deaths <5yr', 'Live births', '×1000', '~32 (India 2020)'],
        ['**Maternal Mortality Ratio**', 'Maternal deaths', '**Live births**', '×100,000', '~97 (India 2020)'],
        ['**Maternal Mortality Rate**', 'Maternal deaths', 'Women 15−44 yrs', '×100,000', '—'],
        ['**Case Fatality Rate**', 'Deaths from disease X', 'Cases of disease X', '×100 (%)', 'Disease-specific'],
        ['**Proportional Mortality**', 'Deaths from cause X', 'Total deaths', '×100 (%)', '—'],
        ['**Standardized Mortality Ratio**', 'Observed deaths', 'Expected deaths', '×100', '>100 = excess mortality'],
    ],
    [38*mm, 38*mm, 32*mm, 18*mm, 33*mm],
    header_bg=C_RED
))
story.append(sp(2))

rates_left = []
rates_left.append(sub_header('MORBIDITY MEASURES', C_TEAL))
rates_left.append(sp(1))
rates_left.append(make_table(
    ['Measure', 'Definition', 'Formula'],
    [
        ['**Incidence Rate**', 'NEW cases / time period', 'New cases / Pop-at-risk × k'],
        ['**Point Prevalence**', 'All cases at one point in time', 'Cases / Total pop × k'],
        ['**Period Prevalence**', 'All cases over a period', 'Cases / Total pop × k'],
        ['**Attack Rate**', 'Cases in exposed group', 'Cases / Exposed × 100'],
        ['**SAR**', 'New cases among contacts', 'New cases / Contacts × 100'],
    ],
    [26*mm, 36*mm, 30*mm], header_bg=C_TEAL
))
rates_left.append(sp(1))
rates_left.append(highlight_box('Prevalence = Incidence × Duration of disease\nHigh prevalence despite low incidence = LONG DURATION disease (e.g., DM, TB)', C_LBLUE, C_TEAL))

rates_right = []
rates_right.append(sub_header('FERTILITY / NATALITY RATES', C_ORANGE))
rates_right.append(sp(1))
rates_right.append(make_table(
    ['Rate', 'Numerator', 'Denominator', '×'],
    [
        ['**CBR** (Crude Birth Rate)', 'Live births', 'Midyear pop', '1000'],
        ['**GFR** (General Fertility)', 'Live births', 'Women 15−44', '1000'],
        ['**TFR** (Total Fertility)', 'Sum of age-specific fertility rates', '—', '—'],
        ['**NRR** (Net Reproduction)', 'Daughters born to one cohort', '—', '—'],
        ['**Sex Ratio at Birth**', 'Males', 'Females × 100', '—'],
    ],
    [28*mm, 28*mm, 24*mm, 8*mm], header_bg=C_ORANGE
))
rates_right.append(sp(1))
rates_right.append(highlight_box('IMR = BEST indicator of: Socioeconomic development, Health services, Community health status\nIndia IMR target: <25/1000 (NHP 2017)', C_LYELLOW, C_ORANGE))

lw4 = W*0.51
rw4 = W - lw4 - 2*mm
story.append(two_col(
    Table([[f] for f in rates_left], colWidths=[lw4]),
    Table([[f] for f in rates_right], colWidths=[rw4]),
    lw=lw4, rw=rw4
))

story.append(PageBreak())

# ──────────────────── PAGE 5: DISEASE CAUSATION & NATURAL HISTORY ────────────
story.append(section_header('🦠  PAGE 5 — DISEASE CAUSATION, NATURAL HISTORY & PREVENTION', C_NAVY))
story.append(sp(2))

nh_left = []
nh_left.append(sub_header('LEVELS OF PREVENTION', C_NAVY))
nh_left.append(sp(1))
nh_left.append(make_table(
    ['Level', 'Target', 'Examples'],
    [
        ['**Primordial**', 'Prevent risk factor emergence', 'Mass media campaigns, policy (no junk food in schools)'],
        ['**Primary**', 'Prevent disease in healthy', 'Vaccination, health education, sanitation, seat belts'],
        ['**Secondary**', 'Early detection & treatment', 'Screening, early diagnosis, case finding'],
        ['**Tertiary**', 'Limit disability, rehabilitate', 'Physiotherapy, prosthesis, occupational therapy'],
    ],
    [22*mm, 30*mm, 52*mm], header_bg=C_NAVY
))
nh_left.append(sp(1))
nh_left.append(sub_header('NATURAL HISTORY OF DISEASE (Leavell & Clark)', C_TEAL))
nh_left.append(sp(1))
nh_left.append(make_table(
    ['Phase', 'Stage', 'Prevention'],
    [
        ['Pre-pathogenesis', 'Susceptibility → interaction of agent-host-env', '**Primary prevention**'],
        ['Pathogenesis (Early)', 'Pathological changes, sub-clinical', '**Secondary prevention** (screening)'],
        ['Pathogenesis (Late)', 'Clinical disease, disability', '**Tertiary prevention**'],
    ],
    [32*mm, 48*mm, 28*mm], header_bg=C_TEAL
))
nh_left.append(sp(1))
nh_left.append(sub_header("BRADFORD HILL'S CRITERIA (Causation)", C_PURPLE))
nh_left.append(sp(1))
nh_left.append(make_table(
    ['Criterion', 'Key Point'],
    [
        ['**Temporality**', '**Only ESSENTIAL criterion** — cause must precede effect'],
        ['Strength', 'High RR/OR'],
        ['Consistency', 'Repeated findings across studies'],
        ['Specificity', 'One cause → one disease'],
        ['Biological gradient', 'Dose-response relationship'],
        ['Plausibility', 'Biologically credible'],
        ['Coherence', 'Consistent with natural history'],
        ['Experiment', 'Removal of cause reduces disease'],
        ['Analogy', 'Similar cause → similar effect'],
    ],
    [28*mm, 62*mm], header_bg=C_PURPLE
))

nh_right = []
nh_right.append(sub_header('EPIDEMIOLOGICAL TRIANGLE', C_RED))
nh_right.append(sp(1))
nh_right.append(make_table(
    ['Component', 'Details'],
    [
        ['**Agent**', 'Biological (bacteria, virus), chemical, physical, nutritional deficiency'],
        ['**Host**', 'Immunity, genetics, age, sex, occupation, nutritional status'],
        ['**Environment**', 'Physical, biological, social, cultural, economic'],
        ['**Time**', 'Incubation period, epidemic threshold, duration of illness'],
    ],
    [20*mm, 58*mm], header_bg=C_RED
))
nh_right.append(sp(1))
nh_right.append(sub_header('WEB OF CAUSATION', C_ORANGE))
nh_right.append(sp(1))
nh_right.append(Paragraph('Multiple interacting factors determine disease. No single cause is sufficient. Used for chronic non-communicable diseases (e.g., CHD). Proposed by MacMahon & Pugh.', BODYSM))
nh_right.append(sp(1))
nh_right.append(sub_header('MODES OF DISEASE TRANSMISSION', C_TEAL))
nh_right.append(sp(1))
nh_right.append(make_table(
    ['Mode', 'Examples'],
    [
        ['**Direct**: Contact', 'STIs, rabies, ringworm'],
        ['**Direct**: Droplet', 'Measles, flu (>5µm, <1m distance)'],
        ['**Indirect**: Airborne', 'TB, measles (droplet nuclei <5µm, >1m)'],
        ['**Indirect**: Vehicle (fomite)', 'Hepatitis A, cholera (water/food)'],
        ['**Indirect**: Vector', 'Malaria (Anopheles), dengue (Aedes)'],
        ['**Vertical**: Transplacental', 'HIV, rubella, syphilis, CMV'],
    ],
    [36*mm, 42*mm], header_bg=C_TEAL
))
nh_right.append(sp(1))
nh_right.append(sub_header('HERD IMMUNITY', C_GREEN))
nh_right.append(sp(1))
nh_right.append(make_table(
    ['Disease', 'Herd Immunity Threshold'],
    [
        ['Measles', '**~95%**'],
        ['Polio', '**~85%**'],
        ['Smallpox', '**~80-85%**'],
        ['Diphtheria', '**~85%**'],
        ['COVID-19 (varies)', '~60-80%'],
    ],
    [40*mm, 38*mm], header_bg=C_GREEN
))

lw5 = W*0.52
rw5 = W - lw5 - 2*mm
story.append(two_col(
    Table([[f] for f in nh_left], colWidths=[lw5]),
    Table([[f] for f in nh_right], colWidths=[rw5]),
    lw=lw5, rw=rw5
))

story.append(PageBreak())

