Frontiers in public healthVictor Luckyboy Dzramado, Obed U Lasim, William Wilberforce Amoah, Samuel Antwi, Joana Edem Koto, Doris Hagan
INTRODUCTION: Infectious disease outbreaks continue to threaten global health security, with resource-limited settings facing disproportionate challenges in surveillance, outbreak investigation, and applied biostatistical capacity. The WHO International Health Regulations 2005 framework defines preparedness through measurable core capacities, and seminal multi-country analyses of IHR State Party Self-Assessment Annual Reporting data from 182 and 186 countries consistently show that sub-Saharan African countries report the largest preparedness gaps globally. Ghana, despite hosting the first Field Epidemiology and Laboratory Training Programme in West Africa, has not previously been the subject of a comprehensive synthesis of its preparedness across all relevant IHR domains. This review therefore addresses a single integrated question: to what extent is the Ghana Health Service prepared for field epidemiology and applied biostatistics, as assessed through surveillance system performance, outbreak investigation capacity, workforce development, laboratory infrastructure, and statistical modeling capacities aligned with WHO IHR core capacity benchmarks? METHODS: This systematic review followed PRISMA 2020 guidelines and was prospectively registered on PROSPERO (CRD420261299788). Searches were conducted in PubMed, African Index Medicus, AJOL, and Google Scholar, supplemented by grey literature, covering January 2000 to February 2026. Two reviewers independently screened 328 unique records, assessed 75 full-text articles, and included 38 studies with Ghana-specific disaggregated data. Quality was assessed using design-specific tools with transparent reconciliation into low, moderate, and high risk-of-bias categories. Narrative synthesis was the principal analytic approach. RESULTS: Surveillance completeness ranged from 71% to 94% (median 82%); timeliness ranged from 48% to 91% (median 76%), with regional performance substantially exceeding district performance. The GFELTP produced 420 graduates from 2007 to 2017, representing 45% of WHO-benchmarked workforce requirements. Outbreak response times improved from 14 to 3 days for comparable outbreaks. Laboratory capacity remained concentrated in 2 to 6 sentinel sites. No included study examined biostatistical modeling capacity. The 2017 WHO Joint External Evaluation rated overall IHR capacity at 67%. DISCUSSION: Ghana demonstrates advancing but incomplete preparedness. Priority interventions should address workforce expansion, peripheral surveillance strengthening, laboratory decentralization, and indigenous biostatistical capacity development. SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261299788, identifier: CRD420261299788.
BMC medical educationFulden Cantas Turkis, Bugra Varol
BACKGROUND: Biostatistics is essential for interpreting evidence and clinical decision-making, yet medical learners may value it while experiencing anxiety, low enjoyment, and perceived difficulty. Average scores may fail to capture distinct attitude patterns. This study aimed to identify latent profiles of attitudes toward biostatistics among first-year medical students and medical residents and compare their sociodemographic and academic characteristics. METHODS: This cross-sectional study included 193 participants: 132 first-year medical students and 61 medical residents. Attitudes were assessed using the 33-item Statistics Attitude Scale, with responses framed in relation to biostatistics in medical education. Item-level responses were treated as continuous indicators in latent profile analysis. Models with one to seven profiles were estimated using an equal-variance, zero-covariance parameterisation. Model selection considered fit indices, profile size, and interpretability. Characteristics were compared across profiles. RESULTS: A five-profile solution was retained because it had the lowest Bayesian information criterion, high classification quality (entropy = 0.970), adequate profile sizes, and clear interpretation. The six- and seven-profile solutions included profiles with only one participant. The retained profiles were Low-Engagement and Anxious (24.4%), Value-Aware but Anxious (28.0%), Moderately Receptive (18.7%), Highly Disengaged and Anxious (10.9%), and Highly Receptive and Confident (18.1%). All five attitude dimensions differed across profiles (all p < 0.001), with the largest between-profile difference observed for Relation of Statistics to Professional Life (ε² = 0.821). The Value-Aware but Anxious profile combined high professional relevance and importance with greater anxiety, lower enjoyment, and greater perceived difficulty. Educational level also differed across profiles (p < 0.001; Cramér's V = 0.449): first-year students were more common in three profiles, whereas residents were more common in the Value-Aware but Anxious and Highly Receptive and Confident profiles. CONCLUSIONS: Attitudes toward biostatistics among medical learners varied and could not be represented on a single scale from negative to positive. Distinguishing learners who valued biostatistics but remained anxious from those showing low engagement may help inform clinically relevant and tailored educational support. The profiles should be regarded as cross-sectional and preliminary rather than as fixed learner types.
