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Data quality (DQ) is the degree to which a given dataset meets a user’s requirements. In the primary healthcare setting, poor quality data can lead to poor patient care, negatively affect the validity and reproducibility of research results and limit the value that such data may have for public health surveillance. To extract reliable and useful information from a large quantity of data and to make more effective and informed decisions, data should be as clean and free of errors as possible. Moreover, because DQ is defined within the context of different user requirements that often change, DQ should be considered to be an emergent construct. As such, we cannot expect that a sufficient level of DQ will last forever. Therefore, the quality of clinical data should be constantly assessed and reassessed in an iterative fashion to ensure that appropriate levels of quality are sustained in an acceptable and transparent manner. This document is based on our hands-on experiences dealing with DQ improvement for the Canadian Primary Care Sentinel Surveillance Network database. The DQ dimensions that are discussed here are accuracy and precision, completeness and comprehensiveness, consistency, timeliness, uniqueness, data cleaning and coherence.

The main objective of this study was to investigate the effectiveness of preharvest 1-methylcyclopropene (1-MCP) treatment on the development of soft scald in ‘Honeycrisp’ apples. In addition, the effects of preharvest 1-MCP on fruit quality at harvest and after storage were examined. For two consecutive years of study, ‘Honeycrisp’ trees were sprayed preharvest with 1-MCP and fruit were harvested twice during each year. Preharvest 1-MCP treatments had little consistent effect on fruit maturity at the time of harvest. In both years of study, preharvest 1-MCP reduced the incidence of soft scald in ‘Honeycrisp’ apples after air storage at 0 or 3 °C for 5 or 6 months. Soggy breakdown developed only in the second year of study and high incidences were reduced by preharvest 1-MCP treatments. Preharvest 1-MCP often reduced flesh firmness loss in ‘Honeycrisp’ during storage, especially during the second year of study, and with 1-MCP application closer to harvest. Malic acid content was often higher in apples with the preharvest 1-MCP spray closer to harvest. Overall, the most important benefit of preharvest 1-MCP treatments on ‘Honeycrisp’ apples was the reduction in soft scald development. Due to the high potential for substantial fruit losses from this disorder, the use of preharvest 1-MCP sprays on ‘Honeycrisp’ apples could be very advantageous.

Identifying patients with rare diseases associated with common symptoms is challenging. Hunter syndrome, or Mucopolysaccharidosis type II is a progressive rare disease caused by a deficiency in the activity of the lysosomal enzyme, iduronate 2-sulphatase. It is inherited in an X-linked manner resulting in males being significantly affected. Expression in females varies with the majority being unaffected although symptoms may emerge over time. We developed a Naïve Bayes classification (NBC) algorithm utilizing the clinical diagnosis and symptoms of patients contained within their de-identified and unstructured electronic medical records (EMR) extracted by the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). To do so, we created a training dataset using published results in the scientific literature and from all MPS II symptoms and applied the training dataset and its independent features to compute the conditional posterior probabilities of having MPS II disease as a categorical dependent variable for 506497 male patients. The classifier identified 125 patients with the highest likelihood for having the disease and 18 features were selected to be necessary for forecasting. Next, a Recursive Backward Feature Elimination algorithm was employed, for optimal input features of the NBC model, using a k-fold Cross-Validation with 3 replicates. The accuracy of the final model was estimated by the Validation Set Approach technique and the bootstrap resampling. We also investigated that whether the NBC is as accurate as three other Bayesian networks. The Naïve Bayes Classifier appears to be an efficient algorithm in assisting physicians with the diagnosis of Hunter syndrome allowing optimal patient management.

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