{'fraction_non_alphanumeric': 0.01790073230268511, 'fraction_numerical': 0.0, 'mean_word_length': 5.40625, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Outlier detection in psychiatric epidemiology

Outliers may be of interest in their own right or they may merely be distractions from the point in question, and a hindrance to generalisation. In the medical field, including psychiatry, concise summary statistics and parsimonious models have tended to be the main aim of many studies. However, partly motivated by the requirements of medical audit and other political and financial considerations, the detection of outliers as an end in itself is becoming a subject of interest in the public domain, and this may extend from medicine in general to psychiatry. The recognition of uncertainty in ranks using statistical methods can place the labels "best" and "worst", when applied to hospitals and consultants, into perspective, and here new developments in Bayesian methods will be important. Other areas of current development include computer intensive methods for multiple, multivariate outliers and for outlier detection tailored to specific situations such as correlational models in factor analysis and reliability studies, and in meta-analysis. These areas are likely to be of particular interest to psychiatric epidemiologists because of the complex nature of their data.