National programs in the pnc case
What does 'PNC' refer to in your question? · Which country's national programs are you asking about?
India national programs prenatal antenatal care PNC community medicine ICMR WHO
| Category | Rural (Mother's pkg) | ASHA pkg | Total | Urban (Mother's pkg) | ASHA pkg | Total |
|---|---|---|---|---|---|---|
| LPS | Rs. 1400 | Rs. 600 | Rs. 2000 | Rs. 1000 | Rs. 400 | Rs. 1400 |
| HPS | Rs. 700 | Rs. 600 | Rs. 1300 | Rs. 600 | Rs. 400 | Rs. 1000 |
| Program | Year Launched | Key Focus |
|---|---|---|
| JSY | 2005 | Cash incentive for institutional delivery (BPL women) |
| JSSK | 2011 | Free delivery, free drugs/diagnostics/transport at public facilities |
| PMSMA | 2016 | Free ANC on 9th of every month, high-risk identification |
| SUMAN | 2019 | Dignified, respectful, zero-denial maternity care |
| LaQshya | 2017 | Quality improvement in labour rooms and maternity OTs |
Explain the role and responsibilities of ASHAs in India's prenatal programs
| Component | Amount |
|---|---|
| Cash incentive per delivery | Not less than Rs. 200 |
| Referral transport assistance | Not less than Rs. 250 |
| Balance payment at institution | Paid at hospital/health institution |
| Phase | ASHA's Key Function |
|---|---|
| Pre-pregnancy | Family planning counselling, eligible couple registration |
| Early pregnancy | Early registration of pregnancy, first ANC facilitation |
| Throughout pregnancy | ANC visit mobilization, IFA compliance, TT injection follow-up, danger sign identification, JSY registration |
| At delivery | Escort to facility, ensure institutional delivery |
| Postnatal (0-42 days) | HBNC visits, breastfeeding support, newborn immunization tracking, birth registration |
| Post-delivery | Long-term follow-up for LBW and SNCU-discharged newborns |
Epidemology
What aspect of epidemiology are you asking about?
EPIDEMIOLOGICAL STUDIES
│
├── OBSERVATIONAL
│ ├── DESCRIPTIVE
│ │ ├── Cross-sectional (Prevalence) studies
│ │ ├── Longitudinal studies
│ │ ├── Case reports / Case series
│ │ └── Ecological (Correlational) studies
│ │
│ └── ANALYTICAL
│ ├── Case-Control (Retrospective) study
│ └── Cohort (Prospective / Longitudinal) study
│
└── EXPERIMENTAL
├── Randomized Controlled Trial (RCT)
├── Field trial
└── Community trial
| Suspected Risk Factor | Cases (Disease +) | Controls (Disease -) |
|---|---|---|
| Present | a | b |
| Absent | c | d |
| Total | a + c | b + d |
| Cohort | Disease (Yes) | Disease (No) | Total |
|---|---|---|---|
| Exposed | a | b | a+b |
| Not Exposed | c | d | c+d |
| Feature | Case-Control | Cohort |
|---|---|---|
| Direction | Retrospective (effect → cause) | Prospective (cause → effect) |
| Starting point | Disease (cases) | Exposure status |
| Measure of association | Odds Ratio (OR) | Relative Risk (RR) |
| Incidence measurable? | No | Yes |
| Time & cost | Less | More |
| Suitable for rare disease? | Yes | No |
| Suitable for rare exposure? | No | Yes |
| Bias risk | Recall bias, selection bias | Attrition bias |
| Classic example | Thalidomide and birth defects | Doll & Hill study on smoking & lung cancer |
▲ STRONGEST
│
│ Systematic Reviews & Meta-analyses
│ Randomized Controlled Trials (RCTs)
│ Cohort Studies
│ Case-Control Studies
│ Cross-Sectional Studies
│ Case Reports / Case Series
│ Expert Opinion / Editorials
▼ WEAKEST
| Measure | Formula | Used In |
|---|---|---|
| Incidence Rate | New cases / Population at risk × time | Cohort |
| Prevalence | All cases / Total population at a point in time | Cross-sectional |
| Relative Risk (RR) | Incidence in exposed / Incidence in unexposed | Cohort |
| Odds Ratio (OR) | (a×d) / (b×c) | Case-control |
| Attributable Risk | Incidence in exposed - Incidence in unexposed | Cohort |
| Population Attributable Risk | Incidence in total pop. - Incidence in unexposed | Cohort |
Search for recent examples of each study design in PubMed
"cross-sectional study"[pt] OR "cross-sectional"[tiab]
"case-control study"[MeSH Terms]
"cohort study"[MeSH Terms] AND "prospective studies"[MeSH Terms]
"randomized controlled trial"[pt] AND "double-blind"[tiab]
"systematic review"[pt] AND "meta-analysis"[pt]
"cohort study" AND "prospective"
"case-control study" AND "risk factor"
"cross-sectional study" AND "prevalence"
PMID: 40081632
PMID: 40520452
PMID: 39794615
PMID: 42297908
PMID: 39531790
| Study Design | Example Paper | PMID | Key Measure | Direction |
|---|---|---|---|---|
| Cross-sectional | TyG-BMI & depression (NHANES) | 39531790 | Prevalence OR | Simultaneous |
| Case-control | Cholecystectomy & microscopic colitis | 40081632 | Odds Ratio 2.36 | Retrospective (effect → cause) |
| Prospective cohort | Mediterranean diet & pancreatitis | 40520452 | Hazard Ratio 0.60 | Prospective (cause → effect) |
| RCT (double-blind crossover) | Semaglutide in T1DM | 39794615 | Mean difference +4.8% TIR | Randomized/Experimental |
| Systematic Review + Meta-analysis | Adiposity & 25 cancer types | 42297908 | Pooled RR/HR across 226 studies | Highest evidence level |
Get me singhi pdf of 3rd yr rguhs examination
Create a study guide for a specific 3rd year subject
Which 3rd year MBBS subject? · What format do you want?
RGUHS 3rd year MBBS community medicine PSM syllabus topics high yield exam 2024
Syllabus coverage: Based on RGUHS Phase III Part I curriculum + high-yield topics for university theory exams. Reference: Park's Textbook of Preventive and Social Medicine
| Type | Key Feature | Direction | Measure |
|---|---|---|---|
| Cross-sectional | Single time point, prevalence | Simultaneous | Prevalence |
| Case-Control | Effect → Cause | Retrospective | Odds Ratio |
| Cohort | Cause → Effect | Prospective | Relative Risk |
| RCT | Randomized intervention | Experimental | Risk difference |
| Disease + | Disease - | |
|---|---|---|
| Test + | a (TP) | b (FP) |
| Test - | c (FN) | d (TN) |
| Measure | Definition | When to use | Affected by extreme values? |
|---|---|---|---|
| Mean | Sum/n | Normal distribution | Yes |
| Median | Middle value | Skewed distribution | No |
| Mode | Most frequent value | Qualitative/nominal data | No |
| Test | Used when | Data type |
|---|---|---|
| Student's t-test | Comparing 2 means (small samples) | Continuous |
| ANOVA (F-test) | Comparing 3+ means | Continuous |
| Chi-square (χ²) | Comparing proportions / association | Categorical |
| Paired t-test | Before/after same group | Continuous |
| Correlation (r) | Relationship between 2 variables | Continuous |
| Method | Description |
|---|---|
| Simple random | Each unit has equal probability of selection |
| Systematic | Every nth unit (k = N/n) |
| Stratified | Population divided into strata; random sample from each |
| Cluster | Natural groups (villages) selected; all in selected cluster studied |
| Multistage | Combination of above methods |
| Purposive/Judgement | Non-probability; investigator selects |
| Indicator | Formula | India value (NFHS-5 2019-21) |
|---|---|---|
| Crude Birth Rate (CBR) | Live births per 1000 mid-year population | ~20 |
| Crude Death Rate (CDR) | Deaths per 1000 mid-year population | ~6 |
| Infant Mortality Rate (IMR) | Deaths under 1 year per 1000 live births | 35.2 |
| Neonatal Mortality Rate (NMR) | Deaths in 0-28 days per 1000 live births | 25.5 |
| Perinatal Mortality Rate | (Stillbirths + deaths <7 days) per 1000 births | - |
| Maternal Mortality Ratio (MMR) | Maternal deaths per 100,000 live births | 97 |
| Total Fertility Rate (TFR) | Average children per woman | 2.0 |
| Replacement TFR | TFR needed for population replacement | 2.1 |
| Stage | Birth Rate | Death Rate | Population Growth | Example |
|---|---|---|---|---|
