Journal of Global Health最新文献

Background: The World Health Organization (WHO) and the United Nations Children's Fund (UNICEF) currently have no benchmarks or 'norms' for scaling up small and sick newborn (SSN) service delivery in health facilities in low- and middle-income countries (LMICs). Our objective was to understand which systematic reviews had addressed the following norms in the last five years: number of SSN beds per live births in a district or similar administrative unit (admission beds); space requirements for SSN units, including mother-infant dyads (space); health workforce ratios in SSN units (workforce); and travel time to health facilities with SSN units (travel time).

Methods: We searched for systematic reviews of admission beds, space, workforce and travel time norms for SSN under 28 days of age and their mothers in all health facilities and countries, regardless of infant gestational age and birth weight, that had been published in the previous five years (2018-23). For beds, space, and workforce norms, we searched for reviews of prevalence, incidence, mean and median estimates. For the travel time norm, we searched for reviews of estimates of effect, i.e. dichotomous (e.g. relative risks) or continuous measures (e.g. mean differences).

Results: We identified 9110 records and included eight systematic reviews published in the last five years: two related to space, five to workforce, and one to travel time norms. We found no reviews for admission bed norms. Two reviews included high income countries only, while three included tertiary neonatal intensive care units only. The reviews provided estimates of mean space requirements in SSN units, health workforce ratios of doctors and nurses, and optimal travel time to health facilities for SSN. Seven of the eight reviews had high risk of bias.

Conclusions: Despite the high burden of SSN in LMICs and the need to scale up hospital care, there have been few systematic reviews into this topic, and rigorous syntheses of evidence are lacking. The WHO and the UNICEF have now commissioned four systematic reviews. The next steps will be to analyse real-world country-level data and develop implementation guidance.

Registration: PROSPERO: CRD42023417847, CRD42023451302, CRD42023478512, CRD42023453644.

Background: In the face of pandemics from infectious diseases, enhancing community resilience is increasingly important. It is, therefore, essential to evaluate community resilience and identify factors that can strengthen it. This study aimed to evaluate community resilience by leveraging a data set comprising user information from Weibo and applying interpretable machine learning (ML) techniques to identify the contributions of various indicators underpinning community resilience.

Methods: This cross-sectional study analysed social media data from December 2022 to January 2023. COVID-19-related user interactions were examined as indicators of community resilience within the context of community response. This study introduced an evaluation framework comprising thirteen indicators. It also described the application of natural language processing (NLP) techniques, the K-means (KM) clustering, a random forest (RF) classifier and SHapley Additive exPlanations (SHAP) to achieve its objectives.

Results: A total of 177 000 Weibo posts were collected for this study. The NLP model demonstrated strong performance in accurately labelling posts, with the area under the curve (AUC) of 0.8862 (95% confidence interval (CI) = 0.8600-0.9102) and accuracy (ACC) of 0.8939 (95% CI = 0.8563-0.9277). This study identified four distinct community resilience levels: low (77.64%), medium-low (9.86%), medium-high (10.55%), and high (1.95%). Further analyses revealed clear regional disparities in community resilience, with higher levels observed in Eastern China. The top five indicators associated with community resilience, as determined by mean SHAP values, were 'Efficacy of performance altruistic response' (0.0101), 'Tangible aid engagement' (0.0051), 'Rapid performance of altruism' (0.0044), 'Sentiment response associated with recording positive posts' (0.0036), and 'Help-seeking response efficacy' (0.0035).

Conclusions: This study is the first to harness social media data to quantify community resilience in mainland China. Five indicators associated with enhanced community resilience are identified as potential predictors that can inform governmental strategies and strengthen decision-making support for improving health emergency responses.

Background: Intervertebral disc degeneration (IDD) is prevalent in orthopaedics, yet lacks effective treatments. This study seeks to discover potential therapeutic targets for IDD to inform clinical therapies and traditional medicine approaches.

Methods: In this study, IDD-related data sets were sourced from the Gene Expression Omnibus, and differential expression analysis was performed to identify differentially expressed genes. Subsequently, candidate genes associated with IDD were recognised using databases such as GeneCards, OMIM, DrugBank, and DisGeNET, with further validation of these genes' biological functions and involvement in signalling pathways through enrichment analyses. Additionally, machine learning algorithms were applied to select candidate targets. The diagnostic value of these targets for IDD was assessed by constructing a nomogram model, and their functional networks and biological processes were revealed using GeneMANIA and Gene Set Enrichment Analysis. Eventually, the research also encompassed immune infiltration analysis and the construction of competing endogenous RNA (ceRNA) networks, as well as predictions for potential drugs and traditional Chinese medicine (TCM) prescriptions.

