Demographic Variables Research Articles

Digital divide and digital engagement in small business owners: an examination of survey data

ABSTRACT This paper examines the digital divide among Small Business Owners (SBO). Most of the existing literature has focused on the digital divide among individuals and communities with little attention given to SBOs. This paper uses the Resources and Appropriation (RA) theory to explore the digital divide in SBOs. Data is analyzed from a survey of residents in the seventh-largest city in the US. The analysis indicates that many characteristics of the digital divide persist when comparing SBOs. However, when controlling for factors such as digital engagement, business characteristics, and expected revenue, demographic variables like race, ethnicity, education, gender, age, and household income did not significantly affect the level of digital engagement or expected revenue of SBOs. The findings support RA theory by demonstrating that access and the reasons for using the Internet play a crucial role among SBOs. The results emphasize the need to create opportunities for minority-owned small businesses to thrive in the digital age.

Autism, intelligence, language, and adaptive behavior, disentangling a complex relationship

IntroductionAutism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by a range of intellectual and language abilities. Its heterogeneity is acknowledged in modern diagnostics, complicating research and necessitating precision medicine and a multidimensional approach for individualized treatment and accurate assessment. Intellectual and language functioning influence adaptive skills and symptomatology. Thus, assessing adaptive functioning in a multidimensional and multi-informant manner is crucial, highlighting the importance of comprehensive evaluations. This study explores the interplay between autistic traits, demographic variables, IQ, adaptive functioning, and the applicability of ICD-11 classifications.MethodsWe analyzed data from the initial global evaluation of 60 diverse autistic children (aged 35 to 120 months; IQ range 16 to 118). Parent-reports using the Vineland Adaptive Behavior Scale (VABS-II) were compared with standardized assessments from the PsychoEducational Profile (PEP-3). Children’s intellectual levels were assessed using Griffiths Scales of Child Development (Griffiths III) and the Autism Diagnostic Observation Schedule (ADOS-2) was used for autistic traits. They were further classified according to the ICD-11 diagnostic system, functional language, and intellectual functioning levels. Correlations among variables, group comparisons, and multivariate analyses were performed.ResultsThe analysis indicates a linear effect of IQ on all adaptive scales and the impact of autistic traits on directly measured adaptive functioning. A factorial effect was observed due to changes concerning specific age, intellectual, and linguistic levels, which do not completely align with ICD-11 categorization. Additionally, a negative correlation between intelligence and measured autistic traits was found. Parental age, education level, and age at childbirth were also found to affect various adaptive scales.DiscussionThe study questions the ICD-11’s proposed distinctions in IQ and language functioning for ASD, advocating for more refined categorization and developmental considerations. It underscores the intricate relationship between autistic traits, IQ, and communication skills, casting doubt on the precision of diagnostic tools across the spectrum. Parental reports and direct assessments are essential for comprehensive evaluation, with parental education and age influencing children’s behaviors and skills. The study calls for a nuanced approach to ASD assessment, integrating various metrics and sources of information for a detailed phenotyping necessary for personalized interventions and biological research.

Access to psychological therapies amongst patients with a mental health diagnosis in primary care: a data linkage study.

Significant numbers of people in England have fallen into a gap between primary care psychological therapies and specialist mental health services. We aim to examine pathways to care by looking at demographic variation in detection and referral to primary and secondary psychological services in south London. Longitudinal descriptive study using a record linkage between a primary care database (Lambeth DataNet) and a secondary care mental health database (CRIS). We extracted data on mental health diagnosis, prescriptions and episodes of care in mental health services for all patients of working age registered from 1 January 2008 to 1 March 2018 (pre-covid era). Of those with a mental disorder detected in primary care (n = 110,419; 26.8%); 33.7% (n = 37,253) received no treatment; 21.3% (n = 23,548) exclusively accessed psychological treatment within NHS Talking Therapies and 7.6% accessed secondary care psychological therapies. People from minoritised groups were more likely to be prescribed psychotropic medication as the only treatment offered compared to the White British group. Men, Black African and Asian groups were less likely to access NHS Talking Therapies. People with a personality disorder diagnosis had the highest prevalence and number of NHS Talking Therapies treatment episodes (48.0%, n = 960), a similar percentage (44.1%, n = 881) received secondary care psychology treatment. Our study highlights marked inequalities in access to psychological therapies for men and people from some minoritised ethnic groups across primary and secondary care and how individuals with personality disorders are offered multiple short-term courses in NHS Talking Therapies even where this is not recommended treatment.

