-
Journal of Multidisciplinary Healthcare 2024Tuberculosis, malaria, and HIV are among the most lethal diseases, with AIDS (Acquired Immune Deficiency Syndrome) being a chronic and potentially life-threatening... (Review)
Review
Tuberculosis, malaria, and HIV are among the most lethal diseases, with AIDS (Acquired Immune Deficiency Syndrome) being a chronic and potentially life-threatening condition caused by the human immunodeficiency virus (HIV). Individually, each of these infections presents a significant health challenge. However, when tuberculosis, malaria, and HIV co-occur, the symptoms can worsen, leading to an increased mortality risk. Mathematical models have been created to study coinfections involving tuberculosis, malaria, and HIV. This systematic literature review explores the importance of coinfection models by examining articles from reputable databases such as Dimensions, ScienceDirect, Scopus, and PubMed. The primary emphasis is on investigating coinfection models related to tuberculosis, malaria, and HIV. The findings demonstrate that each article thoroughly covers various aspects, including model development, mathematical analysis, sensitivity analysis, optimal control strategies, and research discoveries. Based on our comprehensive evaluation, we offer valuable recommendations for future research efforts in this field.
PubMed: 38510530
DOI: 10.2147/JMDH.S446508 -
Gender Bias in Diagnosis, Prevention, and Treatment of Cardiovascular Diseases: A Systematic Review.Cureus Feb 2024Cardiovascular disease (CVDs) has been perceived as a 'man's disease', and this impacted women's referral to CVD diagnosis and treatment. This study systematically... (Review)
Review
Cardiovascular disease (CVDs) has been perceived as a 'man's disease', and this impacted women's referral to CVD diagnosis and treatment. This study systematically reviewed the evidence regarding gender bias in the diagnosis, prevention, and treatment of CVDs. Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed. We searched CINAHL, PubMed, Medline, Web of Science, British Nursing Index, Scopus, and Google Scholar. The included studies were assessed for quality using risk bias tools. Data extracted from the included studies were exported into Statistical Product and Service Solutions (SPSS, v26; IBM SPSS Statistics for Windows, Armonk, NY), where descriptive statistics were applied. A total of 19 studies were analysed. CVDs were less reported among women who either showed milder symptoms than men or had their symptoms misdiagnosed as gastrointestinal or anxiety-related symptoms. Hence, women had their risk factors under-considered by physicians (especially by male physicians). Subsequently, women were offered fewer diagnostic tests, such as coronary angiography, ergometry, electrocardiogram (ECG), and cardiac enzymes, and were referred to less to cardiologists and/or hospitalisation. Furthermore, if hospitalised, women were less likely to receive a coronary intervention. Similarly, women were prescribed cardiovascular medicines than men, with the exception of antihypertensive and anti-anginal medicines. When it comes to the perception of CVD, women considered themselves at lower risk of CVDs than men. This systematic review showed that women were offered fewer diagnostic tests for CVDs and medicines than men and that in turn influenced their disease outcomes. This could be attributed to the inadequate knowledge regarding the differences in manifestations among both genders.
PubMed: 38500942
DOI: 10.7759/cureus.54264 -
BMC Medicine Mar 2024Highlighted by the rise of COVID-19, climate change, and conflict, socially vulnerable populations are least resilient to disaster. In infectious disease management,... (Review)
Review
BACKGROUND
Highlighted by the rise of COVID-19, climate change, and conflict, socially vulnerable populations are least resilient to disaster. In infectious disease management, mathematical models are a commonly used tool. Researchers should include social vulnerability in models to strengthen their utility in reflecting real-world dynamics. We conducted a scoping review to evaluate how researchers have incorporated social vulnerability into infectious disease mathematical models.
METHODS
The methodology followed the Joanna Briggs Institute and updated Arksey and O'Malley frameworks, verified by the PRISMA-ScR checklist. PubMed, Clarivate Web of Science, Scopus, EBSCO Africa Wide Information, and Cochrane Library were systematically searched for peer-reviewed published articles. Screening and extracting data were done by two independent researchers.
RESULTS
Of 4075 results, 89 articles were identified. Two-thirds of articles used a compartmental model (n = 58, 65.2%), with a quarter using agent-based models (n = 24, 27.0%). Overall, routine indicators, namely age and sex, were among the most frequently used measures (n = 42, 12.3%; n = 22, 6.4%, respectively). Only one measure related to culture and social behaviour (0.3%). For compartmental models, researchers commonly constructed distinct models for each level of a social vulnerability measure and included new parameters or influenced standard parameters in model equations (n = 30, 51.7%). For all agent-based models, characteristics were assigned to hosts (n = 24, 100.0%), with most models including age, contact behaviour, and/or sex (n = 18, 75.0%; n = 14, 53.3%; n = 10, 41.7%, respectively).
