Objectives: This study examined the association between food insecurity status and healthcare access, utilization, and quality among adults aged 55 years and older. Methods: Data collected between 2011 and 2016 for the California Health Interview Survey were used. The sample included 72,212 individuals who were divided into three groups: food secure (FS), low food security (L-FS), and very low food security (VL-FS). Results: Logistic regression analyses controlled for demographics. Food insecurity was associated with decreased access to and quality of care and increased utilization. Specifically, VL-FS was more likely to delay care than FS. Additionally, VL-FS and L-FS had greater odds of visiting an emergency room than FS. Furthermore, VL-FS and L-FS were more likely to have a doctor who did not always explain aspects of care carefully compared to FS. Discussion: These findings suggest a need for increased screening for food insecurity in healthcare settings.

Background

Accompanying the rising rates of reported mental distress during the COVID-19 pandemic has been a reported increase in the use of digital technologies to manage health generally, and mental health more specifically.

Objective

The objective of this study was to systematically examine whether there was a COVID-19 pandemic-related increase in the self-reported use of digital mental health tools and other technologies to manage mental health.

Methods

We analyzed results from a survey of 5907 individuals in the United States using Amazon Mechanical Turk (MTurk); the survey was administered during 4 week-long periods in 2020 and survey respondents were from all 50 states and Washington DC. The first set of analyses employed two different logistic regression models to estimate the likelihood of having symptoms indicative of clinical depression and anxiety, respectively, as a function of the rate of COVID-19 cases per 10 people and survey time point. The second set employed seven different logistic regression models to estimate the likelihood of using seven different types of digital mental health tools and other technologies to manage one's mental health, as a function of symptoms indicative of clinical depression and anxiety, rate of COVID-19 cases per 10 people, and survey time point. These models also examined potential interactions between symptoms of clinical depression and anxiety, respectively, and rate of COVID-19 cases. All models controlled for respondent sociodemographic characteristics and state fixed effects.

Results

Higher COVID-19 case rates were associated with a significantly greater likelihood of reporting symptoms of depression (odds ratio [OR] 2.06, 95% CI 1.27-3.35), but not anxiety (OR 1.21, 95% CI 0.77-1.88). Survey time point, a proxy for time, was associated with a greater likelihood of reporting clinically meaningful symptoms of depression and anxiety (OR 1.19, 95% CI 1.12-1.27 and OR 1.12, 95% CI 1.05-1.19, respectively). Reported symptoms of depression and anxiety were associated with a greater likelihood of using each type of technology. Higher COVID-19 case rates were associated with a significantly greater likelihood of using mental health forums, websites, or apps (OR 2.70, 95% CI 1.49-4.88), and other health forums, websites, or apps (OR 2.60, 95% CI 1.55-4.34). Time was associated with increased odds of reported use of mental health forums, websites, or apps (OR 1.20, 95% CI 1.11-1.30), phone-based or text-based crisis lines (OR 1.20, 95% CI 1.10-1.31), and online, computer, or console gaming/video gaming (OR 1.12, 95% CI 1.05-1.19). Interactions between COVID-19 case rate and mental health symptoms were not significantly associated with any of the technology types.

Conclusions

Findings suggested increased use of digital mental health tools and other technologies over time during the early stages of the COVID-19 pandemic. As such, additional effort is urgently needed to consider the quality of these products, either by ensuring users have access to evidence-based and evidence-informed technologies and/or by providing them with the skills to make informed decisions around their potential efficacy.

