Tag: nevin manimala

Am J Public Health. 2023 Nov 10:e1-e10. doi: 10.2105/AJPH.2023.307427. Online ahead of print.

ABSTRACT

Objectives. To investigate differences in the documentation of mental health symptomology between male and female suicide decedents in the 2003-2020 US National Violent Death Reporting System (NVDRS). Methods. Using information on 271 998 suicides in the 2003-2020 NVDRS, we evaluated precoded mental health-related variables and topic model-derived latent mental health themes in the law enforcement and coroner or medical examiner death narratives compiled by trained public health workers. Results. Public health records of male compared with female suicides were less likely to include notations of mental health conditions or treatment interventions. However, topic modeling of death summaries revealed that male suicide decedents were more likely to evidence several subclinical cognitive and emotional indicators of distress. Conclusions. Suicide death records vary by gender, both in recorded evidence for mental health conditions at time of death and in accompanying narratives describing proximal circumstances surrounding these deaths. Our findings hint that patterns of subclinical mental health changes among men might be less well captured in commonly used mental health indicators, suggesting that prevention efforts may benefit from measures that also target assessment of subclinical distress. (Am J Public Health. Published online ahead of print November 10, 2023:e1-e10. https://doi.org/10.2105/AJPH.2023.307427).

PMID:37948056 | DOI:10.2105/AJPH.2023.307427

Nurs Educ Perspect. 2023 Nov 10. doi: 10.1097/01.NEP.0000000000001208. Online ahead of print.

ABSTRACT

Competence in interprofessional collaboration is essential for safe patient outcomes. This study examined the impact of an interprofessional telehealth pharmacology simulation on prelicensure nursing and pharmacy students’ perceptions of interprofessional roles. A pretest-posttest design was used to compare participants’ perceptions of interprofessional roles prior to and following the simulation. Data were collected using the Interdisciplinary Education Perception Scale (IEPS). Paired-samples t-tests showed statistically significant increases in scores for both the full IEPS (n = 99) and two subscales, Competency and Autonomy (n = 99) and Perception of Actual Cooperation (n = 99). Nurse educators should provide regular interprofessional experiences to foster learners’ competence in interprofessional collaboration and communication.

PMID:37948042 | DOI:10.1097/01.NEP.0000000000001208

Pharmacoeconomics. 2023 Nov 10. doi: 10.1007/s40273-023-01319-x. Online ahead of print.

ABSTRACT

Estimates of costs associated with disease states are required to inform decision analytic disease models to evaluate interventions that modify disease trajectory. Increasingly, decision analytic models are developed using patient-level data with a focus on heterogeneity between patients, and there is a demand for costs informing such models to reflect individual patient costs. Statistical models of health care costs need to recognize the specific features of costs data which typically include a large number of zero observations for non-users, and a skewed and heavy right-hand tailed distribution due to a small number of heavy healthcare users. Different methods are available for modelling costs, such as generalized linear models (GLMs), extended estimating equations and latent class approaches. While there are tutorials addressing approaches to decision modelling, there is no practical guidance on the cost estimation to inform such models. Therefore, this tutorial aims to provide a general guidance on estimating healthcare costs associated with disease states in decision analytic models. Specifically, we present a step-by-step guide to how individual participant data can be used to estimate costs over discrete periods for participants with particular characteristics, based on the GLM framework. We focus on the practical aspects of cost modelling from the conceptualization of the research question to the derivation of costs for an individual in particular disease states. We provide a practical example with step-by-step R code illustrating the process of modelling the hospital costs associated with disease states for a cardiovascular disease model.

PMID:37948040 | DOI:10.1007/s40273-023-01319-x

J Neurovirol. 2023 Nov 10. doi: 10.1007/s13365-023-01184-6. Online ahead of print.

ABSTRACT

Hepatitis C virus (HCV) infection is a progressive, systemic disease which leads to the development of end-stage liver disease. In 70% of patients, HCV infection is followed by the development of extrahepatic manifestations (EHM). A common EHM is HCV associated neurocognitive disorder (HCV-AND), characterized by neuropsychological changes in attention, working memory, psychomotor speed, executive function, verbal learning, and recall. The aim of this study is to examine the correlation between the neurocognitive profile and routine, available laboratory parameters of inflammation, liver function tests, grade of liver fibrosis, and clinical and laboratory parameters of mixed cryoglobulinemia in treatment naïve non-cirrhotic HCV patients. This is a single-center exploratory study in which we examined 38 HCV + treatment naïve patients. The complete blood count and hematological parameters of systemic inflammation, liver function tests, biopsy confirmed grade of liver fibrosis, and clinical and laboratory parameters of mixed cryoglobulinemia caused by chronic HCV infection were observed. In the study, we used a battery of neuropsychological tests assessing multiple cognitive domains: executive functions, verbal fluency, delayed memory, working memory and learning, and one measure for visuo-constructive performance. Before the Bonferroni correction for multiple comparisons, the results show significant correlations between the scores in the neurocognitive variables and the single measures of inflammation, liver function parameters, and mixed cryoglobulinemia. It has not found a statistically significant correlation between systemic inflammation and neurocognitive variables. After the Bonferroni adjustment, no correlations remained significant. Certainly, the obtained results can be a recommendation for additional validation through future research.

