Category: Nevin Manimala

Soc Sci Med. 2022 Jan 29;296:114755. doi: 10.1016/j.socscimed.2022.114755. Online ahead of print.

ABSTRACT

Black South Africans accounted for 6.2 out of 6.4 million people living with HIV in South Africa in 2012, highlighting extreme racial disparities in HIV infection. These racial disparities are the result of structural and historical factors, specifically, the racist policies which were facilitated by segregation before, during, and after Apartheid. First, we describe the theoretical context of how racist policies and segregation are linked to HIV prevalence. Next, using data from a 2012 national survey of South Africans (SABSSM IV) and Statistics South Africa (StatsSA), we describe the race-specific geospatial distribution of HIV in South Africa, provide empirical evidence for the impact of Apartheid on important risk factors for HIV infection, and describe the relationship between these risk factors and HIV within racial groups. Using multilevel logistic regression, we find that segregation increases the odds of HIV infection among Black South Africans, even after adjusting for many covariates which are sometimes blamed, in place of structural factors, for a higher HIV prevalence in Black South Africans. We found that the estimated odds of infection in the most segregated municipality was 1.95 (95% CI: 1.15, 3.32) times the odds of infection in the least segregated municipality for Black South Africans. In addition to segregation, we also find other covariates to be differentially associated with HIV infection depending on race, such as gender, age, and sexual behavior. We also find that the HIV infection odds ratio comparing Black and Coloured (i.e., multiple ethnic groups with mixed ancestries from Africa, Asia, and Europe) South Africans varies over space. These results continue to build evidence for the influence of structural and historical factors on the modern geospatial and demographic distribution of HIV.

PMID:35123373 | DOI:10.1016/j.socscimed.2022.114755

Drug Alcohol Depend. 2022 Jan 29;232:109336. doi: 10.1016/j.drugalcdep.2022.109336. Online ahead of print.

ABSTRACT

BACKGROUND: Research on the impact of Medicaid expansion on buprenorphine utilization has largely focused on the Medicaid program. Less is known about its associations with total buprenorphine utilization and non-Medicaid payers.

METHODS: Monthly prescription data (June 2013-May 2018) for proprietary and generic sublingual as well as buccal buprenorphine products were purchased from IQVIA®. Population-adjusted state-level utilization measures were constructed for Medicaid, commercial insurance, Medicare, cash, and total utilization. A difference-in-differences (DID) approach with population weights estimated the association between Medicaid expansion and buprenorphine utilization, while controlling for treatment capacity.

RESULTS: Monthly total buprenorphine prescriptions increased by 68% overall and increased 283% for Medicaid, 30% for commercial insurance, and 143% for Medicare. Cash prescriptions decreased by 10%. The DID estimate for Medicaid expansion was not statistically significant for total utilization (-19.780, 95% CI = -45.118, 5.558, p = .123). For Medicaid buprenorphine utilization, there was a significant increase of 27.120 prescriptions per 100,000 total state residents (95% CI = 9.458, 44.782, p = .003) in expansion states versus non-expansion states post-Medicaid expansion. Medicaid expansion had a negative effect on commercial insurance (DID estimate = -37.745, 95% CI = -62.946, -12.544, p = .004), cash utilization (DID estimate = -6.675, 95% CI = -12.627, -0.723, p = .029), and Medicare utilization (DID estimate = -1.855, 95% CI = -3.697, -0.013, p = .048).

DISCUSSION: The associations between Medicaid expansion and buprenorphine utilization varied across different types of payers, such that the overall impact of Medicaid expansion on buprenorphine utilization was not significant.

PMID:35123365 | DOI:10.1016/j.drugalcdep.2022.109336

J Colloid Interface Sci. 2022 Jan 25;615:50-58. doi: 10.1016/j.jcis.2022.01.141. Online ahead of print.

ABSTRACT

Nanoparticle tracking analysis (NTA) is a single particle tracking technique that in principle provides a more direct measure of particle size distribution compared to dynamic light scattering (DLS). Here, we demonstrate how statistical mixture distribution analysis can be used in combination with NTA to quantitatively characterize the amount and extent of particle binding in a mixture of nanomaterials. The combined approach is used to study the binding of gold nanoparticles to two types of phospholipid vesicles, those containing and lacking the model ion channel peptide gramicidin A. This model system serves as both a proof of concept for the method and a demonstration of the utility of the approach in studying nano-bio interactions. Two diffusional models (Stokes-Einstein and Kirkwood-Riseman) were compared in the determination of particle size, extent of binding, and nanoparticle:vesicle binding ratios for each vesicle type. The combination of NTA and statistical mixture distributions is shown to be a useful method for quantitative assessment of the extent of binding between particles and determination of binding ratios.

PMID:35123359 | DOI:10.1016/j.jcis.2022.01.141

Drug Alcohol Depend. 2022 Jan 29;232:109332. doi: 10.1016/j.drugalcdep.2022.109332. Online ahead of print.

ABSTRACT

BACKGROUND: This study examined associations of local cannabis retail outlet availability and neighborhood disadvantage with cannabis use and related risk factors among young adults.

METHODS: Data were from annual cross-sectional surveys administered from 2015 to 2019 to individuals ages 18-25 residing in Washington State (N = 10,009). As outcomes, this study assessed self-reported cannabis use at different margins/frequencies (any past year, at least monthly, at least weekly, at least daily) and perceived ease of access to cannabis and acceptability of cannabis use in the community. Cannabis retail outlet availability was defined as the presence of at least one retail outlet within a 1-kilometer road network buffer of one’s residence. Sensitivity analyses explored four other spatial metrics to define outlet availability (any outlet within 0.5-km, 2-km, and the census tract; and census tract density per 1000 residents). Census tract level disadvantage was a composite of five US census variables.

RESULTS: Adjusting for individual- and area-level covariates, living within 1-kilometer of at least one cannabis retail outlet was statistically significantly associated with any past year and at least monthly cannabis use as well as high perceived access to cannabis. Results using a 2-km buffer and census tract-level metrics for retail outlet availability showed similar findings. Neighborhood disadvantage was statistically significantly associated with at least weekly and at least daily cannabis use and with greater perceived acceptability of cannabis use.

CONCLUSIONS: Results may have implications for regulatory and prevention strategies to reduce the population burden of cannabis use and related harms.

PMID:35123361 | DOI:10.1016/j.drugalcdep.2022.109332

Poult Sci. 2022 Jan 8;101(4):101700. doi: 10.1016/j.psj.2022.101700. Online ahead of print.

ABSTRACT

Large White male turkey genetic lines (GL) comparison in performance and processing yields under the same conditions are rare in the literature. Two rearing experiments (EXP) were conducted to accomplish 2 objectives. The first objective was to test the effects of poult source and genetic lines on performance and processing yields. The second objective was to extract season and growth patterns when comparing both EXP common treatments. In EXP 1, male poults from 5 different sources were randomly assigned to 48 concrete: litter-covered floor pens. In EXP 2, male poults from 7 different genetic lines were randomly assigned to 48 concrete: litter-covered floor pens. For both EXP, the experimental design was a completely randomized block design with a one-factor arrangement. Both EXP were placed in the same house with the same management and nutrition in two separate seasons of the same year. Bird performance and carcass processing yield were analyzed in SAS 9.4 or JMP 15.1 in a mixed model. In EXP 1 no significant difference in BW or processing yield was observed. However, a similar GL from a commercial hatchery had an improved feed conversion ratio (FCR) over the same GL sourced directly from the genetic company hatchery. In EXP 2, statistical differences were observed in performance and breast meat yield depending on the GL. A season effect was observed when comparing the two EXP. Birds raised in the fall season had a 2 kg BW increase, on average, over their spring counterparts. This difference in BW can also be observed in a statistically higher breast meat yield by the birds raised in the fall over the ones raised in the spring. In conclusion, a comparison between GL resulted in effects due to genetic line, poult source, and rearing season on bird performance and carcass yield.

PMID:35123351 | DOI:10.1016/j.psj.2022.101700

Leg Med (Tokyo). 2022 Jan 31;56:102031. doi: 10.1016/j.legalmed.2022.102031. Online ahead of print.

ABSTRACT

After death human body is subject to the processes of autolysis and putrefaction. Notably, the changes in cadaver during decomposition complicate its forensic analysis and particularly the estimation of the post-mortem interval (PMI). The process and rate of decomposition is impacted by various intrinsic and extrinsic factors that vary across countries and regions. Studying the decomposition pattern in different regions in the world helps us to understand the process and improve the precision of the PMI estimation of decomposed bodies. With the aim to develop a taphonomic model of decomposition in the province of Barcelona (Catalonia, Spain), this study analyses the influence of several intrinsic and extrinsic factors in the pattern and rate of decomposition in this geographical area. Our statistical model concluded that the most significant factors affecting the decomposition pattern and rate are temperature and PMI. Nevertheless, there are other intrinsic factors such as cause, manner of death and underlying pathological conditions which also have an important role. Moreover, considering the various variables studied in this research, two predictive machine learning algorithms were developed as a probabilistic approach to estimate the PMI. Reliable classification results are obtained for three interval groups (1-2 days, 3-10 days, and > 10 days) and two interval groups (>1 week, < 1 week). Machine learning algorithm is a promising tool to gain objectivity in forensic PMI assessments. The results of this study could potentially assist further research in forensic taphonomy.

PMID:35123354 | DOI:10.1016/j.legalmed.2022.102031

Comput Methods Programs Biomed. 2022 Jan 26;216:106663. doi: 10.1016/j.cmpb.2022.106663. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Alert of patient deterioration is essential for prompt medical intervention in the Medical Intensive Care Unit (MICU). Logistic Regression (LR) has been used for the development of most conventional severity-of-illness scoring systems to anticipate the risk of mortality in the MICU. Machine Learning (ML) models such as probabilistic graphical models and Extreme Gradient Boosting (XGB) have demonstrated improved prediction accuracy in patient outcomes compared to LR. The aim was to compare three ML models to the SAPS, SAPS II, SAPS III, SOFA, serial SOFA, LODS, and OASIS for prediction of MICU mortality.

METHODS: A Bayesian Network (BN), Naïve Bayes network (NB), and a XGB model were developed. 9893 adult MICU-stays from the MIMIC-III database were studied. The primary outcome was MICU mortality prediction and the secondary outcome was 1-year mortality prediction. Data analyzed consisted on routine physiological measurements collected during 5 hours in the MICU, demographic and diagnoses/procedure features. The performance was evaluated by accuracy statistics, discrimination and calibration measures. Limitations of the study were discussed.

RESULTS: The AUROC for MICU mortality prediction was 0.919 for XGB, 0.905 for BN, and 0.864 for NB, while the conventional systems displayed much lower values with the serial SOFA having the best value (0.814). The Diagnostic Odds Ratio was ≤7.099 for all the conventional systems, reaching values of 30.115 for XGB and 22.648 for BN. The XGB achieved a sensitivity of 0.831 and specificity of 0.86 assuring an acceptable precision (0.528), whose values were much lower for the conventional systems. The Brier score was better for the ML models, except for the NB (0.119), with 0.072 for XGB and 0.081 for BN.

CONCLUSIONS: The XGB and BN substantially outperformed the conventional systems for discrimination, calibration and the accuracy statistics assessed. The NB showed inferior performance to the XGB and BN but improved the discrimination and all accuracy statistics of the conventional systems except for an inferior calibration and 1-year mortality discrimination. The XGB showed the best performance among all models. These ML models have the potential to improve the monitoring of MICU patients, which must be evaluated in future studies.

PMID:35123348 | DOI:10.1016/j.cmpb.2022.106663

J Psychiatr Res. 2022 Jan 15;148:127-130. doi: 10.1016/j.jpsychires.2022.01.017. Online ahead of print.

ABSTRACT

OBJECTIVE: COVID-19 and associated measures to control the spread of the COVID-19 has significantly impacted clinical research. This study aimed to determine the impact COVID-19 has had on psychiatric clinical trials and to assess whether certain trial areas or trial types were differentially affected.

METHODS: We used information from ClinicalTrials.gov, the largest online database of clinical trial information, to examine changes in psychiatric clinical trials from January 2010-December 2020.

RESULTS: Clinical trial initiation decreased in 2020, with a year-on-year percent change in trial initiation of -5.4% versus an expected percent change based on forecasting observed trends from 2010 to 2019 of 8.6%. When broken down by disease area, the distribution of trials observed in 2020 was significantly different from the predicted distribution (p < 0.00001). The greatest decrease in trial initiation was seen in Schizophrenia-specific trials, with an observed percent change of -29.2% versus an expected percent change of 3.2%. Conversely, anxiety trials saw a significant increase in trial initiation during 2020, with an observed percent change of 24.6% versus an expected percent change of 16.0%. When assessing interventional versus observational studies, data showed a significant increase in initiation of observational psychiatric clinical trials (p < 0.05), and a significant decrease in initiation of interventional psychiatric clinical trials (p < 0.01). When data was analyzed on a month-by-month time scale, 7/12 months in 2020 showed significant decreases when compared to initiation during matching months over prior years, and a single month, June, showed a significant increase.

CONCLUSION: COVID-19 has had significant impacts on the initiation of psychiatric clinical trials over 2020, and this decrease in trial initiation may have long-term impacts on the development and assessment of psychiatric treatments and therapeutics.

PMID:35123324 | DOI:10.1016/j.jpsychires.2022.01.017

Dev Cogn Neurosci. 2022 Jan 15;54:101072. doi: 10.1016/j.dcn.2022.101072. Online ahead of print.

ABSTRACT

Developmental researchers are often interested in event-related potentials (ERPs). Data-analytic approaches based on the observed ERP suffer from major problems such as arbitrary definition of analysis time windows and regions of interest and the observed ERP being a mixture of latent underlying components. Temporal principal component analysis (PCA) can reduce these problems. However, its application in developmental research comes with the unique challenge that the component structure differs between age groups (so-called measurement non-invariance). Separate PCAs for the groups can cope with this challenge. We demonstrate how to make results from separate PCAs accessible for inferential statistics by re-scaling to original units. This tutorial enables readers with a focus on developmental research to conduct a PCA-based ERP analysis of amplitude differences. We explain the benefits of a PCA-based approach, introduce the PCA model and demonstrate its application to a developmental research question using real-data from a child and an adult group (code and data openly available). Finally, we discuss how to cope with typical challenges during the analysis and name potential limitations such as suboptimal decomposition results, data-driven analysis decisions and latency shifts.

PMID:35123341 | DOI:10.1016/j.dcn.2022.101072

Mult Scler Relat Disord. 2022 Jan 10;59:103516. doi: 10.1016/j.msard.2022.103516. Online ahead of print.

ABSTRACT

BACKGROUND: In many clinical situations, ordinal scales afford the primary method of semi-quantifying patient outcomes. In the field of multiple sclerosis, the primary ordinal scale is the Expanded Disability Status Scale. Predominant methods of ordinal scale statistical analysis provide a p-value without effect size or rely heavily on the assumption of proportionality of odds, subjecting them to lack of power and error. The Wilcoxon-Manny-Whitney Odds is a statistical method which provides significant information such as p-value, effect size, number needed to treat, confidence intervals, and is largely assumption-free. However, its utility has not been demonstrated in the field of multiple sclerosis.

METHODS: Three clinical studies in the field of multiple sclerosis were selected which utilized ordinal scale outcomes at group or individual levels. Data from these studies was extracted using WebPlotDigitizer, and a custom Wilxocon-Mann-Whitney Odds software was applied to each dataset to re-analyze the main outcomes of the studies.

RESULTS: Re-analysis of the manuscript by Muraro et al., 2017 demonstrated that autologous stem cell transplantation for relapsing remitting multiple sclerosis resulted in a 65% chance of improving from any Expanded Disability Status Scale category, although not significant. Re-analysis of the manuscript by Songthammawat et al., 2019 demonstrated chance of improvement with intravenous methylprednisolone and concurrent plasma exchange was 185% versus 32% in intravenous methylprednisolone with add-on plasma exchange, although not significant. Re-analysis of Kister et al., 2012 demonstrated the chances of mobility or cognition scores generally favored decline at every 5-year increment of study, and although statistically significant, these were smaller effect sizes ranging from an 11% chance of improvement to a 66% chance of decline over a 5-year interval.

DISCUSSION: The Wilcoxon-Mann-Whitney Odds simplifies ordinal data analysis with its robust largely assumption-free nature. In the place of numerous statistical tests, this single test provides effect size estimate, number needed to treat, p-values, and confidence intervals. Importantly, the Wilcoxon-Mann-Whitney Odds effect size calculation is intuitively applicable to both individual and population-levels. Further, the Wilcoxon-Mann-Whitney Odds allows intuitive description of the progression of large cohorts over time, and we were able to clearly convey the odds of mobility and cognitive decline over 30 years in a large multiple sclerosis cohort. Overall, the Wilcoxon-Mann-Whitney Odds is a powerful and robust statistical test with significant promise within the field of multiple sclerosis.

PMID:35123291 | DOI:10.1016/j.msard.2022.103516