Category: Statistics

Commun Med (Lond). 2023 Feb 10;3(1):21. doi: 10.1038/s43856-023-00250-8.

ABSTRACT

BACKGROUND: Patients with Type 2 diabetes mellitus (T2D) are at risk for micro- and macrovascular complications. Implementable risk scores are needed to improve targeted prevention for patients that are particularly susceptible to complications. The epigenetic clock estimates an individual’s biological age using DNA methylation profiles.

METHODS: In this study, we examined older adults of the Berlin Aging Study II that were reexamined on average 7.4 years after baseline assessment as part of the GendAge study. DNA methylation age (DNAmA) and its deviation from chronological age DNAmA acceleration (DNAmAA) were calculated with the 7-CpG clock (available at both timepoints, n = 1,071), Horvath’s clock, Hannum’s clock, PhenoAge and GrimAge (available at follow-up only, n = 1,067). T2D associated complications were assessed with the Diabetes Complications Severity Index (DCSI).

RESULTS: We report on a statistically significant association between oral glucose tolerance test results and Hannum and PhenoAge DNAmAA. PhenoAge was also associated with fasting glucose. In contrast, we found no cross-sectional association after covariate adjustment between DNAmAA and a diagnosis of T2D. However, longitudinal analyses showed that every additional year of 7-CpG DNAmAA at baseline increased the odds for developing one or more additional complications or worsening of an already existing complication during the follow-up period by 11% in male participants with T2D. This association persisted after covariate adjustment (OR = 1.11, p = 0.045, n = 56).

CONCLUSION: Although our results remain to be independently validated, this study shows promising evidence of utility of the 7-CpG clock in identifying patients with diabetes who are at high risk for developing complications.

PMID:36765171 | DOI:10.1038/s43856-023-00250-8

Nat Methods. 2023 Feb;20(2):259-267. doi: 10.1038/s41592-022-01750-6. Epub 2023 Feb 10.

ABSTRACT

Single-molecule localization microscopy (SMLM) generates data in the form of coordinates of localized fluorophores. Cluster analysis is an attractive route for extracting biologically meaningful information from such data and has been widely applied. Despite a range of cluster analysis algorithms, there exists no consensus framework for the evaluation of their performance. Here, we use a systematic approach based on two metrics to score the success of clustering algorithms in simulated conditions mimicking experimental data. We demonstrate the framework using seven diverse analysis algorithms: DBSCAN, ToMATo, KDE, FOCAL, CAML, ClusterViSu and SR-Tesseler. Given that the best performer depended on the underlying distribution of localizations, we demonstrate an analysis pipeline based on statistical similarity measures that enables the selection of the most appropriate algorithm, and the optimized analysis parameters for real SMLM data. We propose that these standard simulated conditions, metrics and analysis pipeline become the basis for future analysis algorithm development and evaluation.

PMID:36765136 | DOI:10.1038/s41592-022-01750-6

Sci Rep. 2023 Feb 10;13(1):2435. doi: 10.1038/s41598-023-28752-4.

ABSTRACT

One clear aspect of behaviour in the COVID-19 pandemic has been people’s focus on, and response to, reported or observed infection numbers in their community. We describe a simple model of infectious disease spread in a pandemic situation where people’s behaviour is influenced by the current risk of infection and where this behavioural response acts homeostatically to return infection risk to a certain preferred level. This homeostatic response is active until approximate herd immunity is reached: in this domain the model predicts that the reproduction rate R will be centred around a median of 1, that proportional change in infection numbers will follow the standard Cauchy distribution with location and scale parameters 0 and 1, and that high infection numbers will follow a power-law frequency distribution with exponent 2. To test these predictions we used worldwide COVID-19 data from 1st February 2020 to 30th June 2022 to calculate [Formula: see text] confidence interval estimates across countries for these R, location, scale and exponent parameters. The resulting median R estimate was [Formula: see text] (predicted value 1) the proportional change location estimate was [Formula: see text] (predicted value 0), the proportional change scale estimate was [Formula: see text] (predicted value 1), and the frequency distribution exponent estimate was [Formula: see text] (predicted value 2); in each case the observed estimate agreed with model predictions.

PMID:36765110 | DOI:10.1038/s41598-023-28752-4

Sci Data. 2023 Feb 10;10(1):86. doi: 10.1038/s41597-023-01961-2.

