maternal mortality India interventions reduction

maternal mortality rate India 2024 causes programs reduction strategies

https://www.pib.gov.in/PressReleasePage.aspx?PRID=2113800

https://www.pib.gov.in/PressReleasePage.aspx?PRID=2113800

ls -la /tmp/workspace/ 2>/dev/null || mkdir -p /tmp/workspace && echo "created"

mkdir -p /tmp/workspace/maternal-mortality && cat << 'PYEOF' > /tmp/workspace/maternal-mortality/generate_pdf.py from reportlab.lib.pagesizes import A4 from reportlab.lib import colors from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle from reportlab.lib.units import inch, cm from reportlab.platypus import ( SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, HRFlowable, KeepTogether ) from reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_JUSTIFY # Output path output_path = "/tmp/workspace/maternal-mortality/Maternal_Mortality_India.pdf" doc = SimpleDocTemplate( output_path, pagesize=A4, rightMargin=2*cm, leftMargin=2*cm, topMargin=2.5*cm, bottomMargin=2*cm, title="Maternal Mortality in India: Causes, Measures & Programs", author="Orris Medical Reference" ) # ── Colour palette ────────────────────────────────────────────────────────── RED_DARK = colors.HexColor("#B71C1C") RED_MED = colors.HexColor("#D32F2F") RED_LIGHT = colors.HexColor("#FFCDD2") BLUE_DARK = colors.HexColor("#1565C0") BLUE_LIGHT = colors.HexColor("#E3F2FD") GREY_DARK = colors.HexColor("#424242") GREY_LIGHT = colors.HexColor("#F5F5F5") GREEN_DARK = colors.HexColor("#1B5E20") GREEN_LIGHT= colors.HexColor("#E8F5E9") ORANGE = colors.HexColor("#E65100") WHITE = colors.white # ── Styles ─────────────────────────────────────────────────────────────────── styles = getSampleStyleSheet() title_style = ParagraphStyle( 'CustomTitle', parent=styles['Title'], fontSize=22, textColor=WHITE, alignment=TA_CENTER, leading=28, spaceAfter=4 ) subtitle_style = ParagraphStyle( 'Subtitle', parent=styles['Normal'], fontSize=11, textColor=colors.HexColor("#FFCDD2"), alignment=TA_CENTER, leading=16 ) h1_style = ParagraphStyle( 'H1', parent=styles['Heading1'], fontSize=14, textColor=WHITE, spaceAfter=6, spaceBefore=10, leading=18, leftIndent=0 ) h2_style = ParagraphStyle( 'H2', parent=styles['Heading2'], fontSize=12, textColor=RED_DARK, spaceAfter=4, spaceBefore=8, leading=16 ) h3_style = ParagraphStyle( 'H3', parent=styles['Heading3'], fontSize=11, textColor=BLUE_DARK, spaceAfter=3, spaceBefore=6, leading=14 ) body_style = ParagraphStyle( 'Body', parent=styles['Normal'], fontSize=10, textColor=GREY_DARK, leading=15, spaceAfter=4, alignment=TA_JUSTIFY ) bullet_style = ParagraphStyle( 'Bullet', parent=styles['Normal'], fontSize=10, textColor=GREY_DARK, leading=14, leftIndent=18, spaceAfter=3, bulletIndent=6 ) note_style = ParagraphStyle( 'Note', parent=styles['Normal'], fontSize=9, textColor=colors.HexColor("#555555"), leading=13, leftIndent=10, italic=True ) footer_style = ParagraphStyle( 'Footer', parent=styles['Normal'], fontSize=8, textColor=colors.HexColor("#888888"), alignment=TA_CENTER ) def section_header(text, bg_color=RED_MED): """Returns a coloured banner paragraph for section headings.""" p_style = ParagraphStyle( 'SectionHdr', parent=styles['Normal'], fontSize=13, textColor=WHITE, leading=18, spaceBefore=10, spaceAfter=4, leftIndent=8, backColor=bg_color, borderPad=(6, 6, 6, 6) ) return Paragraph(f"<b>{text}</b>", p_style) def sub_header(text): return Paragraph(f"<b><font color='#1565C0'>{text}</font></b>", h2_style) def bullet(text): return Paragraph(f"• {text}", bullet_style) def body(text): return Paragraph(text, body_style) def spacer(h=0.15): return Spacer(1, h*inch) def hrule(color=RED_LIGHT): return HRFlowable(width="100%", thickness=1, color=color, spaceAfter=4, spaceBefore=4) # ── Build story ────────────────────────────────────────────────────────────── story = [] # ── COVER BANNER ───────────────────────────────────────────────────────────── cover_data = [[Paragraph( "<font size=22><b>Maternal Mortality in India</b></font><br/>" "<font size=12 color='#FFCDD2'>Causes, Measures &amp; Programs to Reduce MMR</font>", ParagraphStyle('cover', alignment=TA_CENTER, textColor=WHITE, leading=30) )]] cover_table = Table(cover_data, colWidths=[17*cm]) cover_table.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,-1), RED_DARK), ('TOPPADDING', (0,0), (-1,-1), 18), ('BOTTOMPADDING',(0,0),(-1,-1), 18), ('LEFTPADDING', (0,0), (-1,-1), 14), ('RIGHTPADDING',(0,0), (-1,-1), 14), ('ROUNDEDCORNERS', [8]), ])) story.append(cover_table) story.append(spacer(0.25)) # Status bar status_data = [[ Paragraph("<b>MMR 2023:</b> 88 / 1,00,000 live births", ParagraphStyle('stat', fontSize=10, textColor=WHITE, alignment=TA_CENTER)), Paragraph("<b>Decline since 1990:</b> 86%", ParagraphStyle('stat', fontSize=10, textColor=WHITE, alignment=TA_CENTER)), Paragraph("<b>SDG 3.1 Target:</b> &lt; 70 by 2030", ParagraphStyle('stat', fontSize=10, textColor=WHITE, alignment=TA_CENTER)), ]] status_table = Table(status_data, colWidths=[5.6*cm, 5.6*cm, 5.6*cm]) status_table.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,-1), BLUE_DARK), ('TOPPADDING', (0,0), (-1,-1), 8), ('BOTTOMPADDING',(0,0),(-1,-1), 8), ('INNERGRID', (0,0), (-1,-1), 0.5, WHITE), ('BOX', (0,0), (-1,-1), 0, WHITE), ])) story.append(status_table) story.append(spacer(0.3)) # ── SECTION 1: DEFINITION ──────────────────────────────────────────────────── story.append(section_header("1. Definition of Maternal Death (WHO)")) story.append(spacer(0.1)) def_data = [[Paragraph( '"The death of a woman while pregnant or within <b>42 days</b> of termination of pregnancy, ' 'irrespective of the duration and site of pregnancy, from any cause related to or aggravated ' 'by the pregnancy or its management, but <b>not</b> from unintentional or incidental causes."', ParagraphStyle('quote', fontSize=10, textColor=GREY_DARK, leading=15, leftIndent=10, rightIndent=10, italics=1) )]] def_table = Table(def_data, colWidths=[17*cm]) def_table.