Category: Nevin Manimala

BMC Geriatr. 2022 May 26;22(1):455. doi: 10.1186/s12877-022-03151-y.

ABSTRACT

BACKGROUND: A large body of evidence supports a link between type 2 diabetes mellitus (T2DM) and cognitive function, including dementia. However, longitudinal studies on the association between T2DM and decline of cognitive function are scarce and reported mixed results, and we hence set out to investigate the cross-sectional and longitudinal association between T2DM and global as well as domain-specific cognitive performance.

METHODS: We used multivariable regression models to assess associations of T2DM with cognitive performance and cognitive decline in a subsample of a population-based prospective cohort study (ESTHER). This subsample (n = 732) was aged 70 years and older and had participated in telephone-based cognitive function assessment (COGTEL) measuring global and domain-specific cognitive performance during the 5- and 8-year follow-up.

RESULTS: Total COGTEL scores of patients with prevalent T2DM were 27.4 ± 8.3 and 29.4 ± 8.7 at the 5- and 8-year measurements, respectively, and were roughly two points lower than those of T2DM-free participants after adjustment for age and sex. In cross-sectional models, after adjustment for several potential confounders, performance in verbal short-term and long-term memory tasks was statistically significantly lower in participants with T2DM, but the association was attenuated after further adjustment for vascular risk factors. The difference in total COGTEL scores reflecting global cognitive function by T2DM status after full adjustment for confounders and vascular risk factors was equivalent to a decrement in global cognitive function associated with a four-year age difference. In longitudinal models, a statistically significantly stronger cognitive decline in patients with T2DM was observed for working memory.

CONCLUSIONS: In this sample of older individuals, T2DM was associated with worse performance and stronger decline in a cognitive function test. Memory-related domains were found to be particularly sensitive to T2DM. Further large-scale prospective studies are needed to clarify potential T2DM-related predictors of cognitive decline and possible consequences on the abilities to perform patient self-management tasks in diabetes care.

PMID:35619073 | DOI:10.1186/s12877-022-03151-y

BMC Public Health. 2022 May 27;22(1):1057. doi: 10.1186/s12889-022-13386-4.

ABSTRACT

BACKGROUND: Impulsivity is a formidable cause of waterpipe tobacco smoking among youth, however, it is understudied among African youth. Using PRIME behavioural theory, this study aimed to develop a model that examines the motivators of impulsivity to smoke waterpipe tobacco in linkage to the moderating role of social media normalisation of waterpipe tobacco, specifically among youth in Nigeria who smoke waterpipe tobacco.

METHODS: Data were drawn from 695 respondents who smoke waterpipe tobacco across six Nigerian universities in the South-West zone using the chain-referral sampling procedure. Descriptive analyses of the obtained data were carried out using the Statistical Package for Social Sciences (SPSS) version 25. The constructs in the developed model were validated through Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS version 3.

RESULTS: Among Nigerian youth who smoke waterpipe tobacco, intention (β = 0.442, P < 0.001) was the strongest motivator of impulsivity to smoke waterpipe tobacco as compared to positive evaluations (β = 0.302, P < 0.001). In addition, social media normalisation of waterpipe tobacco acted as a moderator that strengthened the relationship between intention and impulsivity (β = 0.287, P < 0.01), as well as, between positive evaluations and impulsivity (β = 0.186, P < 0.01) among youth.

CONCLUSION: Intention greatly instigates Nigerian youth’s impulsivity to smoke waterpipe tobacco, and social media normalisation of waterpipe tobacco also considerably increases their impulsivity to smoke waterpipe tobacco. Youth-focused educational waterpipe tobacco cessation-oriented programmes that utilise diverse constructive-based learning approaches like illustrative learning and counselling, can help to enlighten and encourage Nigerian youth on the importance of shunning the desirability to smoke waterpipe tobacco.

PMID:35619059 | DOI:10.1186/s12889-022-13386-4

BMC Pregnancy Childbirth. 2022 May 26;22(1):442. doi: 10.1186/s12884-022-04741-9.

