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Neighborhood crime and frequency of police arrest are important indicators for determining neighborhood safety and well-being. Although extant literature has examined crime and neighborhood characteristics and other macro-level determinants, virtually no study has considered the link between neighborhood crime arrest and underage tobacco sales violation (TOB-VIO). This study linked crime arrest data from the Philadelphia Police Department, underage tobacco sales violations from the Philadelphia city database, and sociodemographic/economic data from the Census Bureau to the county shapefile. The principal component analysis (PCA) technique was used to reduce 23 selected variables to four factors: Racial Composition and Neighborhood Disadvantage; Gender Composition; Youth Composition and Poverty; and Ethnic Composition. To account for the effect of spatial dependence in the data, spatial autoregressive and multiscale geographically weighted regression or MGWR techniques were chosen over the nonspatial ordinary least squares regression. Spatial regressions show that crime arrest positively correlates with TOB-VIO and factors from PCA across Philadelphia County. Findings from this study may serve as an area-specific guide for neighborhood policies to improve residents’ safety and well-being and intensify enforcement on the underage tobacco sale violation.

Despite its positive aspects, gentrification has been criticized for displacing the longtime low-income minorities. This study examined the role of social capital and community activism through the...

Though there are extensive studies on neighborhood effects on health, this relationship remains elusive and requires continuous empirical evidence to support existing findings. Gentrification is a process of neighborhood change that affects most longtime residents. This study examined the health impact of the rapidly changing physical and cultural environment using oral history interviews, electronic interviews, and a quantitative structured survey. The study draws on the social determinants of health framework to explain the self-reported chronic health conditions (SR-CHCs) among 331 residents in Austin, Texas. The study employed non-linear techniques suitable for Poisson distribution to estimate the association between gentrification and SR-CHCs and complemented by direct quotes from in-depth interviews (IDIs). Perceived gentrification score significantly vary by marital status (p

The 2019 coronavirus disease (COVID-19) has exacerbated inequality in the United States of America (USA). Black, indigenous, and people of color (BIPOC) are disproportionately affected by the pandemic. This study examines determinants of COVID-19 case fatality ratio (CFR) based on publicly sourced data from January 1 to December 18, 2020, and sociodemographic and rural-urban continuum data from the US Census Bureau. Nonspatial negative binomial Poisson regression and geographically weighted Poisson regression were applied to estimate the global and local relationships between the CFR and predictors—rural-urban continuum, political inclination, and race/ethnicity in 2407 rural counties. The mean COVID-19 CFR among rural counties was 1.79 (standard deviation (SD) = 1.07; 95% CI 1.73-1.84) higher than the total US counties (M = 1.69, SD = 1.18; 95% CI: 1.65-1.73). Based on the global NB model, CFR was positively associated with counties classified as “completely rural” (incidence rate ratio (IRR) = 1.24; 95% CI: 1.12-1.39) and “mostly rural” (IRR = 1.26; 95% CI: 1.15-1.38) relative to “mostly urban” counties. Nonspatial regression indicates that COVID-19 CFR increases by a factor of 8.62, 5.87, 2.61, and 1.36 for one unit increase in county-level percent Blacks, Hispanics, American Indians, and Asian/Pacific Islanders, respectively. Local spatial regression shows CFR was significantly higher in rural counties with a higher share of BIPOC in the Northeast and Midwest regions, and political inclination predicted COVID-19 CFR in rural counties in the Midwest region. In conclusion, spatial and racial/ethnic disparities exist for COVID-19 CFR across the US rural counties, and findings from this study have implications for public health. Supplementary Information The online version contains supplementary material available at 10.1007/s40615-021-01006-7.

Background Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs. Methods We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage. Results We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups. Conclusion The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

Suicide is one of the leading causes of death globally, inherently linked with mental health conditions. Existing studies have also found a significant association between suicide and chronic health conditions such as human immunodeficiency virus (HIV). Although there have been enormous studies on these global health issues, knowledge about their co-existence is elusive. This paper examines the link between HIV and suicide in 186 countries using geospatial techniques and spatial statistics. We used country-level suicide and HIV data obtained from the United Nations Development Program database. The local bivariate spatial association technique was used to determine the covariation of HIV and suicide, while autoregressive models were used to estimate the unexplained country-level variables in the model. Due to evidence of spatial dependencies, we applied multiscale geographically weighted regression (MGWR) to examine the spatial variation in the predictor variables. The local bivariate spatial association technique was used to determine the covariation of HIV and suicide. Analysis indicated that HIV significantly covaried with suicide in regions of Africa and Europe. Our result suggests that HIV is a significant driver of suicide among females in sub-Saharan Africa while it drives suicide among males in many regions including the country of South Africa, Lesotho, Swaziland, Oceania, and Southeast Asia. MGWR indicated that HIV was negatively associated with suicide in the Americas and European countries, but positively associated with suicide in Africa, the Middle East, and Oceania. There is a double burden of HIV and suicide in sub-Saharan Africa. Therefore, investment in both mental healthcare and access to antiretroviral drugs is essential in reducing suicide at both global and local levels.

