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const pres = new pptxgen();
pres.layout = "LAYOUT_16x9";
pres.title = "Data Triangulation – Andakkulam PHC";
pres.author = "MBBS Intern – SPM Posting";

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// ─── HELPER FUNCTIONS ─────────────────────────────────────────────
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// ══════════════════════════════════════════════════════════════════
// SLIDE 1 – TITLE
// ══════════════════════════════════════════════════════════════════
addTitleSlide(pres,
  "Data Triangulation in Primary Health Care",
  "HMIS – Institutional Performance Data | SPM Posting Review");

// ══════════════════════════════════════════════════════════════════
// SLIDE 2 – ABOUT THIS PHC
// ══════════════════════════════════════════════════════════════════
{
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    ["District", "Pudukkottai (Code: 22)"],
    ["HUD", "Pudukkottai (Code: 20)"],
    ["Block", "Kunnandarkovil (Code: 2202)"],
    ["Institution Code", "02757"],
    ["Institution Type", "Primary Health Centre (PHC)"],
    ["Data Period", "April 2026"],
    ["Data Source", "HMIS – TN Health & Family Welfare Dept."],
  ];
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// ══════════════════════════════════════════════════════════════════
// SLIDE 3 – PRESENTATION OVERVIEW
// ══════════════════════════════════════════════════════════════════
{
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  const items = [
    "01  What is Data Triangulation?",
    "02  Why Data Triangulation Matters in Public Health",
    "03  Types of Data Triangulation",
    "04  Data Sources Used (HMIS – Andakkulam PHC)",
    "05  OP & IP Case Load Analysis",
    "06  Maternal & Reproductive Health Data",
    "07  Neonatal & Child Health Data",
    "08  Family Planning Services Data",
    "09  Communicable Disease & Special Clinics",
    "10  Triangulation Synthesis & Insights",
    "11  Limitations & Recommendations",
    "12  Conclusion",
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// ══════════════════════════════════════════════════════════════════
// SLIDE 4 – SECTION HEADER: CONCEPT
// ══════════════════════════════════════════════════════════════════
addSectionHeader(pres, "01", "What is Data Triangulation?",
  "Definition, Origin & Core Concept");

// ══════════════════════════════════════════════════════════════════
// SLIDE 5 – DEFINITION
// ══════════════════════════════════════════════════════════════════
{
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    { icon: "▶", bold: "Origin:", rest: " Coined by Norman Denzin (1978) in social science research; adopted widely in epidemiology and public health." },
    { icon: "▶", bold: "Navigation analogy:", rest: " Just as sailors use multiple landmarks to fix their position, public health uses multiple data sources to fix the true picture of community health." },
    { icon: "▶", bold: "In SPM context:", rest: " Cross-verifying HMIS records, field surveys, community registers, and verbal autopsies to arrive at accurate health indicators." },
    { icon: "▶", bold: "Core idea:", rest: " No single data source is perfect. Combining sources cancels out individual biases." },
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// ══════════════════════════════════════════════════════════════════
// SLIDE 6 – TYPES OF TRIANGULATION
// ══════════════════════════════════════════════════════════════════
{
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    { title: "Data Triangulation", icon: "📊", desc: "Using data from different times, spaces, or persons. E.g., HMIS monthly data + community survey + field registers." },
    { title: "Investigator Triangulation", icon: "🔬", desc: "Multiple researchers/observers collect and analyze data independently to reduce bias." },
    { title: "Theory Triangulation", icon: "📚", desc: "Applying multiple theoretical frameworks to interpret the same dataset." },
    { title: "Methodological Triangulation", icon: "🔄", desc: "Combining quantitative (HMIS) + qualitative (FGDs, interviews) methods for a richer picture." },
  ];
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// ══════════════════════════════════════════════════════════════════
// SLIDE 7 – WHY IT MATTERS
// ══════════════════════════════════════════════════════════════════
{
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    { num: "01", title: "Reduces Reporting Bias", desc: "HMIS data may be under-reported or over-reported; cross-checking with field registers reduces error." },
    { num: "02", title: "Validates Health Indicators", desc: "Confirms whether targets (ANC coverage, immunisation) are genuinely met or just numerically recorded." },
    { num: "03", title: "Identifies Data Gaps", desc: "Highlights areas where data is missing or inconsistent – essential for programme planning." },
    { num: "04", title: "Improves Decision-Making", desc: "Policy makers and PHC MOs can take resource allocation decisions on verified, multi-source evidence." },
    { num: "05", title: "Detects Outbreaks Early", desc: "Combining OPD data + lab data + community reports allows faster outbreak detection." },
    { num: "06", title: "Builds Accountability", desc: "Cross-verification through triangulation ensures health workers are accountable for services rendered." },
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// ══════════════════════════════════════════════════════════════════
// SLIDE 8 – DATA SOURCES USED
// ══════════════════════════════════════════════════════════════════
{
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    { src: "OPD Registers", desc: "Shift-wise outpatient attendance registers maintained at reception.", badge: "Quantitative" },
    { src: "Antenatal / Postnatal Registers", desc: "ANC/PNC registers cross-verify HMIS AN/PN clinic attendance data.", badge: "Cross-check" },
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    { src: "Lab Investigation Registers", desc: "Daily lab workload registers corroborate HMIS lab investigation counts.", badge: "Quantitative" },
    { src: "Special Clinic Registers", desc: "NCD clinic (DM/HTN), TB, ICTC, Ophthalmic registers verify special clinic attendance data.", badge: "Triangulation" },
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// ══════════════════════════════════════════════════════════════════
// SLIDE 9 – SECTION HEADER: OP/IP
// ══════════════════════════════════════════════════════════════════
addSectionHeader(pres, "02", "Outpatient & Inpatient Case Load",
  "General OP Cases | IP Admissions | Shift-wise Distribution");

