Search

Some evidence suggests that neighborhood socioeconomic disadvantage is associated with dementia-related outcomes. However, prior research is predominantly among non-Latino Whites.We evaluated the association between neighborhood disadvantage (Area Deprivation Index [ADI]) and dementia incidence in Asian American (n = 18,103) and non-Latino White (n = 149,385) members of a Northern California integrated health care delivery system aged 60 to 89 at baseline. Race/ethnicity-specific Cox proportional hazards models adjusted for individual-level age, sex, socioeconomic measures, and block group population density estimated hazard ratios (HRs) for dementia.Among non-Latino Whites, ADI was associated with dementia incidence (most vs. least disadvantaged ADI quintile HR = 1.09, 95% confidence interval [CI] = 1.02-1.15). Among Asian Americans, associations were close to null (e.g., most vs. least disadvantaged ADI quintile HR = 1.01, 95% CI = 0.85-1.21).ADI was associated with dementia incidence among non-Latino Whites but not Asian Americans. Understanding the potentially different mechanisms driving dementia incidence in these groups could inform dementia prevention efforts.

Literature shows lower dementia incidence in Asian American groups versus whites, varying by Asian ethnicity. One hypothesized driver is nativity differences (eg, healthy immigrant effect).We followed a cohort of 6243 Chinese, 4879 Filipino, 3256 Japanese, and 141,158 white Kaiser Permanente Northern California members for incident dementia (2002 to 2020), estimating age-adjusted dementia incidence rates by ethnicity and nativity, and hazard ratios (HR) for nativity on dementia incidence using ethnicity-stratified age- and sex-adjusted Cox proportional hazards models.Dementia incidence appeared higher in foreign- versus US-born Filipinos (HR, 95% confidence interval: 1.39, 1.02 to 1.89); differences were small in Japanese (1.07, 0.88 to 1.30) and Chinese (1.07, 0.92 to 1.24). No nativity differences were observed among whites (1.00, 0.95 to 1.04).Nativity does not explain lower dementia incidence in Asian Americans versus whites, but may contribute to heterogeneity across Asian ethnicities. Future research should explore differential impacts of social and cardiometabolic factors.

ImportanceHigh education protects against dementia, but returns on educational attainment may be different across sociodemographic groups owing to various social factors. Asian American individuals are a growing and diverse group, but little research has assessed dementia determinants in this population.ObjectiveTo examine the association of education with dementia in a large cohort of Asian American individuals, stratifying by ethnicity and nativity.Design, Setting, and ParticipantsThis cohort study used electronic health record (EHR) and survey data from the Research Program on Genes, Environment, and Health and the California Men’s Health Study surveys (2002-2020). Data are from Kaiser Permanente Northern California, an integrated health care delivery system. This study used a volunteer sample who completed the surveys. Participants included Chinese, Filipino, and Japanese individuals who were aged 60 to less than 90 years without a dementia diagnosis in the EHR at the time of the survey (baseline) and who had 2 years of health plan coverage before baseline. Data analysis was performed from December 2021 to December 2022.ExposuresThe main exposure was educational attainment (college degree or higher vs less than a college degree), and the main stratification variables were Asian ethnicity and nativity (born in the US or born outside the US).Main Outcomes and MeasuresThe primary outcome was incident dementia diagnosis in the EHR. Dementia incidence rates were estimated by ethnicity and nativity, and Cox proportional hazards and Aalen additive hazards models were fitted for the association of college degree or higher vs less than a college degree with time to dementia, adjusting for age (timescale), sex, nativity, and an interaction between nativity and college degree.ResultsAmong 14 749 individuals, the mean (SD) age at baseline was 70.6 (7.3) years, 8174 (55.4%) were female, and 6931 (47.0%) had attained a college degree. Overall, among individuals born in the US, those with a college degree had 12% lower dementia incidence (HR, 0.88; 95% CI, 0.75-1.03) compared with those without at least a college degree, although the confidence interval included the null. The HR for individuals born outside the US was 0.82 (95% CI, 0.72-0.92; P = .46 for the college degree by nativity interaction). The findings were similar across ethnicity and nativity groups except for Japanese individuals born outside the US.Conclusions and RelevanceThese findings suggest that college degree attainment was associated with lower dementia incidence, with similar associations across nativity. More work is needed to understand determinants of dementia in Asian American individuals and to elucidate mechanisms linking educational attainment and dementia.

Concentrated animal feeding operations (CAFOs) present highly efficient means of meeting food demands. CAFOs create unique conditions that can affect the health and environment of animals and humans within and outside operations, leading to potential epidemiological concerns that scale with operational size. One such arena meriting further investigation is their possible contribution to novel influenzas. CAFOs present opportunities for cross-species transmission of influenza as demonstrated by reports of swine flu and avian influenza outbreaks. Conditions and pathways leading to novel influenza strains are complex and require varied prevention and intervention approaches. Current challenges for prevention of respiratory viruses entering or leaving swine and poultry CAFOs are multifaceted and include adherence of personal safety measures, lack of training and safety provisions for personnel, and incomplete standardized federal, state, and/or county regulation and enforcement coverage across agricultural systems. This report acknowledges that any proposed CAFO-associated influenza intervention should be cross-organizational, and no single intervention should be expected to provide full resolution. Proposed interventions affect multiple components of the One Health triad, and include seasonal human influenza immunization, PPE regulation and adherence, alternative waste management, general biosecurity standardization and an industry best practices incentive program. Due to the complexity of this problem, multiple anticipated communication, enforcement, and logistical challenges may hinder the full implementation of proposed solutions. General and operation-specific (swine and poultry) biosecurity practices may mitigate some of the risks associated with influenza virus reassortment across species. Education and advocacy can help protect workers, communities, veterinarians and consumers from CAFO-associated influenza virus. To achieve this, there must be more complete communication between CAFOs, governing agencies, health services, animal services, researchers, and consumers to better explore the potential health outcomes associated with CAFOs.

