Childhood predictors of cardiovascular disease in adulthood. A systematic review and meta-analysis.

Childhood obesity predicts the risk of adult adiposity, which is associated with the earlier onset of cardiovascular disease [adult atherosclerotic cardiovascular disease, ACVD: hypertension, increased carotid intima media thickness (CIMT) stroke, ischemic heart disease (IHD)] and dysglycaemia. Because it is not known whether childhood obesity contributes to these diseases, we conducted a systematic review of studies that examine the ability of measures of obesity in childhood to predict dysglycaemia and ACVD. Data sources were Web of Science, MEDLINE, PubMed, CINAHL, Cochrane, SCOPUS, ProQuest and reference lists. Studies measuring body mass index (BMI), skin fold thickness and waist circumference were selected; of 1,954 studies, 18 met study criteria. Childhood BMI predicted CIMT: odds ratio (OR), 3.39 (95% confidence interval (CI), 2.02 to 5.67, P < 0.001) and risk of impaired glucose tolerance in adulthood, but its ability to predict ACVD events (stroke, IHD; OR, 1.04; 95% CI, 1.02 to 1.07; P < 0.001) and hypertension (OR, 1.17, 95% CI 1.06 to 1.27, P = 0.003) was weak-moderate. Body mass index was not predictive of systolic BP (r -0.57, P = 0.08) and weakly predicted diastolic BP (r 0.21, P = 0.002). Skin fold thickness in childhood weakly predicted CIMT in female adults only (rs 0.09, P < 0.05). Childhood BMI predicts the risk of dysglycaemia and abnormal CIMT in adulthood, but its ability to predict hypertension and ACVD events was weak and moderate, respectively. Skin fold thickness was a weak predictor of CIMT in female adults.

Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks. Contribution of the IMIA Primary Healthcare Working Group.

BACKGROUND: Generally benefits and risks of vaccines can be determined from studies carried out as part of regulatory compliance, followed by surveillance of routine data; however there are some rarer and more long term events that require new methods. Big data generated by increasingly affordable personalised computing, and from pervasive computing devices is rapidly growing and low cost, high volume, cloud computing makes the processing of these data inexpensive. OBJECTIVE: To describe how big data and related analytical methods might be applied to assess the benefits and risks of vaccines. METHOD: We reviewed the literature on the use of big data to improve health, applied to generic vaccine use cases, that illustrate benefits and risks of vaccination. We defined a use case as the interaction between a user and an information system to achieve a goal. We used flu vaccination and pre-school childhood immunisation as exemplars. RESULTS: We reviewed three big data use cases relevant to assessing vaccine benefits and risks: (i) Big data processing using crowdsourcing, distributed big data processing, and predictive analytics, (ii) Data integration from heterogeneous big data sources, e.g. the increasing range of devices in the "internet of things", and (iii) Real-time monitoring for the direct monitoring of epidemics as well as vaccine effects via social media and other data sources. CONCLUSIONS: Big data raises new ethical dilemmas, though its analysis methods can bring complementary real-time capabilities for monitoring epidemics and assessing vaccine benefit-risk balance.