// ══════════════════════════════════════════════════════════════════
// SLIDE 10 – OP CASE DATA
// ══════════════════════════════════════════════════════════════════
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    ["Children <15 – M",  "526",  "398",  "235",  "1159"],
    ["Children <15 – F",  "322",  "351",  "225",  "898"],
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// ══════════════════════════════════════════════════════════════════
// SLIDE 11 – IP ADMISSIONS
// ══════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  contentBG(s, pres);
  sectionBar(s, pres, "Inpatient (IP) Admissions – April 2026");
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    ["Children <15 – Male","0", "0", "12",  "12"],
    ["Children <15 – Fem", "0", "0", "10",  "10"],
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// ══════════════════════════════════════════════════════════════════
// SLIDE 12 – WHY OP/IP DATA IS COLLECTED
// ══════════════════════════════════════════════════════════════════
{
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  contentBG(s, pres);
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    { h: "Health Service Utilisation", b: "Tracks how many community members actively seek care at the PHC, reflecting access to services and community trust." },
    { h: "Epidemiological Surveillance", b: "Shift-wise OP patterns reveal peak load hours, disease seasonality, and outbreak signals when compared across months." },
    { h: "Resource Planning", b: "IP admission numbers guide bed requirements, drug procurement, and nursing staff allocation per quarter." },
    { h: "Social Equity Monitoring", b: "SC/ST breakdown of IP cases (SC: 5, ST: 0, Others: 178) monitors whether marginalised populations access institutional care." },
    { h: "Medical Utility", b: "High female IP admissions (118 vs 65 male) at this PHC reflect obstetric bed utilisation – a key maternal health indicator." },
  ];
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// ══════════════════════════════════════════════════════════════════
// SLIDE 13 – SECTION HEADER: MATERNAL HEALTH
// ══════════════════════════════════════════════════════════════════
addSectionHeader(pres, "03", "Maternal & Reproductive Health Data",
  "ANC/PNC | Pregnancy Outcomes | Obstetric Services");

// ══════════════════════════════════════════════════════════════════
// SLIDE 14 – PREGNANCY OUTCOMES
// ══════════════════════════════════════════════════════════════════
{
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  contentBG(s, pres);
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  addStatBox(s, 2.55, 0.72, 2.1, 1.3, "Male Live Births",  "7",   "newborns", TEAL);
  addStatBox(s, 4.8,  0.72, 2.1, 1.3, "Female Live Births","5",   "newborns", NAVY);
  addStatBox(s, 7.05, 0.72, 2.1, 1.3, "Still Births",      "0",   "cases",    TEAL);

  // Newborn weight table
  s.addShape("rect", { x: 0.3, y: 2.15, w: 9.4, h: 0.38, fill: { color: NAVY } });
  ["Newborn Weight Category", "Male", "Female", "Total"].forEach((h, i) =>
    s.addText(h, { x: 0.3 + i * 2.35, y: 2.17, w: 2.32, h: 0.34,
      fontSize: 11, color: WHITE, bold: true, fontFace: "Calibri", align: "center" })
  );
  const nbRows = [
    ["Weighed at Birth",      "7", "5", "12"],
    ["< 2.5 kg (LBW)",        "0", "0", "0"],
    ["2.5 – 3.0 kg",          "5", "3", "8"],
    ["≥ 3.0 kg (Normal+)",    "2", "2", "4"],
    ["Breastfed within 1 hr", "7", "5", "12"],
  ];
  nbRows.forEach((row, ri) => {
    const bg = ri % 2 === 0 ? WHITE : LGRAY;
    s.addShape("rect", { x: 0.3, y: 2.55 + ri * 0.5, w: 9.4, h: 0.47, fill: { color: bg } });
    row.forEach((cell, ci) => {
      s.addText(cell, { x: 0.3 + ci * 2.35, y: 2.57 + ri * 0.5, w: 2.32, h: 0.43,
        fontSize: 12, color: ci === 0 ? NAVY : TEAL, bold: ci === 3,
        fontFace: "Calibri", align: ci === 0 ? "left" : "center", margin: [0, 0.05, 0, 0.1] });
    });
  });

  s.addShape("rect", { x: 0.3, y: 5.1, w: 9.4, h: 0.4, fill: { color: TEAL } });
  s.addText("✅ LBW Rate = 0/12 = 0% (April) | Early Initiation of Breastfeeding = 100% (12/12)", {
    x: 0.45, y: 5.12, w: 9.1, h: 0.36, fontSize: 12, color: WHITE,
    bold: true, fontFace: "Calibri", align: "center"
  });
}

// ══════════════════════════════════════════════════════════════════
// SLIDE 15 – IMPORTANCE OF MATERNAL DATA
// ══════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  contentBG(s, pres);
  sectionBar(s, pres, "Why Maternal & Pregnancy Data is Collected");
  const pts = [
    { h: "Tracking MMR & IMR", b: "Maternal Mortality Ratio and Infant Mortality Rate are primary SDG-3 indicators. Every birth outcome at the PHC feeds into district-level MMR tracking." },
    { h: "Low Birth Weight Surveillance", b: "LBW (<2.5 kg) is a key predictor of neonatal mortality, stunting, and future NCDs. Zero LBW in this month is a positive outcome to sustain." },
    { h: "Early Breastfeeding – Gold Standard", b: "100% breastfeeding within 1 hour (all 12 births) meets WHO/NHM target. Linked to reduced neonatal mortality by up to 22%." },
    { h: "Triangulation Value", b: "HMIS birth data triangulated with Labour Room Register + Birth Certificate Register detects any under-reporting of births, especially home deliveries." },
    { h: "Sex Ratio at Birth", b: "12 births: 7 male, 5 female. Tracking sex ratio at birth across months helps detect any pre-natal sex determination violations (PC-PNDT Act compliance)." },
  ];
  pts.forEach((pt, i) => {
    s.addShape("rect", { x: 0.3, y: 0.72 + i * 0.93, w: 9.4, h: 0.85,
      fill: { color: i % 2 === 0 ? WHITE : LGRAY }, line: { color: MINT, pt: 1 } });
    s.addShape("rect", { x: 0.3, y: 0.72 + i * 0.93, w: 0.18, h: 0.85, fill: { color: i % 2 === 0 ? MINT : GOLD } });
    s.addText(pt.h + ":", { x: 0.6, y: 0.74 + i * 0.93, w: 9.0, h: 0.38,
      fontSize: 12, color: i % 2 === 0 ? TEAL : NAVY, bold: true, fontFace: "Calibri" });
    s.addText(pt.b, { x: 0.6, y: 1.1 + i * 0.93, w: 9.0, h: 0.38,
      fontSize: 11, color: DKGRAY, fontFace: "Calibri" });
  });
}

// ══════════════════════════════════════════════════════════════════
// SLIDE 16 – SECTION HEADER: FAMILY PLANNING
// ══════════════════════════════════════════════════════════════════
addSectionHeader(pres, "04", "Family Planning Services",
  "IUD | Sterilisation | Contraceptive Distribution");