Prediction of Healing Trajectory of Chronic Wounds Using a Machine Learning Approach.

Objective: New treatment options are emerging for chronic wounds, which represent a growing problem because of population ageing and increasing burden of chronic disease. While promising, the existing evidence for advanced modalities is commonly derived from small and/or poorly controlled studies and clear criteria for selecting patients, who are likely to benefit from these expensive options are lacking. In this study, we develop and validate a machine learning model to predict if a chronic wound, independent of etiology, is expected to heal within 12 weeks to identify cases in potential need of advanced treatment options. Approach: Retrospective analysis of electronic health record data from 2014 to 2018 covering 532 wound care clinics in the United States and 261,398 patients with 620,356 unique wounds. Prediction of 12-week healing trajectories with a machine learning model. Results: The best-performing model in a training dataset of a randomly drawn 75% subset of wounds contained variables for patient demographics, comorbidities, wound characteristics at initial presentation, and changes in wound dimensions over time, with the latter group being the most influential predictors. The final machine learning model had a high predictive accuracy with area under the receiver operating characteristic curves of 0.9 and 0.92 after 4 and 5 weeks of treatment, respectively. Innovation: A machine learning model can identify chronic wounds at risk of not healing by week 12 with high accuracy in the early weeks of treatment. Conclusions: If embedded in real-world care, the generated information could be able to guide effective and efficient treatment decisions.

Sustainable practices: organisational citizenship behaviour and psychological contract fulfilment

PurposeSustainable practices are the modern-day necessities for organisations as the world is becoming highly dynamic. The purpose of this study is to examine the influence of sustainable training and creativity practices (STP and SCP) on organisational citizenship behaviour (OCB-individual and OCB-organisation) via the mediating role of psychological contract fulfilment (PCF).Design/methodology/approachA sample of 326 white-collar Indian service industry employees was collected. The data are analysed using structural equation modelling and random forest regression supervised learning (RFRSL).FindingsThe findings indicate that sustainable training practices (STP) had an indirect impact on organisational citizenship behaviour (OCB-I, OCB-O) via the mediating effect of transactional (T-PCF) and relational psychological contract fulfilment (R-PCF). In terms of sustainable creative practices (SCP), the impact on OCB-I was indirect due to T-PCF. In addition, R-PCF acts as a mediator between SCP and OCB-O. In the latter portion of the analysis, the RFRSL approach created a prediction model for T-PCF, R-PCF, OCB-I and OCB-O, with demographic characteristics such as industry experience, gender, age, etc. playing a constructive role.Originality/valueThe study conducts a combination of both traditional and newer technology (machine learning), resulting in highlighting the uniqueness of the relationship between variables and the role of demographic variables.

Resilience and spiritual well-being as resources for coping with radiotherapy and surviving in patients with glioblastoma.

