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"Statistical analysis of data"
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Prehistoric adaptation in the American Southwest
This resource is about post-Pleistocene adaptive change among the aboriginal cultures of the mountains and deserts of Arizona and New Mexico.
Book
The prevalence of insomnia in the general population in China: A meta-analysis
This is the first meta-analysis of the pooled prevalence of insomnia in the general population of China. A systematic literature search was conducted via the following databases: PubMed, PsycINFO, EMBASE and Chinese databases (China National Knowledge Interne (CNKI), WanFang Data and SinoMed). Statistical analyses were performed using the Comprehensive Meta-Analysis program. A total of 17 studies with 115,988 participants met the inclusion criteria for the analysis. The pooled prevalence of insomnia in China was 15.0% (95% Confidence interval [CI]: 12.1%-18.5%). No significant difference was found in the prevalence between genders or across time period. The pooled prevalence of insomnia in population with a mean age of 43.7 years and older (11.6%; 95% CI: 7.5%-17.6%) was significantly lower than in those with a mean age younger than 43.7 years (20.4%; 95% CI: 14.2%-28.2%). The prevalence of insomnia was significantly affected by the type of assessment tools (Q = 14.1, P = 0.001). The general population prevalence of insomnia in China is lower than those reported in Western countries but similar to those in Asian countries. Younger Chinese adults appear to suffer from more insomnia than older adults.
CRD 42016043620.
Journal Article
Mental health concerns during the COVID-19 pandemic as revealed by helpline calls
Mental health is an important component of public health, especially in times of crisis. However, monitoring public mental health is difficult because data are often patchy and low-frequency
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. Here we complement established approaches by using data from helplines, which offer a real-time measure of ‘revealed’ distress and mental health concerns across a range of topics
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. We collected data on 8 million calls from 19 countries, focusing on the COVID-19 crisis. Call volumes peaked six weeks after the initial outbreak, at 35% above pre-pandemic levels. The increase was driven mainly by fear (including fear of infection), loneliness and, later in the pandemic, concerns about physical health. Relationship issues, economic problems, violence and suicidal ideation, however, were less prevalent than before the pandemic. This pattern was apparent both during the first wave and during subsequent COVID-19 waves. Issues linked directly to the pandemic therefore seem to have replaced rather than exacerbated underlying anxieties. Conditional on infection rates, suicide-related calls increased when containment policies became more stringent and decreased when income support was extended. This implies that financial relief can allay the distress triggered by lockdown measures and illustrates the insights that can be gleaned from the statistical analysis of helpline data.
Data collected from crisis helplines during the COVID-19 pandemic show that pandemic-related issues replaced rather than exacerbated underlying anxieties, and demonstrate that helpline data are useful indicators of public mental health.
Journal Article
Human damage assessments of coastal flooding for Hong Kong and the Pearl River Delta due to climate change-related sea level rise in the twenty-first century
The adverse impact of climate change-associated extreme weather events is becoming more significant globally, particularly the flood impact on coastal and low-lying areas such as the Pearl River Delta (PRD). This study applied the framework to obtain order-of-magnitude estimations of human damages from future flood disasters caused by sea level rise for Hong Kong and the PRD region in southern China by 2050 and 2100. The assessment framework employs statistical analysis to combine global historical flood damage data with national development indicators and local sea level characteristics to assess the potential damages. Following the terminology of the Intergovernmental Panel on Climate Change Special Report on Extreme Events, the three determinants of disaster risk (climate extreme, exposure and vulnerability) are quantified in our framework. It is found that without adaptation, sea level rise will significantly increase the flood risk in this region. For instance, in the PRD region, with a 75-cm sea level rise by 2100, the deaths and displacements from a 100-year flood are estimated to be around 200 and 1.5 million, respectively. Our results provide motivation for regional authorities to adopt a long-term adaptation plan to reduce exposure and vulnerability to flooding, thus managing the risks in this region. Furthermore, with appropriate datasets available, our framework allows the assessment of the effects of flooding in other areas and/or the quantitative evaluation of potential losses from other climate-related hazards such as heat waves.
