Tag: statistics

Qual Manag Health Care. 2023 Jan-Mar 01;32(Suppl 1):S35-S44. doi: 10.1097/QMH.0000000000000396.

ABSTRACT

BACKGROUND AND OBJECTIVES: Although at-home coronavirus disease-2019 (COVID-19) testing offers several benefits in a relatively cost-effective and less risky manner, evidence suggests that at-home COVID-19 test kits have a high rate of false negatives. One way to improve the accuracy and acceptance of COVID-19 screening is to combine existing at-home physical test kits with an easily accessible, electronic, self-diagnostic tool. The objective of the current study was to test the acceptability and usability of an artificial intelligence (AI)-enabled COVID-19 testing tool that combines a web-based symptom diagnostic screening survey and a physical at-home test kit to test differences across adults from varying races, ages, genders, educational, and income levels in the United States.

METHODS: A total of 822 people from Richmond, Virginia, were included in the study. Data were collected from employees and patients of Virginia Commonwealth University Health Center as well as the surrounding community in June through October 2021. Data were weighted to reflect the demographic distribution of patients in United States. Descriptive statistics and repeated independent t tests were run to evaluate the differences in the acceptability and usability of an AI-enabled COVID-19 testing tool.

RESULTS: Across all participants, there was a reasonable degree of acceptability and usability of the AI-enabled COVID-19 testing tool that included a physical test kit and symptom screening website. The AI-enabled COVID-19 testing tool demonstrated overall good acceptability and usability across race, age, gender, and educational background. Notably, participants preferred both components of the AI-enabled COVID-19 testing tool to the in-clinic testing.

CONCLUSION: Overall, these findings suggest that our AI-enabled COVID-19 testing approach has great potential to improve the quality of remote COVID testing at low cost and high accessibility for diverse demographic populations in the United States.

PMID:36579707 | DOI:10.1097/QMH.0000000000000396

Qual Manag Health Care. 2023 Jan-Mar 01;32(Suppl 1):S29-S34. doi: 10.1097/QMH.0000000000000397.

ABSTRACT

BACKGROUND AND OBJECTIVES: COVID-19 symptoms change after onset-some show early, others later. This article examines whether the order of occurrence of symptoms can improve diagnosis of COVID-19 before test results are available.

METHODS: In total, 483 individuals who completed a COVID-19 test were recruited through Listservs. Participants then completed an online survey regarding their symptoms and test results. The order of symptoms was set according to (a) whether the participant had a “history of the symptom” due to a prior condition; and (b) whether the symptom “occurred first,” or prior to, other symptoms of COVID-19. Two LASSO (Least Absolute Shrinkage and Selection Operator) regression models were developed. The first model, referred to as “time-invariant,” used demographics and symptoms but not the order of symptom occurrence. The second model, referred to as “time-sensitive,” used the same data set but included the order of symptom occurrence.

RESULTS: The average cross-validated area under the receiver operating characteristic (AROC) curve for the time-invariant model was 0.784. The time-sensitive model had an AROC curve of 0.799. The difference between the 2 accuracy levels was statistically significant (α < .05).

CONCLUSION: The order of symptom occurrence made a statistically significant, but small, improvement in the accuracy of the diagnosis of COVID-19.

PMID:36579706 | DOI:10.1097/QMH.0000000000000397

Biometrics. 2022 Dec 29. doi: 10.1111/biom.13816. Online ahead of print.

ABSTRACT

In recent years, the study of species’ occurrence has benefited from the increased availability of large-scale citizen-science data. Whilst abundance data from standardized monitoring schemes are biased towards well-studied taxa and locations, opportunistic data are available for many taxonomic groups, from a large number of locations and across long timescales. Hence, these data provide opportunities to measure species’ changes in occurrence, particularly through the use of occupancy models, which account for imperfect detection. These opportunistic datasets can be substantially large, numbering hundreds of thousands of sites, and hence present a challenge from a computational perspective, especially within a Bayesian framework. In this paper, we develop a unifying framework for Bayesian inference in occupancy models that account for both spatial and temporal autocorrelation. We make use of the Pólya-Gamma scheme, which allows for fast inference, and incorporate spatio-temporal random effects using Gaussian processes (GPs), for which we consider two efficient approximations: Subset of Regressors and Nearest neighbour GPs. We apply our model to data on two UK butterfly species, one common and widespread and one rare, using records from the Butterflies for the New Millennium database, producing occupancy indices spanning 45 years. Our framework can be applied to a wide range of taxa, providing measures of variation in species’ occurrence, which are used to assess biodiversity change. This article is protected by copyright. All rights reserved.

