Analysis Of Longitudinal Data Research Articles

Formation mechanisms of intra-organizational membership overlap: a longitudinal network analysis of membership data from the International Communication Association

Abstract Social entities are linked and interdependent through shared members, a phenomenon described as niche overlap. Using archival data on yearly affiliations of International Communication Association (ICA) members with divisions and interest groups over a 9-year period, this study conducts a longitudinal network analysis to examine the formation mechanisms of intra-organizational niche overlap among organizational subunits. The results of the temporal exponential random graph model suggest the absence of dominant ICA subunits whose topic areas are broad enough to integrate different specializations through membership overlap. Triadic closure facilitates the sharing of members among three subunits, leading to a cohesive internal structure and limited brokerage opportunities. Subunits with smaller membership sizes are more likely to have membership overlap, particularly with larger subunits. Positive affinity relations between subunits, characterized by the sharing of scarce resources within the organization, contribute to the formation of membership overlap.

At-home, telehealth-supported ketamine treatment for depression: Findings from longitudinal, machine learning and symptom network analysis of real-world data

BackgroundImproving safe and effective access to ketamine therapy is of high priority given the growing burden of mental illness. Telehealth-supported administration of sublingual ketamine is being explored toward this goal. MethodsIn this longitudinal study, moderately-to-severely depressed patients received four doses of ketamine at home over four weeks within a supportive digital health context. Treatment was structured to resemble methods of therapeutic psychedelic trials. Patients receiving a second course of treatment were also examined. Symptoms were assessed using the Patient Health Questionnaire (PHQ-9) for depression. We conducted preregistered machine learning and symptom network analyses to investigate outcomes (osf.io/v2rpx). ResultsA sample of 11,441 patients was analyzed, demonstrating a modal antidepressant response from both non-severe (n = 6384, 55.8 %) and severe (n = 2070, 18.1 %) baseline depression levels. Adverse events were detected in 3.0–4.8 % of participants and predominantly neurologic or psychiatric in nature. A second course of treatment helped extend improvements in patients who responded favorably to initial treatment. Improvement was most strongly predicted by lower depression scores and age at baseline. Symptoms of Depressed mood and Anhedonia sustained depression despite ongoing treatment. LimitationsThis study was limited by the absence of comparison or control groups and lack of a fixed-dose procedure for ketamine administration. ConclusionsAt-home, telehealth-supported ketamine administration was largely safe, well-tolerated, and associated with improvement in patients with depression. Strategies for combining psychedelic-oriented therapies with rigorous telehealth models, as explored here, may uniquely address barriers to mental health treatment.

Persisting exercise ventilatory inefficiency in subjects recovering from COVID-19. Longitudinal data analysis 34 months post-discharge

BackgroundSARS-CoV-2 infection has raised concerns about long-term health repercussions. Exercise ventilatory inefficiency (EVin) has emerged as a notable long-term sequela, potentially impacting respiratory and cardiovascular health. This study aims to assess the long-term presence of EVin after 34 months and its association with cardiorespiratory health in post-COVID patients.MethodsIn a longitudinal study on 32 selected post-COVID subjects, we performed two cardiopulmonary exercise tests (CPETs) at 6 months (T0) and 34 months (T1) after hospital discharge. The study sought to explore the long-term persistence of EVin and its correlation with respiratory and cardiovascular responses during exercise. Measurements included also V̇O2peak, end-tidal pressure of CO2 (PETCO2) levels, oxygen uptake efficiency slope (OUES) and other cardiorespiratory parameters, with statistical significance set at p < 0.05. The presence of EVin at both T0 and T1 defines a persisting EVin (pEVin).ResultsOut of the cohort, five subjects (16%) have pEVin at 34 months. Subjects with pEVin, compared to those with ventilatory efficiency (Evef) have lower values of PETCO2 throughout exercise, showing hyperventilation. Evef subjects demonstrated selective improvements in DLCO and oxygen pulse, suggesting a recovery in cardiorespiratory function over time. In contrast, those with pEvin did not exhibit these improvements. Notably, significant correlations were found between hyperventilation (measured by PETCO2), oxygen pulse and OUES, indicating the potential prognostic value of OUES and Evin in post-COVID follow-ups.ConclusionsThe study highlights the clinical importance of long-term follow-up for post-COVID patients, as a significant group exhibit persistent EVin, which correlates with altered and potentially unfavorable cardiovascular responses to exercise. These findings advocate for the continued investigation into the long-term health impacts of COVID-19, especially regarding persistent ventilatory inefficiencies and their implications on patient health outcomes.

DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification.

Biomedical research now commonly integrates diverse data types or views from the same individuals to better understand the pathobiology of complex diseases, but the challenge lies in meaningfully integrating these diverse views. Existing methods often require the same type of data from all views (cross-sectional data only or longitudinal data only) or do not consider any class outcome in the integration method, which presents limitations. To overcome these limitations, we have developed a pipeline that harnesses the power of statistical and deep learning methods to integrate cross-sectional and longitudinal data from multiple sources. In addition, it identifies key variables that contribute to the association between views and the separation between classes, providing deeper biological insights. This pipeline includes variable selection/ranking using linear and nonlinear methods, feature extraction using functional principal component analysis and Euler characteristics, and joint integration and classification using dense feed-forward networks for cross-sectional data and recurrent neural networks for longitudinal data. We applied this pipeline to cross-sectional and longitudinal multiomics data (metagenomics, transcriptomics and metabolomics) from an inflammatory bowel disease (IBD) study and identified microbial pathways, metabolites and genes that discriminate by IBD status, providing information on the etiology of IBD. We conducted simulations to compare the two feature extraction methods.

Evaluating a nurse-led narrative interview intervention with cancer patients with a first diagnosis: A feasibility study

Narrative Medicine and Nursing are clinical-supporting intervention methodologies that aim to enhance professionals’ communication skills and place patients at the heart of their therapeutic path. A narrative interview (NI) is a communication tool between practitioner and patient. The role of NI is debated extensively in the literature, but no studies focus on its use by nurses responsible for first-diagnosed cancer patients.This study aimed to evaluate the feasibility and utility of NI, carried out by Nurses, in managing people recently diagnosed with cancer.A pilot mixed-methods study with before-and-after-intervention evaluation, qualitative longitudinal data analysis, and concurrent data triangulation was conducted. The Mini-Mental Adjustment to Cancer assessed disease adaptation, while the Psychological Distress Inventory investigated psychological distress. The qualitative analysis of the narrative interviews assessed the usefulness of this intervention.13 out of 14 eligible patients took voluntary part in the study. Of those, 9 (69 %) completed T1 and T2 questionnaires and NI. The following five themes emerged from thematic analysis: reaction to the disease, feelings related to the new condition of life, changes, importance of relationships and perception of care. Patients reported being highly stressed and recognized the importance of a supporting social network for better coping with the condition.The adoption of an NI approach at the time of cancer diagnosis is feasible. Due to the limited sample size, it is unclear if NI may positively impact psychological distress in this patient population. Further studies would benefit from the integration of an additional investigation tool aiming to clarify whether NI promotes disease awareness in cancer patients. Furthermore, the recruitment of a larger sample is equally recommended.

Prevalence, incidence, mortality and healthcare resource use for generalized pustular psoriasis, palmoplantar pustulosis and plaque psoriasis in England: a population-based cohort study.

