Nevin Manimala

Diabetes Metab Syndr. 2022 Mar 16;16(4):102463. doi: 10.1016/j.dsx.2022.102463. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: To determine the suitability of 11 basic statistical models for estimating child-growth of under-five children and to bring-forth estimated growth curves for mean height & mean weight by their selected birth-weight categories for Central Region of India.

METHODS: The study used fourth round of National Family Health Survey-4 (NFHS-4) data of India, consisting of 75,645 under-five children, belonging to 3 Indian States – Chhattisgarh, Madhya Pradesh & Uttar Pradesh. The children of the Region were first divided into 4 sub categories according to their birth-weight: (i) < 2000 gm, (ii) 2000-2499 gm, (iii) 2500-2999 gm (iv) 3000+gm, growth curve for mean height and mean weight were estimated for two sexes.

RESULTS: The significant association of 7 socio-demographic factors studied, namely – age & sex of child, birth-order, BMI, mother’s highest level of education, place of residence and wealth index. Further, Cubic Model and Power Model, demonstrated best-fit to height & weight data of under-five children, belonging to different birth-weight categories, for estimating growth of boys & girls separately. These models enabled us to estimate mean height and mean weight, with 95% CI, for boys and girls separately by different birth-weight categories.

CONCLUSIONS: Study concluded that 7 socio-demographic factors were significantly associated with birth-weight. Further, Cubic Model and Power Model were most suitable for estimating child growth in terms of mean height & mean weight for boys and girls – considering specific birth-weight categories.

PMID:35334409 | DOI:10.1016/j.dsx.2022.102463

Dev Cogn Neurosci. 2022 Mar 14;54:101096. doi: 10.1016/j.dcn.2022.101096. Online ahead of print.

ABSTRACT

Electroencephalography (EEG) has been widely adopted by the developmental cognitive neuroscience community, but the application of machine learning (ML) in this domain lags behind adult EEG studies. Applying ML to infant data is particularly challenging due to the low number of trials, low signal-to-noise ratio, high inter-subject variability, and high inter-trial variability. Here, we provide a step-by-step tutorial on how to apply ML to classify cognitive states in infants. We describe the type of brain attributes that are widely used for EEG classification and also introduce a Riemannian geometry based approach for deriving connectivity estimates that account for inter-trial and inter-subject variability. We present pipelines for learning classifiers using trials from a single infant and from multiple infants, and demonstrate the application of these pipelines on a standard infant EEG dataset of forty 12-month-old infants collected under an auditory oddball paradigm. While we classify perceptual states induced by frequent versus rare stimuli, the presented pipelines can be easily adapted for other experimental designs and stimuli using the associated code that we have made publicly available.

PMID:35334336 | DOI:10.1016/j.dcn.2022.101096

Food Chem. 2022 Mar 22;386:132746. doi: 10.1016/j.foodchem.2022.132746. Online ahead of print.

ABSTRACT

To prevent PGI (Protected Geographical Indication) cherry tomato of Pachino (Sicily, Italy) from frauds, an alternative method, which includes chemometric treatments, was proposed. The content of 32 inorganic elements (macro-micronutrients and lanthanides) present in 16 PGI and 24 not PGI cherry tomato samples cv Naomy, and in 16 PGI and 8 not PGI soil samples, was determined by Inductively Coupled Plasma – Mass Spectrometer (ICP-MS). To identify the elements able to differentiate PGI and not PGI cherry tomato samples, Principal Components Analysis (PCA) and Canonical discriminant analysis (CDA) were performed. The first two principal components (PC1-PC2) explain a total variance of 71,41% between PGI and not PGI group, whereas CDA showed Zn, Cd, Mn and Ca as inorganic markers able to correctly classify the 100% of samples. Furthermore, with a translocation factor (K), evaluated in soil/plant chain, the comparison of absorption trends for PGI and not PGI samples was realized.

PMID:35334318 | DOI:10.1016/j.foodchem.2022.132746

Addict Behav. 2022 Mar 17;130:107308. doi: 10.1016/j.addbeh.2022.107308. Online ahead of print.

ABSTRACT

Cost-of-play information is one public health intervention recommended to help reduce gambling-related harm. In the UK, this information is given on electronic gambling machines in a format known as the “return-to-player”, e.g., “This game has an average percentage payout of 90%.” However, previous evidence suggests that this information could be improved by equivalently restating it in terms of the “house-edge”, e.g., “This game keeps 10% of all money bet on average.” A “volatility warning,” stating that this information applies only in the statistical long-run, has also been recommended to help gamblers understand cost-of-play information. However, there is no evidence comparing these information provisions’ effect on gamblers’ behavior. An experiment tested US gamblers'(N = 2433) incentivized behavior in an online slot machine, where this information was manipulated between-participants along with a counter showing the total amount bet. Preregistered analyses showed that participants gambled significantly less when house-edge information or a volatility warning were shown compared to standard return-to-player information, with no effect of the total amount bet counter, and no significant interaction effects. However, these significant findings had small effect sizes, suggesting that a public health approach to gambling should not rely on informational provisions only. Subject to supportive evidence from more ecologically-valid designs such as field studies, these results suggest that improved cost-of-play information could lead to reduced rates of gambling expenditure and therefore benefit a public health approach to gambling.

