Shaban-Nejad Arash

Chronic diseases impose heavy burdens onhealth systems, economies, andsocieties (1). Half of all Americans live with at least one of the chronic conditions and more than 75% of health care cost is associated with people with chronic diseases (2). Multimorbidity, the coexistence of two ormore chronic conditions in an individual or a population, often require complex and ongoing care and a deep understanding of different risk factors, and their indicators.Multimorbidity has been increased over the past years and the trend is expected to continue across the U.S. Knowing how different chronic conditions are related to one another andwhat are the underlying socioeconomic factorsis crucial to design and implement effective health interventions. We introduce multimorbidity network affinity, which measures the degree of how multiple chronic conditions are clustered within a geographic unit. Accurate estimations of how chronic conditions are spatially clustered and linked to other sociomarkers(3) and socio-economic disadvantages facilitate designing effective interventions.

Objective: We study how multimorbidity prevalence is related to socio-economic conditions in Memphis, TN. In addition, we demonstrate that the accumulation of chronic conditions, which is measured by affinity in multimorbidity, is unevenly distributed throughout thecity. Our research shows that not only are socio-economic disadvantages linked to a higher prevalence in each major chronic condition, but also major chronic conditions are heavily clustered in socially disadvantaged neighborhoods.

Adverse Childhood Experiences (ACEs) have been linked to a variety of detrimental health and social outcomes. In the last 20 years, the association between ACEs with several adult health risk behaviors, conditions, and diseases including suicides, and substance abuse, mental health disturbances and impaired memory, nervous, endocrine and immune systems impairments, and criminal activities have been studied. One of the challenges in studying and timely diagnosis of ACEs is that the links between specific childhood experiences and their health outcomes are not totally clear. Similarly, an integrated dataset builtfrom multiple sources is often required for effective ACEs surveillance. The SPACES project aims at providing a semantic infrastructure to facilitate data sharing and integration and answer causal queries to improve ACEs surveillance.

Objective: We introduce the Semantic Platform for Adverse Childhood Experiences (ACEs) Surveillance (SPACES). It facilitates the access to the relevant integrated information, enables discovering the causality pathways and assists researchers, clinicians, public health practitioners, social workers, and health organization in studying the ACEs, identifying the trends, as well as planning and implementing preventive and therapeutic strategies.

In 2015, there were 212 million new cases of malaria, and about 429,000 malaria death, worldwide. African countries accounted for almost 90% of global cases of malaria and 92% of malaria deaths. Currently, malaria data are scattered across different countries, laboratories, and organizations in different heterogeneous data formats and repositories. The diversity of access methodologies makes it difficult to retrieve relevant data in a timely manner. Moreover, lack of rich metadata limits the reusability of data and its integration. The current process of discovering, accessing and reusing the data is inefficient and error-prone profoundly hindering surveillance efforts. As our knowledge about malaria and appropriate preventive measures becomes more comprehensive malaria data management systems, data collection standards, and data stewardship are certain to change regularly. Collectively these changes will make it more difficult to perform accurate data analytics or achieve reliable estimates of important metrics, such as infection rates. Consequently, there is a critical need to rapidly re-assess the integrity of data and knowledge infrastructures that experts depend on to support their surveillance tasks.

Objective:

Malaria is one of the top causes of death in Africa and some other regions in the world. Data driven surveillance activities are essential for enabling the timely interventions to alleviate the impact of the disease and eventually eliminate malaria. Improving the interoperability of data sources through the use of shared semantics is a key consideration when designing surveillance systems, which must be robust in the face of dynamic changes to one or more components of a distributed infrastructure. Here we introduce a semantic framework to improve interoperability of malaria surveillance systems (SIEMA).

A socio-marker is a measurable indicator of social conditions where a patient is embedded in and exposed to, being analogous with a biomarker indicating the severity or presence of some disease state. Social factors are one of the most clinical health determinants, which play a critical role in explaining health outcomes. Socio-markers can help medical practitioners and researchers to reliably identify high-risk individuals in a timely manner.

Objective:

Asthma is one of the most common chronic childhood diseases in the United States. In addition to its pervasiveness, pediatric asthma shows high sensitivity to the environment. Combining medical-social dataset with machine learning methods we demonstrate how socio-markers play an important role in identifying patients at risk of hospital revisits due to pediatric asthma within a year.