Objectives We examined the association between incorrect knowledge of ovulation and unintentional pregnancy and child among young women in sub-Saharan Africa countries. Methods Using Pearson’s Chi-square, t test, multiple logistic regression, and likelihood ratio test, we analyzed Demographic and Health Survey data (2008–2017) of 169,939 young women (15–24 year). Results The range of prevalence of incorrect knowledge of ovulation was 51% in Comoros and 89.6% in Sao Tome and Principe, while unintentional pregnancy ranged between 9.4% in the Republic of Benin and 59.6% in Namibia. The multivariate result indicates a strong association between incorrect knowledge of ovulation and unintentional pregnancy (OR = 1.17; p

Accurate prognostic prediction for head and neck cancer (HNC) is important for the improvement of clinical management. We aimed to compare the prognostic value of various machine learning techniques (MLTs) and statistical Cox regression model for different types of HNC.Clinical data of HNC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 1974 to 2016. The prediction performance of five ML models, including random forest (RF), gradient boosting decision tree (GBDT), support vector machine (SVM), neural network (NN) and deep learning (DL), were compared with the statistical Cox regression model by estimating the concordance index (C-index), integrated Brier score (IBS), time-dependent receiver operating characteristic (ROC) curve and the area under the curve (AUC).Our results showed that the RF model outperformed all other models in prognostic prediction for all tumor sites of HNC, particularly for major salivary gland cancer (MSGC, C-index: 88.730 ± 0.8700, IBS: 7.680 ± 0.4800), oral cavity cancer (OCC, C-index: 84.250 ± 0.6700, IBS: 11.480 ± 0.3300) and oropharyngeal cancer (OPC, C-index: 82.510 ± 0.5400, IBS: 10.120 ± 0.1400). Meanwhile, we analyzed the importance of each clinical variable in the RF model, in which age and tumor size presented the strongest positive prognostic effects. Additionally, similar results can be observed in the internal (6The RF model is a promising prognostic prediction tool for HNC patients, regardless of the anatomic subsites.

Social networking sites (SNS), such as Facebook and Twitter, have attracted users worldwide by providing a means to communicate and share opinions and experiences of daily lives. When empowered by ...

This research examines the consumer nutrition environment in the selected neighborhoods identified as food deserts, food swamps, and food oases in Austin, Texas, by considering food availability, food price, food quality, and food labeling. A food auditing instrument M-TxNEA-S (He Jin, San Marcos, TX, USA) was developed to capture the unique dietary culture and food preferences in Texas. A total of 93 food items in 14 grocery stores and supermarkets (GS) and 32 convenience stores (CS) were surveyed. The GS in food swamps and food oases were found to offer significantly more healthy foods than the CS. The availability of healthy food in the GS in the food swamps and food oases is significantly higher than that of the GS from the food deserts, CS in the three neighborhoods did not exhibit a significant difference in healthy food availability. There was no significant difference between the price for the healthy items (lower fat, lower calorie, and whole grain) and that for the regular food options. No significant difference was found for food quality or food labeling between the stores from the different types of neighborhoods. The GS in food deserts are small grocery stores carrying limited ranges of foods. The establishment of larger food stores in the food deserts might not be very rewarding, but opening more small grocery stores with healthier options may alleviate food issues.

Background: Ancient literature of Traditional Chinese Medicine (TCM) contains massive clinical experiences which are important ingredient of TCM knowledge and valuable for TCM clinical practice of nowadays. However, it is difficult for TCM professionals to acquire such valuable experiences due to their massive volume and broad occurrence in the literature. Furthermore, different characteristics of ancient Chinese language from the modern one lead to additional challenges for analyzing the literature, regardless of how to perform the analyzing, manually or automatically with a software toolkit. Methods: In order to overcome the aforementioned challenges, we formalize a novel information extraction task for ancient literature of TCM, and the entities to be extracted are Disease-Specific Clinical Experiences (DSCEs) occurring in the literature. For the purpose, we have collected two corpora from ancient literature of TCM and annotated them manually with DSCEs occurrence information for the diseases pregnant abdominalgia and colporrhagia (妊娠腹痛及下血) and jaundice (黄疸) respectively. We further propose a deep learning and CRF-based algorithmic framework with character encoding of ancient Chinese, thus avoiding the special difficulty in word segmentation for ancient Chinese texts. We investigate the framework with different methods for contextual encoding of characters in a sentence, including CNN, Bi-LSTM and BERT, and diverse approaches to aggregate contextual information of characters into a sentence encoding, such as max-pooling and attention mechanism. After that all the encoded sentences in a section of the literature are passed through a Bi-LSTM-based sequence labelling model with CRF inference on its top to obtain an optimal label sequence for the sentences in the section. Results: We conduct a series of experiments on the two corpora to verify the effectiveness of our framework for the task, and evaluate its effectiveness with different metrics in two granularities of labelling, namely accuracy/F1-value in sentence-level labelling and precision/recall/F1-value in correct recognition of the whole DSCEs. Conclusion: The experimental results demonstrate that the deep learning and CRF-based framework with character encoding of ancient Chinese could achieve an accuracy of 80.40%±1.64% and an F1-value of 76.73%±1.59% for the sentence labelling, while for recognition of the whole DSCEs, it is able to obtain the recall of 44.97%±2.16% and the precision of 51.13%±2.64%, meaning that the framework is a promising baseline for further development of the novel information extraction task for TCM.

