Trends of child undernutrition in rural Ecuadorian communities with differential access to roads, 2004–2013

Road access can influence protective and risk factors associated with nutrition by affecting various social and biological processes. In northern coastal Ecuador, the construction of new roads created a remoteness gradient among villages, providing a unique opportunity to examine the impact of roads on child nutritional outcomes 10 years after the road was built. Anthropometric and haemoglobin measurements were collected from 2,350 children <5 years in Esmeraldas, Ecuador, from 2004 to 2013 across 28 villages with differing road access. Logistic generalized estimating equation models assessed the longitudinal association between village remoteness and prevalence of stunting, wasting, underweight, overweight, obesity, and anaemia. We examined the influence of socio‐economic characteristics on the pathway between remoteness and nutrition by comparing model results with and without household‐level socio‐economic covariates. Remoteness was associated with stunting (OR = 0.43, 95% CI [0.30, 0.63]) and anaemia (OR = 0.56, 95% CI [0.44, 0.70]). Over time, the prevalence of stunting was generally decreasing but remained higher in villages closer to the road compared to those farther away. Obesity increased (0.5% to 3%) over time; wasting was high (6%) but stable during the study period. Wealth and education partially explained the better nutritional outcomes in remote vs. road villages more than a decade after some communities gained road access. Establishing the extent to which these patterns persist requires additional years of observation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144663/1/mcn12588.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144663/2/mcn12588_am.pd

A Latent Profile Analysis of Aggression and Victimization across Relationship Types Among Veterans Who Use Substances

Objective: This study examined patterns of violence victimization and aggression in both intimate partner and non-partner relationships among veterans, and used latent profile analysis to identify subtypes of violence involvement. Methods: Participants were 841 substance use treatment-seeking veterans (94% male) from a large VA Medical Center who completed screening measures for a randomized controlled trial. Self-report measures were: substance use, legal problems, depression, and violence involvement. Results: Past year violence involvement, including both intimate partner (IPV) and non-partner (NPV) were common in the sample; although NPV occurred at somewhat higher rates. When including either IPV or NPV aggression or victimization, over 48% reported involvement with physical violence, 31% with violence involving injury and 86% with psychological aggression. Latent profile analysis including both aggression and victimization in partner and non-partner relationships indicated a four profile solution: no-low violence (NLV, n = 701), predominantly IPV (n = 35), predominantly NPV (n = 83), and high general violence (HGV, n = 22). Multinomial logistic regression analyses revealed that compared to the no-low violence group, the remaining three groups differed in demographics, depressive symptoms, alcohol and other drug use, and legal involvement. Individuals within each profile had different patterns of substance use and legal involvement with the participants with an HGV profile reporting the most legal problems. Conclusions: IPV and NPV are relatively common among veterans seeking substance use treatment. Characteristics of violence and associated substance use, mental health, and legal difficulties may be useful in considering how to tailor substance use and mental health services

The Role of Mobile Genetic Elements in the Spread of Antimicrobial-Resistant Escherichia coli from Chickens to Humans in Small-Scale Production Poultry Operations in Rural Ecuador

© The Author(s) 2018. Small-scale production poultry operations are increasingly common worldwide. To investigate how these operations influence antimicrobial resistance and mobile genetic elements (MGEs), Escherichia coli isolates were sampled from small-scale production birds (raised in confined spaces with antibiotics in feed), household birds (no movement constraints; fed on scraps), and humans associated with these birds in rural Ecuador (2010-2012). Isolates were screened for genes associated with MGEs as well as phenotypic resistance to 12 antibiotics. Isolates from small-scale production birds had significantly elevated odds of resistance to 7 antibiotics and presence of MGE genes compared with household birds (adjusted odds ratio (OR) range = 2.2-87.9). Isolates from humans associated with small-scale production birds had elevated odds of carrying an integron (adjusted OR = 2.0; 95% confidence interval (CI): 1.06, 3.83) compared with humans associated with household birds, as well as resistance to sulfisoxazole (adjusted OR = 1.9; 95% CI: 1.01, 3.60) and trimethoprim/sulfamethoxazole (adjusted OR = 2.1; 95% CI: 1.13, 3.95). Stratifying by the presence of MGEs revealed antibiotic groups that are explained by biological links to MGEs; in particular, resistance to sulfisoxazole, trimethoprim/sulfamethoxazole, or tetracycline was highest among birds and humans when MGE exposures were present. Small-scale production poultry operations might select for isolates carrying MGEs, contributing to elevated levels of resistance in this setting