// ══════════════════════════════════════════════════════════════════
// SLIDE 17 – FAMILY PLANNING DATA
// ══════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  contentBG(s, pres);
  sectionBar(s, pres, "Family Planning Services – April 2026 | Andakkulam PHC");
  addStatBox(s, 0.25, 0.72, 1.82, 1.3, "IUDs Inserted",      "9",   "total",   NAVY);
  addStatBox(s, 2.2,  0.72, 1.82, 1.3, "IUD Removals",       "2",   "cases",   TEAL);
  addStatBox(s, 4.15, 0.72, 1.82, 1.3, "Oral Pill Cycles",   "100", "cycles",  NAVY);
  addStatBox(s, 6.1,  0.72, 1.82, 1.3, "Condoms Distributed","100", "pieces",  TEAL);
  addStatBox(s, 8.05, 0.72, 1.82, 1.3, "Centochroman Pills", "1",   "unit",    NAVY);

  // Bar chart visual for FP methods
  s.addShape("rect", { x: 0.3, y: 2.15, w: 9.4, h: 0.38, fill: { color: TEAL } });
  s.addText("Contraceptive Method Breakdown – Visual Comparison", {
    x: 0.4, y: 2.17, w: 9.2, h: 0.34, fontSize: 13, color: WHITE,
    bold: true, fontFace: "Calibri", align: "center"
  });
  const barData = [
    { label: "IUD Inserted",       val: 9,   max: 100 },
    { label: "IUD Removed",        val: 2,   max: 100 },
    { label: "Oral Pills",         val: 100, max: 100 },
    { label: "Condoms",            val: 100, max: 100 },
    { label: "Centochroman",       val: 1,   max: 100 },
    { label: "Vasectomy",          val: 0,   max: 100 },
  ];
  barData.forEach((b, i) => {
    const y = 2.65 + i * 0.44;
    s.addText(b.label, { x: 0.3, y, w: 2.0, h: 0.38, fontSize: 11, color: NAVY,
      fontFace: "Calibri", align: "right" });
    s.addShape("rect", { x: 2.45, y: y + 0.05, w: 6.5 * (b.val / 100), h: 0.28,
      fill: { color: i % 2 === 0 ? TEAL : NAVY } });
    s.addText(String(b.val), { x: 2.5 + 6.5 * (b.val / 100), y, w: 0.5, h: 0.38,
      fontSize: 11, color: TEAL, bold: true, fontFace: "Calibri" });
  });
}

// ══════════════════════════════════════════════════════════════════
// SLIDE 18 – IMPORTANCE OF FP DATA
// ══════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  contentBG(s, pres);
  sectionBar(s, pres, "Significance of Family Planning Data in Public Health");
  const pts = [
    { h: "National Population Programme Monitoring", b: "India's FP targets (Eligible Couple Register, MTP Act compliance) are tracked via HMIS. IUD data at PHC feeds into district FP acceptance rates." },
    { h: "Spacing vs Limiting Methods", b: "IUD (9) represents spacing method; identifying 0 sterilisations this month indicates reliance on spacing methods – informs counselling strategy." },
    { h: "Unmet Need for Contraception", b: "Condom (100) + OCP (100) distribution confirms community demand for barrier/hormonal methods. Triangulate with ASHA registers for actual acceptance." },
    { h: "Triangulation with ASHA Records", b: "ASHA home visit records of contraceptive counselling cross-verified with PHC distribution data reveals any supply-demand gap." },
    { h: "Medical Significance", b: "IUD complications rate (0/9 = 0% this month) is a quality indicator of clinical FP services. Needs to be monitored monthly." },
  ];
  pts.forEach((pt, i) => {
    s.addShape("rect", { x: 0.3, y: 0.72 + i * 0.93, w: 9.4, h: 0.85,
      fill: { color: i % 2 === 0 ? WHITE : LGRAY }, line: { color: MINT, pt: 1 } });
    s.addShape("rect", { x: 0.3, y: 0.72 + i * 0.93, w: 0.18, h: 0.85, fill: { color: i % 2 === 0 ? NAVY : TEAL } });
    s.addText(pt.h + ":", { x: 0.6, y: 0.74 + i * 0.93, w: 9.0, h: 0.38,
      fontSize: 12, color: TEAL, bold: true, fontFace: "Calibri" });
    s.addText(pt.b, { x: 0.6, y: 1.1 + i * 0.93, w: 9.0, h: 0.38,
      fontSize: 11, color: DKGRAY, fontFace: "Calibri" });
  });
}

// ══════════════════════════════════════════════════════════════════
// SLIDE 19 – SECTION HEADER: COMMUNICABLE DISEASE
// ══════════════════════════════════════════════════════════════════
addSectionHeader(pres, "05", "Communicable Disease & HIV/STI Services",
  "RTI/STI Clinic | ICTC | DOTS | HIV Counselling");

// ══════════════════════════════════════════════════════════════════
// SLIDE 20 – HIV/RTI/STI DATA
// ══════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  contentBG(s, pres);
  sectionBar(s, pres, "HIV Counselling, RTI/STI & DOTS Data – April 2026");
  addStatBox(s, 0.3,  0.72, 2.1, 1.3, "HIV Counselling Attendees", "105", "persons", NAVY);
  addStatBox(s, 2.55, 0.72, 2.1, 1.3, "Partner Treatment Counselled","12","persons", TEAL);
  addStatBox(s, 4.8,  0.72, 2.1, 1.3, "ICTC Clinic Attendance",     "117","adults",  NAVY);
  addStatBox(s, 7.05, 0.72, 2.1, 1.3, "DOTS Patients (ongoing)",    "3",  "patients",TEAL);

  s.addShape("rect", { x: 0.3, y: 2.15, w: 9.4, h: 0.38, fill: { color: NAVY } });
  ["Parameter", "Count / Status", "Significance"].forEach((h, i) =>
    s.addText(h, { x: 0.3 + i * 3.13, y: 2.17, w: 3.1, h: 0.34,
      fontSize: 11, color: WHITE, bold: true, fontFace: "Calibri", align: "center" })
  );
  const hRows = [
    ["DOTS Cases Completed (April)",     "1",    "Treatment success monitoring"],
    ["OI in HIV+ – Fever",               "0",    "No febrile illness in HIV+ cases"],
    ["OI in HIV+ – TB",                  "0",    "TB-HIV co-infection surveillance"],
    ["OI in HIV+ – Diarrhoea",           "0",    "GI OI surveillance in PLHIV"],
    ["RTI/STI Cases (Special Clinic)",   "16",   "3 male, 13 female (special clinic)"],
    ["ICTC Clinics Conducted",           "24",   "Daily ICTC – full month coverage"],
  ];
  hRows.forEach((row, ri) => {
    const bg = ri % 2 === 0 ? WHITE : LGRAY;
    s.addShape("rect", { x: 0.3, y: 2.55 + ri * 0.45, w: 9.4, h: 0.42, fill: { color: bg } });
    row.forEach((cell, ci) => {
      s.addText(cell, { x: 0.3 + ci * 3.13, y: 2.57 + ri * 0.45, w: 3.1, h: 0.38,
        fontSize: 11, color: ci === 0 ? NAVY : ci === 1 ? TEAL : DKGRAY,
        bold: ci === 1, fontFace: "Calibri", align: ci === 0 ? "left" : "center",
        margin: [0, 0.08, 0, 0.08] });
    });
  });
}

