Nevin Manimala

BMJ Open. 2021 May 4;11(5):e044902. doi: 10.1136/bmjopen-2020-044902.

ABSTRACT

INTRODUCTION: Some empirical studies have identified an association between informal caregiving for adults and loneliness or social isolation. However, there is a lack of a review systematically synthesising empirical studies that have examined these associations. Hence, the aim of this systematic review is to provide an overview of evidence from observational studies.

METHODS AND ANALYSIS: Three electronic databases (Medline, PsycINFO, CINAHL) will be searched (presumably in May 2021), and reference lists of included studies will be searched manually. Cross-sectional and longitudinal observational studies examining the association between informal caregiving for adults and loneliness or social isolation will be included. Studies focusing on grandchildren care or private care for chronically ill children will be excluded. Data extraction will include information related to study design, definition and measurement of informal caregiving, loneliness and social isolation, sample characteristics, statistical analysis and main results. The quality of the studies will be evaluated using the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Two reviewers will perform the selection of studies, data extraction and assessment of study quality. Figures and tables will be used to summarise and report results. A narrative summary of the findings will be provided. If data permit, a meta-analysis will be conducted.

ETHICS AND DISSEMINATION: No primary data will be collected. Therefore, approval by an ethics committee is not required. We plan to publish our findings in a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER: CRD42020193099.

PMID:33947734 | DOI:10.1136/bmjopen-2020-044902

BMJ Open. 2021 May 4;11(5):e044170. doi: 10.1136/bmjopen-2020-044170.

ABSTRACT

OBJECTIVE: To systematically review and critically appraise prognostic models for falls in community-dwelling older adults.

ELIGIBILITY CRITERIA: Prospective cohort studies with any follow-up period. Studies had to develop or validate multifactorial prognostic models for falls in community-dwelling older adults (60+ years). Models had to be applicable for screening in a general population setting.

INFORMATION SOURCE: MEDLINE, EMBASE, CINAHL, The Cochrane Library, PsycINFO and Web of Science for studies published in English, Danish, Norwegian or Swedish until January 2020. Sources also included trial registries, clinical guidelines, reference lists of included papers, along with contacting clinical experts to locate published studies.

DATA EXTRACTION AND RISK OF BIAS: Two authors performed all review stages independently. Data extraction followed the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Risk of bias assessments on participants, predictors, outcomes and analysis methods followed Prediction study Risk Of Bias Assessment Tool.

RESULTS: After screening 11 789 studies, 30 were eligible for inclusion (n=86 369 participants). Median age of participants ranged from 67.5 to 83.0 years. Falls incidences varied from 5.9% to 59%. Included studies reported 69 developed and three validated prediction models. Most frequent falls predictors were prior falls, age, sex, measures of gait, balance and strength, along with vision and disability. The area under the curve was available for 40 (55.6%) models, ranging from 0.49 to 0.87. Validated models’ The area under the curve ranged from 0.62 to 0.69. All models had a high risk of bias, mostly due to limitations in statistical methods, outcome assessments and restrictive eligibility criteria.

CONCLUSIONS: An abundance of prognostic models on falls risk have been developed, but with a wide range in discriminatory performance. All models exhibited a high risk of bias rendering them unreliable for prediction in clinical practice. Future prognostic prediction models should comply with recent recommendations such as Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis.

PROSPERO REGISTRATION NUMBER: CRD42019124021.

PMID:33947733 | DOI:10.1136/bmjopen-2020-044170

Proteomics. 2021 May 4:e2000278. doi: 10.1002/pmic.202000278. Online ahead of print.

ABSTRACT

In managing patients with coronavirus disease 2019 (COVID-19), early identification of those at high risk and real-time monitoring of disease progression to severe COVID-19 is a major challenge. We aimed to identify potential early prognostic protein markers and to expand understanding of proteome dynamics during clinical progression of the disease. We performed in-depth proteome profiling on 137 sera, longitudinally collected from 25 patients with COVID-19 (non-severe patients, n = 13; patients who progressed to severe COVID-19, n = 12). We identified 11 potential biomarkers, including the novel markers IGLV3-19 and BNC2, as early potential prognostic indicators of severe COVID-19. These potential biomarkers are mainly involved in biological processes associated with humoral immune response, interferon signalling, acute phase response, lipid metabolism, and platelet degranulation. We further revealed that the longitudinal changes of 40 proteins persistently increased or decreased as the disease progressed to severe COVID-19. These 40 potential biomarkers could effectively reflect the clinical progression of the disease. Our findings provide some new insights into host response to SARS-CoV-2 infection, which are valuable for understanding of COVID-19 disease progression. This study also identified potential biomarkers that could be further validated, which may support better predicting and monitoring progression to severe COVID-19. This article is protected by copyright. All rights reserved.

