Volunteering in adolescence and young adulthood crime involvement: a longitudinal analysis from the add health study

Background: Experiences in adolescence may have a lasting impact on adulthood. The objective of this study is to evaluate the association between adolescent (12–18 years of age) volunteerism with the incidence of illegal behaviors, arrests, and convictions in adulthood (>18 years of age). Methods: We conducted a retrospective cohort study using secondary data from the National Longitudinal Study of Adolescent to Adult Health. Students from grades 7–12 were recruited in 1994–1995 (n = 20,745), and then followed in 2001–2002 (n = 14,322) and in 2008–2009 (n = 12,288). In 2000–2001, participants were retrospectively asked about their volunteering experience from 12 to 18 years of age. Consequently, participants were divided into non-volunteers, self-volunteers, adult-required volunteers, and court-ordered volunteers. Groups were compared for rates of illegal behaviors, arrest, and convictions in adulthood (>18 years of age) using weighted generalized linear mixed negative binomial models while accounting for sampling design. Results: Relative to non-volunteers, self-volunteers reported 11 % fewer illegal behaviors (RR: 0.89, 95 % CI: 0.80, 0.99), 31 % fewer arrests (RR: 0.69, 95 %: 0.57, 0.85), and 39 % fewer convictions (RR: 0.61, 95 % CI: 0.47, 0.79) by age 18–28 years, and 28 % fewer illegal behaviors, 53 % fewer arrests, and 36 % fewer convictions by age 24–34. In comparison the adult-required volunteers also reported fewer arrests and convictions; however, they reported more illegal behaviors than the non-volunteers. The court-ordered volunteers reported higher rates of criminal involvement than the non-volunteers, throughout. Conclusion: This study suggests that volunteering in adolescence may reduce crime involvement in adulthood

Public health application of predictive modeling: An example from farm vehicle crashes

Background: The goal of predictive modelling is to identify the likelihood of future events, such as the predictive modelling used in climate science to forecast weather patterns and significant weather occurrences. In public health, increasingly sophisticated predictive models are used to predict health events in patients and to screen high risk individuals, such as for cardiovascular disease and breast cancer. Although causal modelling is frequently used in epidemiology to identify risk factors, predictive modelling provides highly useful information for individual risk prediction and for informing courses of treatment. Such predictive knowledge is often of great utility to physicians, counsellors, health education specialists, policymakers or other professionals, who may then advice course correction or interventions to prevent adverse health outcomes from occurring. In this manuscript, we use an example dataset that documents farm vehicle crashes and conventional statistical methods to forecast the risk of an injury or death in a farm vehicle crash for a specific individual or a scenario. Results: Using data from 7094 farm crashes that occurred between 2005 and 2010 in nine mid-western states, we demonstrate and discuss predictive model fitting approaches, model validation techniques using external datasets, and the calculation and interpretation of predicted probabilities. We then developed two automated risk prediction tools using readily available software packages. We discuss best practices and common limitations associated with predictive models built from observational datasets. Conclusions: Predictive analysis offers tools that could aid the decision making of policymakers, physicians, and environmental health practitioners to improve public health

Geographical Detector-Based Risk Assessment of the Under-Five Mortality in the 2008 Wenchuan Earthquake, China

On 12 May, 2008, a devastating earthquake registering 8.0 on the Richter scale occurred in Sichuan Province, China, taking tens of thousands of lives and destroying the homes of millions of people. Many of the deceased were children, particular children less than five years old who were more vulnerable to such a huge disaster than the adult. In order to obtain information specifically relevant to further researches and future preventive measures, potential risk factors associated with earthquake-related child mortality need to be identified. We used four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) based on spatial variation analysis of some potential factors to assess their effects on the under-five mortality. It was found that three factors are responsible for child mortality: earthquake intensity, collapsed house, and slope. The study, despite some limitations, has important implications for both researchers and policy makers

Optimizing sobriety checkpoints to maximize public health benefits and minimize operational costs

Background Sobriety checkpoints are a highly effective strategy to reduce alcohol-impaired driving, but they are used infrequently in the USA. Recent evidence from observational studies suggests that using optimized sobriety checkpoints—operating for shorter duration with fewer officers—can minimize operational costs without reducing public health benefits. The aim of this research was to conduct a pilot study to test whether police can feasibly implement optimized sobriety checkpoints and whether researchers can examine optimized sobriety checkpoints compared to usual practice within a non-randomized controlled trial study design. Methods The study site was the Town of Apex, NC. We worked with Apex Police Department to develop a schedule of sobriety checkpoints during calendar year 2021 that comprised 2 control checkpoints (conducted according to routine practice) and 4 optimized checkpoints staffed by fewer officers. Our primary operations aim was to test whether police can feasibly implement optimized sobriety checkpoints. Our primary research aim was to identify barriers and facilitators for conducting an intervention study of optimized sobriety checkpoints compared to usual practice. A secondary aim was to assess motorist support for sobriety checkpoints and momentary stress while passing through checkpoints. Results Apex PD conducted 5 of the 6 checkpoints and reported similar operational capabilities and results during the optimized checkpoints compared to control checkpoints. For example, a mean of 4 drivers were investigated for possibly driving while impaired at the optimized checkpoints, compared to 2 drivers at control checkpoints. The field team conducted intercept surveys among 112 motorists at 4 of the 6 checkpoints in the trial schedule. The survey response rate was 11% from among 1,045 motorists who passed through these checkpoints. Over 90% of respondents supported sobriety checkpoints, and momentary stress during checkpoints was greater for motorists who reported consuming any alcohol in the last 90 days compared to nondrinkers (OR = 6.7, 95%CI: 1.6, 27.1). Conclusions Results of this study indicate the sobriety checkpoints can feasibly be optimized by municipal police departments, but it will be very difficult to assess the impacts of optimized checkpoints compared to usual practice using an experimental study design

