A Spatial Risk Prediction Model for Drug Overdose

Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy from 2015 to 2018. Overdose deaths, especially from opioids, have also been recognized in recent years as a significant public health issue. To address this public health problem, this study sought to identify neighborhood-level (e.g., block group) factors associated with drug overdose and develop a spatial model using machine learning (ML) algorithms to predict the likelihood or risk of drug overdoses across South Carolina. This study included block group level socio-demographic factors and drug use variables which may influence the incidence of drug overdose. In particular, this study developed a new index of access to measure spatial access to treatment facilities and incorporated these variables to assess the relationship between drug overdose and accessibility to the treatment centers. We explored different ML algorithms (e.g., XGBoost, Random Forest) to identify optimum predictors in each category. The categories were combined into a final ensemble predictive model that addressed spatial dependency. An evaluation was conducted to validate that the final model generalized well across the different datasets and geographical areas. Results of the study identified strong neighborhood-level predictors of a drug overdose, pinpointing the most critical neighborhood-level factor(s) that place a community at risk and protect communities from developing such problems. These factors included proportion of households receiving food stamps, households with income less than $35,000, high opioid prescription rates, smoking accessories expenditures, and low accessibility to opioid treatment programs and hospitals. The generalized error of spatial models did not increase considerably in spatial cross-validation compared to the error estimated from normal cross-validation. Our model also outperformed the geographic weighted regression method. Our Results show that variables regarding socio-demographic factors, drug use variables, and protective resources can assist in spatial drug overdose prediction. Our finding highlights several specific pathways toward community-level intervention targeted to a vulnerable population facing potentially high burdens of drug abuse and overdose

Facility Attractiveness and Social Vulnerability Impacts on Spatial Accessibility to Opioid Treatment Programs in South Carolina

Opioid dependence and opioid-related mortality have been increasing in recent years in the United States. Available and accessible treatments may result in a reduction of opioid-related mortality. This work describes the geographic variation of spatial accessibility to opioid treatment programs (OTPs) and identifies areas with poor access to care in South Carolina. The study develops a new index of access that builds on the two-step floating catchment area (2SFCA) method, and has three dimensions: a facility attractiveness index, defined by services rendered incorporated into the Huff Model; a facility catchment area, defined as a function of facility attractiveness to account for variable catchment size; and a Social Vulnerability Index (SVI) to account for nonspatial factors that mitigate or compound the impacts of spatial access to care. Results of the study indicate a significant variation in access to OTPs statewide. Spatial access to OTPs is low across the entire state except for in a limited number of metropolitan areas. The majority of the population with low access (85%) live in areas with a moderate-to-high levels of social vulnerability. This research provides more realistic estimates of access to care and aims to assist policymakers in better targeting disadvantaged areas for OTP program expansion and resource allocation

Depremde Tasarım Faktörünün Rolü ve Yerel Malzemelerle Yapısal Yenileme: İran-Bam Kentindeki Büyük Depremde Kültürel Mekânların Kırılganlığı

Günümüzde, doğal, biyolojik, teknolojik ve sosyal afetler, toplumların normal yaşam düzenini bozan ve onun uyum sağlama kapasitesini aşarak yardıma gereksinim duyuran olaylardır. Toprak/hava/su kirliliği, salgın hastalıklar, nükleer/radyolojik/kimyasal kazalar, savaş, terör saldırıları ve göç gibi afetlerin bir kısmı direk insan kaynaklıdır. Bu değerlendirmeyle deprem gibi doğal afetlerde, depremlerin ani ve beklenmeyen bir zamanda olmaları sebebiyle, insan faktörü az etkili görülmektedir. Ancak gerekli mühendislik ve mimarlık hizmeti almış yapılar depremlerle uyumlu davranış sergileyebilmektedir. Bu makalede depremde insan kaynaklı faktörlerin sebep olduğu yıkım, İran’ın Bam kenti bağlamında ele alınmıştır. UNESCO Dünya Mirası Listesi’nde yer alan Bam, önemli tarihi ve kültürel mekânlarıyla İran'ın öne çıkan şehirlerindendir. 2003 yılında meydana gelen 6.6 büyüklüğündeki deprem, kentteki dini ve tarihi yapıları yıkıcı bir şekilde etkilemiştir. Bu makalede kentin önemli yapıları arasındaki Arg-ı Bam(Bam Kalesi), Ulu Camii ve Hanasayi Fabrikası'nın insan kaynaklı tasarım faktörlerinden nasıl etkilendiği ve deprem dirençli hale getirilmeleri süreci, yapım tekniği ve kerpiç malzeme bağlamında açıklanmıştır