Journal of global healthMichal Ordak, Lise Korsten, George Oyamo Osanjo, Adey Desta
Despite increasing research output across Africa, weaknesses in statistical methodology continue to compromise the quality, reproducibility, and practical value of health research. Although substantial investments have been made in research capacity strengthening, limitations in statistical training and access to biostatistical expertise remain widespread. Existing initiatives, including regional postgraduate programmes and institution-specific training schemes, have contributed to advances in biostatistical capacity but remain fragmented, geographically limited, and insufficiently integrated into routine research practice. In this viewpoint, we argue that strengthening statistical capacity in Africa now requires a coordinated, continent-wide training network embedded within existing academic and research systems, rather than continued reliance on isolated programmes. The proposed model emphasises scalable training infrastructure, integration of statistical expertise throughout the research process, and structured collaboration between institutions facing similar methodological challenges. Central components include accessible training resources, mentorship, incorporation of statistics into existing curricula, and practical support linked to ongoing research activities. Emphasis is placed on standardising core competencies in statistical practice, including appropriate method selection, assessment of assumptions, transparent reporting, and accurate interpretation of findings. By embedding statistical thinking from study design through publication, the proposed network aims to improve methodological consistency, strengthen research credibility, and enhance the global impact and usability of health research conducted across Africa.
Frontiers in public healthVictor Luckyboy Dzramado, William Wilberforce Amoah, Samuel Antwi, Joana Edem Koto, Doris Hagan
BACKGROUND: Infectious disease outbreaks pose significant threats to global health security, with resource-limited settings in West Africa bearing a disproportionate burden. Despite sustained investments in field epidemiology training and surveillance system strengthening, no comprehensive systematic synthesis exists of Ghana Health Service preparedness for field epidemiology and applied biostatistics. This protocol addresses the primary research question: What is the current level of preparedness of the Ghana Health Service for field epidemiology and applied biostatistics, as assessed across World Health Organization International Health Regulations core capacity domains? Preparedness is operationally defined as the measurable capacity of the Ghana Health Service to detect, investigate, confirm, and respond to infectious disease events across the eight WHO IHR core capacity domains, including surveillance, human resources, laboratory systems, and response mechanisms. METHODS AND ANALYSIS: This systematic review follows PRISMA 2020 guidelines and is registered with PROSPERO (CRD420261299788). Searches will be conducted in PubMed/MEDLINE, African Index Medicus, African Journals Online, and grey literature sources for studies published from January 2010 to present. Eligible studies include those describing field epidemiology capacity, surveillance system performance, outbreak investigation preparedness, biostatistical capacity, training program outcomes, infrastructure, or health workforce within the Ghana Health Service. Two independent reviewers will screen citations, extract data, and assess quality using study-design-appropriate tools including the JBI Critical Appraisal Checklist, CASP Qualitative Checklist, and Mixed Methods Appraisal Tool. Primary outcomes are overall field epidemiology preparedness level measured using Joint External Evaluation and State Party Annual Reporting scores, and surveillance system performance with outbreak response capacity. Secondary outcomes include field epidemiology workforce capacity, statistical modeling and biostatistical capacity, and infrastructure and governance systems. Narrative synthesis is the primary analytic approach. Meta-analysis will be conducted where sufficient comparable studies with acceptable methodological homogeneity are identified. DISCUSSION: This review will provide the first comprehensive assessment of Ghana Health Service field epidemiology preparedness mapped against WHO IHR core capacities, generating actionable evidence-based recommendations applicable to similar resource-limited settings across Africa. ETHICS AND DISSEMINATION: No ethical approval is required. Results will be disseminated through peer-reviewed publication, conference presentations, and policy briefs for the Ghana Health Service, Ministry of Health, and international stakeholders. SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261299788, identifier CRD420261299788.
Biostatistics (Oxford, England)Jincheol Park, Meng Wang, Emerson Webb, Victor Jin, Shili Lin
The three dimensional (3D) spatial organization of the genome is closely linked to biological functions and can be captured by Hi-C assays through interrogating genome-wide chromatin interactions. Methodologies for inferring 3D structures from Hi-C data summarized as a two-dimensional (2D) contact matrix can be broadly placed within the paradigms of optimization-based and sampling-based. Many optimization-based methods are capable of constructing whole genome 3D structures but do not account for spatial dependency in the 2D data matrix nor cell heterogeneity in bulk Hi-C data, which provide an average over millions of cells. Sampling-based methods, on the other hand, are probabilistic model-based and can account for not only dependency, heterogeneity, but also other features inherent in Hi-C data, such as over-dispersion and sparsity. However, whole-genome 3D structure recapitulation is too computationally expensive for sampling-based methods, while chromosome-by-chromosome strategies for sampling-based methods ignore important information on inter-chromosomal contacts. To address these issues, we propose the truncated Random effect EXpression-cut and paste (tREX-cap) method, which applies the tREX model within a divide and conquer strategy. The resulting method inherits the good data-feature-cognizant properties of tREX and, in the meantime, can efficiently infer the whole genome 3D structure. We demonstrate the performance of tREX-cap through an extensive simulation study and analyses of a Hi-C lymphoblastoid dataset and a Hi-C IMR90 dataset.