# ──────────────────── PAGE 6: IMMUNIZATION ──────────────────────────────────
story.append(section_header('💉  PAGE 6 — IMMUNIZATION & VACCINES', C_NAVY))
story.append(sp(2))

story.append(sub_header('UNIVERSAL IMMUNIZATION PROGRAMME (UIP) — INDIA', C_TEAL))
story.append(sp(1))
story.append(make_table(
    ['Age', 'Vaccine', 'Route', 'Dose', 'Notes'],
    [
        ['**At birth**', 'BCG', 'Intradermal (left arm)', '0.05 mL (<1 yr), 0.1 mL (>1 yr)', 'Scar appears 2-4 wks; PPD+ at 6 wks'],
        ['**At birth**', 'OPV-0 (Birth dose)', 'Oral', '2 drops', 'Given within 24 hrs'],
        ['**At birth**', 'Hepatitis B (HepB-0)', 'IM (antero-lateral thigh)', '0.5 mL', 'Within 24 hrs of birth'],
        ['**6, 10, 14 wks**', 'OPV-1,2,3 + IPV', 'Oral + IM', '2 drops + 0.5mL', 'IPV at 6 & 14 wks'],
        ['**6, 10, 14 wks**', 'Pentavalent (DPT+HepB+Hib)', 'IM', '0.5 mL', 'Antero-lateral thigh'],
        ['**6, 10, 14 wks**', 'Rotavirus vaccine', 'Oral', '5 drops', 'Under UIP 2016'],
        ['**9-12 months**', 'MR (Measles-Rubella)', 'SC (right arm)', '0.5 mL', '**No MR after 9 months**'],
        ['**9-12 months**', 'JE vaccine (endemic areas)', 'SC', '0.5 mL', 'Endemic areas only'],
        ['**9-12 months**', 'Vitamin A (1st dose)', 'Oral', '1 lakh IU', 'With MR vaccine'],
        ['**16-24 months**', 'DPT Booster-1 + OPV Booster', 'IM + Oral', '0.5mL + 2 drops', '—'],
        ['**16-24 months**', 'MR Booster', 'SC', '0.5 mL', '—'],
        ['**5-6 years**', 'DPT Booster-2', 'IM', '0.5 mL', 'School entry'],
        ['**10 & 16 years**', 'Td (Tetanus+low-dose diphtheria)', 'IM', '0.5 mL', 'Replaces TT'],
        ['**Pregnant women**', 'Td (2 doses or 1 booster)', 'IM', '0.5 mL', 'TT-1 early; TT-2 4 wks later'],
    ],
    [22*mm, 32*mm, 26*mm, 22*mm, 55*mm],
    header_bg=C_TEAL, fontsize=6.5
))
story.append(sp(2))

vac_left = []
vac_left.append(sub_header('COLD CHAIN TEMPERATURES', C_RED))
vac_left.append(sp(1))
vac_left.append(make_table(
    ['Level', 'Temperature'],
    [
        ['State / Regional store (ILR)', '**+2°C to +8°C**'],
        ['District / PHC level (ILR)', '+2°C to +8°C'],
        ['Walk-in cooler', '+2°C to +8°C'],
        ['Deep Freezer (OPV, Measles)', '**−15°C to −25°C**'],
        ['Vaccine carrier (field)', '+2°C to +8°C (ice packs)'],
        ['VVM (Vaccine Vial Monitor)', 'Color change = discard'],
    ],
    [42*mm, 30*mm], header_bg=C_RED
))

vac_right = []
vac_right.append(sub_header('VACCINE TYPES & EXAMPLES', C_PURPLE))
vac_right.append(sp(1))
vac_right.append(make_table(
    ['Type', 'Examples'],
    [
        ['**Live attenuated**', 'BCG, OPV, MMR, Varicella, Yellow fever, Rotavirus'],
        ['**Killed/Inactivated**', 'IPV (Salk), Rabies (HDCV), Hep A, Pertussis (whole cell), TCV'],
        ['**Toxoid**', 'Tetanus (TT/Td), Diphtheria'],
        ['**Subunit/recombinant**', 'Hepatitis B, HPV, Acellular pertussis'],
        ['**Conjugate**', 'Hib, PCV, MenACWY'],
        ['**mRNA**', 'COVID-19 (Pfizer, Moderna)'],
    ],
    [28*mm, 44*mm], header_bg=C_PURPLE
))
vac_right.append(sp(1))
vac_right.append(highlight_box('OPV: Sabin (live oral) — can cause VAPP (1/2.4M doses)\nIPV: Salk (killed injectable) — cannot cause VAPP\nBCG contraindicated in HIV+ (if symptomatic)', C_LRED, C_RED))

lw6 = W*0.48
rw6 = W - lw6 - 2*mm
story.append(two_col(
    Table([[f] for f in vac_left], colWidths=[lw6]),
    Table([[f] for f in vac_right], colWidths=[rw6]),
    lw=lw6, rw=rw6
))

story.append(PageBreak())

# ──────────────────── PAGE 7: COMMUNICABLE DISEASE CONTROL ──────────────────
story.append(section_header('🦟  PAGE 7 — COMMUNICABLE DISEASES: KEY FACTS FOR FMGE', C_NAVY))
story.append(sp(2))

story.append(sub_header('IMPORTANT INCUBATION PERIODS', C_RED))
story.append(sp(1))
story.append(make_table(
    ['Disease', 'Incubation Period', 'Disease', 'Incubation Period'],
    [
        ['**Cholera**', '6 hrs – 5 days (usually 1-3d)', '**Measles**', '10-14 days (range 7-21d)'],
        ['**Typhoid**', '1-3 weeks (range 3-60d)', '**Mumps**', '14-21 days'],
        ['**Rabies**', '2-8 weeks (range 10d-2yr)', '**Rubella**', '14-21 days'],
        ['**Polio**', '7-14 days (range 3-35d)', '**Chickenpox**', '14-16 days (range 10-21d)'],
        ['**Diphtheria**', '2-5 days', '**Hepatitis A**', '15-50 days (avg 28d)'],
        ['**Tetanus**', '3-21 days (avg 10d)', '**Hepatitis B**', '45-180 days (avg 60-90d)'],
        ['**Pertussis**', '7-10 days', '**HIV/AIDS**', '2-4 wks (seroconversion)'],
        ['**Meningococcal**', '2-10 days', '**Plague**', '2-6 days'],
        ['**Malaria (P.falciparum)**', '7-14 days', '**Malaria (P.vivax)**', '12-17 days'],
        ['**Dengue**', '4-7 days (range 3-14d)', '**COVID-19**', '2-14 days (avg 5-6d)'],
    ],
    [28*mm, 36*mm, 28*mm, 36*mm],
    header_bg=C_RED, fontsize=6.5
))
story.append(sp(2))

