To measure the prevalence of cooking dinner at home in the USA and test whether home dinner preparation habits are associated with socio-economic status, race/ethnicity, country of birth and family ...structure.
Cross-sectional analysis. The primary outcome, self-reported frequency of cooking dinner at home, was divided into three categories: 0-1 dinners cooked per week ('never'), 2-5 ('sometimes') and 6-7 ('always'). We used bivariable and multivariable regression analyses to test for associations between frequency of cooking dinner at home and factors of interest.
The 2007-2008 National Health and Nutrition Examination Survey (NHANES).
The sample consisted of 10 149 participants.
Americans reported cooking an average of five dinners per week; 8 % never, 43 % sometimes and 49 % always cooked dinner at home. Lower household wealth and educational attainment were associated with a higher likelihood of either always or never cooking dinner at home, whereas wealthier, more educated households were more likely to sometimes cook dinner at home (P < 0·05). Black households cooked the fewest dinners at home (mean = 4·4, 95 % CI 4·2, 4·6). Households with foreign-born reference persons cooked more dinners at home (mean = 5·8, 95 % CI 5·7, 6·0) than households with US-born reference persons (mean = 4·9, 95 % CI 4·7, 5·1). Households with dependants cooked more dinners at home (mean = 5·2, 95 % CI 5·1, 5·4) than households without dependants (mean = 4·6, 95 % CI 4·3, 5·0).
Home dinner preparation habits varied substantially with socio-economic status and race/ethnicity, associations that likely will have implications for designing and appropriately tailoring interventions to improve home food preparation practices and promote healthy eating.
The aim of this policy statement is to provide a comprehensive review of the scientific evidence evaluating the use of telemedicine in cardiovascular and stroke care and to provide consensus policy ...suggestions. We evaluate the effectiveness of telehealth in advancing healthcare quality, identify legal and regulatory barriers that impede telehealth adoption or delivery, propose steps to overcome these barriers, and identify areas for future research to ensure that telehealth continues to enhance the quality of cardiovascular and stroke care. The result of these efforts is designed to promote telehealth models that ensure better patient access to high-quality cardiovascular and stroke care while striving for optimal protection of patient safety and privacy.
Medicaid managed care insurers play a crucial role in facilitating access to buprenorphine to treat opioid use disorder. Using a novel set of provider directory and prescription claims data, we ...examined variation in access to in-network buprenorphine-prescribing primary care providers among Medicaid managed care enrollees. Approximately 32.2 percent of enrollees had fewer than one in-network buprenorphine prescriber per 100,000 county residents. On average, there were a greater number of in-network buprenorphine-prescribing primary care providers in states with higher compared with lower overdose death rates. However, most enrollees lived in areas with a shortage of these providers. We found that a 25 percent higher network participation rate by prescribers compared with nonprescribers could improve the probability that enrollees see a prescriber by approximately 25 percent. Policies to improve access within Medicaid managed care include using primary care provider assignment algorithms to match patients with buprenorphine prescribers and requiring that networks include a minimum number of buprenorphine prescribers.
Medicare Advantage (MA) plans often establish restrictive networks of covered providers. Some policy makers have raised concerns that networks may have become excessively restrictive over time, ...potentially interfering with patients' access to providers. Because of data limitations, little is known about the breadth of MA networks. Taking a novel approach, we used Medicare Part D claims data for 2011-15 to examine how primary care physician networks have changed over time and what demographic and plan characteristics are associated with varying levels of network breadth. Our findings indicate that the share of MA plans with broad networks increased from 80.1 percent in 2011 to 82.5 percent in 2015. Enrollment in broad-network plans grew from 54.1 percent to 64.9 percent over the same period. In an adjusted analysis, we detected no significant time trend. In addition, narrow networks were associated with urbanicity, higher income, higher physician density, and more competition among plans. Health maintenance organizations had narrower networks than did point-of-service plans, whose networks were narrower than those of preferred provider organizations.
Objective
Limited consumer use of health care report cards may be due to the large amount of information presented in report cards, which can be difficult to understand. These limitations may be ...overcome with summary measures. Our objective was to evaluate consumer response to summary measures in the setting of nursing homes.
Data Sources/Study Setting
2005–2010 nursing home Minimum Data Set and Online Survey, Certification and Reporting (OSCAR) datasets.
Study Design
In December 2008, Medicare converted its nursing home report card to summary or star ratings. We test whether there was a change in consumer demand for nursing homes related to the nursing home's star rating after the information was released.
Principal Findings
The star rating system was associated with a significant change in consumer demand for low‐ and high‐scoring facilities. After the star‐based rating system was released, 1‐star facilities typically lost 8 percent of their market share and 5‐star facilities gained over 6 percent of their market share.
Conclusions
The nursing home star rating system significantly affected consumer demand for high‐ and low‐rated nursing homes. These results support the use of summary measures in report cards.