Evidence on environmental justice studies linking adverse health effects and on-roadair pollution showing spatial nonstationarity is limited.This study uses cancer and noncancer risk estimates from on-road sources of hazardous air pollutants modeled by the National-Scale Air Toxics Assessment (NATA) at the census tract (N = 5265) level and sociodemographic variables from U.S. Census Bureau to examine the nonstationarity spatial relationship by comparing aspatial and spatial regression modelsglobal ordinary least squares, spatial error model, geographically weighted regression, and multiscale geographically weighted regression. At first glance, census tracts within the highest quartile of cancer and noncancer risks were clustered in the major urban areas. Spatial regression indicates that cancer and non-cancer risk were associated with census tract level percentages of Black, Indigenous, and People of Color (BIPOC). These findings can serve as geospatial guidance for intervening in the processes that drive socio-spatial disparity in air pollution exposure.

Several studies have examined the impacts of water, sanitation, hygiene, and general housing conditions on childhood health in developing countries. However, up-to-date knowledge and change pattern in common childhood illness prevalence over time are scarce in Nigeria. To contribute toward meeting the Sustainable Development Goals three and six, we used pooled data ( N = 94,053) from the Nigeria Demographic Health Surveys from 2008 to 2018 to examine the trend and determinants of four childhood illnesses: diarrhea, fever, cough, and respiratory infection. Multivariate logistic regression was used to estimate the determinants of the four health outcomes. Our results indicate that between 2008 and 2018, the prevalence of childhood diarrhea, cough, and fever slowly declined. However, there was a drastic decline in childhood-related respiratory illness. Housing conditions, sanitation facilities, and water sources were significantly associated with childhood illness based on the logistic regression analysis. Based on our findings, we recommend general improvement in housing conditions that can eradicate childhood cough, fever, and respiratory infections.

Previous works on journey-to-crime research failed to examine the role of geographic background. This study investigates the journey-to-crime patterns of electric-bicycle thieves in Beijing. To examine the impact of geographic background on offenders’ travel behavior, the offenders are classified into native and migrant groups and their residing places into urban core and suburb areas. The native offenders who lived in urban core areas were found to make the shortest journey-to-crime trips; the offenders who lived in suburb areas, regardless of their migration status, traveled farther than their counterparts. Compared to the native offenders, the migrant offenders were more likely to make cross-boundary journey-to-crime trips, meaning that they make more trips from urban core areas to suburb or vice versa. Distance decay effect was revealed for all offender groups; but the effect is the strongest for the native offenders who lived in the urban core areas. In addition to the distribution of an offender’s activity anchor locations and the offense opportunities, we believe that an offender’s journey-to-crime trips are related to their spatial knowledge of the city. Our finding of the migrant offenders making longer journey-to-crime trips than their native offender counterparts may suggest that their trips are largely defined by their activity spaces and spatial awareness of the City when compared with the native offenders.

Some scholars emphasize the global cities network and suggest the declining of the national power. On the contrary, many studies insist on the role of the national economy on global cities. However, there is no specific model to show this relation and no evidence to conform which factor at national level impact this connectivity. The aim of this paper is to set up a specific model to illustrate the relationship between the national economic size and global cities connectivity, and to find the factor at national level impacting on world city connectivity. Bootstrap regression is adopted to set up the model for the relation. The results reveal that the national economic size has significant effectiveness on the global city connectivity with logarithmic function. This finding gives an explicit approach to clarify the idea of ‘glocal’ states with the combination of global city connectivity and national urban system.

