The Spatial Scale and Spread of Child Victimization.

Previous research shows that large, densely populated urban areas have higher rates of child victimization that have persisted over time. However, few investigations have inquired about the processes that produce and sustain hot and cold spots of child victimization. As a result, the mechanisms that produce the observed spatial clustering of child victimization, and hence "why" harms against children tend to cluster in space, remains unknown. Does the likelihood of being a victim of violence in one location depend on a similar event happening in a nearby location within a specified timeframe? Rather, are child victims of violence more likely to reside in suboptimal neighborhood conditions? This paper aims to present an analytical and theoretical framework for distinguishing between these locational (point) processes to determine whether the empirical spatial patterns undergirding child victimization are more reflective of the "spread" via contagion (i.e., dependency) or whether they are produced by neighborhood structural inequality resulting from spatial heterogeneity. To detect spatial dependence, we applied the inhomogeneous K-function to Los Angeles Medical Examiner data on child homicide victim locations while controlling for regional differences in victimization events (i.e., heterogeneity). Our analysis found strong evidence of spatial clustering in child victimization at small spatial scales but inhibition at larger scales. We further found limited support for the spatiotemporal clustering of child victimization indicative of a contagion effect. Overall, our results support the role of neighborhood structural vulnerability in the underlying mechanisms producing patterns of child victimization across Los Angeles County. We conclude by discussing the policy implications for understanding this spatial patterning in geographical context and for developing effective and targeted preventive interventions.

Assessing the effects of the Sacramento Neighborhood Alcohol Prevention Project (SNAPP) on child abuse and neglect.

BACKGROUND: The role of alcohol use in the etiology of abusive and neglectful parenting is significant. We examined how the Sacramento Neighborhood Alcohol Prevention Project (SNAPP) may have reduced rates of substantiated child maltreatment, entries into foster care, and entries into foster care where alcohol use was a factor. PARTICIPANTS AND SETTING: The study sample is 326 Census block groups: 21 and 16 in the South and North intervention areas, respectively, and 289 in the At-Large comparison area in Sacramento, California. METHODS: SNAPP used a quasi-experimental design to reduce alcohol supply and alcohol-related problems among 15-29 year olds in two economically, racially, and ethnically diverse neighborhoods. The dependent variables are substantiated child abuse and neglect; total foster care entries; and alcohol-related foster care entries. RESULTS: Substantiated child abuse and neglect was inconclusive for both intervention areas. In the North, total (RR = 0.822, 95 % CI [0.721, 0.933]) and alcohol-related (RR = 0.760, 95 % CI [0.634, 0.914]) foster care entries decreased by 17.8 % and 24.0 %, respectively. Intervention effects in the South were not well-supported for foster care entries (RR = 1.118, 95 % CI [0.988, 1.258]), but increased alcohol-related foster care entries (RR = 1.264, 95 % CI [1.075, 1.484]). CONCLUSIONS: Environmental intervention strategies may be effective at reducing child abuse and neglect. However, given the mixed findings from our work, we need to identify under what conditions these interventions work best and whether some components of these strategies (e.g. awareness vs. enforcement of underage sales) differentially affect child abuse and neglect.

Contrasting Methods of Measurement in Spatial Analyses Examining the Alcohol Environment and Child Maltreatment.

Child physical abuse is a major public health issue in the United States. Environmental child welfare research has focused on neighborhood characteristics and the influence of alcohol and marijuana establishments. To our knowledge, child welfare studies have singularly examined the outcome in terms of victims, that is, at the level of child population, and have not considered the parent population. Thus, in this exploratory study, we use spatial scan statistics to analyze patterns of child physical abuse at the child and household level, and we use Bayesian hierarchical spatial conditional autoregressive models to determine the relative influence of alcohol availability and other environmental factors. We find that household clusters are nested in child clusters and that controlling for alcohol establishments reduces cluster size. In the Bayesian regression models, alcohol availability increased risk slightly, while neighborhood diversity (measured using Blau's Index) elevated risk considerably. Immediate implications for child welfare agencies are discussed.

Does Increasing Access to Prenatal Care Reduce Racial Disparities in Birth Outcomes?