cd_left = []
cd_left.append(sub_header('VECTORS & DISEASES', C_TEAL))
cd_left.append(sp(1))
cd_left.append(make_table(
    ['Vector', 'Disease'],
    [
        ['**Anopheles** mosquito', 'Malaria, Filariasis (also Culex)'],
        ['**Aedes aegypti**', '**Dengue**, Chikungunya, Yellow fever, Zika'],
        ['**Culex** mosquito', 'Filariasis (W.bancrofti), JE, West Nile'],
        ['**Phlebotomus** (sandfly)', 'Kala-azar (Leishmaniasis)'],
        ['**Ixodes** tick', 'Lyme disease, TBE'],
        ['**Ornithodoros** tick', 'Relapsing fever'],
        ['**Body louse** (Pediculus)', 'Epidemic typhus, Relapsing fever'],
        ['**Rat flea** (Xenopsylla)', 'Plague, Endemic typhus'],
        ['**Tsetse fly**', 'African trypanosomiasis (sleeping sickness)'],
        ['**Blackfly** (Simulium)', 'Onchocerciasis (river blindness)'],
        ['**Cyclops** (water flea)', 'Guinea worm (Dracunculiasis)'],
    ],
    [30*mm, 52*mm], header_bg=C_TEAL, fontsize=6.5
))

cd_right = []
cd_right.append(sub_header('DISEASE SURVEILLANCE & NOTIFICATION', C_PURPLE))
cd_right.append(sp(1))
cd_right.append(make_table(
    ['Category', 'Examples'],
    [
        ['**Notifiable diseases (India)**', 'Cholera, Plague, Yellow fever, Smallpox (internationally notifiable IHR)'],
        ['**IHR 2005 — Always notify**', 'Smallpox, Polio (wild), SARS, Human influenza (new subtype)'],
        ['**Zero-dose surveillance**', 'AFP, Measles, Neonatal tetanus'],
        ['**Sentinel surveillance**', 'HIV, Influenza (selected sites)'],
    ],
    [28*mm, 54*mm], header_bg=C_PURPLE
))
cd_right.append(sp(1))
cd_right.append(sub_header('REPRODUCTIVE NUMBER (R₀)', C_ORANGE))
cd_right.append(sp(1))
cd_right.append(make_table(
    ['Disease', 'R₀'],
    [
        ['**Measles**', '**12–18** (highest)'],
        ['Chickenpox', '8–10'],
        ['Mumps', '4–7'],
        ['Polio', '5–7'],
        ['COVID-19 (original)', '2–3'],
        ['Influenza (seasonal)', '1.2–1.4'],
        ['HIV', '2–5'],
    ],
    [38*mm, 24*mm], header_bg=C_ORANGE
))
cd_right.append(sp(1))
cd_right.append(highlight_box('R₀ > 1 = epidemic potential | R₀ < 1 = epidemic will die out\nEffective R (Rt) accounts for immunity in population', C_LYELLOW, C_ORANGE))

lw7 = W*0.52
rw7 = W - lw7 - 2*mm
story.append(two_col(
    Table([[f] for f in cd_left], colWidths=[lw7]),
    Table([[f] for f in cd_right], colWidths=[rw7]),
    lw=lw7, rw=rw7
))

story.append(PageBreak())

# ──────────────────── PAGE 8: NUTRITION ─────────────────────────────────────
story.append(section_header('🥗  PAGE 8 — NUTRITION & NUTRITIONAL DISORDERS', C_NAVY))
story.append(sp(2))

nut_left = []
nut_left.append(sub_header('PROTEIN-ENERGY MALNUTRITION (PEM)', C_RED))
nut_left.append(sp(1))
nut_left.append(make_table(
    ['Feature', 'Kwashiorkor', 'Marasmus'],
    [
        ['**Deficiency**', 'Protein (adequate calories)', 'Calories + Protein (starvation)'],
        ['**Age**', '1-3 years', '<1 year (weaning)'],
        ['**Oedema**', '**Present** (pitting)', 'Absent'],
        ['**Skin changes**', 'Flaky paint dermatosis', 'Wrinkled, loose skin'],
        ['**Hair**', 'Flag sign (depigmentation)', 'Thin, sparse'],
        ['**Wt for age**', '60-80% of expected', '<60% of expected'],
        ['**Appetite**', 'Poor', 'Good (voracious)'],
        ['**Mood**', 'Miserable, apathetic', 'Alert, hungry'],
        ['**Fatty liver**', '**Present**', 'Absent'],
    ],
    [22*mm, 36*mm, 36*mm], header_bg=C_RED
))
nut_left.append(sp(1))
nut_left.append(sub_header('ANTHROPOMETRIC INDICES (WHO)', C_TEAL))
nut_left.append(sp(1))
nut_left.append(make_table(
    ['Index', 'Reflects', 'Cut-off (Z-score)'],
    [
        ['**Wt/Age** (underweight)', 'Overall malnutrition', '< −2 SD = underweight'],
        ['**Ht/Age** (stunting)', '**Chronic** malnutrition', '< −2 SD = stunted'],
        ['**Wt/Ht** (wasting)', '**Acute** malnutrition', '< −2 SD = wasted'],
        ['**MUAC**', 'Rapid field assessment', '<11.5 cm = SAM (<5yr)'],
        ['**BMI for age**', 'Overweight/obesity', '>+2 SD = overweight'],
    ],
    [28*mm, 28*mm, 32*mm], header_bg=C_TEAL
))

nut_right = []
nut_right.append(sub_header('MICRONUTRIENT DEFICIENCIES', C_ORANGE))
nut_right.append(sp(1))
nut_right.append(make_table(
    ['Nutrient', 'Deficiency Disease', 'Key Features'],
    [
        ['**Vitamin A**', 'Xerophthalmia', 'Night blindness → Bitot\'s spots → Corneal ulcer → Keratomalacia'],
        ['**Vitamin D**', 'Rickets (child) / Osteomalacia (adult)', 'Bow legs, Harrison\'s sulcus, Looser\'s zones on X-ray'],
        ['**Vitamin C**', 'Scurvy', 'Perifollicular haemorrhage, bleeding gums, Corkscrew hair, Frankel\'s line'],
        ['**Vitamin B1**', 'Beriberi', 'Wet (cardiac), Dry (neurological), Wernicke\'s (Wernicke-Korsakoff)'],
        ['**Vitamin B2**', 'Ariboflavinosis', 'Angular stomatitis, cheilosis, corneal vascularisation'],
        ['**Niacin (B3)**', 'Pellagra', '**4 Ds**: Dermatitis, Diarrhoea, Dementia, Death'],
        ['**Folate / B12**', 'Megaloblastic anaemia', 'Neural tube defects (folate), subacute combined degeneration (B12)'],
        ['**Iron**', 'IDA', 'Koilonychia, angular stomatitis, Plummer-Vinson syndrome'],
        ['**Iodine**', 'Goitre / Cretinism', 'Endemic goitre, hypothyroid, deaf-mutism'],
        ['**Zinc**', 'Growth retardation', 'Acrodermatitis enteropathica, hypogonadism, ageusia'],
        ['**Fluoride**', 'Dental/Skeletal fluorosis (>1.5 ppm)', 'Mottled enamel (0.5-1 ppm = optimal)'],
    ],
    [18*mm, 28*mm, 58*mm], header_bg=C_ORANGE, fontsize=6.5
))
nut_right.append(sp(1))
nut_right.append(highlight_box('Vitamin A prophylaxis (UIP): 1 lakh IU at 9m, then 2 lakh IU every 6m up to 5yr\nOptimal fluoride in water: 0.5-0.8 ppm (India: 1 ppm)\nIodized salt: ≥30 ppm at production, ≥15 ppm at consumer level', C_LYELLOW, C_ORANGE))

lw8 = W*0.49
rw8 = W - lw8 - 2*mm
story.append(two_col(
    Table([[f] for f in nut_left], colWidths=[lw8]),
    Table([[f] for f in nut_right], colWidths=[rw8]),
    lw=lw8, rw=rw8
))

story.append(PageBreak())