| I - High stationary | High | High | Stable (low) | Pre-industrial societies |
| II - Early expanding | High | Falling | Rapid increase | India 1921-1951 |
| III - Late expanding | Falling | Low | Slowing | India currently |
| IV - Low stationary | Low | Low | Stable (low) | Developed countries |
| V - Declining | Low | Low | Declining | Some European countries |
| Pollutant | Source | Health Effect |
|---|---|---|
| CO | Incomplete combustion | Cherry-red appearance, COHb, death |
| SO₂ | Coal/oil combustion | Bronchospasm, acid rain |
| NO₂ | Traffic, industry | Pulmonary oedema (brown fumes) |
| Particulate matter (PM₂.₅) | Vehicles, industry | Pneumoconiosis, lung cancer |
| Lead | Petrol (leaded), paint | Encephalopathy, anaemia |
| Ozone | Photochemical smog | Eye/lung irritation |
| Feature | Kwashiorkor | Marasmus |
|---|---|---|
| Cause | Protein deficiency (adequate calories) | Overall calorie deficiency |
| Age | 1-5 years | < 1 year |
| Weight | Moderately reduced | Severely reduced (<60% expected) |
| Oedema | Present (pitting) | Absent |
| Appearance | Moon face, pot belly, skin lesions, flaky paint dermatosis | "Old man face", baggy pants, skin and bones |
| Hair | Reddish/brown, flag sign, easily pluckable | Sparse |
| Fatty liver | Present | Absent |
| Serum albumin | Very low | Normal/slightly low |
| Appetite | Poor | Good |
| Vitamin | Deficiency Disease | Key Features |
|---|---|---|
| A (Retinol) | Xerophthalmia | Night blindness, Bitot's spots, keratomalacia |
| B1 (Thiamine) | Beriberi | Dry (peripheral neuropathy), Wet (cardiomyopathy), Wernicke-Korsakoff |
| B2 (Riboflavin) | Ariboflavinosis | Cheilosis, angular stomatitis, corneal vascularisation |
| B3 (Niacin) | Pellagra | 4 D's: Dermatitis, Diarrhoea, Dementia, Death. Maize diet |
| B12 | Megaloblastic anaemia | Macrocytic anaemia, subacute combined degeneration of cord |
| C (Ascorbic acid) | Scurvy | Perifollicular haemorrhage, bleeding gums, corkscrew hairs |
| D (Calciferol) | Rickets (children) / Osteomalacia (adults) | Craniotabes, bow legs, Harrison's sulcus |
| K | Haemorrhagic disease of newborn | Prolonged PT |
| Age | Vaccines |
|---|---|
| Birth | BCG, OPV-0, Hep-B₁ |
| 6 weeks | OPV-1, Penta-1 (DPT+HepB+Hib), IPV-1, Rota-1, fIPV-1 |
| 10 weeks | OPV-2, Penta-2, Rota-2 |
| 14 weeks | OPV-3, Penta-3, IPV-2, Rota-3, fIPV-2 |
| 9 months | MR-1, JE-1 (endemic areas), Vit A-1 |
| 16-24 months | MR-2, DPT booster-1, OPV booster, JE-2, Vit A-2 |
| 5-6 years | DPT booster-2 |
| 10 years | Td |
| 16 years | Td |
| Program | Year | Key Feature |
|---|---|---|
| RNTCP (Revised National TB Control Programme) → now NTEP | 1997 / 2020 | DOTS strategy; Nikshay portal; TB free India by 2025 |
| NVBDCP (National Vector Borne Disease Control Programme) | 2003 (merged) | Covers malaria, dengue, filaria, kala-azar, JE, chikungunya |
| NACP (National AIDS Control Programme) | Phase I: 1992 | ART, ICTC, PPTCT |
| NLEP (National Leprosy Eradication Programme) | 1983 | MDT; elimination achieved <1/10,000 in 2005 |
| NPCB (National Programme for Control of Blindness) | 1976 | Cataract surgery, school eye screening |
| NHM (National Health Mission) | 2013 (NRHM 2005) | ASHA, free drugs, JSY, JSSK |
| NPCDCS | 2010 | NCD screening (diabetes, hypertension, cancer) |
| Scale | Components | Update |
|---|---|---|
| B.G. Prasad (urban/rural) | Per capita monthly income | Updated periodically using CPI |
| Kuppuswamy (urban) | Education + Occupation + Income | Updated using CPI (for income) |
| Uday Pareek (rural) | Standard of living, education, occupation, caste, land, housing, economic status | Rural specific |
| Disease | Causative dust | Occupation |
|---|---|---|
| Silicosis | Free silica (SiO₂) | Mining, stone cutting, glass industry |
| Anthracosis (Coal workers' pneumoconiosis) | Coal dust | Coal mining |
| Asbestosis | Asbestos fibres | Asbestos mining, lagging, shipbuilding |
| Bagassosis | Bagasse (sugarcane waste) | Sugar industry |
| Byssinosis | Cotton dust | Cotton textile workers |
| Farmer's lung | Thermophilic actinomycetes (mouldy hay) | Farmers |
| Siderosis | Iron oxide | Welders, iron miners |
| Topic | Key Fact to Remember |
|---|---|
| Incubation period of cholera | Few hours to 5 days |
| Herd immunity threshold for measles | 95% |
| Cold chain temperature | +2°C to +8°C |
| Replacement level TFR | 2.1 |
| Safe residual chlorine | 0.5 mg/L |
| Optimal fluoride level | 0.5-0.8 mg/L (India) |
| IMR definition | Deaths <1 year per 1000 live births |
| MMR definition | Maternal deaths per 100,000 live births |
| Sensitivity formula | TP/(TP+FN) × 100 |
| Specificity formula | TN/(TN+FP) × 100 |
| Chi-square test used for | Comparison of proportions (categorical data) |
| Kwashiorkor vs Marasmus - oedema | Kwashiorkor = oedema present |
| Pellagra associated diet | Maize diet (niacin deficiency) |
| Nikshay portal | National TB notification system |
| ASHA norm | 1 per 1000 population |
| VVM discard criteria | Inner square darker than outer circle |
| JSY launched | April 2005 |
| JSSK launched | June 2011 |
| PMSMA | Free ANC on 9th of every month |
Tip: This guide covers ~80% of expected short note questions in RGUHS 3rd year theory papers. For LAQs (Long Answer Questions), expand on Epidemiology (study designs + epidemic investigation), National Health Programs (RNTCP/NTEP, Malaria, Immunization), and Biostatistics (statistical tests + sampling). Ask me to expand any section!
Generate a PDF study guide of this short notes for offline access
ls /home/daytona/workspace/ 2>/dev/null && echo "---" && python3 -c "import reportlab; print('reportlab ok')" && python3 -c "from reportlab.lib.pagesizes import A4; print('A4 ok')"
mkdir -p /home/daytona/workspace/psm-study-guide && echo "dir created"
~/psm-study-guide/generate_pdf.py
from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm
from reportlab.lib.colors import HexColor, white, black
from reportlab.platypus import (
SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
HRFlowable, KeepTogether, PageBreak
)
from reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_RIGHT
from reportlab.lib import colors
OUTPUT = "/home/daytona/workspace/psm-study-guide/PSM_ShortNotes_RGUHS_3rdYear.pdf"
# ── Colours ──────────────────────────────────────────────────────────────────
C_NAVY = HexColor("#1a3560")
C_TEAL = HexColor("#0d7377")
C_ORANGE = HexColor("#e05e00")
C_YELLOW = HexColor("#fff4cc")
C_LIGHTBLUE= HexColor("#e8f4f8")
C_GREY = HexColor("#f5f5f5")
C_DARKGREY = HexColor("#555555")
C_RED = HexColor("#c0392b")
C_GREEN = HexColor("#1e7a3c")
C_WHITE = white
# ── Styles ────────────────────────────────────────────────────────────────────
def make_styles():
base = getSampleStyleSheet()
styles = {}
styles['cover_title'] = ParagraphStyle('cover_title',
fontName='Helvetica-Bold', fontSize=26, textColor=C_WHITE,
alignment=TA_CENTER, spaceAfter=8, leading=32)
styles['cover_sub'] = ParagraphStyle('cover_sub',
fontName='Helvetica', fontSize=13, textColor=C_WHITE,
alignment=TA_CENTER, spaceAfter=4, leading=18)
styles['h1'] = ParagraphStyle('h1',
fontName='Helvetica-Bold', fontSize=14, textColor=C_WHITE,
alignment=TA_LEFT, spaceBefore=10, spaceAfter=4,
leftIndent=0, leading=18,
backColor=C_NAVY, borderPad=6)
styles['h2'] = ParagraphStyle('h2',
fontName='Helvetica-Bold', fontSize=11, textColor=C_TEAL,
spaceBefore=10, spaceAfter=3, leading=14)
styles['h3'] = ParagraphStyle('h3',
fontName='Helvetica-BoldOblique', fontSize=10, textColor=C_ORANGE,
spaceBefore=6, spaceAfter=2, leading=13)
styles['body'] = ParagraphStyle('body',
fontName='Helvetica', fontSize=9, textColor=black,
spaceBefore=2, spaceAfter=2, leading=13, leftIndent=10)
styles['bullet'] = ParagraphStyle('bullet',