Generalized pustular psoriasis (GPP) and palmoplantar pustulosis (PPP) are chronic inflammatory skin conditions. Accumulating evidence shows that GPP and PPP have different characteristics to plaque psoriasis and are distinct clinical entities. To assess the epidemiology, comorbidities, mortality and healthcare use for patients in England with GPP and PPP versus those with plaque psoriasis. We carried out a cohort study involving analyses of longitudinal electronic health record data in the Clinical Practice Research Datalink Aurum database and linked hospital and mortality data between 2008 and 2019. The primary study outcome was the incidence and prevalence rates of GPP, PPP and plaque psoriasis in England. Secondary outcomes included survival rates and healthcare resource use (HCRU) by disease type. We identified 373 patients with GPP, 1828 with PPP and 224 223 with plaque psoriasis. Mean (SD) age was 55.9 (18.6) years for patients with GPP, 51.5 (16.4) years for those with PPP and 48.5 (19.1) years for those with plaque psoriasis; 62.5% and 65.9% of patients with GPP and PPP, respectively, were women, vs. 49.4% of those with plaque psoriasis. About half of patients were overweight or obese at baseline (GPP 48.6%, PPP 56.0%, plaque psoriasis 45.9%). The incidence rates for GPP, PPP and plaque psoriasis were 0.25 [95% confidence interval (CI) 0.21-0.28], 2.01 (95% CI 1.92-2.11) and 103.2 (95% CI 102.5-103.9) per 100 000 person-years, respectively. From 2008 to 2019, the prevalence rates per 100 000 persons ranged from 1.61 to 3.0 for GPP, from 1.1 to 18.7 for PPP and from 1771.0 to 1903.8 for plaque psoriasis. Survival rates were lower for patients with GPP, particularly those who were > 55 years of age and those with a history of one or more comorbidities in each cohort. HCRU was lower in the cohort with plaque psoriasis and highest in the cohort with GPP, particularly among those who had more than one GPP flare. Our results provide further evidence that, in England, GPP is a distinct disease with different epidemiology, lower survival and higher HCRU than plaque psoriasis.

Methionine as a regulator of bone remodeling with fasting.

Caloric restriction improves metabolic health but is often complicated by bone loss. We studied bone parameters in humans during a 10-day fast and identified candidate metabolic regulators of bone turnover. Pro-collagen 1 intact N-terminal pro-peptide (P1NP), a bone formation marker, decreased within 3 days of fasting. Whereas dual-energy x-ray absorptiometry measures of bone mineral density were unchanged after 10 days of fasting, high-resolution peripheral quantitative CT demonstrated remodeling of bone microarchitecture. Pathway analysis of longitudinal metabolomics data identified one-carbon metabolism as fasting dependent. In cultured osteoblasts, we tested the functional significance of one-carbon metabolites modulated by fasting, finding that methionine - which surged after 3 days of fasting - affected markers of osteoblast cell state in a concentration-dependent manner, in some instances exhibiting a U-shaped response with both low and high concentrations driving putative antibone responses. Administration of methionine to mice for 5 days recapitulated some fasting effects on bone, including a reduction in serum P1NP. In conclusion, a 10-day fast in humans led to remodeling of bone microarchitecture, potentially mediated by a surge in circulating methionine. These data support an emerging model that points to a window of optimal methionine exposure for bone health.

Recommendations on Methods for Assessing Multimorbidity Changes Over Time: Aligning the Method to the Purpose.

The rapidly growing field of multimorbidity research demonstrates that changes in multimorbidity in mid- and late-life have far reaching effects on important person-centered outcomes, such as health-related quality of life. However, there are few organizing frameworks and comparatively little work weighing the merits and limitations of various quantitative methods applied to the longitudinal study of multimorbidity. We identify and discuss methods aligned to specific research objectives with the goals of (i) establishing a common language for assessing longitudinal changes in multimorbidity, (ii) illuminating gaps in our knowledge regarding multimorbidity progression and critical periods of change, and (iii) informing research to identify groups that experience different rates and divergent etiological pathways of disease progression linked to deterioration in important health-related outcomes. We review practical issues in the measurement of multimorbidity, longitudinal analysis of health-related data, operationalizing change over time, and discuss methods that align with 4 general typologies for research objectives in the longitudinal study of multimorbidity: (i) examine individual change in multimorbidity, (ii) identify subgroups that follow similar trajectories of multimorbidity progression, (iii) understand when, how, and why individuals or groups shift to more advanced stages of multimorbidity, and (iv) examine the coprogression of multimorbidity with key health domains. This work encourages a systematic approach to the quantitative study of change in multimorbidity and provides a valuable resource for researchers working to measure and minimize the deleterious effects of multimorbidity on aging populations.