# ──────────────────── PAGE 9: MATERNAL & CHILD HEALTH ───────────────────────
story.append(section_header('👶  PAGE 9 — MATERNAL & CHILD HEALTH + FAMILY PLANNING', C_NAVY))
story.append(sp(2))

mch_left = []
mch_left.append(sub_header('ANTENATAL CARE (ANC) — MINIMUM CONTACTS', C_TEAL))
mch_left.append(sp(1))
mch_left.append(make_table(
    ['Contact', 'Timing', 'Key Activities'],
    [
        ['**ANC-1**', '<12 weeks', 'Confirm pregnancy, BP, Hb, blood group, urine, counselling, IFA, TT-1'],
        ['**ANC-2**', '14-16 wks', 'Review tests, TT-2, weight, BP'],
        ['**ANC-3**', '28-32 wks', 'Lie/presentation, BP, Hb repeat, plan delivery'],
        ['**ANC-4**', '36 wks', 'Final check, plan place of delivery, danger signs'],
    ],
    [15*mm, 18*mm, 68*mm], header_bg=C_TEAL
))
mch_left.append(sp(1))
mch_left.append(sub_header('DANGER SIGNS IN PREGNANCY (ABCDEFG)', C_RED))
mch_left.append(sp(1))
mch_left.append(make_table(
    ['Letter', 'Sign'],
    [
        ['A', 'Absent or reduced fetal movements'],
        ['B', 'Bleeding P/V'],
        ['C', 'Convulsions'],
        ['D', 'Difficulty in breathing'],
        ['E', 'Excessive vomiting'],
        ['F', 'Fever'],
        ['G', 'Generalized oedema of face/hands'],
    ],
    [10*mm, 80*mm], header_bg=C_RED
))
mch_left.append(sp(1))
mch_left.append(sub_header('BREAST FEEDING', C_GREEN))
mch_left.append(sp(1))
mch_left.append(make_table(
    ['Term', 'Definition'],
    [
        ['**Colostrum**', 'First 3-4 days; rich in SIgA, vitamin A, protein; yellow, thick'],
        ['**Exclusive BF**', 'Only breast milk for **first 6 months** (no water, no food)'],
        ['**Complementary feeds**', 'Start at **6 months** (while continuing BF up to 2 yrs)'],
        ['**Rooming-in**', 'Mother-baby together 24hr; promotes BF initiation'],
    ],
    [22*mm, 68*mm], header_bg=C_GREEN
))

mch_right = []
mch_right.append(sub_header('FAMILY PLANNING METHODS', C_PURPLE))
mch_right.append(sp(1))
mch_right.append(make_table(
    ['Method', 'Failure Rate (Pearl Index)', 'Notes'],
    [
        ['**OCP (Combined)**', '0.1 (perfect); 3-8 (typical)', 'No protection vs STIs'],
        ['**POP (Mini-pill)**', '0.3-3', 'Suitable for lactating mothers'],
        ['**Cu-IUD (380A)**', '**0.6-0.8%**; lasts 10 yrs', 'Emergency contraception within 5 days'],
        ['**Condom (male)**', '2 (perfect); 15 (typical)', 'Only method protecting vs STIs'],
        ['**Vasectomy**', '**<0.1%**; failure if early intercourse', 'Simpler than tubectomy'],
        ['**Tubectomy (TL)**', '**0.5%**; Minilap under LA', 'Most effective LARC'],
        ['**Depo-Provera**', '0.3%', 'Injectable; every 3 months'],
        ['**LAM**', '<2% (if criteria met)', 'Amenorrhoea + exclusive BF + <6 months PP'],
        ['**Emergency contraception**', '75-85% effective', 'Levonorgestrel 1.5mg within 72 hrs'],
    ],
    [30*mm, 30*mm, 48*mm], header_bg=C_PURPLE, fontsize=6.5
))
mch_right.append(sp(1))
mch_right.append(sub_header('GROWTH MILESTONES', C_ORANGE))
mch_right.append(sp(1))
mch_right.append(make_table(
    ['Age', 'Weight', 'Height', 'Milestone'],
    [
        ['Birth', '3 kg', '50 cm', 'Head circumference ~33 cm'],
        ['**3 months**', '**Double birth weight** not yet', '—', 'Neck holding'],
        ['**5 months**', '**Double birth weight** (~6 kg)', '—', 'Roll over'],
        ['**1 year**', '**Triple birth weight** (~9 kg)', '**75 cm**', 'Walk with support'],
        ['**2 years**', '**4× birth wt** (~12 kg)', '**87 cm**', 'Runs, 2-3 word sentences'],
        ['5 years', '~18-20 kg', '~110 cm', 'Skip, full sentences'],
    ],
    [14*mm, 20*mm, 16*mm, 40*mm], header_bg=C_ORANGE, fontsize=6.5
))

lw9 = W*0.51
rw9 = W - lw9 - 2*mm
story.append(two_col(
    Table([[f] for f in mch_left], colWidths=[lw9]),
    Table([[f] for f in mch_right], colWidths=[rw9]),
    lw=lw9, rw=rw9
))

story.append(PageBreak())

# ──────────────────── PAGE 10: ENVIRONMENT & WATER ──────────────────────────
story.append(section_header('🌍  PAGE 10 — ENVIRONMENTAL HEALTH, WATER & AIR QUALITY', C_NAVY))
story.append(sp(2))

env_left = []
env_left.append(sub_header('WATER QUALITY STANDARDS (WHO / BIS)', C_TEAL))
env_left.append(sp(1))
env_left.append(make_table(
    ['Parameter', 'Desirable / Permissible Limit'],
    [
        ['**Turbidity**', '1 NTU (desirable); <5 NTU (permissible)'],
        ['**pH**', '**6.5 – 8.5**'],
        ['**Fluoride**', '**1.0 ppm** (India); 0.5-0.8 ppm optimal'],
        ['**Nitrate**', '<45 mg/L (WHO: <50 mg/L); >45 = methaemoglobinaemia'],
        ['**Arsenic**', '0.01 mg/L (WHO); 0.05 mg/L (India)'],
        ['**Total Dissolved Solids**', '<500 mg/L (desirable); <2000 mg/L (permissible)'],
        ['**E. coli (coliforms)**', '**0** per 100 mL (treated supply)'],
        ['**Hardness**', '<200 mg/L (desirable); <600 mg/L (permissible)'],
        ['**Chlorine residual**', '**0.5 ppm** at consumer end'],
        ['**Chlorine demand**', 'Amount needed to disinfect; = Applied chlorine – Residual chlorine'],
    ],
    [32*mm, 62*mm], header_bg=C_TEAL
))
env_left.append(sp(1))
env_left.append(sub_header('WATER PURIFICATION METHODS', C_NAVY))
env_left.append(sp(1))
env_left.append(make_table(
    ['Method', 'Effect / Notes'],
    [
        ['**Sedimentation**', 'Removes suspended matter; coagulants (alum) used'],
        ['**Filtration (slow sand)**', 'Removes 99% bacteria; Schmutzdecke biological layer'],
        ['**Filtration (rapid sand)**', 'Faster; needs coagulation first; does NOT remove bacteria alone'],
        ['**Chlorination**', '**Most important** disinfection; kills bacteria and viruses'],
        ['**Boiling**', 'Most effective household method; kills all pathogens'],
        ['**UV radiation**', 'Kills bacteria/viruses; does not remove chemicals'],
        ['**Reverse osmosis**', 'Removes dissolved salts, arsenic, fluoride, bacteria'],
    ],
    [32*mm, 62*mm], header_bg=C_NAVY
))

