Tag: statistics

Gut Pathog. 2025 May 22;17(1):32. doi: 10.1186/s13099-025-00709-0.

ABSTRACT

Diarrheal infection remains a major public health problem in low and middle-income countries (LMICs). Prevention and control of diarrheal diseases are considered a global health priority. This case-control study aims to describe the prevalence of diarrhea etiologic agents and antimicrobial resistance in bacterial enteropathogens for acute diarrhea among children, adult civilians, and military personnel in Cambodia, detecting over 20 bacterial species, viruses, and parasites. A total of 918 subjects with acute diarrhea (cases), 791 aged-matched subjects without diarrhea (controls), and 675 follow-up cases were enrolled from five hospitals in Battambang and Oddor Meanchey provinces from 2020 to 2023. Pathogens were identified from collected stool samples via bacteriology, molecular techniques, immunoassays, and microscopy. Bacterial isolates were tested for antibiotic resistance patterns. From enrolled diarrhea cases, 533 stool samples (58%) were positive for enteric pathogens, compared to 389 samples (49%) in controls, underscoring the high carriage rate of enteric pathogens in this population as well as the difficulties in establishing the etiology of diarrhea cases. The most common enteric pathogens in cases were enteric bacteria with Aeromonas (15%), followed by Plesiomonas (12%), and enteroaggregative E. coli (EAEC) (10%). Shigella (p < 0.05), enterotoxigenic E. coli with heat-stable toxins (ETEC-ST) (p < 0.01), and Plesiomonas (p < 0.01) had a statistically significant association with acute diarrhea cases. Rotavirus was the most common virus found (51% of cases with virus), followed by norovirus (19%), and sapovirus (16%). In terms of antimicrobial resistance, 84% of Shigella isolates were highly resistant to trimethoprim/sulfamethoxazole (SXT), almost 80% of Campylobacter jejuni isolates were resistant to ciprofloxacin (82%) and nalidixic acid (85%). Over 50% of ETEC, Shigella, and EAEC isolates were resistant to ceftriaxone, ciprofloxacin, and SXT, respectively. Overall, our study highlights the high endemicity of enteric bacterial pathogens and the significant carriage rates of these pathogens even in individuals without overt symptoms. Although the overall antimicrobial resistance was moderate, prevalent isolates harbor a significant resistance to the first-line of treatment. This highlights the importance of ongoing diarrhea etiology and antimicrobial resistance (AMR) surveillance efforts to guide the development and implementation of an effective AMR management program in diarrheal infections.

PMID:40405224 | DOI:10.1186/s13099-025-00709-0

Biomed Eng Online. 2025 May 22;24(1):64. doi: 10.1186/s12938-025-01396-3.

ABSTRACT

BACKGROUND: Neurological disorders, ranging from common conditions like Alzheimer’s disease that is a progressive neurodegenerative disorder and remains the most common cause of dementia worldwide to rare disorders such as Angelman syndrome, impose a significant global health burden. Altered facial expressions are a common symptom across these disorders, potentially serving as a diagnostic indicator. Deep learning algorithms, especially convolutional neural networks (CNNs), have shown promise in detecting these facial expression changes, aiding in diagnosing and monitoring neurological conditions.

OBJECTIVES: This systematic review and meta-analysis aimed to evaluate the performance of deep learning algorithms in detecting facial expression changes for diagnosing neurological disorders.

METHODS: Following PRISMA2020 guidelines, we systematically searched PubMed, Scopus, and Web of Science for studies published up to August 2024. Data from 28 studies were extracted, and the quality was assessed using the JBI checklist. A meta-analysis was performed to calculate pooled accuracy estimates. Subgroup analyses were conducted based on neurological disorders, and heterogeneity was evaluated using the I² statistic.

RESULTS: The meta-analysis included 24 studies from 2019 to 2024, with neurological conditions such as dementia, Bell’s palsy, ALS, and Parkinson’s disease assessed. The overall pooled accuracy was 89.25% (95% CI 88.75-89.73%). High accuracy was found for dementia (99%) and Bell’s palsy (93.7%), while conditions such as ALS and stroke had lower accuracy (73.2%).