The primary aims of this multicenter, prospective observational study were to investigate spiritual well-being, resilience, and psychosocial distress in an Italian sample of glioblastoma patients undergoing radiochemotherapy. The secondary aim was to explore the influence of demographic, clinical, and psychological characteristics on survival. The assessment was conducted only once, within the first week of radiochemotherapy treatment. Spiritual well-being was evaluated by the Functional Assessment of Chronic Illness Therapy-Spiritual Well-being (FACIT-Sp-12), and religious/spiritual beliefs and practices were evaluated by the System of Belief Inventory. Resilience was evaluated by the Connor-Davidson Resilience Scale (CD-RISC). Psychosocial distress was evaluated the by Distress Thermometer and Hospital Anxiety Depression Scale. We conducted an univariable analysis of overall survival (OS) using data from the most recent follow-up available, considering demographic and clinical variables that could influence survival. Follow-up was defined as either the time of death or the latest follow-up visit recorded. We recruited 104 patients, and the median follow-up time was 18.3 months. "Distressed" patients had lower scores than "not distressed" patients on the FACIT-Sp-12 and CD-RISC. While OS was not significant according to the FACIT-Sp-12 threshold, the Kaplan-Meier log-rank test was 0.05 according to the CD-RISC threshold. Among demographic variables, age showed significant associations with OS (p = 0.011). Resilience showed significant associations with OS (p = 0.025). Data showed that high spiritual well-being was associated with high resilience and an absence of psychosocial distress in our sample of glioblastoma patients undergoing radiochemotherapy. Patients with greater resilience survived longer than those with lesser resilience. Profiling spiritual well-being and resilience in glioblastoma patients undergoing radiochemotherapy can be seen as a resource to identify novel characteristics to improve clinical take-in-charge of glioblastoma patients.

Willingness to Pay for Improved Groundwater due to BMPs in PEI.

This study uses stated preference techniques to evaluate the willingness to pay for improved groundwater in PEI, which allows us to estimate the social cost of nitrogen in this region. 'Beneficial Management Practices' (BMPs) are often employed in PEI's agricultural sector, to improve crop yields but also reduce adverse environmental effects such as nitrogen leaching that can affect ground and surface waters. PEI residents were surveyed to estimate a dollar value of improved water quality due to reduced nitrogen leaching due to BMPs. From the responses to a double-bounded dichotomous choice survey, models using maximum likelihood estimation showed that residents of PEI were willing to pay roughly $230 per year to see a reduction in nitrogen of either 20% or 50%, which translated to $5-13 per kg of nitrogen reduced. However, excluding 'yeah-sayers' and protest votes suggests that WTP is lower for the 20% reduction and increases as nitrogen reduction increases. Thus, a social cost of $13/kg should be considered an upper bound in PEI. WTP values, as well as estimates of the social cost of nitrogen are similar to values in previous literature. We find that variables such as cell phone usage, belief that farmers should pay for BMPs, and spending on water filtration have a statistically significant impact on WTP. Other variables are significant in some model specifications, however many demographic variables such as employment status and education do not affect WTP.

Assessing the Determinants of Generative AI Integration: A Study on Google's Gemini Adoption Among Thai University Graduate Students

The advent of Generative Artificial Intelligence (Gen AI) in educational tools like Google's Gemini represents a significant evolution. These technologies aim to revolutionize learning by creating human-like content in various formats, enhancing personalized and engaging educational experiences. This research investigates the factors influencing Thai graduate students' adoption of Google's Gemini, employing a quantitative approach with data from 826 respondents via closed-ended questionnaires through convenience sampling. The analysis included demographic, socio-economic, and media platform preference variables. Using binary logistic regression, the study evaluated how these factors affect graduate students' intentions to use Google’s Gemini in Thailand. Key determinants identified include age, education level, occupational status, and preferences for social media platforms like Facebook and YouTube. The study provides insights into the factors influencing Thai graduate students' use of Google's Gemini, useful for stakeholders in digital literacy and technology adoption. It stresses the importance of tailoring digital environments to learners' diverse needs, influenced by demographics and social media, and offers strategies for enhancing digital literacy and engagement. The research also sets the stage for further academic inquiry into educational technology, advocating for an interdisciplinary approach to better understand technology's educational impact.

Simplification of a registry-based algorithm for ejection fraction prediction in heart failure patients: Applicability in cardiology centres of the Netherlands.

