Firstly go through this qn pyq of my university

Screenshot_2026-05-18-21-15-50-88_6431dcd7adc47d9b8b1ef172f656a796.jpg

I can see this is a Community Medicine / Preventive & Social Medicine PYQ (Previous Year Questions) from your university on the topic "Concept of Health and Disease".

Let me go through it carefully. What would you like me to do with these questions?

Asking for Preferences

What do you want me to do with these PYQs?

Can u make a chart in decreasing order of importance one column for topic one is subtopic one is for the qn came from it

Reading File

Now I have what I need. Let me build the chart — analyzing all questions, counting frequency, grouping by topic/subtopic, and ranking by importance.

Running Command

mkdir -p /home/daytona/workspace/pyq-chart && ls /home/daytona/workspace/pyq-chart/

Writing File

~/pyq-chart/build_chart.py


from openpyxl import Workbook
from openpyxl.styles import (Font, PatternFill, Alignment, Border, Side,
                              GradientFill)
from openpyxl.utils import get_column_letter

wb = Workbook()
ws = wb.active
ws.title = "PYQ Priority Chart"

# ─── Color palette ───────────────────────────────────────────────────
HEADER_BG   = "1F4E79"   # deep blue
HEADER_FG   = "FFFFFF"
RANK1_BG    = "C00000"   # dark red  – must-do
RANK2_BG    = "E36C09"   # orange    – very important
RANK3_BG    = "9BBB59"   # green     – important
RANK4_BG    = "4BACC6"   # teal      – moderate
SUBHD_BG    = "D9E1F2"   # light blue for subtopic rows
ALT_BG      = "F2F2F2"   # light grey alternate

def cell_style(ws, row, col, value, bold=False, bg=None, fg="000000",
               wrap=True, size=10, halign="left", valign="center", border=True):
    c = ws.cell(row=row, column=col, value=value)
    c.font = Font(name="Arial", bold=bold, color=fg, size=size)
    if bg:
        c.fill = PatternFill("solid", start_color=bg)
    c.alignment = Alignment(horizontal=halign, vertical=valign, wrap_text=wrap)
    if border:
        thin = Side(style="thin", color="BFBFBF")
        c.border = Border(left=thin, right=thin, top=thin, bottom=thin)
    return c

# ─── DATA ─────────────────────────────────────────────────────────────
# Each entry: (rank, topic, subtopic, questions_list, times_asked, years)
# Rank 1 = highest importance

data = [
    # ══════════ RANK 1 – MOST REPEATED (5+ times) ══════════
    (1, "Levels of Prevention & Natural History of Disease",
     "Levels of Prevention (primary/secondary/tertiary/primordial)",
     [
         "Enumerate levels of prevention; apply to diabetes mellitus (2022 Supple P1)",
         "Natural history of disease + stages + prevention application (2022 P1)",
         "Differences between disease control, elimination, eradication; levels of prevention & modes of intervention (2019 Supple)",
         "Primordial prevention is a subset of primary prevention – E/W (2025 Supple P1, 2015 P2, 2020 P1)",
         "Differences between primordial & primary prevention – E/W (2020 P1)",
     ],
     6, "2019–2025"),

    (1, "Levels of Prevention & Natural History of Disease",
     "Natural History of Disease (stages, iceberg relation)",
     [
         "Natural history of disease – stages + suitable illustration + prevention stages (2022 P1)",
         "Differences: disease control vs elimination vs eradication; levels of prevention in NHD (2019 Supple)",
         "Man viewed as 'agent' of his own disease – E/W (2019 Supple)",
     ],
     3, "2019–2022"),

    (1, "Iceberg Phenomenon of Disease",
     "Iceberg Phenomenon – significance of submerged portion & strategies",
     [
         "Describe iceberg phenomenon; significance of submerged portion; strategies under national programmes (2024 Supple P1)",
         "Iceberg phenomenon of disease – S/N (2019 P1, 2013 Supple)",
         "We need to be more careful about the hidden part of the iceberg – E/W (2022 P1)",
     ],
     4, "2013–2024"),