PMID:36579700 | DOI:10.1111/biom.13816

Psychiatry Clin Neurosci. 2022 Dec 29. doi: 10.1111/pcn.13525. Online ahead of print.

ABSTRACT

AIM: This study aimed to examine the effect of Japanese policies for appropriate hypnotics use and novel hypnotics (e.g., melatonin receptor agonist [MRA] and orexin receptor antagonist [ORA]) on long-term prescriptions of hypnotics.

METHODS: This retrospective study was conducted using a large-scale health insurance claims database. Among subscribers prescribed hypnotics at least once between April 2005 and March 2021, those prescribed hypnotics for the first time after being included in the database in three periods (period 1, April 2012-March 2013; period 2, April 2016-March 2017; and period 3, April 2018-March 2019) were eligible. These were set considering the timing of the 2014 and 2018 medical fee revisions (2014 for polypharmacy of three or more hypnotics, 2018 for long-term prescription of benzodiazepine receptor agonists for >12 months). The duration of consecutive prescriptions of hypnotics over 12 months was evaluated. Factors associated with short-term prescriptions of hypnotics were also investigated.

RESULTS: In total, 186,535 participants were newly prescribed hypnotics. The mean duration of prescriptions was 2.9 months, and 9.3% of participants were prescribed hypnotics for 12 months. Prescription periods were not associated with short-term prescriptions of hypnotics. ORA use was associated with short-term prescriptions of hypnotics (adjusted hazard ratio, 1.077; 95% confidence interval, 1.035-1.120; p<0.001), but MRA use was not.

CONCLUSION: Japanese policies had no statistically significant effect on long-term prescriptions of hypnotics. Although this study suggests initiating ORA for insomniacs as a candidate strategy to prevent long-term prescriptions of hypnotics, further research is necessary to draw conclusions. This article is protected by copyright. All rights reserved.

PMID:36579672 | DOI:10.1111/pcn.13525

J Sleep Res. 2022 Dec 29:e13801. doi: 10.1111/jsr.13801. Online ahead of print.

ABSTRACT

Obstructive sleep apnea is a common disorder that leads to sleep fragmentation and is potentially bidirectionally related to a variety of comorbidities, including an increased risk of heart failure and stroke. It is often considered a consequence of anatomical abnormalities, especially in the head and neck, but its pathophysiology is likely to be multifactorial in origin. With geometric morphometrics, and a large sample of adults from the Study for Health in Pomerania, we explore the association of craniofacial morphology to the apnea-hypopnea index used as an estimate of obstructive sleep apnea severity. We show that craniofacial size and asymmetry, an aspect of morphological variation seldom analysed in obstructive sleep apnea research, are both uncorrelated to apnea-hypopnea index. In contrast, as in previous analyses, we find evidence that brachycephaly and larger nasal proportions might be associated to obstructive sleep apnea severity. However, this correlational signal is weak and completely disappears when age-related shape variation is statistically controlled for. Our findings suggest that previous work might need to be re-evaluated, and urge researchers to take into account the role of confounders to avoid potentially spurious findings in association studies.

PMID:36579627 | DOI:10.1111/jsr.13801

Dis Model Mech. 2022 Dec 29:dmm.049816. doi: 10.1242/dmm.049816. Online ahead of print.

ABSTRACT

Accurate predictions of pathogenicity for mutations associated with genetic disease are key to the success of precision medicine. Inherited, missense coding mutations in the myocilin gene (MYOC), within its olfactomedin (OLF) domain, comprise the strongest genetic link to primary open angle glaucoma via a toxic gain of function, and MYOC is an attractive precision medicine target. However, not all mutations in MYOC cause glaucoma, and common variants are expected to be neutral polymorphisms. The gnomAD database lists ∼100 missense variants documented within OLF, all of which are relatively rare (allele frequency <0.001%) and nearly all are of unknown pathogenicity. To disambiguate disease from benign OLF variants, we first characterized the most prevalent population-based variants using a suite of cellular and biophysical assays, and identify two variants with features of aggregation-prone familial disease variants. Next, we consider all available biochemical and clinical data to demonstrate that pathogenic and benign variants can be differentiated statistically based on a single metric, thermal stability of OLF. Our results motivate genotyping MYOC in patients for clinical monitoring of this widespread, painless, and irreversible age-onset disease.

