The home environment and its relation to bottle feeding outcomes in the first year of life.

BACKGROUND: The home environment and caregiver interactions have an impact on infant development. However, there is a paucity of research surrounding the home environment and its relation to early feeding outcomes within the first year of life. Therefore, the aim of this study was to examine the relationship between the home environment and infant bottle feeding outcomes at 3 and 12 months of age. METHODS: Seventy-two full-term infants completed this study at 3 months of age and fifty-five infants completed the study at 12 months. Data in the current study were collected from a larger, ongoing study completed in the infant's home at 3 and 12 months of age. The Infant-Toddler Home Observation for Measurement of Environment Inventory (IT-HOME) was utilized to assess the infant's home environment. The Oral Feeding Skills (OFS) scale was completed while the infant was observed during a bottle feed. Caregivers completed a questionnaire about their infant's feeding abilities via the Neonatal Eating Assessment Tool (Neo-EAT) at 3 months and Pediatric Eating Assessment Tool (Pedi-EAT) at 12 months. RESULTS: At 3 months of age, the IT-HOME Involvement subscale was associated with an increase in the amount of milk provided in the infant's bottle. There were no significant associations between the IT-HOME and caregiver report of feeding at 3 months of age. At 12 months of age, the IT-HOME Acceptance subscale was associated with an increase in oral transfer rate and the IT-HOME Variety subscale was associated with a decrease in oral transfer rate. Additionally, the IT-HOME Organization subscale was associated with caregiver report of feeding on the following Pedi-EAT scales: Mealtime Behaviors, Selective Restrictive Eating, and Oral Processing at 12 months. CONCLUSIONS: These findings reveal that the home environment is significantly related to different infant bottle feeding outcomes over the first year of life. At 3 months, IT-HOME Involvement was associated with the amount of milk offered in the bottle, whereas at 12 months of age, subscales of the IT-HOME were associated with oral feeding transfer rate and caregiver report of feeding. Clinically, these findings point to the importance of considering the infant's bottle feeding skills in conjunction with certain aspect within the infant's environment. Additional research is needed to further explore these relationships in greater detail, with a larger sample size and across patient populations.

Reprint of "Using multiple imputation by super learning to assign intent to nonfatal firearm injuries".

The number of nonfatal firearm injuries in the US by intent (e.g., due to assault) is not reliably known: First, although the largest surveillance system for hospital-treated events, the Healthcare Cost and Utilization Project Nationwide Emergency Department Sample (HCUP-NEDS), provides accurate data for the number of nonfatal firearm injuries, injury intent is not coded reliably. Second, the system that reliably codes intent, the CDC's National Electronic Injury Surveillance System - Firearm Injury Surveillance Study (NEISS-FISS), while large enough to produce stable estimates of the distribution of intent, is too small to produce stable estimates of the number of these events. Third, a large proportion of cases in NEISS-FISS, notably in early years of the system, are coded as of "undetermined intent." Trends in the proportion of nonfatal firearm injuries by intent in NEISS-FISS thus depend on whether these cases are treated as a distinct category, or, instead, can be re-classified through imputation. We contrast the distributions of nonfatal firearm injury by intent generated using multiple imputation with those generated using complete-case analyses and analyses that consider "undetermined intent" as a distinct category. We produce estimates of the annual number of firearm injuries by intent in a two-step process. First, we impute intent for cases coded as "undetermined" using Multiple Imputation by Super Learning (MISL). Second, we apply MISL-derived distributions to aggregate count data from HCUP-NEDS. The proportion of non-fatal firearm assaults appears to increase over time when injuries coded as undetermined are included as a category. By contrast, the proportion of assaults remains relatively constant over time in complete-case and multiply imputed analyses. Differences between complete-case and multiple imputation approaches become apparent in subgroup analyses. Trends in the number of nonfatal firearm injuries by intent, 2006-2016, derived in our two-step process, are relatively flat. Multiple imputation strategies recovered intent distribution trends that differed from trends derived using methods that are not designed to account for the multiple complex relationships of missingness present in NEISS - FISS data. When applied to NEISS - FISS, MISL imputation produces plausible distributional estimates of firearm injury by intent.

