Adiposity has differing associations with incident coronary heart disease and mortality in the Scottish population: cross-sectional surveys with follow-up

Objective: Investigation of the association of excess adiposity with three different outcomes: all-cause mortality, coronary heart disease (CHD) mortality and incident CHD. Design: Cross-sectional surveys linked to hospital admissions and death records. Subjects: 19 329 adults (aged 18–86 years) from a representative sample of the Scottish population. Measurements: Gender-stratified Cox proportional hazards models were used to estimate hazard ratios (HRs) for all-cause mortality, CHD mortality and incident CHD. Separate models incorporating the anthropometric measurements body mass index (BMI), waist circumference (WC) or waist–hip ratio (WHR) were created adjusted for age, year of survey, smoking status and alcohol consumption. Results: For both genders, BMI-defined obesity (greater than or equal to30 kg m−2) was not associated with either an increased risk of all-cause mortality or CHD mortality. However, there was an increased risk of incident CHD among the obese men (hazard ratio (HR)=1.78; 95% confidence interval=1.37–2.31) and obese women (HR=1.93; 95% confidence interval=1.44–2.59). There was a similar pattern for WC with regard to the three outcomes; for incident CHD, the HR=1.70 (1.35–2.14) for men and 1.71 (1.28–2.29) for women in the highest WC category (men greater than or equal to102 cm, women greater than or equal to88 cm), synonymous with abdominal obesity. For men, the highest category of WHR (greater than or equal to1.0) was associated with an increased risk of all-cause mortality (1.29; 1.04–1.60) and incident CHD (1.55; 1.19–2.01). Among women with a high WHR (greater than or equal to0.85) there was an increased risk of all outcomes: all-cause mortality (1.56; 1.26–1.94), CHD mortality (2.49; 1.36–4.56) and incident CHD (1.76; 1.31–2.38). Conclusions: In this study excess adiposity was associated with an increased risk of incident CHD but not necessarily death. One possibility is that modern medical intervention has contributed to improved survival of first CHD events. The future health burden of increased obesity levels may manifest as an increase in the prevalence of individuals living with CHD and its consequences

Causal mechanisms proposed for the Alcohol Harm Paradox - a systematic review

Background and Aims The Alcohol Harm Paradox (AHP) posits that disadvantaged groups suffer from higher rates of alcohol-related harm compared with advantaged groups, despite reporting similar or lower levels of consumption on average. The causes of this relationship remain unclear. This study aimed to identify explanations proposed for the AHP. Secondary aims were to review the existing evidence for those explanations and investigate whether authors linked explanations to one another. Methods Systematic review. We searched MEDLINE (1946-January 2021), EMBASE (1974 – January 2021) and PsycINFO (1967 – January 2021), supplemented via manual searching of grey literature. Included papers either explored the causes of the AHP or investigated the relationship between alcohol consumption, alcohol-related harm, and socioeconomic position. Papers were set in Organisation for Economic Co-operation and Development high income countries. Explanations extracted for analysis could be evidenced in the empirical results or suggested by researchers in their narrative. Inductive thematic analysis was applied to group explanations. Results Seventy-nine papers met the inclusion criteria and initial coding revealed these papers contained 41 distinct explanations for the AHP. Following inductive thematic analysis, these explanations were grouped into 16 themes within six broad domains: Individual, Lifestyle, Contextual, Disadvantage, Upstream and Artefactual. Explanations related to risk behaviours, which fit within the Lifestyle domain, were the most frequently proposed (n=51) and analysed (n=21). Conclusions While there are many potential explanations for the Alcohol Harm Paradox, most research focuses on risk behaviours while other explanations lack empirical testing

Temporal join processing with hilbert curve space mapping

Management of data with a time dimension increases the overhead of storage and query processing in large database applications especially with the join operation, which is a commonly used and expensive relational operator. The join evaluation is difficult because temporal data are intrinsically multidimensional. The problem is harder since tuples with longer life spans tend to overlap a greater number of joining tuples thus; they are likely to be accessed more often. The proposed index-based Hilbert-Temporal Join (Hilbert-TJ) join algorithm maps temporal data into Hilbert curve space that is inherently clustered, thus allowing for fast retrieval and storage. An evaluation and comparison study of the proposed Hilbert-TJ algorithm determined the relative performance with respect to a nested-loop join, a sort-merge, and a partition-based join algorithm that use a multiversion B+ tree (MVBT) index. The metrics include the processing time (disk I/O time plus CPU time) and index storage size. Under the given conditions, the expected outcome was that by reducing index redundancy better performance was achieved. Additionally, the Hilbert-TJ algorithm offers support to both valid-time and transaction-time data

Accelerating Queries on Very Large Datasets

In this chapter, we explore ways to answer queries on large multi-dimensional data efficiently. Given a large dataset, a user often wants to access only a relatively small number of the records. Such a selection process is typically performed through an SQL query in a database management system (DBMS). In general, the most effective technique to accelerate the query answering process is indexing. For this reason, our primary emphasis is to review indexing techniques for large datasets. Since much of scientific data is not under the management of DBMS systems, our review includes many indexing techniques outside of DBMS systems as well. Among the known indexing methods, bitmap indexes are particularly well suited for answering such queries on large scientific data. Therefore, more details are given on the state of the art of bitmap indexing techniques. This chapter also briefly touches on some emerging data analysis systems that don’t yet make use of indexes. We present some evidence that these systems could also benefit from the use of indexes