Pandemic management demands a multifaceted strategy that integrates disease transmission control, resource allocation, and effective public health interventions. This study explores how combining clinical decision-making frameworks (CDMFs) can improve decision-making and adaptive strategies during public health emergencies. The goal is to provide a synergistic approach that enhances the speed, effectiveness, and equity of pandemic responses. We conducted a review of literature published up to January 2025 to evaluate the contributions and limitations of these frameworks in pandemic preparedness and response. The review emphasizes how each framework supports adaptability, risk identification, and strategic planning, while also addressing challenges related to equity and data quality. The SOAR framework fosters adaptability and creativity, while risk assessment provides a systematic method for threat identification and mitigation. Artificial intelligence (AI)-driven decision support system (DSS) leverage machine learning and predictive analytics to provide immediate insights and improve strategic planning, although issues of data quality and equity must be addressed. The DECIDE framework ensures comprehensive decision-making, balancing strategic planning with the urgency of a crisis. The review highlights the potential of AI to improve decision-making efficiency, while underscoring the need for careful oversight to maintain transparency and prevent the perpetuation of health inequalities. Integrating AI into CDMFs offers significant opportunities to improve future pandemic responses. Evolving these frameworks and incorporating AI-DSS, while carefully addressing ethical considerations and data quality, will lead to more scientifically sound, practical, and equitable solutions to global health problems, enhancing overall pandemic preparedness and resilience.

Background: Non-communicable diseases (NCDs) present a significant public health challenge, particularly among India’s tribal populations. This study aims to implement community-based screening to assess the current risk factors for NCDs in a tribal population, estimate the proportion at high risk using a Community-Based Assessment Checklist (CBAC), evaluate the program’s acceptability and fidelity, identify determinants of high-risk groups, and explore reasons for non-attendance at health centers by high-risk individuals.Methods: A mixed-methods cross-sectional study was conducted among 238 urbanized tribal individuals aged 30-60. Data were collected using a pre-designed, structured, and validated questionnaire in the local language and analyzed with SPSS version 26. Qualitative data were subjected to thematic analysis to provide a comprehensive understanding of the findings.Results: Of the participants, 88 (37%) were identified as high-risk, while 129 (54.2%) exhibited inadequate awareness of NCDs. The program demonstrated high acceptability (90.5%) but moderate fidelity (45.46%). Among the 88 high-risk individuals, 48 (54.54%) did not visit the primary health center (PHC). In-depth interviews with 10 randomly selected defaulters revealed key barriers, including financial and time constraints, inaccessibility of facilities, and a lack of seriousness and awareness regarding NCDs.Conclusion: The findings underscore an urgent need for multifaceted awareness-raising initiatives to improve NCD prevention and management in tribal populations. Enhanced education and accessibility to healthcare services are crucial for reducing the burden of NCDs in these communities.

Background: This study examined the association between changes in diet quality—assessed using the healthy eating index-2015 (HEI-2015) and the dietary inflammatory index (DII)—and lipid profiles and glycemic control in adults with type 2 diabetes.Methods: In this longitudinal study, data were collected from 103 adults with type 2 diabetes at two time points, six years apart (baseline and reassessment). The main predictors were changes in HEI-2015 and DII scores over time. The primary outcome measures were lipid profile components (LDL, HDL, total cholesterol, triglycerides) and glycemic control (FBS). Associations were examined using regression models adjusted for age, sex, body mass index (BMI), and energy intake. Results: No statistically significant associations were observed between HEI-2015 or DII scores and lipid or glycemic outcomes in the overall sample. However, subgroup analyses based on adjusted models revealed reduced odds of LDL elevation among individuals aged>60 (OR: 0.14, 95% CI: 0.02–0.91) and those with BMI≥30 (OR: 0.15, 95% CI: 0.02–0.90) in the highest tertile of DII change. These effects were not observed consistently across other subgroups. Conclusion: While no significant associations were found in the overall cohort, subgroup analyses revealed that individuals over 60 and those with BMI≥30 had reduced odds of LDL elevation with higher DII scores. These findings suggest potential population-specific effects of dietary inflammation on lipid metabolism. Despite limitations such as a small sample size and wide confidence intervals, this study provides valuable exploratory evidence and underscores the need for larger, targeted investigations to confirm whether anti-inflammatory diets can improve metabolic outcomes in high-risk subgroups.

