Full Sample Model Research Articles

Momentary Associations Across Specific Affective States and Dysregulated Eating Experiences Among Children and Adolescents With Loss of Control Eating Symptoms.

Dysregulated eating is common among youth and is associated with trait-level negative affect and emotion regulation difficulties. Despite the transient nature of affect, momentary associations among affect and eating behavior are unclear, which limits development of more impactful treatment tools, such as "just-in-time" intervention approaches (JITAI). The current study (N = 62) drew from two ecological momentary assessment (EMA) studies involving children and adolescents who endorsed loss of control (LOC) eating symptoms during a two-week assessment period. Intensive time series network analysis tested concurrent and prospective relationships across six specific affective states (i.e., upset, guilty, scared, tired, excited, attentive) and four eating-related experiences (e.g., LOC, overeating, hunger, craving) in real time. Additionally, we repeated these models within demographic subgroups of the sample based on age, race, and sex. In the full-sample models, contemporaneously assessed guilt was associated with craving and LOC eating, and tiredness was associated with LOC eating. In the prospective analysis, tiredness was negatively predicted by LOC eating and positively predicted by overeating at the previous timepoint, and attentiveness positively predicted craving. Differences in affect-eating relationships were identified across teens and preteens as well as male and female participants. These results suggest that specific affective states are associated with dysregulated eating-related experiences in real time among youth, and associations may differ depending on demographic characteristics. Findings may be used to inform the development and tailoring of momentary interventions.

Fairness of Machine Learning Algorithms for Predicting Foregone Preventive Dental Care for Adults

Access to routine dental care prevents advanced dental disease and improves oral and overall health. Identifying individuals at risk of foregoing preventive dental care can direct prevention efforts toward high-risk populations. To predict foregone preventive dental care among adults overall and in sociodemographic subgroups and to assess the algorithmic fairness. This prognostic study was a secondary analyses of longitudinal data from the US Medical Expenditure Panel Survey (MEPS) from 2016 to 2019, each with 2 years of follow-up. Participants included adults aged 18 years and older. Data analysis was performed from December 2022 to June 2023. A total of 50 predictors, including demographic and socioeconomic characteristics, health conditions, behaviors, and health services use, were assessed. The outcome of interest was foregoing preventive dental care, defined as either cleaning, general examination, or an appointment with the dental hygienist, in the past year. Among 32 234 participants, the mean (SD) age was 48.5 (18.2) years and 17 386 participants (53.9%) were female; 1935 participants (6.0%) were Asian, 5138 participants (15.9%) were Black, 7681 participants (23.8%) were Hispanic, 16 503 participants (51.2%) were White, and 977 participants (3.0%) identified as other (eg, American Indian and Alaska Native) or multiple racial or ethnic groups. There were 21 083 (65.4%) individuals who missed preventive dental care in the past year. The algorithms demonstrated high performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.84 (95% CI, 0.84-0.85) in the overall population. While the full sample model performed similarly when applied to White individuals and older adults (AUC, 0.88; 95% CI, 0.87-0.90), there was a loss of performance for other subgroups. Removing the subgroup-sensitive predictors (ie, race and ethnicity, age, and income) did not impact model performance. Models stratified by race and ethnicity performed similarly or worse than the full model for all groups, with the lowest performance for individuals who identified as other or multiple racial groups (AUC, 0.76; 95% CI, 0.70-0.81). Previous pattern of dental visits, health care utilization, dental benefits, and sociodemographic characteristics were the highest contributing predictors to the models' performance. Findings of this prognostic study using cohort data suggest that tree-based ensemble machine learning models could accurately predict adults at risk of foregoing preventive dental care and demonstrated bias against underrepresented sociodemographic groups. These results highlight the importance of evaluating model fairness during development and testing to avoid exacerbating existing biases.