PMID:36579626 | DOI:10.1242/dmm.049816

Glob Chang Biol. 2022 Dec 29. doi: 10.1111/gcb.16564. Online ahead of print.

ABSTRACT

Intensification of land use by humans has led to a homogenization of landscapes and decreasing resilience of ecosystems globally due to a loss of biodiversity, including the majority of forests. Biodiversity-ecosystem functioning (BEF) research has provided compelling evidence for a positive effect of biodiversity on ecosystem functions and services at the local (α-diversity) scale, but we largely lack empirical evidence on how the loss of between-patch β-diversity affects biodiversity and multifunctionality at the landscape scale (γ-diversity). Here, we present a novel concept and experimental framework for elucidating BEF patterns at α-, β-, and γ-scales in real landscapes at a forest management-relevant scale. We examine this framework using 22 temperate broadleaf production forests, dominated by Fagus sylvatica. In 11 of these forests, we manipulated the structure between forest patches by increasing variation in canopy cover and deadwood. We hypothesized that an increase in landscape heterogeneity would enhance the β-diversity of different trophic levels, as well as the β-functionality of various ecosystem functions. We will develop a new statistical framework for BEF studies extending across scales and incorporating biodiversity measures from taxonomic to functional to phylogenetic diversity using Hill numbers. We will further expand the Hill number concept to multifunctionality allowing the decomposition of γ-multifunctionality into α- and β-components. Combining this analytic framework with our experimental data will allow us to test how an increase in between patch heterogeneity affects biodiversity and multifunctionality across spatial scales and trophic levels to help inform and improve forest resilience under climate change. Such an integrative concept for biodiversity and functionality, including spatial scales and multiple aspects of diversity and multifunctionality as well as physical and environmental structure in forests, will go far beyond the current widely applied approach in forestry to increase resilience of future forests through the manipulation of tree species composition.

PMID:36579623 | DOI:10.1111/gcb.16564

J Magn Reson Imaging. 2022 Dec 29. doi: 10.1002/jmri.28578. Online ahead of print.

ABSTRACT

BACKGROUND: Characterization of the dynamics of functional brain network has gained increased attention in the study of depression. However, most studies have focused on single temporal dimension, while ignoring spatial dimensional information, hampering the discovery of validated biomarkers for depression.

PURPOSE: To integrate temporal and spatial functional MRI variability features of dynamic brain network in machine-learning techniques to distinguish patients with major depressive disorder (MDD) from healthy controls (HCs).

STUDY TYPE: Prospective.

POPULATION: A discovery cohort including 119 patients and 106 HCs and an external validation cohort including 126 patients and 124 HCs from Rest-meta-MDD consortium.

FIELD STRENGTH/SEQUENCE: A 3.0 T/resting-state functional MRI using the gradient echo sequence.

ASSESSMENT: A random forest (RF) model integrating temporal and spatial variability features of dynamic brain networks with separate feature selection method (M_SFS ) was implemented for MDD classification. Its performance was compared with three RF models that used: temporal variability features (M_TVF ), spatial variability features (M_SVF ), and integrated temporal and spatial variability features with hybrid feature selection method (M_HFS ). A linear regression model based on M_SFS was further established to assess MDD symptom severity, with prediction performance evaluated by the correlations between true and predicted scores.

STATISTICAL TESTS: Receiver operating characteristic analyses with the area under the curve (AUC) were used to evaluate models’ performance. Pearson’s correlation was used to assess relationship of predicted scores and true scores. P < 0.05 was considered statistically significant.

RESULTS: The model with M_SFS achieved the best performance, with AUCs of 0.946 and 0.834 in the discovery and validation cohort, respectively. Additionally, altered temporal and spatial variability could significantly predict the severity of depression (r = 0.640) and anxiety (r = 0.616) in MDD.

