Cost-Related Access Barriers, Medical Debt, and Dissatisfaction with Care Among Privately Insured Americans.

BACKGROUND: Understanding experiences with private important to improving the quality of health care coverage. OBJECTIVE: To examine the association of health with cost-related access barriers, medical debt, and dissatisfaction with care among privately insured Americans. DESIGN: We classified Americans with private insurance by self-reported health status into five groups (excellent, very good, good, fair, and poor health). We examined self-reported difficulty seeing a doctor due to costs, not taking medications due to costs, medical debt, and dissatisfaction with care among individuals with differing health status. We used logistic regression to examine the association of health status with individuals' experiences after accounting for baseline characteristics. The analysis was repeated among individuals with different forms of private insurance. Odds ratios were converted to risk ratios to improve ease of interpretation of the results. SETTING: Behavioral Risk Factor Surveillance System of Americans in 17 states RESULTS: The sample included 82,494 US adults with private insurance. Following adjustment, compared to individuals with excellent health those in very good health, good health, fair health, and poor health reported increasingly higher risks of difficulty seeing a doctor due to costs with risk ratios of 1.02 (95% CI 1.01, 1.03), 1.07 (95% CI 1.06, 1.08), 1.18 (95% CI 1.17, 1.20), and 1.29 (95% CI 1.27, 1.31), respectively. Compared to individuals with excellent health, those in very good health, good health, fair health, and poor health reported increasingly higher risks of not taking medication due to costs, outstanding medical debt, and dissatisfaction with care. Similar relationships were seen across individually purchased and employer-sponsored insurance. CONCLUSION: Cost-related access barriers, medical debt, and dissatisfaction with care were common among individuals with private insurance and most pronounced among those with fair and poor health who likely need and use their health insurance the most.

A national population-based study of cannabis use and correlates among U.S. veterans prescribed opioids in primary care.

BACKGROUND: Cannabis is marketed as a treatment for pain. There is limited data on the prevalence of cannabis use and its correlates among Veterans prescribed opioids. OBJECTIVE: To examine the prevalence and correlates of cannabis use among Veterans prescribed opioids. DESIGN: Cross-sectional study. PARTICIPANTS: Veterans with a urine drug test (UDT) from Primary Care 2014-2018, in 50 states, Washington, D.C., and Puerto Rico. A total of 1,182,779 patients were identified with an opioid prescription within 90 days prior to UDT. MAIN MEASURES: Annual prevalence of cannabis positive UDT by state. We used multivariable logistic regression to assess associations of demographic factors, mental health conditions, substance use disorders, and pain diagnoses with cannabis positive UDT. RESULTS: Annual prevalence of cannabis positive UDT ranged from 8.5% to 9.7% during the study period, and in 2018 was 18.15% in Washington, D.C. and 10 states with legalized medical and recreational cannabis, 6.1% in Puerto Rico and 25 states with legalized medical cannabis, and 4.5% in non-legal states. Younger age, male sex, being unmarried, and marginal housing were associated with use (p < 0.001). Post-traumatic stress disorder (adjusted odds ratio [AOR] 1.17; 95% confidence interval [CI] 1.13-1.22, p < 0.001), opioid use disorder (AOR 1.14; CI 1.07-1.22, p < 0.001), alcohol use disorder or positive AUDIT-C (AOR 1.34; 95% CI 1.28-1.39, p < 0.001), smoking (AOR 2.58; 95% CI 2.49-2.66, p < 0.001), and other drug use disorders (AOR 1.15; 95% CI 1.03-1.29, p = 0.02) were associated with cannabis use. Positive UDT for amphetamines AOR 1.41; 95% CI 1.26-1.58, p < 0.001), benzodiazepines (AOR 1.41; 95% CI 1.31-1.51, p < 0.001) and cocaine (AOR 2.04; 95% CI 1.75-2.36, p < 0.001) were associated with cannabis positive UDT. CONCLUSIONS: Cannabis use among Veterans prescribed opioids varied by state and by legalization status. Veterans with PTSD and substance use disorders were more likely to have cannabis positive UDT. Opioid-prescribed Veterans using cannabis may benefit from screening for these conditions, referral to treatment, and attention to opioid safety.

Comparative Effectiveness of Carotid Stenting to Medical Therapy Among Patients With Asymptomatic Carotid Stenosis.