env_right = []
env_right.append(sub_header('AIR QUALITY & POLLUTION', C_RED))
env_right.append(sp(1))
env_right.append(make_table(
    ['Pollutant', 'Sources', 'Health Effect'],
    [
        ['**PM2.5 / PM10**', 'Combustion, dust', 'Respiratory & CVS disease'],
        ['**CO**', 'Incomplete combustion, vehicles', 'Binds Hb (COHb); headache, death'],
        ['**SO₂**', 'Coal burning, smelters', 'Acid rain, bronchoconstriction'],
        ['**NO₂**', 'Vehicles, power plants', 'Respiratory inflammation'],
        ['**Ozone (O₃)**', 'Photochemical smog', 'Eye/lung irritation; high altitude = protective'],
        ['**Lead**', 'Leaded petrol, paint', 'Neurotoxic, anaemia, nephrotoxic'],
        ['**Benzene**', 'Petrol, solvents', 'Leukaemia (AML)'],
        ['**Asbestos**', 'Insulation, old buildings', 'Mesothelioma, asbestosis, lung Ca'],
        ['**Radon**', 'Soil/rock, building materials', 'Lung cancer (2nd commonest cause)'],
    ],
    [18*mm, 24*mm, 32*mm], header_bg=C_RED, fontsize=6.5
))
env_right.append(sp(1))
env_right.append(sub_header('IMPORTANT POLLUTION DISASTERS', C_ORANGE))
env_right.append(sp(1))
env_right.append(make_table(
    ['Disaster', 'Location', 'Cause', 'Disease'],
    [
        ['**Minamata**', 'Japan', 'Methyl mercury in fish', 'Neurological damage'],
        ['**Itai-itai**', 'Japan', 'Cadmium in rice/water', 'Osteoporosis, renal failure'],
        ['**Bhopal gas tragedy**', 'India, 1984', 'MIC (methyl isocyanate)', 'Pulmonary oedema, death'],
        ['**London smog**', 'UK, 1952', 'Sulphur dioxide + fog', '4000+ deaths'],
    ],
    [22*mm, 18*mm, 26*mm, 26*mm], header_bg=C_ORANGE, fontsize=6.5
))
env_right.append(sp(1))
env_right.append(sub_header('SOLID WASTE & BIOMEDICAL WASTE', C_GREEN))
env_right.append(sp(1))
env_right.append(make_table(
    ['Category (BMW Rules 2016)', 'Color Bag/Container', 'Examples'],
    [
        ['Yellow bag', '**Yellow**', 'Anatomical waste, soiled items, expired medicines'],
        ['Red bag', '**Red**', 'Plastic waste, IV sets, syringes (without needles)'],
        ['White (puncture-proof)', '**White**', 'Needles, sharps, scalpels'],
        ['Blue box', '**Blue**', 'Glassware, metallic implants'],
    ],
    [38*mm, 20*mm, 38*mm], header_bg=C_GREEN, fontsize=6.5
))

lw10 = W*0.52
rw10 = W - lw10 - 2*mm
story.append(two_col(
    Table([[f] for f in env_left], colWidths=[lw10]),
    Table([[f] for f in env_right], colWidths=[rw10]),
    lw=lw10, rw=rw10
))

story.append(PageBreak())

# ──────────────────── PAGE 11: NATIONAL HEALTH PROGRAMS ─────────────────────
story.append(section_header('🏥  PAGE 11 — NATIONAL HEALTH PROGRAMMES (INDIA)', C_NAVY))
story.append(sp(2))

story.append(sub_header('KEY NATIONAL PROGRAMS — QUICK REFERENCE', C_TEAL))
story.append(sp(1))
story.append(make_table(
    ['Program', 'Year Launched', 'Target', 'Key Features'],
    [
        ['**RNTCP → NTP** (Nikshay)', '1997 → 2020', 'TB', 'DOTS strategy; Nikshay Poshan Yojana ₹500/month; End TB by 2025'],
        ['**NVBDCP**', '2003 (merged)', 'Vector-borne diseases', 'Malaria, Dengue, Kala-azar, JE, Filariasis, Chikungunya'],
        ['**NACP** (HIV/AIDS)', 'Phase 1: 1992', 'HIV/AIDS', '4th phase (NACP-IV); free ART; ICTC; blood safety'],
        ['**NPCDCS**', '2010', 'NCDs', 'Cancer, Diabetes, CVD, Stroke at district/sub-district level'],
        ['**NPCB** (Blindness)', '1976', 'Blindness', 'Cataract surgery, School eye screening, Vitamin A'],
        ['**NLEP**', '1983', 'Leprosy', 'MDT (Rifampicin, Dapsone, Clofazimine); elimination <1/10,000'],
        ['**NAMP**', '—', 'Mental health', 'District Mental Health Program (DMHP)'],
        ['**NPCDCS (Cancer)**', '2010', 'Cancer', 'Cervical, Breast, Oral cancer screening'],
        ['**NPPCF**', '2014', 'Fluorosis', 'Prevent dental/skeletal fluorosis'],
        ['**NPHED**', '—', 'Hearing impairment', 'Neonatal hearing screening'],
        ['**RASHTRIYA KISHOR SWASTHYA KARYAKRAM**', '2014', 'Adolescents 10-19yr', 'Nutrition, sexual health, mental health, substance abuse'],
        ['**Surakshit Matritva Aashwasan (SUMAN)**', '2019', 'Maternal/newborn', 'Free, respectful, quality care; zero tolerance for denial'],
        ['**PM-ABHIM**', '2021', 'Health infra', 'Strengthen public health infrastructure post-COVID'],
    ],
    [38*mm, 18*mm, 26*mm, 73*mm],
    header_bg=C_TEAL, fontsize=6.3
))
story.append(sp(2))

nhp_left = []
nhp_left.append(sub_header('AYUSHMAN BHARAT', C_NAVY))
nhp_left.append(sp(1))
nhp_left.append(make_table(
    ['Component', 'Details'],
    [
        ['**Health & Wellness Centres (HWC)**', '1.5 lakh sub-centres & PHCs converted; Comprehensive Primary Health Care'],
        ['**PM-JAY (Pradhan Mantri Jan Arogya Yojana)**', '**₹5 lakh/family/year** health cover; 10.74 crore poor families; cashless hospitalization'],
    ],
    [36*mm, 53*mm], header_bg=C_NAVY
))
nhp_left.append(sp(1))
nhp_left.append(sub_header('HEALTH SYSTEM STRUCTURE (Rural)', C_GREEN))
nhp_left.append(sp(1))
nhp_left.append(make_table(
    ['Level', 'Population Served', 'Facility'],
    [
        ['Sub-centre (SC)', 'Plain: 5000 / Hilly: 3000', '1 ANM + 1 MPW(M)'],
        ['**PHC**', 'Plain: **30,000** / Hilly: 20,000', '1 MBBS doctor; 4-6 beds; 24×7 delivery'],
        ['**CHC**', '**1,20,000** (4 PHCs)', '30 beds; 4 specialists (surgery, medicine, OBG, paediatrics)'],
        ['Sub-district hospital', '5-6 lakh', 'Specialist care'],
        ['**District hospital**', '**10-12 lakh**', 'Full specialist care; blood bank'],
    ],
    [26*mm, 28*mm, 56*mm], header_bg=C_GREEN
))

nhp_right = []
nhp_right.append(sub_header('MDGs → SDGs', C_PURPLE))
nhp_right.append(sp(1))
nhp_right.append(make_table(
    ['Goal', 'Target'],
    [
        ['**SDG 3**', 'Good health and well-being'],
        ['End AIDS, TB, malaria by 2030', '—'],
        ['UHC (Universal Health Coverage)', 'By 2030'],
        ['Reduce neonatal mortality', '<12/1000 LB by 2030'],
        ['Reduce maternal mortality', '<70/100,000 LB by 2030'],
        ['End preventable deaths', '<25/1000 LB under-5 by 2030'],
    ],
    [52*mm, 20*mm], header_bg=C_PURPLE
))
nhp_right.append(sp(1))
nhp_right.append(sub_header('NHP 2017 TARGETS (INDIA)', C_RED))
nhp_right.append(sp(1))
nhp_right.append(make_table(
    ['Indicator', 'Target by 2025'],
    [
        ['**IMR**', '< 28/1000 LB → <20 by 2025'],
        ['**U5MR**', '< 23/1000 LB'],
        ['**MMR**', '< 100/1,00,000 LB'],
        ['**TFR**', '**2.1** (replacement level)'],
        ['Life expectancy at birth', '70 years'],
        ['Malnutrition (under-5)', 'Reduce by 40%'],
        ['Health expenditure (public)', 'Increase to 2.5% of GDP'],
    ],
    [36*mm, 36*mm], header_bg=C_RED
))

lw11 = W*0.52
rw11 = W - lw11 - 2*mm
story.append(two_col(
    Table([[f] for f in nhp_left], colWidths=[lw11]),
    Table([[f] for f in nhp_right], colWidths=[rw11]),
    lw=lw11, rw=rw11
))

story.append(PageBreak())