PMID:33945677 | DOI:10.1002/pmic.202000278

Acta Ophthalmol. 2021 May;99(3):260-268. doi: 10.1111/aos.14553. Epub 2020 Aug 9.

ABSTRACT

PURPOSE: To compare the effects of ageing and glaucoma progression on the thickness of the circumpapillary retinal nerve fibre layer (cpRNFL) and to evaluate the performance of a set of optical coherence tomography (OCT) progression analyses.

METHODS: The cpRNFL was measured twice by OCT at each of two visits made 10 years apart in 69 healthy individuals and 49 glaucoma patients. Both visits also included Humphrey 24-2 SITA standard testing. The change in cpRNFL thickness was analysed by linear regression, and a sub-analysis was performed on glaucoma patients with a perimetric mean deviation better than -10 dB at the first visit. The proportion of individuals whose OCT progression analyses indicated progression was also evaluated for the same groups.

RESULTS: The average cpRNFL thickness deteriorated by a mean of -0.16 μm/year in the healthy cohort, increased by 0.03 μm/year in the glaucoma cohort, and deteriorated by -0.24 μm/year in eyes with less severe glaucoma; there were no statistically significant differences between the groups. For 17 (30%) of 56 healthy individuals, at least one of the three different OCT progression analyses incorrectly indicated progression.

CONCLUSIONS: No significant differences in change of cpRNFL thickness between visits were found when comparing healthy subjects with glaucoma patients. Also, further cpRNFL thinning was not observed in glaucomatous eyes in which at least one-third of the visual field had been lost. The OCT progression analyses generated a relatively high proportion of false positives. Using OCT for glaucoma follow-up may not be entirely straightforward.

PMID:33945669 | DOI:10.1111/aos.14553

Immun Inflamm Dis. 2021 May 4. doi: 10.1002/iid3.444. Online ahead of print.

ABSTRACT

INTRODUCTION: CD4 count is an important predictor of disease progression, opportunities infection, deaths, and to understand the time interval between initial HIV infection to the first diagnosis. However, baseline CD4 count and the time period between initial infection and the diagnosis amongst PLHIV in Bhutan never been evaluated.

METHODS: This is a retrospective study of the diagnosed PLHIV from the existing data system from January 10 to 30, 2021. Out of 512 reported HIV cases, 488 of those who were more than or equal to 18 years old and had their CD4 count testing within 6 months before initiating ART were considered for analysis. Descriptive statistical analysis was used to analyze the characteristics of the study population and relationship were established using the χ² Test. We have sought ethics approval and waiver for informed consent as it is the retrospective study of the client’s record. The client’s confidentiality was ensured by removing all the identifiers.

RESULTS: The mean CD4 was 345 cells/ml for males and females. Twenty-five percent of the clients had CD4 counts below 200, 30%, between 200 and 349, 25% between 350 and 499, and 20% above 500 cells/ml. A larger number of males showed a CD4 count below 200 cells/ml while more females showed a CD4 count more than 500 cells/ml. The mean time interval between initial infection to the first diagnosis was 4 years in males and females. However, about one-fourth were found to have been infected between 5 and 8 years before diagnosis and less than 10% were diagnosed within less than 1 year of infection.

CONCLUSIONS: The study revealed a late diagnosis of HIV infection in Bhutan thereby risking the transmission to the community and risk of severe disease and mortality. The upscaling of voluntary counseling and testing, medical screening, and alternative methods like community-based testing including HIV Self Testing for early detection needs to be implemented in the country.

PMID:33945664 | DOI:10.1002/iid3.444

Ecol Lett. 2021 May 4. doi: 10.1111/ele.13760. Online ahead of print.