ABSTRACT

Understanding the fine scale and subnational spatial distribution of reproductive, maternal, newborn, child, and adolescent health and development indicators is crucial for targeting and increasing the efficiency of resources for public health and development planning. National governments are committed to improve the lives of their people, lift the population out of poverty and to achieve the Sustainable Development Goals. We created an open access collection of high resolution gridded and district level health and development datasets of India using mainly the 2015-16 National Family Health Survey (NFHS-4) data, and provide estimates at higher granularity than what is available in NFHS-4, to support policies with spatially detailed data. Bayesian methods for the construction of 5 km × 5 km high resolution maps were applied for a set of indicators where the data allowed (36 datasets), while for some other indicators, only district level data were produced. All data were summarised using the India district administrative boundaries. In total, 138 high resolution and district level datasets for 28 indicators were produced and made openly available.

PMID:36765058 | DOI:10.1038/s41597-023-01961-2

Nat Commun. 2023 Feb 10;14(1):761. doi: 10.1038/s41467-022-35712-5.

ABSTRACT

The anticipation of progression of Alzheimer’s disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.

PMID:36765056 | DOI:10.1038/s41467-022-35712-5

BDJ Open. 2023 Feb 10;9(1):5. doi: 10.1038/s41405-023-00131-1.

ABSTRACT

OBJECTIVES: To determine the overall estimated financial impact and related expenses incurred over the duration of the undergraduate Dental Degree in Thailand.

METHODS: A cross-sectional survey was conducted among all 658 dental undergraduates in Mahidol University, Thailand. Data was collected through a self-administered questionnaire, including the following information: (1) “Background and Demographics”: household income, hometown, residence during study and source(s) of any financial aid received; (2) “Living Expenses”: Living costs including food, transportation, rent, utility bills, and recreational expenses; (3) “Education related expenses”: Including textbooks, stationeries, uniforms, and student activities fees. A cost-median was used as a baseline representation for the actual cost of each item. The mean differences of all expenses between groups before estimation was assessed by using the analysis of variance (ANOVA) method. The statistically significant differences were identified at p < 0.001.

RESULTS: The estimated adjusted cost of becoming a dentist in Thailand is THB1,265,027 (36,143.63 USD) for students living at home and THB1,823,027 (52,086.49 USD) for those renting accommodation. Students who rented accommodation incurred significantly higher yearly living expenses than those who were living at home. (p < 0.001). The majority of participants (78.4%) were in households having a middle-to-high socioeconomic status. Ninety-five percentages of the participants’ received 100% financial support from their families with no additional source of income, which reflects no real diversity in the socioeconomic background of Dental Degree students.

CONCLUSION: The cost of a higher education Dental Degree in Thailand can be a significant barrier to entry and financial burden, especially for students from disadvantaged socioeconomic backgrounds. Government and Educational Policy makers need to pay more attention to this issue in order to provide equal opportunities for obtaining a University Dental Degree for all Thai students wishing to pursue this career path.

PMID:36765031 | DOI:10.1038/s41405-023-00131-1

J Occup Environ Med. 2023 Feb 11. doi: 10.1097/JOM.0000000000002807. Online ahead of print.

ABSTRACT

OBJECTIVE: This prospective case-control study aimed to investigate the forms and conditions of respiratory effects in workers working in an Aluminum Profile Factory.

METHODS: All male (42 person, mean age: 32.2 ± 6.9) workers working in an Aluminum Profile Factory were compared with 33 controls.

RESULTS: The urinary aluminum levels of the workers were significantly higher than the control group. Complaints of cough, sputum, shortness of breath and wheezing were statistically significantly higher than the control group. In aluminum workers, those with dyspnea had a significantly higher urinary Al level than those without dyspnea.

CONCLUSION: It is thought that primary and secondary prevention are both important in the workplaces with aluminum exposure. Urinary aluminum level monitoring could be key to protecting the respiratory health of the workers.

PMID:36765029 | DOI:10.1097/JOM.0000000000002807

Pain Ther. 2023 Feb 10. doi: 10.1007/s40122-023-00479-0. Online ahead of print.

ABSTRACT

Acute low back pain (LBP) stands as a leading cause of activity limitation and work absenteeism, and its associated healthcare expenditures are expected to become substantial when acute LBP develops into a chronic and even refractory condition. Therefore, early intervention is crucial to prevent progression to chronic pain, for which the management is particularly challenging and the most effective pharmacological therapy is still controversial. Current guideline treatment recommendations vary and are mostly driven by expertise with opinion differing across different interventions. Thus, it is difficult to formulate evidence-based guidance when the relatively few randomized clinical trials have explored the diagnosis and management of LBP while employing different selection criteria, statistical analyses, and outcome measurements. This narrative review aims to provide a critical appraisal of current acute LBP management by discussing the unmet needs and areas of improvement from bench-to-bedside, and proposes multimodal analgesia as the way forward to attain an effective and prolonged pain relief and functional recovery in patients with acute LBP.