Left ventricular ejection fraction (EF) is used to categorize heart failure (HF) into phenotypes but this information is often missing in electronic health records or non-HF registries. We tested the applicability of a simplified version of a multivariable algorithm, that was developed on data of the Swedish Heart Failure Registry to predict EF in patients with HF. We used data from 4,868 patients with HF from the Cardiology Centers of the Netherlands database, an organization of 13 cardiac outpatient clinics that operate between the general practitioner and the hospital cardiologist. The algorithm included 17 demographical and clinical variables. We tested model discrimination, model performance and calculated model sensitivity, specificity, positive and negative predictive values for EF ≥ vs. <50% and EF ≥ vs. <40%. We additionally performed a multivariable multinomial analysis for all three separate HF phenotypes (with reduced, mildly reduced and preserved EF) HFrEF vs. HFmrEF vs. HFpEF. Finally, we internally validated the model by using temporal validation. Mean age was 66 ±12 years, 44% of patients were women, 68% had HFpEF, 17% had HFrEF, and 15% had HFmrEF. The C-statistic was of 0.71 for EF ≥/< 50% (95% CI: 0.69-0.72) and of 0.74 (95% CI: 0.73-0.75) for EF ≥/< 40%. The model had the highest sensitivities for EF ≥50% (0.72, 95% CI: 0.63-0.75) and for EF ≥40% (0.70, 95% CI: 0.65-0.71). Similar results were achieved by the multinomial model, but the C-statistics for predicting HFpEF vs HFrEF was lower (0.61, 95% CI 0.58-0.63). The internal validation confirmed good discriminative ability. A simple algorithm based on routine clinical characteristics can help discern HF phenotypes in non-cardiology datasets and research settings such as research on primary care data, where measurements of EF is often not available.

Mycobacterium avium complex (MAC) infection in severely immunocompromised people living with HIV: Findings from a five-year cohort.

We sought to clarify the current incidence, risk factors and symptoms of disseminated Mycobacterium avium complex (dMAC) infection in admitted people living with HIV in a hospital in the Southeast of Spain. 5-years observational, retrospective and single-centre study. Demographic, clinical and analytical variables, along with microbiological, treatment and follow-up were collected. Five cases of dMAC infection in severely immunocompromised people living with HIV people living were found. dMAC was diagnosed in 22.7% of patients under 100 CD4. All patients presented with fever and clinical manifestations of pneumonia, lymphadenopathy, or gastrointestinal symptoms. Despite low CD4 levels and high viral loads in some cases, primary prophylaxis had not been previously administered. Until 2018, U.S. American guidelines recommended antimycobacterial prophylaxis for patients with low CD4 cell counts, a practice not adopted in Europe. Untreated dMAC infection is associated with high morbidity and mortality rates. dMAC infection represents a prevalent disease in severely immunosuppressed people living with HIV. dMAC requires a high index of suspicion in this population, in order to perform mycobacterial cultures from different samples.

Spike and Slab Regression for Nonstationary Gaussian Linear Mixed Effects Modeling of Rapid Disease Progression

ABSTRACTSelect measures of social and environmental determinants of health (referred to as “geomarkers”), predict rapid lung function decline in cystic fibrosis (CF), defined as a prolonged decline relative to patient and/or center‐level norms. The extent to which hyper‐localization, defined as increasing the spatiotemporal precision of geomarkers, aids in prediction of rapid lung decline remains unclear. Linear mixed effects (LME) models with specialized covariance functions have been used for predicting rapid lung function decline, but there are few options to properly incorporate spatial correlation into the covariance functions while inducing simultaneous variable selection. Our innovative Bayesian model uses a spike and slab prior for simultaneous variable selection and offers additional advantages when coupled with nonstationary Gaussian LME modeling. This model also incorporates spatial correlation through an additional random effect term that accounts for spatial correlation based on ZIP code distances. We validated the model with simulations and applied it to real CF data from a Midwestern CF Center. We demonstrate how a combination of demographic, clinical, and geomarker variables can be selected as optimal predictors using Bayesian false discovery rate controlling rule. Our results indicate that incorporating spatiotemporal effects and geomarkers into this novel Bayesian stochastic LME model enhances the dynamic prediction of rapid CF disease progression.