Generating the fewest redundancy-free scheme trees from conceptual-model hypergraphs is NP-hard [17]. We show, however, that the problem has a polynomial-time solution if the conceptual-model hypergraph is acyclic. We define conceptual-model hypergraphs, cycles, and scheme trees, and then present a polynomial-time algorithm and show that it generates the fewest redundancy-free scheme trees. As a practical application for the algorithm, we comment on its use for the design of ''good'' XML schemas for data storage.

Almost all enterprises use relational databases to handle real time business operations and most need to generate various XML documents for data exchanges internally among various departments and externally with business partners. Exporting data in a relational database to an XML document can be considered a data conversion process. Based on the four approaches for data conversion: Customized program, Interpretive transformer, Translator generator, and Logical level translation, this paper proposes a new interpretive approach using Structured Export Markup Language (SEML) interpreter for converting relational data into XML documents. The frameworks and languages proposed by other researchers are neither generic nor able to generate arbitrary XML documents. Therefore, SEML interpreter is a simple, user friendly, and complete solution with a new mark-up language ? SEML ? for data conversion. The solution can be used as a generic tool for extracting, transforming, and loading (ETL) purposes. In other words, the SEML interpreter is a solution for relational databases similar to what X-Query is for XML databases.

Given a hypergraph and a set of embedded functional dependencies, we investigate the problem of determining the conditions under which we can efficiently generate redundancy-free XML storage structures with as few scheme trees as possible. Redundancy-free XML structures guarantee both economy in storage space and the absence of update anomalies, and having the least number of scheme trees requires the fewest number of joins to navigate among the data elements. We know that the general problem is intractable. The problem may still be intractable even when the hypergraph is acyclic and each hyperedge is in Boyce-Codd normal form (BCNF). As we show here, however, given an acyclic hypergraph with each hyperedge in BCNF, a polynomial-time algorithm exists that generates a largest possible redundancy-free XML storage structure. Successively generating largest possible scheme trees from among hyperedges not already included in generated scheme trees constitutes a reasonable heuristic for finding the fewest possible scheme trees. For many practical cases, this heuristic finds the set of redundancy-free XML storage structures with the fewest number of scheme trees. In addition to a correctness proof and a complexity analysis showing that the algorithm is polynomial, we also give experimental results over randomly generated but appropriately constrained hypergraphs showing empirically that the algorithm is indeed polynomial.

For a company with many databases in different data models, it is necessary to consolidate them into one interchangeable data model and present data in more than one data model concurrently to different users or individual users who need to access the data in more than one data model. The benefit is to let the user stick to his/her own data model to access database in another data model. This paper presents a semantic metadata to preserve database constraints for data materialization to support the user's view of database on an ad hoc basis. The semantic metadata can store the captured semantics of a relational or an XML-enabled database into classes. The stored constraints and data can be materialized into a target database upon user request. The user is allowed to perform data materialization many times alternatively. The process can provide a relational as well as an XML view to the users simultaneously. This concurrent data materialization function can be applied into data warehouse to consolidate heterogeneous database into a fact table in a data model of user's choice. Furthermore, a user can obtain either a relational view or an XML view of the same dataset of an XML-enabled database interchangeably.

Extensible Markup Language (XML) has been used for data-transport and data-transformation while the business sector continues to store critical business data in relational databases. Extracting relational data and formatting it into XML documents, and then converting XML documents back to relational structures, becomes a major daily activity. It is important to have an efficient methodology to handle this conversion between XML documents and relational data. This paper aims to perform data conversion from XML documents into relational databases. It proposes a prototype and algorithms for this conversion process. The pre-process is schema translation using an XML schema definition. The proposed approach is based on the needs of an Order Information System to suggest a methodology to gain the benefits provided by XML technology and relational database management systems. The methodology is a stepwise procedure using XML schema definition and Extensible Stylesheet Language Transformations (XSLT) to ensure that the data constraints are not scarified after data conversion. The implementation of the data conversion is performed by decomposing the XML document of a hierarchical tree model into normalized relations interrelated with their artifact primary keys and foreign keys. The transformation process is performed by XSLT. This paper will also demonstrate the entire conversion process through a detailed case study.

The extensible markup language (XML) has become a standard for persistent storage and data interchange via the Internet due to its openness, self-descriptiveness, and flexibility. This article proposes a systematic approach to reverse engineer arbitrary XML documents to their conceptual schema, extended DTD graphs, which are DTD graphs with data semantics. The proposed approach not only determines the structure of the XML document, but also derives candidate data semantics from the XML element instances by treating each XML element instance as a record in a table of a relational database. One application of the determined data semantics is to verify the linkages among elements. Implicit and explicit referential linkages are among XML elements modeled by the parent-children structure and ID/IDREF(S), respectively. As a result, an arbitrary XML document can be reverse engineered into its conceptual schema in an extended DTD graph format.