Biostatistics (Oxford, England)Brian Gilbert, Iván Díaz, Kara Rudolph, Nicholas Williams, Tat-Thang Vo
This paper introduces a novel decomposition framework to explain heterogeneity in causal effects observed across different studies, considering both observational and randomized settings. We present a formal decomposition of between-study heterogeneity, identifying sources of variability in treatment effects across studies. The proposed methodology allows for robust estimation of causal parameters under various assumptions, addressing differences in pre-treatment covariate distributions, mediating variables, and the outcome mechanism. Our approach is validated through a simulation study and applied to data from the Moving to Opportunity (MTO) study, demonstrating its practical relevance. This work contributes to the broader understanding of causal inference in multi-study environments, with potential applications in evidence synthesis and policy-making.
Biostatistics (Oxford, England)Federico Castelletti, Laura Ferrini
Quantifying the causal effect of a treatment on a disease is a crucial task in medical science for the administration of effective therapies. Typically, such causal effects are inferred from multivariate data that are collected on patients and recorded in the form of categorical variables, including risk factors involved in disease progression, treatment assignments, and disease status. This feature motivates an approach to causal inference based on categorical Directed Acyclic Graphs (DAGs), which provide an effective framework for causal reasoning in complex multivariate settings. In this context, traditional DAG-based methods assume population homogeneity and accordingly attribute a unique causal effect to all subjects. However, this assumption is often unrealistic in clinical contexts, since patients may exhibit heterogeneous characteristics, possibly linked to unmeasured features. To address this issue, we propose a Bayesian nonparametric methodology based on a Dirichlet Process mixture of categorical DAGs, which allows treatment effects to vary across individuals because of underlying clustering structures in the data. We develop a Markov chain Monte Carlo algorithm for Bayesian posterior inference and evaluate our methodology through simulation studies. We then analyze patients affected by HER2+ breast cancer undergoing therapies that may cause cardiotoxic side effects. Importantly, our findings show that approaches neglecting population heterogeneity may produce biased results, since they can over- or under-estimate the risk of cardiotoxicity across patients.
Biostatistics (Oxford, England)David McCoy, Wenxin Zhang, Alan Hubbard, Mark van der Laan, Alejandro Schuler
In epidemiology, identifying subpopulations that are particularly vulnerable to exposures and those who may benefit differently from exposure-reducing interventions is essential. Factors such as age, gender-specific vulnerabilities, and physiological states such as pregnancy are critical for policymakers when setting regulatory guidelines. However, current semiparametric methods for estimating heterogeneous treatment effects are often limited to binary exposures and can function as black boxes, lacking clear, interpretable rules for subpopulation-specific policy interventions. This study introduces a novel method that uses cross-validated targeted minimum loss-based estimation (TMLE) paired with a data-adaptive target parameter strategy to identify subpopulations with the most significant differential impact of simulated policy interventions that reduce exposure. Our approach is assumption-lean, allowing for the integration of machine learning while still yielding valid confidence intervals. We demonstrate the robustness of our methodology through simulations and an application to data from the National Health and Nutrition Examination Survey. Our analysis of NHANES data on persistent organic pollutants (POPs) and leukocyte telomere length (LTL) identified age as a significant effect modifier. Specifically, we found that exposure to 3,3',4,4',5-pentachlorobiphenyl (PCB; NHANES analyte LBXPCBLA) consistently had a differential impact on LTL, with a 1-SD reduction in exposure leading to a more pronounced increase in LTL among younger populations than in older ones. We offer our method as an open-source software package, EffectXshift, enabling researchers to investigate the effect modification of continuous exposures. The EffectXshift package provides clear and interpretable results, informing targeted public health interventions and policydecisions.
Biostatistics (Oxford, England)Lu You, Falastin Salami, Carina Törn, Åke Lernmark, Kendra Vehik, Xiang Liu, Peihua Qiu, Roy Tamura
This paper presents a joint model of multivariate longitudinal data and multistate data with application to modeling and predicting autoantibody development in The Environmental Determinants of Diabetes in the Young (TEDDY) study. The model quantifies the risks of state transitions based on observed time-varying and non-time-varying risk factors. Based on the estimated model, a dynamic prediction approach is suggested to predict future state occupation probabilities using historical data. The proposed method can handle uncertainties in the observed data, due to measurement errors in the observed longitudinal data and interval censoring or missing information in the observed multistate data. For evaluating the predictions by the proposed approach, some performance metrics and their estimation are discussed. The proposed method is evaluated by some simulation studies. It is discussed in detail how this method can be used in analyzing the TEDDY data by properly handling the missing information and predicting future disease status using the proposed dynamic prediction algorithm.