Challenges and directions for digital twin implementation in otorhinolaryngology.

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.

MD3F: Multivariate Distance Drift Diffusion Framework for High-Dimensional Datasets.

High-dimensional biomedical datasets have become easier to collect in the last two decades with the advent of multi-omic and single-cell experiments. These can generate over 1000 measurements per sample or per cell. More recently, focus has been drawn toward the need for longitudinal datasets, with the appreciation that important dynamic changes occur along transitions between health and disease. Analysis of longitudinal omics data comes with many challenges, including type I error inflation and corresponding loss in power when thousands of hypothesis tests are needed. Multivariate analysis can yield approaches with higher statistical power; however, multivariate methods for longitudinal data are currently limited. We propose a multivariate distance-based drift-diffusion framework (MD3F) to tackle the need for a multivariate approach to longitudinal, high-throughput datasets. We show that MD3F can result in surprisingly simple yet valid and powerful hypothesis testing and estimation approaches using generalized linear models. Through simulation and application studies, we show that MD3F is robust and can offer a broadly applicable method for assessing multivariate dynamics in omics data.

The well-being equation: How inner fulfilment drives the impact of older persons on the Ghanaian society.

Understanding and managing the complex processes of ageing is a critical function of gerontological nursing, especially when it comes to older people's well-being and their contributions to society. Globally, older persons contribute in many ways to families and communities. However, the relationship between older person's overall well-being and their propensity to contribute to society remains an important gap in research. The study examined the association between well-being and the impact of older persons on Ghanaian society. A secondary analysis of longitudinal survey data of the 2014/15 Study on Global Ageing and Adult Health (SAGE Wave 2) conducted by the World Health Organization was used. The multilevel logistic regression technique was used to examine four dimensions of well-being and their associations with high social contribution among older persons. The output was reported as odds ratios (OR). The results show that older persons who had high physical and psychological well-being were more likely to contribute to society (OR = 1.25, 95% CI = 0.93, 1.68), (OR = 1.75, 95% CI = 1.32, 2.33). However, those with high levels of emotional and spiritual well-being were less likely to make social contributions (OR = 0.66, 95% CI = 0.49, 0.88), (OR = 0.88, 95% CI = 0.66, 1.18). This study shows a positive association between well-being and older persons' societal impact. Good mental and physical health encourage societal involvement among older persons, while high emotional and spiritual well-being may lead to less societal contribution. These findings are important for nursing policies promoting social contribution and well-being among older persons 60 years and over in Ghana.

Interventional analytics in skilled nursing facilities associated with reduced readmissions.

To assess differences in longitudinal profiles for 30-day risk-adjusted readmission rates in skilled nursing facilities (SNFs) associated with Penn Medicine's Lancaster General Hospital (LGH) that implemented an interventional analytics (IA) platform vs other LGH facilities lacking IA vs other SNFs in Pennsylvania vs facilities in all other states. Retrospective longitudinal analysis of CMS readmissions data from 2017 through 2022, and cross-sectional analysis using CMS quality metrics data. CMS SNF quality performance data were aggregated and compared with risk-adjusted readmissions by facility and time period. Each SNF was assigned to a cohort based on location, referral relationship with LGH, and whether it had implemented IA. Multivariable mixed effects modeling was used to compare readmissions by cohort, whereas quality measures from the fourth quarter of 2022 were compared descriptively. LGH profiles differed significantly from both state and national profiles, with LGH facilities leveraging IA demonstrating an even greater divergence. In the most recent 12 months ending in the fourth quarter of 2022, LGH SNFs with IA had estimated readmission rates that were 15.24, 12.30, and 13.06 percentage points lower than the LGH SNFs without IA, Pennsylvania, and national cohorts, respectively (all pairwise P < .0001). SNFs with IA also demonstrated superior CMS claims-based quality metric outcomes for the 12 months ending in the fourth quarter of 2022. SNFs implementing the studied IA platform demonstrated statistically and clinically significant superior risk-adjusted readmission rate profiles compared with peers nationally, statewide, and within the same SNF referral network (P < .0001). A more detailed study on the use of IA in this setting is warranted.