BACKGROUND: No completed trials have compared carotid artery stenting (CAS) to medical therapy (MT). We examined the effectiveness of CAS compared with MT in patients with asymptomatic carotid stenosis. METHODS: We conducted a retrospective cohort study of 219 979 Veterans ≥65 years who received carotid imaging for asymptomatic carotid stenosis between 2005 and 2009 in the US Veterans Health Administration. We constructed a sample of patients who received MT (n=2509) and comparable patients who received CAS (n=551) and followed them for 5 years. Using target trial methodology, we computed weighted Kaplan-Meier curves and estimated the risk of fatal and nonfatal stroke in each group over 5 years of follow-up. We also estimated the cumulative incidence functions for fatal and nonfatal stroke accounting for nonstroke deaths as competing risks. RESULTS: Five hundred fifty-one patients received CAS, and 2509 patients received MT. The observed rate of stroke or death (perioperative complications) within 30 days in the CAS arm was 2.2%. Using the target trial methodology, the 5-year risk of fatal and nonfatal stroke was similar among patients assigned to CAS (6.9%) compared with patients assigned to MT (7.1%; risk difference, -0.1% [95% CI, -2.6% to 2.7%]). In an analysis that incorporated the competing risk of death, the risk difference between the two arms remained nonsignificant (risk difference, -1.5% [95% CI, -3.0% to 0.3%]). CONCLUSIONS: In this sample of older male adults, we found no difference between MT and CAS in the treatment of asymptomatic carotid stenosis. Future studies in other settings are needed to confirm these findings.

Predictors of Regret among Older Men after Stress Urinary Incontinence Treatment Decisions.

PURPOSE: When seeking treatment for male stress urinary incontinence (mSUI), patients are faced with weighing complex risks and benefits in making treatment decisions within their individual context. We sought to quantify the frequency of decisional regret among this population and to determine factors associated with regret. MATERIALS AND METHODS: A cohort of 130 males aged ≥65 years seen for initial mSUI consultation at the University of California, San Francisco Medical Center and the San Francisco Veterans Affairs Medical Center between June 2015 and March 2020 was developed. Using retrospective chart review and telephone interviews, we ascertained decisional regret as well as other patient-, disease- and treatment-related characteristics. Decisional regret was analyzed by treatment type and patient-, disease- and treatment-related factors. Multivariable logistic regression models were built to examine the factors most associated with decisional regret. RESULTS: Among the entire cohort, 22% reported moderate to severe decisional regret. Regret was highest among those electing conservative management, with 34.7% having decisional regret (vs with surgery: 8.3% sling, 8.2% sphincter; p <0.001). In multivariable analysis, depression, lower rating of shared decision making and higher current incontinence scores were significantly associated with decisional regret. CONCLUSIONS: Recognition of depression, improved efforts at shared decision making and more individualized treatment counseling have the potential to improve patient satisfaction with treatment choice. In addition, given high levels of regret among those electing conservative treatment, we may be underutilizing mSUI surgery in this population.

Association of Social Determinants of Health and Their Cumulative Impact on Hospitalization Among a National Sample of Community-Dwelling US Adults.