BACKGROUND: Diabetes-related multi-morbidity and cultural factors place Latinas with diabetes at increased risk for stress, which can threaten illness management. Families provide an ideal focus for interventions that seek to strengthen interpersonal resources for illness management and, in the process, to reduce stress. The current study sought to examine whether participating in a dyadic intervention was associated with reduced perceived stress and, furthermore, whether this association was mediated by persuasion and pressure, two forms of health-related social control. METHOD: Latina mothers with diabetes and their at-risk adult daughters participated in either (1) a dyadic intervention that encouraged constructive collaboration to improve health behaviors and reduce stress, or (2) a usual-care minimal control condition. Actor-partner interdependence model analysis was used to estimate the effect of the intervention on dyads perceived stress, and mother-daughter ratings of health-related social control as potential mediators. RESULTS: Results revealed that participating in the intervention was associated with significantly reduced perceived stress for daughters, but not for mothers (β =  - 3.00, p = 0.02; β =  - 0.57, p = 0.67, respectively). Analyses also indicated that the association between the intervention and perceived stress was mediated by persuasion, such that mothers who experienced more health-related persuasion exhibited significantly less post-intervention perceived stress (indirect effect =  - 1.52, 95% CI = [- 3.12, - 0.39]). Pressure exerted by others, however, did not evidence a mediating mechanism for either mothers or daughters. CONCLUSION: These findings buttress existing research suggesting that persuasion, or others attempts to increase participants healthy behaviors in an uncritical way, may be a driving force in reducing perceived stress levels.

Purpose

The average delay from first seizure to diagnosis of psychogenic non-epileptic seizures (PNES) is over 7 years. The reason for this delay is not well understood. We hypothesized that a perceived decrease in seizure frequency after starting an anti-seizure medication (ASM) may contribute to longer delays, but the frequency of such a response has not been well established.

Methods

Time from onset to diagnosis, medication history and associated seizure frequency was acquired from the medical records of 297 consecutive patients with PNES diagnosed using video-electroencephalographic monitoring. Exponential regression was used to model the effect of medication trials and response on diagnostic delay.

Results

Mean diagnostic delay was 8.4 years (min 1 day, max 52 years). The robust average diagnostic delay was 2.8 years (95% CI: 2.2-3.5 years) based on an exponential model as 10 to the mean of log10 delay. Each ASM trial increased the robust average delay exponentially by at least one third of a year (Wald t=3.6, p=0.004). Response to ASM trials did not significantly change diagnostic delay (Wald t=-0.9, p=0.38).

Conclusion

Although a response to ASMs was observed commonly in these patients with PNES, the presence of a response was not associated with longer time until definitive diagnosis. Instead, the number of ASMs tried was associated with a longer delay until diagnosis, suggesting that ASM trials were continued despite lack of response. These data support the guideline that patients with seizures should be referred to epilepsy care centers after failure of two medication trials.

Background and objectives

Although moderate and severe traumatic brain injury (TBI) can cause posttraumatic epilepsy (PTE), many patients with functional seizures (FS) also report a history of mild TBI. To determine whether features of TBI history differ between patients with epileptic seizures (ES) and FS, we compared patient reports of TBI severity, symptoms, and causes of injury.

Methods

We recruited patients undergoing video-EEG evaluation for the diagnosis of ES, FS, mixed ES and FS, or physiologic seizure-like events at an academic, tertiary referral center. Patients and their caregivers were interviewed before final video-EEG diagnosis regarding their TBI histories, including concussive symptoms and causes of injury.

Results

Of 506 patients, a greater percentage of patients with FS reported a history of TBI than patients with ES (70% vs 59%, aOR = 1.75 [95% CI: 1.00-3.05], p = 0.047). TBI with loss of consciousness (LOC) lasting less than 30 minutes was more frequently reported among patients with FS than with ES (27% vs 13%, aOR = 2.38 [1.26-4.47], p < 0.01). The proportion of patients reporting other neurologic symptoms immediately after TBI was not significantly different between FS and ES (40% vs 29%, p = 0.08). Causes of TBI were found to differ, with TBIs caused by falls from a height (17% vs 10%, aOR = 2.24 [1.06-4.70], p = 0.03) or motor vehicle collisions (27% vs 11%, aOR = 2.96 [1.54-5.67], p < 0.01) reported more frequently in FS than ES.

Discussion

Our findings further the association of mild TBI with FS and prompt reconsideration of typical assumptions regarding the significance of a reported TBI history in patients with previously undifferentiated seizures. Although common in both groups, TBI with LOC less than 30 minutes and causes of injury that are commonly believed to be more severe were reported more frequently in FS than ES. This suggests that a patient or caregiver reporting of these features does not imply that PTE is a more probable diagnosis than FS. Although a history of TBI with LOC and presumed high-risk causes of injury intuitively raises suspicion for PTE, clinicians should be cautioned that these historical factors also were a frequent finding in patients with FS.

