Risk factors affecting the failed low-density lipoprotein level achievement rate in working-age male population at high cardiovascular risk

Systematic literature review and meta-analysis of cardiovascular risk factor management in selected Asian countries

Aim: There is a need to understand the management status of hypertension, dyslipidemia/hypercholesterolemia, and diabetes mellitus in the Asia-Pacific region (APAC). Methods: We conducted a systematic literature review and meta-analysis to summarize the awareness, treatment, and/or control rates of these risk factors in adults across 11 APAC countries/regions. Results: We included 138 studies. Individuals with dyslipidemia had the lowest pooled rates compared with those with other risk factors. Levels of awareness with diabetes mellitus, hypertension, and hypercholesterolemia were comparable. Individuals with hypercholesterolemia had a statistically lower pooled treatment rate but a higher pooled control rate than those with hypertension. Conclusion: The management of hypertension, dyslipidemia, and diabetes mellitus was suboptimal in these 11 countries/regions.

Characterization and selection of Japanese electronic health record databases used as data sources for non-interventional observational studies

BACKGROUND

Interventional studies are the fundamental method for obtaining answers to clinical questions. However, these studies are sometimes difficult to conduct because of insufficient financial or human resources or the rarity of the disease in question. One means of addressing these issues is to conduct a non-interventional observational study using electronic health record (EHR) databases as the data source, although how best to evaluate the suitability of an EHR database when planning a study remains to be clarified. The aim of the present study is to identify and characterize the data sources that have been used for conducting non-interventional observational studies in Japan and propose a flow diagram to help researchers determine the most appropriate EHR database for their study goals.

METHODS

We compiled a list of published articles reporting observational studies conducted in Japan by searching PubMed for relevant articles published in the last 3 years and by searching database providers' publication lists related to studies using their databases. For each article, we reviewed the abstract and/or full text to obtain information about data source, target disease or therapeutic area, number of patients, and study design (prospective or retrospective). We then characterized the identified EHR databases.

RESULTS

In Japan, non-interventional observational studies have been mostly conducted using data stored locally at individual medical institutions (663/1511) or collected from several collaborating medical institutions (315/1511). Whereas the studies conducted with large-scale integrated databases (330/1511) were mostly retrospective (73.6%), 27.5% of the single-center studies, 47.6% of the multi-center studies, and 73.7% of the post-marketing surveillance studies, identified in the present study, were conducted prospectively. We used our findings to develop an assessment flow diagram to assist researchers in evaluating and choosing the most suitable EHR database for their study goals.

CONCLUSIONS

Our analysis revealed that the non-interventional observational studies were conducted using data stored local at individual medical institutions or collected from collaborating medical institutions in Japan. Disease registries, disease databases, and large-scale databases would enable researchers to conduct studies with large sample sizes to provide robust data from which strong inferences could be drawn.

[Development and Application for Drug-safety and Efficacy Using a Large Claims Data]

Recent years, evidences for medical safety and efficacy are accelerated-developing using medical big data. Medical big data were adequate for analyzing 1) rare events that difficult for finding in each hospital, 2) for comparison of bench marks obtained routine work between average data in large number of hospitals and specific hospital data and 3) prescription surveys etc. As so far, these analyses using medical big data were conducted by academia and/or researcher. However, in these days, evidences using medical big data were focused on hospital pharmacists little by little. In this review, we show 3 researches using large claims data such as 1) risk factors assessing for failed low-density lipoprotein level achievement in members of the working-age population, 2) prevalence of drug-drug interaction in atrial fibrillation patients and 3) assessment of "look-alike" packaging designs related to medication errors using information technology and large claims data. Medical big data such as large claims data analysis is useful and suitable for building evidences according to medical staffs-needs.