Results: A total of 89 differentially expressed genes were identified through bioinformatics analysis, and further analysis led to the determination of 16 candidate genes associated with IDD. Seven candidate targets were found from the candidate genes using machine learning methods. Two of these targets, cytochrome P450 family 1 subfamily B member 1 (CYP1B1) and tumour necrosis factor alpha-induced protein 6 (TNFAIP6), were chosen as key targets because they demonstrated a significant difference in expression in IDD. Following, it was also found that CYP1B1 and TNFAIP6, as well as the nomogram, indicated good predictive performance for IDD. Furthermore, gamma-delta T cells were more prevalent in IDD. CYP1B1 and TNFAIP6 showed strong correlations with gamma delta T cells, indicating a tight link between these key targets and the pathology of IDD. Eventually, 11 natural small molecules corresponding to key targets were discovered. Three of these compounds (Quercetin, Genistein, Apigenin) were found in six TCM. This could offer new theoretical references for the clinical treatment of IDD.

Conclusions: This study identified CYP1B1 and TNFAIP6 as important targets for IDD, developed a predictive nomogram, and explored the application of TCM herbal formulae, providing new insights into the clinical treatment and prescription development of IDD.

Background: High-quality healthcare for pregnant women and newborns, particularly postnatal care (PNC) and small and/or sick newborn care (SSNC), is essential to reducing maternal and newborn morbidity and mortality in low- and middle-income countries (LMICs). Poor quality of care (QoC) is a major contributor to preventable morbidity and mortality, emphasising the need for its improvement in health service delivery through systematic measurement and monitoring. Although indicators measuring QoC have been identified, there is a current gap in the availability of composite indicators that can summarise its complex, multidimensional nature. Here we present three systematically developed composite QoC indices for maternal PNC, newborn PNC, and SSNC, feasible to measure using existing data in LMICs.

Methods: We developed a four-step process to define the indices. First, we identified interventions by reviewing global clinical guidelines and QoC frameworks. Second, we extracted discrete items recommended for delivery of each of the selected interventions from intervention-specific guidelines. Third, we mapped these items to health facility survey data to assess their alignment with standardised tools. Finally, we developed a quality readiness index (QRI) for each service area based on QoC frameworks, available data, and clinical guidelines.

Results: The maternal PNC-QRI includes 12 interventions and contains 24 items, the newborn PNC-QRI includes three interventions and contains 16 items, and the SSNC-QRI includes eight interventions and contains 48 items. Data gaps across all three indices led us to exclude some evidence-based interventions and include a limited number of items. No data on provision/experience of care were available for maternal PNC, newborn PNC, or SSNC, so the indices reflect only facility readiness.

Conclusions: The three QRIs provide composite measures for maternal and newborn PNC and SSNC readiness that could be adapted at the country level and operationalised using health facility assessment survey data, facilitating their use by decision-makers for planning and resource allocation. Revision of existing health facility assessments to address gaps in readiness and provision/experience of care measurement for PNC and SSNC would bolster efforts to monitor and improve care quality for mothers and newborns.

Background: The electronic routine health information system (eRHIS) is crucial for policy and planning. However, its effectiveness depends on end-users' capabilities in utilising it. Using a mixed-methods approach, we evaluated end-users' data in the Central African Republic (CAR), Ethiopia, Tanzania, and Uganda in one of the first standardised cross-country assessment of eRHIS users' capabilities focussed on newborn and stillbirth indicators.

Methods: We collected data in 12 regions and 3 city administrations between November 2022 and July 2024 using the Every Newborn-Measurement Improvement for Newborn & Stillbirth Indicators (EN-MINI) Performance of Routine Information System Management (PRISM) tools. All data but staff opinions were collected through direct observation. We analysed quantitative questions and reported them as frequencies/normalised PRISM scores, both on the overall sample and by country. We analysed qualitative data using thematic analysis.

Results: We included end-users of the eRHIS from 151 sites (56 data offices, 95 facilities). Their capabilities in utilising the eRHIS varied and were mainly higher in Uganda, followed by Tanzania, Ethiopia, and the CAR. End-users' capabilities also varied by type of abilities, being in general higher for track report completeness (with Tanzania, Uganda, Ethiopia, and CAR having 6/10, 5/10, 4/10, and 2/10 indicators at >80%, respectively), compared to skills in data analysis and visualisation (with only Uganda showing 2/6 indicators >80% for both domains and the other countries having no indicators at >80%). Practical skills scores were low in all countries, particularly on plotting, problem-solving, and use of information. 'Champion/good performer' emerged in each country, with staff at higher health system levels showing the highest capabilities. End-users' suggestions to improve the eRHIS (n = 127) were focussed on technical/software improvements (n = 73, 57.5%) and functionalities for data quality checks and data analysis (n = 36, 28.3%).