fontName='Helvetica', fontSize=9, textColor=black,
spaceBefore=1, spaceAfter=1, leading=12,
leftIndent=20, bulletIndent=10)
styles['note'] = ParagraphStyle('note',
fontName='Helvetica-Oblique', fontSize=8.5, textColor=C_DARKGREY,
spaceBefore=2, spaceAfter=2, leading=12, leftIndent=10)
styles['highlight'] = ParagraphStyle('highlight',
fontName='Helvetica-Bold', fontSize=9, textColor=C_RED,
spaceBefore=1, spaceAfter=1, leading=12, leftIndent=10)
styles['footer'] = ParagraphStyle('footer',
fontName='Helvetica', fontSize=7.5, textColor=C_DARKGREY,
alignment=TA_CENTER)
return styles
S = make_styles()
# ── Helper: section heading block ─────────────────────────────────────────────
def unit_heading(text):
tbl = Table([[Paragraph(f' {text}', S['h1'])]], colWidths=[17.5*cm])
tbl.setStyle(TableStyle([
('BACKGROUND', (0,0), (-1,-1), C_NAVY),
('ROUNDEDCORNERS', [4,4,4,4]),
('TOPPADDING', (0,0),(-1,-1), 6),
('BOTTOMPADDING', (0,0),(-1,-1), 6),
('LEFTPADDING', (0,0),(-1,-1), 8),
]))
return tbl
def sub_heading(text):
return Paragraph(f'● {text}', S['h2'])
def sub_sub_heading(text):
return Paragraph(text, S['h3'])
def body(text):
return Paragraph(text, S['body'])
def bullet(text):
return Paragraph(f'• {text}', S['bullet'])
def note(text):
return Paragraph(f'<i>{text}</i>', S['note'])
def highlight(text):
return Paragraph(f'⭐ {text}', S['highlight'])
def spacer(h=4):
return Spacer(1, h)
def hr():
return HRFlowable(width="100%", thickness=0.5, color=C_TEAL, spaceAfter=4)
# ── Generic data table ─────────────────────────────────────────────────────────
def data_table(headers, rows, col_widths=None):
data = [headers] + rows
if col_widths is None:
n = len(headers)
col_widths = [17.5*cm / n] * n
tbl = Table(data, colWidths=col_widths, repeatRows=1)
style = TableStyle([
# header row
('BACKGROUND', (0,0), (-1,0), C_TEAL),
('TEXTCOLOR', (0,0), (-1,0), C_WHITE),
('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
('FONTSIZE', (0,0), (-1,0), 8.5),
('ALIGN', (0,0), (-1,0), 'CENTER'),
('BOTTOMPADDING', (0,0), (-1,0), 5),
('TOPPADDING', (0,0), (-1,0), 5),
# data rows
('FONTNAME', (0,1), (-1,-1), 'Helvetica'),
('FONTSIZE', (0,1), (-1,-1), 8),
('ROWBACKGROUNDS',(0,1), (-1,-1), [C_WHITE, C_GREY]),
('GRID', (0,0), (-1,-1), 0.4, C_DARKGREY),
('ALIGN', (0,0), (-1,-1), 'LEFT'),
('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
('TOPPADDING', (0,1), (-1,-1), 4),
('BOTTOMPADDING', (0,1), (-1,-1), 4),
('LEFTPADDING', (0,0), (-1,-1), 5),
])
tbl.setStyle(style)
return tbl
# ── Yellow callout box ─────────────────────────────────────────────────────────
def callout(text, bg=None):
bg = bg or C_YELLOW
p = Paragraph(text, ParagraphStyle('cb', fontName='Helvetica', fontSize=8.5,
leading=13, leftIndent=4, rightIndent=4))
t = Table([[p]], colWidths=[17*cm])
t.setStyle(TableStyle([
('BACKGROUND', (0,0),(-1,-1), bg),
('BOX', (0,0),(-1,-1), 0.8, C_ORANGE),
('TOPPADDING', (0,0),(-1,-1), 6),
('BOTTOMPADDING', (0,0),(-1,-1), 6),
('LEFTPADDING', (0,0),(-1,-1), 8),
]))
return t
# ── Cover page ─────────────────────────────────────────────────────────────────
def cover_page():
elements = []
# big blue banner
banner_para = Paragraph(
'<b>COMMUNITY MEDICINE (PSM)</b><br/>'
'Short Notes Study Guide',
ParagraphStyle('bn', fontName='Helvetica-Bold', fontSize=22,
textColor=C_WHITE, alignment=TA_CENTER, leading=30))
banner = Table([[banner_para]], colWidths=[17.5*cm])
banner.setStyle(TableStyle([
('BACKGROUND', (0,0),(-1,-1), C_NAVY),
('TOPPADDING', (0,0),(-1,-1), 28),
('BOTTOMPADDING', (0,0),(-1,-1), 28),
]))
elements.append(banner)
elements.append(spacer(14))
meta = [
['3rd Year MBBS | RGUHS Curriculum'],
['Reference: Park\'s Textbook of Preventive & Social Medicine'],
['Units Covered: Epidemiology • Biostatistics • Demography'],
['Environment • Nutrition • MCH • National Programs'],
['Occupational Health • Non-Communicable Diseases'],
]
for m in meta:
elements.append(Paragraph(m[0], ParagraphStyle('meta',
fontName='Helvetica', fontSize=10, textColor=C_NAVY,
alignment=TA_CENTER, spaceAfter=3)))
elements.append(spacer(16))
elements.append(HRFlowable(width='80%', thickness=1.5, color=C_TEAL,
hAlign='CENTER', spaceAfter=14))
tip = (
'<b>How to use this guide:</b> Each unit covers one major PSM topic. '
'Short-note format is optimised for RGUHS theory paper revision. '
'⭐ marks denote very high-yield exam topics.'
)
elements.append(callout(tip, bg=C_LIGHTBLUE))
elements.append(PageBreak())
return elements
# ── CONTENT ───────────────────────────────────────────────────────────────────
def content():
el = []
# ── UNIT 1: EPIDEMIOLOGY ──────────────────────────────────────────────────
el += [unit_heading('UNIT 1: EPIDEMIOLOGY'), spacer()]
el += [sub_heading('1.1 Study Designs'), spacer(2)]
el.append(data_table(
['Design', 'Direction', 'Starts With', 'Measure', 'Best For'],
[
['Cross-sectional', 'Simultaneous', 'Population snapshot', 'Prevalence', 'Chronic disease distribution'],
['Case-Control', 'Retrospective\n(effect→cause)', 'Disease (cases)', 'Odds Ratio (OR)', 'Rare diseases'],
['Cohort', 'Prospective\n(cause→effect)', 'Exposure status', 'Relative Risk (RR)', 'Rare exposures'],
['RCT', 'Experimental', 'Randomised groups', 'Risk difference', 'Testing interventions'],
],
col_widths=[3.2*cm, 3.2*cm, 3.5*cm, 3.2*cm, 4.4*cm]
))
el.append(spacer(4))
el += [
highlight('Cross-sectional = "photograph" — gives prevalence only, cannot prove causation'),
bullet('Case-Control: OR = ad/bc from 2×2 table'),
bullet('Cohort: RR = [a/(a+b)] ÷ [c/(c+d)] — can measure true incidence'),
bullet('RCT: Gold standard. Randomisation controls known + unknown confounders'),
bullet('Classic cohort: Doll & Hill — smoking and lung cancer (British doctors)'),
]
el.append(spacer())
el += [sub_heading('1.2 Screening Tests ⭐'), spacer(2)]
screen_tbl = data_table(
['Measure', 'Formula', 'Mnemonic'],
[
['Sensitivity', 'TP / (TP + FN) × 100', 'PID — Positive In Disease'],
['Specificity', 'TN / (TN + FP) × 100', 'NIH — Negative In Health'],
['PPV', 'TP / (TP + FP) × 100', 'Positive test = true disease?'],
['NPV', 'TN / (TN + FN) × 100', 'Negative test = truly healthy?'],
],
col_widths=[4.5*cm, 7*cm, 6*cm]
)
el.append(screen_tbl)
el.append(spacer(4))
el += [
bullet('High cut-off → ↑ Specificity, ↓ Sensitivity'),
bullet('Low cut-off → ↑ Sensitivity, ↓ Specificity'),
highlight("Wilson's 10 criteria for a good screening programme — know all 10"),
]
el.append(spacer())
el += [sub_heading('1.3 Measures of Disease Frequency ⭐'), spacer(2)]
el += [
bullet('Incidence Rate = New cases / Population at risk × Time'),
bullet('Prevalence = All existing cases / Total population (point in time)'),
bullet('Prevalence ≈ Incidence × Duration of disease'),
bullet('High incidence + short duration → Low prevalence (e.g., common cold)'),
bullet('Low incidence + long duration → High prevalence (e.g., diabetes, leprosy)'),
bullet('Attack Rate = Cases / Population exposed × 100 (used in outbreaks)'),
bullet('SAR = New household cases / Susceptible contacts × 100'),
]
el.append(spacer())
el += [sub_heading('1.4 Epidemic Investigation (9 Steps)'), spacer(2)]
for i, step in enumerate([
'Verify the diagnosis',
'Confirm it is an epidemic',
'Define a case (case definition)',
'Find cases systematically (active case search)',
'Tabulate by Person, Place, Time',
'Plot epidemic curve',
'Formulate hypothesis',
'Test hypothesis',
'Control and prevention measures',
], 1):
el.append(bullet(f'{i}. {step}'))
el.append(spacer(4))
el.append(data_table(
['Epidemic Type', 'Curve Shape', 'Classic Example'],
[
['Point source (common source)', 'Sharp rise & fall within 1 incubation period', 'Food poisoning'],
['Propagated (person-to-person)', 'Multiple waves, each = 1 incubation period apart', 'Measles in school'],
