pandoc --track-changes=all /home/daytona/workspace/attachments/370b96f7-25fe-472…

Duncan Chanda antimicrobial stewardship Zambia

Mudenda Kapolowe antibiotic stewardship Zambia point prevalence survey

Duncan Chanda Zambia ICARS antibiotic stewardship hospital

OMOP CDM antimicrobial stewardship LMIC data standardisation

OMOP common data model medication standardization low income countries

Chizimu Zambia antibiotic WHO AWaRe AMR surveillance

PMID: 40149095

PMID: 39399696

PMID: 39464860

PMID: 41301589

PMID: 40149077

Duncan Chanda UTH Zambia ICARS antimicrobial stewardship published papers PubMed

PMID: 41301620

PMID: 41301621

community engagement antimicrobial stewardship AMR Africa LMIC

community engagement antimicrobial stewardship low income countries patient participation

RxNorm drug terminology standardization hospital Africa interoperability

ls /home/daytona/workspace/ 2>/dev/null && mkdir -p /home/daytona/workspace/conc…

~/concept-note-revised/build.js

const {
  Document, Packer, Paragraph, TextRun, HeadingLevel, AlignmentType,
  BorderStyle, Table, TableRow, TableCell, WidthType, ShadingType,
  PageBreak, NumberingLevel
} = require('docx');
const fs = require('fs');

// Helper: normal paragraph with optional highlight colour
function para(runs, opts = {}) {
  return new Paragraph({
    alignment: opts.alignment || AlignmentType.JUSTIFIED,
    spacing: { after: 120, line: 276 },
    children: Array.isArray(runs) ? runs : [runs],
  });
}

function heading1(text) {
  return new Paragraph({
    heading: HeadingLevel.HEADING_1,
    spacing: { before: 240, after: 120 },
    children: [new TextRun({ text, bold: true, size: 24, color: '1F3864' })],
  });
}

function heading2(text) {
  return new Paragraph({
    heading: HeadingLevel.HEADING_2,
    spacing: { before: 200, after: 80 },
    children: [new TextRun({ text, bold: true, size: 22, color: '2E5090' })],
  });
}

function t(text, opts = {}) {
  return new TextRun({ text, size: 20, font: 'Calibri', ...opts });
}

// Highlighted run (yellow = improvement marker)
function hi(text, opts = {}) {
  return new TextRun({
    text,
    size: 20,
    font: 'Calibri',
    highlight: 'yellow',
    ...opts,
  });
}

function bullet(runs, level = 0) {
  return new Paragraph({
    bullet: { level },
    spacing: { after: 80 },
    children: Array.isArray(runs) ? runs : [runs],
  });
}

function refLine(num, text) {
  return new Paragraph({
    spacing: { after: 100 },
    indent: { left: 720, hanging: 360 },
    children: [
      new TextRun({ text: `${num}. `, size: 18, font: 'Calibri', bold: true }),
      new TextRun({ text, size: 18, font: 'Calibri' }),
    ],
  });
}

function pageBreak() {
  return new Paragraph({ children: [new PageBreak()] });
}

// ─── DOCUMENT ──────────────────────────────────────────────────────────────

const doc = new Document({
  styles: {
    default: {
      document: {
        run: { font: 'Calibri', size: 20 },
        paragraph: { spacing: { line: 276 } },
      },
    },
  },
  sections: [{
    properties: {
      page: {
        margin: { top: 1080, bottom: 1080, left: 1080, right: 1080 },
      },
    },
    children: [

      // ── TITLE BLOCK ──────────────────────────────────────────────────────
      new Paragraph({
        alignment: AlignmentType.CENTER,
        spacing: { after: 60 },
        children: [t('CONCEPT NOTE', { bold: true, size: 28, allCaps: true })],
      }),
      new Paragraph({
        alignment: AlignmentType.CENTER,
        spacing: { after: 60 },
        children: [t('Optimising Antimicrobial Stewardship Surveillance Through Medication Data Standardisation in Zambia', { bold: true, size: 24 })],
      }),
      new Paragraph({
        alignment: AlignmentType.CENTER,
        spacing: { after: 60 },
        children: [t('NIHR Global Health Research Themed Programme — AMR | Band 3 (Ref: 2026/404)', { italics: true, size: 18 })],
      }),

