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BackgroundCigarette smoking is a risk factor for severe COVID-19 disease. Understanding smokers’ responses to the pandemic will help assess its public health impact and inform future public health and provider messages to smokers.ObjectiveTo assess risk perceptions and change in tobacco use among current and former smokers during the COVID-19 pandemic.DesignCross-sectional survey conducted in May–July 2020 (55% response rate)Participants694 current and former daily smokers (mean age 53, 40% male, 78% white) who had been hospitalized pre-COVID-19 and enrolled into a smoking cessation clinical trial at hospitals in Massachusetts, Pennsylvania, and Tennessee.Main MeasuresPerceived risk of COVID-19 due to tobacco use; changes in tobacco consumption and interest in quitting tobacco use; self-reported quitting and relapse since January 2020.Key Results68% (95% CI, 65–72%) of respondents believed that smoking increases the risk of contracting COVID-19 or having a more severe case. In adjusted analyses, perceived risk was higher in Massachusetts where COVID-19 had already surged than in Pennsylvania and Tennessee which were pre-surge during survey administration (AOR 1.56, 95% CI, 1.07–2.28). Higher perceived COVID-19 risk was associated with increased interest in quitting smoking (AOR 1.72, 95% CI 1.01–2.92). During the pandemic, 32% (95% CI, 27–37%) of smokers increased, 37% (95% CI, 33–42%) decreased, and 31% (95% CI, 26–35%) did not change their cigarette consumption. Increased smoking was associated with higher perceived stress (AOR 1.49, 95% CI 1.16–1.91). Overall, 11% (95% CI, 8–14%) of respondents who smoked in January 2020 (pre-COVID-19) had quit smoking at survey (mean, 6 months later) while 28% (95% CI, 22–34%) of former smokers relapsed. Higher perceived COVID-19 risk was associated with higher odds of quitting and lower odds of relapse.ConclusionsMost smokers believed that smoking increased COVID-19 risk. Smokers’ responses to the pandemic varied, with increased smoking related to stress and increased quitting associated with perceived COVID-19 vulnerability.

This national survey finds that only 4% of physicians use an extensive, fully functional system for electronic health records, and 13% use some form of basic electronic records. Those who use electronic records are generally satisfied with the systems and believe that they improve the quality of care that patients receive. Only 4% of physicians use an extensive, fully functional system for electronic health records, and 13% use some form of basic electronic records. Those who use electronic records believe that they improve the quality of care that patients receive. Health-information technology, such as sophisticated electronic health records, has the potential to improve health care. 1 – 3 Nevertheless, electronic-records systems have been slow to become part of the practices of physicians in the United States. 4 , 5 To date, there have been no definitive national studies that provide reliable estimates of the adoption of electronic health records by U.S. physicians. Recent estimates of such adoption by physicians range from 9 to 29%. 4 , 5 These percentages were derived from studies that either had a small number of respondents or incompletely specified definitions of an electronic health record. 5 , 6 To provide clearer estimates of . . .

