Jennifer King

Objective: This study assessed college food insecurity (FI) longitudinally during COVID-19. Participants: Convenient sample (n = 551) college students took an online survey between December 2020-February 2021 [Y1]. Follow-up (December 2021-February 2022) [Y2] from Y1, (n = 131). Methods: FI measured via USDA Short Form Food Security Module. Stress and depression measured via PSS-10 and CES-D. Results: Y1, 31.3% FI. FI had lower mean GPA, were less likely to tell a parent about a food shortage, but more likely to be on financial aid, part-time employed, non-white, first-generation, full-time, and live off-campus compared to food secure [FS] (p ≤ .05* for all). FI had higher mean CES-D [28.45 ± 8.38 vs. 23.50 ± 7.98] and PSS-10 [24.39 ± 3.76 vs. 23.40 ± 3.69] (p < .01*). Y2, FI increased to 39.7%. FI had higher CES-D scores [29.11 ± 10.24 vs. 21.30 ± 6.80] and PSS-10 scores [23.81 ± 3.53 vs. 22.44 ± 3.25] (p ≤ .03*). Conclusions: FI, stress, and depression worsened from Y1 to Y2 due to increased employment, financial aid, and limited familial support.

Background: User demographics are often hidden in social media data due to privacy concerns. However, demographic information on substance use (SU) can provide valuable insights, allowing public health policy makers to focus on specific cohorts and develop efficient prevention strategies, especially during global crises such as the COVID-19 pandemic. Objective: This study aimed to analyze SU trends at the user level across different demographic dimensions, such as age, gender, race, and ethnicity, with a focus on the COVID-19 pandemic. The study also establishes a baseline for SU trends using social media data. Methods: The study was conducted using large-scale English-language data from Twitter (now known as X) over a 3-year period (2019, 2020, and 2021), comprising 1.13 billion posts. Following preprocessing, the SU posts were identified using our custom-trained deep learning model (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach [RoBERTa]), which resulted in the identification of 9 million SU posts. Then, demographic attributes, such as user type, age, gender, race, and ethnicity, as well as sentiments and emotions associated with each post, were extracted via a collection of natural language processing modules. Finally, various qualitative analyses were performed to obtain insight into user behaviors based on demographics. Results: The highest level of user participation in SU discussions was observed in 2020, with a 22.18% increase compared to 2019 and a 25.24% increase compared to 2021. Throughout the study period, male users and teenagers increasingly dominated the SU discussions across all substance types. During the COVID-19 pandemic, user participation in prescription medication discussions was notably higher among female users compared to other substance types. In addition, alcohol use increased by 80% within 2 weeks after the global pandemic declaration in 2020. Conclusions: This study presents a large-scale, fine-grained analysis of SU on social media data, examining trends by age, gender, race, and ethnicity before, during, and after the COVID-19 pandemic. Our findings, contextualized with sociocultural and pandemic-specific factors, provide actionable insights for targeted public health interventions. This study establishes social media data (powered with artificial intelligence and natural language processing tools) as a valuable platform for real-time SU surveillance and prevention during crises.