Associations Between Relative Lower and Upper Body Strength and Hyperinsulinemia in US Adults: 1999-2002 and 2011-2014 NHANES.

Churilla, JR, Boyer, WR, Richardson, MR, and Williams, CC. Associations between relative lower and upper body strength and hyperinsulinemia in U.S. Adults: NHANES 1999-2002 and 2011-2014. J Strength Cond Res XX(X): 000-000, 2024-Skeletal muscle (SM) is an insulin-sensitive tissue that aids in glucose homeostasis. Insulin resistance leads to chronic hyperglycemia and type 2 diabetes. Previous evidence suggests that greater SM strength and size improve insulin dynamics. The primary aim of this study was to examine the association(s) between relative lower and upper body strength and hyperinsulinemia in a nationally representative sample of US adults. Samples of adult (≥50 years) subjects in the 1999-2002 (N = 1,097) and adults (≥20 years) in the 2011-2014 (N = 2,576) National Health and Nutrition Examination Survey were used in the analyses. Significance was set at p ≤ 0.05 for regression models. Quartiles (Q) of relative lower body strength (RLBS [N·BMI-1]) and relative grip strength (RGS [kg·BMI-1]) were created. Hyperinsulinemia was calculated using the weighted 75th percentile of log-fasted insulin among adults without diabetes. Inverse dose-response relationships were found for decreasing prevalence estimates of hyperinsulinemia by increasing Q of both RLBS and RGS. Similar dose-response associations were revealed for increasing Q of both RLBS and RGS and mean insulin concentrations. Following adjustment for demographic and lifestyle variables, subjects in Q2, Q3, and Q4 of RLBS were found to have significantly lower odds of hyperinsulinemia (OR 0.58 [P = 0.05], OR 0.38, OR 0.22 [p < 0.05 for both], respectively). Subjects in Q2, Q3, and Q4 of RGS were also found to have lower odds of hyperinsulinemia (OR 0.30; OR 0.14; OR 0.05 [p < 0.0001 for all]), respectively. These data suggest RLBS and RGS may both be favorably associated with insulin homeostasis.

Exploring the substitution of cannabis for alcohol and other drugs among a large convenience sample of people who use cannabis

BackgroundThe substitution of cannabis for alcohol and other drugs has been conceptualised in a harm reduction framework as where cannabis is used to reduce the negative side-effects, addiction potential, and social stigma of other drugs. There is currently mixed evidence with recent reviews suggesting cannabis co-use patterns may vary by age and ethnicity. Yet few studies have had large enough samples to examine this demographic variation in detail.AimsTo explore the co-use of cannabis with alcohol and other drugs within demographic subgroups of a large sample of people who use cannabis. Specifically: (1) whether cannabis is being substituted for other drugs, and (2), whether cannabis use leads to more, less or the same level of other drug use.MethodOnline convenience survey promoted via Facebook™ completed by 23,500 New Zealand respondents. Those who had used cannabis and any of eight other substances in the same six-month period were asked if their use of cannabis had any impact on their use of each other substance (“a lot more”, “little more”, “no impact/same”, “little less”, “a lot less”). Frequency and quantity used of each other drug was compared by co-use group. Generalised logistic regression models were developed to predict co-use categories.ResultsSignificant proportions reported cannabis use led to “less” alcohol (60%), synthetic cannabinoid (60%), morphine (44%) and methamphetamine (40%) use. Those who reported using “less” had lower frequency and amount used of other drugs. Approximately seven-out-ten reported cannabis use had “no impact” on LSD, MDMA, and cocaine use. One-in-five reported using cannabis led to “more” tobacco use. Young adults (21–35-years) were more likely to report cannabis use led to “less” drinking and methamphetamine use. Adolescent co-users (16–20 years) reported mixed impacts. Māori were more likely to report cannabis use resulted in “less” alcohol, tobacco, methamphetamine, and LSD use. Students and those living in cities were less likely to report cannabis use lowering use of other substances.ConclusionCannabis and other drug co-use patterns are moderated by life stages, lifestyles, cultural perspectives, and urbanicity. Harm reduction initiatives and policy reforms should take account of these moderating factors.

