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Providers currently rely on universal screening to identify health-related social needs (HRSNs). Predicting HRSNs using EHR and community-level data could be more efficient and less resource intensive. Using machine learning models, we evaluated the predictive performance of HRSN status from EHR and community-level social determinants of health (SDOH) data for Medicare and Medicaid beneficiaries participating in the Accountable Health Communities Model. We hypothesized that Medicaid insurance coverage would predict HRSN status. All models significantly outperformed the baseline Medicaid hypothesis. AUCs ranged from 0.59 to 0.68. The top performance (AUC = 0.68 CI 0.66–0.70) was achieved by the “any HRSNs” outcome, which is the most useful for screening prioritization. Community-level SDOH features had lower predictive performance than EHR features. Machine learning models can be used to prioritize patients for screening. However, screening only patients identified by our current model(s) would miss many patients. Future studies are warranted to optimize prediction of HRSNs.

Online weight loss programs are increasingly popular. However, little is known about outcomes and associations with website usage among members of free online weight loss programs. This retrospective cohort study examined the association between website usage and weight loss among members of a free commercial online weight loss program (SparkPeople). We conducted a retrospective analysis of a systematic random sample of members who joined the program during February 1 to April 30, 2008, and included follow-up data through May 10, 2010. The main outcome was net weight change based on self-reported weight. Measures of website usage included log-ins, self-monitoring entries (weight, food, exercise), and use of social support tools (discussion forums, friendships). The main sample included 1258 members with at least 2 weight entries. They were 90.7% female, with mean (SD) age 33.6 (11.0) and mean (SD) BMI 31.6 (7.7). Members with at least one forum post lost an additional 1.55 kg (95% CI 0.55 kg to 2.55 kg) relative to those with no forum posts. Having at least 4 log-in days, weight entry days, or food entry days per 30 days was significantly associated with weight loss. In the multiple regression analysis, members with at least 4 weight entry days per 30 days reported 5.09 kg (95% CI 3.29 kg to 6.88 kg) more weight loss per 30 days than those with fewer weight entry days. After controlling for weight entry days, the other website usage variables were not associated with weight change. Weekly or more frequent self-monitoring of weight is associated with greater weight loss among members of this free online weight loss program.

Background: Despite the high prevalence of blood pressure (BP) measurement errors in the outpatient setting, little is known about why primary care clinics struggle to achieve consistently accurate BP measurements in routine practice. We investigated barriers affecting measurement of BP for adult patients in primary care. Methods: We conducted a qualitative evaluation in 6 adult primary care clinics. BP measurement was observed during 54 routine patient encounters. Six managers completed semistructured interviews and 18 clinical staff members participated in focus group discussions. We used an inductive, data-driven approach to identify and organize findings into cohesive, overarching themes describing factors affecting BP measurement. Results: Observed errors in BP measurement spanned the entire spectrum of steps required to obtain BP properly. Barriers to proper BP measurement were related to staff knowledge and behavior (inadequate knowledge, training, and feedback); workflow constraints (need to multitask, inadequate time); and equipment issues (BP monitors, seating). Patient characteristics and behavior also affected BP measurement. Conclusions: Correct measurement of BP is affected by a wide range of factors and is challenging to accomplish consistently in primary care. These findings may inform the design of performance improvement programs to maximize the quality of BP measurement in the outpatient setting.

Background and objective: Understanding the mental health impact of the COVID-19 pandemic on persons receiving COVID-19 testing will help guide mental health interventions. We aimed to determine the association between sociodemographic factors and mental health symptoms at 8 weeks (baseline) after a COVID-19 test, and compare prevalence of mental health symptoms at baseline to those at 16-week follow-up. Materials and Methods: Prospective cohort study of adults who received outpatient COVID-19 testing at primary care clinics. Logistic regression analyses were used to assess the association between sociodemographic characteristics and COVID-19 test results with mental health symptoms. Mental health symptoms reported at baseline were compared to symptoms at 16 weeks follow-up using conditional logistic regression analyses. Results: At baseline, a total of 124 (47.51%) participants reported at least mild depressive symptoms, 110 (42.15%) participants endorsed at least mild anxiety symptoms, and 94 participants (35.21%) endorsed hazardous use of alcohol. Females compared to males were at increased risk of at least mild depressive symptoms at baseline (Adjusted Odds Ratio (AOR): 2.08; 95% CI: 1.14-3.79). The odds of at least mild depressive symptoms was significantly lower among those residing in zip codes within the highest quartile compared to lowest quartile of household income (AOR: 0.37; 95% CI: 0.17-0.81). Also, non-Hispanic Whites had significantly higher odds of reporting hazardous alcohol use compared to non-Whites at baseline (AOR: 1.94; 95% CI: 1.05-3.57). The prevalence of mental health symptoms remained elevated after 16 weeks. Conclusion and Relevance: We found a high burden of symptoms of depression and anxiety as well as hazardous alcohol use in a diverse population who received testing for COVID-19 in the primary care setting. Primary care providers need to remain vigilant in screening for symptoms of mental health disorders in patients tested for COVID-19 well after initial testing.

