The effects of graduated driver licensing on teenage body weight.

The graduated driver licensing (GDL) program requires teenage drivers to pass through an intermediate stage, which contains specific driving restrictions such as a night curfew or a limit on the number of teen passengers to be carried, before earning full driving privileges. Using individual data from the 1999 to 2017 biennial Youth Risk Behavior Surveillance System (YRBS) combined with state-level GDL policy variations, we estimate the effects of GDL on teenage body weight in the United States. We find that the presence of GDL raises adolescents' body mass index Z-score and their likelihood of being overweight or obese. Among the restrictions imposed, a night curfew implemented together with a passenger restriction makes the most significant impact. These estimated effects are concentrated among states with more restrictive GDL policies. We also find that the presence of GDL reduces adolescent physical activity and heavy smoking, while increasing their time spent watching TV and milk intake, perhaps contributing to youth weight gain. An event study analysis reveals that the effects of GDL on adolescent weight increase may be transitory.

Food recalls associated with foodborne disease outbreaks, United States, 2006-2016.

About 800 foodborne disease outbreaks are reported in the United States annually. Few are associated with food recalls. We compared 226 outbreaks associated with food recalls with those not associated with recalls during 2006-2016. Recall-associated outbreaks had, on average, more illnesses per outbreak and higher proportions of hospitalisations and deaths than non-recall-associated outbreaks. The top confirmed aetiology for recall-associated outbreaks was Salmonella. Pasteurised and unpasteurised dairy products, beef and molluscs were the most frequently implicated foods. The most common pathogen-food pairs for outbreaks with recalls were Escherichia coli-beef and norovirus-molluscs; the top pairs for non-recall-associated outbreaks were scombrotoxin-fish and ciguatoxin-fish. For outbreaks with recalls, 48% of the recalls occurred after the outbreak, 27% during the outbreak, 3% before the outbreak, and 22% were inconclusive or had unknown recall timing. Fifty per cent of recall-associated outbreaks were multistate, compared with 2% of non-recall-associated outbreaks. The differences between recall-associated outbreaks and non-recall-associated outbreaks help define the types of outbreaks and food vehicles that are likely to have a recall. Improved outbreak vehicle identification and traceability of rarely recalled foods could lead to more recalls of these products, resulting in fewer illnesses and deaths.

Association Between Incorrect Posture and Adolescent Idiopathic Scoliosis Among Chinese Adolescents: Findings From a Large-Scale Population-Based Study.

Objectives: Adolescent idiopathic scoliosis (AIS) affects between 1 and 4% of adolescents, and severe curvature may be related to their adverse long-term outcomes. However, whether the change in body appearance is related to AIS remains largely unclear. We aimed to explore the association between incorrect posture and AIS among Chinese adolescents. Methods: Data were collected from a population-based (595,057) school scoliosis screening program in China. A sample of 3,871 adolescents was classified as cases with a diagnosed radiological lateral Cobb angle ≥10°, and 3,987 control subjects with a Cobb angle <10° were randomly selected from the screening system. Adolescents were accessed with demographic information and incorrect posture measured by visual inspection of physical signs, Adam's forward bending test (FBT), and the angle of trunk rotation (ATR). Logistic regression (LR) models were used to examine the associations. Results: Multivariate LR showed that shoulder-height difference, scapula tilt, lumbar concave, and pelvic tilt were associated with AIS. Adolescents with angle of thoracic rotation ≥5° [adjusted odds ratio (AOR) = 5.33-14.67, P < 0.001], thoracolumbar rotation ≥5° (AOR = 4.61-5.79, P < 0.001), or lumbar rotation ≥5° (AOR = 7.49-7.85, P < 0.001) were at especially higher risk for AIS than those with ATR <5°. Conclusions: Incorrect posture may be the potential risk factor for developing AIS, and ATR ≥5° was an important indicator for predicting the occurrence of scoliosis. Early monitoring of incorrect posture for school adolescents should be considered as a routine intervention to effectively identify the progress of scoliosis.

Predicting Adolescent Idiopathic Scoliosis among Chinese Children and Adolescents.

OBJECTIVE: Adolescent idiopathic scoliosis (AIS) affects 1%-4% of adolescents in the early stages of puberty, but there is still no effective prediction method. This study aimed to establish a prediction model and validated the accuracy and efficacy of this model in predicting the occurrence of AIS. METHODS: Data was collected from a population-based school scoliosis screening program for AIS in China. A sample of 884 children and adolescents with the radiological lateral Cobb angle ≥ 10° was classified as an AIS case, and 895 non-AIS subjects with a Cobb angle < 10° were randomly selected from the screening system. All selected subjects were screened by visual inspection of clinical signs, the Adam's forward-bending test (FBT), and the measurement of angle of trunk rotation (ATR). LR and receiver operating characteristic (ROC) curves were used to preliminarily screen the influential factors, and LR models with different adjusted weights were established to predict the occurrence of AIS. RESULTS: Multivariate LR and ROC curves indicated that angle of thoracic rotation (adjusted odds ratios (AOR) = 5.18 - 10.06), angle of thoracolumbar rotation (AOR = 4.67 - 7.22), angle of lumbar rotation (AOR = 6.97 - 8.09), scapular tilt (area under the curve (AUC) = 0.77, 95% CI: 0.75-0.80), shoulder-height difference, lumbar concave, and pelvic tilt were the risk predictors for AIS. LR models with different adjusted weights (by AOR, AUC, and AOR+AUC) performed similarly in predicting the occurrence of AIS compared with multivariate LR. The sensitivity (82.55%-83.27%), specificity (82.59%-83.33%), Youden's index (0.65-0.67), positive predictive value (82.85%-83.58%), negative predictive value (82.29%-83.03%), and total accuracy (82.57%-83.30%) manifested that LR could accurately identify patients with AIS. CONCLUSIONS: LR model is a relatively high accurate and feasible method for predicting AIS. Increased performance of LR models using clinically relevant variables offers the potential to early identify high-risk groups of AIS.

Assessment of Area-Level Disease Control and Surveillance Vulnerabilities: An Application to Visceral Leishmaniasis in Brazil.

The large number of activities contributing to zoonoses surveillance and control capability, on both human and animal domains, and their likely heterogeneous implementation across administrative units make assessment and comparisons of capability performance between such units a complex task. Such comparisons are important to identify gaps in capability development, which could lead to clusters of vulnerable areas, and to rank and subsequently prioritize resource allocation toward the least capable administrative units. Area-level preparedness is a multidimensional entity and, to the best of our knowledge, there is no consensus on a single comprehensive indicator, or combination of indicators, in a summary metric. We use Bayesian spatial factor analysis models to jointly estimate and rank disease control and surveillance capabilities against visceral leishmaniasis (VL) at the municipality level in Brazil. The latent level of joint capability is informed by four variables at each municipality, three reflecting efforts to monitor and control the disease in humans, and one variable informing surveillance capability on the reservoir, the domestic dog. Because of the large volume of missing data, we applied imputation techniques to allow production of comprehensive rankings. We were able to show the application of these models to this sparse dataset and present a ranked list of municipalities based on their overall VL capability. We discuss improvements to our models, and additional applications.