Psychosocial and structural stressors and engagement in medical care among young sexual minority men across racial identities

Psychosocial and structural stressors and low engagement in medical care likely contribute to the disproportionate burden of chronic disease among sexual minority men (SMM) across the life course. However, how these stressors impact engagement in medical care among young SMM (YSMM) across racial identities remains understudied. The association of psychosocial and structural stressors with forgoing care among YSMM across racial identities was examined using race-stratified adjusted logistic regression of cross-sectional data. Among 737 HIV-negative SMM aged 16–24 years, nearly all (93%) experienced discrimination in their daily lives. Non-Hispanic/Latinx Black participants reported significantly higher levels of discrimination, exposure to community violence, and food insecurity. Medical mistrust and mental health were not significantly different across racial groups. In the full sample model, education, food insecurity, and discrimination were associated with forgoing care. Among the non-Hispanic/Latinx White sample, medical mistrust and discrimination were associated with forgoing care. Among the non-Hispanic/Latinx Black sample, discrimination was associated with forgoing care. Among the Hispanic/Latinx sample, food insecurity was associated with forgoing care. Psychosocial and structural stressors were common in this YSMM cohort, and significantly different across racial/ethnic identities. Race-stratified analysis revealed differences in the association of stressors with forgoing care among YSMM across racial identities, not appreciated in the analysis limited to the total study population. Our findings may support efforts to address health inequity and improve engagement in medical care among SMM.

29 Examining the Relationship between Symptom and Performance Validity Measures Across Referral Subtypes

Objective:Performance validity (PVT) and symptom validity tests (SVT) have become standard practice in assessing credibility of neuropsychological profiles and symptom report. While PVTs assess cognitive task engagement, SVTs assess credibility of patient symptom report. Although prior research aimed to conceptualize the relationship between the two validity measure types, it generally focused on SVTs from the Minnesota Multiphasic Personality Inventory (MMPI-2 &RF) and the Structured Inventory of Malingered Symptoms (SIMS; Ord et al., 2021, MMPI-2; Van Dyke et al., 2013). Further studies have demonstrated mixed results, with many studies concluding that symptom and performance validity are separate but related constructs. The current study aimed to assess the relationship between PVTs and SVTs utilizing symptom validity measures from the Personality Assessment Inventory (PAI) across three samples, including neurodevelopmental, psychiatric, and traumatic brain injury groups.Participants and Methods:Participants included 634 individuals consecutively referred for neuropsychological assessment who completed the Test of Memory Malingering (TOMM) and the PAI (mean Age = 41.7, SD = 15.7; mean Education = 13.7, SD = 2.7; 53% female; 89% Caucasian). Participants were divided into three groups based on referral, including neurodevelopmental (mean Age = SD = 10.7; mean Education = 13.4, SD = 2.5; 39% female; 79% Caucasian), psychiatric (mean Age = 44.7, SD = 15.0; mean Education = 13.8, SD = 2.8; 58% female; 90% Caucasian), and traumatic brain injury samples (mean Age = SD = 15.5; mean Education = 13.3, SD = 2.3; 50% female; 91% Caucasian). Four structural equation models (latent variable models) were constructed. The first model was fit across the entire sample while the remaining three were fit for the aforementioned subsamples. TOMM trials modeled the performance validity latent variable while SVTs from the PAI modeled the symptom validity latent variable (Positive Impression Management and Defensiveness Index modeled underreporting; Negative Impression Management, Malingering Index, and Cognitive Bias Scale modeled overreporting).Results:In the full sample model overreporting significantly predicted performance validity (p < 0.001, r = -0.31), indicating higher symptom overreporting related to poorer performance validity while symptom underreporting did not significantly predict performance validity (p = 0.09, r = 0.08). In the neurodevelopmental model overreporting did not significantly predict performance validity (p = 0.44, r = 0.10). Further, symptom underreporting did not significantly predict performance validity (p = 0.40, r = 0.10). Similarly, for the TBI model, overreporting did not significantly predict performance validity (p = 0.82, r = -0.02) and symptom underreporting did not significantly predict performance validity (p = 0.50, r = -0.08). For the psychiatric sample symptom underreporting did not significantly predict performance validity (p = 0.06, r = 0.11); however, symptom overreporting significantly predicted performance validity (p < 0.001, r = - 0.39).Conclusions:The current study expands on prior research comparing the relationship between SVTs and PVTs in neuropsychological evaluation utilizing SVTs from the PAI. Results of the present study suggest the relationship between the SVTs and PVTs varies by referral type and further supports using both PVTs and SVTs in neuropsychological assessment.