CONCLUSIONS: Deep learning models, particularly CNNs, show strong potential in detecting facial expression changes for neurological disorders. However, further work is needed to standardize data sets and improve model robustness for motor-related conditions.

PMID:40405223 | DOI:10.1186/s12938-025-01396-3

Int J Equity Health. 2025 May 22;24(1):146. doi: 10.1186/s12939-025-02485-4.

ABSTRACT

The objective of this study is to analyze the changes in catastrophic health expenditures (CHE) and out-of-pocket expenditures (OOP) before and during the COVID-19 pandemic, as well as to examine their determinants in Meta, Colombia, a region affected by armed conflict. We used data from the Conflicto, Paz y Salud (CONPAS) survey and applied mixed-effects logistic regression models. The analysis places particular emphasis on mental health as a key determinant, comparing the odds of incurring OOP and CHE between individuals with and without a tendency to present mental health disorders (SRQ + versus SRQ-).The results show that the odds of incurring CHE increased in 2020 compared to 2018, while the odds of incurring OOP decreased during the same period. Individuals living in less wealthy households (quintiles 1, 2, and 3 of the Household Wealth Index) have more odds of incurring CHE than those in the wealthiest group (quintile 5). Similarly, individuals aged 45 to 60 years or over 60 years and have more odds of incurring CHE than younger individuals (18 to 44 years). Those who fell sick or were hospitalized also have more odds of incurring CHE compared to those who did not. Additionally, we found that individuals with SRQ + have significantly higher odds of incurring OOP and marginally significantly higher odds of incurring CHE compared to SRQ- individuals. Additionally, those who have been displaced due to the conflict have higher odds of incurring OOP compared to those who have not.This study underscores the heightened vulnerability of regions impacted by violence; a situation further exacerbated by the COVID-19 pandemic. It emphasizes the need for targeted financial safeguards and comprehensive mental health programs to support marginalized communities, enhance economic resilience, and advance progress toward the Sustainable Development Goals (SDGs), particularly SDG 3, which aims to promote good health and well-being. The findings shed light on health disparities in violence-affected areas, highlighting the urgency of policies designed to improve financial security and healthcare access for individuals with mental health conditions.

PMID:40405222 | DOI:10.1186/s12939-025-02485-4

J Occup Med Toxicol. 2025 May 22;20(1):17. doi: 10.1186/s12995-025-00462-9.

ABSTRACT

BACKGROUND: During the COVID-19 pandemic, several industries were deemed essential. However, information on infection risk in occupational settings outside of healthcare workers and medical staff (HCWs) remain scarce. Thus, a systematic review with meta-analysis was conducted to compile the risk of infection to SARS-CoV-2 in non-healthcare workers (non-HCWs).

METHODS: We screened three databases (EMBASE, PubMed, medRχiv) for studies on SARS-CoV-2 infection risk in working population. Several stages of severity (infection, hospitalisation, admission to intensive care unit (ICU), mortality) were eligible. Occupational specifications were harmonised according to the German classification of professions (KldB). All reported risk estimators were considered. Studies were analysed for their risk of bias. Results of random-effects meta-analyses were assessed for their evidence according to GRADE. Subgroup analyses were run for ‘outcome’, ‘comparison group’, and ‘risk of bias’.

RESULTS: Of 9,081 publications identified, 25 were recognised as eligible, mainly describing the first year of the pandemic. For 20 occupations, we were able to carry out meta-analyses on KldB-4-level by integrating all stages of severity. Nine occupations were identified with a statistically significantly increased risk of infection for SARS-CoV-2, four of which had a relative risk (RR) of > 2: Occupations in meat processing (RR = 3.58 [95%-CI 1.46; 8.77]), occupations in building cleaning services (RR = 2.55 [95%-CI 1.51; 4.31]), occupations in cargo handling (RR = 2.52 [95%-CI 2.27; 2.79]) and cooks (RR = 2.53 [95%-CI 1.75; 3.67]). The certainty of evidence of eight results was found moderate or high.