CONCLUSIONS
Given the importance of equitable and effective infectious disease management, there is potential to further the field. Our findings demonstrate that social vulnerability is not considered holistically. There is a focus on incorporating routine demographic indicators but important cultural and social behaviours that impact health outcomes are excluded. It is crucial to develop models that foreground social vulnerability to not only design more equitable interventions, but also to develop more effective infectious disease control and elimination strategies. Furthermore, this study revealed the lack of transparency around data sources, inconsistent reporting, lack of collaboration with local experts, and limited studies focused on modelling cultural indicators. These challenges are priorities for future research.
Topics: Humans; Social Vulnerability; Communicable Diseases; COVID-19; Communicable Disease Control; Models, Theoretical
PubMed: 38500147
DOI: 10.1186/s12916-024-03333-y -
The Lancet. Child & Adolescent Health May 2024Febrile infants presenting in the first 90 days of life are at higher risk of invasive and serious bacterial infections than older children. Modern clinical practice... (Meta-Analysis)
Meta-Analysis
Diagnostic test accuracy of procalcitonin and C-reactive protein for predicting invasive and serious bacterial infections in young febrile infants: a systematic review and meta-analysis.
BACKGROUND
Febrile infants presenting in the first 90 days of life are at higher risk of invasive and serious bacterial infections than older children. Modern clinical practice guidelines, mostly using procalcitonin as a diagnostic biomarker, can identify infants who are at low risk and therefore suitable for tailored management. C-reactive protein, by comparison, is widely available, but whether C-reactive protein and procalcitonin have similar diagnostic accuracy is unclear. We aimed to compare the test accuracy of procalcitonin and C-reactive protein in the prediction of invasive or serious bacterial infections in febrile infants.
METHODS
For this systematic review and meta-analysis, we searched MEDLINE, EMBASE, Web of Science, and The Cochrane Library for diagnostic test accuracy studies up to June 19, 2023, using MeSH terms "procalcitonin", and "bacterial infection" or "fever" and keywords "invasive bacterial infection*" and "serious bacterial infection*", without language or date restrictions. Studies were selected by independent authors against eligibility criteria. Eligible studies included participants aged 90 days or younger presenting to hospital with a fever (≥38°C) or history of fever within the preceding 48 h. The primary index test was procalcitonin, and the secondary index test was C-reactive protein. Test kits had to be commercially available, and test samples had to be collected upon presentation to hospital. Invasive bacterial infection was defined as the presence of a bacterial pathogen in blood or cerebrospinal fluid, as detected by culture or quantitative PCR; authors' definitions of serious bacterial infection were used. Data were extracted from selected studies, and the detection of invasive or serious bacterial infections was analysed with two models for each biomarker. Diagnostic accuracy was determined against internationally recognised cutoff values (0·5 ng/mL for procalcitonin, 20 mg/L for C-reactive protein) and pooled to calculate partial area under the curve (pAUC) values for each biomarker. Optimum cutoff values were identified for each biomarker. This study is registered with PROSPERO, CRD42022293284.
FINDINGS
Of 734 studies derived from the literature search, 14 studies (n=7755) were included in the meta-analysis. For the detection of invasive bacterial infections, pAUC values were greater for procalcitonin (0·72, 95% CI 0·56-0·79) than C-reactive protein (0·28, 0·17-0·61; p=0·016). Optimal cutoffs for detecting invasive bacterial infections were 0·49 ng/mL for procalcitonin and 13·12 mg/L for C-reactive protein. For the detection of serious bacterial infections, procalcitonin and C-reactive protein had similar pAUC values (0·55, 0·44-0·69 vs 0·54, 0·40-0·61; p=0·92). For serious bacterial infections, the optimal cutoffs for procalcitonin and C-reactive protein were 0·17 ng/mL and 16·18 mg/L, respectively. Heterogeneity was low for studies investigating the test accuracy of procalcitonin in detecting invasive bacterial infection (I=23·5%), high for studies investigating procalcitonin for serious bacterial infection (I=75·5%), and moderate for studies investigating C-reactive protein for invasive bacterial infection (I=49·5%) and serious bacterial infection (I=28·3%). The absence of a single definition of serious bacterial infection across studies was the greatest source of interstudy variability and potential bias.
INTERPRETATION
Within a large cohort of febrile infants, a procalcitonin cutoff of 0·5 ng/mL had a superior pAUC value to a C-reactive protein cutoff of 20 mg/L for identifying invasive bacterial infections. In settings without access to procalcitonin, C-reactive protein should therefore be used cautiously for the identification of invasive bacterial infections, and a cutoff value below 20 mg/L should be considered. C-reactive protein and procalcitonin showed similar test accuracy for the identification of serious bacterial infection with internationally recognised cutoff values. This might reflect the challenges involved in confirming serious bacterial infection and the absence of a universally accepted definition of serious bacterial infection.