We employed a regionalization algorithm called regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) to analyze the Body Mass Index (BMI) of school students in Texas and its association with social economic status (SES). The study includes 741 school districts in Texas. BMI data were extracted from the Physical Fitness Assessment Initiative program managed by Texas Education Agency. SES was described using six variables that cover two aspects, Household SES and Neighborhood SES. The study period was the 2012–2013 academic year. We used three REDCAP algorithms to delineate regions considering both spatial contiguity and attribute homogeneity. The result shows that Full-order-CLK algorithm of REDCAP is most effective in producing regions to delineate the patterns of Texas students’ BMI and its relationship with SES. Moreover, two- and six -class regions are the optimal numbers for such a regionalization. Our study reveals the regional patterns of school youth obesity in Texas when connecting to SES—particularly in regions with high rate of obesity and low SES, such as the regions in South and West Texas, and the three school districts in San Antonio. The findings can provide guidance for regionalized policy and practice to fight against school youth obesity in Texas.

Most existing food deserts/swamps studies adopted non-spatial approaches,overlooking the role of spatial dependence in the associations between food access and socioeconomic status. This study aims to fill the gap by examining this relationship with a spatial perspective. Using Austin, Tx as a case study, we adopted a multi-mode Huff-based 2SFCA method to measure the spatial access to food stores. Eight socioeconomic variables were represented by two indices per factor analysis:Economic Deprivation Index (EDI) and Sociocultural Deprivation Index (SDI). We proposed a SAR-Gi* model to characterize food deserts/swamps in Austin. Our analyses reveal that EDI is a significant predictor for access to healthy food (β = -0.054, p = 0.037), and SDI is significantly associated with access to unhealthy food (β= 0.160, p = 0.000). We also found that food deserts and swamps in Austin are concentrated in the east and northeast of Austin, respectively. The noticeable difference between the spatial patterns of food deserts and food swamps as identified by our study and those based on the USDA definition or other traditional methods speaks to the great potential of the SAR-Gi* model in reflecting geographical patterns and relations embedded in food access and socioeconomic status of neighborhoods.

Co-offending refers to illegal activities that are committed by multiple parties together. Little attention has been paid to co-offenders' geographic background when examining co-offender groups. T...

Goals 3 and 5 of the United Nations Sustainable Development Goals are to promote good health and well-being and to achieve gender equality, respectively. To successfully move toward these goals in the area of gender equality, there is the need to understand the underlying legislative or laws that protect women and girls from all forms of domestic violence (DV), including gender-based violence (GBV). The cardinal objective of this study, therefore, was to examine the risk factors of GBV and the physiological effects of GBV. To date, few studies have quantified the relationship between laws on DV and the incidence of DV/GBV. This article fills that gap by using Demographic and Health Surveys (DHS) data of 12 African countries. We applied multivariate logistic regression to estimate the association of the absence of laws on DV, men dominant power, history of violence, alcohol consumption, women’s attitude toward men’s violence perpetration, and decision-making power with the scores of GBV and physiological effects of GBV. Group Kruskal–Wallis Rank test was used to determine the variation of the two outcomes among the 12 countries. Results show significant disparities in the score of GBV, H test (11) = 168,217, p < .001, and score of physiological effects, H test (11) = 122,127, p < .001, among the 12 countries. Specifically, Ghana, Namibia, Rwanda, Mozambique, Zimbabwe, Malawi, Sierra Leone, and Togo reported the highest physiological effect of GBV. Presence of DV laws, male dominance, alcohol consumption, history of abuse, and women empowerment predict GBV and the physiological effect of GBV. Thus, building strong legal frameworks against all forms of DV and empowering women may reduce the incidence of GBV and physiological effects of GBV for all African women.

Summary Analysis of soil nematode feeding groups and functional guilds were used as a valuable tool to detect heavy metal pollution. Effects of cadmium (Cd) at 5 mg/kg, mercury (Hg) at 20mg/kg, combined Cd and Hg at 5+20mg/kg on the nematode communities were studied after three months application. Nematodes were collected from soil in rhizosphere of Morning glories (Pharhiris nil) which were applied as heavy metal accumulators and were grown in the experimental pots. Both single and combined heavy metals had marked effects on the nematode abundance, life-history strategies and feeding type composition. Bacteriovores and c-p 2 group were found to be the most abundant trophic group and functional guild, respectively. Acrobeloides and Pratylenchus were the most two abundant genera, decreasing number of them was responsible for the significant difference between control and polluted treatments. Cd-5 and Cd-Hg 5+20 presented lower values of nematode diversity index (H’) and evenness index (J’) than Hg-20. The combination of Cd and Hg showed lower nematode trophic diversity (TD), in comparison with single Cd or Hg. Conversely, heavy metals addition exhibited no pronounced effect on Maturity index (MI), structural index (SI) and enrichment index (EI). Our results demonstrate that genera composition is a better indicator to short-term heavy metal effects than some common indicator indices and emphasize that deeper assemblage analyses are needed for a correct interpretation of short-term disturbance on soil nematodes.