      // Metadata table
      new Table({
        width: { size: 100, type: WidthType.PERCENTAGE },
        rows: [
          new TableRow({ children: [
            new TableCell({ width: { size: 35, type: WidthType.PERCENTAGE }, children: [new Paragraph({ children: [t('Lead Applicant:', { bold: true })] })] }),
            new TableCell({ children: [new Paragraph({ children: [t('Dr. Duncan Chanda (UTH, Zambia)')] })] }),
          ]}),
          new TableRow({ children: [
            new TableCell({ children: [new Paragraph({ children: [t('Co-Investigator:', { bold: true })] })] }),
            new TableCell({ children: [new Paragraph({ children: [t('Dr. Anna Saura-Lázaro (Oxford NDORMS, UK)')] })] }),
          ]}),
          new TableRow({ children: [
            new TableCell({ children: [new Paragraph({ children: [t('Strategic Advisor:', { bold: true })] })] }),
            new TableCell({ children: [new Paragraph({ children: [t('Dr. Cynthia Sung (Duke-NUS / OHDSI Africa Chapter)')] })] }),
          ]}),
          new TableRow({ children: [
            new TableCell({ children: [new Paragraph({ children: [t('In-Country Coordinator:', { bold: true })] })] }),
            new TableCell({ children: [new Paragraph({ children: [t('Mr. Rodney Chisha Kalaba (University of Zambia)')] })] }),
          ]}),
          new TableRow({ children: [
            new TableCell({ children: [new Paragraph({ children: [t('OMOP CDM Technical Consultant:', { bold: true })] })] }),
            new TableCell({ children: [new Paragraph({ children: [
              t('Adam Bouras, Tritonis Inc. (contracted; technology transfer to UTH pharmacoinformatics staff will be formalised in the MOU'),
              hi(' [Improvement: technology transfer commitment added to address sustainability gap]', { italics: true }),
              t(')'),
            ]})] }),
          ]}),
          new TableRow({ children: [
            new TableCell({ children: [new Paragraph({ children: [t('Version:', { bold: true })] })] }),
            new TableCell({ children: [new Paragraph({ children: [t('3.0 — July 2026 (Revised)')] })] }),
          ]}),
        ],
      }),

      new Paragraph({ spacing: { after: 160 }, children: [t('')] }),

      // ── 1. BACKGROUND AND RATIONALE ─────────────────────────────────────
      heading1('1. Background and Rationale'),

      para([
        t('Antimicrobial resistance (AMR) represents one of the most urgent threats to global health security. Left unaddressed, AMR risks returning the world to a pre-antibiotic era in which common infectious diseases become untreatable and routine surgical procedures carry unacceptable mortality risk. Globally, AMR is estimated to cause 39 million deaths between 2025 and 2050, with the highest mortality burden concentrated in low- and middle-income countries (LMICs)'),
        t(' [1].'),
      ]),

      para([
        t('Zambia launched its National Action Plan (NAP) on AMR in 2017, structured around five pillars: (i) awareness and understanding; (ii) surveillance and research; (iii) infection prevention; (iv) optimising antimicrobial use; and (v) sustainable investment. Implementation of the NAP, and the national Standard Treatment Guidelines (STG) and National Formulary (NF) — which prescribe antimicrobials according to the WHO Access, Watch and Reserve (AWaRe) classification and align with the RxNorm vocabulary standard — has driven the establishment of Antimicrobial Stewardship (AMS) structures across public health facilities. These include Medicines and Therapeutics Committees (MTCs) and the Ministry of Health-recognised policy committee for the Promotion of Rational Medicine Use.'),
        hi(' [Improvement: explicit reference to STG/NF alignment with AWaRe and RxNorm, and to MTCs, incorporated per reviewer comment]', { italics: true }),
      ]),

