Journal of Family Medicine and Primary Care

Background: Lung cancer is leading cause of cancer related death globally. It is second most prevalent cancer among women worldwide and ranks third among females in Nepal. Contributing factors are smoking, tobacco use, air pollution, and delayed diagnosis. Image-guided fine needle aspiration cytology (FNAC) is rapid diagnostic technique for evaluating lung lesions. It is minimally invasive procedure with less complications. This study examine histocytologic makeup of lung lesions and link the results. Materials and Methods: This cross-sectional observational study included 65 patients irrespective of age and sex presenting with lung masses at Chitwan Medical College and Teaching Hospital from April 2023 to September 2024. After clinical and radiologic evaluation, all cases underwent image-guided FNAC and biopsy. Only specimens with unequivocal malignant features were classified positive. Histopathology served as diagnostic reference standard. Results: FNAC diagnosed 90.8% as malignant and 9.2% as benign. Biopsy confirmed malignancy in 92.3% of cases. FNAC demonstrated a sensitivity of 98.33%, specificity of 100%, positive predictive value(PPV) of 100%, and negative predictive value (NPV) of 83.33%. Concordance between FNAC and histopathological subtyping was 98.46%. Adenocarcinoma was most common subtype, followed by Squamous cell carcinoma(SCC) and small cell carcinoma. Smoking was most common contributing factor associated with malignancy. Conclusion and implications: Image-guided FNAC is an excellent diagnostic accuracy tool which possess higher level of concordance with biopsy in evaluating lung masses. It should be considered as frontline diagnostic tool, especially in resource limited settings. Keywords: FNAC, Lung cancer, Biopsy, SCC, Adenocarcinoma, Small cell carcinoma, Nepal

Introduction Hearing loss (HL) affects over 1{middle dot}5 billion people globally and India shares a disproportionately high burden including Disabling Hearing Loss (DHL). HL affects an Individual socio-economically, but there are limited studies on the broader societal economic consequences of HL in India.Methods Using Cost-of-Illness (COI) approach, we studied the societal economic burden of HL in India. This study uses epidemiological and macroeconomic data and modelling to estimate the loss of Gross National Income (GNI) due to HL and DHL across three economic pathways. Uncertainty is evaluated using deterministic and Probabilistic Sensitivity Analyses (PSA).Results The model estimates that there are in India, 289 million and 85{middle dot}9 million people with HL and DHL respectively. Direct Loss of GNI and Indirect Loss of GNI (Caregiver burden) are estimated as INR 4,648{middle dot}4 billion (USD 55{middle dot}6 billion) and INR 3,268 billion (USD 39 billion) respectively. The Loss of GNI due to Low Education amongst those with HL is estimated as INR 1,041{middle dot}9 billion (USD 12{middle dot}45 billion).Discussion Economic burden of HL is presented across three pathways with Direct Loss of GNI due to DHL being the greatest. It also presents age stratified caregiver economic burden. The findings of the study help in estimating similar cost pathways, advocacy, and policy decisions towards reducing HL prevalence in India and LMICs. This study also highlights the need for India specific estimations related to the HL attributable low education, state-wise disaggregates, and prevalence studies. Funding This study has not received any funding.

Introduction: Professionalism is a conceptualization of obligations, attributes, interactions, attitudes, and role behaviors required for professionals with individual clients and to the society as a whole. Despite there is a known effect of low professionalism of nurses on health care delivery system, the study determining professionalism and affecting factors in Ethiopia is limited in to a few numbers. Therefore this study was aimed to assess perceived level of professionalism and associated factors among nurses working in Hadiya Zone public hospitals, south Ethiopia. Methods: An institution based concurrent mixed cross sectional study was conducted from May 09 to June 09/2022. A total of 376 nurses were selected by using Computer generated Simple random sampling techniques for quantitative study whereas Purposive sampling technique was used to select qualitative study participants. Epi data version 4.6 was used to enter the data whereas Statistical package for social science version 25 was used for the analysis of quantitative data. Ordinal logistic regression model was used to identify effects of independents on outcome variable. Results: A total of 360 nurses participated in the study with a response rate of 95.7%. The overall low perceived professionalism among nurses working in Hadiya zone found to be 40.3%. Variables such as Educational status (AOR= 0.045 (0.021, 0.096), Sense of accomplishment (AOR= 3.21(1.85, 5.55), and participating in professional associations (AOR= 5.302(2.82, 9.96) were significantly associated with professionalism. Qualitative study finding showed Salary, training, upgrading education; undermining nursing profession, inadequate facilities were the raised influencing factors of professionalism. Conclusion: This study revealed that professionalism among nurses was low and the associated factors were; level of education, taking training, participating in professional organizations, and sense of accomplishment. Ethiopian nursing association and Ministry of Health should focus on developing nurses professionalism.