CONCLUSIONS: The first systematic review and meta-analysis of occupational SARS-CoV-2 infection risk in occupations other than HCWs revealed a considerably elevated risk in individual related services as well as in commercial services.

TRIAL REGISTRATION: PROSPERO CRD42021297572.

PMID:40405221 | DOI:10.1186/s12995-025-00462-9

Health Res Policy Syst. 2025 May 22;23(1):65. doi: 10.1186/s12961-025-01344-6.

ABSTRACT

BACKGROUND: In realization of the importance of health policy, several scholars have examined health policies, contributing to the development of the field of health policy and systems research (HPSR). A growing body of literature within HPSR systematically analyses published articles in journals to examine how specific topics are dealt with in HPSR journals. The focus has extensively been on how research shapes policies and transfer of research to policy and practice. The present study takes a new approach in thinking about policy documents by exploring how policy documents are positioned in HPSR publications. In other words, the study answers the question of how policy documents are positioned in HPSR journal articles.

METHODS: The study examined how policy documents were positioned in selected health policy journal articles. It analysed articles in Q1 journals indexed in Scopus under the journal subject health policy and published in 2022. A total of 52 articles were included in the full analysis. A data collection tool was created to tabulate extracted data which included data about journals, articles, and policy and methodological descriptions. Data were interpreted and analysed using descriptive statistics.

RESULTS: Original research articles represented the majority of the analysed articles (72%). Multiple authorship was most common (63%), with authors from Western countries contributing the most to the publications. Analysed policies had a wide variety of foci and were mainly national-level policies (63%). Most of the articles included more than one policy for analysis (86%). Document analysis of policy documents was the only source of data in 33% of the articles. In addition, 27% of the articles lacked depth or details on how document analysis was conducted. The majority of the articles were aligned with policymaking as one phase of the policy process (31%).

CONCLUSIONS: The study reflects a primary effort to examine how policy documents are positioned in HPSR articles. The study’s findings contribute to the extant literature on the limited use of document analysis in HPSR. It further extends the research policy gap by understanding policy documents as a primary data source for researchers with a clear lack of its consideration for policy implementation or evaluation. The study’s findings introduce implications and provide recommendations for research, policy and practice.

PMID:40405213 | DOI:10.1186/s12961-025-01344-6

BMC Oral Health. 2025 May 22;25(1):763. doi: 10.1186/s12903-025-06149-1.

ABSTRACT

BACKGROUND: The objective of this study was to evaluate the performance of ScholarGPT, ChatGPT-4o and Google Gemini in responding to queries pertaining to endodontic apical surgery, a subject that demands advanced specialist knowledge in endodontics.

METHODS: A total of 30 questions, including 12 binary and 18 open-ended queries, were formulated based on information on endodontic apical surgery taken from a well-known endodontic book called Cohen’s pathways of the pulp (12th edition). The questions were posed by two different researchers using different accounts on the ScholarGPT, ChatGPT-4o and Gemini platforms. The responses were then coded by the researchers and categorised as ‘correct’, ‘incorrect’, or ‘insufficient’. The Pearson chi-square test was used to assess the relationships between the platforms.

RESULTS: A total of 5,400 responses were evaluated. Chi-square analysis revealed statistically significant differences between the accuracy of the responses provided applications (χ² = 22.61; p < 0.05). ScholarGPT demonstrated the highest rate of correct responses (97.7%), followed by ChatGPT-4o with 90.1%. Conversely, Gemini exhibited the lowest correct response rate (59.5%) among the applications examined.

CONCLUSIONS: ScholarGPT performed better overall on questions about endodontic apical surgery than ChatGPT-4o and Gemini. GPT models based on academic databases, such as ScholarGPT, may provide more accurate information about dentistry. However, additional research should be conducted to develop a GPT model that is specifically tailored to the field of endodontics.

