National programs in the pnc case
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
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
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 repo…
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
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 |