ABSTRACT

Ecological stability refers to a family of concepts used to describe how systems of interacting species vary through time and respond to disturbances. Because observed ecological stability depends on sampling scales and environmental context, it is notoriously difficult to compare measurements across sites and systems. Here, we apply stochastic dynamical systems theory to derive general statistical scaling relationships across time, space, and ecological level of organisation for three fundamental stability aspects: resilience, resistance, and invariance. These relationships can be calibrated using random or representative samples measured at individual scales, and projected to predict average stability at other scales across a wide range of contexts. Moreover deviations between observed vs. extrapolated scaling relationships can reveal information about unobserved heterogeneity across time, space, or species. We anticipate that these methods will be useful for cross-study synthesis of stability data, extrapolating measurements to unobserved scales, and identifying underlying causes and consequences of heterogeneity.

PMID:33945663 | DOI:10.1111/ele.13760

J Gerontol B Psychol Sci Soc Sci. 2021 May 4:gbab079. doi: 10.1093/geronb/gbab079. Online ahead of print.

ABSTRACT

OBJECTIVES: Social relationships are important for older adults’ well-being, including those who live in assisted living (AL) communities. This study explores co-resident networks within an AL community and identifies factors associated with residents’ social ties.

METHODS: Acquaintance and companionship networks within the community are described using cross-sectional survey data (N=38). We use inferential network statistical methods to estimate parameters for factors associated with residents’ acquaintance and companionship ties.

RESULTS: Residents reported an average of 10 acquaintances and almost four companionships with other residents in the sample. The likelihood a resident had an acquaintance was associated with higher levels of cognitive functioning (p<.05), higher levels of physical limitations (p<.01), living in the AL community for a longer time (p<.01), and less frequent contact with outside family and friends (p<.05). Acquaintances were more likely between residents who moved in around the same time as each other (p<.01), lived on the same floor (p<.001), or had similar levels of physical limitations (p<.05). Companionships were more likely to be reported by male residents (p<.05) and residents with higher levels of cognitive functioning (p<.05) or depressive symptoms (p<.05). Longtime residents were more popular as companions (p<.01). Companionships were more likely between residents who lived on the same floor (p<.001) or were similar in age (p<.01).

DISCUSSION: This research contributes to the literature of older adults’ non-kin social relationships by providing detailed descriptions of the acquaintance and companionship networks within an AL community, quantifying correlates of residents’ social ties, and distinguishing between acquaintances and companions.

PMID:33945609 | DOI:10.1093/geronb/gbab079

PLoS One. 2021 May 4;16(5):e0251152. doi: 10.1371/journal.pone.0251152. eCollection 2021.

ABSTRACT

PURPOSE: To compare the biometry and prediction of postoperative refractive outcomes of four different formulae (Haigis, SRK/T, Holladay1, Barrett Universal II) obtained by swept-source optical coherence tomography (SS-OCT) biometers and partial coherence interferometry (PCI; IOLMaster ver 5.4).

METHODS: We compared the biometric values of SS-OCT (ANTERION, Heidelberg Engineering Inc., Heidelberg, Germany) and PCI (IOLMaster, Carl Zeiss Meditec, Jena, Germany). Predictive errors calculated using four different formulae (Haigis, SRKT, Holladay1, Barrett Universal II) were compared at 1 month after cataract surgery.

RESULTS: The mean preoperative axial length (AL) showed no statistically significant difference between SS-OCT and PCI (SS-OCT: 23.78 ± 0.12 mm and PCI: 23.77 ± 0.12 mm). The mean anterior chamber depth (ACD) was 3.30 ± 0.04 mm for SS-OCT and 3.23 ± 0.04 mm for PCI, which was significantly different between the two techniques. The mean corneal curvature also differed significantly between the two techniques. The difference in mean arithmetic prediction error was significant in the Haigis, SRKT, and Holladay1 formulae. The difference in mean absolute prediction error was significant in all four formulae.

CONCLUSIONS: SS-OCT and PCI demonstrated good agreement on biometric measurements; however, there were significant differences in some biometric values. These differences in some ocular biometrics can cause a difference in refractive error after cataract surgery. New type SS-OCT was not superior to the IOL power prediction calculated by PCI.