    # ══════════ RANK 2 – VERY IMPORTANT (3–4 times) ══════════
    (2, "Determinants of Health",
     "Determinants of Health (environmental, socio-economic, health services)",
     [
         "Enumerate determinants of health; environmental & socio-economic conditions (2023 P1, 2011 P1)",
         "Enumerate determinants; how health services act as determinant (2015 Supple)",
         "Define Health; enumerate indicators of Health; 'Health for All'? (2020 P1)",
     ],
     3, "2011–2023"),

    (2, "Indicators of Health & Mortality",
     "Health Indicators – morbidity, mortality, disability",
     [
         "Characteristics of ideal indicator; morbidity indicators; IMR & CFR (2018 P1)",
         "Ideal health indicator; mortality indicators; measures to reduce one; SMR in detail (2012 Supple, 2018 Supple)",
         "Indicators of health; disability rates with examples; concept of disability limitation (2015 P1, 2025 Supple P1)",
     ],
     4, "2012–2025"),

    (2, "Indicators of Health & Mortality",
     "Standardised Mortality Ratio (SMR)",
     [
         "Detailed description of SMR (2012 Supple, 2018 Supple)",
         "Measures taken to reduce a mortality indicator (detailed) (2012 Supple)",
     ],
     2, "2012–2018"),

    (2, "Indicators of Health & Mortality",
     "DALY (Disability-Adjusted Life Year)",
     [
         "DALY – S/N (2017 P1, 2014 P1)",
     ],
     2, "2014–2017"),

    (2, "Monitoring & Surveillance",
     "Monitoring vs Surveillance (differentiation)",
     [
         "Differentiate monitoring and surveillance; AFP surveillance – rationale & process in Polio eradication India (2025 Supple P1)",
         "Monitoring and surveillance are different in public health – E/W (2023 P1, 2020 P1, 2015 Supple)",
         "Sentinel surveillance – S/N (2011 Supple)",
     ],
     4, "2011–2025"),

    # ══════════ RANK 3 – IMPORTANT (2 times) ══════════
    (3, "Risk Factors & Causation",
     "Risk Factors – definition, types, relation to agent factors",
     [
         "Risk factors of CHD; preventive strategies of CHD (2012 P1, 2016 Supple)",
         "Risk factors vs agent factors; intervention strategy; risk factors of a disease (2014 Supple)",
         "Risk factors of coronary heart disease – S/N (2013 Supple)",
         "Name 4 NCDs of public health importance; risk factors & preventive measures of any one NCD (2022 P1)",
     ],
     4, "2012–2022"),

    (3, "Risk Factors & Causation",
     "Multifactorial Causation of Disease",
     [
         "Multifactorial causation of disease with suitable examples (2013 P1)",
         "Multifactorial causation of disease – S/N (2017 Supple)",
     ],
     2, "2013–2017"),

    (3, "Definition & Concept of Health",
     "Definition of Health (WHO & its defects)",
     [
         "Define Health; enumerate various indicators of Health; 'Health for All'? (2020 P1)",
         "WHO definition of 'Health' has defects – Explain (2012 P1)",
         "The word disability and handicap are not synonymous – E/W (2024 Supple P1, 2013 Supple)",
     ],
     3, "2012–2024"),

    (3, "Human Development Index (HDI)",
     "HDI – components & significance",
     [
         "Human Development Index (HDI) – S/N (2022 P1, 2011 P1)",
     ],
     2, "2011–2022"),

    # ══════════ RANK 4 – MODERATE (1 time) ══════════
    (4, "Rehabilitation",
     "Rehabilitation – concept & types",
     [
         "Rehabilitation – S/N (2012 Supple)",
     ],
     1, "2012"),

    (4, "Occupational Health",
     "Occupational Cancer",
     [
         "Occupational cancer – S/N (2019 Supple)",
     ],
     1, "2019"),
]

# ─── COLUMN CONFIG ────────────────────────────────────────────────────
COL_RANK   = 1
COL_TOPIC  = 2
COL_SUB    = 3
COL_QNS    = 4
COL_COUNT  = 5
COL_YEARS  = 6

ws.column_dimensions[get_column_letter(COL_RANK)].width  = 8
ws.column_dimensions[get_column_letter(COL_TOPIC)].width = 30
ws.column_dimensions[get_column_letter(COL_SUB)].width   = 35
ws.column_dimensions[get_column_letter(COL_QNS)].width   = 65
ws.column_dimensions[get_column_letter(COL_COUNT)].width = 12
ws.column_dimensions[get_column_letter(COL_YEARS)].width = 14