Background: Guidelines recommend that physicians screen all adults for obesity and offer an intensive counseling and behavioral interventions for weight loss for obese adults. Current trends of weight-related counseling are unknown in the setting of the US obesity epidemic. Objectives: To describe primary care physician (PCP) weight-related counseling, comparing counseling rates in 1995-1996 and 2007-2008. Research Design: Data analysis of outpatient PCP visits in 1995-1996 and 2007-2008, as reported in the National Ambulatory Medical Care Survey. Subjects: A total of 32,519 adult primary care visits with PCPs. Measures: Rates of counseling for weight, diet, exercise, and a composite variable, weight-related counseling (defined as counseling for weight, diet, or exercise) between survey years. Adjusted analyses controlled for patient and visit characteristics. Results: Weight counseling declined from 7.8% of visits in 1995-1996 to 6.2% of visits in 2007-2008 [adjusted odds ratios, 0.64; 95% confidence intervals, 0.53, 0.79]. Rates of receipt of diet, exercise, and weight-related counseling similarly declined. Greater declines in odds of weight-counseling receipt were observed among those with hypertension (47%), diabetes (59%), and obesity (41%), patients who stand the most to gain from losing weight. Conclusions: Rates of weight counseling in primary care have significantly declined despite increased rates of overweight and obesity in the United States. Further, these declines are even more marked in patients with obesity and weight-related comorbidities, despite expectations to provide such care by both patients and policymakers. These findings have implications for determining deliverable, novel ways to engage PCPs in addressing the obesity epidemic.

Publically available internet programs or cell phone apps, such as SparkPeople and Fitbit, show promise for weight loss in African American women with breast cancer. Abstract Weight management after breast cancer (BC) treatment in African American (AA) women is crucial to reduce comorbid conditions and health disparities. We examined feasibility and potential efficacy of commercial eHealth/mHealth tools for weight management in AA BC survivors in New Jersey. Participants (N = 35) were randomized to an intervention (SparkPeople) plus activity tracker, Fitbit Charge (n = 18), or wait-list active control group (Fitbit only, n = 17). Anthropometric, behavioral, and quality of life (QOL) outcomes were collected at baseline, 3, 6, and 12 months. Differences in outcomes were assessed using intent-to-treat analysis. Retention was 97.1%. Both groups lost weight, with no significant differences between groups. At month 6, mean weight change was: intervention: −1.71 kg (SD 2.33; p = .006), 33.3% lost ≥3% of baseline weight; control: −2.54 kg (SD 4.00, p = .002), 23.5% lost ≥3% weight. Intervention participants achieved significant improvements in waist circumference (−3.56 cm, SD 4.70, p = .005), QOL (p = .030), and use of strategies for healthy eating (p = .025) and decreasing calories (p < .001). Number of days logged food per week was associated with decreases in waist circumference at 6 months (β −0.79, 95% CI, −1.49, −0.09, p = .030) and 12 months (β −2.16, 95% CI, −4.17, −0.15, p = .038). Weight loss was maintained at 12 months. This is the first study to demonstrate potential efficacy of commercial eHealth/mHealth tools for weight loss in AA BC survivors, without additional counseling from the research team. If effective, they may be convenient weight loss tools that can be easily and widely disseminated. Clinical Trials registration: ClinicalTrials.gov NCT02699983

Background After-hours out-of-hospital phone consultations require physicians to make decisions based on information provided by a nurse over the phone. Methods We conducted a simulation study to evaluate physicians’ actions following communication of key information. 22 nurses were asked to call physicians with six cases based on the six most common reasons for after-hours phone calls. We evaluated physicians’ actions following the communication of key clinical information: A situation cue described a patient's problem (eg, confusion). A background cue described a specific clinical finding regarding the cause of the problem (eg, patient's sodium is low). For each cue we defined a list of indicators, based on the medical literature, to ascertain whether physicians acted upon the provided information (which was defined as addressing at least one of the indicators). Results A total of 108 phone consultations (containing 88 situation and 93 background cues) were analysed. Situation cues were communicated in 90% (79/88) of the calls and background cues in 33% (31/93). Physician acted upon the provided information in 57% (45/79) and 48% (15/31) of the communicated situation and background cues, respectively. When the background cues were not communicated, physicians asked questions expected to elicit the cue in 12% of the cases. Responding to the situation cue was associated with longer conversations and active inquiry by the physician. Conclusions After-hours phone calls are error prone. Both nurse communication and physician decision-making are problematic. Efforts to improve patient safety in this setting must address both communication and decision-making.