In-roads to the spread of antibiotic resistance: regional patterns of microbial transmission in northern coastal Ecuador

The evolution of antibiotic resistance (AR) increases treatment cost and probability of failure, threatening human health worldwide. The relative importance of individual antibiotic use, environmental transmission and rates of introduction of resistant bacteria in explaining community AR patterns is poorly understood. Evaluating their relative importance requires studying a region where they vary. The construction of a new road in a previously roadless area of northern coastal Ecuador provides a valuable natural experiment to study how changes in the social and natural environment affect the epidemiology of resistant Escherichia coli. We conducted seven bi-annual 15 day surveys of AR between 2003 and 2008 in 21 villages. Resistance to both ampicillin and sulphamethoxazole was the most frequently observed profile, based on antibiogram tests of seven antibiotics from 2210 samples. The prevalence of enteric bacteria with this resistance pair in the less remote communities was 80 per cent higher than in more remote communities (OR = 1.8 [1.3, 2.3]). This pattern could not be explained with data on individual antibiotic use. We used a transmission model to help explain this observed discrepancy. The model analysis suggests that both transmission and the rate of introduction of resistant bacteria into communities may contribute to the observed regional scale AR patterns, and that village-level antibiotic use rate determines which of these two factors predominate. While usually conceived as a main effect on individual risk, antibiotic use rate is revealed in this analysis as an effect modifier with regard to community-level risk of resistance

Multi-site External Validation and Improvement of a Clinical Screening Tool for Future Firearm Violence

Interventions in clinical settings, such as the emergency department (ED), are an opportunity for interpersonal firearm violence prevention, particularly among youth, whom interpersonal firearm violence disproportionately affects. A crucial prerequisite to successful clinical interventions is an accurate gauge of risk, to ensure the judicious allocation of scarce resources; providing that missing prerequisite is the primary goal of the proposed work. Machine learning methods, in contrast to traditional inferential statistical models, are distinguished by their emphasis on prospective prediction, and have enhanced clinical prediction in several fields, including heart disease, cancer diagnosis and outcomes, PTSD, suicide risk, and substance use, among others. Yet, with the exception of the SAFETY score—developed by the current investigative team—machine learning methods have not been leveraged to prospectively predict firearm violence. In this proposed work our research objectives are two-fold: 1) Externally validate the SAFETY score by determining its ability to predict firearm violence involvement within the next year on a new data set; and 2) Improve the SAFETY score by conducting a comparative analysis of four powerful machine learning methods: elastic net penalized logistic regression, random forests, support vector machines, and boosting (ensemble) methods. In this way, we are responding to Objective One: Research to help inform the development of innovative and promising opportunities to enhance safety and prevent firearm-related injuries, deaths, and crime. This approach is innovative because it builds upon the only work to apply machine learning methods to firearm violence prediction, and it is a promising opportunity to prevent firearm injuries because it will a) provide an explicit gauge of future firearm violence risk; and b) characterize risk factor effects in terms of their prospective prediction ability, unlike any prior research. Thus this research will both identify individuals in most need of intervention, and also point to potentially modifiable predictive factors. Properly addressing this research question in a generalizable way requires a contemporary data set with 1) a focus on a high-need, yet broad, study population; 2) comprehensive baseline measures that provide a broad basis for prediction; and 3) geographic variability (Midwest, West Coast, and East Coast) that enhances generalizability. Thus, we will recruit 1,500 youth age 18-24 from urban EDs in three broadly different locales—Flint, Philadelphia, and Seattle—and administer a baseline survey covering several domains of potential risk factors for future violence, and follow up with those youth at 6- and 12-months to ascertain the primary outcome—firearm violence involvement (as victim or perpetrator)—as well as the secondary outcomes: high-risk firearm behaviors, non-firearm violence, and violent injury. Because this work requires a prospective longitudinal study, we are applying for Option B. This work will lay the ground for future research involving the development and testing of interventions for interpersonal firearm violence both by identifying potential high-leverage modifiable predictive factors, and by identifying youth most in need of intervention

Contributions to Modeling the Dynamic Association Structure in Longitudinal Data Sets.