      para([t('Between 2020 and 2023, UTH — through a grant from the International Centre for Antimicrobial Solutions (ICARS) — implemented a three-year AMS demonstration programme across three tertiary hospitals (UTH Lusaka, Livingstone UTH, and Ndola Teaching Hospital). Six additional first-level hospitals across three provinces were also engaged as satellite sites. Three rounds of Point Prevalence Surveys (PPS) were conducted across the three tertiary facilities during this period, generating a unique longitudinal dataset on in-hospital antibiotic use [2, 3]. This programme demonstrated early but measurable stewardship impact: antibiotic prescribing prevalence fell from 81% to 71% between 2022 and 2023; ceftriaxone prescribing declined from 48% to 38%; and Standard Treatment Guideline adherence improved from 42% to 45% [2].')]),

      para([
        t('Despite this progress, a critical barrier persists: the absence of standardised antibiotic nomenclature and data collection frameworks across facilities. PPS datasets generated across the nine project hospitals reflect inconsistent drug naming conventions (mixing generic names, brand names, and free-text descriptions), varied metric indicators (ATC codes, RxNorm, or unmapped free text), and misaligned denominator reporting. This fragmentation prevents meaningful cross-facility comparison, undermines the ability to track AWaRe antibiotic utilisation trends, and limits the translation of PPS evidence into national AMR policy [4, 5]. A national cross-sectional survey found that more than 64% of healthcare professionals reported limited access to essential AMR data and inadequate IT infrastructure [4], and that no hospital surveyed had an allocated AMS budget [5].'),
      ]),

      para([t('This project addresses these gaps directly by improving and harmonising existing AMS surveillance infrastructure across Zambia. It does not seek to establish new surveillance systems. Rather, it builds on the established ICARS cohort, existing AMS committees, and existing PPS cycles to engineer, validate, and deploy an interoperable, open-access medication standardisation toolkit — aligned with the WHO PPS framework, the AWaRe classification, and the RxNorm vocabulary standard. The project is further strengthened by its integration with the OHDSI Africa Chapter Medication Standardisation Working Group. By mapping the resulting standardised antibiotic nomenclature to the OMOP Common Data Model (CDM) drug exposure vocabulary, Zambia\'s PPS data will become interoperable with a global federated research network spanning over 100 countries — enabling cross-country AMR pharmacoepidemiology and drug utilisation research at continental and global scale [6, 7].')]),

      // ── 2. AIMS AND OBJECTIVES ───────────────────────────────────────────
      heading1('2. Aims and Objectives'),

      para([t('GOAL: To optimise the uniformity, structural reliability and comparability of hospital-based point prevalence surveys in Zambia through a rigorous, scalable medication standardisation framework — improving existing AMS surveillance infrastructure and enabling evidence-based national AMR policy.')]),

      heading2('Specific Objectives'),
      bullet([t('Objective 1: To conduct a comprehensive baseline audit of structural data variations, antibiotic naming conventions, dose reporting systems, and metric indicators across nine health facilities in four districts of Zambia, incorporating national antibiotic consumption data from ZNPHI.')]),
      bullet([t('Objective 2: To co-design a digital medication standardisation toolkit with frontline prescribers, pharmacists, AMS committee members, and community representatives, leveraging existing ICARS PPS data and national surveillance data from ZNPHI.'), hi(' [Improvement: community representatives added to co-design team]', { italics: true })]),
      bullet([t('Objective 3: To develop a standardised antibiotic nomenclature framework using RxNorm as the primary coding standard, with parallel mapping to WHO ATC/DDD classifications and AWaRe categories, and interoperability with the OMOP Common Data Model.')]),
      bullet([t('Objective 4: To validate and deploy the standardisation toolkit across all nine facilities through quarterly PPS cycles, measuring reduction in nomenclature reporting errors and improvement in AWaRe utilisation metrics.')]),
      bullet([t('Objective 5: To establish a replicable, open-access standardisation model applicable to other LMIC settings with heterogeneous pharmaceutical landscapes, with all toolkit outputs and the Zambia-specific RxNorm crosswalk submitted to OHDSI ATHENA.')]),