Conclusions: Our findings suggest several common gaps in end-users' capabilities in utilising the eRHIS, particularly in the CAR, and in all countries at facility levels.

Background: The growing burden of respiratory disease, particularly in Asia, where mortality is higher and awareness and policy engagement lag, could be mitigated through rapidly advancing digital health tools that offer opportunities for improved management, prevention, and personal health empowerment. We aimed to map the existing evidence, technologies, opportunities, and gaps related to respiratory digital health interventions in South and Southeast Asia and propose relevant recommendations.

Methods: We used a scoping review methodology, where we searched MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Library, Web of Science, PakMediNet, and MyMedR along with grey literature databases (ProQuest Thesis and Dissertations, Digital Health Atlas, Global Digital Health Monitor, Global Index Medicus) for reports on any technological interventions for pneumonia, tuberculosis, asthma, chronic obstructive pulmonary disease, and environmentally induced respiratory disease (air quality, smoking). We used the World Health Organization's Classification of Digital Health Interventions to categorise digital interventions and assessed how completely they were reported via the mHealth Evidence Reporting and Assessment checklist.

Results: We extracted and analysed data from 87 studies conducted in 14 South and Southeast Asian countries and found that digital health interventions are primarily used for communication with patients and between patients and providers. Interventions targeting tuberculosis were the most numerous. There was a high prevalence of pilot interventions which failed to significantly address the respiratory health needs in the region. Artificial intelligence and machine learning interventions are promising, but lack clear guidelines and adherence to best ethical and equity practices.

Conclusions: We collated and synthesised information and knowledge about the current state of digital health interventions. Our findings can inform future interventions so that they are planned, deployed, scaled, and evaluated to have long-lasting positive impacts on population health.

Registration: Evans L, Evans J, Fletcher M, Abdullah A, Ahmed Z. Mapping Respiratory Health Digital Interventions in South and Southeast Asia: Protocol for a Scoping Review. 2024;13:e52517.

Background: Traditional, complementary, and integrative (TCI) medicine is an essential component of health systems worldwide, especially in low- and middle-income countries. Despite its widespread use, existing research on the safety, efficacy, and integration of TCI medicine within conventional healthcare systems is fragmented. This fragmentation highlights the urgent need for a clearly defined global research agenda to guide future studies, secure funding, and inform governance in this field.

Methods: The Traditional, Complementary, and Integrative Medicine Unit at the World Health Organization Headquarters in Geneva, Switzerland coordinated an international research priority-setting exercise using the Child Health and Nutrition Research Initiative (CHNRI) method between June and December 2023. We invited a purposive sample of 120 experts from established academic networks to participate; 53 experts (44.16% response rate) contributed, and 34 of them scored 157 unique research ideas according to five CHNRI criteria: feasibility, effectiveness, deliverability, equity, and potential for disease burden reduction. Additionally, we performed a comparative analysis by generating research priorities using large language models (LLMs), including ChatGPT-4o, Claude 3.5, and Grok 3, and these outputs were compared with the expert-derived priorities.

Results: Top-ranked research priorities focused on chronic disease management (e.g. diabetes, dyslipidemia), geriatric safety (e.g. herb-drug interactions), mental health (e.g. resilience and mood disorders), and integration of TCI into health systems. Priorities varied by income setting. Comparison with LLM-generated lists showed thematic overlap in efficacy and safety but divergence in focus, with LLMs emphasising research capacity, policy, and systems-level priorities.

Conclusions: We established a global, expert-informed research agenda to guide the future direction of TCI medicine and ensure alignment with public health needs. The comparison with LLMs highlights the complementary potential of artificial intelligence in research governance and agenda-setting.

Background: Tribal communities in India experience a very high burden of tuberculosis (TB), estimated at 7030 per million. The diagnosis and notification gaps are substantial, partly due to the geographical remoteness of these populations. Within an overarching study to design an intervention for finding the 'missing millions' among tribal communities, we conducted a systematic review to identify the barriers and enablers of tuberculosis diagnosis and notification, with the aim of developing a contextually relevant intervention.

Methods: We searched PubMed, Embase and Web of Science using terms related to TB, diagnosis, notification, barriers, enablers, and interventions. Studies from lower- and lower-middle-income countries (LICs and LMICs) published between 2000-2023 were included. Qualitative and quantitative studies were assessed using the Critical Appraisal Skills Programme tool and Newcastle Ottawa scale, respectively. Narrative and thematic analyses were performed, applying the socio-ecological model (SEM) to categorise barriers and enablers of diagnosis and notification, and the consolidated framework for implementation research (CFIR) to assess intervention implementation.