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,-1), BLUE_LIGHT), ('BOX', (0,0), (-1,-1), 1.5, BLUE_DARK), ('TOPPADDING', (0,0),(-1,-1), 8), ('BOTTOMPADDING',(0,0),(-1,-1), 8), ('LEFTPADDING', (0,0),(-1,-1), 12), ])) story.append(def_table) story.append(spacer(0.1)) story.append(bullet("<b>Direct obstetric deaths:</b> From complications of pregnancy, labour and puerperium, interventions, omissions or incorrect treatment (e.g. PPH, eclampsia, sepsis).")) story.append(bullet("<b>Indirect obstetric deaths:</b> From pre-existing disease aggravated by pregnancy (e.g. cardiac disease, anaemia, TB, malaria).")) story.append(bullet("<b>Late maternal death:</b> Death from direct/indirect obstetric causes, >42 days but <1 year after termination of pregnancy.")) story.append(spacer(0.2)) # ── SECTION 2: CAUSES ──────────────────────────────────────────────────────── story.append(section_header("2. Causes of Maternal Mortality in India")) story.append(spacer(0.1)) sub_header_direct = sub_header("A. Direct Obstetric Causes") story.append(sub_header_direct) causes_data = [ [Paragraph("<b>Cause</b>", body_style), Paragraph("<b>Proportion (%)</b>", body_style), Paragraph("<b>Key Notes</b>", body_style)], ["Haemorrhage (PPH / APH)", "~47%", "Single largest cause; uterine atony main trigger"], ["Pregnancy-related infection / Sepsis", "~12%", "Post-delivery sepsis, chorioamnionitis"], ["Hypertensive disorders\n(pre-eclampsia / eclampsia)", "~7%", "More common in wealthier states (Kerala, TN, MH)"], ["Unsafe abortion complications", "~5%", "Mainly in states with poor access to MTP services"], ["Other direct causes\n(embolism, obstructed labour, anaesthesia)", "~20%", "Includes AFE, obstructed labour, VTE"], ] causes_table = Table( causes_data, colWidths=[6.5*cm, 3.5*cm, 7*cm] ) causes_table.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,0), RED_DARK), ('TEXTCOLOR', (0,0), (-1,0), WHITE), ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'), ('FONTSIZE', (0,0), (-1,-1), 9), ('ROWBACKGROUNDS', (0,1), (-1,-1), [WHITE, RED_LIGHT]), ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor("#BDBDBD")), ('TOPPADDING', (0,0), (-1,-1), 5), ('BOTTOMPADDING',(0,0),(-1,-1), 5), ('LEFTPADDING',(0,0),(-1,-1), 7), ('VALIGN', (0,0), (-1,-1), 'MIDDLE'), ])) story.append(causes_table) story.append(spacer(0.15)) story.append(sub_header("B. Indirect Obstetric Causes (~10%)")) story.append(bullet("Severe anaemia (major underlying factor - often potentiates haemorrhage deaths)")) story.append(bullet("Cardiac disease (rheumatic heart disease most common in India)")) story.append(bullet("Viral hepatitis E (especially dangerous in pregnancy)")) story.append(bullet("Tuberculosis, Malaria, HIV/AIDS")) story.append(bullet("Renal disease")) story.append(spacer(0.15)) story.append(sub_header("C. Underlying Socio-Determinant Factors")) socio_items = [ ("Poor access to healthcare", "Half of maternal deaths occurred at home (SRS data)"), ("Low female literacy & education", "Correlates strongly with MMR across states"), ("Adolescent pregnancy", "Early marriage drives obstetric risk"), ("Malnutrition & anaemia", "Iron/folic acid deficiency extremely prevalent"), ("Poor ANC utilisation", "Many women receive zero antenatal care visits"), ("Regional disparity", "Assam MMR ~195 vs Kerala ~19; UP, Bihar, Rajasthan lag behind"), ("Gender inequity", "Limited autonomy over reproductive decisions"), ("Geographic barriers", "Remote, hilly, tribal areas lack facilities"), ] for item, detail in socio_items: story.append(bullet(f"<b>{item}:</b> {detail}")) story.append(spacer(0.2)) # ── SECTION 3: MEASURES ────────────────────────────────────────────────────── story.append(section_header("3. Measures to Reduce Maternal Mortality", bg_color=colors.HexColor("#1565C0"))) story.append(spacer(0.1)) measures = [ ("Antenatal Care (ANC)", ["Minimum 4 ANC visits (WHO recommends 8 contacts); early 1st-trimester registration", "Screening & management of anaemia, hypertension, gestational diabetes", "Universal IFA (Iron-Folic Acid) supplementation — shown to significantly improve outcomes (meta-analysis)", "Calcium supplementation to prevent pre-eclampsia (supported by RCT meta-analysis of 26 trials)", "TT/Td immunisation; birth preparedness counselling"]), ("Skilled Birth Attendance", ["All deliveries attended by a Skilled Birth Attendant (SBA)", "Active Management of Third Stage of Labour (AMTSL): oxytocin 10 IU IM, controlled cord traction, uterine massage", "Institutional deliveries dramatically reduce mortality vs home births"]), ("Emergency Obstetric Care (EmOC)", ["Basic EmOC (PHC level): oxytocics, antibiotics, anticonvulsants, assisted vaginal delivery, manual placenta removal", "Comprehensive EmOC (FRU/district hospital): Basic EmOC + caesarean section + blood transfusion", "Functional referral transport to higher centres"]), ("Postnatal Care (PNC)", ["Minimum 3 PNC visits; first 24–48 hrs most critical (PPH peak risk)", "Monitoring for haemorrhage, hypertension, sepsis"]), ("Safe