The influence of genetic risk for obesity on food choice behaviors is unknown and may be in the causal pathway between genetic risk and weight gain. The aim of this study was to examine associations between genetic risk for obesity and food choice behaviors using objectively assessed workplace food purchases. This study is a secondary analysis of baseline data collected prior to the start of the \"ChooseWell 365\" health-promotion intervention randomized control trial. Participants were employees of a large hospital in Boston, MA, who enrolled in the study between September 2016 and February 2018. Cafeteria sales data, collected retrospectively for 3 months prior to enrollment, were used to track the quantity (number of items per 3 months) and timing (median time of day) of purchases, and participant surveys provided self-reported behaviors, including skipping meals and preparing meals at home. A previously validated Healthy Purchasing Score was calculated using the cafeteria traffic-light labeling system (i.e., green = healthy, yellow = less healthy, red = unhealthy) to estimate the healthfulness (quality) of employees' purchases (range, 0%-100% healthy). DNA was extracted and genotyped from blood samples. A body mass index (BMI) genome-wide polygenic score (BMIGPS) was generated by summing BMI-increasing risk alleles across the genome. Additionally, 3 polygenic risk scores (PRSs) were generated with 97 BMI variants previously identified at the genome-wide significance level (P < 5 × 10-8): (1) BMI97 (97 loci), (2) BMICNS (54 loci near genes related to central nervous system [CNS]), and (3) BMInon-CNS (43 loci not related to CNS). Multivariable linear and logistic regression tested associations of genetic risk score quartiles with workplace purchases, adjusted for age, sex, seasonality, and population structure. Associations were considered significant at P < 0.05. In 397 participants, mean age was 44.9 years, and 80.9% were female. Higher genetic risk scores were associated with higher BMI. The highest quartile of BMIGPS was associated with lower Healthy Purchasing Score (-4.8 percentage points [95% CI -8.6 to -1.0]; P = 0.02), higher quantity of food purchases (14.4 more items [95% CI -0.1 to 29.0]; P = 0.03), later time of breakfast purchases (15.0 minutes later [95% CI 1.5-28.5]; P = 0.03), and lower likelihood of preparing dinner at home (Q4 odds ratio [OR] = 0.3 [95% CI 0.1-0.9]; P = 0.03) relative to the lowest BMIGPS quartile. Compared with the lowest quartile, the highest BMICNS quartile was associated with fewer items purchased (P = 0.04), and the highest BMInon-CNS quartile was associated with purchasing breakfast at a later time (P = 0.01), skipping breakfast (P = 0.03), and not preparing breakfast (P = 0.04) or lunch (P = 0.01) at home. A limitation of this study is our data come from a relatively small sample of healthy working adults of European ancestry who volunteered to enroll in a health-promotion study, which may limit generalizability. In this study, genetic risk for obesity was associated with the quality, quantity, and timing of objectively measured workplace food purchases. These findings suggest that genetic risk for obesity may influence eating behaviors that contribute to weight and could be targeted in personalized workplace wellness programs in the future. Clinicaltrials.gov NCT02660086.

To conduct a pilot study to determine if improving the visibility and quality of fresh produce (choice architecture) in corner stores would increase fruit/vegetable purchases by families participating in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Six stores were randomly assigned to choice architecture intervention or control. Store-level WIC sales data were provided by the state. Primary outcomes were WIC fruit/vegetable voucher and non-fruit/vegetable voucher sales, comparing trends from baseline (December 2012-October 2013) with the five-month intervention period (December 2013-April 2014). Secondary outcomes were differences in customer self-reported fruit/vegetable purchases between baseline and end of the intervention. Chelsea, MA, USA, a low-income urban community. Adult customers (n 575) completing store exit interviews. During baseline, WIC fruit/vegetable and non-fruit/vegetable sales decreased in both intervention and control stores by $US 16/month. During the intervention period, WIC fruit/vegetable sales increased in intervention stores by $US 40/month but decreased in control stores by $US 23/month (difference in trends: $US 63/month; 95 % CI 4, 121 $US/month; P=0·036); WIC non-fruit/vegetable sales were not different (P=0·45). Comparing baseline and intervention-period exit interview responses by customers participating in WIC (n 134), intervention store customers reported increased fruit/vegetable purchases compared with control store customers (18 v. -2 %), but this did not achieve statistical significance (P=0·11). Placement of fruits/vegetables near the front of corner stores increased purchase of produce by customers using WIC. New policies that incentivize stores to stock and prominently display good-quality produce could promote healthier food choices of low-income families.

Introduction People with low socioeconomic status (SES) or serious psychological distress (SPD) in the U.S. face ongoing and future disparities in tobacco smoking. We sought to estimate how smoking disparities contribute to disparities in life expectancy and aggregate life-years in these marginalized subpopulations. Methods We used the Simulation of Tobacco and Nicotine Outcomes and Policy (STOP) microsimulation model to project life expectancy as a function of subpopulation (low SES, higher SES, SPD, or non-SPD) and cigarette smoking status. Low SES was defined as having at least one of the following: income below poverty, less than high school education, or Medicaid insurance. Higher SES individuals belonged to none of these categories. SPD was defined as Kessler-6 score ≥ 13; non-SPD was a Kessler-6 score < 13. To project individual life expectancy losses from smoking, we simulated 40-year-olds stratified by gender, subpopulation (by SES or by SPD, with no change), and smoking status (current/never, with no change). To project time to reach 5% cigarette smoking prevalence (U.S.) – reflecting one tobacco “endgame” threshold – in each subpopulation, we simulated the entire subpopulations of people with low SES, higher SES, SPD, and non-SPD, incorporating corresponding distributions of gender, age, and smoking status and accounting for changes in smoking behaviors and secular smoking trends. We then estimated total life-years accumulated under status quo and alternate scenarios in which smoking dynamics in the marginalized subpopulations matched those of their less marginalized counterparts. Results The model showed that, for individuals with low SES or SPD, smoking is associated with substantial loss of life expectancy (9.8-11.5y). Marginalized subpopulations would reach 5% smoking prevalence 20y (low SES) and 17y (SPD) sooner if smoking trends mirrored their less marginalized counterparts; these differences result in 5.3 million (low SES) and 966,000 (SPD) excess life-years lost over 40y. Conclusions Differences in cigarette smoking portend substantial ongoing and future disparities in life expectancy and time to reach 5% smoking prevalence. Reducing tobacco-related disparities in the U.S. will require an explicitly equity-focused vision, and the tobacco endgame will only be truly achieved when it includes all groups.