PMID:40405212 | DOI:10.1186/s12903-025-06149-1

Hum Resour Health. 2025 May 22;23(1):24. doi: 10.1186/s12960-025-00991-8.

ABSTRACT

BACKGROUND: The English National Health Service (NHS) strives for a fair, diverse, and inclusive workplace, but Black and Minority Ethnic (BME) representation in senior leadership roles remains limited. To address this, a large multi-hospital acute NHS Trust introduced an inclusive recruitment programme, requiring ethnically and gender diverse interview panels and a letter to the Chief Executive Officer (CEO) explaining hiring manager’s candidate choice. This generated large amount of valuable structured and free-text data, but manual analysis to derive actionable insights is challenging, limiting efforts to evaluate and improve such equality, diversity, and inclusion (EDI) recruitment initiatives.

METHODS: Using this routinely collected recruitment data from the programme between September 2021 to January 2024, we used natural language processing artificial intelligence techniques, triangulated with secondary data analysis, to evaluate the programme’s effectiveness in increasing the number of BME appointees to senior leadership roles. Multivariate logistic regression identified recruitment factors that influence the odds of BME candidates applying, being shortlisted or offered a role compared to white candidates. Topic and sentiment analysis revealed thematic trends and tone of candidate assessments, stratified by hiring manager and candidate characteristics. Normalised average interview scores were also compared by job grades and candidate characteristics.

RESULTS: The requirement for hiring managers to write a letter to the CEO explaining recruitment decisions raised the odds of a BME candidate being offered a role by 1.7 times [95% CI 1.2-2.3] compared to white candidates. However, white candidates still had higher overall odds of being offered senior roles. BME candidates scored lower in interviews, with BME women twice as likely (p < 0.05) to receive negative assessments compared to white women.

CONCLUSIONS: The Letter to the CEO component of the inclusive recruitment programme increased BME representation in senior leadership roles, but inequities still persist in the recruitment process, reflecting national NHS recruitment trends. While the initiative marks progress, further strategies are needed to ensure equitable recruitment, career development, and retention. Artificial intelligence tools, such as natural language processing, provide effective methods to evaluate and enhance EDI recruitment initiatives by analysing routinely collected recruitment data to identify areas for improvement and establish best practices.

PMID:40405205 | DOI:10.1186/s12960-025-00991-8

Malar J. 2025 May 22;24(1):161. doi: 10.1186/s12936-025-05249-1.

ABSTRACT

BACKGROUND: Malaria remains a major public health problem in Cameroon where it accounts for high rates of morbidity and mortality. The management of the disease has been made worst in the North West region of Cameroon and in Dumbu in particular by the on-going socio-political crisis since 2016, which has limited the transport of drugs to this community and has also forced the inhabitants of this community to rely on traditional concoctions for treatment with the notion that it is cheap. The aim of this study was to determine the prevalence of malaria, assess the malaria awareness level, the treatment-seeking behaviours, and its implications on the prevalence of malaria in the Dumbu community.

METHODS: Questionnaires were administered to consented individuals. Blood samples were collected by finger prick using sterile lancets and blood films prepared on well labelled glass slides. The dry blood films were stained using a 3% Giemsa staining solution for 30 min. Data was collected were later analysed using SPSS.

RESULTS: Out of the 385 persons screened, malaria was recorded in 107 persons (27.8%). Those in the age group 11-25 years old were the most infected with malaria prevalence of 32.3% (41/127) while those in the age group ≥ 50 years recorded the least prevalence [19.3% (11/57)] and the difference was not significant (χ² = 3.716, p = 0.294). Yaoundé quarter recorded the highest prevalence 32.7% (35/107) and males were more infected than females. On awareness level, they have heard of malaria and knew its causal agent to be an infected mosquito, 48.3% considered fever as the sign of the infection. Sixty-nine-point 8 percent (69.8%) of the population rushes to the health centre for treatment while 5.6% prefers herbal treatment.