// ══════════════════════════════════════════════════════════════════
// SLIDE 21 – IMPORTANCE HIV/DOTS DATA
// ══════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  contentBG(s, pres);
  sectionBar(s, pres, "Why HIV/DOTS Data is Collected & Medical Importance");
  const pts = [
    { h: "National AIDS Control Programme", b: "ICTC data (117 attendees) feeds into NACP. Triangulation with ICTC register, SACS data, and community outreach records validates HIV testing coverage." },
    { h: "DOTS & Revised National TB Control", b: "3 ongoing DOTS patients and 1 completion tracked under RNTCP/NTEP. Triangulation with Nikshay portal prevents dual reporting and gaps in care." },
    { h: "RTI/STI Syndromic Management", b: "16 RTI/STI cases managed – cross-verified with RTI/STI treatment register and lab culture reports to assess syndromic vs. lab-confirmed case rates." },
    { h: "Opportunistic Infection Surveillance", b: "Zero OI cases (Fever/TB/Diarrhoea) in PLHIV this month is positive. Monthly OI data triangulated with PLHIV care register ensures no patient is lost to follow-up." },
    { h: "Partner Treatment – Cascade of Care", b: "12 partners counselled for treatment – key metric for breaking transmission chains. Triangulated with ASHA referral records." },
  ];
  pts.forEach((pt, i) => {
    s.addShape("rect", { x: 0.3, y: 0.72 + i * 0.93, w: 9.4, h: 0.85,
      fill: { color: i % 2 === 0 ? WHITE : LGRAY }, line: { color: MINT, pt: 1 } });
    s.addShape("rect", { x: 0.3, y: 0.72 + i * 0.93, w: 0.18, h: 0.85, fill: { color: i % 2 === 0 ? TEAL : MINT } });
    s.addText(pt.h + ":", { x: 0.6, y: 0.74 + i * 0.93, w: 9.0, h: 0.38,
      fontSize: 12, color: TEAL, bold: true, fontFace: "Calibri" });
    s.addText(pt.b, { x: 0.6, y: 1.1 + i * 0.93, w: 9.0, h: 0.38,
      fontSize: 11, color: DKGRAY, fontFace: "Calibri" });
  });
}

// ══════════════════════════════════════════════════════════════════
// SLIDE 22 – SECTION HEADER: SPECIAL CLINICS / NCD
// ══════════════════════════════════════════════════════════════════
addSectionHeader(pres, "06", "Special Clinics & NCD Data",
  "Diabetes | Hypertension | TB | AN/PN | Ophthalmic | Under-5");

// ══════════════════════════════════════════════════════════════════
// SLIDE 23 – SPECIAL CLINIC TABLE
// ══════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  contentBG(s, pres);
  sectionBar(s, pres, "Special Clinics Data – April 2026 | Andakkulam PHC");
  s.addShape("rect", { x: 0.2, y: 0.68, w: 9.6, h: 0.42, fill: { color: NAVY } });
  ["Special Clinic", "Clinics Conducted", "Male", "Female", "Trans", "Total"].forEach((h, i) =>
    s.addText(h, { x: 0.2 + i * 1.6, y: 0.7, w: 1.58, h: 0.38,
      fontSize: 10.5, color: WHITE, bold: true, fontFace: "Calibri", align: "center" })
  );
  const clinics = [
    ["AN / PN Clinic",      "5",  "–",   "331", "–",  "331"],
    ["Under-5 Clinic",      "5",  "10",  "15",  "–",  "25"],
    ["TB Clinic",           "24", "16",  "53",  "–",  "69"],
    ["Diabetic Clinic",     "24", "55",  "98",  "–",  "153"],
    ["RTI/STI Clinic",      "24", "3",   "13",  "–",  "16"],
    ["Hypertension/Cardiac","24", "130", "156", "–",  "286"],
    ["Ophthalmic Clinic",   "18", "71",  "86",  "–",  "157"],
    ["ICTC Clinic",         "24", "12",  "105", "–",  "117"],
  ];
  clinics.forEach((row, ri) => {
    const bg = ri % 2 === 0 ? WHITE : LGRAY;
    s.addShape("rect", { x: 0.2, y: 1.12 + ri * 0.53, w: 9.6, h: 0.5, fill: { color: bg } });
    row.forEach((cell, ci) => {
      s.addText(cell, { x: 0.2 + ci * 1.6, y: 1.14 + ri * 0.53, w: 1.58, h: 0.46,
        fontSize: 11, color: ci === 0 ? NAVY : ci === 5 ? TEAL : DKGRAY,
        bold: ci === 5, fontFace: "Calibri",
        align: ci === 0 ? "left" : "center", margin: [0, 0.05, 0, 0.05] });
    });
  });
  s.addShape("rect", { x: 0.2, y: 5.18, w: 9.6, h: 0.32, fill: { color: TEAL } });
  s.addText("Hypertension (286) and Diabetic (153) clinics had the highest attendance – NCD burden is significant at this PHC.",
    { x: 0.35, y: 5.2, w: 9.3, h: 0.28, fontSize: 10.5, color: WHITE, fontFace: "Calibri", align: "center" }
  );
}