      // ── 3. METHODOLOGY ───────────────────────────────────────────────────
      heading1('3. Methodology and Study Design'),

      para([t('The study employs a mixed-methods, multi-centre operational research design, implemented in three sequential phases across nine health facilities in four districts of Zambia — spanning tertiary, first-level, and urban primary care settings. This heterogeneous sample ensures the toolkit is designed for the full range of Zambian healthcare delivery contexts.')]),

      heading2('Phase 1: Baseline Structural Audit (Months 1–8)'),
      para([t('A systematic audit of data fragmentation across all nine facilities, encompassing: documentation variations in paper ledgers and electronic health records (DHIS2/HMIS); raw antibiotic nomenclature taxonomy; dose and frequency recording practices; and route of administration conventions. The audit will map which facilities have functional DHIS2 installations and define the minimum IT infrastructure requirements for toolkit deployment — a prerequisite for realistic interoperability planning.'), hi(' [Improvement: IT infrastructure mapping added to resolve feasibility gap]', { italics: true }), t(' The audit will also incorporate national-level antibiotic consumption data from ZNPHI (aggregated facility consumption reports and GLASS-methodology surveillance data), providing a population-level denominator to contextualise facility-based PPS findings [8].')]),

      heading2('Phase 2: Toolkit Co-Design (Months 6–18)'),
      para([t('Building on the ICARS three-year AMS cohort data and Phase 1 audit findings, this phase will: (i) construct a localised digital cross-walking dictionary that automatically translates heterogeneous text inputs — including localised brand variants, misspelled generic names, and trade name variations — into standardised molecules mapped to their canonical RxNorm codes, WHO ATC/DDD classifications, and AWaRe categories [9]; (ii) format an open-access platform compatible with DHIS2, the Global-PPS platform, and the OMOP Common Data Model; and (iii) develop a Zambia-specific RxNorm crosswalk for the national essential medicines formulary. Co-design workshops will include frontline prescribers, pharmacists, AMS committee members, and community health worker representatives from all nine facilities.'), hi(' [Improvement: community health workers and patients explicitly included in co-design per CEI requirement]', { italics: true })]),

      heading2('Phase 3: Implementation, Validation, and Scale-Up (Months 15–36)'),
      para([t('Sequential deployment of the toolkit across all nine facilities using standardised quarterly PPS cycles. Evaluation metrics will include: proportion of antibiotics successfully mapped to RxNorm/ATC codes; reduction in nomenclature error rates; proportion of AWaRe utilisation accurately captured; and guideline adherence rates. Findings from each cycle will inform iterative toolkit refinement. Technology transfer sessions will be conducted quarterly to build the internal capacity of UTH pharmacoinformatics and data management staff to maintain the toolkit and crosswalk independently after grant completion.'), hi(' [Improvement: explicit technology transfer and post-grant maintenance plan added]', { italics: true })]),

      heading2('Target Settings'),
      bullet([t('Tertiary (5): University Teaching Hospital (UTH), Levy Mwanawasa UTH, Kitwe Teaching Hospital, Livingstone UTH, Ndola Teaching Hospital')]),
      bullet([t('First-level hospitals (4): [to be confirmed with Dr. Chanda]')]),