Disease Course of Clinically Isolated Optic Neuritis.

Optic neuritis is the most common optic neuropathy in young adults and a frequent manifestation of multiple sclerosis. Its clinical course is pertinent to the design of visual pathway neuroprotection trials. This is a secondary analysis of longitudinal data from the TONE trial, which included 103 patients from 12 German academic tertiary centers with acute unilateral optic neuritis as a clinically isolated syndrome and baseline high-contrast visual acuity <0.5 decimal. Patients were randomized to 1,000 mg methylprednisolone i.v./d plus either erythropoietin (33,000 IU/d) or placebo (saline solution) for 3 days. They were followed up at standardized intervals with a battery of tests including high-contrast visual acuity, low-contrast letter acuity, contrast sensitivity, visual fields, visual evoked potentials, and retinal optical coherence tomography. At 6 months, participants answered a standardized questionnaire on vision-related quality of life (NEI-VFQ 25). We describe the disease course with mixed-effects piecewise linear models and calculate structure-function correlations using Pearson r. Because erythropoietin had no effect on the visual system, we use pooled (treatment-agnostic) data. Patients experienced initial rapid and then decelerating improvements of visual function with thinning of inner and thickening of outer retinal layers. At 6 months, visual parameters were positively correlated with inner and negatively correlated with outer retinal thickness changes. Peripapillary retinal nerve fiber layer thinning predominantly occurred in sectors without previous swelling. At 6 months, macular ganglion cell and inner plexiform layer thinning was weakly correlated with the P100 peak time (r = -0.11) and moderately correlated with the amplitude of visual evoked potentials (r = 0.35). Only functional outcomes were at least moderately correlated with vision-related quality of life. The longitudinal data from this large study cohort may serve as a reference for the clinical course of acute optic neuritis. The pattern of correlation between visual evoked potentials and inner retinal thinning may argue that the latter is mostly due to ganglion cell loss, rather than dysfunction. Visual pathway neuroprotection trials with functional outcomes are needed to confirm that candidate drugs will benefit patients' vision-related quality of life. ClinicalTrials.gov, NCT01962571.

Factors Associated With Change in S3-NIV Score Over Time in People With Chronic Respiratory Failure Treated With Long-Term Home Noninvasive Ventilation

IntroductionMonitoring changes in symptoms over time during long-term nocturnal home non-invasive ventilation (NIV) using patient-reported outcome measures is crucial. This study aimed to identify factors associated with changes in the S3-NIV total score, its two domains (“respiratory symptoms” and “sleep and NIV-related side effects”) and individual item responses. MethodsWe conducted a retrospective, longitudinal data analysis of a cohort of adults with chronic respiratory failure treated with NIV. Data were obtained from a French homecare provider. Multivariate linear and multinomial ordinal mixed effect models were used to identify factors associated with changes in S3-NIV scores over time. ResultsMedian follow-up was 2 years for 2135 participants. Each participant completed a median of five S3-NIV questionnaires; totaling 11,359 analyzed questionnaires. Type of respiratory condition, sex, age and time since NIV initiation were associated with change in S3-NIV score over time. NIV adherence was not associated with total S3-NIV score but high adherence was associated with more severe respiratory symptoms and an improvement in sleep and NIV-related side effects during the follow-up. Intensity of pressure support was associated with a lower total S3-NIV score and more side effects. Face masks and supplemental oxygen were associated with a lower S3-NIV total score. ConclusionChanges in S3-NIV scores over time are associated with the individual's characteristics and NIV settings. Analysis of the two domains and individual items of the S3-NIV could increase understanding of the difficulties experienced by people on NIV.