PMID:35334298 | DOI:10.1016/j.addbeh.2022.107308

Asian J Psychiatr. 2022 Mar 11;72:103063. doi: 10.1016/j.ajp.2022.103063. Online ahead of print.

ABSTRACT

PURPOSE: Antenatal depression is as prevalent as postpartum depression and studies on it are very few. It has been relatively neglected leading to adverse effects on the growing child as well as the mother. Hence screening of depression in high risk individuals, planning and adopting important strategies for prevention needs to be undertaken. Our study aimed to assess the modifiable social and obstetric risk factors of antenatal depression.

METHODS: Third trimester pregnant women of 18-40 years attending obstetric out-patient department and admitted in tertiary hospitals who had no past psychiatric illness were screened using Edinburgh postnatal depression scale after obtaining written consent, socio-demographic and obstetric details. Statistical analysis was calculated using IBM version SPSS 23.

RESULTS: Among 222 women recruited, 25.6% had antenatal depression. Significant associations were found between lower level of education (p = 0.02,O.R=1.87), urban population (p = 0.04,O.R=5.139), intimate partner violence (p = 0.01,O.R=15.769), daily alcohol use by husband (p < 0.00,O.R=15.281), poor relationship with in-laws (p < 0.000,O.R=21.733) and parents (p < 0.000,O.R=15.281), number of previous pregnancies (p = 0.026,O.R=5.545), parity (p = 0.04,O.R=4.187), previous abortions (p = 0.007,O.R=2.834), fear of labour (p < 0.000,O.R=5.77) and complications during pregnancy (p < 0.000,O.R=3.017) with antenatal depression. Living in urban area (p = 0.023, A.O.R=3.132), fear of labour (p < 0.000, A.O.R=7.398), intimate partner violence (p = 0.026, A.O.R=36.655), poor relationship with in-laws (p = 0.001, A.O.R=36.855) and parents (p = 0.042, A.O.R=8.377) were found to be predictors of antenatal depression.

CONCLUSION: Antenatal depression is multifactorial in origin and requires a multifactorial approach in prevention and treatment. Routine antenatal screening for depression must be conducted with efforts to build strong family, peer and social support at community level.

PMID:35334285 | DOI:10.1016/j.ajp.2022.103063

Int J Biostat. 2022 Mar 25. doi: 10.1515/ijb-2021-0014. Online ahead of print.

ABSTRACT

The use of cumulative incidence functions for characterizing the risk of one type of event in the presence of others has become increasingly popular over the past two decades. The problems of modeling, estimation and inference have been treated using parametric, nonparametric and semi-parametric methods. Efforts to develop suitable extensions of machine learning methods, such as regression trees and ensemble methods, have begun comparatively recently. In this paper, we propose a novel approach to estimating cumulative incidence curves in a competing risks setting using regression trees and associated ensemble estimators. The proposed methods use augmented estimators of the Brier score risk as the primary basis for building and pruning trees, and lead to methods that are easily implemented using existing R packages. Data from the Radiation Therapy Oncology Group (trial 9410) is used to illustrate these new methods.

PMID:35334192 | DOI:10.1515/ijb-2021-0014

Port J Card Thorac Vasc Surg. 2022 Jan 4;28(4):31-36. doi: 10.48729/pjctvs.221.

ABSTRACT

OBJECTIVES: Identify risk factors for major perioperative complications (MPC) after anatomical lung resection for NonSmall-Cell Lung Cancer (NSCLC) and establish a scoring system.

METHODS: Single center retrospective study of all consecutive patients diagnosed with NSCLC submitted to anatomical lung resection from 2015 to 2019 (N=564).

EXCLUSION CRITERIA: previous lung surgery, concomitant non-lung cancer related procedures, urgency surgery.

STUDY POPULATION: 520 patients.

PRIMARY END-POINT: MPC defined as a composite endpoint including at least one of the in-hospital complications. Univariable and Multivariable analyses were developed to identify predictors of perioperative complications and create a risk score. Discrimination was assessed using the C-statistic. Calibration was evaluated by Hosmer and Lemeshow test and internal validation was obtained by means of bootstrap replication.

RESULTS: Mean age of 65 years and 327 (62.9%) were males. Mean hospital stay of 9 days after surgery. Overall MPC rate was 23.3%. Male gender, hypertension, FEV1<75%, thoracotomy, bilobectomy/pneumectomy and additional resection were independent predictors of MPC. A risk score based on the odds ratios was developed – Major Perioperative Complications of Lung Resection (MPCLR) scoring system – and ranged between 0 and 14 points. It was divided in 5 groups: 1-2 points (positive preditive value 15%); 3-4 (PPV 25%); 5-7 (PPV 35%); 8-9 (PPV 60%); >10 points (PPV 88%). The score showed rea- sonable discrimination (C-statistic=0.70), good calibration (P=.643) and it was internally validated (C-statistic=0,70 BCa95% CI,0.65-0.79).