Background: Abortion is safe when carried out using a method recommended by the World Health Organization (WHO), appropriate to the pregnancy duration, and by someone with the necessary skills. lobally, around 73 million induced abortions take place each year. Around 95% of maternal deaths occur in developing countries due to childbirth and pregnancy-related complications. Methodology: A descriptive cross-sectional study was used for the study among the married reproductive (20-45 years) age women using a non-probability purposive sampling technique. A self-developed semi-structured questionnaire was used via face-to-face interview for data collection. The collected data were analyzed using SPSS 16.0 version descriptive and inferential statistics were used to find the association between variables. Results: The findings of the study show that among 118 respondents, nearly two-thirds (57.6%) of the respondents had adequate knowledge and less than half (42.4%) of the respondents had inadequate knowledge regarding safe abortion. The study further shows that there was a significant association between the level of knowledge regarding safe abortion and education status and education level with p-value <0.05. Conclusion: The study concludes that nearly two third of the respondents have adequate knowledge regarding safe abortion. Educational status significantly influenced their level of knowledge. Thus, we could provide correct knowledge, education, and enhance awareness to married women in the reproductive age group.

Abstract Objective. To identify and rank the leadership traits most valued by medical staff in public hospitals, and to compare them with an established generic instrument and a generative artificial intelligence source. Design. Sequential exploratory qualitative-quantitative (QUAL QUAN) mixed-methods study: focus groups followed by an online ranking survey, with cross-comparison against the Northouse Leadership Traits Questionnaire (LTQ) and a ChatGPT derived list (LAIT). Setting. Major public hospital affiliated with Monash University, Melbourne, Australia, in 2023. Participants. Twenty-four senior medical staff (16 men, 8 women; 18 clinicians, 6 administrators) recruited through opportunistic sampling. Main outcome measures. Weighted ranking of the ten most desired leadership traits (Leadership Enabling Traits Survey, LETS); internal consistency (Cronbach s alpha); agreement between LETS and LTQ self-scores; and strong overlap with the AI-derived list. Results. The first most-weighted LETS traits were integrity (1.526), communication (1.435), compelling vision (1.404), emotional intelligence (1.040) and empathy (0.969), the same five identified by the AI source. Integrity was weighted 3.6 times more heavily than rebelliousness (0.424). Both LETS and LTQ achieved Cronbach s alpha > 0.7. Unweighted total self-scores did not differ between LETS (77.4 +/- 7.6) and LTQ (78.4 +/-6.9); weighted emotional intelligence related and other-trait sub-scores diverged significantly (p<0.001). Survey power was 45% at alpha=0.05.

Background Non-alcoholic fatty liver disease (NAFLD) has become a common metabolic disorder in paediatric age groups and is a major contributor to the burden of health economics. However, there is a lack of comprehensive data on the prevalence of this condition among children. Methods English language literature published from inception until April 2025 was searched from the electronic databases, i.e., PubMed/Medline, Scopus, Embase, and CINAHL. Original data published in any form that reported NAFLD prevalence among children and/or adolescents in India were included. The subgroup analysis of prevalence was done based on the risk category, i.e., average risk group and high risk group (obesity or overweight, metabolic syndrome, etc.). The prevalence estimates were pooled using the random-effects model. Results A total of 11 studies (six in high-risk populations and 5 in the average-risk general population) comprising data from 3512 individuals were found eligible and were included. The overall pooled estimate of NAFLD prevalence among the children and adolescents was 35.4% (95% CI: 19.7% to 52.9%) with very high heterogeneity(I2=99.0%). The pooled prevalence of NAFLD among average/low risk children and adolescents was 10.7% (95%CI: 5.2% to 20.5%) with high heterogeneity across the studies (I2= 96.6%, p=0.001) whereas the pooled prevalence of NAFLD in high risk overweight/obese children and adolescents was found to be 59.7% (95% CI:55.2% to 64.1%) with moderate heterogeneity across the studies (I2=50.90%; H2=2.04; Q (5) = 10.03; p=0.07) Conclusion This systematic review demonstrates that non-alcoholic fatty liver disease (NAFLD) poses a growing health concern among Indian children and adolescents, as 1 out of 3 children/adolescents is suffering from it, with a disproportionately high burden observed in those who are overweight or obese.