Exploring Drivers Shaping the Choice of Alternative-Fueled New Vessels

The urgent imperative for maritime decarbonization has driven shipowners to embrace alternative marine fuels. Using a robust orderbook dataset spanning from January 2020 to July 2023 (encompassing 4712 vessels, 281 shipyards, and 967 shipping companies), four distinct multinomial logit models were developed. These models, comprising a full-sample model and specialized ones for container vessels, dry bulk carriers, and tankers, aim to identify the key determinants influencing shipowners’ choices of alternative fuels when ordering new vessels. It is interesting to find that alternative fuels (e.g., liquefied natural gas) are the most attractive choice for gas ships and ro-ro carriers; others prefer to use conventional fuels. Furthermore, this study reveals that shipowners’ choices of new fuels significantly correlate with their nationality. While it is well-established that economic factors influence shipowners’ choices for new ship fuel solutions, the impacts of bunker costs, freight rates, and CO2 emission allowance prices remain relatively limited. It is evident that the policies of the International Maritime Organization (IMO) to reduce carbon emissions have increased the demand for building new energy ships. This research contributes to bridging research gaps by shedding light on the intricate interplay of factors that influence shipowners’ preferences for alternative marine fuels amidst global regulatory shifts. It also offers valuable insights for policymakers aiming to incentivize shipowners to transition towards sustainable energy sources.

The Determinants of East African Economic Growth

This paper focuses on examining the effect of government external debt on economic growth, investigating the effect of interest rate on economic growth, establishing the effect of government domestic debt on economic growth, and examining the effect of budget deficit on economic growth. The study was carried out for three East African countries, Kenya, Tanzania and Uganda, over the study period of 1980-2019. Autoregressive Distributed Lag (ADL) Panel Approach was used to specifically establish the effect of government external debt, interest rate, government domestic debt, and budget deficit on economic growth. Pooled Mean Estimator (PMG) was used to estimate both the general and full sample model. The results showed that in the long run, government external debt and interest rate had negative but not statistically significant effect on economic growth for the three countries. On the other hand, government domestic debt and budget deficit had positive effect but not statistically significant effect on economic growth. The results of the short term showed that government external debt, government domestic debt, and budget deficit had a negative but not statistically significant effect on economic growth for the three countries, while interest rate had a negative and statistically significant effect on economic growth for the three countries. Firstly, in order to achieve a positive economic growth, it is recommended that the East African countries, Kenya, Tanzania and Uganda, should design and implement debt, interest rates, and budget deficit policies to fit and align with other macroeconomics policies. Secondly, they should restrain their expenditure to sustainable levels to maintain a balanced if not surplus budget. Thirdly, they should endeavor to put economic and legal framework in place to eradicate corruption of public funds. Fourthly, they should design and implement policies to broaden tax base to support a balance budget. Fifthly, the governments should avoid a crowding through huge borrowings in the domestic market at the expense of the private sector.

Sociodemographic predictors of depression and anxiety symptomatology among parents in rural Côte d'Ivoire

BackgroundIn Côte d'Ivoire, cocoa farming is a widespread practice in rural households, an occupation with increased risks of depression and anxiety exacerbated by economic instability. We used the Goldberg-18 Depression and Anxiety diagnostic tool to identify predictors of depressive and anxiety symptomatology among a sample of parents in rural cocoa farming communities. MethodsIn a cross-sectional survey, the Goldberg-18 was administered to Ivorian parents (N = 2471). Confirmatory factor analysis (CFA) was conducted to confirm the factor structure of the assessment tool, and Ordinary Least Squares (OLS) regression with clustered standard errors was used to identify sociodemographic predictors of symptomatology. ResultsCFA showed adequate fit statistics for a two-factor model measuring depressive and anxiety symptoms. Among respondents, 87 % screened positive for requiring further referral for clinical diagnosis. Sociodemographic predictors of depressive and anxiety symptoms were similar for males and females. For the total sample, higher monthly income, more years of education, and belonging to the Mandinka ethnic group predicted fewer depressive and anxiety symptoms. In contrast, higher depressive and anxiety symptomatology were associated with age. Single marital status predicted increased anxiety but not depressive symptoms for the full sample model and the female only sample, but not the male sample. LimitationsThis is a cross-sectional study. ConclusionsThe Goldberg-18 measures distinct domains of depressive and anxiety symptoms in a rural Ivorian sample. Age and single marital status are predictors of increased symptoms. Higher monthly income, higher education, and certain ethnic affiliations are protective factors.