Background: This study aimed to identify some risk factors associated with time to diabetes type II events using artificial intelligence (AI) survival models (SM) in a population cohort from East Azerbaijan, Iran. Methods: Data from Azar-Cohort spanning from 2014 to 2020 was analyzed using the random forest (RF) variable selection method along with Cox regression to identify the most relevant risk factors associated with diabetes. We then developed prediction models using RF survival analysis. Lasso-variable selection and RF variable selection were used to select the most important variables. The concordance index (C-index) was used to evaluate the concordance of the prediction models. Results: Our LASSO-Cox regression identified six factors to be significantly associated with diabetes: age, mean corpuscular hemoglobin concentration (MCHC), waist circumference (WC), body mass index (BMI), use of sleep medication, and hypertension stage 1 and stage 2. The model included all variables with a C-index of 76.3%. In contrast, the RF analysis identified 21 important variables predicting a higher probability of having diabetes. Of those, WC, MCHC, triglyceride, and age were the most important predictors of diabetes. The RF model converged after 500 trees with an out-of-bag (OOB) of 0.28 and a C-index of 79.5%.Conclusion: RF machine learning algorithms and LASSO-Cox regression analyses consistently identified WC, hypertension, and MCHC as the main risk factors for developing diabetes. The RF approach demonstrated slightly better accuracy in predicting the likelihood of diabetes at different time points.

Step up for wellness: Reducing sedentary lifestyles for a healthier future Comment on "Intervening to reduce sedentary behavior among African American elders: the "Stand Up and Move More" intervention"

This study aimed to analyze the geographic distribution of diabetes and sedentary behavior across Florida counites, identifying high-risk areas to inform targeted public health interventions. We seek to inform targeted public health interventions and address health disparities, particularly in rural areas. Data from the 2017-2019 Florida Behavioral Risk Factor Surveillance System (BRFSS) were analyzed, among adults aged 18 years and older. Diabetes status was determined by self-reported diagnosis, and sedentary behavior was assessed based on physical activity participation. Respondents who declined to answer or were unsure were excluded. The statewide prevalence of diabetes diagnosis in Florida was 11.7% (95% CI: 10.8-12.6), with substantial geographic variation across counties. Sedentary prevalence varied significantly across the state, compared to the statewide rate of 26.5% (95% CI: 25-28). Counties with high diabetes prevalence often had elevated rates of sedentary behavior. Rural northern panhandle counties had higher concentrations of both diabetes and sedentary behavior. BRFSS design variables and weights ensured representative estimates for Florida. This study highlights the elevated prevalence of diabetes and sedentary lifestyles in the northern counties of Florida. The geographic patterns underscore the need for targeted, county-level interventions. Despite troubling rise in diabetes and sedentary behavior, there is a significant opportunity to mobilized community and outreach services, ensuring they are effectively deployed to these high-risk areas.

To evaluate a multi-setting lifestyle intervention's effect on body mass index (BMI) across the entire spectrum in a middle-income adult population over 15 years. This pragmatic interventional study included 5153 adults (≥20 years) from a middle-income community, followed for over 15 years with five follow-ups. A multi-setting intervention (school, family, community) aimed to promote healthy lifestyles. The lambda-mu sigma (LMS) method and quantile regression model were used to analyze changes in BMI percentiles (10th-90th) by sex and intervention group. The intervention showed modest effects on BMI percentiles. In men, it lowered BMI at the 40th and 70th percentiles (overweight/obesity onset) at the first follow-up (β=-0.16, 95% CI: -0.33, -0.001 and β=-0.21, 95% CI: -0.38, -0.04 respectively). In women, the effect emerged later (second follow-up) at the 20th (β=-0.39, 95% CI: -0.60, -0.18), 30th (β=-0.27, 95% CI: -0.49, -0.04), and 60th (β=-0.20, 95% CI: -0.39, -0.02) percentiles (overweight risk), extending to more overweight percentiles (20th-50th) in the third follow-up (βs ranged from -0.28 until -0.26). Our results indicated the effectiveness of a practical lifestyle intervention to control rising trend of BMI at the onset of overweight and obesity in a middle-income population. These findings can be useful for planning obesity prevention programs in communities with similar socioeconomic statuses.

