Language Outcomes in Children who are Deaf or Hard of Hearing
Dr. Jareen Meinzen-Derr, PhD
, associate professor in the Division of Biostatistics and Epidemiology received funding to conduct a randomized trial using augmentative and alternative communication (AAC) delivered on iPads to help children who are deaf, or hard of hearing, develop and learn language. Despite early identification and intervention for hearing loss, many children who are deaf, or hard of hearing, continue to have significant gaps in language development. These gaps in language can have significant downstream effects on other areas of child development, including social functioning and academics. This trial, funded by the March of Dimes
and National Institute of Disability, Independent Living, and Rehabilitation Research
, is the first to introduce AAC technology into therapy for children with hearing loss to close the gap between language and cognitive skills. Early results of this innovative work have shown that children who are deaf, or hard of hearing, benefit from the use of AAC technology as a tool to support development of more complex language skills. Many of the participants have made substantial improvements in their language, comprehension, and social skills as a result of the intervention.
Phenotypes from Electronic Health Research
Clinical data repositories like patient registries present unique challenges and opportunities for biostatisticians and epidemiologists in the quest to positively impact child health. In many chronic pediatric diseases, use of these registries as a means of epidemiologic surveillance are now interrogated to improve medical monitoring, and develop targeted therapies. New approaches to the analysis of registry data are beginning to enable clinicians to track child health outcomes, and employ decision support tools to slow disease progression in these populations. One approach is to phenotype patients’ longitudinal trajectories, which allows analysts to classify severity of each patient’s clinical course and accurately predict onset of severe events in ways that were previously not possible. The American Journal of Respiratory and Critical Care Medicine
recently published a study by Drs. Rhonda Szczesniak, PhD
; Cole Brokamp, PhD; and Ms. Su of the Division of Biostatistics, and Epedimiology and members from the Divisions of Pulmonary Medicine
and Biomedical Informatics
, has identified phenotypes of rapid lung function decline by applying a novel phenotyping approach to data from the national cystic fibrosis patient registry. Rapid lung function decline is a prolonged drop in lung function that cystic fibrosis patients are susceptible to throughout life. Their study confirmed the severity and timing of this event described in previous epidemiologic studies, which suggests that it occurs most dramatically during adolescence and early adulthood, and it also revealed that children with the highest lung function early in life suffer the greatest loss of lung function by that time. The study also shows associations with infections and comorbidities according to phenotype. These results pave the way to more targeted approaches for early treatment of rapid decline prior to irreversible loss of lung damage, and extended for other chronic diseases and disorders.
Methods and Tools Development
In FY 2017, faculty and staff from the division developed novel methods and tools, and applied innovative statistical approaches, to advance pediatric science and medicine. Examples of new methods and tools included Land Use Random Forest (LURF) models for elemental components of particulate matter (PM) in urban cities (Drs. Cole Brokamp, PhD; and Patrick Ryan, PhD, MS); CerebroMatic–a versatile toolbox for spline-based MRI template (Dr. Mekbib Altaye, PhD); nonrespondent subsample multiple imputation for two-phase sampling (Dr. Nanhua Zhang, PhD); and Nomograms for the extrahepatic bile duct diameter in children (Ms. Resmi Gupta and Dr. Lin Fei, PhD). Innovative applications of advanced statistical methods included application of inverse-probability of treatment weighting to study racial disparities in child asthma readmission (Drs. Bin Huang, PhD; Patrick Ryan, PhD; and Chen Chen); semiparametric approach to identify sensitive time points during gestation (Ms. Resmi Gupta, Drs. Jane Khoury, PhD; Mekbib Altaye, PhD; and Rhonda Szczesniak, PhD); functional approach to study longitudinal patterns of glycemic control and blood pressure in pregnant women with Type 1 diabetes mellitus (Drs. Szczesniak, Altaye, Khoury, PhD), and time-varying propensity score matching to examine causal risk factors for cognitive impairment (Dr. Zhang). Dr. Bin Huang, is leading a Patient-Centered Outcomes Research Institute (PCORI) Methods Award to develop patient centered adaptive treatment strategies.
Professional journals in which our new methods and innovative applications in FY 17 appear include JAMA Pediatrics, Atmospheric Environment, Frontiers in Computational Neuroscience, Journal of Pediatric Gastroenterology and Nutrition, American Journal of Perinatology, Journal of Official Statistics, and Communications in Statistics. Both intramural and extramural awards funds this work. Shared interest groups in causal inference, big data, and adaptive clinical trials met regularly within the division to promote research collaborations in these important areas of pediatric research. A newly formed special interest group focusing on statistical process control includes members of Division of Biostatistics and Epidimiology and of the James H. Anderson Center for Health Systems Excellence who support quality improvement projects at Cincinnati Children’s and within the Learning Networks.
Dr. Patrick Ryan, PhD
, associate professor in the Division of Biostatistics and Epidemiology, is leading the Ecological Momentary Assessment and Personal Particle Exposure (EcoMAPPE) Study to elucidate the health effects associated with ultrafine particles (UFP) exposure. A growing body of research suggests that exposure to UFP associates with adverse respiratory outcomes and may trigger inflammation in multiple systems. UFPs are a form of air pollution and are a product of various processes such as diesel combustion and cigarette smoke. The EcoMAPPE Study will enroll adolescent children with, and without asthma. During a one-week study period, participants will wear a personal sensor that simultaneously measures UFPs and tracks location using a GPS. Participants will also complete breathing tests and questionnaires regarding their surroundings, activities, and respiratory symptoms throughout the day via smartphone applications. Integration and analysis of the data from these multiple platforms will help to identify locations and scenarios associated with high UFP exposure. Further, we will evaluate the effect of UFP exposures on respiratory health and inflammatory responses. Increased inflammatory response associates with multiple chronic diseases including heart disease and stroke, as well as many forms of cancer. Thus, the UFP-inflammation link may represent a major modifiable risk factor with a high impact on health and survival through the lifespan.
Dr. Cole Brokamp, PhD, recipient of the 2016 Strauss Fellowship, led innovative research into machine learning methods for estimating the history of an individual’s exposure to specific components of air pollution from the home address. Unlike other methods currently available, Dr. Brokamp’s approach allows identifying pollution hotspots in time and space, opening new horizons in epidemiologic research, environmental management, and policy making.