The perspectives of clinical level medical students on animal-assisted therapy: a study in Ghana

BackgroundThe role of animal-assisted therapy (AAT) in complementary and alternative medicine is well acknowledged. AAT is widely patronized, in developed countries such as USA, Canada, and many European countries, but less so in developing countries in Africa including Ghana. For persons in developing African countries and elsewhere to benefit from AAT, healthcare professionals must be acquainted with it recommend it to their patients when necessary. This study therefore assessed the perspectives of clinical-level medical students on AAT.MethodA semi-structured questionnaire was used to collect data in a cross-sectional study from 206 randomly selected clinical-level medical students of the University for Development Studies, Tamale, Ghana. The data was analyzed using Microsoft Excel and SPSS (Version 26) and the results were presented in Tables and charts. The association between demographic variables and the knowledge and attitude of the students were determined using ANOVA, while bivariate Pearson’s correlation was used to measure the relationships between continuous variables. Associations were considered significant when p-value < 0.05.ResultsThe knowledge about AAT among the medical students was very poor (0.971 ± 2.407 over 10; 9.7%); almost all of them (≈ 99.0%) had very little or no exposure to AAT in school or at home. The attitude of the students was however averagely positive (3.845 ± 0.748 over 7; 54.9%), with a perceived health benefit of ATT score of 4.768 ± 1.002 (68.1%). The motivation of the students to acquire more knowledge and skills about AAT mostly through lectures and practical sessions (70.9%) was good (4.809 ± 1.221; 68.7%). Female students were significantly more knowledgeable about AAT than their male counterparts (1.5 versus 0.6; p-value = 0.006). No other sociodemographic characteristics had any significant association with knowledge, attitude, and perception of benefit variables. However, a significant positive relationship was found to exist between the students’ knowledge, attitude and perception of animal-assisted therapy (r ≥ 0.236; p-value ≤ 0.001).ConclusionWe conclude that the knowledge about AAT among medical students is inadequate and this is worrying given the beneficial complementary role of AAT in achieving SDG 3. Medical schools and their regulators in Ghana should incorporate complementary and alternative medicine (CAM) lessons in their training curricula. For practising medical doctors, topics on CAM should be discussed during their Continuous Professional Development sessions. With better knowledge, physicians would possibly be more willing and confident in recommending this useful complementary and alternative medicine to patients who may seek other forms of therapy besides conventional medicine or which will augment the orthodox medication the patients may have been using to treat their conditions.

Radiomics analysis in differentiating osteosarcoma and chondrosarcoma based on T2-weighted imaging and contrast-enhanced T1-weighted imaging

This study was performed to investigate the diagnostic value of radiomics models constructed by fat suppressed T2-weighted imaging (T2WI-FS) and contrast-enhanced T1-weighted imaging (CET1) based on magnetic resonance imaging (MRI) for differentiation of osteosarcoma (OS) and chondrosarcoma (CS). In this retrospective cohort study, we included all inpatients with pathologically confirmed OS or CS from Second Xiangya Hospital of Central South University (Hunan, China) as of October 2020. Demographic and imaging variables were extracted from electronic medical records and compared between OS and CS group. Totals of 530 radiomics features were extracted from CET1 and T2WI-FS sequences based on MRI. The least absolute shrinkage and selection operator (LASSO) method was used for screening and dimensionality reduction of the radiomics model. Multivariate logistic regression analysis was performed to construct the radiomics model, and receiver operating characteristic curve (ROC) was generated to evaluate the diagnostic accuracy of the radiomics model. The training cohort and validation cohort included 87 and 29 patients, respectively. 8 CET1 features and 15 T2WI-FS features were screened based on the radiomics features. In the training group, the area under the receiver-operator characteristic curve (AUC) value for CET1 and T2WI-FS sequences in the radiomics model was 0.894 (95% CI 0.817–0.970) and 0.970 (95% CI 0.940–0.999), respectively. In the validation group, the AUC value for CET1 and T2WI-FS sequences in the radiomics model was 0.821 (95% CI 0.642–1.000) and 0.899 (95% CI 0.785–1.000), respectively. In this study, we developed a radiomics model based on T2WI-FS and CET1 sequences to differentiate between OS and CS. This model exhibits good performance and can help clinicians make decisions and optimize the use of healthcare resources.