Background: Gastric carcinoma remains a major cause of cancer related mortality worldwide, with tumor progression increasingly recognized as a consequence of complex interactions within the tumor microenvironment. Hypoxia induced signaling, cancer associated fibroblast (CAF) heterogeneity, and immune checkpoint activation play critical roles in tumor progression and immune evasion. However, their integrated relationship in gastric carcinoma remains insufficiently characterized. Objectives: To evaluate the expression of Hypoxia inducible factor 1 alpha and its association with cancer-associated fibroblast subtypes and Programmed death-ligand 1 expression in gastric carcinoma. Methods: This cross sectional analytical study included 100 histologically confirmed gastric carcinoma cases from Satkhira Medical College. Immunohistochemistry was performed for HIF 1 alpha, smooth muscle actin (SMA), fibroblast activation protein (FAP), and PD L1. CAFs were subclassified into myofibroblastic CAFs (myCAFs) and inflammatory CAFs (iCAFs). Associations between biomarkers and clinicopathological variables were analyzed using chi square test, Spearman correlation, and multivariate logistic regression. Receiver operating characteristic (ROC) curve analysis was used to assess model performance. Result: High HIF 1 alpha expression was observed in 55% of cases and demonstrated significant association with poor differentiation (p = 0.001), advanced tumor stage (p = 0.002), and lymph node metastasis (p = 0.001). iCAF predominance was significantly associated with poor differentiation (p = 0.003), advanced stage (p = 0.004), and nodal metastasis (p = 0.004). High PD L1 expression was significantly associated with poor differentiation (p = 0.03), advanced stage (p = 0.001), and lymph node metastasis (p = 0.002). Multivariate logistic regression identified high HIF 1 alpha expression (OR = 3.8, p = 0.001), iCAF dominance (OR = 4.5, p < 0.001), and advanced tumor stage (OR = 2.9, p = 0.004) as independent predictors of high PD L1 expression. Combined high HIF 1 alpha expression and CAF activation demonstrated the highest rate of PD L1 positivity (76.7%, p < 0.001). ROC curve analysis demonstrated good predictive performance of the model with an area under the curve of 0.81. Conclusion: The present study demonstrates a significant interaction between hypoxia, stromal remodeling, and immune checkpoint activation in gastric carcinoma. High HIF 1 alpha expression and inflammatory CAF predominance are strongly associated with aggressive clinicopathological features and increased PD L1 expression, supporting the existence of a coordinated hypoxia stroma immune axis in gastric carcinoma progression. These findings may have potential implications for prognostic stratification and combined targeted therapeutic strategies.

Objective: Malnutrition and micronutrient deficiency are significant public health problems affecting the well-being of both the mother and her offspring. It is important to first quantify their burden in underserved communities and then to tackle this problem. The objective of this study was to assess the nutritional status, micronutrient deficiency profile, and associated determinants among pregnant women attending antenatal care clinics under PMSMA in selected government health facilities across three districts of Jharkhand, India. Design: The study employed a cross-sectional study design to assess the burden of malnutrition and micronutrient deficiency among pregnant women. Setting: The study was conducted in 4 health facilities in 3 districts of Jharkhand. The selected facilities were Rajendra Institute of Medical Sciences (RIMS), Ranchi; District Hospital (Sadar), Ranchi; District Hospital (Sadar), Godda; and Community Health Centre (Gamharia, Saraikela) under RHTC, Department of Community Medicine, Manipal Tata Medical College, Jamshedpur. The study was conducted during the period of September to December, 2022. Participant: Eligible pregnant women attending ANC clinics under PMSMA in the selected health facilities were enrolled until the required sample size of 977 was achieved. Pregnant women who were critically ill or those who presented with some emergency conditions were excluded from the study. Result: Based on BMI assessment, 38.6% of participants were malnourished, with 17.7% being underweight and 20.9% overweight or obese. Anaemia affected 69.3% of women, while clinical features suggestive of iron deficiency were observed among 21.1% of the participants. Vitamin A deficiency, iodine deficiency, and fluoride excess were also identified among a smaller proportion of women. Conclusion: The present study highlights a substantial burden of both malnutrition and micronutrient deficiency among pregnant women in Jharkhand and justifies the need for integrated maternal nutrition strategies during antenatal care.

