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Record W4410331314 · doi:10.33540/2935

From Early detection to Outcome: Transforming Congenital Heart Disease Care in Tanzania

2025· dissertation· en· W4410331314 on OpenAlexaff
Naizihijwa Gadi Majani

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsTanzaniaOutcome (game theory)MedicineHeart diseaseIntensive care medicineInternal medicineGeographyEconomicsEnvironmental planning

Abstract

fetched live from OpenAlex

This thesis investigates the burden, diagnosis, and management of congenital heart disease (CHD) in Tanzania, emphasising the critical importance of early detection through pulse oximetry (POX) screening. The findings are presented in three thematic parts that collectively offer a comprehensive understanding of the CHD landscape and practical strategies to improve outcomes. Part 1: Global Perspective and Burden of CHD in Tanzania (Chapters 1–3) The first section explores global and regional CHD burden, noting CHD affects 1 in 100 live births globally, with CCHD accounting for 2.5–3 per 1,000 and contributing to nearly 10% of neonatal deaths. In Tanzania, an estimated 13,600 children are born annually with CHD, around 4,000 of whom have CCHD. Chapter 2 analyzes echocardiographic data from over 6,000 children referred to JKCI, revealing that 80% of diagnosed heart defects were CHDs, mostly detected late (median age: 1 year). Ventricular septal defects (VSD), patent ductus arteriosus (PDA), and Tetralogy of Fallot (TOF) were predominant. Challenges include late referrals, a 40% hospitalization rate, and poor referral systems. Chapter 3 details the Tanzanian POX Study, which screened over 10,000 newborns and found a CCHD birth prevalence of 3.27 per 1,000—higher than previously reported. Surgical intervention significantly improved survival (88% vs. 40%). These chapters advocate for integrating POX screening and strengthening referral pathways. Part 2: Surgical Outcomes and Quality of Life (Chapters 4–6) This section highlights Tanzania’s growing surgical capacity at JKCI, where annual pediatric surgeries rose from 34 in 2015 to 350 in 2023, with 70% performed by local teams. Chapter 4 focuses on TOF repairs, showing most surgeries occur after age 1, with underweight status and severe cyanosis linked to poorer outcomes. In-hospital mortality was 5.9%, but no deaths occurred in children operated on before one year. Chapter 5 shows significant improvement in children’s health-related quality of life (HRQoL) after surgery, with financial hardship as a major barrier. Chapter 6 examines caregiver QoL using the Swahili PedsQL Family Impact Module, revealing improved emotional well-being post-surgery but persistent family stress, underscoring the need for psychosocial support. Part 3: Feasibility and Impact of POX Screening (Chapters 7–12) This section assesses POX screening’s feasibility and cost-effectiveness. Chapter 7 describes a prospective cohort study enrolling 30,000 newborns at two hospitals. Chapter 8 reports high specificity (99.5%) and moderate sensitivity (50%), improving with dual screening and mandatory physical exams. Chapter 9’s systematic review confirms that combining POX and physical exams increases sensitivity to 93%. Chapter 10 shows POX also detects non-cardiac neonatal conditions like sepsis and PPHN. Chapter 11 shares healthcare workers’ positive views on scalability. Chapter 12 confirms POX’s affordability (USD 6–8 per newborn) and cost-effectiveness (USD 264.87 per QALY), with a return of USD 20 for every USD 1 invested. Altogether, this thesis demonstrates that early CHD detection via POX is feasible, life-saving, and economically sound, offering a scalable strategy to improve child survival in Tanzania and similar settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.306
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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