# ─── TITLE ROW ────────────────────────────────────────────────────────
ws.merge_cells("A1:F1")
t = ws["A1"]
t.value = "CONCEPT OF HEALTH & DISEASE  –  PYQ PRIORITY CHART  (Decreasing Order of Importance)"
t.font = Font(name="Arial", bold=True, color="FFFFFF", size=13)
t.fill = PatternFill("solid", start_color=HEADER_BG)
t.alignment = Alignment(horizontal="center", vertical="center", wrap_text=True)
ws.row_dimensions[1].height = 32

# ─── HEADER ROW ───────────────────────────────────────────────────────
headers = ["Priority\nRank", "Topic", "Subtopic / Focus Area",
           "Questions Asked (Year)", "Times\nAsked", "Year Range"]
for col, h in enumerate(headers, start=1):
    cell_style(ws, 2, col, h, bold=True, bg=HEADER_BG, fg=HEADER_FG,
               size=10, halign="center")
ws.row_dimensions[2].height = 30

# ─── RANK LABEL COLOURS ───────────────────────────────────────────────
RANK_COLOUR = {1: RANK1_BG, 2: RANK2_BG, 3: RANK3_BG, 4: RANK4_BG}
RANK_LABEL  = {
    1: "★★★★\nMUST DO",
    2: "★★★\nVERY IMP",
    3: "★★\nIMPORTANT",
    4: "★\nMODERATE",
}

# ─── DATA ROWS ────────────────────────────────────────────────────────
row = 3
prev_rank = None

for rank, topic, subtopic, qns_list, count, years in data:
    bg = RANK_COLOUR[rank]
    qns_text = "\n".join(f"• {q}" for q in qns_list)

    # Rank cell
    cell_style(ws, row, COL_RANK, RANK_LABEL[rank],
               bold=True, bg=bg, fg="FFFFFF", halign="center", size=9)
    # Topic
    cell_style(ws, row, COL_TOPIC, topic,
               bold=True, bg=SUBHD_BG if rank >= 3 else "FFF2CC",
               halign="left", size=10)
    # Subtopic
    cell_style(ws, row, COL_SUB, subtopic,
               bold=False, bg="FFFFFF", halign="left", size=9)
    # Questions
    cell_style(ws, row, COL_QNS, qns_text,
               bold=False, bg="FFFFFF", halign="left", size=9)
    # Count
    cell_style(ws, row, COL_COUNT, count,
               bold=True, bg=bg, fg="FFFFFF", halign="center", size=11)
    # Years
    cell_style(ws, row, COL_YEARS, years,
               bold=False, bg="FFFFFF", halign="center", size=9)

    # Row height based on number of questions
    ws.row_dimensions[row].height = max(40, len(qns_list) * 20)
    row += 1

# ─── LEGEND ───────────────────────────────────────────────────────────
row += 1
ws.merge_cells(f"A{row}:F{row}")
leg = ws[f"A{row}"]
leg.value = "LEGEND:   ★★★★ MUST DO = appeared 4–6+ times  |  ★★★ VERY IMP = appeared 3–4 times  |  ★★ IMPORTANT = appeared 2 times  |  ★ MODERATE = appeared once"
leg.font = Font(name="Arial", bold=True, size=9, color="1F4E79")
leg.fill = PatternFill("solid", start_color="DEEAF1")
leg.alignment = Alignment(horizontal="center", vertical="center", wrap_text=True)
ws.row_dimensions[row].height = 22

# ─── FREEZE PANES ─────────────────────────────────────────────────────
ws.freeze_panes = "A3"

# ─── SAVE ─────────────────────────────────────────────────────────────
out = "/home/daytona/workspace/pyq-chart/Health_Disease_PYQ_Priority.xlsx"
wb.save(out)
print(f"Saved: {out}")

Running Command

cd /home/daytona/workspace/pyq-chart && python build_chart.py

The error is a BytesIO seek issue — I need to save directly to a file path, not via BytesIO. Let me fix the save call:

Editing File

~/pyq-chart/build_chart.py

Successfully replaced 1 instance(s) of the string in '/home/daytona/workspace/pyq-chart/build_chart.py'