Predicting the Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Using an Explainable AI Approach

Mild Cognitive Impairment (MCI) is a cognitive state frequently observed in older adults, characterized by significant alterations in memory, thinking, and reasoning abilities that extend beyond typical cognitive decline. It is worth noting that around 10–15% of individuals with MCI are projected to develop Alzheimer’s disease, effectively positioning MCI as an early stage of Alzheimer’s. In this study, a novel approach is presented involving the utilization of eXtreme Gradient Boosting to predict the onset of Alzheimer’s disease during the MCI stage. The methodology entails utilizing data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Through the analysis of longitudinal data, spanning from the baseline visit to the 12-month follow-up, a predictive model was constructed. The proposed model calculates, over a 36-month period, the likelihood of progression from MCI to Alzheimer’s disease, achieving an accuracy rate of 85%. To further enhance the precision of the model, this study implements feature selection using the Recursive Feature Elimination technique. Additionally, the Shapley method is employed to provide insights into the model’s decision-making process, thereby augmenting the transparency and interpretability of the predictions.

Differential sex-related effect of left ventricular ejection fraction trajectory on the risk of mortality and heart failure readmission following hospitalization for acute heart failure: A longitudinal study.

There is limited information on the sex-specific longitudinal changes of left ventricular ejection fraction (LVEF) after an acute heart failure (AHF) hospitalization. We aimed to investigate whether LVEF trajectories over time and their impact on mortality and AHF readmission rates differ between men and women. We conducted a retrospective sex-specific analysis of longitudinal LVEF measurements (n = 9581) in 3383 patients with an index hospitalization for AHF in a single tertiary-level hospital. Statistical techniques suited for longitudinal data analysis were used. The mean age of the sample was 73.8 ± 11.2 years, and 47.9% were women. The mean LVEF was 49.4 ± 15.3%. At a median follow-up of 2.58 years (interquartile range 0.77-5.62), we registered 2197 deaths (64.9%) and 2597 AHF readmissions in 1302 (38.5%) patients. The longitudinal analysis showed that women had consistently higher LVEF values throughout the follow-up with both trajectories characterized by an early peak-approximately at 1 year-followed by decreasing values in men but a plateau in women. Multivariate between-sex comparisons across LVEF categories revealed that women had lower rates of AHF readmissions when LVEF ≤40%. On the contrary, women displayed an excess risk of AHF readmissions when LVEF >60%. A trend in the same direction was found for cardiovascular and all-cause mortality. Sex was a significant factor in determining the follow-up trajectory of LVEF and predicting differences in outcomes after an AHF admission. The findings suggest that women have a higher risk of AHF readmissions at higher LVEF values, while men have a higher risk at lower LVEF values. For all-cause and cardiovascular mortality, the same direction of the association was inferred but they were not significant.

Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas.

There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method's value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial.

Trends and determinants of mental illness in humanitarian migrants resettled in Australia: Analysis of longitudinal data.