# ──────────────────── PAGE 12: OCCUPATIONAL HEALTH ─────────────────────────
story.append(section_header('🏭  PAGE 12 — OCCUPATIONAL HEALTH & NON-COMMUNICABLE DISEASES', C_NAVY))
story.append(sp(2))

occ_left = []
occ_left.append(sub_header('OCCUPATIONAL DISEASES (EXPOSURE → DISEASE)', C_RED))
occ_left.append(sp(1))
occ_left.append(make_table(
    ['Exposure / Occupation', 'Disease'],
    [
        ['**Coal dust** (miners)', '**Coal workers\' pneumoconiosis (CWP)**; Anthracosis'],
        ['**Silica** (quarry, sand-blasting)', '**Silicosis** (most dangerous pneumoconiosis); ↑TB risk'],
        ['**Asbestos** (insulation, ship-building)', '**Asbestosis**, Mesothelioma (pleural), Lung Ca'],
        ['**Cotton dust** (textile)', '**Byssinosis** (Monday fever); progressive dyspnoea'],
        ['**Bagasse** (sugar cane)', '**Bagassosis** (extrinsic allergic alveolitis)'],
        ['**Bird droppings** / Farmer\'s lung', '**Extrinsic allergic alveolitis (EAA)**'],
        ['**Lead** (paint, battery, printing)', 'Lead poisoning: Burton\'s line, anaemia, basophilic stippling, colic, foot drop'],
        ['**Mercury** (thermometers, gold mining)', 'Minamata disease; tremor, gingivitis, erethism'],
        ['**Arsenic** (pesticides, smelting)', 'Mees\' lines, rain-drop pigmentation, Bowen\'s disease, skin/lung Ca'],
        ['**Benzene** (solvents, petrol)', 'AML (leukaemia), aplastic anaemia'],
        ['**Vinyl chloride**', 'Hepatic angiosarcoma'],
        ['**Vibration** (chain saw)', 'Raynaud\'s phenomenon, white finger'],
        ['**Noise > 85 dB** (8hrs)', 'NIHL (noise-induced hearing loss); 4000 Hz first affected'],
        ['**UV radiation** (welders)', 'Arc eye (photokeratitis), skin cancer (SCC, BCC, melanoma)'],
        ['**Radiation** (X-ray workers)', 'Aplastic anaemia, leukaemia, cataracts, thyroid Ca'],
    ],
    [40*mm, 62*mm], header_bg=C_RED, fontsize=6.3
))

occ_right = []
occ_right.append(sub_header('NON-COMMUNICABLE DISEASES (NCDs)', C_TEAL))
occ_right.append(sp(1))
occ_right.append(make_table(
    ['Disease', 'Key Risk Factor', 'FMGE Point'],
    [
        ['**CHD**', 'Smoking, HTN, DM, dyslipidaemia', 'Framingham score; Metabolic syndrome (NCEP-ATP III)'],
        ['**Hypertension**', 'Obesity, salt, alcohol, sedentary', 'JNC 8: ≥140/90; Stage 1: 130-139/80-89 (AHA 2017)'],
        ['**DM Type 2**', 'Obesity, sedentary, family history', 'FPG ≥126; 2-hr PG ≥200; HbA1c ≥6.5%'],
        ['**Cancer (lung)**', 'Smoking (80%), radon', '2nd most common cancer worldwide'],
        ['**Cancer (cervix)**', '**HPV (16, 18)**', 'Most common Ca in developing countries (India)'],
        ['**Cancer (breast)**', 'BRCA1/2, obesity, HRT', 'Most common Ca in women worldwide'],
        ['**COPD**', 'Smoking, biomass fuel', 'FEV1/FVC < 0.70 post-bronchodilator'],
        ['**Stroke**', 'HTN, AF, diabetes', 'FAST: Face, Arm, Speech, Time'],
    ],
    [20*mm, 26*mm, 54*mm], header_bg=C_TEAL, fontsize=6.5
))
occ_right.append(sp(1))
occ_right.append(sub_header('METABOLIC SYNDROME (NCEP-ATP III)', C_ORANGE))
occ_right.append(sp(1))
occ_right.append(make_table(
    ['Criterion', 'Value'],
    [
        ['Waist circumference', 'Men >102 cm; Women >88 cm (Asian: men >90, women >80)'],
        ['Triglycerides', '≥150 mg/dL'],
        ['HDL cholesterol', 'Men <40 mg/dL; Women <50 mg/dL'],
        ['Blood pressure', '≥130/85 mmHg'],
        ['Fasting glucose', '≥100 mg/dL'],
        ['**Diagnosis: ≥3 of above criteria**', '—'],
    ],
    [36*mm, 52*mm], header_bg=C_ORANGE
))

lw12 = W*0.51
rw12 = W - lw12 - 2*mm
story.append(two_col(
    Table([[f] for f in occ_left], colWidths=[lw12]),
    Table([[f] for f in occ_right], colWidths=[rw12]),
    lw=lw12, rw=rw12
))

story.append(PageBreak())

# ──────────────────── PAGE 13: MALARIA, TB, LEPROSY ─────────────────────────
story.append(section_header("🦠  PAGE 13 — MALARIA, TB & LEPROSY (HIGH-YIELD DISEASES)", C_NAVY))
story.append(sp(2))

mal_left = []
mal_left.append(sub_header('MALARIA', C_RED))
mal_left.append(sp(1))
mal_left.append(make_table(
    ['Feature', 'P. vivax / P. ovale', 'P. falciparum'],
    [
        ['Incubation', '12-17 days', '7-14 days'],
        ['Fever cycle', '**48 hr (Benign tertian)**', '**36-48 hr (Malignant tertian)**'],
        ['Relapse', '**Yes** (hypnozoites in liver)', 'No relapse (recrudescence only)'],
        ['Dangerous', 'No', '**Yes** — Cerebral malaria, severe anaemia'],
        ['RBC affected', 'Young RBCs', 'All RBCs'],
        ['Schuffner\'s dots', 'Present', 'Absent (Maurer\'s clefts)'],
        ['Anti-relapse Rx', 'Primaquine 14 days', 'Not needed'],
    ],
    [26*mm, 36*mm, 36*mm], header_bg=C_RED
))
mal_left.append(sp(1))
mal_left.append(make_table(
    ['Treatment', 'Drug'],
    [
        ['**P. vivax**', 'Chloroquine 3 days + Primaquine 14 days'],
        ['**P. falciparum**', 'ACT (Artemether-Lumefantrine) 3 days + Primaquine single dose'],
        ['**Severe malaria**', 'IV Artesunate (replaces quinine)'],
        ['**Prevention**', 'LLIN (Long-lasting insecticidal nets), IRS (DDT/Deltamethrin)'],
    ],
    [22*mm, 76*mm], header_bg=C_RED
))
mal_left.append(sp(1))
mal_left.append(sub_header('TB (RNTCP/NTP)', C_TEAL))
mal_left.append(sp(1))
mal_left.append(make_table(
    ['Category', 'Regimen (old DOTS)', 'Notes'],
    [
        ['**New cases**', '2HRZE / 4HR', 'H=INH, R=Rifampicin, Z=Pyrazinamide, E=Ethambutol'],
        ['**Previously treated**', '2HRZES/1HRZE/5HRE', 'Category II (being phased out for DST-guided)'],
        ['**DR-TB (MDR)**', 'Bedaquiline + Linezolid-based regimen', 'Resist INH + Rifampicin; culture confirmation'],
        ['**XDR-TB**', 'Resistant to INH, Rif + FQ + injectable', 'Newer drugs: Bedaquiline, Delamanid'],
        ['**Latent TB**', '6H or 3HP (isoniazid + rifapentine)', 'IGRA or TST positive; no active disease'],
    ],
    [20*mm, 38*mm, 40*mm], header_bg=C_TEAL, fontsize=6.3
))