Demographic variation in truancy in adolescence and its effects on educational attainment and income in early adulthood

Although prior research has linked adolescent truancy with lower educational attainment and income in young adulthood, less is known about how these relationships are conditioned by demographics, such as gender, race, ethnicity, and receiving public assistance. Using data from two waves of the National Longitudinal Study of Adolescent to Adult Health, we find that adolescent truancy predicts lower educational attainment in young adulthood, and the effect is significantly stronger for males relative to females and significantly weaker for recipients of public assistance relative to their peers. Truancy's effect on educational attainment was largely invariant across race and ethnicity. We found little evidence that truancy in adolescence is significantly related to income in young adulthood, net of controls. This study highlights the importance of examining the nuanced relationship between truancy and later life outcomes.

Health Disparities in General Medicine: A Systematic Review of Socioeconomic and Demographic Influences

Health disparities in general medicine are a critical concern, with socioeconomic and demographic factors such as income, education, race, gender, and geographic location contributing to unequal healthcare access, quality, and outcomes. This systematic review aims to assess how these factors influence health disparities in general medical settings. A comprehensive search was conducted across PubMed, Embase, and MEDLINE, focusing on studies published in the last decade that examined the role of socioeconomic and demographic variables in health outcomes within general healthcare. Forty studies met the inclusion criteria, including cohort and cross-sectional studies. Findings reveal significant health disparities associated with lower socioeconomic status, with low-income individuals facing higher morbidity and mortality risks and reduced access to preventive care. Racial and ethnic minorities frequently experience poorer health outcomes and encounter systemic biases in medical treatment, while rural populations report limited access to healthcare services. The review underscores the need for policy interventions to address structural inequalities in healthcare. Future research should focus on developing targeted interventions to mitigate health disparities across diverse populations and healthcare settings.

Investigating the risk factors of cardiomyopathy in pregnant mothers

BackgroundCardiomyopathy during pregnancy significantly impacts maternal health, contributing to high morbidity and mortality. Identifying risk factors for this condition is crucial for enhancing prenatal care and preventing adverse outcomes. Study designThis population-based case-control study investigates the risk factors associated with cardiomyopathy in pregnant women. MethodsWe analyzed data from 5133 pregnant women (1686 cases with cardiomyopathy and 3447 controls without cardiomyopathy) who received care at healthcare facilities affiliated with Mashhad University of Medical Sciences between March 1, 2017, and June 20, 2024. Risk factors were assessed through logistic regression models, which calculated adjusted odds ratios (AORs) with 95 % confidence intervals (CIs) for various demographic, medical, and pregnancy-related variables. ResultsSignificant risk factors for cardiomyopathy in pregnancy included obesity (AOR = 2.11, 95 % CI: 1.43–2.02), higher education level (AOR = 1.84, 95 % CI: 1.25–1.86), mental health issues (AOR = 2.07, 95 % CI: 1.7–2.5), domestic violence (AOR = 2.02, 95 % CI: 1.08–3.6), twin pregnancy (AOR = 2.6, 95 % CI: 1.57–4.6), and preeclampsia (AOR = 6.9, 95 % CI: 2.78–17.4). Additional risk factors included lack of physical fitness (AOR = 1.5, 95 % CI: 1.13–2.007), history of infectious disease during pregnancy (AOR = 1.4, 95 % CI: 1.11–1.76), anemia (AOR = 1.57, 95 % CI: 1.15–2.09), and hypertension (AOR = 1.55, 95 % CI: 1.18–2.02). Smoking increased risk but was not statistically significant (AOR = 1.29, 95 % CI: 0.69–2.4). ConclusionAddressing modifiable and non-modifiable risk factors in prenatal care is crucial to reducing cardiomyopathy incidence and improving maternal cardiovascular health.