Journal Article
COVID-19 is rapidly changing: Examining public perceptions and behaviors in response to this evolving pandemic
Since the emergence of SARS-CoV-2, the virus that causes coronavirus disease (COVID-19) in late 2019, communities have been required to rapidly adopt community mitigation strategies rarely used before, or only in limited settings. This study aimed to examine the attitudes and beliefs of Australian adults towards the COVID-19 pandemic, and willingness and capacity to engage with these mitigation measures. In addition, we aimed to explore the psychosocial and demographic factors that are associated with adoption of recommended hygiene-related and avoidance-related behaviors.
A national cross-sectional online survey of 1420 Australian adults (18 years and older) was undertaken between the 18 and 24 March 2020. The statistical analysis of the data included univariate and multivariate logistic regression analysis.
The survey of 1420 respondents found 50% (710) of respondents felt COVID-19 would 'somewhat' affect their health if infected and 19% perceived their level of risk as high or very high. 84·9% had performed ≥1 of the three recommended hygiene-related behaviors and 93·4% performed ≥1 of six avoidance-related behaviors over the last one month. Adopting avoidance behaviors was associated with trust in government/authorities (aOR: 6.0, 95% CI 2.6-11·0), higher perceived rating of effectiveness of behaviors (aOR: 4·0, 95% CI: 1·8-8·7), higher levels of perceived ability to adopt social distancing strategies (aOR: 5.0, 95% CI: 1·5-9.3), higher trust in government (aOR: 6.0, 95% CI: 2.6-11.0) and higher level of concern if self-isolated (aOR: 1.8, 95% CI: 1.1-3.0).
In the last two months, members of the public have been inundated with messages about hygiene and social (physical) distancing. However, our results indicate that a continued focus on supporting community understanding of the rationale for these strategies, as well as instilling community confidence in their ability to adopt or sustain the recommendations is needed.
Journal Article
Lifestyle and prevalence of dysmenorrhea among Spanish female university students
The aim of this study was to determine the prevalence of primary dysmenorrhea in a sample of Spanish university students, and to describe their menstrual characteristics, lifestyle habits and associated risk factors. This cross-sectional study was conducted with a total of 258 young female university students recruited from the Ciudad Real Faculty of Nursing, with a mean age of 20.63± 3.32 years. An anonymous self-report questionnaire was used to collect data from students. This included sociodemographic characteristics, lifestyle habits, gynecological personal history and the severity of pain using the visual analogue scale. The statistical analysis of the data included calculation of the mean, percentages, chi-square analysis of the data and logistic regression. The prevalence of dysmenorrhea was of 74.8% (n = 193) with a mean pain severity of 6.88 (±1.71). Our results show that 38.3% of students described their menstrual pain as severe and 58% as moderate. The bivariate analysis showed statistically significant differences between students with and without dysmenorrhea: a higher proportion of women with dysmenorrhea had a greater duration of the menstruation flow (p = .003), a longer duration of the menstrual cycle (p = .046), were not using the oral contraceptive pill (p = .026) and had a family history of dysmenorrhea (p = .001). Backward step-wise binary logistic regression analysis using all the significant bivariate variables including lifestyle variables revealed the following risk factors: drinking cola drinks, duration of the menstrual flow, eating meat and having a first-degree relative affected by dysmenorrhea.
Journal Article
PoPoolation: A Toolbox for Population Genetic Analysis of Next Generation Sequencing Data from Pooled Individuals
Recent statistical analyses suggest that sequencing of pooled samples provides a cost effective approach to determine genome-wide population genetic parameters. Here we introduce PoPoolation, a toolbox specifically designed for the population genetic analysis of sequence data from pooled individuals. PoPoolation calculates estimates of θ(Watterson), θ(π), and Tajima's D that account for the bias introduced by pooling and sequencing errors, as well as divergence between species. Results of genome-wide analyses can be graphically displayed in a sliding window plot. PoPoolation is written in Perl and R and it builds on commonly used data formats. Its source code can be downloaded from http://code.google.com/p/popoolation/. Furthermore, we evaluate the influence of mapping algorithms, sequencing errors, and read coverage on the accuracy of population genetic parameter estimates from pooled data.
Journal Article
Prediction of crime occurrence from multi-modal data using deep learning
In recent years, various studies have been conducted on the prediction of crime occurrences. This predictive capability is intended to assist in crime prevention by facilitating effective implementation of police patrols. Previous studies have used data from multiple domains such as demographics, economics, and education. Their prediction models treat data from different domains equally. These methods have problems in crime occurrence prediction, such as difficulty in discovering highly nonlinear relationships, redundancies, and dependencies between multiple datasets. In order to enhance crime prediction models, we consider environmental context information, such as broken windows theory and crime prevention through environmental design. In this paper, we propose a feature-level data fusion method with environmental context based on a deep neural network (DNN). Our dataset consists of data collected from various online databases of crime statistics, demographic and meteorological data, and images in Chicago, Illinois. Prior to generating training data, we select crime-related data by conducting statistical analyses. Finally, we train our DNN, which consists of the following four kinds of layers: spatial, temporal, environmental context, and joint feature representation layers. Coupled with crucial data extracted from various domains, our fusion DNN is a product of an efficient decision-making process that statistically analyzes data redundancy. Experimental performance results show that our DNN model is more accurate in predicting crime occurrence than other prediction models.
Journal Article
Contribution of the Interdecadal Pacific Oscillation to the Recent Abrupt Decrease in Tropical Cyclone Genesis Frequency over the Western North Pacific since 1998
Previous studies have documented an abrupt decrease of tropical cyclone (TC) genesis frequency over the western North Pacific (WNP) since 1998. In this study, results from an objective clustering analysis demonstrated that this abrupt decrease is primarily related to the decrease in a cluster of TCs (C1) that mostly formed over the southeastern WNP, south of 15°N and east of the Philippines, and possessed long tracks. Further statistical analyses based on both best track TC data and global reanalysis data during 1980–2015 revealed that the genesis of C1 TCs was significantly modulated by the interdecadal Pacific oscillation (IPO), whose recent negative phase since 1998 corresponded to a La Niña–like sea surface temperature anomaly (SSTA) pattern, which strengthened the Walker circulation in the tropical Pacific and weakened the WNP monsoon trough, suppressing genesis of C1 TCs in the southeastern WNP and predominantly contributing to the decrease in TC genesis frequency over the entire WNP basin. These findings were further confirmed by results from similar analyses based on longer observational datasets and also the outputs from a 500-yr preindustrial general circulation model experiment using the Geophysical Fluid Dynamics Laboratory (GFDL) Coupled Model, version 3. Additional analysis indicates that the decrease in C1 TC genesis frequency in the recent period was dominated during August–October, with the largest decrease in October.
Journal Article
Comparability of Mixed IC50 Data – A Statistical Analysis
The biochemical half maximal inhibitory concentration (IC50) is the most commonly used metric for on-target activity in lead optimization. It is used to guide lead optimization, build large-scale chemogenomics analysis, off-target activity and toxicity models based on public data. However, the use of public biochemical IC50 data is problematic, because they are assay specific and comparable only under certain conditions. For large scale analysis it is not feasible to check each data entry manually and it is very tempting to mix all available IC50 values from public database even if assay information is not reported. As previously reported for Ki database analysis, we first analyzed the types of errors, the redundancy and the variability that can be found in ChEMBL IC50 database. For assessing the variability of IC50 data independently measured in two different labs at least ten IC50 data for identical protein-ligand systems against the same target were searched in ChEMBL. As a not sufficient number of cases of this type are available, the variability of IC50 data was assessed by comparing all pairs of independent IC50 measurements on identical protein-ligand systems. The standard deviation of IC50 data is only 25% larger than the standard deviation of Ki data, suggesting that mixing IC50 data from different assays, even not knowing assay conditions details, only adds a moderate amount of noise to the overall data. The standard deviation of public ChEMBL IC50 data, as expected, resulted greater than the standard deviation of in-house intra-laboratory/inter-day IC50 data. Augmenting mixed public IC50 data by public Ki data does not deteriorate the quality of the mixed IC50 data, if the Ki is corrected by an offset. For a broad dataset such as ChEMBL database a Ki- IC50 conversion factor of 2 was found to be the most reasonable.
Journal Article