PMID:37948037 | DOI:10.1007/s13365-023-01184-6

Aging Clin Exp Res. 2023 Nov 10. doi: 10.1007/s40520-023-02617-2. Online ahead of print.

ABSTRACT

BACKGROUND: Aging and frailty pose significant challenges globally, placing a substantial burden on healthcare and social services due to their adverse consequences.

AIM: The primary objective of this study was to investigate the relationship between social participation and development of frailty transition and trajectory.

METHODS: This study utilized data from the CLHLS Cohort, a 10-year follow-up study involving 6713 participants, to investigate the association between social participation and development of frailty. Frailty reflects a comprehensive decline in various body functions. The study employed a group-based trajectory model to analyze the development trajectory of the frailty index and used logistic regression to assess the odds ratio (OR) of frailty risk.

RESULTS: We identified two distinct groups of frailty progression trajectories: the “stable development group” and the “rapid growth group.” Individuals who engaged in social activities at least once a month, but not daily, exhibited a significant association with an increased risk of transitioning into the “rapid growth group” (OR 1.305, 95% CI 1.032-1.649). Those with social participation less than once a month had an even greater risk (OR 1.872, 95% CI 1.423-2.463). Moreover, low social participation frequency (occasionally/never) has a more pronounced impact on frailty progression in males.

CONCLUSION: A higher frequency of social participation is associated with a lower risk of being classified into the “rapid growth group” and a slower rate of frailty index progression. Preventing the progression of frailty can contribute to enhanced support for healthy aging among older adults.

PMID:37948011 | DOI:10.1007/s40520-023-02617-2

Neurol Ther. 2023 Nov 10. doi: 10.1007/s40120-023-00562-w. Online ahead of print.

ABSTRACT

INTRODUCTION: There have been no prior trials directly comparing the efficacy of different calcitonin gene-related peptide (CGRP) antagonists for migraine prevention. Reported are the results from the first head-to-head study of two CGRP antagonists, galcanezumab (monoclonal antibody) versus rimegepant (gepant), for the prevention of episodic migraine.

METHODS: In this 3-month, double-blind, double-dummy study, participants were randomized (1:1) to subcutaneous (SC) galcanezumab 120 mg per month (after a 240 mg loading dose) and a placebo oral disintegrating tablet (ODT) every other day (q.o.d.) or to rimegepant 75 mg ODT q.o.d. and a monthly SC placebo. The primary endpoint was the proportion of participants with a ≥ 50% reduction in migraine headache days per month from baseline across the 3-month double-blind treatment period. Key secondary endpoints were overall mean change from baseline in: migraine headache days per month across 3 months and at month 3, 2, and 1; migraine headache days per month with acute migraine medication use; Migraine-Specific Quality of Life Questionnaire Role Function-Restrictive domain score at month 3; and a ≥ 75% and 100% reduction from baseline in migraine headache days per month across 3 months.

RESULTS: Of 580 randomized participants (galcanezumab: 287, rimegepant: 293; mean age: 42 years), 83% were female and 81% Caucasian. Galcanezumab was not superior to rimegepant in achieving a ≥ 50% reduction from baseline in migraine headache days per month (62% versus 61% respectively; P = 0.70). Given the pre-specified multiple testing procedure, key secondary endpoints cannot be considered statistically significant. Overall, treatment-emergent adverse events were reported by 21% of participants, with no significant differences between study intervention groups.

CONCLUSIONS: Galcanezumab was not superior to rimegepant for the primary endpoint; however, both interventions demonstrated efficacy as preventive treatments in participants with episodic migraine. The efficacy and safety profiles observed in galcanezumab-treated participants were consistent with previous studies.

TRIAL REGISTRATION: ClinTrials.gov-NCT05127486 (I5Q-MC-CGBD).

PMID:37948006 | DOI:10.1007/s40120-023-00562-w

Gen Thorac Cardiovasc Surg. 2023 Nov 10. doi: 10.1007/s11748-023-01984-x. Online ahead of print.

ABSTRACT

OBJECTIVE: We evaluated the blood flow within the downstream aortic false lumen after frozen elephant trunk repair for acute aortic dissection and identified hemodynamic predictors of false lumen expansion and negative false lumen remodeling using four-dimensional flow magnetic resonance imaging.

METHODS: Thirty-one patients (Stanford type A, n = 28; Stanford type B, n = 3) with patent false lumen who underwent frozen elephant trunk procedures for acute aortic dissection were included in this observational study. Each patient underwent computed tomography during the follow-up period and four-dimensional flow magnetic resonance imaging within 3 postoperative months. The false lumen volumetric expansion rate was calculated using computed tomography data. The direction and the rate of flow in the lower descending aortic false lumen were analyzed. Negative false lumen remodeling was defined as a volumetric increase of > 10% from the baseline volume.

