Heat waves are one of the most common direct impacts of anthropogenic climate change and excess mortality their most apparent impact. While Turkey has experienced an increase in heat wave episodes between 1971 and 2016, no epidemiological studies have examined their potential impacts on public health so far. In this study excess mortality in Istanbul attributable to extreme heat wave episodes between 2013 and 2017 is presented. Total excess deaths were calculated using mortality rates across different categories, including age, sex, and cause of death. The analysis shows that three extreme heat waves in the summer months of 2015, 2016, and 2017, which covered 14 days in total, significantly increased the mortality rate and caused 419 excess deaths in 23 days of exposure. As climate simulations show that Turkey is one of the most vulnerable countries in the Europe region to the increased intensity of heat waves until the end of the 21st century, further studies about increased mortality and morbidity risks due to heat waves in Istanbul and other cities, as well as intervention studies, are necessary.

Game meat from animals killed by lead ammunition may expose consumers to lead. We assessed the risk related to lead intake from meat consumption of white-tailed deer and moose killed by lead ammunition and documented the perception of hunters and butchers regarding this potential contamination. Information on cervid meat consumption and risk perception were collected using a mailed self-administrated questionnaire which was addressed to a random sample of Quebec hunters. In parallel, 72 samples of white-tailed deer (n = 35) and moose (n = 37) meats were collected from voluntary hunters and analysed for lead content using inductively coupled plasma-mass spectrometry. A risk assessment for people consuming lead shot game meat was performed using Monte Carlo simulations. Mean lead levels in white-tailed deer and moose killed by lead ammunition were 0.28 and 0.17 mg kg(-1) respectively. Risk assessment based on declared cervid meat consumption revealed that 1.7% of the surveyed hunters would exceed the dose associated with a 1 mmHg increase in systolic blood pressure (SBP). For consumers of moose meat once, twice or three times a week, simulations predicted that 0.5%, 0.9% and 1.5% of adults would be exposed to a dose associated with a 1 mmHg increase in SBP, whereas 0.9%, 1.9% and 3.3% of children would be exposed to a dose associated with 1 point intelligence quotient (IQ) decrease, respectively. For consumers of deer meat once, twice or three times a week, the proportions were 1.6%, 2.9% and 4% for adults and 2.9%, 5.8% and 7.7% for children, respectively. The consumption of meat from cervids killed with lead ammunition may increase lead exposure and its associated health risks. It would be important to inform the population, particularly hunters, about this potential risk and promote the use of lead-free ammunition.

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BackgroundType 1 diabetes is one of the most common chronic diseases in childhood with a worldwide incidence that is increasing by 3–5% per year. The incidence of type 2 diabetes, traditionally viewed as an adult disease, is increasing at alarming rates in children, paralleling the rise in childhood obesity. As the rates of diabetes increase in children, accurate population-based assessment of disease burden is important for those implementing strategies for health services delivery. Health administrative data are a powerful tool that can be used to track disease burden, health services use, and health outcomes. Case validation is essential in ensuring accurate disease identification using administrative databases.AimThe aim of our study was to define and validate a pediatric diabetes case ascertainment algorithm (including any form of childhood-onset diabetes) using health administrative data.Research design and methodsWe conducted a two-stage method using linked health administrative data and data extracted from charts. In stage 1, we linked chart data from a large urban region to health administrative data and compared the diagnostic accuracy of various algorithms. We selected those that performed the best to be validated in stage 2. In stage 2, the most accurate algorithms were validated with chart data within two other geographic areas in the province of Quebec.ResultsAccurate identification of diabetes in children (ages ≤15 years) required four physician claims or one hospitalization (with International Classification of Disease codes within 1 year (sensitivity 91.2%, 95% confidence interval [CI] 89.2–92.9]; positive predictive value [PPV] 93.5%, 95% CI 91.7–95.0) or using only four physician claims in 2 years (sensitivity 90.4%, 95% CI 88.3–92.2; PPV 93.2%, 95% CI 91.7–95.0). Separating the physician claims by 30 days increased the PPV of all algorithms tested.ConclusionPatients with child-onset diabetes can be accurately identified within health administrative databases providing a valid source of information for health care resource planning and evaluation.

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