ABSTRACT

BACKGROUND: Perinatal depression is estimated to affect ~ 12% of pregnancies and is linked to numerous negative outcomes. There is currently no model to predict perinatal depression at multiple time-points during and after pregnancy using variables ascertained early into pregnancy.

METHODS: A prospective cohort design where 858 participants filled in a baseline self-reported survey at week 4-10 of pregnancy (that included social economics, health history, various psychiatric measures), with follow-up until 3 months after delivery. Our primary outcome was an Edinburgh Postnatal Depression Score (EPDS) score of 12 or more (a proxy for perinatal depression) assessed during each trimester and again at two time periods after delivery. Five gradient boosting machines were trained to predict the risk of having EPDS score > = 12 at each of the five follow-up periods. The predictors consisted of 21 variables from 3 validated psychometric scales. As a sensitivity analysis, we also investigated different predictor sets that contained: i) 17 of the 21 variables predictors by only including two of the psychometric scales and ii) including 143 additional social economics and health history predictors, resulting in 164 predictors.

RESULTS: We developed five prognostic models: PND-T1 (trimester 1), PND-T2 (trimester 2), PND-T3 (trimester 3), PND-A1 (after delivery 1) and PND-A2 (delayed onset after delivery) that calculate personalised risks while only requiring that women be asked 21 questions from 3 validated psychometric scales at weeks 4-10 of pregnancy. C-statistics (also known as AUC) ranged between 0.69 (95% CI 0.65-0.73) and 0.77 (95% CI 0.74-0.80). At 50% sensitivity the positive predictive value ranged between 30%-50% across the models, generally identifying groups of patients with double the average risk. Models trained using the 17 predictors and 164 predictors did not improve model performance compared to the models trained using 21 predictors.

CONCLUSIONS: The five models can predict risk of perinatal depression within each trimester and in two post-natal periods using survey responses as early as week 4 of pregnancy with modest performance. The models need to be externally validated and prospectively tested to ensure generalizability to any pregnant patient.

PMID:35619056 | DOI:10.1186/s12884-022-04741-9

Ann N Y Acad Sci. 2022 May 26. doi: 10.1111/nyas.14797. Online ahead of print.

ABSTRACT

Statistical knowledge is a key competency for psychologists in order to correctly interpret assessment outcomes. Importantly, when learning statistics (and its mathematical foundations), self-efficacy (defined as an individual’s belief to successfully accomplish specific performance attainments) is a central predictor of students’ motivation to learn, learning engagement, and actual achievement. Therefore, it is crucial to gain a better understanding of students’ self-efficacy for statistics and its interrelations with statistics anxiety and students’ belief in the relevance of statistics. Here, we present results showing development and validation of a self-assessment questionnaire for examining self-efficacy for statistics in psychology students (Self-Efficacy for Learning Statistics for Psychologists, SES-Psy). Upon using different methodological approaches, we demonstrate that the SES-Psy questionnaire has (1) sound psychometric properties, and within our sample of university students, (2) a robust latent structure disclosing three clearly distinctive profiles that are characterized by a complex and nonlinear interplay between perceived self-efficacy (for basic and advanced statistics), statistics anxiety, and students’ belief in the relevance of statistics. Implications for educational settings and future research are discussed.

PMID:35619040 | DOI:10.1111/nyas.14797

Environ Sci Pollut Res Int. 2022 May 27. doi: 10.1007/s11356-022-20660-w. Online ahead of print.

ABSTRACT

Noise pollution as a result of urbanization and socioeconomic development threatens human health and has become a major environmental problem worldwide, particularly for urban residents. Based on observed equivalent noise data of 113 major Chinese cities, a Bayesian spatiotemporal hierarchy model (BSTHM) was employed to investigate the spatiotemporal characteristics of urban noise pollution in China from 2007 to 2019. Meanwhile, the BART model was adopted to explore the drivers of urban noise pollution. The mean and medium of the equivalent noise of the 113 major cities decreased from 2007 to 2011 but increased from 2011 to 2019; the corresponding annual growth is 0.0793 dB and 0.0947 dB per year. The overall spatial pattern has a certain geographical feature. The cities located in the eastern and north-eastern coastal regions generally have a higher level of noise pollution, and the western and southwestern cities have a lower level. One hundred cities not only have greater noise pollution but also an increasing trend. Although the 52 cities located in Western China have less noise pollution, they have increasing local trends. The results indicate that economic and social factors are the main drivers of noise pollution of China; the explanatory power is 46.2%. Traffic factors are also relatively important drivers, of which bus ridership is the leading one. In terms of the natural environment, climatic factors, including temperature and relative humidity, and presence of green areas containing parkland and general green land are the main determinants.

