Although stuttering is regarded as a speech-specific disorder, there is a growing body of evidence suggesting that subtle abnormalities in the motor planning and execution of non-speech gestures exist in stuttering individuals. We hypothesized that people who stutter (PWS) would differ from fluent controls in their neural responses during motor planning and execution of both speech and non-speech gestures that had auditory targets. Using fMRI with sparse sampling, separate BOLD responses were measured for perception, planning, and fluent production of speech and non-speech vocal tract gestures. During both speech and non-speech perception and planning, PWS had less activation in the frontal and temporoparietal regions relative to controls. During speech and non-speech production, PWS had less activation than the controls in the left superior temporal gyrus (STG) and the left pre-motor areas (BA 6) but greater activation in the right STG, bilateral Heschl's gyrus (HG), insula, putamen, and precentral motor regions (BA 4). Differences in brain activation patterns between PWS and controls were greatest in the females and less apparent in males. In conclusion, similar differences in PWS from the controls were found during speech and non-speech; during perception and planning they had reduced activation while during production they had increased activity in the auditory area on the right and decreased activation in the left sensorimotor regions. These results demonstrated that neural activation differences in PWS are not speech-specific.

We examined trends and neighborhood and sociobehavioral determinants of sleep problems in US children aged 6–17 between 2003 and 2012. The 2003, 2007, and 2011-2012 rounds of the National Survey of Children's Health were used to estimate trends and differentials in sleep problems using logistic regression. Prevalence of sleep problems increased significantly over time. The proportion of children with <7 days/week of adequate sleep increased from 31.2% in 2003 to 41.9% in 2011-2012, whereas the prevalence of adequate sleep <5 days/week rose from 12.6% in 2003 to 13.6% in 2011-2012. Prevalence of sleep problems varied in relation to neighborhood socioeconomic and built-environmental characteristics (e.g., safety concerns, poor housing, garbage/litter, vandalism, sidewalks, and parks/playgrounds). Approximately 10% of children in neighborhoods with the most-favorable social environment had serious sleep problems, compared with 16.2% of children in neighborhoods with the least-favorable social environment. Children in neighborhoods with the fewest health-promoting amenities or the greatest social disadvantage had 37%–43% higher adjusted odds of serious sleep problems than children in the most-favorable neighborhoods. Higher levels of screen time, physical inactivity, and secondhand smoke exposure were associated with 20%–47% higher adjusted odds of sleep problems. Neighborhood conditions and behavioral factors are important determinants of sleep problems in children.

Childhood mental, behavioral, and developmental disorders (MBDDs) are associated with adverse outcomes that can persist into adulthood (1,2). Pediatric clinical settings are important for identifying and treating MBDDs (3). Early identification and treatment of MBDDs can promote healthy development for all children (4), especially those living in poverty who are at increased risk for MBDDs (3,5) but might have reduced access to care (6). CDC analyzed data from the 2016 National Survey of Children’s Health (NSCH) on MBDDs, risk factors, and use of federal assistance programs (e.g., Supplemental Nutrition Assistance Program [SNAP]) to identify points to reach children in poverty. In line with previous research (3,6), compared with children in higher-income households, those in lower-income households more often had ever received a diagnosis of an MBDD (22.1% versus 13.9%), and less often had seen a health care provider in the previous year (80.4% versus 93.8%). Among children living below 200% of the federal poverty level (FPL) who did not see a health care provider in the previous year, seven of 10 were in families receiving at least one public assistance benefit. Public assistance programs might offer collaboration opportunities to provide families living in poverty with information, co-located screening programs or services, or connection to care.

Results of the survey demonstrated that whereas most families of children with special health care needs feel they are partners in the care of their child, further work is needed, particularly for poor, uninsured, and minority children, as well as those with functional limitations. The survey results also demonstrate the importance of partnership; children whose care met the partnership core outcome experienced improved access to care and well-being.

We examined parent-reported adverse childhood experiences (ACEs) and associated outcomes among American Indian and Alaska Native (AI/AN) children aged 0–17 years from the 2011-2012 National Survey of Children's Health. Bivariate and multivariable analyses of cross-sectional data on 1,453 AI/AN children and 61,381 non-Hispanic White (NHW) children assessed race-based differences in ACEs prevalence and differences in provider-diagnosed chronic emotional and developmental conditions, health characteristics, reported child behaviors, and health services received as a function of having multiple ACEs. AI/AN children were more likely to have experienced 2+ ACEs (40.3% versus 21%), 3+ ACEs (26.8% versus 11.5%), 4+ ACEs (16.8% versus 6.2%), and 5+ ACEs (9.9% versus 3.3%) compared to NHW children. Prevalence rates for depression, anxiety, and ADHD were higher among AI/AN children with 3+ ACEs (14.4%, 7.7%, and 12.5%) compared to AI/ANs with fewer than 2 ACEs (0.4%, 1.8%, and 5.5%). School problems, grade failures, and need for medication and counseling were 2-3 times higher among AI/ANs with 3+ ACEs versus the same comparison group. Adjusted odds ratio for emotional, developmental, and behavioral difficulties among AI/AN children with 2+ ACEs was 10.3 (95% CI = 3.6–29.3). Race-based differences were largely accounted for by social and economic-related factors.