Diversity–performance nexus in an emerging economy: an investigation of family and non-family firms

PurposeThe empirical study aims to examine the impact of board diversity with respect to gender and nationality on firm performance in an emerging economy. This research further splits the sample into family and non-family domains and investigates the diversity–performance nexus in isolation.Design/methodology/approachThe sample consists of 183 listed companies in Bangladesh over the period 2007 to 2017. This study employed the generalised method of moments (GMM) technique to address the possible endogeneity issue in the governance–performance connection. To underscore the strength of diversity, three distinctive assessment measures were used: percentage representation of females and foreign directors, the Blau index and the Shannon index.FindingsThe results for the full sample models reveal that board heterogeneity regarding both female and foreign directors positively and significantly influences firm performance as measured by return on assets (ROA). Further to this, female directors in family-owned businesses have a positive association with profitability, whereas foreign nationals demonstrate a significant positive association with performance in non-family firms. Additionally, at least three women directors are needed to make a positive difference in profitability; however, a sole director with foreign nationality is capable of demonstrating a similar impact on performance.Practical implicationsThe findings are significant for policymakers and organisations that advocate diversity on corporate boards of directors, and the minimum number of diverse board members needs to be considered depending on the identity to bring about a significant change in organisational outcome. Therefore, the findings of this study may be applied to other emerging economies with similar institutional characteristics.Originality/valueThis study reinforces the existing stock of knowledge on the impact of board diversity on the profitability of firms, especially in the context of an emerging economy – Bangladesh. Irrespective of the given backdrop, this study finds that both gender and nationality diversity in the case of Bangladesh is found to have a positive and significant effect on financial performance with respect to all the diversity metrics, i.e. the proportionate number of female and foreign directors on the boards, the Blau index and the Shannon index.

Exploring the factors contributing to increase in facility child births in Bangladesh between 2004 and 2017–2018

BackgroundAlthough Bangladesh has gained rapid improvement in births at health facilities, yet far behind to achieve the SDG target. Assessing the contribution of factors in increased use of delivery at facilities are important to demonstrate. ObjectiveTo explore the determinants and their contribution in explaining increased use of facility child births in Bangladesh. ParticipantsReproductive-aged women (15–49 years) of Bangladesh. Methods and materialsWe used the latest five rounds (2004, 2007, 2011, 2014, 2017–2018) of Bangladesh Demographic and Health Surveys (BDHSs). The regression based classical decomposition approach has been used to explore the determinants and their contribution in explaining the increased use of facility child birth. ResultsA sample of 26,686 reproductive-aged women were included in the analysis, 32.90% (8780) from the urban and 67.10% (17,906) from the rural area. We observed a 2.4-fold increase in delivery at facilities from 2004 to 2017–2018, in rural areas it is more than three times higher than the urban areas. The change in mean delivery at facilities is about 1.8 whereas, the predicted change is 1.4. In our full sample model antenatal care visits contribute the largest predicted change of 22.3%, wealth and education contributes 17.3% and 15.3% respectively. For the rural area health indicator (prenatal doctor visit) is the largest drivers contributing 42.7% of the predicted change, hereafter education, demography and wealth. However, in urban area education and health contributed equally 32.0% of the change followed by demography (26.3%) and wealth (9.7%). Demographic variables (maternal BMI, birth order, age at marriage) contributing more than two-thirds (41.2%) of the predicted change in the model without the health variables. All models showed more than 60.0% predictive power. ConclusionHealth sector interventions should focus both coverage and quality of maternal health care services to sustain steady improvements in child birth facilities.

Private Tutoring Classes and Emotional Well-Being in China: Bidirectional Relationship and the Role of Academic Achievement

The Chinese education system is characterized as highly meritocratic and examination-oriented. Scores in high-stakes public standardized examinations mainly determine upward educational transitions. Private tutoring classes (PTCs) are a typical measure to boost students’ academic achievement. Mental health is also assumed to affect one’s behaviors and educational outcomes, playing a key role in the stratification process. However, few studies discuss (1) the bidirectional relationship between PTC attendance and emotional well-being, and (2) the role of academic achievement in determining PTC attendance and moderating the relationship between the two variables. This study fills these gaps by analyzing the 2013–14 and 2014–15 waves of data from the China Educational Panel Survey. Using a cross-lagged model, the author only finds a positive association between prior emotional well-being and later PTC attendance. Moreover, the positive association between students’ prior academic achievement and their later PTC attendance suggests that PTCs tend to serve higher-performing students. Examining the heterogeneity of the bidirectional relationship in terms of academic achievement, the author finds that only low-achievers follow a similar pattern as shown in the full sample model. This study will enrich the understanding on how mental health together with PTCs attendance contributes to the educational stratification process.