Secondhand smoke remains a health concern for individuals living in multiunit housing, where smoke has been shown to easily transfer between units. Building-wide smoke-free policies are a logical step for minimizing smoke exposure in these settings. This evaluation sought to determine whether buildings with smoke-free policies have less secondhand smoke than similar buildings without such policies. Furthermore, this study assessed potential secondhand smoke transfer between apartments with and without resident smokers. Fine particulate matter (PM2.5), airborne nicotine, and self-reported smoking activity were recorded in 15 households with resident smokers and 17 households where no one smoked in 5 Boston Housing Authority developments. Of these, 4 apartment pairs were adjacent apartments with and without resident smokers. Halls between apartments and outdoor air were also monitored to capture potential smoke transfer and to provide background PM2.5 concentrations. Households within buildings with smoke-free policies showed lower PM2.5 concentrations compared to buildings without these policies (median: 4.8 vs 8.1 µg/m(3)). Although the greatest difference in PM2.5 between smoking-permitted and smoke-free buildings was observed in households with resident smokers (14.3 vs 7.0 µg/m(3)), households without resident smokers also showed a significant difference (5.1 vs 4.0 µg/m(3)). Secondhand smoke transfer to smoke-free apartments was demonstrable with directly adjacent households. This evaluation documented instances of secondhand smoke transfer between households as well as lower PM2.5 measurements in buildings with smoke-free policies. Building-wide smoke-free policies can limit secondhand smoke exposure for everyone living in multiunit housing.

Tobacco smoke exposure (TSE) in public multi-unit housing (MUH) is of concern. However, the validity of self-reports for determining TSE among non-smoking residents in such housing is unclear. We analyzed data from 285 non-smoking public MUH residents living in non-smoking households in the Boston area. Participants were interviewed about personal TSE in various locations in the past 7 days and completed a diary of home TSE for 7 days. Self-reported TSE was validated against measurable saliva cotinine (lower limit of detection (LOD) 0.02 ng/ml) and airborne apartment nicotine (LOD 5 ng). Correlations, estimates of inter-measure agreement, and logistic regression assessed associations between self-reported TSE items and measurable cotinine and nicotine. Cotinine and nicotine levels were low in this sample (median = 0.026 ng/ml and 0.022 μg/m(3), respectively). Prevalence of detectable personal TSE was 66.3% via self-report and 57.0% via measurable cotinine (median concentration among those with cotinine>LOD: 0.057 ng/ml), with poor agreement (kappa = 0.06; sensitivity = 68.9%; specificity = 37.1%). TSE in the home, car, and other peoples' homes was weakly associated with cotinine levels (Spearman correlations rs = 0.15-0.25), while TSE in public places was not associated with cotinine. Among those with airborne nicotine and daily diary data (n = 161), a smaller proportion had household TSE via self-report (41.6%) compared with measurable airborne nicotine (53.4%) (median concentration among those with nicotine>LOD: 0.04 μg/m(3)) (kappa = 0.09, sensitivity = 46.5%, specificity = 62.7%). Self-report alone was not adequate to identify individuals with TSE, as 31% with measurable cotinine and 53% with measurable nicotine did not report TSE. Self-report of TSE in private indoor spaces outside the home was most associated with measurable cotinine in this low-income non-smoking population.

