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One reviewer conducted a full abstraction of all data, and 2 reviewers (E.R.W. All-cause mortality and mortality due to natural causes (eg, acute and chronic illnesses) and unnatural causes (eg, suicide and unintentional injury) were abstracted separately. We also abstracted the observed number of deaths and/or rate of death among people with mental disorders (when possible, we did not include substance use or dementia), expected number of deaths and/or rate of death among people with mental disorders, risk of mortality (eg, standardized mortality ratio, relative risk, odds ratio, hazard ratio, and years of potential life lost ), and adjustment variables. We sought to provide comprehensive estimates of individual- and population-level mortality rates related to mental disorders.įrom all eligible articles, we abstracted the first author’s name, year of publication, country, setting, year of baseline, years of follow-up, sample source, sample size, mental disorders included, population of people with mental disorders (eg, inpatient, outpatient, and community), method of diagnosing mental disorders, diagnostic system, control or comparison group, and assessment of mortality. The purpose of this study was to systematically review the literature to examine the excess mortality rate of people with mental disorders, extending existing reviews of individual disorders. Quantifying and understanding the excess mortality among people with mental disorders can inform approaches for addressing this persistent issue and widen discussion of the effect of mental disorders on mortality. In turn, these behaviors contribute to the high rates of chronic medical conditions among people with mental disorders.
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People with mental disorders have high rates of adverse health behaviors, including tobacco smoking, substance use, physical inactivity, and poor diet. 11, 12 Another complicating factor is that mental disorders are associated with risk factors for mortality. The link between mental disorders and mortality is complicated because most people with mental disorders do not die of their condition rather, they die of heart disease and other chronic diseases, infections, suicide, and other causes. The studies 11, 12 on the global burden of disease illustrate the growing burden of mental disorders, although this burden has largely been reflected in disability rather than mortality. Since then, numerous studies and reviews have been conducted on the mortality risks of people with a variety of mental disorders 2– 6 and specific diagnoses (eg, schizophrenia, 7 depression, 8, 9 and bipolar disorder 10). In 1937, Malzberg 1 reported that psychiatric inpatients had a mortality rate that was 6 times greater than the rate in the general population of New York.
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Keywords: vader,sentiment,analysis,opinion,mining,nlp,text,data,text analysis,opinion analysis,sentiment analysis,text mining,twitter sentiment,opinion mining,social media,twitter,social,mediaĬlassifier: Development Status :: 5 - Production/StableĬlassifier: Intended Audience :: Science/ResearchĬlassifier: License :: OSI Approved :: MIT LicenseĬlassifier: Programming Language :: Python :: 3.Researchers have consistently reported that people with mental disorders have elevated mortality compared with the general population. VADER has been ported to Java, JavaScript, PHP, Scala, C#, Rust, and Go (see details and links to these on the.Details about the scoring are provided on the.Eighth International Conference on Weblogs and Social Media (ICWSM-14).
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VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text.
Citing comprehensive meta analysis 3.3 code#
If you use either the dataset or any of the VADER sentiment analysis tools (VADER sentiment lexicon or Python code for rule-based sentiment analysis engine) in your research, please cite the above paper. It is fully open-sourced under the (we sincerely appreciate all attributions and readily accept most contributions, but please don’t hold us liable). VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media.