Conectar
by Subhanwita Manna, Bijaya Kumar Mishra, Krishna Kant Yadav, Pratibha Dhiman, Smiteerekha Sahoo, Chiranjeet Mishra, Tanveer Rehman, Ranjan Kumar Prusty, Ragini Kulkarni, Mahadev Bhise, Ashok Kumar Pandey, Ravikumar B. S., Ipsita Pal Bhowmick, Subrata Kumar Palo, Narayana Swamy D. M., Neha Srivastava, Umaer Alam, Reeta Singh, Manish Barvaliya, Vani Kandpal, Barsha Gadapani Pathak, Reema Mukherjee, Sanghamitra Pati
BackgroundUnsafe abortion continues to be a significant cause of avoidable maternal morbidity and mortality especially in low- and middle-income countries. Despite India’s permissive Medical Termination of Pregnancy (MTP) Act, operational gaps continue to prevent women from accessing safe abortion services. There is still a dearth of multi-centric mixed-methods research from rural areas on women’s experiences, provider perspectives, and facility readiness. This study aims to assess the status of comprehensive abortion care (CAC) in rural India by evaluating public-sector facility preparedness, provider experiences, care-seeking pathways, and identifying key barriers and facilitators as per CAC guidelines (2023). The findings will guide the development of a preliminary implementation approach (Model 0+) as well as future phases focused on testing, adaptation, and scale up to strengthen CAC availability, quality, and uptake.
MethodsThis study employs a convergent mixed-methods design across six geographically varied rural blocks in India (North, South, East, West, Central, and Northeast). There will be two participant groups: married women aged 18–49 years who are current, previous, or potential abortion service users, and public health system stakeholders involved in Comprehensive Abortion Care (CAC). A multistage cluster survey will enroll approximately 854 married women per site across 20 clusters. The qualitative component will comprise interviews with women who have had abortions in the last two years and healthcare practitioners, for a total of around 240 interviews. Additionally, 18 focus group discussions with Accredited Social Health Activists and Anganwadi Workers (three per site), will capture frontline worker’s perspectives. Quantitative data will be collected using KoboCollect and analysed using STATA (or an equivalent statistical software). Qualitative data will undergo combined phenomenological and thematic analysis using NVivo or equivalent software.
DiscussionThis study constitutes the first phase of a multi-stage implementation research programme. The study builds upon national CAC guidelines as a foundation of implementation of “Model 0” for abortion care and assesses their functioning in routine public-sector settings. Insights from women, frontline workers, and providers will inform development of a refined implementation strategy (“Model 0+”) grounded in real-world contexts. This evidence-driven approach is expected to yield a practical, acceptable, and scalable model for subsequent piloting, adaptation, and wider health system uptake.
by Dhirendra Prasad Yadav, Bhisham Sharma, Julian L. Webber, Abolfazl Mehbodniya
Colorectal cancer is the third most common malignancy worldwide. Manual screening requires expertise and resources. However, advancements in AI (artificial intelligence) have reduced the computation burden and time. Machine and deep learning have recently been used to diagnose colorectal lesions. The requirement of handcrafted features makes machine learning models expertise-dependent. At the same time, classical CNN (convolutional neural network) miss the global attention of the features. This work presents CDCTNet (colorectal diagnosis convolution transformer network), a hierarchical model for colorectal disease detection. Our model utilized two convolution blocks for the local high-dimensional spatial features from the lesion. In addition, the ViT encoder is used in parallel with the CNN block to provide a global correlation of the feature map. Furthermore, we designed an IEM block for the interaction of the features between the convolution block and ViT encoder to improve the attention on the features. The CDCTNet is evaluated on Kather and Kvasir datasets and obtained a precision and Kappa score of 96.60% and 95.02%, respectively. At the same time, CDCTNet has recall and F1 scores of 98.08% and 97.94%.
Chronic respiratory diseases (CRDs), such as asthma and chronic obstructive pulmonary disease (COPD), are heterogeneous conditions with a high multimorbidity burden. However, existing risk assessment instruments prioritise physiological measures while overlooking systemic comorbidities. We aim to develop and validate an electronic health record (EHR)-embedded artificial intelligence (AI) model—AiRES (AI in patients with RESpiratory disease)—to predict the 30-day, 90-day and 180-day risks of all-cause and index-disease hospitalisations. This model represents a first step towards a clinical decision support tool for personalised multimorbidity management in patients with CRD.
Patients aged ≥18 years with a validated case definition of asthma and COPD will be identified from Singapore health administrative data (2012–2020). Candidate predictors will include age, sex, ethnicity, housing type, and comorbidities, measured across multiple care settings as visit frequency, grouped at quarterly intervals in Year 1 and annually for Years 2 and 3 over a 3-year lookback window. We will predict 30-day, 90-day, and 180-day risks of (1) all-cause and (2) asthma/COPD-specific hospital admissions using up to five randomly selected index dates per individual. Three machine learning algorithms—logistic regression (LR) with Lasso regularisation, eXtreme Gradient Boosting, and Categorical Boosting—will be trained using 10-fold cross-validation (CV) with an ensemble feature selection strategy. The optimal model, selected based on performance and feature importance, will be benchmarked against two reference models: a full LR and a Zero-Inflated Negative Binomial regression with hospitalisation history as the sole predictor. Discrimination and calibration will be assessed using internal-external cluster-based and temporal CV. Clinical utility will be evaluated using decision curve analysis.
This study obtained ethics approval from the National University of Singapore (NUS-IRB-2024-849). Results will be published in international peer-reviewed journals.
To collate and appraise evidence from existing systematic reviews and meta-analyses on interventions to prevent stillbirth and reduce perinatal mortality across the reproductive continuum, including preconception, antenatal, intrapartum and immediate newborn periods.
Umbrella review synthesising evidence from systematic reviews, including meta-analyses where available.
A comprehensive search was conducted in CENTRAL (via Cochrane Register of Studies Online), PubMed, Embase and Web of Science, along with trial registries (WHO International Clinical Trials Registry Platform, ClinicalTrials.gov and ISRCTN Registry), from inception to 12 January 2026.
Systematic reviews and meta-analyses synthesising randomised controlled trials or quasi-experimental studies that reported stillbirth, perinatal mortality, fetal loss or fetal death were included. Reviews focused exclusively on predefined high-risk populations were excluded.
Two reviewers independently extracted data and assessed methodological quality using A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2). Grading of Recommendations, Assessment, Development and Evaluation (GRADE) certainty ratings were extracted as reported by the original review authors. Evidence synthesis followed a structured framework adapted from Ota et al, integrating direction of effect and certainty of evidence based on pooled estimates and GRADE assessments. Publication overlap was assessed using the Corrected Covered Area index where relevant.
A total of 116 systematic reviews were included, synthesising evidence from randomised controlled and quasi-experimental studies across preconception, antenatal, intrapartum and immediate newborn periods. Evidence from individual reviews showed clear benefit for several interventions, including balanced energy-protein supplementation, home visits by community health workers, birth preparedness interventions, labour induction at or beyond 37 weeks of gestation and skilled or community-based intrapartum care, primarily for reducing perinatal mortality. Reduced antenatal visit schedules compared with standard care were associated with a possible increase in stillbirth or perinatal mortality, indicating potential harm. Many interventions—such as group antenatal care (ANC), nutritional education, case-note provision, routine ultrasound or Doppler monitoring, antibiotic treatment for bacterial vaginosis, antiretroviral therapy in pregnancy and several pharmacological or hormonal interventions—demonstrated unknown or inconclusive effects on stillbirth or perinatal mortality, largely due to imprecision and heterogeneity.
This umbrella review identifies a range of interventions with evidence of effectiveness across the reproductive continuum, particularly those addressing maternal nutrition, continuity of ANC and quality intrapartum and newborn care. However, substantial evidence gaps remain, especially for interventions widely implemented without strong supporting evidence. These findings highlight the need for context-specific implementation research and prioritisation of proven strategies in low- and middle-income countries, where the burden of stillbirth remains highest.
CRD42024531100.
by Afsana Anwar, Mahmood Parvez, Farhan Azim, Uday Narayan Yadav, Saruna Ghimire, Ateeb Ahmad Parray, Shovon Bhattacharjee, ARM Mehrab Ali, Rashidul Alam Mahumud, Md Irteja Islam, Md Nazmul Huda, Mohammad Enamul Hoque, Probal Kumar Mondal, Abu Ansar Md Rizwan, Suvasish Das Shuvo, Sabuj Kanti Mistry
BackgroundFrailty and disability often emerge with ageing and affect quality of life. Older adults residing in Rohingya refugee camp in Bangladesh are particularly susceptible to frailty and disability due to adverse physical and social environment along with limited health and social care services available in the camp. This study aimed to investigate the prevalence and factors associated with frailty and disability among Rohingya older adults living in Bangladesh.
MethodsThis cross-sectional study was conducted among older adults aged ≥60 years residing in the Rohingya refugee settlement in Bangladesh. The primary outcomes were frailty and disability, explored using the ‘Frail Non-Disabled (FiND) questionnaire. Data were collected face-to-face during November-December 2021, using a semi-structured questionnaire. A multinomial logistic regression model was used to identify the factors associated with frailty and disability.
ResultsThe majority of participants (n = 864) were aged 60–69 years (72.34%), male (56.25%), married (79.05%), and without formal education (89.0%). The study revealed a high prevalence of frailty (36.92%) and disability (55.21%) among the participants. The multinomial regression analysis showed that the likelihood of experiencing disability was significantly higher among participants who were aged 70–79 years (RRR = 2.65, 95% CI: 1.25, 5.66) and ≥80 years (RRR = 8.06, 95% CI: 1.05, 61.80), were female (RRR = 3.93, 95% CI: 1.88, 8.1.9), had no formal education (RRR = 4.34, 95% CI: 2.19, 8.63), were living in a large family (RRR = 1.82, 95% CI: 1.05, 3.18) and were suffering from non-communicable diseases (RRR = 2.36, 95% CI: 1.32, 4.22) compared to their respective counterparts. The regression analysis also revealed that frailty was significantly higher among participants who were female (RRR = 2.82, 95% CI: 1.34, 5.94), were suffering from non-communicable diseases (RRR = 2.28, 95% CI: 1.27, 4.09), and had feeling of loneliness (RRR = 2.16, 95% CI: 1.11, 4.22).
ConclusionsThe findings underscore the need for long-term care and health promotion activities to alleviate the burden of frailty and disability among older adults in humanitarian settings. Efforts should particularly target the most vulnerable groups- older individuals (≥80 years), women, those without formal education, those living in large families, and those with non-communicable diseases.
FreshRSS