PMID:36765012 | DOI:10.1007/s40122-023-00479-0

Environ Sci Pollut Res Int. 2023 Feb 11. doi: 10.1007/s11356-023-25720-3. Online ahead of print.

ABSTRACT

Karnataka state has the second highest rainfed agricultural land in India, where agricultural output relies heavily on rainfall. The Shimsha basin, a sub-basin of Cauvery in the state, comes under a semi-arid region and predominantly consists of rainfed agricultural land. Rainfall patterns have changed dramatically with time resulting in frequent floods and droughts. Understanding the spatiotemporal distribution of rainfall and its change patterns in the area would benefit sustainable agriculture planning and water resources management practices. The current study aims to determine the variability and trend in rainfall. The daily rainfall data of the Shimsha basin from 1989 to 2018 is collected, and the annual, seasonal, and monthly rainfall totals and the number of rainy days are derived. All the time series are subjected to statistical methods to examine rainfall variability and trend. The mean, standard deviation, coefficient of variation (CV), and Standardized Anomaly Index are used for the preliminary and variability analysis, while the coefficient of skewness and kurtosis are used to understand the rainfall distribution characteristics. The homogenous and serially independent series are identified by homogeneity and serial correlation tests. The trend in the homogenous and serially independent series is identified by Mann-Kendall and Spearman’s rank correlation tests, while the magnitude of the trend is quantified using the Sen’s slope technique, and the trend change point is evaluated using the sequential Mann-Kendall test. Based on the study, the average rainfall in the study area is 801.86 mm, with CV ranging from 43.3 to 22.27%. The southwest monsoon (SWM) season brings the greatest rain to the basin, followed by the post-monsoon (PM), summer, and winter seasons. In the annual time frame, except one station, all other stations have shown significant or insignificant increasing trends. The seasonal rainfall has shown insignificant rising trends during the summer and winter seasons while insignificant increasing and decreasing trends during the PM season. The SWM season has indicated significant increasing trends, insignificant increasing and decreasing trends. Overall, the study area has noticed an increased annual and seasonal rainfall except for the post-monsoon season, during which the rainfall showed a considerable decline. The findings of the study are helpful in water resource management, agricultural planning, and socioeconomic development in the study area.

PMID:36764993 | DOI:10.1007/s11356-023-25720-3

J Neural Eng. 2023 Feb 10. doi: 10.1088/1741-2552/acbb2b. Online ahead of print.

ABSTRACT

OBJECTIVE: Suboptimal electrode placement during subthalamic deep brain stimulation (STN DBS) surgery may arise from several sources, including frame-based targeting errors and intraoperative brain shift. We present a computer algorithm that can accurately localize intraoperative microelectrode recording (MER) tracks on preoperative magnetic resonance imaging (MRI) in real-time, thereby predicting deviation between the surgical plan and the MER trajectories.

APPROACH: Random forest (RF) modeling was used to derive a statistical relationship between electrophysiological features on intraoperative MER and voxel intensity on preoperative T2-weighted MR imaging. This model was integrated into a larger algorithm that can automatically localize intraoperative MER recording tracks on preoperative MRI in real-time. To verify accuracy, targeting error of both the planned intraoperative trajectory (“planned”) and the algorithm-derived trajectory (“calculated”) was estimated by measuring deviation from the final DBS lead location on postoperative high-resolution computed tomography (“actual”).

MAIN RESULTS: MR imaging and MERs were obtained from 24 STN DBS implant trajectories. The cross-validated RF model could accurately distinguish between gray and white matter regions along MER trajectories (AUC 0.84). When applying this model within the localization algorithm, the calculated MER trajectory estimate was found to be significantly closer to the actual DBS lead when compared to the planned trajectory recorded during surgery (1.04 mm vs 1.52 mm deviation, p<0.002), with improvement shown in 19/24 cases (79%). When applying the algorithm to simulated DBS trajectory plans with randomized targeting error, up to 4 mm of error could be resolved to <2 mm on average (p<0.0001).

SIGNIFICANCE: This work presents an automated system for intraoperative localization of electrodes during STN DBS surgery. This neuroengineering solution may enhance the accuracy of electrode position estimation, particularly in cases where high-resolution intraoperative imaging is not available.

PMID:36763997 | DOI:10.1088/1741-2552/acbb2b