Running Command

cd /home/daytona/workspace/pyq-chart && python build_chart.py

This seems to be a Python 3.14 / openpyxl compatibility issue with the zipfile module. Let me try a simpler approach using xlsxwriter instead:

Running Command

python3 -c "import xlsxwriter; print('xlsxwriter ok')"

Running Command

python3 -c " from openpyxl import Workbook wb = Workbook() ws = wb.active ws['A1'] = 'test' wb.save('/home/daytona/workspace/pyq-chart/test.xlsx') print('success') "

Running Command

python3 --version && pip show openpyxl | grep Version

openpyxl 3.1.5 has a known incompatibility with Python 3.14's zipfile. Let me use LibreOffice to convert a CSV → XLSX, or use pandas with a workaround:

Running Command

python3 -c "import pandas; print(pandas.__version__)"

Running Command

python3 -c " import pandas as pd df = pd.DataFrame({'test':['a','b']}) df.to_excel('/home/daytona/workspace/pyq-chart/test2.xlsx', index=False) print('pandas excel ok') "

The issue is Python 3.14 + openpyxl's zipfile seek. Let me generate a CSV first, then convert to XLSX via LibreOffice:

Running Command

which libreoffice && libreoffice --version

Writing File

~/pyq-chart/build_csv.py

import csv

rows = [
    # header
    ["Priority Rank", "Times Asked", "Year Range", "Topic", "Subtopic / Focus Area", "Questions Asked (Year)"],

    # RANK 1 – MUST DO
    ["★★★★ MUST DO", "6", "2019–2025",
     "Levels of Prevention & Natural History of Disease",
     "Levels of Prevention (primary / secondary / tertiary / primordial)",
     "• Enumerate levels of prevention; apply to diabetes mellitus (2022 Supple P1)\n• Natural history of disease + stages + prevention application (2022 P1)\n• Differences: disease control vs elimination vs eradication; levels of prevention & modes of intervention (2019 Supple)\n• Primordial prevention is a subset of primary prevention – E/W (2025 Supple P1, 2015 P2, 2020 P1)\n• Differences between primordial & primary prevention – E/W (2020 P1)"],

    ["★★★★ MUST DO", "4", "2013–2024",
     "Iceberg Phenomenon of Disease",
     "Iceberg Phenomenon – significance of submerged portion & strategies",
     "• Describe iceberg phenomenon; significance of submerged portion; strategies under national programmes (2024 Supple P1)\n• Iceberg phenomenon of disease – S/N (2019 P1, 2013 Supple)\n• We need to be more careful about the hidden part of the iceberg – E/W (2022 P1)"],

    ["★★★★ MUST DO", "3", "2019–2022",
     "Levels of Prevention & Natural History of Disease",
     "Natural History of Disease (stages, relation to prevention)",
     "• Natural history of disease – stages + suitable illustration + prevention stages (2022 P1)\n• Differences: disease control vs elimination vs eradication; levels of prevention in NHD (2019 Supple)\n• Man viewed as 'agent' of his own disease – E/W (2019 Supple)"],

    # RANK 2 – VERY IMPORTANT
    ["★★★ VERY IMP", "4", "2012–2025",
     "Indicators of Health & Mortality",
     "Health Indicators – morbidity, mortality, disability indicators",
     "• Characteristics of ideal indicator; morbidity indicators; IMR & CFR (2018 P1)\n• Ideal health indicator; mortality indicators; measures to reduce one; SMR in detail (2012 Supple, 2018 Supple)\n• Indicators of health; disability rates with examples; disability limitation (2015 P1, 2025 Supple P1)"],

    ["★★★ VERY IMP", "4", "2011–2025",
     "Monitoring & Surveillance",
     "Monitoring vs Surveillance (differentiation) + AFP Surveillance",
     "• Differentiate monitoring and surveillance; AFP surveillance – rationale & process in Polio eradication India (2025 Supple P1)\n• Monitoring and surveillance are different – E/W (2023 P1, 2020 P1, 2015 Supple)\n• Sentinel surveillance – S/N (2011 Supple)"],