Using multiple imputation by super learning to assign intent to nonfatal firearm injuries.

The number of nonfatal firearm injuries in the US by intent (e.g., due to assault) is not reliably known: First, although the largest surveillance system for hospital-treated events, the Healthcare Cost and Utilization Project Nationwide Emergency Department Sample (HCUP-NEDS), provides accurate data for the number of nonfatal firearm injuries, injury intent is not coded reliably. Second, the system that reliably codes intent, the CDC's National Electronic Injury Surveillance System - Firearm Injury Surveillance Study (NEISS-FISS), while large enough to produce stable estimates of the distribution of intent, is too small to produce stable estimates of the number of these events. Third, a large proportion of cases in NEISS-FISS, notably in early years of the system, are coded as of "undetermined intent." Trends in the proportion of nonfatal firearm injuries by intent in NEISS-FISS thus depend on whether these cases are treated as a distinct category, or, instead, can be re-classified through imputation. We contrast the distributions of nonfatal firearm injury by intent generated using multiple imputation with those generated using complete-case analyses and analyses that consider "undetermined intent" as a distinct category. We produce estimates of the annual number of firearm injuries by intent in a two-step process. First, we impute intent for cases coded as "undetermined" using Multiple Imputation by Super Learning (MISL). Second, we apply MISL-derived distributions to aggregate count data from HCUP-NEDS. The proportion of non-fatal firearm assaults appears to increase over time when injuries coded as undetermined are included as a category. By contrast, the proportion of assaults remains relatively constant over time in complete-case and multiply imputed analyses. Differences between complete-case and multiple imputation approaches become apparent in subgroup analyses. Trends in the number of nonfatal firearm injuries by intent, 2006-2016, derived in our two-step process, are relatively flat. Multiple imputation strategies recovered intent distribution trends that differed from trends derived using methods that are not designed to account for the multiple complex relationships of missingness present in NEISS - FISS data. When applied to NEISS - FISS, MISL imputation produces plausible distributional estimates of firearm injury by intent.

The home environment and its relation to vocalizations in the first year of life.

Background: Prior data has shown that the home environment impacts child development; however, there remains a paucity of research on how the home environment relates to child and adult words. Therefore, the aim of this prospective and quantitative study was to examine the relationship between the home environment and the quantity of vocalizations or words, and conversational turns produced by infants and parents at 3 and 12 months of age. Methods: Seventy-two (56% male) full-term infants were assessed at 3 and 12 months of age. The home environment was assessed in person via interview and observation of the child's home using the Infant-Toddler Home Observation for Measurement of the Environment (IT-HOME) Inventory subscales. Vocalizations were measured using the Language Environment Analysis (LENA) device, which measures the adult word count, child vocalization count and conversational turn count. These measures were then averaged for the most voluble, or vocal hour, in the recording period. Results: At 3 months, IT-HOME Learning Materials scores were significantly associated with a decrease in adult words. We found a statistically significant difference in LENA outcomes between 3 and 12 months when stratified by sex. Specifically, male infants had significantly fewer vocalizations at 12 months when compared to 3 months, whereas females had more vocalizations. There was also a statistically significant difference in IT-HOME Learning Materials, Organization, Responsivity, and Total scores when comparing 3- and 12-month time points. Conclusions: These findings reveal that the home environment changes significantly over the first year of life. At 3 months, Learning Materials in the home were related to adult words, while between 3 and 12 months, several aspects of the home significantly changed. Male children had reduced vocalizations between 3 and 12 months; whereas, female children had increased vocalizations during the same time points. Future research should focus on examining these outcomes with multiple measures, time points, and patient populations.

MISL: Multiple imputation by super learning.