mal_right = []
mal_right.append(sub_header('LEPROSY', C_PURPLE))
mal_right.append(sp(1))
mal_right.append(make_table(
    ['Feature', 'Tuberculoid (TT)', 'Lepromatous (LL)'],
    [
        ['Immunity', 'High cell-mediated', 'Low/absent CMI'],
        ['Lesions', 'Few (1-5), well-defined', 'Many, ill-defined, symmetrical'],
        ['Sensation', '**Absent** in lesion', 'Reduced but not absent early'],
        ['Bacteria', '**Very few** (PB)', '**Many** (MB); Globi present'],
        ['Lepromin test', '**Positive**', '**Negative**'],
        ['Nerve damage', 'Early, one nerve', 'Late, multiple nerves'],
        ['Transmissibility', 'Low', 'High'],
    ],
    [20*mm, 38*mm, 40*mm], header_bg=C_PURPLE
))
mal_right.append(sp(1))
mal_right.append(make_table(
    ['Classification', 'Criteria', 'MDT Regimen'],
    [
        ['**PB (Paucibacillary)**', '1-5 patches, skin smear −ve', '**6 months**: Rifampicin 600mg monthly + Dapsone 100mg daily'],
        ['**MB (Multibacillary)**', '>5 patches, smear +ve or borderline', '**12 months**: Rifampicin + Dapsone + Clofazimine'],
        ['**Single lesion PB**', '1 patch, no nerve', 'ROM (single dose: Rifampicin + Ofloxacin + Minocycline)'],
    ],
    [18*mm, 30*mm, 52*mm], header_bg=C_PURPLE, fontsize=6.3
))
mal_right.append(sp(1))
mal_right.append(sub_header('LEPROSY REACTIONS', C_ORANGE))
mal_right.append(sp(1))
mal_right.append(make_table(
    ['Reaction', 'Type', 'Treatment'],
    [
        ['**Type 1 (Reversal)**', 'Occurs in BT, BB, BL; Delayed hypersensitivity', 'Prednisolone 40-60mg/day'],
        ['**Type 2 (ENL)**', 'Occurs in BL, LL; Immune complex (Type III)', 'Thalidomide (drug of choice) or steroids'],
        ['**Lucio phenomenon**', 'Only in LL; vasculitis with skin ulceration', 'Continue MDT'],
    ],
    [22*mm, 36*mm, 40*mm], header_bg=C_ORANGE, fontsize=6.3
))

lw13 = W*0.5
rw13 = W - lw13 - 2*mm
story.append(two_col(
    Table([[f] for f in mal_left], colWidths=[lw13]),
    Table([[f] for f in mal_right], colWidths=[rw13]),
    lw=lw13, rw=rw13
))

story.append(PageBreak())

# ──────────────────── PAGE 14: HEALTH INDICATORS & CONCEPTS ─────────────────
story.append(section_header('📋  PAGE 14 — HEALTH INDICATORS, CONCEPTS & DEFINITIONS', C_NAVY))
story.append(sp(2))

ind_left = []
ind_left.append(sub_header('WHO DEFINITION & DIMENSIONS OF HEALTH', C_TEAL))
ind_left.append(sp(1))
ind_left.append(Paragraph('<b>WHO 1948:</b> "Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity"', BODYSM))
ind_left.append(sp(1))
ind_left.append(make_table(
    ['Dimension', 'Components'],
    [
        ['**Physical**', 'Normal physiological function; no disease'],
        ['**Mental**', 'Absence of mental illness; emotional well-being'],
        ['**Social**', 'Ability to function in society; relationships'],
        ['**Spiritual**', 'Sense of purpose, meaning, values'],
        ['**Vocational**', 'Ability to perform productive work'],
    ],
    [22*mm, 66*mm], header_bg=C_TEAL
))
ind_left.append(sp(1))
ind_left.append(sub_header('COMPOSITE HEALTH INDICATORS', C_NAVY))
ind_left.append(sp(1))
ind_left.append(make_table(
    ['Indicator', 'Definition'],
    [
        ['**HALE**', 'Health-Adjusted Life Expectancy; years in full health'],
        ['**DALY**', 'Disability-Adjusted Life Year = YLL + YLD; measures disease burden'],
        ['**YLL**', 'Years of Life Lost due to premature death'],
        ['**YLD**', 'Years Lived with Disability (weighted)'],
        ['**QALY**', 'Quality-Adjusted Life Year; used in cost-effectiveness'],
        ['**HDI**', 'Human Development Index: Life expectancy + Education + Income'],
        ['**PQLI**', 'Physical Quality of Life Index: Infant literacy + Life expectancy at age 1 + IMR'],
    ],
    [16*mm, 76*mm], header_bg=C_NAVY
))
ind_left.append(sp(1))
ind_left.append(sub_header('PRIMARY HEALTH CARE (Alma-Ata 1978)', C_GREEN))
ind_left.append(sp(1))
ind_left.append(Paragraph('<b>Declaration of Alma-Ata 1978</b> — "Health For All by 2000 AD" (goal unmet → renewed as HFA in SDGs 2030)', BODYSM))
ind_left.append(sp(1))
ind_left.append(make_table(
    ['Element (SAFE WMEN)', 'Full Form'],
    [
        ['S', 'Safe water & sanitation'],
        ['A', 'Adequate nutrition'],
        ['F', 'Immunization (Fundamental prevention)'],
        ['E', 'Essential drugs provision'],
        ['W', 'Women & child health, family planning'],
        ['M', 'Maternal health'],
        ['E', 'Endemic disease control'],
        ['N', 'Non-communicable disease treatment'],
    ],
    [10*mm, 82*mm], header_bg=C_GREEN
))

ind_right = []
ind_right.append(sub_header('IMPORTANT DEFINITIONS', C_PURPLE))
ind_right.append(sp(1))
ind_right.append(make_table(
    ['Term', 'Definition'],
    [
        ['**Epidemic**', 'Occurrence clearly in excess of normal expectancy in area/period'],
        ['**Endemic**', 'Constant presence of disease in a given geographic area'],
        ['**Pandemic**', 'Worldwide epidemic'],
        ['**Sporadic**', 'Irregular occurrence, not constant'],
        ['**Hyperendemic**', 'Consistently high levels, evenly distributed across age groups'],
        ['**Holoendemic**', 'Very high in children; lower in adults due to acquired immunity (e.g., malaria)'],
        ['**Eradication**', 'Permanent reduction to zero worldwide (e.g., Smallpox 1980)'],
        ['**Elimination**', 'Reduction to zero in specific geographic area (not globally)'],
        ['**Control**', 'Reduction of disease to acceptable level; ongoing control needed'],
        ['**Herd immunity**', 'Indirect protection of susceptibles when large proportion immune'],
        ['**Surveillance**', 'Ongoing systematic collection, analysis & use of health data'],
        ['**Quarantine**', 'Restriction of movement of exposed (well) persons for max incubation period'],
        ['**Isolation**', 'Separation of **infected/ill** persons from others'],
        ['**Case finding**', 'Active search for cases among known at-risk groups'],
        ['**Screening**', 'Presumptive identification of unrecognized disease in apparently healthy'],
        ['**Sentinel surveillance**', 'Selected sites for monitoring trends (not representative)'],
    ],
    [28*mm, 80*mm], header_bg=C_PURPLE, fontsize=6.3
))
ind_right.append(sp(1))
ind_right.append(highlight_box('SMALLPOX eradicated 1980 | POLIO: India polio-free since 2014 | GUINEA WORM: Near eradication | LEPROSY: Eliminated in India (<1/10,000) 2005', C_LYELLOW, C_GREEN))

lw14 = W*0.47
rw14 = W - lw14 - 2*mm
story.append(two_col(
    Table([[f] for f in ind_left], colWidths=[lw14]),
    Table([[f] for f in ind_right], colWidths=[rw14]),
    lw=lw14, rw=rw14
))

story.append(PageBreak())