This dissertation considers several modeling problems involving clustered longitudinal data. Interest focuses on the association structure rather than the means and, in particular, on its change over time. Concentration on this non-stationary, or ``dynamic'' aspect of the association structure is motivated by applications involving the study of behavioral traits in children observed from early childhood to adulthood. To begin we consider cases where the longitudinal measurements are comprised of multiple variables measured on an individual at each time point. A natural approach to characterizing the dynamic association structure in this setting is to ``regress" a univariate measure on time. Applications of this framework include analyzing temporally dependent comorbidity patterns among traits. In this section we consider binary associations quantified by the log odds ratio, although analogous models may be formulated for continuous variables. The first method we present uses penalized maximum likelihood to estimate the log odds ratio trajectory semi-parametrically as a smooth function of time in the bivariate case. A second method, appropriate for any number of variables, is proposed that allows for the pairwise log odds ratio trajectories to be estimated in isolation. By using a composite, conditional likelihood approach we no longer need to model means or dependencies of secondary interest. We next consider the setting where the longitudinal data are observed in clusters (e.g. siblings). The children in a family are exposed to events that occur at specific calendar times, and also are influenced by developmental processes that depend are age-specific. Since the children in different families have different birth spacings, these two influences are offset to varying degrees in different families, prompting us to ask whether both age and time are modulating the association structure and can we disaggregate these effects? Existing methods for such data only account for a single timing variable, effectively marginalizing over the other. We present a modeling framework for jointly estimating how age and time distinctly affect the association structure and extensive empirical results are presented to clarify our ability to decompose these effects. Difficult computational problems arise, requiring the development of new estimators and computing techniques.Ph.D.StatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77719/1/jasoneg_1.pd

Design and implementation of a parent guide for coaching teen drivers

Introduction: Teens beginning to drive independently are at significant increased risk of motor-vehicle crashes relative to their other life stages. There is, however, little guidance for parents as to how best to supervise learning to drive. Method: This study sought to undertake an informed approach to development and implementation of a Parent Guide. We included a multi-stage development process, using theory, findings from a Delphi-study of young driver traffic-safety experts, and parent focus groups. This process informed the development of a Guide that was then evaluated for feasibility and acceptability, comparing a group that received the Guide with a control group of parent and teen dyads. Both members of the dyads were surveyed at baseline, again at the approximate time teens would be licensed to drive independently (post-test), and again three months later. Results: We found no difference in the proportion of teens who became licensed between those given the new Guide and control teens (who received the state-developed booklet); that is the Guide did not appear to promote or delay licensure. Teens in the Guide group reported that their parents were more likely to use the provided resource compared with control teens. Responses indicated that the Parent Guide was favorably viewed, that it was easy to use, and that the logging of hours was a useful inclusion. Parents noted that the Guide helped them manage their stress, provided strategies to keep calm, and helped with planning practice. In contrast, control parents noted that their booklet helped explain rules. Among licensed teens there was no significant difference in self-reported risky driving at the three-month follow-up. We discuss the challenges in providing motivation for parents to move beyond a set number of practice hours to provide diversity of driving practice. (C) 2018 National Safety Council and Elsevier Ltd. All rights reserved

Commentary: Reducing youth firearm violence and the associated health disparities requires enhanced surveillance and modern behavioral intervention strategies – a commentary on Bottiani et al. (2021)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167533/1/jcpp13421_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167533/2/jcpp13421.pd

Data missingness in the Michigan NEMSIS (MI-EMSIS) dataset: a mixed-methods study

Abstract Objective The study was done to evaluate levels of missing and invalid values in the Michigan (MI) National Emergency Medical Services Information System (NEMSIS) (MI-EMSIS) and explore possible causes to inform improvement in data reporting and prehospital care quality. Methods We used a mixed-methods approach to study trends in data reporting. The proportion of missing or invalid values for 18 key reported variables in the MI-EMSIS (2010–2015) dataset was assessed overall, then stratified by EMS agency, software platform, and Medical Control Authorities (MCA)—regional EMS oversight entities in MI. We also conducted 4 focus groups and 10 key-informant interviews with EMS participants to understand the root causes of data missingness in MI-EMSIS. Results Only five variables of the 18 studied exhibited less than 10% missingness, and there was apparent variation in the rate of missingness across all stratifying variables under study. No consistent trends over time regarding the levels of missing or invalid values from 2010 to 2015 were identified. Qualitative findings indicated possible causes for this missingness including data-mapping issues, unclear variable definitions, and lack of infrastructure or training for data collection. Conclusions The adoption of electronic data collection in the prehospital setting can only support quality improvement if its entry is complete. The data suggest that there are many EMS agencies and MCAs with very high levels of missingness, and they do not appear to be improving over time, demonstrating a need for investment in efforts in improving data collection and reporting.http://deepblue.lib.umich.edu/bitstream/2027.42/173260/1/12245_2021_Article_343.pd