Estimating the impacts of nonoptimal temperatures on mortality: A study in British Columbia, Canada, 2001-2021.

Background: Studies show that more than 5.1 million deaths annually are attributed to nonoptimal temperatures, including extreme cold and extreme heat. However, those studies mostly report average estimates across large geographical areas. The health risks attributed to nonoptimal temperatures in British Columbia (BC) are reported incompletely or limit the study area to urban centers. In this study, we aim to estimate the attributable deaths linked to nonoptimal temperatures in all five regional health authorities (RHAs) of BC from 2001 to 2021. Methods: We applied the widely used distributed lag nonlinear modeling approach to estimate temperature-mortality association in the RHAs of BC, using daily all-cause deaths and 1 × 1 km gridded daily mean temperature. We evaluated the model by comparing the model-estimated attributable number of deaths during the 2021 heat dome to the number of heat-related deaths confirmed by the British Columbia Coroners Service. Results: Overall, between 2001 and 2021, we estimate that 7.17% (95% empirical confidence interval = 3.15, 10.32) of deaths in BC were attributed to nonoptimal temperatures, the majority of which are attributed to cold. On average, the mortality rates attributable to moderate cold, moderate heat, extreme cold, and extreme heat were 47.04 (95% confidence interval [CI] = 45.83, 48.26), 0.94 (95% CI = 0.81, 1.08), 2.88 (95% CI = 2.05, 3.71), and 3.10 (95% CI = 1.79, 4.4) per 100,000 population per year, respectively. Conclusions: Our results show significant spatial variability in deaths attributable to nonoptimal temperatures across BC. We find that the effect of extreme temperatures is significantly less compared to milder nonoptimal temperatures between 2001 and 2021. However, the increased contribution of extreme heat cannot be ruled out in the near future.

Validation of case definition algorithms for the ascertainment of congenital anomalies.

BACKGROUND: Congenital anomalies (CA) are one of the leading causes of infant mortality and long-term disability. Many jurisdictions rely on health administrative data to monitor these conditions. Case definition algorithms can be used to monitor CA; however, validation of these algorithms is needed to understand the strengths and limitations of the data. This study aimed to validate case definition algorithms used in a CA surveillance system in British Columbia (BC), Canada. METHODS: A cohort of births between March 2000 and April 2002 in BC was linked to the Health Status Registry (HSR) and the BC Congenital Anomalies Surveillance System (BCCASS) to identify cases and non-cases of specific anomalies within each surveillance system. Measures of algorithm performance were calculated for each CA using the HSR as the reference standard. Agreement between both databases was calculated using kappa coefficient. The modified Standards for Reporting Diagnostic Accuracy guidelines were used to enhance the quality of the study. RESULTS: Measures of algorithm performance varied by condition. Positive predictive value (PPV) ranged between approximately 73%-100%. Sensitivity was lower than PPV for most conditions. Internal congenital anomalies or conditions not easily identifiable at birth had the lowest sensitivity. Specificity and negative predictive value exceeded 99% for all algorithms. CONCLUSION: Case definition algorithms may be used to monitor CA at the population level. Accuracy of algorithms is higher for conditions that are easily identified at birth. Jurisdictions with similar administrative data may benefit from using validated case definitions for CA surveillance as this facilitates cross-jurisdictional comparison.

Corrigendum to "Spontaneous Resolution of Symptomatic Hepatic Sarcoidosis".

[This corrects the article DOI: 10.1155/2018/1535049.].

Spontaneous Resolution of Symptomatic Hepatic Sarcoidosis.

Sarcoidosis is an inflammatory process of unknown etiology, characterized by noncaseating granulomas. Isolated extrapulmonary disease is rare. We present a case of a 60-year-old woman with chronically elevated alkaline phosphatase. Upon obtaining a liver biopsy, granulomatous hepatitis was observed, suggestive of sarcoidosis. No particular treatment was initiated, and 3 years following the onset of elevated alkaline phosphatase, her levels decreased spontaneously.

Frame-to-frame image realignment assessment tool for dynamic brain positron emission tomography.

PURPOSE: Subject motion during positron emission tomography (PET) brain scans can reduce image quality and may lead to incorrect biological outcome measures, especially for data acquired with high resolution tomographs. A semiautomatic method for assessing the quality of frame-to-frame image realignments to compensate for subject motion in dynamic brain PET is proposed and evaluated. METHODS: A test set of 256 11C-raclopride (a dopamine D2-type receptor antagonist) brain PET image frames was used to develop and evaluate the proposed method. The transformation matrix to be applied to each image to achieve a frame-to-frame realignment was calculated with two independent methods: Using motion data measured with the Polaris Vicra optical tracking device and using the image-based realignment algorithm AIR (automated image registration). The quality assessment method is based on the observation that there is a very low probability that two independent approaches to motion detection will produce equal, but incorrect results. Agreement between transformation matrices was taken to be a signature of an accurate motion determination and thus realignment. Each pair of realignment matrices was compared and used to calculate a metric describing the frame-to-frame image realignment accuracy. In order to determine the range of values of the metric that correspond to a successful realignment, a comparison was made to a detailed visual inspection of the frame-to-frame realigned images for each image in the test set. The threshold on the metric for realignment acceptance was then selected to optimize the numbers of true-positives (realignments accepted by both the protocol and the operator) and minimize the number of false-positives (accepted by the protocol but not the operator). RESULTS: The proposed method categorized 53% of the image realignments in the test dataset as successful, of which 11% were incorrectly categorized (6% of the total dataset). Implementation of the proposed assessment tool resulted in a 45% time savings compared to the same visual inspection applied to all image realignments. CONCLUSIONS: The frame-to-frame image realignment assessment tool presented here required less operator time to evaluate realignment success compared to a method requiring visual inspection of all realigned images, while maintaining the same level of accuracy in the realigned dataset. This practical method can be easily implemented at any center with motion monitoring capabilities or, for centers lacking this technology, methods of estimating image realignment parameters that use independent information. In addition, the procedure is flexible, allowing modifications to be made for different tracer types and/or downstream analysis goals.

Over half of the far-infrared background light comes from galaxies at z >or= 1.2.

Submillimetre surveys during the past decade have discovered a population of luminous, high-redshift, dusty starburst galaxies. In the redshift range 1 or= 1.2 accounting for 70% of it. As expected, at the longest wavelengths the signal is dominated by ultraluminous galaxies at z > 1.