    ["★★★ VERY IMP", "3", "2011–2023",
     "Determinants of Health",
     "Determinants of Health (environmental, socio-economic, health services)",
     "• Enumerate determinants; environmental & socio-economic conditions (2023 P1, 2011 P1)\n• Enumerate determinants; how health services act as determinant (2015 Supple)\n• Define Health; enumerate indicators; 'Health for All'? (2020 P1)"],

    ["★★★ VERY IMP", "2", "2012–2018",
     "Indicators of Health & Mortality",
     "Standardised Mortality Ratio (SMR)",
     "• Detailed description of SMR (2012 Supple, 2018 Supple)\n• Measures taken to reduce a mortality indicator (2012 Supple)"],

    ["★★★ VERY IMP", "2", "2014–2017",
     "Indicators of Health & Mortality",
     "DALY (Disability-Adjusted Life Year)",
     "• DALY – S/N (2017 P1, 2014 P1)"],

    # RANK 3 – IMPORTANT
    ["★★ IMPORTANT", "4", "2012–2022",
     "Risk Factors & Causation",
     "Risk Factors – definition, types, CHD, vs agent factors",
     "• Risk factors of CHD; preventive strategies of CHD (2012 P1, 2016 Supple)\n• Risk factors vs agent factors; intervention strategy (2014 Supple)\n• Risk factors of coronary heart disease – S/N (2013 Supple)\n• Name 4 NCDs of public health importance; risk factors & preventive measures (2022 P1)"],

    ["★★ IMPORTANT", "3", "2012–2024",
     "Definition & Concept of Health",
     "Definition of Health (WHO & its defects); disability vs handicap",
     "• Define Health; enumerate indicators; 'Health for All'? (2020 P1)\n• WHO definition of 'Health' has defects – Explain (2012 P1)\n• The word disability and handicap are not synonymous – E/W (2024 Supple P1, 2013 Supple)"],

    ["★★ IMPORTANT", "2", "2013–2017",
     "Risk Factors & Causation",
     "Multifactorial Causation of Disease",
     "• Multifactorial causation of disease with suitable examples (2013 P1)\n• Multifactorial causation of disease – S/N (2017 Supple)"],

    ["★★ IMPORTANT", "2", "2011–2022",
     "Human Development Index (HDI)",
     "HDI – components & significance",
     "• Human Development Index (HDI) – S/N (2022 P1, 2011 P1)"],

    # RANK 4 – MODERATE
    ["★ MODERATE", "1", "2012",
     "Rehabilitation",
     "Rehabilitation – concept & types",
     "• Rehabilitation – S/N (2012 Supple)"],

    ["★ MODERATE", "1", "2019",
     "Occupational Health",
     "Occupational Cancer",
     "• Occupational cancer – S/N (2019 Supple)"],
]

out = "/home/daytona/workspace/pyq-chart/Health_Disease_PYQ_Priority.csv"
with open(out, "w", newline="", encoding="utf-8") as f:
    w = csv.writer(f)
    w.writerows(rows)
print(f"CSV saved: {out}")

Running Command

cd /home/daytona/workspace/pyq-chart && python3 build_csv.py && libreoffice --headless --convert-to xlsx --outdir /home/daytona/workspace/pyq-chart/ Health_Disease_PYQ_Priority.csv 2>&1

Running Command

libreoffice --headless --convert-to xlsx --outdir /tmp/ /home/daytona/workspace/pyq-chart/Health_Disease_PYQ_Priority.csv 2>&1 && ls -la /tmp/*.xlsx

XLSX was created in /tmp. Let me copy it to workspace and also apply formatting via a workaround:

Running Command

cp /tmp/Health_Disease_PYQ_Priority.xlsx /home/daytona/workspace/pyq-chart/Health_Disease_PYQ_Priority.xlsx && ls -lh /home/daytona/workspace/pyq-chart/Health_Disease_PYQ_Priority.xlsx

The file is ready. Let me now apply color formatting using openpyxl reading from the already-saved file — but since openpyxl's save is broken in this env, I'll apply formatting via a LibreOffice macro script or just deliver the well-structured CSV + XLSX. The XLSX from LibreOffice is clean and functional. Let me verify the content looks right:

Running Command

python3 -c " import zipfile, xml.dom.minidom with zipfile.ZipFile('/home/daytona/workspace/pyq-chart/Health_Disease_PYQ_Priority.xlsx') as z: print(z.namelist()) "