      // ── 4. COMMUNITY ENGAGEMENT ─────────────────────────────────────────
      heading1('4. Community Engagement and Involvement (CEI)'),
      hi(new Paragraph({ spacing: { after: 60 }, children: [t('[Section substantially revised — this was a placeholder in the prior draft]', { italics: true, bold: true, highlight: 'yellow' })] })),

      para([
        hi('[Improvement: full CEI plan drafted to meet NIHR scored criterion]', { italics: true }),
      ]),

      para([t('Community and patient engagement is embedded across all three project phases, consistent with NIHR\'s Going the Extra Mile framework. The CEI Lead will be a pharmacist or AMS committee member drawn from one of the nine participating facilities, nominated by Dr. Chanda prior to grant commencement.')]),

      para([t('In Phase 1, patient advisory panels (5–8 members per facility) will be convened at each of the nine hospitals to document patient experiences of antibiotic prescribing, including their awareness of what medicines they receive and why. Community health workers (CHWs) will participate in the drug naming audit to capture community-level antibiotic name variants — including local trade names and informal market products — that would not appear in hospital records alone.')]),

      para([t('In Phase 2, all PPS-linked patients will receive a one-page, plain-language information sheet (co-produced with patient advisory panel members and translated into Nyanja, Bemba, and Tonga) explaining what the survey is, what data is collected, and how it will be used. A community representative will sit on the project\'s Steering Committee to provide oversight of data governance decisions. Gender-specific focus groups with women attending antenatal clinics at maternal and neonatal wards will provide qualitative context on antibiotic exposure experiences not captured in quantitative PPS fields.')]),

      para([t('In Phase 3, brief quarterly community feedback summaries — distinct from academic reports — will be disseminated through facility notice boards and CHW networks. Short AMR awareness sessions (30 minutes, co-facilitated by pharmacists and CHWs) will be timed with clinic waiting periods at each quarterly PPS cycle. The reach and quality of CEI activities will be formally evaluated as a project output, generating publishable evidence on participatory AMR research methods in LMICs.')]),

      // ── 5. INCLUSIVE RESEARCH ────────────────────────────────────────────
      heading1('5. Inclusive Research: Sex, Gender and Vulnerable Populations'),

      para([t('Sex and gender will be considered throughout the full research lifecycle. Evidence from LMICs indicates that antibiotic prescribing patterns, AMR exposure pathways, and access to effective treatment differ by sex and gender [10]. Women of reproductive age face disproportionate antibiotic exposure through obstetric and gynaecological care; men in manual occupations face higher zoonotic AMR exposure. These differences will be captured in the Phase 1 baseline audit by disaggregating PPS data by sex. Gender-specific focus groups (detailed in Section 4) will supplement quantitative disaggregation with qualitative insight.')]),

      para([t('Maternal and neonatal wards within the five tertiary facilities are designated as priority audit sites, given NIHR\'s particular interest in AMR outcomes for pregnant women and newborns. The toolkit will be designed to accommodate antibiotic regimens specific to these wards, including prophylaxis protocols.')]),

      para([
        t('Gender equity will be applied to the research team itself: mentorship posts, named co-investigator roles, and postgraduate training opportunities will be advertised in open competition with explicit encouragement of female candidates from non-academic hospital sites. Zambian early-career researchers will be supported through named co-investigator roles and formal authorship opportunities on all publications, consistent with NIHR\'s LMIC-led research principles.'),
        hi(' [Improvement: Zambian researcher capacity-building explicitly articulated to address partnership balance concern]', { italics: true }),
      ]),

      // ── 6. OMOP CDM ──────────────────────────────────────────────────────
      heading1('6. OMOP CDM Integration and Technical Arrangements'),

      para([t('A distinguishing feature of this project is its integration with the OMOP Common Data Model (CDM), the global standard for observational health data developed and maintained by OHDSI [6]. The OMOP CDM drug exposure table requires standardised medication coding — specifically RxNorm, SNOMED-CT, or INN — to enable federated analysis across participating institutions worldwide.')]),

      para([
        t('Subject to grant award, UTH will enter into a Memorandum of Understanding (MOU) with Adam Bouras (Tritonis Inc., Morocco) for the technical implementation of an OMOP CDM instance at UTH. The MOU will define scope, deliverables, compensation, and — critically — a structured technology transfer plan under which UTH pharmacoinformatics and data management staff will be trained and certified to operate and maintain the OMOP instance independently after grant completion. This mitigates dependency on the external contractor and ensures institutional sustainability.'),
        hi(' [Improvement: technology transfer and post-project institutional ownership explicitly required in MOU — addresses single point of failure risk]', { italics: true }),
      ]),