Multiple imputation techniques are commonly used when data are missing, however, there are many options one can consider. Multivariate imputation by chained equations is a popular method for generating imputations but relies on specifying models when imputing missing values. In this work, we introduce multiple imputation by super learning, an update to the multivariate imputation by chained equations method to generate imputations with ensemble learning. Ensemble methodologies have recently gained attention for use in inference and prediction as they optimally combine a variety of user-specified parametric and non-parametric models and perform well when estimating complex functions, including those with interaction terms. Through two simulations we compare inferences made using the multiple imputation by super learning approach to those made with other commonly used multiple imputation methods and demonstrate multiple imputation by super learning as a superior option when considering characteristics such as bias, confidence interval coverage rate, and confidence interval width.

Predictors of Service Utilization of Young Children and Families Enrolled in a Pediatric Primary Care Mental Health Promotion and Prevention Program.

Understanding early childhood mental health service utilization in community-based clinical settings is important. Project Linking Actions for Unmet Needs in Children's Health (Project LAUNCH) provided mental health-related services for young children and families within pediatric medical homes. Using data from the Project LAUNCH evaluation (n = 106), we implemented negative binomial regression models to determine if baseline variables were associated with service utilization, defined as the number of encounters between the family and the team. Past-year homelessness emerged as a significant predictor of service utilization. Encounters for families with children who experienced homelessness within the last 12 months occurred at a rate 34.5% lower than those who had not experienced homelessness. Results highlight the importance of addressing homelessness as a barrier to mental health service utilization for families. Screening for recent housing insecurity and developing interventions that integrate housing support services into mental health programs may inform strategies to increase attendance for families with young children.

Changes in infant non-nutritive sucking throughout a suck sample at 3-months of age.

The goal of this study was to compare how infants' non-nutritive suck (NNS) changes throughout a suck sample. Fifty-four full-term infants (57% male) completed this study at, on average, 3.03 (SD .31) months of age. These infants sucked on our custom research pacifier for approximately five minutes. Infants produced, on average, 14.50 suck bursts during the sample. NNS data was pooled across subjects and breakpoint analyses were completed to determine if there were changes in their NNS patterning. Breakpoints were evident for NNS cycles per burst at burst numbers 18 and 34, and for amplitude (cmH20) at burst numbers 18 and 29. No breakpoints were present for NNS frequency. Infants exhibit changes in their suck physiology across burst number. When assessing suck, developmental specialists should observe more than one suck burst to attain a more valid and appropriate scope of the infant's suck ability.

Cohort profile: Center for Research on Early Childhood Exposure and Development in Puerto Rico.

PURPOSE: Puerto Rican children experience high rates of asthma and obesity. Further, infants born in Puerto Rico are more at risk for being born prematurely compared with infants on the mainland USA. Environmental exposures from multiple sources during critical periods of child development, potentially modified by psychosocial factors, may contribute to these adverse health outcomes. To date, most studies investigating the health effects of environmental factors on infant and child health have focused on single or individual exposures. PARTICIPANTS: Infants currently in gestation whose mother is enrolled in Puerto Rico Testsite for Exploring Contamination Threats (PROTECT) cohort, and infants and children already born to mothers who participated in the PROTECT study. FINDINGS TO DATE: Data collection and processing remains ongoing. Demographic data have been collected on 437 mother-child pairs. Birth outcomes are available for 420 infants, neurodevelopmental outcomes have been collected on 319 children. Concentrations of parabens and phenols in maternal spot urine samples have been measured from 386 mothers. Center for Research on Early Childhood Exposure and Development mothers have significantly higher urinary concentrations of dichlorophenols, triclosan and triclocarban, but lower levels of several parabens compared with reference values from a similar population drawn from the National Health and Nutrition Examination Survey. FUTURE PLANS: Data will continue to be collected through recruitment of new births with a target of 600 children. Seven scheduled follow-up visits with existing and new participants are planned. Further, our research team continues to work with healthcare providers, paediatricians and early intervention providers to support parent's ability to access early intervention services for participants.