Background: The COVID-19 pandemic intensified the challenges associated with mental health and substance use (SU), with societal and economic upheavals leading to heightened stress and increased reliance on drugs as a coping mechanism. Centers for Disease Control and Prevention data from June 2020 showed that 13% of Americans used substances more frequently due to pandemic-related stress, accompanied by an 18% rise in drug overdoses early in the year. Simultaneously, a significant increase in social media engagement provided unique insights into these trends. Our study analyzed social media data from January 2019 to December 2021 to identify changes in SU patterns across the pandemic timeline, aiming to inform effective public health interventions. Objective: This study aims to analyze SU from large-scale social media data during the COVID-19 pandemic, including the prepandemic and postpandemic periods as baseline and consequence periods. The objective was to examine the patterns related to a broader spectrum of drug types with underlying themes, aiming to provide a more comprehensive understanding of SU trends during the COVID-19 pandemic. Methods: We leveraged a deep learning model, Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa), to analyze 1.13 billion Twitter (subsequently rebranded X) posts from January 2019 to December 2021, aiming to identify SU posts. The model’s performance was enhanced by a human-in-the-loop strategy that subsequently enriched the annotated data used during the fine-tuning phase. To gain insights into SU trends over the study period, we applied a range of statistical techniques, including trend analysis, k-means clustering, topic modeling, and thematic analysis. In addition, we integrated the system into a real-time application designed for monitoring and preventing SU within specific geographic locations. Results: Our research identified 9 million SU posts in the studied period. Compared to 2019 and 2021, the most substantial display of SU-related posts occurred in 2020, with a sharp 21% increase within 3 days of the global COVID-19 pandemic declaration. Alcohol and cannabinoids remained the most discussed substances throughout the research period. The pandemic particularly influenced the rise in nonillicit substances, such as alcohol, prescription medication, and cannabinoids. In addition, thematic analysis highlighted COVID-19, mental health, and economic stress as the leading issues that contributed to the influx of substance-related posts during the study period. Conclusions: This study demonstrates the potential of leveraging social media data for real-time detection of SU trends during global crises. By uncovering how factors such as mental health and economic stress drive SU spikes, particularly in alcohol and prescription medication, we offer crucial insights for public health strategies. Our approach paves the way for proactive, data-driven interventions that will help mitigate the impact of future crises on vulnerable populations.

BACKGROUND User demographics are often hidden in social media data due to privacy concerns. However, demographic information on substance use (SU) can provide valuable insights, allowing public health policy makers to focus on specific cohorts and develop efficient prevention strategies, especially during global crises such as the COVID-19 pandemic. OBJECTIVE This study aimed to analyze SU trends at the user level across different demographic dimensions, such as age, gender, race, and ethnicity, with a focus on the COVID-19 pandemic. The study also establishes a baseline for SU trends using social media data. METHODS The study was conducted using large-scale English-language data from Twitter (now known as X) over a 3-year period (2019, 2020, and 2021), comprising 1.13 billion posts. Following preprocessing, the SU posts were identified using our custom-trained deep learning model (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach [RoBERTa]), which resulted in the identification of 9 million SU posts. Then, demographic attributes, such as user type, age, gender, race, and ethnicity, as well as sentiments and emotions associated with each post, were extracted via a collection of natural language processing modules. Finally, various qualitative analyses were performed to obtain insight into user behaviors based on demographics. RESULTS The highest level of user participation in SU discussions was observed in 2020, with a 22.18% increase compared to 2019 and a 25.24% increase compared to 2021. Throughout the study period, male users and teenagers increasingly dominated the SU discussions across all substance types. During the COVID-19 pandemic, user participation in prescription medication discussions was notably higher among female users compared to other substance types. In addition, alcohol use increased by 80% within 2 weeks after the global pandemic declaration in 2020. CONCLUSIONS This study presents a large-scale, fine-grained analysis of SU on social media data, examining trends by age, gender, race, and ethnicity before, during, and after the COVID-19 pandemic. Our findings, contextualized with sociocultural and pandemic-specific factors, provide actionable insights for targeted public health interventions. This study establishes social media data (powered with artificial intelligence and natural language processing tools) as a valuable platform for real-time SU surveillance and prevention during crises.