Purpose

Descriptions of seizure manifestations (SM), or semiology, can help localize the symptomatogenic zone and subsequently included brain regions involved in epileptic seizures, as well as identify patients with dissociative seizures (DS). Patients and witnesses are not trained observers, so these descriptions may vary from expert review of seizure video recordings of seizures. To better understand how reported factors can help identify patients with DS or epileptic seizures (ES), we evaluated the associations between more than 30 SMs and diagnosis using standardized interviews.

Methods

Based on patient- and observer-reported data from 490 patients with diagnoses documented by video-electoencephalography, we compared the rate of each SM in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic seizure-like events (PSLE), mixed DS and ES, and inconclusive testing.

Results

In addition to SMs that we described in a prior manuscript, the following were associated with DS: light triggers, emotional stress trigger, pre-ictal and post-ictal headache, post-ictal muscle soreness, and ictal sensory symptoms. The following were associated with ES: triggered by missing medication, aura of déjà vu, and leftward eye deviation. There were numerous manifestations separately associated with mixed ES and DS.

Conclusions

Reported SM can help identify patients with DS, but no manifestation is pathognomonic for either ES or DS. Patients with mixed ES and DS reported factors divergent from both ES-alone and DS-alone.

The definitive diagnosis of the type of epilepsy, if it exists, in medication-resistant seizure disorder is based on the efficient combination of clinical information, long-term video-electroencephalography (EEG) and neuroimaging. Diagnoses are reached by a consensus panel that combines these diverse modalities using clinical wisdom and experience. Here we compare two methods of multimodal computer-aided diagnosis, vector concatenation (VC) and conditional dependence (CD), using clinical archive data from 645 patients with medication-resistant seizure disorder, confirmed by video-EEG. CD models the clinical decision process, whereas VC allows for statistical modeling of cross-modality interactions. Due to the nature of clinical data, not all information was available in all patients. To overcome this, we multiply-imputed the missing data. Using a C4.5 decision tree, single modality classifiers achieved 53.1%, 51.5% and 51.1% average accuracy for MRI, clinical information and FDG-PET, respectively, for the discrimination between non-epileptic seizures, temporal lobe epilepsy, other focal epilepsies and generalized-onset epilepsy (vs. chance, p<0.01). Using VC, the average accuracy was significantly lower (39.2%). In contrast, the CD classifier that classified with MRI then clinical information achieved an average accuracy of 58.7% (vs. VC, p<0.01). The decrease in accuracy of VC compared to the MRI classifier illustrates how the addition of more informative features does not improve performance monotonically. The superiority of conditional dependence over vector concatenation suggests that the structure imposed by conditional dependence improved our ability to model the underlying diagnostic trends in the multimodality data.

Objective

Early and accurate diagnosis of patients with psychogenic nonepileptic seizures (PNES) leads to appropriate treatment and improves long-term seizure prognosis. However, this is complicated by the need to record seizures to make a definitive diagnosis. Suspicion for PNES can be raised through knowledge that patients with PNES have increased somatic sensitivity and report more positive complaints on review-of-systems questionnaires (RoSQs) than patients with epileptic seizures. If the responses on the RoSQ can differentiate PNES from other seizure types, then these forms could be an early screening tool.

Methods

Our dataset included all patients admitted from January 2006 to June 2016 for video-electroencephalography at UCLA. RoSQs prior to May 2015 were acquired through retrospective chart review (n=405), whereas RoSQs from subsequent patients were acquired prospectively (n=190). Controlling for sex and number of comorbidities, we used binomial regression to compare the total number of symptoms and the frequency of specific symptoms between five mutually exclusive groups of patients: epileptic seizures (ES), PNES, physiologic nonepileptic seizure-like events (PSLE), mixed PNES plus ES, and inconclusive monitoring. To determine the diagnostic utility of RoSQs to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and the number of medical comorbidities.

Results

On average, patients with PNES or mixed PNES and ES reported more than twice as many symptoms than patients with isolated ES or PSLE (p<0.001). The prospective accuracy to differentiate PNES from ES was not significantly higher than naïve assumption that all patients had ES (76% vs 70%, p>0.1).