Results: Thirty-four eligible studies from 15 LICs and LMICs were included in the review. At community level, limited knowledge, illiteracy, stigma, geographical inaccessibility, and financial constraints were key barriers of diagnosis. At health system level, active case finding was the major intervention; however, inadequate diagnostic facilities, shortage of trained staff, insufficient incentives, weak counselling, and inadequate budget were the major barriers. Reported enablers were: increasing awareness about TB in the community to reduce stigma, encouragement from family members and TB survivors, mobilising human resources, regular capacity-building and monetary incentives to health workers.

Conclusions: This systematic review identified barriers and enablers at multiple levels of the SEM and CFIR frameworks. To addressed the interconnected challenges, multifaceted and context-specific strategies are essential. Approaches that combine community engagement along with health system strengthening are essential for reducing the diagnosis and notification gaps among tribal populations.

Registration: PROSPERO: CRD42023439841.

Background: Migrants and refugees in Europe carry a disproportionate burden of chronic hepatitis B and C and face barriers accessing health systems. Community-based interventions can improve screening, prevention, and care, yet no framework exists to track their performance. This study aimed to generate a consensus set of indicators for monitoring and evaluating such interventions.

Methods: A scoping review of PubMed from January 2005 to June 2024 identified 70 studies and 275 candidate indicators. After removing redundancies, 38 primary and 17 additional indicators were submitted to a two-round online Delphi panel. Fourteen experts from six countries (five from Spain, three from the UK, two each from Italy and from Greece, and one each from Belgium and the USA) rated each indicator on relevance, measurability, accuracy, ethics and clarity. Indicators with >67% combined 'agree/somewhat agree' were revised and advanced to Round 2 (R2), and were re-rated and ranked by experts.

Results: Thirty-eight primary indicators and 10/17 additional indicators advanced to R2. Fifteen indicators were re-rated in R2; none were rejected. The final set comprised 50 indicators across six domains: Prevention (six), Testing (nine), Linkage to care (six), Treatment & Care (nine), Morbidity (seven) and Health System (13). Overall combined agreement averaged 95.3% (standard deviation = 7.0), with 29 indicators achieving unanimous support. Testing and Morbidity domains showed the strongest consensus. Ranking highlighted screening acceptability, infection prevalence, rapid testing results, referral success and treatment initiation as highest priorities.

Conclusions: This Delphi study delivers the first consensus-driven indicator set for monitoring and evaluating community hepatitis B/C services targeting migrants and refugees. Adoption of the 50-indicator framework, and its streamlined core set, can harmonise monitoring, guide resource allocation and strengthen data-driven progress toward elimination goals.

Registration: Open Science Framework.

Background: Oxidative stress contributes to hypertension and its complications. The oxidative balance score (OBS) could therefore provide insight into the relationship with mortality in hypertensive individuals. We aimed to investigate the association between OBS and all-cause mortality using data from National Health and Nutrition Examination Survey 2007-18.

Methods: Our sample comprised 11 196 hypertensive participants. We calculated OBS based on dietary and lifestyle factors and categorised participants accordingly into quartiles. We used Cox proportional hazards model to assess associations between OBS and mortality, and restricted cubic spline (RCS) analyses to determine dose-response relationships. Lastly, we conducted Kaplan-Meier survival curves and stratified/sensitivity analyses.

Results: 1764 deaths occurred during a median follow-up time of 73.4 months. Higher OBS was significantly associated with lower mortality risk, whereby participants in the highest OBS quartile had a 34% reduced mortality risk compared to those in the lowest quartile (hazard ratio (HR) = 0.66; 95% confidence interval (CI) = 0.51-0.84, P = 0.001). Each unit increase in OBS reduced mortality risk by 2% (HR = 0.98; 95% CI = 0.96-0.99, P < 0.001). We observed similar results for dietary and lifestyle OBS. RCS analyses indicated a nearly linear dose-response relationship between OBS and the risk of all-cause mortality (P-value for nonlinearity >0.05). Kaplan-Meier curves confirmed better survival in those with higher OBS (log-rank P-value <0.001). Stratified analyses showed stronger protective effects in individuals with middle incomes and those without a history of cancer (P-value for interaction <0.05). Sensitivity analyses confirmed the robustness of these findings.

Conclusions: Higher OBS levels, along with its dietary and lifestyle subscale scores, are significantly associated with a reduced risk of all-cause mortality among hypertensive individuals. These findings highlight the importance of oxidative balance and the potential benefits of antioxidant-rich diets and healthy lifestyles in reducing mortality risk for this population.