IMPORTANCE: While the association between Social Determinants of Health (SDOH) and health outcomes is well known, few studies have explored the impact of SDOH on hospitalization. OBJECTIVE: Examine the independent association and cumulative effect of six SDOH domains on hospitalization. DESIGN: Using cross-sectional data from the 2016-2018 National Health Interview Surveys (NHIS), we used multivariable logistical regression models controlling for sociodemographics and comorbid conditions to assess the association of each SDOH and SDOH burden (i.e., cumulative number of SDOH) with hospitalization. SETTING: National survey of community-dwelling individuals in the US PARTICIPANTS: Adults ≥18 years who responded to the NHIS survey EXPOSURE: Six SDOH domains (economic instability, lack of community, educational deficits, food insecurity, social isolation, and inadequate access to medical care) MEASURES: Hospitalization within 1 year RESULTS: Among all 55,186 respondents, most were ≤50 years old (54.2%), female (51.7%, 95% CI 51.1-52.3), non-Hispanic (83.9%, 95% CI 82.4-84.5), identified as White (77.9%, 95% CI 76.8-79.1), and had health insurance (90%, 95% CI 88.9-91.9). Hospitalized individuals (n=5506; 8.7%) were more likely to be ≥50 years old (61.2%), female (60.7%, 95% CI 58.9-62.4), non-Hispanic (87%, 95% CI 86.2-88.4), and identify as White (78.5%, 95% CI 76.7-80.3), compared to those who were not hospitalized. Hospitalized individuals described poorer overall health, reporting higher incidence of having ≥5 comorbid conditions (38.9%, 95% CI 37.1-40.1) compared to those who did not report a hospitalization (15.9%, 95% CI 15.4-16.5). Hospitalized respondents reported higher rates of economic instability (33%), lack of community (14%), educational deficits (67%), food insecurity (14%), social isolation (34%), and less access to health care (6%) compared to non-hospitalized individuals. In adjusted analysis, food insecurity (OR: 1.36, 95% CI 1.22-1.52), social isolation (OR: 1.17, 95% CI 1.08-1.26), and lower educational attainment (OR: 1.12, 95% CI 1.02-1.25) were associated with hospitalization, while a higher SDOH burden was associated with increased odds of hospitalization (3-4 SDOH [OR: 1.25, 95% CI 1.06-1.49] and ≥5 SDOH [OR: 1.72, 95% CI 1.40-2.06]) compared to those who reported no SDOH. CONCLUSIONS: Among community-dwelling US adults, three SDOH domains: food insecurity, social isolation, and low educational attainment increase an individual's risk of hospitalization. Additionally, risk of hospitalization increases as SDOH burden increases.

Long-term Psychoactive Medications, Polypharmacy, and Risk of Suicide and Unintended Overdose Death Among Midlife and Older Women Veterans.

BACKGROUND: Rates of suicide and unintended overdose death are high among midlife and older women, yet there is paucity of data identifying women at greatest risk. Psychoactive medications, commonly prescribed and co-prescribed in this population, may serve as salient indicators of risk for these outcomes. OBJECTIVE: To determine whether long-term psychoactive medications and psychoactive polypharmacy predict risk of suicide and unintended overdose death among midlife and older women Veterans above and beyond other recognized factors. DESIGN: Longitudinal cohort study PARTICIPANTS: Women Veterans aged ≥ 50 with at least one Veterans Health Administration (VHA) clinical encounter in FY2012-2013. MAIN MEASURES: Long-term psychoactive medications (opioids, benzodiazepines, sedative-hypnotics, antidepressants, antipsychotics, and antiepileptics, prescribed for ≥ 90/180 days) and psychoactive polypharmacy (overlapping for ≥ 1 day) from VHA pharmacy records; suicide and unintended overdose death through December 31, 2018. KEY RESULTS: In this national sample of 154,558 midlife and older women Veterans (mean age 63.4, SD 9.3 years), 130 died by suicide and 175 died from unintentional overdose over an average of 5.6 years. In fully adjusted models, long-term opioids (hazard ratio (HR) 2.01, 95% CI 1.21-3.35) and benzodiazepines (HR 2.99, 95% CI 1.82-4.91) were associated with death by suicide; opioids (HR 3.62, 95% CI 2.46-5.34), benzodiazepines (HR 2.77, 95% CI 1.73-4.42), sedative-hypnotics (HR 1.87, 95% CI 1.06-3.29), antidepressants (HR 1.47, 95% CI 1.03-2.12), antipsychotics (HR 1.81, 95% CI 1.02-3.22), and antiepileptics (HR 2.17, 95% CI 1.48-3.19) were associated with unintended overdose death. Women who were co-prescribed ≥ 3 psychoactive medications had over 2-fold increased risk of suicide (HR 2.83, 95% CI 1.65-4.84) and unintended overdose death (HR 2.60, 95% CI 1.72-3.94). CONCLUSIONS: Long-term psychoactive medications and psychoactive medication polypharmacy were important indicators of risk for death by suicide and death by unintended overdose among midlife and older women Veterans, even after accounting for psychiatric and substance use disorders.

The Heart and Cannabis (THC) Cohort: Differences in Baseline Health and Behaviors by Cannabis Use.