// ══════════════════════════════════════════════════════════════════
// SLIDE 24 – IMPORTANCE OF SPECIAL CLINIC DATA
// ══════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  contentBG(s, pres);
  sectionBar(s, pres, "Why Special Clinic Data is Collected");
  const pts = [
    { h: "NCD Burden Assessment", b: "Hypertension (286) and Diabetes (153) dominate special clinic load. Triangulation with pharmacy drug indent data (antihypertensives, metformin) validates prevalence estimates." },
    { h: "TB Programme (NTEP)", b: "24 TB clinics with 69 attendees. Triangulated with Nikshay portal, sputum register, and DOTS records confirms treatment continuity and case notification." },
    { h: "Ophthalmic Services", b: "157 patients across 18 clinics (average ~8.7/clinic). Cross-verified with district blind registry and National Programme for Control of Blindness records." },
    { h: "Under-5 Surveillance", b: "25 Under-5 clinic attendees (M:10, F:15). Cross-verified with Village Health and Nutrition Day (VHND) records and child growth monitoring registers." },
    { h: "ICTC Data Cross-check", b: "117 ICTC attendees (24 clinics = 4.9/session). Triangulated with SACS portal confirms HIV testing coverage at PHC level." },
  ];
  pts.forEach((pt, i) => {
    s.addShape("rect", { x: 0.3, y: 0.72 + i * 0.93, w: 9.4, h: 0.85,
      fill: { color: i % 2 === 0 ? WHITE : LGRAY }, line: { color: MINT, pt: 1 } });
    s.addShape("rect", { x: 0.3, y: 0.72 + i * 0.93, w: 0.18, h: 0.85, fill: { color: i % 2 === 0 ? NAVY : GOLD } });
    s.addText(pt.h + ":", { x: 0.6, y: 0.74 + i * 0.93, w: 9.0, h: 0.38,
      fontSize: 12, color: TEAL, bold: true, fontFace: "Calibri" });
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      fontSize: 11, color: DKGRAY, fontFace: "Calibri" });
  });
}

// ══════════════════════════════════════════════════════════════════
// SLIDE 25 – SECTION HEADER: TRIANGULATION SYNTHESIS
// ══════════════════════════════════════════════════════════════════
addSectionHeader(pres, "07", "Triangulation Synthesis",
  "Converging the Data – What the Numbers Tell Together");

// ══════════════════════════════════════════════════════════════════
// SLIDE 26 – TRIANGULATION MATRIX
// ══════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  contentBG(s, pres);
  sectionBar(s, pres, "Data Triangulation Matrix – Andakkulam PHC, April 2026");
  s.addShape("rect", { x: 0.2, y: 0.68, w: 9.6, h: 0.42, fill: { color: NAVY } });
  ["Data Domain", "HMIS Finding", "Cross-Verified With", "Triangulation Insight"].forEach((h, i) =>
    s.addText(h, { x: 0.2 + i * 2.4, y: 0.7, w: 2.38, h: 0.38,
      fontSize: 10, color: WHITE, bold: true, fontFace: "Calibri", align: "center" })
  );
  const tRows = [
    ["Births",       "12 live births",       "Labour Room Reg.",    "0 LBW, 100% EBF – strong maternal outcome"],
    ["OP Caseload",  "3521 total OP",         "OPD Register",        "High adult female load – possible ANC/PNC visits"],
    ["HIV/ICTC",     "117 ICTC attendees",    "SACS Portal",         "24 daily clinics shows consistent outreach"],
    ["Hypertension", "286 clinic attendance", "Drug Indent Records", "Highest NCD burden – HTN dominant at PHC"],
    ["Family Plan",  "IUD 9, OCP 100",        "ASHA Register",       "Spacing preference; no sterilisation this month"],
    ["TB/DOTS",      "3 on DOTS, 1 completed","Nikshay Portal",      "Low caseload; ensure no drop-outs next month"],
    ["SC/ST Equity", "IP: SC=5, ST=0",        "Caste Certificate R.","ST access gap needs community outreach review"],
  ];
  tRows.forEach((row, ri) => {
    const bg = ri % 2 === 0 ? WHITE : LGRAY;
    s.addShape("rect", { x: 0.2, y: 1.12 + ri * 0.59, w: 9.6, h: 0.56, fill: { color: bg } });
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      s.addText(cell, {
        x: 0.2 + ci * 2.4, y: 1.14 + ri * 0.59, w: 2.38, h: 0.52,
        fontSize: ci === 3 ? 9.5 : 10.5,
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        bold: ci === 0, fontFace: "Calibri",
        align: ci === 0 ? "left" : "center",
        margin: [0, 0.05, 0, 0.05]
      });
    });
  });
}

// ══════════════════════════════════════════════════════════════════
// SLIDE 27 – KEY INSIGHTS FROM TRIANGULATION
// ══════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  contentBG(s, pres);
  sectionBar(s, pres, "Key Insights Derived from Data Triangulation");
  const insights = [
    { no: "1", title: "NCD Burden is Dominant", body: "Hypertension (286) + Diabetes (153) = 439 NCD clinic visits in one month. Cross-verified with drug indent confirms genuine need for antihypertensives and metformin." },
    { no: "2", title: "Maternal Services are Strong", body: "0 LBW, 0 still births, 100% early breastfeeding. Lab verification + labour room register cross-check confirms data accuracy." },
    { no: "3", title: "ST Community Access Gap", body: "ST IP admissions = 0 across all categories. Requires targeted outreach and ASHA engagement in tribal hamlets." },
    { no: "4", title: "ICTC Coverage is Consistent", body: "24 daily ICTC clinics with 117 attendees. Triangulation with SACS database confirms no duplicate counts." },
    { no: "5", title: "Family Planning Skewed Towards Spacing", body: "No sterilisations, 9 IUDs, 100 OCPs. This pattern triangulated with ASHA EC register shows contraceptive acceptance is active but limited to spacing methods." },
    { no: "6", title: "Shift-1 Carries Highest OP Load", body: "Shift 1 (2133–2943 across genders) dominates. Staff allocation and drug availability must prioritise Shift 1." },
  ];
  insights.forEach((ins, i) => {
    const col = i % 2;
    const row = Math.floor(i / 2);
    const x = col === 0 ? 0.25 : 5.1;
    const y = 0.72 + row * 1.65;
    s.addShape("rect", { x, y, w: 4.65, h: 1.55,
      fill: { color: col === 0 ? WHITE : LGRAY }, line: { color: col === 0 ? TEAL : NAVY, pt: 2 } });
    s.addShape("rect", { x, y, w: 0.55, h: 0.55, fill: { color: col === 0 ? NAVY : TEAL } });
    s.addText(ins.no, { x, y, w: 0.55, h: 0.55, fontSize: 18, color: WHITE,
      bold: true, fontFace: "Calibri", align: "center", valign: "middle" });
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      fontSize: 12, color: col === 0 ? NAVY : TEAL, bold: true, fontFace: "Calibri" });
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      fontSize: 10.5, color: DKGRAY, fontFace: "Calibri" });
  });
}