ObjectivesLoneliness is a major public health problem and an estimated 17% of adults aged 18–70 in the USA reported being lonely. We sought to characterise the (online) lives of people who mention ...the words ‘lonely’ or ‘alone’ in their Twitter timeline and correlate their posts with predictors of mental health.Setting and designFrom approximately 400 million tweets collected from Twitter in Pennsylvania, USA, between 2012 and 2016, we identified users whose Twitter posts contained the words ‘lonely’ or ‘alone’ and compared them to a control group matched by age, gender and period of posting. Using natural-language processing, we characterised the topics and diurnal patterns of users’ posts, their association with linguistic markers of mental health and if language can predict manifestations of loneliness. The statistical analysis, data synthesis and model creation were conducted in 2018–2019.Primary outcome measuresWe evaluated counts of language features in the users with posts including the words lonely or alone compared with the control group. These language features were measured by (a) open-vocabulary topics, (b) Linguistic Inquiry Word Count (LIWC) lexicon, (c) linguistic markers of anger, depression and anxiety, and (d) temporal patterns and number of drug words. Using machine learning, we also evaluated if expressions of loneliness can be predicted in users’ timelines, measured by area under curve (AUC).ResultsTwitter timelines of users (n=6202) with posts including the words lonely or alone were found to include themes about difficult interpersonal relationships, psychosomatic symptoms, substance use, wanting change, unhealthy eating and having troubles with sleep. Their posts were also associated with linguistic markers of anger, depression and anxiety. A random forest model predicted expressions of loneliness online with an AUC of 0.86.ConclusionsUsers’ Twitter timelines with the words lonely or alone often include psychosocial features and can potentially have associations with how individuals express and experience loneliness. This can inform low-resource online assessment for high-risk individuals experiencing loneliness and interventions focused on addressing morbidities in this condition.
The objective of this study was to provide national estimates of psychotropic medication use among Medicaid-enrolled children with autism spectrum disorders and to examine child and health system ...characteristics associated with psychotropic medication use.
This cross-sectional study used Medicaid claims for calendar year 2001 from all 50 states and Washington, DC, to examine 60,641 children with an autism spectrum disorder diagnosis. Logistic regression with random effects was used to examine the child, county, and state factors associated with psychotropic medication use.
Of the sample, 56% used at least 1 psychotropic medication, 20% of whom were prescribed > or = 3 medications concurrently. Use was common even in children aged 0 to 2 years (18%) and 3 to 5 years (32%). Neuroleptic drugs were the most common psychotropic class (31%), followed by antidepressants (25%) and stimulants (22%). In adjusted analyses, male, older, and white children; those who were in foster care or in the Medicaid disability category; those who received additional psychiatric diagnoses; and those who used more autism spectrum disorder services were more likely to have used psychotropic drugs. Children who had a diagnosis of autistic disorder or who lived in counties with a lower percentage of white residents or greater urban density were less likely to use such medications.
Psychotropic medication use is common among even very young children with autism spectrum disorders. Factors unrelated to clinical presentation seem highly associated with prescribing practices. Given the limited evidence base, there is an urgent need to assess the risks, benefits, and costs of medication use and understand the local and national policies that affect medication use.
Hospital readmission prediction models often perform poorly, but most only use information collected until the time of hospital discharge. In this clinical trial, we randomly assigned 500 patients ...discharged from hospital to home to use either a smartphone or wearable device to collect and transmit remote patient monitoring (RPM) data on activity patterns after hospital discharge. Analyses were conducted at the patient-day level using discrete-time survival analysis. Each arm was split into training and testing folds. The training set used fivefold cross-validation and then final model results are from predictions on the test set. A standard model comprised data collected up to the time of discharge including demographics, comorbidities, hospital length of stay, and vitals prior to discharge. An enhanced model consisted of the standard model plus RPM data. Traditional parametric regression models (logit and lasso) were compared to nonparametric machine learning approaches (random forest, gradient boosting, and ensemble). The main outcome was hospital readmission or death within 30 days of discharge. Prediction of 30-day hospital readmission significantly improved when including remotely-monitored patient data on activity patterns after hospital discharge and using nonparametric machine learning approaches. Wearables slightly outperformed smartphones but both had good prediction of 30-day hospital-readmission.
Background
Despite the importance of high-quality and patient-centered substance use disorder treatment, there are no standardized ratings of specialized drug treatment facilities and their services. ...Online platforms offer insights into potential drivers of high and low patient experience.
Objective
We sought to analyze publicly available online review content of specialized drug treatment facilities and identify themes within high and low ratings.
Design
This was a retrospective analysis of online ratings and reviews of specialized drug treatment facilities in Pennsylvania listed within the 2016 National Directory of Drug and Alcohol Abuse Treatment Facilities. Latent Dirichlet Allocation, a machine learning approach to narrative text, was used to identify themes within reviews. Differential Language Analysis was then used to measure correlations between themes and star ratings.
Setting
Online reviews of Pennsylvania’s specialized drug treatment facilities posted to Google and Yelp (July 2010–August 2018).
Results
A total of 7823 online ratings were posted over 8 years. The distribution was bimodal (43% 5-star and 34% 1-star). The average weighted rating of a facility was 3.3 stars. Online themes correlated with 5-star ratings were the following: focus on recovery (
r
= 0.53), helpfulness of staff (
r
= 0.43), compassionate care (
r
= 0.37), experienced a life-changing moment (
r
= 0.32), and staff professionalism (
r
= 0.29). Themes correlated with a 1-star rating were waiting time (
r
= 0.41), poor accommodations (0.26), poor phone communication (
r
= 0.24), medications given (0.24), and appointment availability (
r
= 0.23). Themes derived from review content were similar to 9 of the 14 facility-level services highlighted by the Substance Abuse and Mental Health Services Administration’s National Survey of Substance Abuse Treatment Services.
Conclusions
Individuals are sharing their ratings and reviews of specialized drug treatment facilities on online platforms. Organically derived reviews of the patient experience, captured by online platforms, reveal potential drivers of high and low ratings. These represent additional areas of focus which can inform patient-centered quality metrics for specialized drug treatment facilities.
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