BACKGROUND: Evidence on the cardiovascular health effects of cannabis use is limited. We designed a prospective cohort study of older Veterans (66 to 68 years) with coronary artery disease (CAD) to understand the cardiovascular consequences of cannabis use. We describe the cohort construction, baseline characteristics, and health behaviors that were associated with smoking cannabis. OBJECTIVE: To understand the cardiovascular consequences of cannabis use. DESIGN: We designed a prospective cohort study of older Veterans (66 to 68 years) with CAD. PARTICIPANTS: A total of 1,015 current cannabis smokers and 3,270 non-cannabis smokers with CAD. MAIN MEASURES: Using logistic regression, we examined the association of baseline variables with smoking cannabis in the past 30 days. RESULTS: The current cannabis smokers and non-current smokers were predominantly male (97.2% vs 97.1%, p=0.96). Characteristics associated with recent cannabis use in multivariable analyses included lack of a high school education (odds ratio [OR] 2.15, 95% confidence interval [CI]: 1.10 to 4.19), financial difficulty (OR 1.47, 95% CI: 1.02 to 2.11), tobacco use (OR 3.02, 95% CI: 1.66 to 5.48), current drug use (OR 2.82, 95% CI: 1.06 to 7.46), and prior drug use (OR 2.84, 95% CI: 2.11 to 3.82). In contrast, compared to individuals with 0 to 1 comorbid conditions, those with 5 chronic conditions or more (OR 0.43, 95% CI: 0.27 to 0.70) were less likely to smoke cannabis. CONCLUSIONS: In this older high-risk cohort, smoking cannabis was associated with higher social and behavioral risk, but with fewer chronic health conditions.

Capitated versus fee-for-service reimbursement and quality of care for chronic disease: a US cross-sectional analysis.

BACKGROUND: Upcoming alternative payment models Primary Care First (PCF) and Kidney Care Choices (KCC) incorporate capitated payments for chronic disease management. Prior research on the effect of capitated payments on chronic disease management has shown mixed results. We assessed the patient, physician, and practice characteristics of practices with capitation as the majority of revenue, and evaluated the association of capitated reimbursement with quality of chronic disease care. METHODS: We performed a cross-sectional analysis of visits in the United States' National Ambulatory Medical Care Survey (NAMCS) for patients with hypertension, diabetes, or chronic kidney disease (CKD). Our predictor was practice reimbursement type, classified as 1) majority capitation, 2) majority FFS, or 3) other reimbursement mix. Outcomes were quality indicators of hypertension control, diabetes control, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker (ACEi/ARB) use, and statin use. RESULTS: About 9% of visits were to practices with majority capitation revenue. Capitated practices, compared with FFS and other practices, had lower visit frequency (3.7 vs. 5.2 vs. 5.2, p = 0.006), were more likely to be located in the West Census Region (55% vs. 18% vs. 17%, p < 0.001), less likely to be solo practice (21% vs. 37% vs. 35%, p = 0.005), more likely to be owned by an insurance company, health plan or HMO (24% vs. 13% vs. 13%, p = 0.033), and more likely to have private insurance (43% vs. 25% vs. 19%, p = 0.004) and managed care payments (69% vs. 23% vs. 26%, p < 0.001) as the majority of revenue. The prevalence of controlled hypertension, controlled diabetes, ACEi/ARB use, and statin use was suboptimal across practice reimbursement types. Capitated reimbursement was not associated with differences in hypertension, diabetes, or CKD quality indicators, in multivariable models adjusting for patient, physician, and practice characteristics. CONCLUSIONS: Practices with majority capitation revenue differed substantially from FFS and other practices in patient, physician, and practice characteristics, but were not associated with consistent quality differences. Our findings establish baseline estimates of chronic disease quality of care performance by practice reimbursement composition, informing chronic disease care delivery within upcoming payment models.

Comparing mental and physical health of U.S. veterans by VA healthcare use: implications for generalizability of research in the VA electronic health records.

OBJECTIVE: The Department of Veterans Affairs' (VA) electronic health records (EHR) offer a rich source of big data to study medical and health care questions, but patient eligibility and preferences may limit generalizability of findings. We therefore examined the representativeness of VA veterans by comparing veterans using VA healthcare services to those who do not. METHODS: We analyzed data on 3051 veteran participants age ≥ 18 years in the 2019 National Health Interview Survey. Weighted logistic regression was used to model participant characteristics, health conditions, pain, and self-reported health by past year VA healthcare use and generate predicted marginal prevalences, which were used to calculate Cohen's d of group differences in absolute risk by past-year VA healthcare use. RESULTS: Among veterans, 30.4% had past-year VA healthcare use. Veterans with lower income and members of racial/ethnic minority groups were more likely to report past-year VA healthcare use. Health conditions overrepresented in past-year VA healthcare users included chronic medical conditions (80.6% vs. 69.4%, d = 0.36), pain (78.9% vs. 65.9%; d = 0.35), mental distress (11.6% vs. 5.9%; d = 0.47), anxiety (10.8% vs. 4.1%; d = 0.67), and fair/poor self-reported health (27.9% vs. 18.0%; d = 0.40). CONCLUSIONS: Heterogeneity in veteran sociodemographic and health characteristics was observed by past-year VA healthcare use. Researchers working with VA EHR data should consider how the patient selection process may relate to the exposures and outcomes under study. Statistical reweighting may be needed to generalize risk estimates from the VA EHR data to the overall veteran population.

