Pre- and Perinatal Correlates of Ideal Cardiovascular Health during Early Childhood: A Prospective Analysis in the Healthy Start Study.

OBJECTIVES: To characterize prevalence of ideal cardiovascular health (ICVH) during early childhood (4-7 years of age), and to identify pre- and perinatal biological, sociodemographic, metabolic, and behavioral correlates of ICVH. STUDY DESIGN: Among 350 mother-child pairs in the Healthy Start Study, we defined ICVH as no exposure to second hand smoke; ≥1 hour/day of moderate-to-vigorous physical activity; body mass index ≤85th percentile; systolic and diastolic blood pressure <90th percentile; cholesterol <170 mg/dL, fasting glucose <100 mg/dL; and healthy diet, per the American Heart Association. Pre- and perinatal characteristics were obtained from questionnaires, medical records, and in-person visits. Because of low prevalence of ICVH, we focused on prevalence of meeting ≥6 metrics in the analysis. We examined bivariate associations of each characteristic with % meeting ≥6 metrics and included those that were significant (P < .05) in a multivariable logistic regression model. RESULTS: ICVH prevalence at mean ± SD age 4.7±0.6 years was 6.9%; boys had twice the prevalence as girls (9% vs 4.4%). Most (>85%) children met criteria for second hand smoke, body mass index, blood pressure, cholesterol, and glucose, and only one-third met criteria for physical activity (31.4%) and diet (35.1%). In multivariable analyses, key correlates of ICVH were maternal weight status (ORoverweight/obese vs nonoverweight/obese = 0.41 [0.23, 0.73]) and offspring sex (ORmale vs female = 2.14 [1.22, 3.65]). CONCLUSIONS: At age 4-7 years, ICVH prevalence is already low, with diet and adequate physical activity being the limiting factors. Healthy maternal weight prior to pregnancy and male sex are potential determinants of childhood ICVH. Additional work is required to explore associations of early-life ICVH with future health outcomes.

Advanced Data Analytics for Clinical Research Part I: What are the Tools?

The concept of Big Data is changing the way that clinical research can be performed. Cardiothoracic surgeons need to understand the dynamic digital transformation taking place in the healthcare industry. In the last decade, technological advances and Big Data analytics have become powerful tools for businesses. In healthcare, rapid expansion of Big Data infrastructure has occurred in parallel with attempts to reduce cost and improve outcomes. Many hospitals around the country are augmenting traditional relational databases with Big Data infrastructure. Advanced data capture and categorization tools such as natural language processing and optical character recognition are being developed for clinical and research use, while Internet of Things in the form of wearable technology serves as an additional source of data usable for research. As cardiothoracic surgeons seek ways to innovate, novel approaches to data acquisition and analysis enable a more rigorous level of investigatory efforts.

Advanced Data Analytics for Clinical Research Part II: Application to Cardiothoracic Surgery.

In the first part of this series, we introduced the tools of Big Data, including Not Only Standard Query Language data warehouse, natural language processing (NLP), optical character recognition (OCR), and Internet of Things (IoT). There are nuances to the utilization of these analytics tools, which must be well understood by clinicians seeking to take advantage of these innovative research strategies. One must recognize technical challenges to NLP, such as unintended search outcomes and variability in the expression of human written texts. Other caveats include dealing written texts in image formats, which may ultimately be handled with transformation to text format by OCR, though this technology is still under development. IoT is beginning to be used in cardiac monitoring, medication adherence alerts, lifestyle monitoring, and saving traditional labs from equipment failure catastrophes. These technologies will become more prevalent in the future research landscape, and cardiothoracic surgeons should understand the advantages of these technologies to propel our research to the next level. Experience and understanding of technology are needed in building a robust NLP search result, and effective communication with the data management team is a crucial step in successful utilization of these technologies. In this second installment of the series, we provide examples of published investigations utilizing the advanced analytic tools introduced in Part I. We will explain our processes in developing the research question, barriers to achieving the research goals using traditional research methods, tools used to overcome the barriers, and the research findings.

The determinants of health-seeking behaviour during the A/H1N1 influenza pandemic: an ecological study.

BACKGROUND: This is an ecological study that examines the relationship between antiviral drug collection during the 2009/2010 A/H1N1 influenza pandemic, and area-level ethnicity, socioeconomic deprivation and distance from an antiviral collection point (ACP). METHODS: Age-standardized antiviral collection rates (ACR) were calculated for each super output area (geographic areas representing a population of ∼1500) in Sandwell, UK for all residents who received an antiviral drug for influenza-like illness between 23 July 2009 and 7 February 2010. Multivariable regression was used to examine the relationship between ACR and ethnicity (percentage population non-white), socioeconomic deprivation (index of multiple deprivation, IMD) and distance from an ACP. RESULTS: Socioeconomic deprivation, ethnicity and distance from an ACP were independently associated with a reduction in ACR. Each one-point increase in the IMD score was associated with a drop in the ACR of 15.7 prescriptions per 100 000 population (P= 0.013). CONCLUSIONS: Socioeconomic deprivation, ethnicity and distance from an ACP may have influenced health-seeking behaviour during the 2009/2010 influenza pandemic. This suggests possible inequalities in access to antivirals during the most recent influenza pandemic. Qualitative research is needed to examine the reasons for this. Individual-level data on ethnicity should be routinely collected in the event of a future pandemic.