Conectar
To conduct a systematic review and narrative synthesis to identify barriers, facilitators and pre-existing interventions and describe the current status of initiatives/interventions aimed at improving access to quality trauma healthcare after injury in Pakistan.
Systematic review and narrative synthesis
MEDLINE (Ovid), Embase (Ovid), Web of Science (Clarivate Analytics), Cochrane (Wiley), Scopus and ProQuest, as well as grey literature.
Full-text peer-reviewed publications, including cross-sectional studies, cohort studies, case-control studies, randomised controlled trials and qualitative studies published in English from January 2013 to December 2023.
Two independent reviewers used a standardised tool to extract data variables to Excel. The quality of the included studies was evaluated using the CASP checklist. The barriers, facilitators and pre-existing interventions were mapped using the four delays framework, the Institute of Medicine (IOM) quality domains and the WHO health systems building blocks. The data were synthesised narratively to improve access to quality trauma care in Pakistan. This review was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines.
The review included 20 studies. 19 studies reported 58 barriers to access to quality care. Six studies reported 20 facilitators, and eight studies described initiatives or interventions aimed at improving access to quality trauma healthcare after injury. According to the four delays framework, the receiving care stage of access to care was primarily studied in 16 studies, which identified 37 barriers and 13 facilitators across 5 studies. Regarding the quality of care according to IOM domains, the effectiveness of quality trauma care after an injury was studied in 15 studies, which identified 19 barriers and 10 facilitators across four studies. According to the WHO health system building blocks, most studies (n=15) described challenges in healthcare service delivery, with these 15 studies identifying 23 barriers and 3 studies identifying 4 facilitators.
Our findings highlighted the scarcity of available literature, identified barriers and facilitators and pre-existing interventions, which informed the need to develop feasible, sustainable and contextually relevant interventions to improve access to quality trauma care after injury in Pakistan.
CRD42024545786
by Claude Emmanuel Koutouan, Marie Louisa Ramaroson, Angelina El Ghaziri, Laurent Ogé, Abdelhamid Kebieche, Raymonde Baltenweck, Patricia Claudel, Philippe Hugueney, Anita Suel, Sébastien Huet, Linda Voisine, Mathilde Briard, Jean Jacques Helesbeux, Latifa Hamama, Valérie Le Clerc, Emmanuel Geoffriau
Resistance of carrot to Alternaria leaf blight (ALB) caused by Alternaria dauci is a complex and quantitative trait. Numerous QTL for resistance (rQTLs) to ALB have been identified but the underlying mechanisms remain largely unknown. Some rQTLs have been recently proposed to be linked to the flavonoid content of carrot leaves. In this study, we performed a metabolic QTL analysis and shed light on the potential mechanisms underlying the most significant rQTL, located on carrot chromosome 6 and accounting for a large proportion of the resistance variation. The flavonoids apigenin 7-O-rutinoside, chrysoeriol 7-O-rutinoside and luteolin 7-O-rutinoside were identified as strongly correlated with resistance. The combination of genetic, metabolomic and transcriptomic approaches led to the identification of a gene encoding a bHLH162-like transcription factor, which may be responsible for the accumulation of these rutinosylated flavonoids. Transgenic expression of this bHLH transcription factor led to an over-accumulation of flavonoids in carrot calli, together with significant increase in the antifungal properties of the corresponding calli extracts. Altogether, the bHLH162-like transcription factor identified in this work is a strong candidate for explaining the flavonoid-based resistance to ALB in carrot.by Rana Muhammad Amir Latif, Tahir Iqbal, Ismaeel Abdel Qader, Atif Ikram, Hadeel Alsolai, Bayan Alabdullah, Fatimah Alhayan, Taher M. Ghazal
Urban air pollution remains a critical challenge for public health and environmental sustainability. This study investigates the predictive capabilities of five machine learning (ML) models: Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) for forecasting the Air Quality Index (AQI) using the widely adopted Air Quality dataset from the UCI ML Repository. Although collected in 2004–2005, the dataset continues to serve as a benchmark in recent literature and provides a reproducible testbed for methodological evaluation. After structured pre-processing, feature engineering, and chronological train–validation–test splitting, models were rigorously tuned and assessed using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2), with 95% bootstrap confidence intervals and corrected resampled t-tests confirming statistical significance. Ensemble models achieved the best performance, with Random Forest obtaining the lowest RMSE (12.48) and MAE (9.35), and XGBoost achieving the highest R2 (0.89). Feature importance analysis identified NOx, PM2.5, and CO as the most influential predictors. We incorporated Shapley Additive exPlanations (SHAP) analyses and case-level visualizations to support interpretability, providing transparent insights for practical decision-making. While the study is limited by the absence of external validation and genetic variables (e.g., APOE), it establishes a reproducible, interpretable, and computationally efficient ML framework for AQI forecasting. The findings highlight the continuing relevance of benchmark datasets for reproducible evaluation and demonstrate the potential of interpretable ML-based approaches for smart city air quality management and public health policy.The rising burden of non-communicable diseases (NCDs), including mental health disorders (MHDs) such as anxiety and depression, poses a significant public health challenge globally. Evidence suggests that both diabetes and hypertension, the two most prevalent NCDs, are linked to a higher prevalence of MHDs. However, there is a lack of evidence on prevalence of generalised anxiety disorder (GAD) and depression among adults living with both diabetes and hypertension in Bangladesh. We aimed to assess the prevalence of GAD and depression and explore the associated factors among adults living with diabetes and hypertension comorbidity in rural Bangladesh.