Incidence of Severe COVID-19 Illness Following Vaccination and Booster With BNT162b2, mRNA-1273, and Ad26.COV2.S Vaccines.

Importance: Evidence describing the incidence of severe COVID-19 illness following vaccination and booster with BNT162b2, mRNA-1273, and Ad26.COV2.S vaccines is needed, particularly for high-risk populations. Objective: To describe the incidence of severe COVID-19 illness among a cohort that received vaccination plus a booster vaccine dose. Design, Setting, and Participants: Retrospective cohort study of adults receiving care at Veterans Health Administration facilities across the US who received a vaccination series plus 1 booster against SARS-CoV-2, conducted from July 1, 2021, to May 30, 2022. Patients were eligible if they had received a primary care visit in the prior 2 years and had documented receipt of all US Food and Drug Administration-authorized doses of the initial mRNA vaccine or viral vector vaccination series after December 11, 2020, and a subsequent documented booster dose between July 1, 2021, and April 29, 2022. The analytic cohort consisted of 1 610 719 participants. Exposures: Receipt of any combination of mRNA-1273 (Moderna), BNT162b2 (Pfizer-BioNTech), and Ad26.COV2.S (Janssen/Johnson & Johnson) primary vaccination series and a booster dose. Main Outcomes and Measures: Outcomes were breakthrough COVID-19 (symptomatic infection), hospitalization with COVID-19 pneumonia and/or death, and hospitalization with severe COVID-19 pneumonia and/or death. A subgroup analysis of nonoverlapping populations included those aged 65 years or older, those with high-risk comorbid conditions, and those with immunocompromising conditions. Results: Of 1 610 719 participants, 1 100 280 (68.4%) were aged 65 years or older and 132 243 (8.2%) were female; 1 133 785 (70.4%) had high-risk comorbid conditions, 155 995 (9.6%) had immunocompromising conditions, and 1 467 879 (91.1%) received the same type of mRNA vaccine (initial series and booster). Over 24 weeks, 125.0 (95% CI, 123.3-126.8) per 10 000 persons had breakthrough COVID-19, 8.9 (95% CI, 8.5-9.4) per 10 000 persons were hospitalized with COVID-19 pneumonia or died, and 3.4 (95% CI, 3.1-3.7) per 10 000 persons were hospitalized with severe pneumonia or died. For high-risk populations, incidence of hospitalization with COVID-19 pneumonia or death was as follows: aged 65 years or older, 1.9 (95% CI, 1.4-2.6) per 10 000 persons; high-risk comorbid conditions, 6.7 (95% CI, 6.2-7.2) per 10 000 persons; and immunocompromising conditions, 39.6 (95% CI, 36.6-42.9) per 10 000 persons. Subgroup analyses of patients hospitalized with COVID-19 pneumonia or death by time after booster demonstrated similar incidence estimates among those aged 65 years or older and with high-risk comorbid conditions but not among those with immunocompromising conditions. Conclusions and Relevance: In a US cohort of patients receiving care at Veterans Health Administration facilities during a period of Delta and Omicron variant predominance, there was a low incidence of hospitalization with COVID-19 pneumonia or death following vaccination and booster with any of BNT162b2, mRNA-1273, or Ad26.COV2.S vaccines.

Examining the association of social risk with heart failure readmission in the Veterans Health Administration.

