Dataset Information

Nursing Informatics and Epigenetics: Methodological Considerations for Big Data Analysis.

ABSTRACT: Nursing informatics requires an understanding of patient-centered data and clinical workflow, and epigenetic research requires an understanding of data analysis. The purpose of this article is to document the methodology that nursing informatics specialists can use to conduct epigenetic research and subsequently strengthen patient-centered care. A pilot study of a secondary methylation data analysis using The Cancer Genome Atlas data from individuals with colon cancer is utilized to illustrate the methodology. The steps for conducting the study using public and free resources are discussed. These steps include finding a data source; downloading and analyzing differentially methylated regions; annotating differentially methylated region, gene ontology and function analysis; and reporting results. A model of epigenetic testing workflow is provided, as is a list of publicly available data and analysis sources that can be used to conduct epigenetic research.

SUBMITTER: Milner JJ

PROVIDER: S-EPMC10241417 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Nursing Informatics and Epigenetics: Methodological Considerations for Big Data Analysis.

Milner John J JJ Zadinsky Julie K JK Shiao S Pamela K SPK

Computers, informatics, nursing : CIN 20230601 6

Nursing informatics requires an understanding of patient-centered data and clinical workflow, and epigenetic research requires an understanding of data analysis. The purpose of this article is to document the methodology that nursing informatics specialists can use to conduct epigenetic research and subsequently strengthen patient-centered care. A pilot study of a secondary methylation data analysis using The Cancer Genome Atlas data from individuals with colon cancer is utilized to illustrate t ...[more]

PMID: 36728378

Similar Datasets

Project description:BackgroundMuch research is being carried out using publicly available Twitter data in the field of public health, but the types of research questions that these data are being used to answer and the extent to which these projects require ethical oversight are not clear.ObjectiveThis review describes the current state of public health research using Twitter data in terms of methods and research questions, geographic focus, and ethical considerations including obtaining informed consent from Twitter handlers.MethodsWe implemented a systematic review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, of articles published between January 2006 and October 31, 2019, using Twitter data in secondary analyses for public health research, which were found using standardized search criteria on SocINDEX, PsycINFO, and PubMed. Studies were excluded when using Twitter for primary data collection, such as for study recruitment or as part of a dissemination intervention.ResultsWe identified 367 articles that met eligibility criteria. Infectious disease (n=80, 22%) and substance use (n=66, 18%) were the most common topics for these studies, and sentiment mining (n=227, 62%), surveillance (n=224, 61%), and thematic exploration (n=217, 59%) were the most common methodologies employed. Approximately one-third of articles had a global or worldwide geographic focus; another one-third focused on the United States. The majority (n=222, 60%) of articles used a native Twitter application programming interface, and a significant amount of the remainder (n=102, 28%) used a third-party application programming interface. Only one-third (n=119, 32%) of studies sought ethical approval from an institutional review board, while 17% of them (n=62) included identifying information on Twitter users or tweets and 36% of them (n=131) attempted to anonymize identifiers. Most studies (n=272, 79%) included a discussion on the validity of the measures and reliability of coding (70% for interreliability of human coding and 70% for computer algorithm checks), but less attention was paid to the sampling frame, and what underlying population the sample represented.ConclusionsTwitter data may be useful in public health research, given its access to publicly available information. However, studies should exercise greater caution in considering the data sources, accession method, and external validity of the sampling frame. Further, an ethical framework is necessary to help guide future research in this area, especially when individual, identifiable Twitter users and tweets are shared and discussed.Trial registrationPROSPERO CRD42020148170; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=148170.

Project description:PurposeThe decision whether to continue antidepressant use for depression during pregnancy requires weighing maternal and child risks and benefits. Little is known about the effectiveness of antidepressant therapy during pregnancy. The goal of this study is to evaluate whether standard administrative claims data can be used to evaluate the effectiveness of antidepressants.MethodsUsing prescription and healthcare visit Medicaid claims (2000-2007), we identified 28 493 women with a depression diagnosis and antidepressant fill in the 90 days before their last menstrual period. Antidepressant continuation was defined based on prescription fills during the first trimester. Depression hospitalizations and deliberate self-harm served as measures of the effectiveness of treatment continuation during pregnancy. Propensity score and instrumental variable analyses were used to attempt to account for confounding.ResultsRelative to women who discontinued antidepressant therapy, women who continued were more likely to have a depression inpatient stay (odds ratio [OR] = 2.2, 95% confidence interval [95%CI]: 2.0-2.4) and deliberate self-harm code (OR = 1.4, 95%CI: 0.7-2.7). Accounting for measured covariates in the propensity score analysis, including age, race, comorbidities, comedications, features of the depression diagnosis, and antidepressant class, led to slightly attenuated estimates (OR = 2.0, 95%CI: 1.8-2.2; OR = 1.1, 95%CI: 0.5-2.4). Similar associations were estimated in subgroups with different levels of baseline depression severity. Proposed preference-time, calendar-time-based, and geography-based instruments were unlikely to meet the required conditions for a valid analysis.ConclusionsOur findings suggest that either antidepressant medications do not reduce the risk of depression relapse in pregnant women, or that administrative data alone could not be used to validly estimate the effectiveness of psychotropic medications during pregnancy.

Dataset Information

Nursing Informatics and Epigenetics: Methodological Considerations for Big Data Analysis.

Publications

Nursing Informatics and Epigenetics: Methodological Considerations for Big Data Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets