Tag: pubmed

JAMA Netw Open. 2023 Aug 1;6(8):e2327421. doi: 10.1001/jamanetworkopen.2023.27421.

ABSTRACT

IMPORTANCE: Early-life socioeconomic adversity may be associated with poor cognitive health over the life course.

OBJECTIVE: To examine the association of childhood and midlife neighborhood socioeconomic position (nSEP) with cognitive decline.

DESIGN, SETTING, AND PARTICIPANTS: This cohort study included 5711 men and women enrolled in the community-based Atherosclerosis Risk in Communities (ARIC) Study with repeated cognitive data measured over a median 27.0 years (IQR, 26.0-27.9 years) (1990-2019). Statistical analysis was performed from December 2022 through March 2023.

EXPOSURE: Residence addresses for ARIC Study cohort participants were obtained at midlife (1990-1993) and as recalled addresses at 10 years of age (childhood). A composite nSEP z score was created as a sum of z scores for US Census-based measures of median household income; median value of owner-occupied housing units; percentage of households receiving interest, dividend, or net rental income; percentage of adults with a high school degree; percentage of adults with a college degree; and percentage of adults in professional, managerial, or executive occupations. Childhood nSEP and midlife nSEP were modeled as continuous measures and discretized into tertiles.

MAIN OUTCOMES AND MEASURES: A factor score for global cognition was derived from a battery of cognitive tests administered at 5 in-person visits from baseline to 2019. The rate of cognitive decline from 50 to 90 years of age was calculated by fitting mixed-effects linear regression models with age as the time scale and adjusted for race, sex, birth decade, educational level, and presence of the apolipoprotein E ε4 allele.

RESULTS: Among 5711 ARIC Study participants (mean [SD] baseline age, 55.1 [4.7] years; 3372 women [59.0%]; and 1313 Black participants [23.0%]), the median rate of cognitive decline was -0.33 SDs (IQR, -0.49 to -0.20 SDs) per decade. In adjusted analyses, each 1-SD-higher childhood nSEP score was associated with a slower (β, -9.2%; 95% CI, -12.1% to -6.4%) rate of cognitive decline relative to the sample median. A comparable association was observed when comparing the highest tertile with the lowest tertile of childhood nSEP (β, -17.7%; 95% CI, -24.1% to -11.3%). Midlife nSEP was not associated with the rate of cognitive decline.

CONCLUSIONS AND RELEVANCE: In this cohort study of contextual factors associated with cognitive decline, childhood nSEP was inversely associated with trajectories of cognitive function throughout adulthood.

PMID:37540511 | DOI:10.1001/jamanetworkopen.2023.27421

Cancer Epidemiol Biomarkers Prev. 2023 Aug 4:EPI-23-0132. doi: 10.1158/1055-9965.EPI-23-0132. Online ahead of print.

ABSTRACT

BACKGROUND: Cervical cancer disparities exist in the United States with the highest incidence in Hispanic women and the highest mortality in Black women. Effective control of cervical cancer in the population requires targeted interventions tailored to community composition in terms of race, ethnicity, and social determinants of health (SDOH).

METHODS: Using cancer registry and SDOH data, geospatial hot spot analyses were carried out to identify statistically significant neighborhood clusters with high numbers of cervical cancer cases within the catchment area of an NCI-designated cancer center. The locations, racial and ethnic composition, and SDOH resources of these hot spots were used by the center’s community outreach and engagement office to deploy mobile screening units (MSU) for intervention in communities with women facing heightened risk for cervical cancer.

RESULTS: Neighborhood hot spots with high numbers of cervical cancer cases in South Florida largely overlap with locations of poverty. Cervical cancer hot spots are associated with a high percentage of Hispanic cases and low SDOH status, including low income, housing tenure, and education attainment.

CONCLUSIONS: A geospatially referenced cancer surveillance platform integrating cancer registry, SDOH, and cervical screening data can effectively identify targets for cervical cancer intervention in neighborhoods experiencing disparities.

IMPACT: Guided with a data-driven surveillance system, MSUs proactively bringing prevention education and cervical screening to communities with more unscreened, at-risk women are an effective means for addressing disparities associated with cervical cancer control.

PMID:37540496 | DOI:10.1158/1055-9965.EPI-23-0132

Vet Res Commun. 2023 Aug 4. doi: 10.1007/s11259-023-10183-1. Online ahead of print.

ABSTRACT

Leptospirosis is a zoonotic disease with significant public health and economic impact worldwide. Rapid and accurate diagnosis is essential for effective prevention and treatment. This study optimized a loop-mediated isothermal amplification (LAMP) assay using BFo isothermal DNA polymerase with different colorimetric indicators. LAMP was able to detect DNA from pathogenic and intermediate leptospires, while non-pathogenic leptospires and other non-leptospiral microorganisms were negative. LAMP assay combined with calcein showed a tenfold higher limit of detection (1 ng of leptospiral DNA per reaction) than LAMP combined with hydroxynaphthol blue or end-point PCR lipL32 (10 ng of DNA per reaction). Animal samples were collected from infected and non-infected Golden Syrian hamsters (Mesocricetus auratus) to evaluate and compare the performance of LAMP and PCR. These techniques showed a substantial agreement according to Cohen’s kappa statistic, being both useful techniques for detecting leptospiral DNA in clinical samples. Overall, this study demonstrates that the LAMP assay is a sensitive, specific, rapid, and simple tool for the detection of leptospiral DNA. It has the potential to facilitate the diagnosis of leptospirosis, particularly in low-income regions with limited diagnosis resources.

PMID:37540477 | DOI:10.1007/s11259-023-10183-1

J Epidemiol Glob Health. 2023 Aug 4. doi: 10.1007/s44197-023-00141-0. Online ahead of print.

ABSTRACT

BACKGROUND: Evaluating mortality effects of the COVID-19 pandemic using all-cause mortality data for national populations is inevitably associated with the risk of masking important subnational differentials and hampering targeted health policies. This study aims at assessing simultaneously cause-specific, spatial and seasonal mortality effects attributable to the pandemic in Germany in 2020.

METHODS: Our analyses rely on official cause-of-death statistics consisting of 5.65 million individual death records reported for the German population during 2015-2020. We conduct differential mortality analyses by age, sex, cause, month and district (N = 400), using decomposition and standardisation methods, comparing each strata of the mortality level observed in 2020 with its expected value, as well as spatial regression to explore the association of excess mortality with pre-pandemic indicators.

RESULTS: The spatial analyses of excess mortality reveal a very heterogenous pattern, even within federal states. The coastal areas in the north were least affected, while the south of eastern Germany experienced the highest levels. Excess mortality in the most affected districts, with standardised mortality ratios reaching up to 20%, is driven widely by older ages and deaths reported in December, particularly from COVID-19 but also from cardiovascular and mental/nervous diseases.

CONCLUSIONS: Our results suggest that increased psychosocial stress influenced the outcome of excess mortality in the most affected areas during the second lockdown, thus hinting at possible adverse effects of strict policy measures. It is essential to accelerate the collection of detailed mortality data to provide policymakers earlier with relevant information in times of crisis.

PMID:37540473 | DOI:10.1007/s44197-023-00141-0

Behav Res Methods. 2023 Jul 24. doi: 10.3758/s13428-023-02132-2. Online ahead of print.

ABSTRACT

Outcome reporting bias (ORB) refers to the biasing effect caused by researchers selectively reporting outcomes within a study based on their statistical significance. ORB leads to inflated effect size estimates in meta-analysis if only the outcome with the largest effect size is reported due to ORB. We propose a new method (CORB) to correct for ORB that includes an estimate of the variability of the outcomes’ effect size as a moderator in a meta-regression model. An estimate of the variability of the outcomes’ effect size can be computed by assuming a correlation among the outcomes. Results of a Monte-Carlo simulation study showed that the effect size in meta-analyses may be severely overestimated without correcting for ORB. Estimates of CORB are close to the true effect size when overestimation caused by ORB is the largest. Applying the method to a meta-analysis on the effect of playing violent video games on aggression showed that the effect size estimate decreased when correcting for ORB. We recommend to routinely apply methods to correct for ORB in any meta-analysis. We provide annotated R code and functions to help researchers apply the CORB method.

PMID:37540470 | DOI:10.3758/s13428-023-02132-2

Behav Res Methods. 2023 Jul 19. doi: 10.3758/s13428-023-02108-2. Online ahead of print.

ABSTRACT

pyWitness is a python toolkit for recognition memory experiments, with a focus on eyewitness identification (ID) data analysis and model fitting. The current practice is for researchers to use different statistical packages to analyze a single dataset. pyWitness streamlines the process. In addition to conducting key data analyses (e.g., receiver operating characteristic analysis, confidence accuracy characteristic analysis), statistical comparisons, signal-detection-based model fits, simulated data generation, and power analyses are also possible. We describe the package implementation and provide detailed instructions and tutorials with datasets so that users can follow. There is also an online manual that is regularly updated. We developed pyWitness to be user-friendly, reduce human interaction with pre-processing and processing of data and model fits, and produce publication-ready plots. All pyWitness features align with open science practices, such that the algorithms, fits, and methods are reproducible and documented. While pyWitness is a python toolkit, it can also be used from R for users more accustomed to this environment.

PMID:37540469 | DOI:10.3758/s13428-023-02108-2

Behav Res Methods. 2023 Aug 1. doi: 10.3758/s13428-023-02117-1. Online ahead of print.

ABSTRACT

Multiple Imputation (MI) is one of the most popular approaches to addressing missing values in questionnaires and surveys. MI with multivariate imputation by chained equations (MICE) allows flexible imputation of many types of data. In MICE, for each variable under imputation, the imputer needs to specify which variables should act as predictors in the imputation model. The selection of these predictors is a difficult, but fundamental, step in the MI procedure, especially when there are many variables in a data set. In this project, we explore the use of principal component regression (PCR) as a univariate imputation method in the MICE algorithm to automatically address the many-variables problem that arises when imputing large social science data. We compare different implementations of PCR-based MICE with a correlation-thresholding strategy through two Monte Carlo simulation studies and a case study. We find the use of PCR on a variable-by-variable basis to perform best and that it can perform closely to expertly designed imputation procedures.

PMID:37540467 | DOI:10.3758/s13428-023-02117-1

Behav Res Methods. 2023 Aug 1. doi: 10.3758/s13428-023-02099-0. Online ahead of print.

ABSTRACT

Principal component analysis (PCA) is an important tool for analyzing large collections of variables. It functions both as a pre-processing tool to summarize many variables into components and as a method to reveal structure in data. Different coefficients play a central role in these two uses. One focuses on the weights when the goal is summarization, while one inspects the loadings if the goal is to reveal structure. It is well known that the solutions to the two approaches can be found by singular value decomposition; weights, loadings, and right singular vectors are mathematically equivalent. What is often overlooked, is that they are no longer equivalent in the setting of sparse PCA methods which induce zeros either in the weights or the loadings. The lack of awareness for this difference has led to questionable research practices in sparse PCA. First, in simulation studies data is generated mostly based only on structures with sparse singular vectors or sparse loadings, neglecting the structure with sparse weights. Second, reported results represent local optima as the iterative routines are often initiated with the right singular vectors. In this paper we critically re-assess sparse PCA methods by also including data generating schemes characterized by sparse weights and different initialization strategies. The results show that relying on commonly used data generating models can lead to over-optimistic conclusions. They also highlight the impact of choice between sparse weights versus sparse loadings methods and the initialization strategies. The practical consequences of this choice are illustrated with empirical datasets.

PMID:37540466 | DOI:10.3758/s13428-023-02099-0

Mol Biol Rep. 2023 Aug 4. doi: 10.1007/s11033-023-08680-2. Online ahead of print.

ABSTRACT

BACKGROUND: Acute myeloid leukemia (AML) is a type of blood cancer that affects the bone marrow and blood cells. AML is characterized by the rapid growth and accumulation of abnormal white blood cells, known as myeloblasts, which interfere with the production of normal blood cells.

AIMS: The main aim was to determine the relationship between these genetic alterations and the clinico-haematological parameters and prognostic factors with therapy for Iraqi patients with AML.

METHODS: We used Sanger Sequencing to detect the mutations in 76 AML patients. Clinical data of AML patients were retrospectively analysed to compare the prognosis of each gene mutation group.

RESULTS: Somatic mutations were identified in 47.4% of the enrolled patients in a core set of pathogenic genes, including FLT3 (18 patients, 23.7%), DNMT3A (14, 18.4%), NPM1 (11, 14.5%) and TP53 (5, 6.8%). As multiple mutations frequently coexisted in the same patient, we classified patients into 10 further groups. Two novel mutations were detected in FLT3-ITD, with new accession numbers deposited into NCBI GenBank (OP807465 and OP807466). These two novel mutations were computationally analysed and predicted as disease-causing mutations. We found significant differences between patients with and without the detected mutations in disease progression after induction therapy (remission, failure and death; pv = < 0.001) and statistically significant differences were reported in total leukocyte count (pv = < 0.0001).

CONCLUSION: These genes are among the most frequently mutated genes in AML patients. Understanding the molecular and clinical significance of these mutations is important for guiding treatment decisions and predicting patient outcomes.

PMID:37540457 | DOI:10.1007/s11033-023-08680-2

Environ Sci Pollut Res Int. 2023 Aug 4. doi: 10.1007/s11356-023-29038-y. Online ahead of print.

ABSTRACT

Roads and traffic are important elements of urbanization, but their spatial associations with surface water quality in watersheds have been seldom studied. In this study, the spatially varying associations of three urbanization indicators, including road density, traffic density, and percentages of urban land, with twenty water quality indicators, including dissolved oxygen (DO), specific conductance (SC), dissolved solids (DS), suspended solids (SS), biochemical oxygen demand (BOD), dissolved nutrients, dissolved ions, heavy metals, and coliform bacteria, across the watersheds in the northern part of the state of Georgia, USA, have been examined by a conventional statistical method, ordinary least squares regression (OLS), and a spatial statistical method, geographically weighted regression (GWR). The results from OLS show that the urbanization indicators all have significant positive associations with the majority of the studied water pollutants, indicating that water pollution is significantly contributed by human activities related to urbanization in northern Georgia. In contrast, GWR results show that the associations vary across the watersheds affected by their urbanization levels. Significant positive associations are found between each urbanization indicator and each of the studied water pollutants, but not in all watersheds. The associations of suspended solids, nitrogen nutrients, and coliform bacteria with all three urbanization indicators are more significant in less-urbanized watersheds, while the associations of dissolved ions, BOD, and orthophosphate (PO₄) with road density and traffic density are more significant than those with urban land in more-urbanized watersheds, indicating that those water pollutants are more contributed by human activities associated with roads and traffic than other activities in more-urbanized areas. As a pilot study to explore how and why the associations of surface water quality with roads and traffic change across watersheds with different urbanization levels, its findings suggest that the policies of watershed management, land-use planning, and transportation planning should be tailored in local areas based on the locally important water pollutants and their associated urbanization indicators.

PMID:37540414 | DOI:10.1007/s11356-023-29038-y