Discussion

This analysis of RoSQs confirms that patients with PNES with and without comorbid ES report more symptoms on a population level than patients with epilepsy or PSLE. While these differences help describe the population of patients with PNES, the consistency of RoSQ responses was neither accurate nor specific enough to be used solely as an early screening tool for PNES. Our results suggest that the RoSQ may help differentiate PNES from ES only when, based on other information, the pre-test probability of PNES is at least 50%.

Objective

Low-cost evidence-based tools are needed to facilitate the early identification of patients with possible psychogenic nonepileptic seizures (PNES). Prior to accurate diagnosis, patients with PNES do not receive interventions that address the cause of their seizures and therefore incur high medical costs and disability due to an uncontrolled seizure disorder. Both seizures and comorbidities may contribute to this high cost.

Methods

Based on data from 1,365 adult patients with video-electroencephalography-confirmed diagnoses from a single center, we used logistic and Poisson regression to compare the total number of comorbidities, number of medications, and presence of specific comorbidities in five mutually exclusive groups of diagnoses: epileptic seizures (ES) only, PNES only, mixed PNES and ES, physiologic nonepileptic seizurelike events, and inconclusive monitoring. To determine the diagnostic utility of comorbid diagnoses and medication history to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and age, trained using a retrospective database and validated using a prospective database.

Results

Our model differentiated PNES only from ES only with a prospective accuracy of 78% (95% confidence interval =72-84%) and area under the curve of 79%. With a few exceptions, the number of comorbidities and medications was more predictive than a specific comorbidity. Comorbidities associated with PNES were asthma, chronic pain, and migraines (p < 0.01). Comorbidities associated with ES were diabetes mellitus and nonmetastatic neoplasm (p < 0.01). The population-level analysis suggested that patients with mixed PNES and ES may be a population distinct from patients with either condition alone.

Significance

An accurate patient-reported medical history and medication history can be useful when screening for possible PNES. Our prospectively validated and objective score may assist in the interpretation of the medication and medical history in the context of the seizure description and history.

OBJECTIVE:Psychogenic nonepileptic seizure (PNES) is a common diagnosis after evaluation of medication resistant or atypical seizures with video-electroencephalographic monitoring (VEM), but usually follows a long delay after the development of seizures, during which patients are treated for epilepsy. Therefore, more readily available diagnostic tools are needed for earlier identification of patients at risk for PNES. A tool based on patient-reported psychosocial history would be especially beneficial because it could be implemented in the outpatient clinic. METHODS:Based on the data from 1375 patients with VEM-confirmed diagnoses, we used logistic regression to compare the frequency of specific patient-reported historical events, demographic information, age of onset, and delay from first seizure until VEM in five mutually exclusive groups of patients: epileptic seizures (ES), PNES, physiologic nonepileptic seizure-like events (PSLE), mixed PNES plus ES, and inconclusive monitoring. To determine the diagnostic utility of this information to differentiate PNES only from ES only, we used multivariate piecewise-linear logistic regression trained using retrospective data from chart review and validated based on data from 246 prospective standardized interviews. RESULTS:The prospective area under the curve of our weighted multivariate piecewise-linear by-sex score was 73%, with the threshold that maximized overall retrospective accuracy resulting in a prospective sensitivity of 74% (95% CI: 70-79%) and prospective specificity of 71% (95% CI: 64-82%). The linear model and piecewise linear without an interaction term for sex had very similar performance statistics. In the multivariate piecewise-linear sex-split predictive model, the significant factors positively associated with ES were history of febrile seizures, current employment or active student status, history of traumatic brain injury (TBI), and longer delay from first seizure until VEM. The significant factors associated with PNES were female sex, older age of onset, mild TBI, and significant stressful events with sexual abuse, in particular, increasing the likelihood of PNES. Delays longer than 20years, age of onset after 31years for men, and age of onset after 40years for women had no additional effect on the likelihood of PNES. DISCUSSION:Our promising results suggest that an objective score has the potential to serve as an early outpatient screening tool to identify patients with greater likelihood of PNES when considered in combination with other factors. In addition, our analysis suggests that sexual abuse, more than other psychological stressors including physical abuse, is more associated with PNES. There was a trend of increasing frequency of PNES for women during childbearing years and plateauing outside those years that was not observed in men.