PURPOSE: To examine the effectiveness of early and adequate prenatal care (PNC) in reducing racial disparities in pre-term birth (PTB) among low-income women. DESIGN AND METHODS: This retrospective study examined birth records for 14,950 low-income Black and White women. The primary outcome of interest was racial disparities in PTB. Exposures of interest were first trimester entry into, and adequacy of, PNC. Maternal residential proximity to nearest PNC provider was calculated. Bivariate analyses were performed for PTB by race. Binary logistic regression was performed, controlling for maternal age, smoking status and racial segregation. Attributable risk of PTB for no or late entry into PNC, and percent difference by race was calculated. RESULTS: We find that early and adequate PNC significantly decreases the risk of preterm birth, however, we find no evidence that this reduces racial disparities. Low income black females in a large metropolitan county have greater geographic access to and utilization of PNC than low-income white females, yet racial disparities in preterm birth remain. Attributable risk of PTB for no or late entry into PNC was lower for Black women (32.2%) than White women (39.4%). CONCLUSIONS: Our findings suggest that adequate PNC alone does not reduce the marked racial disparities in preterm birth. PRACTICE IMPLICATIONS: Public health agencies and health care providers need to look beyond access to care, to achieve racial equity in birth outcomes. Expansion of evidence-based, comprehensive nursing interventions shown to reduce preterm birth, such as the Nurse Family Partnership home visiting program, could contribute to these efforts.

Long-term mental health services use in children referred to a clinical intervention.

BACKGROUND: Recent advancements in pediatric mental health (MH) increased accessibility of evidence-based interventions. Yet, accessibility alone does not explain the rise in MH services use (MHSU). Maltreatment-related adversity, symptom severity, and access to early interventions have been linked to ongoing need for services, yet their joint contributions to continuities in MHSU remain unclear. OBJECTIVE: The study examines the role of maltreatment, externalizing symptom severity, and referral for early intervention in pediatric MHSU across five years. To evaluate engagement in treatment, we accounted for treatment progress and referral type, comparing MHSU in court-mandated and voluntary participants. PARTICIPANTS AND SETTING: Participants were 321 children (M = 4.3 years; 58.9 % boys) referred to parent-child interaction therapy (PCIT), an evidence-based intervention for families struggling with child disruptive behaviors and maltreating families involved with child welfare. Services were provided at a university-affiliated clinic in a metropolitan county. METHOD: Symptom severity was assessed with standardized questionnaires; maltreatment history and referral type were obtained from casefile reviews. MHSU was tracked through county behavioral health diagnostic reports. The data were analyzed using structural equation modeling. RESULTS: Results indicated that for the 44.9 % of children with onward referrals, the frequency of service use, but not progress in treatment, predicted ongoing services. Maltreatment emerged as a universal predictor, while externalizing predicted MHSU only in court-mandated participants, suggesting referral type contributes to quantifiable differences in MH needs. CONCLUSIONS: Findings emphasize importance of ongoing funding for pediatric MH services, and the need to explore mechanisms underlying continuous MHSU in vulnerable children.

The spatio-temporal distribution of naloxone administration events in rural Ohio 2010-16.

INTRODUCTION: In 2017, Ohio had the second highest rate of drug overdose deaths in the United States. Current opioid related epidemiologic literature has begun to uncover the environmental level influences on the opioid epidemic and how the end results may ultimately manifest over space and time. This work is still nascent however, with most clustering research conducted at a spatial unit such as county level, which (1) can obscure differences between urban and rural communities, (2) does not consider dynamics that cross county lines, and (3) is difficult to interpret directly into strategic and localized intervention efforts. We address this gap by describing, at the Census block level, the spatial-temporal clustering of opioid related events in rural Ohio. METHODS: We use the outcome of the administration of naloxone emergency medical service (EMS) calls in rural Ohio Census blocks during 2010-16 in a Poisson model of spatial scan statistics. RESULTS: We found that naloxone event clustering in rural Ohio in the recent decade was widely dispersed over time and space, with clusters that average 17 times the risk of having an event compared to areas outside the cluster. Many of the larger spatial clusters crossed administrative boundaries (i.e., county lines) suggesting that opioid misuse may be less responsive to county level policies than to other factors. DISCUSSION: Timely identification of localized overdose event clustering can guide affected communities toward rapid interventions aimed at minimizing the morbidity and mortality resulting from contagious opioid misuse.

Disparity in child welfare referrals from public schools: An example of Simpson's Paradox?