In Ghana, several qualitative studies have explored users' perspectives on conventional sources of sexual and reproductive health (SRH) information and factors which influence provision of and access in rural settings. However, there is a dearth of qualitative studies on healthcare provider (HCP) perspectives on factors that deter access to conventional sources of SRH information among young people in rural Ghana and innovative ways for addressing barriers. This study explored perspectives on barriers to traditional sources of SRH information and services and innovative ways of using mHealth technologies for addressing provision and access challenges among young people in rural Ghana. This study used a qualitative approach using in-depth interviews. Semi-structured in-depth interviews were conducted with HCPs in rural areas in three regions of Ghana between May and August 2021. Participants were selected from rural communities using the convenience snowball sampling and were interviewed via Zoom. The interviews explored the experiences and perceptions of HCPs on conventional SRH information and services and young people's access to this information and services. The interviews were audio recorded and transcribed verbatim. Data were analysed thematically using NVivo software version 12, following the approach outlined by Braun and Clarke. Twenty HCPs were interviewed for this study. The participants identified different sources of SRH information and services used by rural young people. Peers or friends, health facilities, health providers, and community settings were reported as the main services and sources of SRH information. Participants reported several barriers and challenges to the provision of and access to SRH information to young people, including socio-cultural norms, religious beliefs, unfriendly health facility environments, negative health providers' attitudes, lack of privacy and confidentiality resulting in unfriendly youth SRH services, distance, and financial challenges due to costs of transportation which limits rural young people's access to, and use of, SRH services. All the participants indicated that in addressing provision and access barriers, the use of mobile phones could be beneficial. This study highlights several barriers and challenges that deter provision of, and access to, SRH information and services for young people in rural Ghana. The findings indicate the use of innovative mobile health (mHealth) technologies may be one solution to some of the barriers and challenges.

Digital technologies play an essential role in health systems by providing new solutions to reduce the burden of mental illnesses and disorders. However, in many cases, user preferences and expectations are not considered in the design of portals. This study aims to explore the preferences and expectations of end-users from the features and capabilities of mental health educational portals. This qualitative study was conducted from January 2022 to January 2023 using the conventional content analysis approach. The participants were 20 individuals, selected through purposive sampling, ranging in age from 18 to 61, all of whom had prior experience using an educational portal. Data were collected via individual semi-structured interviews. Qualitative analysis was performed using MAXQDA 10 software. After analyzing the data, the preferences and expectations of end-users were categorized into five main themes: to be reliable, mutual interaction capability, to be accessible, creating a stylish and attractive design, and attention to the quality and structure of the content. Considering the expectations and needs of users will enhance their acceptance and satisfaction with the portals. From the end-users' perspectives, the content, appearance, and structural or technical features a mental health educational portal are crucial for its effectiveness.

The aim of this study is to evaluate the validity and reliability of the Food Craving Acceptance and Action Questionnaire (FAAQ) in university students. The study included 394 undergraduate students at Ankara Yıldırım Beyazıt University. The study included individuals who volunteered to participate, were at least 18 years old, and did not have any severe psychological issues. Explanatory and confirmatory factor analyses of the scale were conducted by dividing the data set into two groups. Cronbach's α coefficient was analyzed and a test-retest was conducted with 94 students. It was determined that the Food Craving Acceptance and Action Questionnaire had 2 factors (acceptance and willingness). The fit values of the scale were found to be CMIN/df=2.26; GFI=0.92; AGFI=0.87; CFI=0.85; RMSEA=0.08. The acceptance, willingness subscales and total Cronbach α coefficients of the scale were 0.761, 0.716 and 0.761, respectively. Intraclass correlation coefficient (ICC) values were 0.84, 0.81 and 0.80 for acceptance, willingness and total scale score, respectively. Statistically significant negative correlations were found between the 'acceptance', 'willingness' subscale and total DEBQ, FCQ-T scores, subscale scores and mYFAS 2.0 symptom count (P<0.05). The FAAQ was found to have a two-factor structure and the fit values were found to be within the acceptable range. The age range for university students is considered to be that of adults, and this scale can also be applied to adults in general.