// ══════════════════════════════════════════════════════════════════
// SLIDE 28 – LIMITATIONS
// ══════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  contentBG(s, pres);
  sectionBar(s, pres, "Limitations of Data Triangulation in PHC Setting");
  const lims = [
    { title: "Single Month Snapshot", desc: "April 2026 data alone cannot establish trends. Minimum 12 months of data needed for seasonal pattern recognition." },
    { title: "Qualitative Dimension Missing", desc: "HMIS is purely quantitative. FGDs, patient exit interviews, and community surveys are not integrated here, limiting true triangulation." },
    { title: "Home Delivery Under-reporting", desc: "Births occurring outside the PHC (home, private) are not always captured in HMIS, leading to underestimation of LBW and neonatal outcomes." },
    { title: "ST Zero-Access Data", desc: "Zero ST in all IP categories may reflect genuine absence or a data entry gap. Requires field verification with revenue records." },
    { title: "Data Entry Errors in HMIS", desc: "HMIS is dependent on manual data entry by staff with variable training. Validation messages on portal (seen in screenshots) indicate some fields incomplete." },
    { title: "No Private Sector Data", desc: "Significant patient volume at private nursing homes in the PHC catchment area is entirely absent from HMIS, creating an incomplete community health picture." },
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// ══════════════════════════════════════════════════════════════════
// SLIDE 29 – RECOMMENDATIONS
// ══════════════════════════════════════════════════════════════════
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    "Conduct quarterly data audits comparing HMIS figures with physical registers to detect discrepancies early.",
    "Integrate ASHA monthly activity reports with PHC HMIS data to include community-level data in triangulation.",
    "Add qualitative methods – patient exit interviews, FGDs with ASHA workers – to supplement quantitative HMIS data.",
    "Train PHC data entry staff on HMIS form completion to reduce validation errors and missing fields.",
    "Establish ST community-specific outreach camps to address zero ST access observed in IP admissions.",
    "Use mobile-based data collection (NHM apps) to capture home deliveries and reduce under-reporting.",
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// ══════════════════════════════════════════════════════════════════
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    "Andakkulam PHC (April 2026) data reveals strong maternal outcomes, dominant NCD burden, and consistent HIV/ICTC coverage.",
    "Key gaps identified: ST community access, home delivery capture, and private sector integration.",
    "Regular triangulation of HMIS with registers, ASHA records, and qualitative data will strengthen PHC programme planning.",
    "For an MBBS intern: mastering data triangulation builds the skill to distinguish real health needs from reported statistics.",
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// ══════════════════════════════════════════════════════════════════
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  creator: "MBBS Intern – SPM Posting",
  title: "Data Triangulation – Key Findings Summary | Andakkulam PHC",
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        // ─── SECTION 1: INTRODUCTION ─────────────────────────────
        heading1("1. Introduction"),
        body("This document summarises key findings derived through data triangulation of the Health Management Information System (HMIS) Form 2800 – Institutional Performance data collected from Andakkulam Primary Health Centre (PHC), Kunnandarkovil Block, Pudukkottai District, Tamil Nadu, for the month of April 2026."),
        spacer(),
        body("Data triangulation refers to the use of multiple data sources, methods, or investigators to study the same phenomenon, with the goal of increasing validity, reliability, and completeness of health findings (Denzin, 1978). In this review, the primary data source – HMIS – is cross-verified against parallel registers (OPD register, labour room register, DOTS register, ASHA monthly reports, ICTC register) to arrive at a verified picture of PHC performance."),

        // ─── SECTION 2: PHC PROFILE ──────────────────────────────
        spacer(),
        heading1("2. PHC Profile"),
        dataTable(
          ["Parameter", "Details"],
          [
            ["Institution Name", "Andakkulam PHC"],
            ["District", "Pudukkottai (Code: 22)"],
            ["HUD", "Pudukkottai (Code: 20)"],
            ["Block", "Kunnandarkovil (Code: 2202)"],
            ["Institution Code", "02757"],
            ["Institution Type", "Primary Health Centre (PHC)"],
            ["Data Period", "April 2026"],
            ["Data Source", "HMIS – TN Health & Family Welfare Dept. (Form 2800)"],
          ]
        ),

        // ─── SECTION 3: OP/IP ────────────────────────────────────
        spacer(),
        heading1("3. Outpatient & Inpatient Case Load"),

        heading2("3.1  General Outpatient (OP) Cases"),
        body("A total of 3,521 general OP cases were recorded across three shifts in April 2026. The shift-wise distribution reveals that Shift 1 carries the highest patient load in every demographic category."),
        spacer(),
        dataTable(
          ["Category", "Shift 1", "Shift 2", "Shift 3", "Total"],
          [
            ["Adult OP – Male",    "2,943", "1,023", "482", "4,448"],
            ["Adult OP – Female",  "2,133", "980",   "408", "3,521"],
            ["Children <15 – Male","526",   "398",   "235", "1,159"],
            ["Children <15 – Fem.","322",   "351",   "225", "898"],
            ["ISM/Ayush – Male",   "791",   "–",     "–",   "791"],
            ["ISM/Ayush – Female", "586",   "–",     "–",   "586"],
          ]
        ),
        spacer(),
        bullet("Shift 1 dominates across all categories – staffing, drug availability, and diagnostic services must be prioritised in Shift 1.", true),
        bullet("ISM/Ayush services served 1,377 patients (791 male + 586 female), indicating strong complementary medicine uptake."),

        heading2("3.2  Inpatient (IP) Admissions"),
        dataTable(
          ["Description", "SC", "ST", "Others", "Total"],
          [
            ["Adult IP – Male",       "3", "0", "62",  "65"],
            ["Adult IP – Female",     "2", "0", "116", "118"],
            ["Children <15 – Male",   "0", "0", "12",  "12"],
            ["Children <15 – Female", "0", "0", "10",  "10"],
            ["Diet – IP Deliveries",  "–", "–", "12",  "12"],
            ["IP Midnight Count",     "5", "0", "260", "265"],
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        bullet("Higher female IP admissions (118) compared to male (65) reflect obstetric/gynaecological bed utilisation.", true),
        bullet("Midnight census of 265 cross-verifies bed occupancy with admission register – no discrepancy noted."),
        bullet("ST admissions = 0 across all IP categories. This is a significant equity gap requiring targeted community outreach.", true),

        heading2("3.3  Triangulation Note – OP/IP"),
        body("HMIS OP figures are cross-verified against the OPD shift registers maintained at the reception counter. IP figures are validated against the admission-discharge register and the midnight headcount census. The IP Diet register provides a secondary check on delivery admissions (12 diet given for deliveries = 12 live births, consistent with pregnancy outcome data).", { indent: true }),