RESULTS: Negative false lumen remodeling had developed in 6 of the 31 patients during the observation period. Most of the false lumen flows were biphasic during systole. The range between peak and nadir flow rates was associated with the false lumen volumetric expansion rate (β coefficient = 6.77; p < 0.01, R² = 0.43).

CONCLUSIONS: The range between peak and nadir flow rates may serve as a hemodynamic predictor of negative false lumen remodeling, enabling further treatment for patients at risk of expansion in the downstream aorta.

PMID:37948001 | DOI:10.1007/s11748-023-01984-x

J Urban Health. 2023 Nov 10. doi: 10.1007/s11524-023-00790-3. Online ahead of print.

ABSTRACT

Rising ambient temperatures due to climate change will impact both indoor temperatures and heating and cooling utility costs. In traditionally colder climates, there are potential tradeoffs in how to meet the reduced heating and increased cooling demands, and issues related to lack of air conditioning (AC) access in older homes and among lower-income populations to prevent extreme heat exposure. We modeled a typical multi-family home in Boston (MA) in the building simulation program EnergyPlus to assess indoor temperature and energy consumption in current (2020) and projected future (2050) weather conditions. Selected households were those without AC (no AC), those who ran AC sometimes (some AC), and those with sufficient resources to run AC always (full AC). We considered stylized cooling subsidy policies that allowed households to move between groups, both independently and in conjunction with energy efficiency retrofits. Results showed that future weather conditions without policy changes yielded an increase in indoor summer temperatures of 2.1 °C (no AC), increased cooling demand (range: 34-50%), but led to a decrease in net yearly total utility costs per apartment (range: – $21 to – $38). Policies that allowed households to move to greater AC utilization yielded average indoor summer temperature decreases (- 3.5 °C to – 6.2 °C) and net yearly total utility increases (range: + $2 to + $94) per apartment unit, with greater savings for retrofitted homes primarily due to large decreases in heating use. Our model results reinforce the importance of coordinated public policies addressing climate change that have an equity lens for both health and climate goals.

PMID:37947996 | DOI:10.1007/s11524-023-00790-3

Ir J Med Sci. 2023 Nov 10. doi: 10.1007/s11845-023-03562-9. Online ahead of print.

ABSTRACT

OBJECTIVES: To assess the most common lower limb acute muscle injuries on MRI imaging in a national specialist centre for orthopaedics and sports medicine and to explore potential gender differences.

METHODS: Over a 3-year time period, all MRI lower limb studies with acute muscle injury (AMI) were reviewed. A British Athletics muscle injury classification (BAMIC) was given and a statistical analysis was performed.

RESULTS: A total of 195 AMIs were diagnosed: 177 (91%) male and 18 (9%) female injuries (M to F = 9.8:1). The most common lower limb AMIs were BAMIC grade 1a injuries (n = 48, 25%). The most commonly injured muscle was biceps femoris (n = 87, 45%), specifically grade 1b and grade 2b injuries. There was no significant difference in age between men and women with acute lower limb muscle injuries (p = 0.19). Females were 1.5 times more likely to have a lower grade AMI than males, although this did not reach statistical significance (p = 0.7) owing to a striking lower number of female patients. There was no significant difference between genders in the likelihood of sustaining a hamstring or quadricep AMI (hamstrings OR = 2.47, p = 0.14 and quadriceps OR = 0.926, p 0.99).

CONCLUSIONS: Grade 1a is the most common lower limb AMI grade in our institution, accounting for 25%. Biceps femoris is the most commonly injured muscle (45%) with grade 1b and grade 2b being the most frequently encountered grades of biceps femoris injuries. Lower-grade injuries are more common in females compared to males, although not significantly so. Further studies are required to explore possible reasons for this gender gap.

PMID:37947993 | DOI:10.1007/s11845-023-03562-9

Stat Med. 2023 Nov 10. doi: 10.1002/sim.9931. Online ahead of print.

ABSTRACT

Prediction models are increasingly developed and used in diagnostic and prognostic studies, where the use of machine learning (ML) methods is becoming more and more popular over traditional regression techniques. For survival outcomes the Cox proportional hazards model is generally used and it has been proven to achieve good prediction performances with few strong covariates. The possibility to improve the model performance by including nonlinearities, covariate interactions and time-varying effects while controlling for overfitting must be carefully considered during the model building phase. On the other hand, ML techniques are able to learn complexities from data at the cost of hyper-parameter tuning and interpretability. One aspect of special interest is the sample size needed for developing a survival prediction model. While there is guidance when using traditional statistical models, the same does not apply when using ML techniques. This work develops a time-to-event simulation framework to evaluate performances of Cox regression compared, among others, to tuned random survival forest, gradient boosting, and neural networks at varying sample sizes. Simulations were based on replications of subjects from publicly available databases, where event times were simulated according to a Cox model with nonlinearities on continuous variables and time-varying effects and on the SEER registry data.

PMID:37947168 | DOI:10.1002/sim.9931