FUNDING
None.
Topics: Infant; Child; Humans; Adolescent; C-Reactive Protein; Procalcitonin; Fever; Biomarkers; Bacterial Infections; Diagnostic Tests, Routine
PubMed: 38499017
DOI: 10.1016/S2352-4642(24)00021-X -
JMIR Public Health and Surveillance Mar 2024Burnout is a multidimensional psychological syndrome that arises from chronic workplace stress. Health care workers (HCWs), who operate in physically and emotionally...
BACKGROUND
Burnout is a multidimensional psychological syndrome that arises from chronic workplace stress. Health care workers (HCWs), who operate in physically and emotionally exhausting work contexts, constitute a vulnerable group. This, coupled with its subsequent impact on patients and public economic resources, makes burnout a significant public health concern. Various self-care practices have been suggested to have a positive effect on burnout among HCWs. Of these, physical activity stands out for its ability to combine psychological, physiological, and biochemical mechanisms. In fact, it promotes psychological detachment from work and increases self-efficacy by inhibiting neurotransmitters and neuromodulators, increasing endorphin levels, enhancing mitochondrial function, and attenuating the hypothalamic pituitary-adrenal axis response to stress.
OBJECTIVE
Our objective was to conduct a systematic review of the evidence on the association between physical activity and burnout among HCWs.
METHODS
We considered HCWs, physical activity, and burnout, framing them as population, exposure, and outcome, respectively. We searched APA PsycArticles, MEDLINE, and Scopus until July 2022. We extracted relevant data on study design, methods to measure exposure and outcome, and statistical approaches.
RESULTS
Our analysis encompassed 21 independent studies. Although 10% (2/21) of the studies explicitly focused on physical activity, the remaining investigations were exploratory in nature and examined various predictors, including physical activity. The most commonly used questionnaire was the Maslach Burnout Inventory. Owing to the heterogeneity in definitions and cutoffs used, the reported prevalence of burnout varied widely, ranging from 7% to 83%. Heterogeneity was also observed in the measurement tools used to assess physical activity, with objective measures rarely used. In total, 14% (3/21) of the studies used structured questionnaires to assess different types of exercise, whereas most studies (18/21, 86%) only recorded the attainment of a benchmark or reported the frequency, intensity, or duration of exercise. The reported prevalence of physically active HCWs ranged from 44% to 87%. The analyses, through a variety of inferential approaches, indicated that physical activity is often associated with a reduced risk of burnout, particularly in the domains of emotional exhaustion and depersonalization. Furthermore, we compiled and classified a list of factors associated with burnout.
CONCLUSIONS
Our comprehensive overview of studies investigating the association between physical activity and burnout in HCWs revealed significant heterogeneity in definitions, measurements, and analyses adopted in the literature. To address this issue, it is crucial to adopt a clear definition of physical activity and make thoughtful choices regarding measurement tools and methodologies for data analysis. Our considerations regarding the measurement of burnout and the comprehensive list of associated factors have the potential to improve future studies aimed at informing decision-makers, thus laying the foundation for more effective management measures to address burnout.
Topics: Humans; Exercise; Health Personnel; Psychological Tests; Self Report; Burnout, Professional
PubMed: 38498040
DOI: 10.2196/49772 -
MedRxiv : the Preprint Server For... Mar 2024As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out...
As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.
PubMed: 38496570
DOI: 10.1101/2024.03.04.24303726 -
TheScientificWorldJournal 2024Multilevel models have gained immense popularity across almost every discipline due to the presence of hierarchy in most data and phenomena. In this paper, we present a...
INTRODUCTION
Multilevel models have gained immense popularity across almost every discipline due to the presence of hierarchy in most data and phenomena. In this paper, we present a systematic review on the adoption and application of multilevel models and the important information reported on the results generated from the use of these models.
METHODS
The review was performed by searching Google Scholar for original research articles on the application of multilevel models published between 2010 and 2020. The search strategy involved topics such as "multilevel models," "hierarchical linear models," and "mixed models with hierarchy." The search placed more emphasis on the application of hierarchical models in any discipline but excluded software methodological development and related articles.