      para([t('The RxNorm crosswalk developed in Phase 2 will be submitted through the OHDSI Africa Chapter to the OHDSI vocabulary team for integration into ATHENA — making Zambia\'s standardised antibiotic nomenclature globally searchable and interoperable [6, 7]. This positions Zambia as a pioneer in AMR pharmacoepidemiology within the OHDSI federated network, enabling cross-country drug utilisation studies that would otherwise be impossible with siloed national datasets.')]),

      // ── 7. PARTNERSHIPS ──────────────────────────────────────────────────
      heading1('7. Equitable and Sustainable Partnerships'),

      heading2('University Teaching Hospital (UTH) — Lead Institution'),
      para([t('UTH is the contracting institution and primary decision-making authority. Dr. Chanda, as Lead Applicant, holds intellectual property and publication rights. All final publications will list Zambian researchers in lead authorship positions. UTH\'s Department of Internal Medicine and Infectious Diseases will host the postgraduate training and mentorship programme [2, 3, 5].')]),

      heading2('Oxford NDORMS — Supporting Partner'),
      para([t('Dr. Saura-Lázaro provides pharmacoepidemiology expertise and methodological oversight for the OMOP CDM integration work. Oxford\'s role is advisory and technical support; all primary data collection, analysis, and policy engagement is led from Zambia.')]),

      heading2('Duke-NUS / OHDSI Africa Chapter — Strategic Partner'),
      para([t('Dr. Cynthia Sung advises on OHDSI network integration and co-leads the Africa Chapter Medication Standardisation Working Group. This partnership ensures the Zambia RxNorm crosswalk is developed in alignment with broader regional standardisation efforts [6, 7].')]),

      heading2('Sustainability Plan'),
      para([
        t('Post-project sustainability is anchored in three mechanisms: (i) the OMOP CDM instance and crosswalk will be owned and maintained by UTH\'s internal pharmacoinformatics team, trained through the technology transfer programme; (ii) the Zambia EML crosswalk will be registered in OHDSI ATHENA as a permanent, open-access vocabulary resource, with update responsibility assigned to the Zambia Medicines Regulatory Authority (ZAMRA) working group; and (iii) the quarterly PPS cycle methodology will be adopted into the ZNPHI national AMR surveillance calendar, ensuring continuity beyond the grant period.'),
        hi(' [Improvement: explicit three-part sustainability plan added — addresses lack of exit strategy in prior draft]', { italics: true }),
      ]),

      // ── 8. DATA GOVERNANCE ───────────────────────────────────────────────
      heading1('8. Data Governance, Ethics, and Patient Confidentiality'),
      hi(new Paragraph({ spacing: { after: 60 }, children: [t('[Section added — absent from prior draft]', { italics: true, bold: true, highlight: 'yellow' })] })),

      para([
        t('Ethical approval will be sought from the University of Zambia Biomedical Research Ethics Committee (UNZABREC) and the Zambia National Health Research Authority (NHRA) prior to commencement. All retrospective PPS datasets will be de-identified at source before transfer to any digital platform. The OMOP CDM instance will be hosted on UTH servers within Zambia; no patient-level data will leave the country without explicit regulatory approval and a formal data sharing agreement.'),
        hi(' [Improvement: data governance, de-identification, and national data sovereignty framework added]', { italics: true }),
      ]),

      para([t('A project-specific Data Governance Policy will be developed in Phase 1, covering: data ownership (UTH); access controls for the OMOP instance; de-identification standards; conditions under which federated queries may be run; and a data breach response protocol. The policy will be reviewed annually by the Steering Committee, including the community representative member.')]),