Background: Diabetes mellitus and hypertension are increasingly prevalent non-communicable diseases that often coexist due to their interrelated pathophysiology and commonalities of risk factors. Effective management of these two comorbid conditions often involves polypharmacy, defined as the concurrent use of five or more medications, which for therapeutically relevant outcomes requires a high-medication adherence. Limited data exist on the extent of polypharmacy and its impact on adherence among Tanzanian patients with these comorbidities. This study therefore aimed at evaluating the prevalence of polypharmacy and its impact on medication adherence levels among this population. Methodology: A cross-sectional study involving 396 outpatients was conducted at Muhimbili National Hospital. Consecutive sampling was used to recruit eligible participants. Data was collected using structured-questionnaire which captured information on socio-demographics, clinical characteristics and adherence behaviors. Polypharmacy was defined as using five or more medications. Medication adherence was assessed using the Medication Adherence Report Scale (MARS-5). Multivariable logistic regression was performed to identify factors associated with adherence. Results: 71% of the study participants were on five or more medications, indicating high polypharmacy prevalence, with a median of six medications. Medication adherence was reported at 55.1%. Factors associated with lower adherence included moderate (APR: 0.83, P = 0.001) and high fasting glucose (APR: 0.66, P < 0.001), herbal medicine use (APR: 0.72, P < 0.001), and uncontrolled blood pressure. Conclusion: This study reveals a high prevalence of polypharmacy with moderate medication adherence among patients with comorbid T2DM and hypertension. These findings suggest a targeted intervention utilizing such as patient education and medication reviews are essential to improve adherence and management in Tanzania.

Abstract Background: Even though significant advances in diagnosis, treatment, and prevention strategies have been implemented, neonatal sepsis remains a common concern in clinical practice, especially in low-resource countries. It is one of the major causes of death during the first month of life. This study aimed to assess clinical outcomes and predictors of mortality among neonates with neonatal sepsis admitted to public hospitals in selected Hospitals in Addis Ababa, Ethiopia. Methods: A hospital-based prospective cohort study design was conducted among 466 neonates admitted with neonatal sepsis from September 2024 to January 2025. All neonates who were admitted to selected Hospitals of Addis Ababa city after being clinically or laboratory-diagnosed with neonatal sepsis by the attending physician were included in the study. Data were entered into EpiData 4.2 and analyzed by SPSS version 26. Bivariate and multivariate Cox regression were used to identify the relationship between dependent and independent variables. Finally, variables with p-value [&le;] 0.05 were taken as significant factors associated with poor clinical outcome. Results: The study was conducted among 466 neonates admitted with neonatal sepsis. Of all neonates admitted with neonatal sepsis, 372 (79.8%) were discharged with good outcomes, and 94 (20.2%) had a poor outcome/died. Duration of ruptured membrane being >12hr (AOR=7.02, 95 % (CI: 1.85, 26.57), marital status /divorced (AOR=3.12, 95 % (CI: 1.67, 7.45),rural residence (AOR= 6.05, 95 % (CI: 2.03-16.53), assisted instrumental delivery (AOR= 5.99, 95 % (CI: 1.46-17.11), meconium-stained amniotic fluid ((AOR= 9.48, 95 % (CI: 0.49-18.61)), no initiate exclusive breast feed within one hour (AOR= 3.20, 95 % (CI: 0.90-7.52), chest in drawing (AOR= 5.81, 95 % (CI: 1.75-11.23) were significantly associated with neonatal mortality. Conclusion: Neonatal mortality was moderately high. Meconium-stained amniotic fluid, prolonged duration of ruptured membrane (>12hr), Mode of delivery (instrumental delivery), and chest in drawing are among the predictors of neonatal mortality. Keywords: -Clinical outcome,Neonatal sepsis, Mortality, predictors, Ethiopia.