RESULTS
A total of 121 articles were initially obtained from the search results. However, 65 articles met the inclusion criteria for the review. Out of the 65 articles reviewed, 46.2% were related to health/epidemiology, 15.4% to education and psychology, and 16.9% to social life. The majority of the articles (78.5%) were two-level models, and most of these studies modelled univariate responses. However, the few that modelled more than one response modelled them separately. Moreover, 83.1% were cross-sectional design, and 9.2% and 6.2% were longitudinal and repeated measures, respectively. Moreover, a little over half (55.4%) of articles reported on the intraclass correlation measure, and all articles indicated the response variable distribution where most (47.7%) were normally distributed. Only 58.5% of articles reported on the estimation methods used as Bayesian (20%) and MLE (18.5%). Again, model validation measures and statistical software were reported in 70.8% and 90.8% articles, respectively.
CONCLUSION
There is an increase in the utilization of multilevel modelling in the last decade, which could be attributed to the presence of clustered and hierarchically correlated data structures. There is a need for improvement in the area of measurement and reporting on the intraclass correlation, parameter estimation, and variable selection measures to further improve the quality of the application of multilevel models. The integration of spatial effects into multilevel models is very limited and needs to be explored in the future.
PubMed: 38495479
DOI: 10.1155/2024/4658333 -
Metabolomics : Official Journal of the... Mar 2024Untargeted direct mass spectrometric analysis of volatile organic compounds has many potential applications across fields such as healthcare and food safety. However,...
INTRODUCTION
Untargeted direct mass spectrometric analysis of volatile organic compounds has many potential applications across fields such as healthcare and food safety. However, robust data processing protocols must be employed to ensure that research is replicable and practical applications can be realised. User-friendly data processing and statistical tools are becoming increasingly available; however, the use of these tools have neither been analysed, nor are they necessarily suited for every data type.
OBJECTIVES
This review aims to analyse data processing and analytic workflows currently in use and examine whether methodological reporting is sufficient to enable replication.
METHODS
Studies identified from Web of Science and Scopus databases were systematically examined against the inclusion criteria. The experimental, data processing, and data analysis workflows were reviewed for the relevant studies.
RESULTS
From 459 studies identified from the databases, a total of 110 met the inclusion criteria. Very few papers provided enough detail to allow all aspects of the methodology to be replicated accurately, with only three meeting previous guidelines for reporting experimental methods. A wide range of data processing methods were used, with only eight papers (7.3%) employing a largely similar workflow where direct comparability was achievable.
CONCLUSIONS
Standardised workflows and reporting systems need to be developed to ensure research in this area is replicable, comparable, and held to a high standard. Thus, allowing the wide-ranging potential applications to be realised.
Topics: Metabolomics; Volatile Organic Compounds; Mass Spectrometry; Reference Standards; Workflow
PubMed: 38491298
DOI: 10.1007/s11306-024-02104-3 -
Computers in Biology and Medicine Apr 2024Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension... (Review)
Review
Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.
Topics: Humans; Artificial Intelligence; Electrocardiography; Hypertension; Magnetic Resonance Angiography; Medicine
PubMed: 38489986
DOI: 10.1016/j.compbiomed.2024.108207 -
European Psychiatry : the Journal of... Mar 2024We employed a Bayesian network meta-analysis for comparison of the efficacy and tolerability of US Food and Drug Administration (FDA)-approved atypical antipsychotics... (Meta-Analysis)
Meta-Analysis Review
We employed a Bayesian network meta-analysis for comparison of the efficacy and tolerability of US Food and Drug Administration (FDA)-approved atypical antipsychotics (AAPs) for the treatment of bipolar patients with depressive episodes. Sixteen randomized controlled trials with 7234 patients treated by one of the five AAPs (cariprazine, lumateperone, lurasidone, olanzapine, and quetiapine) were included. For the response rate (defined as an improvement of ≥50% from baseline on the Montgomery-Åsberg Depression Rating Scale [MADRS]), all AAPs were more efficacious than placebo. For the remission rate (defined as the endpoint of MADRS ≤12 or ≤ 10), cariprazine, lurasidone, olanzapine, and quetiapine had higher remission rates than placebo. In terms of tolerability, olanzapine was unexpectedly associated with lower odds of all-cause discontinuation in comparison with placebo, whereas quetiapine was associated with higher odds of discontinuation due to adverse events than placebo. Compared with placebo, lumateperone, olanzapine, and quetiapine showed higher odds of somnolence. Lumateperone had a lower rate of ≥ weight gain of 7% than placebo and other treatments. Olanzapine was associated with a significant increase from baseline in total cholesterol and triglycerides than placebo. These findings inform individualized prescriptions of AAPs for treating bipolar depression in clinical practice.
Topics: United States; Humans; Antipsychotic Agents; Bipolar Disorder; Quetiapine Fumarate; Olanzapine; Lurasidone Hydrochloride; Network Meta-Analysis; United States Food and Drug Administration; Bayes Theorem; Treatment Outcome
PubMed: 38487836
DOI: 10.1192/j.eurpsy.2024.25