The mediating effect of feedback-seeking behavior on the relationship between ideal L2 writing self and story continuation writing performance: A multigroup structural equation modeling approach

The existing literature on the “the motivation → behavior → outcome chain” has implied that ideal L2 writing self should be equipped with appropriate behavioral strategies to facilitate writing achievement. Feedback-seeking behavior (FSB) includes both feedback monitoring and feedback inquiry and offers a novel perspective. Using a sample of 589 Chinese high school students with low, mid, high levels of writing proficiency, this study explores the mediating role of FSB in the relationship between ideal L2 writing self and story continuation writing performance. Structural equation modeling (SEM) results showed no significant mediating effect in the full sample model. However, such an effect varied across the different levels of L2 writing competence: (1) for the low-achieving students, the indirect path from ideal L2 writing self to writing performance via FSB was not evident; (2) for the mid-achieving students, feedback monitoring positively mediated the relationship between ideal L2 writing self and writing performance, whereas feedback inquiry had a negative mediation effect on this relationship; (3) for the high-achieving students, feedback monitoring had a significantly negative mediation effect, while feedback inquiry had a marginally positive mediation effect. The implications of the findings are discussed.

‘Can we predict aggression?’—Determining the predictors of aggression among individuals with substance use disorder in China undergoing enforced detoxification through machine learning

BackgroundThe general aggression model has shown that both individual and situational factors can predict aggression. However, past research has tended to discuss these two factors separately, which might lead to inconsistency. This study addresses this gap by examining the importance of each predictor of aggression in a Chinese compulsory drug treatment population and further explores the predictors of aggression in various substance use disorder populations. MethodAnalyses were conducted using a sample of 894 male participants (mean = 38.30, SD = 8.38) in Chinese compulsory drug rehab. A machine learning model named LightGBM was employed to make predictions. We then used a game-theoretic explanatory technique, SHAP, to estimate the effect of predictors. ResultsIn the full-sample model, psychological security, parental conflict, and impulsivity were the top 3 predictors. Depression, childhood abuse, and alexithymia positively predicted aggression, whereas psychological security, family cohesion, and gratitude negatively predicted aggression. There were significant differences in the predictive effects of depressants and stimulants. Although the importance of predictors varied between drug-use groups, several individual and situational factors were consistently the most important predictors. LimitationsAll participants in this study were male, and the data were acquired through self-reports from the participants. Domestic and nondomestic aggression are not distinguished. Additionally, our findings cannot support causal conclusions. ConclusionThis study tested a series of classical theories of the predictors of aggression in China's compulsory drug treatment context and extended the ideas of the GAM to various substance use disorder groups. The findings have important implications for aggression treatment.

The double burden of overweight or obesity and anemia among women married as children in India: A case of the Simpson’s paradox

BackgroundChild marriage, defined as marriage before age 18 years, could impact women’s nutritional status through biological as well as environmental and socioeconomic attributes affecting diet and lifestyle behaviors. This study aims to examine whether women married as children have a differential risk of individual level double burden of overweight/obesity and anemia at adult age compared to women married as adults. MethodsUsing nationally representative data from India we estimated multinomial logistic regressions to obtain relative risk ratios (RRR) in favor of mutually exclusive anemia and overweight/ obesity conditions among women aged 20–49 years. We estimated the model for full sample and for sub-samples by household wealth groups. ResultsWe find that women who were married as children had a lower relative risk (RRR=0.941) of the double burden of anemia and overweight/obesity in the full sample. However, when sociodemographic correlates were accounted for and assessed in sub-groups by wealth groups, they had a higher relative risk (ARRR ranging from 1.079 to 1.204) of the double burden compared to women married as adults. ConclusionOur results thus portray a classic case of the Simpson’s paradox by documenting a reversal of association between child marriage and occurrence of the double burden of malnutrition in the subgroup level than that in the general population. This finding provides a critical policy insight for effective public health interventions to improve women’s health and wellbeing, particularly in low resource settings.

Perceived Neighborhood Racial Composition and Depressive Symptoms Among Black Americans Across Adulthood: Evaluating the Role of Psychosocial Risks and Resources.