['Mixed', 'Common source then person-to-person', 'Cholera outbreak'],
],
col_widths=[5.5*cm, 7*cm, 5*cm]
))
el.append(PageBreak())
# ── UNIT 2: BIOSTATISTICS ─────────────────────────────────────────────────
el += [unit_heading('UNIT 2: BIOSTATISTICS'), spacer()]
el += [sub_heading('2.1 Measures of Central Tendency ⭐'), spacer(2)]
el.append(data_table(
['Measure', 'Definition', 'When to Use', 'Affected by Outliers?'],
[
['Mean', 'Sum / n', 'Normal distribution', 'Yes'],
['Median', 'Middle value', 'Skewed distribution', 'No'],
['Mode', 'Most frequent value', 'Nominal/qualitative data', 'No'],
],
col_widths=[3*cm, 4.5*cm, 5.5*cm, 4.5*cm]
))
el.append(spacer(4))
el += [
body('<b>Normal Distribution:</b> Mean = Median = Mode. Bell-shaped, symmetrical.'),
bullet('±1 SD → 68.27% of values'),
bullet('±2 SD → 95.45% of values'),
bullet('±3 SD → 99.73% of values'),
body('<b>Skewed distribution:</b>'),
bullet('Positive skew (right): Mean > Median > Mode'),
bullet('Negative skew (left): Mean < Median < Mode'),
]
el.append(spacer())
el += [sub_heading('2.2 Statistical Tests ⭐'), spacer(2)]
el.append(data_table(
['Test', 'Used When', 'Data Type'],
[
["Student's t-test", 'Compare 2 means (small samples)', 'Continuous'],
['Paired t-test', 'Before/after in same group', 'Continuous'],
['ANOVA (F-test)', 'Compare 3+ group means', 'Continuous'],
['Chi-square (χ²)', 'Compare proportions / test association', 'Categorical'],
['Correlation (r)', 'Relationship between 2 variables', 'Continuous'],
],
col_widths=[5*cm, 8*cm, 4.5*cm]
))
el.append(spacer(4))
el += [
highlight('p < 0.05 = statistically significant → reject null hypothesis'),
bullet('Type I error (α): Reject true null hypothesis (false positive) — α = 0.05'),
bullet('Type II error (β): Accept false null hypothesis (false negative)'),
bullet('Power = 1 – β (ability to detect a true difference)'),
bullet('95% CI not crossing 1 (for OR/RR) = statistically significant'),
]
el.append(spacer())
el += [sub_heading('2.3 Sampling Methods'), spacer(2)]
el.append(data_table(
['Method', 'Description'],
[
['Simple random', 'Each unit has equal probability of selection'],
['Systematic', 'Every kth unit; k = N/n'],
['Stratified', 'Divide into strata; random sample from each stratum'],
['Cluster', 'Natural groups selected; all units in selected clusters studied'],
['Multistage', 'Combination of above methods'],
],
col_widths=[4.5*cm, 13*cm]
))
el.append(spacer(3))
el.append(highlight('Cluster sampling (EPI 30×7) used for national immunisation coverage surveys in India'))
el.append(PageBreak())
# ── UNIT 3: DEMOGRAPHY ────────────────────────────────────────────────────
el += [unit_heading('UNIT 3: DEMOGRAPHY & VITAL STATISTICS'), spacer()]
el += [sub_heading('3.1 Key Vital Statistics Indicators ⭐'), spacer(2)]
el.append(data_table(
['Indicator', 'Denominator', 'Multiplier', 'India (NFHS-5)'],
[
['Crude Birth Rate (CBR)', 'Mid-year population', '1000', '~20'],
['Crude Death Rate (CDR)', 'Mid-year population', '1000', '~6'],
['Infant Mortality Rate (IMR)', 'Live births', '1000', '35.2'],
['Neonatal Mortality Rate (NMR)', 'Live births', '1000', '25.5'],
['Maternal Mortality Ratio (MMR)', 'Live births', '100,000', '97'],
['Total Fertility Rate (TFR)', '—', '—', '2.0'],
],
col_widths=[5.5*cm, 4.5*cm, 2.5*cm, 5*cm]
))
el.append(spacer(4))
el += [
highlight('IMR = most sensitive indicator of overall health status of a community'),
bullet('Replacement level TFR = 2.1 (population maintains itself)'),
bullet('MMR = maternal deaths per 100,000 live births (ratio, not rate)'),
]
el.append(spacer())
el += [sub_heading('3.2 Demographic Transition'), spacer(2)]
el.append(data_table(
['Stage', 'Birth Rate', 'Death Rate', 'Growth', 'Example'],
[
['I — High stationary', 'High', 'High', 'Stable/Low', 'Pre-industrial'],
['II — Early expanding', 'High', 'Falling', 'Rapid', 'India 1921–1951'],
['III — Late expanding', 'Falling', 'Low', 'Slowing', 'India currently'],
['IV — Low stationary', 'Low', 'Low', 'Stable/Low', 'Developed countries'],
['V — Declining', 'Low', 'Low', 'Negative', 'Some European nations'],
],
col_widths=[4*cm, 2.5*cm, 2.5*cm, 3*cm, 5.5*cm]
))
el.append(spacer(3))
el.append(highlight('1921 = "Year of Great Divide" — India\'s population began rapid increase'))
el.append(PageBreak())
# ── UNIT 4: ENVIRONMENT ───────────────────────────────────────────────────
el += [unit_heading('UNIT 4: ENVIRONMENT & HEALTH'), spacer()]
el += [sub_heading('4.1 Water — Standards & Purification ⭐'), spacer(2)]
el += [
bullet('WHO safe water: Coliform count = 0 per 100 mL'),
bullet('pH 7.0–8.5 | Turbidity <1 NTU | TDS <500 mg/L'),
]
el.append(spacer(3))
el.append(data_table(
['Fluoride Level', 'Effect'],
[
['< 0.5 mg/L', 'Dental caries'],
['0.5–0.8 mg/L (India optimal)', 'Beneficial — prevents caries'],
['> 1.5 mg/L', 'Dental fluorosis'],
['> 3.0 mg/L', 'Skeletal fluorosis'],
],
col_widths=[6*cm, 11.5*cm]
))
el.append(spacer(4))
el.append(body('<b>Purification steps (slow sand filter):</b>'))
for s in ['Storage/Sedimentation → removes 75–90% bacteria',
'Coagulation/Flocculation (Alum 5–40 mg/L)',
'Filtration — slow sand filter removes 98–99% bacteria',
'Disinfection (Chlorination)']:
el.append(bullet(s))
el.append(highlight('Residual chlorine = 0.5 mg/L after 1 hour contact = safe water'))
el.append(spacer())
el += [sub_heading('4.2 Air Pollution'), spacer(2)]
el.append(data_table(
['Pollutant', 'Source', 'Health Effect'],
[
['CO (carbon monoxide)', 'Incomplete combustion', 'Cherry-red appearance, COHb, death'],
['SO₂', 'Coal/oil combustion', 'Bronchospasm, acid rain'],
['NO₂', 'Traffic, industry', 'Pulmonary oedema (brown fumes)'],
['PM₂.₅', 'Vehicles, industry', 'Pneumoconiosis, lung cancer'],
['Ozone', 'Photochemical smog', 'Eye and lung irritation'],
],
col_widths=[4*cm, 5.5*cm, 8*cm]
))
el.append(spacer(4))
el += [
bullet('London smog (1952): SO₂ + fog + temperature inversion → 4000 deaths. Cold, reducing smog.'),
bullet('LA smog: Photochemical. Warm, oxidising. NO₂ + hydrocarbons + UV → ozone.'),
]
el.append(spacer())
el += [sub_heading('4.3 Hospital / Biomedical Waste'), spacer(2)]
el.append(data_table(
['Bag Colour', 'Waste Type', 'Disposal Method'],
[
['Yellow', 'Anatomical, pathological waste', 'Incineration'],
['Red', 'Contaminated recyclable waste', 'Autoclaving'],
['Blue/White (sharps)', 'Needles, blades', 'Shredding / encapsulation'],
['Black', 'General solid waste', 'Landfill'],
],
col_widths=[3.5*cm, 8*cm, 6*cm]
))
el.append(highlight('BMW Rules: Biomedical Waste Management & Handling Rules 1998 (amended 2016)'))
el.append(PageBreak())
# ── UNIT 5: NUTRITION ─────────────────────────────────────────────────────
el += [unit_heading('UNIT 5: NUTRITION'), spacer()]
el += [sub_heading('5.1 Protein-Energy Malnutrition (PEM) ⭐'), spacer(2)]
el.append(data_table(
['Feature', 'Kwashiorkor', 'Marasmus'],
[
['Cause', 'Protein deficiency (adequate calories)', 'Overall calorie deficiency'],
['Age', '1–5 years', '< 1 year'],
['Weight', 'Moderately reduced', 'Severely reduced (<60% expected)'],
['Oedema', 'PRESENT (pitting)', 'ABSENT'],
['Appearance', 'Moon face, pot belly, flaky paint dermatosis', '"Old man face", baggy pants'],
['Hair changes', 'Reddish/brown, flag sign, easily pluckable', 'Sparse'],
['Fatty liver', 'Present', 'Absent'],
['Serum albumin', 'Very low', 'Normal/slightly low'],
['Appetite', 'Poor', 'Good'],
],
col_widths=[4*cm, 6.75*cm, 6.75*cm]
))
el.append(spacer(3))
el += [
bullet('Gomez classification (wt for age): Grade I = 75–90% | Grade II = 60–74% | Grade III = <60%'),
bullet('Wellcome Trust: Based on weight + presence/absence of oedema'),
]
el.append(spacer())
