Chu-Chang Ku

Biographic Data

ID6068069
NAMEChu-Chang Ku
GIVEN NAMESChu-Chang
FAMILY NAMEKu
SIGNATUREKU C
AFFILIATIONSChu-Chang Ku is with the School of Health and Related Research, University of Sheffield, Sheffield, UK.
VERIFIEDNo
TOTAL WORKS4
TOTAL CITATIONS1
WORKS AS AUTHOR4
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2020
LATEST PUBLICATION YEAR2025
H-INDEX1

  • Estimating the impact of nutritional transition and ending hunger on tuberculosis in 12 high-burden countries

    Open AccessChieh-Yin Wu, Chu-Chang Ku et al.ARTICLEBMJ Global Health2025

    BACKGROUND: Nutrition is a critical determinant of tuberculosis (TB), providing a protective effect at high body mass index (BMI) and incurring an increased risk of TB disease at low BMI. Global nutritional transition and interventions to end hunger could directly affect the TB epidemic in high TB burden countries. METHODS: We constructed dynamic TB transmission models for 12 high TB burden countries with low HIV prevalence. We explicitly account…

  • Public Health Insight From Excess Mortality During the Covid-19 Pandemic

    Chu-Chang KuARTICLEAmerican Journal of Public Health2021References: 10

    Public Health Insight From Excess Mortality During the COVID-19 Pandemic Chu-Chang Ku PhD Affiliation Chu-Chang Ku is with the School of Health and Related Research, University of Sheffield, Sheffield, UK. CopyRightCorrespondence should be sent to Chu-Chang Ku, 30 Regent St, Sheffield City Centre, Sheffield S1 4DA, UK (e-mail: [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the "Reprints" link. https://doi.org/10…

  • Patient pathways of tuberculosis care-seeking and treatment

    Open AccessChu-Chang Ku, Chu Chang Ku et al.ARTICLEBMJ Global Health2020

    INTRODUCTION: Patients with tuberculosis (TB) often experience difficulties in accessing diagnosis and treatment. Patient pathway analysis identifies mismatches between TB patient care-seeking patterns and service coverage, but to date, studies have only employed cross-sectional aggregate data. METHODS: We developed an algorithmic approach to analyse and interpret patient-level routine data on healthcare use and to construct patients' pathways fr…

  • Improving the Use of Mortality Data in Public Health

    Ta-Chou Ng, Ta-Chou Vincent Ng et al.ARTICLEAmerican Journal of Public Health2020Cited by: 1References: 10

    Objectives. To describe and compare 3 garbage code (GC) redistribution models: naïve Bayes classifier (NB), coarsened exact matching (CEM), and multinomial logistic regression (MLR). Methods. We analyzed Taiwan Vital Registration data (2008–2016) using a 2-step approach. First, we used non-GC death records to evaluate 3 different prediction models (NB, CEM, and MLR), incorporating individual-level information on multiple causes of death (MCDs) an…