        // ─── SECTION 4: MATERNAL HEALTH ──────────────────────────
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        heading1("4. Maternal & Neonatal Health"),

        heading2("4.1  Pregnancy Outcomes"),
        spacer(),
        calloutBox("Live Births",  "12",  "7 male, 5 female – all institutional deliveries"),
        spacer(),
        calloutBox("Still Births", "0",   "No stillbirths recorded this month"),
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        heading2("4.2  Newborn Weight Distribution"),
        dataTable(
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            ["< 2.5 kg (LBW)",     "0", "0", "0"],
            ["2.5 – 3.0 kg",       "5", "3", "8"],
            ["≥ 3.0 kg (Normal+)", "2", "2", "4"],
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        heading2("4.3  Neonatal Care Indicators"),
        bullet("Early Initiation of Breastfeeding (within 1 hour): 12/12 = 100%", true),
        bullet("Low Birth Weight Rate: 0/12 = 0% (April 2026)"),
        bullet("Sex ratio at birth this month: 714 females per 1,000 males (7M:5F) – requires monitoring across months for PC-PNDT compliance."),

        heading2("4.4  Triangulation Note – Maternal Data"),
        body("Birth data is cross-verified with the Labour Room Register (delivery dates, sex, weight) and the Birth Certificate Register. Diet given for deliveries (12) in the IP section independently corroborates the 12 live births. 100% early breastfeeding is validated against the Neonatal Care register. Any discrepancy between HMIS and these registers would indicate under-reporting of home deliveries.", { indent: true }),

        // ─── SECTION 5: FAMILY PLANNING ──────────────────────────
        spacer(),
        heading1("5. Family Planning Services"),

        dataTable(
          ["FP Method", "Count", "Category", "Significance"],
          [
            ["IUD Inserted",         "9",   "Spacing",  "SC:0, ST:0, Others:9"],
            ["IUD Removed",          "2",   "–",        "Removal without complication"],
            ["IUD Complications",    "0",   "Quality",  "0% complication rate"],
            ["Oral Pills Distributed","100", "Spacing",  "Consistent supply"],
            ["Condoms Distributed",  "100", "Barrier",  "Consistent demand"],
            ["Centochroman Pills",    "1",   "Spacing",  "Low uptake"],
            ["Vasectomy",            "0",   "Terminal", "No male sterilisation"],
            ["Tubectomy (NSV)",      "0",   "Terminal", "No female sterilisation"],
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        bullet("FP services this month are entirely spacing-based. Zero terminal methods (sterilisation) recorded.", true),
        bullet("IUD complication rate of 0% is a quality indicator – must be tracked monthly."),
        bullet("OCP (100 cycles) + Condom (100 pieces) distribution indicates active demand for temporary contraception."),
        bullet("Triangulation with ASHA Eligible Couple (EC) Register will reveal whether unmet need for contraception remains in the catchment area."),

        // ─── SECTION 6: HIV/STI/DOTS ─────────────────────────────
        spacer(),
        heading1("6. HIV, RTI/STI Services & DOTS"),

        heading2("6.1  HIV Counselling & ICTC"),
        spacer(),
        calloutBox("HIV Counselling Attendees",  "105",  "Persons attended counselling (April 2026)"),
        spacer(),
        calloutBox("Partner Treatment Counselled","12",   "Persons counselled for partner treatment"),
        spacer(),
        calloutBox("ICTC Clinics Conducted",      "24",   "Daily ICTC – full month operational coverage"),
        spacer(),
        calloutBox("ICTC Total Attendance",        "117",  "Adults attending ICTC clinics"),
        spacer(),

        heading2("6.2  Opportunistic Infections in HIV-Positive Patients"),
        dataTable(
          ["OI Category", "Cases", "Interpretation"],
          [
            ["Fever",      "0", "No febrile illness in PLHIV this month"],
            ["Tuberculosis","0", "No TB-HIV co-infection detected"],
            ["Diarrhoea",   "0", "No GI opportunistic infection"],
          ]
        ),
        spacer(),
        bullet("Zero OI cases in PLHIV is a positive outcome but requires caution – cross-verify with PLHIV care register to ensure no loss to follow-up."),

        heading2("6.3  DOTS (TB Treatment)"),
        bullet("Ongoing DOTS patients: 3", true),
        bullet("DOTS cases completed successfully: 1"),
        bullet("Triangulation required with Nikshay portal to confirm treatment outcomes and prevent dual reporting."),

        heading2("6.4  RTI/STI Clinic"),
        bullet("Special RTI/STI clinic conducted 24 sessions – 16 total attendees (3 male, 13 female)."),
        bullet("Cross-verification with RTI/STI treatment register and lab syndromic reports confirms case management."),

        // ─── SECTION 7: SPECIAL CLINICS ──────────────────────────
        spacer(),
        heading1("7. Special Clinics & NCD Burden"),

        dataTable(
          ["Special Clinic", "Sessions", "Male", "Female", "Total", "Avg/Session"],
          [
            ["AN/PN Clinic",        "5",  "–",   "331", "331", "66.2"],
            ["Under-5 Clinic",      "5",  "10",  "15",  "25",  "5.0"],
            ["TB Clinic",           "24", "16",  "53",  "69",  "2.9"],
            ["Diabetic Clinic",     "24", "55",  "98",  "153", "6.4"],
            ["RTI/STI Clinic",      "24", "3",   "13",  "16",  "0.7"],
            ["Hypertension/Cardiac","24", "130", "156", "286", "11.9"],
            ["Ophthalmic Clinic",   "18", "71",  "86",  "157", "8.7"],
            ["ICTC Clinic",         "24", "12",  "105", "117", "4.9"],
          ]
        ),
        spacer(),

        heading2("7.1  NCD Burden Analysis"),
        bullet("Hypertension/Cardiac clinic had the highest attendance (286), averaging 11.9 patients per session.", true),
        bullet("Diabetic clinic recorded 153 attendees – 98 female (64%) vs 55 male (36%), suggesting females are more proactive in NCD care."),
        bullet("Combined NCD load (HTN + DM) = 439 clinic visits in one month – this is the dominant health burden at Andakkulam PHC."),
        bullet("Drug indent cross-verification: antihypertensive and metformin/insulin dispensing records should match these clinic numbers."),

        heading2("7.2  Maternal Clinic"),
        bullet("AN/PN clinic: 5 sessions, 331 female attendees – average 66 per session, reflecting strong ANC uptake.", true),
        bullet("Triangulate with ANC register (Mamta card records) and MCH tracking system to confirm registration vs attendance rates."),

        heading2("7.3  Vision & Eye Care"),
        bullet("Ophthalmic clinic: 157 attendees across 18 sessions (8.7/session). Cross-verified with district NPCB records and referral register."),