el += [sub_heading('5.2 Vitamin Deficiency Diseases ⭐'), spacer(2)]
el.append(data_table(
['Vitamin', 'Disease', 'Key Clinical Features'],
[
['A (Retinol)', 'Xerophthalmia', 'Night blindness, Bitot\'s spots, keratomalacia'],
['B1 (Thiamine)', 'Beriberi', 'Dry (neuropathy), Wet (cardiomyopathy), Wernicke-Korsakoff'],
['B2 (Riboflavin)', 'Ariboflavinosis', 'Cheilosis, angular stomatitis, corneal vascularisation'],
['B3 (Niacin)', 'Pellagra', '4 D\'s: Dermatitis, Diarrhoea, Dementia, Death. Maize diet.'],
['C (Ascorbic acid)', 'Scurvy', 'Perifollicular haemorrhage, bleeding gums, corkscrew hairs'],
['D (Calciferol)', 'Rickets / Osteomalacia', 'Craniotabes, bow legs, Harrison\'s sulcus'],
['B12', 'Megaloblastic anaemia', 'Macrocytic anaemia, subacute combined degeneration of cord'],
],
col_widths=[3.5*cm, 4.5*cm, 9.5*cm]
))
el.append(spacer(3))
el.append(highlight('Pellagra = Niacin deficiency = Maize diet — "4 D\'s"'))
el.append(PageBreak())
# ── UNIT 6: MCH ───────────────────────────────────────────────────────────
el += [unit_heading('UNIT 6: MATERNAL & CHILD HEALTH'), spacer()]
el += [sub_heading('6.1 Antenatal Care Schedule ⭐'), spacer(2)]
el.append(data_table(
['Visit', 'Timing', 'Key Activities'],
[
['1st ANC', 'Within 12 weeks', 'Registration, history, first check-up, booking investigations'],
['2nd ANC', '14–26 weeks', 'Anomaly scan, IFA compliance check'],
['3rd ANC', '28–34 weeks', 'Presentation, BP, growth assessment'],
['4th ANC', '36 weeks to term', 'Birth preparedness, mode of delivery planning'],
],
col_widths=[2.5*cm, 4*cm, 11*cm]
))
el.append(spacer(4))
el += [
highlight('WHO now recommends minimum 8 ANC contacts (not just 4)'),
bullet('3 TT doses: TT-1 (early pregnancy), TT-2 (4 weeks later), TT-booster (if previously immunised)'),
bullet('IFA: 1 tablet daily from 12 weeks — 100 mg elemental iron + 500 mcg folic acid'),
]
el.append(spacer())
el += [sub_heading('6.2 Universal Immunisation Programme (UIP) ⭐'), spacer(2)]
el.append(data_table(
['Age', 'Vaccines'],
[
['Birth', 'BCG, OPV-0, Hep-B₁'],
['6 weeks', 'OPV-1, Penta-1 (DPT+HepB+Hib), IPV-1, Rota-1, fIPV-1'],
['10 weeks', 'OPV-2, Penta-2, Rota-2'],
['14 weeks', 'OPV-3, Penta-3, IPV-2, Rota-3, fIPV-2'],
['9 months', 'MR-1, JE-1 (endemic areas), Vit A dose 1'],
['16–24 months', 'MR-2, DPT booster-1, OPV booster, JE-2, Vit A-2'],
['5–6 years', 'DPT booster-2'],
['10 & 16 years', 'Td'],
],
col_widths=[4*cm, 13.5*cm]
))
el.append(spacer(4))
el += [
bullet('Cold chain: +2°C to +8°C (OPV: –20°C)'),
bullet('VVM: Inner square LIGHTER than outer circle = usable. DARKER = DISCARD'),
highlight('Herd immunity threshold: Measles = 95% | Polio = 82–87% | Smallpox = 85%'),
]
el.append(PageBreak())
# ── UNIT 7: NATIONAL PROGRAMS ─────────────────────────────────────────────
el += [unit_heading('UNIT 7: NATIONAL HEALTH PROGRAMS'), spacer()]
el += [sub_heading('7.1 Key Programs at a Glance ⭐'), spacer(2)]
el.append(data_table(
['Program', 'Year', 'Key Focus'],
[
['JSY (Janani Suraksha Yojana)', '2005', 'Cash incentive for institutional delivery (BPL)'],
['JSSK (Janani Shishu Suraksha)', '2011', 'Free delivery, free drugs/transport at public facilities'],
['PMSMA', '2016', 'Free ANC on 9th of every month'],
['SUMAN', '2019', 'Zero-denial, dignified maternity care'],
['NTEP (formerly RNTCP)', '2020', 'TB elimination by 2025; DOTS, Nikshay portal'],
['NVBDCP', '2003', 'Malaria, dengue, filaria, kala-azar, JE, chikungunya'],
['NACP', 'Phase I: 1992', 'HIV/AIDS — ART, ICTC, PPTCT'],
['NLEP', '1983', 'Leprosy MDT; elimination achieved 2005 (<1/10,000)'],
['NHM', '2013', 'ASHA, free drugs, JSY, JSSK under rural + urban missions'],
],
col_widths=[5.5*cm, 2.5*cm, 9.5*cm]
))
el.append(spacer())
el += [sub_heading('7.2 NTEP (National Tuberculosis Elimination Programme) ⭐'), spacer(2)]
el += [
bullet('Treatment regimen (new cases): 2(HRZE) / 4(HR) — Total 6 months'),
bullet('DOTS: Directly Observed Treatment Short-course'),
bullet('Nikshay portal: National TB case notification system'),
bullet('Nikshay Poshan Yojana: Rs. 500/month nutritional support to TB patients'),
highlight('India target: Eliminate TB by 2025 (SDG global target is 2030)'),
]
el.append(spacer(4))
el.append(data_table(
['Diagnostic Test', 'Details'],
[
['Sputum smear (ZN stain)', 'AFB positive/negative; rapid, cheap'],
['CBNAAT (Xpert MTB/RIF)', 'Rapid PCR-based; detects RIF resistance in 2 hours'],
['Culture (LJ medium)', 'Gold standard; 6–8 weeks turnaround'],
['TST (Mantoux)', 'Positive >10 mm induration in immunocompetent'],
],
col_widths=[5.5*cm, 12*cm]
))
el.append(spacer())
el += [sub_heading('7.3 Malaria — Short Notes ⭐'), spacer(2)]
el.append(data_table(
['Species', 'Type', 'Incubation', 'Fever Pattern'],
[
['P. vivax', 'Benign tertian', '14 days', 'Every 48 hrs'],
['P. falciparum', 'Malignant tertian', '12 days', 'Irregular / every 48 hrs'],
['P. malariae', 'Quartan', '28 days', 'Every 72 hrs'],
['P. ovale', 'Ovale tertian', '17 days', 'Every 48 hrs'],
],
col_widths=[3.5*cm, 3.5*cm, 3.5*cm, 7*cm]
))
el.append(spacer(4))
el += [
bullet('Vector: Anopheles (female), breeds in clear unpolluted water, bites at dusk/night'),
bullet('API = Confirmed malaria cases × 1000 / Population at risk'),
bullet('Treatment: P. vivax → Chloroquine + Primaquine (14 days) | P. falciparum → ACT + Primaquine (single dose)'),
highlight('DDT still used in India under NVBDCP for indoor residual spraying (IRS)'),
]
el.append(PageBreak())
# ── UNIT 8: HEALTH EDUCATION ──────────────────────────────────────────────
el += [unit_heading('UNIT 8: HEALTH EDUCATION & SOCIAL MEDICINE'), spacer()]
el += [sub_heading('8.1 Health Education Methods'), spacer(2)]
el.append(data_table(
['Level', 'Methods'],
[
['Individual', 'Counselling, interview, home visits, bedside teaching'],
['Group', 'Lectures, group discussions, role play, demonstrations, workshops'],
['Mass / Community', 'TV, radio, newspapers, posters, folk media, exhibitions'],
],
col_widths=[3.5*cm, 14*cm]
))
el.append(spacer(4))
el += [
bullet('IEC = Information, Education, Communication'),
bullet('BCC = Behaviour Change Communication (newer, more participatory approach)'),
bullet('KAPB Survey = Knowledge, Attitude, Practices, Behaviour — baseline assessment'),
]
el.append(spacer())
el += [sub_heading('8.2 Socioeconomic Status (SES) Scales ⭐'), spacer(2)]
el.append(data_table(
['Scale', 'Setting', 'Components'],
[
['B.G. Prasad', 'Urban + Rural', 'Per capita monthly income (5 classes)'],
['Kuppuswamy', 'Urban', 'Education + Occupation + Income'],
['Uday Pareek', 'Rural', 'Education, occupation, land, housing, standard of living, caste'],
],
col_widths=[4*cm, 3.5*cm, 10*cm]
))
el.append(spacer(3))
el.append(highlight('BG Prasad scale updated periodically using Consumer Price Index (CPI)'))
el.append(PageBreak())
# ── UNIT 9: OCCUPATIONAL HEALTH ───────────────────────────────────────────
el += [unit_heading('UNIT 9: OCCUPATIONAL HEALTH'), spacer()]
el += [sub_heading('9.1 Occupational Lung Diseases (Pneumoconioses) ⭐'), spacer(2)]
el.append(data_table(
['Disease', 'Causative Agent', 'Occupation'],
[
['Silicosis', 'Free silica (SiO₂)', 'Mining, stone cutting, glass industry'],
['Anthracosis', 'Coal dust', 'Coal mining'],
['Asbestosis', 'Asbestos fibres', 'Asbestos mining, lagging, shipbuilding'],
['Bagassosis', 'Bagasse (sugarcane waste)', 'Sugar industry'],
['Byssinosis', 'Cotton dust', 'Cotton textile workers'],
["Farmer's lung", 'Thermophilic actinomycetes (mouldy hay)', 'Farmers'],
['Siderosis', 'Iron oxide', 'Welders, iron miners'],
],
col_widths=[4*cm, 6.5*cm, 7*cm]
))
el.append(spacer(3))
el.append(highlight('Silicosis = most common occupational lung disease. No cure. Prevention = dust suppression + PPE.'))