Natural Hazards and Climate Change Impacts on Food Security and Rural–Urban Livelihoods in Mozambique—A Bibliometric Analysis and Framework

Mozambique is confronted with numerous risks related to food security and natural disasters. The study conducted a literature review on natural hazards and food security. This can help to identify gaps and further areas of research. A bibliometric analysis was conducted using standardized text search terms, and the VOSviewer tool was used to analyze over 7000 scientific articles and cluster over 60,000 keyword co-occurrences. The results show that research on natural hazards for food security needs to be integrated. The priority topic of disasters focuses on specific hazards such as climate change, floods, and hurricanes, which are also linked to demographic and other social variables. More studies on food security, such as droughts, sustainable development, and other human and social conditions, are being conducted. Resilience as an emerging research paradigm needs to be addressed in comparison. One result is an analytical framework on impacts on food security in the context of disaster risk, based on the empirical findings of the literature review. It shows how everyday risks such as disease or food security can be conceptually better linked to natural hazards and resilience. It shows that further research is needed on the interlinkages of multiple risks, of which Mozambique is an outstanding example. The methodology presented is also applied to provide a framework for linking multiple risks to food security and natural hazards. The innovative dimension of the research is that this inquiry constitutes one of the pioneering attempts to conduct a bibliometric analysis of the linkages between natural hazards, food security, and resilience in Mozambique. Another noteworthy contribution is introducing a novel analytical framework that integrates food security and disaster risks.

Efficacy of Diagnostic In-Office Shoulder Ultrasound in the Surgical Treatment of Full-Thickness Rotator Cuff Tears.

Early surgical management of full-thickness traumatic rotator cuff tears (RCTs) may optimize functional outcomes, prioritizing timely diagnoses. Ultrasound and magnetic resonance imaging (MRI) are highly sensitive and specific modalities for RCT diagnosis, yet MRI remains the gold standard diagnostic tool despite increased costs and potential delays in care. Ultrasound can provide same-day diagnosis, thus possibly expediting care. The use of diagnostic shoulder ultrasound alone in the orthopaedic surgeon's office could increase the efficiency of diagnosis and care of traumatic full-thickness RCTs compared with MRI. Cohort study; Level of evidence, 3. A retrospective chart review of patients with full-thickness traumatic RCT diagnosed via ultrasound or MRI who subsequently underwent rotator cuff repair with 1 of 2 ultrasound-trained surgeons between January 1, 2014, and December 31, 2019, was performed. Inclusion criteria included patients ≥18 years old, documentation of a first-time traumatic event, and the patient's desire to have immediate surgical management. Revision surgeries, long-term shoulder instability, and nontraumatic injuries were excluded. Basic demographic data were collected, and the number of preoperative office visits and the timing from initial evaluation to diagnosis and surgery were calculated for the ultrasound and the MRI cohorts. A power analysis of 0.8 with an alpha of .05 required 38 patients per group. Overall, 133 patients were diagnosed via MRI compared with 76 via ultrasound. Besides body mass index, there were no significant differences in demographic variables or insurance status. Compared with the MRI cohort, patients in the ultrasound cohort received their diagnosis almost 2 weeks faster (P < .0001), were scheduled for surgery almost 3 weeks faster (P < .0001), and underwent surgery 2 weeks faster after initial evaluation (P < .0001) while requiring nearly half as many clinical visits (P < .0001). Regression analysis confirmed that ultrasound significantly reduced time to imaging, scheduling, and surgery after initial evaluation while requiring fewer clinical visits (P < .05). The study findings indicated that ultrasound was a time-saving alternative diagnostic modality for traumatic RCT compared with MRI without compromising standard of care. These results were achieved independent of patient insurance status, disability index, or other demographic variables.