Objectives: To evaluate the relationships between perceived neighborhood racial composition (PNRC), psychosocial risks and resources, and depressive symptoms among young (ages 22-35), middle-aged (ages 36-49), and older (ages 50+) Black Americans. Methods: Full sample and age-stratified linear regression models estimated the PNRC-depressive symptoms association and the extent to which it persisted after accounting for psychosocial risks (i.e., neighborhood disorder, other social stressors) and resources (i.e., mastery, social support, racial identity) among 627 Black Americans in the Nashville Stress and Health Study. Results: Living in racially integrated and predominately White neighborhoods was associated with elevated depressive symptoms. While psychosocial risks and resources explained a substantial portion of these associations, patterns varied across age groups. Discussion: PNRC impacts depressive symptoms among Black Americans by shaping psychosocial risks and resources. Findings underscore interconnections between contextual and psychosocial factors, as well as the distinct mental health significance of these processes across stages of adulthood.

We Want You Back: Uncovering the Effects on In-Person Instructional Operations in Fall 2020.

Postsecondary institutions’ responses to COVID-19 are a topic of immediate relevance. Emergent research suggests that partisanship was more strongly linked to institutions offering in-person instruction for Fall 2020 than was COVID-19. Using data from the College Crisis Initiative and a multiple group structural equation modeling approach, we tested the relationships between our outcome of interest (in-person instruction in Fall 2020) and state and county sociopolitical features, state and county COVID-19 rates, and state revenue losses. Our full-sample model suggested that County Political Preferences had the strongest association with in-person instruction, followed by Pandemic Severity and State Sociopolitical Features. Because institutional sectors may be uniquely sensitive to these factors, we tested our models separately on 4-year public, 4-year private, and 2-year public and 2-year private institutions. State Sociopolitical Features were significantly related to in-person instruction for 4-year private and 2-year public institutions but were strongest for 4-year public institutions. For 4-year private and 2-year public institutions, County Political Preferences’ effect sizes were 2–3 times stronger than effects from State Sociopolitical Features. Pandemic Severity was significantly, negatively related to in-person instruction for 4-year private and 2-year public institutions–similar in magnitude to State Sociopolitical Features. Our analysis revealed that COVID-19 played a stronger role in determining in-person instruction in Fall 2020 than initial research using less sophisticated methods suggested—and while State Sociopolitical Features may have played a role in the decision, 4-year private and 2-year public institutions were more sensitive to county-level preferences.

IoT-Assisted Hybrid Intelligent Learning Architecture Based on Digital Education in a Diverse Society

The existing research paradigm has caused the research of educational theory to be criticized for lack of logic and scientificity. With the gradual deepening and refinement of the development of smart education platforms, the educational process can be quantified, and the research paradigm of educational science has moved from a sampling model to a full-sample model, and it has become a real empirical science. This paper combines the Internet of Things technology to construct a hybrid intelligent learning system and applies it to digital education in a diverse society to construct a functional structure of the extraction system. Moreover, starting from the actual situation of intelligent education, this paper combines a simulation system to implement an IoT-assisted hybrid intelligent learning architecture and evaluates the teaching effect of the system. Through experimental research, it can be known that the IoT-assisted hybrid intelligent learning framework based on social diversified digital education has certain effects and has a certain reference effect for the development of subsequent intelligent education.

Price premiums for certified roundwood: evidence from auction sales in Turkey

ABSTRACT Around 6.5 million ha state-owned forests are certified by Forest Stewardship Council in Turkey. Roundwood originated from certified forests has been in the market for nearly a decade. Little is known about the impact of forest certification on the roundwood price. Price models were constructed to explore the price premium for certified roundwood using sales data collected from state forest enterprises in Bolu. Full sample models for sawlog and pulpwood revealed that forest certification provided a limited premium. Sawlogs had a 0.54% premium while pulpwood had none. In tree-type models, scots pine sawlog received a price premium of 1.74%, while black pine pulpwood and scots pine pulpwood fetched price premium by 1.9% and 2.2% respectively. Results of the study provide useful information to forest policymakers.