Crash characteristics and patterns of injury among hospitalized motorised two-wheeled vehicle users in urban India

<p>Abstract</p> <p>Background</p> <p>Traffic crashes and consequent injuries represent a growing public health concern in India, particularly in light of increasing motorization. Motorised two-wheeled vehicles (MTV) constitute a large portion of the vehicle fleet in India. We report the crash characteristics and injury patterns among a cohort of MTV riders and pillions presenting to hospital post-crash.</p> <p>Methods</p> <p>Consecutive MTV riders and pillions, whether alive or dead, injured in a road traffic crash presenting to the emergency departments of two government hospitals and three branches of a private hospital in urban Hyderabad, India, were recruited to this study.</p> <p>Results</p> <p>378 MTV users were enrolled to the study of whom 333 (88.1%) were male, 252 (66.7%) were riders and median age was 31.3 years. A total of 223 (59%) MTV users were injured in multi-vehicle crashes while one-third had a frontal impact. The majority (77%) were assessed as having a Glasgow coma score (GCS) of 13–15, 12% a GCS of 9–12 and 11% a GCS of 3–8. No difference was seen in the severity distribution of injuries based on GCS among riders and pillions. Open wounds and superficial injuries to the head (69.3%) and upper extremity (27%) and lower extremity (24%) were the most common injuries. 43 (11%) sustained an intracranial injury, including 12 (28%) with associated fracture of the bones of the head. There were few differences in types of injuries sustained by riders and pillions though riders had a significantly lower risk of crush injuries of the lower extremity than pillions (relative risk, RR 0.25, 95% CI 0.08–0.81) and female pillions were at a significantly lower risk of sustaining fractures of the lower extremity than male pillions (RR 0.30, 95% CI 0.09 – 0.94). Overall, 42 (11%) MTV users died, of which 42.8% died before reaching the hospital. Only 74 (19.6%) MTV users had worn a helmet correctly and failure to wear a helmet was associated with a five times greater risk of intracranial injury (RR 4.99, 95% CI 1.23–20.1). Of the 19 pre-hospital deaths, 16 (84%) had not worn a helmet.</p> <p>Conclusion</p> <p>Head injuries accounted for the major proportion of injuries sustained in MTV users. Non-helmet use was associated with increased risk of serious head injuries. The data presented on the nature and severity of injuries sustained by MTV users can assist with planning to deal with these consequences as well as prevention of these injuries given the high use of MTV in India.</p

Implementing Telemedicine in Medical Emergency Response: Concept of Operation for a Regional Telemedicine Hub

A regional telemedicine hub, providing linkage of a telemedicine command center with an extended network of clinical experts in the setting of a natural or intentional disaster, may facilitate future disaster response and improve patient outcomes. However, the health benefits derived from the use of telemedicine in disaster response have not been quantitatively analyzed. In this paper, we present a general model of the application of telemedicine to disaster response and evaluate a concept of operations for a regional telemedicine hub, which would create distributed surge capacity using regional telemedicine networks connecting available healthcare and telemedicine infrastructures to external expertise. Specifically, we investigate (1) the scope of potential use of telemedicine in disaster response; (2) the operational characteristics of a regional telemedicine hub using a new discrete-event simulation model of an earthquake scenario; and (3) the benefit that the affected population may gain from a coordinated regional telemedicine network

Fall Classification by Machine Learning Using Mobile Phones

Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls–left and right lateral, forward trips, and backward slips–while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls

Cobertura efectiva del manejo de la violencia contra mujeres en municipios Mexicanos: límites de la métrica

El estudio estimó la cobertura efectiva de los servicios en salud de primer nivel de atención para el manejo de la violencia doméstica contra la mujer en tres municipios mexicanos. Se estimó la prevalencia y severidad de la violencia usando una escala validada, y la cobertura efectiva con la propuesta de Shengelia y colaboradores, con modificaciones. Se consideró atención con calidad cuando hubo sugerencia de hacer la denuncia a las autoridades. La utilización y calidad de la atención fue baja en los tres municipios analizados, siendo más frecuente la utilización cuando hubo violencia sexual o física. La cobertura efectiva en Guachochi, Jojutla y Tizimín fue de 29.41%, 16.67% y cero, respectivamente. El indicador de cobertura efectiva tiene dificultades para medir eventos y respuestas no se fundamentan en modelos biomédicos. Los hallazgos sugieren que el indicador puede ser mejorado al incorporar otras dimensiones de la calidad