PMID:35619016 | DOI:10.1007/s11356-022-20660-w

Environ Sci Pollut Res Int. 2022 May 26. doi: 10.1007/s11356-022-20918-3. Online ahead of print.

ABSTRACT

The case of a sea breeze where the mean daily PM10 concentration reached the recommended limit value for human health for the period from 2007 to 2009 at the air quality station AMS3 Split-1 in Split, Croatia, is analysed. The Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) atmospheric model MM5 and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model are used to simulate the lower atmospheric flow and dispersion within 100 km from the coast. The simulation is made with four point sources in the hinterland of Split: Kaštel-Sućurac, Solin and Klis, where the local cement plants are located, and Vranjic, where an asbestos-cement plant is located. Statistical analysis and rotational statistics showed good agreement of the measurement data with the modelled wind speed and direction at 10 m and temperature at 2 m height. The backward trajectories show that the pollutants are caught in the sea breeze circulation from all sources and in the early afternoon the plume is placed over parts of Split from all sources. From the peak concentrations during the selected day, it can be concluded that pollution from Kaštel-Sućurac had the greatest impact on the high PM10 concentrations measured at the Split-1 station.

PMID:35619005 | DOI:10.1007/s11356-022-20918-3

Environ Sci Pollut Res Int. 2022 May 27. doi: 10.1007/s11356-022-20189-y. Online ahead of print.

ABSTRACT

This paper examined the impact of green finance and financial technology on economic growth by utilizing statistics between 1990 to 2000 gathered from 50 US states and regions. A two-step generalized GMM was used to analyze the link between green finance, financial technology, and continued economic growth using panel regression analysis. We found that the US green finance sector had grown significantly and such growth was accompanied by an increase in the usage of non-combustible energy and an advancement in the green finance economic forum. This research concludes that green financing has a beneficial impact on all three components of continued financial development. There is a favorable influence of financial technology on green finance in environmental and economic dimensions, although the link between green banking and investment performance is only somewhat affected by technology. When total emissions increased, non-oil energy usage did not grow and initiatives for renewable energy initiatives were lacking. As a result, there was a decline in the growth of green finance. Green funding and total emissions had a significant impact on the US non-combustible energy usage as did explicit policy changes. The recommendation is also canvassed to strengthen the adoption of green financing policies, increase the use of non-combustible energy, and build an alternative energy economy. It also offers three policy recommendations for policymakers, namely, to improve the integration of banking technology with green finance, develop a corporate environmental approach to manage and control state authorities in increasing green finance productivity, and generate medium- and long-term favorable steps to support green finance in the financial market.

PMID:35619012 | DOI:10.1007/s11356-022-20189-y

Environ Sci Pollut Res Int. 2022 May 26. doi: 10.1007/s11356-022-20767-0. Online ahead of print.