Facility Attractiveness and Social Vulnerability Impacts on Spatial Accessibility to Opioid Treatment Programs in South Carolina

Opioid dependence and opioid-related mortality have been increasing in recent years in the United States. Available and accessible treatments may result in a reduction of opioid-related mortality. This work describes the geographic variation of spatial accessibility to opioid treatment programs (OTPs) and identifies areas with poor access to care in South Carolina. The study develops a new index of access that builds on the two-step floating catchment area (2SFCA) method, and has three dimensions: a facility attractiveness index, defined by services rendered incorporated into the Huff Model; a facility catchment area, defined as a function of facility attractiveness to account for variable catchment size; and a Social Vulnerability Index (SVI) to account for nonspatial factors that mitigate or compound the impacts of spatial access to care. Results of the study indicate a significant variation in access to OTPs statewide. Spatial access to OTPs is low across the entire state except for in a limited number of metropolitan areas. The majority of the population with low access (85%) live in areas with a moderate-to-high levels of social vulnerability. This research provides more realistic estimates of access to care and aims to assist policymakers in better targeting disadvantaged areas for OTP program expansion and resource allocation

The Leading Neighborhood-Level Predictors of Drug Overdose: A Mixed Machine Learning and Spatial Approach

Background: Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy during recent years. To combat this health issue, this study aims to identify the leading neighborhood-level predictors of drug overdose and develop a model to predict areas at the highest risk of drug overdose using geographic information systems and machine learning (ML) techniques. Method: Neighborhood-level (block group) predictors were grouped into three domains: socio-demographic factors, drug use variables, and protective resources. We explored different ML algorithms, accounting for spatial dependency, to identify leading predictors in each domain. Using geographically weighted regression and the best-performing ML algorithm, we combined the output prediction of three domains to produce a final ensemble model. The model performance was validated using classification evaluation metrics, spatial cross- validation, and spatial autocorrelation testing. Results: The variables contributing most to the predictive model included the proportion of households with food stamps, households with an annual income below $35,000, opioid prescription rate, smoking accessories ex- penditures, and accessibility to opioid treatment programs and hospitals. Compared to the error estimated from normal cross-validation, the generalized error of the model did not increase considerably in spatial cross- validation. The ensemble model using ML outperformed the GWR method. Conclusion: This study identified strong neighborhood-level predictors that place a community at risk of expe- riencing drug overdoses, as well as protective factors. Our findings may shed light on several specific avenues for targeted intervention in neighborhoods at risk for high drug overdose burdens

Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?

Twitter’s APIs are now the main data source for social media researchers. A large number of studies have utilized Twitter data for diverse research interests. Twitter users can share their precise real-time location, and Twitter APIs can provide this information as longitude and latitude. These geotagged Twitter data can help to study human activities and movements for different applications. Compared to the mostly small-scale data samples in different domains, such as social science, collecting geotagged data offers large samples. There is a fundamental question whether geotagged users can represent non-geotagged users. While some studies have investigated the question from different perspectives, they did not investigate profile information and the contents of tweets of geotagged and non-geotagged users. This empirical study addresses this limitation by applying text mining, statistical analysis, and machine learning techniques on Twitter data comprising more than 88,000 users and over 170 million tweets. Our findings show that there is a significant difference (p-value < 0.001) between geotagged and non-geotagged users based on 73% of the features obtained from the users’ profiles and tweets. The features can also help to distinguish between geotagged and non-geotagged users with around 80% accuracy. This research illustrates that geotagged users do not represent the Twitter population

Potential voriconazole associated posterior reversible leukoencephalopathy in children with malignancies: Report of two cases

The fungal infection has become severe morbidity amongst patients with malignancy. Voriconazole, a new generation of triazole, has shown excellent results in treating invasive fungal infections. Herein, we report two cases of posterior reversible encephalopathy syndrome (PRES), which induced after voriconazole exposure. Magnetic resonance imaging, and the serum level of voriconazole were investigated in both patients to assess toxicity. The role of methotrexate, as one of the possible causes of PRES, is weakened significantly through precise assessing diffusion-weighted images on magnetic resonance imaging. These unique cases emphasize that voriconazole can induce PRES even at therapeutic levels. Therefore, in the case of neurotoxicity, PRES must be considered, and voriconazole should discontinue. The prognosis seemed promising when voriconazole stopped immediately after clinical suspicion