The experience of COVID-19 among people with depression: Impact on daily life and coping strategies.

BACKGROUND: The prevalence of depression symptoms among U.S. adults increased dramatically during the early months of the COVID-19 pandemic. We sought to understand the impact of the pandemic on people with a history of depression. METHODS: In June 2020, a national sample of 5023 U.S. adults, including 760 reporting past/current diagnoses of depression, completed survey measures related to the COVID experience, coping, anxiety, depression, and PTSD. RESULTS: After adjusting for sociodemographic characteristics, a history of depression increased the odds of negative effects of pandemic on multiple aspects of life: routines, access to mental health treatment, alcohol use, prescription painkiller use, and other drug use. Those with a history of depression also scored significantly higher on the PHQ-8, GAD-7, and PDS-5 (all ps < 0.0001). Greater use of adaptive coping strategies was significantly associated with lower scores, and greater use of maladaptive strategies with higher scores. Individuals reporting a history of depression reported greater use of both adaptive and maladaptive strategies. CONCLUSIONS: Adaptive coping strategies appear to be protective and help regulate symptomatology, suggesting that particular focus during the clinical encounter on developing tools to promote well-being, alleviate stress, and decrease perceptions of helplessness could mitigate the effects.

The risks of attrition bias in longitudinal surveys of the impact of COVID-19.

As the COVID-19 pandemic continues, interest in mental health impacts is shifting from short-term to long-term outcomes. As part of a longitudinal online survey study examining mental health impacts of the pandemic, we assessed the risk of attrition bias related to a history of depression-a condition research shows can increase challenges of recruitment and retention. Among 5023 participants who completed the baseline survey, significantly more reporting a history of depression were lost to follow-up: baseline to 3 months: 497/760 (65.4%) vs 2228/4263 (52.3%), P < 0.001; 3 to 6 months: 179/263 (68.1%) vs 1183/2035 (58.1%), P = 0.002. Participants reporting a history of depression also had greater adjusted odds of a Patient Health Questionnaire-8 score ≥10 (odds ratio [OR] = 3.97, 95% confidence interval [CI] 3.27, 4.84), Generalized Anxiety Disorder-7 score ≥10 (OR = 3.77, 95% CI 3.07, 4.62), and Posttraumatic Diagnostic Scale for DSM V score ≥ 28 (OR = 7.17, 95% CI 4.67, 11.00) at baseline, indicating a need to account for attrition bias when examining these outcomes. Similar considerations likely apply to other longitudinal survey studies and are important to address to ensure accurate evidence is available to support policy decisions regarding resource allocation and funding.

Income Differences and COVID-19: Impact on Daily Life and Mental Health.

The COVID-19 pandemic has caused disproportionate suffering among vulnerable and socioeconomically disadvantaged portions of the population. Low-income and minority populations are likely to experience disparate disease and mental health burdens. Currently, there is little evidence regarding how the experience of the early months of the US COVID-19 outbreak differed by income level, and how that related to mental health symptoms. The present study used data from a national sample of US adults (n = 5023) who completed measures related to the COVID experience, the COVID-19 Fear Scale, the Generalized Anxiety Disorder-7 (GAD-7), and the Patient Health Questionnaire-8 (PHQ-8). Multivariable regression was performed to determine whether income level (low: <$45,000 vs high: ≥$75,000) was significantly associated with COVID experience measures, PHQ-8, GAD-7, and COVID fear scores. Among the low-income group, COVID-19 had a significantly greater negative impact on: family income/employment, access to food, access to mental health treatment, and stress and discord in the family. Participants in the low-income group also had greater odds of a PHQ-8 score ≥10 (odds ratio [OR] = 1.38, 95% confidence interval [CI] 1.08, 1.77) and a GAD-7 score ≥10 (OR = 1.65, 95% CI 1.27, 2.14) compared to those in the high-income group. Study findings suggest substantial differences in how COVID-19 impacted daily life and mental health between adults living in low-income households compared to high-earning households during the early months of the pandemic.

The Pandemic Experience for People with Depressive Symptoms: Substance Use, Finances, Access to Treatment, and Trusted Sources of Information.

Background: Mental health impacts of the COVID-19 pandemic are well recognized, but little is known about the pandemic experience among people experiencing mental health symptoms. Methods: In June 2020, a national sample of 5023 U.S. adults, including 785 scoring ⩾10 on the PHQ-8 for symptoms of depression, completed survey measures related to their pandemic experience. Results: After adjusting for sociodemographic characteristics, aspects of the COVID-19 pandemic experience for which participants scoring PHQ-8 ⩾ 10 had the greatest increase in odds of reporting moderate/severe negative impacts included: mental health treatment access (odds ratio [OR], 95% confidence interval [CI] = 8.81, 6.70-11.57), family stress/discord (OR, 95% CI = 5.21, 4.24-6.42), food access (OR, 95% CI = 3.76, 2.97-4.77), and income/employment (OR, 95% CI = 3.19, 2.66-3.83). They were also significantly more likely to report increased use of prescription painkillers (OR, 95% CI = 8.46, 4.50-15.92) and other drugs (OR, 95% CI = 4.43, 2.85-6.89), and less trust in healthcare authorities/providers, family/friends, and employers, and more trust in websites/blogs/social media, for COVID-19 information (P-values < .05). Conclusions: The interplay among depressive symptoms, substance use, lack of trust in healthcare authorities, and negative impact of the pandemic on family, finances, and access to mental health treatment and food indicate the need for robust social and behavioral health safety nets to buffer communities from the shadow epidemics of depression, family violence, and overdose deaths during public health disasters.

Predicting opioid-induced oversedation in hospitalised patients: a multicentre observational study.

OBJECTIVES: Opioid-induced respiratory depression (OIRD) and oversedation are rare but potentially devastating adverse events in hospitalised patients. We investigated which features predict an individual patient's risk of OIRD or oversedation; and developed a risk stratification tool that can be used to aid point-of-care clinical decision-making. DESIGN: Retrospective observational study. SETTING: Twelve acute care hospitals in a large not-for-profit integrated delivery system. PARTICIPANTS: All inpatients ≥18 years admitted between 1 July 2016 and 30 June 2018 who received an opioid during their stay (163 190 unique hospitalisations). MAIN OUTCOME MEASURES: The primary outcome was occurrence of sedation or respiratory depression severe enough that emergent reversal with naloxone was required, as determined from medical record review; if naloxone reversal was unsuccessful or if there was no evidence of hypoxic encephalopathy or death due to oversedation, it was not considered an oversedation event. RESULTS: Age, sex, body mass index, chronic obstructive pulmonary disease, concurrent sedating medication, renal insufficiency, liver insufficiency, opioid naïvety, sleep apnoea and surgery were significantly associated with risk of oversedation. The strongest predictor was concurrent administration of another sedating medication (adjusted HR, 95% CI=3.88, 2.48 to 6.06); the most common such medications were benzodiazepines (29%), antidepressants (22%) and gamma-aminobutyric acid analogue (14.7%). The c-statistic for the final model was 0.755. The 24-point Oversedation Risk Criteria (ORC) score developed from the model stratifies patients as high (>20%, ≥21 points), moderate (11%-20%, 10-20 points) and low risk (≤10%, <10 points). CONCLUSIONS: The ORC risk score identifies patients at high risk for OIRD or oversedation from routinely collected data, enabling targeted monitoring for early detection and intervention. It can also be applied to preventive strategies-for example, clinical decision support offered when concurrent prescriptions for opioids and other sedating medications are entered that shows how the chosen combination impacts the patient's risk.