BACKGROUND: Previous research has found that social risk factors are associated with an increased risk of 30-day readmission. We aimed to assess the association of 5 social risk factors (living alone, lack of social support, marginal housing, substance abuse, and low income) with 30-day Heart Failure (HF) hospital readmissions within the Veterans Health Affairs (VA) and the impact of their inclusion on hospital readmission model performance. METHODS: We performed a retrospective cohort study using chart review and VA and Centers for Medicare and Medicaid Services (CMS) administrative data from a random sample of 1,500 elderly (≥ 65 years) Veterans hospitalized for HF in 2012. Using logistic regression, we examined whether any of the social risk factors were associated with 30-day readmission after adjusting for age alone and clinical variables used by CMS in its 30-day risk stratified readmission model. The impact of these five social risk factors on readmission model performance was assessed by comparing c-statistics, likelihood ratio tests, and the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS: The prevalence varied among the 5 risk factors; low income (47 % vs. 47 %), lives alone (18 % vs. 19 %), substance abuse (14 % vs. 16 %), lacks social support (2 % vs. <1 %), and marginal housing (< 1 % vs. 3 %) among readmitted and non-readmitted patients, respectively. Controlling for clinical factors contained in CMS readmission models, a lack of social support was found to be associated with an increased risk of 30-day readmission (OR 4.8, 95 %CI 1.35-17.88), while marginal housing was noted to decrease readmission risk (OR 0.21, 95 %CI 0.03-0.87). Living alone (OR: 0.9, 95 %CI 0.64-1.26), substance abuse (OR 0.91, 95 %CI 0.67-1.22), and having low income (OR 1.01, 95 %CI 0.77-1.31) had no association with HF readmissions. Adding the five social risk factors to a CMS-based model (age and comorbid conditions; c-statistic 0.62) did not improve model performance (c-statistic: 0.62). CONCLUSIONS: While a lack of social support was associated with 30-day readmission in the VA, its prevalence was low. Moreover, the inclusion of some social risk factors did not improve readmission model performance. In an integrated healthcare system like the VA, social risk factors may have a limited effect on 30-day readmission outcomes.

Changes in Preventative Health Care After Medicaid Expansion.

BACKGROUND: Medicaid expansion substantially increased health insurance coverage, but its effect on the delivery of preventative health care is unclear. OBJECTIVE: The objective of this study was to assess the impact of Medicaid expansion on the receipt of 15 different measures of preventive care including cancer screening, cardiovascular risk reduction, diabetes care, and other primary care measures. RESEARCH DESIGN: We performed serial cross-sectional analysis of Behavioral Risk Factor Surveillance System (BRFSS) survey data from 2012 to 2017. We used a quasi-experimental design with difference-in-differences (DiD) analyses to examine changes in preventative health care delivery over 3 time periods in Medicaid expansion compared with nonexpansion states. SUBJECTS: We included low-income (<138% federal poverty level) nonelderly (age younger than 65 y) adults residing in 46 US states. MEASURES: Our predictor was residing in a Medicaid expansion state (24 states) versus nonexpansion state (19 states). Our primary outcomes were preventative health care services, which we categorized as cancer screening (breast cancer, cervical cancer, and colorectal cancer); cardiovascular risk reduction (serum cholesterol screening in low-risk groups, serum cholesterol monitoring in high-risk groups, and aspirin use); diabetes care (serum cholesterol monitoring, hemoglobin A1c monitoring, foot examination, eye examination, and influenza vaccination, and pneumonia vaccination); and other primary care measures [influenza vaccination, alcohol use screening, and human immunodeficiency virus (HIV) screening]. RESULTS: Survey responses from 500,495 low-income nonelderly adults from 2012 to 2017 were included in the analysis, representing 68.2 million US adults per year. Of the 15 outcomes evaluated, we did not detect statistically significant differences in cancer screening (3 outcomes), cholesterol screening or monitoring (2 outcomes), diabetes care (6 outcomes), or alcohol use screening (1 outcome) in expansion compared with nonexpansion states. Aspirin use (DiD 8.8%, P<0.001), influenza vaccination (DiD 1.4%, P=0.016), and HIV screening (DiD 1.9%, P=0.004) increased in expansion states compared with nonexpansion states. CONCLUSIONS: Medicaid expansion was associated with an increase in aspirin use, influenza vaccination, and HIV screening in expansion states. Despite improvements in access to care, including health insurance, having a primary care doctor, and routine visits, Medicaid expansion was not associated with improvements in cancer screening, cholesterol monitoring, diabetes care, or alcohol use screening. Our findings highlight implementation challenges in delivering high-quality primary care to low-income populations.

Medical Reasons for Marijuana Use, Forms of Use, and Patient Perception of Physician Attitudes Among the US Population.