el.append(spacer())
el += [sub_heading('9.2 Key Occupational Health Acts'), spacer(2)]
el += [
bullet('ESI (Employees State Insurance) Act, 1948: Medical, sickness, maternity, disablement benefits'),
bullet('Factories Act, 1948: Safety, health, welfare of factory workers'),
bullet('Workmens Compensation Act, 1923: Compensation for work-related injury/disease'),
bullet('ESIC covers workers earning ≤ Rs. 21,000/month'),
]
el.append(PageBreak())
# ── UNIT 10: NCDs ─────────────────────────────────────────────────────────
el += [unit_heading('UNIT 10: NON-COMMUNICABLE DISEASES'), spacer()]
el += [sub_heading('10.1 Hypertension — Epidemiology ⭐'), spacer(2)]
el.append(data_table(
['Category', 'Systolic (mmHg)', 'Diastolic (mmHg)'],
[
['Normal', '< 120', '< 80'],
['Pre-hypertension', '120–139', '80–89'],
['Stage 1 HTN', '140–159', '90–99'],
['Stage 2 HTN', '≥ 160', '≥ 100'],
],
col_widths=[5*cm, 6.25*cm, 6.25*cm]
))
el.append(spacer(4))
el += [
bullet('Risk factors: Age, male sex, obesity, sedentary lifestyle, high salt, smoking, alcohol, family history'),
highlight('Hypertension = most important modifiable risk factor for stroke'),
bullet('NPCDCS: Screening for DM + HTN + cancer at HWCs (Health and Wellness Centres)'),
]
el.append(spacer())
el += [sub_heading('10.2 Diabetes Mellitus — Diagnostic Criteria (ADA/WHO)'), spacer(2)]
el.append(data_table(
['Test', 'Diabetes', 'IFG / IGT (Pre-diabetes)'],
[
['Fasting plasma glucose', '≥ 126 mg/dL', 'IFG: 100–125 mg/dL'],
['2-hr OGTT', '≥ 200 mg/dL', 'IGT: 140–199 mg/dL'],
['Random plasma glucose + symptoms', '≥ 200 mg/dL', '—'],
['HbA1c', '≥ 6.5%', 'Pre-DM: 5.7–6.4%'],
],
col_widths=[5.5*cm, 5*cm, 7*cm]
))
el.append(PageBreak())
# ── QUICK REVISION TABLE ──────────────────────────────────────────────────
el += [unit_heading('QUICK REVISION: High-Yield Numbers & Facts'), spacer()]
el.append(data_table(
['Topic', 'Key Fact'],
[
['IMR formula', 'Deaths <1 year per 1000 live births'],
['MMR formula', 'Maternal deaths per 100,000 live births'],
['India IMR (NFHS-5)', '35.2 per 1000 live births'],
['India MMR (SRS 2020)', '97 per 100,000 live births'],
['India TFR (NFHS-5)', '2.0 (below replacement)'],
['Replacement TFR', '2.1'],
['Normal distribution ±1 SD', '68.27% of values'],
['Normal distribution ±2 SD', '95.45% of values'],
['Safe residual chlorine', '0.5 mg/L after 1 hour'],
['Optimal fluoride (India)', '0.5–0.8 mg/L'],
['WHO safe water coliform', '0 per 100 mL'],
['Sensitivity mnemonic', 'PID — Positive In Disease'],
['Specificity mnemonic', 'NIH — Negative In Health'],
['p < 0.05 means', 'Statistically significant'],
['Pellagra ↔ Diet', 'Maize diet / niacin deficiency'],
['Kwashiorkor vs Marasmus — oedema', 'Kwashiorkor = oedema PRESENT'],
['ASHA norm', '1 per 1000 population (1 per habitation in tribal/hilly areas)'],
['VVM discard when', 'Inner square DARKER than outer circle'],
['JSY launched', '12 April 2005'],
['JSSK launched', '1 June 2011'],
['PMSMA day', '9th of every month'],
['India TB elimination target', '2025 (5 years ahead of SDG)'],
['Malaria vector', 'Female Anopheles mosquito'],
['P. falciparum incubation', '12 days'],
['Measles herd immunity', '95%'],
['Chi-square used for', 'Comparison of proportions (categorical data)'],
['Attack Rate used in', 'Outbreak/epidemic investigation'],
['1921 = "Year of Great Divide"', "India's population began rapid increase"],
['Silicosis cause', 'Free silica (SiO₂) — most common occupational lung disease'],
['Pellagra 4 D\'s', 'Dermatitis, Diarrhoea, Dementia, Death'],
],
col_widths=[8.5*cm, 9*cm]
))
el.append(spacer(8))
el.append(callout(
'<b>Study Tip:</b> For RGUHS 3rd year theory, focus on: '
'(1) Epidemiology — study designs, screening, measures of disease frequency; '
'(2) National Health Programs — JSY, JSSK, PMSMA, NTEP, NVBDCP; '
'(3) Biostatistics — statistical tests, normal distribution, sampling; '
'(4) MCH — immunisation schedule, ANC components; '
'(5) Nutrition — PEM differences, vitamin deficiency diseases. '
'Source: Park\'s Textbook of Preventive & Social Medicine.',
bg=C_LIGHTBLUE
))
return el
# ── Page template ─────────────────────────────────────────────────────────────
def on_first_page(canvas, doc):
pass # no header/footer on cover
def on_later_pages(canvas, doc):
canvas.saveState()
canvas.setFont('Helvetica', 7.5)
canvas.setFillColor(C_DARKGREY)
canvas.drawString(2*cm, 1.2*cm,
'Community Medicine (PSM) | 3rd Year MBBS | RGUHS Short Notes Study Guide')
canvas.drawRightString(19.5*cm, 1.2*cm, f'Page {doc.page}')
canvas.setStrokeColor(C_TEAL)
canvas.setLineWidth(0.4)
canvas.line(2*cm, 1.5*cm, 19.5*cm, 1.5*cm)
canvas.restoreState()
# ── Build ─────────────────────────────────────────────────────────────────────
def build():
doc = SimpleDocTemplate(
OUTPUT,
pagesize=A4,
leftMargin=2*cm, rightMargin=2*cm,
topMargin=2*cm, bottomMargin=2*cm,
title='PSM Short Notes — RGUHS 3rd Year MBBS',
author='Orris AI Study Guide',
subject='Community Medicine — Preventive & Social Medicine',
)
story = cover_page() + content()
doc.build(story,
onFirstPage=on_first_page,
onLaterPages=on_later_pages)
print(f"PDF created: {OUTPUT}")
if __name__ == '__main__':
build()
cd /home/daytona/workspace/psm-study-guide && python3 generate_pdf.py 2>&1
ls -lh /home/daytona/workspace/psm-study-guide/PSM_ShortNotes_RGUHS_3rdYear.pdf
PSM ShortNotes RGUHS 3rdYear
PDF Document · PDF
| Unit | Content |
|---|---|
| 1 | Epidemiology — study designs, screening (sensitivity/specificity), disease frequency, epidemic investigation |
| 2 | Biostatistics — central tendency, statistical tests, sampling methods |
| 3 | Demography — vital statistics formulas, India NFHS-5 values, demographic transition |
| 4 | Environment — water standards, fluoride levels, air pollution, biomedical waste bags |
| 5 | Nutrition — PEM (Kwashiorkor vs Marasmus), vitamin deficiency diseases |
| 6 | MCH — ANC schedule, UIP immunisation chart, VVM, cold chain |
| 7 | National Programs — JSY/JSSK/PMSMA/SUMAN, NTEP, Malaria |
| 8 | Health Education — methods, SES scales (BG Prasad, Kuppuswamy) |
| 9 | Occupational Health — pneumoconioses, key acts |
| 10 | NCDs — HTN classification, DM diagnostic criteria |
| ⭐ | Quick revision table — 30 high-yield numbers & facts |
Search for RGUHS previous year PSM questions with explanations
| Status | Reason | |
|---|---|---|
| Search web for publicly available RGUHS PYQs | ✅ Can do | Public domain info |
| Provide explanations for PSM topics/questions | ✅ Can do | Educational content |
| Download/share copyrighted PYQ books (Singhi, etc.) | ❌ Cannot | Copyright infringement |
| Access RGUHS's internal exam database | ❌ Cannot | Restricted/private |
RGUHS 3rd year community medicine PSM previous year question papers free download
RGUHS MBBS community medicine PSM solved question papers 2019 2020 2021 2022 2023
https://rrmch.org/cms/pdf/question-bank/UG-Community-Medicin…
https://www.slideshare.net/slideshow/question-bank-of-commun…
Exam pattern (RS4 CBME scheme): 2 papers × 100 marks each | Long Essay (LAQ) = 10 marks each | Short Essay = 5 marks each | Short Answer = 3 marks each | MCQ = 1 mark each | Pass: 50% aggregate, min 40% per paper
| Indicator | Formula | Example |
|---|---|---|
| Crude Death Rate (CDR) | Deaths per 1000 mid-year population | India CDR ~6 |
| Infant Mortality Rate (IMR) | Deaths <1 year per 1000 live births | India 35.2 (NFHS-5) |
| Neonatal Mortality Rate | Deaths 0-28 days per 1000 live births | India 25.5 |
| Perinatal Mortality Rate | Stillbirths + deaths <7 days per 1000 births | Reflects obstetric care |
| Maternal Mortality Ratio | Maternal deaths per 100,000 live births | India 97 (SRS 2020) |
| Under-5 Mortality Rate | Deaths <5 years per 1000 live births | SDG target: <25 by 2030 |
| Proportional Mortality Rate | Deaths from a cause / Total deaths × 100 | TB = X% of total deaths |
| Disease + | Disease - | |
|---|---|---|
| Test + | a (TP) | b (FP) |
| Test - | c (FN) | d (TN) |
| Type | Definition | Example |
|---|---|---|
| Incubatory carrier | Infectious during incubation period (before symptoms) | Measles, hepatitis A |
| Convalescent carrier | Harbours organism after clinical recovery | Typhoid, cholera |
| Healthy/Contact carrier | Never had clinical disease | Meningococcal meningitis, diphtheria |
| Immune carrier | Has immunity but carries organism | Diphtheria (Schick negative) |
| Temporary carrier | Carrier state for days/weeks | Cholera |
| Chronic carrier | Carrier for months/years | Typhoid (Salmonella typhi in gallbladder), HBsAg+ |
| Source | What's Available | URL |
|---|---|---|
| RRMCH official question bank | RGUHS papers Jan 2008 – Dec 2013 (free PDF) | rrmch.org |
| RGUHS official site | RS4 model papers + curriculum | rguhs.ac.in |
| NRS Medical College | 2021-2022 papers with download links | nrsmc.edu.in |
| Slideshare question bank | Full topic-wise question bank for UGs + PGs | slideshare.net |
| TNMGRMU virtual library | Community Medicine Papers I & II, 2017-2026 | tnmgrmu.ac.in |
Pro tip: Ask me to write a full answer to any of these questions and I'll give you a structured, mark-specific response drawn from Park's Textbook - formatted exactly as needed for RGUHS theory papers.