# ──────────────────── PAGE 15: RAPID FIRE ONE-LINERS ────────────────────────
story.append(section_header('⚡  PAGE 15 — RAPID FIRE ONE-LINERS & EXAM DAY QUICK RECALL', C_NAVY))
story.append(sp(2))

# Two columns of one-liners
rf_data_l = [
    ['**Best indicator of community health**', '→ IMR'],
    ['**Best indicator of nutritional status**', '→ Weight for age (Wt/Age)'],
    ['**Gold standard study design**', '→ RCT (Double-blind RCT)'],
    ['**Best for rare disease study**', '→ Case-control study'],
    ['**Incidence measured by**', '→ Cohort study'],
    ['**Prevalence = Incidence ×**', '→ Duration of disease'],
    ['**OR ≈ RR when**', '→ Disease prevalence < 10%'],
    ['**SnNout**', '→ High Sensitivity → Negative rules OUT'],
    ['**SpPin**', '→ High Specificity → Positive rules IN'],
    ['**Screening needs high**', '→ Sensitivity'],
    ['**Confirmatory test needs high**', '→ Specificity'],
    ['**PPV/NPV depend on**', '→ Prevalence'],
    ['**Type I error (α)**', '→ False positive; α = 0.05'],
    ['**Type II error (β)**', '→ False negative; β = 0.20'],
    ['**Power of study**', '→ 1 − β = 0.80 (80%)'],
    ['**p < 0.05 means**', '→ Statistically significant'],
    ['**95% CI excludes 1 (RR/OR)**', '→ Significant association'],
    ['**Mean ± 1.96 SD covers**', '→ 95% of population'],
    ['**Mean = Median = Mode**', '→ Normal distribution'],
    ['**Most important Hill\'s criterion**', '→ Temporality'],
    ['**Best to control confounding**', '→ Randomization (in RCT)'],
    ['**Recall bias occurs in**', '→ Case-control studies'],
    ['**Lead time bias occurs in**', '→ Screening studies (false prolonged survival)'],
    ['**Berkson\'s bias**', '→ Hospital-based case-control studies'],
    ['**Ecological fallacy**', '→ Ecological/correlation studies'],
]

rf_data_r = [
    ['**IMR denominator**', '→ LIVE births (not total births)'],
    ['**MMR denominator**', '→ Live births (per 1,00,000)'],
    ['**Perinatal MR denominator**', '→ Stillbirths + Live births'],
    ['**Best single indicator of childbirth services**', '→ MMR'],
    ['**Herd immunity threshold for measles**', '→ ~95%'],
    ['**R₀ highest**', '→ Measles (12-18)'],
    ['**VAPP caused by**', '→ OPV (not IPV)'],
    ['**BCG route**', '→ Intradermal (left deltoid)'],
    ['**MR vaccine age**', '→ 9-12 months + 16-24 months'],
    ['**DPT booster-2 age**', '→ 5-6 years'],
    ['**Optimal fluoride in water**', '→ 0.5-0.8 ppm; India standard = 1 ppm'],
    ['**Residual chlorine at consumer end**', '→ 0.5 ppm'],
    ['**Slow sand filter removes**', '→ 99% bacteria (Schmutzdecke)'],
    ['**Rapid sand filter**', '→ Requires coagulation; does NOT remove bacteria alone'],
    ['**Minamata disease**', '→ Methyl mercury poisoning'],
    ['**Itai-itai disease**', '→ Cadmium poisoning'],
    ['**Bhopal tragedy gas**', '→ MIC (Methyl isocyanate)'],
    ['**Silicosis risk**', '→ ↑ susceptibility to TB'],
    ['**Noise: frequency first affected**', '→ 4000 Hz (NIHL)'],
    ['**Mesothelioma caused by**', '→ Asbestos'],
    ['**Leukaemia (AML) caused by**', '→ Benzene'],
    ['**Kwashiorkor has**', '→ Oedema, fatty liver, flaky paint rash'],
    ['**Marasmus has**', '→ No oedema, wasted, hungry'],
    ['**Pellagra: 4 Ds**', '→ Dermatitis, Diarrhoea, Dementia, Death'],
    ['**HPV types causing cervical Ca**', '→ 16 & 18'],
]

def rf_table(data, bg):
    rows = [[Paragraph(a, ParagraphStyle('rfa', fontName='Helvetica-Bold', fontSize=6.3, leading=8, textColor=C_BLACK)),
             Paragraph(b, ParagraphStyle('rfb', fontName='Helvetica', fontSize=6.3, leading=8, textColor=C_BLACK))]
            for a, b in [(d[0], d[1]) for d in data]]
    t = Table(rows, colWidths=[50*mm, 34*mm])
    style_list = [
        ('ROWBACKGROUNDS', (0,0), (-1,-1), [C_WHITE, C_LGREY]),
        ('GRID', (0,0), (-1,-1), 0.2, colors.HexColor('#DDDDDD')),
        ('TOPPADDING', (0,0), (-1,-1), 1),
        ('BOTTOMPADDING', (0,0), (-1,-1), 1),
        ('LEFTPADDING', (0,0), (-1,-1), 3),
        ('RIGHTPADDING', (0,0), (-1,-1), 3),
        ('VALIGN', (0,0), (-1,-1), 'TOP'),
    ]
    t.setStyle(TableStyle(style_list))
    return t

lw15 = W*0.5 - 2*mm
rw15 = W - lw15 - 2*mm

left_block = []
left_block.append(sub_header('EPIDEMIOLOGY & BIOSTATISTICS', C_TEAL))
left_block.append(sp(1))
left_block.append(rf_table(rf_data_l, C_TEAL))

right_block = []
right_block.append(sub_header('DISEASE CONTROL, NUTRITION & ENVIRONMENT', C_RED))
right_block.append(sp(1))
right_block.append(rf_table(rf_data_r, C_RED))

story.append(two_col(
    Table([[f] for f in left_block], colWidths=[lw15]),
    Table([[f] for f in right_block], colWidths=[rw15]),
    lw=lw15, rw=rw15
))

story.append(sp(3))

# Footer banner
footer = Table([[
    Paragraph('Park\'s Textbook of Preventive & Social Medicine  |  FMGE Community Medicine Cheat Sheet  |  All rights reserved  |  For examination revision only', 
              ParagraphStyle('foot', fontName='Helvetica-Oblique', fontSize=6, textColor=C_WHITE, alignment=TA_CENTER)),
]], colWidths=[W])
footer.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,-1), C_NAVY),
    ('TOPPADDING', (0,0), (-1,-1), 3),
    ('BOTTOMPADDING', (0,0), (-1,-1), 3),
]))
story.append(footer)

# ── Build ──────────────────────────────────────────────────────────────────────
doc.build(story)
print("PDF generated successfully!")

cd /home/daytona/workspace/fmge-cheatsheet && python generate_cheatsheet.py 2>&1

ls -lh /home/daytona/workspace/fmge-cheatsheet/FMGE_Community_Medicine_Cheatshee…