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Can accurate demographic information about people who use prescription medications nonmedically be derived from Twitter?

ABSTRACT: Traditional substance use (SU) surveillance methods, such as surveys, incur substantial lags. Due to the continuously evolving trends in SU, insights obtained via such methods are often outdated. Social media-based sources have been proposed for obtaining timely insights, but methods leveraging such data cannot typically provide fine-grained statistics about subpopulations, unlike traditional approaches. We address this gap by developing methods for automatically characterizing a large Twitter nonmedical prescription medication use (NPMU) cohort (n = 288,562) in terms of age-group, race, and gender. Our natural language processing and machine learning methods for automated cohort characterization achieved 0.88 precision (95% CI:0.84 to 0.92) for age-group, 0.90 (95% CI: 0.85 to 0.95) for race, and 94% accuracy (95% CI: 92 to 97) for gender, when evaluated against manually annotated gold-standard data. We compared automatically derived statistics for NPMU of tranquilizers, stimulants, and opioids from Twitter with statistics reported in the National Survey on Drug Use and Health (NSDUH) and the National Emergency Department Sample (NEDS). Distributions automatically estimated from Twitter were mostly consistent with the NSDUH [Spearman r: race: 0.98 (P < 0.005); age-group: 0.67 (P < 0.005); gender: 0.66 (P = 0.27)] and NEDS, with 34/65 (52.3%) of the Twitter-based estimates lying within 95% CIs of estimates from the traditional sources. Explainable differences (e.g., overrepresentation of younger people) were found for age-group-related statistics. Our study demonstrates that accurate subpopulation-specific estimates about SU, particularly NPMU, may be automatically derived from Twitter to obtain earlier insights about targeted subpopulations compared to traditional surveillance approaches.

SUBMITTER: Yang YC

PROVIDER: S-EPMC9974473 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Can accurate demographic information about people who use prescription medications nonmedically be derived from Twitter?

Yang Yuan-Chi YC Al-Garadi Mohammed Ali MA Love Jennifer S JS Cooper Hannah L F HLF Perrone Jeanmarie J Sarker Abeed A

Proceedings of the National Academy of Sciences of the United States of America 20230214 8

Traditional substance use (SU) surveillance methods, such as surveys, incur substantial lags. Due to the continuously evolving trends in SU, insights obtained via such methods are often outdated. Social media-based sources have been proposed for obtaining timely insights, but methods leveraging such data cannot typically provide fine-grained statistics about subpopulations, unlike traditional approaches. We address this gap by developing methods for automatically characterizing a large Twitter n ...[more]

PMID: 36787355

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Project description:BackgroundA lack of safety data on postpartum medication use presents a potential barrier to breastfeeding and may result in infant exposure to medications in breastmilk. The type and extent of medication use by lactating women requires investigation.MethodsData were collected from the CHILD Cohort Study which enrolled pregnant women across Canada between 2008 and 2012. Participants completed questionnaires regarding medications and non-prescription medications used and breastfeeding status at 3, 6 and 12 months postpartum. Medications, along with self-reported reasons for medication use, were categorized by ontologies [hierarchical controlled vocabulary] as part of a large-scale curation effort to enable more robust investigations of reasons for medication use.ResultsA total of 3542 mother-infant dyads were recruited to the CHILD study. Breastfeeding rates were 87.4%, 75.3%, 45.5% at 3, 6 and 12 months respectively. About 40% of women who were breastfeeding at 3 months used at least one prescription medication during the first three months postpartum; this proportion decreased over time to 29.5% % at 6 months and 32.8% at 12 months. The most commonly used prescription medication by breastfeeding women was domperidone at 3 months (9.0%, n = 229/2540) and 6 months (5.6%, n = 109/1948), and norethisterone at 12 months (4.1%, n = 48/1180). The vast majority of domperidone use by breastfeeding women (97.3%) was for lactation purposes which is off-label (signifying unapproved use of an approved medication). Non-prescription medications were more often used among breastfeeding than non-breastfeeding women (67.6% versus 48.9% at 3 months, p < 0.0001), The most commonly used non-prescription medications were multivitamins and Vitamin D at 3, 6 and 12 months postpartum.ConclusionsIn Canada, medication use is common postpartum; 40% of breastfeeding women use prescription medications in the first 3 months postpartum. A diverse range of medications were used, with many women taking more than one prescription and non-prescription medicines. The most commonly used prescription medication by breastfeeding women were domperidone for off-label lactation support, signalling a need for more data on the efficacy of domperidone for this indication. This data should inform research priorities and communication strategies developed to optimize care during lactation.