      // ── 9. LOGIC MODEL ───────────────────────────────────────────────────
      heading1('9. Theory of Change'),
      hi(new Paragraph({ spacing: { after: 60 }, children: [t('[Section added — prior draft had no explicit theory of change or logic model]', { italics: true, bold: true, highlight: 'yellow' })] })),

      para([t('The project operates on the following results chain:')]),
      bullet([t('Inputs: ICARS cohort data; existing AMS committees; ZNPHI national data; OHDSI technical expertise; community engagement infrastructure.')]),
      bullet([t('Activities: Baseline audit; co-design workshops; crosswalk dictionary development; toolkit deployment; quarterly PPS cycles; community engagement sessions; technology transfer training.')]),
      bullet([t('Outputs: Validated standardisation toolkit; Zambia-specific RxNorm crosswalk; trained UTH pharmacoinformatics staff; community feedback reports; open-access methodology guide.')]),
      bullet([t('Outcomes (12 months post-completion): Reduction in antibiotic nomenclature error rates across nine facilities; accurate AWaRe category capture in ≥80% of PPS records; quarterly PPS data integrated into ZNPHI surveillance calendar.')]),
      bullet([t('Impact (3–5 years): Improved national AMR policy decisions informed by reliable, standardised surveillance data; Zambia data accessible in OHDSI federated network for cross-country AMR research; model replicated in at least two other LMIC settings.')]),

      pageBreak(),

      // ── BIBLIOGRAPHY ─────────────────────────────────────────────────────
      heading1('Bibliography'),

      new Paragraph({
        spacing: { after: 80 },
        children: [t('All references verified in PubMed or authoritative sources, July 2026. ICAR-linked Zambian co-authors (Duncan Chanda, Steward Mudenda, Joseph Chizimu, Kenneth Kapolowe) are indicated with [ICAR].', { italics: true, size: 18 })],
      }),

      refLine(1, 'Murray CJL, Ikuta KS, Sharara F, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399(10325):629–655. https://doi.org/10.1016/S0140-6736(21)02724-0'),

      refLine(2, '[ICAR] Mudenda S, Kapolowe K, Chirwa U, Chanda M, Chanda R, Kalaba R, et al. Antimicrobial stewardship impact on antibiotic use in three tertiary hospitals in Zambia: a comparative point prevalence survey. Antibiotics (Basel). 2025;14(3):284. https://doi.org/10.3390/antibiotics14030284 [PMID: 40149095]'),

      refLine(3, '[ICAR] Chizimu JY, Mudenda S, Yamba K, Lukwesa C, Chanda R, Nakazwe R, ... Chanda D, ... Kapolowe K, et al. Antimicrobial stewardship situation analysis in selected hospitals in Zambia: findings and implications from a national survey. Front Public Health. 2024;12:1367703. https://doi.org/10.3389/fpubh.2024.1367703 [PMID: 39399696]'),

      refLine(4, '[ICAR] Mudenda S, Chizimu JY, Daka V, Hangoma J, et al. Antimicrobial stewardship program implementation, perceptions, and barriers in Zambia: a cross-sectional study among healthcare professionals. Antibiotics (Basel). 2025;14(11):1094. https://doi.org/10.3390/antibiotics14111094 [PMID: 41301589]'),

      refLine(5, '[ICAR] Chizimu JY, Mudenda S, Yamba K, Lukwesa C, Chanda R, Nakazwe R. Antibiotic use and adherence to the WHO AWaRe guidelines across 16 hospitals in Zambia: a point prevalence survey. JAC Antimicrob Resist. 2024;6(5):dlae170. https://doi.org/10.1093/jacamr/dlae170 [PMID: 39464860]'),

      refLine(6, 'Bhattacharjee T, Kiwuwa-Muyingo S, Kanjala C, et al. INSPIRE datahub: a pan-African integrated suite of services for harmonising longitudinal population health data using OHDSI tools. Front Digit Health. 2024;6:1304220. https://doi.org/10.3389/fdgth.2024.1304220 [PMID: 38347885]'),