DATA CONCLUSION: Integration of temporal and spatial variability features provides potential assistance for clinical diagnosis and symptom prediction of MDD.

EVIDENCE LEVEL: 2.

TECHNICAL EFFICACY: Stage 2.

PMID:36579618 | DOI:10.1002/jmri.28578

J Pers Med. 2022 Nov 8;12(11):1871. doi: 10.3390/jpm12111871.

ABSTRACT

Studies observed that women infected with SARS-CoV-2 during pregnancy had a higher risk of preterm birth. Although it is likely that COVID-19 during the late trimester of pregnancy can trigger premature birth, prematurity remains a concern, and it is vital to study additional clinical and biological patient factors that are highly associated with this negative pregnancy outcome and allow for better management based on the existing predictors. In order to achieve this goal, the current study retrospectively recruited 428 pregnant patients that were separated into three study groups using a 1:2:4 matching ratio and a nearest-neighbor matching method. Sixty-one pregnant patients had a history of COVID-19 during pregnancy and gave birth prematurely; 124 pregnant patient controls had COVID-19 and gave birth full-term, while the second control group of 243 pregnant patients had a premature birth but no history of COVID-19. It was observed that a symptomatic SARS-CoV-2 infection during the third trimester was significantly more likely to be associated with premature birth. Even though the rate of ICU admission was higher in these cases, the mortality rate did not change significantly in the COVID-19 groups. However, SARS-CoV-2 infection alone did not show statistical significance in determining a premature birth (β = 1.09, CI = 0.94-1.15, p-value = 0.067). Maternal anemia was the strongest predictor for prematurity in association with SARS-CoV-2 infection (β = 3.65, CI = 1.46-5.39, p-value &lt; 0.001), followed by elevated CRP (β = 2.11, CI = 1.20-3.06, p-value &lt; 0.001), and respectively IL-6 (β = 1.92, CI = 1.20-2.47, p-value = 0.001. SARS-CoV-2 infection is associated with an increased risk of preterm birth, as shown by our data. If SARS-CoV-2 infection arises during the third trimester, it is recommended that these patients be hospitalized for surveillance of clinical evolution and biological parameters, such as anemia and high inflammatory markers, which have a multiplicative influence on the pregnancy result.

PMID:36579593 | DOI:10.3390/jpm12111871

J Pers Med. 2022 Nov 7;12(11):1857. doi: 10.3390/jpm12111857.

ABSTRACT

Suboptimal states of vitamin D may play a role in psoriasis evolution, but the interconnections have been studied over the past years with controversial results. Although a peerless therapy among moderate to severe types of psoriasis, the therapeutic effectiveness of biological therapy may vary unforeseeably between patients and leads to biologics switch. We conducted a pilot study in patients diagnosed with psoriasis and treated with biologics, the purpose of which was to explore the prevalence of suboptimal states of vitamin D, especially in the group of patients characterized by the failure of previous biologics, and to investigate the associations between vitamin D levels and psoriasis, regarding aspects such the severity of the disease and quality of life. Their current result of latent tuberculosis infection (LTBI) was also considered concerning a feasible relationship with vitamin D levels. From July to December 2021, 45 patients corresponding to our inclusion criteria were assessed. Variables such as Psoriasis Area and Severity Index (PASI) score and the Dermatology Life Quality Index (DLQI) score, as well as vitamin D serum concentrations and their LTBI result, were recorded for them. Lower serum concentrations of vitamin D were not more common in patients characterized by failure to previous biologics (p = 0.443), but we concluded a weak correlation between the DLQI score and vitamin D (rho = -0.345, p-value = 0.020), although a statistically insignificant result was obtained between vitamin D and the PASI score (rho = -0.280, p-value = 0.062), and with the LTBI result (rho = -0.053, p-value = 0.728). These results establish a connection between higher levels of vitamin D and a better outcome of psoriasis from the perspective of the patient’s quality of life, with no significant association with psoriasis severity and no significant prevalence of suboptimal states among patients that failed previous biologics compared to those with a continuously good response.

PMID:36579586 | DOI:10.3390/jpm12111857