ABSTRACT

The groundwater vulnerability assessment is known as a useful tool for predicting and prevention of groundwater pollution. This study targets the DRASTIC, evidential belief function (EBF), and logistic regression (LR) models to assess vulnerability in Kabul aquifers, Afghanistan Country. The growth of urban sprawl, groundwater overexploitation, and lack of suitable municipal sewage systems as anthropogenic sources have been the main potential to increase groundwater contaminants such as nitrate in the study area. The vulnerability map has been developed based on various effective factors including altitude, slope (percentage rise), aspect, curvature, land-use type, drainage density, distance from river, annual mean precipitation, net recharge, geology/lithology units, the impact of the vadose zone, aquifer media, depth to water (unsaturated zone), saturated zone, drawdown, and hydraulic conductivity. To identify groundwater pollution, the spatial variation of nitrate concentration data in 2018 was considered indication of groundwater pollution. Based on descriptive statistics, the value of 2.65 mg/l (the median of the pixel values of nitrate map) was selected as a threshold to differentiate the occurrence and non-occurrence of pollution. The groundwater quality data were selected and randomly divided into two datasets for training and validation, including 70% and 30%, respectively. The success-rate and prediction-rate curves were computed based on the receiver operating characteristic (ROC) curve and the area under the curve (AUC) to estimate the efficiency of models. The ROC-AUC of success rates for EBF, LR, and DRASTIC models were estimated to be 67%, 66%, and 52%, respectively. Moreover, the ROC-AUC of the prediction rates of the EBF, LR, and DRASTIC models were obtained 61%, 63%, and 55%, respectively. Based on correlation between mean nitrate concentration and the mean vulnerability indexes in each model, the EBF model is the most compatible with the current developed vulnerability zones as the role of mankind in changing the environment in real conditions in comparison to LR and DRASTIC models.

PMID:35619000 | DOI:10.1007/s11356-022-20767-0

Trends Psychiatry Psychother. 2022 May 26;44. doi: 10.47626/2237-6089-2021-0396. Online ahead of print.

ABSTRACT

INTRODUCTION: Autism Spectrum Disorder is characterized by persistent deficits in social communication, social interaction, and restricted and repetitive patterns of behavior. Some studies have shown that substances derived from Cannabis sativa improve the quality of life of autistic children without causing serious adverse effects, thus providing a therapeutic alternative.

METHOD: This was a randomized, double-blind, placebo-controlled clinical trial to evaluate the efficacy and safety of a cannabis extract rich in cannabidiol (CBD) in autistic children. Sixty children, aged between 5 and 11 years, were selected and divided into two groups: the treatment group, which received the CBD-rich cannabis extract, and the control group, which received the placebo, both used the product for a period of 12 weeks. Statistical analysis was done by two-factor mixed analysis of variance (ANOVA two way).

RESULTS: Significant results were found for social interaction [F(1,116)=14.13, p=0.0002)], anxiety [F(1,116)=5.99, p=0.016], psychomotor agitation [F(1,116)=9.22, p=0.003)], number of meals a day [F(1,116)=4.11, p=0.04)] and concentration [F (1,48)=6.75, p=0.01], the latter being significant only in mild autism spectrum disorder. Regarding safety, it was found that only three children in the treatment group (9.7%) had adverse effects, namely dizziness, insomnia, colic and weight gain.

CONCLUSION: CBD-rich cannabis extract was found to improve one of the diagnostic criteria for ASD (social interaction), as well as often co-existing features, and to have few serious adverse effects.

PMID:35617670 | DOI:10.47626/2237-6089-2021-0396

J Head Trauma Rehabil. 2022 May 26. doi: 10.1097/HTR.0000000000000788. Online ahead of print.

ABSTRACT

OBJECTIVE: To examine motor vehicle crash frequency and risk factors following moderate-to-severe traumatic brain injury (TBI).

SETTING: Eight TBI Model Systems sites. Participants: Adults (N = 438) with TBI who required inpatient acute rehabilitation.

DESIGN: Cross-sectional, observational design.

MAIN MEASURES: Driving survey completed at phone follow-up 1 to 30 years after injury.

RESULTS: TBI participants reported 1.5 to 2.5 times the frequency of crashes noted in the general population depending on the time frame queried, even when accounting for unreported crashes. Most reported having no crashes; for those who experienced a crash, half of them reported a single incident. Based on logistic regression, age at survey, years since injury, and perception of driving skills were significantly associated with crashes.

CONCLUSION: Compared with national statistics, crash risk is higher following TBI based on self-report. Older age and less time since resuming driving were associated with lower crash risk. When driving was resumed was not associated with crash risk. These results do not justify restricting people from driving after TBI, given that the most who resumed driving did not report experiencing any crashes. However, there is a need to identify and address factors that increase crash risk after TBI.

PMID:35617669 | DOI:10.1097/HTR.0000000000000788