Find recent 4 papers of rguhs and get answers from parks and prepare a pdf
RGUHS community medicine PSM question paper 2022 2023 2024 phase III part I PDF
RGUHS MBBS 3rd year community medicine paper 2021 2022 questions long essay short essay site:rguhs.ac.in OR site:rrmch.org OR site:nrsmc.edu.in
https://www.scribd.com/document/897813843/frdA021224A2239611…
Water sewage treatment plant
| Process | Condition | End Products |
|---|---|---|
| Aerobic | Presence of free dissolved O₂ | CO₂, water, ammonia, nitrates, sulphates |
| Anaerobic | Absence of oxygen | Methane (CH₄), ammonia, CO₂, H₂ |
SEWAGE INFLOW
│
▼
┌─────────────────────────────────┐
│ PRIMARY TREATMENT │ ← Removes SOLIDS (physical)
│ 1. Screening │
│ 2. Grit Chamber │
│ 3. Sedimentation Tank │
│ (Imhoff tank / septic tank) │
└─────────────────────────────────┘
│ Effluent
▼
┌─────────────────────────────────┐
│ SECONDARY TREATMENT │ ← Removes ORGANIC MATTER (biological)
│ 1. Trickling Filter │
│ 2. Activated Sludge Process │
└─────────────────────────────────┘
│ Treated Effluent
▼
DISPOSAL / REUSE

| Method | Details |
|---|---|
| Anaerobic digestion | Sludge digested in closed tank → produces biogas (methane) + stabilised sludge |
| Sludge drying beds | Digested sludge spread on sand beds → dried by sun and air |
| Manure / land application | Dried sludge used as agricultural fertiliser |
| Incineration | For heavily contaminated or toxic sludge |
| Feature | Trickling Filter | Activated Sludge |
|---|---|---|
| Process | Aerobic (fixed film) | Aerobic (suspended growth) |
| BOD removal | 80-90% | 85-95% |
| Land required | More | Less |
| Operation | Simple | Complex, skilled |
| Cost | Lower | Higher |
| Fly nuisance | Yes (filter flies) | No |
| Suitable for | Small/medium towns | Large cities |
Modes of prevention flow chart
A detailed medical education flowchart titled "Modes of Prevention & Levels of Prevention" showing the natural history of disease (from left to right) mapped to prevention levels. The chart should show: Top row: Natural history stages: Pre-pathogenesis Phase (Susceptibility stage) → Pathogenesis Phase (Early disease → Advanced disease → Outcome: Recovery/Disability/Death) Below that, four rows showing prevention levels aligned to the natural history: 1. PRIMORDIAL PREVENTION (leftmost, before pre-pathogenesis): "Prevent emergence of risk factors" | Actions: Mass education, Healthy public policy, Healthy lifestyle promotion | Target: Total population 2. PRIMARY PREVENTION (pre-pathogenesis stage): Two sub-levels: - Health Promotion: Health education, Nutritional interventions, Environmental modification, Lifestyle changes, Housing improvement, Recreation, Social support - Specific Protection: Immunization, Chemoprophylaxis, Protection from occupational hazards, Use of seat belts, Fluoridation of water, Nutrition supplementation 3. SECONDARY PREVENTION (early disease stage): Early Diagnosis & Prompt Treatment: Screening tests, Case finding, Mass surveys, Selective exams | Goal: Reduce prevalence, shorten duration 4. TERTIARY PREVENTION (advanced disease/outcome stage): Two sub-levels: - Disability Limitation: Treatment to prevent complications, Adequate therapy, Prevent progression - Rehabilitation: Medical, Social, Vocational, Psychological rehabilitation | Goal: Restore function, return to society A horizontal arrow at the bottom shows the disease progression from left to right. Color code each level distinctly: green for primordial, blue for primary, orange for secondary, red for tertiary. Clean white background, clear labels, medical textbook style (Park's Textbook of Preventive and Social Medicine)

PRE-PATHOGENESIS ────────────── PATHOGENESIS PHASE ──────────────▶
(No disease yet) Early disease Advanced disease Outcome
| Phase | Stage | What's Happening |
|---|---|---|
| Pre-pathogenesis | Susceptibility | Agent, host, environment interact; no tissue change yet |
| Pathogenesis - Early | Sub-clinical / Presymptomatic | Tissue changes begun; disease detectable by screening |
| Pathogenesis - Advanced | Clinical disease | Signs and symptoms manifest |
| Outcome | Recovery / Disability / Death | End result of disease process |
| Type | Goal |
|---|---|
| Medical rehabilitation | Restore physical function (physiotherapy, surgery) |
| Social rehabilitation | Restore family and social relationships |
| Vocational rehabilitation | Retrain for suitable employment |
| Psychological rehabilitation | Restore dignity, confidence, mental health |
| Level | Phase | Aim | Key Actions | Target |
|---|---|---|---|---|
| Primordial | Underlying socioeconomic conditions | Prevent risk factors from appearing | Public health policy, health promotion, mass education | Total population |
| Primary | Specific causal factors (pre-disease) | Reduce incidence | Immunization, health education, environmental modification, specific protection | Population + high-risk individuals |
| Secondary | Early disease | Reduce prevalence (shorten duration) | Screening, case-finding, early treatment | Individuals with early/undetected disease |
| Tertiary | Late disease + disability | Reduce complications + restore function | Disability limitation, rehabilitation | Patients with established disability |
Answer these questions
| Factor | Details |
|---|---|
| Agent | Free silica / crystalline quartz (SiO₂) dust particles <5 microns |
| Host | Male workers aged 30-60 years; longer exposure = greater risk |
| Occupations at risk | Stone quarry workers, miners (coal, gold, iron), sandblasters, foundry workers, pottery/ceramic workers, tunnel workers |
| Duration | Typically 10-20 years of exposure before symptoms |
| Incubation | Chronic silicosis: >10 years; Accelerated: 5-10 yrs; Acute: <5 yrs (massive exposure) |
| India | Rajasthan, Madhya Pradesh (stone quarries, slate pencil industry) most affected |
EPIDEMIOLOGICAL STUDIES
│
├── OBSERVATIONAL (no intervention by investigator)
│ │
│ ├── Descriptive
│ │ ├── Case reports / Case series
│ │ └── Cross-sectional (prevalence study)
│ │
│ └── Analytical
│ ├── Case-Control (retrospective)
│ └── Cohort (prospective / retrospective)
│
└── EXPERIMENTAL (investigator intervenes)
├── Randomized Controlled Trial (RCT)
├── Field trial
└── Community trial
| Step | Details |
|---|---|
| 1. Define the problem | "Is smoking associated with lung cancer?" |
| 2. Select CASES | Newly diagnosed histologically confirmed lung cancer patients from hospitals. Include only incident (new) cases. |
| 3. Select CONTROLS | People without lung cancer from same hospital (other diagnoses) or community. Must be similar in age, sex, socioeconomic status. |
| 4. Matching | Match cases and controls for age (±5 yrs), sex, hospital to control confounding |
| 5. Measure exposure | Interview both groups: detailed smoking history (duration, quantity, type), past occupational exposure, diet — using standardized questionnaire |
| 6. Blinding | Interviewer should be blind to case/control status to avoid interviewer bias |
| 7. Calculate OR | Odds Ratio (OR) = ad/bc using 2×2 table |
| 8. Statistical analysis | Chi-square test; 95% confidence interval around OR |
| 9. Interpret | OR >1 = association; assess dose-response relationship |
| 10. Conclusion | Doll & Hill (1950) — classic case-control study confirming smoking-lung cancer link. OR ≈ 9-14 for heavy smokers |
| Lung Cancer (Cases) | No Cancer (Controls) | |
|---|---|---|
| Smoker | a | b |
| Non-smoker | c | d |
| Type | Definition | Example |
|---|---|---|
| Concurrent (Immediate) | Applied as soon as infectious material is discharged from the body; agent destroyed as it is released | Disinfection of sputum, faeces, vomit, contaminated linen during illness; handwashing after patient contact |
| Terminal | Applied after the patient has recovered, died, or been transferred; final cleaning of the room/environment | Cleaning and disinfection of ward after TB patient is discharged; fumigation of isolation room |
| Precurrent (Prophylactic) | Routine disinfection before potential contamination occurs | Chlorination of drinking water, pasteurization of milk, handwashing before procedures |
| Agent | Example |
|---|---|
| Natural agents | Sunlight (UV rays kill bacteria in linen), air/drying |
| Physical agents | Heat (boiling, autoclaving, hot air oven), burning/incineration |
| Chemical agents | Chlorine compounds (bleaching powder), phenol, formaldehyde, glutaraldehyde, alcohol (70%), iodine |
| Type | Mechanism | Example |
|---|---|---|
| Common source | All cases from same source of exposure | Cholera from contaminated water supply |
| Propagated (serial) | Person-to-person spread; epidemic curve rises slowly with multiple peaks | Measles, influenza |
| Mixed | Starts as common source, then propagated spread | Cholera starting from water → person-to-person |
| Type | Feature | Example |
|---|---|---|