BACKGROUND: The use of marijuana for medical purposes is increasing in parallel with expanding legalization and decreased public perception of harm. Despite this increase in use, it is unclear which medical conditions patients are attempting to treat with marijuana and whether they are communicating with medical providers about their use. OBJECTIVE: To understand the medical reasons for marijuana use, forms of marijuana used for medical purposes, and disclosure of use to physicians. DESIGN: National, probability-based online survey. SETTING: The USA, 2017. PARTICIPANTS: 16,280 US adults. MAIN MEASURE: Proportion of US adults who agreed with a statement. KEY RESULTS: A total of 9003 participants completed the survey (55% response rate). Five hundred ninety-one (7% of US adults) reported using marijuana for medical purposes. The most common medical reasons for marijuana use were anxiety (49%), insomnia (47%), chronic pain (42%), and depression (39%). The most common forms of use for all medical conditions were smoking and edibles, followed by vaping, concentrate, and topical. We found women were more likely to use marijuana for posttraumatic stress disorder, sleep, anxiety, and migraines. We did not find substantial variation in medical reasons for marijuana use by race. Among those using marijuana for medical purposes, 21% did not have a doctor. Among those with doctors, 33% did not inform them, 28% reported their doctor was neutral on their use, 32% reported their doctor was supportive, and 8% reported their doctor was not supportive. Those who lived in states where medical marijuana was illegal were less likely to disclose use to their doctor. LIMITATION: The online format of the survey may have caused selection bias. Wording of the questions may have affected interpretation. Doctors were not queried directly, rather participants were asked about their perception of doctor attitudes. CONCLUSION: Americans are using marijuana to treat medical conditions despite lack of evidence of efficacy.

Sources of Information and Beliefs About the Health Effects of Marijuana.

BACKGROUND: Marijuana is currently legal for recreational use in 10 states and Washington DC while a total of 34 states have implemented varying degrees of medical marijuana. The commercialization of marijuana has been accompanied by a proliferation of false claims regarding the therapeutic potential of marijuana, which are popularized by several different information sources. To date, no study has examined where US adults get their information regarding marijuana. OBJECTIVE: To determine the sources of information associated with believing unsupported claims about marijuana. DESIGN: Probability-based online survey PARTICIPANTS: 16,820 adults, with a response rate of about 55% (N = 9003) MAIN MEASURES: Most influential sources of information about marijuana and belief of statements consistent with misinformation, for example, smoking marijuana has preventative health benefits, secondhand marijuana smoke or use during pregnancy is completely or somewhat safe, and marijuana is not at all addictive. KEY RESULTS: There were 9003 respondents (response rate 55%). Forty-three percent believed unsupported claims about marijuana. The most influential sources of information were health professionals, traditional media, friends/relatives, and social media/internet. Individuals reporting social media or the Internet (1.46 CI [1.30, 1.64]), the marijuana industry (e.g., advertisements, dispensaries) (2.88 CI [2.15, 3.88]), and friends or relatives (1.41 CI[1.26, 1.58]) as the most influential source of information about marijuana were more likely to believe any statement consistent with misinformation about marijuana in comparison with those who reported other sources as most influential. CONCLUSIONS: Individuals reporting the most significant source of information regarding marijuana was from social media or the Internet, the marijuana industry, or friends or relatives were more likely to believe unsupported claims about marijuana. Public health campaigns to counter the misinformation about marijuana to the public are needed.

Associations Between Marijuana Use and Cardiovascular Risk Factors and Outcomes: A Systematic Review.

Background: Marijuana use is increasing in the United States, and its effect on cardiovascular health is unknown. Purpose: To review harms and benefits of marijuana use in relation to cardiovascular risk factors and clinical outcomes. Data Sources: PubMed, MEDLINE, EMBASE, PsycINFO, and the Cochrane Library between 1 January 1975 and 30 September 2017. Study Selection: Observational studies that were published in English, enrolled adults using any form of marijuana, and reported on vascular risk factors (hyperglycemia, diabetes, dyslipidemia, and obesity) or on outcomes (stroke, myocardial infarction, cardiovascular mortality, and all-cause mortality in cardiovascular cohorts). Data Extraction: Study characteristics and quality were assessed by 4 reviewers independently; strength of evidence for each outcome was graded by consensus. Data Synthesis: 13 and 11 studies examined associations between marijuana use and cardiovascular risk factors and clinical outcomes, respectively. Although 6 studies suggested a metabolic benefit from marijuana use, they were based on cross-sectional designs and were not supported by prospective studies. Evidence examining the effect of marijuana on diabetes, dyslipidemia, acute myocardial infarction, stroke, or cardiovascular and all-cause mortality was insufficient. Although the current literature includes several long-term prospective studies, they are limited by recall bias, inadequate exposure assessment, minimal marijuana exposure, and a predominance of low-risk cohorts. Limitation: Poor- or moderate-quality data, inadequate assessment of marijuana exposure and minimal exposure in the populations studied, and variation in study design. Conclusion: Evidence examining the effect of marijuana on cardiovascular risk factors and outcomes, including stroke and myocardial infarction, is insufficient. Primary Funding Source: National Heart, Lung, and Blood Institute. (PROSPERO: CRD42016051297).