Given the frequent exposure of humanitarian migrants to traumatic or stressful circumstances, there exists a potential predisposition to mental illness. Our objective was to pinpoint the trends and determinants of mental illness among humanitarian migrants resettled in Australia. This study considered five waves of longitudinal data involving humanitarian migrants resettled in Australia. Post-traumatic stress disorder (PTSD) and psychological distress were assessed using PTSD-8 and Kessler-6 screening tools. Through a Generalised Linear Mixed model (GLMM), variables displaying a 95% CI that excluded the value of 1.0 for the odds ratio were identified as associated factors for both PTSD and elevated psychological distress. The selection of multivariable covariates was guided by causal loop diagrams and least absolute shrinkage and selection operators methods. At baseline, there were 2399 humanitarian migrants with 1881 retained and at the fifth yearly wave; the response rate was 78.4%. PTSD prevalence decreased from 33.3% (95% CI: 31.4-35.3) at baseline to 28.3% (95% CI: 26.2-30.5) at year 5. Elevated psychological distress persisted across all waves: 17.1% (95% CI: 15.5-18.6) at baseline and 17.1% (95% CI: 15.3-18.9) at year 5. Across the five waves, 34.0% of humanitarian migrants met screening criteria for mental illness, either PTSD or elevated psychological distress. In the multivariate model, factors associated with PTSD were loneliness (AOR 1.5, 95% CI: 1.3-1.8), discrimination (AOR 1.6: 1.2-2.1), temporary housing contract (AOR 3.7: 2.1-6.7), financial hardship (AOR 2.2:1.4-3.6) and chronic health conditions (AOR 1.3: 1.1-1.5), whereas the associated factors for elevated psychological distress were loneliness (AOR 1.8: 1.5-2.2), discrimination (AOR 1.7: 1.3-2.2) and short-term lease housing (AOR 1.6: 1.0-1.7). The prevalence, persistence and consequential burden of mental illness within this demographic underscore the urgent need for targeted social and healthcare policies. These policies should aim to mitigate modifiable risk factors, thereby alleviating the significant impact of mental health challenges on this population.

Psychological and physical factors related to social integration of older adults in Ghana

BackgroundGlobally, older adults aged 60 years and over are outnumbering children under 5 and young people aged 15–24. Much evidence exists on the importance of high social integration and positive quality of life and health outcomes. However, evidence on how older adults are socially integrated in Ghanaian communities is limited. This study examined how self-reported well-being and quality of life (psychological and physical (psycho-physical) factors) predict the social integration of older adults in Ghana.MethodsA secondary analysis of longitudinal survey data of the 2014/15 Study on Global Ageing and Adult Health (SAGE Wave 2) conducted by the World Health Organization was applied. Older adults aged 60 years and older (n = 1,927) were included in this study. Multilevel logistic regression analyses were used to examine psycho-physical factors associated with high social integration among older adults. The output was reported as odds ratios (OR).ResultsIn general, social integration varied based on older adults’ demographic characteristics. Those in rural communities had lower odds of having high social integration (OR = 0.76, 95% CI = 0.56,1.03) when compared with older adults in urban areas. Having high physical and psychological well-being was associated with high social integration (OR = 1.90, 95% CI = 1.41, 2.57), (OR = 2.05, 95% CI = 1.56, 2.69). However, older adults with high levels of emotional and spiritual well-being were 9% and 7% (respectively) less likely to experience a high level of social integration (OR = 0.94, 95% CI = 0.71,1.24), (OR = 0.79, 95% CI = 0.60,1.04).ConclusionThe higher the level of self-reported psychological and physical well-being, the higher the social integration for older adults aged 60 years and over. However, the higher the level of self-reported emotional well-being and spiritual well-being, the less likely to have high social integration. Improved social integration or participation in society for older adults with high emotional and spiritual well-being is needed. The findings of this study highlight the need for policymakers and stakeholders to consider psycho-physical factors as an important public health tool and metric to encourage more research on the well-being of older adults in Ghana.

A review of common methods used in the analysis of human microbiome sequencing data

The past two decades have seen a rapid rise in the development and use of sequencing technologies. The advent of high-throughput sequencing (HTS) has allowed scientists sequence the genomic content of entire microbial communities rapidly and affordably. Methods for data management, visualization, and analysis of this microbiome sequencing data are now widespread in open and closed source software tools. In this review, we identify common analysis methods used in microbiome sequencing studies, including methods for normalisation of abundance table data, visualization of high-dimensional microbiome data and the analysis of longitudinal microbiome sequencing data. In particular we find that a very small proportion of metagenomic studies made use of compositional data analysis methods and none that made use of compositional methods for longitudinal microbiome data.