疾病空间分布的“等值线－面积”多重分形模型及其应用

目的：探讨“等值线－面积”多重分形模型在识别疾病空间插值地图的绘图等级界限中的应用。方法：将分形理论与统计学方法相结合，以ArcGIS9.0为数据分析平台和绘图工具，构建疾病空间分布的“等值线－面积”多重分形模型。结果：构建了疾病空间分布的“等值线－面积”多重分形模型，提出了建模的基本步骤，并以肾综合征出血热空间异质性结构及其界限的识别为例，验证了模型的科学性和适用性。结论：利用“等值线－面积”多重分形模型所确定的疾病空间异质性结构及其界限能准确反映疾病空间异质性变化的本质规律。     

Persistent variations in average height between countries and between socio-economic groups: an overview of 10 European countries

Primary objectives: This paper aims to provide an overview of variations in average height between 10 European countries, and between socio-economic groups within these countries.

Data and methods: Data on self-reported height of men and women aged 20-74 years were obtained from national health, level of living or multipurpose surveys for 1987-1994. Regression analyses were used to estimate height differences between educational groups and to evaluate whether the differences in average height between countries and between educational groups were smaller among younger than among older birth cohorts.

Results: Men and women were on average tallest in Norway, Sweden, Denmark and the Netherlands and shortest in France, Italy and Spain (range for men: 170-179 cm; range for women: 160-167 cm). The differences in average height between northern and southern European countries were not smaller among younger than among older birth cohorts. In most countries average height increased linearly with increasing birth-year (≈0.7-0.8cm/5 years for men and ≈0.4cm/5 years for women). In all countries, lower educated men and women on average were shorter than higher educated men (range of differences: 1.6-3.0 cm) and women (range of differences: 1.2-2.2 cm). In most countries, education-related height differences were not smaller among younger than among older birth cohorts.

Conclusions: The persistence of international differences in average height into the youngest birth cohorts indicates a high degree of continuity of differences between countries in childhood living conditions. Similarly, the persistence of education-related height differences indicates continuity of socio-economic differences in childhood living conditions, and also suggests that socio-economic differences in childhood living conditions will continue to contribute to socio-economic differences in health at adult ages.     

Spatial learning in a virtual reality-based task is altered in very preterm children

Very preterm births prevent a complete development of the nervous system. The hippocampus is especially vulnerable in this population since the perinatal period is critical for its growth and development. Learning and memory abilities, like spatial memory, depend on the hippocampal integrity. In this study we applied virtual-reality-based tasks to assess spatial memory in a sample of 20 very preterm children of 7 and 8 years of age. Two different conditions of difficulty were used. Very preterm children performed poorly in the task in comparison with the control group. They committed more errors than controls searching for the rewarded positions. However, no significant differences were observed in the mean speed, an index of the motor abilities and joystick handling. These results suggest that the hippocampal function is affected in this sample. Nevertheless, other variables to consider are discussed.     

Effects of buffer size and shape on associations between the built environment and energy balance

Uncertainty in the relevant spatial context may drive heterogeneity in findings on the built environment and energy balance. To estimate the effect of this uncertainty, we conducted a sensitivity analysis defining intersection and business densities and counts within different buffer sizes and shapes on associations with self-reported walking and body mass index. Linear regression results indicated that the scale and shape of buffers influenced study results and may partly explain the inconsistent findings in the built environment and energy balance literature.     