Abstract Background: Sub Saharan Africa is experiencing an accelerating epidemiological transition characterized by a growing burden of non-communicable diseases. Although excess body weight is an established risk factor for cardiovascular disease significant variations in hypertension status exist among overweight and obese adults due to individual traits and community environments. This study aimed to identify individual and community level determinants of hypertension among overweight and obese adults in Ethiopia using nationally representative Demographic and Health Survey data. Methods: We analyzed nationally representative data from non pregnant adults aged eighteen years and older with a Body Mass Index of 25 kilograms per meter squared or higher from the two stage cluster sampled 2024-25 Ethiopia Demographic and Health Survey across 797 enumeration areas. The primary outcome was hypertension, defined by elevated blood pressure or current antihypertensive medication use. Two level multivariable logistic regression evaluated fixed effect Adjusted Odds Ratios with 95% Confidence Intervals, alongside cluster random effects and model performance using Intra Class Correlation, Median Odds Ratio, Proportional Change in Variance and the Akaike Information Criterion. Results: Among a total weighted sample of three thousand eight hundred forty-two overweight and obese adults across six hundred twelve clusters, the weighted national prevalence of hypertension was 24.8%. In the final multivariable multilevel model, advancing age, male sex, higher educational status and upper wealth index categories were significant individual level risk factors. At the community level, residing in urban clusters and high community level wealth concentration significantly elevated hypertension odds. The null model revealed substantial clustering, which dropped substantially in the final model, demonstrating that contextual factors account for much of the cluster variations. Conclusions: Both individual metabolic and demographic drivers alongside community level economic and urban environments influence hypertension risk among overweight and obese Ethiopian adults. Cardiovascular health strategies should combine clinical targeted screening with urban structural modifications that facilitate active living environments.

Background: Retail red meat may act as a source of foodborne pathogens, antimicrobial resistant bacteria, and antibiotic residues, posing a significant public health concern in Bangladesh. Objectives: This study aimed to isolate and identify major bacterial pathogens from retail red meat, determine their antimicrobial susceptibility patterns, assess the prevalence of antibiotic resistant bacteria, and detect antibiotic residues in meat samples. Methods: A cross-sectional study was conducted from January to June 2019 using 60 retail red meat samples (20 cattle, 20 goat, and 20 buffalo) collected from Rajshahi and Naogaon districts. Bacterial isolates were identified using standard cultural, morphological, staining, and biochemical techniques. Antimicrobial susceptibility was evaluated by the Kirby Bauer disc diffusion method according to CLSI guidelines. Antibiotic residues were screened in 15 representative samples using thin layer chromatography (TLC). Results: Overall prevalence of Escherichia coli, Salmonella spp., and Staphylococcus aureus was 10.0%, 13.3%, and 28.3%, respectively. E. coli showed complete resistance to penicillin (100%) and high resistance to amoxicillin (83.3%), while remaining highly susceptible to ciprofloxacin (83.3%) and gentamicin (66.7%). Salmonella spp. exhibited highest resistance to penicillin (87.5%) and tetracycline (75.0%), whereas gentamicin (87.5%) and ciprofloxacin (75.0%) remained the most effective agents. S. aureus demonstrated marked resistance to penicillin (94.1%), ampicillin (58.8%), tetracycline (47.1%), and amoxicillin (47.1%), but high susceptibility to gentamicin (88.2%) and ceftriaxone (70.6%). TLC detected ciprofloxacin and oxytetracycline residues in one cattle meat sample each (6.7%). Conclusions: Retail red meat marketed in the study areas harbored multidrug-resistant bacterial pathogens and detectable antibiotic residues, highlighting potential risks to food safety and public health. Continuous surveillance, prudent antimicrobial use, improved slaughterhouse hygiene, and strict compliance with antibiotic withdrawal periods are essential to minimize antimicrobial resistance and residue contamination.