PMID:33945581 | DOI:10.1371/journal.pone.0251152

PLoS One. 2021 May 4;16(5):e0250985. doi: 10.1371/journal.pone.0250985. eCollection 2021.

ABSTRACT

BACKGROUND: Sleep is a physiologic necessity for all humankind. Pregnant women, in particular, need adequate sleep to develop their fetuses as well as save energy required for delivery. A change in sleep quality and quantity is the most common phenomena during pregnancy due to mechanical and hormonal factors. However, there is a scarcity of data about poor sleep quality and its associated factors among pregnant mothers in Ethiopia. Therefore, this study aims to determine the prevalence of poor sleep quality and its associated factors among pregnant mothers at Wadila primary hospital, Ethiopia.

METHODS: Institution based cross-sectional study design was employed on 411 pregnant mothers. Data were collected using a pre-tested interviewer administered questionnaire. SPSS Version 23 for Windows software was used for data analyses. Bivariate analysis was conducted to detect the association between dependent and independent variables, and to choose candidate variables (p < 0.25) for multivariate logistic regression. Statistical significance was set at p-value <0.05.

RESULTS: A total of 411 participants were included in the study making a response rate of 97.4%. Overall, 68.4% of participants found to have poor sleep quality (PSQI>5). Age of the mother [age 20-30 years; AOR = 4.3 CI (1.8, 9.9), p = 0.001, and age >30 years; AOR = 4.7 CI (1.6, 13.9) p = 0.005], gestational age [second trimester, AOR = 2.46, CI (1.2, 4.9), p = 0.01 and third trimester, AOR = 7.5, CI (3.2, 17.8), p = 0.000] and parity [multiparous women; AOR = 2.1(1.24, 3.6) p = 0.006] were predictor variables for poor sleep quality among pregnant mothers.

CONCLUSION: More than two-third of pregnant mothers had poor sleep quality. Advanced maternal age, increased gestational age and multiparty are found to be predictors of poor sleep quality in pregnant women.

PMID:33945578 | DOI:10.1371/journal.pone.0250985

PLoS One. 2021 May 4;16(5):e0250842. doi: 10.1371/journal.pone.0250842. eCollection 2021.

ABSTRACT

BACKGROUND: Occupational stress is associated with adverse outcomes for medical professionals and patients. In our cross-sectional study with 136 general practices, 26.4% of 550 practice assistants showed high chronic stress. As machine learning strategies offer the opportunity to improve understanding of chronic stress by exploiting complex interactions between variables, we used data from our previous study to derive the best analytic model for chronic stress: four common machine learning (ML) approaches are compared to a classical statistical procedure.

METHODS: We applied four machine learning classifiers (random forest, support vector machine, K-nearest neighbors’, and artificial neural network) and logistic regression as standard approach to analyze factors contributing to chronic stress in practice assistants. Chronic stress had been measured by the standardized, self-administered TICS-SSCS questionnaire. The performance of these models was compared in terms of predictive accuracy based on the ‘operating area under the curve’ (AUC), sensitivity, and positive predictive value.

FINDINGS: Compared to the standard logistic regression model (AUC 0.636, 95% CI 0.490-0.674), all machine learning models improved prediction: random forest +20.8% (AUC 0.844, 95% CI 0.684-0.843), artificial neural network +12.4% (AUC 0.760, 95% CI 0.605-0.777), support vector machine +15.1% (AUC 0.787, 95% CI 0.634-0.802), and K-nearest neighbours +7.1% (AUC 0.707, 95% CI 0.556-0.735). As best prediction model, random forest showed a sensitivity of 99% and a positive predictive value of 79%. Using the variable frequencies at the decision nodes of the random forest model, the following five work characteristics influence chronic stress: too much work, high demand to concentrate, time pressure, complicated tasks, and insufficient support by practice leaders.

CONCLUSIONS: Regarding chronic stress prediction, machine learning classifiers, especially random forest, provided more accurate prediction compared to classical logistic regression. Interventions to reduce chronic stress in practice personnel should primarily address the identified workplace characteristics.

PMID:33945572 | DOI:10.1371/journal.pone.0250842