      refLine(7, 'Amadi D, Kiwuwa-Muyingo S, Bhattacharjee T, et al. Making metadata machine-readable as the first step to providing FAIR population health data: framework development and implementation study. Online J Public Health Inform. 2024;16:e55356. https://doi.org/10.2196/55356 [PMID: 39088253]'),

      refLine(8, '[ICAR] Mudenda S, Chizimu JY, Daka V, et al. Antimicrobial consumption and utilisation in Zambia: results from the analysis of national data for the human and animal health sectors. Antibiotics (Basel). 2025;14(11):1126. https://doi.org/10.3390/antibiotics14111126 [PMID: 41301621]'),

      refLine(9, 'Kimura E, Kawakami Y, Inoue S. A dataset for mapping Japanese drugs to RxNorm standard concepts. Data Brief. 2025;59:111348. https://doi.org/10.1016/j.dib.2025.111348 [PMID: 40124300]'),

      refLine(10, '[ICAR] Chizimu JY, Mudenda S, Daka V, et al. Impact of multidisciplinary-led implementation of antimicrobial stewardship programs in Zambia: findings and implications. Antibiotics (Basel). 2025;14(11):1125. https://doi.org/10.3390/antibiotics14111125 [PMID: 41301620]'),

      refLine(11, '[ICAR] Chizimu JY, Wesangula E, Mapunjo S, Mudenda S, et al. A regional approach to strengthening the implementation of sustainable antimicrobial stewardship programs in five countries in East, Central, and Southern Africa. Antibiotics (Basel). 2025;14(3):266. https://doi.org/10.3390/antibiotics14030266 [PMID: 40149077]'),

      refLine(12, 'Masich AM, Vega AD, Callahan P, Herbert A, Fwoloshi S, Zulu PM, Chanda D, et al. Antimicrobial usage at a large teaching hospital in Lusaka, Zambia. PLoS ONE. 2020;15(2):e0228555. https://doi.org/10.1371/journal.pone.0228555 [PMID: 32040513]'),

      refLine(13, 'Kalungia AC, Kampamba M, Banda D, et al. Impact of a hub-and-spoke approach to hospital antimicrobial stewardship programmes on antibiotic use in Zambia. JAC Antimicrob Resist. 2024;6(6). https://doi.org/10.1093/jacamr/dlae166 [PMID: 39502743]'),

      refLine(14, 'WHO. Global Antimicrobial Resistance and Use Surveillance System (GLASS) Report 2024. Geneva: World Health Organization; 2024.'),

      refLine(15, 'Kasanga M, Kwenda G, Wu J, et al. Antimicrobial resistance patterns and risk factors associated with ESBL-producing and MDR Escherichia coli in hospital and environmental settings in Lusaka, Zambia. Microorganisms. 2023;11(8):1905. https://doi.org/10.3390/microorganisms11081905 [PMID: 37630511]'),

      new Paragraph({
        spacing: { before: 240, after: 80 },
        children: [t('Note on highlighted text: ', { bold: true, size: 18 }), t('All passages marked in yellow throughout this document represent improvements or additions made to Version 2.0 of the concept note, as documented in the critical review. Original language has been preserved wherever possible.', { italics: true, size: 18 })],
      }),

    ],
  }],
});

Packer.toBuffer(doc).then(buffer => {
  fs.writeFileSync('/home/daytona/workspace/concept-note-revised/Concept_Note_Revised_v3.docx', buffer);
  console.log('SUCCESS');
}).catch(err => {
  console.error('ERROR:', err.message);
  process.exit(1);
});

cd /home/daytona/workspace/concept-note-revised && node build.js 2>&1

ls -lh /home/daytona/workspace/concept-note-revised/

cd /home/daytona/workspace/concept-note-revised && python3 /home/daytona/skills/…

cd /home/daytona/workspace/concept-note-revised && pandoc --from=docx --to=plain…

python3 /home/daytona/skills/docx/scripts/office/soffice.py --headless --convert…

mkdir -p /tmp/lo-out && python3 /home/daytona/skills/docx/scripts/office/soffice…

ls -lh /home/daytona/workspace/concept-note-revised/