CONCLUSIONS: This study proposes a simple and daily-life risk score system that was able to predict the incidence of perioperative complications.

PMID:35334178 | DOI:10.48729/pjctvs.221

Public Health Nurs. 2022 Mar 25. doi: 10.1111/phn.13074. Online ahead of print.

ABSTRACT

OBJECTIVE: Determine the effectiveness of a COVID-19 remote monitoring and management program in reducing preventable hospital utilization.

DESIGN: A retrospective cohort study utilizing data from electronic health records.

SAMPLE: Two hundred and ninety-third patients who tested positive for COVID-19 at a drive-through testing site in Michigan. The intervention group, consisting of 139 patients, was compared to a control group of 154 patients.

MEASUREMENTS: The primary outcome was the 30-day probability of hospital utilization. The covariates included in the analysis were age, gender, tobacco use, body mass index (BMI), race, and ethnicity.

INTERVENTION: A nurse-led, telephone-based active management protocol for COVID-19 patients who were isolating at home.

RESULTS: The intervention group had a non-statistically significant 42% reduction in risk of hospital utilization within 30 days of a positive COVID-19 test when compared to the control group (HR = 0.578, p-value .111, HR 95% CI [0.29, 1.13]).

CONCLUSIONS: A nurse-led remote monitoring and management program for COVID-19 reduced the probability of 30-day hospital utilization. Although the findings were not statistically significant, the program yielded practical significance by reducing hospital utilization, in-person interaction, and the risk of infection for healthcare workers.

PMID:35334128 | DOI:10.1111/phn.13074

Spec Care Dentist. 2022 Mar 25. doi: 10.1111/scd.12714. Online ahead of print.

ABSTRACT

AIMS: A Virtual Dental Home (VDH) is an alternative care model using teledentistry technology to provide care in community settings for special care populations. The Dental Hygiene Department at Idaho State University developed a VDH educational program to deliver preventive and therapeutic care at an assisted-living (AL) facility for memory care residents. The purpose of the educational program was to design and implement a VDH model for AL residents, and subsequently to evaluate the effectiveness of the educational program through students’ knowledge, clinical confidence, and perspectives.

METHODS AND RESULTS: Senior dental hygiene students (n = 32) completed didactic, laboratory, and clinical experiences on implementing an VDH. Using a pretest/posttest design, data were collected using a self-generated questionnaire; validity and reliability of the questionnaire were established prior to administration. Likert data were analyzed (n = 22, 69%) using the Wilcoxon Signed Rank test and Bonferroni correction. The change in scores of all three variables was statistically significant. The educational program was effective for increasing knowledge, clinical confidence, and perspectives of the dental hygiene students.

CONCLUSION: Educational programs that include didactic, laboratory, and clinical experiences prepare graduates for using alternative care models, thereby, enhancing the potential to improve access to care for vulnerable populations in community settings.

PMID:35334117 | DOI:10.1111/scd.12714

Int Endod J. 2022 Mar 25. doi: 10.1111/iej.13736. Online ahead of print.

ABSTRACT

BACKGROUND: A comprehensive effort to evaluate outcomes of primary root canal treatment (RCT) between 1966 and 2002 was published by Ng et al. (2007, 2008). Changes in endodontic materials and treatment methods warrants an updated analysis of outcomes.

OBJECTIVES: This study aimed to 1) quantify the success rates of primary RCT published between 2003 and 2020; and 2) investigate the influence of some characteristics known/ suspected to be associated with treatment outcomes.

METHODS: An electronic search was performed in the following databases (01-01-2003 to 12-31-2020): Pubmed, Embase, CINHAL, Cochrane and Web of Science. Included study designs were longitudinal clinical studies (randomized control trials, cohort studies, retrospective observational studies). Studies with at least twelve-months of post-operative review and success rates based on clinical and radiographic criteria were analyzed. The terms ‘strict’ (complete resolution of periapical lesion) or ‘loose’ (reduction in size of existing periapical lesion) were used to describe the outcome criteria. Weighted, pooled success rates were calculated. Random effects meta-regression models were used to investigate potential sources of statistical heterogeneity. The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach was used to evaluate for quality assessment of the included studies.

RESULTS: Forty-two studies were included in the review. Meta-analyses showed that the weighted pooled success rates were estimated to be 92.6% (95% CI: 90.5-94.8%) under ‘loose criteria’ and 82.0% (95% CI: 79.3-84.8%) under ‘strict’ criteria. The most significant areas of study heterogeneity were year of publication and qualification of operator. The majority (64.29%) of studies were considered to be of low quality of evidence.

DISCUSSION: Biological factors continue to have the most significant impact on RCT outcomes. The technological method of instrumentation had no significant effect. The quality of evidence, was based primarily on study design and only randomized control trials were considered to be “high” quality of evidence.

CONCLUSIONS: The reported success rates show improvement over time. Weighted success rates for studies with a minimum of four-year follow-up had better outcomes, compared to those with less than four years, when ‘strict criteria’ were used.

PMID:35334111 | DOI:10.1111/iej.13736