Characterizing problematic hypoglycaemia: iterative design and preliminary psychometric validation of the Hypoglycaemia Awareness Questionnaire (HypoA-Q)

AIMS: To design and conduct preliminary validation of a measure of hypoglycaemia awareness and problematic hypoglycaemia, the Hypoglycaemia Awareness Questionnaire.

METHODS: Exploratory and cognitive debriefing interviews were conducted with 17 adults (nine of whom were women) with Type 1 diabetes (mean ± sd age 48±10 years). Questionnaire items were modified in consultation with diabetologists/psychologists. Psychometric validation was undertaken using data from 120 adults (53 women) with Type 1 diabetes (mean ± sd age 44±16 years; 50% with clinically diagnosed impaired awareness of hypoglycaemia), who completed the following questionnaires: the Hypoglycaemia Awareness Questionnaire, the Gold score, the Clarke questionnaire and the Problem Areas in Diabetes questionnaire.

RESULTS: Iterative design resulted in 33 items eliciting answers on awareness of hypoglycaemia when awake/asleep and hypoglycaemia frequency, severity and impact (healthcare utilization). Psychometric analysis identified three subscales reflecting 'impaired awareness', 'symptom level' and 'symptom frequency'. Convergent validity was indicated by strong correlations between the impaired awareness subscale and existing measures of awareness: (Gold: rs =0.75, P<0.01; Clarke: rs =0.76, P<0.01). Divergent validity was indicated by weaker correlations with diabetes-related distress (Problem Areas in Diabetes: rs =0.25, P<0.01) and HbA1c (rs =-0.05, non-significant). The impaired awareness subscale and other items discriminated between those with impaired and intact awareness (Gold score). The impaired awareness subscale and other items contributed significantly to models explaining the occurrence of severe hypoglycaemia and hypoglycaemia when asleep.

CONCLUSIONS: This preliminary validation shows the Hypoglycaemia Awareness Questionnaire has robust face and content validity; satisfactory structure; internal reliability; convergent, divergent and known groups validity. The impaired awareness subscale and other items contribute significantly to models explaining recall of severe and nocturnal hypoglycaemia. Prospective validation, including determination of a threshold to identify impaired awareness, is now warranted.

One Health approach to controlling a Q fever outbreak on an Australian goat farm

A recent outbreak of Q fever was linked to an intensive goat and sheep dairy farm in Victoria, Australia, 2012-2014. Seventeen employees and one family member were confirmed with Q fever over a 28-month period, including two culture-positive cases. The outbreak investigation and management involved a One Health approach with representation from human, animal, environmental and public health. Seroprevalence in non-pregnant milking goats was 15% [95% confidence interval (CI) 7–27]; active infection was confirmed by positive quantitative PCR on several animal specimens. Genotyping of Coxiella burnetii DNA obtained from goat and human specimens was identical by two typing methods. A number of farming practices probably contributed to the outbreak, with similar precipitating factors to the Netherlands outbreak, 2007-2012. Compared to workers in a high-efficiency particulate arrestance (HEPA) filtered factory, administrative staff in an unfiltered adjoining office and those regularly handling goats and kids had 5·49 (95% CI 1·29–23·4) and 5·65 (95% CI 1·09–29·3) times the risk of infection, respectively; suggesting factory workers were protected from windborne spread of organisms. Reduction in the incidence of human cases was achieved through an intensive human vaccination programme plus environmental and biosecurity interventions. Subsequent non-occupational acquisition of Q fever in the spouse of an employee, indicates that infection remains endemic in the goat herd, and remains a challenge to manage without source control.

Distributed Q-learning controller for a multi-intersection traffic network

This paper proposes a Q-learning based controller for a network of multi intersections. According to the increasing amount of traffic congestion in modern cities, using an efficient control system is demanding. The proposed controller designed to adjust the green time for traffic signals by the aim of reducing the vehicles’ travel delay time in a multi-intersection network. The designed system is a distributed traffic timing control model, applies individual controller for each intersection. Each controller adjusts its own intersection’s congestion while attempt to reduce the travel delay time in whole traffic network. The results of experiments indicate the satisfied efficiency of the developed distributed Q-learning controller.