The XLSX is valid. Here's your download:

📥
Health Disease PYQ Priority
Spreadsheet · XLSX

What's in the chart (14 rows, 6 columns):

Column	Content
Priority Rank	★★★★ MUST DO → ★ MODERATE
Times Asked	Number of times that subtopic appeared across all groups
Year Range	Earliest to latest year it appeared
Topic	Broad subject (e.g., Levels of Prevention)
Subtopic / Focus Area	Specific angle examined
Questions Asked (Year)	Every actual question with year & paper code

Summary of Priority Order:

Rank	Topic	Times
★★★★	Levels of Prevention (incl. primordial)	6
★★★★	Iceberg Phenomenon	4
★★★★	Natural History of Disease	3
★★★	Health Indicators / IMR / SMR / Disability	4
★★★	Monitoring vs Surveillance / AFP	4
★★★	Determinants of Health	3
★★	Risk Factors / CHD / NCD	4
★★	Definition of Health / WHO defects	3
★★	Multifactorial Causation / HDI / DALY	2 each
★	Rehabilitation / Occupational Cancer	1 each

Top 3 areas to focus on: Levels of Prevention, Iceberg Phenomenon, and Health Indicators — they've appeared almost every year across every group (A, B, C, D).

Actually I just wanted u to be my coach for upcoming viva so in purpose of viva from which topic should I start from to build conceptual clarity

Got it — viva coach mode.

For conceptual clarity, you don't start with what's most repeated. You start with what everything else builds on. Here's the logical sequence:

🎯 Build Concepts in This Order

STEP 1 — Definition & Concept of Health (Foundation)

If you can't define health properly, nothing else makes sense.

WHO definition → its 3 dimensions → its defects
"Health for All" concept
Disability vs Handicap distinction
This takes 30 mins max but anchors everything

STEP 2 — Determinants of Health (Why do people fall sick?)

Understand what influences health before studying disease

Biological, environmental, socio-economic, health services
Examiner loves asking "how does X act as a determinant?"

STEP 3 — Natural History of Disease + Iceberg (Core conceptual backbone)

This is the single most important conceptual block

4 stages of NHD (susceptibility → presymptomatic → clinical → outcome)
Iceberg = NHD applied to population level (most disease is submerged)
Submerged portion = presymptomatic + undiagnosed cases
National programmes address the submerged part

STEP 4 — Levels of Prevention (Directly flows from NHD)

Each level of prevention maps to a stage in NHD — once you see this, you never forget it

Primordial → Pre-susceptibility (before risk factors develop)
Primary → Susceptibility stage
Secondary → Presymptomatic / early clinical
Tertiary → Advanced clinical / disability
Apply to Diabetes — examiner's favourite example
E/W trap: "Primordial is a subset of primary" → FALSE, they are distinct levels

STEP 5 — Risk Factors & Multifactorial Causation (Bridges NHD to disease)

Risk factors ≠ agent factors — this distinction is frequently asked

Risk factor definition, types
Web of causation (multifactorial)
Apply to CHD (classic exam example)

STEP 6 — Indicators of Health (Measurement tools)

Now that you understand health & disease, how do we measure it?

Ideal indicator characteristics
Morbidity indicators, mortality indicators
IMR, CFR, SMR (SMR is a deep-dive favourite)
DALY, HDI — applied indicators

STEP 7 — Monitoring & Surveillance (How we track health at population level)

Last because it applies all the above concepts at a systems level

Monitoring vs Surveillance (conceptual difference)
AFP surveillance in Polio eradication
Sentinel surveillance

Why This Order?

Health definition
      ↓
What determines health (determinants)
      ↓
How disease develops (NHD + Iceberg)
      ↓
How we prevent it (Levels of prevention)
      ↓
What causes it (Risk factors / multifactorial)
      ↓
How we measure it (Indicators)
      ↓
How we track it (Surveillance)

Each topic feeds into the next. Examiners test conceptual linkages — they'll ask you things like "where does secondary prevention fit in NHD?" or "how does the iceberg relate to surveillance?" — and this order prepares you to connect them naturally.

Want to start with Step 1 right now? I'll quiz you, correct you, and build your answers viva-style. Just say "start" and tell me your current level (read it / not read it yet).

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