CONCLUSION: Malaria is still a health challenge in this area and people infected should be advised to seek treatment, whenever they have malaria from a health facility to ensure that the treatment given is appropriate.

PMID:40405204 | DOI:10.1186/s12936-025-05249-1

BMC Palliat Care. 2025 May 22;24(1):141. doi: 10.1186/s12904-025-01753-y.

ABSTRACT

BACKGROUND: Pediatric palliative care (PPC) aims to alleviate suffering, improve quality of life, and facilitate decision-making for patients, families, and healthcare professionals. Specifically, PPC in neonatal patients influences their quality of life by considering the fragility and complexity of their diagnoses when performing clinical interventions. However, to date, data on newborn patients and their specialized palliative care needs is limited. Therefore, this study aims to describe the clinical characteristics of neonatal patients requiring pediatric palliative care in a specialized center of health in a low- and middle-income country in Latin America.

METHODS: We conducted a cross-sectional study. Neonatal patients with at least 40 weeks of corrected gestational age were included. A review of medical records was conducted to obtain information about clinical outcomes and medical management. A descriptive statistical analysis was performed considering the sociodemographic and clinical characteristics of the patients. The therapeutic strategies implemented were described, comparing deceased and surviving patients using the Chi2 test, Mann-Whitney U test, or Fisher’s Exact test.

RESULTS: 263 individuals were included, of which 55.13% (n = 145) were males. The median gestational age was 35 weeks (IQR 28-38), and the median birth weight was 1,119 g (IQR 610-1760). The most frequent diagnosis was trisomy 21 in 30% (n = 79), followed by severe congenital heart disease in 25.5% (n = 67). The median length of hospitalization was 25 days (IQR 8-53), adherence to the established palliative care plan was 99.6% (n = 262), and 58% (n = 152) of patients required weekly follow-up by pediatric palliative care. Additionally, 81.37% (n = 214) received social work support, and 94.68% received spiritual support. Regarding clinical outcomes, 140 patients died. Among these, respiratory distress (n = 135, 96.42%) and seizures (n = 87, 54.37%) were the most frequent symptoms in the last 24 h of life. Deceased patients had a higher NEOMOD score and lower gestational age, which were statistically significant compared to surviving patients.

CONCLUSIONS: The implementation of a palliative care program in a neonatal unit facilitates multidisciplinary care that provides comfort to patients with life-limiting conditions and supports their families. Our findings highlight the importance of strengthening advanced care planning both prenatal and postnatal, being essential strategies in care.

PMID:40405201 | DOI:10.1186/s12904-025-01753-y

Comput Biol Chem. 2025 May 18;119:108511. doi: 10.1016/j.compbiolchem.2025.108511. Online ahead of print.

ABSTRACT

Diagnosing Alzheimer’s disease based on gene expression data extracted from microarrays is still an open field of research. Due to the availability of whole-genome data through microarrays technology, diagnosis accuracy is expected to be improved. Despite the high potential of the data prepared by the technology, their analysis on different platforms shows that they may differ for different samples concerning biomarker status. This affects the diagnosis accuracy because of the existing bias between two experimental conditions. To address this problem, we propose a pipeline-based approach to diagnose Alzheimer’s disease using statistical analysis of biological data combined with artificial intelligence techniques. At first, the B-statistics and a new score based on a gene interaction network are used to evaluate genes. The B-statistics helps us to find differentially expressed genes. The new score, called the evidence score, measures the compliance level of the differentially expressed genes with past biological evidence. Next, we use artificial intelligence methods to find the subset of genes that define high separability between normal and affected samples. To this end, we employed a genetic algorithm to find the optimal subset. The performance of the pipeline was compared with other state-of-the-art methods. The results indicate that the proposed method can obtain fruitful predictive performance for diagnosing Alzheimer’s disease. All the codes implemented in this study are available online at https://github.com/HamedKAAC/AD-gene-selection.

PMID:40403352 | DOI:10.1016/j.compbiolchem.2025.108511