Background While leisure-time physical activity (PA) has been associated with reduced risk of cardiometabolic disease, less is known about the relationship between work-related PA and health. Work-related PA is often not a chosen behavior and may be associated with lower socioeconomic status and less control over job-related activities. This study examined whether high work-related PA and leisure-time PA reported by hospital employees were associated with healthier dietary intake and reductions in cardiometabolic risk. Methods This was a cross-sectional analysis of 602 hospital employees who used workplace cafeterias and completed the baseline visit for a health promotion study in 2016–2018. Participants completed the International Physical Activity Questionnaire and clinical measures of weight, blood pressure, HbA1c, and lipids. Healthy Eating Index (HEI) scores were calculated from two 24-h dietary recalls, and a Healthy Purchasing Score was calculated based on healthfulness of workplace food/beverage purchases. Regression analyses examined Healthy Purchasing Score, HEI, and obesity, hypertension, hyperlipidemia, and diabetes/prediabetes by quartile of work-related PA, leisure-time PA, and sedentary time. Results Participants’ mean age was 43.6 years (SD = 12.2), 79.4% were female, and 81.1% were white. In total, 30.3% had obesity, 20.6% had hypertension, 26.6% had prediabetes/diabetes, and 32.1% had hyperlipidemia. Median leisure-time PA was 12.0 (IQR: 3.3, 28.0) and median work-related PA was 14.0 (IQR: 0.0, 51.1) MET-hours/week. Higher leisure-time PA was associated with higher workplace Healthy Purchasing Score and HEI (p’s < 0.01) and lower prevalence of obesity, diabetes/prediabetes, and hyperlipidemia (p’s < 0.05). Work-related PA was not associated with Healthy Purchasing Score, HEI, or cardiometabolic risk factors. Increased sedentary time was associated with lower HEI ( p  = 0.02) but was not associated with the workplace Healthy Purchasing Score. Conclusions Employees with high work-related PA did not have associated reductions in cardiometabolic risk or have healthier dietary intake as did employees reporting high leisure-time PA. Workplace wellness programs should promote leisure-time PA and healthy food choices for all employees, but programs may need to be customized and made more accessible to meet the unique needs of employees who are physically active at work. Trial registration This trial was prospectively registered with clinicaltrials.gov (Identifier: NCT02660086 ) on January 21, 2016. The first participant was enrolled on September 16, 2016.

Introduction Economic evaluations are helpful for efficient resource use. This paper aims to clarify the relationship between economic evaluation methods and two types of health care efficiency, aiding implementation scientists in selecting the appropriate approach for their research. Methods We clarify the connection between cost-effectiveness analysis (CEA) and allocative efficiency, and explain how budget impact analysis (BIA) more closely connects with productive efficiency. We also discuss other methods that researchers can use to analyze an organization's productive efficiency, given increasing pressure for health care organizations to be efficient. Results Allocative efficiency seeks to maximize social welfare through optimal resource distribution. Productive efficiency focuses on an organization’s ability to maximize its output given its resource constraints. CEA, particularly when incorporating a societal perspective, assesses allocative efficiency. BIA, which often has a short time horizon and more focused perspective, assesses productive efficiency. When organizational leaders ask implementation scientists for an economic evaluation, it is important to determine whether they want a CEA or a BIA, given they answer different questions, often employing different methods. We also present other methods for measuring efficiency and causes of inefficiency stemming from fixed costs, scale, scope, regulations, labor, and decision-making. Conclusions Implementation scientists must recognize that CEA and BIA serve distinct purposes and are not interchangeable. Choosing the right economic evaluation tool is crucial for answering specific research questions and building research teams. Future implementation work will also need to measure efficiency so that it is sustainable.

Introduction/Objectives: Systematic screening for social determinants of health (SDOH), such as food and housing insecurity, is increasingly implemented in primary care, particularly in the context of Accountable Care Organizations (ACO). Despite the importance of developing effective systems for SDOH resource linkage, there is limited research examining these processes. The objective of the study was to explore facilitators and barriers to addressing SDOH identified by systematic screening in a healthcare system participating in a Medicaid ACO. Methods: This qualitative case study took place between January and March 2020. Semi-structured interviews were conducted with fifteen staff (8 community resource staff and 7 managers) from community health centers and hospitals affiliated with a large healthcare system. Interviews were transcribed, coded, and analyzed using the Framework Method. Results: Facilitators for addressing SDOH included maintaining updated resource lists, collaborating with community organizations, having leadership buy-in, and developing a trusting relationship with patients. Barriers to addressing SDOH included high caseloads, time constraints, inefficiencies in tracking, lack of community resources, and several specific patient characteristics. Further, resource staff expressed distress associated with having to communicate to patients that they were unable to address certain needs. Conclusions: Health system, community, and individual-level facilitators and barriers should be considered when developing programs for addressing SDOH. Specifically, the psychological burden on resource staff is an important and underappreciated factor that could impact patient care and lead to staff burnout.