Project description:BackgroundUse of the social media website Twitter is highly prevalent and has led to a plethora of Web-based social and health-related data available for use by researchers. As such, researchers are increasingly using data from social media to retrieve and analyze mental health-related content. However, there is limited evidence regarding why people use this emerging platform to discuss mental health problems in the first place.ObjectivesThe aim of this study was to explore the reasons why individuals discuss mental health on the social media website Twitter. The study was the first of its kind to implement a study-specific hashtag for research; therefore, we also examined how feasible it was to circulate and analyze a study-specific hashtag for mental health research.MethodsText mining methods using the Twitter Streaming Application Programming Interface (API) and Twitter Search API were used to collect and organize tweets from the hashtag #WhyWeTweetMH, circulated between September 2015 and November 2015. Tweets were analyzed thematically to understand the key reasons for discussing mental health using the Twitter platform.ResultsFour overarching themes were derived from the 132 tweets collected: (1) sense of community; (2) raising awareness and combatting stigma; (3) safe space for expression; and (4) coping and empowerment. In addition, 11 associated subthemes were also identified.ConclusionsThe themes derived from the content of the tweets highlight the perceived therapeutic benefits of Twitter through the provision of support and information and the potential for self-management strategies. The ability to use Twitter to combat stigma and raise awareness of mental health problems indicates the societal benefits that can be facilitated via the platform. The number of tweets and themes identified demonstrates the feasibility of implementing study-specific hashtags to explore research questions in the field of mental health and can be used as a basis for other health-related research.

Project description:BackgroundAs survivors of childhood cancer age, development of cancer treatment-related chronic health conditions often occur. This study aimed to describe the pattern of chronic prescription medication use and identify factors associated with polypharmacy among survivors of childhood cancer.MethodsThis was a retrospective study conducted at the pediatric oncology long-term follow-up clinic in Hong Kong. Eligible subjects included survivors who were (1) diagnosed with cancer before 18 years old, (2) were at least 3 years post-cancer diagnosis and had completed treatment for at least 30 days, and (3) receiving long-term follow-up care at the study site between 2015 and 2018. Dispensing records of eligible survivors were reviewed to identify medications taken daily for ≥30 days or used on an "as needed" basis for ≥6 months cumulatively within the past 12-month period. Polypharmacy was defined as the concurrent use of ≥5 chronic medications. Multivariable log-binomial modeling was conducted to identify treatment and clinical factors associated with medication use pattern and polypharmacy.ResultsThis study included 625 survivors (mean current age = 17.9 years, standard deviation [SD] = 7.2 years) who were 9.2 [5.2] years post-treatment. Approximately one-third (n = 219, 35.0%) of survivors were prescribed at least one chronic medication. Frequently prescribed medication classes include systemic antihistamines (26.5%), sex hormones (19.2%), and thyroid replacement therapy (16.0%). Overall prevalence of polypharmacy was 5.3% (n = 33). A higher rate of polypharmacy was found in survivors of CNS tumors (13.6%) than in survivors of hematological malignancies (4.3%) and other solid tumors (5.3%) (P = .0051). Higher medication burden was also observed in survivors who had undergone cranial radiation (RR = 6.31; 95% CI = 2.75-14.49) or hematopoietic stem-cell transplantation (HSCT) (RR = 3.53; 95% CI = 1.59-7.83).ConclusionAlthough polypharmacy was observed in a minority of included survivors of childhood cancer, chronic medication use was common. Special attention should be paid to survivors of CNS tumors and survivors who have undergone HSCT or cranial radiation. These individuals should be monitored closely for drug-drug interactions and adverse health outcomes that may result from multiple chronic medications, particularly during hospitalization in an acute care setting.

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Can accurate demographic information about people who use prescription medications nonmedically be derived from Twitter?

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Can accurate demographic information about people who use prescription medications nonmedically be derived from Twitter?

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