BACKGROUND: Black children continue to be found in child welfare outcome measures at rates nearly double those of White children in the United States. Researchers have turned from bias theory to risk theory, arguing that disparity disappears when considering only the subgroup of children in poverty. In this study, we consider whether this phenomenon is an example of Simpson's Paradox, where aggregate findings are confounded by a third factor. PARTICIPANTS: We created a dataset by matching child welfare data to schools in a metropolitan California county. METHODS: We consider measures of poverty and racial-ethnic student composition as possible confounders, utilizing compositional data analysis for the latter. Traditional linear and ridge regression models were used to calculate the unadjusted and adjusted effects of each independent variable. RESULTS: We find only partial evidence of Simpson's Paradox, in that Black to White disparity only disappears in the highest quartile of poverty. Holding poverty constant, only increasing student population non-White composition was significantly associated with reducing Black to White disparity ratios. CONCLUSION: In a small, exploratory study, we find that while poverty may serve as an equalizer, diversity racial/ethnic student body composition may serve as a neutralizer. We find that underlying causes of disparity are complex and caution against endorsement of single theories to explain the disproportionate representation of Black children in child welfare. We find utility in analyzing child welfare data with concepts and techniques common in other disciplines and highlight several weaknesses of current child welfare informatics which impact both program evaluation and research.

Community-level Adverse Experiences and Emotional Regulation in Children and Adolescents.

PURPOSE: The association of adverse childhood experiences (ACEs) with negative health outcomes is well established, and the concept of allostatic load has been proposed as a possible causal mechanism. Most studies measure conventional (household) ACE exposures without accounting for non-conventional (community) ACE exposures, which may underrepresent the adversity experienced by racial/ethnic minorities. We address this gap by calculating the prevalence of both types of ACE exposures for racial/ethnic subgroups. We also examine associations of ACE exposures and emotional regulation in school aged children and youth. DESIGN AND METHODS: This study used data (n = 65,680) for a nationally representative sample of children ages 6 to 17 years in the National Survey of Children's Health (2011-2012). Confirmatory factor analysis, descriptive statistics and regression models were used to examine the relationships between ACEs and emotional regulation. RESULTS: Community level ACE events disproportionately affect ethnic minorities. Some but not all ACEs were significantly and inversely associated with the ability to emotionally regulate in children. Experiencing racism had the strongest negative effect of all ACE variables. The strength of the child-caregiver relationship was associated with increased odds of emotional regulation, independent of exposure to ACEs. CONCLUSIONS: The study supports the need to refine and expand ACE health screenings to fully capture the adversity faced by all children. Emotional regulation is identified as a possible intervention point. PRACTICE IMPLICATIONS: Expansion of programs that strengthen the child-caregiver relationship and reduce ACEs in early childhood may be a key approach to increasing coping abilities in youth.

Registered nurses' preferences for rural and urban jobs: A discrete choice experiment.

BACKGROUND: Recruitment and retention of nurses is an ongoing challenge for employers in rural areas worldwide. There is limited information available regarding influences on nurses' job choice in the U.S. and little understanding of how nurses make trade-offs between desired and less desirable job characteristics when choosing between jobs. OBJECTIVES: The purpose of this research was to examine the hospital job preferences of registered nurses in the U.S. The specific objectives of the study were: 1) To identify the relative importance of key job attributes on registered nurse job choice, and 2) To predict the impact of changes in the levels of attributes on the probability of registered nurses choosing one job over another. DESIGN AND SETTING: A discrete choice experiment was developed and applied in the U.S. using California as a study site. PARTICIPANTS: 190 registered nurses currently working in nursing or intending to return to work in nursing from urban, large-, small- and isolated-rural communities. METHODS: The survey instrument was developed through a literature review and semi-structured interviews with nurse experts, utilizing a hypothetical job in a hospital medical / surgical unit. Experimental design principles were applied to create a discrete choice experiment which was pilot tested with urban and rural nurses. The survey was mailed to a random sample of 1000 licensed registered nurses in California. A mixed logit model was used to estimate nurses' preferences for different levels of the job attributes. Willingness to pay estimates and simulations of job uptake rates were calculated. RESULTS: Eight factors were identified as important to job choice: earnings, nursing voice in management, tuition reimbursement, scheduling, patient care team, leadership, location and nursing sensitive patient care outcomes. Respondents valued a cohesive patient care team (coefficient 1.95, [SE 0.23]) and a strong nursing voice in management (coefficient 1.56, [SE 0.22]) highest. A job in a large urban inland location was negatively valued (coefficient -0.69, [SE 0.25]). Around 72% of respondents chose to stay in their current job when this choice was offered. While earnings were important, nurses were willing to sacrifice earnings to secure other valued job characteristics when choosing between jobs. CONCLUSIONS: Study findings provide information on how job characteristics are valued by nurses in California. Findings suggest job seekers may be 65-75 percent more likely to choose a job when valued job characteristics are present. Our findings are particularly relevant to rural hospitals with limited financial resources.

The use of model based recursive partitioning as an analytic tool in child welfare.

Child welfare agencies are tasked with investigating allegations of child maltreatment and intervening when necessary. Researchers are turning to the field of predictive analytics to optimize data analysis and data-driven decision making. To demonstrate the utility of statistical algorithms that preceded the current predictive analytics, we used Model Based (MOB) recursive partitioning, a variant of regression analysis known as decision trees, on a dataset of cases and controls with a binary outcome of serious maltreatment (defined as hospitalization or death). We ran two models, one which split a robust set of variables significantly correlated with the outcome on the partitioning of a proxy variable for environmental poverty, and one which ran the same variable set partitioned on a variable representing confirmed prior maltreatment. Both models found that what most differentiated children was spending greater than 2% of the timeframe of interest in foster care, and that for some children, lack of Medicaid eligibility almost doubled or tripled the odds of serious maltreatment. We find that decision trees such as MOB can augment risk assessment tools and other data analyses, informing data-driven program and policy decision making. We caution that decision trees, as with any other predictive tool, must be evaluated for inherent biases that may be contained in the proxy variables and the results interpreted carefully. Predictive analytics, as a class, should be used to augment, but not replace, critical thinking in child welfare decision making.

Environmental and individual attributes associated with child maltreatment resulting in hospitalization or death.

Maltreatment continues to be a leading cause of death for young children. Researchers are beginning to uncover which neighborhood attributes may be associated with maltreatment outcomes. However, few studies have been able to explore these influences while controlling for individual family attributes, and none have been able to parse out the most severe outcomes-injuries resulting in hospitalization or death. This study utilizes a retrospective, case-control design on a dataset containing both individual and environmental level attributes of children who have been hospitalized or died due to maltreatment to explore the relative influence of attributes inside and outside the household walls. Binary conditional logistic regression was used to model the outcome as a function of the individual and environmental level predictors. Separate analyses also separated the outcome by manner of maltreatment: abuse or neglect. Finally, a sub-analysis included protective predictors representing access to supportive resources. Findings indicate that neighborhood attributes were similar for both cases and controls, except in the neglect only model, wherein impoverishment was associated with higher odds of serious maltreatment. Dense housing increased risk in all models except the neglect only model. In a sub-analysis, distance to Family Resource Centers was inversely related to serious maltreatment. In all models, variables representing more extreme intervention and/or removal of the victim and/or perpetrator from the home (foster care or criminal court involvement) were negatively associated with the risk of becoming a case. Medi-Cal insurance eligibility of a child was also negatively associated with becoming a case. Government interventions may be playing a critical role in child protection. More research is needed to ascertain how these interventions assert their influence.

The temporal-spatial distribution of seriously maltreated children.

This descriptive study utilized Bernoulli and Poisson spatial scan statistical models in SatScan v.9.4 to examine the distribution in space and time of residence of maltreatment cases-operationalized as families with serious maltreatment (resulting in death or hospitalization) of children under 6 years-for the presence of clusters ("hot spots"). In the Poisson model, a population dataset of serious maltreatment cases were non-randomly dispersed in four major areas, with these "hot spots" moving over time and space. Most cases were outside these clusters. In the Bernoulli model, the geographic distribution of a case-control dataset of families with serious maltreatment who were previously investigated by child welfare did not differ compared to controls previously investigated by child welfare with no serious maltreatment. Findings suggest that child fatality prevention efforts such as Back to Sleep and Never Shake a Baby campaigns should continue to be universal efforts, targeted to all parents.

Risk factors for fatal and non-fatal child maltreatment in families previously investigated by CPS: A case-control study.

The objective of this study was to identify individual, family and caregiver risk factors for serious child maltreatment, resulting in hospitalization or death, among children and families investigated by Child Protective Services (CPS). We conducted a matched case-control study of 234 children who sustained fatal or serious nonfatal maltreatment due to physical abuse or neglect and whose mother was named in a CPS investigation between 1999 and 2013. A total of 702 children and their caregivers were included in the study with 234 cases matched 2:1,resulting in 468 controls. Data on potential risk factors were abstracted from three county administrative databases. Differences between cases and controls were calculated and multivariable conditional logistic regression was used to estimate risk models. Variables associated with increased risk for serious maltreatment included male child gender,younger caregivers, three or more children under the age of 5 living in the home, families in which a biologic child was not living with either parent, and scoring moderate or high on the Structured Decision Making Risk Tool®. Caregiver involvement in intimate partner violence (IPV) and child enrollment in public health insurance appears to mitigate the risk of serious maltreatment.