Abortion Services", ["Access to MTP Act services at all levels", "Medical abortion with mifepristone + misoprostol reduces unsafe abortion deaths"]), ("Management of Specific Causes", ["PPH: Oxytocin 10 IU IM/IV routinely; misoprostol as backup; Bakri balloon; uterine compression sutures; hysterectomy last resort", "Eclampsia: Magnesium sulphate (MgSO4) — WHO Essential Medicine; antihypertensives (nifedipine, labetalol, hydralazine)", "Sepsis: prophylactic antibiotics, sterile delivery technique", "Anaemia: universal IFA supplementation, dietary counselling, IV iron / transfusion if severe"]), ("Community Mobilisation", ["ASHA-led household visits for ANC counselling, identification of danger signs, and facility referral", "Women's group meetings (systematic review evidence confirms reduction in maternal and neonatal mortality)", "Male partner involvement and family sensitisation"]), ] for title_text, points in measures: story.append(KeepTogether([ Paragraph(f"<b><font color='#1565C0'>{title_text}</font></b>", ParagraphStyle('mh', fontSize=11, textColor=BLUE_DARK, leading=15, spaceBefore=8, spaceAfter=3)), *[bullet(pt) for pt in points], spacer(0.05) ])) story.append(spacer(0.1)) # ── THREE DELAYS BOX ───────────────────────────────────────────────────────── delay_data = [[Paragraph( "<b><font color='#E65100'>Three Delays Model (Key Framework)</font></b><br/><br/>" "<b>Delay 1 — Decision to seek care:</b> Address via community awareness, ASHA mobilisation, female education<br/>" "<b>Delay 2 — Reaching care:</b> Address via transport schemes (108 ambulance, Janani Express), road infrastructure<br/>" "<b>Delay 3 — Receiving adequate care:</b> Address via skilled providers, EmOC capacity, blood availability, referral protocols", ParagraphStyle('delays', fontSize=10, textColor=GREY_DARK, leading=16, leftIndent=6, rightIndent=6) )]] delay_table = Table(delay_data, colWidths=[17*cm]) delay_table.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,-1), colors.HexColor("#FFF8E1")), ('BOX', (0,0), (-1,-1), 2, ORANGE), ('TOPPADDING', (0,0),(-1,-1), 10), ('BOTTOMPADDING',(0,0),(-1,-1), 10), ('LEFTPADDING', (0,0),(-1,-1), 12), ])) story.append(delay_table) story.append(spacer(0.25)) # ── SECTION 4: GOVERNMENT PROGRAMS ────────────────────────────────────────── story.append(section_header("4. Government Programs in India (NHM / RMNCAH+N)", bg_color=GREEN_DARK)) story.append(spacer(0.1)) prog_data = [ [Paragraph("<b>Program</b>", body_style), Paragraph("<b>Year</b>", body_style), Paragraph("<b>Key Features</b>", body_style)], [Paragraph("<b>Janani Suraksha Yojana (JSY)</b>", body_style), "2005", "Cash incentive for BPL/SC/ST women for institutional delivery; ASHA incentivised; massively increased institutional births"], [Paragraph("<b>Janani Shishu Suraksha Karyakram (JSSK)</b>", body_style), "2011", "Free & cashless services for mother & newborn in public facilities: delivery, C-section, drugs, diagnostics, blood, diet, transport"], [Paragraph("<b>PM Surakshit Matritva Abhiyan (PMSMA)</b>", body_style), "2016", "Fixed-day (9th of month) free quality ANC; focus on high-risk pregnancies; >5.9 crore women examined (as of 2025); extended e-PMSMA with financial incentives"], [Paragraph("<b>LaQshya</b>", body_style), "2017", "Improves quality of care in labour rooms & maternity OTs; respectful maternity care; reduction of preventable intrapartum deaths"], [Paragraph("<b>PMMVY (Maternity Benefit)</b>", body_style), "2017", "Cash transfer of Rs. 5,000 in instalments; compensates wage loss; promotes early ANC registration & institutional delivery"], [Paragraph("<b>Surakshit Matritva Aashwasan (SUMAN)</b>", body_style), "2019", "Guarantees no denial of free healthcare; zero tolerance for disrespect or harm in maternity care"], [Paragraph("<b>Midwifery Services Initiative (MSI)</b>", body_style), "2018", "Establishes Nurse Practitioner Midwives (NPM); builds skilled midwife cadre in high-burden districts"], [Paragraph("<b>ASHA Programme</b>", body_style), "2005", "1+ million ASHAs at village level; first contact for ANC counselling, referral, accompaniment to facility"], ] prog_table = Table(prog_data, colWidths=[4.5*cm, 1.5*cm, 11*cm]) prog_table.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,0), GREEN_DARK), ('TEXTCOLOR', (0,0), (-1,0), WHITE), ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'), ('FONTSIZE', (0,0), (-1,-1), 9), ('ROWBACKGROUNDS',(0,1),(-1,-1), [WHITE, GREEN_LIGHT]), ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor("#BDBDBD")), ('TOPPADDING', (0,0), (-1,-1), 5), ('BOTTOMPADDING',(0,0), (-1,-1), 5), ('LEFTPADDING', (0,0), (-1,-1), 6), ('VALIGN', (0,0), (-1,-1), 'TOP'), ])) story.append(prog_table) story.append(spacer(0.15)) story.append(sub_header("Other Strategic Interventions")) other = [ "First Referral Units (FRUs) — 24x7 emergency obstetric care at block/sub-district level", "National Ambulance Services (108 / Janani Express) — free referral transport", "Maternal Death Review (MDR) — mandatory community-based & facility-based review of every maternal death", "Skilled Birth Attendant (SBA) training for ANMs and nurses", "Blood storage units at FRU level for haemorrhage management", "Iron-Folic Acid (IFA) supplementation program — universal distribution to adolescent girls & pregnant women", "Obstetric HDU/ICU at district hospitals; dedicated maternity wings", ] for o in other: story.append(bullet(o)) story.append(spacer(0.2)) # ── SECTION 5: STATUS & GAPS ───────────────────────────────────────────────── story.append(section_header("5. Achievements & Remaining Challenges", bg_color=colors.HexColor("#6A1B9A"))) story.append(spacer(0.1)) ach_data = [ [Paragraph("<b>Achievements</b>", ParagraphStyle('ah', fontSize=10, textColor=GREEN_DARK)), Paragraph("<b>Remaining Challenges</b>", ParagraphStyle('ch', fontSize=10, textColor=RED_DARK))], ["MMR declined from 254 (2004-06) to 88 (2023)\n— a 65% fall in under 20 years", "High-burden states (UP, Rajasthan, Bihar, Assam)\nstill 2-3x the national MMR"], ["India achieved MDG-5 target; outpaced\nglobal MMR reduction trend", "SDG 3.1 target (<70) still not met;\nrequires further acceleration"], ["Institutional delivery rate rose steeply\n(JSY, JSSK impact)", "Quality of care gaps persist; skilled provider\nshortage in rural areas"], ["PMSMA reached 5.9 crore pregnant women\nfor quality ANC (2025)", "Anaemia & malnutrition still highly prevalent;\nunderlying indirect deaths persist"], ["Nationwide ASHA network ensures\nlast-mile outreach", "Barriers to obstetric care (cost, distance,\nsocio-cultural norms) remain in lagging states"], ] ach_table = Table(ach_data, colWidths=[8.5*cm, 8.5*cm]) ach_table.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,0), colors.HexColor("#EDE7F6")), ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'), ('FONTSIZE', (0,0), (-1,-1), 9), ('ROWBACKGROUNDS',(0,1),(-1,-1), [WHITE, colors.HexColor("#F9F9F9")]), ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor("#BDBDBD")), ('TOPPADDING', (0,0), (-1,-1), 6), ('BOTTOMPADDING',(0,0), (-1,-1), 6), ('LEFTPADDING', (0,0), (-1,-1), 8), ('VALIGN', (0,0), (-1,-1), 'TOP'), ('BACKGROUND', (0,1), (0,-1), colors.HexColor("#E8F5E9")), ('BACKGROUND', (1,1), (1,-1), colors.HexColor("#FFEBEE")), ])) story.append(ach_table) story.append(spacer(0.2)) # ── REFERENCES ─────────────────────────────────────────────────────────────── story.append(hrule()) story.append(Paragraph("<b>References & Sources</b>", ParagraphStyle('ref_hdr', fontSize=10, textColor=GREY_DARK, spaceBefore=4, spaceAfter=4))) refs = [ "Park's Textbook of Preventive and Social Medicine — Maternal Mortality Ratio (MMR) chapter", "Creasy & Resnik's Maternal-Fetal Medicine: Principles and Practice — Causes & Preventability of Maternal Mortality", "Government of India, PIB — India's Success in Reducing Maternal Mortality (pib.gov.in, 2025)", "SRS-based national MMR estimates (PMC9292773) — Trends in maternal mortality in India over two decades", "Gupta M et al. (2023). Home visits by paramedical professionals in LMICs. Public Health [PMID: 36682079]", "Jaiswal V et al. (2024). Calcium supplementation and pre-eclampsia meta-analysis. Curr Probl Cardiol [PMID: 38013011]", "Srivastava M et al. (2025). Iron-folic acid supplementation: systematic review & meta-analysis. Nutr Health [PMID: 39569449]", "UNICEF — Maternal Mortality Rates and Statistics (data.unicef.org, 2024)", "Data For India — Maternal mortality in India (dataforindia.com, updated Mar 2026)", ] for r in refs: story.append(Paragraph(f"• {r}", ParagraphStyle('ref', fontSize=8, textColor=GREY_DARK, leading=12, leftIndent=12, spaceAfter=2))) story.append(spacer(0.15)) story.append(hrule()) story.append(Paragraph("Generated by Orris Medical Reference | July 2026 | For educational use only.", footer_style)) # ── Build PDF ───────────────────────────────────────────────────────────────── doc.build(story) print(f"PDF created: {output_path}") PYEOF echo "Script written"

cd /tmp/workspace/maternal-mortality && python generate_pdf.py

ls -lh /tmp/workspace/maternal-mortality/Maternal_Mortality_India.pdf

infant mortality India interventions reduction neonatal

infant mortality rate India 2024 causes prevention social measures programs IMR reduction

https://m.economictimes.com/news/india/is-india-winning-the-…

mkdir -p /tmp/workspace/infant-mortality && cat << 'PYEOF' > /tmp/workspace/infant-mortality/generate_pdf.py from reportlab.lib.pagesizes import A4 from reportlab.lib import colors from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle from reportlab.lib.units import inch, cm from reportlab.platypus import ( SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, HRFlowable, KeepTogether ) from reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_JUSTIFY output_path = "/tmp/workspace/infant-mortality/Infant_Mortality_India.pdf" doc = SimpleDocTemplate( output_path, pagesize=A4, rightMargin=2*cm, leftMargin=2*cm, topMargin=2.5*cm, bottomMargin=2*cm, title="Infant Mortality in India: Causes, Prevention & Social Measures", author="Orris Medical Reference" ) # Colours TEAL_DARK = colors.HexColor("#004D40") TEAL_MED = colors.HexColor("#00695C") TEAL_LIGHT = colors.HexColor("#B2DFDB") BLUE_DARK = colors.HexColor("#1565C0") BLUE_LIGHT = colors.HexColor("#E3F2FD") RED_DARK = colors.HexColor("#B71C1C") RED_LIGHT = colors.HexColor("#FFCDD2") ORANGE = colors.HexColor("#E65100") ORANGE_LT = colors.HexColor("#FFF3E0") PURPLE = colors.HexColor("#6A1B9A") PURPLE_LT = colors.HexColor("#F3E5F5") GREEN_DARK = colors.HexColor("#1B5E20") GREEN_LT = colors.HexColor("#E8F5E9") GREY_DARK = colors.HexColor("#424242") GREY_LIGHT = colors.HexColor("#F5F5F5") WHITE = colors.white styles = getSampleStyleSheet() body_style = ParagraphStyle('Body', parent=styles['Normal'], fontSize=10, textColor=GREY_DARK, leading=15, spaceAfter=4, alignment=TA_JUSTIFY) bullet_style = ParagraphStyle('Bullet', parent=styles['Normal'], fontSize=10, textColor=GREY_DARK, leading=14, leftIndent=18, spaceAfter=3, bulletIndent=6) sub_style = ParagraphStyle('Sub', parent=styles['Normal'], fontSize=11, textColor=BLUE_DARK, leading=16, spaceBefore=8, spaceAfter=3) footer_style = ParagraphStyle('Footer', parent=styles['Normal'], fontSize=8, textColor=colors.HexColor("#888888"), alignment=TA_CENTER) ref_style = ParagraphStyle('Ref', parent=styles['Normal'], fontSize=8, textColor=GREY_DARK, leading=12, leftIndent=12, spaceAfter=2) def section_header(text, bg=TEAL_MED): s = ParagraphStyle('SH', parent=styles['Normal'], fontSize=13, textColor=WHITE, leading=18, spaceBefore=10, spaceAfter=4, leftIndent=8, backColor=bg, borderPad=(6,6,6,6)) return Paragraph(f"<b>{text}</b>", s) def sub_header(text, color=BLUE_DARK): return Paragraph(f"<b><font color='#{color.hexval()[2:]}' >{text}</font></b>", sub_style) def bullet(text): return Paragraph(f"• {text}", bullet_style) def body(text): return Paragraph(text, body_style) def spacer(h=0.15): return Spacer(1, h*inch) def hrule(c=TEAL_LIGHT): return HRFlowable(width="100%", thickness=1, color=c, spaceAfter=4, spaceBefore=4) story = [] # COVER cover_data = [[Paragraph( "<font size=22><b>Infant Mortality in India</b></font><br/>" "<font size=11 color='#B2DFDB'>Causes, Prevention &amp; Social Measures to Reduce IMR</font>", ParagraphStyle('cov', alignment=TA_CENTER, textColor=WHITE, leading=30) )]] ct = Table(cover_data, colWidths=[17*cm]) ct.setStyle(TableStyle([ ('BACKGROUND',(0,0),(-1,-1),TEAL_DARK), ('TOPPADDING',(0,0),(-1,-1),18),('BOTTOMPADDING',(0,0),(-1,-1),18), ('LEFTPADDING',(0,0),(-1,-1),14),('RIGHTPADDING',(0,0),(-1,-1),14), ])) story.append(ct) story.append(spacer(0.2)) # Stats bar stat_data = [[ Paragraph("<b>IMR 2024 (SRS):</b> 24 / 1,000 live births", ParagraphStyle('s',fontSize=10,textColor=WHITE,alignment=TA_CENTER)), Paragraph("<b>Neonatal share:</b> 73% of infant deaths", ParagraphStyle('s',fontSize=10,textColor=WHITE,alignment=TA_CENTER)), Paragraph("<b>SDG 3.2 Target:</b> ≤ 12 by 2030", ParagraphStyle('s',fontSize=10,textColor=WHITE,alignment=TA_CENTER)), ]] st = Table(stat_data, colWidths=[5.6*cm,5.6*cm,5.6*cm]) st.setStyle(TableStyle([ ('BACKGROUND',(0,0),(-1,-1),BLUE_DARK), ('TOPPADDING',(0,0),(-1,-1),8),('BOTTOMPADDING',(0,0),(-1,-1),8), ('INNERGRID',(0,0),(-1,-1),0.5,WHITE), ])) story.append(st) story.append(spacer(0.25)) # SECTION 1: DEFINITION & COMPONENTS story.append(section_header("1. Definition & Components of Infant Mortality")) story.append(spacer(0.1)) def_data = [[Paragraph( "<b>Infant Mortality Rate (IMR)</b> = Number of deaths of children under 1 year of age in a year " "÷ Number of live births in the same year × <b>1,000</b><br/><br/>" "IMR is universally regarded as the most important indicator of community health status and the " "effectiveness of MCH (Mother and Child Health) services.", ParagraphStyle('qd',fontSize=10,textColor=GREY_DARK,leading=15,leftIndent=10,rightIndent=10) )]] dt = Table(def_data, colWidths=[17*cm]) dt.setStyle(TableStyle([ ('BACKGROUND',(0,0),(-1,-1),BLUE_LIGHT), ('BOX',(0,0),(-1,-1),1.5,BLUE_DARK), ('TOPPADDING',(0,0),(-1,-1),8),('BOTTOMPADDING',(0,0),(-1,-1),8), ('LEFTPADDING',(0,0),(-1,-1),12), ])) story.append(dt) story.append(spacer(0.1)) comp_data = [ [Paragraph("<b>Component</b>",body_style), Paragraph("<b>Period</b>",body_style), Paragraph("<b>India (2024)</b>",body_style), Paragraph("<b>Significance</b>",body_style)], ["Neonatal Mortality Rate (NMR)","0–28 days of life","17–18 / 1,000","73% of all infant deaths; highest risk period"], ["Early Neonatal Mortality","0–7 days","~12 / 1,000","Birth asphyxia, LBW, sepsis peak here"], ["Late Neonatal Mortality","8–28 days","~5 / 1,000","Sepsis, jaundice, feeding problems"], ["Post-Neonatal Mortality","1–12 months","~6–7 / 1,000","Diarrhoea, ARI, malnutrition dominant"], ["Perinatal Mortality Rate","Stillbirths + deaths 0–7 days","~25–30 / 1,000","Obstetric care quality indicator"], ] comp_tbl = Table(comp_data, colWidths=[3.8*cm,2.5*cm,2.5*cm,8.2*cm]) comp_tbl.setStyle(TableStyle([ ('BACKGROUND',(0,0),(-1,0),TEAL_DARK),('TEXTCOLOR',(0,0),(-1,0),WHITE), ('FONTNAME',(0,0),(-1,0),'Helvetica-Bold'),('FONTSIZE',(0,0),(-1,-1),9), ('ROWBACKGROUNDS',(0,1),(-1,-1),[WHITE,TEAL_LIGHT]), ('GRID',(0,0),(-1,-1),0.5,colors.HexColor("#BDBDBD")), ('TOPPADDING',(0,0),(-1,-1),5),('BOTTOMPADDING',(0,0),(-1,-1),5), ('LEFTPADDING',(0,0),(-1,-1),6),('VALIGN',(0,0),(-1,-1),'MIDDLE'), ])) story.append(comp_tbl) story.append(spacer(0.2)) # SECTION 2: CAUSES story.append(section_header("2. Causes of Infant Mortality", bg=RED_DARK)) story.append(spacer(0.1)) story.append(Paragraph("<b><font color='#B71C1C'>A. Medical Causes</font></b>", sub_style)) med_data = [ [Paragraph("<b>Neonatal (0–4 weeks)</b>",body_style), Paragraph("<b>Post-Neonatal (1–12 months)</b>",body_style)], ["1. Low birth weight (LBW) & prematurity — leading cause (~57%)", "1. Diarrhoeal diseases & dehydration"], ["2. Birth asphyxia / difficult labour","2. Acute respiratory infections (ARI/pneumonia)"], ["3. Neonatal sepsis (cord infection, hospital-acquired)","3. Other communicable diseases (measles, pertussis)"], ["4. Congenital anomalies","4. Malnutrition (PEM, micronutrient deficiencies)"], ["5. Haemolytic disease of the newborn","5. Congenital anomalies"], ["6. Conditions of placenta and cord","6. Accidents and injuries"], ["7. Neonatal tetanus (declining with TT immunisation)",""], ["8. Acute respiratory infections",""], ] med_tbl = Table(med_data, colWidths=[8.5*cm,8.5*cm]) med_tbl.setStyle(TableStyle([ ('BACKGROUND',(0,0),(-1,0),colors.HexColor("#FFEBEE")), ('FONTNAME',(0,0),(-1,0),'Helvetica-Bold'),('FONTSIZE',(0,0),(-1,-1),9), ('ROWBACKGROUNDS',(0,1),(-1,-1),[WHITE,RED_LIGHT]), ('GRID',(0,0),(-1,-1),0.5,colors.HexColor("#BDBDBD")), ('TOPPADDING',(0,0),(-1,-1),5),('BOTTOMPADDING',(0,0),(-1,-1),5), ('LEFTPADDING',(0,0),(-1,-1),6),('VALIGN',(0,0),(-1,-1),'TOP'), ])) story.append(med_tbl) story.append(spacer(0.1)) story.append(Paragraph("<b><font color='#1565C0'>B. Biological Factors</font></b>", sub_style)) bio = [ ("Birth weight","LBW (<2.5 kg) is the single most important determinant; virtually all <1000 g infants die without NICU care. Poor maternal nutrition is the root cause."), ("Mother's age","IMR highest when mother is <19 or >30 years; teenage mothers are also poorer and less educated."), ("Birth order","Highest IMR among 1st-born; escalates sharply after 3rd child. 5th+ children have 3-4× higher nutrition-related mortality."), ("Birth spacing","IMR highest when interval <1 year; lowest when >4 years. Risk 2-4× higher for babies born <2 years apart (WHO)."), ("Multiple births","Twins/triplets carry higher LBW risk, hence higher mortality."), ("Family size","Large families dilute food and parental attention; malnutrition and infection risk rise."), ("High fertility","High fertility and high IMR are directly correlated."), ] for factor, detail in bio: story.append(bullet(f"<b>{factor}:</b> {detail}")) story.append(spacer(0.1)) story.append(Paragraph("<b><font color='#E65100'>C. Economic Factors</font></b>", sub_style)) story.append(bullet("Poverty is the overarching driver: IMR highest in slums, lowest in wealthy localities.")) story.append(bullet("Socioeconomic status determines access to quality healthcare, nutrition, and safe living environment.")) story.append(spacer(0.1)) story.append(Paragraph("<b><font color='#6A1B9A'>D. Social & Cultural Factors</font></b>", sub_style)) soc = [ "Breast-feeding: Early weaning and bottle-feeding under poor hygienic conditions greatly increase infant deaths.", "Early marriage: Babies of teenage mothers have highest neonatal and post-neonatal mortality risk.", "Gender discrimination: Female infants receive less care in many parts of India — post-neonatal female IMR exceeds male.", "Quality of mothering: Even under poverty, an efficient, educated mother substantially reduces infant deaths.", "Maternal illiteracy: The strongest social barrier. Kerala example: high female literacy → IMR of 7-8 vs states like UP/MP (40+).", "Harmful customs: Discarding colostrum, applying cow dung to umbilical cord, early/faulty weaning, frequent purgation.", "Untrained dais: Unhygienic delivery practices by traditional birth attendants contribute significantly to neonatal deaths.", "Bad environmental sanitation: Lack of safe water, poor housing, overcrowding, open defecation increase diarrhoea and ARI deaths.", "Religion, caste and socio-cultural patterns: Influence child-feeding, hygiene, and health-seeking behaviour.", ] for s in soc: story.append(bullet(s)) story.append(spacer(0.2)) # SECTION 3: PREVENTIVE & SOCIAL MEASURES story.append(section_header("3. Preventive & Social Measures to Reduce IMR", bg=GREEN_DARK)) story.append(spacer(0.1)) note_data = [[Paragraph( "<b>Key Principle:</b> Since the aetiology of infant mortality is multifactorial, " "nothing less than a <b>multipronged approach</b> will reduce IMR. " "There is no single programme that alone can achieve this reduction. " "<i>(Park's Textbook of Preventive & Social Medicine)</i>", ParagraphStyle('note',fontSize=10,textColor=GREY_DARK,leading=15,leftIndent=6,rightIndent=6) )]] nt = Table(note_data, colWidths=[17*cm]) nt.setStyle(TableStyle([ ('BACKGROUND',(0,0),(-1,-1),GREEN_LT), ('BOX',(0,0),(-1,-1),2,GREEN_DARK), ('TOPPADDING',(0,0),(-1,-1),8),('BOTTOMPADDING',(0,0),(-1,-1),8), ('LEFTPADDING',(0,0),(-1,-1),12), ])) story.append(nt) story.append(spacer(0.15)) measures = [ ("1. Prenatal Nutrition", [ "Adequate nutrition for the mother during pregnancy AND before (adolescent nutrition is critical)", "IFA (Iron-Folic Acid) supplementation — systematic review evidence confirms significant reduction in LBW and neonatal deaths (PMID 39569449)", "Protein and calorie supplementation for undernourished pregnant women", "Treatment of anaemia: oral iron, IV iron, or transfusion as needed", "Calcium supplementation to prevent pre-eclampsia and its effect on neonatal outcomes", ]), ("2. Maternal Care & Antenatal Services", [ "Early ANC registration (1st trimester); minimum 4-8 ANC visits", "Identification and management of high-risk pregnancies (toxaemia, APH, diabetes, twin pregnancy)", "Hospitalisation of high-risk pregnancies for monitored delivery", "Tetanus Toxoid (TT/Td) immunisation to prevent neonatal tetanus", "Detection and treatment of reproductive tract infections (RTI/STI)", ]), ("3. Care at Birth", [ "Institutional delivery with skilled birth attendance — critical for preventing asphyxia and sepsis", "Aseptic cord care: clean cutting, sterile tie; no application of harmful substances", "Active resuscitation of the asphyxiated newborn (bag and mask ventilation)", "Immediate thermal care: dry, warm, and wrap; prevent hypothermia", "Immediate breastfeeding and skin-to-skin contact", ]), ("4. Breast-feeding Promotion", [ "Exclusive breastfeeding for the first 6 months — provides nutrition, immunological protection, and reduced diarrhoea/ARI risk", "Colostrum