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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.

Objectives. To determine if blight remediation of abandoned buildings and vacant lots can be a cost-beneficial solution to firearm violence in US cities. Methods. We performed quasi-experimental analyses of the impacts and economic returns on investment of urban blight remediation programs involving 5112 abandoned buildings and vacant lots on the occurrence of firearm and nonfirearm violence in Philadelphia, Pennsylvania, from 1999 to 2013. We adjusted before–after percent changes and returns on investment in treated versus control groups for sociodemographic factors. Results. Abandoned building remediation significantly reduced firearm violence −39% (95% confidence interval [CI] = −28%, −50%; P < .05) as did vacant lot remediation (−4.6%; 95% CI = −4.2%, −5.0%; P < .001). Neither program significantly affected nonfirearm violence. Respectively, taxpayer and societal returns on investment for the prevention of firearm violence were$5 and $ 79 for every dollar spent on abandoned building remediation and$26 and $ 333 for every dollar spent on vacant lot remediation. Conclusions. Abandoned buildings and vacant lots are blighted structures seen daily by urban residents that may create physical opportunities for violence by sheltering illegal activity and illegal firearms. Urban blight remediation programs can be cost-beneficial strategies that significantly and sustainably reduce firearm violence.

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.

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.

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.

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.

Ambient AI “digital scribes” are rapidly moving into routine practice, easing documentation burden and physician burnout. Early evidence suggests these tools can increase billing and risk-adjustment coding intensity, prompting payer responses such as downcoding and risk-score recalibration. This Policy Brief contrasts their implications in fee-for-service and Medicare Advantage models, notes relevance for systems blending encounter-based and capitated payment, and outlines steps to preserve value without fueling a coding arms race.

BackgroundDespite 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.ObjectiveWe sought to analyze publicly available online review content of specialized drug treatment facilities and identify themes within high and low ratings.DesignThis 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.SettingOnline reviews of Pennsylvania’s specialized drug treatment facilities posted to Google and Yelp (July 2010–August 2018).ResultsA 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.ConclusionsIndividuals 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.

Acute kidney injury (AKI) is one of the most common complications after noncardiac surgery. Yet current postoperative AKI risk stratification models have substantial limitations, such as limited use of perioperative data. To examine whether adding preoperative and intraoperative data is associated with improved prediction of noncardiac postoperative AKI. A prognostic study using logistic regression with elastic net selection, gradient boosting machine (GBM), and random forest approaches was conducted at 4 tertiary academic hospitals in the United States. A total of 42 615 hospitalized adults with serum creatinine measurements who underwent major noncardiac surgery between January 1, 2014, and April 30, 2018, were included in the study. Serum creatinine measurements from 365 days before and 7 days after surgery were used in this study. Postoperative AKI (defined by the Kidney Disease Improving Global Outcomes within 7 days after surgery) was the primary outcome. The area under the receiver operating characteristic curve (AUC) was used to assess discrimination. Among 42 615 patients who underwent noncardiac surgery, the mean (SD) age was 57.9 (15.7) years, 23 943 (56.2%) were women, 27 857 (65.4%) were white, and the most frequent surgery types were orthopedic (15 718 [36.9%]), general (8808 [20.7%]), and neurologic (6564 [15.4%]). The rate of postoperative AKI was 10.1% (n = 4318). The progressive addition of clinical data improved model performance across all modeling approaches, with GBM providing the highest discrimination by AUC. In GBM models, the AUC increased from 0.712 (95% CI, 0.694-0.731) using prehospitalization variables to 0.804 (95% CI, 0.788-0.819) using preoperative variables (inclusive of prehospitalization variables) (P < .001 for AUC comparison). The AUC further increased to 0.817 (95% CI, 0.802-0.832) when adding intraoperative variables (P < .001 for comparison vs model using preoperative variables). However, the statistically significant improvements in discrimination did not appear to be clinically significant. In particular, the AKI rate among patients classified as high risk improved from 29.1% to 30.0%, a net of 15 patients were appropriately reclassified as high risk, and an additional 15 patients were appropriately reclassified as low risk. The findings of the study suggest that electronic health record data may be used to accurately stratify patients at risk of perioperative AKI, but the modest improvements from adding intraoperative data should be weighed against challenges in using intraoperative data.

The design of the Affordable Care Act's online health insurance Marketplaces can improve how consumers make complex health plan choices. We examined the choice environment on the state-based Marketplaces and HealthCare.gov in the third open enrollment period. Compared to previous enrollment periods, we found greater adoption of some decision support tools, such as total cost estimators and integrated provider lookups. Total cost estimators differed in how they generated estimates: In some Marketplaces, consumers categorized their own utilization, while in others, consumers answered detailed questions and were assigned a utilization profile. The tools available before creating an account (in the window-shopping period) and afterward (in the real-shopping period) differed in several Marketplaces. For example, five Marketplaces provided total cost estimators to window shoppers, but only two provided them to real shoppers. Further research is needed on the impact of different choice environments and on which tools are most effective in helping consumers pick optimal plans.