Background: Health and administrative professionals in tertiary hospitals face high levels of occupational stress, mental illness, and multimorbidity. The integrated measurement of these multidimensional health aspects is essential for informing effective workplace health promotion strategies. Objective: To describe the general health status of federal public hospital staff and correlate the measured health dimensions to inform institutional health promotion initiatives. Methods: A cross-sectional, online survey study was conducted at Hospital Universitario dos Servidores do Estado (HUSE) between November and December 2025. Data collection was performed via online REDCap questionnaires covering sociodemographic profiles and validated instruments (SRQ-20, MIDAS, AUDIT, WHOQOL-BREF, PHI, WHOQOL-SRPB BREF, CBI, GPAQ, and EPSO). Descriptive statistics, comparisons across employment ties (permanent vs. contracted staff), and Spearman correlation matrices were calculated. Results: Among 197 accesses, 117 completed the informed consent, and 86 finished all questionnaires. Participants were predominantly female, aged 40 to 60 years, and Christian. Screening positivity was 25% for common mental disorders, 21% for headache-related disability, and 10% for harmful drinking. Burnout scores clustered in the second quartile, while quality of life, happiness, and spirituality scores were in the upper third. Median physical activity was 670 min/week. Mental symptoms (SRQ-20), headache (MIDAS), and burnout (CBI) correlated positively with each other and negatively with quality of life, happiness, spirituality, and institutional support (EPSO). Conclusion: The set of instruments proved feasible for situational health diagnosis among hospital staff. Although the sample size was limited in this baseline wave, the initiative fostered workplace health awareness, driving concrete initiatives, including an on-site functional gym and workplace vaccination campaigns.

Background. The clinical course of coronavirus disease 2019 (COVID-19), and the ability to anticipate which patients will require intensive care, were poorly characterized in Central Asia during the first pandemic wave. We aimed to describe the clinical features of hospitalized COVID-19 patients at the Tashkent State Medical University, Uzbekistan, and to identify risk factors for intensive care unit (ICU) admission. Methods. In this single-centre cross-sectional study, we reviewed the records of 2500 consecutive patients hospitalized between 11 April and 8 August 2020. Patients were grouped as asymptomatic or symptomatic, and symptomatic patients were compared by ICU versus non-ICU status. Groups were compared with chi-square or Fisher's exact and Mann-Whitney U tests. Univariable and multivariable logistic regression identified risk factors for ICU admission. Results. Of 2500 patients (median age 36 years; 60.9% male), 989 (39.6%) were asymptomatic and 1511 (60.4%) symptomatic. In total, 129 (5.2%) were admitted to the ICU and 38 (1.5%) died. ICU patients were older (median 56 vs 40.5 years) and more often had bilateral pneumonia, oxygen desaturation and cardiometabolic comorbidity. In the multivariable model (AUC 0.82), the independent predictors of ICU admission were ischemic heart disease (aOR 4.20), shortness of breath (aOR 3.22), hypertensive heart disease (aOR 2.93) and male sex (aOR 2.00). Conclusions. Older age, cardiometabolic comorbidity and respiratory compromise identified patients at high ICU risk. As one of the first clinical COVID-19 descriptions from Uzbekistan, these data provide a baseline for preparedness in Central Asia.

Background Waterlogging, a form of chronic, stagnant flooding, is increasing in the Ganges-Brahmaputra delta in Bangladesh due to the compounding effects of land-use change, including the expansion of brackish shrimp farming, poor water management, and changing rainfall regimes. Its effects are negatively impacting livelihoods and health; its association with mental wellbeing is less known. Methods We hypothesised that 1-recent waterlogging, 2-social disadvantage (women, older individuals, the poorest, the least educated, those with chronic illness, and religious minorities) would be associated with lower wellbeing, and 3-chronic illness would modify the association between waterlogging and mental wellbeing. 1260 respondents from 595 households in Tala upazila, southwest Bangladesh, were interviewed about their mental wellbeing, chronic illness, and exposure to waterlogging in the 12 months prior to data collection, in August and September 2022. Associations between WHO-5 wellbeing scores and waterlogging and covariates were assessed using multi-level linear mixed-effects models with household random effects and cluster fixed effects. Results Our results confirm our hypotheses: wellbeing was lower among disadvantaged groups and chronic health vulnerability modifies the association between waterlogging and wellbeing: waterlogging exposure was associated with 18.31-point lower WHO-5 scores among individuals with chronic illness (95% CI -26.45 to -10.17), an association markedly attenuated among those without chronic illness (interaction {beta}=14.21, 95% CI 5.48 to 22.95, p=0.001). Conclusion These results suggest that chronic illness may increase vulnerability to the mental health burden associated with waterlogging. As waterlogging is increasing, policies addressing both its environmental drivers and the needs of vulnerable populations should be considered.