must be given (NOT discarded) — contains sIgA, lactoferrin, growth factors", "Correct latch and positioning — prevents early discontinuation", "Continued breastfeeding up to 2 years alongside complementary foods", "Baby-Friendly Hospital Initiative (BFHI): hospital policies to support breastfeeding", ]), ("5. Immunisation", [ "National Immunisation Schedule: BCG, OPV, Hepatitis B, Pentavalent (DPT-HepB-Hib), IPV, PCV, Rotavirus, MR, JE, Typhoid", "Preventing measles, pertussis, Hib meningitis, rotavirus diarrhoea — collectively reduce infant deaths", "Cold chain maintenance and outreach immunisation in rural/tribal areas", "Intensified Mission Indradhanush (IMI) for hard-to-reach unimmunised children", ]), ("6. Family Planning", [ "Birth spacing ≥2 years substantially reduces infant mortality risk (confirmed by WHO and Khanna Study)", "Limiting births to ≤3 children improves child survival through better nutrition and care", "Delaying first pregnancy beyond teenage years", "Access to reversible contraception and counselling for eligible couples", ]), ("7. Environmental Sanitation & Safe Water", [ "Safe drinking water supply — reduces diarrhoeal deaths (leading post-neonatal killer)", "Sanitation: construction and use of toilets; ODF (Open Defecation Free) villages under Swachh Bharat Mission", "Safe food storage and preparation; handwashing with soap before feeding", "Proper housing: ventilation, reduction of indoor air pollution (biomass fuel) — reduces ARI deaths", "Vector control: malaria prevention (insecticide-treated bed nets, IRS)", ]), ("8. Primary Health Care Provision", [ "Accessible 24x7 PHCs and CHCs with skilled personnel for paediatric and obstetric emergencies", "Special Care Newborn Units (SCNUs) / Neonatal ICUs for LBW and sick newborns", "Kangaroo Mother Care (KMC) for stable LBW infants — proven by meta-analysis to reduce neonatal mortality (PMID 37277198)", "Oral Rehydration Therapy (ORT) corners at every health facility and community level", "IMNCI (Integrated Management of Neonatal and Childhood Illness): algorithm-based care for sick neonates and children", "Functional referral system and transport for sick infants to higher centres", ]), ("9. Socio-economic Development", [ "Female education is the single most powerful determinant of IMR reduction — Tamil Nadu mid-day meal case study showed IMR fall from 90 to 57 per 1,000 in 7 years", "Poverty alleviation and livelihood support for marginalised households", "Safe housing and improved living conditions", "Growth of agriculture, rural infrastructure, and communication networks", ]), ("10. Education & Behaviour Change Communication (BCC)", [ "Community awareness on danger signs in newborns (hypothermia, jaundice, poor feeding, respiratory distress)", "Health education on correct child-feeding practices and weaning", "Elimination of harmful customs: educating families against discarding colostrum, cord contamination, etc.", "Male partner and family involvement in newborn care", "ASHA-led home visits for essential newborn care counselling", ]), ] for title_text, points in measures: story.append(KeepTogether([ Paragraph(f"<b><font color='#004D40'>{title_text}</font></b>", ParagraphStyle('mh',fontSize=11,textColor=TEAL_DARK,leading=15, spaceBefore=8,spaceAfter=3)), *[bullet(pt) for pt in points], spacer(0.05) ])) story.append(spacer(0.1)) # SECTION 4: GOVERNMENT PROGRAMS story.append(section_header("4. Government Programs (India) — NHM & Beyond", bg=BLUE_DARK)) story.append(spacer(0.1)) prog_data = [ [Paragraph("<b>Program</b>",body_style), Paragraph("<b>Year</b>",body_style), Paragraph("<b>Relevance to IMR Reduction</b>",body_style)], [Paragraph("<b>Janani Suraksha Yojana (JSY)</b>",body_style),"2005", "Incentivises institutional delivery; eliminates dai-related neonatal deaths; raised medically-attended births from <50% to >95%"], [Paragraph("<b>Janani Shishu Suraksha Karyakram (JSSK)</b>",body_style),"2011", "Free delivery, drugs, diagnostics, blood, diet and transport for mother AND newborn; cashless care removes financial barrier"], [Paragraph("<b>National Immunisation Programme / Mission Indradhanush</b>",body_style),"Ongoing/2015", "Full immunisation for all children under 2 years; Intensified Mission Indradhanush targets unreached children"], [Paragraph("<b>IMNCI (Integrated Mgmt. of Neonatal & Childhood Illness)</b>",body_style),"2003", "Standardised algorithm for recognition and management of sick neonates and children at community and facility level"], [Paragraph("<b>Special Care Newborn Units (SCNUs) / NICUs</b>",body_style),"Under NHM", "Facility-based care for LBW, preterm and sick newborns; Kangaroo Mother Care promoted"], [Paragraph("<b>Home-Based Newborn Care (HBNC)</b>",body_style),"2011", "ASHA visits on days 1, 3, 7, 14, 28 after birth to assess, counsel and refer sick newborns in high-burden rural settings"], [Paragraph("<b>Rashtriya Bal Swasthya Karyakram (RBSK)</b>",body_style),"2013", "Child health screening for 4Ds: Defects at birth, Diseases, Deficiencies, Developmental delays — early intervention"], [Paragraph("<b>Poshan Abhiyaan (National Nutrition Mission)</b>",body_style),"2018", "Targets stunting, underweight, anaemia in children <5 and pregnant/lactating women; reduces malnutrition-linked infant deaths"], [Paragraph("<b>Swachh Bharat Mission</b>",body_style),"2014", "ODF villages, toilet construction, handwashing — directly reduces diarrhoeal and ARI deaths in infants"], [Paragraph("<b>LaQshya (Labour Room Quality)</b>",body_style),"2017", "Improves intrapartum care quality; reduces birth asphyxia and neonatal deaths from preventable intrapartum events"], [Paragraph("<b>Pradhan Mantri Matru Vandana Yojana (PMMVY)</b>",body_style),"2017", "Maternity cash benefit promotes early ANC registration, nutrition and institutional delivery — indirect IMR benefit"], ] prog_tbl = Table(prog_data, colWidths=[4.5*cm,1.5*cm,11*cm]) prog_tbl.setStyle(TableStyle([ ('BACKGROUND',(0,0),(-1,0),BLUE_DARK),('TEXTCOLOR',(0,0),(-1,0),WHITE), ('FONTNAME',(0,0),(-1,0),'Helvetica-Bold'),('FONTSIZE',(0,0),(-1,-1),9), ('ROWBACKGROUNDS',(0,1),(-1,-1),[WHITE,BLUE_LIGHT]), ('GRID',(0,0),(-1,-1),0.5,colors.HexColor("#BDBDBD")), ('TOPPADDING',(0,0),(-1,-1),5),('BOTTOMPADDING',(0,0),(-1,-1),5), ('LEFTPADDING',(0,0),(-1,-1),6),('VALIGN',(0,0),(-1,-1),'TOP'), ])) story.append(prog_tbl) story.append(spacer(0.2)) # SECTION 5: REGIONAL PICTURE story.append(section_header("5. Regional Disparities (India, 2024)", bg=PURPLE)) story.append(spacer(0.1)) reg_data = [ [Paragraph("<b>Category</b>",body_style), Paragraph("<b>States/UTs</b>",body_style), Paragraph("<b>IMR (2024)</b>",body_style)], ["Lowest IMR (best performers)","Goa, Sikkim","7"], ["","Kerala","8"], ["","Tamil Nadu, Delhi","11"], ["National Average","India","24"], ["High-burden belt (above national avg)","Uttar Pradesh, MP, Rajasthan, Bihar, Assam, Chhattisgarh, Odisha","28–38+"], ["Widest rural-urban gap","Assam","Rural: 31 vs Urban: 14"], ] reg_tbl = Table(reg_data, colWidths=[4.5*cm,7.5*cm,5*cm]) reg_tbl.setStyle(TableStyle([ ('BACKGROUND',(0,0),(-1,0),PURPLE),('TEXTCOLOR',(0,0),(-1,0),WHITE), ('FONTNAME',(0,0),(-1,0),'Helvetica-Bold'),('FONTSIZE',(0,0),(-1,-1),9), ('ROWBACKGROUNDS',(0,1),(-1,-1),[WHITE,PURPLE_LT]), ('GRID',(0,0),(-1,-1),0.5,colors.HexColor("#BDBDBD")), ('TOPPADDING',(0,0),(-1,-1),5),('BOTTOMPADDING',(0,0),(-1,-1),5), ('LEFTPADDING',(0,0),(-1,-1),6),('VALIGN',(0,0),(-1,-1),'MIDDLE'), ('BACKGROUND',(0,4),(2,4),colors.HexColor("#FFF9C4")), ])) story.append(reg_tbl) story.append(spacer(0.15)) # Progress box prog_box = [[Paragraph( "<b><font color='#1B5E20'>India's IMR Progress:</font></b><br/>" "1911-15: 204 | 1970: 129 | 1990: 88 | 2000: ~68 | 2010: ~47 | 2019: 30 | 2024: <b>24</b><br/>" "71% decline in IMR from 1990 to 2024 — surpassing global reduction of 58%<br/>" "Medically-attended births rose from &lt;83% (2019) to &gt;95% (2024); " "Neonatal deaths now account for 73% of infant deaths — future gains depend on quality neonatal care.", ParagraphStyle('pb',fontSize=10,textColor=GREY_DARK,leading=16,leftIndent=6) )]] pbt = Table(prog_box, colWidths=[17*cm]) pbt.setStyle(TableStyle([ ('BACKGROUND',(0,0),(-1,-1),GREEN_LT), ('BOX',(0,0),(-1,-1),2,GREEN_DARK), ('TOPPADDING',(0,0),(-1,-1),10),('BOTTOMPADDING',(0,0),(-1,-1),10), ('LEFTPADDING',(0,0),(-1,-1),12), ])) story.append(pbt) story.append(spacer(0.2)) # SECTION 6: KEY EVIDENCE story.append(section_header("6. Key Evidence-Based Interventions", bg=ORANGE)) story.append(spacer(0.1)) evidence = [ ("Kangaroo Mother Care (KMC)", "Systematic review & meta-analysis (Sivanandan & Sankar, BMJ Glob Health 2023) confirmed KMC reduces neonatal mortality in LBW/preterm infants. [PMID 37277198]"), ("Iron-Folic Acid Supplementation", "Meta-analysis (Srivastava et al., Nutr Health 2025) confirmed IFA reduces LBW and improves neonatal outcomes. [PMID 39569449]"), ("ASHA Home Visits", "Meta-analysis (Gupta et al., Public Health 2023): home visits by paramedical workers + community group meetings significantly improve neonatal and maternal outcomes in LMICs. [PMID 36682079]"), ("Barriers to obstetric care", "Systematic review (Singh & Rajak, BMC Pregnancy Childbirth 2024) documented financial, geographic, provider-related and socio-cultural barriers to obstetric care in India — targeting these is key. [PMID 38166775]"), ] for intv, evidence_text in evidence: story.append(bullet(f"<b>{intv}:</b> {evidence_text}")) story.append(spacer(0.2)) # REFERENCES story.append(hrule()) story.append(Paragraph("<b>References</b>", ParagraphStyle('rh',fontSize=10,textColor=GREY_DARK,spaceBefore=4,spaceAfter=4))) refs = [ "Park's Textbook of Preventive and Social Medicine — IMR Definition, Causes, Factors & Preventive Measures", "Creasy & Resnik's Maternal-Fetal Medicine — Perinatal and Infant Mortality chapter", "SRS Report 2024, Office of the Registrar General of India — State-wise IMR data", "Economic Times (2025): India's IMR fell to 24/1,000 in 2024 (SRS 2024 Report)", "PIB, Government of India — India's Success in Reducing Child Mortality (2025)", "Sivanandan S, Sankar MJ (2023). Kangaroo Mother Care meta-analysis. BMJ Glob Health. [PMID 37277198]", "Srivastava M et al. (2025). IFA supplementation outcomes meta-analysis. Nutr Health. [PMID 39569449]", "Gupta M et al. (2023). Home visits in LMICs meta-analysis. Public Health. [PMID 36682079]", "Singh S, Rajak R (2024). Barriers to obstetric care in India. BMC Pregnancy Childbirth. [PMID 38166775]", "UNICEF/UNIGME Report 2025 — India Child Mortality Progress", ] for r in refs: story.append(Paragraph(f"• {r}", ref_style)) story.append(spacer(0.1)) story.append(hrule()) story.append(Paragraph("Generated by Orris Medical Reference | July 2026 | For educational use only.", footer_style)) doc.build(story) print(f"PDF created: {output_path}") PYEOF python /tmp/workspace/infant-mortality/generate_pdf.py

ls -lh /tmp/workspace/infant-mortality/Infant_Mortality_India.pdf