Risks and Benefits of Marijuana Use: A National Survey of U.S. Adults.

Background: Despite insufficient evidence regarding its risks and benefits, marijuana is increasingly available and is aggressively marketed to the public. Objective: To understand the public's views on the risks and benefits of marijuana use. Design: Probability-based online survey. Setting: United States, 2017. Participants: 16 280 U.S. adults. Measurements: Proportion of U.S. adults who agreed with a statement. Results: The response rate was 55.3% (n = 9003). Approximately 14.6% of U.S. adults reported using marijuana in the past year. About 81% of U.S. adults believe marijuana has at least 1 benefit, whereas 17% believe it has no benefit. The most common benefit cited was pain management (66%), followed by treatment of diseases, such as epilepsy and multiple sclerosis (48%), and relief from anxiety, stress, and depression (47%). About 91% of U.S. adults believe marijuana has at least 1 risk, whereas 9% believe it has no risks. The most common risk identified by the public was legal problems (51.8%), followed by addiction (50%) and impaired memory (42%). Among U.S. adults, 29.2% agree that smoking marijuana prevents health problems. About 18% believe exposure to secondhand marijuana smoke is somewhat or completely safe for adults, whereas 7.6% indicated that it is somewhat or completely safe for children. Of the respondents, 7.3% agree that marijuana use is somewhat or completely safe during pregnancy. About 22.4% of U.S. adults believe that marijuana is not at all addictive. Limitation: Wording of the questions may have affected interpretation. Conclusion: Americans' view of marijuana use is more favorable than existing evidence supports. Primary Funding Source: National Heart, Lung, and Blood Institute.

Marijuana Use, Respiratory Symptoms, and Pulmonary Function: A Systematic Review and Meta-analysis.

Background: The health effects of smoking marijuana are not well-understood. Purpose: To examine the association between marijuana use and respiratory symptoms, pulmonary function, and obstructive lung disease among adolescents and adults. Data Sources: PubMed, Embase, PsycINFO, MEDLINE, and the Cochrane Library from 1 January 1973 to 30 April 2018. Study Selection: Observational and interventional studies published in English that reported pulmonary outcomes of adolescents and adults who used marijuana. Data Extraction: Four reviewers independently extracted study characteristics and assessed risk of bias. Three reviewers assessed strength of evidence. Studies of similar design with low or moderate risk of bias and sufficient data were pooled. Data Synthesis: Twenty-two studies were included. A pooled analysis of 2 prospective studies showed that marijuana use was associated with an increased risk for cough (risk ratio [RR], 2.04 [95% CI, 1.02 to 4.06]) and sputum production (RR, 3.84 [CI, 1.62 to 9.07]). Pooled analysis of cross-sectional studies (1 low and 3 moderate risk of bias) showed that marijuana use was associated with cough (RR, 4.37 [CI, 1.71 to 11.19]), sputum production (RR, 3.40 [CI, 1.99 to 5.79]), wheezing (RR, 2.83 [CI, 1.89 to 4.23]), and dyspnea (RR, 1.56 [CI, 1.33 to 1.83]). Data on pulmonary function and obstructive lung disease were insufficient. Limitation: Few studies were at low risk of bias, marijuana exposure was limited in the population studied, cohorts were young overall, assessment of marijuana exposure was not uniform, and study designs varied. Conclusion: Low-strength evidence suggests that smoking marijuana is associated with cough, sputum production, and wheezing. Evidence on the association between marijuana use and obstructive lung disease and pulmonary function is insufficient. Primary Funding Source: None. (PROSPERO: CRD42017059224).