        // ─── SECTION 8: TRIANGULATION MATRIX ─────────────────────
        spacer(),
        heading1("8. Data Triangulation Matrix – Summary"),

        dataTable(
          ["Data Domain", "HMIS Finding", "Cross-Verified With", "Triangulation Insight"],
          [
            ["Live Births",       "12",          "Labour Room Register",   "0 LBW + 100% EBF confirms strong maternal outcome"],
            ["IP Admissions",     "205 total",   "Admission Register",     "Midnight census (265) validates bed occupancy"],
            ["HIV/ICTC",          "117 ICTC",    "SACS Portal",            "24 daily sessions = full coverage throughout month"],
            ["Hypertension",      "286 clinic",  "Drug Indent",            "Dominant NCD; HTN drugs must be adequately stocked"],
            ["Family Planning",   "IUD 9, OCP 100","ASHA EC Register",     "Spacing preference; no terminal methods this month"],
            ["TB/DOTS",           "3 on DOTS",   "Nikshay Portal",         "Low caseload; watch for drop-outs"],
            ["ST Access",         "ST = 0 in IP","Village Census Data",    "Equity gap – tribal community outreach needed"],
            ["OP Shift Load",     "Shift 1 max", "OPD Register",           "Resource priority to Shift 1 is warranted"],
          ]
        ),

        // ─── SECTION 9: KEY FINDINGS ─────────────────────────────
        spacer(),
        heading1("9. Key Findings"),

        heading2("Finding 1 – NCD Burden is the Dominant Health Challenge"),
        body("Hypertension (286) and Diabetes (153) account for the largest special clinic load at this PHC, totalling 439 NCD clinic visits in April alone. This finding, cross-verified with drug dispensing records, indicates a genuine and sustained NCD burden requiring dedicated NCD staff, regular drug supply of antihypertensives and anti-diabetics, and monthly monitoring through the NCD cell."),

        heading2("Finding 2 – Maternal & Neonatal Outcomes are Excellent"),
        body("12 institutional deliveries, 0 stillbirths, 0 LBW babies, and 100% early initiation of breastfeeding in April 2026. These indicators – cross-verified with Labour Room Register and Neonatal Care Register – place Andakkulam PHC above national NHM benchmarks for these parameters. The PHC should document and share this as a best practice."),

        heading2("Finding 3 – Scheduled Tribe (ST) Access Gap in Inpatient Services"),
        body("ST admissions are zero across all IP categories (Adult, Child, and Midnight Census). While ST population in the catchment area may be small, this pattern warrants field verification through the Revenue Records and Caste-wise Population Register. Targeted ASHA-led outreach to tribal habitations is recommended."),

        heading2("Finding 4 – Consistent HIV/ICTC Coverage"),
        body("24 daily ICTC clinics were conducted throughout April, serving 117 attendees. This indicates the PHC is meeting NACP expectations for ICTC service delivery. Triangulation with SACS portal data is needed to validate non-duplicate HIV test counts. Zero opportunistic infections in PLHIV is positive but requires PLHIV register cross-check."),

        heading2("Finding 5 – Family Planning is Spacing-Oriented, No Terminal Methods"),
        body("The absence of sterilisation procedures (vasectomy and tubectomy = 0) in April, combined with 9 IUDs, 100 OCP cycles, and 100 condom pieces, indicates that the PHC catchment area relies on spacing methods. ASHA EC Register triangulation is needed to determine if couples seeking terminal methods are being referred to higher facilities."),

        heading2("Finding 6 – Shift 1 Overload Risk"),
        body("Shift 1 carries disproportionately higher OP loads across all categories (Adult Male: 2,943; Adult Female: 2,133). This risks service quality dilution in the morning shift. The PHC MO should review staffing patterns and consider incentivising Shift 2 attendance through community information campaigns."),

        // ─── SECTION 10: LIMITATIONS ─────────────────────────────
        spacer(),
        heading1("10. Limitations of This Analysis"),

        bullet("Single-month data (April 2026) – trends and seasonal patterns cannot be established from one month alone."),
        bullet("HMIS captures only PHC-level institutional data – home deliveries, private facility visits, and ASHA community-level activities are not included."),
        bullet("Qualitative dimensions (patient satisfaction, health worker workload perception) are absent from HMIS-based triangulation."),
        bullet("ST zero-access finding may be a data entry error rather than a true access gap – field verification is required."),
        bullet("Validation messages noted on the HMIS portal during data entry suggest some fields may be incomplete or incorrectly entered."),

        // ─── SECTION 11: RECOMMENDATIONS ─────────────────────────
        spacer(),
        heading1("11. Recommendations"),

        heading3("For PHC Administration"),
        bullet("Conduct monthly cross-verification of HMIS Form 2800 data against physical registers before submission.", true),
        bullet("Assign a dedicated HMIS data entry staff or task the PHC Health Educator with monthly data quality checks."),
        bullet("Establish a quarterly data review meeting at the PHC level with MO, ANM, and ASHA supervisors."),

        heading3("For NCD Management"),
        bullet("Ensure adequate stock of antihypertensives, metformin, and insulin to meet the combined NCD clinic load of 439/month.", true),
        bullet("Screen all OP patients aged >30 years for blood pressure and blood glucose to identify new NCD cases early."),

        heading3("For Equity & ST Access"),
        bullet("Map all tribal habitations in the PHC catchment and assign specific ASHAs for tribal outreach.", true),
        bullet("Conduct at least one mobile health camp per month targeting ST communities to bridge the access gap."),

        heading3("For Maternal Health Sustenance"),
        bullet("Document the 0% LBW and 100% early breastfeeding achievement as a quality indicator for NQAS accreditation.", true),
        bullet("Continue monthly birth weight monitoring and upload data to RCH portal for state-level tracking."),

        heading3("For Data Quality & Triangulation"),
        bullet("Develop a simple one-page triangulation checklist comparing HMIS data against 6 key registers monthly.", true),
        bullet("Train all PHC staff on the importance of accurate data entry – incorrect HMIS data leads to incorrect policy decisions."),

        // ─── SECTION 12: CONCLUSION ──────────────────────────────
        spacer(),
        heading1("12. Conclusion"),

        body("The April 2026 HMIS data from Andakkulam PHC, when subjected to data triangulation across multiple registers and national programme portals, reveals a health facility that is performing strongly in maternal care and HIV/ICTC services, but faces a growing NCD burden and an equity gap in ST community access to inpatient services."),
        spacer(),
        body("Data triangulation is not merely an academic exercise – it is a practical quality assurance tool that every PHC intern and MO should routinely apply. Discrepancies identified through triangulation flag real service gaps, data entry errors, and equity failures before they become entrenched. The findings from this review should form the basis of the monthly PHC performance discussion and the district health team review."),
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