Big Data Needs and Challenges to Advance Research on Racial and Ethnic Inequities in Maternal and Child Health

Perez, Lilian G.; Peet, Evan D.; Vegetabile, Brian G.; Shih, Regina A.

doi:10.1016/j.whi.2021.11.003

Cited by 1 publication

(4 citation statements)

References 25 publications

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“…Similarly, outcomes of underrepresented minorities can be improved with advocacy for engagement and adherence to follow-up in order to obtain complete long-term data. 1 “Interactions between subjects” is an application of value to find the connections between subjects (e.g., shared environments, twins), which can increase the statistical power to discover disease mechanisms. “Heterogeneity and diversity” of a cohort to include appropriate representation of the real-world population (factors such as age, sex, ethnicity, socioeconomic status, exposure to different social determinants of health) contribute to the variety of data.…”

Section: Big Data Characteristics and Strategies For Analysismentioning

confidence: 99%

“…The rapid growth of Big Data utilization in healthcare has unmasked many limitations and challenges. Data quality, accuracy, completeness, and availability are major hurdles in using large healthcare datasets 4,34 and can lead to inaccurate analysis, 1 biased inference, and false discoveries. 35 Similarly, management and storage of large amounts of data present challenges with maintaining data security and accuracy over time, archiving data, managing data warehouses, and removing/disposing of information.…”

Section: Big Data Challengesmentioning

confidence: 99%

“…Despite living in the richest economy in the world, children in the United States have worse health outcomes as assessed by quantitative, standardized metrics than children in other upper income countries. 1 Improvements in child health outcomes over the past 30 years associated with availability of vaccines against common childhood diseases, greater chance of survival after preterm birth, and improved nutrition have not reduced race-, ethnicity-, geography-, and poverty-based disparities in child health. 2 , 3 Access to large medical, biological, and environmental data sets and progress in data science provide multidimensional data and data analytics that more fully capture the dynamic and complex interactions among genetic background, culture, social determinants of health, development, environment, and biologic risk.…”

Section: Introductionmentioning

confidence: 99%

“…2,3 Access to large medical, biological, and environmental data sets and progress in data science provide multidimensional data and data analytics that more fully capture the dynamic and complex interactions among genetic background, culture, social determinants of health, development, environment, and biologic risk. 1,[4][5][6][7][8] These new tools build upon the progress from systems biology omics-based algorithms in which mechanistic explanations of outcomes were anchored in biologic problems to permit integration of environmental (e.g., pollution), cultural, demographic, and geographic factors that interact with child development and biology to create adverse health outcomes. 6,9,10 For example, machine learning algorithms (approaches that improve automatically with additional data experience), a branch of artificial intelligence, 11 recognize and apply patterns in multidimensional data that train models for prediction and stratification of disease risk based on biologic risk as well as developmental, cultural, and environmental risk determinants of adverse child health outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Improving child health through Big Data and data science

2022

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Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. Impact Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.

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Section: Big Data Characteristics and Strategies For Analysismentioning

confidence: 99%