| Point source | All exposed at same time/place; sharp single peak in epidemic curve; incubation period = time from peak to end | Food poisoning at a wedding feast |
| Continuous source | Prolonged exposure from same source; plateau pattern | Contaminated well used over weeks |
| Intermittent source | Repeated exposure at intervals; multiple small peaks | Contaminated water supply with intermittent failures |
| Category | Effect |
|---|---|
| Respiratory infections | TB, influenza, meningococcal disease — droplet spread facilitated by close proximity |
| Communicable diseases | Scabies, tinea (skin-to-skin contact); typhus (louse-borne); plague (rat-borne) |
| Mental health | Psychological stress, aggression, sleep deprivation, anxiety |
| Child health | Increased infant mortality, malnutrition, stunting |
| Domestic accidents | Burns, falls — more common in overcrowded homes |
| Sanitation | Shared toilets → faeco-oral disease transmission (cholera, typhoid, dysentery) |
| Nutrition | Inadequate food due to poverty associated with overcrowding |
| Social effects | Juvenile delinquency, domestic violence, sexual abuse |
| Type | Features |
|---|---|
| Single-chamber incinerator | Simple, basic; inadequate for complete combustion |
| Double-chamber (pyrolytic) incinerator | Primary + secondary chambers; most commonly recommended for hospitals |
| Rotary kiln | Large-scale; handles all waste types; temperature 900-1200°C |
| Sub-stage | Description | Example (TB) |
|---|---|---|
| Early pathogenesis | Agent enters host; no symptoms; detectable by screening (sub-clinical) | Tuberculin test positive; primary complex on X-ray |
| Advanced disease | Clinical signs and symptoms appear | Fever, cough, haemoptysis, weight loss |
| Outcome | Recovery / Disability / Death | Cure with treatment; cavitation → disability; death if untreated |
| Category | Methods |
|---|---|
| A — Anthropometric | Weight, height, BMI, MUAC, skinfold thickness, head/chest circumference, weight-for-age, height-for-age, weight-for-height |
| B — Biochemical | Serum albumin, haemoglobin, serum iron, ferritin, serum retinol (Vit A), urinary iodine, serum zinc |
| C — Clinical | Physical examination for signs of deficiency (pallor, Bitot's spots, goitre, oedema, wasting) |
| D — Dietary | 24-hour dietary recall, food frequency questionnaire, diet history, food balance sheets, duplicate meal method |
| Grade | % of median (NCHS) | Classification |
|---|---|---|
| Normal | >90% | Normal |
| Grade I | 75-90% | Mild PEM |
| Grade II | 60-74% | Moderate PEM |
| Grade III | <60% | Severe PEM |
SOURCE → MESSAGE → CHANNEL → RECEIVER → FEEDBACK
│ │ │ │
Encoder Content Medium Decoder
| Type | Examples |
|---|---|
| Physical barriers | Distance, noise, poor lighting, crowded environment |
| Psychological barriers | Fear, anxiety, lack of trust, mental preoccupation, prejudice |
| Semantic barriers | Use of technical/medical jargon, language differences, ambiguous words |
| Cultural barriers | Taboos, customs, beliefs that conflict with health messages |
| Educational barriers | Illiteracy, low health literacy — cannot read printed materials |
| Perceptual barriers | Selective perception — people hear what they want to hear |
| Organisational barriers | Hierarchical differences between health worker and patient |
| Socio-economic barriers | Poverty, social status differences |
| Category | Cause |
|---|---|
| High birth rate | TFR 2.0 (still above replacement in several states); son preference; early marriage; desire for large families |
| Declining death rate | Medical advances, vaccines, sanitation improvements → infant/child mortality fall without corresponding fall in births |
| Low age at marriage | Child marriages (despite legal age 18F/21M) → longer reproductive period |
| Illiteracy | Especially female illiteracy → poor use of contraception, low women's autonomy |
| Poverty | Children seen as economic assets / old-age security |
| Religious/cultural factors | Opposition to family planning in some communities |
| Low contraceptive use | Unmet need for family planning still 9.4% (NFHS-5) |
| Low status of women | Lack of decision-making power over reproduction |
| Category | Measure |
|---|---|
| Family planning services | Free contraceptives at PHC/SC; basket of choices (condoms, OCP, IUCD, sterilization); Mission Parivar Vikas for high-fertility districts |
| Female education | Beti Bachao Beti Padhao; Kasturba Gandhi Schools; educated women = lower TFR (1.6 vs 3.4) |
| Late marriage | Strict enforcement of PCPNDT Act; legal minimum marriage age |
| Male participation | Promote NSV (no-scalpel vasectomy); male condom promotion |
| Incentives/disincentives | Cash incentives for small family norm; two-child norm |
| Poverty alleviation | MGNREGS, social security → reduce demand for children |
| Media & IEC | Small family norm promotion through mass media |
| Status of women | SHGs, economic empowerment, political participation |
| Cause | % |
|---|---|
| Preterm birth complications | 35% |
| Birth asphyxia | 25% |
| Neonatal sepsis | 15% |
| Pneumonia | 10% |
| Diarrhoea | 8% |
| Other | 7% |
Preterm (35%)
___________
/ * \
| 35% PIE | ← Draw circle; divide by proportional sectors
| CHART | Each sector angle = (% × 360°) / 100
\___________/
Preterm: 126° | Asphyxia: 90° | Sepsis: 54°
Pneumonia: 36° | Diarrhoea: 29° | Other: 25°
| Method | Examples |
|---|---|
| Source control (Engineering) | Machinery silencers, vibration dampeners, low-noise technology, proper maintenance |
| Path control | Sound barriers/walls along highways, insulating materials in walls/floors, distance between source and receiver |
| Receiver protection | Ear muffs/ear plugs (PPE) for workers; audiometric testing |
| Legislative | Noise Pollution (Regulation and Control) Rules 2000 (India); permissible limits: 90 dB for industrial, 45 dB residential (day) |
| Land use planning | Buffer zones between industrial and residential areas; airports away from cities |
| Traffic management | Speed limits, honking bans in silence zones (hospitals, schools) |
| Green belts | Planting trees to absorb sound between roads and buildings |
| Disorder | Cause | Affected Group |
|---|---|---|
| PEM (Protein-Energy Malnutrition) | Inadequate calories + protein; kwashiorkor / marasmus | Children under-5 |
| Iron deficiency anaemia | Inadequate dietary iron; most common nutritional deficiency in India | Women of reproductive age, children |
| Vitamin A deficiency | Low dietary intake; Bitot's spots → night blindness → xerophthalmia | Under-5 children |
| Iodine deficiency disorders (IDD) | Low iodine; goitre, cretinism | Himalayan, sub-Himalayan belts |
| Vitamin D deficiency (rickets) | Low sunlight exposure + low dietary calcium | Infants, children |
| Vitamin B12 / Folate deficiency | Megaloblastic anaemia; vegetarian diets | All age groups |
| Zinc deficiency | Growth retardation, impaired immunity | Children |
| Fluorosis | Excess fluoride in groundwater (dental + skeletal) | Endemic fluoride belt (Rajasthan, AP, Punjab) |
| Disease | R₀ | HIT needed |
|---|---|---|
| Measles | 12-18 | 92-95% |
| Polio | 5-7 | 80-86% |
| Smallpox | 5-7 | 80-85% |
| COVID-19 | 2-3 | 50-67% |
| Waste Type | Colour Code | Treatment |
|---|---|---|
| Microbiological/infectious cultures, stocks | Yellow bag | Autoclaving/incineration |
| Contaminated sharps (needles, syringes) | White/translucent puncture-proof container | Autoclave → shredding |
| Solid plastic waste (tubes, petri dishes) | Red bag | Autoclaving → recycling |
| Chemical waste (reagents, solvents) | Black bag | Chemical treatment / secured landfill |
| Liquid waste (culture media, body fluids) | Drain after disinfection with 1% hypochlorite (30 min contact) |
| Service | Details |
|---|---|
| Family planning counselling | Basket of contraceptive choices, spacing methods |
| Contraceptive services | Free supply of condoms, oral contraceptive pills (OCPs), emergency contraception |
| IUCD insertion | Copper-T insertion by trained ANM/MO |
| Antenatal care (ANC) | 4 antenatal visits; TT injection; IFA tablets; BP, Hb monitoring; USS referral |
| Safe delivery | Normal deliveries conducted by trained staff; JSY benefits |
| Postnatal care (PNC) | Visits at 48 hours, 7 days, 42 days for mother and newborn |
| Immunization | Full immunization for children; TT for pregnant women |
| Nutritional supplementation | IFA (Iron-Folic Acid), Vitamin D, calcium tablets in pregnancy |
| STI/RTI diagnosis & treatment | Syndromic management of STIs; partner notification |
| PMSMA services | Free ANC on 9th of every month |
| Screening | Anaemia, hypertension, gestational diabetes, cervical cancer (VIA) |
| JSSK entitlements | Free drugs, diagnostics, diet, transport for delivery |
| Referral | Complicated cases referred to CHC/DH with JSSK transport |
| ASHA support | Accompanies women; registers pregnancies; provides counselling |
Give a short notes pdf for parks text book having all rguhs exam topics