We implemented a cross-sectional study.
The study was conducted in Chirirbandar, a sub-district of Dinajpur, Bangladesh.
We interviewed a total of 387 adults living with diabetes and hypertension comorbidity.
We had two primary outcome measures: GAD and depression. Individuals scoring ≥10 on the General Anxiety Disorder-7 scale were considered as having GAD and individuals scoring ≥10 on the Patient Health Questionnaire-9 scale were considered as having depression. The outcome variables were dichotomised based on these scores.
The prevalence of GAD was 7.24% (95% CI 5.04 to 10.29). Education level (grades 5–9) (adjusted OR (AOR): 3.40, 95% CI 1.26 to 9.19) and household wealth status (highest wealth tertile) (AOR: 0.12, 95% CI 0.02 to 0.62) were associated with GAD. The prevalence of depression was 17.83% (95% CI 14.32 to 21.98). Socioeconomic factors associated with depression included unemployment (AOR: 3.26, 95% CI 1.05 to 10.10) and household wealth status (highest wealth tertile) (AOR: 0.45, 95% CI 0.21 to 0.98). Higher odds of depression were also observed among participants with controlled hypertension (AOR: 3.88, 95% CI 1.81 to 8.35). Other factors, such as tobacco use, dietary diversity and physical activity, were not associated with GAD or depression.
A high prevalence of GAD and depression was observed among adults living with diabetes and hypertension comorbidity. The findings from the study emphasise the need for integration of mental health services into the existing non-communicable disease care. The identified factors associated with GAD or depression should be considered to develop targeted interventions for people with hypertension and diabetes comorbidity in Bangladesh.
by Mohammed Hadi Bestaoui, Ali Lounici, Amar Tebaibia, Latifa Henaoui, Nawal Brikci-Nigassa, Houssem Baghous, Amel Bensefia
BackgroundVisceral adipose tissue (VAT) is associated with several cardiometabolic risk factors, particularly metabolic syndrome and insulin resistance. Reference values for VAT vary across populations, genders, and ages. Data on visceral fat in the Algerian population are lacking. This study aimed to establish reference values for VAT in a general adult population. The secondary objectives were to determine cardiometabolic consequences and to propose suggested threshold values for VAT to predict metabolic syndrome.
Materials and methodsThis cross-sectional, analytical study randomly selected participants from the electoral list of Tlemcen, Algeria. VAT was measured using dual-energy X-ray absorptiometry (DXA) General Electric Healthcare© Lunar iDXA.
ResultsA total of 301 adults (147 men and 154 women) with a mean age of 49.3 ± 15.1 years participated. The median (25th-75th percentiles) VAT mass was 1364 g (690–2049) in men and 1060 g (585–1590) in women. Binary logistic regression analyses demonstrated that cardiometabolic risk factors, including hypertension, type 2 diabetes, dyslipidemia, metabolic syndrome, insulin resistance according to HOMA2-IR, hepatic steatosis, and sleep apnea syndrome, were significantly dependent on VAT mass. Threshold values for VAT to predict metabolic syndrome (according to International Diabetes Federation) were ≥ 1369 g in men (sensitivity: 86.2%, specificity: 74.2%, Youden’s index: 0.604) and ≥ 1082 g in women (sensitivity: 76.3%, specificity: 76.9%, Youden’s index: 0.532).
ConclusionThis study provides reference values for VAT in an urban Algerian adult population and highlights its importance in assessing cardiometabolic risk.
Pharmacogenomic testing could potentially reduce the number of adverse drug reactions and improve treatment outcomes through tailoring treatment to an individual’s genetic makeup. Despite its benefits and the ambitions to integrate into routine care, the implementation of pharmacogenomic testing in primary care settings remains limited. This study aims to qualitatively explore the views of healthcare professionals (HCPs) and patients on implementing pharmacogenomic testing in the UK National Health Service (NHS) primary care setting and to estimate the cost-effectiveness of service-delivery implementation by comparing different HCPs’ models of care.
This study consists of three workstreams (WS). WS1 is semi-structured interviews with General Practitioners, pharmacists, nurses and patients (24 participants) to explore implementation issues, including the perceived barriers and facilitators to delivering a pharmacogenomic service. WS2 consists of focus groups (between 24–36 participants) with genomic experts to develop practical pharmacogenomic-guided clinical pathways for primary care. WS3 will estimate the cost-effectiveness of implementing pharmacogenomic testing when led by different HCPs incorporating parameters from the literature, expert opinions, as well as data from WS1 and WS2.
Thematic analysis will be used to analyse the qualitative data from WS1 and WS2, mapping findings onto the Consolidated Framework for Implementation Research domains, which will also be used as the theoretical framework. WS3 will be a decision-analytic model developed in Microsoft Excel to compare the cost-effectiveness of pharmacist-led, GP-led, nurse-led or multidisciplinary pathways.
This study has been approved by the NHS Health Research Authority and Health and Care Research Wales (24/PR/1088). Findings will be disseminated through peer-reviewed publications, conference presentations and engagement with NHS policymakers and Genomics England.
FreshRSS 