Association of Unexpected Newborn Deaths With Changes in Obstetric and Neonatal Process of Care

The death of a healthy term infant may signal patient safety and quality issues. Various initiatives aim to encourage clinicians to learn from these events, but little evidence exists regarding how exposure to an unexpected newborn death may alter clinician practice. To examine the association between an unexpected newborn death and changes in obstetric and newborn procedures that may be used in response to potential fetal distress or newborn complications. This cross-sectional study used difference-in-differences analysis of 2011 to 2017 US vital statistics data from 477 US counties experiencing an unexpected newborn death during the study period. All in-hospital live births in the 477 counties during the study period were included. Data were analyzed from September 2019 to September 2020. The death of an infant aged 0 to 7 days following an unremarkable pregnancy owing to causes other than birth defects, accidents/assaults, or sudden infant death syndrome. Primary outcomes included binary variables capturing intervention in labor/delivery (induction, augmentation, cesarean delivery, forceps/vacuum) and procedures to avert and mitigate newborn complications (assisted ventilation, surfactant replacement therapy, antibiotics for suspected sepsis, neonatal intensive care unit admission). The main sample included 5.72 million births (2.54 million during preexposure time). Mean (SD) maternal age was 27.3 (5.8) years; 67% of mothers were White, and 12% were Black. Associations varied across the 4 estimated models. Following an unexpected newborn death, there was no significant increase in the probability of cesarean delivery in the full sample model (0.28 percentage points [pp]; 95% CI, -0.01 to 0.57 pp), but a significant increase in the other 3 models, with values ranging from 0.55 pp (95% CI, 0.21 to 0.88 pp) in the full sample model with matching to 0.66 pp (95% CI, 0.13 to 1.19 pp) in the 1-hospital county subsample with matching. There was a significant increase in the probability of newborn assisted ventilation in the full sample model with matching (0.46 pp; 95% CI, 0.08 to 0.83 pp), but no significant increase in the other 3 models, with estimates ranging from 0.33 pp (95% CI, -0.04 to 0.71 pp) to 0.69 pp (95% CI, -0.02 to 1.40 pp). An unexpected newborn death was not associated with a significant increase in antibiotic use in the full sample models (without matching: 0.19 pp; 95% CI, -0.00 to 0.39 pp; with matching: 0.22 pp; 95% CI: -0.02 to 0.46 pp), but was associated with a significant increase in both of the 1-hospital county subsample models (without matching: 0.38 pp; 95% CI, 0.02 to 0.73 pp; with matching: 0.39 pp; 95% CI, 0.01 to 0.77 pp). In some study models, an unexpected newborn death was associated with statistically significant increases in subsequent use of procedures to avert and mitigate fetal distress and newborn complications, which could reflect increases in identifying and proactively addressing serious potential complications or increased clinician caution applied across all cases. Future research should address whether these changes affect patient outcomes.

Does economic growth reduce childhood stunting? A multicountry analysis of 89 Demographic and Health Surveys in sub-Saharan Africa

BackgroundThere is mixed evidence and lack of consensus on the impact of economic development on stunting, and likewise there is a dearth of empirical studies on this relationship in the...

Demographic, psychological, behavioral, and cognitive correlates of BMI in youth: Findings from the Adolescent Brain Cognitive Development (ABCD) study.

Previous research has implicated demographic, psychological, behavioral, and cognitive variables in the onset and maintenance of pediatric overweight/obesity. No adequately-powered study has simultaneously modeled these variables to assess their relative associations with body mass index (BMI; kg/m2) in a nationally representative sample of youth. Multiple machine learning regression approaches were employed to estimate the relative importance of 43 demographic, psychological, behavioral, and cognitive variables previously associated with BMI in youth to elucidate the associations of both fixed (e.g. demographics) and potentially modifiable (e.g. psychological/behavioral) variables with BMI in a diverse representative sample of youth. The primary analyses consisted of 9-10 year olds divided into a training (n = 2724) and test (n = 1123) sets. Secondary analyses were conducted by sex, ethnicity, and race. The full sample model captured 12% of the variance in both the training and test sets, suggesting good generalizability. Stimulant medications and demographic factors were most strongly associated with BMI. Lower attention problems and matrix reasoning (i.e. nonverbal abstract problem solving and inductive reasoning) and higher social problems and screen time were robust positive correlates in the primary analyses and in analyses separated by sex. Beyond demographics and stimulant use, this study highlights abstract reasoning as an important cognitive variable and reaffirms social problems and screen time as significant correlates of BMI and as modifiable therapeutic targets. Prospective data are needed to understand the predictive power of these variables for BMI gain.