Integrating online weight-loss programs into the primary care setting could yield substantial public health benefit. Little is known about primary care providers' perspectives on online weight-loss programs. To assess primary care providers' perspectives on online weight-loss programs. We conducted focus group discussions with providers in family medicine, internal medicine, and combined internal medicine/pediatrics in Texas and Pennsylvania, USA. Open-ended questions addressed their experience with and attitudes toward online weight-loss programs; useful characteristics of existing online weight-loss programs; barriers to referring patients to online weight-loss programs; and preferred characteristics of an ideal online weight-loss program. Transcripts were analyzed with the grounded theory approach to identify major themes. A total of 44 primary care providers participated in 9 focus groups. The mean age was 45 (SD 9) years. Providers had limited experience with structured online weight-loss programs and were uncertain about their safety and efficacy. They thought motivated, younger patients would be more likely than others to respond to an online weight-loss program. According to primary care providers, an ideal online weight-loss program would provide-at no cost to the patient-a structured curriculum addressing motivation, psychological issues, and problem solving; tools for tracking diet, exercise, and weight loss; and peer support monitored by experts. Primary care providers were interested in receiving reports about patients from the online weight-loss programs, but were concerned about the time required to review and act on the reports. Primary care providers have high expectations for how online weight-loss programs should deliver services to patients and fit into the clinical workflow. Efforts to integrate online weight-loss programs into the primary care setting should address efficacy and safety of online weight-loss programs in clinic-based populations; acceptable methods of sending reports to primary care providers about their patients' progress; and elimination or reduction of costs to patients.

Background Our objective was to analyze subjective explanations for unsuccessful weight loss among bariatric surgery candidates. Methods This was a retrospective analysis of 909 bariatric surgery candidates (78.2% female, average body mass index [BMI] 47.3) at a university center from 2001 to April 2007 who answered an open-ended question about why they were unable to lose weight. We generated a coding scheme for answers to the question and established inter-rater reliability of the coding process. Associations with demographic parameters and initial BMI were tested. Results The most common categories of answers were nonspecific explanations related to diet (25.3%), physical activity (21.0%), or motivation (19.7%), followed by diet-related motivation (12.7%) and medical conditions or medications affecting physical activity (12.7%). Categories related to time, financial cost, social support, physical environment, and knowledge occurred in less than 4% each. Men were more likely than women to cite a medical condition or medication affecting physical activity (19.2% vs 10.8%, P  = 0.002, odds ratio [OR] = 1.96, 95% confidence interval [CI] = 1.28–2.99) but less likely to cite diet-related motivation (7.1% vs 14.2%, P  = 0.008, OR = 0.46, 95% CI = 0.26–0.82). Conclusions Our findings suggest that addressing diet, physical activity, and motivation in a comprehensive approach would meet the stated needs of obese patients. Raising patient awareness of under-recognized barriers to weight loss, such as the physical environment and lack of social support, should also be considered. Lastly, anticipating gender-specific attributions may facilitate tailoring of interventions.

Background Some models based on clinical information have been reported to predict which patients have Coronavirus Disease-2019 (COVID-19) pneumonia but have failed so far to yield reliable results. We aimed to determine if physicians were able to accurately predict which patients, as described in clinical vignettes, had, or did not have this infection using their clinical acumen and epidemiological data. Methods Of 1177 patients under investigation for COVID-19 admitted, we selected 20 and presented them in a vignette form. We surveyed physicians from different levels of training (<5, and five or more years after graduation from medical school) and included non-medical participants as a control group. We asked all participants to predict the result of the PCR test for COVID-19. We measured the accuracy of responses as a whole, and at three stages of the pandemic associated with a growing incidence of COVID-19 in the community. We calculated the inter-rater reliability, sensitivity, and specificity of the clinical prediction as a whole and by pandemic stage.  Results Between June 8 and August 28, 2020, 82 doctors and 20 non-medical participants completed the survey. The accuracy was 58% (59% for doctors and 52% for non-medical, p=0.002). The lowest accuracy was noted for cases in the pandemic middle stage; years of post-graduate training represented no difference. Of the 2040 total answers, 1176 were accurate and 864 inaccurate (349 false positives and 515 false negatives). Conclusion The influence of symptomatic positivity, confirmation bias, and rapid expertise acquisition on accuracy is discussed, as the disease is new, time after graduation made no difference in the response accuracy. The limited clinical diagnostic capacity emphasizes the need for a reliable diagnostic test.