BACKGROUND The COVID-19 pandemic intensified the challenges associated with mental health and substance use (SU), with societal and economic upheavals leading to heightened stress and increased reliance on drugs as a coping mechanism. Centers for Disease Control and Prevention data from June 2020 showed that 13% of Americans used substances more frequently due to pandemic-related stress, accompanied by an 18% rise in drug overdoses early in the year. Simultaneously, a significant increase in social media engagement provided unique insights into these trends. Our study analyzed social media data from January 2019 to December 2021 to identify changes in SU patterns across the pandemic timeline, aiming to inform effective public health interventions. OBJECTIVE This study aims to analyze SU from large-scale social media data during the COVID-19 pandemic, including the prepandemic and postpandemic periods as baseline and consequence periods. The objective was to examine the patterns related to a broader spectrum of drug types with underlying themes, aiming to provide a more comprehensive understanding of SU trends during the COVID-19 pandemic. METHODS We leveraged a deep learning model, Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa), to analyze 1.13 billion Twitter (subsequently rebranded X) posts from January 2019 to December 2021, aiming to identify SU posts. The model’s performance was enhanced by a human-in-the-loop strategy that subsequently enriched the annotated data used during the fine-tuning phase. To gain insights into SU trends over the study period, we applied a range of statistical techniques, including trend analysis, k-means clustering, topic modeling, and thematic analysis. In addition, we integrated the system into a real-time application designed for monitoring and preventing SU within specific geographic locations. RESULTS Our research identified 9 million SU posts in the studied period. Compared to 2019 and 2021, the most substantial display of SU-related posts occurred in 2020, with a sharp 21% increase within 3 days of the global COVID-19 pandemic declaration. Alcohol and cannabinoids remained the most discussed substances throughout the research period. The pandemic particularly influenced the rise in nonillicit substances, such as alcohol, prescription medication, and cannabinoids. In addition, thematic analysis highlighted COVID-19, mental health, and economic stress as the leading issues that contributed to the influx of substance-related posts during the study period. CONCLUSIONS This study demonstrates the potential of leveraging social media data for real-time detection of SU trends during global crises. By uncovering how factors such as mental health and economic stress drive SU spikes, particularly in alcohol and prescription medication, we offer crucial insights for public health strategies. Our approach paves the way for proactive, data-driven interventions that will help mitigate the impact of future crises on vulnerable populations.

Tolvaptan (a selective V2 receptor antagonist) is licensed for the inpatient management of SIADH induced hyponatraemia, a common complication in patients with malignancy. Licensed daily doses start at 15 mg but there is evidence that some patients have a rise in serum sodium (Na) of >12 mmol/l per 24 h in response to this. Lower initial doses (7.5 mg) may therefore be appropriate^1,2. Methods: A retrospective case note audit was performed. Thirty-four oncology inpatients (mean (±S.D.) age 67±9 years, 15 males) were treated with tolvaptan (Nov 2009–Sept 2013). Twenty-six patients had small cell lung cancer (remaining diagnoses: renal cell carcinoma, oesophageal cancer, myeloma, testicular teratoma, cholangiocarcinoma and adrenal liposarcoma). Results: Thirty-one patients had a trial of demeclocycline and fluid restriction prior to tolvaptan; 29 received endocrine team review. Nineteen met full biochemical criteria for SIADH (plasma osmolality 100 mOsm/kg, urinary sodium >20 mmol/l). Three patients died. No incidents of central pontine myelinosis were recorded. Mean (±S.D.) (Na) pre-tolvaptan was 117±5 mmol/l. In the 28 patients receiving 7.5 mg, the initial increase (Na)/24 h was 7.4±5 mmol/l (27/28 showed a rise in (Na)); 4/28 patients had a rise in (Na)/24 h≥12 mmol/l. To achieve (Na) >130 mmol/l, three patients required escalation to a 15 mg dose. Twelve patients achieved a (Na) >130 mmol/l within 48 h with eight requiring tolvaptan on discharge. Mean (±S.D.) (Na) was 131±6 mmol/l on discharge. The six patients receiving an initial dose of 15 mg showed a mean (±S.D.) rise in (Na)/24 h of 13.8±4 mmol/l (5/6 were ≥12 mmol/l). Conclusion: Tolvaptan 7.5 mg safely and effectively increased (Na) to >130 mmol/l in this group of patients. Higher doses of tolvaptan were more likely to cause a 24 h rise in (Na) outside of recommended limits. Further work is required to improve investigation and monitoring of hyponatremia in oncology patients.

A Time for Dreams: The Right to Education for First Nations Children and Youth Living On-Reserve was published in Youth, Education, and Marginality on page 93.