Primer of statistics in dental research: Part II

The Part I of Primer of Statistics in Dental Research covered five topics that are often mentioned in statistical check list of many peer-review journals [1], [2], [3] including (1) statistical graph, (2) how to deal with outliers, (3) p-value and confidence interval, (4) testing equivalence, and (5) multiplicity Adjustment [4]. The Part II of the series covers another set of important topics in dental statistics including (1) selecting the proper statistical tests, (2) repeated measures analysis, (3) epidemiological consideration for causal association, and (4) analysis of agreement. First, a guide in selecting the proper statistical tests based on the research question will be laid out in text and with a table so that researchers choose the univariable statistical test by answering five simple questions. Second, the importance of utilizing repeated measures analysis will be illustrated. This is a key component of data analysis as in many dental studies, observations are considered repeated in a single patient (several teeth are measured in a single patient). Third, concepts of confounding and the use of regression analysis are explained by going over a famous observational cohort study. Lastly, the use of proper agreement analysis vs. correlation for study of agreement will be discussed to avoid a common pitfall in dental research.     

Small area estimation of obesity prevalence and dietary patterns: A model applied to Rio de Janeiro city,Brazil

We propose the use of previously developed small area estimation techniques to monitor obesity and dietary habits in developing countries and apply the model to Rio de Janeiro city. We estimate obesity prevalence rates at the Census Tract through a combinatorial optimization spatial microsimulation model that matches body mass index and socio-demographic data in Brazil's 2008–9 family expenditure survey with Census 2010 socio-demographic data. Obesity ranges from 8% to 25% in most areas and affects the poor almost as much as the rich. Male and female obesity rates are uncorrelated at the small area level. The model is an effective tool to understand the complexity of the problem and to aid in policy design.     

The involvement of Type II Neuregulin-1 in rat visuospatial learning and memory

The cognitive deficits observed in schizophrenia are considered a core feature of the disease. Neuregulin-1 is a risk gene for schizophrenia that is involved in many neurodevelopmental and synaptic plasticity-related processes relevant to schizophrenia. Here, we have utilized a rat model (Nrg1^Tn), which is hypomorphic for the neuregulin-1 (Nrg1) gene, to test whether reduced Type II NRG1 in the rat brain leads to cognitive deficits relevant to schizophrenia. Wild-type and homozygous Nrg1^Tn male rats were tested in memory tasks that evaluated spatial memory (Morris water maze) and visuospatial working and reference memory (Can Test). Nrg1^Tn rats were not impaired on the Morris water maze, but did show a deficit in the appetitive visuospatial discrimination test. Nrg1^Tn rats committed more reference and working memory errors in this test. These results indicate that decreased Type II NRG1 in the brain may lead to deficits in visuospatial learning and memory.     

Correlates of county-level nonviral sexually transmitted infection hot spots in the US: application of hot spot analysis and spatial logistic regression

PurposeWe used a combination of hot spot analysis (HSA) and spatial regression to examine county-level hot spot correlates for the most commonly reported nonviral sexually transmitted infections (STIs) in the 48 contiguous states in the United States (US).MethodsWe obtained reported county-level total case rates of chlamydia, gonorrhea, and primary and secondary (P&S) syphilis in all counties in the 48 contiguous states from national surveillance data and computed temporally smoothed rates using 2008–2012 data. Covariates were obtained from county-level multiyear (2008–2012) American Community Surveys from the US census. We conducted HSA to identify hot spot counties for all three STIs. We then applied spatial logistic regression with the spatial error model to determine the association between the identified hot spots and the covariates.ResultsHSA indicated that ≥84% of hot spots for each STI were in the South. Spatial regression results indicated that, a 10-unit increase in the percentage of Black non-Hispanics was associated with ≈42% (P < 0.01) [≈22% (P < 0.01), for Hispanics] increase in the odds of being a hot spot county for chlamydia and gonorrhea, and ≈27% (P < 0.01) [≈11% (P < 0.01) for Hispanics] for P&S syphilis. Compared with the other regions (West, Midwest, and Northeast), counties in the South were 6.5 (P < 0.01; chlamydia), 9.6 (P < 0.01; gonorrhea), and 4.7 (P < 0.01; P&S syphilis) times more likely to be hot spots.ConclusionOur study provides important information on hot spot clusters of nonviral STIs in the entire United States, including associations between hot spot counties and sociodemographic factors.