Background The epidemiology of mental health disorders has important theoretical and practical implications for health care service and planning. The recent increase in big data storage and subsequent development of analytical tools suggest that mining search databases may yield important trends on mental health, which can be used to support existing population health studies. Objective This study aimed to map depression search intent in the United States based on internet-based mental health queries. Methods Weekly data on mental health searches were extracted from Google Trends for an 11-year period (2010-2021) and separated by US state for the following terms: “feeling sad,” “depressed,” “depression,” “empty,” “insomnia,” “fatigue,” “guilty,” “feeling guilty,” and “suicide.” Multivariable regression models were created based on geographic and environmental factors and normalized to the following control terms: “sports,” “news,” “google,” “youtube,” “facebook,” and “netflix.” Heat maps of population depression were generated based on search intent. Results Depression search intent grew 67% from January 2010 to March 2021. Depression search intent showed significant seasonal patterns with peak intensity during winter (adjusted P<.001) and early spring months (adjusted P<.001), relative to summer months. Geographic location correlated with depression search intent with states in the Northeast (adjusted P=.01) having higher search intent than states in the South. Conclusions The trends extrapolated from Google Trends successfully correlate with known risk factors for depression, such as seasonality and increasing latitude. These findings suggest that Google Trends may be a valid novel epidemiological tool to map depression prevalence in the United States.
In this report, we present a case series involving four patients placed on the Clinical Institute Withdrawal Assessment for Alcohol, Revised (CIWA-Ar) protocol for alcohol or sedative-hypnotic withdrawal syndromes, who developed delirium on sustained or increasing symptom-triggered benzodiazepine dosages. In each of the four cases, delirium was not present on admission and resolved in the hospital itself with fixed benzodiazepine tapers. Cases were selected from an electronic medical record database of patients admitted to a United States-based university hospital and placed on CIWA-Ar between 2017 and 2018. This case series illustrates the major limitations of CIWA-Ar including its subjective nature, its susceptibility to inappropriate patient selection, and its requirement for providers to consider alternative etiologies to alcohol and benzodiazepine withdrawal syndromes. These cases demonstrate the necessity of considering other assessment and treatment options such as objective alcohol withdrawal scales, fixed benzodiazepine tapers, and even antiepileptics. An effective systems-based approach to overcoming these challenges may include setting time limits on CIWA-Ar orders within the electronic health record (EHR) system.
Treatment of Central Pain Syndrome (CPS) is known to be extremely challenging. Current therapies are unsatisfactory as patients report only mild to moderate pain relief. We report a case of using ketamine as a patient-controlled analgesia (PCA) for the treatment of CPS. A 58-year-old male with CPS presented with severe generalized body pain refractory to multiple pharmacological interventions. He was started on a basal infusion rate at 0.3 mg/kg/h with a ketamine PCA bolus of 10 mg with a 10-minute lockout period. Over the next 7 days, the basal infusion rate was titrated up to 2.1 mg/kg/h relative to the number of times the patient pressed the PCA. At the end of the trial, the patient reported 0/10 pain with lightheadedness on the first day being the only side effect reported. He was discharged home with his regular pain regimen, with significant decrease in pain over the next few months. Rather than trying to establish a "one size fits all" protocol for ketamine infusions, this case illustrates a shift in pain management focus by allowing patients to self-titrate and demonstrates the potential for using ketamine PCA as a treatment option for CPS.
BACKGROUND The epidemiology of mental health disorders has important theoretical and practical implications for healthcare service and planning. The recent increase in big data storage and subsequent development of analytical tools suggests that mining search databases may yield important trends on mental health, which can be used to replace or support existing population health studies. OBJECTIVE This study aimed to map out depression search intent in the United States based on internet mental health queries. METHODS Weekly data on mental health searches were extracted from Google Trends for an 11-year period (2010-2021) and separated by US state for the following terms: “feeling sad,” “depressed,” “depression,” “empty,” “insomnia,” “fatigue,” “guilty,” “feeling guilty,” and “suicide”. Multivariable regression models were created based on geographic and environmental factors and normalized to control terms “sports,” “news,” “google,” “youtube,” “facebook,” and “netflix”. Heat maps of population depression were generated based on search intent. RESULTS Depression search intent grew 67% from January 2010 to March 2021. Depression search intent showed significant seasonal patterns with peak intensity during winter (adjusted P < 0.001) and early spring months (adjusted P < 0.001), relative to summer months. Geographic location correlated to depression search intent with states in the Northeast (adjusted P = 0.01) having higher search intent than states in the South. CONCLUSIONS The trends extrapolated from Google Trends successfully correlate with known risk factors for depression, such as seasonality and increasing latitude. These findings suggest that Google Trends may be a valid novel epidemiological tool to map out depression prevalence in the United States.
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