Background Stroke remains one of the leading causes of mortality and long-term disability worldwide, with low- and middle-income countries bearing a disproportionate share of the global disease burden. In India, delays in risk identification, fragmented referral pathways, and limited continuity of preventive care present significant challenges, particularly in rural communities. As a frontline health worker Accredited Social Health Activists (ASHAs) are strategically positioned to support community-based stroke prevention; however, existing workflows are frequently constrained by multi-tasking, predominantly paper-based documentation and fragmented digital systems. Advances in mobile health, artificial intelligence along with digital health ecosystem provided by Ayushman Bharat Digital Mission (ABDM) provide an opportunity to strengthen community healthcare through integrated digital platforms. Objective This protocol describes the design, system architecture, and prospective evaluation framework of ASHA Assist India, an integrated AI-assisted mobile health platform intended to support community-based stroke prevention by connecting citizens, ASHA workers, Primary Health Centres (PHCs), and higher levels of healthcare facilities within a unified digital ecosystem. Methods ASHA Assist India has been designed as a modular, cloud-based digital health platform supporting standardized data collection, longitudinal health monitoring, referral management, and AI-assisted clinical decision support. The proposed system comprises four user-facing applications corresponding to citizens, ASHA workers, PHCs, and referral hospitals, integrated through a centralized backend providing authentication, secure data management, interoperability, analytics, and notification services. The AI framework includes three planned analytical modules: (i) population-level stroke risk stratification, (ii) longitudinal stroke risk prediction, and (iii) acute stroke symptom recognition. A prospective implementation study is planned to evaluate platform usability, feasibility, workflow integration, implementation outcomes, and operational performance within routine community healthcare settings. Future validation of the AI modules will be conducted using prospectively collected longitudinal datasets. Expected Impact The proposed platform aims to strengthen community-based stroke prevention by improving digital workflow integration, facilitating coordinated referral pathways, and supporting longitudinal monitoring through the existing healthcare providers at health and wellness centres like ASHA, Community Health Officers (CHOs), ANM, etc. Beyond stroke prevention, the modular architecture is intended to provide a scalable framework for future digital health programmes addressing multiple non-communicable diseases within primary healthcare systems. Publication of this protocol establishes a transparent implementation and evaluation framework that may guide future research, digital health innovation, and implementation science in resource-constrained settings.

People with intellectual disabilities and their communities are rarely involved in setting priorities for research. Our study addressed this gap through consultations with the UK communities of three genetic syndromes in which intellectual disabilities are common: Down syndrome (DS), Fragile X syndrome (FXS) and Williams syndrome (WS). The study aimed to provide an understanding of (1) the views of the DS, FXS and WS communities on current UK research; (2) their priorities for future research; and (3) participants views of engaging with UK research. We conducted focus group discussions with 39 community members including: children and adults with DS, FXS and WS; parent/carers of people with DS, FXS and WS; practitioners and researchers who work with these communities. Our study was carried out in collaboration with a Steering Group and two Advisory Groups of DS, FXS and WS community members. We identified three themes. First, participants shared their dissatisfaction with the current research landscape and wanted a more balanced landscape, with more research with direct application to the daily lives of people with DS, FXS and WS. Second, community members emphasised the importance of translating research into practice, advocating for better access to research and more meaningful participation to research of individuals with lived experience. Third, our study not only identified what should be the focus of future research on DS, FXS and WS, but also how researchers should conduct their research. Whilst including children in our sample was a strength, there were some limitations to the diversity of our sample; children with FXS were not represented and gender, ethnic and geographic diversity could have been broader. Nevertheless, we hope that our findings will change the future of research in this field so that research carried out in the name of individuals with intellectual disabilities such as DS, FXS and WS, is of direct use to these communities.

Since model dose-response is critical when assessing caries lesion development over time, this study evaluated the influence of fluoride dose and treatment duration on caries progression in a rat caries model. Streptococcus mutans-infected Sprague-Dawley rats were treated with deionized water, 226 ppm F-, or 2,260 ppm F- twice daily for 3, 4, or 5 weeks. Caries lesions were assessed using Larson's modification of the Keyes scoring system and complemented by micro-computed tomography (microCT). Intraoral fluoride availability, serum and bone fluoride concentrations and microbial counts were also determined. Fluoride reduced caries severity in a dose- and time-dependent manner. While early enamel lesions were detected in all groups, extensive dentine lesions increased over time, in a dose-dependent manner, in the control and 226 ppm F- groups, and were not observed in the 2,260 ppm F- group after 5 weeks. Intraoral and bone fluoride availability increased significantly with fluoride concentration and treatment duration, whereas serum fluoride levels reflected fluoride dose instead of treatment duration. MicroCT-derived enamel volume correlated negatively with both total and extensive caries scores, supporting its utility as an objective measure of lesion severity. In conclusion, extending model length from 3 to 5 weeks increased the severity of caries lesions in a dose-dependent manner. Fluoride intraoral availability and bone fluoride also demonstrated a dose and time-dependent response.

Background: Low birth weight remains a primary driver of neonatal and infant mortality in Ethiopia. Machine learning models can assist early risk identification, yet clinical adoption is often limited by black box algorithms and late pregnancy predictor variables. This study aimed to develop and validate interpretable machine learning models using early pregnancy and sociodemographic features from a national survey dataset. Methods: Secondary data from the nationwide Ethiopian Demographic and Health Survey were analyzed. Predictors were restricted to features accessible during early antenatal visits. Six machine learning algorithms were trained and evaluated on an independent holdout test set: Logistic Regression, Decision Tree, Support Vector Machine, Gradient Boosting, Random Forest and Extreme Gradient Boosting (XGBoost). Imbalance was addressed using synthetic oversampling on the training set. Model explainability was established through Shapley Additive exPlanations (SHAP). Results: Out of 12876 births, 4249 (33%) were categorized as low birth weight / small birth size. XGBoost achieved superior predictive performance with an AUC-ROC of 0.947 (95% CI: 0.910-0.938) on the test set, outperforming standard logistic regression (0.8088). Key global predictive drivers identified by SHAP values included maternal anemia status, short inter pregnancy interval (< 18 months), low maternal BMI (< 18.5 kg/m^2), rural residence, lowest household wealth quintile and delayed or non-attendance of first trimester antenatal care. Conclusion: Machine learning models trained on early pregnancy and demographic features can accurately predict low birth weight risk in Ethiopia. Integrating interpretable frameworks into primary healthcare decision support tools provides a viable strategy for early risk stratification and targeted interventions in resource-limited settings.

Depression has become a serious concern for students worldwide. Aligned with the WHO Helping Adolescents Thrive (HAT) Guidelines and the Social Determinants of Health (SDoH) model, this study isolates five academically relevant factors academic pressure, work/study hours, study satisfaction, sleep duration, and financial stress from a dataset of 27,880 university students in India and quantifies their associations with depression. Unlike prior work that maximises classification accuracy, this study prioritises interpretability: logistic regression provides odds ratios (OR) with 95% confidence intervals, Random Forest (RF) and XGBoost rank predictors by feature importance, and SHAP (SHapley Additive exPlanations) values extend the analysis to individual-level risk explanation. SMOTE oversampling was applied exclusively to the training set, and performance was evaluated on the original imbalanced test set (n = 5,576). Both ensemble models achieve approximately 77-78% accuracy and an AUC of 0.845, confirmed by 5-fold pipeline cross-validation (CV AUC [~] 0.843). Academic pressure is the dominant risk factor (OR = 2.271; RF importance = 0.481; mean |SHAP| = 0.174), while study satisfaction (OR = 0.796) and sleep duration (OR = 0.835) are protective. The RF model yields a tipping point at academic pressure > 4.02, and interaction plots reveal how depression risk is amplified by low sleep, high financial stress, and extended study hours. These findings provide data-driven thresholds aligned with WHO-endorsed modifiable determinants to support early detection and institutional counselling.